i 

Disaster Resilience: A Guide to the Literature 

Stanley W. Gilbert 

U.S. Department of Commerce

National Institute of Standards and 

Technology 

Office of Applied Economics

Building and Fire Research Laboratory 

Gaithersburg, Maryland 20899 

NIST Special Publication 1117 
ii
i 

Disaster Resilience: A Guide to the Literature 

Stanley W. Gilbert 

Sponsored by: 

National Institute of Standards and Technology 

Building and Fire Research Laboratory 

September 2010 

U.S. DEPARTMENT OF COMMERCE 

Gary Locke, Secretary 

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 

Patrick D. Gallagher, Director 

U.S. Department of Commerce

National Institute of Standards and 

Technology 

Office of Applied Economics

Building and Fire Research Laboratory 

Gaithersburg, Maryland 20899

NIST Special Publication 1117 
ii
iii 

Abstract 

Although there is a great deal of high-quality information available on resilience-related 

topics—hazard assessment, vulnerability assessment, risk assessment, risk management, 

and loss estimation—as well as disaster resilience itself, there is no central source of data 

and tools to which the owners and managers of constructed facilities, community 

planners, policy makers, and other decision makers can turn for help in defining and 

measuring the resilience of their structures and communities. The purpose of this 

document is to provide a survey of the literature and an annotated bibliography of printed 

and electronic resources that serves as that central source of data and tools to help readers 

develop methodologies for defining and measuring the disaster resilience of their 

structures and communities. 

The report covers resilience-related topics at two different levels: (1) individual 

constructed facilities and correlated collections of constructed facilities that form a 

network (e.g., hospitals) and (2) community/regional scale frameworks (e.g., physical 

infrastructure, business and economic relationships, population and employment 

demographics). Thus, the first level focuses on physical infrastructure, whereas the 

second takes a broader look at how the physical infrastructure interacts with other 

activities that collectively define modern communities. The reason for taking this 

approach is to establish a foundation for developing methodologies for defining and 

measuring the disaster resilience of structures. This step is especially important because 

physical infrastructure enables the community to function as a place of employment, a 

window to the regional and national economy, and a home for individuals. Developing 

better metrics and tools for defining and measuring the resilience of structures is an 

important step in meeting the challenge of measuring disaster resilience at the community 

scale. 

Keywords 

Buildings; communities; constructed facilities; disasters; economic analysis; insurance; 

natural and man-made hazards; resilience; risk assessment; vulnerability assessment 
iv 

   
v 

Preface 

This study was conducted by the Office of Applied Economics in the Building and Fire 

Research Laboratory at the National Institute of Standards and Technology. The study 

provides a survey of the literature and an annotated bibliography of printed and electronic 

resources that serves as a central source of data and tools to owners and managers of 

constructed facilities, community planners, policy makers, and other decision makers 

concerned with the disaster resilience of their constructed facilities and communities. 

The intended audience is the National Institute of Standards and Technology, the 

National Oceanic and Atmospheric Administration, the Department of Homeland 

Security’s Science and Technology Directorate and Federal Emergency Management 

Agency, the U.S. Geological Survey, the National Science Foundation, insurance 

industry organizations, and standards and codes development organizations interested in 

developing metrics and tools for defining and measuring the disaster resilience of 

constructed facilities and communities. 

Disclaimer 

Certain trade names and company products are mentioned in the text in order to 

adequately specify the technical procedures and equipment used. In no case does such 

identification imply recommendation or endorsement by the National Institute of 

Standards and Technology, nor does it imply that the products are necessarily the best 

available for the purpose. 

Cover Photographs Credits 

Clockwise from the bottom right are as follows: 

Path of named North Atlantic Tropical Cyclone Tracks in 2005, National Oceanic and 

Atomospheric Administration, National Hurricane Center; Tampa and vicinity map, 2009 

National Geographic Society, i-cubed; Seismic Hazard Map of the Continental United 

States, nationalatlas.gov; DigitalVision Construction in Action clips gallery image used 

in compliance with DigitalVision’s royalty free digital stock photgraphy use policy; and 

Microsoft Clip Gallery Images used in compliance with Microsoft Corporations noncommercial use policy. 
vi 

 
vii 

Acknowledgements 

The author wishes to thank all those who contributed so many excellent ideas and 

suggestions for this report. They include Mr. Stephen A. Cauffman, Deputy Chief of the 

Materials and Construction Research Division of the Building and fire Research 

Laboratory (BFRL) at the National Institute of Standards and Technology (NIST), and 

Dr. Jack Hayes, Director of the National Earthquake Hazards Reduction Program at 

BFRL/NIST, for their leadership, technical guidance, suggestions, and programmatic 

support. Special appreciation is extended to Dr. David T. Butry, Dr. Robert E. Chapman, 

and Dr. Harold E. Marshall of BFRL’s Office of Applied Economics for their thorough 

reviews and many insights, to Mr. Douglas Thomas for his assistance in generating the 

charts used on the cover page, and to Ms. Carmen Pardo for her assistance in preparing 

the manuscript for review and publication. The report has also benefitted from the 

review and technical comments provided by Dr. Nicos Matrys of BFRL’s Materials and 

Construction Research Division. 

 
viii 
ix 

Table of Contents

Abstract ............................................................................................................................. iii

Preface ................................................................................................................................ v

Acknowledgements ......................................................................................................... vii

1 Introduction ............................................................................................................... 1

1.1 Background .......................................................................................................... 1

1.2 Purpose ................................................................................................................. 1

1.3 Scope and Approach ............................................................................................. 2

2 Disaster Resilience: A Survey of the Literature ..................................................... 5

2.1 Preliminaries......................................................................................................... 8

2.1.1 Definitions................................................................................................... 8

2.1.2 What we are protecting ............................................................................. 11

2.1.3 Dimensions of Harm ................................................................................. 13

2.1.4 Disaster Cycle ........................................................................................... 14

2.2 Response............................................................................................................. 15

2.2.1 Observations from Research ..................................................................... 16

2.2.2 Ways to Improve Response ...................................................................... 18

2.3 Recovery ............................................................................................................. 19

2.3.1 Full Recovery ............................................................................................ 20

2.3.2 “Instant Urban Renewal” .......................................................................... 21

2.3.3 Population Recovery ................................................................................. 22

2.3.4 Physical Infrastructure .............................................................................. 22

2.3.5 Economy ................................................................................................... 23

2.3.6 Social Networks, Government Services and the Environment ................. 25

2.3.7 Simulating Recovery ................................................................................. 25

2.3.8 Ways to Improve Recovery ...................................................................... 29

2.4 Preparedness ....................................................................................................... 30

2.4.1 Warning Systems ...................................................................................... 31

2.4.2 Evacuation................................................................................................. 31

2.5 Mitigation ........................................................................................................... 33

2.5.1 Individual Behavior .................................................................................. 33

2.5.2 Vulnerability ............................................................................................. 34

2.5.3 Hazard Models and Damage Prediction ................................................... 35

2.5.4 Optimal Mitigation .................................................................................... 36

2.5.5 Optimal Insurance ..................................................................................... 38

2.6 Data and Measurement ....................................................................................... 39

2.6.1 Recovery Measurement ............................................................................ 42

2.6.2 Individual and Family ............................................................................... 43

2.6.3 Physical Infrastructure .............................................................................. 43

2.6.4 Economy ................................................................................................... 43

2.6.5 Resilience .................................................................................................. 46
x 

3 Summary and Recommendations for Further Research .................................... 49

3.1 Summary ............................................................................................................ 49

3.2 Recommendations for Further Research ............................................................ 50

3.2.1 Analysis of Key Structure Types .............................................................. 50

3.2.2 Cost Analysis ............................................................................................ 51

3.2.3 Analysis of Expected Property Losses ...................................................... 51

3.2.4 Analysis of Expected Business Interruption Costs ................................... 52

Appendix A Disaster Resilience: An Annotated Bibliography ............................... 53

Appendix B Selected Databases, Software Tools, and Web Portals ...................... 93

Appendix C Mathematical Appendix ....................................................................... 99

C.1 What is the Optimal Recovery Path from Disaster ............................................ 99

C.2 A Rationalization of the Multiplicative Resilience Index ................................ 101

References ...................................................................................................................... 105

List of Figures

Figure 1: Real U.S. disaster losses, in billions of dollars ................................................... 5

Figure 2: U.S. deaths due to natural disasters ..................................................................... 6

Figure 3: Hypothetical Trajectories of recovery ............................................................... 14

Figure 4: Disaster Cycle.................................................................................................... 15

Figure 5: Percent recovery versus time (in years) for different levels of destruction in a 

disaster .............................................................................................................................. 28

Figure 6: Time to recovery relative to initial capital and consumption ............................ 29

Figure 7: Conceptual degree of evacuation versus risk .................................................... 32

Figure 8: Elements of mitigation, with dependencies ....................................................... 33

Figure 9: Time line of production for an individual firm following a disaster ................. 44

List of Tables 

Table 1: 2009 Swiss Re threshold for disasters .................................................................. 9

Table 2: MCEER PEOPLES framework .......................................................................... 12

Table 3: What we want to protect ..................................................................................... 13
1 

1 Introduction 

1.1 Background 

Despite significant progress in disaster-related science and technology, natural and man-made 

disasters in the United States are responsible for an estimated $57 billion (and growing) in 

average annual costs in terms of injuries and lives lost, disruption of commerce and financial 

networks, property damaged or destroyed, the cost of mobilizing emergency response personnel 

and equipment, and recovery of essential services. Natural hazards—including earthquakes, 

community-scale fires, hurricane-strength winds and hurricane-borne storm surge, and 

tsunamis—are a continuing and significant threat to U.S. communities. Preventing natural and 

man-made hazards from becoming disasters depends upon the disaster resilience of our 

structures and communities. While the overall goal is to improve resilience at the community 

scale, buildings play a critical role in assuring community resilience. Thus, measuring the 

resilience of buildings provides a foundation for the more complex problem of measuring the 

resilience of communities. 

Measuring the resilience of structures and communities to natural and man-made hazards poses 

difficult technical challenges due to a lack of adequate: (1) understanding of the natural 

processes that create hazards to the built environment and information relative to such hazards 

for use by design professionals, standards developers, and emergency managers; (2) predictive 

technologies and mitigation strategies to improve the performance of complex structural systems 

and means to transfer the results of research into practice and to promote risk-informed behavior; 

and (3) standard methods to assess the disaster resilience of structures and communities for use 

in making disaster preparedness and mitigation decisions. Buildings are systems of systems with 

many interactions and interdependencies among these systems. The performance of the 

structural system can have a direct impact on the performance of other systems (e.g., 

architectural, mechanical, electrical, etc.), in addition to utility infrastructure that can be housed 

inside, atop, or below the building. Thus the resilience of the structure has a substantial 

influence on the resilience of the building as a whole and in turn on the community. Although 

the focus of this report is on a critical review of and commentary on resilience-related literature, 

it serves to strengthen the linkage between establishing technically valid measures of resilience 

at the level of the individual building and at the level of the community. 

1.2 Purpose 

Although there is a great deal of high-quality information available on resilience-related topics—

hazard assessment, vulnerability assessment, risk assessment, risk management, and loss 

estimation—as well as disaster resilience itself, there is no central source of data and tools to 

which the owners and managers of constructed facilities, community planners, policy makers, 

and other decision makers can turn for help in defining and measuring the resilience of their 

structures and communities. The purpose of this document is to provide a survey of the literature 
2 

and an annotated bibliography of printed and electronic resources that serves as that central 

source of data and tools to help readers develop methodologies for defining and measuring the 

disaster resilience of their structures and communities. 

The report covers resilience-related topics at two different levels: (1) individual constructed 

facilities and correlated collections of constructed facilities that form a network (e.g., hospitals) 

and (2) community/regional scale frameworks (e.g., business and economic relationships, 

population and employment demographics). Thus, the first level focuses on physical 

infrastructure, whereas the second takes a broader look at how the physical infrastructure 

interacts with other activities that collectively define modern communities. The reason for 

taking this approach is to establish a foundation for developing methodologies for defining and 

measuring the disaster resilience of structures. This step is especially important because physical 

infrastructure enables the community to function as a place of employment, a window to the 

regional and national economy, and a home for individuals. Developing better metrics and tools 

for defining and measuring the resilience of structures is an important step in meeting the 

challenge of measuring disaster resilience at the community scale. 

The report is being published both as a printed document and as a Web-based publication. Both 

documents are intended as a ready reference for researchers, public and private sector decisionmakers, and practitioners—owners and managers of constructed facilities—concerned with 

defining and measuring disaster resilience. In the Web-based version downloadable from the 

NIST Website, all Web links are active as of the print date, enabling readers to browse 

documents and data sources. Furthermore, many of the Web links permit documents and data 

files to be downloaded for future reference and use. 

1.3 Scope and Approach 

The report contains two chapters and two appendices in addition to the Introduction. Chapter 2 

and Appendix A are the core components of the report. 

Chapter 2 is a survey of the literature on disaster resilience. The chapter begins with a discussion 

of the key concepts—hazards, vulnerability, losses, and disasters—that feed into any 

methodology for defining and measuring disaster resilience. The discussion then focuses on the 

various definitions of disaster resilience that are found in the literature. For the purpose of 

setting the scope of the literature review found in this report, disaster resilience is defined as the 

ability to minimize the costs of a disaster, return to the status quo, and to do so in the shortest 

feasible time. A conceptual model of the “disaster cycle” is then introduced. The disaster cycle 

model consists of four stages: (1) response; (2) recovery; (3) preparedness; and (4) mitigation. 

The first two stages are “reacting to” a disaster, whereas the second two are “preparing for” a 

disaster. The conceptual model provides an ideal framework for highlighting the interactions 

between the four stages and how decision-making at the individual asset and community levels 
3 

differ. The chapter then describes data and measurement issues associated with loss estimation. 

These include critical analyses of case studies, insurance claims, direct measurement of losses, 

and survey-based methods. The discussion covers both direct losses due to damages to buildings 

and other constructed facilities and indirect losses due to business interruption and other secondorder, disaster-related effects. The chapter concludes with a mathematical treatment of two 

resilience-related topics—the optimal recovery path following a disaster and the theoretical basis 

for a multiplicative resilience index. 

Chapter 3 provides a summary and recommendations for further research. Four 

recommendations for further research are put forward with an aim of strengthening the linkage 

between defining and measuring disaster resilience at the level of individual structures and at the 

community level. 

Appendix A presents an annotated bibliography on disaster resilience. The appendix begins with 

a synopsis of the key reference documents that provide the basis for much of the literature on 

resilience-related topics. References are listed in alphabetical order by author, where the author 

may be a person, a company, an organization, or a government entity. An abstract is provided 

for each reference that summarizes the salient points of the reference. 

Appendix B presents an annotated bibliography of selected databases, software tools, and Web 

portals. An abstract is provided for each reference that summarizes the salient points of the 

reference. A URL is provided whenever a reference is available in electronic format. 

 
4 

 
5 

2 Disaster Resilience: A Survey of the Literature 

Disaster losses have been increasing exponentially, and many researchers argue that the 

increasing trend is unsustainable (e.g., see Mileti 1999; White, Kates, and Burton 2001), and that 

eventually the costs of disasters will become more than we can afford. Real U.S. Disaster losses 

since 1960 are shown in Figure 1. The trend, shown in the graph, shows that disaster losses are 

increasing at a 4.8 % annual rate. A similar trend is visible in international data as well. One 

factor influencing the results in the figure is that the data are biased down.1

 The bias tends to be 

stronger the older the data. 

Figure 1: Real U.S. disaster losses, in billions of dollars 

Source: Data drawn from SHELDUS. 

A number of researchers have investigated the contributors to this trend (Collins and Lowe 2001; 

Cutter and Emrich 2005; Dlugolecki 2008; Landsea et al. 2003; Miller, Muir-Wood, and 

Boissonnade 2008; Pielke and Downton 2000; Pielke and Landsea 1998; Vranes and Pielke 

2009). These authors have attempted to determine how much of the trend is due to increases in 

population and wealth, spatial distribution of assets, and climate change. Confounding the 

analysis is the fact that there was a lull in Atlantic hurricane activity between about 1970 and 

1990 (Landsea et al. 2003). Most analyses have found that damage trends are flat or down after 

adjusting for population and wealth. Pielke and Downton (2000) confirmed those results but also 

found that damaging floods are most strongly correlated with the number of 2-day heavy rainfall 

events, and that such events are increasing. 

                                                           

1

 That is, the values in the figure are probably lower than the true damages. 

1

1

2

4

8

16

32

64

128

1960 1970 1980 1990 2000 2010
6 

Further analysis reveals that the trend since the early 1990’s is essentially flat (see Figure 1). 

Similar results are present in international data (not shown). Disaster loss data from the Center of 

Research on the Epidemiology of Disasters and from Swiss Re both have a break in trend and 

level of damages somewhere around 1989, with the trend flattening. In the Swiss Re data the 

structural break occurs in insured losses from both weather-related disasters and in man-made 

disasters. It is not clear at this point whether the flattening of the trend is due to changes in 

frequency and severity of hazards, improved mitigation, changes in data collection and reporting, 

or statistical chance. 

In contrast to property losses, deaths and injuries due to disasters have been flat for decades (see 

Figure 2). Similar results hold for international losses (not shown). Analysis of data from Swiss 

Re finds no significant trend in deaths internationally due to natural hazards. However, there is a 

significant upward trend internationally in deaths due to man-caused disasters. No similar data 

set for man-caused disasters was readily available for the U.S.. 

Figure 2: U.S. deaths due to natural disasters 

Source: Data drawn from SHELDUS. 

Different hazards cause property damage versus deaths. The property loss record in the U.S. is 

dominated by hurricanes, flooding and other coastal hazards (for example Stuart Miller et al. 

2008). Deaths are dominated by heat, thunderstorms and lightning, and severe winter weather 

(Borden and Cutter 2008). 

Data quality issues will be discussed in more detail later, but two items from the above figures 

illustrate some of the problems with disaster-loss data. Total damages for 1992 in Figure 1 are 

about $7 billion. However, in 1992 Hurricane Andrew struck south Florida and did in excess of 

$30 billion in damages. Similarly in Figure 2, deaths from Hurricane Katrina do not appear. 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1960 1970 1980 1990 2000 2010
7 

Neither omission changes significantly the results discussed above, but they do illustrate that 

data quality is an issue. 

Disaster losses are highly skewed. For example, the top 10 % of damaging floods do 

approximately 80 % of the property damage.2

 It is easy to pick the Northridge Earthquake and 

Hurricane Katrina out of the graph in Figure 1. The spikes in the number of deaths in Figure 2 

are almost all individual heat waves, including 1980, 1995 and 1999. After normalizing for 

population growth, wealth and inflation, the disaster record suggests that hurricanes are the most 

destructive type of U.S. disaster, with about $1.05 trillion in aggregate normalized losses from 

hurricanes since 1900 followed by earthquakes with about $432 billion in aggregate normalized 

losses since 1900 (Vranes and Pielke 2009). However, the largest events for hurricanes and 

earthquakes are of comparable size, with the largest U.S. disaster in terms of normalized 

damages probably being the 1906 San Francisco Earthquake. 

A number of groups have been warning that the U.S. is becoming more vulnerable to disasters 

(e.g., Burby 1998, Berke et al. 2008). First, people have been moving into areas with greater 

exposure to natural disasters. The percentage of the population living in areas with high exposure 

to hurricanes has been steadily increasing. 

Second, many warn that U.S. infrastructure has become inherently more vulnerable. For 

example, the American Society of Civil Engineers in its 2009 Report Card for America’s 

Infrastructure (2009) gives most infrastructure systems a ‘D’ grade, and warns of dire 

consequences if infrastructure is not improved. For example, with regard to waste water, it warns 

that “the nation’s wastewater systems are not resilient in terms of current ability to properly fund 

and maintain, prevent failure, or reconstitute services.” Furthermore “if the nation fails to meet 

the investment needs of the next 20 years, it risks reversing public health, environmental, and 

economic gains of the past three decades.” 3

Because of this, the National Science Technology Council developed a set of Grand Challenges 

for Disaster Reduction (2005) whose objective was to increase disaster resilience and reduce 

harm from natural and man-made disasters. The grand challenges were: 

Grand Challenge #1—Provide hazard and disaster information where and when it is needed. 

