WADE IN THE WATER: RACE, CLASS, LOCAL GOVERNMENT AND HURRICANE
KATRINA
By
Davia Cox Downey

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Political Science
2011

ABSTRACT
WADE IN THE WATER: RACE, CLASS, LOCAL GOVERNMENT AND HURRICANE
KATRINA
By
Davia Cox Downey

This dissertation investigates the role race and class play in the economic recovery of the
states of Louisiana and Mississippi after Hurricanes Katrina and Rita in 2005. Race and class
have been shown to affect economic development of localities elsewhere in the country. This
project informs the debate on how race and class affect economic recovery after an unplanned
event. Instead of focusing on New Orleans as a case, this research explores the effects of race
and class on recovery efforts all of the parishes in Louisiana and all counties in Mississippi
affected by the storm. Two main indicators of recovery: new employment and new housing
construction--are used to test the hypothesis that race and class have a negative impact on parish
recovery. The theoretical perspective looks specifically at the tipping point theory of
socioeconomic composition of places to inform how the demographic characteristics of each
location affect recovery rates. Using data from years 2003 to 2007 collected from the census and
the state’s department of emergency management, results show for job recovery, race and class
play a significant role. The economic health of the parish is measured in the numbers of jobs
reclaimed and housing units restored. Housing recovery data also confirm that racial competition
and tipping points play a role in the effectiveness of economic recovery in these states.

Copyright by
Davia Cox Downey
2010

DEDICATION
I dedicate this project to my parents who always believed in me, to my husband for his
unwavering support, to the people of the Gulf Coast whose tenacity never ceases to amaze me,
and to my son without whom I would not have had the motivation to finish.

iv

ACKNOWLEDGEMENTS
Thank you to Jennifer Budjos Lathom for assistance with mapping; Shane Singh, William Myers
Dr. Mark Skidmore for statistical support; and finally, John Downey, Dr. Laura Reese and Dr.
Cynthia Jackson-Elmoore for editing assistance.

v

TABLE OF CONTENTS
List of Tables .................................................................................................................... ix
List of Figures .................................................................................................................... xi
CHAPTER ONE: Introduction
Introduction..............................................................................................................1
Background ..............................................................................................................5
Research Questions..................................................................................................9
Research Design and Methods...............................................................................11
Race and Class Variables.......................................................................................15
Fiscal and Economic Capacity Variables. .............................................................15
Housing Variables..................................................................................................15
Federal and State Funding. ....................................................................................15
Sources of Invalidity and Secondary Data Source Issues......................................16
Chapter Outline and Concluding Thoughts. ..........................................................16
Bibliography ..........................................................................................................18
CHAPTER TWO: Literature Review and Theory
Introduction............................................................................................................27
Race, Class and Local Government Institutions ....................................................28
Race, Class and Public Policy Development .........................................................30
Race vs. Class ........................................................................................................32
Race, Class and Economic Outcomes....................................................................34
Theory Construction...............................................................................................35
Theory ...................................................................................................................36
Bibliography ..........................................................................................................41
CHAPTER THREE: Methodology
Introduction............................................................................................................47
Case Selection........................................................................................................47
Data and Sources....................................................................................................48
Variable Creation ...................................................................................................52
Panel Analysis Methodology .................................................................................54
Random vs. Fixed Effects Models . .......................................................................55
Panel Analysis vs. Linear Regression....................................................................57
Bibliography ..........................................................................................................59
CHAPTER FOUR: Louisiana
Introduction............................................................................................................62
Social Institutions and History...............................................................................63
Economic Institutions and History.........................................................................65
Local Government Institutions ..............................................................................65
The Levee System..................................................................................................67

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Levee Boards and Levee Districts. ........................................................................67
Louisiana Pre-Katrina ............................................................................................68
Demographics of Panel ..........................................................................................71
Theoretical Impact .................................................................................................78
Panel Results..........................................................................................................79
Conclusion .............................................................................................................93
Bibliography .........................................................................................................95
CHAPTER FIVE: New Orleans
Introduction............................................................................................................99
Social Institutions and History...............................................................................99
Economic Institutions: History and Present Day .................................................101
Local Government Institutions ............................................................................103
The Levee System................................................................................................105
New Orleans, Pre-Katrina....................................................................................108
Data and Data Sources .........................................................................................109
Demographics of Panel. .......................................................................................110
Panel Results........................................................................................................116
Conclusion ...........................................................................................................129
Bibliography ........................................................................................................131
CHAPTER SIX: Mississippi
Introduction..........................................................................................................135
Social Institutions and History.............................................................................136
Economic Institutions: History and Present Day .................................................139
Local Government Institutions ............................................................................140
The Levee System ...............................................................................................142
Mississippi Pre-Katrina........................................................................................143
Data and Data Sources. ........................................................................................144
Panel Results: The State of Mississippi ...............................................................145
Panel Results: The Gulfport CSA ........................................................................163
Conclusion ...........................................................................................................172
Bibliography ........................................................................................................174
CHAPTER EIGHT: Concluding Thoughts
Introduction..........................................................................................................177
Theory Revisited..................................................................................................178
Results ..................................................................................................................180
Hypotheses Revisited...........................................................................................183
Policy Implications ..............................................................................................185
Future Research Implications ..............................................................................188
The Importance of Rural Study............................................................................188
Data Needs ...........................................................................................................189
Next Steps ............................................................................................................189
Conclusion ...........................................................................................................190
Bibliography ........................................................................................................191

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LIST OF TABLES
Table 1.1. Types of Parishes and Counties Examined on Race and Class Dimensions ....14
Table 2.1: Literature on the Effects of Minority Presence and Poverty ............................26
Table 3.1: Variables and Sources.......................................................................................51
Table 4.1: Summary of Funding and Occupied Damage...................................................72
Table 4.2: Minority and Class Proportions by Year ..........................................................73
Table 4.3: Summary Statistics of the State of Louisiana ...................................................75
Table 4.4: Job Starts and Housing Permits ........................................................................77
Table 4.5: Effect of Poverty and Race on New Hires........................................................81
Table 4.6: Effect of Poverty and Race on New Home Permitting.....................................83
Table 4.7: Race and Class Characteristics of Louisiana Parishes......................................85
Table 4.8: Correlation Table of Race and Class Variables ...............................................86
Table 4.9: Effect of Socioeconomic Categorization on New Hires and Housing .............87
Table 4.10: Effect of External Funding on New Home Permitting and Job Creation in Louisiana
............................................................................................................................................89
Table 4.11: Effect of All Variables on Economic Recovery .............................................91
Table 5.1: Summary Statistics of the New Orleans MSA Compared to the Entire State of
Louisiana..........................................................................................................................110
Table 5.2: Summary Statistics of the Greater New Orleans MSA ..................................111
Table 5.3: Correlations of Concentration of Minority Residents and Residents Living Under the
Poverty Line (2003-2007)................................................................................................112
Table 5.4: Correlations of Individual and Public Assistance Dollars, Road Home Dollars and
Percentage of Damage (2003-2007) ................................................................................116
Table 5.5: Effect of Poverty and Race on New Hires in the Greater New Orleans Area 118
Table 5.6: Effect of Poverty and Race on New Hires in the Greater New Orleans Area 120
Table 5.7: Effect of Poverty and Race on New Homes in the Greater New Orleans Area ...
..........................................................................................................................................121

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Table 5.8: Effect of Poverty and Race on New Homes in the Greater New Orleans Area ...
..........................................................................................................................................123
Table 5.9: Two-sample t test with unequal variances (New Hires).................................124
Table 5.10: Two-sample t test with unequal variances (New Home Permits).................125
Table 6.1: Summary of Funding and Occupied Damage.................................................141
Table 6.2: Minority and Class Distribution by Year........................................................142
Table 6.3: Summary Statistics of the State of Mississippi...............................................143
Table 6.4: Job Starts and Housing Permits ......................................................................146
Table 6.5: Effect of Poverty and Race on New Hires and New Homes in Mississippi...153
Table 6.6: Race and Class Characteristics of Mississippi Counties ...............................154
Table 6.7: Correlation Table of Race and Class Variables..............................................154
Table 6.8: Effect of Socioeconomic Categorization on New Hires and Housing ...........156
Table 6.9: Effect of Poverty and Race on New Home Construction ...............................158
Table 6.10: Summary Statistics of the Gulfport CSA Compared to the Entire State of Mississippi
..........................................................................................................................................159
Table 6.11: Summary Statistics of the Gulfport CSA......................................................159
Table 6.12: Correlations of Concentration of Minority Residents and Residents Living Under
Poverty Line (2003-2007) ...............................................................................................162
Table 6.13: Correlations of Individual and Public Assistance Dollars, HAP and % Housing
Damage (2003-2007) .......................................................................................................163
Table 6.14: Effect of Race and Class on New Hires and New Homes in the Greater Gulfport
Area..................................................................................................................................164
Table 6.15: Effect of Hurricane Katrina on New Hires in the Gulfport Area .................165
Table 6.16. Two-sample t test with equal variances (New Hires) ...................................166
Table 6.17: Two-sample t test with unequal variances (New Hires)...............................167
Table 6.18: Effect of External Funding, Poverty and Minority Population on Recovery in
Mississippi .......................................................................................................................169

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Table 6.19: Effect of External Funding, Poverty and Minority Population on Recovery in
Mississippi .......................................................................................................................170
Table 6.20: Effect of All Variables on Economic Recovery ...........................................171

x

LIST OF FIGURES
Figure 1.1 Pattern of Emergency Response in Natural Disasters ......................................13
Figure 4.1: Race and Class Distribution by Parish ............................................................76
Figure 4.3: Effect of IA Funds on New Hires....................................................................93
Figure 5.1: Race and Class Distributions (New Orleans Metropolitan Area) .................112
Figure 5.2: New Housing Permit Change 2003-2007 (New Orleans MSA Only) ..........113
Figure 5.3: New Hires Change 2003-2007 (New Orleans MSA Only)...........................114
Figure 5.3: Effect of IA Funds on New Hires (Greater New Orleans Area) ...................126
Figure 6.1: Race and Class Distributions.........................................................................149
Figure 6.2: New Housing Permit Change 2003-2007......................................................151
Figure 6.3: New Hires Change 2003-2007 ......................................................................152
Figure 6.4: Race and Class Distributions (Gulfport CSA) ..............................................160
Figure 6.5: New Housing Permit Change 2003-2007 (Gulfport CSA) ...........................161
Figure 6.6: New Hires Change 2003-2007 (Gulfport CSA) ............................................161
Figure 6.7. Effect of IA Funds on New Hires..................................................................168

xi

Chapter One: Introduction
Introduction
When Hurricanes Katrina and Rita hit the gulf in 2005, few understood what the impact
of these storms would be. The facts of both disasters are now well known: an estimated $81
billion of damage to the gulf region during Hurricane Katrina, $4 to 5 billion dollars worth of
damage attributed to Rita, 250,000 residents displaced after Katrina, an additional 3 million
residents evacuated during Rita, and over 1800 lives, disproportionately African-American and
poor, lost to the flood as reported by the New York Times, the Federal Emergency Management
Agency (FEMA) and various other news outlets (Department of Homeland Security 2006;
Development 2006; Dewan 2006; Drew 2006; Saulny 2006a; Saulny 2006b). In the wake of
these natural disasters, disaster preparedness, mitigation and local revitalization took center stage
as federal, state, and local government entities took stock and created plans to rebuild the gulf
region. To date, much of the scholarship on Hurricane Katrina in particular, has focused on
failures of emergency disaster planning, namely FEMA’s lack of response, (Eikenberry et al.
2007; Gerber 2007; Petak 1985; Schneider 2005), the race and class divide after the storm,
(Elliott and Pais 2006; Lavelle and Feagin 2006; Stivers 2007; Tynes et al. 2006), and difficulties
that local and state governments face when attempting to rebuild after a natural disaster
(Eckdish-Knack 2006; Lewis 2005; Liu 2006; Olshansky 2006).
What is missing is attention to how smaller municipalities were affected by the storm. In
fact, nearly all of the accumulated scholarship on Hurricane Katrina has been case studies of the
city of New Orleans and its attempts to rebuild. Public administration and policy scholars have
largely been silent on issues of race and class and the effect these have on creating public policy

1

at the local level. In addition, studies on the effectiveness of FEMA have neglected to discuss the
issues of local emergency planning or recovery.
While New Orleans was the largest local unit to suffer during the storms, the path of both
hurricanes passed over Florida, Louisiana, Mississippi, Texas, and Alabama before dissipating in
Tennessee and Kentucky. Thousands of residents living in small towns in these states suffered
damage from the storm yet little media or scholarly attention has been paid to their plight.
Smaller units of government may lack the types of networks available to assist in disaster
planning. Further, issues of race and class may be exacerbated in rural and sparsely populated
areas (Browne and Hadwiger 1982; Long 1987; Marshall 2000; Quigley 2002).
This project adds to the scholarship on racial politics by exploring issues of race and class
at the state and local level, focusing on how communities with larger minority populations deal
with disasters. It also adds to the debate currently raging in public administration over effective
and efficient government (Frederickson 1996; Osborne and Gaebler 1992; Thompson and
Riccucci 1998). This project uncovers the impact of exogenous events on local government
operations by looking at the rate of recovery in the two states most affected by the storm,
Louisiana and Mississippi. It looks at the capacity of local areas to rebuild after a natural disaster
and answers questions posed by many scholars and pundits after the storm regarding how race
and class complicate rebuilding communities. This project uncovers how counties governments
of various sizes and race and class compositions rebounded economically post-disaster.
Central to this project is a theory regarding how socioeconomic diversity affects
economic outcomes in the wake of crisis. This theory rests on the premise that, in areas that are
racially and ethnically diverse, groups will compete with the dominant racial group (typically
whites) when creating public policy and communicating preferences for public services. The key

2

question in this line of research is whether close proximity to racial minorities will facilitate
hostility (Bobo and Kluegel 1993; Gay 2004; Hutchings and Valentino 2004; Mendelberg and
Oleske 2000; Morgan and Mareschal 1999; Sharp 2005; White 2007) the effect of which can
depress voter turnout, political participation and dampen economic investment. The racial
composition of Louisiana and Mississippi provides an interesting test of this theory. Many
counties identified by FEMA as eligible for aid differ dramatically along race and class lines,
providing the kind of variation needed to test the assumption of whether heterogeneous areas are
likely to recover after storms at a different pace than their homogenous counterparts. The term
homogenous--used liberally in this project--is defined as areas that are both racially and
economically similar (i.e., white and black versus rich and poor). The remainder of this chapter
will provide a brief timeline of the disaster, discuss the literature on race and class as it relates to
local government management and public policy, provide background on public administration
and emergency management issues, highlight the effect of race on political preferences and
policy making at the local level, and discuss the importance of institutional constraints on the
creation of public policies at the local level.
Emergency planning has long been a difficult policy area for political actors and analysts
to discuss. Effectively planning for events that have a low probability of occurrence but a high
hazard for damage to a community has consistently challenged government officials. Obstacles
affecting this type of planning include organizational arrangements, institutional inertia, and the
behavioral and cognitive limitations of emergency management planners (Gerber 2007). In the
United States, emergency and disaster management planning systems began as a local
government function. Issues of federalism and state’s rights further complicate planning. The
phases of disaster management--mitigation, preparedness, response, and recovery--require both

3

horizontal and vertical relationships among political actors (Burby 2006). The highly politicized
nature of policymaking at the local level involves greater economic costs in mitigating hazardous
events than calculable benefits to a community (Congress 2005; Long 1987). Emergency relief
and disaster planning have typically been considered functions of fire and law enforcement in
many towns and counties across the United States rather than as separate local government
functions. And, emergency and disaster planning occupy a small part of law enforcement
budgets (Petak 1985). After 9/11, the additional requirements on local and state governments to
tie emergency planning policy to eliminating threats of terrorism further complicated this area of
policy (Sylves 2008). As a result, disaster or emergency planning is best characterized as a
bottom-up network which, if capacity is minimal at the local level, can result in limited response
during and after a disaster (Waugh Jr. 2006a, 2007).
In addition to the difficulties associated with planning for disasters, many communities in
the gulf region suffer from high levels of poverty; disparities along racial and economic lines
came into sharp focus in the wake of Katrina. Following the storm, media attention was integral
to highlighting many issues that faced the gulf region after the 2005 hurricane season. The most
striking to many outside observers was the intersection of race and class in the wake of a natural
disaster. The broadcast news images that flooded the airwaves during and after the storm showed
nearly all left behind in the city of New Orleans were African-American—thus the conclusion
was drawn (by the media) that racism was a major contributor to the lack of coordination and
response as the storm passed and thereafter. Thus the question bears asking, is it insufficiency in
the planning process that results in economic disparities in disaster recovery or do issues of race
and class which have proven intractable in other areas of policy, make recovery more
challenging?

4

Class also played an important part in who was left behind in New Orleans. Being poor
means having fewer resources, such as access to a car, or the ability to purchase a home in a
safer area, resulting in difficulties recovering assets post disaster. In New Orleans, safe ground is
higher ground and large portions of the city, specifically Orleans, St. Bernard and Jefferson
parishes, lie below sea level (ASCE 2007). These factors contributed to a lack of mobilization of
citizens out of the path of the storm. In the United States, race and class have always been
closely intertwined and public policy has, at times, promoted institutionalized racism (Better
2008; Leon 1979). Thus how this region will revive itself after the storm is directly connected to
how policymakers and the public at large react to issues of race and class.
Background:
Before launching into a more detailed discussion of race, class, public administration,
urban politics, and disaster relief policy, a quick summary of the extent of damage to the gulf
region is useful. In particular, a review of events leading up to the landfall of the hurricanes and
subsequent evacuation orders is needed to understand local government’s role in notifying
residents of an impending disaster and execution of emergency plans.
The National Hurricane Center (NHC) issued its first advisory about the system that was
to become Hurricane Katrina on August 23, 2005. Over the next two days, the storm
strengthened and came ashore as a category one hurricane in North Miami Beach, Florida. Two
casualties were reported. As the storm crossed into the Gulf, it strengthened due to its warm
waters. On August 26, the NHC predicted the storm would become a major hurricane and
Louisiana governor Kathleen Blanco declared a state of emergency. On the 27th, the NHC
upgraded Katrina to a category three storm; its trajectory was projected to hit New Orleans
directly and pass through Louisiana and into Mississippi within 48 hours. At this time, Mayor

5

Ray Nagin issued a voluntary evacuation of New Orleans residents. Mississippi’s Governor
Haley Barbour declared a state of emergency. On the 28th, the storm was upgraded to category
four and quickly intensified into a category five hurricane before hitting land. Mayor Nagin then
issued a mandatory evacuation of the city, less than 24 hours before the storm reached land
(Townsend 2006). The hurricane made several landfalls during the evening of August 29, the
third recorded near the Mississippi-Louisiana border, over a hundred miles from New Orleans.
On the 29th, the first of three levees within New Orleans failed leaving most of the city under
water. It would be weeks before it would be habitable and the 150,000 residents unable to leave
the city were in the Superdome and later the New Orleans Convention Center for shelter from the
sweltering heat and stagnant waters outside (Comfort 2006; Drye 2005).
Hurricane Katrina's winds and storm surge reached the Mississippi coastline on the
afternoon of August 28, 2005, beginning a two-day path of destruction through central
Mississippi; by 10 a.m. CDT on August 29, 2005, the eye of Katrina began traveling up the
entire state, only slowing from hurricane-force winds at Meridian at seven p.m. and entering
Tennessee as a tropical storm. Many coastal towns of Mississippi (and Louisiana) had been
obliterated in a single night (Congress 2005; McClendon 2010). Hurricane-force winds reached
coastal Mississippi by two a.m. and lasted over 17 hours, spawning 11 tornadoes and a 28-foot
storm surge flooding 6-12 miles inland in places. Many, unable to evacuate, survived by
climbing to attics or rooftops, or swimming to higher buildings and trees. The worst property
damage from Katrina occurred in coastal Mississippi, where all towns flooded over 90% in
hours, and waves destroyed many historic buildings, with others gutted to the third story
(Editorial 2005b). Afterward, over 235 people died in Mississippi, and all counties in Mississippi
were declared disaster areas, 49 for full federal assistance. Post-Katrina, a regulation for the

6

placement of emergency centers and floating casinos were changed. Emergency command
centers were moved higher because all three coastal centers flooded at 30 feet above sea level
during the storm.
Tropical Storm Rita developed on September 18, 2005. The storm increased in intensity,
reaching category 2 status over the next 48 hours. Rita reached the Florida Keys on the 20th and
swung out towards the Gulf of Mexico, reaching category 5 strength as it passed through the
warm waters of the gulf. On the 23rd of September, Rita made landfall at the Texas-Louisiana
boarder at category 3 strength (Lawrimore 2005). Unlike Katrina, the number of deaths caused
by Rita was low; only seven (Knabb et al. 2006). Early evacuation measures taken by state
officials reduced the amount of lives lost to the storm. In addition, power outages and costs to
repair damages from Rita have been significantly less than Katrina. Although Rita has been
identified as the fourth most intense hurricane ever recorded, the proximity of Rita to Katrina
staved off a repeat of mistakes made one month earlier.
It is important to note that the outline above provides the barest detail of events occurring
before, during, and after the storms. Most notably, this outline, as well as many media reports
after Katrina, neglected to discuss the role of regional aid systems that were in place prior to the
storm. Nor does it discuss the local level planning efforts made by cities in the damage path. This
project rectifies that by analyzing the networks of emergency aid agreements in place before the
storm. Typically, communities have mutual aid agreements so they can borrow fire, police, and
emergency medical services in the event that their own departments are overwhelmed (Office
2007; Waugh Jr. 2006a, 2007).
In the case of Hurricane Katrina, the governors of Mississippi and Louisiana had the
Emergency Management Assistance Compact (EMAC) to aid them in mitigating the impact of

7

the storm. This compact, created in the wake of Hurricane Andrew in 1992, represents a
consortium of southern states to supplement supplies and manpower in the early stages of a
disaster. The compact serves as a mechanism for collaboration at the local, state, and federal
levels and facilitates sharing resources across and within state borders. The use of the EMAC and
its resources was significantly different between Louisiana and Mississippi, however. Later
chapters will discuss this issue in detail, but suffice it to say here that Louisiana Governor
Blanco’s use of the EMAC system was significantly less than her Mississippi counterpart,
Governor Barbour’s (Waugh Jr. 2007).
In the days leading up to Hurricane Katrina, the message to residents of the gulf region
had a subdued tone. The area had seen its share of storms and very few had caused the kinds of
problems that would be attributed to this particular one. The very fact that the mayor of New
Orleans waited until a day before landfall to issue a mandatory evacuation of the city, reflects the
nonchalance people in the region felt towards “another” hurricane. This is the main problem with
emergency and disaster planning. It is difficult to know when a disaster will outpace networks in
place. In addition, it is very difficult to make people leave their homes during a natural disaster,
as fears of looting and the inability to protect private property after a storm may outweigh the
benefits of packing up and leaving (2005; Cutter 2009; 2005a, c; Richard 2008).
So what happened? What made Katrina different from all the other storms? First, the
scope of the storm has contributed to a reduction of population throughout the Gulf, most notably
in New Orleans (Liu 2006). In addition, the entire racial composition of the Gulf has been altered
due to the numbers of people displaced by the storm. Rebuilding infrastructure and restoring
critical public services were delayed for months. New Orleans, once a city with a majority
African-American population, according to the American Community Survey, has seen that

8

African-American population decreased by 57% while the white population decreased by just
36% (Frey 2006). Issues of where and how to rebuild are still being fought at city hall in New
Orleans and across Louisiana and Mississippi. Finally, community building efforts have suffered
a major blow in the wake of this storm. Many survivors are hesitant to come back due to lack of
resources, failure of a plan laid out by local officials as to where to move back to, and a lack of
guidance from the federal government regarding what the role of FEMA and other federal
agencies will be if another storm of this magnitude hits the region.
Research Questions:
This project seeks to determine whether race and class play a part in facilitating or
inhibiting recovery after an exogenous event, in this case, Hurricanes Katrina and Rita. Because
racial diversity can act as an impediment to cooperation when citizens deliberate and form policy
(Frymer 1999; Hutchings and Valentino 2004; LeMay 2005), it could be shown that as the
number of minorities within a community increases, those communities will have more
challenges in recovery after a storm. On the other hand, the impact of racial segregation and
subsequent emancipation of African-Americans in the south may have a different effect on the
cooperation/competition theory of race than initially expected. Historically, southerners have had
a more difficult relationship with race than in other parts of the country exhibited by the adoption
of Jim Crow laws in the wake of emancipation, and resistance to change after the passage of the
Civil Rights Act of 1965(Oakes 1985, 1998; Shepard 2007). It could be that smaller,
homogenous, and less populated areas have chosen to cooperate rather than compete over scare
resources, leading to declining conflict over policy decisions. This type of cooperation could
occur in places predominantly black and rich, white and rich, black and poor or white and poor.
In effect, the research questions here pertain to whether changes in the ratio of minorities helps

9

or hinders the coordination necessary for disaster planning and recovery. Thus a heterogeneous
community, one with racial and income diversity may be less likely to recover economically.
The second question is to ascertain whether class plays a role separate from race in
recovery. If class acts as a separate variable affecting response and planning capacity, this
analysis should show that counties with lower average incomes, and fewer economic resources in
terms of taxable income, have difficulty participating in mutual aid sharing and lack the capacity
to plan for emergency relief. It is assumed that better off communities will have access to the
resources and additional personnel to aid in planning for disaster relief and should reflect a faster
recovery time after a disaster.
The third question that has gone unanswered so far is that of what recovery means. For
this project, the ability of a community to recover will be tracked by the ratio of new jobs created
after the storms. In addition, this project will look at housing, namely the number of new
construction projects permitted post-storm. Both of these variables are at the heart of any
community’s economic and social strength. Thus tracking these variables over time allows for a
better picture of the ability of these communities to persevere post-disaster. The Brookings
Institute has assessed recovery of the Gulf Region using a variety of measures, ranging from the
population returning to the area post-disaster, to the number of jobs created in the area, to the
number of schools reopened in the wake of Hurricane Katrina (Liu 2006). Other scholars have
tracked recovery by looking at the reconstruction of rental housing in New Orleans (Lubell
2006), transportation infrastructure (Weinberger 2006) or have used qualitative techniques to
uncover the emotional and psychological recovery of storm survivors (Harris-Lacewell 2008).
The questions this project seeks to answer are:
1. Does a high concentration of minority residents lessen economic recovery postdisaster?

