RETHINKING RACE AND ETHNICITY AS DISASTER RISK FACTORS

A Critique of Social Vulnerability Indices

NORA LOUISE SCHWALLER

Nora Louise Schwaller is a PhD candidate at the University of North Carolina, Chapel Hill in the Department of City and Regional Planning, and a registered architect in the state of North Carolina. Her research interests focus on migration, climate change adaptation, and equitable recoveries.

JORDAN BRANHAM

Jordan Branham is a PhD candidate at the University of North Carolina at Chapel Hill in the Department of City and Regional Planning, with a specialization in land use and environmental planning. His research interests center around two major areas: coastal climate adaptation and land use planning.

ATTICUS JARAMILLO

Atticus Jaramillo is a PhD candidate at the University of North Carolina at Chapel Hill in the Department of City and Regional Planning, with a specialization in housing and community development planning. His research focuses on federal housing policy, neighborhood inequality, and residential mobility behavior.

MAI THI NGUYEN

Dr. Mai Thi Nguyen teaches in the Housing and Community Development specialization. Her research focuses on housing policy, social and spatial inequality, and resilient communities. She has worked as the MURAP Program Director. She has also served as the Director of New Faculty Programs at the Institute for Arts and Humanities. Dr. Nguyen has recently accepted a role to be the new Director of the Design Lab at the University of California-San Diego.

ABSTRACT

Problem Approach & Findings

As hazards become more prevalent in the era of climate change, there have been a number of initiatives to develop ‘big data’ indices to define areas most at risk. Across a number of these indices, place-based vulnerability is attributed, in part, to the racial and ethnic composition of a given neighborhood, city, or region. Within these vulnerability frameworks, areas with high concentrations of black, indigenous, and people of color (BIPOC) are often labeled as most vulnerable. We argue that this practice is problematic because it implies but does not make explicit an important link between BIPOC, vulnerability, and a long history of racialized development, while failing to illuminate and address the underlying causes.

Implications

Using BIPOC as a defining measure for vulnerability perpetuates stereotypes and devalues the protection of BIPOC communities in the face of anthropogenic climate threats. Considering the widespread adoption of social vulnerability indices in hazard mitigation and recovery plans, failing to critically analyze how these indices contribute to the preservation and enhancement of historical patterns of inequality risks the continuation of inherently biased recovery.

INTRODUCTION Disasters do not inflict the same level of harm on all communities. Rather, the adverse effects of hazards vary by space and time, by the architecture of place and physical geographies, and by the supports available to impacted communities. For example, the Chicago heatwave of 1995 overwhelmed the City’s capacity to consistently supply electricity and water to all neighborhoods. By the time the heat broke, there were 525 medically confirmed heat-related deaths, and many of those deaths were concentrated in predominantly African American and lowincome neighborhoods (Klinenberg 1999). The heatwave also disproportionately claimed the lives of seniors – a population that largely lacked the resources needed to escape the heatwave and that was not equitably supported by public assistance and community support programs (Klinenberg 1999).

As awareness of the unequal distribution of harm caused by natural disasters – such as the 1995 Chicago heatwave – has grown, so too have planning and policy efforts to mitigate those unequal impacts (Thomas et al. 2009). One way that researchers have attempted to respond to this issue is by developing ‘big data’ indices that use multiple risk indicators to identify areas most vulnerable to disasters (Cutter et al. 2003; Flanagan et al. 2011). The main argument for these indices is that, by identifying high risk areas and populations before disaster strikes, they can help direct essential resources to those most in-need, reduce the loss of life and property, and mitigate inequality (Van Zandt et al. 2012). At the same time, however, vulnerability indices suffer limitations and are imperfect predictors of disaster impacts and post-disaster outcomes. For these reasons, it is vitally important for researchers and practitioners to be aware of the potential drawbacks of vulnerability indices as planning tools.