Grand Challenge #2—Understand the natural processes that produce hazards. 

Grand Challenge #3—Develop hazard mitigation strategies and technologies. 

                                                           

2 The results from Vranes and Pielke (2009) suggest that damages from earthquakes are even more highly skewed 

than from hurricanes or floods. 

3 There are two main problems with the report card. First there are no clear criteria for the grades they give; so there 

is no way to independently evaluate the report and no way to determine what is required to raise the grades. Second 

their grades do not appear to be performance based. 
8 

Grand Challenge #4—Recognize and reduce vulnerability of interdependent critical 

infrastructure. 

Grand Challenge #5—Assess disaster resilience using standard methods. 

Grand Challenge #6—Promote risk-wise behavior. 

This report is a step in the process of meeting several of these grand challenges, but primarily in 

meeting Grand Challenge 5.

2.1 Preliminaries 

2.1.1 Definitions 

A Disaster according to the dictionary4 is “a sudden or great misfortune.” Researchers have 

defined disaster in a number of different ways. Rubin (1985) defined disaster as “an 

unscheduled, overwhelming event that causes death, injury, and extensive property damage.” 

Mileti (1999) quotes several definitions of disaster, including “a breaking of the routines of 

social life in such a way that extraordinary measures are needed for survival,” and “disruptions in 

cultural expectations that cause a loss of faith in the institutions that are supposed to keep 

hazards under control.” 

Fritz (1961) developed one of the earliest and most influential definitions, when he defined a 

disaster as: 

an event, concentrated in time and space, in which a society, or a relatively selfsufficient subdivision of a society, undergoes severe danger and incurs such losses 

to its members and physical appurtenances that the social structure is disrupted 

and the fulfillment of all or some of the essential functions of the society is 

prevented 

Swiss Re (2010) operationally defines a disaster as any event in which insured losses exceed the 

thresholds listed in Table 1. Swiss Re adjusts the thresholds annually. Operational definitions of 

this sort are common, but are not consistent across databases. For example, the SHELDUS 

database now includes all geologic and weather-related hazards since 1970 that have killed at 

least one person, and Property Claims Services defines a disaster as an “event that causes $25 

million or more in direct insured losses to property....” 

 

                                                           

4 Webster’s Third New International Dictionary, Unabridged. Merriam-Webster, 2002. 

http://unabridged.merriam-webster.com (18 Jun. 2010). 
9 

Table 1: 2009 Swiss Re threshold for disasters 

Type of Disaster Losses ($M) 

Insured Claims Maritime Disasters 17.1 

Aviation 34.3 

Other Losses 42.6 

or Total Economic Losses 85.2 

or Casualties Dead or Missing 20 

Injured 50 

Homeless 2,000 

For the purposes of this document, a Disaster is defined as any event appearing in a disaster 

database, with the understanding that the boundaries of the definition are very fuzzy at the lower 

end. 

A Hazard is a natural or man-made phenomenon capable of inflicting harm on communities. A 

disaster is the intersection of some hazard with a vulnerable community (National Science 

Technology Council 2005). In the discussion Hazard is used to mean any phenomenon capable 

of causing harm, whether it does so or not, and Disaster to refer to any situation where harm has 

been caused on a wide scale in a community. 

Resilience has a wide variety of definitions as well. The basic idea is that “a community’s hazard 

resilience is ... its ability to absorb disaster impacts and rapidly return to normal socioeconomic 

activity” (Lindell in NEHRP 2010). In general, definitions for resilience fall into two broad 

categories. The first are outcome-oriented definitions, and the second are process-oriented 

definitions. 

Outcome-oriented definitions define resilience in terms of end results. An outcome-oriented 

definition would define resilience in terms of degree of recovery, time to recovery, or extent of 

damage avoided. For example, the Heinz Center (2000) defines a “disaster resilient community” 

as “a community built to reduce losses to humans, the environment, and property as well as the 

social and economic disruptions caused by natural disasters.” Similarly Bruneau et al (2003) 

define resilience as “the ability of social units...to mitigate hazards, contain the effects of 
10 

disasters when they occur, and carry out recovery activities in ways that minimize social 

disruptions and mitigate the effects of future [disasters].” 

Process-oriented definitions have become preferred by disaster researchers from the social 

sciences. For example, Comfort (1999) defines resilience as “the capacity to adapt existing 

resources and skills to new situations and operating conditions.” Norris et al. (2008) define 

resilience as “a process linking a set of adaptive capacities to a positive trajectory of functioning 

and adaptation after a disturbance.” 

Manyena (2006), arguing forcefully for a process-oriented definition of resilience, says that 

[t]he danger of viewing disaster resilience as an outcome is the tendency to 

reinforce the traditional practice of disaster management, which takes a reactive 

stance. Disaster management interventions have a propensity to follow a 

paternalistic mode that can lead to the skewing of activities towards supply rather 

than demand. Activities such as community capacity building, mitigation and 

emergency preparedness planning, which impact greatly on response and recovery 

operations, may be neglected. ... Outcome-oriented disaster resilience 

programmes are inclined to adopt command and control styles that risk preserving 

the status quo, and which might entrench exclusion, and take attention away from 

the inequality, oppression and entitlement loss that results in cases of proneness to 

insecurity and disaster. 

Furthermore he argues that 

Viewing disaster resilience as a deliberate process (leading to desired outcomes) 

that comprises a series of events, actions or changes to augment the capacity of 

the affected community when confronted with ... shocks and stresses, places 

emphasis on the human role in disasters. Disaster resilience is seen as a quality, 

characteristic or result that is generated or developed by the processes that foster 

or promote it. Put differently, resilience is not a science nor does it deal with 

regularities in our experience, but rather, it is an art that addresses singularities as 

we experience them. For instance, recognising the human role in disasters, taking 

responsibility for action, having a disaster plan, building capabilities to implement 

the plan, purchasing insurance and sharing information on recovery priorities are 

steps that can enhance the resilience and hence the ability ... to deal with 

[disasters]. 

Basically, Manyena is arguing three things. First, he argues that by focusing on desired 

outcomes, communities tend to overlook important measures that would improve their ability to 

withstand and respond to disasters. Second, there are some process-oriented items that are 

valuable in their own right. Here would belong “recognising the human role in disasters, [and] 

taking responsibility for action.” Third, is a debate about the end point. Assume serious damage 
11 

has occurred. Resilience would entail a prompt return to some improved state, but what state? A 

return to the status quo ante is never completely possible. However, a return to a state as good as 

the status quo ante, with a minimum of change presumably is. Manyena essentially argues that 

the status quo ante was flawed, and the disaster should be seen as an opportunity to fix some of 

the flaws. For example, he argues that the end-point for resilience should include correcting 

problems with “inequality, oppression and entitlement loss.” 

An outcome-oriented definition better suits our purposes than a process-oriented one. Process in 

dealing with disasters is only valuable to the extent that it serves specific desirable outcomes: 

saving lives, preventing the destruction of property and the environment, maintaining the flow of 

valuable goods and services, etc. A process-oriented definition begs the key question of what 

process? That is a question that cannot be answered without reference to the desired outcomes. 

An outcome-based definition and measure would enable us to more rigorously test processes to 

see which ones are effective at minimizing the destruction associated with disasters. 

Therefore for this report Resilience is defined as the ability to minimize the costs of a disaster, to 

return to a state as good as or better than the status quo ante, and to do so in the shortest feasible 

time. As such, greater resilience entails a return to some ‘normal’ as soon as possible, and the 

best ‘normal’ reasonably achievable. Note that this is general enough that it leaves room for a 

substantial amount of community reorganization or redistribution if the community desires it. 

However, at the same time it does not take it into account. Reorganization of the community may 

very well be desirable and desired by the citizens of the community, but community redesign and 

improvement are different from resilience. 

Norris et al. (2008) make a distinction between resilience and Resistance. In their terminology 

resilient communities and people bounce back from disasters,5

 while Resistant communities and 

people do not suffer harm from hazards in the first place. In this paper Resistance is used to 

mean the ability to withstand a hazard without suffering much harm. Resilience in this paper will 

include resistance but will also include the ability to recover after suffering harm from a hazard. 

2.1.2 What we are protecting 

In order to effectively measure and improve resilience, we need to define what bad thing it is we 

are trying to avoid. That is, we need to know what it is we are trying to protect. In the literature 

that has usually been left implicit. Generally the focus is on protecting either lives or property. 

The Heinz Center (2000) divided losses into four categories: the built environment, the business 

environment, the social environment, and the natural environment. Losses to the built 

environment include destruction of houses and businesses as well as destruction of lifeline 

infrastructure. Losses to the business environment included what are usually called “indirect” 

business interruption losses as well as losses of things like inventory, tools, and equipment. 

                                                           

5 ...to some extent. Their definition is process oriented and so is deliberately vague about how much the resilient 

community or person bounces back.
12 

Losses to the social environment included deaths, injuries, hedonic losses due to life and 

community disruption, and lost wages. Losses to the natural environment included among other 

things crop losses. 

The MCEER (2010) recently developed a draft framework for measuring disaster resilience. 

There are seven elements in the framework (represented by the acronym ‘PEOPLES’) which 

represent both what we want to protect and resources that provide protection. The framework to 

some extent seems to confound the roles of ‘protected’ and ‘protection’ in the sense that each 

element in the framework fills both roles. The framework does not make the respective roles of 

‘protected’ versus ‘protection’ clear. Here we are interested in the elements of the framework as 

things we want to protect. 

The elements in the framework are listed in Table 2. Most elements are fairly self-explanatory. 

However, Lifestyle and Community Competence and Social-Cultural Capital are not clearly 

separated. 

Table 2: MCEER PEOPLES framework

Population and Demographics 

Environmental/Ecosystem 

Organized Governmental Services 

Physical Infrastructure 

Lifestyle and Community Competence 

Economic Development 

Social-Cultural Capital 

For the purposes of this paper a slight modification of the PEOPLES framework is used. The list 

used in this paper is shown in Table 3. Protecting people means, among other things, preventing 

deaths and injuries and preventing people from being made homeless. Protecting physical 

infrastructure means limiting damage to buildings and structures, including most lifeline 

infrastructure. Protecting the economy means preventing job losses and business failures, and 

preventing business interruption losses. Protecting key government services includes (among 

other things) ensuring that emergency services are still functioning. Protecting social networks 

and systems includes (among many other things) ensuring that people are not separated from 

friends and family. 
13 

Table 3: What we want to protect 

People 

Physical Infrastructure 

Economy 

Key Governmental Services 

Social Networks and Systems 

Environment 

2.1.3 Dimensions of Harm 

In measuring the harm done by a disaster there are a number of dimensions that need to be 

considered. 

First, we need to consider both direct and indirect damages. Direct damages are those that are 

directly caused by the hazard. For example, this would include people killed or injured in an 

earthquake, buildings destroyed in a hurricane, forest area burned in a wildfire, etc. Direct 

damages are what the traditional measures of harm tend to measure. 

However, there are also indirect damages. Indirect damages are those damages that are caused, 

not by the hazard itself, but as a ‘ripple effect’ from the initial damages. For example, this would 

include business interruption costs due to power outages, deaths resulting from a lack of medical 

services, etc. Indirect effects need not always be negative. For example, Hurricane Hugo 

devastated the Frances Marion National Forest, thus doing long-term harm to the forestry 

industry in South Carolina. However, the destruction in the Forest produced a short-run boom as 

salvage operations cleaned up after the hurricane (Guimaraes, Hefner, and Woodward 1993). 

Trajectory of recovery also matters to the total amount of damages (see Figure 3). If direct harm 

is the same for two disasters, but recovery time is longer for one (for example, the power is out 

longer, or people are unable to return to their homes for a longer time) then the longer recovery 

time results in greater total harm. Similarly, a disaster where recovery is complete will suffer less 

harm than one where recovery is only partial. In some cases recovery may be (temporarily) more 

than complete (see Figure 3). 

 
14 

Figure 3: Hypothetical Trajectories of recovery 

As a general rule we tend to measure harm at the local and regional level. However, effects may 

spill over into the economy as a whole. First, few if any regions are truly autonomous. An 

earthquake that interrupts business in Los Angeles will have secondary effects throughout the 

country. Businesses in Los Angeles will have customers in Denver and suppliers in Seattle. 

Furthermore, when recovery is incomplete, one region’s losses may be another region’s gains. 

Population losses to New Orleans due to Hurricane Katrina were population gains for Houston 

and Memphis. 

2.1.4 Disaster Cycle 

Most recent researchers use the ‘disaster cycle’ as a filter for understanding and evaluating 

response to hazards. By portraying it as a cycle, we convey the idea that in recovering from one 

disaster we are preparing for the next one. 

Different researchers use different versions of the disaster cycle depending on their purposes and 

objectives. For example, MCEER (2010) uses a disaster cycle with six stages. Rubin (1991) on 

the other hand divides the disaster cycle into four stages. This report uses the disaster cycle from 

Rubin (1991), shown in Figure 4. 

The stages in the disaster cycle can be thought of as falling into two groups. Response and 

Recovery form the first group, while Mitigation and Preparedness form the second group. 

Response and Recovery occur in the immediate aftermath of a disaster and are generally aimed 

at dealing with the disaster that has just hit. Mitigation and Preparedness occur mainly during 

normal circumstances and are largely aimed at preparing for the next disaster. 

 

time

Degree of recovery

100%
15 

Figure 4: Disaster Cycle 

2.2 Response 

Response includes the period during and immediately following a disaster in which efforts are 

directed at limiting the harm done by the disaster (Haas, Kates, and Bowden 1977). The NFPA 

(2007) defines response as the “immediate and ongoing activities, tasks, programs, and systems 

to manage the effects of an incident that threatens life, property, operations, or the environment.” 

This includes measures like evacuation, mass emergency sheltering and feeding, emergency 

response, search and rescue, and initial clearing of the main arteries. During the response period 

normal social and economic activities are stopped. 

Harrald, (2006) divides the response period into four phases. 

• An initial response phase. This is conducted by local resources while external resources 

are being mobilized. 

• An integration phase. Once external resources have arrived, time is required to structure 

these resources into a functioning organization. 

• A production phase. During the production phase, the response organization is fully 

productive, delivering needed services as a matter of routine. 

• A demobilization phase. 
16 

2.2.1 Observations from Research 

There have been many case studies on response, among which are Comfort (1999), Comfort and 

Haase (2006), Quarantelli (1982, 1983), and Wenger et al. (1986, 1989) (See Quarantelli 1989 

for a more complete bibliography). There are a number of very consistent results regarding the 

response period from the case studies. Unfortunately, it is also very hard to determine from the 

literature what measures can be taken by communities to improve response. 

Virtually all studies agree on a number of things (see for example Perry and Lindell 2007). 

Citizens in disasters do not panic; they do not react with shock or passivity. People are almost 

always more prosocial after disasters, and act rationally given their understanding of the 

situation. Similarly, “people with officially-defined disaster roles will execute those roles...” but 

will worry about their families until they find out how they are doing. That is, they do not 

abandon their posts (Perry and Lindell 2007). 

“Uninjured victims are usually the first to search for survivors, care for those who are injured, 

and assist others in protecting property.” In fact many report that the majority of search and 

rescue is performed by bystanders rather than professional emergency responders (ibid.). 

Very few people use mass emergency shelters. In general 5 % - 15 % of evacuees stay in 

emergency shelters (Tierney, Lindell, and Perry 2001). Most people who evacuate stay in hotels 

or with family or friends. Emergency shelters also tend to empty out very quickly (Quarantelli 

1982). 

In relatively small disasters, there are far more resources than can be used. In the initial stages of 

a disaster the scale of the disaster it often not clear, so there is a tendency to dispatch more 

resources than turn out to be needed. In addition, many emergency response units self-dispatch. 

For example, in the well-studied 1974 Xenia Ohio tornado, fire units from Dayton traveled the 

15 miles to Xenia to assist in search and rescue without any requests for assistance from Xenia 

(Quarantelli 1982). 

Even in larger disasters resources are not usually scarce. For example, in Hurricane Hugo in 

response to the persistent national appeals by the mayor of Charleston, South Carolina for food 

and clothing, the region was inundated by donations, many of which simply had to be thrown 

away (Rubin 1991). The biggest resource problems in both small and large disasters tend to be 

getting the right resources6

 and distributing the available resources to those who need them. 

Local emergency-response agencies will be the first official responders to disasters. In small 

disasters they may be the only such responders. In large disasters they typically provide the sole 

                                                           

6 Rubin (1991) reports in the Charleston, SC case that the food and clothing donations “contributed to significant 

roadway congestion and ... a flood of telephone calls that diverted disaster workers’ attention.” In addition, it “got 

in the way of shipments of chainsaws and other needed goods.” 
17 

emergency response for hours to days after the disaster. Generally they respond in ways very 

similar to their behavior in normal situations (Quarantelli 1983; Wenger et al. 1989). 

Police and Fire departments do not reach much beyond their regular duties and day-to-day 

experience in disasters. Basically, they do what they always do. Firefighters put out fires, engage 

in search and rescue, and provide emergency medical treatment (at least, those departments that 

provide EMT services normally) and then go back to the station. Police departments engage in 

search and rescue and provide site control. Emergency medical personnel stabilize victims and 

transport them to the nearest hospital. All these agencies and personnel mostly do their job, with 

very little modification, the way they always do it. 

This has prompted a fair amount of criticism for the agencies from researchers for failing to 

adapt to the environment of the disaster. Disasters are different. As already discussed, many 

EMS tasks are carried out by people outside the traditional emergency system. In larger disasters 

agencies may have to temporarily ration services. Interagency coordination becomes much more 

important. And often changes in routines can help alleviate some of the problems that develop. 

On the other hand, there are benefits to sticking mostly to what they do normally. In many of the 

case studies (which were all small disasters) the coordination problems that could have 

developed generally did not precisely because the agencies basically limited themselves to their 

normal roles. Furthermore, since agencies rarely went beyond their training, the work was 

carried out expeditiously and well. 

One partial exception to this was emergency medical care. The main study on this is Quarantelli 

(1983), which is almost 30 years old. The much more recent paper by Auf der Heide (2006) 

repeats the same lessons. 

The main objective of all participants in emergency medical treatment is to get victims to the 

nearest hospital as quickly as possible. Triage in the field simply was not performed. Some 40 % 

of patients were not transported to the hospital by ambulance (Auf der Heide 2006 suggests that 

the number tops 50 %). As a result less-injured patients tend to arrive first at the hospital. 

Communication was poor to nonexistent. What information hospitals had generally came from 

ambulance drivers and early-arriving patients. So, hospitals did not generally know what to 

expect. As usual throughout the emergency response system they usually prepared for a much 

bigger disaster than they actually ended up facing. 

Due to the tendency to transport patients to the nearest hospital, one hospital was frequently 

overwhelmed with patients, while other nearly hospitals had few if any patients. Occasionally 

this ended up costing lives because hospitals drew personnel from other critical areas to deal 

with the large patient influx. 
18 

An important observation is that in disasters hospitals just do not keep good records of patients, 

injuries, and treatments, especially for those with minor injuries. So, data on injuries is likely to 

be unreliable. 

The big problems in response, through many disasters, over many years, in many countries, were 

communication, task assignment and coordination, and authority relationships. Essentially, you 

had independent units working, not talking to each other, and no one (that everyone agreed upon) 

in charge (see for example Comfort 1999; Quarantelli 1983; Wenger et al. 1986). 

In small disasters this was not generally a big issue since interagency coordination was either not 

needed or could be handled implicitly by assuming the other agencies would fulfill their 

traditional roles. However in larger disasters this becomes a major problem. When outside 

agencies are dispatched (or self-dispatch) coordination with the local agencies is often poor. So, 

for example, fire fighting units may sit unused because no one says ‘you are needed over there.’ 

