“Baby Won't You Please Come Home:” Studying Ethnoracial Segregation Trends in New Orleans Pre and Post Hurricane Katrina

Written by
Nathan Babb
May 5, 2021

By Nathan Babb


This paper explores the ethnoracial segregation trends of New Orleans, Louisiana between the years 2000, 2010, and 2018. It studies the effect of Hurricane Katrina—which struck in August 2005—on population figures and racial composition within two geographic units of study in Orleans parish: neighborhoods and census tract block groups. Since Hurricane Katrina, White residents have returned in larger numbers than Black residents, and particularly so in neighborhoods that were predominantly Black before the storm. In 2019, New Orleans had 100,000 fewer people than before the storm—nearly the same as the number of Black residents who have not returned. Using a Gibbs-Martin index, which measures racial diversity, the paper finds that decreases in population at the census block group level are associated with racial “diversifying.” This trend invites a conversation on the normative interpretations of racial heterogenization, its causes, and its consequences: who bears the costs of increased “diversity” and what is the historical backdrop it operates under?

Introduction and Motivation

Hurricane Katrina struck the Gulf Coast on August 29, 2005 as a Category 3 storm centered just east of New Orleans, Louisiana. Though areas such as Biloxi and Gulfport, Mississippi received stronger wind damage, New Orleans received much of the national media's attention due to the city’s breached levees and subsequent flooding. Compounding the damage was the ineptitude and disastrous response of the Federal Emergency Management Agency (FEMA), which left many New Orleanians stranded on rooftops, desolate and helpless.

The storm had an impact on more than just the city. It left an indelible footprint on the entire region; a diaspora of people fled the city—and the southeast Louisiana region—and scattered across the Gulf Coast. Some fled to family in Houston and Atlanta. Some were evacuated away from family. This would permanently alter the tight-knit familial networks that so identify this region of the United States. Where once lonely rest stops on the I-10 interstate stood, now there were gumbo shops and beignet cafes. “New Orleans style” snow cones used to only be found in the city itself. Now, one can get them in the Florida panhandle from an elderly couple who evacuated the city before Katrina struck. Hurricane Katrina was, and is, much more than just another instance of flooding in the “Big Easy.” It was an event with permanent effects.

While it is true that the city as a whole has not returned to its pre-Katrina population level, this fact is misleading; the population has returned and—in some cases exceeded—its pre-Katrina level, but only for some racial groups.

The consequences of the storm are still being felt today. At the time of writing—15 years later—the city’s population has still not recovered to its pre-Katrina level. [1] It may never recover. It remains permanently affected by the storm: destroyed and razed houses, lack of investment in historically Black neighborhoods below sea level, the inevitability of stronger storms and rising seas due to climate change keeping people away—the list goes on.

In the narrative of the city’s “return,” an important facet is often overlooked. Who makes up the city’s return is not often disaggregated, even though it remains an essential part of the data. More importantly, it remains an essential part of what makes the city so beloved, cosmopolitan, and unique. While it is true that the city as a whole has not returned to its pre-Katrina population level, this fact is misleading; the population has returned and—in some cases exceeded—its pre-Katrina level, but only for some racial groups. The one racial group where this is unquestionably, and unfortunately, not the case is Black New Orleanians. In 2019, there were nearly 100,000 fewer Black New Orleanians in the city than in 2000, accounting for a nearly six percent drop in the share of the city’s population. This confirmed a hypothesis that New Orleans would appear “whiter and wealthier as a result of selective return migration” following Hurricane Katrina (Fussell 2010).

To study New Orleans before and after Katrina is thus a study in resiliency. It is a study of place, identity, and access. It is also a study in disadvantage, poverty, segregation, and injustice. This paper looks more discretely at the who and where: by using both neighborhood-specific and the smaller census block group population data, and constructing a variable to measure racial homogeneity, this paper highlights the ways that racial heterogeneity is illusorily “achieved” when White residents return at higher rates than Black residents. Hurricane Katrina’s effects have been well-studied and this paper contributes a novel statistical relationship not found in a review of the literature. 