In this article, we assess the drawbacks of using race and ethnicity variables in indices as indicators of “social vulnerability” – defined by Blaikie et al. (1994, 9) as “the characteristics of a person or group in terms of their capacity to anticipate, cope with, resist and recover from the impacts of a natural hazard.” In many social vulnerability indices, communities with high concentrations of black, indigenous, and people of color (BIPOC) are labelled as the most vulnerable. While this approach acknowledges that many BIPOC communities are at an increased physical risk for disasters, we contend that labeling communities as vulnerable merely due to their racial and ethnic composition is also problematic. For instance, while historical development has often led to conditions where BIPOC have settled in more physically vulnerable areas, this is not always the case. Therefore, race is an inferior indicator for vulnerability, compared to measuring vulnerability by the physical composition of land and structures, and many social vulnerability indices obscure the relationship between race and physical vulnerability.

Specifically, we argue that this practice is problematic because it implies but does not make explicit an important link between BIPOC, vulnerability, and a long history of racialized development (e.g., redlining, restrictive housing covenants, etc.). In our view, this shortcoming of social vulnerability indices is reason for concern because it

may reinforce inequities in the disaster recovery process and perpetuate stereotypes that harm communities with high concentrations of BIPOC. To help provide context for these arguments, the article first reviews the history of social vulnerability indices and highlights case-studies that exemplify why BIPOC communities are often most prone to hazards. We then provide justification for our argument and conclude by discussing why it is important to reframe disaster recovery and hazard mitigation planning in antiracist frameworks. By this, we mean that disaster recovery should be used as an opportunity to mitigate the legacy of racialized development in a manner that prioritizes BIPOC communities that have historically been relegated to residing in more physically vulnerable areas. This requires an understanding beyond that which is provided by social vulnerability indices, which are disassociated from historical development processes.

SOCIAL VULNERABILITY INDEXES The social dimensions of disaster vulnerability have been documented in the literature for some time, as efforts to understand and assess vulnerability have long moved beyond a narrow focus on physical exposure (e.g., White and Haas 1975). In recent decades, the study of social vulnerability has evolved to encompass the systematic creation of indices designed to measure social vulnerability across large geographical scales, with the intention of illuminating how social vulnerability varies across space and interacts with physical vulnerabilities. In this section, we describe two such indices, focusing in particular on how these indices use race and ethnicity variables.

Social Vulnerability Index (SoVI)

The Social Vulnerability Index (SoVI) was developed by Cutter et al. (2003) in an effort to create a “consistent set of metrics” (245) that could reliably and repeatedly be used to assess social vulnerability to environmental hazards. Drawing on previous work that examined the intersection of social and physical vulnerability (Cutter, Mitchell, and Scott 2000), this team specifically identified seven dimensions of social vulnerability that represented “those characteristics that influence social vulnerability most often found in the literature” (Cutter et al. 2003, 245). They then collected 250 county-level variables related to these dimensions from the U.S. Census. After testing for multicollinearity, and normalizing these data, they whittled the list of variables down to 42. They then used factor analysis to create an even shorter list of variables that adequately captured the explanatory power of the larger 42 variable set. In their final index, race and ethnicity account for a substantial proportion of the variables underlying the index, with 4 of the 11 final variables measuring either race or ethnicity (percent African American, percent Asian, percent Native American, and percent Hispanic). Unsurprisingly, therefore, many of the most vulnerable regions identified by SoVI are those with highly diverse, multi-racial communities (Hazards & Vulnerability Research Institute, 2016).

The inclusion of race and ethnicity as a factor in the study of vulnerability is justified by previous studies that find that disasters tend to have disproportionate adverse impacts on certain racial groups (e.g., Bolin and Stanford 1998). It is also justified by research that documents the role that racism and structural inequalities of power play in creating and perpetuating environmental racism (Pulido 2000b). In their prior work studying vulnerability in Georgetown County, SC, Cutter, Mitchell, and Scott (2000) note the fundamental causes of social vulnerability, which include a “lack of access to resources, including information and knowledge; limited access to political power and representation; certain beliefs and customs; weak buildings or weak individuals; infrastructure and lifelines” (726). Race, as Cutter et al. (2003) later reasons, contributes to the circumstances that often become an underlying cause of vulnerability, such as political marginalization; thus, in the case of SoVI, race becomes a demographic indicator serving as a proxy for the measurement of these fundamental causes.