10

2. Does a high concentration of poor residents lesson economic recovery postdisaster?
3. Does the interaction of race and class create barriers to recovery?
4. Are problems with economic recovery post-disaster policy-based or based in the
discrimination of minority groups?
Research Design and Methods:
This project is based in part on research the Brookings Institute has conducted on the city
of New Orleans in the aftermath of Hurricane Katrina (Liu 2006). Starting in 2005, Brookings
began tracking the social and economic recovery of the gulf region using the Katrina Index. That
project has focused primarily on the Greater New Orleans area, thus it makes no distinction
between other areas or states affected by the storm. Brookings focuses, as this project does, on
the following variables: Rebuilding Damaged Housing Stock, and Fiscal and Economic
Conditions.
The project identifies cities within the counties FEMA classified as eligible for public
assistance and individual aid following the hurricanes. In Louisiana, 37 parishes (counties) were
identified and in Mississippi, 49 counties were so identified. The primary independent variable
for this project is the ratio of minority-to-majority population. The secondary independent
variable of interest is the ratio of citizens living under the poverty line to those living over it
from 2003 to 2007. There is some debate regarding the desirability of using the concentration of
minorities within a geographic area or other measures of heterogeneity such as a dissimilarity
index in race research with some authors promoting the use of commonly used measures of
dissimilarity (Massey and Denton 1988; Massey et al. 1996) to those who desire a redefinition of
in vs. out group measures of dissimilarity (White et al. 2005). Dissimilarity indexes measure the
evenness with which two groups (usually races) are distributed across a geographic area (Duncan
and Duncan 1955). The index score that is the measure of dissimilarity can be interpreted as the

11

percentage of one of the two groups included in the calculation that would have to move to
different geographic areas in order to produce a completely even distribution. This measure can
also be used as a measure of income inequality (Massey et al. 1987). The percentage (i.e.,
concentration) of minority residents and those living in poverty is used in this project. Although
there is a substantial literature on the effects and causes of dissimilarity in the United States
(Baldassare 1992; Blandy 2003; Cohen 1993; Massey 1988; Myrdal et al. 1944), there also is a
substantial number of studies that use concentration of minority population as well (see
(Hutchings and Valentino 2004). Due to the fact that dissimilarity scores are based on Census
data and the availability of relatively current scores will not be available until 2011, this project
uses minority proportion instead. It should also be noted, that correcting for dissimilarity in a
geographic area in some urban policy studies does not ensure that policy outcomes or societal
outcomes improve (Galster 1987, 1996; Massey 1985); most notably, in cities with highly
integrated neighborhoods, such as New York, Chicago, and other metropolitan areas with large
ethnic populations, there are many examples of disparate policy and economic effects.
This project uses a descriptive, longitudinal panel design to collect data on each variable
from the year 2003, before the hurricanes reached land, through the year 2007 (Campbell and
Stanley 1963; Wooldridge 2002). This time-series panel design allows for an in depth look at the
conditions before the storm hit and how each county in the analysis recovered. It utilizes a
combination of census, state, and local government data sources. The cases will be stratified on
two metrics. First, counties will be divided into four groups, based on the socio-economic
composition of residents living in the area before the storm. Additionally, the concentration
(ratio) of minority and class populations will be imputed in the original continuous-level variable

12

form in other models to directly test assumptions from the cooperation and competition theories
of race and class (Leavitt 2008).
Another issue in disaster relief and recovery is the amount of capacity a community has
before a storm (Ink 2007; Marshall 1979). Schneider (1995) notes that successful governmental
response to natural disasters is dependent on the extent to which post-disaster behavior
corresponds to prior government expectation and planning. Therefore, the ability of a community
to recover may not only be tied to how race and class interact in the policymaking process, but
may also be attributed to the ability of policymakers at the state and local levels to prepare plans
and create bureaucratic norms of response that come into play once an emergency is imminent.
Sabatier’s (1986) discussion of bottom-up policy implementation provides a good
visualization of how emergency planning for disasters operates. In a bottom-up approach,
emergency response should move upwards from the local level to other levels of government.
The illustration below shows the pattern of response during a disaster:
Figure 1. Pattern of Emergency Response During Natural Disasters
(For interpretation of the references to color in this and all other figures, the reader is referred to
the electronic version of this thesis (or dissertation).
Local

State

Federal

In this scenario, local officials activate plans to handle disasters, state officials await
direction from their local counterparts, and the federal government, if needed, coordinates
outside assistance from the Red Cross, National Guards, and other governmental agencies in the
wake of a disaster. On the other hand, poor planning and emergency relief infrastructure at the
local level might require the replacement of the bottom up approach with a top down approach
where the federal government takes over the normal response process. Somewhere in the middle,
a confused response occurs leaving local, state and federal agents unsure of what actions to take

13

to alleviate the emergency (Sabatier 1986; Schneider 1995). Therefore the amount of planning
and infrastructure at the local and state level should be considered as an outside factor that could
affect the ability of a community to recover whether heterogeneous or homogenous in
composition. Since both Louisiana and Mississippi had emergency planning policies in place,
instead of including a variable for this in the longitudinal model, a content analysis of the plans
looking specifically at mentions of planning for low-income and minority populations before and
since Hurricanes Katrina and Rita is conducted. Recovery, then, in this project will be measured
by the effect of the race and class metrics on job creation and housing permitting levels after the
disasters as well as on mentions of these populations in planning documents. It is hypothesized
that those parishes and counties with less heterogeneity will recover to pre-storm job and housing
levels more quickly than those places with greater economic and income diversity/disparity.
Poverty and racial diversity are not necessarily mutually exclusive categories. For example, a
community could be primarily African-American and wealthy, conversely another community
could be primarily white and poor. Finally, a community could be a mix between predominately
one race and socioeconomic status.
Table 1.1: Types of Parishes and Counties Examined on Race and Class Dimensions

Class Dimension

Black and Wealthy
Black and Poor

Race Dimension
White and Wealthy
White and Poor

Lack of diversity, or homogeneity is the key component of the theory to follow. The
more alike residents are, the more likely policy planning can occur without conflict, leading to
the activation of emergency plans in a timely manner which will result in faster economic
recovery. There is a substantial literature, which concludes that in areas that are
socioeconomically homogenous, economic outcomes are increased in comparison to more

14

heterogeneous counterparts (Alesina and Ferrara 2000, 2005; Alesina and La Ferrara 2002;
Frank and Mark 2005; Habyarimana et al. 2006; Montalvo and Reynal-Querol 2005). Thus,
research into the economic effects of natural disasters in diverse locales is both timely and
necessary to informing the development of public policy.
Race and Class Variables:
This project will measure the minority population before, during, and after the storm. It
will also track the percent of minority residents living in each area pre-post storm and the rate of
return to the area for these groups. The minority population information comes from the US
Census Bureau’s population estimate dataset (Census 2008). Poverty estimates come from the
Small Area Income Poverty Estimates dataset also compiled by the US Census.
Fiscal and Economic Capacity Variables:
This project’s main dependent variable is the ratio of new jobs prior to and following the
storms (Census 2007). This information comes from the Local Employment Dynamics Quarterly
Workforce Indicators data which is a subsection of the Longitudinal Employer Household
Dynamics dataset compiled by the Census Bureau.
Housing Variables:
This secondary dependent variable will track the number of residential building permits
issued by each county prior to and following the storms. Information on new residential
construction is gathered from the Manufacturing, Mining and Construction Statistics data
collected by the Census Bureau.
Federal and State Funding:
This project also tracks the amount of funding given to each county in the analysis to
ascertain the effect of outside resources on recovery capacity. This information was gathered

15

through a FOIA request to Regions VI and IV for individual and public assistance funding. State
funding numbers were requested and received from each state’s office of emergency
management.
Sources of Invalidity and Secondary Data Source Issues:
As Donald Campbell and Julian Stanley (1963) note, utilizing time series designs in
social research introduces issues of internal validity. Although the effects of history are spread
over the time in a longitudinal design, history effects pose a particular problem here since the
study compares two states with very distinct historical backgrounds. External validity issues of
interactions between testing and the test group should be minimized as this study relies on
secondary data. Secondary data do, however, have their own problems such as difficulties
verifying sources of original data, age of data used, and understanding the methodology used to
gather data (McNabb 2004; Pierce 2008). Verification of variables and evaluations for each
database accessed is conducted.
Chapter Outline and Concluding Thoughts:
Chapter two discusses the literature on race and class and presents a theory of
socioeconomic diversity as it affects a geographic area after a natural disaster. Chapter three
provides the methodology used in the quantitative analyses. Chapter four looks at the state of
Louisiana, examining recovery of jobs and housing, the effect of race and class on recovery as
well as make comparisons to the state of Louisiana’s recovery efforts. Chapter five examines the
Greater New Orleans metropolitan area and makes comparisons to recovery in the rest of the
state on the same metrics. Chapter six analyzes the state of Mississippi looking at the recovery of
jobs and housing post-disaster and tracking the effects of race and class on recovery of these
dependent variables over time; and chapter seven concludes the project. Because both states have

16

historical and institutional differences, each chapter takes care to provide a look at historical
development as well as the relationship of local governments to state infrastructure.
This project sheds light on three issues: race, class, and the operations of local
government and how these variables interact, creating differing outcomes for municipalities. The
project uncovers the impact of exogenous events on the economic well being of an area postdisaster. It also explores whether areas with large numbers of minority residents and poorer
citizens rebuild differently.
The larger question of whether to rebuild some areas at all came to the forefront soon
th

after Katrina devastated the 9 Ward in New Orleans. The normative question of whether it
makes sense to invest in some areas, particularly when a pattern of destruction by natural
disasters is well documented has long been a part of policy discussions (Schneider 1995; Waugh
Jr. 2006b; Webb 2002). This project looks at whether racial and class composition affects
recovery. If this project is able to isolate these factors as an impediment to recovery, it could be
that planning efforts should be modified in communities that are smaller, poorer and have more
minorities. What we have seen so far in New Orleans is stratification along race and class lines
producing an untenable variance in who gets to return to the city and who does not. This project
takes these findings and applies the same logic to smaller municipal units to uncover whether
New Orleans is a unique case that has set its recovery trajectory apart from other cities that have
suffered massive losses in a natural disaster, or if issues of race and class retain their affect on
smaller communities.

17

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18

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26

Chapter Two: Literature Review and Theory
Introduction:
The argument presented in this project is that highly diverse areas have more difficulty
determining what issues should be on the policy agenda, resulting in uneven policy outcomes in
terms of how issues of race and class affect the recovery stage of emergency management
planning (Nemeth 1986; Nemeth et al. 1974; Park and Vargas-Ramos 2002). This chapter
presents several literatures relevant to uncovering the intersection of race, class, and their effect
on policy outcomes. It discusses the literature on how economic outcomes are affected by
socioeconomic diversity. The first literature includes a discussion of power, the concentration of
power, and how it is wielded during the policy process. The second literature discusses
municipal reform and its affect on political representation of minorities within cities and states.
Finally, the third discusses economic outcomes in the face of diversity in localities.
Table 2.1: Literature on the Effects of Minority Presence and Poverty
Effects on Minorities
Effect on Poverty
Power and Group Politics
Limited access to political
Limited access to political
agenda (Cooper 2005; Dahl
agenda (Cohen 1993a;
1961b; Mills 1956 ).
Fleischmann et al. 1992;
Robert 2003).
Municipal Reform
Declines in minority
Same findings (Cohen and
representation, limited policy
Dawson 1993; Hill and
development directed at
Leighley 1992; Leighley and
minorities (Browning et al.
Nagler 1992).
1984; Eisinger 1982; Joyce
1997; Lineberry 1977;
McClain 1993b).
Economics of Diversity
Decreased investment, stunted Same findings.
economic outputs, service
sector job dominated,
educationally limited
workforce (Alesina and
Ferrara 2000; Collier 2000;
Montalvo and Reynal-Querol
2005; Quigley 2002).

27

Table one shows how each literature tackles the effects of minority presence and poverty
on different aspects of political life. Each of these components is critical to understating the
nature of how localities plan for and recover from disasters. This diverse collection of literature
shows how diversity at various levels of government inhibit political participation, cooperation
among groups, decreases outside investment in the economy and promotes white flight, all of
which can complicate economic recovery in places affected by natural disasters. In addition,
these literatures exhibit how diversity in terms of race and class can inhibit the creation of policy
that is careful to pay attention to the needs of these groups and require special attention in terms
of policy development in the area of emergency management by policy experts.
As emergency plans act as roadmaps for mitigating and recovering from an event, the
crux of this project is the attempt to determine how the atmosphere surrounding the creation of
such policies interacts with the socioeconomic character of a place, and as result affects the
economic outputs after a disaster has passed. This chapter will conclude with a discussion of
theory and hypotheses to be tested empirically in the quantitative and qualitative chapters that
follow.
Race, Class and Local Government Institutions
The earliest research on local governance and policy discusses the issue of pluralism and
elitism as they affect the policymaking process (Abney and Lauth 1985; Cooper 2005; Davis et
al. 1997; Mills 1956 ; Truman 1951). This research also debates the importance of power
(Cooper 2005; Dahl 1961a; Hunter 1953; Kettl 1993; Moe 2005; Ordeshook and Schwartz
1987). The question, “who gets what and when,” remains central in most political research. The
connection to public policy lies in who maintains political power within cities, counties, states,
and nations. The majority has the power to dictate the direction of policy and if the minority is

28

not cohesive or large enough to make a significant dent in the desires of the majority, public
policy will be slanted toward majority desires. Therefore, participation in the political process, be
it in the area of presenting alternatives to policy problems or voting for candidates that represent
needs, is integral to who is in power and what the policy ramifications of that political choice
will be on residents in a locale (Nelson 1979; Oliver 2000; Pelissero 2003). How people vote can
be highly dependent on the local government institutions a particular municipality adopts. If a
minority population grows within a locality, it is inferred that changes in political preferences
and policies will result.
Municipal reforms in the United States transformed the way in which the electorate as a
whole participated in the political process. Prior to the reform movement in the United States,
many cities had developed a strong, party-based system that controlled nominations to public
office using a network of party officials and material rewards to garner votes during election
time (Alford 1968; Bobo 1990; Carmines 1995; Pelissero 2003). Minorities played an important
part in the development of political machines because of the ability of party bosses to give
material rewards to new voters in return for support for party candidates. This mobilizing
function allowed the machine to retain its power and at the same time enhanced the political
participation of those connected to parties. The effects of the political machine on AfricanAmerican political participation was inherently limited due to the institutional arrangements in
place that made it impossible for blacks to vote, however. Only in the 1960’s and after the
passage of the Voting Rights Act did political science and society at large see the emergence of a
new voting bloc. The enfranchisement of African-Americans coupled with the decline of the
political machine due to the development of federal programs which provided many of the
material benefits machine bosses used to provide to minorities, ushered in a new flurry of

29

political studies on minority representation (Guterbock 1983; Leighley 1999; Lopez and Pantoja
2004; McKenzie 2004; Mladenka 1989).
As the African-American population growth in urban areas remained unchanged in the
1980s and 1990s, other ethnic and minority groups grew in proportion (Shrestha 2006). Scholars
began to study the emergence of coalitional politics, and this line of research quickly subsumed
previous studies on single racial groups. As it were, research on minorities has tended to focus on
the interaction between a majority group (namely whites) and blacks in the political process. As
the Latino population grew in the United States, scholars noticed that these newly emerging
groups had differing political preferences and could not simply be lumped together when looking
at minority/majority dynamics. In the beginning, Latinos often voted with blacks as an ethnic
bloc. When immigration and English-only policies gained traction in the political debate, the
policy preferences of these two groups diverged (McClain 1993a; McClain 1993c). Policy
differences have produced conflict, particularly in southern states where the Latino population
has grown, and in New Orleans, the growth of the Latino population in the wake of the storm has
changed not only the racial makeup of the city, but the political landscape as well (Saulny 2006).
As recovery began in both Louisiana and Mississippi, the influx of Hispanics seeking jobs in
construction and tourism changed the political landscape within both urban and rural areas. And
although emergency planning is not directly concerned with the changing composition of races
within an area, the types of skills sets that these new groups bring are integral to the quality of
recovery experienced in certain areas.
Race, Class and Public Policy Development:
Early pluralist scholars noted that in the United States, ethnic integration was
unavoidable in a pluralistic democracy (Dahl 1961a). As minority groups gained their

30

independence, public policies changed to meet the demands of these newly enfranchised groups.
Pluralists determined that in a representative democracy, in order to have policy preferences
realized, ethnic groups should compete through electoral contests and economic development in
order to be assimilated into the mainstream of urban political life. Critiques of this literature
point out that this theory on the whole fails to account for the political reality of economic
inequality in the US. Additionally, those who occupy the lower classes within the United States
typically are disorganized in creating the necessary group power to affect change in the political
process, resulting in policies that may not necessarily benefit them (Dwyre et al. 1994; Pippen et
al. 2002).
Schattschneider’s research on the mobilization of bias also notes that economic equality
is a key factor in the realization of some group’s policy preferences over others (1960). To him,
conflict is the central fact in a free society. Further, it is the contagiousness of conflict that
characterizes the American system of government. On the face of it, this social theory of conflict
seems very simplistic; who wins the fight is a product of who is involved. Schattscheider’s
theory has an important caveat about the confidence and size of the minority, however; if they
feel they will be penalized for their actions, they won’t fight (Schattschneider 1960, 8 emphasis
added). His research on the interest group community in Washington D.C. led him to conclude
that the middle and upper classes were better organized and more moneyed. Therefore, this
“middle and upper class bias” of interest groups limits the amount of representation of lower
class and minority groups. Furthermore, if groups become more inclusive of the minority, the
power of these groups would be mitigated because, “ if everybody gets into the act, the unique
advantages shared by the pressure group would be destroyed…”(Schattschneider 1960, 34).

31

This connection is relevant to the problems exhibited in Hurricane Katrina. The
emergency planning policies in place neglected to consider the special needs of minorities who
did not have access to cars, the time it would take to evacuate the larger urban areas completely,
and the need for housing temporarily displaced persons, as well as for the sick (Lubell 2006).
The policies surrounding emergency planning and subsequent recovery reflect an upper class
bias as those who had access to cars, as well as things like homeowner’s insurance policies on
their property were able to evacuate in a much more orderly fashion and recover their homes
more quickly than those left behind. This problem illustrates the “mobilization of bias” in action.
Race vs. Class
Poverty in the local and state contexts also affects the ability of citizens to access the
political system. Banfield and Wilson’s City Politics was one of the first efforts to explore the
effects of municipal reforms (Banfield and Wilson 1963). Their political ethos theory pitted
private-regarding immigrant working class values, which favored sustaining traditional political
machines, against public-regarding white Anglo Protestant middle-class values, which supported
municipal reform. Thus, lower-class workers had very different policy preferences than whitecollar counterparts, but institutional reforms made it difficult for poorer residents to participate in
the political process. This theory also left little role for African-Americans in the municipal
reform movement since, at the time, African Americans could not vote. Since the adoption of
municipal reforms, scholars examining black voter turnout in elections which have no blacks
running for office often found low rates of participation by blacks and other minority groups
(Oliver 2000; Verba 1993; Wolfinger 1965; Wood 2002). Those in the lower classes also have
dismal voting participation rates (Avery and Peffley 2005; Leighley and Nagler 1992;
Rosenstone 1982).

32

Scholars of public policy have also found significant differences in economic impacts of
municipal reforms. Elaine Sharp (1991) found that unreformed cities provide minorities more
access to local decision makers, resulting in greater attention to minority economic development
needs. However, James Clingermayer (1994) found that the development policies of unreformed
cities had the strongest impact on the formation and implementation of zoning policies that were
restrictive towards minority groups.
Cohen and Dawson, in their exploration of the effects of neighborhood poverty, find that
the devastation of the urban core as first described by Banfield and Wilson (1963), has
propagated a decline in black voting, particularly in areas that are well below the poverty level
(Cohen 1993a). Decline affects blacks in particular, because after the massive influx of freed
slaves during emancipation, the city core continues to be populated by African-Americans and
poor whites who do not have the economic resources to move from the city center (Banfield and
Wilson 1963). This situation decreases the effectiveness of institutions that promote political
participation in these areas (community groups, party offices, churches that support political
candidates), thus reducing the political efficacy of residents.
J. Eric Oliver and Tali Mendelberg (2000a; 1999, 2000, 2001) have studied in depth the
effects of socioeconomic status, city size and segregation on political participation. In
considering the social composition of American localities, Oliver and Mendelberg convincingly
show the impacts of socioeconomic status and residential segregation on the dynamics of
democratic government. Their findings indicate that individual political behavior varies
systematically across economic contexts. They do not find a positive relationship between
affluence and participation they do, however; find that political participation is lower in cities
with higher median household incomes and in those more economically homogenous.

33

Mendleberg and Oleske find similar results in an exploration of the political
communication patterns in various California neighborhoods. They found that “subordinate
groups cannot rely on deliberation to secure equality” and therefore ensure political outcomes
that are favorable to them, because deliberation must have equality and community as
preconditions to succeed and in the areas they explored, these preconditions were not found
(Mendelberg and Oleske 2000). Most importantly, once individual-level characteristics were
taken into account, participation rates for contacting, attending meetings and voting diminished
ten percent from economically homogenous upper-middle class cities to economically diverse
middle-class cities. Therefore, while homogenous municipalities participate less, they also seem
to have fewer things to debate.
These findings lend additional credence to racial participation studies by showing that
competition over resources matters to who participates and what the policy results will be. In the
case of disaster recovery, particularly when outside federal assistance is in play, we may see
poorer communities benefit more because they receive more money. This effect may be limited
by the heterogeneity of residents in a locale, however, or the influx of dollars may mitigate
competition strategies between residents. In terms of disaster planning, a lack of participation by
minority or poor groups during the planning process inhibits the ability of an area to consider the
needs of these populations, resulting in retarded economic recovery after a natural disaster.
Race, Class and Economic Outcomes:
The literature discussed above has dealt with the political ramifications of diversity on
policy outcomes, voting participation and participation. There is some literature that deals with
the effects of minority and poverty population on the economic outputs of a locale. This aspect

34

of the effects of diversity is critical to the theory developed here as emergency management
policies are connected to returning economic normalcy to an area once a disaster has passed.
Alesina and La Ferrara (2005) survey the literature on the economics of diversity and find
the production of public goods in ethnically and racially fragmented societies (studied at the
village, city, state and country levels) are lower than in places where racial and ethnic
homogeneity is present. Additional work by Alesina and LaFerrara (2000; 2002) determine that
trust and social capital are critical to ameliorating issues of public good production. Looking at
economic outputs and demographic fragmentation, the authors find that private investment is
lower in places that have high fragmentation.
Paul Collier finds that diversity is tied to lower overall economic growth in places where
political rights are limited (Collier 2000). Given the South’s history of suppressing the political
participation of minorities in various ways, and the current position of both Louisiana and
Mississippi at the lower ends of economic investment compared to other states in the union, the
conclusions Colliers presented are worth mentioning here. Finally, Ferraro and Cummings
(2007) find the economic behavior in societies is directly related to differences in ethnicity, race
and religion. Research into the effects of income stratification has yielded similar results (Moller
et al. 2009; Tomaskovic-Devey and Roscigno 1996). Thus, higher fragmentation on racial, ethnic
and/or class lines yields lower economic investment and outputs at the state, city and country
level (Ottaviano and Peri 2005).
Theory Construction:
Public policy scholar Elinor Ostrom (1990) highlights the importance of informal and
formal groups coming together to determine the decision rules to be used in protecting scare
resources. The political power theory discussed by early political scientists Hunter (1953), Dahl

35

(1961b) and Mills (1956 ) and later, with scholars studying suburban political behavior (Oliver
2001), highlight the importance of people coalescing around a specific policy need. From this,
the theory presented here takes the premise that in communities of homogeneity (i.e., those
where the majority of the population have similar economic or racial characteristics)
policymaking is easier because people with similar socioeconomic situations desire similar
policy changes. Thus it would follow that in communities that have a more mixed socioeconomic
makeup, policy creation is more challenging and, as a result, policy outputs are decreased as
well. Additionally, communities of color and poverty often lack cohesion around policy desires
because their policy needs are more diverse (Cohen 1993b; Robert 2003). The end result of
socioeconomic and racial diversity is a policy atmosphere that is combative resulting in policy
outcomes that are disparate among groups.
Theory:
The theoretical perspective presented here has three parts. The first concerns the racial
composition of a locale. Following the assumptions of the theory of competition and cooperation
as it pertains to minority representation in local government, this perspective focuses on racial
dynamics at the local level to predict recovery outcomes. To this end this project looks at the
ratio of minority presence as an impediment (or promoter) of recovery efforts.
1. H1: Increased ratios of minority concentration decrease the amount of economic
recovery post disaster.
2. H2: Lower class concentration decreases the amount of economic recovery post
disaster
3. H3: Increased ratios of both minority and lower class concentration decrease the
amount of economic recovery post disaster.
4. H3a: Differing Racial and lower class concentrations combinations may act as
tipping points, which, once a critical threshold is passed, decrease economic
recovery.
5. H4: Increased funding mitigates racial and economic disparities in terms of
economic recovery post-disaster
36

Consistent with the scare resources or “commons” theories of policy, it is believed that race
and class both act to decrease the likelihood that citizens will be able to cooperate when disaster
strikes. Secondly, the class component of this theory predicts that class has a separate and
distinct effect on recovery (Hamilton 1972; Hill and Leighley 1992). Class variables may also
work in tandem with race to exacerbate barriers to community recovery. Finally, the racial
makeup and class structure of a community may interact causing differing outcomes in recovery
efforts resulting in what is termed Racialized Response Theory. Thus, this project will also
attempt to test whether areas that are diverse will experience greater difficulty recovering
economically post-disaster. The question to be answered by the dissertation is how race and class
composition of a locale affect both the policy process as well as the recovery process postdisaster. It is hypothesized that at the outset, there is competition over the nature of public policy,
that race and lower class play a significant role in inhibiting areas with high concentrations of
minority and poor residents to plan accordingly for disasters as well as to recover economically
from disasters once the threat has passed, resulting in decreased economic activity in the
recovery stage of disaster relief.
The distinction of the racialized response theory from traditional theories of competition
and cooperation is that instead of thinking of race as a characteristic that set New Orleans apart
from other natural disasters of this type, I propose that high concentrations of minority and lower
class within any local area make it difficult for important needs to be met in terms of economic
recovery. This occurs in several ways. At the policy development level, an increase in minority
population may result in more groups at the table during the policy formation stage, resulting in
specific mentions of how these groups are to be evacuated during an emergency if cooperative
strategies are present within the community. This is not an assurance that these groups’ needs are

37

met, but it may allow for policy planning and emergency response to be more sensitive to group
needs. However, if competition is present at the outset before disasters strike, then the policy
planning process is more at risk of leaving out these populations resulting in differing economic
outcomes in the recovery phase due to the “closed circle of deliberation” that dominates
policymaking in areas that are diverse (Mendelberg and Oleske 2000; Oliver and Mendelberg
2000b).
At the economic level, we may see that the institutionalized effects of race are
exacerbated in communities with high competition. Thus, if emergency planning makes no
mention of how minority and poor populations should be treated during and after storms (i.e., in
terms of evacuation, temporary housing, job placement services, etc.), the previous race and class
composition of the community may be a critical part of why economic outcomes post-disaster in
heterogeneous areas are less positive than in homogenous counterparts. And as the population of
minorities grows in a locale, divergent demands for policy grow. This affect will permeate all
types of policy, including disaster relief. I seek to determine if the presence of minorities in an
area restricts economic recovery, particularly in the face of massive outside investment.
There may be some question regarding whether this project merely shows the effects
found only show how race and class exacerbate recovery much like they inhibit economic
development in times of peace as opposed to crises. While institutional history and the effect of
past racism may be present in the statistical results presented, the planning for response to a
disaster is not, as it is purported to be, a neutral activity undertaken by political and policy
experts. Both Mississippi and Louisiana have high rates of minorities living within their borders
as well as high numbers of residents living in extreme poverty. Neglecting to include these
populations throughout the policy making process or in formal policy documents will have direct

38

effects on how locales are able to regain their footing economically. Planning is a purposeful
process undertaken in communities and states to itemize priorities for an area’s recovery after an
emergency. This project sheds light on whether race and class are simply insurmountable
obstacles that impede economic recovery in the face of outside investment. If this is case, it
could be that nothing federal, state or local officials can do in the face of emergencies like these.
The question remains how best to identify and measure how this racialized effect plays
out in the recovery process. This research investigates the number of jobs created in an area once
a disaster has hit, as well as housing production and population shifts. Better planning and
cooperation before a disaster should result in a shorter, more temporary effect on the
demographic character of a parish and it is hypothesized that jobs and housing should return to
more homogenous areas faster than racially mixed areas. This is due to the lack of conflict over
what needs to be included in a local response plan and the ability of residents to evacuate and
return more quickly (i.e., richer residents have means to leave and return). The reason for this
difference in return to normalcy is tied to differences in socioeconomic composition,
communication and inclusion in the planning process, and organization of recovery in these
places that make it easier for homogenous areas to return to previous states faster than those that
are more heterogeneous.
There are a few ways that racial and class diversity could affect the policy process and
subsequent economic recovery of areas following a natural disaster. The Racialized Response
Theory surmises that diversity leads to conflict, conflict leads to incoherent policies and, as a
result, recovery is disjointed. Another perspective would theorize that diversity (along racial and
class lines) creates an atmosphere of general malaise at the local level, and in places with high
instances of minorities or poor there is consistent lack of government and social resources,

39

business investment and coherent policy planning (Ford 1994; McFarlane 1999). As a result,
economic recovery would be suspect. Finally, an institutional racism perspective would posit that
diversity leads to a targeted lack of investment in certain areas due to discrimination or decision
rules that affect economic recovery in distinctive ways for certain parts of the population, namely
those in minority groups and those who are impoverished (Bullock and Rodgers 1976; Smith
1997). This project tests the first perspective and theorizes that it is diversity itself that causes
disjoint in planning, leading to disjointed recovery in areas that are highly diverse. As it is not
possible to directly observe the policy process itself pre-Katrina, this project looks specifically at
the effect of diversity on economic indicators over time. The results should shed light on whether
this effect is systemic and will illuminate how the policy process itself can be informed to take
into account how diversity might need to be managed for future disasters.
This project connects the literatures on power, policy, race and class to explore how
extraordinary events affect the ability of places to regain stability. It tests empirically the effects
race and class has on economic recovery as well as explores the dynamics of policy planning in
diverse socioeconomic situations. This project makes a unique contribution to the literature on
policy planning as well as the various literatures on minority competition and cooperation,
intergroup conflict, and the political power studies that once dominated the literature in political
science. The following methodology chapter describes at the development of variables, discusses
the rationale for case choice as well as describe the methodology employed to analyze the data
collected.

