Content uploaded by Melanie Gall
Author content
All content in this area was uploaded by Melanie Gall on Apr 03, 2019
Content may be subject to copyright.
A preview of the PDF is not available
Indices of Social Vulnerability to Natural Hazards: A Comparative Evaluation
... The reason is that indices are "measure(s) of an abstract theoretical construct in which two or more indicators of the construct are combined to form a single summary score" (Carmines and Wood, 2004: 485). Indices are beneficial measures since they condense a complex reality into simple terms (Diener and Suh, 1997;Gall, 2007). Recent studies on climate change vulnerability have attempted to develop global level, general vulnerability indicators for climate change assessment. ...
... Others have cited difficulties in the interpretation of indices (Eakin and Luers, 2006, p. 377). Similarly, Gall (2007) attempted to compare seven national-level indices of social vulnerability to climate change with a particular focus on natural hazards. The indices from various sources and fields of practice (see Table 1) include: the Human Development Index (HDI) of the UNDP; Human Wellbeing Index (HWI) by Prescott-Allen (2001); Prevalent Vulnerability Index (PVI) by Cardona (2007); the Index of Social Vulnerability to Climate ...
... In this study, Gall (2007) suggested that there are "significant shortcomings in the construction of most of the evaluated indices with particular gaps in empirical validity and methodological robustness" (p.vi). She noted further that the key issues that "shape the variability and uncertainty contained in the current vulnerability indices are: "subjective interpretation of vulnerability concepts, ignorance of sound statistical practices, limited data availability, and absence of reliable approaches to calibrate social vulnerability indices" (Ibid.), a position we strongly share in this study. ...
In order to make a good assessment of vulnerability to climate change, it is im- portant first, to distinguish between weather and climate, climate variability and climate change. This is also necessary for the development of an effective meth- odology and good assessment tools for vulnerability analysis. While weather refers to daily observed atmospheric conditions, particularly temperature, humidity, pre- cipitation and air movements and/or quality; climate is much more complex and “scientific”. Climate refers to aggregate weather conditions (temperature, precipita- tion, wind and other meteorological conditions) collected over a long period of not less than 30 years (UN-Habitat, 2012) that characteristically prevail in a particular region. It includes some variability in day-to-day, year-to-year, decade-to-decade conditions, known as weather cycles (WBI, 2013); as well as from place to place. Variability may be due to natural internal processes within the climate system (de- scribed as internal variability), or to variations in natural or anthropogenic external forces (external variability) (IPCC, 2001). Climate change describes the variations that persist for periods of 30 years or more in a region (Ibid). Here, the IPCC define climate change as the statistically significant variation of the climate due to natural or human related causes (IPCC, 2007; CARE, 2009). Humans and their activities are the most affected by climate change; hence, assessing what is likely to change, who are most likely to be impacted upon by climate change, and how those exposed to climate hazards are prepared to respond to the change become imperative. This is the concept of vulnerability and this study seeks to define a methodology that can characterise, assess and monitor local vulnerability to climate change.
... To date, very few attempts have been made to validate vulnerability index outcomes (Tate, 2012;Bakkensen et al., 2017). The most widely used approach so far is based on the use of independent proxy data referring to real observable outcomes, such as emergency service requests (Kontokosta and Malik, 2018), mortality (Adger et al., 2004;Gall, 2007;Peacock et al., 2010;Cardozo and Monteiro, 2019), building damage or losses (Peacock et al., 2010;Papathoma-Köhle, 2016;Papathoma-Köhle et al., 2019;Rufat et al., 2019), number of disasters (Cutter et al., 2003;Debortoli et al., 2017), post-event surveys (Fekete, 2009;Sherrieb et al., 2010) and qualitative information from focus groups (Oulahen et al., 2015). In other cases, validation is addressed by comparing results with other reference indices (Gall, 2007;Peacock et al., 2010;Sherrieb et al., 2010;Estoque and Murayama, 2014). ...
... The most widely used approach so far is based on the use of independent proxy data referring to real observable outcomes, such as emergency service requests (Kontokosta and Malik, 2018), mortality (Adger et al., 2004;Gall, 2007;Peacock et al., 2010;Cardozo and Monteiro, 2019), building damage or losses (Peacock et al., 2010;Papathoma-Köhle, 2016;Papathoma-Köhle et al., 2019;Rufat et al., 2019), number of disasters (Cutter et al., 2003;Debortoli et al., 2017), post-event surveys (Fekete, 2009;Sherrieb et al., 2010) and qualitative information from focus groups (Oulahen et al., 2015). In other cases, validation is addressed by comparing results with other reference indices (Gall, 2007;Peacock et al., 2010;Sherrieb et al., 2010;Estoque and Murayama, 2014). The approaches described above are included in what is known as external validation, and they are usually implemented J o u r n a l P r e -p r o o f when natural hazard impacts happen at regional or national scale (e.g. ...