Comfort (1999) studied communications extensively in her work on disaster response 

internationally. In all her case studies communications were a serious problem. In general she 

found that the better communications were handled the better the response was. 

2.2.2 Ways to Improve Response 

The research has been much less effective at finding ways to improve response. Some of the 

regularities that researchers have identified in response suggest ways (in theory at least) that 

response can be improved. The big observation of all the researchers cited is that good 

interagency communications is important to good disaster response. The importance of good 

communications increases as the size of the disaster response increases—and hence the number 

of different agencies increases. Unfortunately that seems to be difficult to do. As Wenger et al. 

(1986) said, “[r]esearchers have noted these issues for the past two decades. Even in the face of 

concerted efforts to improve planning, they continue to plague disaster response.” 

Disaster planning correlates poorly with good disaster response (Tierney et al. 2001; Wenger et 

al. 1986). Wenger, et al. (1986), in their case studies, found some evidence that planning helped, 

but even in their work the correlation was poor. The best predictors of effective response were 

not having a plan, a local emergency management office, or anything else. The best predictor 

was experience. The communication problems mentioned still occur, but they were less severe 

and less common. 

Harrald (2006) argued that both agility and discipline are critical factors for success in disaster 

response. Agility is needed because much of what happens in a disaster will be unanticipated. 

Discipline is needed because it increases the effectiveness of the actions taken. He also argues 

that they are not incompatible. 
19 

A number of authors have criticized the various incident management systems that have been 

used in this country, including the National Incident Management System (NIMS). Harrald 

(2006) argued that the NIMS is rigid and tends to sacrifice agility. Wenger, et al. (1989), looking 

at an earlier version of NIMS, argued that the Incident Management System was in effect a Fire 

Management System. In the case studies they evaluated,7

 the Incident Management System was 

used exclusively by fire departments. When multiple agencies were involved, police departments 

and other groups were not a part of the Incident Management System. 

2.3 Recovery 

...ὁμοίως σμικρὰ καὶ μεγάλα ἄστεα ἀνθρώπων [ἐπέξειμι]. τὰ γὰρ τὸ πάλαι μεγάλα 

ἦν, τὰ πολλὰ σμικρὰ αὐτῶν γέγονε: τὰ δὲ ἐπ ̓ ἐμεῦ ἦν μεγάλα, πρότερον ἦν σμικρά.

τὴν ἀνθρωπηίην ὤν ἐπιστάμενος εὐδαιμονίην οὐδαμὰ ἐν τὠυτῷ μένουσαν,

ἐπιμνήσομαι ἀμφοτέρων ὁμοίως. Herodotus, Histories, 1.5.3-4. 

I will treat cities with both small and large populations alike. For many of the 

cities which were large in ancient times now have become small. Those that are 

large in my time formerly were small. Knowing that good fortune in a place does 

not last, I will treat them both alike. 

Recovery is the time period in which activities and programs are pursued with the objective of 

returning conditions to an acceptable level (National Fire Protection Association 2007). During 

the recovery period, rubble is cleared, people are provided with interim housing, normal social 

and economic activities are resumed, and the area is rebuilt (Haas et al. 1977). 

Kates and Pijawka (1977), part of the larger work of Haas et al. (1977), break recovery down 

into four different sub-periods: an Emergency Period, a Restoration Period, a Replacement 

Reconstruction Period, and a Commemorative, Betterment, Developmental Reconstruction 

Period. Their Emergency Period is approximately comparable to the Response period in section 

2.2. During the Restoration period infrastructure is repaired and normal activities are resumed. 

The Replacement Reconstruction Period rebuilds capital stock to predisaster levels. Finally the 

Commemorative-Betterment period is when memorials are built and attempts are made to 

improve the community. They argued that the length of time for each period was fairly 

predictable, with each period lasting about 10 times as long as the previous period (except for the 

Commemorative-Betterment Period). 

The periods were overlapping, with restoration activities often begun before the emergency 

activities were complete, and replacement reconstruction begun before restoration was fully 

complete. As a general rule recovery was faster for the wealthy than the poor. So Replacement 

                                                           

7 Their case studies date from the 1980’s. It is not clear from the literature how much of their observations still hold. 
20 

may be well underway in one segment of the city even while Restoration is ongoing in a 

different portion of the city. 

Many more recent researchers have criticized this framework (for example Berke, Kartez, and 

Wenger 2008; Miles and Chang 2003). However, much of the criticism is really a result of the 

more recent researchers being interested in different questions. The more recent researchers are 

often interested in differences in impact and recovery far more than they are interested in the fact 

of recovery. As Miles and Chang point out “this more recent literature has been concerned with 

disparities and equalities in recovery, and with conceptualizing disaster recovery as a social 

process involving decision-making, institutional capacity, and conflicts between interest groups.” 

The results of Kates and Pijawka were based on four case studies of major disasters throughout 

the world. The best-studied case was the 1906 San Francisco Earthquake. As such the regularity 

of the lengths of recovery periods is not well supported. The model itself is a useful way of 

thinking about some aspects of recovery, but, as the criticism suggests, its usefulness decreases 

as the focus shifts away from restoration of the physical city. 

Berke et al. (2008) propose a model of ‘recovery’ intended to compete with Haas et al. However, 

it is really a model of aid. Their model is further hampered by their exclusive focus on 

government action — the private sector simply does not exist in their model. 

2.3.1 Full Recovery 

Usually we think of full recovery as a return to conditions as good as those that would have 

obtained had the disaster never occurred. Operationally, this is often defined as a return to 

conditions as good as or better than the ex ante state (e.g., Chang 2010). 

By the latter definition, communities almost always fully recover. Kates and Pijawka (1977) 

analyze the time required for full recovery of population for cities hit by disaster between 1200 

and 1800. These include a number of cities that lost more than 50 % of their population in 

disasters (often wars). Nearly all the cities recovered. Vigdor (2008) does a similar analysis for 

modern disasters and finds the same result. He cites Hamburg where nearly half the city’s 

housing was destroyed in allied bombing, yet the city’s population had exceeded its prewar level 

by the early 1950’s. The only modern example he could find where a city did not recovery was 

Dresden after World War II. Similarly, Quarantelli (1989) notes that “disasters are not new 

phenomena. But more important, recovery from them [is] also not new. In fact, there is almost 

always recovery from disasters.” 

Wright et al. (1979) analyzed the long-range effects of disasters on communities. They analyze 

all disasters to hit the U.S. in the 1960’s, by county and census tract. As usual the overwhelming 

majority of the disasters are small: the median tornado, for example, killed no one and caused 

less than $500 000 in damage. Based on their database, they could find no evidence of long-term 

impact from a disaster on either the county or census tract it was in. 
21 

Vigdor suggests (and most researchers agree) that cities recover fully unless they are already in 

decline. His example of Dresden supports this since Dresden was already experiencing a 

population decline before the war. Vigdor argues on that basis that New Orleans likely will not 

recovery fully since it was already in a long-term decline. 

Kobe does not seem to have recovered fully since the Great Hanshin Earthquake in 1995, and 

there is no sign of earlier decline (Chang 2010). In the case of Kobe, its port was an important 

economic foundation, and the port was completely destroyed. By the time the port was rebuilt 

three years later, business had moved elsewhere. Container traffic had been a major part of the 

port’s business and had been growing before the earthquake. The earthquake was associated with 

a severe decline in container traffic, and ten years later container transshipments are down 90 %.8

2.3.2 “Instant Urban Renewal” 

We have so far assumed that the objective of recovery is return to ex ante conditions as far as 

possible. For the most part we will continue to make that assumption in the remainder of this 

report. However, it is not the only possible objective, and many researchers argue that disasters 

are opportunities to reengineer the social environment in ways that improve resilience and social 

amenity. As a dramatic example, Foster and Giegengack (2006) essentially argue that New 

Orleans is irremediably vulnerable to hurricanes and other coastal disasters and should largely be 

abandoned. Mileti (1999) spins an imaginary scenario in which in the aftermath of a major 

hurricane hitting south Florida, the people decide to dramatically reduce the population of south 

Florida, largely abandon the coastline, and prohibit private automobiles. Proponents of using 

disasters as an opportunity for “instant urban renewal” on a smaller scale include Berke et al. 

(2008), Burby (1998), Godschalk et al. (1999), and Rubin (1985), among others. 

There can almost never be a complete return to conditions ex ante. The deceased will never come 

back. Capital destroyed may be replaced, but only by either reducing consumption or foregoing 

capital investment elsewhere. In major disasters the layout of the city is almost always altered 

permanently (as discussed in more detail below). Furthermore, a jurisdiction hit by a disaster 

may decide to use the opportunity for redesign of some aspects of the community. Any measure 

of recovery will need to be robust enough to allow for these possibilities. 

                                                           

8 Similarly, looking at ancient disasters, a number of cities lost population and took many centuries to recover (Data 

on population taken from Chandler (1987)). Baghdad was sacked in 1258 and took many centuries to recover. 

Similarly, Rome and Constantinople suffered major declines after being sacked. Rome did not return to its peak 

population for more than 1,000 years. 

However, in each of these cases, the ‘disaster’ was as much political and economic as military. Rome and Bagdad 

had been the capitals of major empires. In each of those cases either the empire ceased to exist or the capital was 

moved. In the case of Constantinople the decline in population was associated with a decline in the empire of which 

it was the capital. In all these cases, the city’s reason for being went away, and the population seemed to follow it. 
22 

However, the possibilities for conscious redesign of a community are probably limited. Haas et 

al. (1977) argues that “despite the best efforts to shape the character of the reconstructed city, 

fundamental change is unlikely.” “Overambitious plans to accomplish ... goals [such as 

improved efficiency, equity, amenity] tend to be counterproductive.” They argue that private 

actors want a prompt return to normal. The longer the government delays, the more likely it is 

that people will rebuild, with or without government support. 

Rubin (1985) supports these conclusions. She repeats the observation that if government delays 

too long in planning for a post-disaster future, the more likely it is that private action will make 

such planning moot. Furthermore she notes that consensus on reshaping a community is not 

always easy to obtain. In her case study of the 1983 Coalinga Earthquake she notes that Coalinga 

had trouble precisely because there were two competing visions and no consensus. 

2.3.3 Population Recovery 

As discussed above, community populations almost always recover to their previous levels. For 

the most part, exceptions are limited to cities that are already in long-term decline. A particularly 

dramatic example of long-term recovery is the city of Warsaw, Poland. During World War II, 

Warsaw was almost completely destroyed, and had lost more than 80 % of its pre-war 

population. But within 15 years its population returned to pre-war levels. 

2.3.4 Physical Infrastructure 

Studies of time to rebuild commercial and industrial infrastructure do not seem to exist. Any idea 

on the rebuilding of commercial and industrial infrastructure must be inferred from data on the 

restoration of business services. Similarly, little data exists on recovery of inventory losses. 

There are a number of models regarding recovery of lifeline infrastructures (like power, gas, 

water, transportation). There are very few regarding longer term lifeline recovery. Models of 

response and recovery include Isumi et al. (1985), Kozin and Zhau (1990), and Liu et al. (2007). 

Business recovery is heavily dependent on restoration of lifelines. For example, Tierney (1997a), 

in her survey of businesses following the Northridge earthquake, found that 58 % of business 

closures were in part due to lack of electricity, 56 % due to the inability of employees to get to 

work, and 50 % due to lack of telephone service (multiple reasons were cited by most 

businesses). Gordon et al. (1998) concluded that 27 % of business interruptions were due (in 

part) to transportation interruptions, with job ‘losses’ of more than 15,700 person-years. 

Following the Kobe earthquake, electric power took nearly a week to restore, 

telecommunications two weeks, water and natural gas about two and a half months, railway 

seven months, and highway transportation 21 months (Chang 2010). Electrical power was 

restored within 28 hours following the Northridge earthquake in all but two service areas (Rose 

and Lim 2002). Median restoration times for water and natural gas were about two days; 

restoring sewage services took an additional day for affected businesses (Tierney 1997a). 
23 

Quarantelli (1982) argued that there are four stages involved in housing recovery: 

The first stage is emergency shelter, which consists of unplanned and spontaneously sought 

locations that are intended only to provide protection from the elements. This period typically 

lasts only a few hours, and falls in the Response period. 

The next step is temporary shelter, which includes food preparation and sleeping facilities that 

usually are sought from friends and relatives or are found in commercial lodging, although ‘mass 

care’ facilities in school gymnasiums or church auditoriums are acceptable as a last resort. As a 

rule this lasts only for a few days, and also falls in the Response period. This was discussed 

briefly there. 

The third step is temporary housing, which allows victims to reestablish household routines in 

nonpreferred locations or structures. 

The last step is permanent housing, which reestablishes household routines in preferred locations 

and structures. In some cases temporary housing ends up becoming permanent housing (Bolin 

and Stanford 1991), usually against the intentions of the agencies providing the temporary 

housing. 

Bowden, et al. (1977) observed a number of regularities in rebuilding. Their main example is the 

1906 San Francisco Earthquake. The wealthy reestablished housing first. The poor were several 

years in finding new houses, and many who lived there during the disaster left. Initially, with 

50 % of the housing stock destroyed, and the population not seriously depleted, housing prices 

were very high. As time went on, and the building stock was restored, prices fell. They found 

similar price effects in the Rapid City, SD flood of 1972. Bolin and Stanford (1991) found that 

earthquakes tended remove the bottom tier of housing from the market. The bottom tier was 

typically either destroyed or had to be upgraded to such an extent that the poor were priced out of 

the housing market. 

In San Francisco, the city expanded and reduced its density. The Central Business District grew 

in physical size, and residences moved further out. Sorting became much stronger. So, rich and 

poor neighborhoods that had been somewhat intermixed before became strongly sorted. Ethnic 

enclaves became much sharper. The business district became much more sorted with the 

financial district cleanly separated from the garment district, from the theater district etc. 

Other researchers have found similar results. Chang (2010) found after the Kobe (1995) 

earthquake that there was increased suburbanization. Friesma (1979) found the same result in 

Topeka, KS after flooding there. 

2.3.5 Economy 

Friesma et al. (1979) suggest that disasters can be good for the economy. The basic argument is 

that it results in replacement of capital stock with newer and better, which in turn makes the 

community more competitive. The argument is difficult to support. If companies and regions 

would become more competitive by upgrading their capital, they have a powerful incentive to do 

so even absent a disaster. 
24 

What the disaster may do is get someone else to fund capital improvement. Money transfers to a 

disaster-stricken area can be quite large. With aid, the region may end up economically better off 

than it was before, but the nation is still worse off. The money that went to fund reconstruction 

still has to be financed either out of consumption or by deferring or foregoing some other 

investment. 

Even with aid, however, it seems unlikely that regions are often better off for disasters. Chang 

(1983) evaluated the effect of Hurricane Frederick on the finances of the City of Mobile, AL. He 

found that the impact of the hurricane was net negative. Chang (2010), in her analysis of the 

recovery of Kobe, found that Kobe never fully recovered. There was an initial construction boom 

“followed by settlement at a ‘new normality’ that was roughly ten per cent lower than the predisaster normal.” Guimaraes et al. (1993) estimated the economic impact of Hurricane Hugo on 

the Charleston, SC economy and found a similar construction boom followed by a construction 

lull. Apparently much of the repair, maintenance, and improvement that would have normally 

been spread out over several years was compressed into the time period immediately after the 

hurricane. A similar effect was visible in forestry. The hurricane did enormous damage to the 

Frances Marion National Forest. The forestry industry went through a short-term boom after the 

hurricane due to a massive salvage effort, but the boom was followed by a bust due to the dearth 

of harvestable trees. 

Bowden, et al. (1977) in their analysis of recovery from the 1906 San Francisco Earthquake 

found that trends in terms of business types accelerated. So manufacturing was already moving 

out of San Francisco, and that trend accelerated. Chang (2010) found a similar result for Kobe 

after the 1995 Earthquake. In Japan, over the long-term the proportion of small businesses had 

been declining. In Kobe that trend was greatly accelerated by the earthquake. Webb et al. (2000) 

found that businesses in declining industries tended to do worse in disasters. 

Most studies find that small businesses are hardest hit by disasters (Tierney 1997a, 1997b; Webb 

et al. 2000). These results do not attempt to take into account normal business turnover. Even 

without disasters small businesses have a much higher turnover than large businesses. It is not 

clear from these studies how much of the small business losses in comparison to large business 

are attributable to their higher vulnerability and how much to normal turnover. 

Business preparedness had no measurable impact on business recovery. Webb et al. discuss some 

reasons why preparedness does not seem to impact recovery. ‘Preparedness’ is usually 

operationally defined as having taken one or more of a specified set of measures. The measures 

are determined by the experimenter and are usually measures that are typically recommended by 

experts. Webb et al. argued that the measures that operationally define preparedness are largely 

aimed at improving emergency response and have little impact on recovery. 

Construction does better after disasters than do other businesses and typically financial and realestate firms do worse. Businesses as a rule effectively self-insure against disasters. Very few in 

any study have insurance against losses. 

Webb et al. found that aid does not have any positive impact on recovery, while Dahlhamer and 

Tierney (1998) found that aid was actually negatively correlated with recovery. The most likely 

explanation is that firms don’t apply for aid unless they are in bad financial shape. In addition, 
25 

business aid nearly always takes the form of loans. The additional leverage could make business 

survivability worse. If so, then only businesses on the brink of bankruptcy will be willing to 

apply for such aid. 

In all of these studies it is not clear to what extent survivor bias9

 plays a role in the conclusions. 

Most of the studies of contributors to business recovery (including Chang and Falit-Baiamonte 

2002; Dahlhamer and Tierney 1998; Tierney 1997a, 1997b; Webb et al. 2000) pass out surveys 

to businesses identified in the affected area. Efforts are made to target businesses that had been 

in the area before the disaster struck, but it is not clear how effective those efforts are nor to what 

extent response is correlated with survival. 

2.3.6 Social Networks, Government Services and the Environment 

There is very little in the literature on recovery of social networks, government services or the 

environment from disasters. 

Disasters in many ways produce behavior that is strongly prosocial. People help out their 

neighbors, crime and looting are rare, abundant aid comes in from outside to the affected area 

(Waugh and Tierney 2007). However, there are also sources of conflict as well. Much of the 

conflict is over distribution and use of aid (Berke et al. 2008), the shape of the post-disaster 

community (Rubin 1985), or temporary housing (Bolin and Stanford 1991). 

Temporary housing is typically disruptive of social networks. Specifically, it is generally not 

designed in such a way as to respect preexisting social networks and so ends up breaking them 

up (Bowden et al. 1977). 

2.3.7 Simulating Recovery 

Miles and Chang (2003) develop a numerical model simulating recovery. Their model includes 

modules for households, businesses and lifeline systems. The households, businesses and lifeline 

systems are organized into neighborhoods and communities. There are minimal markets for labor 

and consumer goods. The model allows both businesses and households to leave the region. 

The major factors in the behavior of the model end up being lifelines and household income. If 

lifelines go down, negative impacts on the model are large and all the households may end up 

leaving the region. 

They attempted to simulate the aftereffects of the Kobe earthquake using the model. The model 

did not have a very good fit. In at least one simulation, using parameters that seemed reasonable, 

all the simulated population of Kobe left. 

Their model does not account well for the economics of such a situation. Markets for labor and 

goods are minimal. And the decision criterion for households leaving is somewhat ad hoc. Their 

                                                           

9 “Survivor Bias” is the bias that results when your data comes primarily from survivors. In this case, if businesses 

that survive are more likely to be interviewed (or to respond to surveys) then the significance of any factor that 

correlates with survival will be misidentified. 
26 

model would provide one interesting place to start in simulating disaster recovery, but more 

work is needed. 

Glaeser and Gottlieb (2009) discuss location choice in the context of urban economics. Urban 

economics assumes that people choose where they work and live so as to maximize their utility. 