The paper proceeds as follows: Section 2 will provide a brief overview of the literature on this topic and Section 3 contains the research design. This is followed by the analyses in Section 4. Section 5 concludes with limitations to this approach and discussion for future research.

Literature Review

There is a rich body of analytical literature on the subject of segregation in cities and neighborhoods. Cutler, Glaeser, and Vigdor (1999) study the development of the “American ghetto” by constructing an index of dissimilarity and an index of isolation. They find that neighborhood racial segregation increased from 1890 until circa 1970, when the trend began to reverse. Cutler et al. posit that segregation within cities has instead been replaced by segregation between cities: that Whites began to discriminate on housing price and location as the federal subsidization and growth of suburbs took off. Cutler et al. use the Great Migration—the decades-long migration of Black Americans from the South to the North—as the backdrop for their research. In contrast to Cutler et al.’s use of a slow-moving, decades-long trend, this paper relies on the more discrete event of Hurricane Katrina to study the out-migration of Black Americans away from a particular city.

Turning to the city of New Orleans, Frymer, Strolovitch, and Warren (2006) encourage researchers to use a racial lens when analyzing trends in the city. They argue that any analysis of socio-economic trends needs to consider the role of race. It influences this paper by linking the quantifiable aspects of Hurricane Katrina on the racial dispersion and migration rates to normative questions about the desirability of reducing segregation.

Several studies have advanced Frymer et al.’s call by studying the recovery patterns in New Orleans in an intersectional way, albeit not always with a racial lens. Finch, Emrich, and Cutter (2010) look at the way in which recovery and repopulation trends are related to what they term “social vulnerability"—the socioeconomic characteristics that influence a community’s ability to prepare, respond, cope, and recover from a hazard event including inequalities in health care, social capital, and social stratification. They find that groups with low flood exposure and low social vulnerability risk were able to return and rebuild relatively quickly. Private and public sources assisted those at high risk and high vulnerability, allowing them to rebuild, but people in the middle risk group showed the slowest rate of recovery. 

Go (2018) studies community civic structures as a proxy for rebuilding and asks whether spatial inequality—the inequitable allocation of or access to resources—is exacerbated or lessened because of it. Go finds that the stronger the civic structure, the more likely spatial inequality will be deepened in the rebuilding effort. Go also finds that this occurs along racial lines: White residents concentrate in the geographically safer areas above sea level, while Black residents are left with the low-lying flood-prone areas.

Perhaps the best analysis to date examining how repopulation efforts interact with race is that of Gotham and Campanella (2013). They study neighborhood resiliency through quantitative analysis and qualitative interviews and find that New Orleans has experienced modest increases in ethnoracial diversity and a retrenchment of socio-spatial inequalities. Using the quantitative methodology of Gotham and Campanella, this paper digs deeper into the residential patterns grouped by neighborhood and census block group, a level of data granularity more likely to be racially homogenous.

Research Design

This paper uses the 2000 and 2010 Census data equidistant from the 2005 storm to measure the impacts on ethnoracial segregation within the city. [2] It also uses the 2018 American Community Survey’s (ACS) 5-year estimate to compare whether the general population and racial composition trends observed in 2010 persist, desist, or remain neutral.

All three data sets have observations at the census block group level. Furthermore, these census block groups can be aggregated into the neighborhoods of the city as defined by The Data Center (formerly named the Greater New Orleans Community Data Center). [3] The data is then grouped by the years 2000, 2010, and 2018 and analyzed across two different geographic units (block groups and neighborhoods) to study whether parallel trends are observed between the units across years.

Social Explorer, a mapping tool that uses Census data overlaid on a geographic area, is used to create the visual narrative, creating snapshots of New Orleans in the years 2000, 2010, and 2018 using the same data sets. The population densities are plotted by race. These maps are shown in Section 4.1.