... This is because the required data are not normally available (Fekete, 2009) and because flash flooding events are not simultaneously triggered in all urban areas of interest. Thus, an increasingly being used alternative is to validate vulnerability indices internally through an analysis of how changes in index inputs affect modeled results (Gall, 2007;Tate, 2012Tate, , 2013. Internal validation is usually performed by means of a sensitivity and uncertainty analysis, Monte Carlo simulations being the most frequently used approach (Davidson and Lambert, 2001;Gall, 2007;Tate, 2012Tate, , 2013, although other statistical tools such as correlation and regression analysis and cross-validation have also been employed (Schmidtlein et al., 2008;Cai et al., 2016;Anderson et al., 2019;Marzi et al., 2019;Nazeer and Bork, 2019). ...
... In response to the need to improve risk management and adaptation strategies, academics and . Therefore, vulnerability is also a function of 86 exposure (i.e., people and assets at risk), sensitivity (i.e., the level of impact on people and assets at 87 risk), and resilience (i.e., the ability of the social system to resist, absorb, cope with, adapt, and recover The most widely used methodology to analyze vulnerability so far is the development of results (Gall, 2007;Tate, 2012Tate, , 2013. Internal validation is usually performed by means of a sensitivity 134 and uncertainty analysis, Monte Carlo simulations being the most frequently used approach (Davidson 135 and Lambert, 2001; Gall, 2007;Tate, 2012Tate, , 2013, although other statistical tools such as correlation 136 and regression analysis and cross-validation have also been employed (Schmidtlein et Therefore, the goal of this paper is to construct and validate at regional scale an Integrated Socio- 139 Economic Vulnerability Index (ISEVI) in urban areas prone to flash flooding. ...
... Therefore, vulnerability is also a function of 86 exposure (i.e., people and assets at risk), sensitivity (i.e., the level of impact on people and assets at 87 risk), and resilience (i.e., the ability of the social system to resist, absorb, cope with, adapt, and recover The most widely used methodology to analyze vulnerability so far is the development of results (Gall, 2007;Tate, 2012Tate, , 2013. Internal validation is usually performed by means of a sensitivity 134 and uncertainty analysis, Monte Carlo simulations being the most frequently used approach (Davidson 135 and Lambert, 2001; Gall, 2007;Tate, 2012Tate, , 2013, although other statistical tools such as correlation 136 and regression analysis and cross-validation have also been employed (Schmidtlein et Therefore, the goal of this paper is to construct and validate at regional scale an Integrated Socio- 139 Economic Vulnerability Index (ISEVI) in urban areas prone to flash flooding. It considers all 140 vulnerability components (i.e., exposure, sensitivity and resilience) and it takes into account two of the 141 most influential dimensions in the urban environment (i.e., social and economic dimensions). ...
Flash flooding is the natural hazard provoking the largest number of casualties, so adequately characterizing vulnerability is key to improve flood risk analysis and management. Developing composite indices is the most widely used methodology in vulnerability analysis. However, very few studies have so far assessed vulnerability in urban areas prone to flash flooding and the resulting research presents two main drawbacks: i) a fragmented approach is often pursued, i.e. without jointly considering the vulnerability components (exposure, sensitivity and resilience) and the two most influential dimensions in urban environments (social and economic); and ii) vulnerability indices are not usually validated because an ancillary dataset is not generally available and flash flooding events do not happen simultaneously in all urban areas of a particular region. Considering the above gaps, this paper describes the construction of an Integrated Socio-Economic Vulnerability Index (ISEVI) at the regional scale, which considers all vulnerability components and social and economic dimensions. ISEVI was subsequently validated through an uncertainty and sensitivity analysis using the Monte Carlo method. Further, regional spatial patterns of vulnerability were identified implementing a Latent Class Cluster Analysis. Uncertainty analysis reveals the high stability of vulnerability categories of the ISEVI and sensitivity analysis shows that the type and the conservation state of buildings are the vulnerability factors that cause a greater variability in ISEVI scores. The method deployed here may allow specific strategies for vulnerability reduction to be developed based on disaggregating the validated ISEVI into dimensions and components and using the regional spatial patterns characterized.
