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Hurricane Katrina: Who Received Aid

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Running Head: HURRICANE KATRINA: WHO RECEIVED AID

Hurricane Katrina:

A Closer Look at Those Affected and How They Received Aid

Laura Giannotti

Senior Distinction

Sociology Department

Emmanuel College

© Laura Giannotti


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Abstract

Why did counties receive differing amounts of aid, following Hurricane Katrina? Although one

expects it would be based on the damage a county had, I hypothesize, based upon social capital

theory, that the amount of aid received was also associated with the number of businesses in the

county, the poverty rate, racial make-up, and net population size. I conduct secondary data

analysis on data collected by the U.S. Census, FEMA, and other national databases. In support

of my hypotheses, I find positive correlations between a rising poverty rate and the percentage of

black residents, respectively, and the amount of aid received by a county, while a negative

relationship exists between structural damage and aid received across county lines. A positive

relationship exists between the number of non-profits in a county and aid, suggesting that non-

profits may advocate for aid, making up for limited social capital at the individual level.


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Introduction:

The morning of August 29, 2005 was not a typical morning for the millions of people

living along the Gulf Coast states. Early that morning, Hurricane Katrina made landfall in the

region. Winds reached as high as 140 mph in parts of Southeastern Louisiana (NCDC, 2005).

The storm surge caused by onshore winds reached at least 30 feet, flooding towns and cities

(NCDC, 2005). Months later, I travelled to Harrison County, MS to aid in the recovery efforts.

Cement slabs covered the landscape with scattered piles of debris from where houses had once

been. Palm trees no longer stood tall but at 45° angles. Houses that still stood carried memories

of the storm, with watermarks high off the ground and the smell of mold. One could see large

orange “X-shaped” grids painted on the sides of buildings to indicate when the search date

occurred. Experiences with locals remained close to my heart as the years passed by. Locals

brought to light the injustices of aid distribution, mainly the issue of aid distribution and its

inequalities in poor areas. Another apparent theme from Mississippi residents was anger towards

the attention and recovery efforts directed at New Orleans, but not other affected areas. The

stories I heard years ago serve as my motivation for researching this topic. What factors

determined how much aid a county received? Did some counties receive more aid than others?

Prior Research:

Hurricane Katrina was one of the worst natural disasters that America has seen. For

many, the terms “Hurricane Katrina” and “New Orleans” became synonymous during and after

2005. Millions were displaced and billions of dollars were needed to rebuild the affected areas

(Bush, 2005).

An Overview of the Effects of Hurricane Katrina:


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Hurricane Katrina was very costly in terms of destruction and amount of lives lost. John

S. Petterson, Laura D. Stanley, Edward Glazier, and James Philipp (2006) found that homes,

businesses, and public buildings along the Gulf Coast received the vast majority of the damage

from the storm. The authors carried out a preliminary assessment of social and economic

impacts. These authors researched the effect Hurricane Katrina had on energy, finance,

construction, housing, and gaming. To obtain data, the authors interviewed evacuees. Petterson

et al (2006) found that many lacked food, water, safety, evacuation aid, and medical resources.

Why were there so many errors in recovery efforts? Did some have access to resources while

others did not?

Understanding Hurricane Katrina though Stratification Theory:

In every society, not all are equal in terms of economic, or class, status. Different class

levels are stratified and have created multiple social classes. Kingsley Davis and Wilbert E.

Moore (1945) declared members of a functioning society have different statuses and carry out

their designated roles. This functionalist perspective of stratification theory suggests that while

parts of society function as a whole, individuals have specific tasks to complete in order to keep

society functioning; these specific tasks rank individuals in a particular social class. Rewards for

completed task may include prestige or income (Davis & Moore, 1945). The position’s

importance along with the amount of training and talent required for a specific position

determines the amount of rewards an individual obtains (Davis & Moore, 1945). If a position

does not require much skill or training, individuals that hold the position will not receive many

rewards and will become potentially replaceable. The positions in society not highly important

to the functioning of society become the jobs of the working and poor class.


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The class differences that stem from Davis and Moore’s stratification theory (1945)

illustrate differences in societies across the world, including the counties affected by Hurricane

Katrina. In the counties affected by the storm, the class and racial divisions were clear. Those

from lower classes and minority groups did not have the resources available in order to evacuate

(i.e. enough money for transportation or a vehicle in order to leave).

Timothy Brezina (2008) addressed the lack of governmental aid made available to

survivors, both pre- and post-storm. Brezina (2008) used the Survey of Hurricane Katrina

Evacuees, targeting individuals who were hit the hardest by the storm, in order to analyze

attitudes and evacuation patterns. Those who evacuated early were predominately wealthy,

white women (Brezina, 2008). The majority of those who participated in government-assisted

post-evacuation programs were unemployed, poor, African American females (Brezina, 2008).

In addition, during recovery, many of the poor had to rely on outside sources to recover.

Brezina’s findings (2008) further show the stratification between different classes and racial

groups.

Much like Brezina (2008), James R. Elliott and Jeremy Pais (2006), addressed the role of

class differences and government aid, post-Katrina. Elliott and Pais (2006) compared a variety

of characteristics including race, gender, age, homeownership, and income distributions, to short-

term recovery efforts, stress and emotional support available, and the likelihood of victims

returning home. While examining who did not evacuate the affected areas, findings show that

the vast majority of residents eventually evacuated but consistent with Brezina’s study (2008),

those who braved the storm were heavily low-income African Americans. This was due to the

stratification of classes and races and the overall lack of resources available to the African-

American population. Unemployment after the storm and the stress that victims experienced


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were higher among African Americans than their white counterparts (Elliott & Pais, 2006).