CDC Social Vulnerability Index (SVI)

Developed by Flanagan et al. in 2011, the CDC Social Vulnerability Index (SVI) is also commonly used in the U.S. In contrast to SoVI, which was originally constructed at the county level, the SVI is constructed at the Census tract level using data from the American Community Survey (ACS 5-year estimates) and is put forth as a tool that can be used in aiding emergency preparedness and targeting post-disaster relief (CDC 2020). Drawing in part

on the work of Cutter et al. (2003), the authors note that social and economic marginalization and discrimination have produced more vulnerable populations, specifically asserting that “African Americans; Native Americans; and populations of Asian, Pacific Islander, or Hispanic origin are correlated with higher vulnerability rates” (Flanagan et al. 2011, 5). Accounting for this, the authors include ‘percent minority’, measured as the percentage of the population that is not white, as one of 15 variables comprising their index.

Importantly, the CDC’s vulnerability measure is broken into four broad themes - Socioeconomic Status, Household Composition, Race/Ethnicity/Language, and Housing/ Transportation - with each tract receiving a separate ranking for each theme as well as a composite score. This disaggregation helps distinguish between the inputs that contribute to a community’s vulnerability and allows for a more nuanced assessment of risk and resulting community needs. However, it still aggregates diverse populations into vulnerable categories, without attention given to historical factors or nuanced local-level impacts.

Racism, Inequality, and Disaster Risk

While both SoVI and SVI rightfully acknowledge that communities with high concentrations of BIPOC are often at an elevated risk for disasters, these indices are problematic because they imply but do not make explicit an important link between BIPOC, vulnerability, and a long history of racialized development. Racial inequity is built into spatial patterns across the United States (Soja 2010). This is a product of both conscious hostile acts, such as instances of physical and personal attacks, as well as structural manifestations of white privilege, such as the development of restrictive housing covenants based on race (Trounstine 2020; Peacock et al. 2014). Further, areas predominantly populated by BIPOC tend to have more limited resources and services due to historical disinvestment in both the people and the places, which occurred in tandem with the production of advantages for white neighborhoods (Goetz, Williams, and Damiano, 2020; Williams 2019). As a result, BIPOC neighborhoods have traditionally faced issues not seen in intentionally privileged white areas. This trend extends to environmental justice, where the spatial application of racism contributes to environmental racism and can lead to situations where BIPOC face outsized exposure to environmental hazards (Pulido 2000). The remainder of this section outlines specific examples of how environmental racism can lead to such situations. These examples help contextualize why it is problematic that social vulnerability indices do not make explicit the important link between BIPOC, vulnerability, and a long history of racialized development.

Princeville, NC

Princeville, NC, originally named Freedom Hill, was the first U.S. town to be governed by African Americans. It was founded in 1865, in the shadow of the Civil War, along the swampy banks of the Tar River and south of the predominantly white city of Tarboro, which laid on higher ground. This development was possible because, even in the mid 1800s, the land was so susceptible to flooding that it was cheap enough for freed slaves to afford and undesirable enough that their white neighbors found its development by their black neighbors, who provided cheap labor, to be advantageous rather than distressing (Philips, Stukes, and Jenkins 2012).

The development offered a chance for formerly enslaved men and women to develop a community and insulate themselves, partially, from larger patterns of violent racism. However, in many ways, this was a false promise. Princeville continued to flood. In 1999, five rain events caused flooding that exceeded a 500-year event. There was a two-week period where the town was fully inundated; the floodwaters rose to sweep the land clean of structures, and with them, the photographs, furniture, and other trappings of a history of a lived life, so that only the land and legacy remained (Philips, Stukes, and Jenkins 2012). More recently, Princeville has had to continually reckon with its physical vulnerability as a result of Hurricanes Matthew (2016) and Florence (2018), which has led to a dramatic decline in population despite recently promised federal investment. The disparate impact these storms had can be seen in Figure 1, which shows the flooding extent of Hurricane Matthew, and how it disproportionately impacted Princeville, compared to Tarboro right across

FIGURE 1 - Flood extents from Hurricane Matthew show a disproportionate impact on Princeville, which is

located on low ground

the river. Princeville is just one example of centuries of racist policies and actions producing conditions in which communities of color are more susceptible to natural hazards.