... CONNECTION TO NATURE: Biophilia: Connection to all of life (1) [37] CULTURAL FULFILLMENT: Cultural Activity: Performing arts attendance (1) [38]; Belonging to religious denomination (1) [37] EDUCATION: Basic Skills of Youth: Standard math test achievement (2) [39]; Standard reading test achievement (2) [39]; Standard science test achievement (2) [39] Participation/Attainment: Adult illiteracy rate (3) [40]; High school graduation rate (14) [32]; Post-secondary education enrollment (14) [32]; Post-secondary education graduation (14) [32] Social Aspects: Children feeling unsafe at school (5) [41]; Children's health (4) [42]; Children's social behavior (5) [41] HEALTH: Care: Regular doctor visits (17) [43]; Satisfaction with hospital care (2) [43] Life Expectancy/Mortality: Asthma mortality rate (16) [44]; Cancer mortality rate (16) [44]; Diabetes mortality rate (16) [44]; Heart disease mortality rate (16) [44]; Infant mortality rate (18) [44]; Life expectancy at birth (18) [45]; Suicide mortality rate (16) [44] Personal Well-Being: Happiness (1) [37]; Life satisfaction (6) [43]; Perceived health (18) [43] Lifestyle/Behavior: Alcoholic beverage consumption (17) [43]; Healthy Behaviors Index (9) [43]; Teen pregnancy rate (18) [43]; Teen smoking rate (4) [41] Physical/Mental Conditions: Lifetime adult asthma rate (18) [43]; Lifetime adult cancer rate (8) [43]; Lifetime child asthma rate (13) [43]; Lifetime adult depression rate (9) [43]; Lifetime adult diabetes rate (18) [43]; Lifetime adult heart attack rate (14) [43]; Lifetime adult heart disease rate (14) [43]; Adult obesity rate (18) [43]; Lifetime adult stroke rate (14) [43] LEISURE TIME: Leisure Activity: Physical activity participation (18) [43]; Time spent on vacation (10) [46] Time Spent: Time spent on leisure or relaxing (1) [47] Working Age Adults: Percent working long work hours (12) [48]; Percent daytime work hours (12) [48] LIVING STANDARDS: Basic Necessities: Food security (1) [49]; Home ownership cost/income ratio (13) [32] Income: Median household income (13) [32]; Poverty rate (13) [32]; Persistent poverty rate (11) [32] Wealth: Median home value (13) [32]; Mortgage debt (13) [32] Work: No fear of job loss (13) [50]; Job satisfaction (1) [51] SAFETY/SECURITY: Actual Safety: Accidental death rate (16) [44]; Natural event injury/death rate (15) [52]; Millions of dollars in natural event damage (17) [52]; Property crime rate (17) [53]; Violent crime rate (17) [53] Perceived Safety: Perceived safety (1) [54] Risk: Social Vulnerability Index (1) [55] SOCIAL COHESION: Attitude Toward Community: Trust in people (1) [37]; City satisfaction (6) [56]; Feeling close to one's town or city (1) [57]; Perception that others are helpful (1) [37] Democratic Engagement: Interest in politics (1) [37]; Registered voters (4) [58]; Satisfaction with democracy (1) [37]; Trust in government (1) [37]; Voice in government (1) [37]; Voter turnout (4) [58] ...
Ecosystem services, including availability of greenspace, clean air, and clean water, can have benefits to human well‐being, but their relative importance compared to economic or social services is often overlooked. In Puerto Rico, for example, improving community well‐being, including economic and cultural opportunities, human health, and safety, are often overarching goals of environmental management decisions, but the degree to which improvements in ecological condition and provision of ecosystem services could impact local communities is complicated by wide variation in social and economic conditions. This study quantifies and maps neighborhood-scale indicators of human well‐being and ecosystem services for Puerto Rico to better understand the degree to which ecosystem services provisioning, alongside co‐occurring social and economic services, explains variability in a number of indicators of human well‐being. In Puerto Rico, variability in indicators of human well‐being were predominately explained by economic services related to accumulating income and personal savings, and social services, including availability of family services, healthcare services, and access to communication technology. Despite the large explanatory power of economic and social services, however, the analysis detected that substantial portions of well‐being, in particular education and human health, could be explained by variability in ecosystem services over space and time, especially availability of greenspace. Linking ecosystem services to multivariate elements of human well‐being can serve to complement more traditional community planning or environmental management efforts by helping identify potential unintended consequences or overlooked benefits of decisions.