Individuals in the lower ranks of society received fewer rewards and thus did not have the

resources to evacuate before the storm (Davis & Miller, 1945). Similarly, in a study by Lee

Clarke (2006), Clarke found that poor African-Americans could not evacuate prior to the storm.

Both the conditions that minority groups and lower social classes experienced were a result of

stratification. The stratification of social classes took a toll on minority groups. Since the

founding of the United States, African-Americans, stereotypically, occupied the lowest classes in

society. After the Civil War, and even the Civil Rights Movement, many still experienced

inequalities and segregation. This placed minority groups in the lower classes of society and

forced them to receive the smallest amount of rewards, which has put them at a significant

disadvantage.

The Presence of Anomie in the Aftermath of Hurricane Katrina:

Anomie, a term coined by Emile Durkheim (1897), or weak societal norms and the

absence of shared values, results in a lack of social control and purpose in society. This idea,

often described as “normlessness”, expresses the absence of norms present in a society

(Durkheim, 1897). In the direct aftermath of the storm, communities experienced anomie, both

on a macro and micro level. Many businesses and local organizations, such as police stations,

local governments, churches, and hospitals received extensive damage, remained closed, or

became unable to carry out their duties. This may have left counties in a state of chaos,

confusion, and isolation. In order to rid the affected counties of this feeling of anomie, the

federal government, private organizations, and NGOs came in and alleviated the burdens;

restored norms, and reestablished a sense of order. This enabled counties to function properly

again.


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The question of interest for Lee Clarke (2006), what more could have been done in order

to diminish the loss of life and disorder among the areas affected by Hurricane Katrina, remained

unanswered. Clarke (2006) states that Hurricane Katrina will become the most costly hurricane

in US history because of the widespread and catastrophic damage. Following the storm’s

destruction, the disappearance of social order, resulting, in part, from the many police officers

who went missing, occurred immediately after the storm hit. This disorder worsened conditions

and established a sense of anomie and disorganization. Evacuations were not carried out in a

timely manner, nor could the Federal Emergency Management Agency (FEMA) respond to the

areas affected without being summoned by state or local governments. Furthermore, FEMA had

to in turn contact the US military for supplies, but failed to do so. The counties that experienced

anomie had to rely on outside organizations to send monetary and physical relief. The social

networks in areas with anomie were limited and caused victims to find recovery on their own or

turn to the government for help.

The Great Flood of 1927 in Louisiana, the Vanport Flood of 1948 in Oregon, and

Hurricane Katrina of 2005 held similarities to each other and to the concept of anomie. Rivera

and Miller (2007) reported white elites of New Orleans sabotaged the levees in 1927 in order to

avoid large amounts of damage in the city, but by doing so, the poorer surrounding areas that

housed African-Americans became flooded. In Vanport, Oregon in 1948, policies of

discrimination and housing segregation were greatly enforced (Rivera & Miller, 2007). Social

injustices and racial biases arose after Hurricane Katrina left the affected areas. Black men looted

stores and raped women throughout New Orleans; this caused black Americans to be labeled as

criminals, although white men took part in these acts as well (Rivera & Miller, 2007). In the

past, recovery efforts turned to African-Americans after all others received help (Rivera &


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Miller, 2007). These actions of oppression by white elites produced anomie in black

neighborhoods. Directly after disasters, specifically Hurricane Katrina, normlessness occurred.

Outside forces, often the federal government, needed to step in and restore normalcy.

The Role of Social Capital in Recovery Efforts:

Pierre Bourdieu (1986) defined the term social capital as the acquired benefits from

personal social networks and connections. Social capital, one’s access to resources including

networks, information, and advocacy programs, is also dependent on an individual’s race and

class status. Those of lower economic classes and of a minority are likely to have a lower social

capital and access to resources. These networks and connections offer aid including monetary

and physical donations, and/or emotional support to those within their networks who are in need.

Mutual benefit in the form of cooperation and coordination typically result from social

networks and norms (Putnam 1995). The social capital that causes this mutual benefit can be

formal or informal, dense or thin, but all should envelope reciprocity (Putnam 1995). The civic

commitment of norms and networks affects the performance of the government (Putnam, 1995).

When individuals are socially active in society, the performance of the government is oftentimes

beneficial and helpful to the society as a whole. According to Putnam (1995), areas with higher

social capital are easier to live in than areas with low social capital. This may be because of the

greater number of connections and sense of community.

Understanding Hurricane Katrina through Social Capital:

In the aftermath of Hurricane Katrina, survivors sought out resources from the

government, non-governmental organizations (NGOs), and social and kinship networks to aid

them in recovery efforts. Those with a higher level of social capital were able to access their

resources with ease and did not have to rely on the government for aid.


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Richard Forgette, David A. Swanson, and Mark Van Boening (2008) researched the role

of social and kinship networks and their relationship to the amount of informal aid an individual

received. Forgette et al. (2008) believed that the size of one’s social network affected the amount

of aid one received. Findings show that whites and women are more likely than men and

minorities to have network ties (2008). The vast majority of these networks are made of families

and friends, a view that is consistent with Putnam’s findings on social capital (Forgette et al.,

2008; Putnam, 1995). Furthermore, Forgette et al. (2008) argued the strength of ties depends

upon whether or not the network existed pre-disaster, formed during the disaster, or was

stationary. The level of social capital was significant to the amount of aid a person received.

Individuals with lower income, and of the minority, had smaller, weaker, and poorer networks.