This would apply in particular to the decision as to whether to leave an area after a disaster. To 

illustrate this idea, suppose there are only two cities in the world, which we will dub New 

Orleans and Memphis. Prices, wages, and local amenities are different in each city. Amenities 

are assumed to be public goods (and hence free). The choice sets of consumption goods are 

identical between the two cities, although, as mentioned, their prices may be very different. 

Assume a person lives in New Orleans. Then the utility of living in New Orleans is: 

ܷேை ൌ max ௖ ݑሺܿ, ܽேைሻ

Subject to the budget constraint: 

ܭݎ ൅ ܮேைݓ൑ܿேை݌

Where: 

aNO = the amenities New Orleans offers (which could be positive or negative) 

c = the consumption vector, 

pNO = the price vector in New Orleans, 

wNO = wage in New Orleans, 

L = labor 

r = rate of return on capital (assumed to be identical in both cities) 

K = savings 

Similarly for Memphis we have: 

ܷெ ൌ max ௖ ݑሺܿ, ܽெሻ

Subject to the budget constraint: 

݉ െ ܭݎ ൅ ܮெݓ൑ܿெ݌

The only new variable here is m, which represents moving costs. 

Before the disaster strikes, the equilibrium assumption implies that UNO ≥ UM for residents of 

New Orleans. The disaster would tend to cause amenity (aNO) and wage (wNO) to decrease, and 

price level (pNO) to increase. All of these serve to increase the likelihood of ex-migration. In 

addition, K will likely decrease for residents of New Orleans, however its effect on ex-migration 

is ambiguous. Price level would increase due to the scarcity of goods. In particular if a 

significant fraction of the housing stock is destroyed, home prices and rents will increase 

significantly, increasing cost of living. 
27 

This simple model suggests the most important factors in preventing ex-migration are a rapid 

restoration of jobs and housing. Depending on the importance of amenity in people’s 

preferences, rapid restoration of amenity may also be important. Since there are fixed costs to 

moving, it seems likely that such changes would have to be large or of long duration before 

significant migration effects appeared. 

To see the impact of capital loss (e.g., destruction of an owner-occupied dwelling), assume there 

is only one consumption good, and utility is based solely on consumption (that is, amenity is 

irrelevant). Then the equilibrium assumption implies that consumption in New Orleans is greater 

than consumption in Memphis. That implies that: 

ேைݓ

ேை݌

൅ ܮ

1

ேை݌

൒ ܭݎ

ெݓ

ெ݌

൅ ܮ

1

ெ݌

ܭݎ

Reorganization gives: 

൬

ேைݓ

ேை݌

െ ݓெ

ெ݌

൰ ܮ ൅ ൬ 1

ேை݌

െ 1

ெ݌

0 ൒ ܭݎ ൰

Then 

ሺ݌ெݓேை െ ݌ேைݓெሻܮ ൅ ሺ݌ெ െ ݌ேைሻܭݎ ൒ 0

A reduction in capital increases the likelihood of ex-migration if ݌ெ ൐ ݌ேை. Otherwise, it 

reduces it. In other words, reductions in owner-capital favor high-priced regions and disfavor 

low-priced ones. 

Some researchers suggest that in addition to the factors discussed above a home serves as a 

liquidity constraint that limits the ability of people to move. In such a case, destruction of the 

house could serve to lift the liquidity constraint. Alternatively, by destroying assets (and not 

debts) the disaster could inhibit migration by making it more difficult to finance moving. 

A theoretical macroeconomic model of recovery can also be constructed. Assume there is a 

single representative consumer, with access to a Cobb-Douglass production technology. Assume 

Constant Elasticity of Substitution preferences. Then the representative consumer seeks to 

maximize present expected utility of consumption: 

ܷൌන ݁ିఘ௧ ܥଵିఙ െ 1

ݐ݀ ߪെ1

∞

଴

Subject to the budget constraint: 

ܥ൅ܭሶ ൑ ሺ݁௫௧ሻଵିఈܭఈ െ ܭߜ
28 

Where 

C = Consumption 

K = Capital 

α = Cobb-Douglass production parameter; related to capital intensity 

δ = depreciation rate of capital 

ρ = discount rate 

σ = preferences parameter; related to the intertemporal elasticity of substitution 

x = exogenous economic growth rate. 

Mathematical development is in the mathematical appendix. 

Assume that the pre-disaster city is on the balanced growth path. Then a disaster is a reduction of 

capital below the balanced growth path. Recovery time is modeled as the amount of time 

required to return approximately to the balanced growth path.10 Estimated time to recovery is 

shown in Figure 5. This is best thought of as time required to return to the state that would have 

obtained had the disaster not occurred. Figure 6 shows the amount of time required to return to 

the levels of capital and consumption that obtained before the disaster. The values are different 

because the productive capacity of the economy is growing even as the region is recovering from 

the disaster. 

Figure 5: Percent recovery versus time (in years) for different levels of 

destruction in a disaster 

The model makes two unrealistic assumptions that impact the results. First, the model assumes 

that the region impacted by the disaster is a closed economy. The region depends entirely on its 

                                                           

10 Strictly speaking, in this model, capital levels approach the balanced growth path asymptotically. Approximate 

return is defined as arriving within a predetermined distance of the balanced growth path. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 100 200 300 400

loss = 90%

loss = 50%

loss = 25%

loss = 10%
29 

own resources for recovery. Second, there is no construction sector, so scarcity of construction 

cannot impact time to recovery. The first assumption significantly extends the time to recovery, 

while the second assumption shortens it. On net, this model probably overstates significantly 

time to recovery. A more complete model, correcting for the limitations mentioned above, would 

provide a more realistic estimate of recovery time. 

Figure 6: Time to recovery relative to initial capital and consumption 

2.3.8 Ways to Improve Recovery 

As with response, it is difficult to identify clear lessons for improving recovery. Preparedness 

does not correlate well to recovery (Tierney et al. 2001; Webb et al. 2000). Aid seems to 

influence individual recovery, but not always for the better (Bolin and Stanford 1991). On the 

other hand, aid seems to be at best uncorrelated to business recovery (Webb et al. 2000). 

Bolin and Stanford suggest that aid aimed primarily at providing permanent housing can enhance 

individual and family recovery, while aid aimed primarily at providing temporary housing can 

impede it. 

Rubin (1985) suggested that three things mattered to recovery: Leadership, the Ability to Act, 

and Knowledge. She argued that recovery was enhanced when the community settled promptly 

on a vision for the rebuilt community (Leadership), effectively interfaced with aid agencies while 

maintaining local control over the spending of resources (Ability to Act), and knew how to 

obtain aid and resources (Knowledge). 

Berke et al. (2008) argue that good aid programs are responsive to household needs and are 

implemented by strong organizations capable of achieving program goals. They argue that many 

aid programs fail because the aid does not get to the groups that most need it. Such groups often 

do not have the information to seek out aid that is available or the political weight to convince 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

loss = 90%

loss = 50%

loss = 25%

loss = 10%
30 

aid donors to provide them with aid. Aid also often fails because the agencies providing aid do 

not have the local knowledge to tailor aid to the needs of the community. 

They argue that good aid has four characteristics: (1) Goodness of fit to local needs and 

opportunities; (2) reliance on local resources in planning, distribution and implementation; (3) 

local knowledge about how to obtain disaster assistance; and (4) flexibility and willingness to 

learn in process. 

2.4 Preparedness 

The NFPA (2007) defines preparedness as “Activities, tasks, programs, and systems developed 

and implemented prior to an emergency that are used to support the prevention of, mitigation of, 

response to, and recovery from emergencies.” Preparedness is defined in this paper as measures 

taken in advance of a disaster with the objective of improving response and recovery. It 

specifically includes warning and evacuation. 

Preparedness usually is operationally defined as fulfilling a checklist pre-selected by the 

researcher (e.g., Godschalk et al. 1999 who operationally define preparedness as meeting the 

requirements of the Stafford Act). 

As discussed above, preparedness by businesses does not correlate with loss or recovery. Similar 

results hold for communities. Tierney et al. (2001) observe that planning does not correlate well 

with effective response or recovery. Wenger et al. (1986) noted in their case studies that planning 

did have some positive effect, but many of the jurisdictions with plans did not handle disaster 

response well. They noted that “more problems and difficulties arose in those communities 

which had no planning or very limited planning activities. Second, while moderate extensive 

planning activities tended to facilitate an effective response they did not guarantee it.” In their 

case studies the key to effective response really was extensive previous experience. 

Why does planning have such ambiguous effect? There are at least three possibilities. 

Possibility 1: In practice there are limits to what can be planned for. Resource and human 

constraints limit us. Regarding resource constraints, nature has more resources than we do and 

will occasionally overwhelm even the best of plans. Regarding human constraints, there are 

limits to how far people will depart from their normal behavior even in disasters. 

Possibility 2: Information constraints. It is not clear what a plan should include. The check-lists 

may be made up of items that have little to do with resilience and recovery. Unfortunately, 

learning is hard. 

Possibility 3: Checklists are necessarily made up of things that can be easily verified. It may well 

be that the things that actually contribute to resilience and recovery are not easily verified. In this 

case, the checklist is essentially noise covering up the signal of how well prepared a jurisdiction 

actually is. 
31 

All three probably play a role, although their relative contribution is not known. 

2.4.1 Warning Systems 

Warning and evacuation systems are generally regarded by researchers as one of the most 

effective ways to prevent death and injury in disasters. 

Mileti and Sorensen (1990) did a meta-analysis of studies of warning systems and suggested 

several lessons learned from the literature. First, they repeated the lesson that people don’t panic 

when warned of an impending disaster. Their reaction to warnings is generally to seek more 

information; people generally want more information than they get. They also tend to want 

information from multiple sources. 

People do not simply do what they are told—they make their own decisions about what to do. 

However, most people can be persuaded. Again, that requires providing people with the 

information they need to make their decision. 

They suggest that false alarms do not necessarily diminish the effectiveness of future warnings if 

the reasons for the false alarm are explained. They point out that, at the time they wrote their 

paper, about 70 % of hurricane evacuation warnings were false alarms. It seems likely that the 

false-alarm issue is more complicated than they suggest. A false alarm is (at best) evidence of 

ignorance, and reacting to warnings is costly. If false alarms represent a high enough percentage 

of warnings then the cost of reacting to a warning will exceed the expected benefits. At that 

point, people will quit paying attention to them. However, people react to warnings by seeking 

more information. Since the cost of seeking more information is relatively low, people will 

tolerate a relatively high percentage of false alarms before they begin to ignore them. 

They suggest that sirens aren’t much use as warning tools. People do not know what the siren is 

warning against. The normal response to warning sirens is to seek more information about what 

the hazard is. However, they note that very few warning systems are capable of providing shortterm warnings (under three hours). 

The standard progression for how people react to warnings is Hearing, Understanding, Believing, 

Personalizing (is it relevant to me or not), Deciding and Responding, and Confirming (going 

back to check the current status). 

2.4.2 Evacuation 

The research on evacuation starts with two standard observations. First, when evacuations are 

ordered not everyone leaves (Tierney et al. 2001). Second, some people leave even though they 

have not been ordered to evacuate. The latter phenomenon is called an ‘evacuation shadow.’ 

Fothergill (1998) reports that women are more likely to evacuate than men. Women generally 

have better social networks than men and so are more likely to hear about an impending hazard. 
32 

Women are also more risk averse, and so are more likely to evacuate once they have heard about 

the hazard. Family behavior falls somewhere in between, with men often agreeing to evacuate at 

their wives’ insistence. 

Some people stay because they don’t hear, believe, or personalize the warning. Tierney et al. 

note for example that some ethnic groups are routinely distrustful of authority and may not 

believe warnings when given. Some people cannot leave: for example some of the poor do not 

have transportation, and some people are disabled. In the U.S., the assumption has been that 

people have their own transportation and can manage their own evacuation. Hurricane Katrina 

showed that that was not always true. 

Figure 7: Conceptual degree of evacuation versus risk 

Evacuation is risky, difficult, and expensive. When southeast Texas evacuated ahead of 

Hurricane Rita a number of people died as a result of the evacuation (e.g., Belli and Falkenberg 

2005).11 Spending several days somewhere other than in one’s own home carries both financial 

and hedonic costs. 

In general we would expect the decision to evacuate to be based on a balancing of the risks 

associated with remaining against the costs and risks of evacuation. A person’s degree of risk 

aversion will mediate that decision. Risks of staying, risks of evacuating, costs of evacuating, 

ability to delay decision-making and seek better information, and risk tolerance will be different 

for different people. It is therefore unsurprising that evacuation behavior is heterogeneous. 

Conceptually, as shown in Figure 7, we would expect degree of evacuation to decrease as the 

risk from the hazard decreases. An evacuation order (hopefully) provides additional information 

to people in making their decisions, but it seems unlikely that they will completely delegate such 

                                                           

11 The author personally knows someone whose elderly mother died as a result of being evacuated ahead of 

Hurricane Rita. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Evacuation

%

Hazard Risk (decreasing)

Evacuation

Order
33 

a decision to distant authorities who do not know their individual circumstances and cannot take 

them into account. 

2.5 Mitigation 

The NFPA (2007) defines mitigation as those “activities taken to reduce the severity or 

consequences of an emergency.” In this paper mitigation will be defined as measures taken 

before a disaster strikes with the objective of improving disaster resistance. We explicitly 

exclude preparedness. However much of the discussion below applies to preparedness as well. 

A number of factors contribute to the determination of optimal mitigation (see Figure 8). An 

understanding of individual behavior is needed because it impacts and limits our ability to 

implement specific mitigation measures. In order to determine optimal mitigation we also need 

to know the type, frequency, and severity of the hazards; how vulnerable our communities are, 

and where those vulnerabilities lie. Those enable us to estimate probabilistic damages, which in 

turn we can use to estimate optimal mitigation and insurance. Each of these topics is investigated 

below. 

Figure 8: Elements of mitigation, with dependencies 

2.5.1 Individual Behavior 

It is generally believed that people under-invest in insurance and mitigation compared to what is 

optimal (e.g., H. Kunreuther, Meyer, and Van den Bulte 2004). A number of research papers 

have explored reasons why people do not optimally invest in mitigation and ways to encourage 

optimal investment. 

Kunreuther (2001) suggests that the reasons people fail to invest optimally in mitigation include 

bounded rationality and moral hazard induced by the expectation of disaster aid. Bounded 

rationality reduces investment because boundedly rational people tend to underestimate the risks 
34 

of low-probability events.12 Moral hazard reduces investment because when people expect a 

portion of their losses to be paid for by disaster aid (or insurance) they have less of an incentive 

to invest in loss avoidance. Similarly, Muermann and Kunreuther (2008) analyze a theoretical 

model in which people fail to invest optimally in mitigation due to externalities.

Alternatively, Braun and Muermann (2004) suggest that if regret is a factor people will be more 

likely to buy insurance, but less likely to buy full insurance than if they were expected utility 

maximizers. Regret theory (Loomes and Sugden 1982) suggests that people’s enjoyment of a 

specific outcome is diminished if there is a better outcome that they could have enjoyed had their 

choices been different. People for whom regret is important are less likely to invest in full 

insurance because if there is no disaster they will regret having bought insurance. On the other 

hand, they are less likely to buy no insurance because if there is a disaster they will regret not 

having bought insurance. 

Lindell and Perry (2000) and Lindell and Prater (2000) find that the best predictors of adoption 

of mitigation are risk perception and personalization of risk. Demographic variables (race, sex, 

age, etc.) fare poorly. Lindell and Prater (2000) note that hazard experience matters to risk 

perception. They also find that women are more risk averse, but do not do more mitigation. 

2.5.2 Vulnerability 

Vulnerability differs across people. Poverty, lack of education, disability, and minority race are 

all correlated to vulnerability (Tierney 2006). However, since the factors listed are highly 

correlated, it is not clear how much they independently contribute to vulnerability. Tierney 

observes that one of the contributors to their vulnerability is that disaster preparedness and 

assistance programs are not all that well tailored for the poor and disadvantaged. This includes, 

among other things, that wealthier victims are also better able to search for and find assistance. 

This does not hold universally for all disasters. Gordon et al. (2002) attempt to estimate the 

distributional effects of an earthquake on the Elysian Park fault in Los Angeles. In that case they 

find that the well-off suffer a disproportionate share of the damage. They suggest that the reason 

is that information on spatial distribution of earthquake hazards may be harder to come by, so the 

relatively well-off may not be able to avoid the risk. 

Cutter et al. (2003) estimate a Social Vulnerability Index for counties in the United States. They 

took 42 variables, drawn from 1990 census data, which are associated with increased individual 

vulnerability. They used a principle-components analysis to reduce those variables to 11. The 11 

variables were then added together (and rescaled) to form the Social Vulnerability Index 

(SoVITM). They tested the index against Presidential disaster declarations to estimate its 

explanatory power. The index was negatively correlated with Presidential disaster declarations. 

                                                           

12 The standard observation is that people either under- or over-estimate the risks of low-probability events. 
35 

In a separate paper, Cutter (2008) applies the framework to estimate the spatial distribution of 

vulnerability in New Jersey. 

The negative correlation with Presidential disaster declarations is not as problematic as it seems. 

Disaster declarations are known to have political components. Downton and Pielke (2001) found 

that disaster declarations for floods are more likely in election years, even after taking weather 

into account. Garrett and Sobel (2003) found that counties are more likely to receive disaster 

declarations if they are in a ‘battleground’ state in a Presidential election year or if they are 

represented by a member of congress who sits on one of the FEMA oversight committees. The 

same factors that contribute to vulnerability also tend to reduce political weight. 

Nevertheless, the SoVI has serious flaws. Income at the county level, which is one of their main 

variables, is problematic. Prices vary dramatically between counties, so real income levels have 

much less variation than any measure of simple wealth (Glaeser and Gottlieb 2009). Second, 

they do not attempt to correlate their vulnerability factors to actual harm done in disasters. As a 

result, it is not at all clear whether some of the factors are overweighted or underweighted. 

Estimating against actual harm would be difficult because major hazards are rare and spatially 

random, but failure to do so reduces the predictive value of the index. 

Using similar methods Cutter (2008) develops an Infrastructure Vulnerability Index and an 

Environmental Vulnerability Index for the State of New Jersey. Borden et al. (2007) develop a 

Built-Environment Vulnerability Index and a Hazard Vulnerability Index for counties containing 

132 metropolitan areas in the United States. 

Factors that are believed to contribute specifically to community vulnerability are economic and 

social diversity, social cohesion, and communication among members of the community (Norris 

et al. 2008; Tierney et al. 2001). 

Business vulnerability is strongly associated with being small and being in retail (among others, 

Chang and Falit-Baiamonte 2002). However, as noted earlier, these results do not take into 

account normal turnover. These same types of businesses tend to have high turnover even 

without disasters. 

2.5.3 Hazard Models and Damage Prediction 

In order to determine the optimal mitigation, we need to have an estimate of how likely disasters 

are and how much damage they will do. Grossi et al. (2005) discuss the basic approach used to 

estimate this. A number of models and services provide probabilistic estimates of damage as 

described in Grossi et al., including HAZUS-MH provided by FEMA, the EQECAT catastrophe 

modeling system published by EQECAT, Inc., and the catastrophe modeling and consulting 

services of RMS Inc. All these systems include estimates of hazard probability and estimates of 

damage resulting from a given hazard. Damage estimates include estimates of lives lost, damage 
36 

to the built environment, and indirect economic losses. The private models are aimed primarily at 

insurance companies and are intended to estimate insured losses. 

Estimates of damage to the built environment are typically based on fragility curves for different 

classes of structures, combined with location-coded inventories of structures (for example 

Kappos and Dimitrakopoulos 2008). 

Estimates of indirect losses and losses to the economy are based on simplified regional 

macroeconomic models. For example, HAZUS-MH has the regional accounting system from 

IMPLAN at the core of its indirect economic cost module (Rose 2002). This has been a very 

active part of the literature in the last 15 years, with a number of techniques developed and 

substantial improvements and refinements to the approaches. Most of these will be discussed in 

more detail below. 