40

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46

Chapter Three: Methodology
Introduction:
Before proceeding with a discussion of emergency management planning at the local and
state level and the analysis of how race and class affect the economic recovery of Louisiana and
Mississippi, a discussion of the panel analysis methodology, and variable creation, as well as the
qualitative analysis procedure is needed. This chapter provides a detailed discussion of how the
cases for analysis were chosen, data sources, how each variable used in the quantitative chapters
was created, the methodology employed to analyze the data, as well as the qualitative content
analysis procedure used to analyze state emergency management plans. To begin, this chapter
will discuss the justification for choosing the cases included in the project and the decision to
utilize panel analysis.
Case Selection:
The cases in this project include all of the counties and parishes (Louisiana’s etymology
uses “parishes” to delineate county governments) in the states of Louisiana and Mississippi that
were classified by FEMA as eligible for both Individual and Public Assistance post-Katrina.
Mississippi was selected as a case due to the fact that it had a large number of counties affected
by the hurricane with varying degrees of class and racial diversity. Additionally, since a project
of this nature has not yet been conducted, the opportunity to explore the dynamics of urban
issues, emergency management, and race and class in a state not typically the focus of posthurricane analysis provides a unique opportunity to contribute to the literature. Also, because of
the heavy focus on New Orleans we don’t know much about the rest of Louisiana or any other
state that suffered damage at the hands of Hurricane Katrina. Tracking two years before the
storm and two years after the storm ascertains the effect race and class had on each locale in the
47

dataset. This provides a baseline of the economic conditions prior to the storm and allows for an
examination of how quickly a locale was able to return to a baseline state post-disaster.
Additionally, it should be noted here that this project provides a snapshot of the immediate
pre/post socioeconomic changes in the states affected.
Data and Data Sources:
Counties are used as the unit of analysis due to the lack of available data for both
Louisiana and Mississippi’s smaller cities. The main dependent variable—the number of job
starts, is defined by the census as employees in newly created jobs that were not employed by the
same employer in a previous quarter. This information is divided by the total number of jobs in
each place minus the total number of job separations (i.e., firings, leaves of absence or
downsized positions) for each year and each location in the dataset.
New Hires =

New Jobs Added
Total Jobs- Job Separations

This information is provided by the Quarterly Workforce Indicators tracked by the
Longitudinal Employer Household Dynamic dataset for the Census. The New Jobs Added
portion of the New Hires equation is taken from the U.S. Census Local Employment Dynamics
Quarterly Workforce Indicators dataset. For each entity (county or parish) included in the data
set, this number reflects the “estimated number of workers who started a new job (Abowd
2011).” Specifically, this number reflects the total number of workers that, while they worked for
an employer in the specified quarter, were not employed by that same employer in any of the
previous year. Next, the Total Jobs component of the dependent variable reflects the beginning
of the quarter estimate of the total number of jobs in the economy on the first day of the
reference quarter (Abowd 2011). Thus a worker is counted in this equation if he or she has
48

positive earnings in t-1 and t. Finally, the Job Separation portion of the dependent variable is
calculated as the estimated number of workers who had a job for at least a full quarter and then
the job ended (Abowd 2006). Thus a worker is defined as separated if he or she had positive
earnings in t,t-1, but no earnings in t+1. Each component of the formula above answers a
different question about the economy. Separations highlight which economies are losing stable
workers, while New Hires highlight which economies are hiring new workers. Total
Employment is a reflection of which economies at the beginning of each quarter performing the
best, who is being employed by various industries and are there discernable trends among local
economies (i.e., are industries growing or shrinking at similar rates). Since the dataset collects
information at the county and parish level, information about individual industries or businesses
within each area is not included in the data but is available through the Quarterly Workforce
Industries data tools.
Another ratio level variable is created for the other dependent variable—the number of
new housing permits issued by each parish/counties during the same period. The data for new
housing permits come from the Building Permit Estimates dataset as reported by the Census. The
New Housing Permits are reported to the Mining, Manufacturing, and Construction Statistics
division of the U.S. Census by local permit-issuing jurisdictions by a building or zoning permit.
Not all areas of the country require a building or zoning permit. Thus, these statistics only
represent those areas that do require a permit (2009). Total Housing Stock reflects the annual
housing estimates reported by the Population Division of the U.S. Census.
New Homes = New Housing Permits Issued
Total Housing Stock

49

The main independent variables for this study are the yearly percentage of minority
residents in a county and the percentage of residents living under the poverty level in each
county for the years under study. This information was collected using Population Estimates
(Census 2008b) and the Small Area Income Population Estimates data (Census 2008a) for the
counties and parishes under examination. Other variables of interest are the amount of federal
funds approved by FEMA as well as the amount of damage assessed by FEMA mitigation teams
post-disaster.
A note on the percentage of minority residents’ variable: the United States Census
categorized those of Hispanic origin as a separate ethnic group. So respondents answering race
and ethnicity questions may choose Hispanic or non-Hispanic origin and a race category. The
census uses six categories to delineate race: White Alone, Black Alone, American Indian and/or
Alaskan Native alone, Asian Alone, Native Hawaiian and/or Other Pacific Islander Alone, or two
or more races. For the purposes of this study, minority population includes the respondents who
were of Hispanic Origin and any race, as well as individuals who were non-Hispanic in origin,
but non-white. It should be also noted that the percentage of residents of Hispanic origin, as well
as other racial groups aside from African-Americans is very low in the states of Louisiana and
Mississippi until recently, (Brunsma 2007) African-Americans were the dominant racial group.
FEMA funds are the total amount of funds approved for each county under examination.
This information came from the office of Research and Special Projects housed in the Louisiana
Recovery Authority (Office of Community Development 2008), and Mississippi’s Emergency
Management Agency Public Information Office (MEMA) as well as through a Freedom of
Information Request to FEMA. The dollar amounts include Individual Assistance (IA) and
Public Assistance (PA) funds dedicated to recovery in each county. FEMA’s Individuals and
50

Households Program (IHP) assists homeowners and renters with reconstruction after a declared
disaster (Federal Emergency Management Agency 2008). Funds help applicants pay for
temporary housing, repair, replacement, disaster related medical services, burial costs, and
moving expenses for example. FEMA’s Public Assistance (PA) Grant Program directs funding
to states, local governments, tribal areas, and certain non-profit organizations to assist in helping
them respond during and after a declared disaster. For the purpose of this study, the percentage
of total funds received by each parish/county was recorded.
The Louisiana Road Home program was designed as a separate, state-level effort to
provide compensation to homeowners affected by Hurricanes Katrina and Rita. It offers up to
$150,000 in compensation to homeowners for losses sustained after the hurricanes. It also
provides compensation in the form of loans and grants to owners of rental properties to rebuild.
Finally the program compiled a list of participating building professionals so that homeowners
can access guidance on rebuilding new homes to post-Katrina building standards (Home 2007).
The amount of funds contributed under the Road Home program was recorded for each county.
Again, the percentage of total funding received for each parish was used.
Mississippi had a similar program, however instead of providing funds for all affected
counties, Mississippi aimed their rebuilding efforts in the devastated Gulfport area. The Housing
Assistance Program, or HAP, provides homeowners with money to rebuild homes that sustained
damage during the storm. The program had two phases. Phase one was directed at homeowners
with homeowner’s insurance and phase two was directed at homeowners with a household
income beneath 120% AMI with Hurricane Katrina storm surge damage. Like the Road Home
program, HAP offered up to 150,000 dollars in compensation for hurricane victims (Authority
2005).
51

The Office of the Federal Coordinator for Gulf Coast Rebuilding at the Department of
Homeland Security, in cooperation with FEMA, the Small Business Administration (SBA) and
the Department of Housing and Urban Development (HUD), compiled data to assess the extent
of damage (type, tenure, insurance status and housing type) after Hurricanes Katrina, Rita and
Wilma (Department of Homeland Security 2006). The report lists the percentage of occupied
housing units with damage (severe, major or minor). This information was recorded for all
counties under examination. Table one below lists all the variables used in the project as well as
there source.
Table 3.1 Variables and Sources
Variable
New Hires
New Home Construction
Percentage of Minority Residents to White
Residents
Percentage of Residents Living Under Poverty
Line to Those Living Above It
% of Individual Assistance Received
% of Public Assistance Received
% of State Program Funding Issued
Katrina/Rita
Year

Source
Local Employment and Household Dynamics
(LEHD) dataset
Manufacturing, Mining, and Construction
Statistics (MMCS) dataset
Population Estimates dataset
Small Area Income and Poverty Estimates
(SAIPE)
Federal Emergency Management Agency
Federal Emergency Management Agency
Road Home Program, Housing Assistance
Program.
Dummy Variable coded 0 prior to storms and 1
during and following storms.
Coded 1 for year of interest, 0 for all other
years.

Variable Creation:
This section discusses how each variable was calculated for use in the analyses. The U.S.
Census defines New Hires as the total number of ascensions that were not employed by an
employer during the previous four quarters. In plainer language, this number tracks the number
of new hires working for an employer in the current quarter. This allows a researcher to
52

determine which industries are hiring new workers, which geographic areas are doing the most
hiring, or who (old or young) is being hired for work. The new hires variable, which tracks the
jobs created during the duration of the study, was created by taking the quarterly QWI New
Hires numbers and dividing them by the quarterly Total Employment numbers. Total
employment is calculated by the Census by tracking the beginning of quarter employment total
of workers who were employed by the same employer in both the current and previous quarter.
Thus, new hires that were employed in quarter one who retained their job in quarter two would
be rolled into the total employment number for that particular county in the following quarter.
The result is a percentage or ratio of new hires to total employment that changes for each
year in the data set. Years were used in the project because data for housing construction are only
collected for certain areas on a yearly basis, thus making annual instead of quarterly comparisons
between job creation and housing construction necessary. Ratio level variables allow for data to
be arranged in such a matter that each county has a unique number attributed to job or housing
growth throughout each year. Ratio level variables are ordered, mutually exclusive and have an
absolute zero point that is meaningful for analysis (Johnson et al. 2008).
The Manufacturing, Mining, and Construction Statistics department of the US Census
Bureau tracks monthly and annual new home permits by county for all 50 states. A new home
permit is defined as a new privately-owned residential housing unit authorized by building
permits. Other items that could be analyzed using these data include: number of buildings, units,
and construction costs from new privately-owned residential building permits issued. These data
are updated monthly. Most of the permit-issuing jurisdictions are municipalities; the remainder
are county offices, townships or unincorporated towns. In some cases, counties are large enough
to be tracked monthly and others only yearly.
53

The Katrina variable is a dummy variable. For the years in the panel, Katrina is coded 0
for the years prior to the storm making land (2003 and 2004). For years affected, the variable is
coded 1 (2005, 2006 and 2007). In some models year dummy variables were added to ascertain
the magnitude of difference of the race and/or class effect on economic development for each
year since the storm had passed. The imputation of data for panel analysis requires that each year
be represented in the panel. Therefore, in the analysis of the effect of each year on local job
creation and housing, a dummy is created to separate that year’s observation from the other years
in the dataset.
Panel Analysis Methodology:
Before presenting the results of the panel analysis, this section discusses the rationale to
use the panel model to estimate changes in employment and housing. Time series analysis (of
which panel analysis is a part) permits the analysis of a consistent set of variables with data
collected on units of analysis over multiple periods. This approach has many uses, for example,
the analysis of performance between different operating units (i.e., stores) over time. Within the
social sciences, panel analysis enables researchers to analyze, among other things, political
behavior and organizations over time. Since the data collected create a natural panel by which a
deeper exploration of how the dynamics of minority and class diversity affect economic output,
panel analysis techniques were used. In all cases, panel analysis is used to analyze the temporal
effects of the independent and dependent variables. Because the variables include demographic
changes from before, during and after the storm, the model controls for any significant changes
in racial or class composition.

54

Random vs. Fixed Effects Models:
Panel data analysis includes both temporal and spatial dimensions. The spatial component
refers to a set of cross-sectional units. These could be firms, counties, states, or even individuals.
The temporal dimension is the periodic observations of a set of variables describing the crosssectional units over a particular time span. Fixed effects models explore the relations between
predictor and outcomes variables within an entity (in this case counties). Thus, a fixed effect
model assumes that there are individual level characteristics that bias predictor outcomes and
should be controlled. The fixed effect model removes the impact of these individual (or timeinvariant) level characteristics to assess the predictor variables net effect.
(1) Fixed Effects: Y = " X + # + u
it
1 it
i it
Where: !it is the unknown intercept for each entity
!
Yit is the dependent variable where i represent the entity and t is equal to time

Xit is an independent variable
"1 is the coefficient for the independent variable
uit is the error term where i represents the entity and t is equal to time (Gujarati 1995;
Stock 2003)
In a random effects model, it is assumed that the variation across entities (in this case counties) is
random and uncorrelated with the independent variables included in the model (Greene 2003).
Thus, the alpha term (which is an unobservable effect of each individual entity) is treated as an
unknown parameter in a fixed effect model and a random parameter in the random effect model
(Hsiao 2003). The following equation models the random effect relationship:
55

(2) Random Effects: Yit= !Xit+ "+ uit+ #it
Where: !it is a random intercept for each entity (i) at each t (time),
Yit is the dependent variable where i represent the entity and t is equal to time
Xit is an independent variable
" is the coefficient for the first independent variable
uit is the between-entity error term where i represent the entity and t is equal to time, and
#it is the within-entity error term where i represent the entity and t is equal to time
(Stock 2003)
Since it is assumed that the variation across each entity (county) is unrelated to the independent
variables included in the model, using random effects is appropriate (Greene 2003).
Additionally, random effects models have the distinct advantage of allowing for time-invariant
variables to be included among the regressors (Yaffee 2003). Since this project is interested in
ascertaining the effect of federal and state funding on job and housing creation post-disaster, it is
necessary to use random effects estimation. Finally, using a random effects model allows a
researcher to make inferences about entities not included in the sample.
The generally accepted rule of thumb for choosing between the fixed and random effect
models is to use the fixed effect model when you want to control for omitted variables that differ
between cases but are constant over time. Random effects are typically used when some omitted
variables may be constant over time and vary between entities or may be fixed between cases but
vary over time. Essentially, random effects allow a researcher to control for both types of

56

omitted variable bias by estimating the weighted average of the fixed and between effects
estimator (see formula 2 above).
One way to determine which model is most useful is by running a Hausman test. A
Hausman test checks a more efficient model against a less efficient but consistent model to make
sure that the more efficient model also gives consistent results (Stock 2003; Wooldridge 2002).
The Hausman test tests the null hypothesis that the coefficients produced by the random effects
estimator are the same as the ones produced by the fixed effects model. If they are insignificant
with a chi-square larger than .05, then one should use the random effects model; if not, one
should use the fixed effects model. A Hausman test was run for each model presented in
subsequent chapters to determine if a fixed effects or random effects model was indeed the
correct model to be used.
Another way of thinking about fixed versus random effects models is that a fixed-effects
analysis can only support inference about the group of measurements (subjects, states, etc.) you
actually have - the actual subject pool you looked at. A random-effects analysis, by contrast,
allows you to infer something about the population from which you drew the sample. If the
effect size in each subject relative to the variance between your subjects is large enough, you can
guess (given a large enough sample size) that your population exhibits that effect. Thus when
random effects models are reported in the quantitative chapters, some generalizations to other
counties affected by Hurricane Katrina that are not included in this study can be (tentatively)
made (Wooldridge 2002).
Panel Analysis vs. Linear Regression:
Panel analysis allows a researcher to conduct a longitudinal data analysis. While a
researcher could simply produce a linear regression model and use a cluster function to group
57

cases by a time period, panel analysis allows a researcher to control for some types of omitted
variable bias, something linear regression, even when using a cluster function, cannot (Yaffee
2003). Further, because panel analysis has various functions (i.e., fixed effects, random effects
and between effects), a researcher has more control over determining which model fit is best for
a particular panel of data. Additionally, panel analysis allows for the examination of balanced
and unbalanced panels (i.e., variables may be present in one year but not another, or data may be
available for one case in a certain period but not for another), something linear regression cannot
do (Greene 2003).

58

BIBLIOGRAPHY

59

Bibliography
2009. New Residential Construction Definitions. Ed. U.s. Census Bureau. Washington D.C.
Abowd, John and Bryce Stephens and Lars Vilhuber. 2006. Detailed QWI Definitions.
Washington D.C.: United States Census.
Abowd, John and Bryce Stephens and Lars Vilhuber. 2011. LED: Quarterly Workforce
Indicators 101. Washington D.C.: United States Census.
Authority, Mississippi Development. 2005. Mississppi Home Help- Hurricane Katrina
Homeowner Grants.
Brunsma, David J. Stevn Picou David Overfelt. 2007. The Sociology of Katrina: Perspectives on
a Modern Catastrophe.
Census, U.S. 2008a. Small Area Income and Poverty Estimates. Washington D.C.: U.S. Census.
Census, U.S. 2008b. State Population Projections. U.S. Census Bureau.
Department of Homeland Security, FEMA. 2006. Current Housing Unit Damage Estimates:
Hurricanes Katrina, Rita and Wilma. Washington D.C.
Federal Emergency Management Agency, FEMA. 2008. DIsaster Assistance Available from
FEMA. Ed. Department of Homeland Security: Federal Emergency Management Agency.
Greene, William H. 2003. Econometric analysis Greene, William H., 1951-: Greene, William H.,
1951-.
Gujarati, Damodar N. 1995. Basic econometrics. New York: McGraw-Hill.
Home, Road. 2007. The Road Home Program. Baton Rouge, LA.
Hsiao, Cheng. 2003. Analysis of panel data. Cambridge ; New York: Cambridge University
Press.
60

Johnson, Janet Buttolph, H. T. Reynolds, and Jason D. Mycoff. 2008. Political science research
methods. Washington, D.C.: CQ Press.
MEMA. 2010. Mississippi Emergency Management Agency.
Office of Community Development, Louisiana Recovery Authority. 2008. Parish Quick Facts
Report Baton Rouge, LA: Disaster Recovery Unit, a Division of Administration.
Stock, James H. and Mark W. Watson. 2003. Introduction to Econometrics: Addison Wesley.
Wooldridge, Jeffrey M. 2002. Econometric Analysis of Cross Section and Panel Data.
Cambridge, MA: MIT Press.
Yaffee, Robert. 2003. A Primer for Panel Analysis. New York University.

61

Chapter Four: Louisiana
Introduction:
The state of Louisiana has an economic, political and social history unlike any other state
in the union. There is a rich diversity of people, including Native Americans, as well as settlers
from France, Spain, England, Germany, Acadia, the West Indies, Africa, Ireland and Italy (S. o.
Louisiana, 2009, pp. 584-586). French colonists initially settled Louisiana and were soon joined
by Spanish and Acadian explorers. Later, French aristocrats fleeing slave revolts in the West
Indies or the French Revolution joined the mix. As part of this French colonial legacy, counties
in Louisiana are called parishes. In addition the judicial system in Louisiana has its roots in
Napoleonic law, not English common law which is practiced in the rest of the 49 states in the
Union (Court, 2009).
The state has been governed under ten different flags beginning in 1541 with de Soto’s
claim of the region for Spain. La Salle, a French explorer later claimed it for France and for
many years Louisiana was a subject of Great Britain, Napoleonic France, and the Lone Star flag
of West Florida. During the Civil War, Louisiana became an independent republic before joining
the Confederacy (S. o. Louisiana, 2009). In 1803, the United States had a stake in Louisiana due
to its importance to increasing global trade and for the security of the growing south and midwest regions of America. President Thomas Jefferson negotiated the Louisiana Purchase from
Napoleon I in 1803.
Louisiana’s early economy was marked by the importance of the region as a trading and
financial center. Although the Civil War shattered the plantation economy that created much of
Louisiana’s wealth in the 1700 to1800’s, the state continued to be a powerful agricultural center

62

after the war. The land was fertile and Louisiana soon had a significant place in the growing US
economy as the leading producer of sugar, cotton, indigo, and later sulphur, oil and natural gas.
Social Institutions and History:
During the Antebellum period, state government authorities granted large pieces of land,
called concessions, to influential white men. Most colonists at the time received smaller
holdings, called habitations and as time went by habitations became smaller. When sugar and
cotton became profitable in the 1800s, many concession owners sought to consolidate habitations
into large plantations (Museum, 2009). To run these plantations, white landowners relied on
slaves to maintain the crops. Cotton was important for Louisiana’s economy and in 1860;
Louisiana produced nearly one-third of all cotton exported from the United States overseas.
Sugar was also an important crop and during the antebellum period, nearly all sugar consumed in
the United States came from Louisiana.
Slaves made up slightly less than half of Louisiana’s population in 1850. Nearly threefifths of these slaves lived on plantations outside New Orleans (Museum, 2009). In 1811, the
largest slave revolt in the history of Louisiana broke out. During the uprising, two white men and
sixty-six slaves were killed and several plantations were destroyed by fire. Slaves, who were
caught, were tried and killed as a warning to other potential rebel slaves. Slavery continued in
Louisiana until the end of the Civil War. During the civil war, Louisiana played an important
role in providing munitions and other goods to Confederate forces. In 1862, the Union occupied
New Orleans and remained in the area for the duration of the war (Museum, 2009).
Louisiana played an important role during Reconstruction; the state was the only area in
the Deep South where Union authorities implemented Reconstructionist policies during the Civil
War. Since Union forces occupied New Orleans, southern Louisiana served as the experimental

63

ground for early integration attempts. Lincoln’s Emancipation Proclamation did not apply to
Union-held territories, thus slavery continued in the 13 parishes occupied by the Union.
Lincoln’s plan to readmit states to the union by selecting delegates had a liberating effect on
slaves in the state; delegates to the constitutional convention agreed to abolish slavery without
compensation to slave masters, although they did not extend voting rights to black men.
The constitution adopted in 1864 extended voting rights to black men who fought for the
Union, owned property, or were literate (Museum, 2009). The Freedman’s Bureau, an agency
created by the United States Congress tried to solve issues pertaining to the ending of slavery,
but was unsuccessful in affecting change in postwar economic and social relations. Louisiana
and other southern states enacted Black Codes in 1865, a pre-cursor to Jim Crow laws, to curtail
black electoral participation.
Radical Republicans, a loose faction within the Republican Party, who opposed Lincoln’s
terms for reinstating southern states after the Civil War, led the riot of 1866 in the city of New
Orleans. Federal troops were called in, but arrived after much of the carnage had abated. Listed
as one of the bloodiest riots during the Reconstruction era, it claimed 37 lives (34 black and 3
white) and wounded 146. Radical Reconstruction in Louisiana was very contentious with
segregationists and white supremacists on one side, and Reconstructionist supporters on the
other. The Louisiana Constitution of 1868 marked a huge victory for Reconstructionist causes as
it extended voting rights to black males, established integrated schools and guaranteed equal
access to public accommodations. The document did little to end racial discrimination, however,
and the color line remained.

64

Economic Institutions and History:
With the end of the Civil War and slavery, the economic institutions of the state changed
dramatically. Ex-slaves moved in droves from plantations to the cities of Baton Rouge and New
Orleans seeking their fortunes. These large increases in population placed a strain on the
economic development of cities as well as on the provision of social services. Plantations were
broken up and agricultural advances automated the harvesting of many crops; sharecropping
replaced many of the grand plantations of old. In the city, the building of a railroad offset the
decline of steamboat traffic. In addition, the widening of the Mississippi River into the Gulf
allowed larger ships to utilize New Orleans as a port. Repeated floods posed problems and levee
construction increased during this time. In 1927, the federal government took over the
construction of the levee system. Finally, the discovery of natural gas and oil in the 1900s
spurred a new economy and industries capitalizing on the production of these new goods carried
th

the state into the 20 century (S. o. Louisiana, 2009).
Local Government Institutions:
Parishes are the territorial subdivisions that manage the functions of local government
within the state of Louisiana; the 64 parishes operate much like counties in other states. The
main functions attributed to parishes range from bridge construction and maintenance to
promoting economic development and tourism (P. J. A. o. Louisiana, 2009). Louisiana is unique
in that its parishes are governed in most cases by policy juries. The policy jury form of
government is similar to the traditional commissioner form of government. The jury performs
legislative functions such as enacting ordinances, setting policy, and establishing programs. It
also serves an administrative function as juries prepare the budget, hire and fire personnel and

65

negotiate contracts. In 1974, the state granted home rule authority to parishes and municipalities
and since then, some parishes have adopted the police jury form. In addition, parish government
in the state include President-Council charters, Council-Administrator, Parish-Commission forms
of government as well as City-Parish consolidated forms. Four of Louisiana’s eight metropolitan
areas have adopted this latter form, including New Orleans.
The Levee System:
Since this project deals with the management of natural disasters, a discussion of the
complexity of the levee management system is needed. As stated earlier, Louisiana has suffered
its share of hurricanes, floods and tropical storms since its discovery by the Spanish in the 1500s
(Roth, 2003). In 1717, the first man-made levee system was started by Bienville, New Orleans’
founder and first mayor. Before this time, reinforcing natural levees that existed was the solution
to assuaging storms. The original levees only reached three feet in height and the original levee
system was completed in 1727 (Addison, 2000). After this time, private interests expanded the
system. The unorganized levee system was turned over to the Army Corps of Engineers in the
1850s. Several Congressional studies pointed to issues of settlement and expansion of the city as
major impediments to levee efficiency. The Army Corps of Engineers overlooked many of these
findings and the city continued to grow. As time progressed and the city expanded, a new
problem emerged.
Lake Pontchartrain is an estuary that leads to the Gulf of Mexico. In the 1920s, the
Industrial Canal, a project funded by the state government, was built, and connected the lake to
th

the Mississippi River. The canal passes through the 9 ward of the city. Like other canals
throughout the city, it is managed by a lock system. During hurricanes, storm surges can build up
in the lake and floodwaters often spill into the city as evidenced not only by Hurricane Katrina,

66

but also by several hurricanes since its construction. In 1947 when Hurricane Betsy
overwhelmed the levee system, the Army Corps of Engineers undertook several projects to raise
levees encircling the city and outlying parishes. But due to cost concerns, the levees were only
built to withstand a category three storm.
The problems with the levee system in Louisiana have many facets. First, wetland
degradation due to the vast expansion of the city of New Orleans post-Civil War has contributed
to issues of flooding. In addition, the canalization system implemented by the ACE spurred by
the burgeoning shipping industry in the 1950s increases the likelihood of diversion of
floodwaters to places in the city and lake instead of the Gulf. Finally, the siphoning of sediment
rich water into the gulf further erodes the natural protection provided by the surrounding
landscape (Addison, 2000).
The Army Corps of Engineers stated that Hurricane Katrina was simply too massive, and
that the swells created by it exceeded the walls causing much of the damage. Research has
indicated that the levees constructed in the 1950s and 60s had flawed design features, inadequate
construction and poor maintenance leading to the breeches that damaged much of the city
(Grunwald, 2005). Essentially, the growth of the city, compromises in planning due to
bureaucratic intervention and lack of funding resulted in much of the flooding that occurred in
the city of New Orleans and the surrounding parishes in the wake of Katrina.
Levee Boards and Levee Districts:
Before the topic of local governmental institutions involved in hurricane planning and
protection is completed, the levee board organizational structure should be discussed. Levee
boards were created by the Louisiana state legislature in 1890 (Louisiana, 2007). Initially the
board was created to protect the city of New Orleans from flooding, however other parish

67

governments in the state soon adopted similar levee boards. Before the creation of the board
system, communities on the Mississippi River were responsible for creating and monitoring their
own levee systems. In New Orleans, the levee board was responsible for the maintenance of over
126 miles of levee systems, floodwalls and other related structures.
The Orleans Levee District coordinates with the Army Corps of Engineers and also
shares costs. In addition to the original responsibilities granted to the boards in 1890, in 1928 the
state legislature authorized districts to construct, dedicate, operate and maintain public parks,
beaches, marinas and other similar facilities (District, 2009). The operation of such boards has
not come without scrutiny however. In the wake of Hurricane Katrina, the Orleans Levee District
was investigated for malfeasance in relation to the maintenance of the levees under its purview.
Further investigations revealed that levee boards often were mismanaged, highly political, and
disliked by residents. The Orleans board admitted to accounting irregularities leading to the
cessation of local board operations and approval by the state legislature for a state run board
system in 2007 (Anthony L. Vogt, 2002; Hudson, 2005; Konigsmark, 2005; Nossiter, 2005,
2006).
Louisiana Pre-Katrina:
th

In 2005, per capita personal income in Louisiana ranked 50 in the United States
(Analysis, 2005). The pupil-teacher ratio in elementary and secondary schools lagged behind the
national average by 1%. National expenditure for education in 2004 was $8,711, in Louisiana;
the figure was $7,669 per student, over a thousand dollars less per pupil. Average graduation
rates in the state were 69.4% compared to the national average of 74.3% (N. C. f. E. Statistics,
2005). Finally, state unemployment in 2004 was 5.7 percent compared to the national average of

68

5.5. In 2005, the unemployment rate increased to 7.1 compared to the nation’s average of 5.1%
(B. o. L. Statistics, 2009).
Louisiana’s economic development has a long history starting with early successes in
agri-business and recently marred attempts to rejuvenate natural gas and oil production. The state
is a study in contrasts. It is split between two Federal Reserve Districts. Additionally, the
economic heartbeat of the state (New Orleans and Baton Rouge) is located south of I-10. These
are the areas hardest hit by hurricanes (Wall, 2008). Each time a hurricane hits the state, the
heavily exported crops grown in the south are affected. In addition, since Louisiana is home to
10% of U.S. oil reserves and 25% of natural gas supplies, a storm like Katrina can and does shut
down these operations for long periods.
In the 1970s, much of the state’s revenue from the burgeoning oil industry was used to
improve schools and highways (""Louisiana"," 2000). When world oil prices dropped in the
1980s, the Louisiana economy suffered. Since the 1980s, Louisiana has had some of the highest
unemployment figures for women and the highest percentages of individuals and families living
in poverty (""Louisiana"," 2000). In the 1990s, Louisiana attempted to revitalize the economy by
legalizing riverboat and casino gambling. In 1992, the state legislature voted to approve
gambling in New Orleans (Staff, 1992). This change spurred the service industry and
employment in that sector increased throughout the 90s. Shipping and chemicals also played a
large part in the economic sustainability of the state throughout the 90s. Despite increases in
certain employment sectors, however, Louisiana’s household income distribution has always
been skewed (""Louisiana"," 2000, pp. 584-586). Currently, Louisiana suffers from a “brain
drain” and although nearly 50,000 of the 250,000 residents displaced by the storm have returned,

69

it is yet to be seen if Louisiana can equalize economic benefits throughout its citizenry (Liu,
2006; Wall, 2008).
Politically, before the storm the state was marked by distinct disconnects between the
hedonistic southern part of the state (New Orleans) and the conservative north (Baton Rouge).
The contentious relationship between Governor Kathleen Blanco and New Orleans mayor Ray
Nagin was exposed during the hurricane. Unfortunately, intergovernmental relationships did not
improve as rebuilding began. As New Orleans grew as a political and economic powerhouse, the
politics of the city often clashed with the politics of the overall state.
The story of Louisiana politics usually begins with the tale of Huey Long, who in 1928
made Louisiana the nation’s first welfare state (Laborde, 1985). He ran a campaign that railed
against the bourgeois and upper classes and won the support of the economically disadvantaged
in large numbers. Although Long did not serve a full term, he continued many of the pork barrel
projects that garnered so much support.
From the 1940s through 1960, the Long family remained a large part of Louisiana
politics. Politics during this time swung between anti-Long and pro-Long types of policies and
governing styles. Earl Long, Huey’s brother continued much of the spending programs his
brother championed while serving as governor, thus creating a political culture of intense loyalty
and cronyism unmatched in other states. Interestingly, the voting coalition that elected Long
three times was largely black and poor. Where other legislators won throughout the South during
this period running on platforms of racial hate and segregation, Earl Long ran campaigns that
were colorblind.
Modern Louisiana politics is quite different from the politics of the Long brothers. As
machines and cronyism declined in state politics, the rise of state and national media outlets

70

changed the nature of politicking. In 1979 Louisiana elected its first Republican governor since
the year 1877, David C. Treen (Laborde, 1985). Although Treen capitalized on the
suburbanization of the southern part of the state, the margin that elected him was narrow.
Interestingly, Treen grew up as a Democrat, and his gubernatorial style was one of two party
politics. His lieutenant governor, Robert Freeman, was a Democrat, as were many of the
members of his cabinet (Rees III, 1979). Following the election of Treen, Louisiana has seen
governors that are both Democratic and Republican. Republicans such as Murphy Foster (19962004) ran against gambling and for the business lobby. Democrats such as Edwin Edwards
(1984-1988 and 1992-1996) ran campaigns much like the Long’s, capitalizing on the growing
strength of the black population and urbanization of the southern half of Louisiana.
Those that govern Louisiana do so in a state that is highly fragmented; there is a true
north-south division, with Baptists and Protestants living in the north, and the south being more
French-Catholic (Rees III, 1979). Additionally, the geography of the land is very different, with
cotton fields to the north and swamps and bayous in the south. Thus, the politics of the state
remain divided on cultural and geographic lines.
Demographics of Panel:
Before delving into the panel analysis of the data, an overview of the basic demographics
of the cases under study is presented here. Table 1 summarizes the following variables:
Individual Assistance and Public Assistance funds received during Katrina and Rita, Road Home
funds received from the state of Louisiana and the percentage of housing damage in each parish.