These individuals were therefore more reliant on formal systems, such as the federal

government. Individuals with larger social networks and a higher amount of social capital

received more aid through connections. Conversely, individuals with a small amount of social

capital will receive larger amounts of aid from government-funded programs.

In “Determinants of Governmental Aid to Katrina Survivors: Evidence from Survey

Data”, William F. Chappell, Richard Forgette, Bryan Dettrey, Mark Van Boening, and David A.

Swanson (2007) measured the government aid made available to survivors and its effectiveness.

Chappell et al. (2007) surveyed 400 survivors from Hancock and Harrison counties in

Mississippi in order to determine whether the government distributed aid based on need or other

criteria. The survey conducted measured physical, psychological, social, and economic well-

being before and after the storm in addition to social and kinship networks, and common

descriptive statistics such as age, gender, and education (Chappell et al., 2007). Chappell et al.

(2007) found that those affected reported the majority of their aid came from religious


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organizations and kinship networks; only 25% of those interviewed thought the government

provided the most aid. The importance of social capital reflects findings by Chappell et al.

(2007). The majority living in the Hancock and Harrison Counties relied on social networks and

affiliations with religious organizations in order to obtain the aid needed to recover from the

storm. With only 25% of the sample reporting aid coming from the government, one can see the

overwhelming presence of social capital.

The makeup of core network ties determined the amount of informal support available to

individuals, according to Jeanne Hurlbert, Valerie Haines, & John Beggs (2000). Individuals

with networks that were denser, younger, and comprised of more family members and males had

access to a greater amount of informal support (Hurlbert et al., 2000). Social capital, with the

number of providers and activated ties available, may broaden in order to receive aid from others

in the case of disasters, such as Hurricane Katrina. The studies carried out by Forgette et al.

(2008), Chappell et al. (2007), and Hurlbert et al. (2000) support the idea that social capital is

oftentimes the first line of defense and is very important when individuals are faced with

devastation.

Factors that Matter on the County Level:

Marc Landy (2008) assessed the governmental aid survivors received at the local, state,

and federal levels in both Louisiana and Mississippi in his article “Mega-Disasters and

Federalism”. Landy (2008) placed blame on New Orleans' residents and on its local government

for not evacuating effectively before Hurricane Katrina hit. The limited evacuation was the

result of many factors. The order to evacuate, issued less than 24 hours before the storm hit the

city, played a major role in causing chaos. In questioning why New Orleans had a higher death

toll than more rural areas, Landy (2008) found that Mississippi mayors had ordered timely


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evacuations. In the aftermath of the storm, Mississippi towns were able to begin clean up

immediately because of some money that was set aside for events such as a hurricane; Louisiana,

in contrast, had to wait for government aid (Landy, 2008). Landy (2008) acknowledges that the

federal government under President Bush gave over $51 billion in emergency spending, in

addition to providing personnel and services. FEMA provided housing for nearly half a million

victims (Landy, 2008). Unfortunately, despite the efforts from the government, the ineffective

distribution process causes many to overlook their efforts. FEMA, the US Department of

Housing and Urban Development, and the Corps of Engineers, required lengthy application

forms, reviews, and requirements, which significantly slowed the recovery and relief process

(Landy, 2008). The government feared the corruption of aid which often prevented individuals

from receiving aid in a timely manner, if they received aid at all. Fortunately for survivors, state

governments called in National Guard reserves immediately, which began aiding survivors 24

hours after Katrina hit the area (Landy, 2008). Landy (2008) found that many from Mississippi

were grateful for the government aid they received, while those from New Orleans expressed

hostility and defiance towards the government.

Limitations to Previous Studies:

Although these previous studies are informative and thorough, there are limitations. The

articles do not address whether or not those in different social classes or with varying amounts of

damage received differing amounts of aid. The articles addressed aid from personal networks,

but failed to analyze the aid provided from the government, private donations, and NGOs, with

the exception of perception. Furthermore, the articles do not address the role local businesses

had on communities in terms of the amount of aid received. Lastly, the majority of the articles


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addressed the victims from New Orleans but often dismissed those living along the Gulf Coast in

Mississippi or Alabama.

The studies conducted by Landy (2008) and Chappell et al. (2007) examined the

government’s involvement in the recovery efforts throughout Mississippi and Louisiana.

Similarly, Landy (2008) discussed the role the government played after the storm. Landy (2008)

found that although the government provided a large amount of emergency spending, the

restrictions and application process made it difficult to access the aid. It is possible that the

federal government distributed aid unjustly based on the stratification of classes and races; lower

classes and minorities might not have had the information or resources available to access

government aid. Chappell et al. (2007) discussed the effectiveness of the aid survivors received,

as well as which organizations supplied the most aid. Findings from this study show that

religious organizations and kinship networks, a part of one’s social capital, were more likely seen

as the major contributors to the relief efforts rather than the government.

In this study, I will begin to address some of the limitations listed above. During

recovery, social capital may play important roles in one’s life. I will analyze the relationships

between different types of federal aid made available to counties based on various factors. I will

determine whether there is a relationship between the amount of damage from the hurricane and

the amount of aid distributed to counties by the government. Potential relationships exist

between amount of government aid received and the number of local businesses open for

business. In addition, the study will determine potential relationships between aid and both the

racial and economic composition of the counties, based on stratification and social capital

theories.


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Hypotheses:

Question 1: Is the amount of aid a county receives influenced by the amount of damage

experienced?

Hypothesis 1: Counties with more structural damage would receive more aid.

As the amount of damage increases, the cost of repairs increases. Many victims of the

storm relied on others for aid. I believe the government supplied more aid to victims with

widespread structural damage because the damage would become too costly for network ties.