... Sensitivity analysis assesses the contribution of the individual source of uncertainty to the output variance. While uncertainty analysis is used more often than sensitivity analysis and is almost always treated separately, the iterative use of uncertainty and sensitivity analysis during the development of a composite indicator could improve its structure (Saisana et al., 2005a;Tarantola et al., 2000;Gall, 2007). Ideally, all potential sources of uncertainty should be addressed: selection of individual indicators, data quality, normalisation, weighting, aggregation method, etc. ...
“Vulnerable populations” are experiencing a (re)emphasis in climate change adaptation research and practice even though the concept has long been contested. Adaptation planning is increasingly expected to restore past inequalities and address systemic injustices. Yet, we know little about the role local environmental agencies, bureaucrats, and policy practitioners (can) play in addressing “vulnerable populations”. Drawing from qualitative empirical research in Atlanta, Georgia, the United States, and Jinhua, Zhejiang in China, the local problem recognition about “vulnerable populations” and adaptation decision-making was examined. The findings reveal severe limitations in the way “vulnerable populations” are approached, with certain groups being politically contested and being considered difficult to be prioritized. In both cases, accidental forms of adaptation stand out, which mainly focus on blue-green infrastructure interventions and neighborhood revitalization programs, some of which recreated “vulnerable populations”. The findings hint to vulnerability being more deeply rooted in external conditions to the individual, which requires different policy interventions. The article presents a novel understanding by conceptualizing “vulnerable populations” as an instance of vulnerable political institutions. There’s a need to explore the nature of our political systems, how much inequality we allow and which redistribution mechanisms the state has for addressing interdependent dimensions of inequality. To make “vulnerable populations” finally a front and center concern begs us to radically engage the outside of the conventional adaptation box. Inequality studies offers synergies with adaptation justice discourses and different policy instruments that address the root causes of vulnerability.
Disaster management protocols are applicable to pandemics as they fall under biological category of disasters [1,2]. “Capacity development for effective response and recovery” [3] is critical to disaster preparedness. The alarming pace at which pandemics spread demands continuous assessment and monitoring of disaster response capacity. Absence of a metric to assess pandemic disaster response capacity calls for identification of measurable indicators of capacity to gauge it. Through a literature review on different assessment frameworks in the realm of disaster management across the globe; six categories of indicators for capacity development from a generic disaster preparedness perspective were drawn. Questionnaire Surveys among 151 expert respondents ascertained the relative weightings and criticality of indicators. Measurable indicator attributes were identified through key informant interviews, and a metric was modelled as a linear function of these variables. To arrive at a fewer set of measurable, manageable and actionable critical indicators sensitive to the pandemic context, model reduction was performed through l2 norm-based sensitivity analysis and using coefficient of variation on pertinent data from 123 districts of India. Results show that critical attributes are number of relief and health service personnel, road density, percentage literacy, efficacy of operational procedures and active NGOs. A case study of Kerala state in India is presented to demonstrate the results. The authors formulate a tool-kit with measurable critical indicators to assess pandemic preparedness of a region; applicable to wider contexts; which would help sensitize decision makers on the capacity of emergency management systems to handle a pandemic.