R, a statistical program, is used for quantitative analysis. Simple correlative regressions are run against the variables of interest to gauge any statistically significant relationships. Important limitations to this regression approach will be discussed in Section 5.1. No causal interpretations can be made from these regressions. Rather, they highlight correlations. Potential theories underlying the results are then posited.

The qualitative research section includes an interview with one resident of New Orleans. This resident grew up in the city, left for a handful of years, and returned before Hurricane Katrina struck. His experience does not constitute a representative sample. However, it illustrates an experienced history of New Orleans borne out in this paper’s statistical analysis. A brief literature review supplements this interview with additional voices and helpful context.


Visual Analysis

The analysis starts with a visual presentation of New Orleans as defined by the boundaries of Orleans parish. [4] Henceforth, references to New Orleans are interchangeable with Orleans parish. This is common in Katrina-specific research on New Orleans; while the metro area extends across parish borders into neighboring Metairie and Kenner (Jefferson parish), New Orleans itself is fully captured within the boundaries of Orleans parish.The quantitative analysis of this paper uses Orleans parish data alone. 

Figure 1 shows the city is sandwiched between Lake Pontchartrain to the north and the Mississippi River to the south. The smaller units of the city are neighborhoods whose boundaries are defined by The Data Center. These boundaries are well-accepted and were the geographic definitions used to aggregate and group the census block group data. 

Figure 1: Boundaries of Orleans Parish 



Figure 1: Boundaries of Orleans Parish.
Image by Nathan Babb.

The visual analysis of density and depopulation—the loss of residents over time—starts with the Social Explorer maps from the years 2000, 2010, and 2018. The first two time periods use the Census data while 2018 uses the ACS 5-year estimates data to construct population density maps. In Figures 2, 3, and 4, green dots represent those who self-identify as White, orange dots for Black, and purple dots for Asian. These are the three dominant racial groups in New Orleans for which data are available. [5] Each dot represents five people.

Figure 2 shows the density of the city in 2000. Visually, segregated boundaries are very explicit for the three racial groups. The purple triangle in the top-right corner represents Village East, a predominantly Vietnamese-American neighborhood on the border of the parish. City Park, the large gray rectangle in the map, effectively separates Whites from Blacks, with the former living on the west side of the park in Lakeview and the latter on the east side in Gentilly Terrace. Uptown is the thin green strip that snakes its way through two largely Black-dominant areas in the saddle of the Mississippi River. It is centered upon the St. Charles Avenue and Streetcar Line. It is a wealthy neighborhood above sea level. Other examples of neighborhood-level racial segregation exist; by merging this picture with the neighborhood definitions and data sets, this paper quantitatively shows to what extent these geographic areas remain defined by race.

Figure 2: New Orleans Population Density (Year 2000)



Figure 2: Year 2000 - New Orleans Population Density 
Green dots = White; Orange dots = Black; Purple dots = Asian. One dot represents five people. Image by Nathan Babb.

Figure 3: New Orleans Population Density (2010) 



Figure 3: Year 2010 - New Orleans Population Density
Green dots = White; Orange dots = Black; Purple dots = Asian. One dot represents five people. Image by Nathan Babb.

The same map for the year 2010 shows some stark differences. Firstly and most importantly, the city lost a substantial number of residents, evidenced through the “thinning” of the maps. In particular, the city lost a substantial number of its Black residents—there are significantly fewer orange dots in Figure 3 than in Figure 2.

Secondly, race-dominant regions still exist, albeit in a much less concentrated form than in 2000. This makes sense: the neighborhoods lost people after Hurricane Katrina, some more than others, and in a few cases almost their entire population. This speaks to the resiliency of pre-codified physical racial barriers arising from historical, cultural, and discriminatory legacies. Individuals and families are moving back by 2010 but their return patterns, perhaps obviously, are happening largely in ways that recreate the racial segregation distributions seen in 2000, five years before Hurricane Katrina.