Based on Landy’s study (2008), I hypothesize that counties with an increasing number of

catastrophic structural damages recorded will receive more aid.

Question 2: Is the amount of aid a county receives influenced by its poverty rate and

social capital?

Hypothesis 2A: Counties with a higher rate of poverty would receive more aid.

Those in greater poverty have fewer informal networks and are more reliant on

government structures. Brezina (2008), found that those who took part in government-assisted

post-evacuation programs were often poor, unemployed, and African-American. Elliott et al.

(2006) found individuals living in poverty were more likely to seek assistance in evacuating from

the government because they lacked sufficient resources. These findings allow me to

hypothesize that poorer counties will not have adequate funds for recovery and will have to turn

to the government for aid.

Hypothesis 2B: Counties with a higher rate of poverty would receive less aid.

Social capital plays an important role here. Those in poverty typically have a smaller

amount of social capital, which further limits their resources and connections to the federal

government. Stratification theory (Davis & Moore, 1945) and social capital (Bourdieu, 1986)


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suggest those of poor classes have fewer rewards and fewer social networks. This might limit

access to resources resulting in less aid distributed on the federal government level.

Question 3: Is the presence of businesses in a county related to the amount of aid the

county receives?

Hypothesis 3: Counties with a greater number of businesses would receive less aid.

Petterson et al. (2006) found a positive relationship between casinos and other businesses in

Mississippi and a faster rate of recovery in those areas. In addition, the social capital of locals

would increase with more businesses aiding the local economy. Petterson et al.’s findings (2006)

and social capital theory led me to hypothesize that counties with a greater number of active

businesses would receive less aid.

Question 4: Is race a contributing factor to the amount of aid a county receives?

Hypothesis 4A: Counties with more African-Americans would receive more aid.

Reliance on formal aid and inferior housing conditions are experiences by African-

Americans. It was reported that African-Americans live in disaster prone areas because the

living conditions available to them due to housing segregation (Rivera and Millar 2006). I

hypothesize that this population would receive more aid than their white counterparts since they

live in areas where the amount of potential damage was greater due to geographic location and

poorer housing stock.

Hypothesis 4B: Counties with more African-Americans would receive less aid.

The racial stratification, more homogenous social networks, and less social capital are

reasons that support this hypothesis. As a racial group, African-Americans are often placed on

the bottom of society. This causes them to have fewer opportunities and limit their social capital.

Additionally, housing segregation causes social networks to be made up of homogenous


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members, having limited support but no real economic opportunity. The limits placed on social

capital due to the stratification may result in less aid overall, including government aid.

Methodology:

To study the aid distribution and its possible inequalities that followed the aftermath of

Hurricane Katrina, I chose to conduct a quantitative analysis using secondary data. This data

was collected from sources including but not limited to the United States Census, Federal

Emergency Management Agency, Gulf Coast News, Governor Barbour reports, and the US

Bureau of Labor Statistics. My sample size was made up of ten counties. This sample is made

up of the parishes and counties most affected by Hurricane Katrina in Louisiana, Mississippi, and

Alabama. The counties include Jefferson, Orleans, Plaquemines, St. Bernard, St. Charles, and

St. Tammany Parishes of Louisiana; Hancock, Harrison, and Jackson Counties of Mississippi;

and Mobile, Alabama. The data was compiled within thirteen months after Hurricane Katrina,

although some reports were not published until 2010. Variables regarding the overall population

as well as the racial breakdown of the population were recorded one month before and five

months after Hurricane Katrina made landfall.

After entering all secondary data into SPSS, I conducted various statistical tests. I

calculated means and frequency distributions for population, race, income, and employment. I

also examined damages, which were measured into five categories: limited damage, moderate

damage, extensive damage, catastrophic damage, and flooding damage (FEMA, 2005).

The variables for structural damage are broken up into five categories. Damages 1

through 4 are made up of FEMA reports that declared limited, moderate, extensive, and

catastrophic damage respectively. Flooding is also examined but considered a separate variable


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because it has specific effects whereas the other four variables for damage include damage from

wind, tornadoes, and/or storm surge. Limited damage (Damage 1) is defined as surface damage

to both solid and mobile structures. Moderate damage (Damage 2) includes exterior damage to

solid structures while mobile homes may be destroyed, thus causing displacement. Solid

structures that are either destroyed or are damaged on the interior and exterior and the

destruction of mobile homes are all defined as extensive damage (Damage 3). Catastrophic

damage (Damage 4) includes most solid structures and all mobile homes completely destroyed

(FEMA, 2005).

In order to test my hypotheses, I carried out bivariate and partial bivariate correlations.

While the bivariate correlations show the overall relationships between an independent and

dependent variable, the partial bivariate correlations control for a variable, such as population,

before Hurricane Katrina hit. In order to find a potential relationship between race, poverty, and

amount of aid together, I carried out a linear regression, a type of multivariate statistical test.

With this test, I was able to control for the net population before the storm. The results are able

to show the statistical significance along with any relationship between the variables. Each of

these tests provided statistics for the counties affected and allowed me to examine what county

specific factors played a role in the distribution of funds.

When compiling my dataset from a variety of sources, I encountered a few limitations.

The variables I was interested in studying had to be collected through numerous databases. This

meant data were collected at different points in time. Since my variables were conducted from

multiple databases, not all counties have information available for every variable. This limited

my sample size significantly while analyzing specific variables, such as the variable for non-

profits. Another restriction arose while finding data. In the cases of all three counties in


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Mississippi, no data were available under the variable “community development grant” for the

economy, housing, or infrastructure. Although I was able to obtain similar information, there

may be some error in these numbers since they were researched by different state officials.

