## Abstract

Natural hazards and disasters distress populations and inflict damage on the built environment, but existing studies yielded mixed results regarding their lasting demographic implications. I leverage variation across three decades of block group exposure to an exogenous and acute natural hazard—severe tornadoes—to focus conceptually on social vulnerability and to empirically assess local net demographic change. Using matching techniques and a difference-in-difference estimator, I find that severe tornadoes result in no net change in local population size but lead to compositional changes, whereby affected neighborhoods become more White and socioeconomically advantaged. Moderation models show that the effects are exacerbated for wealthier communities and that a federal disaster declaration does not mitigate the effects. I interpret the empirical findings as evidence of a displacement process by which economically disadvantaged residents are forcibly mobile, and economically advantaged and White locals rebuild rather than relocate. To make sense of demographic change after natural hazards, I advance an unequal replacement of social vulnerability framework that considers hazard attributes, geographic scale, and impacted local context. I conclude that the natural environment is consequential for the sociospatial organization of communities and that a disaster declaration has little impact on mitigating this driver of neighborhood inequality.

## Introduction

Given the mounting evidence that links anthropogenic climate change to the increasing severity of extreme weather (Diffenbaugh et al. 2013, 2017), a pressing empirical question is how these environmental shocks affect American communities. One potential impact is on the demographic profile of neighborhoods. A multidisciplinary literature on the demographic consequences of natural hazards has developed, recently advancing beyond case studies to test generalizable hypotheses and refine theoretical concepts of vulnerability (e.g., Elliott and Pais 2010; Fussell et al. 2017; Logan et al. 2016). A primary conclusion of this literature is that vulnerability to hazards differs across demographic groups and is a driver of population change by making groups differentially likely to stay in place or move away in response to both direct impacts (e.g., housing damage) and indirect impacts (e.g., housing costs, availability, and labor markets) (Blaikie et al. 1994; Fothergill and Peek 2004; Fothergill et al. 1999; Pais and Elliott 2008). Despite this consensus and recent methodological advancements, contradictory evidence remains regarding both the direction and magnitude of the effect of natural hazards on local demographic change.

In terms of total population, some studies have found that the typical disaster leads to net population gain or an acceleration in total growth (Pais and Elliott 2008; Schultz and Elliott 2013). Other studies have documented a net out-migration (Boustan et al. 2017), a reduction in population growth (Logan et al. 2016), or even no change at all (Deryugina 2017). Many studies also have highlighted considerable effect heterogeneity (Elliott and Pais 2010; Fussell et al. 2017; Logan et al. 2016). Beyond the effects on total population, sociologists and demographers also model changes among various demographic subgroups. Although some have found a net out-migration of disadvantaged groups, such as racial minorities and impoverished individuals (Boustan et al. 2017; Elliott 2015), other studies have suggested that wealthy and White people move away and that lower-income and racial minorities are stuck in place (Logan et al. 2016). These disparate findings signal a need for further research with rigorous empirical strategies and refined theoretical models. In this study, I use a novel research design that assesses net demographic change after the apparent population of a particularly exogenous and understudied type of natural hazard—severe tornadoes (n = 1,016)—in 25 U.S. states from 1980 to 2009. In doing so, this research advances the literature in three ways.

First, given tornadoes’ exogeneity and locals’ inability to make residential place decisions based on tornado risk, I argue that my empirical case allows me to isolate the constitutive demographic elements of social vulnerability by controlling for locational vulnerability to natural hazards. These are two distinct concepts that scholars have theorized to drive demographic change but have yet to disaggregate empirically because patterned residential sorting around flood plains occurs in cases of hurricanes or floods, which is the focus of most previous research on post-disaster demographic change.

Second, my empirical analyses deploy causal inference methods and use the smallest geographic unit possible: census block groups. This choice represents an improvement on prior studies conducted using change score ordinary least squares (OLS) regression at the county level.

Third, I match the National Oceanic and Atmospheric Administration (NOAA) Severe Weather GIS database to the Federal Emergency Management Agency (FEMA) disaster database to identify the severe tornadic events declared by the U.S. President as a disaster, which releases federal aid that may moderate post-disaster change. Thus, to my knowledge this is the first study to empirically test how a presidential disaster declaration mitigates or exacerbates demographic change, net of magnitude.

I interpret these findings as aligning with the displacement hypothesis, driven by a combination of an out-migration of socially vulnerable residents from affected block groups and a maintenance or in-migration of relatively more advantaged residents. In light of these findings and those from previous research, I advance a hybrid framework of unequal replacement of local residents after natural hazards, which suggests that critical variation in this process will be a function of hazard attributes, local context, and the geographic scale under consideration. In what follows, I first review the relevant literature on the demographic consequences of natural hazards and disasters in the United States. I then describe the research design, in which I use NOAA and FEMA data to plot the exact coordinates of severe tornado paths onto standardized block group boundaries to estimate net population change from four censuses: 1980, 1990, 2000, and 2010.