Thirdly, in some cases, neighborhoods are becoming more diverse due to new residents. But, as this paper will show, diversity can also be increased by losing population. This is studied in Section 4.2 by constructing a Gibbs-Martin index, a measure of geographic diversity by race, and studying its changes over time.

Figure 4: New Orleans Population Density (2018)



Figure 4: Year 2018 - New Orleans Population Density
Green dots = White; Orange dots = Black; Purple dots = Asian. One dot represents five people. Image by Nathan Babb.

Figure 4 shows the population density of the city in 2018 using the ACS 5-year estimate data. [6] The trends remain fairly noticeable even by 2018: the orange regions across the city have thinned relative to the 2000 levels.

The green dots have also thinned relative to 2000 but in a much less severe way. The purple dots remain clustered in the same location observed in 2000. It does not appear that New Orleans has returned to its pre-Katrina level of density and this is particularly true among Black residents. The statistical analysis bears this out. 

Statistical Analysis

Figure 5: Population Totals by Race, 2000 and 2019



Figure 5: Population Totals by Race, 2000 and 2019

Figure 5: Population Totals by Race, 2000 and 2019

When looking at the population totals for the parish overall broken down by race, displayed in Figure 5, two important features of re-population trends in New Orleans through 2019 are evident:

  1. The parish is still below its pre-Katrina population benchmark in 2000, and
  2. This is driven almost entirely by the disappearance of Black residents

This long-term displacement of Black residents is an important cultural loss for the city, and drives a relationship between depopulation and diversity.

Racial diversity for each geographic unit (both at the census block group and neighborhood level) can be measured using the Gibbs-Martin index from Gotham and Campanella (2013); the Gibbs-Martin index measures the probability that two randomly selected persons from a geographic area will be of different racial or ethnic backgrounds. The Gibbs-Martin index is sometimes referred to as the Blau index or the Gini-Simpson index. It is the inverse of the Simpson index (sometimes known as the Herfindahl-Hirschman index), which measures the probability that persons will be of the same racial or ethnic group. For this paper, the Gibbs-Martin index is preferable to the Herfindahl-Hirschman index or the dissimilarity and isolation indices of Cutler, Glaeser, and Vigdor (1999), both for ease of computation and interpretation.

The Gibbs-Martin index is computed as follows:



Gibbs-Martin Index equation

where i represents a racial group and p is the share of that racial group in the geographic unit of study. R is the total number of racial groups in an area. D is then the Gibbs-Martin index ∈ (0,1). A value of zero represents a geographic unit composed entirely of one racial group. Values closer to one indicate highly heterogeneous areas. This index will be the primary metric for comparing the “diversification” of a neighborhood or block group over the time horizon under study.

D is computed for each area for every year. Differences are then taken across the years 2000-2010, 2000-2018, and 2010-2018 and plotted against the population changes observed across the same time periods for each area.

Analysis of these changes begins at the neighborhood level as shown in Figures 6 and 7. The x-axis shows the changes of the neighborhoods’ Gibbs-Martin index score relative to their population changes on the y-axis. Figure 6 shows the difference between 2000 and 2010 and Figure 7 builds out by 8 years. This extension is to gauge whether the trend observed in the first five years after the storm persists, desists, or strengthens.

Figure 6



Figure 6: 2000-2010 changes, neighborhood level
Image by Nathan Babb.

 Figure 7



Figure 7: 2000 - 2018 changes, neighborhood level
Image by Nathan Babb.

At the neighborhood level there is a slight negative correlation between the Gibbs-Martin index and population changes, both between 2000-2010 and 2000-2018, where the trend seems more pronounced. It appears that increases in the Gibbs-Martin index are associated with falling population figures. The observation points are population weighted with N = 55 neighborhoods included. [7] However, when the simple regression for the line of best fit is run, no statistically significant coefficient is obtained for either time period. Thus, at the neighborhood level, the association observed cannot be understood as statistically significant.

However, at the census block groups which compose these neighborhoods, the conclusion is different and decisive.