71

Table 4.1: Summary of Funding and Occupied Damage

Mean
Std. Dev.

Katrina IA
Funds (by
1,000)
136113.9
486183.7

Katrina
PA Funds
(by 1,000)
82989.97
244182.7

Min
Max

0
66.812
2760094.33 1277477

Rita IA
Funds (by
1,000)
14023.45
38667.64

Rita PA
Funds (by
1,000)
7349.83
22107.94

Road
Home
Funds
41219.15
13437.47

%
Damage
(rounded)
36.65%
23.20%

0
233324

0
108982.1

25819.72
80590.85

6.4%
90.2%

Sources: FEMA, Louisiana Governor’s Office of Homeland Security and Emergency
Preparedness, Mississippi Emergency Management Agency.
The above table details the dollars dedicated to the state of Louisiana as of June of 2007.
FEMA funding devoted to the region under the Individual and Households Assistance (IHP)
program has two funding classifications: Other Needs Assistance (ONA) and Housing
Assistance (HA). IHP eligibility is based on residence within a declared disaster area. Moreover,
to be considered, the affected home must be the individual's primary residence and it must be
located in the disaster area designated for Individual Assistance. Additionally, within each
household applying for assistance, an individual or a pre-disaster member of the household must
be a United States citizen, a non-citizen national, or a qualified alien.
According to the Federal Emergency Management Agency’s website, these three funding
streams allow for residents affected by disasters to qualify for funding to pay for temporary
housing, housing repair and replacement and for permanent housing construction. In addition,
applicants may also be eligible to receive money for disaster related medical bills, funeral costs,
clothing and other household items as well as moving and storage expenses related to a disaster
under the ONA classification (Agency, 2009). Five counties: Allen, Evangeline, Sabine, St.
Landry and Vernon parishes received no funding although each parish sent applications for
assistance. Orleans parish received the most funding, $2,760,094,329.58, with 278,499

72

applications coming from the IHP program, 276,349 individuals receiving assistance under the
HA program and 111,622 receiving funding under the ONA program.
Table 4.2 details the proportion of minority residents living within all affected counties
over the period under examination. Orleans parish, home of New Orleans had (and continues to
have) the highest concentration of minorities and residents living under the poverty line as
reported by the Census. Livingston parish, part of the Baton Rouge metropolitan statistical area,
is located approximately 62 miles from Orleans parish, has (and continues to have) the lowest
concentration of minorities in the panel.
Table 4.2: Minority and Class Proportions by Year
Minority Proportion (in
Class Proportion (in percentages)
percentages)
2003
Mean
30.66
17.90
Standard Deviation 14.40
3.68
Min 5.30
10.50
Max 70.40
25.50
2004
Mean
30.76
18.92
Standard Deviation 14.37
3.92
Min 5.30
11.2
Max 70.40
27.0
2005
Mean
30.84
19.77
Standard Deviation 14.33
4.75
Min 5.4
11.0
Max 70.4
30.50
2006
Mean
31.21
20.13
Standard Deviation 14.23
4.74
Min 4.90
9.90
Max 64.00
30.30
2007
Mean
31.40
19.17
Standard Deviation 14.19
4.53
Min 5.10
10.80
Max 65.40
30.50
Year

As far as poverty is concerned, St. Tammany and Ascension parishes had the lowest
mean concentration of residents living under the poverty line in the dataset. Evangeline and St.
Landry top the list with mean concentrations of 27.16% and 27.24% respectively. Evangeline
73

parish is located 173 miles from New Orleans and is in the center of the lower half of the state.
St. Landry parish is located directly east of Evangeline parish and its county seat, Opelousas, is
approximately 153 east of New Orleans. Orleans parish follows close behind with an average
concentration of 24.7% of residents living below the poverty line for the period under
observation.
Table 4.3 details the five year average of: the proportion of minority residents to whites,
the concentration of residents living under poverty, percentage of IA and PA funds received out
of the total sent to the state, percentage of housing damage and percentage of Road Home funds
received by each parish in the year 2006. Interestingly, there seems to be little relationship
between the amount of damage reported and money received from various sources, save Orleans
parish. This could be due to differences in the population and housing density of other parishes
affected by the storm, however a closer look at insurance claims, home ownership rates, and
existing infrastructure among parishes might reveal more information on why funding was so
dramatically different within the state.

74

Parish
Name

Acadia
Allen
Ascension
Assumption
Beauregard
Calcasieu
Cameron
East Baton
Rouge
East
Feliciana
Evangeline
Iberia
Iberville
Jefferson
Davis
Lafayette
Lafourche
Livingston
Orleans
Plaquemines
Point
Coupee
Sabine
St. Bernard
St. Charles
St. Helena
St. James
St. John the
Baptist
St. Landry
St. Martin
St. Mary
St.
Tammany
Tangipahoa
Terrebonne
Vermillion

Table 4.3: Summary Statistics of the state of Louisiana
Minority
Class
Housing
FEMA IA FEMA PA
Ratio (in
Ratio (in
Damage
Funds (out Funds (out
%)
%)
(in %)
of 100 %) of 100%)
19.5
27.36
21.64
32.94
15.3
26.12
5.2
46.1

21.92
21.64
11.74
20.2
26.02
17.04
14.04
18.68

25.2
44.9
15.8
24.8
51.0
64.4
90.2
11.0

.0003
0
.0018
.0011
0
.0003
0
.0071

.0001
0
.0009
.0003
.0004
.0006
.0001
.012

Road
Home
Funds
(out of
100%
.0012
.0019
.0008
.0008
.0036
.0543
.0118
.0008

46.0

21.62

17.2

.0006

.0005

.0001

30.42
34.72
50.78
30.29
18.72

27.16
21.44
22.06
15.4
18.9

13.1
25.7
21.0
53.3
50.3

0
.0006
.001
.2221
0

.0001
.0002
.0005
.0807
0

.0002
.0054
.0003
.1573
.0034

27.22
16.68
6.04
68.12
28.8
38.34

16.2
16.2
12.54
24.7
16.32
21.94

29.1
29.1
21.9
71.5
80.0
11.2

.0039
.0039
.0032
.5445
.0243
.0004

.006
.006
.0013
.416
.0805
.0002

.0041
.0041
.0011
.4728
.0197
.0001

25.48
11.76
28.5
53.16
50.32
49.26

18.94
16.86
12.78
22.1
17.32
16.64

16.4
80.6
49.5
45.4
35.2
46.3

0
.0699
.0066
.0007
.0015
.0048

.0002
.2363
.0077
.0003
.0007
.0011

.0001
.1207
.0049
.0012
.0015
.0009

43.12
33.08
35.76
13.4

27.24
19.5
22.1
10.76

15.7
14.9
24
70.5

0
.0001
.001
.0791

.0003
.0001
.0004
.106

.0003
.0036
.0865
.004

30.02
24.66
17.22

22.82
17.88
19.48

44.4
31.6
38.8

.0112
.0047
.0001

.0055
.0015
.0001

.0074
.0139
.01

75

Table 4.3 Cont,d
Vernon
Washington
West Baton
Rouge
West
Feliciana

19.68
32.32
37.06

16.6
25.26
16.56

24.3
67.7
14.1

0
.0086
.0003

.0001
.0353
.0003

.0004
.0045
.0001

50.96

20.92

8.5

.0001

.0001

0

Figure 4.1: Race and Class Distribution by Parish

Figure 4.1 above shows the variation of race and class proportions for all parishes
included in the dataset. Table 4 below details the main dependent variables of interest for all
parishes; the ratio of new job starts during the period under observation and the ratio of new

76

housing construction permits to overall existing housing within each parish issued during the
same period.
Year
2003

Mean
Standard Deviation
Min
Max
2004
Mean
Standard Deviation
Min
Max
2005
Mean
Standard Deviation
Min
Max
2006
Mean
Standard Deviation
Min
Max
2007
Mean
Standard Deviation
Min
Max

Table 4.4: Job Starts and Housing Permits
Ratio of New Hires (in
Ratio of New Housing Permits
percentages)
(in percentages)
19.15
.011
3.67
.009
11.20
0
28.50
.04
18.46
.011
3.54
.010
11.90
0
26.80
.04
21.79
.012
4.26
.011
11.90
0
26.80
.040
24.37
.015
4.65
.013
15.30
0
32.50
.05
22.01
.01
3.62
.01
15.70
0
32.9
.03

The ratio of new job hires to the overall job situation of the parishes under examination
fluctuated very little over the period. Looking at the average percentage of jobs created in the
year directly following Katrina and Rita, there was an increase in hires of 2.58%. The parishes
that fared best in the aftermath of the hurricane were Cameron, St. Tammany, Tangipahoa, and
Vernon. These four parishes had vast differences in the amount of damage suffered from Katrina,
with FEMA/HUD reporting 90.2% severe damage reported in Cameron parish to just 44.4%
severe damage in Tangipahoa (Research, 2006).

77

Theoretical Impact:
As mentioned in the preceding chapter, natural disasters usually involve a disruption to
the local economy. In the cases of Katrina and Rita, the economic impact was higher than usual,
causing extreme damage to housing stock and building infrastructure as well as affecting the
health of the human infrastructure needed to restart the economy once each area received the all
clear. Evacuation numbers from Hurricane Katrina totaled over 1 million residents over the age
of 16 (Kosanovich, 2006). For residents who fled the storm, the length of stay in out-of-state
areas varied from short to long term, and in some cases was permanent. Decision makers charged
with economic recovery need to consider how to handle future disasters, thus they require
information on how the in and out-migration of populations throughout each parish affects
economic recovery.
In addition to these economic considerations, an examination of the changing dynamics
of race and class within each parish also needs to be explored. A substantial body of research has
investigated the consequences of various disasters in the United States on labor market
outcomes, primarily unemployment and employment ((Ewing, 2005; Webb, 2002). These studies
generally find that the negative effects of natural disasters are short lived, with recovery levels of
employment often outpacing pre-disaster levels. The short-term effects of Katrina on
employment were described in the August 2006 Monthly Labor Review. Findings showed that
employment declined in the affected states and counties but varied significantly by region,
industry, and evacuation rates. Louisiana, in particular, had the most potential for labor market
effects, as 17% of the state’s employment was in the FEMA designated damage zone. In
comparison, Mississippi’s FEMA designated zone only contained 5% of the state’s employment.

78

We now turn to the empirical testing of hypotheses developed in chapter two. The focus
of the analysis here is on the effect of Katrina and Rita on the production of new jobs and new
homes in each parish in FEMA designated areas. In the analysis that follows, a random effects
panel model is used to determine labor market outcomes. The first model tests the hypothesis for
new hires in each parish and includes the following variables: ratio of minorities living in each
county over the time period, the ratio of residents living under the poverty line for each parish,
per capita income for each parish, total population of each county as well as dummy variables to
capture the effect of time before, during and after the storm. The second model tests the
hypothesis that race and class have an effect on new housing permits in the dataset. Finally, a
third model tests the hypothesis that funding has a mitigating effect on economic recovery. In
this model we would expect the coefficients for race and class to be positive or not significant
indicating that an influx of funding helped communities to overcome socioeconomic disparities
post-disaster.
1. H1: Increased ratios of minority concentration decrease the amount of economic
recovery post disaster.
2. H2: Lower class concentration decreases the amount of economic recovery post
disaster
3. H3: Increased ratios of both minority and lower class concentration decrease the
amount of economic recovery post disaster.
4. H4: Increased funding mitigates racialized effects in terms of economic recovery.
Panel Results:
In table 4.5, the dependent variable is the ratio of yearly new hires in each parish in the
dataset. The quarterly data were totaled for each year in the panel to be consistent with the
housing data collected from other census sources. Also in the model is a variable that has the
ratio of minorities living within each parish (Minority Proportion). Other explanatory variables
included in the model are a variable calculating the number of residents living in poverty for

79

each year of the dataset (Class Proportion). Class and poverty estimates were derived from the
Small Area Income and Poverty Estimates (SAIPE) also collected by the Census (Census, 2008).
Finally, dummy variables for years 2004 through 2007 are included to determine if there is a
time effect in the model. Year 2003 is removed to act as a reference category in the model so that
problems of collinearity are avoided. The second model (Table 4.6) estimates the multiplicative
effect of race and class on job creation. The formula below is the mathematical representation of
the theoretical implications:
1) Yit = "X it + .... + " k X kit + # + $it + %it
-or2) New Hires= Minority Proportion+ Class Proportion+ Minority*Class Proportion+ 2004+ 2005
!
(Year of Katrina and Rita)+ 2006+ 2007
Where Y is the number of new jobs created in each parish for each year of the dataset,
!Xit…!Xkit are the coefficients for the indepedent variables for each parish (i) for each year
(t) in the dataset,
where " is the random intercept for each parish (i) at each year (t) in the dataset,
where # is the between parish (i) error term for each year (t) in the dataset,
and where $ is the within parish (i) error term for each year (t) in the dataset.

80

Table 4.5:
Effect of Poverty and Race on New Hires
Variables

New Hires Model

Minority Proportion

-.004**
(.002)

Class Proportion

-.009**
(.004)

Minority x Class Proportion

.0001
(.0001)

2004

-.004
(.008)

2005

.069***
(.009)

2006

.109***
(.010)

2007

.064***
(.007)

Constant

.469***
(.066)

No. of observations
185
No. of groups
37
2
1
R (overall)
.4510
Rho= .63599024
2
Prob > chi = 0.7136 (Hausman test)
2

Prob > chi = 0.0000 (Breusch-Pagan Lagrange Multiplier test (BPLM))

1

Overall: The average R-squared from the between and within mean-deviated regression, which
is equivalent to the ordinary r-squared from running OLS on the transformed data.

81

Interpreting the coefficients in random effects models is difficult because they include
both within and between entity effects as illustrated by the formula above. In the case of time
series, cross-sectional data the coefficient can be interpreted as the average effect of X over Y
when X changes across both time and entity by one unit. In table 6 we see that the proportion of
minority residents (H1) within each parish has a small, yet significant and negative effect on a
one-unit change in job production within each parish in each year in the dataset. The individual
year effect illustrates that the passage of time post-Katrina has a strongly positive effect on each
unit of job production for each time period in the dataset.
Class also has a small, significant and negative effect on job production verifying
hypothesis two. Interestingly, there is no evidence of a multiplicative effect of race and class on
the production of new jobs throughout the panel time period, thus hypothesis three is not
confirmed. The hurricane has a strong, positive affect on job creation during the years 2005,
2006 and 2007. The overall r-squared for the new hires model is .45, illustrating that the
variables used to estimate this model explain 45 percent of the variation in the dependent
variable. The Hausman test examines the null hypothesis that the unique error terms (ui) are
correlated with the regressors (as they are in the fixed effect model). In this case the value .7136
is greater than .05, thus using a random effects model is appropriate. Finally, the Breusch-Pagan
Lagrange multiplier test tests the assumption that variances across all entities in the panel are
zero, meaning there are no significant differences across units. To reject the null hypothesis that
the random effects model is appropriate, the result of this test must be less than .05, since the
threshold is .0000, we reject the null hypothesis that random effects is not appropriate and
conclude that there is significant differences across the parishes in the data.

82

Table 4.6:
Effect of Poverty and Race on New Home Permitting
Variables

New Home Permits (Random)

New Home Permits (Fixed)

Minority Proportion

-.0006***
(.0002)

-.001
(.001)

Class Proportion

-.0011**
(.0005)

.0005
(.001)

Minority x Class Proportion

.00002**
(.00001)

-1.22e-06
(.00002)

2004

.0009
(.001)

.0001
(.0011)

2005

.002
(.001)

.0002
(.002)

2006

.005***
(.001)

.003*
(.002)

2007

.0006
(.001)

.0000
(.002)

Constant

.036***
(.010)

.0333
(.040)

No. of observations
No. of groups
2
2
R (overall)
2

Prob > chi =
2

185
37
.2747

185
37
2
3
R (within)
2

.0117 (Hausman test)

Prob> chi =
2

Prob > chi = .0000 (BPLM test)

.1388
.0117 (Hausman test)

Prob> chi = .0000 (Wald test)

2

Overall: The average R-squared of the between and within mean-deviated regression, which is
equivalent to the ordinary r-squared from running OLS on the transformed data.
3
Within: Reflects the amount of variance explained by X after having taken out the fixed effect
of the unobservable variables that affect each entity in the dataset.
83

In terms of new housing, the proportion of minority residents again is significant and
negative, the expected direction based on earlier hypotheses (H1). Class also has a negative and
significant effect (-.011), on each unit of housing production in the panel (H2). Race and class do
not have an effect on housing production and the sign of the coefficient is in the wrong direction
(H3). The yearly effect of time is strong and positive, but only in the year 2006. The overall rsquared value for the new homes model is .27 (using random effects), illustrating that the
variables used to estimate this model are not accounting for as much of the variation in the
dependent variable as is the case for job creation.
The Hausman test for the new home permitting model indicated that using a fixed effect
model was more appropriate and the second column shows the coefficients of the same variables.
A Wald test for heteroskedasticity confirmed that heteroskedicity was present indicating that
robust standard errors should be used for this model. The r-squared value using fixed effects is
.13 indicating that the variables hypothesized to have an effect on new home permitting postdisaster have very little explanatory effect on the dependent variable. One variable that is thought
to be missing from the new homes model is rental data from each parish. Unfortunately, the
census only collects these data from a sample of parishes and the data on rental rates at the time
of the analysis were 5 years old. It could be possible that rental data as it becomes more readily
available might help to provide more explanatory power in the model. However, due to the fact
that thess data are not available for the time period under examination, nor are available for all
parishes included in the dataset, they cannot be used here.

84

The sample was then divided into four categories marking the amount of racial and class
disparity to measure evidence of a tipping point (H3a). To create this variable, the cases were
4

split by the concentration of poverty and minority presence into four groups.

Class (Income)
Wealthy

Poor

Table 4.7: Race and Class Characteristics of Louisiana Parishes
Race
Black
White
Examples include:
Assumption, Jefferson, St.
James, and St. John the
Baptist

Examples include:
Plaquemines, Terrebonne, La
Fourche, Vernon, and St.
Charles

Examples include:
Examples include: Acadia,
Tangipahoa, Orleans,
Beauregard, Vermillion, and
Iberville, and St. Martin
Sabine
Four dummy variables delineating a parish’s socioeconomic composition were added to

the database. Table 4.7 above shows the parishes that would fall under each classification
throughout the panel. Table 4.8 shows the correlation table between the newly created variables
and the original, untransformed dependent variables. The untransformed race and class
proportion variables are moderately correlated with each other at .69 while the relationship
between the newly created variables to the original untransformed variable range from a high of
.66 between the High Black High Poor parishes to Class Proportion to a low of -.62 between the
Low Black High Rich category and untransformed Minority Proportion variable.

4

The minority proportion variable has a range from a minimum of 4.9% to 70.4%, class
proportion ranges from 9.9% to 30.5%. To create this variable all parishes that had averages
above the mean were categorized as high, and those below the mean were grouped as low for
each category. Example: Parishes with a minority proportion above 30.9 and a class proportion
above 19.1 are classified as High Minority/High Poor.
85

Table 4.8: Correlation Table of Race and Class Variables
Minority
Proportion
Minority
Proportion
Class
Proportion
High Black
High Rich
High Black
High Poor
Low Black
High Poor
Low Black
High Rich

Class
Proportion

High Black
High Rich

High Black
High Poor

Low Black
High Poor

Low
Black
High
Rich

1.00
0.6995*

1.00

0.3221*

-0.2141*

1.00

0.5864*

0.6614*

-0.3012*

1.00

-0.2649*

0.2595*

-0.1561*

-0.2875*

1.00

-0.6282*

-0.6816*

-0.3119*

-0.5744*

-0.2978*

1.00

Note: * Significant and the .05 level.
Then a model (table 4.9) to estimate the separate socioeconomic effect of each type of
community was run. The following equations illustration the model specifications in as linear
regression form.
(3) New Hires= Minority Proportion +Class Proportion+ Race x Class Proportion+
2004+ 2005+ 2006+2007+ Low Minority*High Poor+ High Minority*High Poor+ High
Minority*Low Poor
(4) New Homes= Minority Proportion+ Class Proportion+ Race x Class Proportion+
2004+ 2005+ 2006+2007+ Low Minority*High Poor+ High Minority*High Poor+ High
Minority*Low Poor

86

Table 4.9: Effect of Socioeconomic Categorization on New Hires and Housing
Variables

New Hires Model

New Homes Model

Minority Proportion

-.005**
(.002)

-.001
(.001)

Class Proportion

-.008**
(.004)

5

.0006
(.0014)

Minority x Class Proportion .0001
(.000)

-2.89e-06
(.000)

2004

-.003
(.008)

.0005
(.0011)

2005

.069***
(.008)

.0001
(.002)

2006

.109***
(.010)

.0033*
(.002)

2007

.063***
(.008)

-.00007
(.002)

Low Minority*High Poor

-.0001
(.017)

-.001
(.010)

High Minority* High Poor

-.002
(.023)

.002
(.002)

High Minority* Low Poor

.005
(.019)

.031
(.002)

Constant

.472***
(.069)

.031
(.041)

No. of observations
No. of groups
2
R (overall)

185
37
.4549

185
37
.1444

2

Prob > chi =
2

0.7338 (Hausman test)

.0099 (Hausman test)

Prob> chi =

.0000 (Wald test)

2

Prob > chi = 0.0000 (BPLM test)

5

2

Prob> chi =

Fixed effect model.
87

Notes: Numbers in parenthesis are robust standard errors.* significant at 10%, ** significant at
5%, ***significant at 1%
Both models estimate a negative effect of racial diversity on new hires and housing
permits in the parishes during the time period under examination. In the new jobs model, both
the proportion of minorities and poor residents has a negative and significant effect on job
creation. An increase of .005 minority proportion in a parish results in a decrease of one unit of
new hiring activity. An increase of .008 in poverty concentration results in a decrease of one unit
of job creation. The passage of time again has a strong, positive affect on economic activity;
however categorizing the parishes by socioeconomic differences has no effect what so ever on
economic activity (H3a). The overall r-squared value for the new hires model is .45, which
reflects a moderate explanatory power of explaining the variance in job creation during this time
period. The second model estimating the effects of the independent variables on new home
permitting perform poorly. None of the variables that were hypothesized to have an effect on
home permitting reached statistical significance.
To ascertain the effect of funding on economic activity throughout the panel, two other
models were run to assess its ability to mitigate the affects race and class in the panel. Because
outside funding was only recorded as a total by FEMA and GOSHEP, and not in yearly
distributions to each parish, using a linear regression model instead of a panel regression is
appropriate here. The formulas below reflect the linear regression specification for each model.
(5) New Hires= Minority Proportion +Class Proportion+ % of Public Assistance
Received+ % of Individual Assistance Receive+ % of Road Home Assistance Received
(6) New Homes= Minority Proportion +Class Proportion+ % of Public Assistance
Received+ % of Individual Assistance Receive+ % of Road Home Assistance Received

88

Table 4.10:
Effect of External Funding on New Home Permitting and Job Creation in Louisiana
Variables

New Hires

New Home Permits

Minority Proportion

-.0022*
(.0011)

.00002
(.0001)

Class Proportion

-.006**
(.002)

-.0012***
(.0004)

% Individual
Assistance Rcvd.

-.012
(.237)

.070
(.045)

% Public
Assistance Rcvd.

.103
(.176)

-.018
(.041)

% Road Home
Assistance Received

.332
(.271)

-.086**
(.032)

Constant

.508***
(.041)

.036
(.009)

No. of observations
2
R
.3613
Prob > F=
0.0001

37

37
2
R
.3819
Prob> F= 0.0009

Notes: Numbers in parenthesis are robust standard errors.* significant at 10%, ** significant at
5%, ***significant at 1%
In table 4.10, which illustrates the effect of funding on new hires, an increase in minority
proportion decreases job creation .0022 (H1). Similarly, poverty affects job creation by .006 for
every unit of job creation in the dataset. Federal Public and Individual assistance have no
statistically significant affect on job creation, nor did the influx of state Road Home funds. The rsquared for the new hires model is .36, reflecting moderate explanatory power. Turning to new
homes, minority proportion has a positive, but insignificant affect on permitting, thus hypothesis
one is not supported when looking at home permitting. Class has a significant and negative

89

effect. And of the various funding streams, only the state’s program had an effect on home
permitting in the panel. The r-squared for the new homes model is .38, a moderate fit to the data.
A model estimating all variables was run to determine the effect of race and class on
economic recovery in the state. The results in table 4.11 illustrate that both the proportion of
minority and poor residents for both model performed as hypothesized, each having a significant
and negative affect on new hiring and new home permitting throughout the panel. Only Road
Home funding had an affect on new home permitting in the state, however the effect negative
and significant, not positive as expected. The yearly effect was strong for new job production,
but limited in new home permitting. Overall the fit of each model is moderate, with the variables
predicting 44% of the variance in new hiring and 32% of new home permitting respectively.

90

Table 4.11 Effect of All Variables on Economic Recovery
New Hires Model

New Homes Model

Minority Proportion

-.009**
(-.003)

-.001***
(-.0002)

Class Proportion

-.015**
(-.004)

-0.00252***
(-.0006)

Race x Class Proportion

.0004*
(-.0001)

.000004***
(-.00001)

High Black High Rich

.030
(-.0299)

.005
(-.00459)

High Black High Poor

-.0118
(-.0283)

.006
(-0.00374)

Low Black High Poor

.0004
(-.0248)

.00096
(-.003)

2004

-.00333
(-.00619)

.00141
(-.000846)

2005

.0689***
(-0.0106)

.00373*
(-0.0014)

2006

.110***
(-.0114)

.00632***
(-.0016)

2007

.0652***
(-.0106)

.00137
(-.00155)

% Individual Assistance

-.278
(-.204)

.0563
(-.036)

% Public Assistance

-.0461
(-.117)

-.0263
(-.0298)

% Road Home

.365
(-.268)

-.0710***
(-.0194)

Constant

.593***
(-.0644)

.060***
(-.0101)

91

Table 4.11 Cont,d.
No. of observations
2
Adjusted R
Prob>F

185

185

0.442
.0000

0.327
.0000

Notes: OLS Regression. Numbers in parenthesis are standard errors.* significant at 10%, **
significant at 5%, ***significant at 1%
Overall, hypotheses one and two testing the effects of poverty and minority proportion on
job activity post-disaster were largely confirmed. Hypothesis one predicted that the higher
minority proportion within an area, a decrease in job and housing creation would result.
Hypothesis two predicted that an increase in the proportion of residents living in poverty would
result in lower job and housing creation throughout the panel. Hypotheses one and two were not
confirmed when exploring the relationship between race, class and home permitting throughout
the panel. Further, evidence of a multiplicative effect of race and class was not borne out in any
of the analyses above. Finally, the effect of outside funding on promoting job activity and home
permitting was negligible.
Finally, to further explore the relationship between funding on job creation and home
construction, the following graphs illustrate the prediction of the dependent variable (in this case,
New Hires or New Home Permitting) from a linear regression and plot the values predicted by
the model as a line in graphic form. This linear prediction plots show in graphical form the fit
between the amounts of funding received on the creation of new jobs throughout the panel. This
is the same as running a regression modeling a single independent variable and dependent
variable, predicting the fitted values from the regression model for each entity and plotting those
fitted values against a scatterplot of the original data. Graph 4.7 estimates the effect of the
Individual Assistance funding streams new job creation for the all the years in the dataset. For
sake of space, the linear prediction plots for the new housing variable are not shown but the

92

effect is nearly identical. In the case of the other funding streams and their relationships to the
other dependent variable, New Home Permitting, which were estimated but not shown here, the
effect of funding was very small. Additionally, the large amount of funding directed to Orleans
parish in all cases acts as an outlier pulling the regression line upward.