New Orleans, LA

The City of New Orleans was originally founded as La Nouvelle-Orléans by the French in 1718. It is a palimpsest of urban history through the colonization of the Americas, and the policies that guided the U.S.’s growth through to the 21st century. Through the first century of its growth, development in New Orleans focused on the thin strip of land along the Mississippi river, which was both advantageously located for shipping, and on higher ground, built up from centuries of silt deposited by the river. However, after a series of technological advances in the late 19th century and early 20th century, the swampland on the lake-side of the city was drained and developed. These areas are below sea-level and need to be pumped dry whenever it rains (BondGraham 2007). Around the same time when low-lying lands were opened to increased development, Jim Crow laws and white flight resulted in an outmigration of white residents to the growing suburbs, which were largely unavailable to nonwhite Americans. Consequently, BIPOC became heavily concentrated in these low-lying areas. Because these were still the conditions when Hurricane Katrina struck in 2005, black neighborhoods were the areas that faced the most damage and displacement, the highest rates of mortality and illness, and the greatest loss of wealth (Germany 2007).

Relationship Back to Social Vulnerability Indices

These examples illustrate that racism, not racial composition, led to situations where communities with

high proportions of BIPOC are more likely to be physically vulnerable to natural disasters compared to majority-white communities. This is part of the reasoning behind using race as a socioeconomic vulnerability indicator; other aspects include how being non-white is associated with less accumulated wealth, less political power, etc. (Cutter 1996). However, using race as a shorthand for physical vulnerability obscures the path between racism and vulnerability to disasters and is therefore a less useful measure for indexing vulnerability compared to measuring actual physical risk (e.g., relative elevation, housing quality, etc.).

In the context of our case studies, both unveil a history of racism that drove the settlement of predominantly black residents in primarily low-lying and physically vulnerable areas. This would be captured in the SoVI, which would highlight these areas as particularly vulnerable to disruption. But it ignores the why, and would be better captured by simply measuring the relative elevation and the quality of housing stock. Therefore, in areas where low-lying lands are valued higher (e.g., in Miami, where low-lying coastal lands are both particularly vulnerable and particularly desirable; Keenan, Hill, and Gumber 2018), using race as a shorthand for vulnerable development obscures and complicates the relationship.

A RACIAL EQUITY CRITIQUE OF SOCIAL VULNERABILITY INDICES As noted in the previous section, social vulnerability indices imply but do not make explicit an important link between BIPOC, vulnerability, and a long history of racialized development. This section provides a more in-depth discussion about why we believe this practice is problematic and outlines our broader concerns with social vulnerability indices. The main argument we advance is that this practice may reinforce inequities in the disaster recovery process and perpetuate stereotypes that harm communities with high concentrations of BIPOC.

OBSCURING THE ROLE OF RACE AND ETHNICITY Because social vulnerability indices collapse numerous indicators into a single measure of vulnerability, they make it difficult to understand why a specific area is labeled as “highly vulnerable.” One cannot determine, for example, if a given county has a high score due to the overlap between poverty and percent population living in rural areas or, in contrast, due to the overlap between people who identify as black or African American and median home values. Thus, these indices may help identify at-risk areas, but provide little information about what specific combination of factors makes those areas particularly susceptible to natural hazards and disasters. With respect to race and ethnicity, this caveat makes it difficult to understand whether a community is deemed “highly vulnerable” due to the concentration of BIPOC in that community or due to some other factor (Cutter et al. 2003; Flanagan 2011).