A couple of examples from the literature where estimates of losses were made for possible future 

disasters are Ellson et al. (1984) who estimate the impacts of an earthquake in the Charleston, SC 

area, and Rose et al. (1997) who estimate indirect losses in Memphis, TN due to electrical 

outages caused by an earthquake along the New Madrid fault system. 

Bernknopf et al. (2001) proposed a simplified approach to damage estimation for certain types of 

earthquake damage. They demonstrate their technique by performing an assessment of the risks 

of earthquake-induced lateral-spread ground failure in Watsonville, CA. They use property tax 

records to provide a reconnaissance level estimate of building inventory and type. They use soil 

and geologic maps and topographic maps to estimate the regional distribution of soil types. 

Using a ground-shaking model they estimate the risk of failure for each soil type. That combined 

with the property-tax inventory allows them to estimate damages from an event. 

Complicating the problem of hazard estimation is that disasters often have other follow-on 

problems that are caused by the initial disaster. The standard example is fire following 

earthquake. In the 1906 San Francisco Earthquake the majority of buildings destroyed were 

destroyed by fire rather than the earthquake itself. Many modern hazard models include this as 

part of the model. 

2.5.4 Optimal Mitigation 

There are a number of standards relating to mitigation, including ASTM Standard E 2506 (2006) 

and NFPA Standard 1600 (2007). 

In general the approach to determining optimal mitigation consists of (ASTM 2006): 

• Establishing risk mitigation objectives and constraints; 

• Identifying and selecting candidate combinations of risk mitigation strategies; 
37 

• Computing measures of economic performance for each candidate combination, taking 

into account uncertainty and risk; 

• Analyzing results and recommending the most cost-effective combination of risk 

mitigation strategies. 

Risk mitigation objectives must balance the benefits of mitigation (fewer deaths and injuries, less 

value of buildings destroyed, less business interruption costs, etc.) against the costs. In many 

cases that is simply a comparison of dollars to dollars.13 However, when the objective involves 

avoiding non-monetary damages (like deaths or injuries avoided, environmental damage 

avoided, etc.) the problem becomes more difficult. In order to make that decision, a monetary 

value for lives saved (or environmental damage avoided, etc.) must be explicitly or implicitly 

arrived at. 

Approaches to estimating such values include attempts to determine how much value people 

themselves place on avoiding death (or injury, or environmental damage, etc.). Methods to do 

this include contingent valuation surveys where people are essentially asked how much they 

value the death avoided using a carefully-drawn questionnaire (Carson and Hanemann 2005), 

and econometric studies that estimate how much people give up to avoid premature death in their 

daily lives. A relevant example of the former is Viscusi (2009), who estimated willingness to pay 

to avoid deaths due to natural disasters and terrorism as compared to traffic deaths. He found that 

natural disaster deaths are worth half of traffic deaths. Terrorism deaths are worth about the same 

as traffic deaths. 

A second approach to valuing deaths effectively compares lives saved to lives forfeit. Life 

expectancy is a normal good—the greater people’s income the more they are willing to spend to 

increase life expectancy. Spending money to avoid one hazard reduces the amount people have 

available to spend to reduce other hazards. If enough money is spent to save lives from (say) 

natural disasters, then the reductions in spending on other hazards will result in a life forfeit from 

other causes. Lutter et al. (1999) investigated this and estimated the value of spending per life 

saved at which lives lost due to income effects equal lives gained from the spending program at 

about $15 million per life saved. Programs which cost more than that per life saved effectively 

cost more lives than they save. 

Virtually all mitigation measures discussed in the literature fall into four categories: improved 

building and construction practices, avoiding hazardous areas, protective structures (both natural 

and man-made), and warning and evacuation (e.g., Godschalk et al. 1999; Mileti 1999). Warning 

and evacuation was already discussed under Preparedness. 

                                                           

13 Note, however, that the comparison is typically of guaranteed dollars today (for the mitigation) to uncertain 

dollars sometime in the future (for the avoided damages). That requires decisions regarding degree of risk aversion 

and time preference in order to make those comparisons. 
38 

An example of improve building and construction practices is the report by the San Francisco 

Planning & Urban Research Association (SPUR). They produced a report (SPUR 2009) that 

proposed a series of improvements to building codes and construction practices for San 

Francisco. The improvements were intended to improve the survivability of the built 

infrastructure of the city in a major earthquake. They developed a set of performance standards 

for buildings and lifeline infrastructure and a series of recommendations regarding improvements 

to building codes and construction practices intended to help the city meet those standards. 

Protective structures would include man-made structures like dams and levees, but would also 

include natural barriers like protective coastal dunes (Godschalk et al. 1999) and wetlands 

(Brody et al. 2008). 

Avoiding hazardous areas is the mitigation measure strongly preferred by the sociology literature 

(essentially, “don’t build your house where there is driftwood in the trees”). The most frequently 

mentioned examples are the removal of housing in flood plains and on barrier islands in 

hurricane-prone regions. 

A number of papers have been written on identifying optimal mitigation measures. Most use a 

damage-estimation model, run the model several times using different candidate mitigation 

policies, compare them using some predetermined preference relation, and identify the best from 

the set. 

The series of papers by Dodo et al. (2005, 2007) and Xu et al. (2007) use HAZUS as their 

damage estimation model. They evaluate building upgrade policies. The number of policies 

evaluated in their examples numbers in the thousands. They use linear programming techniques 

to estimate the optimal policy, and develop numerical methods for efficient finding of a solution. 

Chapman and Rushing (2008) and FEMA (2005) are implementations of the ASTM standard, 

and are intended to aid in determining optimal mitigation. FEMA (2005) is primarily focused on 

hazards due to terrorism. It provides detailed guidance regarding the first two elements of the 

standard, but provides relatively little guidance regarding computing measures of economic 

performance. In contrast Chapman and Rushing (2008) largely assume that the first two steps 

have been completed and provide a detailed protocol and software tool for estimating economic 

performance.14 They also allow the separate explicit modeling of non economic damages from 

hazards. Thomas and Chapman (2008) provide a detailed guide and annotated bibliography of 

sources useful in carrying out this evaluation. 

2.5.5 Optimal Insurance 

In general, insurance does not reduce damage in disasters. Rather, insurance is a means of 

redistributing the losses. In that sense, disaster aid programs serve as a form of insurance. It is 

                                                           

14 They allow the user to determine the rate of time preference, but implicitly assume that their users are risk neutral. 
39 

generally believed that people exposed to catastrophic risk under-insure (Howard Kunreuther 

and Michel-Kerjan 2009). 

Insurance in disasters presents special problems that do not apply to insurance in general. From 

an insurance perspective disasters are characterized by having highly correlated losses. For 

example, with homeowners insurance, normally if one house burns down it is highly likely that 

all the other houses in the area are still intact. In a normal housing insurance market, houses burn 

down all the time, but they are a small and predictable proportion of the entire population of 

houses. 

In disasters, however, many houses are destroyed at the same time. That means that an insurance 

company insuring against disasters must maintain much higher reserves to stay solvent and still 

pay off on its policies. One of the strategies for dealing with this is reinsurance. Ermoliev et al. 

(2000)and Amendola et al. (2000) both analyze the optimal reinsurance problem for insurance 

companies exposed to catastrophic risk. 

The U.S. GAO (2007) analyzes the catastrophe insurance market in the United States. The report 

responded to the impression that the catastrophe insurance market is failing. They suggest that a 

private insurance market where firms charge premiums that fully reflect actual risks, where there 

is broad participation by insurance firms, and where the market limits costs to taxpayers both 

before and after a disaster may be unobtainable, especially given the incentives by state 

insurance regulators to keep premiums below prices that reflect actual risk. 

When liquidity constraints are binding, it is possible for insurance and disaster aid to reduce 

damages in disasters. Investment is often necessary to limit indirect effects from disasters. For 

example, a business may shut down due to the destruction of vital machinery in an earthquake. 

The ongoing losses due to business interruption may continue until the damaged or destroyed 

machinery is replaced. However, if liquidity constraints are binding, the investment may be 

delayed or foregone, thus allowing the loss to continue. It is unclear whether any theoretical or 

empirical research has been conducted into this. 

2.6 Data and Measurement 

Data generally fall into four types. Case studies, insurance claims, direct measurements, and 

survey methods. 

Case studies are used extensively in the social science literature studying disasters. They have 

the advantage of providing a lot of detail about the community, preparedness (e.g., Wenger et al. 

1986), mitigation measures (e.g., Brody et al. 2008), response and recovery (e.g., Comfort 1999; 

Haas et al. 1977). That detail can be used to evaluate the effectiveness of these measures. 

However, estimates of damage, vulnerability or resilience are usually developed by the methods 

discussed below. The estimates are also usually qualitative, at least in application. Case studies 

have so far been the best source of information regarding the effectiveness of various mitigation 
40 

and preparedness measures, and on response and recovery. However, their usefulness is limited 

by the fact that such studies have very small sample sets. Furthermore different studies are not 

usually comparable, and so the ability to do meta-analyses across multiple studies is limited. 

Insured losses for disasters are compiled by a number of companies including Swiss Re, Munich 

Re, and (in the United States) by Property Claims Services, a service of Verisk Analytics, Inc. 

FEMA, which offers flood insurance, also collects insured loss data and data on disaster 

assistance. Insured losses have the advantage of providing a numerical estimate of losses due to a 

disaster, and are provided by organizations whose incentives are to get accurate estimates of 

losses. However, they are limited to insured losses. Insurance coverage is not complete, and not 

all types of losses are covered by insurance. Therefore estimates of total loss are biased down 

and have limited coverage. Using insurance penetration estimates it is possible to estimate total 

losses to the built environment, but data are too thin to estimate losses to other things we want to 

protect. 

Direct measurements of losses have generally been collected by local agencies or the National 

Weather Service, and compiled by various government agencies (Cutter, Gall, and Christopher 

Emrich 2008), including FEMA (disaster declarations), NOAA (weather-related disasters), and 

the National Geophysical Data Center (geologic disasters). Damage estimates have often been 

based on ‘windshield counts’ (Downton, Miller, and Pielke 2005), and until 1995 were usually 

reported only to the nearest order of magnitude.15 More recently other methodologies including 

remote sensing (e.g., Adams et al. 2004; RMS Inc. 2010) have begun to be used in the estimation 

of damages. Such damage estimates have largely focused on human losses and losses to the built 

environment. However, the breadth of coverage has increased over time. 

Recently, damages from a small handful of disasters have been estimated using survey methods. 

The surveys were intended to estimate a certain class of damages, usually damage to the 

economy, and in particular business interruption costs. One example of such an estimate is 

Tierney (1997a) who used a survey to estimate business losses—and in particular business 

interruption losses—due to the Northridge Earthquake. Surveys are potentially subject to selfselection biases in terms of who responds to the surveys and to survivor biases. The degree of 

such biases is unknown. 

Business interruption costs have begun to be estimated using a variety of econometric and 

regional macroeconomic models. The models, which are discussed in more detail below, attempt 

to estimate the economic impact of a disaster on the region compared to what conditions would 

have been like without the disaster. Using such models, estimates can be made as to business 

                                                           

15 The National Weather Service, who collected and reported much of the disaster loss information, until 1995 

reported loss estimates in preset categories. The categories were $50 000 - $500 000, $500 000 - $5 Million, $5 

Million - $50 Million, etc. 
41 

interruption costs, and losses of production and jobs due to a disaster. However, such models are 

relatively hard to calibrate against actual losses. 

Howe and Cochrane (1993) develop a set of guidelines for estimation of losses in disasters. Their 

guidelines are primarily aimed at estimating losses to the physical infrastructure, but they also 

discuss loss estimation techniques for business interruption, damages to historical monuments, 

and valuing losses of human capital and natural capital. 

One of the complications in estimating damages is the fact that construction prices go up during 

recovery as construction capacity becomes relatively scarce compared to demand. 

The Natural Research Council (1999) in its Framework for Loss Estimation, noted that “there is 

no system in either the public or private sector for consistently compiling information about their 

economic impacts.” Nevertheless, a number of databases aggregate loss estimates from a number 

of sources to report loss data from disasters. These include SHELDUS, a product of the Hazards 

and Vulnerability Research Institute at the University of South Carolina, which compiles loss 

data for natural disasters in the U.S. since about 1960. The database www.flooddamagedata.com

provides flood damage data for the U.S. between 1925 and 2000. The EM-DAT database is 

published by the Center for Research on the Epidemiology of Disasters, a division of the World 

Health Organization, and publishes data on losses from disasters world-wide. 

All of these databases rely on many of the same data sources, and so cannot be considered 

independent of each other. SHELDUS consciously biases its estimates low. When the raw 

sources list a range of possible losses SHELDUS reports the low point in the range. The EMDAT database routinely estimates losses higher than those drawn from other databases. It is also 

not particularly transparent, and it is not clear for any particular disaster what its source of 

information is (Gall, Borden, and Cutter 2009; Vranes and Pielke 2009). The database EM-DAT 

likely includes some indirect loss estimates, while SHELDUS and the flood damage database 

explicitly do not. They also reflect the underlying temporal bias of their sources, where more 

recent disasters tend to be better reported, and more classes of loss are included. 

While, as discussed above, there are sources of data for human losses, losses to the built 

environment, and general economic losses, there are essentially no estimates for losses to Social 

Networks, Government Services and the Environment. 

The National Research Council (1999) recommends that a single federal agency be placed in 

charge of compiling estimated losses—they recommend that the Bureau of Economic Analysis 

be given the task. They recommend that loss information should include who initially bears the 

loss (e.g., government, insurers, businesses, individuals, nongovernmental organizations), and 

the type of loss (e.g., property, agricultural products, human losses, cleanup and response costs, 

adjustment costs). They also recommend that efforts be made to estimate indirect losses such as 

(temporary) job losses and business interruption losses. 
42 

2.6.1 Recovery Measurement 

There have been very few attempts in the literature to measure recovery. Recovery is usually 

measured in terms of time to full recovery. In attempting to measure recovery, there are two 

basic questions that must be answered. First, what are we measuring the recovery of? Most 

recovery measurements have measured recovery of population following major disasters. A few 

have measured recovery of the economy. The second question is what constitutes full recovery. 

It is generally argued that the proper comparison for recovery is not before versus after, but with 

versus without (for example Friesma et al. 1979). That is, a community has fully recovered when 

it has arrived at a state as good as what would have obtained had there been no disaster. 

However, as a practical matter, most measurements of recovery have considered the return to the 

ex ante state as full recovery. A few researchers suggest that the proper measure of ‘full’ 

recovery is attainment of some ‘new normal’ (Chang 2010). 

Kates and Pijawka (1977) measured the recovery of a number of cities since 1200. They used 

population as the measure, and full recovery was defined as returning to the predisaster 

population. Their most severe disasters were products of war. In some of their disasters, 

population declines during the disaster exceeded 90 %. All of their cities eventually recovered. 

Cities with population declines of less than 70 % usually recovered in less than 50 years. 

Population declines of greater than 70 % tended to require centuries for recovery. 

Chang (2010) develops a framework for the measurement of recovery and applies it to the 1995 

Kobe Earthquake. She measures recovery of both population and of the economy. She considers 

all three definitions16 of full recovery. Kobe returned to pre-disaster population levels in about 

ten years. It never returned to predisaster levels of economic activity, and it never returned to the 

former trends in either population or economic activity. 

Chang develops an approach to operationalize attainment of a ‘new normal.’ She selects the 

greatest annual change from the few years preceding the disaster. When the annual rate of 

change following the disaster declines to within that level, a new normal is considered to have 

been achieved. She found that the economy attained a new normal within about 6 years, although 

some sectors (such as trade and services) attained a new normal level much more quickly. 

Most researchers suggest that the proper comparison for recovery is with the disaster compared 

to without: that is, how does the region after a disaster differ from what would have obtained had 

there been no disaster. This, however, is not easy to operationalize. Both Chang (2010) and 

Friesma et al. (1979) operationalize it as return to trend. However, that is not the same thing. 

Other things are affecting a region at the same time as the disaster recovery is ongoing. For 

                                                           

16 As described above, the definitions are: 

1. When the community has arrived at a state as good as what would have obtained had there been no disaster. 

2. When the community has arrived at a state as good as the ex ante state. 

3. When some ‘new normal’ has been attained. 
43 

example, a recession hitting at the same time as the disaster would ensure that “with versus 

without” was not the same as return to previous trend. Chang (1983) evaluated the impact of 

Hurricane Frederic (in 1979) on municipal finances for Mobile, AL. His analysis was able to 

take into account the recession of the early 1980’s for comparison to actual outcomes. 

2.6.2 Individual and Family 

Records of deaths due to disasters go back more than 100 years. As with all estimates of losses, 

older records are less accurate, and have poorer coverage. Records of numbers injured are less 

accurate than deaths and do not have the length of record. Quarantelli (1983) comments that in 

disasters, hospitals tend to skimp on record-keeping. So numbers of people treated and released, 

nature of injuries, and nature of treatment often is difficult or impossible to obtain. Records on 

population changes are usually available, although they are relatively low resolution. 

2.6.3 Physical Infrastructure 

Estimates of damage to physical infrastructure are available back to at least 1960. In some cases 

(flood damage for example) they are available back further than that. As always, the older the 

data is, the less reliable it is. Estimates on physical damage are almost always accurate to within 

an order of magnitude, and usually are more accurate than that (Downton and Pielke 2005). 

2.6.4 Economy 

There has been very little measurement of impacts on economic activity before 1990. Even since, 

measurement is highly incomplete. The insurance industry reports claims against its businessinterruption insurance lines as part of insured losses. However, since the great majority of 

businesses effectively self-insure (e.g., Chang and Falit-Baiamonte 2002; Tierney 1997b), 

reported losses substantially underestimate total losses. In addition, there have been a handful of 

efforts to use surveys to estimate losses (e.g., Tierney 1997a, 1997b). But surveys have been 

inconsistent, less than comprehensive, and are potentially affected by self-selection issues and 

survivor bias. 

Regional econometric models have been used to estimate losses in disasters, in particular in 

Hurricane Hugo (Guimaraes et al. 1993), Hurricane Andrew (West and Lenze 1994), the 

Northridge Earthquake (Rose and Lim 2002), and Hurricane Katrina (Hallegatte 2008). Business 

interruption losses have generally been less than 50 % of direct losses, although Hallegatte’s 

simulations suggest that business interruption losses increase much more rapidly than direct 

losses. She suggests that if direct losses from hurricane Katrina doubled (to about $200 billion) 

indirect losses would exceed direct losses. 

Most models used in loss estimation are either modified Input-Output models or are Computable 

General Equilibrium models. Input-Output models assume that input proportions for each good 

in the economy change slowly. In practice, the input proportions as usually assumed to be fixed 
44 

for the duration of the model. So, a product for which electricity is one of its inputs will have its 

production reduced by half if the electricity delivered during the period of the model is reduced 

by half. This allows the effect of lifeline interruptions to be analyzed and to propagate the effects 

of business interruption through the economy. In practice, a number of modifications are made to 

the model to take into account limited input substitution and various forms of business resilience. 

Computable General Equilibrium models assume that all markets are in equilibrium at all times. 

Inherently, they allow for much greater input substitution than Input-Output models, and they 

allow for the effects of lifeline interruptions to be allocated to the sectors where they have the 

least impact. Clearly, in the short term after a disaster, markets will not be in equilibrium. 

However as time goes on, the assumption becomes a closer approximation to reality. Rose 

(2004) recommends that “I-O models, with an adjustment for inventories, are probably better 

suited to recovery periods of less than one week, but CGE models are better suited to all other 

cases....” 

Rarely have regional accounts been used in estimating losses from disasters. Guimaraes et al. 

(1993) compared regional accounts to a baseline regional econometric model of Charleston, SC 

to get estimates of losses from Hurricane Hugo. Hallegatte (2008) used regional accounts to 

validate her model of indirect losses from Hurricane Katrina. 

Rose and Lim (2002) discuss the important distinction between stocks and flows in estimating 

losses to the economy. Stocks in this context would be losses that occur at a single point in time. 