Findings:

Frequency Distributions of Independent Variables:

Profiles of the ten counties that make up my sample are illustrated in the descriptive

statistics displayed in Tables 1 and 2. In Table 1, the population means from one month before

and a few months after Hurricane Katrina made landfall are provided. The difference in the

means are shown as well. The means and differences in population for the white and black

populations are displayed above. The population before and after the storm decreased drastically

on all fronts especially with almost a third of all whites migrating elsewhere. Table 2 shows the

descriptive statistics of the counties including their socioeconomic profiles and the amount of

housing units and mobile homes. The outcomes of the means outline amount of education

attainment and number of workers above the age of sixteen, which is further broken down into

homeowners and those who rent their homes or apartments. The median household income,

Table 1: County Statistics; N=10 Mean Difference in

Means Pre-Hurricane Katrina population (2005)

212602.5 -

Post-Hurricane Katrina population (2006)

176081.7 -36520.8

White population 2005 127341.1 - White population 2006 86617.7 -40723.4

Black population 2005 68600.5 -

Black population 2006 48655.7 -19944.8

Source: US Census 2005 and 2006


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number of mobile homes and housing units, and those living in or below poverty further helped

represent the socioeconomic status and profile of the counties studies.

The percent of employment change is also provided. Just over three out of four individuals in

these counties graduated high school while not even one in five are college educated. The results

from comparing the population statistics with employment show that roughly half of the

individuals in these counties are unemployed. This does not take into account children under the

age of 16, those not employed for health factors, and those who are retired. Unemployment is

still high, however. This high unemployment rate increases the rate of poverty, as do the low

levels of education and types of occupations. I examine all three measurements of a county’s

socioeconomic profile (average unemployment, average income, and average level of education)

in the bivariate analyses in relation to the amount of aid received.

Findings Regarding Structural Damage and Aid:

Hypothesis one tests the potential relationship between amount of structural damage

experienced and the amount of aid received. The amount of cases reported for each level of

Table 2: Descriptive Statistics; N=10

% employment change (2005 to 2006) -15% % HS graduates 78% % College graduates 18% Workers 16+ 87083 Number of home owners with a job 49232 Number of renters with a job 27828 Median household income 37636 Total mobile homes 7146 Total housing units 84778 Total living in or below poverty 234585 Source: US Census 2005, BLS monthly review


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structural damage is shown in Figure 1.

H1: Counties with more structural damage would receive more aid.

The potential relationship between the variables of damage previously discussed and the

amount of aid received were examined. After conducting a partial bivariate correlation and

controlling for population size before Hurricane Katrina made landfall, some support exists for

my hypothesis. Strong, negative, and statistically significant relationships between Damage 1

and the majority of the aid variables can be seen in Table 3. The outcomes for Damage 1 and the

community development block grants for the economy, the block grant for infrastructure, and the

federal grant were not statistically significant. All three variables were marginally significant

with p=0.073 or less. These results confirm that as the amount of limited damages recorded

increases, the amount of aid distributed to those counties decreases. This creates mixed support

for my hypothesis; there is no strong, significant relationship between the other levels of damage

and aid.

Findings Regarding Poverty and Aid:

The two sections of hypothesis two address the effects poverty had on the amount of aid

distributed through the federal government, and specific organizations, such as FEMA. The

average number of individuals living in or below the poverty line in the ten counties studied is

3914.7 549.2 1339.5

132470.9 110551.4

0

50000

100000

150000

Damage 4 Damage 3 Damage 2 Damage 1 Flooding

Figure 1: Amount of Structural Damage Recorded; N=10

Damage

Sources: Library of Congress, FEMA


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234585 individuals (Table 2). Partial bivariate correlations between poverty and aid, controlling

for the pre-Hurricane Katrina population were tested.

H 2A: Counties with a higher rate of poverty would receive more government aid.

I hypothesized that non-profits would have an effect on the amount of aid received by

county because of their presence in social networks advocating for people. This hypothesis is

refuted; although, there is a positive relationship between poverty rates and aid distribution.

Strong, positive, and statistically significant relationships are found among the majority of tests

between my independent and dependent variables. The federal government grant in 2006 and the

number living below poverty yielded a correlation of 0.845**(Table 4). The number of

government funded projects and the number living below poverty had a correlation of

0.825**(Table 4). These were both very strong, positive, and statistically significant

relationships. The community development block grant for infrastructure, the federal share

obligated for projects, and the number of non-profits also fall under this category of strong,

positive, and significant correlations. These results support my hypothesis; as the amount of

poverty increases, the amount of aid increases.

Table 3: Amount of Aid Received per County in Regards to Recorded Damages; N= 10 Damage 1 Damage 2 Damage 3 Damage 4 Flooding FEMA -.838** -.381 -.339 -.248 .328 Economic grant -.608 .161 .513 .420 .114 Housing grant1 -.933** -.210 -.314 .100 .313 Infrastructure grant -.625 -.086 .252 .421 -.027 Number of projects -.837** -.222 -.010 .165 .153 Federal money -.843** -.252 -.204 .119 .236 Federal grant -.629 -.311 .113 .335 -.114 Federal direct loans -.857** -.057 .003 .177 .288 (Statistically Significant:*p ≤0.05, **p≤0.01**);

1N=7

Sources: US Census Bureau, Barbour, Governor H., FEMA


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H2B : Counties with a higher rate of poverty would receive less aid.