.1

.2

.3

.4

.5

Graph 4.1: Effect of IA Funds on New Hires

0

.2

.4

.6

% INDIVIDUAL ASSISTANCE
FITTED VALUES

NEW HIRES

Conclusion:
The state of Louisiana suffered significant damage after hurricanes Katrina and Rita
made ground. In terms of job creation, we see that higher diversity acted as a significant
impediment to job creation post-disaster. In term of lower class, the effect was less apparent but
did depress job creation. In terms of housing development in parishes following the storms,
higher proportions of minorities are negatively correlated with the amount of new permit issued
post-disaster. Class was also shown to have negative effects on new hiring activity but not in the
area of new home permitting. This effect remained when taking into account the massive
amounts of outside investment from FEMA and the state to the parishes as well.
93

As we move forward to analyzing the greater New Orleans area, it is expected that the
findings will remain similar to what was found here. Minority composition and lower class are
expected to act as significant deterrents to economic activity in both housing and job creation.
Funding, however, may bolster economic activity as Orleans parish received the most funding of
all parishes. As New Orleans was the most damaged during the hurricane season of 2005
however, and suffers from the greatest poverty contrast of the parishes, results could show that
class, not race, has a more significant effect in the models to follow.

94

BIBLIOGRAPHY

95

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Addison, James D. (2000). The Mississippi Levee System and the Old River Control Sturcture,
2009, from http://www.tulane.edu/~bfleury/envirobio/enviroweb/FloodControl.htm
Agency, Federal Emergency Management. (2009). Disaster Assistance Available from FEMA
Analysis, Bureau of Economic. (2005). State BEARFACTS 1995-2005. U.S. Department of
Commerce.
294 F.3d 684 (5th Cir 2002).
Census, U.S. (2008). Small Area Income and Poverty Estimates
Court, Louisiana Supreme. (2009). Louisiana Supreme Court History, 2009
District, Orleans Levee. (2009). Mission Statement, 2009, from www.orleanslevee.com
Ewing, Bradley T. (2005). Hurricanes and Unemployment. The Center for Natural Hazards
Research. Working Paper. East Carolina University.
Grunwald, Michael and Susan B. Glasser. (2005). Experts Say Faulty Levees Caused Much of
Flooding, Washington Post.
Hudson, Audrey. (2005). Malfeasance cited in Katrina flooding; Engineers report power
struggles, The Washington Times.
Konigsmark, Anne Rochell. (2005, December 23, 2005). Residents push to rid levee boards of
politics, USA Today.
Kosanovich, karen. (2006). The Labor Market Impact of Hurricane Katrina: An Overview.
Monthly Labor Review, 129(8), 3-10.
Laborde, Errol. (1985). Louisiana Politics. PS, 18(3), 593-600.
Liu, Amy, Matt Fellowes & Mia Mabanta. (2006). Katrina Index: Tracking Variables of PostKatrina Recovery. Washington D.C.: The Brookings Institute: Metropolitan Policy Program.
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Louisiana, Policy Jury Association of. (2009). Parish Government Structure, from
www.lpgov.org
Louisiana, State of. (2009). About Louisiana Retrieved
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Louisiana, The Association of Levee Boards of. (2007). ALBL Mission, 2009, from
www.albl.org
. "Louisiana". (2000). In Thomas Carson & Mary Bonk (Eds.), Gale Encyclopedia of U.S.
Economic History (Vol. 2, pp. 584-586). Detroit: Gale.
Museum, Louisiana State. (2009). Antebellum Louisiana: Agrarian Life. The Cabildo, 2009
Nossiter, Adam. (2005, November 23, 2005). Criticized Levee Boards Survive in Louisiana, New
York Times.
Nossiter, Adam. (2006, February 18, 2006). Louisiana Lawmakers Approve of Merging Levee
Boards, New York Times.
Rees III, Grover. (1979). Dave Treen of Louisiana. Baton Rouge: Moran Publishing.
Research, U.S. Department of Housing and Urban Development's Office of Policy Development
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Federal Emergency Management Agency.
Roth, David. (2003). Louisiana Hurricane History, 2009, from
http://www.srh.noaa.gov/lch/research/lahur.php
Staff. (1992). Hilton Joins Bidding for New Orleans Casino, Los Angeles Times.
Statistics, Bureau of Labor. (2009). U.S. Unemployment Rate by State.
Statistics, National Center for Education. (2005). Digest of Education Statistics. (Table 33,
66,184, 106,144). U.S. Department of Education.
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Webb, Gary R., Kathleen J. Tierney and James M. Dahlhammer. (2002). Predicting Long-term
Business Recovery from Disaster: A Comparison of the Loma Prieta Earthquake and Hurricane
Andrew. Environmental Hazards, 4, 45-58.

98

Chapter Five: New Orleans
Introduction:
In the early 1700’s when Bienville founded the city of New Orleans, it was the only place
among the marshes and thickets that had solid soil and thus, could support a port city (Miller
2008). The initial settlement, named L’Isle de la Nouvelle Orleans (the island of New Orleans),
was virtually surrounded by water. In 1718, the urban landscape began to take form. The first
plan called for a sixty-six block grid until the city was expanded under Spanish rule in the 1760s.
Population growth nearly tripled from 3,100 residents in 1769 to 8,000 –plus residents in 1797.
Trade primarily drove population and sprawl. The Spanish built the Corondelet Canal to
facilitate drainage and navigation. At the same time, two fires (in 1788 and in 1794) ravaged the
inner city and the Spanish rebuilt on a much larger scale following those events. Changes to the
municipal building codes required fire walls to be constructed in new homes and businesses as
well as requirements for using more brick and cement as building materials (Magill 2003 quoted
in Miller 2008). Regardless of the explosion of growth during the early period, building did not
cross the river, thus the city of New Orleans remained within the crescent created by the natural
boundary of the Mississippi River.
Social Institutions and History:
Racial segregation played a major role in the cultural development of New Orleans from
its founding. When the Spanish took over rule of the territory from the French in the late 1700s,
western traditions of ethnocentrism drove much of the social policies enacted in the city.
Coupled with the use of the port city as a major slave trading post, race has been an integral
component of interpersonal relationships in the city for over 300 years.

99

The Spanish authorities made race mixing illegal in the late 18th century (Miller 2008).
Before this change in law however, race mixing was prevalent in the city. The French, the
original settlers of New Orleans had adopted a social classification scheme much like they had
adopted in Haiti, and sorted residents based on their ethnic variations (i.e., Pure Black, ThreeQuarters Black—Sambo or Mangro, half-Black-Mulatto, one quarter black—Quadroon, and less
than one-quarter Black—Octroon, or Pass as White- Kephart, 1948 quoted in Miller, 2008). Thus
after the enactment of race mixing laws in the 1800s, many mixed raced residents passed into
white society in order to benefit from membership in the city’s political and social elite. When
the United States gained authority in the territory, segregationist policies were enforced often
more harshly than under Spanish and French rule.
In fact, after Louisiana was accepted into the union, the politics of segregation (i.e.,
denial of social, economic and political access) began what many observed after Hurricane
Katrina as “extreme stratification” (Dreier et al. 2001). Despite the increase of a racialized
landscape within the city, it continued to grow attracting immigrants from Ireland, Germany, and
Italy. With the onset of Jim Crow coupled with discriminatory lending and housing policies of
th

the late 19 and early 20

th

century, the city of New Orleans began to exhibit a highly stratified

social and economic landscape (Dreier 2006).
By the time Katrina hit, New Orleans was one of the poorest and most segregated cities
in the America. Residential segregation was so prevalent that in order for there to be an equal
distribution of blacks and whites in every neighborhood within the city, 69% of black residents
would have to relocate (Center 2000). Additionally, poverty played a large role in the
stratification of New Orleans with the city experiencing a 12% unemployment rate in 2004,
which at the time of Katrina was double that of the nation (Holzer 2006). In addition, research

100

by the Brookings Institute reveals that in 2000, New Orleans ranked second among all US cities
in concentrated poverty behind El Paso, Texas (Durant 2008).
Economic Institutions: History and Present Day
The slave-based economy flourished in New Orleans under Spanish rule, the second brief
French command, and American control until the Civil War (Graham 2008). After the Civil War,
emancipated slaves left rural parishes and settled in New Orleans. Prior to emancipation, the
city’s residential patterns were organized loosely, with black and white residents living in close
quarters. After the Civil War, during reconstruction and the introduction of Jim Crow, the
residential patterns had a marked shift with clusters of black blocks located within larger census
tracts, with whites and mixed race residents on the outer fringes and main avenues. Additionally,
the concentrating of blacks in close quarters was exacerbated by the custom of building slave
quarters and servant’s quarters in the rear of the master’s house. Little changed until the civil
rights movement of the 1960s, however by this time, the expansion of the city allowed for whites
to flee to other surrounding areas leaving black residents behind in the center (Graham 2008).
New Orleans’ economy has been dominated by four sectors: oil and gas related activities,
tourism, shipbuilding and aerospace manufacturing. After the oil bust of the 1980s, many large
oil-related industries left New Orleans. In the absence of a strong oil and gas industry, New
Orleans turned to tourism as its major economic engine. Tourism is the driving force of the
economy, supplying the city with over 66,000 jobs in 2004 (History 2000). Despite New
Orleans’ heavy focus on tourism as a money maker, it is also a major transportation hub and its
location in the Gulf of Mexico makes it a port of call for many imported goods from Latin and
South America.

101

The influx of nearly 10 million visitors annually to the city is a double-edged sword for
economic prosperity however; many jobs within the tourism industry are low paying and
seasonal. Research has found that retail businesses connected to the tourism industry typically
recover last after natural disasters (Waugh Jr. 2006). Additionally, small and locally owned
business typically fall prey to the same fate. After Hurricane Katrina made ground, the effects of
the city’s reliance on tourism as a backbone of the local economy became evident (Redwood
2008). Directly after the storm, the New Orleans area lost over 200,000 jobs in both the public
and private sectors. In November of 2005, the unemployment rate was 17.4% compared to the
4.6% reported in 2004 (Whelan 2006). Although the unemployment rate fell to 8.2% in
December of 2005 and had mostly recovered by April of 2006 (5.1%), problems with housing
and insurance claim processing exacerbated the ability of businesses to get back on their feet.
Complicating issues even further was the influx of a new minority group post-Katrina.
The H-2B guest worker program, which paid laborers to relocate to New Orleans to assist in
rebuilding, had various problems. First, many Latino workers reported being promised full time
work and wages between $1000-1500 per month (Redwood 2008). But when workers arrived,
most received wages that were substantially below this figure. Additionally, the H-2B visa
program restricted workers from seeking outside employment to supplement their income.
Complicating matters even further was the suspension of the Davis-Bacon Act of 1931, an act
which requires employers to pay prevailing wages on federally financed construction projects
(Edsall 2005). This had a dual effect of depressing the wages paid to black workers returning to
previously held jobs as well as on the newly imported Hispanic workforce.
Private businesses also fared badly in the weeks and months post-Katrina. The major
issue of housing shortages and flooding restricted much of white-collar worker’s ability to come

102

home and restart the economic engine of the city’s downtown area. The infamous FEMA trailers
sent to the area to temporarily house workers faced problems with passing electrical inspection,
NIMBYism regarding the siting of large trailers from FEMA as well as air quality issues inside
the trailers (Hsu 2007; Nossiter 2006a; Saulny 2006). Finally, complications with permission to
rebuild from FEMA delayed many local business construction projects for months. Outside
capital funded construction projects for businesses that could find it. And in the weeks and
months following the hurricane season, delays in public assistance and state funds to the city
coupled with issues of insurance and claims processing led many businesses to close for good.
Local Government Institutions:
The history of New Orleans and its prominent place as the largest city in the southern part
of the state play a large role in the politicization of its citizens. When Louisiana seceded from the
Union in 1861, the federal government took steps to allow parts of Louisiana (New Orleans and
the surrounding area) to have representation in the U.S. Congress during the latter part of the
war. Additionally, for the duration of the Civil War, both the Confederacy and the Union
recognized their own distinct governors (Kean 1915).
The current electoral system in Louisiana dates back to Edwin Edward’s efforts to limit
the power of the Republican Party within the state. In 1976, the democratic governor instituted a
change in the state’s constitution that called for an all-inclusive primary, in which candidates for
office would initially run against all other candidates for the same office, regardless of party
affiliation (Warren 2008). If a candidate receives more than 50% of the vote, there is no run-off
election and the candidate is the winner. If no candidate wins a 50% majority of the popular vote,
the top two vote getters face off in a run off election. Additionally, candidates who lose in the

103

“jungle primary” are free to throw their support behind a candidate in the runoff election in hope
of gaining a position in the coming administration.
Democratic politicians and African American candidates have faired well under these
electoral rules (Bullock 2006). With the enfranchisement of blacks in the 1960’s, the substantial
increase in black voter representation resulted in the election of many black public officials. In
1968, there were 36 black public officials serving at the state level. From 1977 through 2002,
three black public officials have served. Ernest Morial was elected as mayor of New Orleans and
served until 1986. The election of Morial was followed by the election, Sidney Barthelemy, a
creole and black public official, who served from 1986-1994. Marc Morial, son of Ernest Morial,
served from 1994-2002 and was succeeded by Ray Nagin in 2003 (Bullock 2006).
Peter Burns and Matthew Thomas have written extensively on the presence (or lack
thereof) of regime politics in New Orleans pre and post-Katrina. In non-regime cities, the
presence of temporary networks of stakeholders, and brief, but fleeting coalescence around
certain issues mark the day-to-day operations of government, whereas regime cities have the
necessary components of a closely linked political and pro-business agenda, a governing
coalition, political and business resources and a scheme of cooperation to ensure a well
functioning city (Stone 1989). New Orleans typifies a non-regime city in that cooperation
between black leaders and white businesses has been tenuous at best (Burns and Thomas 2006).
This lack of cooperation coupled with severe and continuing declines in corporate sustainability,
has mired New Orleans’ ability to flourish economically in the wake of the disaster.
The organizational structure of the city government is the mayor-council form. Orleans
Parish is coterminous with New Orleans, so in addition to having the mayor-council form of
government, the city charter also provides for city-county consolidation. City-County/Parish

104

consolidation is one structural adaptation that may provide unified government in a metropolitan
area. The advantages of city-county/parish consolidation, according to proponents, lie in
simplifying the governmental structure in the county/parish, consolidating responsibility,
eliminating duplication, mobilizing the resources of the area, promoting the orderly development
of the county/parish, solving major area-wide problems, increasing popular control and
achieving economies of scale (Louisiana 2009).
Although the current New Orleans charter dates from 1952, the city and parish began
their merger in 1805-the nation's first consolidation. The New Orleans charter provides a strong
mayor-council form of government with a seven-member council (five elected from districts and
two at-large). The council selects its own presiding officer. The mayor serves a four-year term,
as does the seven-member council. The city itself has approximately 6000 employees who work
in the various city departments. There are seven municipal districts in the city. The city is also
subdivided into seventeen wards and often residents of New Orleans refer to the area where they
live by ward number. In the early 1900s, these wards had electoral representation in the General
Assembly of Louisiana. Once the Louisiana constitution was changed to reflect a bicameral
legislature, these positions were eliminated, however. These wards are further subdivided into
precincts (police and polling) that manage public safety as well as serve as polling places for
local elections (Kean 1915).
The Levee System:
The first foray into the extensive canal and levee system that dominates the New Orleans
landscape began in 1807 (Mosher 1995). Technological advances of the late 1800s allowed the
city of New Orleans to expand beyond the natural boundaries of the Mississippi River and began
with the construction of a canal that linked the Mississippi to Lake Ponchartrain. The canal went

105

undeveloped and became what is now known as Canal Street. Canal street became a natural
dividing line for new residents moving to the city and served as the dividing line between the
French-Creole sector of the French Quarter and the “American” sector of uptown. These cultural
and social artifacts are seen today with the Vieux Carre (French Quarter) and settlements to the
east populated predominately by minorities, and the Central Business District, Uptown and
University districts populated by whites on the opposite side of Canal Street.
The need for a levee system was evident even before the city’s official founding. By
1812, the settlers of the Louisiana Territory had constructed levees from the east bank of the
Mississippi River to Baton Rouge (over 130 miles) to as far west as Point Coupee (165 milles)
(Miller 2008). Local leaders saw this taming of the swamplands to the north of New Orleans as a
boon to economic development opportunities and as a way to curb the spread of yellow fever.
Once Louisiana became a part of the Union after the Civil War, it worked closely with
the federal government to fund additional levee and canal projects. With the assistance of the US
Army Corps of Engineers, the city of New Orleans has seen the construction of a massive flood
protection system to protect it from river, hurricane and other precipitation-based flooding
(Colten 2006). The Army Corp of Engineers has worked closely with the Orleans Levee Board to
determine the flood protection needs for the parish. The levee board, created by the Louisiana
legislature in 1890, had full control over all levee matters (repair, placement, and maintenance)
and was given taxation powers to fund levee projects.
The Orleans Levee Board has had a checkered history connected to the enforcement of
levee standards for the city. Levees constructed in the 1920s proved inadequate when a 1947
hurricane struck. In the 1960s, the Army Corp of Engineers played a larger role in constructing
and repairing levees and were responsible for the construction of most of the levee system that

106

Hurricane Katrina tested in 2005 (Colten 2006). The levee board system, rife with political
infighting and disorganization, came under intense scrutiny following the hurricane season of
2005. Many projects that were aimed at strengthening the system were discarded over time,
resulting in uneven protection. The board’s composition over the years consisted of patronage
positions doled out by the governor and local governments, thus ignorance of planning for a
cataclysmic event like a category 5 hurricane resulted in huge problems in 2005.
In effect, the city of New Orleans acts like a bowl when flooded and the levee and canal
system, when working properly, acts as a siphon for floodwaters. When breeches in the system
occur, it is quickly overwhelmed. Coupled with long periods of power loss in the wake of
Hurricanes Katrina and Rita, the siphoning effect could not occur, leading to standing water in
many parts of the city for weeks and even months in some cases (Grunwald 2005).
Problems with the levees and levee board management led to a resident-driven initiative
to rid levee boards of political influence. A vote to amend the state’s constitution to merge levee
boards across the state was held in late 2006. The vote passed, with the majority of support
coming from areas worst hit by Hurricane Katrina. The new system consolidates levee boards in
the southern half of the state and creates a governing board staffed by experts from engineering
and hydrology fields to manage flood protection (Donze 2006).
How the levee system came to dominate the socioeconomic distribution of the city is also
important. Both political and economic forces have coalesced over the years to create a
patchwork of faulty protection for residents living within city limits as well as in outlying areas.
Housing development in the city followed the trajectory of many gulf or port cities. Higher
ground along the river was settled first, while later development consisted of single and double
shotgun homes built on low ground (formerly cypress swamps) to accommodate the influx of

107

th

th

immigrants in the late 19 and early 20 centuries (Reichard, 2006). The juxtaposition of
opulence (in the cities’ Garden District, Esplanade, Metairie and Gentilly areas, for example) and
poverty of the Seventh, Eighth and Ninth wards highlighted the socioeconomic inequalities
present in New Orleans before the storm. Post World War II much of the remaining low land was
developed into middle-class, suburban housing with little attention given to the need for correctly
reinforced foundations or levees. The result of the levee breaks for many areas was cataclysmic,
worsened by their inability to slough off water for weeks after the storm. Both middle-class
suburban neighborhoods and inner-city public housing built on low land were affected
(Reichard, 2006).
New Orleans, Pre-Katrina:
We now turn to the socio-economic profile of New Orleans pre-Katrina along with the
backdrop of the state of the city and surrounding area before the storm made ground. The New
Orleans’ poverty rate in 2000 was 28% compared to 12% for the entire nation (Census 2008).
The number of high-poverty census tracts (more than 40% of residents in poverty) grew from 30
in 1980 to 49 in 2000. Additionally, the population within these tracts grew 12% from 96,417 to
108,419 in that same time span. The black poverty rate was more than three times the white
poverty rate (35% vs. 11%), and 43% of blacks lived in poor neighborhoods (Census 2006;
Institute 2005).
According to a measure of racial segregation—the Dissimilarity Index—New Orleans
ranks very high among large cities with minority populations (Center 2000). Blacks and whites
lived in different worlds, marked by geographical separation as well as by other measures of
socioeconomic status. Educationally, New Orleans had a pattern of underachievement that put
minority and poor students behind their white counterparts. In 2005, before Hurricane Katrina

108

struck, only 26% of third graders in New Orleans scored above national averages on
standardized tests collected by the No Child Left Behind Act (Statistics 2005). New Orleans
students in the 10th grade also fared badly. Only 40% of public school students scored at basic
levels for language arts and 39% for basic math skills in 2005.
There is also geographic dissimilarity in the public schools. In 2005, before the hurricane
season, an overwhelming 93.4% of students attending public schools were black; white students
typically attended private or parochial schools within the city. The city’s failure in running
efficient and effective schools prior to and immediately following the storm season has been
documented at length (Casserly 2006; Jervis 2009; Johnson 2007; Maloney 2009; Nossiter
2006b; Toppo 2007). Additionally, New Orleans suffered (and still does) from a brain drain at its
institutions of higher learning (Alfano 2005). This effect complicated by rebuilding efforts
creates a lack of human infrastructure available for jobs created post-storm. Although race, and
poverty are highlighted here, research has shown that the education system, women, the elderly
and the infirm also suffered from similar disparities before the storm (Grey 2006; Louisiana
Recovery Authority 2007).
Data and Data Sources:
Like the previous chapter, counties (parishes) remain the unit of analysis. The same
dependent and independent variables are also used here. One additional variable is added to
delineate membership in the U.S. Census Metropolitan Statistical Area (MSA) as of the year
2000. The parishes included in this group are: Jefferson, Orleans, Plaquemines, St. Barnard, St.
Tammany, and St. John the Baptist so that comparisons between the city and the state can be
made. The population of these seven parishes account for over 1.3 million residents. The major
cities other than New Orleans located within the MSA include: Kenner and Metairie. The latter is

109

an unincorporated community located east of New Orleans and south of Lake Ponchartrain. The
17th street canal marks the border between New Orleans and Metairie. Kenner is located west of
New Orleans. In the analyses that follow, the models will report effects of race and class on
housing and job creation as it pertains to the MSA, as well as making comparisons between the
MSA and the overall state.
Demographics of Panel:
The seven-parish area that comprises the New Orleans MSA is a study in contrasts.
Comparing the MSA to the state, we see that, in most categories, the greater New Orleans area
has more minorities, suffered more damage during and after the storm and received more
individual assistance and public assistance dollars than the rest of the state. Interestingly, the
concentration of residents living under the poverty line is much higher outside the greater New
Orleans area, however the results reported in the previous chapter show that race had a more
significant effect on recovery than class. Even using median income as a proxy for poverty, only
in the area of job creation did poverty have a significant effect on recovery.
Table 5.1: Summary Statistics of the New Orleans MSA Compared to the Entire State of
Louisiana
NO
Minority Class %
% of FEMA IA % of FEMA
% of LA Road
Metro
Ratio (in Ratio Severe
Funds (out of
PA Funds (out Home Funds
Area
%)
(in %) Housing 100%)1
of 100%)
(out of 100%)
Damage
State
30.53
19.87 30.14
27.08
7.17
13.41
($1,372,700,415)

New
Orleans

32.88

16.21

64.53

($220,118,402)

($971,263,095)

72.92

92.83

86.58

($3,695,911,930)

($2,850,510,389)

($6,271,185,414)

In table 5.1 we see some distinct differences between the main variables of interest in the
study. Inside the New Orleans metropolitan statistical area there are slightly high proportions of
1

Table 1 and 2 report the total dollar amount of (HA, ONA, PA and Road Home Funds) funds
approved as of June 2007 by FEMA and the Louisiana Office of Homeland Security &
Emergency Preparedness based on registrations received from affected households in the state.

110

minorities, while slightly lower proportions of poor residents. Housing damage was the greatest
here as New Orleans itself was ground zero when Katrina made landfall in 2005. The New
Orleans MSA also received the most outside investment from federal and state level programs.
Within the New Orleans MSA, the differences are much starker; the range of minority
concentration varies from a mean of 11% in St. Bernard parish to 68% in Orleans parish, home
of New Orleans. We see similar trends in the ratio of residents living under the poverty line as
well as great disparities in funding distributed to the parishes in the Greater New Orleans area.
The number in parenthesis shows the total amount of funds for each funding stream given to the
parishes included in the MSA.
Table 5.2: Summary Statistics of the Greater New Orleans MSA
Parish
Minority Class %
% of FEMA IA
% of FEMA PA % of LA Road
Ratio
Ratio Severe
Funds (out of
Funds (out of
Home Funds
(in %)
(in
Housing 100%)
100%)
(out of 100%)
%)
Damage
Jefferson
30.29
15.40 53.3
.00
.08
.16
($190,911)
($247,863,822)
($1,139,135,312)
Orleans
68.12
24.70 71.5
.54
.42
.47
($2,760,094,329) ($1,277,476,688) ($3,424,578,164)
Plaquemines 28.80
16.32 80.0
.02
.08
.02
($123,048,704)
($247,155,926)
($142,565,171)
St. Bernard 11.76
16.86 80.6
.07
.24
.12
($354,178,698) ($725,582,902)
($874,491,283)
St. Charles
28.50
12.78 49.5
.007
.007
.005
($33,421,855)
($23,648,631)
($35,470,328)
St. John the 49.26
16.64 46.3
.005
.001
.004
Baptist
($24,275,534)
($3,354,449)
($28,608,255)
St.
13.40
10.76 70.5
.08
.11
.09
Tammany
($400,701,896)
($325,427,971)
($626,336,899)
Figure 5.1 shows the variance in racial and class disparities across the New Orleans
metropolitan area. The bars represent the five-year average of the population living under the
poverty line (in blue). The red bar represents the five-year average minority population living in
the MSA. Higher bars signify a larger concentration of poverty or minorities.

111

Figure 5.1: Race and Class Distributions (New Orleans Metropolitan Area)

Turning to the main dependent variables under examination (New Hires and New Home
Permits Issued), we see that there is some variation between years for new home permitting
within the greater New Orleans area. Graph 6.1 below shows the movement in home permits
throughout the time period. In 2003, the seven-parish area averaged 1.4% of new homes
permitted. In 2004, the number of homes permitted dropped to 1.3%. In 2005, the permitting
112

dropped further to 1.2% only to rise after the storms to 1.5% in 2006. The permitting dropped
back to 2004 levels in 2007 with an average ratio of permitting within the seven-parish area of
1.3%. These data are collected on a quarterly basis but are reported here in yearly increments due
to the fact that not all parishes included in the dataset issue monthly housing permit reports.
However, in parishes which report monthly there was a noted increase in permitting in the 3

rd

th

and 4 quarters of 2006 followed by a drastic drop in permitting in all quarters of 2007.

.012

NEW HOME PERMITS
.013
.014
.015

.016

Figure 5.2: New Housing Permit Change 2003-2007 (New Orleans MSA Only)

2003

2004

2005

2006

2007

YEAR

In the area of jobs, we see similar patterns of disparate recovery (Graph 6.2). In 2003, the
average rate of new jobs created in the seven-parish area was 20.5%. In 2004, this number
dipped slightly to 19.75%. During the year of the storm, 2005, the average rate of new job starts
in the MSA was 22%. In 2006, the creation of new jobs rose significantly to 28% in the metro
area, returning to 23.8% in 2007. Comparing these results to the overall state, the rate of new

113

hiring mimicked job creation except for the year directly following Katrina and Rita (2006),
where job creation in the New Orleans MSA outpaced that of the rest of the state by about 5%.

24
20

22

NEW HIRES

26

28

Figure 5.2: New Hires Change 2003-2007 (New Orleans MSA Only)

2003

2004

2005

2006

2007

YEAR

A correlation between the two dependent variables within the Greater New Orleans
Metro Area shows a strongly significant relationship with a correlation coefficient of .76. The
correlation of the same variables for the counties outside the Greater New Orleans area was .39
and significant. Thus, in both areas the production of new homes and new jobs are moving
together throughout the panel. Correlations between the main independent variables of note:
percentage of funds dedicated post-disaster and percent of housing damage also showed
interesting relationships.