Figure 8



Figure 8: 2000 - 2010 changes, census block group
Image by Nathan Babb.

 Figure 9



Figure 9: 2000-2018 changes, census block group
Image by Nathan Babb.

There is now a much larger sample (N = 417 block groups with similar outliers and non-residential areas removed) in which the observations are likely to be much more racially homogeneous. Again there is a negative correlation between the Gibbs-Martin index and population. The line of best fit in both regressions is downward sloping and significant at the one percent level. The result is not casual, only associative: decreases in population at the census block group level between 2000-2010 and 2000-2018 are associated with racial “diversifying” as measured by increases in the Gibbs-Martin index. Because of the racial distribution of the city and re-population trends observed in Figure 5, one hypothesis is that this is predominantly due to the loss of Black residents across the city. Thus, New Orleans has become “more diverse” at the expense of its historic Black residents. This leads to important normative questions about diversity, racial segregation, and the policy and cultural implications for the city, which are addressed in the conclusion section of this paper.

Qualitative Analysis

In an effort to supplement the statistical findings and corroborate the narrative of post-Katrina re-population trends, anecdotal evidence from one New Orleans resident is supplemented with a brief literature review of others’ experiences. New Orleans native and current resident Matt Emerson grew up in the city, graduated from high school in 1979, and left for nearby Baton Rouge to attend university. [8] He did not return to New Orleans as a resident until 2003, but has lived through Hurricane Katrina and moved across several neighborhoods in the time since.

On the topic of whether any neighborhoods seemed to be gentrifying along racial lines prior to the storm: "Yes. By the time I moved into the Marigny in 2003 it was well on its way. As you may know, the French Quarter used to be a real working-class neighborhood, it was very mixed ethnically and on other lines. But in the years leading up to Katrina the area was slowly gentrified: it was becoming more commercial, more tourist-friendly. It was probably the first to go. By the time I moved back in 2003 the French Quarter’s gentrifying process was essentially a done deal. It was like a mini-resort. It had a completely redone feel."

New Orleans has become “more diverse” at the expense of its historic Black residents. This leads to important normative questions about diversity, racial segregation, and the policy and cultural implications for the city.

The data set supports Mr. Emerson’s claim: the French Quarter’s Gibbs-Martin index shrinks from 0.20 in 2000 to 0.16 in 2010 and settles at 0.13 in 2018. The more that one racial group comes to disproportionately represent an area, the lower the Gibbs-Martin index will be. In the French Quarter, White residents’ numbers were growing at faster rates than other racial groups, which remained stable. Given the limitations of the data set, this study cannot interact the change in racial composition with changes in median income or assessed property values (important metrics for the study of gentrification), leaving only part of Mr. Emerson’s claim corroborated. Mr. Emerson continued, now about the Marigny, an adjacent neighborhood to the French Quarter: "The Marigny, when I moved there in 2003, was undergoing that. And the Marigny is the neighborhood just downriver from [adjacent to] the French Quarter. I think I got there as that was happening—it’s hard to say whether Katrina made that happen. It seemed more like a step function: I evacuated for the storm and couldn’t return for three months. By the time I got back it seemed pretty clear that there was a different rate of return along racial and ethnicity lines and income. Rich White people came back, fixed things up, poorer people did not. And poor people, especially in the South in general and in New Orleans in particular tend to be Black. So it seemed more like a step-function over that time."

Here, like the analysis for the French Quarter, the data backs up Mr. Emerson’s claim: the Marigny in 2000 has a Gibbs-Martin index of 0.43, placing it in the top quartile of racially diverse neighborhoods. However, by 2010, this index dropped by twelve basis points to 0.31 and then down to 0.26 by 2018, a staggering drop over this period. The Gibbs-Martin index is changing from two reinforcing population trends: the number of White residents is increasing and the number of Black residents is decreasing. Meanwhile, the total number of residents for this neighborhood is shrinking, thus magnifying the effects. 