Part of this obfuscation has to do with the method used to create some vulnerability indices: principal component analysis (PCA). Within the social sciences, PCA is used to measure “unobservable variables” or “latent constructs” that can only be captured via multiple indicator variables (Vyas & Kumaranayake 2006). Because it is not desirable to include all these indicators in a single statistical model, PCA collapses these variables into multiple “principal components” – each of which measures the degree of intercorrelation among variables that display similar linear trends. Importantly, each of these principal components is assumed to represent a distinct factor explaining the overall latent construct of interest. For example, Cutter et al. (2003) labels one principal component of the SoVI Index as “wealth” because it measures the degree of intercorrelation between several variables that are reflective of wealth accumulation within a county, including: median housing value, percent of households earning over $200,000 annually, median gross rent, percapita income, and percent Asian.

Because each component is assumed to represent an important factor that explains the overall latent construct, it is instructive to analyze the percent of cumulative variance explained by each component: the more cumulative variance explained by the component, the more important it is in explaining the overall latent construct. A close look at both the SoVI and SVI indices shows that the components representing race

and ethnicity account for a substantial amount of the cumulative variance explained. In the SoVI index, the race and ethnicity components account for 26 percent of the total variance explained. In the SVI index, principal components representing race and ethnicity account for nearly 30 percent of the total variance explained. Thus, in both indices, race and ethnicity are primary factors dictating the overall vulnerability score. This raises an important question: if race and ethnicity are such important factors in explaining the overall latent construct measured by the PCA analysis, why discuss that construct in terms of “social vulnerability” rather than “systemic racism”? To this end, one of our primary concerns with social vulnerability indices is that they obscure the centrality of race and ethnicity – and relatedly of racism – in shaping which communities are most adversely impacted by natural disasters.

Unreliable Predictor of Disaster Impacts and Recovery

Another reason that social vulnerability indices are problematic is because they do not appear to serve their stated goals – which are to highlight areas of concern for further investigation or as potential recipients of greater resources and to help communities “build back better” (Cutter 2003; Flanagan et al. 2018; Lue & Wilson 2017; Kim and Olshansky 2014; Oliver-Smith 1990). For example, work has shown that social vulnerability (defined by SoVI) is related to a “recovery divide” in Mississippi post-Katrina, but not in New Jersey post-Sandy (Cutter et al. 2014). Social vulnerability (defined by SoVI) also had a mixed relationship with recovery indicators in post-Katrina New Orleans when controlling for damages and, although SoVI and SVI have both been adopted into local plans, we have not found any studies that show a relationship to post-disaster aid (Finch, Emrich, and Cutter 2010).

A larger scale analysis of social vulnerability indices also yielded similar results. Specifically, this study found that social vulnerability indices were not a consistent predictor of disaster outcomes – including disaster damages (by cost), disaster declarations, and disaster fatalities from 2000 to 2012 – across all counties in the Gulf Coast states (Bakkensen et al. 2017). In this study, SoVI had a positive and statistically significant relationship to property damages and disaster declarations; SVI had a positive and statistically significant relationship to damages and fatalities, but a negative and statistically significant relationship to disaster declarations. In summary, the authors of the study noted that “not all indices perform as expected” (997).

The fact that social vulnerability indices are not highly associated with disaster impacts or recovery implies that these indices do not comprehensively capture the political, social, economic, and natural factors that influence disaster impacts and outcomes. This is important because it suggests that social vulnerability indices are out-of-touch with disaster processes that actually unfold on the ground. Given this disconnect, it is therefore questionable whether these indices improve planning scholars’ and practitioners’ ability to understand and mitigate the inequitable distribution of disaster impacts across communities. Our main concern with this shortcoming is that absent practical utility, these indices merely identify communities as vulnerable rather than – as stated by their goals – encourage the equitable deployment of disaster recovery resources to those communities. As discussed in upcoming sections, this shortcoming of social vulnerability indices may have substantial ramifications for communities with a high concentration of BIPOC.