Vulnerability and fragility are at the heart of the global debate arising from the definition and implementation of the sustainable development goals. This PhD dissertation offers enhanced tools to assess structural vulnerability and fragility from various aspects: economic, social, and environmental. The proposed approach for apprehending these concepts is based on the construction and refinement of composite indicators. It is divided into four chapters.In Chapter 1, we build the retrospective series of the economic vulnerability index (EVI), proposed by the United Nations’ Committee for Development Policy (CDP). Some choices and measures are discussed, such as the methodology used to calculate the instabilities of exports and agricultural production. From our analyses, it appears that the structural economic vulnerability of LDCs is still higher compared to non-LDCs. As well, focusing on the African context, we show that fragile African states are economically more vulnerable than non-fragile African states, and the difference between the two groups of countries seems to come from the difference in the magnitude of shocks. Finally, employing a stochastic dominance approach and using a five-year testing horizon to assess the evolution of the EVI and its main components over time, we observe that there is no real decline of the EVI and its main components at the first order sense. But, an overall decrease can be concluded at the second order sense of dominance.The second chapter focuses on the issue of structural resilience through the Human Assets Index (HAI), another index designed by the UN-CDP for identification of LDCs. We start with a presentation of retrospective series of the HAI and its components, for which, to a limited extend, we have used econometric tools to consistently impute missing data. Secondly, we analyze the HAI’s dynamics by assessing the contributions of each component to this. Finally, we debate about the choice of equal weighting for the four components in the HAI. Taking into account the fact that the correlation between indicators is closely linked to the issue, we propose a new scheme pattern based on the correlation ratio and linearity (or nonlinearity) dependence between components. The third chapter is devoted to the climate change vulnerability. We design a composite indicator called “Physical Vulnerability to Climate Change (PVCCI)”. This indicator based only on the physical characteristics of climate change is independent of present and future country policy, and aims to be used for international allocation of resources. After explaining the specific methodology used to build the PVCCI and presenting the results for developing countries, we investigate the relationship between civil conflict and vulnerability to climate change measured here by the PVCCI. The starting point of the fourth chapter is that African countries are still lagging behind when it comes to attracting Foreign Direct Investments (FDI). We suspect the structural economic vulnerability, measured by the Economic Vulnerability Index (EVI), in part, responsible for the relative lack of interest of foreign investors towards Africa. We estimate a spatial error correction model during the time period from 1980 to 2010 to assess the dynamic relationships between FDI and its determinants. Our finding reveals that in the long run, there is a significant negative relationship between FDI and EVI. The results also suggest that a high EVI in neighboring countries negatively affects the amount of FDI into a host country. Later on, we also observe that structural economic vulnerability plays an important role in explaining the FDI gap between African Low-Income Countries and African Middle-Income Countries. The share of agriculture, forestry and fishery in GDP appears as the strongest contributing factor to this difference.
Human well-being is often an overarching goal in environmental decision-making, yet assessments are often limited to economic, health, or ecological endpoints that are more tangible to measure. Composite indices provide a comprehensive approach to measuring well-being in terms of multi-dimensional components, such as living standards, health, education, safety, and culture. For example, the Human Well-Being Index (HWBI) framework, initially developed for the U.S. fifty states, was recently applied to quantify human well-being for Puerto Rico. However, the paucity of data at spatial scales finer than state or county levels, particularly for social metrics, poses a major limitation to quantifying well-being at neighborhood-scales relevant to decision-making. Here we demonstrate a spatial interpolation method to fill in missing fine-scale data where coarser-scale data is available. Downscaling from municipio (i.e., county-equivalent) to census-tract revealed a greater range of variability in well-being scores across Puerto Rico, in particular, a larger proportion of low well-being scores. Furthermore, while some components of wellbeing (e.g., Education, Health, Leisure Time, Safety and Security, Social Cohesion) showed consistent improvement over time from 2000 to 2017 across Puerto Rico, others (e.g., Connection to Nature, Cultural Fulfillment, Living Standards) were variable among census tracts, increasing for some but declining for others. We use a case study in the San Juan Bay estuary watershed to illustrate how approaches to quantify baseline levels of well-being can be used to explore potential impacts of management actions on communities, including to identify environmental justice inequalities among neighborhoods. Spatial clustering analysis was used to identify statistically significant cold or hot spots in well-being. This study demonstrates how indicators of well-being, coupled with interpolation methods to overcome limitations of data availability, can help to monitor long-term changes over time and to better communicate the potential value of ecosystem restoration or resource management.
Focusing on gender differences, this article synthesizes the literature on gender, risk, and disasters, and presents a comprehensive view of what is known in this area. Data are limited, yet, by using a nine-stage typology to delineate disaster preparedness, impact, and recovery, noteworthy findings are documented and discussed. The literature reveals a pattern of gender differentiation throughout the disaster process. The differences are largely attributed to childcare responsibilities, poverty, social networks, traditional roles, discrimination, and other issues of gender stratification. The emergent patterns have important implications and recommendations for future directions are offered.
Geography in America at the Dawn of the 21st Century surveys American geographers’ current research in their specialty areas and tracks trends and innovations in the many subfields of geography. As such, it is both a ‘state of the discipline’ assessment and a topical reference. It includes an introduction by the editors and 47 chapters, each on a specific specialty. The authors of each chapter were chosen by their specialty group of the American Association of Geographers (AAG). Based on a process of review and revision, the chapters in this volume have become truly representative of the recent scholarship of American geographers. While it focuses on work since 1990, it additionally includes related prior work and work by non-American geographers. The initial Geography in America was published in 1989 and has become a benchmark reference of American geographical research during the 1980s. This latest volume is completely new and features a preface written by the eminent geographer, Gilbert White.