114

Table 5.3: Correlations of Concentration of Minority Residents and Residents Living
Under the Poverty Line (2003-2007)
Outside Greater New Orleans .39*
Metro Area
(N=30)
New Orleans Metro Area
.76*
(N=7)
Note: * significant at .05 level.
Correlations between the main independent variables: percentage of Individual
Assistance, Public Assistance, Road Home Dollars and Percent of Housing Damage tell a
different story. In the parishes located outside the Greater New Orleans Area, Individual
Assistance and Public Assistance funding was the only significant relationship. Within the New
Orleans MSA, the following correlations were significant: the relationship between Indvidual
Assistance and Public Assistance funding, Individual Assistance and Road Home funding and
between Public Assistance and Road Home funding. Thus, outside New Orleans, the federal
funding streams were correlated and high instances of public assistance was correlated to
increased individual assistance dollars being sent to those areas. Inside New Orleans, all streams
of funding were highly correlated with each other. A note of caution interpreting table 5.4; New
Orleans has the highest percentage of residents living within a geographic area within this data
set. So the significant correlation results likely reflect the sheer numbers of applications received
from the New Orleans MSA as compared to the less densely populated areas of the other
parishes. For a chart listing the population and damage tables of all affected parishes, see
Chapter 4. In addition, table 5.4 illustrates that damage and funding, which was expected to be
highly correlated, have little relationship to each other.

115

Table 5.4: Correlations of Individual and Public Assistance Dollars, Road Home Dollars
and Percentage of Damage (2003-2007).
NO Metro Area= % Housing
% IA FEMA
% PA FEMA
% Road Home
No (30)
Damage
Funds
Funds
% Housing
1.00
Damage
% IA FEMA
.1692
1.00
Funds
% PA FEMA
.2781
.6858*
1.00
Funds
% Road Home
.2373
-.0217
-.0546
1.00
NO Metro Area=
Yes (7)
% Housing
1.00
Damage
% IA FEMA
.1816
1.00
Funds
% PA FEMA
.5871
.8425*
1.00
Funds
% Road Home
.2482
.9749*
.9012*
1.00
Note: significance reported is at the .05 level.
Panel Results:
Moving to the results of the panel, like the previous chapter, the dependent variable is the
ratio of yearly new hires in each parish within the GNOA and the number of new homes permits
submitted in the same area during the years 2003-2007. Also in the models are the following
variables: ratio of minorities to white overall population within the parishes of the dataset, and
ratio of residents living under the poverty line to residents living above. Additional control
variables include a variable named Katrina, which marks the number of years from when the
storms made landfall. For the first set of models, a dummy variable for each year is included to
determine the dynamics of the variables for each year of pre and post Katrina activity. The year
2003 is removed as a reference category. Again, like in the previous chapter the formulas below
are the mathematical representation of the theory:
1) Yit = "X it + .... + " k X kit + # + $it + %it

!

116

-or2) New Hires= Minority Proportion + Class Proportion + 2004 + 2005 (Year of Katrina and Rita)
+ 2006 + 2007
-and3) New Hires= Minority Proportion + Class Proportion + Minority Proportion*Class Proportion
+ 2004 + 2005 (Year of Katrina and Rita) + 2006 + 2007
Where Y is the number of new jobs created in each parish for each year of the dataset,
!Xit…!Xkit are the coefficients for the indepedent variables for each parish
(i) for each year (t) in the dataset,
where " is the random intercept for each parish (i) at each year (t) in the dataset,
where # is the between parish (i) error term for each year (t) in the dataset,
and where $ is the within parish (i) error term for each year (t) in the dataset.

117

Table 5.5:
Effect of Poverty and Race on New Hires in the Greater New Orleans Area
Variables

New Hires Model

Minority Proportion

-.002
(.001)

Class Proportion

.009**
(.004)

Year 2004

-.006
(.017)

Year 2005

.058***
(.017)

Year 2006

.146***
(.017)

Year 2007

.063***
(.016)

Constant

.189***
(.064)

No. of observations
No. of group
2
2
R (overall)
Rho= .80027321
2
Prob> chi = (Hausman test)
2

35
7
.5351
.9998

Prob> chi = (Breusch-Pagan Lagrange Multiplier test (BPLM))

.0000

Notes: Numbers in parenthesis are robust standard errors. Variables dropped: Year 2003 dummy.
*significant at 10%, ** significant at 5%, ***significant at 1%
In the new hires model we see that the ratio of minority residents within the GNOA does
not have a significant and negative impact on the amount of new jobs recovered. Class, on the
other hand, does have a positive and significant effect on the dependent variable. The effect of
2

Overall: The average R-squared from the between and within mean-deviated regression is
equivalent to the ordinary r-squared from running OLS on the transformed data.

118

time post-disaster was significant in the years 2005, 2006 and 2007 signifying a large impact of
recovery efforts post-Katrina. The r-square for the new hire model is .5351, a good fit to the
data. In the city of New Orleans, hypothesis one, which predicted that minority proportion would
have a negative impact on job creation, failed to be confirmed. Hypothesis two, which is
concerned with poverty and its effect on job production after hurricane Katrina, also failed to be
confirmed. The Hausman specification tests the null hypothesis that the unique error terms (ui)
are correlated with the regressors (as they are in the fixed effect model). In this case the value
.7136 is greater than .05, thus using a random effects model is appropriate. Finally, the BreuschPagan Lagrange multiplier tests the assumption that variances across all entities (i.e., parishes) in
the panel are zero, meaning there are no significant differences across units. This would mean
that the estimated variances of the residuals from the panel regression are dependent on the
values of the independent variables, confirming the presence of heteroskedasticity in the data.
To reject the null hypothesis that the random effects model is appropriate, the result of this test
must be less than .05. Since the threshold is .0000, we reject the null hypothesis that random
effects are not appropriate and conclude that there are significant differences across the parishes
in the data, thus no heteroskedasticity is present.
A second model was run with a multiplicative term added to assess the interaction
between race and class. The other independent variables remained the same. Here, only the
individual effect of the passage of time (the yearly dummies) had significant effects on the
dependent variable. Hypothesis three, which theorized that race and class would have an
increased negative effect on job production, is not confirmed. Although the overall fit of the
model has an r-squared value of .5417, a slightly better fit than the model without the

119

multiplicative term, it would seem that a random effect captured by the yearly dummy variables
is subsuming the effect of race and/or class on hiring.
Table 5.6:
Effect of Poverty and Race on New Hires in the Greater New Orleans Area
Variables

New Hires Model

Minority Proportion

-.002
(.0039)

Class Proportion

.009
(.007)

Race x Class Proportion

.00001
(.0002)

Year 2004

-.0067
(.017)

Year 2005

.058***
(.018)

Year 2006

.147***
(.019)

Year 2007

.063**
(.019)

Constant

.201
(.129)

No. of observations
No. of group
2
3
R (overall)
Rho= .80894225
2
Prob> chi = (Hausman test)
2

35
7
.5417
.9998

Prob> chi = (Breusch-Pagan Lagrange Multiplier test (BPLM))

3

.0000

Overall: The average R-squared from the between and within mean-deviated regression is
equivalent to the ordinary r-squared from running OLS on the transformed data.

120

Notes: Numbers in parenthesis are robust standard errors. Variables dropped: Year 2003 dummy.
** significant at 5%, ***significant at 1%
Next, models testing the effects of minority and poverty concentration on new home
permitting were analyzed. Table 5.7 shows the effect of the main independent variables without
the multiplicative term (Hypotheses 1 and 2) and Table 5.8 shows the effect of the multiplicative
term of race and class on new home permitting (Hypothesis 3).
Table 5.7:
Effect of Poverty and Race on New Homes in the Greater New Orleans Area
Variables

New Homes Model

Minority Proportion

-.0001*
(.0001)

Class Proportion

-.0003
(.0008)

Year 2004

-.0011
(.0029)

Year 2005

-.0019
(.0022)

Year 2006

.0011
(.004)

Year 2007

-.001
(.006)

Constant

.027*
(.014)

No. of observations
No. of group
2
4
R (overall)
Rho= .31065945
2
Prob> chi = (Hausman test)
4

35
7
.2564
.3782

Overall: The average R-squared from the between and within mean-deviated regression is
equivalent to the ordinary r-squared from running OLS on the transformed data.

121

2

Prob> chi = (Breusch-Pagan Lagrange Multiplier test) .1617
Notes: Numbers in parenthesis are robust standard errors. Variables dropped: Year 2003 dummy.
*significant at 10%, ** significant at 5%, ***significant at 1%
In terms of the main independent variables’ predictive ability on new home permitting
activity through the panel, the model fits very poorly. Hypotheses two and three failed to be
confirmed by the data and the Breusch-Pagan Lagrange Multiplier test. Thus, there is
heteroskedasticity present although robust standard errors where estimated in table 5.7. Minority
proportion does have a small, negative, and significant effect on home permitting, however,
confirming hypothesis one. Table 5.8 below shows that the multiplicative term fails to reach
significance, thus hypothesis three is unconfirmed.

122

Table 5.8:
Effect of Poverty and Race on New Homes in the Greater New Orleans Area
Variables

New Homes Model

Minority Proportion

-.0002
(.0006)

Class Proportion

-.0008
(.0018)

Race x Class Proportion

5.47e-06
(.0003)

Year 2004

-.0009
(.003)

Year 2005

-.0016
(.0023)

Year 2006

.0015
(.003)

Year 2007

-.0009
(.006)

Constant

.032
(.032)

No. of observations
No. of group
2
5
R (overall)
Rho= .23293442
2
Prob> chi = (Hausman test)
2

35
7
.2923
.0345

Prob> chi = (Breusch-Pagan Lagrange Multiplier test (BPLM))

.7081

Notes: Numbers in parenthesis are robust standard errors. Variables dropped: Year 2003 dummy.
** significant at 5%, ***significant at 1%

5

Overall: The average R-squared from the between and within mean-deviated regression is
equivalent to the ordinary r-squared from running OLS on the transformed data.

123

Finally, a t-test was conducted on those parishes outside the Greater New Orleans Metro
Area and those within to test for significant differences between the means of the main
dependent variables for each group. Table 5.9 shows the results of the t-test. The two samples are
of very different sizes (i.e., there are 30 parishes outside New Orleans and only 7 in the metro
area), thus the variance in the two groups is extremely different and the t-tests below report the
unequal variance results.
Group6
0
1
Combined
Diff

Table 5.9: Two-sample t test with unequal variances (New Hires)
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
150
.2964
. 0067172 .0822688
.2831267
.3096733
35
0.3388571 .0143765
.0850526
.3096405
.3680737
185
. 3044324
. 006193
.0842346
.2922139
.316651
.0424571 . 0158684
.0105833
.0743309
diff = mean(0) - mean(1)
t = -2.6756
Ho: diff = 0
Satterthwaite's degrees of freedom = 49.9225
Ha: diff < 0
Ha: diff = 0
Ha: diff > 0
Pr(T < t) = 0.9950
Pr(|T| > |t|) = 0.0101
Pr(T > t) = 0.0050

Assuming unequal variances changes the t-statistic to -2.5017 and the corresponding pvalue is .0101, which falls below .05, thus we can reject the null hypothesis that the two areas
have equal average new hiring throughout the panel. This confirms that there is a difference
between how recovery proceeded in the Greater New Orleans Area and the rest of the state in
terms of new job creation.
In table 5.10 below, running a two-sample t-test assuming unequal variances resulted in
a t-statistic of 1.2099 and a corresponding p-value of .2321, thus in the case of new home
permitting activity the null hypothesis is not rejected and cannot conclude that the difference of
means in new home permitting between rural and urban parishes is different from 0, allowing for

6

Group 0 are the parishes located outside the New Orleans MSA. Group 1 is the 7-parish MSA.
124

differences in variances across groups. Therefore, parishes outside the metropolitan area faired
just as poorly as the city itself in terms of new home permitting activity.
Table 5.10: Two-sample t-test with unequal variances (New Home Permits)
Group
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
0
150
.0111907
.00084
.010288
.0095308
.0128505
1
35
.01366
.0018601
.0110042
.0098799 .01784401
Combined
Diff

185

.0116578
.0007677
.0104416
.0101433
.0131724
.0024693
.0019569
-.0016325 .0065711
diff = mean(0) - mean(1)
t = 1.2099
Ho: diff = 0
Satterthwaite's degrees of freedom = 48.8194
Ha: diff < 0
Pr(T < t) = 0.8839

Ha: diff = 0
Pr(|T| > |t|) = 0.2321

Ha: diff > 0
Pr(T > t) = 0.1161

Finally, as in the previous chapter, the following graphs show the relationships between
funding and new hires for the years 2003-2007. Graph 5.3 shows the effect of funding on new
hiring activity. The linear prediction plots between funding and new hiring in the Greater New
Orleans area during the time period under examination is nearly flat, signifying no relationship
between funding and new hiring. The relationship between the other funding streams (i.e., public
assistance and Road Home program dollars) were similar to the relationship between Individual
Assistance and New Hires and for sake of space are not presented. This is a surprising finding
particularly in light of the massive amount of funding dedicated to individuals, businesses and
local government entities following the storm. In the case of funding and new hires, we see that
funding has a slight negative effect on the recovery of jobs. Thus, it would seem that although
New Orleans and the surrounding areas did see resurgence in jobs post-disaster, this effect had
little long lasting benefit to the economic health of the area. Furthermore, these results suggest
that funding probably acted as a proxy for the severity of damage within the New Orleans
(remember population is highly concentrated in this area of the state) and had little do with the

125

creation of long-lasting jobs (in the public or private sectors) that will significantly affect the
overall economic health of the area in years to come.

.35
.3
.25

% NEW HIRES

.4

Graph 5.4: Effect of IA Funds on New Hires (Greater New Orleans Area)

0

.2

.4

.6

% INDIVIDUAL ASSISTANCE
NEW HIRES

FITTED VALUES

The regression analyses reveal a more disturbing effect of funding on job and housing
creation post-Katrina. Table 11 shows the effect of funding on job creation in the parishes
located outside the greater New Orleans metropolitan area and Table 12 shows the effect of
funding within the city. Outside the city of New Orleans, public assistance dollars went a long
way in helping restore economic normalcy; for every thirteen dollars given in public assistance,
job creation increased by one unit. In terms of individual assistance, we observe that in the 30
parishes outside New Orleans, this funding stream had a negative, insignificant effect on job
creation. The proportion of minorities and poor had a negative and significant effect on job
creation, but it would seem that in places located outside New Orleans, public funding went a
long way toward stabilizing the economy.

126

Table 5.11:
Effect of External Funding, Poverty and Minority Population on Recovery Outside
New Orleans
New Hires
Minority Proportion

-.003**
(.001)

Class Proportion

-.006**
(.003)

% Public Assistance $

13.11***
(3.233)

% Individual Assistance $

-1.54
(.837)

% Road Home $

.416
(.431)

Constant

.518***
(.051)

Number of obs = 30
F( 5, 29) = 9.28
Prob > F
= 0.0000
R-squared = 0.5548
Note: OLS Regression. Numbers in parenthesis are robust standard errors.
In New Orleans, the picture is very different. In table 12 below we see that for every
three and a half dollars of public assistance, there was a positive and significant effect on job
creation, a much smaller effect than what was experienced outside the greater New Orleans area.
Additionally, we see that the Road Home program dollars have a significant, negative effect on
job creation post-Katrina. Again, the proportion of minority residents act a drag on economic
recovery to a degree that is much stronger than the parishes located outside the metropolitan
area. Thus, in this model, hypothesis one and two concerning race is confirmed, but hypothesis

127

two concerning class is not. Hypothesis four which stated that external funding would have a
mitigating affect on racialized response is not confirmed. Despite impressive outside investment,
the proportion of minorities and poor in the area still has a negative affect on job creation.
Finally, table 5.12 shows the effect of external funding on home permitting in the greater New
Orleans area. In Table 5.12, public assistance has a positive and significant effect on new homes,
albeit a much smaller effect than on job creation. The higher proportion of minorities in the area
has a negative and significant affect on economic activity post-Katrina, confirming hypothesis
one. The model itself is a good fit to the data with an r-square value of .927.
Table 5.12.
Effect of External Funding, Poverty and Minority Population on Recovery in New Orleans
New Homes
Minority Proportion

-.012**
(.004)

Class Proportion

.103**
(.035)

% Public Assistance $

3.596**
(1.23)

% Individual Assistance $

-.151
(.291)

% Road Home $

-5.34**
(1.656)

Constant

-.823
(.401)

Number of obs = 7
F( 5, 29) = 10.57
Prob > F
= 0.0062
R-squared = 0.9275
Note: OLS Regression. Numbers in parenthesis are robust standard errors.

128

Conclusion:
The Greater New Orleans area has seen its fair share of triumphs and failures in the area
of economic recovery in the wake of storms throughout history. It would seem however that
Katrina, one of the largest and most deadly storms to hit the Gulf Coast in recent years, presents
a large impediment to recovery inside and outside the city. New Orleans’ focus on the tourism
industry has produced jobs, but those jobs are not high paying and do not attract the types of
white-collar workers or the innovation needed to jump-start the economy. Construction, another
industry to see a boom after the disaster, has also failed to provide long lasting economic
recovery in the area. In the area of housing, the high number of renters within the city has limited
the type of residents that can return to the city center as well as the amount of new development
that can occur within the area.
Race, not class seems to be the overriding factor in depressing recovery in the New
Orleans MSA. Unlike the rest of the state, New Orleans is unique in its socioeconomic makeup
and it seems that the high concentrations of funding and attention post-disaster have been unable
to overcome the problems race can cause in redevelopment. As stated previously, direct tests of
the tipping point, and conflict theories of race are difficult to perform due to the lack of microlevel data for the seven parishes that make up the MSA. It may be that in the future, data will
allow for a more in-depth look at the racial and economic dynamics that played out after the
storm. Additionally, it is important to realize that funding and recovery in the Greater New
Orleans area have a very tenuous relationship. This could be due to the high numbers of renters
affected (who have been largely neglected in the funding process), or it could be due to the poor
planning that marked recovery efforts after residents returned to the city.

129

Comparing the results from the analysis of recovery in New Orleans to the rest of the
state we see some interesting trends. First, class has more of an effect in the greater New Orleans
area than outside the area. This is probably due to the differences in the size of that population
within the metro area as compared to the rest of the state. Additionally, time from the disaster
seems to increase recovery of jobs outside the GNOA, but not within the metro area. It could be
that the dysfunction of local government officials in deciding how to proceed in recovery in the
city of New Orleans and the surrounding area has impeded the process of recovery more so than
outside the metro area. Finally, it would seem that the massive amount of money that was sent to
the state of Louisiana and New Orleans has had little to no effect on the creation of new jobs or
housing units. These dollars may help in other ways, but the impact of funding is not captured by
the data presented here.
Next, as we move forward to analyzing the state of Mississippi, it is expected that race
and class will play a significant part in recovery of the more urban areas of the state. It is
important to note that the geographic area of the disaster path was much larger in Mississippi (55
counties compared to 37 parishes), however the socioeconomic mix is comparable. Additionally,
the affect of funding may or may not play a significant role as the state of Mississippi received
much less than Louisiana. It could be, however, that Mississippi with less funding was able to do
more due to the fact that its organizational structures were sounder. Also, the state level program
dedicated to providing funds, the Homeowners Assistance Program was much smaller in reach
(only residents from Hancock, Harrison, Jackson and Pearl River were eligible). These factors
may play a role in affecting how much or how little race and class affect recovery in Mississippi.
It is still expected that both race and class will act as a negative pull on economic recovery in the
state of Mississippi, however.

130

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131

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the twenty-first century. Lawrence: University Press of Kansas.
Durant, Thomas J. and Dawood Sultan. 2008. The Impact of Hurrican Katrina. In Seeking
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Spatial Relationships. In Seeking Higher Ground, Ed. Manning Marable. New York, NY:
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Grunwald, Michael and Susan B. Glasser. 2005. Experts Say Faulty Levees Caused Much of
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Institute, Brookings. 2005. New Orleans After the Storm: Lessons from the Past, a Plan for the
Future. Washington, D.C.: The Brookings Institution.
Jervis, Rick. 2009. High marks, new starts in New Orleans. USA TODAY.
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Maloney, Stephen. 2009. Quality of charter school system has New Orleans parents opting out of
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December 26th, 2006.
Nossiter, Adam. 2006b. Plan Shifts Power to New Orleans Schools. New York Times.
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Kristen Clarke Marable. New York, NY: Palgrave MacMillan.
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NY: Routlege.

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Chapter Six: Mississippi
Introduction:
The state of Mississippi, like Louisiana, has a colorful political, social and economic
history. The Spanish first explored the area which became the state of Mississippi, in the 1500s.
The region was later claimed by the French and in 1699 a French group established the first
permanent settlement near present-day Ocean Springs (Busbee 2005).
In the 1700s, the British took over the area after the French and Indian Wars. It was
ceded to the United States in 1783 after the American Revolution; however, the Spanish did not
surrender their claims over the area until the early 1800s. When the United State annexed West
Florida from Spain in 1810, the land of South Mississippi was included in the transaction.
Before the Spanish, French, and British began their exploration, Native American groups
mostly inhabited the area. The Chickasaw lived in the north and east parts of the state, the
Choctaw in the central regions and the Natchez in the southwest. The French and Indian Wars
were predicated on the Natchez tribe rising up against the French in the early 1700s (O'Brien
2003). The Chickasaws and British allied with the Natchez to defeat the French in the
northeastern part of the state. At the conclusion of the war, the Treaty of Paris gave England all
of the land east of the Mississippi River. At this time, France effectively ceded all land claims in
the area in the lower Mississippi Valley (save New Orleans), and the British proceeded to split
the territory which spanned most of Florida to Louisiana into two parts: West Florida and the
Natchez district. Spain continued to be a presence in the Natchez district and when the U.S. War
of Independence (also known as the American Revolutionary War) was underway; Spain
regained possession of Florida and occupied Natchez. The occupation dispute between Spain and
England was settled in 1798 at the conclusion of the war, but as stated above, the entirety of the

135

territory which would later become Mississippi was not ceded to the United States from Spain
until 1810 (Williams 2002). In 1817, the original Mississippi Territory was divided into the
th

states of Mississippi and Alabama. In December of that year, Mississippi became the 20 state
to join the union. At the time, the state’s population was around 60,000 people.
Social Institutions and History:
The 1820 and 30s were uneventful, but marked with political transformation as the
number of Jeffersonian Republicans declined in American Politics and were replaced by
Jacksonian Democrats. Additionally, the Native American presence in the region remained
contentious, resulting in the removal of many tribes to Oklahoma (O'Brien 2003). This coupled
with slave uprisings in the South and the “slavery question” in general as part of the political
maneuverings to create a union after the American Revolution, threatened to undo the peace
founded after the War of Independence. These racial, ethnic and political factors created many
cross-pressures across the south and all worked to set the stage for the Civil War.
Slavery grew rapidly in the South during the 1800s; Mississippi was no exception. In
1860, the slave population was nearing half a million, while there were only 350,000 whites in
the state. Most whites were not slave owners, however, and those who were plantation owners
typically had slaves numbering in the teens, not hundreds (Williams 2002). The state’s economy
at the time was based primarily on the production of cotton and depended heavily on the slave
trade to provide necessary labor. Slavery in the state was not only an economic institution, but
also a social one. Many whites believed that blacks were inferior and should not be afforded
basic rights. In fact, a Declaration from the State’s January 1861 convention on whether to
secede from the Union went so far as to state that the position was…”thoroughly identified with
the institutions of slavery—the greatest material interest in the world (2002).”

136

Although the position of the state supported and vehemently defended the institution of
slavery, Mississippi also afforded freedom to blacks in numerous ways. Prior to the 1820s, slaves
could purchase their freedom from slave owners, or could be freed by slave owners. By midcentury, this practice was mainly outlawed. After the 1830s, legal avenues to freedom were
restricted to children born of free mothers, or to children born of parents who petitioned the state
legislature for emancipation. This law, passed in 1822, gave the state legislature the power to
approve or decline petitions for freedom and gave policymakers a direct hand in controlling the
free black population within the state (Davis 2000). Mississippi laws required free blacks to
appear in court to give evidence of their freedom and were also required to carry papers proving
their free status. These registration papers had to be renewed every three years and cost the
equivalent of $25 in today’s dollars.
Free blacks faced many economic and social hurdles pre and post Civil War. The
registration papers issued to them were only good in the county that issued them, so traveling to
another part of the state put free blacks at risk for capture and violence. Further, the types of
economic activity open to these persons were also limited. Free blacks were not able to sell
grocery items or liquor, own printing establishments or houses of entertainment, and could only
sell wares in incorporated towns (Davis 2000). Following the Nat Turner riots of 1831, and
various other slave uprisings, Mississippi attempted to pass a law removing “all free Negros to
Liberia at the expense of the county from which there are removed (Mississippi 1798-1848).”
Despite these limits on freedom of movement and commerce, there were areas within the state
that had large numbers of free blacks, mostly in the Natchez District (present day Adams and
Warren counties), though some groups settled in Jackson and Hinds counties.

137

During the Civil War, the free black population dwindled. After the Civil War, much of
the state lay in ruins both economically and figuratively. Proportionately, Mississippi lost the
most troops to the war. Mississippi also grappled with the political, economic and social
consequences of emancipation. The strained relations between free blacks, former slaves, and
former slave owners were difficult. Like Louisiana, Mississippi passed a new constitution in the
wake of the war (1869) granting voting rights to blacks. In 1870, Mississippi was allowed to
return to the Union, however, the equality of blacks in the political and social realm was short
lived. In 1890, a new state constitution took away the rights of blacks to vote, segregation began
anew and groups like the Ku Klux Klan organized to terrorize black people (Busbee 2005).
Prior to the Civil Rights Movement of the 1960s, much of the political and social
progress of blacks was stalled in the state of Mississippi. In 1955, Reverend George Lee, vice
president of the Regional Council of Negro Leadership and member of the NAACP, was shot in
the face for urging black residents of the delta to vote. Authorities failed to charge anyone with
the incident despite many eyewitness accounts of the white men who perpetrated the crime. This
incident is one of many that occurred during the 50s and early 60s as citizens, white and black,
urged black citizens to vote. A right afforded them by the federal constitution (Austin 2002).
The most notable incident of this era were the murders of James Chaney, Andrew
Goodman and Michael Schwerner, civil rights activists who were investigating a number of
black church bombings in the state during what is commonly known as the Freedom Summer.
The three men traveled to Neshoba County to investigate, they were stopped and arrested by
Neshoba County police who then released, and re-arrested them, finally turning them over to
local Klansmen who beat and murdered them. After weeks of inquiry by the FBI, the bodies
were found and seven Klansmen were brought to trial. A jury of sympathizers found them not

138

guilty. The federal government later found the men guilty of violating the civil rights of the three
murdered men, and the convicted served sentences ranging from two to ten years (Austin 2002).
With the passage of the Civil Rights Act of 1964 and 1965, much of the overt racism
against the state’s black residents subsided. The Freedom Summer helped register AfricanAmericans in Mississippi despite violent reactions from Mississippi whites. Although
Mississippi school districts came up with creative ways to thwart desegregation, continued court
challenges by African-Americans resulted in the desegregation of Mississippi schools by 1970 in
a series of Supreme Court rulings.
Economic Institutions: History and Present Day:
Mississippi’s economic history has followed closely the trajectory of its neighboring
state, Louisiana. It began as an agrarian state and focused heavily on the production of cotton in
th

the late 1800s. Swept along by the high demand for cotton by Europeans in the 19 century,
Mississippi dominated the burgeoning textile industry (Dattel 2006). This dominance in the
textile industry was closely tied to the continuance of the institution of slavery as well as
inventions such as the cotton gin. Before the Civil War, Mississippi was the fifth wealthiest state
in the nation. The war cost the state approximately 30,000 men and after the war, many
plantations were bankrupted by the emancipation of slaves and by the damage Union troops left
during the many battles conducted on Mississippi soil (Williams 2002).
After the Civil War, the Mississippi economy turned towards utilizing the Mississippi
River as a trading route for the United States. The state also entered into the lumber,
th

manufacturing, and catfish farming industries in the early 20 century. Shipbuilding was a
mainstay of the state, enabling Mississippians to maintain a global and national presence during
the Industrial Revolution (Busbee 2005).