Mr. Emerson was also able to provide anecdotal insights into a few other neighborhoods in the data set, particularly the neighborhoods of Freret, Treme’/Laffite, Uptown, and the Bywater. To locals, these are the neighborhoods which represent the most recent trends of post-Katrina gentrification, as defined by increased White in-migration, both relative to Black residents and in absolute terms. The trends of these neighborhoods are mixed: Freret and Treme’/Lafitte both have increases in their Gibbs-Martin indices owing to the fact that in 2000, both neighborhoods were majority Black, and thus had a low Gibbs-Martin index. However, by 2018, these neighborhoods’ White and Black resident shares were near parity and the Gibbs-Martin index, reflecting this parity, increased. The Bywater, on the other hand, was closer to parity in 2000 and saw a decrease in its Gibbs-Martin index during this time as White residents came to account for an overwhelming share of the neighborhood population. The Gibbs-Martin index can tell a straightforward story of racial composition patterns measured over time, but it cannot be taken in isolation. Contextualizing the Gibbs-Martin index alongside the population trends by race helps paint a clearer picture of what is going on. 

Mr. Emerson’s contributions, while personalizing the experience of the city’s transition, offers only one resident’s account. A brief literature review follows. It contextualizes the population recovery patterns following Hurricane Katrina to lend further context to understanding why some residents come back while others do not.

Li et al. (2010) profile the experiences of both Black and Vietnamese-American residents of New Orleans East, a neighborhood on the edge of Orleans parish. Figures 2, 3, and 4 show this neighborhood, which is the purple triangle in the top right corner of the figures. The authors surveyed residents of this neighborhood after the hurricane and found that Vietnamese-American evacuees were more likely to express interest in returning to New Orleans, while Black evacuees were more likely to support staying in their evacuation site. Li et al. (2010) find that the neighborhood Catholic church in the Vietnamese-American community served as a logistical center and community anchor. Interview responses show that many of the Vietnamese-American evacuees of Katrina were themselves refugees from the Vietnam War. They express strong intra-community ties through a shared experience in ways that Black respondents did not.

Research conducted right after the storm sheds light on why some residents are unlikely or unable to return to New Orleans after being displaced. McCarthy et al. (2006) highlight the lack of car ownership among the many poor Black residents as a chief reason: being evacuated by bus but not being provided a means of returning via public transportation makes return costly. An additional reason concerns low-cost renting and the expected increase in rents stemming from redevelopment after the storm, which would have increased the cost of returning to New Orleans.

Paxson and Rouse (2008) use a small, non-representative sample of low-income students enrolled in community college to propose two hypotheses. The first is that “redevelopment plans are designed to discourage low income minority residents from returning” and the second is that “members of this group [find] better opportunities outside of New Orleans and do not want to return” (Paxson and Rouse 2008). They find that flooding exposure is the largest determinant to whether an individual returns. They do not find evidence that improved welfare in relocation sites—such as higher wages or higher social capital—explains return patterns. 

In the book How to Kill a City: Gentrification, Inequality, and the Fight for the Neighborhood, P.E. Moskowitz argues that gentrification was the goal. Moskowitz quotes the then-Governor of Louisiana, Kathleen Blanco: “It took the storm of a lifetime to create the opportunity of a lifetime.” From the book: "What else do you call a coordinated attempt by thought leaders, politicians, and business interests to radically change an entire city “demographically, geographically, and politically,” as one real estate maven put it, except a deliberate attempt to gentrify? To many of the black people here…those words—colonization, occupation, genocide—do not feel sensational. They feel like what happened." (Moskowitz, 18)

Moskowitz reminds readers that policy actions and responses do not happen by coincidence. They are active statements curated with an audience in mind.

If your neighborhood is defined by its people, but many of the people have either perished or scattered due to evacuation plans, then the likelihood of returning is reduced.