This article uses a systemic perspective to identify and analyze the conceptual relations among vulnerability, resilience, and adaptive capacity within socio-ecological systems (SES). Since different intellectual traditions use the terms in different, sometimes incompatible, ways, they emerge as strongly related but unclear in the precise nature of their relationships. A set of diagnostic questions is proposed regarding the specification of the terms to develop a shared conceptual framework for the natural and social dimensions of global change. Also, development of a general theory of change in SESs is suggested as an important agenda item for research on global change.
Global disasters increase through underdevelopment. Case studies include Guatemala (1976 earthquake) and Honduras (Hurricane Fifi) where vulnerable societies suffered, and within those societies the poor suffered most. Describes the process of marginalization and its relationship to disaster.-K.Clayton
Social science research on disasters began in the early twentieth century with the publication of Samuel Henry Prince's sociology doctoral dissertation on the 1917 Halifax explosion (Prince 1920). However, disaster research did not begin to coalesce as a field until pioneering research was carried out by the National Academy of Sciences and the National Opinion Research Center in the early 1950s, as research teams were sent into the field to collect data on individual, group, and organizational responses to disasters (see Fritz and Marks 1954). The Disaster Research Center, established in 1963 at the Ohio State University and now located at the University of Delaware, continued the practice of conducting "quick-response" studies following major disasters, with an emphasis on organizational and community response. Over subsequent decades, other research centers were established both nationally and internationally. The terrorist attacks of September 11, 2001 generated additional interest in disaster research, as questions were raised concerning a range of topics, including behavioral, psychological, and social-psychological responses to terrorism. Classic sociological research on disasters emphasized the pro-social and adaptive dimensions of disaster-related behavior. Studies consistently documented such patterns as widespread helping behavior among community residents, the emergence of new groups focusing on victim and community needs, increases in social cohesion, the convergence of volunteers and material resources into disaster areas, and the suspension of community conflicts as community residents and public and private-sector organizations put aside their pre-disaster agendas in the interest of overcoming disaster-induced challenges. Disasters were framed in the literature as "consensus" crises and contrasted with "conflict" crises such as riots. Outcomes following disasters include the emergence of "therapeutic communities" that support victims and maintain high community morale. Therapeutic communities help to cushion the negative psychological consequences of disasters, and as a result, negative psycho-social reactions tend to be short-lived following disasters (see Fritz 1961; Barton 1969; Dynes 1970; Stallings and Quarantelli 1985; Drabek 1986). Ongoing research on disasters provides additional support for these earlier empirical findings. At the same time, it has become increasingly evident that earlier consensus-oriented perspectives paid insufficient attention to the diverse ways in which individuals, groups, and communities experience disasters. In contrast with classic studies, newer research has emphasized those diverse experiences. Research has also shown how disaster-related experiences are shaped in important ways by the same dimensions of stratification and inequality that influence people's lives during non-disaster times. Disaster scholarship now recognizes that factors such as wealth and poverty, race and ethnicity, gender and age influence vulnerability to hazards, disaster victimization, and disaster recovery outcomes (Blaikie et al. 1994; Peacock, Morrow, and Gladwin 1997; Bolin and Stanford 1998; Fothergill 1998). As a consequence of these developments, disasters are no longer seen as producing common or typical challenges for at-risk populations. While morale and cohesiveness may undoubtedly be high within some groups within a disaster-stricken community, other groups may be excluded. Postdisaster experiences that are therapeutic for some may be corrosive for others. Some groups may be able to return to their pre-disaster status with relatively difficulty, while others may never fully recover. And to a greater degree than has been recognized before, disasters may become arenas not only for consensus-based social action but also for contentious intergroup interactions. Measures taken to deal with disasters may be welcomed by some groups but denounced by others. Relief programs may benefit some within the population while disadvantaging others Research also shows that groups are differentially vulnerable and also differentially resilient in the face of disasters, depending upon their position in the stratification system. The sections that follow discuss recent advances in the study of the social factors that affect disaster vulnerability and that contribute to resilience in the face of disasters. Using examples from both Hurricane Katrina and other U.S. disasters, these discussions illustrate how large-scale social trends, structural forces, and group characteristics influence preparedness for, responses to, and recovery from disasters. A key point made in these discussions is that while Hurricane Katrina revealed the devastating consequences of social inequality more vividly than any recent U.S. disaster, Katrina has a great deal in common with other disasters the nation has experienced. One implication of these findings is that diverse patterns of vulnerability and resilience must be taken into consideration both in programs that provide disaster aid and in overall planning frameworks for disaster loss reduction. Copyright