This hypothesis aimed at determining whether or not social capital played a role in the amount of

aid a county received. Support for this hypothesis is not found; however, the relationship

between non-profits and the aid variables all have very strong, positive, and significant

relationships. Three variables have a significance of p<0.001. The number of nonprofits

increases alongside the amount of aid, suggesting that nonprofits are working as advocacy

organizations.

Findings Regarding Local Businesses and Aid:

Table 4: Poverty Rates and the Amount of Aid Distributed per County; N=10

FEMA .715* Economic grant .523

Housing grant1 .766

Infrastructure grant .731* Number of projects .825** Federal money .743* Federal grant .845** Federal direct loans .617 (Statistically Significant: *p ≤0.05, **p≤0.01**);

1N=7


Table 5: Implications of Race on Amount of Aid Received by County; N=10

White Black Non-profit

1 -.951** .943**

FEMA -.772* .737* Economic grant -.593 .560 Housing grant

1 -.855* .813*

Infrastructure grant -.745* .739* # projects -.878** .854* Federal money .787* .753* Federal grants -.844** .855** Federal loans -.689* .645 (Statistically Significant: *p ≤0.05, **p≤0.01**);

1N=7



22

A relationship between the total number of businesses and amount of aid received was

likely. Since the amount of internal support and local economy were positively influencing the

county, the amount of aid would likely have a negative relationship. In order to test this

relationship, I used the number of businesses in the individual counties in 2006. This variable

shows the amount of businesses functioning after Hurricane Katrina. These businesses

contributed to the local economies not only by providing jobs, but also through aiding the

economy in other ways.

H3: Counties with a greater number of businesses would receive less aid.

My results, which can be seen in Table 6, support this hypothesis. A very strong, negative, and

statistically significant relationship exists between businesses and aid. As the number of

businesses in a particular county increase, the amount of aid decreases. The more businesses in a

community suggest a functioning economy. A bivariate correlation between the different levels

of structural damage (moderate, limited, and flooding damage) and number of businesses

resulted in strong, positive, statistically significant relationships. No relationship was found

between catastrophic or extensive damage and number of businesses. Counties with less

structural damage have more businesses. With more local infrastructures and internal resources

at hand, less need is for external aid.

These findings are consistent with my hypothesis as well.

Findings Regarding Race and Aid:

Another key factor in determining aid distribution is race. Although both Hispanics and

Asians reside in the counties studied, I mainly examined the populations of whites and African-

Americans because they made up the vast majority of the populations across all counties. I

expected to find race as an influencing variable in determining amount of aid a county received.


23

H4A: Counties with more African-Americans would receive more government aid.

I found the support for this hypothesis to be strong and significant. In Table 7, one can see that

the amount of aid received by the black community and white community yielded opposite

results. The majority of my variables depicting amount and distribution of aid hold a positive

relationship in counties with black populations. On the contrary, counties with more whites

received less aid across all aid measurements. My findings support my hypothesis: counties with

a higher concentration of African-Americans receive more aid.

Table 6: Relationships between businesses in 2006 and aid received by county; N=10

Non-profit aid1 -.917** FEMA aid -.734* Block grant for economic development -.735* Block grant for infrastructure -.846** Number of projects -.893*** Federal money for projects -.826** Federal grant -.904*** Federal direct loans -.799** (Statistically Significant: *p ≤0.05, **p≤0.01**, ***p≤0.001);

1N=7

Sources: US Census, FEMA

Table 7: Implications of Race on Amount of Aid Received by County; N=10

White Black Non-profit

1 -.951** .943**

FEMA -.772* .737* Economic grant -.593 .560 Housing grant

1 -.855* .813*

Infrastructure grant -.745* .739* # projects -.878** .854* Federal money .787* .753* Federal grants -.844** .855** Federal loans -.689* .645 (Statistically Significant: *p ≤0.05, **p≤0.01**);

1N=7



24

H4B: Counties with more African-Americans would receive less aid.

This hypothesis received no support. This hypothesis aimed at testing the role of social capital

among African-Americans. The relationship between the non-profits and aid from Table 5

shows its importance to the amount of aid given. When running a bivariate correlation between

race and non-profits, the correlation is very strong. This suggests a potential increase in social

capital.

Examining the Relationships between Poverty, Race, and Aid:

While conducting partial bivariate correlations, I found that both poverty and race held

strong relationships to the aid distributed to counties individually. In order to see if both class

and race affected the amount of aid a county received together, I conducted multivariate analyses

in the form of linear regressions. Before running the regressions, I ran a bivariate correlation

between African-Americans living in the counties in 2005 and the total amount living in or

below the poverty line. I found that race and poverty rates were highly correlated; this result

suggests not many clear relationships will result from linear regressions since it is hard to

determine the difference between the two variables. In a multivariate test between net

population, black population from 2005, education level, and unemployment, I found statistical

significance among the black population and variables of aid while education and unemployment

were held constant. The results shown in Table 8 depict the beta, or standard coefficient, and

their significance. There is a beta of 0.949** for federal grants and the black population while

all other variables are held constant. For every one standard deviation increase in the black

population, 0.949 of a dollar increases in the federal grants. In testing the total number living in

poverty and education level against the multiple aid variables, I found only a few significant

relationships. While controlling for education, the betas between poverty and the number of


25

projects and federal grants and loans were 0.855*, 0.872*, and 0.644 (with a marginal

significance). In these three tests, as poverty increased one standard deviation, the number of

projects and money increases. The independent variables in this statistical test were both black

population from 2005 and the number living in or below poverty, while controlling for the

general population in 2005. The beta for black population within the linear regression measuring

aid from non-profits was 2.779 with marginal significance (p=0.072). This finding shows that if

the general population and number below poverty are constant, the number of nonprofits will

increase 2.779 times a standard deviation every time one standard deviation of the black

population from 2005 increases. These results show that while the variables for race and

economic status are closely correlated, some differences in relationships result. The number of

non-profits increases as race increases while controlling for the general population and keeping

the number living below poverty constant.