139

th

In the mid-20 century, Mississippi assisted in World War I and II by contributing many
farm workers to the effort. In fact during this time, Mississippi made a lasting break from the
production of cotton as a main economic resource, however Mississippi remains to this day one
of the most rural of the 50 states. And while Mississippi was ranked third in the nation for cotton
production and 30% of the state’s economic resources come from agribusiness efforts, it now has
a much more balanced economic profile (Busbee 2005). After World War I and II, Mississippi
entered into the oil refinery business, developing the many oil resources located in the gulf. More
recently, Mississippi has dedicated resources towards the development of petrol-chemical plants,
plastics and wood products.
Despite Mississippi’s efforts to become more economically diversified, it remains a very
st

poor state. In 2003, Mississippi ranked 51 in the nation (ranking includes the District of
Columbia) in terms of per capita income. Educationally, Mississippi ranks in the lower 5% on
th

th

most measures relating to reading and math proficiency for both 4 and 8 grade students
th

(Statistics 2005). It ranks 40 among the 51 states in terms of high school graduation rates and
th

49

in terms of residents who have a bachelor’s degree. This takes its toll on the state in its

efforts to draw in business and diversify its economic portfolio.
Local Government Institutions:
Each of Mississippi’s 82 counties is divided into 5 districts. From these districts, a
member is elected to the county board of supervisors. The Board of Supervisors is responsible
for the management of county government and a number of special districts. The five supervisors
are elected by district to four-year terms by the citizens of each county. There are no term limits.
Board members serve residents in districts of different geographical size, but each district has

140

approximately equal population. The primary duties for the board are to establish a budget, set
annual property tax rates, and adopt policies that promote economic growth and development
(County 2010).
At the local level, a mayor or city council typically administers Mississippi’s
incorporated cities, towns and villages. Some of the smaller, unincorporated places in the state
are run by a commissioner style of government. As of 2002, Mississippi had 296 municipal
governments, 164 public school districts and 458 special districts, including the levee districts in
charge of the management of the Mississippi River Valley (2010).
Republican governor, Haley Barbour, currently runs the state. Barbour won the
gubernatorial election in 2003 from Democratic incumbent, Ronnie Musgrove prior to Hurricane
Katrina. According to the state constitution, the eight statewide officers are chosen by the
number of electoral votes they receive instead of by a direct popular vote. A candidate wins an
electoral vote by winning a plurality of the votes in a state house district; therefore, with there
being 122 house districts, there are 122 electoral votes. The state elected officials are: Governor,
Lieutenant Governor, State Auditor, State Treasurer, Secretary of State, Attorney General, and
Commissioners of Agriculture and Insurance (Services 2010). The governorship in Mississippi is
a relatively weak political position. The governor competes with 7 other popularly elected
officials (listed above) who have significant budgetary and policymaking powers. In fact, it was
not until 1984 that the governor was given sole authority over the state’s budget (Nash 2006).
Mississippi also has an excessively fractured executive branch at the state level that has made
politics overly complicated at times (Krane 1992).
Gubernatorial and national level politics in the state have long been contentious. A
change to the electoral structure in the 1970s, which replaced multi-member legislative districts

141

with single-member districts, has allowed for moneyed (and mostly white) interests to rule the
political field. Additionally, Mississippi’s political culture has long been a traditionalistic one
(Elazar 1984; Nash 2006); whites have dominated most political issues, subjugating black
political participation at every turn. Party politics in the state were shaken post-Civil War,
Reconstruction, and during the realignment that occurred nationally after the Civil Rights era.
The result: the Democratic Party machine in the state remains decidedly weak (Colby 1986;
Greenblatt 2003). In fact, since the Reconstruction, only two black representatives have served
from the state of Mississippi (Freedman 2007) and at the local level, black participation remains
fairly limited to this day (Nash 2006).
The Levee System:
The Mississippi River is one of the most iconic natural resources in the United States. It
spans from Minnesota all the way to the Gulf of Mexico and has long been a major tributary for
trade, leisure and transportation. The Yazoo-Mississippi Delta is an alluvial flood plain, meaning
that the waters of the Mississippi over time have essentially flattened this area, making it highly
susceptible to flooding (District 2010).
Two years after Mississippi became a state, efforts began to control the Mississippi river
by the erection of a levee in Warren County. In the years leading up to the Civil War numerous
attempts were made to control the surges in water that flowed down the Mississippi following
winter in the Midwest. At the time, most levee construction was the responsibility of county
residents and landowners affected by flooding. In the 1877, the Mississippi state legislature
passed an act to establish a board of commissioners who would be responsible for coordinating
the construction and maintenance of the growing levee system. In the beginning, most of the
board’s efforts were limited to southern counties within the state, leaving the upper counties

142

largely unprotected. Floods in 1882, the further development of the upper delta, and growing
local interest led to the creation of the Yazoo-Mississippi Delta Levee District in 1884. The
enlargement of the geographic area of which this new organization was responsible, led to the
development of a wider levee system within the state (District 2010).
In 1917, the United States Congress took a more active role in the control of the
Mississippi River. The passage of the Flood Control Act of 1928 moved the onus of control of
the Mississippi River into the hands of the federal government. The Army Corps of Engineers
took over much of the maintenance and construction activities surrounding levees while the state
levee board managed day-to-day operations of the system. It was not until 1972 when another
major flood devastated areas within the levee system that major construction began again in the
delta. When Hurricane Katrina hit in 2005, levee control again received attention on the national
stage. During the disaster, Mississippi flooding was pervasive, however, the levees held and
protected the delta unlike in Louisiana were levee breeches were severe and led to much death
and destruction.
Mississippi Pre-Katrina:
Mississippi pre-Katrina had economic, social and educational issues. In 2005, the percent
of people living in poverty in the United States was 13.3%, in Mississippi that figure was 21%.
Per capita income in 2005 was $25,318 compared to a national average of $34,586. Minimum
wage as of 2008 was $5.85 compared to a federal minimum wage of $6.55. The state’s
graduation rate was 78.5 percent in 2005 compared to a national average of 84.2 (Bureau 2006).
Mississippi also remains highly stratified along racial lines. According to the Dissimilarity Index,
the Gulfport-Biloxi MSA has a white/black dissimilarity measure of 59.3, meaning that nearly

143

60% of white residents would need to move in order to have a racially balanced residential
distribution (Center 2000).
Unlike Louisiana with its burgeoning music and cultural scene, Mississippi’s tourism
industry has been limited to riverboat cruises, plantation tours and gambling in places located
along the gulf coast of the state. Additionally, Mississippi remains a very rural state, with the
Gulfport-Buloxi metropolitan area its second most populated with just over 250,000 residents.
Only Jackson, the capital city located in Hinds County has more residents. It is the gulf coast of
the state, which took the brunt of the storm, making economic recovery of this area critical to the
state’s recovery.
Data and Data Sources:
Like the previous chapter, counties are the unit of analysis. The same dependent and
independent variables are also used here. One additional variable is added to delineate
membership in the U.S. Census Combined Statistical Area (CSA) as of the year 2000. The
Gulfport-Biloxi-Pascagoula Combined Statistical Area is made up of five counties in the gulf
coast region. The statistical area consists of the Gulfport-Biloxi Metropolitan Statistical Area and
the Pascagoula Metropolitan Statistical Area. As of the 2000 census, the CSA had a population
of 396,754. The counties included in this group are: Jackson, Stone, George, Hancock and
Harrison. In the analyses that follow, the models will report effects of race and class on housing
and job creation as it pertains to the CSA, as well as making comparisons between the CSA and
the overall state.
Two additional changes to the data from previous chapters should be mentioned here.
The jobs data variable, which reports new job starts from 2003-2007, is limited to the years
2004-2007. The reporting agency responsible for collecting data for the state of Mississippi did

144

not report these figures to the Census for dissemination and repeated calls to the state’s economic
development board were unanswered. Additionally, Hurricane Rita was not a declared disaster
for the state of Mississippi in the fall of 2005, therefore no funding dollars were given to the state
in the wake of that disaster for individual assistance or public projects. The following analyses
only include funding dollars contributed for Hurricane Katrina.
Demographics of Panel:
Following previous chapters, the tables illustrate basic demographic information of the
state of Mississippi during the time period under examination. Table 7.1 summarizes the IA and
PA funds received during Hurricane Katrina, Housing Assistance Programs (HAP) dollars and
the percentage housing damage in each of the 48 counties affected by the storm.
Table 6.1: Summary of Funding and Occupied Damage
Katrina IA
Funds (by
1,000)
26001.2

Katrina PA
Funds (by
1,000)
46042.4

Housing Assistance Program
Funds (by 1,000)
450211.2

%
Damage
(rounded)
28.86%

Std.
Dev.
Min

83544.1

181558.4

83544.1

21.26%

0

66,979

4,262,896

3.6%

Max

496,908.362

11624980.10

652,750.442

90%

Mean

Of the Mississippi counties eligible for assistance, Choctaw County received no IA
funding as no application for individual assistance were submitted to FEMA. Harrison County,
the ground zero of Katrina’s wrath, received the most IA ($496,908,361) and PA
($1,162,498,010) funding overall. Table 7.2 summarizes the minority and class distributions for
all counties by year. What is notable about this table is that the percentage of minority residents
within the affected area increases slightly with each passing year. However, what is more notable

145

is that in terms of class, the proportion of residents living in poverty among the counties shifts
dramatically throughout the panel as evidenced by the change in standard deviation from year to
year meaning that in Mississippi, people living in poverty increased throughout the panel despite
heavy public and private investment. Additionally, after 2003, the average number of residents
living in poverty within the counties affected increased by approximately 3%.
Table 6.2: Minority and Class Distribution by Year
Minority Ratio (in percentages) Class Ratio (in percentages)
Mean
41.33
20.11
Standard Deviation 18.55
4.30
Min 8
10.4
Max 87
32.4
2004
Mean
41.45
21.26
Standard Deviation 18.44
4.77
Min 9
10.6
Max 86
33.7
2005
Mean
41.55
23.66
Standard Deviation 18.6
6.89
Min 9
9.1
Max 86
33.7
2006
Mean
41.70
23.31
Standard Deviation 18.7
6.06
Min 8
11
Max 86
39.9
2007
Mean
41.94
23.28
Standard Deviation 18.64
7.06
Min 8
9.4
Max 87
41.7
Year
2003

Table 6.3 lists summary statistics for the entire state by county. It should be noted that
Mississippi had more counties that qualified for disaster assistance, thus there are more counties
included in the dataset. Jefferson and Clairbourne counties have the highest concentration of
minority residents as well as some of the highest numbers of people living in poverty. Holmes
County has the highest number of people living in poverty at 38%, double the national average.

146

County
Name
Adams
Amite
Attala
Choctaw
Clairbourne
Clarke
Copiah
Covington
Forrest
Franklin
George
Greene
Hancock
Harrison
Hinds
Holmes
Humphreys
Jackson
Jasper
Jefferson
Jefferson
Davis
Jones
Kemper
Lamar
Lauderdale
Lawrence
Leake
Lincoln
Lowndes
Madison
Marion
Neshoba
Newton
Noxubee

Table 6.3: Summary Statistics of the state of Mississippi
Minority Class
% of FEMA IA
% of FEMA PA
% of HAP Funds
Ratio
Ratio
Funds (out of 100%) Funds (out of 100%) (out of 100%)
(in %)
(in %)
55.8
26.84
.0014
.0002
.
43.4
21.08
.0015
.0001
.
41.2
22.08
.0004
.0001
.
31
24.08
0
.0001
.
84.2
31
.0007
.0003
.
35
20.48
.0026
.0004
.
51.8
22.5
.0023
.0004
.
36.4
21.18
.0047
.0017
.
36.4
23.38
.0198
.008
.
37.8
20.94
.0005
0
.
10
16.74
.0078
.0011
.
27
22.04
.0033
.0072
.
8.4
16.72
.1938
.2228
.2804
26.2
16.2
.3975
.5153
.3548
65.6
22.24
.0085
.0039
.
80.6
38.3
.0007
.0002
.
72.6
36.14
.0001
.0002
.
24.2
15.18
.2193
.1132
.3625
52.4
21.58
.0043
.0049
.
86.4
32.22
.0011
0
.
58.2

27.32

.0028

.002

.

28
61.6
15.4
41.2
33
43.8
30.4
43.4
39
33
35.4
35
70

20.84
23.94
13.2
21.36
18.7
21.16
18.76
20.06
13.12
24.64
20.86
18.46
30.28

.0171
.0008
.0111
.0073
.0035
.0006
.0031
.0007
.0012
.0073
.0013
.0022
.0008

.0184
.0001
.0011
.0036
.0017
.0001
.0003
.0004
.0017
.0068
.0007
.0003
.0001

.
.
.
.
.
.
.
.
.
.
.
.
.

147

Okittbeha
Pearl River
Perry

40
13.4
23

26.3
19.66
20.32

Pike
Rankin
Scott
Simpson
Smith
Stone
Walthall
Warren

49.4
20.2
39.4
35
24
20.4
45
47

26.02
10.1
18.96
20.78
18.18
17.98
24.2
18.56

Wayne

38.4

Wilkinson
Winston
Yazoo

69
45
55

.0004
.0236
.004
Table 6.3 Cont,d.

.0001
.047
.0003

.
.0024
.

.0075
.0046
.0016
.0039
.0025
.0087
.006
.0016

.0012
.002
.0004
.002
.0006
.0153
.0011
.0003

.
.
.
.
.
.
.
.

22.44

.0038

.0118

.

33.52
22.88
30.24

.0009
.0003
.0007

.0001
.0002
.0002

.
.
.

Figure 6.1 illustrates the race and class distributions statewide from the above table in graphic
form. The red bars illustrate the five-year minority population average within each county while
the blue bars represent the five-year average population of people living under the poverty line in
each county.

148

Figure 6.1: Race and Class Distributions

149

A look at the main dependent variables in table 6.4 reveals slow change in terms of new
housing, but a dramatic difference post-Katrina in new job creation within the state. Compared to
the state of Louisiana, Mississippi on average had 2-3% less jobs created post-Katrina.

Year
2003

Mean
Standard Deviation
Min
Max
2004
Mean
Standard Deviation
Min
Max
2005
Mean
Standard Deviation
Min
Max
2006
Mean
Standard Deviation
Min
Max
2007
Mean
Standard Deviation
Min
Max

Table 6.4: Job Starts and Housing Permits
Ratio of New Hires (in %)
Ratio of New Housing Permits
(in %)
NA
.020
NA
.049
NA
0
NA
.1912
8.43
.020
1.69
.046
5
0
12
.179
19.43
.020
5.27
.046
9
0
35
.2168
19.42
.020
5.27
.045
11
0
26
.2274
18.20
.020
5.96
.047
5
0
35
.2274

150

.0205

NEW HOUSING PERMITS
.021
.0215
.022
.0225

.023

Figure 6.2: New Housing Permit Change 2003-2007

2003

2004

2005

2006

2007

YEAR

Mississippi, although having much more modest housing creation post-Katrina, experienced a
dramatic rise and fall in housing production centered on the year the storm hit as illustrated in
table 6.1. New hiring activity in the state also has seen a dramatic shift, with low job creation
before the storm compared to fairly high rates of new hiring occurring post-Katrina (figure 6.3).

151

5

10

NEW HIRES

15

20

Figure 6.3: New Hires Change 2003-2007

2003

2004

2005

2006

2007

YEAR

Panel Results: The State of Mississippi:
Turning to the panel results, we see that Mississippi was affected even more greatly by
the hurricane than its neighboring state when taking into account racial and economic diversity.
In table 6.5, both independent variables have the hypothesized direction and but only race is
highly significant. Again, the number of years past the disaster has a large, positive effect on job
creation in the state, with the last quarter of 2005 providing a much needed economic boost to
economic health. The new home model performs poorly, with an r-squared value of .20. Finally,

152

in the new homes model we observe that the number of residents living in poverty has a
significant and negative affect on new home permitting

153

Table 6.5:
Effect of Hurricane Katrina on New Hires and New Homes in Mississippi (Fixed
Effects Model)
Variables

New Hires Model

New Homes Model

Minority Ratio

-.088***
(.0283)

.0005
(0004)

Class Ratio

-.054
(.083)

-.004***
(.0019)

Year 2004

.005***
(.002)

Year 2005

10.79***
(.474)

.019***
(0.006)

Year 2006

11.13***
(.638)

.015***
(.005)

Year 2007

9.927***
(.440)

.0148***
(.004)

Constant

13.25***
(1.128)

.014***
(.032)

No. of observations
F(5,48)
Prob> F

196
176.24
0.000

245
2.00
.0842

R

.690

.208

2

1

Notes: OLS Regression. Numbers in parenthesis are robust standard errors. Variables dropped:
Year 2003 and 2004 (New Hires Model only). * significant at 10% ** significant at 5%,
***significant at 1%
Three variables were created to conduct a test of hypothesis 3a, which hypothesized that
differing categorizations of race and class would have negative effects on economic activity.
Table 6.6 below shows some examples of counties that fell into the four categories created.
1

The number of cases in the New Hires model are 196 as there were no job creation numbers
reported for 2003.
154

Table 6.7 is a correlation table showing the relationship between the untransformed minority and
class variables to the newly created categories.
Table 6.6: Race and Class Characteristics of Mississippi Counties
Race
Class (Income)
Black
White
Wealthy

Examples include: Kemper,
Lowndes, Jasper and Leake

Examples include: Newton,
Jones, Hancock, Pearl River

Poor

Examples include:
Examples include: Wayne,
Clairbourne, Jefferson, Yazoo, Oktibbeha, Choctaw, and
and Holmes
Marion

In table 6.7 we see that the untransformed race and class variables are modestly
correlated at .69, while the high minority and high poor category is also highly correlated with
the original race and class variables at .75 and .71 respectively. The other variables are also
moderately related to each other, ranging from a low of -.68 between the Low Minority Low
Poor and Minority Proportion categories to a high of -.14 between the Low Minority High Poor
and Minority Proportion categories.

155

Table 6.7
Correlation Table of Race and Class Variables
High
High
Low
Minority
Minority
Minority
Minority
Class
Proportion Proportion Low Poor
High Poor
High Poor
Minority
Proportion
Class
Proportion
High
Minority
Low Poor
High
Minority
High Poor
Low
Minority
High Poor
Low
Minority
Low Poor

Low
Minority
Low Poor

1.00
0.6995*

1.00

0.1304*

-0.124

1.00

0.7506*

0.7103*

-0.2410*

1.00

-0.1472*

0.1448*

-0.1235

-0.2218*

1.00

-0.6851*

-0.6604*

-0.3503*

-0.6289*

-0.3223*

1.00

Table 6.8 illustrates the effect of socioeconomic categorization on the economy. In both
models, hypothesis 3a failed to be confirmed, however increase proportions of minorities in the
new jobs model and increased proportions of the poor in the new home permitting model (H1
and H2) were confirmed. Finally, the passage of time again proves to be the most significant
indicator of economic recovery for the state, illustrating that the process of rebuilding alone had
significant effects on economic recovery.

156

Table 6.8: Effect of Socioeconomic Categorization on New Hires and Housing
Variables

New Hires Model

New Homes Model

Minority Proportion

-.002**
(.001)

-.002
(.002)

Class Proportion

-.003
(.004)

.0006
(.0014)

Minority x Class Proportion .00006
(.000)

-1.26e-04*
(.000)

2004

0
(.)

.017**
(.005)

2005

.144***
(.009)

.0001
(.002)

2006

.152***
(.011)

.014**
(.004)

2007

.127***
(.008)

.013**
(.004)

High Minority*Low Poor

.003
(.018)

-.006
(.013)

High Minority* High Poor

.013
(.023)

.017
(.014)

Low Minority* High Poor

0.017
(.018)

.026
(.013)

Constant

.262***
(.061)

.258***
(.090)

No. of observations
2
R (overall)

196
.616

245
.299

Notes: OLS Regression. Numbers in parenthesis are standard errors.* significant at 10%, **
significant at 5%, ***significant at 1%
Table 6.9 illustrates the multiplicative effect of racial and class diversity on job and
housing creation during the time period under examination. In terms of new hires, the proportion

157

of minority residents in a county had a significant and negative effect on job creation. Poverty
had a negative, but insignificant effect on job creation. There is a distinct and strong yearly effect
on job activity and the model is a fairly good fit to the data with an r-square value of .69. In the
housing model, class, not race had a negative and significant effect on permitting activity.
Hypothesis 3 which theorized that race and class would have an affect on economic activity is
not confirmed. The coefficient for the multiplicative term is positive, not negative and very
small.

158

Table 6.9: Effect of Poverty and Race on New Hires and Home Construction
Variables

New Hires Model

New Homes Model

Minority Proportion

-.166***
(.062)

-.002
(.001)

Class Proportion

-.224
(.167)

-0.10***
(.003)

Race x Class Proportion

.003
(.002)

.0001***
(.00005)

Year 2004

.005***
(.002)

Year 2005

10.78 ***
(.477)

.020***
(.005)

Year 2006

11.14***
(.646)

.0166***
(.005)

Year 2007

9.88***
(.440)

.0143***
(.004)

Constant

16.98***
(3.24)

.223
(.079)

No. of observations
F(5,48)
Prob> F
2
R

1962
176.24
0.000
.6959

245
2.00
.0287
.3018

Notes: Numbers in parenthesis are robust standard errors Values. ** significant at 5%,
***significant at 1%
We now turn to the Gulfport-Biloxi CSA results. First, a brief overview of the area’s
summary statistics is below. What is most notable about the Gulfport area is its lack of
socioeconomic diversity compared to the rest of the state. Unlike New Orleans, the Gulfport
2

The number of cases in the New Hires model is 196 as there were no job creation numbers
reported for 2003.
159

CSA was whiter and less poor. Table 6.9 summarizes the main variables of interest for the
Gulfport CSA while table 6.10 shows the summary statistics for the five county Gulfport
combined statistical area.
Table 6.10: Summary Statistics of the Gulfport CSA Compared to the Entire State of
Mississippi
GPB
Minority Class Mean % % of FEMA IA % of FEMA
% of MS
Metro
Ratio
Ratio Severe
Funds (out of
PA Funds (out Housing
Area
(Mean in (Mean Housing 100%)
of 100%)
Assistance
%)
in %) Damage
Program Funds
(out of 100%)
State
44.29
22.97 24.25
17.39
13.44
.24
Gulfport- 17.84
Biloxi

16.56

69.44

82.61

86.56

99.77

George

Table 6.11: Summary Statistics of the Gulfport CSA
Minority Class
%
% of FEMA
% of FEMA
Ratio
Ratio
Severe
IA Funds (out
PA Funds (out
(in %)
(in %)
Housing of 100%)
of 100%)
Damage
10
16.74
57.5
.78
.11

Hancock

8.4

16.72

90

19.38

22.28

28.04

Harrison

26.2

16.2

68

39.75

51.53

35.48

Jackson

24.2

15.18

64

21.93

11.32

36.25

Stone

20.4

17.98

67.7

.87

1.53

0

County

% of HAP
Funds (out of
100%)
0

Finally figure 6.4 shows the five-year racial and poverty averages for each county included in the
Gulfport CSA. The red bar denotes the five-year minority population average and the blue bar
represents the five-year poor population average.

160

Figure 6.4: Race and Class Distributions (Gulfport CSA)

Figures 6.5 and 6.6 illustrate the sporadic changes in hiring and home permitting in the
Gulfport-Biloxi area during the time period under examination. Although this area is one of the
more populous in the state and sustained the most damage, the graphs illustrate that recovery in
terms of housing has been intermittent. In terms of job creation, the area’s hiring has been more
stable, but as we move further and further away from the storm, job creation has declined.

161

.06

NEW HOME PERMITS
.07
.08
.09

.1

Figure 6.5: New Housing Permit Change 2003-2007 (Gulfport CSA)

2003

2004

2005

2006

2007

YEAR

10

15

NEW HIRES
20

25

30

Figure 6.6: New Hires Change 2003-2007 (Gulfport CSA)

2003

2004

2005

2006

2007

YEAR

Table 6.12 shows the correlation between the two main independent variables within the
Gulfport-Biloxi CSA. There is a weak and insignificant relationship of .28 between minority and
162

poverty proportions inside the Gulfport area compared to the relationship outside the Gulfport
area. The correlation of the same variables for the counties outside the Greater Gulfport area was
.75 and significant suggesting that heterogeneity may affect the rural areas of the state more than
the CSA, opposite the situation in New Orleans. Correlations between the main independent
variables of note: percentage of funds dedicated post-disaster and percent of housing damage
also showed interesting relationships which can be seen in table 6.11.
Table 6.12: Correlations of Concentration of Minority Residents and Residents Living
Under Poverty Line (2003-2007)
Outside Gulfport-Biloxi CSA .75*
Gulfport-Biloxi CSA
-.28
Note: Significant at the .05 level.
Table 6.12 shows the correlations between the main independent variables: percentage of
Individual Assistance, Public Assistance, HAP Funds, and Percent of Housing Damage. These
results tell a different story than what we observed in New Orleans. In the counties located
outside the Gulfport-Biloxi Combined Statistical Area (GBP) IA and PA funding and housing
damage were significantly correlated. Within the GBP, the relationship between HAP funds and
Housing actually had a negative and significant relationship. This is a paradoxical finding since
the creation of the HAP program was aimed at the rebuilding of the Gulfport area, the one most
affected by Hurricane Katrina. Finally, there is a high inter-correlation between Public
Assistance and Individual Assistance dollars within the GBP area, indicating that FEMA dollars
allocated to this area helped to meet both individual household and public organization needs
post-Katrina.

Table 6.13: Correlations of Individual and Public Assistance Dollars, HAP and % Housing
Damage (2003-2007)
GBP Metro
% Housing
% IA FEMA
% PA FEMA
% HAP
Area= No (44)
Damage
Funds
Funds

163

% Housing
1.00
Damage
% IA FEMA
.5756*
Funds
% PA FEMA
.4501*
Funds
% HAP Funds3
-*
GBP Metro
Area= Yes (5)
% Housing
1.00
Damage
% IA FEMA
.2635
Funds
% PA FEMA
.3083
Funds
% HAP Funds
-.9983*
Note: Significant at the .05 level.

1.00
.7039*

1.00

-*

-*

1.00

1.00
.9390*

1.00

.5259

.1710

1.00

Panel Results: The Gulfport-Biloxi CSA
Moving on to the results of the panel, like the previous chapters, the dependent variables
are the ratio of new hires by year and the number of new home permits issued within the
Gulfport-Biloxi CSA during the years 2003-2007. The same independent variables are minority
proportion; poverty concentration and a variable that marks the number of years elapsed from
Hurricane Katrina’s landfall. For each model, a dummy variable for the years 2004-2007 (2003
is the reference year) is included to determine the singular effect of each year on the dependent
variable.

3

Pearl River County was the only county outside the GBP CSA that was given funding through
the state’s HAP program. The results in Table 12 have suppressed this county from the
correlation table.
164

Table 6.14:
Effect of Race and Class on New Hires and New Homes in the Greater Gulfport Area
Variables

New Hires Model

New Homes Model

Minority Proportion

.126
(.136)

.004
(.003)

Class Proportion

.547
(1.01)

-.029
(.023)

Year 2004

-18.97***
(2.72)

.0255
(.021)

Year 2005

-4.044
(2.488)

-.0002
(.014)

Year 2006

-.009
(-0.71)

Year 2007

-4.39
(2.75)

.026
(0.03)

Constant

16.71
(17.89)

.462
(.380)

No. of observations
R2

20
.8753

25
.5164

Notes: OLS Regression. Numbers in parenthesis are robust standard errors. Variables dropped:
Year 2003 and Year 2006 (New Hires Model). ** significant at 5%, ***significant at 1%
In the new hires model (table 6.14), time seems to be the most important generator of job
creation within the CSA, however based on the result the passage of time has a negative effect on
job creation in the CSA post-Katrina. It should be noted that the fit of this model is very good
with eighty-seven percent of the variance in job creation being explained by the hypothesized
variables. Minority and class proportions are not in the hypothesized direction illustrating that
lower levels of both have positive effects on economic activity. This is to be expected as the
homogenous composition of the Gulfport CSA contributes to the area’s ability to produce jobs
165

and housing. In the new home permits model, none of the hypothesized variables had an effect,
although the model’s fit predicts fifty-one percent of the variance in new home permitting within
the area. Next, an additional variable was created to ascertain the multiplicative affect of race
and class on the CSA (table 6.15). The new home model again had no significance and is not
presented below.
Table 6.15:
Effect of Hurricane Katrina on New Hires in the Gulfport Area
Variables

New Hires Model

Minority Proportion

3.10*
(1.22)

Class Proportion

4.17**
(1.39)

Race x Class Proportion

-.179*
(.078)

2004

-14.05***
(1.25)

2005

1.385
(1.49)

2006

3.95***
(2.89)

Constant

-48.88*
(21.68)

No. of observations
R2 (within)

25
.8925

Notes: OLS Regression. Numbers in parenthesis are robust standard errors. Variables dropped:
Year 2007 dummy.* significant at 10%, ** significant at 5%, ***significant at 1%
The multiplicative term of race and poverty concentration has a small, significant effect
on the model verifying hypothesis three as seen in table 6.13, however again we see that time

166

plays a significant role in the creation of jobs in this area of the state. Next t-tests were run to
assess the differences between the dependent variable within the Gulfport CSA and the rest of
the state. The two samples are of very different sizes (i.e., there are 44 counties outside the
Gulfport CSA and only 5 in the metro area), thus variance in the two groups is extremely
different and table 6.16 below reports the unequal variance results for new hiring activity.
Group4
0
1

Table 6.16: Two-sample t test with unequal variances (New Hires)
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
176
.2019318
.0057252
.0759527
.1906326
.2132311
20
.2975
.0275096
.1230265
.2399218
.3550782

Combined
196
.2116837
Diff
-.0955682
diff = mean(0) - mean(1)
Ho: diff = 0
Ha: diff < 0
Pr(T < t) = 0.0014

.006183
.028099

.0865624

.1994895
.2238779
-.1554058 -.0370775
t = -3.4011
Sattherwaite’s degrees of freedom =20.6773

Ha: diff = 0
Pr(|T| > |t|) = 0.0027

Ha: diff > 0
Pr(T > t) = .9986

The t-statistic is -3.4011 with 20.6773 degrees of freedom with a corresponding twotailed p-value of .0027, which is less than .05. From this, we can confirm the null hypotheses that
that the difference of means in new hiring between rural and urban counties in Mississippi is
different from 0, allowing for differences in variances across groups. These results suggest that
there is a difference between how recovery proceeded in the Gulfport area compared to the rest
of the state. A t-test was also run for the new home permits variable. The results are presented
below in table 6.17. The t-statistics is 4.1321 and the corresponding p-value fall is .0003,
therefore the we again confirm the null hypothesis that the difference of means in new home

4

Group 0 are the counties located outside the Gulfport CSA, or 176 observations over the fiveyear period. Group 1 is the 5-county CSA, or 20 observations over the five-year period. There
were no job data available for the year 2003. In table 6.17, new home permitting has data from
2003, thus the number of observations increase to 220 and 45, respectively.
167

permitting between urban and rural counties is different from 0, allowing for differences in
variances across the two groups.
Table 6.17: Two-sample t test with unequal variances (New Homes)
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
220
.0143373
.0025115 . 0372511 . 0093875
.019287
25
.079432
. 0155518 .0777588 . 0473347 .1115293

Group
0
1
Combined
Diff

245

.0209796
.003017
.0472234
.0150369
.0269223
.0650947
.0157532
.0326676
.0975218
diff = mean(0) - mean(1)
t = 4.1321
Ho: diff = 0
Satterthwaite’s degrees of freedom =25.2662
Ha: diff < 0
Pr(T < t) = 0.9998

Ha: diff = 0
Pr(|T| > |t|) = 0.0003

Ha: diff > 0
Pr(T > t) = .0002

Finally, the following figure (6.5) illustrate the relationship between the Individual
Assistance funding and the dependent variables for the years in the panel. In all cases the amount
of funding directed at Harrison County pulls the regression line upwards indicating that funding
did have an effect on hires and housing but that the results should be interpreted with caution.
The results for the other funding streams were similar, for sake of space only the Individual
Assistance and New Hires relationship is shown.