For example, buildings, equipment, or inventories destroyed would be a stock. Flows refer to 

losses that occur over a period of time. For example, business interruption losses would represent 

a flow. Failure to properly account for them can easily lead to double-counting of losses. Rose 

and Lim argue that flows are the better and more natural loss to count. 

Figure 9 shows a time line of production for an individual firm following a disaster. 

Figure 9: Time line of production for an individual firm following a disaster

Disaster Replacement End Natural Life

Business

Interruption

Losses

Old Equipment

Replacement
45 

For a business like the one shown, total disaster losses to a first order of approximation would 

be: 

L = D + R – V

Where: 

L = Total Losses 

D = Present expected value of production from equipment that was Destroyed. 

R = Present value of net Replacement cost of equipment destroyed. 

V = Present expected Value of production from new equipment. 

Most estimates of damages have counted either D or R,

17 with estimates of replacement value (R) 

predominating. Since a firm will not buy a piece of equipment unless the net revenues it will 

generate exceed its cost, D will always overestimate the losses from a disaster.18

We can reorganize this in the following form: 

L = B + R – ΔV

Where: 

B = Business Interruption Losses. 

ΔV = Difference in the present expected value of production of the new equipment versus the old 

after production is restarted. 

This casts the loss calculation in more traditional terms. Note that ΔV will usually be positive, so 

the sum of replacement costs and business interruption costs will usually overstate the losses to 

business. 

As with economic accounts in general, there are three ways the business interruption losses and 

difference in values can be aggregated. By analogy with economic accounts, we could refer to 

these as the expenditures approach, the income approach, and the value-added approach. 

The expenditures approach would count as business interruption costs all revenues for the 

production of final goods only. Any goods sold as intermediate goods would not get counted as 

part of the costs the disaster since they are already included in the final-goods computation. 

Individual economic losses, like wages, dividends, royalties, etc., would not be counted because 

they are already included in the expenditures. It is not immediately clear using the expenditures 

approach how difference in value due to the replacement of damaged equipment would be 

aggregated. 

                                                           

17 Or other values, like book value, that have little relationship to either. 

18 Unless the company goes bankrupt, in which case there is some ambiguity. 
46 

The income approach would add up all lost profits, wages, royalties, and rents due to business 

interruption, and similarly the change in profits, wages, royalties, and rents due to the 

replacement of the damaged equipment. Lost dividends or capital gains to individuals or firms 

would not be counted as part of the loss from the disaster because those losses are already 

counted a part of lost profits. 

The value-added approach would add up all the value added due to business interruption, and 

similarly the change in value added due to the replacement of the damaged equipment. Lost 

wages would likely be included, since labor adds value to the produced goods. 

2.6.5 Resilience 

There are very few data or measurement results directly on resilience. In general, we assume that 

resilient communities will minimize population loss, per capita economic loss, job loss, the 

amount of time people are without housing or unable to return to their homes, loss of essential 

services, etc. Furthermore we expect resilient communities to be able to establish a new ‘normal’ 

relatively quickly, and we expect that the more resilient the community the better the new 

‘normal’ will be. 

MCEER has developed a preliminary aggregate resilience index (MCEER 2010). They start with 

a series of quality-of-service (QOS) variables: 

ܳ௜ሺݐሻ

Which are normalized to fall in the range between 0 and 1, and where there is a separate QOS 

variable for each lifeline or variable of interest. Each individual QOS index can be treated as a 

resilience index in its own right, with some limitations. MCEER then combines the individual 

indices into a composite resilience index, using an independence assumption. For two indices, 

the composite index is.19

ܳሺݐሻ ൌ ܳଵሺݐሻ · ܳଶሺݐሻ

ܳଵሺݐሻ ൅ ܳଶሺݐሻ െ ܳଵሺݐሻ · ܳଶሺݐሻ

The approach is similar to composite indices used in other work. For example Meacham and 

Johann (2006) discuss a similar multiplicative index for rating risk in structures. 

Given its intended use, any resilience index is effectively a welfare function, with all the 

strengths and weaknesses of welfare functions in general. As such it will be used for such 

purposes as recommending which lifelines should be repaired first, or for whom (e.g., Rose et al. 

1997). 

                                                           

19 An exposition of the general case is in the appendix. 
47 

There have been a handful of efforts directed at measuring elements of resilience. As already 

discussed, Kates and Pijawka (1977) and Vigdor (2008) discuss degree of recovery. Chang 

(2010) develops a methodology for measuring time to recovery, and applies it to Kobe, Japan 

after the earthquake. Rose et al. (2007) estimate the impact of business adjustment to minimize 

losses due to lifeline outages in a disaster. They include such measures as reliance on 

inventories, maintaining electrical generators (for resilience against electrical outages) and 

rescheduling of lost production. In their evaluation of the impact of a 2-week electrical outage in 

Los Angeles County, these forms of business resilience reduced losses by almost 80 %. 

 
48 

 
49 

3 Summary and Recommendations for Further Research 

3.1 Summary 

Although there is a great deal of high-quality information available on resilience-related topics—

hazard assessment, vulnerability assessment, risk assessment, risk management, and loss 

estimation—as well as disaster resilience itself, there is no central source of data and tools to 

which the owners and managers of constructed facilities, community planners, policy makers, 

and other decision makers can turn for help in defining and measuring the resilience of their 

structures and communities. The purpose of this document is to provide a survey of the literature 

and an annotated bibliography of printed and electronic resources that serves as that central 

source of data and tools to help readers develop methodologies for defining and measuring the 

disaster resilience of their structures and communities. 

The survey of the literature on disaster resilience begins with a discussion of the key concepts—

hazards, vulnerability, losses, and disasters—that feed into any methodology for defining and 

measuring disaster resilience. The discussion then focuses on the various definitions of disaster 

resilience that are found in the literature. For the purpose of setting the scope of the literature 

review found in this report, disaster resilience is defined as the ability to minimize the costs of a 

disaster, return to the status quo, and to do so in the shortest feasible time. A conceptual model 

of the “disaster cycle” is then introduced. The disaster cycle model consists of four stages: (1) 

response; (2) recovery; (3) preparedness; and (4) mitigation. The first two stages are “reacting 

to” a disaster, whereas the second two are “preparing for” a disaster. The conceptual model 

provides an ideal framework for highlighting the interactions between the four stages and how 

decision-making at the individual asset and community levels differ. Data and measurement 

issues associated with loss estimation are then described. These include critical analyses of case 

studies, insurance claims, direct measurement of losses, and survey-based methods. The 

discussion covers both direct losses due to damages to buildings and other constructed facilities 

and indirect losses due to business interruption and other second-order, disaster-related effects. 

The survey of the literature concludes with a mathematical treatment of two resilience-related 

topics—the optimal recovery path following a disaster and the theoretical basis for a 

multiplicative resilience index. 

The annotated bibliography on disaster resilience covers technical reports, journal articles, 

software tools, databases, and web portals; it includes a synopsis of the key references as an 

overview and introduction to the materials abstracted. References are listed in alphabetical order 

by author, where the author may be a person, a company, an organization, or a government 

entity. An abstract is provided for each reference that summarizes the salient points of the 

reference. A URL is provided whenever a reference is available in electronic format. 
50 

3.2 Recommendations for Further Research 

The background work for this report uncovered additional areas of research that might be of 

value to owners and managers of constructed facilities, community planners, policy makers, and 

other decision makers concerned with defining and measuring disaster resilience. Four 

recommendations for further research are put forward with an aim of strengthening the linkage 

between defining and measuring disaster resilience at the level of individual structures and at the 

community level. These four recommendations are concerned with: (1) a critical analysis of key 

structure types and their vulnerabilities, how these structure types interact with other physical 

infrastructure components in the event of a disaster, and how their performance would be 

improved if performance-based codes were implemented to address selected vulnerabilities; (2) 

an estimate of the incremental cost of implementing the proposed performance-based codes in 

the various structure types; (3) an estimate of the difference in expected property losses 

associated with implementing the proposed performance-based codes in the various structure 

types; and (4) an estimate of the difference in expected business interruption costs associated 

with implementing the proposed performance-based codes in the various structure types. The 

first three areas of research establish a foundation for measuring the disaster resilience of 

structures. The fourth area is an important link between the disaster resilience of structures and 

the disaster resilience of communities. The benefit of the proposed four-part research plan is that 

it provides the technical basis for using benefit-cost analysis of design and rehabilitation 

approaches to enhance the resilience of buildings. 

3.2.1 Analysis of Key Structure Types 

Buildings are systems of systems with many interactions and interdependencies among these 

systems. The performance of the structural system can have a direct impact on the performance 

of other systems (e.g., architectural, mechanical, electrical, etc.), as well as the utility 

infrastructure that can be housed inside, atop, or below the building. Thus the resilience of the 

structure has a substantial influence on the resilience of the building as a whole and in turn on the 

community. Furthermore, measures of resilience have been limited to assessing the extent of the 

adoption of current practices, standards, and codes for design and construction of buildings and 

infrastructure. This research has the goal of providing the technical basis for performance-based 

approaches to the design of new buildings or the rehabilitation of existing buildings to respond to 

natural hazards—earthquakes, community-scale fires, hurricane-strength winds and hurricaneborne storm surge, and tsunamis—and man-made hazards. 

This research will involve the identification of a class of building types based on national and 

regional statistics. A set of hazards those building types face will then be formulated and a risk 

assessment conducted. The risk assessment will address different hazards using current building 

codes in place for the region of interest to identify vulnerabilities to those hazards. Researchers 
51 

will then formulate a performance-based approach to modify the existing building code to 

address selected vulnerabilities. 

3.2.2 Cost Analysis 

Using national and regional statistics, data on building size, height, structural system, and 

primary use will be compiled for each building type selected for analysis in the first stage. 

These, “building characteristics” data, will be used to develop prototypical designs for the 

selected building types. Each design will have at least two alternative configurations: (1) 

reflecting current code provisions and (2) reflecting the proposed performance-based provisions 

to address selected vulnerabilities identified in the risk assessment. If appropriate, additional 

alternatives will be formulated that represent intermediate steps between the current code 

provisions and those associated with the performance-based approach. The prototypical building 

designs will be of sufficient detail that cost estimates can be developed that highlight the 

differences in the current code provisions and the proposed performance-based code provisions. 

Quantity take-offs and assemblies’ data will be used to further analyze differences among 

alternative configurations. 

Using national and regional statistics, weighting factors for each building type will be developed. 

These weighting factors reflect the proportion of a selected building type in a particular region of 

the country. National-level weighting factors might also prove useful. The purpose of the 

weighting factors is to develop regional-level estimates of the costs of implementing the 

improvements associated with the proposed performance-based provisions. Initial emphasis is 

placed on regional-level estimates because certain hazards affect some regions more than others. 

Consequently, the performance-based provisions recommended for one hazard/region 

combination may differ from those proposed elsewhere. 

3.2.3 Analysis of Expected Property Losses 

A variety of resources are available for estimating the expected property losses following a 

disaster. These include the HAZUS-MH software tool, the HAZUS-MH database, and other 

complementary databases. For purposes of this proposed research effort, HAZUS-MH is best 

suited. Because expected property losses may vary considerably across the different building 

types for a given hazard, a case study approach including both a baseline analysis (i.e., using 

most likely estimates of model input values) and a sensitivity analysis (i.e., reflecting uncertainty 

in the values of model inputs) is in order. The case study will select a geographical region, use 

the HAZUS-MH software tool to perform a series of hazard-specific simulations by varying the 

magnitude of the hazard, and analyze the property losses associated with different levels of 

hazard mitigation as reflected in selected building codes and their corresponding fragility curves. 

The specifications for the HAZUS-MH fragility curves will be examined to analyze ways to 
52 

change key parameters and correlate those changes with the more stringent code requirements 

associated with the performance-based approach. 

3.2.4 Analysis of Expected Business Interruption Costs 

This study has demonstrated that business interruption costs are difficult to quantify. This is 

unfortunate since business interruption costs often equal or exceed property losses experienced 

following a disaster. Consequently, additional research is needed to analyze current and 

proposed approaches for measuring disaster-related, business-interruption costs. Better 

approaches for measuring business interruption costs—both at the microeconomic and 

macroeconomic levels—are crucial to a diverse set of stakeholders (e.g., insurance industry 

organizations, state and local emergency management agencies, and federal entities such as the 

Department of Homeland Security and the Bureau of Economic Analysis). Once a better 

methodology for measuring business interruption costs is available, it will be possible to use 

benefit-cost analysis of design and rehabilitation approaches to enhance the resilience of 

buildings. 

Reducing business interruption costs creates an economic incentive for building owners if they 

occupy the structure. Conversely, if the building owner undertakes mitigation measures that will 

reduce expected business interruption costs of their tenants; they may be able to command a 

rental premium. Furthermore, business interruption costs are an important component of 

indirect, second-order, disaster-related costs. Thus, they are the first link in establishing the 

relationship between the disaster resilience of structures and the disaster resilience of 

communities. 

 
53 

Appendix A Disaster Resilience: An Annotated Bibliography 

The literature on disasters and disaster resilience is vast and draws from a wide number of fields 

including climatology, geography, geology, sociology, political science and economics. Its 

objectives range from answering purely scientific questions to determining optimal responses to 

overt political advocacy. The bibliography on the subsequent pages is only a sampling of it. 

Tierney et al. (2001) probably provide the best overview of the field. They have chapters on 

preparedness, individual and group behavior in disasters, organizational and government 

response in disasters, and factors influencing preparedness and response. 

Waugh and Tierney (2007) write a textbook on emergency management and response to 

disasters. Its intended audience is local government officials who are tasked with developing and 

implementing emergency response plans and managing the local response to disasters. This book 

is different from the one by Tierney et al. in that this one is a more practical and down-to-earth 

guide to disaster management and response while the previous one provides a more general and 

theoretical view of disaster response. 

J. Haas, R. Kates, M. Bowden, eds. (1977) are the seminal work in understanding disaster 

response and recovery. Nearly all subsequent work in understanding response and recovery 

reference them. Much of the subsequent work on recovery uses the framework established by 

Haas et al. However, many of the researchers following them disagree with the Haas framework 

(e.g., Berke et al. 2008, Bolin and Bolton 1986). The problem is two-fold. (1) The Haas 

framework is more of a way of organizing and interpreting information than it is really an 

empirical result. (2) Subsequent researchers are interested in different questions. They are 

interested in differences in impact and recovery far more than they are interested in the degree of 

recovery. 

Miles and Chang (2003) and Chang (2010) develop a set of simulations and models of the 

recovery process. The work is preliminary, but it provides a foundation on which further work 

can be built. 

Bruneau et al. (2003) develop a framework to quantitatively assess disaster resilience. They have 

continued and refined that work since at MCEER. 

There have been a series of studies aimed at estimating indirect costs of disasters, including in 

particular Rose and Liao (2005) who develop Computable General Equilibrium methods for 

estimating disaster losses and apply them to the hypothetical case of an earthquake in Portland 

Oregon. Also West and Lenze (1994) who carefully develop the data that would be needed to 

model disaster impacts in regard to Hurricane Andrew. 

Rose et al. (2007) evaluate the benefits versus the costs of FEMA Hazard Mitigation Grants, and 

find that the benefits exceed the costs of the grants by an average magnitude of about 4:1. 

 
54 

B. Adams, C. Huyck, B. Mansouri, R. Eguchi, M. Shinozuka, 2004. “Application of HighResolution Optical Satellite Imagery for Post-Earthquake Damage Assessment: The 2003 

Boumerdes (Algeria) and Bam (Iran) Earthquakes” MCEER Research Progress and 

Accomplishments 2003-2004: 173-186. 

This is a description of a near-real-time damage assessment algorithm based on the 

comparative analysis of remote sensing images acquired before and after the event. They 

also develop methods for integrating these techniques into field-based reconnaissance 

activities. 

A. Amendola, Y. Ermoliev, T. Ermoliev, V. Gitis, G. Koff, J. Linerooth-Bayer, 2000. “A 

Systems Approach to Modeling Catastrophic Risk and Insurability” Natural Hazards 21 (2-3): 

381-393. 

They develop a methodology for estimating risk of damage, and in particular risk of 

insolvency of an insurance company (or companies). They then apply it to a hypothetical 

earthquake in the Irkutsk region of Russia. Their approach is analytically intractable, so 

they use a Monte Carlo simulation and accounting for the interdependencies in the risks. 

American Society of Civil Engineers, 2009. 2009 report card for America’s infrastructure.

Reston Va.: American Society of Civil Engineers. 

They evaluate the levels of infrastructure investment in the U.S., including Aviation, 

Bridges, Dams, Drinking Water, Energy, Hazardous Waste, Inland Waterways, Levees, 

Public Parks and Recreation, Rail, Roads, Schools, Solid Waste, Transit, and Wastewater. 

All are judged to be underfunded and their resilience is considered to be low. 

P. Berke, J. Kartez, D. Wenger, 2008. “Recovery after Disaster: Achieving Sustainable 

Development, Mitigation and Equity” Disasters 17 (2): 93-109. 

This paper reviews key findings of the disaster recovery literature, but forms a 

perspective different from much of that literature. The authors are interested particularly 

in equity, mitigation, and sustainable development. They are especially interested in the 

impact of redevelopment planning and institutional cooperation on these outcomes. 

They review Haas’ model of recovery (infra) and argue against it. Their main objection is 

that his model is not useful for what they are interested in. As mentioned, they are 

interested in issues of equity and sustainability, and Haas’ model of recovery does not 

address those topics. 

They propose to replace Haas’ model with what is in effect a model of disaster aid rather 

than a model of recovery. Their model is centered around (re)development planning. 

They argue that good aid has four characteristics: 

1. Goodness of fit to local needs and opportunities; 
55 

2. Reliance on local resources. 

3. Local knowledge about how to obtain disaster assistance. 

4. Flexibility and willingness to learn in process. 

Bin, Okmyung, Tom Crawford, Jamie Kruse, and Craig Landry. 2006. “Valuing Spatially 

Integrated Amenities and Risks in Coastal Housing Markets.” Working Paper. 

Separating the value of coastal amenities from the negative value of risk from coastal 

storms is extremely difficult using hedonic methods due to the high correlation between 

the two. In this study, they construct a three-dimensional measure of view, accounting for 

natural topography and built obstructions, that varies independent of the risk to 

disentangle these spatially integrated housing characteristics. A spatial hedonic model is 

developed to provide consistent estimates of the willingness to pay for coastal amenities 

and risk. Their results suggest that such techniques can be successful in isolating risk 

from amenities on the coast. 

R. Bolin, P. Bolton, 1986. Race, Religion, and Ethnicity in Disaster Recovery. Institute of 

Behavioral Science, University of Colorado. 

They study four specific disasters: the Coalinga Earthquake (1983); Paris, TX Tornado 

(1982); Wasatch Front Floods, UT (1983); and Hurricane Iwa, Kauai HI, (1982). They 

are primarily interested in the differences in recovery between ethnic groups. They study 

the psychological resiliency of the various groups and factors affecting it; (differential) 

degree of impact, Economic and Social Recovery Time, and (differential) utilization of 

aid. 

R. Bolin, L. Stanford, 1991. “Shelter, Housing and Recovery: A Comparison of U.S. Disasters” 

Disasters 15 (1): 24-34. 

They examine the issues associated with the temporary sheltering and housing of victims 

after natural disasters in the United States. They are particularly interested in differential 

access to shelter and housing aid according to social class, ethnicity and related 

demographic factors; the relationship between post-disaster shelter and housing and longterm recovery; the role of social support networks in the sheltering of victims; and the 

implications of the research for the provision of shelter and housing aid after disasters. 

Two observations from the paper should be mentioned. First, in effect an earthquake 

erases the bottom tier of housing, either by directly destroying it or by forcing it to be 

upgraded and therefore priced out of the bottom tier housing market. Second, the more 

aid is devoted to temporary housing, the more time it tends to take for permanent housing 

to be completed. 
56 

K. Borden, S. Cutter, 2008. “Spatial patterns of natural hazards mortality in the United States” 

International Journal of Health Geographics 7 (1): 64. 