Discussion:

The issue of fairness in aid distribution was examined through various bivariate and

multivariate statistical tests. Many researchers, including Clarke (2006) and Rivera and Miller

Table 8: The Black Population in 2005 and Variables of Aid; N=10

FEMA 0.611* Economy Grant 0.767 Housing Grant1 0.903* Infrastructure Grant 0.755 Number of Projects 0.928** Federal money obligated for projects 0.625* Federal Grants 0.949** Federal Loans 0.723** (Statistically Significant: *p ≤0.05, **p≤0.01**);

1N=7



26

(2007), believed that the widespread destruction and difficult recovery process could have been

avoided, but was this really the case. Although reports show that, the storm was predicted and

the government had plenty of time to plan for recovery efforts, few plans were initiated and

catastrophic destruction resulted (Clarke, 2006).

Some support was found between the amount of damage and aid counties received. This

is similar to a study conducted by Forgette et al. (2008), who stated those who experienced

minimum damage would be more satisfied with aid. Forgette et al. (2008) declared those with

less damage would receive less aid, and consequently be more satisfied with aid distribution.

This finding provides mixed support for H1. Although no credible relationship exists between

moderate, extensive, catastrophic, and flooding damages correlated individually with amount of

aid counties received, these hypotheses still received limited support. Perhaps those with

moderate, extensive, catastrophic and flooding damage, looked to other sources of aid than those

I tested. Another reason for the lack of relationship could be the presence of their social capital.

Those who experienced widespread damage may have large social capital and did not need to

turn to the government for help. Mirroring the findings provided by Forgette et al. (2008), I

found that the more limited damage a county received, the less aid that county received. This one

finding of counties with a large number of limited damage receiving less aid, refutes that idea of

distribution inequality by supporting my hypothesis.

The hypotheses regarding the economic status and aid, as well as the racial composition

of counties and aid, yield similar findings as Brezina (2008). Brezina (2008) found poor,

unemployed, African-American women predominately made up those who sought government

assistance through post-evacuation programs. In my partial bivariate correlations, I found a

continuation of this view. A positive relationship exists between the number living in or below


27

poverty and the assistance the government, non-profits, and FEMA gave to separate counties.

Hypothesis 2B was refuted, however, as the number of non-profits increased, the amount of aid

increased. This suggests the non-profits were an active part of the communities and the social

capital of locals. Despite the low stratification of survivors, non-profits helped make up their

social capital and may have been used for advocacy in order to apply for government aid. These

findings show aid was given to counties with a greater percent of poverty. Although H2B was

refuted, an increase in non-profits led to an increase in aid. Since many of the poor in these

counties likely did not have other forms of recovery assistance, it is important that these formal

organizations gave the survivors access to the organizations’ resources.

The number of businesses in a county affected the amount of aid that county received.

When analyzing the potential relationship between the two variables listed above, I found a

positive relationship. Instead of looking at just the demographics and amount of damage a

county experienced, I additionally examined the role of businesses in the counties. Petterson et

al. (2006) found the presence of casinos in Mississippi significantly influenced the recovery

speed of the county. Based on this finding, in addition to the negative relationship I found

between the number of businesses and aid, one can infer that local businesses were helping their

communities in recovery efforts, both directly and indirectly. The active businesses in the

aftermath of Hurricane Katrina provided local residents with jobs and helped recreate an

effective capitalist economy. This increased the importance of social capital; the growing

connections between individuals in the workplace and having these networks have money, less

government aid was needed because of stronger, denser, and richer ties. Another reason for this

negative relationship could be that areas with more businesses experienced less damage overall.


28

Regardless of the explanation, local businesses and the amount of aid given to a county has a

strong, negative, and significant relationship.

Returning to the study by Elliott et al. (2006), the majority of individuals who sought

government aid directly after the storm in the form of evacuation assistance were African-

American women. To further develop knowledge on the implications of race and aid. Rivera and

Miller (2007) provided insight on past disasters and their effects on African-Americans.

Segregation, discrimination, and inequalities were present in floods and storms and their relief

efforts across the United States, including Hurricane Katrina (Rivera & Miller, 2007). The

question of whether or not the black population would receive more aid than other racial groups

remained unanswered. The results from partial bivariate correlations from the data I collected

demonstrate a positive relationship between African-Americans and aid received. As the amount

of African-Americans increased, the amount of aid increased; the opposite is true for the increase

in whites. This finding supports my hypothesis 4A. Hypothesis 4B was refuted, although, as

previously states, as the number of non-profits increased, aid increased. Race and non-profits are

highly correlated; as the number of African-Americans increases, the number of nonprofits

increases. Like those living in poverty, non-profits made up a significant portion of their social

capital. They advocated and helped the black population apply for government aid.

I carried out a multivariate linear regression between the black population before the

storm, variables of socioeconomic status, and the amount of aid given. Before carrying out the

regression, I found that the correlation between African-Americans and the number living below

the poverty line was high. This factor impacted my results for the linear regressions since the

two variables are so highly correlated. The correlation between African-Americans and those

living below poverty means there are many poor, African-Americans within the counties studied.