A highly relevant but often underreported impact of Hurricane Katrina on residents and evacuees alike is the role of trauma, distress, and disoriented grief. Malone et al. (2011) study this topic by interviewing 71 Katrina evacuees situated in Austin, Texas about their experience and how it affects them months later. One 27-year-old woman shared her experience of driving into her neighborhood after the flood waters cleared: "… driving into my neighborhood … it's like mile upon mile of nothing where there was something. Of absence, absence of human life, absence of trees that were growing, absence of birds and animals and traffic lights and all the things that signify energy. It's just completely parched. The earth looks scorched … to get to my house it's quite a drive, and I went through gradations of horror. Slowly you just become completely numb …" 

One can easily imagine the trauma resulting from such imagery is enough to keep someone from moving back. It can be hard to separate the memories of life before the storm with the reality of absence, as she describes. A 55-year-old woman recounts the loss of her community at the hands of the storm, saying “We lost our elders. Ninety-nine percent of the people that died were over 50, over 60, over 70. It hit me how much we lost in terms of history.” Indeed, one theory of rebuilding relies on the strength of social networks (McCarthy 2006). If your neighborhood is defined by its people, but many of the people have either perished or scattered due to evacuation plans, then the likelihood of returning is reduced.

For some, the experience was so traumatic that they have decided not to move back. Vanessa Willis-Nelson, in an interview with the Christian Science Monitor in 2005, said that she would stay put in Arizona, the place she had been evacuated to. She had no desire to repeat the hurricane season, the flooding of her home, being “plucked” from her rooftop by a military helicopter (Grier 2005). “I ain't ever going back,” she says.


Limitations and Conclusion

Limitations and Recommendations for Future Research

This paper’s analysis shows a strong correlation between depopulation at the census block group level with increases in diversity as measured by the Gibbs-Martin index. However, three important covariates that could lead to an improvement in the regression are not included. These covariates are (1) flood depth of census block group (as measured by FEMA surveyors while the city was still flooded after Katrina), (2) income, and (3) housing stock declines due to razed houses. No causal interpretation of these findings is warranted without a more specified model. For example, some census block groups cannot return to their pre-Katrina population figures because homes and apartments were destroyed in the storm and never rebuilt. Thus, some data points are necessarily limited to fall below the 0° line on the y-axis. Accounting for the ways in which the census block groups have been permanently altered due to flooding is an important avenue for further research.

Second, the ability to return and rebuild in New Orleans following the storm is likely to be strongly correlated with pre-Katrina wealth and income. This is also related to whether the resident was a homeowner or renter: those who own homes and property may be more likely to return to New Orleans to rebuild whereas a renter may have fewer financial imperatives to return, especially conditional on damage from the storm. Data from the 2006 Displaced New Orleans Residents Pilot Survey show that 62 percent of homeowners had returned while 51 percent of renters had returned. These results are not significantly different from one other (Fussell 2010). From this sample there appears to be a significant relationship between higher housing damage and reduced return rates (Fussell 2010). However, these variables do not interact with one another, providing an avenue for further research.

Third, this analysis does not look at adjacent parishes which may have absorbed residents of Orleans parish. Some of the depopulation trends may thus be persistent as areas farther away from the city center (and, importantly, above sea level) absorb those who fled Katrina in 2005. While these residents may not return to Orleans parish, they may instead return to the city’s suburbs or outlying areas. This conjecture may be true for White residents who moved to Metairie and Kenner (in Jefferson parish) or the Northshore (of Lake Pontchartrain, in St. Tammany parish), but for Black residents this claim is misleading. Ample reporting has documented how Black residents were unable to or proactively prevented from returning after the storm (Casselman 2015; Land 2018; Rivlin 2015; Moskowitz 2017). 

Are there patterns involved as to why so many individuals and families cannot or will not return and will the city’s Black population ever return to its level from 2000? Answers to these questions may prove essential to safeguarding the legacy of New Orleans and historic Black communities across the United States.