They use data from the SHELDUS database (augmented with some supplemental data) to 

estimate the spatial patterns of natural hazard mortality at the county-level for the U.S. 

from 1970–2004 using a combination of geographical and epidemiological methods. 

Note that this excludes Katrina. 

They find that chronic everyday hazards such as severe weather (summer and winter) and 

heat account for the majority of natural hazard fatalities. The regions most prone to 

deaths from natural hazards are the South and intermountain west, but sub-regional 

county-level mortality patterns show more variability. 

The greatest mortality is due to heat, with generic bad weather (excluding hurricanes, 

tornados, floods, and lightning) making up the majority of the deaths. Hurricanes and 

earthquakes are minor players in deaths. 

Event Deaths Pct 

Heat/Drought 3,906 19.57 %

Severe Weather 3,762 18.85 %

Winter Weather 3,612 18.10 %

Flooding 2,788 13.97 %

Tornado 2,314 11.59 %

Lightning 2,261 11.33 %

Coastal 456 2.28 %

Hurricane/Trop. Storm 304 1.52 %

Geophysical 302 1.51 %

Mass Movement 170 0.85 %

Wildfire 84 0.42 %

S. Brody, S. Zahran, W. Highfield, H. Grover, A. Vedlitz, 2008. “Identifying the impact of the 

built environment on flood damage in Texas” Disasters 32 (1): 1–18. 

This study examines the relationship between the built environment and flood impacts in 

Texas. They regress (log) property damage resulting from 423 flood events between 1997 

and 2001 at the county level. The regressors include several natural environment 

variables, the number of dams in the county, wetland alteration in the county, percent 

impervious surface in the county, and median household income. 

Their results suggest that naturally occurring wetlands play a particularly important role 

in mitigating flood damage. They also reproduce the standard result that impervious 

surface increases the amount of flood damage. 
57 

Bruneau, Michel et al. 2003. “A Framework to Quantitatively Assess and Enhance the Seismic 

Resilience of Communities.” Earthquake Spectra 19:733-752. 

This paper presents a conceptual framework to define seismic resilience of communities 

and quantitative measures of resilience that can be useful for a coordinated research effort 

on enhancing this resilience. It is an early version of work that is still underway at 

MCEER. 

In this framework there are 4 dimensions of resilience: Technical (survivability of 

physical systems); Organizational (Capacity of key organizations to respond); Social; and 

Economic. They are divided into two groups, Technical and Organizational, which focus 

primarily on the protective side, and Social and Economic which focus primarily on the 

protected side. 

They also consider 4 ‘aspects’ that they intersect with their 4 dimensions: Robustness; 

Redundancy; Resourcefulness; and Rapidity. 

R. Burby, R. Deyle, D. Godschalk, R. Olshansky, 2000. “Creating Hazard Resilient 

Communities through Land-Use Planning” Natural Hazards Review 1 (2): 99-106. 

They argue that land-use planning is the single most promising approach for bringing 

about sustainable hazard mitigation. This article describes the essential elements of landuse planning for hazard mitigation. It highlights important choices involved in 

formulating planning processes, undertaking hazard assessments, and crafting programs 

to manage urban development so that it is more resilient to natural hazards. 

They suggest that the following planning tools are usable for disaster mitigation: 

• Building standards 

• Development regulations: i.e., zoning. 

• Critical- and public-facilities policies: e.g., don’t build the hospital on the fault. 

• Land and property acquisition: e.g., turn the flood plain into a park. 

• Taxation and fiscal policies: e.g., tax the properties on the wildland interface. 

• Information dissemination. 

They also offer the following Principles for development management: 

• Use clear and authoritative maps of the hazard, with an emphasis on clear. 

• Link clear and realistic design guidelines to the maps: so everyone knows what it 

means for them. 
58 

• Ensure that hazard-free land is available for development: 

• If trying to rearrange or restrict land uses in hazardous areas, do so before the land is 

subdivided. 

• Offer incentives to encourage developers to locate projects outside of hazardous areas 

and to adopt hazard mitigation measures that exceed those required by law. 

• If hazardous land is subdivided and built out, be prepared to purchase selected 

properties. 

• Use project-specific design approaches. 

• Use the post-disaster window of opportunity to encourage individual owners to 

retrofit or relocate. 

S. Chang, C. Chamberlin, 2004. “Assessing the Role of Lifeline Systems in Community Disaster 

Resilience” MCEER Research Progress and Accomplishments 2003-2004: 87-94. 

This is a preliminary effort to model the impacts of infrastructure damage on business. 

The key innovation here is to take into account the overlap in damage. They estimate the 

damage to infrastructure systems (including—or perhaps especially—overlaps). Then 

they distribute the ‘disruptiveness’ randomly across affected businesses. Finally, they 

estimate probabilities of business closure based on how many ‘disruptive’ interruptions 

there are. Finally they propose to apply the model to the Los Angeles Department of 

Water and Power (LADWP). 

S. Chang, 1983. “Disasters and Fiscal Policy: Hurricane Impact on Municipal Revenue” Urban 

Affairs Review 18 (4): 511-523. 

This study investigates the fiscal impact of Hurricane Frederic on the city of Mobile, 

Alabama. The hurricane caused damages of about $1.6 billion in property and other 

losses in the state, led to an influx of $670 million in recovery funds, and resulted in a 

$2.5 million increase in municipal revenue for the next 12 months. The long-term impact 

was negative, however, because secondary effects overwhelmed the revenue effect. For 

example, flooding as a result of tree destruction from the hurricane cost more than 

revenue gains from disaster aid. 

S. Chang, 2010. “Urban disaster recovery: a measurement framework and its application to the 

1995 Kobe earthquake” Disasters 34 (2): 303-327. 

This continues Chang’s recent work on disaster recovery by applying it to Kobe. The 

paper provides a framework for assessing empirical patterns of urban disaster recovery 

through the use of statistical indicators. 
59 

She discusses several different possible meanings for ‘recovery,’ and provides measures 

for each of them in the Kobe analysis. There is an effort also to filter out exogenous 

influences unrelated to the disaster, and to make comparisons across disparate areas or 

events. 

It is applied to document how Kobe City, Japan, recovered from the catastrophic 1995 

earthquake. Findings reflect the results of Haas (1977) in a number of ways. For example, 

while aggregate population regained pre-disaster levels in ten years, population had 

shifted away from the older urban core and become more suburban. Longer-term trends 

in business mix were generally accelerated. In particular there were sectoral shifts toward 

services and large businesses. There was a three to four year temporary boost in 

economic activity from reconstruction activities, 

The city did not seem to fully recover. While population returned to pre-disaster levels 

within ten years (from a 6 % decline), economic activity eventually settled at a level 

some ten per cent below pre-disaster levels. Similarly there were substantial losses of port 

activity that do not seem to be presaged by longer term trends. 

She grades recovery based on “time to recovery” with two standards: return to previous 

level, and “attainment of a new ‘normal.’” 

Return to previous level is intuitive and straight-forward. Return to new normal is defined 

here. She picks a pre-disaster comparison period (in her case, four years), and selects the 

greatest variation in that period. So: 

ߜܺ ൌ max

௜ ൛ห்ܺିሺ௜ାଵሻ െ ்ܺିሺ௜ାଶሻหൟ

Then Normal is judged to be reached when annual variation becomes less than that 

critical amount. 

S. Chang, A. Falit-Baiamonte, 2002. “Disaster vulnerability of businesses in the 2001 Nisqually 

earthquake” Global Environmental Change Part B: Environmental Hazards 4 (2-3): 59-71. 

This paper examines the impacts of the February, 2001, Nisqually earthquake on 

businesses. The study investigates the extent of losses, patterns of disparities, and 

underlying loss factors. 

Results showed that business losses were much greater than what standard statistical data 

would imply. They find that businesses are largely self-insured: 80 % cover losses out of 

their own pocket. They note that since most of the losses were self-financed, they tend to 

be uncounted in most official statistics. That suggests business losses are almost entirely 

uncounted. 

In this case, SMALL and RETAIL were strongly correlated with loss. Preparedness does 

not have any significant relationship at all to loss. Preparedness and Vulnerability 
60 

correlate highly negatively. That is, Big Non-Retail Building owners prepare, while small 

retail renters do not. 

S. Chang, M. Shinozuka, J. Moore, 2000. “Probabilistic Earthquake Scenarios: Extending Risk 

Analysis Methodologies to Spatially Distributed Systems” Earthquake Spectra 16 (3): 557-572. 

This paper proposes a methodology by which probabilistic risk analysis methods can be 

extended to the assessment of urban lifeline systems. The problem with its previous 

application to lifeline systems is that systems such as highway networks, electric power 

grids, and regional health care delivery systems, are geographically distributed, and the 

failure at one point degrades the ability of the entire system to function. So the spatial 

correlation between earthquake ground motion across many sites is important in 

determining system functionality. 

The methodology developed in this paper first identifies a limited set of deterministic 

earthquake scenarios and evaluates infrastructure system-wide performance in each. It 

then assigns hazard-consistent probabilities to the scenarios in order to approximate the 

regional seismicity. The resulting probabilistic scenarios indicate the likelihood of 

exceeding various levels of system performance degradation. 

A demonstration for the Los Angeles study area highway network suggests that there is 

roughly a 50 % probability of exceedance of Northridge-level disruption in 50 years. This 

methodology provides a means for selecting representative earthquake scenarios for 

response or mitigation planning. 

Chapman, Robert, and Amy Rushing. 2008. Users Manual for Version 4.0 of the CostEffectiveness Tool for Capital Asset Protection. NIST IR 7524. Gaithersburg, MD: NIST. 

This paper describes the CET 4.0 software tool. It is intended to implement the ASTM 

2506 protocol for evaluating the cost effectiveness of proposed disaster mitigation 

measures. It takes as inputs costs of the various proposed alternatives, including O&M 

costs; costs associated with relevant disaster events, and the probabilities associated with 

the events. It is capable of handling uncertainty in costs of likelihood of adverse events 

using Monte Carlo methods. 

S. Cho, P. Gordon, J. Moore, H. Richardson, M. Shinozuka, S. Chang, 2001. “Integrating 

Transportation Network and Regional Economic Models to Estimate the Costs of a Large Urban 

Earthquake.” Journal of Regional Science 41 (1): 39-65. 

In this paper they summarize an integrated model of losses due to earthquake impacts on 

transportation and industrial capacity, and how these losses affect the metropolitan 

economy. They divide damage into four groups: Structural Losses, Direct Business 

Losses, Indirect Business Losses, and Costs of Increased Transportation Time. Indirect 
61 

business losses are due entirely to transportation problems; e.g., supplies and labor being 

unable to get to work sites, etc. 

Preliminary results are summarized for a magnitude 7.1 earthquake on the Elysian Park 

blind thrust fault in Los Angeles. Here, costs of increased transportation time are on an 

order with structure losses. Indirect business losses are on an order with direct business 

losses. The amount of indirect losses are underestimated since there are clearly other 

sources of indirect losses than transportation. 

H. Cochrane, 2004. “Indirect Losses from Natural Disasters: Measurement and Myth.” pp. 37-

52. In Okuyama, Yasuhide, and Stephanie Chang, eds. Modeling Spatial and Economic Impacts 

of Disasters. Springer. 

He runs a series of HAZUS simulations to estimate the amount of indirect losses from 

various natural disasters. Based on those simulations he observes: 

• There is no indirect loss if all sectors of the economy are equally damaged. 

• Indirect losses are the predominant loss if a small critical sector goes down. 

• Indirect losses are negative if there is ample excess capacity, and reconstruction is 

financed by outside sources, but for the nation they must be positive. 

• Indirect losses are temporarily negative if reconstruction is financed out of savings or 

borrowing. 

• Indirect losses are less sensitive to economic structure than to damage pattern, degree 

of integration, preexisting conditions, and financing source. 

Collins, D., and S. Lowe. 2001. “A Macro Validation Dataset for U.S. Hurricane Models.” in 

Casualty Actuarial Society Forum. Arlington, VA: Casualty Actuarial Society. 

They have developed a macro validation data set for the U.S. hurricane history between 

1900 and 1999. This should enable a lay person to compare the overall results of modern 

hurricane models to an historical record. 

The macro validation dataset consists of the aggregate insured losses from hurricanes 

affecting the continental United States from 1900 through 1999. The historical losses in 

each county have been “trended”—adjusted from the conditions at the time to those 

existing today. The trending reflects not only estimated changes in price levels, but also 

estimated changes in the value of the stock of properties and contents, and changes in the 

insurance system. 
62 

L. Comfort, 1999. Shared Risk: Complex Systems in Seismic Response. Pergamon. 

She is primarily interested in adaptive response to earthquakes. Communication, 

flexibility and adaptability are her big issues. In this she continues the work of 

Quarantelli, Wenger, and the others at the Disaster Research Center who have been 

identifying communications as a major problem with disaster response for many decades. 

Here case studies ranged from cases where communities “failed to address the larger goal 

of seismic risk reduction,” and did fairly poor jobs at relief and restoration. On the other 

end there were cases where response was highly adaptive. There were frequently 

problems with the response, and the more severe the disaster the greater the problems. 

One key observation is that local governments and organizations are barely in evidence in 

any of the ‘non-adaptive’ case studies. This contrasts with her ‘operative-’ and ‘autoadaptive systems,’ where the response was primarily local. 

Characteristics of good Response based on her comments: 

1. Strong, Independent, Capable local responders. In the case of minor ‘disasters,’ 

they will be able to take care of the problem themselves. In large ‘no warning’ 

disasters, their resources may not be sufficient to the task, but they will still be the 

only ones on site for hours or days, so their reaction is still critical. Note that this 

includes having the locals highly trained and skilled. 

2. Ability to scale up. In large disasters, the ability to call in additional resources 

promptly becomes critical. 

3. Ability to coordinate response. The ability to put resources where they are needed. 

Two observations about the case studies. First, responses are best where they are local. 

That is, the cases where the locals did the main work came out best. Second, 

communication is important when the scale of the disaster begins to overwhelm local 

resources. In Whittier Narrows and Loma Prieta, local resources were (mostly) sufficient 

to the task so the communications problems she identifies are not important to the 

outcome. In Hanshin and Erzincan, where the scale of the disaster overwhelmed local 

resources, lack of communication between local authorities and national/international aid 

sources significantly slowed the response. 

L. Comfort, T. Haase, 2006. “Communication, Coherence, and Collective Action: The Impact of 

Hurricane Katrina on Communications Infrastructure” Public Works Management & Policy 10 

(4): 328-343. 

Here they use the methodology from Comfort (1999) for Katrina, and perform some 

additional analysis. 

Coughlin, K., E. Bellone, T. Laepple, S. Jewson, and K. Nzerem. 2009. “A Relationship between 

all Atlantic Hurricanes and those that make landfall in the U.S.” Quarterly Journal of the Royal 

Meteorological Society 135:371-379. 
63 

They first investigate whether there are statistical breaks in the hurricane record. They 

find that there are three breaks: 1945, which probably representing improved data 

collection; 1967, and 1994. The period from 1967 – 1994 is a well-known lull in Atlantic 

hurricane activity. 

Their results suggest that the proportion of Atlantic hurricanes that hit the U.S. is a 

random process with fixed distribution. Specifically, about 1/3 of all Atlantic hurricanes 

hit the U.S., and the proportion seems to be constant over time. 

S. Cutter, B. Boruff, L. Shirley, 2003. “Social Vulnerability to Environmental Hazards” Social 

Science Quarterly 84 (2): 242-261. 

Their objective is to develop an index of social vulnerability to environmental hazards 

based on readily available county-level socioeconomic and demographic data. 

They pick about 40 variables that people believe are associated with vulnerability to risk. 

They then do a principle-components analysis on the variables, and come up with about 

10 that explain most of the variation in their associated variables. To determine the Social 

Vulnerability Index (SoVI), they do an unweighted sum of the variables. Then they break 

that into 5 bins based on standard deviation of the sum. 

They find that there are some distinct spatial patterns in the SoVI, with the most 

vulnerable counties clustered in metropolitan counties in the east, south Texas, and the 

Mississippi Delta region. 

They do not attempt to correlate any of the factors with actual disaster impacts. Nor is 

county level wealth corrected for regional price variations. The index is negatively

correlated with presidential disaster declarations. However, disaster declarations have a 

strong political component. 

Dahlhamer, James, and Kathleen Tierney. 1998. “Rebounding from Disruptive Events: Business 

Recovery Following the Northridge Earthquake.” Sociological Spectrum 18:121-141. 

They develop a model of business recovery by drawing from existing research on disaster 

recovery and on organizational survival in non disaster contexts and test it by using data 

collected from a stratified random sample of 1 110 Los Angeles area firms affected by 

the 1994 Northridge earthquake. 

Key predictors of recovery were size (positively), degree of disruption (negatively), 

Shaking Intensity (negatively), and whether the business was a recipient of post-disaster 

aid (negatively). 

The negative impact of aid is probably a result of self-selection effects (only the worst-off 

businesses obtained aid) and problems such as higher debt caused by the aid itself. 
64 

They argue that 'ecological factors' are important to business recovery. For example, a 

business that was undamaged and experienced no interruptions may still do poorly if it is 

located in a district where most of the surrounding businesses were destroyed. 

Davidson, Rachel, and Kelly Lambert. 2001. “Comparing the Hurricane Disaster Risk of U.S. 

Coastal Counties.” Natural Hazards Review 2:132-142. 

This paper describes the Hurricane Disaster Risk Index (HDRI), a composite index 

developed to compare the risk of hurricane disaster in U.S. coastal counties. The HDRI is 

a composite of four separate sub-indices: Hazard, Exposure, Vulnerability, and 

Emergency Response and Recovery Capability. The factors contributing to the subindices are weighted according to expert judgment. The sub-indices are combined 

multiplicatively to get the HDRI. The paper includes an analysis of 15 sample counties. 

A. Dlugolecki, 2008. “An overview of the impact of climate change on the insurance industry.” 

pp. 248-278. In Diaz, Henry, and Richard Murnane, eds. Climate extremes and society. 

Cambridge University Press. 

This chapter focuses on the types of extreme weather and climate events that are 

important to property insurers, and it considers evidence on how those risks have been 

changing and how they might change in the future with climate change. 

He does a superficial analysis of insured losses due to flood damage in the UK where 

insurance penetration for flooding is high. He finds a significant upward trend in insured 

flood losses. The analysis is very incomplete and has some flaws (for example, he does 

not correct for trends in Real GDP or insurance penetration), but it seems likely that there 

will be an upward trend even after correcting for those items. 

He believes there is good ground to argue that climate change is already affecting the 

risks, although it is not the only factor that has caused change. He argues that weather 

losses will increase rapidly, and cites others’ models to support this idea. In particular, he 

suggests that extreme losses could increase very rapidly. 

A. Dodo, R. Davidson, N. Xu, L. Nozick, 2007. “Application of regional earthquake mitigation 

optimization” Computers & Operations Research 34 (8): 2478-2494. 

and 

N. Xu, R. Davidson, L. Nozick, A. Dodo, 2007. “The risk-return tradeoff in optimizing regional 

earthquake mitigation investment” Structure and Infrastructure Engineering 3 (2): 133–146. 

These papers introduce a stochastic optimization model developed to help decisionmakers understand the risk-return tradeoff in regional earthquake risk mitigation, and to 

help state and local governments comply with the Disaster Mitigation Act of 2000 

requirement that they develop a mitigation plan. A case study for Central and Eastern Los 

Angeles illustrates an application of the model. Results include a graph of the tradeoff 
65 

between risk and return, quantification of the relative contributions of each possible 

earthquake scenario, and discussion of the effect of risk aversion on the selection of 

mitigation alternatives. 

They develop a method to compute the optimal set of seismic upgrades to buildings in a 

large area. The mitigation alternatives considered are structural upgrading policies for 

groups of buildings. They use the HAZUS model to simulate damage, and they design a 

linear program to estimate how much to spend on pre-earthquake mitigation, and which 

of the many possible mitigation activities to fund so as to minimize overall risk. 