168

Figure 6.7: Effect of IA Funds on New Hires
1

0

.2

.4

.6

0

0

.2

.4

0

.2

.4

% INDIVIDUAL ASSISTANCE
FITTED VALUES

NEW HIRES RATIO

0= OUTSIDE GULPORT CSA 1= GULFPORT CSA

Looking at the linear regression of external funding and its affect on economic recovery
(Table 6.18), we observe that funding has not significant effect on economic recovery over the
time period, however we see that race does. As the proportion of racial diversity of a place
increases, this in turn has a negative effect on the variance of new hiring, providing evidence for
hypothesis one. The fit of the model is moderate, with the variables only predicting 33% of the
variance in new hiring throughout the panel. The Housing Assistance Program funding was made
available only to four counties within the state and was dropped from the model.

169

Table 6.18:
Effect of External Funding, Poverty and Minority Population on Recovery in Mississippi
New Hires
Minority Proportion

-.096***
(.038)

Class Proportion

.016
(.099)

% Public Assistance $

7.844
(8.70)

% Individual Assistance $

6.955
(6.99)

% HAP $
Constant

22.40***
(1.90)

Number of obs = 49
F( 4, 48) = 10.57
Prob > F
= 0.0000
R-squared = 0.3380
Note: OLS Regression. Numbers in parenthesis are robust standard errors.
Turning to new housing (Table 6.19), we observe that individual assistance played a
small role in new home permitting activity throughout the panel. However, neither hypothesis
one or two dealing with the hypothesized negative effect of race and class on economic activity
were confirmed. The fit of the model is moderate, indicating that there again remains an
unknown factor that affected economic activity in the state of Mississippi during this time.

170

Table 6.19:
Effect of External Funding, Poverty and Minority Population on Recovery in Mississippi
New Homes
Minority Proportion

.0008
(.0005)

Class Proportion

-.0039
(.002)

% Public Assistance $

-.111
(.091)

% Individual Assistance $

.438***
(.141)

% HAP $
Constant

.0726
(.0440)

Number of obs = 49
F( 4, 48) = 60.49
Prob > F
= 0.0000
R-squared = 0.3608
Note: OLS Regression. Numbers in parenthesis are robust standard errors.
Finally, a model with all variables was run to determine the effect of race and class on
both new job production and home permitting activity throughout the panel. Table 6.20 shows
that the proportion of minorities in an area negatively impacts the creation of new jobs
throughout the panel, while the proportion of residents living under the poverty line affects new
home permitting negatively. There is a consistent yearly effect between both models and
funding, while hypothesized to have a mitigating effect on the race and class variables, actually
decreases job and home permitting activity in most cases. The fit of each model is moderate with
an r-squared value of .64 for the new hires model and an r-squared value of .31 for the new home
permits model. None of the transformed combination variables (H3a) had a significant effect on
either model.
171

Table 6.20
Effect of All Variables on Economic Recovery
New Hires Model

New Homes Model

Minority Proportion

-.00319**
(-.001)

-.00218
(-.002)

Class Proportion

-.00328
(-.003)

-.0126**
(-.005)

Race x Class Proportion

.0000576
(.000)

.000124*
(.000)

High Black High Poor

.0137
(-.023)

.018
(-.015)

High Black Low Poor

.00566
(-.017)

-.00541
(-.013)

Low Black High Poor

-.0182
(-.017)

.0262
(-.013)

2004

0
(.)

.00584*
(-.002)

2005

.130***
(-.008)

.00898
(-.006)

2006

.151***
(-.011)

.0130**
(-.004)

2007

.127***
(-.008)

.0126**
(-.004)

% Individual Assistance

2.901***
(-.823)

1.695
(-.962)

% Public Assistance

-.826*
(-.374)

-.648
(-.444)

% HAP Funds

-1.412***
(-.368)

-.666
(-.443)

Constant

.251***
(-.057)

.252*
(-.096)

172

Table 6.20 Cont,d.
N
Adjusted R2
Prob >F

196
.648
.0000

245
.316
.0000

Conclusion:
In the state of Mississippi, recovery has been steady, but short lived, even in areas where
there is less heterogeneity in socioeconomic composition. In the case of new hiring, much of the
state’s resources were directed at Harrison County, the county most devastated by the storm.
Mississippi had dismal economic productivity before Hurricane Katrina and it would seem from
the results here that although attention to recovery had an effect on the state, much of this has
been short-lived. The hypotheses of racialized response were supported here in areas where high
heterogeneity was present. In the Gulfport area, where race and class was not an issue, the
passage of time predicted the creation of jobs and homebuilding much better than the
socioeconomic variables. In all, the results here confirm that a racialized response is present in
places where there is a diverse mix of income and racial makeup and shows the lasting effects
race and class can have on economic recovery in populated and less populated areas. And as we
move further and further past this disaster in Mississippi, both housing starts and new hires
decline dramatically, even more so than in the state of Louisiana.
The question of whether directed effort at creating jobs, recovering lost infrastructure and
promoting places that will be “renewed” after a disaster is effective, appears to have been
answered in the negative for both Louisiana and Mississippi. In addition, the metropolitan areas,
while rebuilt, have lost population, infrastructure (both private and public), and economic
stability because of these events. As we turn to the final concluding chapter, a closer look at the

173

ramifications of emergency management policy, local socioeconomic dynamics and the
implications of racialized response for future disasters are explored.

174

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175

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(4).
County, Jackson. 2010. Board of Supervisions. Jackson, Miss.: City of Jackson, Mississippi.
Dattel, Eugene R. 2006. Cotton in a Global Economy: Mississippi (1800-1860). In Mississippi
Now: an online publication of the Mississippi Historical Society, Ed. Mississippi Historical
Society. Jackson, Miss.: Mississippi Historical Society.
Davis, Dernoral. 2000. A Contested Presence: Free Blacks in Antebellum Mississippi, 18201860. In Mississippi History Now: an online publication of the Mississippi Historical Society,
Ed. Mississippi Historical Society. Jackson, Miss.: Mississippi Historical Society.
District, Yazoo-Mississippi Delta Levee. 2010. District Information. Ed. Yazoo-Mississippi
Delta Levee District. Clarksdale, Miss.
Elazar, Daniel Judah. 1984. American federalism : a view from the States New York: Harper &
Row.
Freedman, Eric., and Stephen Jones. 2007. African Americans in Congress : a documentary
history

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Greenblatt, Alan. 2003. The Two-Sided South. Black Democrats and White Republicans in the
American South Governing 16 (10).
Krane, Dale and Stephen D. Schaffer. 1992. Mississippi government & politics : modernizers
versus traditionalists. Lincoln, NE: University of Nebraska Press.
Mississippi, Hutchinson's Code of. 1798-1848. 524-525, 533.
Nash, Jere. 2006. Mississippi politics : the struggle for power, 1976-2006 / Jere Nash and Andy
Taggart ; foreword by John Grisham. Nash, Jere.: Nash, Jere.
O'Brien, Greg. 2003. Chickasaws: The Unconquerable People. In Mississippi History Now: an
online publication of the Mississippi Historical Society, Ed. Mississippi Historical Society.
Jackson, Miss.: Mississippi Historical Society.
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Statistics, National Center for Education. 2005. Digest of Education Statistics. Ed. Institute of
Education Sciences U.S. Department of Education: U.S. Department of Education.
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publication of the Mississippi Historical Society, Ed. Mississippi Historical Society. Jackson,
Miss.: Mississippi Historical Society.

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Chapter Seven: Concluding Thoughts
Introduction:
The 2005 hurricane season 2005 marked one of the worst natural disasters in our nation’s
history. The loss of life, livelihood, culture and community has changed the American South
permanently. The places and spaces that were devastated by the storms will never be the same
and the rebuilding and recovery process is an ongoing task that in recent years seems to have lost
steam. Most recently, the BP oil spill in the Gulf of Mexico illustrates that in the area of
emergency management, both natural and man-made disasters can have untold impacts on
communities (Anonymous 2010; Walsh 2010). More importantly, these disasters illustrate the
failures of emergency planning to attenuate loss of life and property. Political leadership and
community cohesiveness are necessary in order to create plans that allow for communities to
survive emerging issues and thrive once incidents are under control. The development and
fostering of social capital, as well as an effort to be inclusive throughout the policy planning
process is necessary in all communities when planning for disasters (Coleman 1988; Jackman
and Miller 1998; Margit 2006; Richard and David 2010; Robert 2003; Skocpol and Fiorina
1999).
Local policy development is also crucial to ensuring that economic recovery is smooth.
As chapter four illustrates, in Mississippi local governments are charged with the task of being
the first to respond during a disaster but are not provided the tools needed (expertise, funding,
guidance) to execute this role effectively. State and federal oversight is also lacking, further
complicating the ability to assess community needs during an emergent incident. Additionally,
policy planning in this area remains highly technical but has low salience in the public’s eye so
policymakers have not been as committed to ensuring that planning occurs. When considering

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issues of race and class, communities with high numbers of residents living in poverty are doubly
affected, as the financial resources needed to implement policy are nonexistent. In communities
where there are high numbers of minority residents, community trust as well as financial
limitations makes inclusive, comprehensive policymaking difficult (Bond and Saunders 1982;
Hutchings and Valentino 2004; Mendelberg and Oleske 2000). Race and class are separate
sociological phenomenon, but the quantitative evidence shown here illustrates that they both play
a large role in economic health of communities prior to, during, but especially after a disaster
strikes (Col 2007; Holvino 2010). Thus political actors at all levels of government should pay
close attention to this aspect of community when creating all-hazards planning documents.
Access to transportation, availability of housing/shelter, and dissemination of public information
during times of crises should be made available to all people within a community, not just those
with monetary means. These simple considerations during the policy process can stem the loss of
life and property post-disaster.
Theory Revisited:
The theoretical perspective presented had three parts: race, class and the interaction of the
two in the development of emergency plans, the execution of said plans and the subsequent
economic recovery of areas post-disaster. Consistent with findings from other race and class
studies, this project confirms that racial and economic diversity in an area depresses the ability of
communities to recover after an emergency (Hypothesis 1 and 2). High concentrations of poor
residents had significant negative effects in the state of Louisiana.1 The interactive effect of race
and class, however, did not have the expected effect in either state (Hypothesis 3a). Cooperation
and competition theory hypothesizes high concentrations of race and/or class within a locale

1

See tables 4.6, 4.9 and 4.10 in Chapter 4.
178

results in a lack of coherent planning that is inclusive of certain groups resulting in policy
outcomes that are distinctly different for residents of color. The Racialized Response Theory
bares this finding in all three states by illustrating that in places where high concentrations of
poor and/or minority residents reside, there are distinctly different patterns of economic activity
despite massive investment. Competition and cooperation theory suggests this occurs due to the
different needs of groups and the inability of groups to come to a consensus to managing scare
resources (political and economic) (Bobo and Hutchings 1996; Bornstein and Rapoport 1988;
Frymer 1999; McClain 1993; McClain and Karnig 1990; Oliver 1999).
Hypothesis 3a tested different categorizations of socioeconomic combinations (i.e., High
Minority and High Poor versus High White and Low Poor) to determine if places with various
mixes of residents would have similar negative effects on economic activity. However, findings
were not consistent with the notion that once an area reaches a certain level of minority or
poverty concentration, recovery becomes more difficult (Card 2008; Goering 1978). None of the
different combinations had the explanatory power of the original untransformed poverty or
minority proportion variable.2 While it is impossible to track the residents that were displaced by
the storm and determine who returned home with the data used here,3 it is important to note that
tipping points as envisioned as the amount of diversity within a locale may yet play a role in
economic recovery.
Institutionalized racism was mentioned as a possible effect that could taint results found
here. As mentioned in chapter two, it is impossible to ascertain how much of an effect
institutionalized racism could have on these results without conducting a large-scale time series
2
3

See table 4.9 in chapter 4.

The current 2010 census will have a supplement containing this data and could be used in
future research.
179

with variables to track major legislation related to race, class and emergency planning. This type
of project is beyond the scope of this work, but should be undertaken in the future if funding and
data become available.
Results:
From a theoretical perspective, the quantitative results illustrate that in the area of job
recovery, the hypotheses of racialized response were confirmed in both Mississippi and
Louisiana. In Louisiana, race played a substantial role in limiting the economic progress of
places post-disaster. This result held when testing the effect on new home construction, albeit to
a much smaller degree. The regression analyses also show that class also plays a role in limiting
the development of jobs in the state.4 When looking at the effect of external funding, and its role
in mitigating the effects of race and class on economic recovery, funding had no significant
affect on recovery economies in places where racial and class diversity was high5.
In New Orleans, the most devastated area in the state post-Katrina, the effects of race and
class were very different to what was found in the state results. 6 The class variable, expected to
be negative as hypothesis 2 predicted, had a positive effect on new job creation throughout the
panel. Only when looking at new home permitting activity did we see either variable have a
significant, negative effect.7 Overall, the housing results for both states were not very robust in
any instance and while this has been used elsewhere as a marker of economic health, it may not
be a good indicator of recovery in Mississippi and Louisiana due to high concentrations of rental
units in the Gulfport are and New Orleans (Fisher 2006; Lubell 2006; Turner and Rabenhorst

4
5
6
7

See tables 4.6, 4.7 and 4.9
See table 4.10
See tables 5.5 and 5.6
See table 5.7
180

2007). The correlation between the number of residents living in poverty and the number of
minorities within a locale was stronger inside the greater New Orleans metropolitan area as
expected, but the regression results did not bear out this relationship when ascertaining these
variables affect on the economy. This result casts the issues of race and class as a New Orleanscentric narrative in doubt. The additional robust results from chapter five lend credence to the
issues of economic recovery as a state, not big city problem. For as much effort and time put into
rebuilding the city of New Orleans, the results of this project show that much more work is
needed in other areas of the state.
In Mississippi, the theory of racialized response were more mixed. The proportion of
minority resident has a significant effect on economic activity throughout the panel, but when
looking at new home permitting activity class had a negative and significant effect. Results for
job creation in Louisiana and New Orleans were robust, but for new home construction,
weakened when controlling for the passage of time.8 Looking at the results for Gulfport, a place
with a socioeconomic makeup nearly opposite of New Orleans (i.e., 17.8% minority vs. 32.88
minority proportion), neither class nor race had a significant effect on economic recovery. These
results show that in places where racial and economic diversity were low, economic results were
more robust. In addition, the passage of time in Mississippi played the biggest role in predicting
recovery,9 which may indicate that state leaders were better able to mobilize resources to assist in
job creation and housing development. This result should be interpreted with caution, however;
outside the Gulfport CSA job creation and housing development post disaster was dismal
regardless of the socioeconomic makeup of the county. So although the Gulfport area results

8
9

See table 6.5, 6.8
See table 6.8
181

indicate a robust recovery, the stratification on racial and class lines throughout other parts of the
state confirm the racialized response theory.
The results for the Gulfport area show that the effects of class are insignificant, and that
the passage of time is the best predictor of new job creation10. Minority concentration also plays
little role in understanding the recovery of the Gulfport area of the state. Therefore, future
research utilizing the racialized response theory should be updated to include a rural component
in analysis. It would seem that the homogeneity of Harrison County helps recovery and this
provides additional credence to the racialized response theory as these results show the
composition of place does matter. Additionally, comparing the results to areas outside the CSA,
rural communities fared poorly in both states. As the racialized response theory suggests, if the
socioeconomic composition of place matters, policy and planning will reflect this and it is the
circular function of policy, community, and response that make for success or failure after a
disaster strikes.
Although the recovery of both states has been limited in housing and job creation, there
are some critical differences in emergency response and recovery that should be noted here. In
Mississippi, the governor’s office has been quick to create a plan for the Gulfport area that has
had some success in the years post-Katrina. Haley Barbour also utilized EMAC resources to his
advantage immediately following the storm (Waugh Jr. 2007).
Reese (2006) mentions several features that indicated successful recovery from the
flooding of East Grand Forks in 1997. In comparing the community indicators needed to
redevelop a place post-disaster, she illustrates how recovery is possible and may be applicable to
places such as Detroit, Michigan, a place which has long suffered from slow, economic death.

10

See tables 6.13 and Table 6.14
182

Community pride, sense of urgency couple with long-range vision, and a focus on community
hope are all needed to ensure recovery is swift, long lasting, and inclusive. Louisiana’s response,
in contrast to Mississippi, seems to mimic the slow death occurring in Detroit. Post-disaster
development has been limited due to political infighting and pressure to quickly redevelop
wetland areas to recoup economic losses without considering the historic pitfalls of ignoring land
development recommendations (Cox and Reese 2007).
Hypotheses Revisited:
H1: An increased ratio of minority concentration decreases the amount of economic
recovery post disaster.
Increased racial populations in both Louisiana and Mississippi were shown to have
significant and consistent effect on the economic recovery, especially in the area of job creation.
In the case of Louisiana, increased ratios of minorities and poor residents affected the
development of new jobs and in new home permitting, the proportion of poor residents
negatively affected this aspect of the economy. In Mississippi, increased proportions of minority
residents negatively affected job production throughout the panel while increased proportions of
residents in poverty negatively impacted new housing. In New Orleans, both race and poverty
had devastating effects on job and housing production throughout the panel.
H2: Lower class concentration decreases the amount of economic recovery post disaster
Lower class concentrations in both Louisiana and Mississippi had inconsistent effects on
job creation and housing recovery. In Louisiana and New Orleans, the effects of class were
small, but significant, however in Mississippi, outside the Gulfport CSA, lower class
concentrations had a significant effect on job and housing recovery but not within the CSA.
H3: Increased ratios of both minority and lower class concentration decrease the amount of
economic recovery post disaster.

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In Louisiana, the interactive term of race and class proportion produced no significant
results in economic activity throughout the panel (See table 8, Chapter 5). In Mississippi, only in
the new home permitting model did the multiplicative term have an effect on economic activity,
albeit small and positive. In New Orleans, the interactive term produced no significant affect on
new home permitting or new hiring throughout the panel. In the Gulfport CSA, the interactive
term of race and class proportion produced a negative and significant effect on new hiring as
hypothesized, however the variable had no effect on new home permitting.
H3a: Differing Racial and lower class concentrations combinations may act as tipping
points, which, once a critical threshold is passed, decrease economic recovery.
In the case of Louisiana and Mississippi, no evidence for a racial or class tipping point
was found. Testing different combinations of minority and class populations on job and home
permitting produced no significant results in either case.11 Again, it should be noted here that the
literature on tipping points has traditionally tested the public opinion of residents who move in
and out of districts based on public policy preferences. The approach used here does not utilize
public opinion data, but attempts to ascertain the differing effects of socioeconomic
combinations on economic activity. Nonetheless, the approach used in this project produced no
fruitful results for discussion.
H4: The mention of race and class in planning documents increases the effectiveness of
disaster mitigation.
The interactive effect of race and class had no effect on economic recovery in either state.
Evidence for the tipping point hypothesis was supported in both cases explored here. Finally, in
the policy review, lack of consideration for racial and lower class groups was found to have an
effect on the expedience with which emergency plans were executed. Overall, racial composition
explains economic recovery of areas post-disaster best. In terms of policy development, the
11

See table 4.9
184

policy review illustrates a lack of detail on how to best meet the needs of marginalized
populations, particularly minorities and the poor in both states. Thus, the affect of this lack of
attention resulted in a haphazard approach to getting residents out of the cities affected in a
timely manner, providing emergency shelter for residents left behind, and providing housing and
job resources for affected groups post-Katrina. The organizations responsible for the recovery
aspect of emergency planning have very little direction as to how best to engage marginalized
groups, work with local economic leaders, or communicate economic revitalization plans to
communities at-large. This lack of horizontal communication has resulted in stunted economic
growth in both states as evidenced by the results shown in this project.
Policy Implications:
The racialized response theory posits that community composition (namely high
diversity) affects the policymaking process in a negative way leading to incoherent or incomplete
policies and as a result a decreased chance at a sustained recovery. Looking at the economic
activity in places within both states with high diversity compared to those that are most white
and middle-class, this theory has been largely confirmed. There remains one question that is
unanswered however, how do race and class issues emerge in the policy making process? To
answer this question, a look at the state emergency management plans was done.
The policy review shows that, in general, all levels of government typically overlook
community composition when creating public policy. Examples of this include the building of an
interstate highway system that separates the inner cities from the suburbs, the practice of redlining mortgage applications to discourage racial mixing within communities, the resistance to
school busing to integrate public schools, the list goes on and on. Thus in the U.S., place matters,
and for many it can be the difference between life or death as was the case for many residents in

185

low lying areas during Hurricane Katrina, or the ability to return to the place that is home (Dreier
et al. 2001). Community cohesion planning, while popular in Europe (Adam 2006; Craig et al.
2009; David 2005) has not been utilized to the extent it should in most areas of policy
development. Since emergency management is such a turbulent and disruptive process, planning
for these events demands community participation. Black box theory developed by David Easton
(1957), which envisions public policy development as a system of inputs and outputs that
produces policy that is responsive to the demands of interested parties (i.e., the general public,
the government, and political community), states that policymaking is a continuous process that
is made up of an interrelated set of activities, roles and institutions which operate in an
environment that provides inputs to the political system and translates this input in to policy
outputs. In terms of emergency management, the inputs from the political system should include
residents of all communities, policy professionals as well as emergency management expert,
however this area of policy development remains fairly closed and highly specialized. This
closed nature of policy development means that the system is insular and slow to respond to
newly emergent problems that development when new disasters strike.
The policy review also illustrate the need for due diligence on the part of state and federal
officials in understanding their roles during emergencies. The mobilization of needed resources
and collaborative efforts across various agencies should be incorporated. Finally, policymaker’s
aversion to risk (politically) in creating policies that are costly to the American public should be
tamped down by providing the public an opportunity to understand the true costs of inaction or
inadequate planning (Hacker 2004; Shavell 1979; White 2006). Natural disasters, as well as
man-made disasters are not place specific. What occurred in New Orleans was the culmination of

186

years of poor planning, racial and economic strife, as well as political inattention to these
pressing issues.
Since Hurricane Katrina several federal policy changes have been implemented. In 2006,
Congress passed the Post-Katrina Emergency Management Reform Act (Pub. L. 109-295). This
act contains several provisions that make several organizational changes to FEMA and the
Department of Homeland Security including:
•
•
•
•
•
•
•
•
•

Giving FEMA both an elevated status (i.e., it is now considered a distinct entity within
DHS) within Homeland Security and enhanced organizational autonomy to respond
during disasters;
Authorizing the creation of the National Integration Center, whose responsibilities
include the management of both the NIMS and the NRP;
Charges FEMA with the creation of a logistics system to track the location of goods and
services to affected states during disasters;
Allows the FEMA administrator authorization to facilitate disaster response operations
(i.e., Recovery Strategy);
Creates Emergency Response Teams for use during emergencies, as well as Urban
Search and Rescue teams to be used in post-recovery stages;
Charges the FEMA administrator with assembling a group of federal and non-federal
players to develop recovery strategies for a myriad of emergency situations;
Establishes recovery offices in Mississippi, Louisiana, Alabama and Texas
Develops a separate Housing Strategy (related to the Recovery Strategy noted above) to
assist in providing disaster victims with housing resources post-disaster;
Charges the FEMA Administrator to designate a Small State and Rural Advocate to
ensure that rural and small area needs are met (Bea 2006).

At the state level, both Louisiana and Mississippi have updated emergency planning
documents incorporating lessons learned during Katrina (2010). State officials highlight issues of
staff turnover and reluctance of arbitration over FEMA funded projects as major impediments to
economic recovery post disaster. Finally, at the community level New Orleans and the Gulfport
area have both implemented community resiliency panels to determine best practices in the event
another disaster on the scale of Hurricane Katrina occurs (Cigler 2010; Gillette 2007; Northway
2009). Additionally, the impact of newer disasters such as Hurricanes Gustav, Ike and the
Deepwater Horizon oil spill have hindered economic recovery and policy adjustments have yet

187

to be implemented to handle these new developments (2010; Cigler 2010). It would seem that
issues of race and class and community cohesion may indeed be present in the black box, time
will tell whether or not these new inputs will product meaningful policy outputs for those
involved.
Future Research Implications:
Racialized response theory should be tested on other focusing events throughout United
States history. This should be done to determine whether, in different areas of the county,
racialized response remains an issue. Future research on emergency management planning and
the role of race and class in the planning and recovery stages of emergency management can be
tested by utilizing participant observation, case study and in person interview approaches. Using
participant observation of the policy planning process may uncover critical opportunities to
include the community in recovery planning. Case studies of successful planning strategies and
execution of said plans will allow for policymakers to duplicate well-made plans and adjust them
accordingly for specific situations. Finally, in-person interviews may help uncover the thought
processes employed when declaring and executing emergency management plans. These
elements of emergency management planning policy have been closed to researchers and the
public, future work to illuminate these areas is necessary if successful plans are to be developed
in the future.
The Importance of Rural Study:
This project highlighted differences between urban and rural recovery in stark ways. The
socioeconomic composition of both states in its rural areas were very similar to each other, and
although the poor and black were concentrated in New Orleans per the media coverage of
Hurricane Katrina, there seems to be just as big a story (of lack of coordination, communication

188

and consideration of group needs) that is ongoing in the state’s rural areas. Research in this area
is critical to understanding both the policy process as well as the recovery process. Rural needs
typically do not take precedence in state planner’s minds when creating public policy and this
research highlights these needs in the policy planning process as well.
Data Needs:
To conduct a thorough analysis of rural needs, additional information would need to be
collected. Creating a survey instrument to be used to ascertain the planning processes of these
areas would shed much needed light on the ways emergency management and economic
development planning could be improved. Additionally, research into the existing policy
network between the urban and rural in the area of emergency planning policy of both states
should be further explored. Finally, issues of race and class may be even more insurmountable in
places where citizens live further apart. The creation of a dissimilarity index of rural area might
also be an important measure to determine the affect proximity has on planning and recovery.
Next Steps:
Moving forward from this research project, I would like to proceed with the development
of a survey instrument that could be used to gauge the affects of race and the inclusion of
minority groups in the policy planning process at the state and local levels. Additionally, the
development of a measure of dissimilarity for rural areas would be necessary in order to
undertake testing the racialized response theory in other areas of the United States; disasters are
not discriminate in which areas they hit, big city, or small town. Finally, updating the current
data set with 2008, 2009 and 2010 economic numbers would further strengthen the results
founds here. Adding controls for a change in administration as well as additional federal

189

programs (i.e., ARRA) that have since been created to mitigate economic issues in the gulf
should be explored.
Conclusion:
This project set out to determine whether race and class play a decisive role in the
economic recovery of places after a natural disaster. By utilizing a panel design, results show
that race and class do play a role in the rebuilding of communities. Additionally, this project
aimed to determine whether race and class play a role in the policy process surrounding
emergency management. Using a document review approach, the racialized response theory also
was confirmed. The failure to incorporate race and class into planning documents proved to be
problematic in dealing with a natural disaster in a highly stratified place. What occurred in the
gulf coast based on these finding could easily happen in New York City, San Francisco, Miami,
or any place where the race and class divide is significant. What remains to be seen is whether
policy learning will occur in this area of policy. As stated above, emergency management is a
highly labor intensive process and requires technical expertise, risk management, substantial
financial resources as well as horizontal and vertical collaboration between levels of government.

190

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