29

The linear regressions between the population, education level, and unemployment displayed

significant relationships between variables of aid. In addition, between poverty, education level,

and the number of projects, government grants, and government loans, received support. The

number of non-profits and population and the number below poverty had similar results. While

the variables for general population and number living below the poverty line were constant, the

number of non-profits increased as the number of African-Americans increased. These limited

findings depict the difficulty in deciphering what variable has a greater impact on the amount of

aid a county received. By finding some significant relationships in the linear regression, one can

see that race has an important role in influencing aid when socioeconomic status variables are

constant. Likewise, when education level is constant, poverty rates have some impact on the

amount of aid received. The stratification of race and class are highly correlated, however, these

findings listed above suggest race is more of a determining factor than class.

Limitations:

Although I was able to find support for my hypotheses, I encountered a few limitations

throughout the study. The sample size I chose for this study was twelve counties originally,

however I had to drop two cases because of lack of information available. This sample

contained the counties that experienced the most damage from the storm throughout Louisiana,

Mississippi, and Alabama. My results were limited and my findings may not be consistent with

all counties throughout the Unites States.

The collection of secondary data from multiple datasets was the main factor for

limitations. No one previously collected or compiled a dataset that contained all the variables I

was interested in analyzing. Since I had to compile data from multiple datasets, the timing of


30

data collection was not always the same. I only used data that was collected up to thirteen

months after Hurricane Katrina hit in order to help limit any differences among counties. In

addition to the timing of data collection not being consistent for all variables or for all cases,

limitations on variables available existed. For both East and West Baton Rouge in Louisiana,

data was not available for my use for many dependent variables. This influenced my results and

limited my sample size to ten the vast majority of my statistical tests. Comparably, the variables

for the number of non-profits and community development grants for housing yielded a sample

size of seven with just under half of my original sample size not containing data for the variables.

There are also limitations centered on the variables themselves and what they stand for.

The variable for non-profits only includes the number of non-profits in specific counties. The

amount of aid they contributed and the amount of external aid from non-profits outside the

designated counties were not examined since no data exists for these factors as of now.

Likewise, the measurements of other forms of aid posed a limitation. The amount of total aid

was not available for my use for the various counties. In order to combat this limitation I

collected data for nine separate aid measurements. Some overlap in a few counties is possible

since government officials from different counties and states were analyzing different criteria.

Lastly, the measurements for amount of damage alone is a potential limitation. The guidelines

for the varying levels of aid was set by FEMA, however only survivors and limited recovery

workers still in the affected areas after the storm were able to report damage. The number of

houses with catastrophic and extensive damage may not have been reported.

Future Research:


31

After completing my study, I came across a few areas of future research. In future

studies, I would consider broadening the sample size and not just look at the counties most

affected by the storm but include all areas Hurricane Katrina travelled through. The

measurements for aid could be altered as well. I analyzed aid in a variety of ways from the

amount of monetary aid given by FEMA to community and individual grants to the number of

projects and the amount given towards them. Chappell et al. (2007) stated many received aid

from religious organizations and social networks. Although I did not grant access to this data, it

would be interesting to see the implications for amount of aid distributed to different counties

based on private and NGO aid, both domestic and international aid. To further elaborate on

differing sources of aid, the aid received by social networks would be an interesting area of

study. My only measurements that could express social capital were non-profits and the number

of businesses. In addition, when I examined FEMA I only took into account the monetary aid

each county received thirteen months after the storm. In future studies, the number and type of

housing should be examined and the length of time it was available to victims. For example,

how many FEMA trailers and motel rooms were provided to individuals who lost their homes

and how long were they able to use these resources? When measuring the relationships between

economic class and amount of aid distributed to various counties, income levels of individuals

would likely change results. I only measured aid with those living in or below the poverty line.

Lastly, in future studies, more multivariate statistical tests should be tested between the

differences in race and economic class in terms of aid. These two variables are closely

correlated and more research should be conducted in these areas whether it is with amount of aid

or damage.


32

Some recommendations or suggestions for the federal government, based on prior

research, would be to make the application process easier and more time-effective. With the

difficult application process, many became impatient and might not have applied for assistance

(Landy 2008). Planning for the disaster in terms of evacuations and short- and long-term

recovery efforts would be helpful as well. This will reduce the normlessness and chaos that can

result. In areas with the most catastrophic damage, I would suggest the government looking into

the reasons that caused the destruction and address those issues (i.e. build up and improve the

levees so not as much flooding can occur). I would suggest providing a mix of resources

available as well. Areas should not just have grants for housing available but also loans and

grants directed towards businesses specifically so the internal, local economy can help alleviate

its own burdens.

Conclusion:

In order to determine how the amount of formal aid was given to counties that were most

affected by Hurricane Katrina, I conducted a quantitative research study by compiling secondary

data into a dataset. Frequency distributions, partial bivariate and bivariate statistics, and multiple

linear regressions, allowed me to be able to find support for most of my hypotheses. I found that

counties with more limited damage received less aid while counties with a greater number living

in or below poverty receive more aid. Counties with more businesses received less aid than

counties with fewer businesses. Counties with a higher number of African-Americans received

more aid while counties with more whites received less aid. The extent of damages, rate of

poverty, number of businesses, and the racial composition of counties were all factors for aid a

county received. Based on my results, aid was distributed mainly by need. The support found


33

for my hypotheses implies that economic background and the racial composition in counties

were among the main factors that influence the amount of aid distributed. This is the case,

especially since economic status and race have a high correlation in the counties affected

Equally, the number of businesses in a county is a contributing factor for amount of aid a county

received.. Businesses are negatively correlated with amount of aid since areas with businesses

have less of a need for external aid. The stratification of classes and races negatively impacted

those living in disaster-prone areas. The role of their social capital came into play as they either

used network resources or relied on the government for assistance.


34

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