Fourth, this paper focuses on a racial binary that excludes the experiences of other racial groups in New Orleans. The breadth of experience and trauma related to Katrina is not reflected in the analysis and thus should be viewed as incomplete and a point for future research to build upon. 

Fifth, this paper’s qualitative section uses only one interview, hardly a representative sample. Several papers cited in this study do use novel samples of interviewed evacuees, but they too suffer from weak generalizable potential.

Finally, future research may want to explore the reasons why the out-migration of Black New Orleanians has persisted until and through 2018. Are there patterns involved as to why so many individuals and families cannot or will not return and will the city’s Black population ever return to its level from 2000? Answers to these questions may prove essential to safeguarding the legacy of New Orleans and historic Black communities across the United States.


This paper’s findings posit a set of normative questions on racial diversity for a city that is defined, in many peoples’ eyes, by its prominent cultural heritage: at what cost does “diversity” come and at whose expense? As a historical port city that brought together enslaved persons, Caribbean, Haitian, French, German, Italian, English, and Spanish immigrants, ancestral diversity has always defined New Orleans. This brief study offers pause for reconsideration, to dig deeper than the surface-level reading of trends to better expose and understand underlying inequities. Diversity comes from a dynamic process and the context it operates in is equally important to understand.

The effects of increased diversity in cities are the subject of much academic research. One popular argument in favor of racial diversity relies on its economic benefits. Susan Fainstein notes that “diversity attracts human capital, encourages innovation, and ensures fairness and equal access to a variety of groups. Indeed, by this logic, the competitive advantages of cities, and thus the most promising approach to attaining economic success, lies in enhancing the diversity within the society, economic base, and built environment” (Fainstein 2005). This argument is also supported by Richard Florida, author of The Creative Class, who adds that diversity stimulates creativity (Florida 2002). In Florida and Fainstein’s argument, diversity becomes a tool for material advantage—a commodity—rather than an ends unto itself. If diversity is reduced to a commodity to achieve other ends, readers should be aware of the overarching motivations. Furthermore the argument fails to provide a historical rubric to better understand at whose expense the trend of diversity comes. This paper has built off existing literature and written the first draft case for New Orleans. Such an approach in other cities would be welcome. 

This paper’s findings suggest that diversity is “increased” when census blocks do not repopulate to their pre-Katrina levels. This is largely explained by the fact that the Black population of New Orleans is the only racial group to be significantly below its 2000 population level. There are still 100,000 fewer Black New Orleanians today than before Hurricane Katrina. For a city that was, in 2000, 67 percent Black, this is a huge loss. The impacts of that displacement are still felt in New Orleans today.

*This article was edited by Sherrine Boseman-Rives (Pennsylvania State University), Rocio Cara Labrador (Princeton University), and Francis Torres (Princeton University).

About the Author

Nathan Babb is a Masters of Public Affairs candidate at Princeton University in the School of Public and International Affairs. He focuses on economic and U.S. domestic policy topics. 


He would like to thank his JPIA editing team, Professor Patrick Sharkey, Leon Mait, Andrew Schlager, and Clarke Wheeler for their friendly encouragement, thoughtful suggestions, and principled discourse. He can be reached at [email protected].


1. Census data analyzed by author.

2. The 2020 Census data was not available at the time of publication.

3. The Data Center: https://www.datacenterresearch.org

4. In Louisiana, the term “county” is not used. In its place is “parish.” 

5. The group “Hispanic (Any Race)” is slightly larger than “Asian,” but data were not available.

6. Because the ACS data is estimated, there is a higher degree of noise in the data. Visually, this noise obscures parts of the Mississippi River making the image more “blurry” than is real.

7. A few neighborhood observations were discarded due to missing data, being “Development Zones,” or related to the military.

8. The interview took place on December 3, 2020 over Zoom. His comments have been paraphrased for succinctness and approved for publication.


Casselman, Ben. 2015. “Katrina Washed Away New Orleans's Black Middle Class.” FiveThirtyEight, August 24, 2015. 

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