## Natural Hazards and Demographic Change

Influenced by the findings of earlier case studies of extreme disasters (e.g., Bolin and Stanford 1998; Elliott and Pais 2006; Smith and McCarty 1996), the modal mechanism by which scholars hypothesize local post-hazard demographic change is migration: a combination of out-migration of exposed residents and in-migration of new residents. The literature relies heavily on the theoretical concept of vulnerability to make sense of heterogeneity in both baseline exposure to natural hazards and the post-event capacity to recover (Logan et al. 2016). A conceptual emphasis on vulnerability leads to three distinct expectations regarding the processes driving observed demographic change.

First, viewing communities as ecologically resilient, an equilibrium hypothesis predicts that communities will eventually return to a previously achieved equilibrium and experience no net change in demographic characteristics. The earliest sociological research on population effects of natural disasters found support for this thesis. Researchers have argued that places remained largely unchanged and rebounded within a few years to functional recovery (Cochrane 1975; Friesema et al. 1977). For example, Wright et al. (1979) found no difference in changes in the population composition between counties that experienced natural hazards in the 1960s and those that did not. Recent research has updated these analyses with newer data and more sophisticated methodologies, advancing two additional hypotheses.

The concentration hypothesis predicts that vulnerable (or disadvantaged) groups will be stuck in place and unable to deploy resources to move, whereas socially advantaged groups will relocate and even upgrade their residential circumstances. In a rigorous county-level analysis of demographic change after 32 hurricanes from 1970 to 2005, Logan et al. (2016) developed the concept of segmented resilience. They found that advantaged groups—specifically, Whites and young adults—are more likely to move away from counties where a hurricane struck. Disadvantaged groups—defined as Blacks and elderly residents—are more likely to be stuck in place. Similarly, consistent with a concentration hypothesis, other studies documented an increase in poverty rates in counties after natural disasters (Boustan et al. 2017; Smiley et al. 2018).

Finally, the displacement hypothesis suggests that environmental hardship displaces socially vulnerable people, whereas advantaged groups rely on savings, insurance, or wealth to remain in place and rebuild. The displacement hypothesis has also received empirical support and important refinements from other studies. For example, although Elliott (2015) found no racial differences in post-disaster migration, given preexisting inequalities in residential mobility, racial minorities become more mobile in the post-disaster period. In another study, Pais and Elliott (2008) studied three, billion-dollar hurricanes and developed the concept of places as recovery machines, which posits that post-disaster resources and power increase local populations and housing units but in unequal ways. They found an overall increase in total population in affected regions, but the neighborhoods that experienced the most damage became smaller and more White. Schultz and Elliott (2013) further supported the recovery machine hypothesis, showing that damage from natural disasters is associated with an increase in county-level median income but not with changes in poverty, which they interpreted as increasing socioeconomic polarization among residents.

Some studies have offered support for the three hypotheses under various circumstances, namely along dimensions of urbanicity (or population density) and past population trends. Elliott and Pais (2010) exploited the fact that Hurricane Andrew hit both urban Miami and more rural parts of Louisiana. They found that in more rural areas, long-term recovery concentrates disadvantaged residents, whereas recovery processes in urban areas tend to displace disadvantaged residents. Alternatively, Fussell et al. (2017) concluded that trends in past population and past hazards matter significantly for the magnitude and direction of hurricane and tropical storm hazards’ impacts on population change. Finally, Logan et al. (2016) furthered their segmented resilience hypothesis by showing that the negative effects are exacerbated in more affluent counties. This heterogeneity suggests that further research must strategically address these factors, empirically and conceptually. In this article, I carefully tackle these prior issues by first addressing the theoretical drivers of change: vulnerabilities to hazards.

## Social and Locational Vulnerability

In developing the three expected ways that community demographics change after natural hazards or disasters, scholars typically differentiate between two types of vulnerability: locational and social (Logan et al. 2016). On the one hand, locational vulnerability—a concept developed by geographers that captures the “hazardousness of a specific place”—is the physical risk of experiencing an environmental hazard based solely on residential location, such as in a flood plain or directly on the coast (Cutter et al. 2000:731). On the other hand, social vulnerability refers to the sociodemographic characteristics (e.g., income, wealth, and social capital) of locals that render them differentially likely to respond to or to cope with an event (i.e., to display resilience) (Cutter et al. 2003; Finch et al. 2010).

Locational vulnerability is especially important in empirical analyses of hydrological hazards, such as floods, hurricanes, tropical storms, and some types of industrial hazards (Crowder and Downey 2010), given the well-documented processes sorting marginalized families into flood plains and environmentally hazardous areas and also sorting advantaged families to coastal properties. Prior research has documented that individuals, especially in flood-prone areas, make residential decisions based on flood data and risk perceptions (Kousky 2010). Thus, the demographic implications of such events will partially be endogenous to this type of patterned residential sorting along dimensions of race and socioeconomic status.

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