Furthering Disinvestment in BIPOC Communities A major potential drawback of social vulnerability indices is that they may discourage planners and policymakers from making investments in neighborhoods labeled as vulnerable. Because the term vulnerability implies an increased threat of risk and likelihood of loss: why invest in an area that is “vulnerable” to natural destruction? If vulnerability indices are used in such a fashion to withhold investments from BIPOC communities, this practice may compound the failures of existing disaster relief programs for those communities. As studies have shown, disaster recovery programs rely on local governments to have the knowledge and capacity to act as the local administrators of the program and advocates for their citizens – a process that favors wealthier communities with more formal education and staff capacity. Due to historic disinvestment in BIPOC communities, however, such capacity is often lacking. This in turn makes it difficult for these communities to access disaster relief programs.

For example, an analysis by Mach et al. (2019) of 40,000 voluntary buyouts has shown that buyouts are more likely to occur in richer, more populated areas. However, within these areas, buyouts are more likely to be used by poorer, more socially vulnerable citizens. This finding suggests that the institutional capacity, expertise, and money of richer communities helps them connect citizens with post-disaster relief and – by the same token – that poorer communities are disadvantaged in the disaster recovery process due to lack of access to institutional resources and wealth. Recent research by Elliot, Brown, and Loughran (2020) has provided further evidence of this trend, finding that the Federal Emergency Management Agency (FEMA) buyout programs tend to target whiter communities, particularly in urbanized areas, even though history shows that neighborhoods of color are more likely to accept a buyout. Research by Howell and Elliott (2018; 2019) further reveals that aid from FEMA exacerbates the racial wealth gap between black and white residents.

Perpetuating Stereotypes

Finally, social vulnerability indices may perpetuate negative stereotypes of BIPOC communities. By labeling communities as “vulnerable” merely due to the high concentration of BIPOC, these indices implicitly frame predominantly white communities as less vulnerable and more resilient. This, in turn, further reinforces the view that BIPOC communities are inherently less resilient compared to white communities, rather than highlighting the reality that systemic racism is a primary factor that has made BIPOC communities particularly susceptible to natural disasters and hazards. Furthermore, it fails to recognize that BIPOC communities have shown incredible resilience when facing environmental and social hazards throughout history. The potential for social vulnerability indices to perpetuate stereotypes is particularly alarming given that – as described in previous sections – these indices do not appear to help planners identify or mitigate the factors that make BIPOC communities particularly susceptible to disasters. CONCLUSION Planners play an essential role in helping communities ‘build back better’ in the aftermath of a disaster (Kim and Olshansky 2014). However, much of the planning literature on disaster recovery minimizes or ignores the influence of racism on post-disaster outcomes (see, for example: Siembieda 2014). Without accounting for this reality, most recovery processes systematically increase inequality (Howell and Elliott 2019; Peacock et al. 2014). To address these issues, planners need to reframe recovery with an anti-racist framework. By this, we mean that recovery initiatives need to consider and address structural disadvantages that are often inherent in the communities of BIPOC due to a history of racist development. Planners and emergency managers need to explicitly detail the impacts of inequitable development when creating recovery plans that identify social vulnerability as a pre-existing condition. This would help recognize the limitations and problems of defining vulnerability primarily through race, and lead to plans that are more responsive to community needs.

Anti-racism needs to inform how we think of social vulnerability indices. While these indices were developed with the intention of helping to direct more support to those most in need, it is not clear that they are successful in identifying areas most in need of support, or that they are being used appropriately in this manner. To this end, we suggest two solutions. First, future work is needed to identify how, and to what degree, social vulnerability indices are used by state and local governments to inform post-disaster recovery and the allocation of financial support. Second, we must re-conceptualize social vulnerability indices as blunt tools rather than comprehensive databases. While social vulnerability indices may be useful in identifying areas in need of a more nuanced analysis of needs and risk, they should not be used as a definitive marker of pre-existing deficiencies.

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CONFLICT OF INTEREST: The authors attest that they have no financial interest in the materials and subjects discussed in this article.