The model is intended to be used as a tool to support the public regional mitigation 

planning process. In any realistic application, the model would include millions of 

variables, thus requiring a special solution method. This paper focuses on two efficient 

solution algorithms to solve the model. 

K. Donaghy, N. Balta-Ozkan, G. Hewings, 2007. “Modeling Unexpected Events in Temporally 

Disaggregated Econometric Input-Output Models of Regional Economies” Economic Systems 

Research 19 (2): 125-145. 

Their objective is to develop the framework for Regional Econometric Input-Output 

Models (REIMs) at high spatial resolution. Their main focus is on developing a structural 

econometric model for estimating the Input-Output table at the core of the model. They 

conclude by estimating a hypothetical example where a single industry is shut down for 

four weeks, and estimate the impacts on the economy at 2-week intervals. 

Downton, Mary, J Zoe Barnard Miller, and Roger Pielke. 2005. “Reanalysis of U.S. National 

Weather Service Flood Loss Database.” Natural Hazards Review 6:13-22. 

and

M. Downton, R. Pielke, 2005. “How Accurate are Disaster Loss Data? The Case of U.S. Flood 

Damage” Natural Hazards 35 (2): 211-228. 

These papers are an analysis of the accuracy of the flood loss data collected by the U.S. 

National Weather Service since about 1933. The reanalyzed data are posted at 

http://www.flooddamagedata.org/. 

These papers use historical flood damage data in the U.S. to evaluate disaster loss data. 

The NWS estimates are obtained from diverse sources, compiled soon after each flood 

event, and not verified by comparison with actual expenditures. These papers presents 

results of a comprehensive reanalysis of the scope, accuracy, and consistency of NWS 

damage estimates from 1926 to 2000. 

1. Damage estimates for individual flood events are often quite inaccurate. Although, 

the larger the event, the more accurate (in log terms) the estimate. 
66 

2. With the caveats mentioned below, the data seem to be unbiased. 

3. The data are bottom-censored, that is, any event smaller than a specific size is not 

included in the database. That means the totals are probably biased down. 

4. Most of the damage occurs in a few very large events. So, for example, in 

Pennsylvania (which has the highest total damage in the database), something like 

75 % of the total damage (over 75 years) occurred in a single flood event. 

R. Ellson, J. Milliman, R. Roberts, 1984. “Measuring the Regional Economic Effects of 

Earthquakes and Earthquake Predictions” Journal of Regional Science 24 (4): 559-579. 

They build a predictive model of the Charleston, SC area economy. They simulate the 

area with and without a serious earthquake. Their model explicitly takes into account the 

types of supply-side constraints one would expect to encounter in a natural disaster. 

Estimates of deaths and losses of capital are more or less ad hoc. The simulations assume 

a global death and loss rate based on analogy with the Los Angeles area. They assume 

that there is no external aid. 

They run four simulations: a baseline (no earthquake) simulation, an unanticipated 

earthquake, a predicted earthquake, and a false-alarm prediction. 

The simulations are ranked in terms of total losses in the expected order. However, there 

are significant distributional differences in the different simulations. For example, the 

unanticipated disaster results in large increases (relative to the other simulations) in labor 

and proprietor income, largely due to the resulting construction boom. 

Y. Ermoliev, T. Ermolieva, G. MacDonald, V. Norkin, A. Amendola, 2000. “A system approach 

to management of catastrophic risks” European Journal of Operations Research 122: 452-460. 

They are interested in understanding the behavior and optimal choice of an insurance 

company facing correlated (catastrophic) risk. In their models, firms are bankruptcy 

averse. Firms have the option of buying reinsurance or catastrophe bonds to distribute 

their risks. 

The loss and optimization functions are discontinuous, which makes optimizing over 

them particularly difficult. They discuss Stochastic Dynamic Optimization techniques 

that will solve such problems. 

FEMA. 2005. Risk Assessment: A How-To Guide to Mitigate Potential Terrorist Attacks Against 

Buildings. FEMA 452. Washington DC: FEMA. 

This is a how-to guide for the development and evaluation of alternatives for mitigation 

against terrorist attacks. The guidebook uses five steps to complete the process. The first 
67 

step is to list and assess possible threats. Second, they evaluate the value of the protected 

asset. Value in their case means evaluating how serious the consequences would be if the 

building were destroyed or taken out of service. They also evaluate the value of building 

and security components. Third they estimate vulnerability of the building. Fourth is the 

risk assessment, which combines the results of the first three into a single index value. 

Finally (step five), they develop a set of mitigation options, by asking what measures will 

reduce the risk rating, how effective the measures are and how much they cost (in both 

monetary and non-monetary terms). 

A. Fothergill, 1998. “The Neglect of Gender in Disaster Work: An overview of the literature.” 

pp. 11-26. In Enarson, Elaine, and Betty Morrow, eds. The gendered terrain of disaster: through 

women’s eyes. Westport Conn.: Praeger. 

She makes several observations about the behavior of women during the disaster cycle, 

and how they differ from men: 

• Women are more risk averse. They are more likely to evacuate. But they are less 

likely to take mitigation measures. 

• Women are more likely to buy safer homes (and insist that builders follow building 

codes). 

• Women are more likely to hear warnings (due to better peer networks), and take them 

seriously. Men are less likely to take the warnings seriously. Married men often 

evacuate at the behest of their wives, even though they themselves would prefer not 

to. 

• The results on mortality are mostly taken from the third world, since there are very 

few deaths in the U.S. to base results on. In general, if the disaster is outdoors 

(lightning, wind, etc) men die more. If the disaster is indoors (earthquakes, flooding, 

etc.) women die more. 

H. Friesma, J. Caporaso, G. Goldstein, R. Linberry, R. McCleary, 1979. Aftermath: Communities 

after natural disasters. Beverly Hills, Calif: Sage. 

They look at several communities after disasters and try to do an analysis of trends to see 

if there are any long-term effects of the disaster. The disasters are mostly small relative to 

the size of the community. 

• They had a hard time finding an effect on unemployment due to the disasters. 

• Effects on numbers of small businesses are estimated by eye, and the results are 

ambiguous. In some communities there is a decline, in others an increase. In almost 

all cases, the effect is small in comparison to the larger trend. 

• There is no evidence of impact on total sales at the annual level. 
68 

• In two communities there is strong evidence of a temporary drop in divorce that is not 

made up later. That is, some families that would have divorced simply never did due 

to the disaster. 

• The effects on crime are ambiguous. 

• Population figures are only reliable for one community. There they suggest a brief 

decline (less than 2 years) followed by recovery to trend. There is some evidence of 

dispersal of the population to the fringes a la Haas. 

E. Glaeser, J. Gottlieb, 2009. “The wealth of cities: agglomeration economies and spatial 

equilibrium in the United States” Journal of Economic Literature 47 (4): 983–1028. 

This is a survey article and general introduction to urban economics. 

Empirical research on cities starts with a spatial equilibrium condition: workers and firms 

are assumed to be indifferent across space. This condition implies that research on cities 

is different from research on countries, and that work on places within countries needs to 

consider population, income, and housing prices simultaneously. 

• Not only do incomes vary across locations (e.g., San Francisco has a much higher per 

capita income than Brownsville), but so do prices. After factoring in prices, the 

spatial differences in income are less variable. 

• Nevertheless, people express a mild preference for rural as opposed to urban living in 

that they are willing to accept a decrease in standard of living to live in the country. 

• Mobility and spatial equilibrium suggest that poverty in cities is less a product of 

cities being bad (since people can leave). Rather it is a product of cities providing 

better services to the poor (and thus attracting them). 

D. Godschalk, T. Beatley, P. Berke, D. Brower, E. Kaiser, 1999. Natural Hazard Mitigation: 

recasting disaster policy and planning. Washington DC: Island Press. 

The basic questions they ask are whether state mitigation plans meet the requirements of 

the Stafford Act, whether they meet basic standards of quality, and whether they are 

consulted and implemented in disaster recovery. In their six case studies, mitigation plans 

were largely unconsulted in the recovery period and in deciding what mitigation (if any) 

to actually implement. 

P. Gordon, J. Moore, H. Richardson, 2002. Economic-Engineering Integrated Models for 

Earthquakes: Socioeconomic Impacts. Pacific Earthquake Engineering Research Center: College 

of Engineering: University of California, Berkeley. 

They are interested in how the impacts of an earthquake differ depending on (mainly) 

income. The Southern California Planning Model-2 (SCPM-2) is used to model the 

economic impacts of a hypothetical earthquake on Los Angeles’s Elysian Park fault. 
69 

Census data on occupation are used to distribute these impacts across income groups 

within each city in the region. This permits the impacts of such an earthquake, and 

potential mitigation programs, to be assessed in terms of city-specific changes in income 

equity measures. 

In the Elysian Park scenario, the data suggest that the well-off suffer a (slightly) 

disproportionate share of the damage. That is, this is one disaster that strikes the wealthy 

more than the poor. They suggest that information is the main reason. There is no readily 

available information to say where the risks are, so the wealthy can’t really avoid them. 

P. Gordon, H. Richardson, B. Davis, 1998. “Transport-related impacts of the Northridge 

earthquake” Journal of Transportation and Statistics 1 (2): 21–36. 

This research estimates the transport-related business interruption impacts of the 1994 

Northridge earthquake using a spatial allocation model, the Southern California Planning 

Model (SCPM), and surveys of businesses and individuals. 

Total business interruption losses are estimated at more than $6.5 billion, sizeable but 

much smaller than total structural damage (over $25 billion), with an associated job loss 

of 69,000 person-years. 

The four types of transport-related interruptions (commuting, inhibited customer access, 

and shipping and supply disruptions) totaled more than $1.5 billion, or 27.3 % of all local 

business interruptions, with a job loss of more than 15 700 person-years. In addition, 

there were commuting travel time losses of at least $33 million and some dislocation of 

shopping patterns and frequencies. These losses would have been much higher had it not 

been for the substantial redundancy in Los Angeles’ highway system. 

P. Gordon, H. Richardson, 1995. The Business Interruption Effects of the Northridge 

Earthquake. Lusk Center Research Institute: University of Southern California. 

They do a preliminary analysis of aggregate economic statistics for the LA area 

surrounding the Northridge quake and find no discernable impact. There are issues with 

such an analysis, but what that does is place an upper limit on the amount of business 

interruption that occurs. 

Then they use a survey of businesses in the impact zone with the idea of feeding it into 

the Southern California Planning Model to distribute regional impacts. Among other 

things, they find: 

• Total business interruption losses are about $6 billion, which is in the range of 20 % 

of total losses. This is not small, but it is less than earlier hypothetical studies had 

suggested. About 2/3 of the losses are outside the direct zone of impact, and about 1⁄4 

of the business losses are outside the region. 
70 

• Employment losses (not job losses) amounted to approximately 75,500 person years 

or about 1.2 % of the total employment in the area. 

• The support for the usual finding that small businesses are more vulnerable is weak. 

They do not account for survivor bias, or for forward impacts. 

P. Guimaraes, F. Hefner, D. Woodward, 1993. “Wealth and Income Effects of Natural Disasters: 

An Econometric Analysis of Hurricane Hugo” Review of Regional Studies 23: 97-114. 

They use a regional economic model, calibrated to the period before Hurricane Hugo, as 

the baseline for comparison. The model takes into account the 1991 recession and the 

impacts of the Gulf War. Deviations from the model are assumed to be due to the 

hurricane. 

They “found that South Carolina’s total personal income suffered a major drop in the 

third quarter of 1989 due to the hurricane. ... To a large extent, however, the personal 

income effect is an accounting, not economic artifact. ... The decline in South Carolina’s 

personal income in the third quarter of 1989 largely reflected damage to structures caused 

by the hurricane and figured in the calculation of imputed rental income.” 

“One unexpected result to emerge from the analysis is the length of time the hurricane 

affected the economy. ... [T]here is a negative economic after-shock two years after the 

disaster. Apparently, residents and businesses undertake construction projects that may 

have been part of normal maintenance, along with storm damage repair. This boosts 

construction income in the quarters following the disaster, but dampens it in later 

quarters.” 

H. John Heinz III Center for Science, Economics, and the Environment., 2000. The hidden costs 

of coastal hazards: implications for risk assessment and mitigation. Washington D.C.: Island 

Press. 

They define a “Disaster Resilient Community” as “a community built to reduce losses to 

humans, the environment, and property as well as the social and economic disruptions 

caused by natural disasters. It is the safest community that can be designed and built 

given the status quo and budget and resource constraints.” 

They divide losses into four categories: 

• Built environment 

• Business environment: mostly ‘indirect’ business interruption losses. But they would 

also include things like loss of inventory, tools, equipment, etc., in this category 

• Social Environment: deaths, injuries, hedonic losses due to life and community 

disruption. They also include lost wages in this category. 

• Natural Environment: they include crop losses in this category. 
71 

They also discuss a number of considerations in risk evaluation and mitigation for the 

environments we want to protect. 

J. Haas, R. Kates, M. Bowden, eds. 1977. Reconstruction following disaster. Cambridge Mass.: 

MIT Press. 

This is a classic work on the subject of recovery from natural disaster. They conducted 

case studies of several major disasters, ranging from the Great San Francisco Earthquake 

to the Managua (Nicaragua) earthquake of 1972. 

Much of the subsequent work on recovery uses the framework established by Haas et al. 

However, many of the researchers after Haas at al., disagree with the Haas framework. 

The problem is two-fold. (1) The Haas framework is more of a way of organizing and 

interpreting information than it is really an empirical result. (2) Subsequent researchers 

are interested in different questions. They are interested in differences in impact and 

recovery far more than they are interested in the degree of recovery. As Chang (2003) 

points out, “this more recent literature has been concerned with disparities and equalities 

in recovery, and with conceptualizing disaster recovery as a social process involving 

decision-making, institutional capacity, and conflicts between interest groups.” So they 

are simply asking a different set of questions. 

Kates and Pijawka (pp. 1-24) describe the model of recovery. Recovery is broken up into 

four different recovery periods: (1) the Emergency Period, (2) Restoration Period, (3) the 

Replacement Reconstruction Period, and (4) the Commemorative, Betterment, 

Developmental Reconstruction Period. They explicitly note that these periods may 

overlap, and different parts of the city may be in different stages at the same time. 

They note that there is no recent example of a damaged/destroyed city not rebuilding. In 

a historical analysis covering several hundred years they could find no more than one or 

two cities which did not eventually recover their full populations after a disaster. 

Haas et al. (pp. 25-68) discuss general observations about recovery, and the choices that 

public authorities make in guiding it. Disasters tend to accelerate suburbanization. They 

observe that disasters tend to accelerate existing trends in a city. So, in a city where 

business is shifting from one industry to another, a trend that might have taken several 

years will be completed in only a short time. 

They observe that private actors want a prompt return to normal. The longer the 

government delays, the more likely it is that people will rebuild, with or without 

permission, and so the longer the planners take to make their decisions, the more likely it 

is that the decisions will be made for them. 

Planners often have the idea that disasters serve as a form of “instant urban renewal,” and 

represent an opportunity to rebuild the city so as to provide improved efficiency, equity, 

or amenity. Haas et al. observe that “despite the best efforts to shape the character of the 

reconstructed city, fundamental change is unlikely. Past trends will be accelerated in most 

cases.” “Overambitious plans to accomplish [these] goals tend to be counterproductive.” 

They also note that “a basic error of the professional community is to assume that formal 
72 

studies...are requirements for reconstruction when there is already such a plan in the 

minds of the community inhabitants—the predisaster city.” 

Bowden et al. (pp. 69-145) discuss the details of the rebuilding of houses, businesses and 

neighborhoods. They are focused primarily on location. 

In their analysis of San Francisco (1906) they note several details (the other case studies 

tended to support these observations): 

• First, the wealthy reestablish housing first. The poor were several years in finding 

new houses, and many who lived there during the disaster left. 

• Initially, with 50 % of the housing stock destroyed and the population not seriously 

depleted, housing prices were sky high. As time went on and the building stock was 

restored, prices fell. 

• In San Francisco, the city expanded and reduced its density. The Central Business 

District grew in physical size, and residences moved further out. 

• Sorting became much stronger. So rich and poor neighborhoods that had been 

somewhat intermixed before became strongly sorted. Ethnic enclaves became much 

sharper. The business district became much more sorted with the financial district 

cleanly separated from the garment district, from the theater district etc. Trends in 

terms of business types accelerated. So manufacturing was already moving out of San 

Francisco, and that trend accelerated. 

• Naturally, small businesses were fairly likely to fail in the large disasters. That 

however does not address the question of how readily new businesses arose to take 

their place. 

J. Harrald, 2006. “Agility and Discipline: Critical Success Factors for Disaster Response” The 

Annals of the American Academy of Political and Social Science 604 (1): 256-272. 

For more than thirty years, the U.S. emergency management community has been 

increasing its ability to structure, control, and manage a large response. The result of this 

evolution is a National Response System based on the National Response Plan and the 

National Incident Management System. 

Over the same period, social scientists and other disaster researchers have been 

documenting and describing the nonstructural factors such as improvisation, adaptability, 

and creativity that are critical to coordination, collaboration, and communication and to 

successful problem solving. 

This article argues that these two streams of thought are not in opposition, but form 

orthogonal dimensions of discipline and agility that must both be achieved. The critical 
73 

success factors that must be met to prepare for and respond to an extreme event are 

described, and an organizational typology is developed. 

He divides response into four phases. (1) “The initial response is conducted by resources 

on the ground ... while external resources are mobilized.” (2) “An integration phase is 

required to structure these resources into a functioning organization.” (3) “A production 

phase is reached where the response organization is fully productive, delivering needed 

services as a matter of routine.” (4) “Finally, the large external presence is diminished 

during a demobilization and transition to recovery stage.” 

K. Hosseini, M. Jafari, M. Hosseini, B. Mansouri, S. Hosseinioon, 2009. “Development of urban 

planning guidelines for improving emergency response capacities in seismic areas of Iran” 

Disasters 33 (4): 645-664. 

This presents an evaluation of the disaster response to several large earthquakes in Iran 

between 1990 and 2006. The response and recommendations are based on experts’ 

opinions as to the best ways to conduct emergency response. 

The research concentrated on emergency response operations, emergency medical care, 

emergency transportation, and evacuation. They are primarily thinking of preplanning 

and prepositioning of resources so that when disaster strikes, everything is ready. 

They propose portable medical triage units scattered throughout an impacted region. 

These have the ability to absorb casualties when they are high. Such units would deal 

with the lightly wounded without burdening hospitals and such, and stabilize the more 

severely wounded for transport to hospitals. In that vein, they note that 80 % of medical 

cases treated did not require hospitalization. 

R. Iman, M. Johnson, C. Watson, 2005. “Sensitivity Analysis for Computer Model Projections of 

Hurricane Losses” Risk Analysis 25 (5): 1277-1297. 

and 

R. Iman, M. Johnson, C. Watson, 2005. “Uncertainty Analysis for Computer Model Projections 

of Hurricane Losses” Risk Analysis 25 (5): 1299-1312. 

Florida requires disaster models used in justifying insurance rates be approved by the 

Florida Commission on Hurricane Loss Projection Methodology. As a part of the 

approval process the Commission requires that sensitivity and uncertainty analyses be 

conducted of the models. A sensitivity analysis would show how sensitive the results are 

to changes in specific input variables. An uncertainty analysis would show how much of 

the variance in output of the model is attributable to variance in specific input variables. 

These papers discuss methods for conducting these analyses. 
74 

S. King, A. Kiremidjian, N. Basöz, K. Law, M. Vucetic, M. Doroudian, R. Olson, J. Eidinger, K. 

Goettel, G. Horner, 1997. “Methodologies for Evaluating the Socio-Economic Consequences of 

Large Earthquakes” Earthquake Spectra 13 (4): 565-584. 

They developed a comprehensive methodology for evaluating the socio-economic 

impacts of large earthquakes. New models were developed for some of the methodology 

components, such as the identification and ranking of critical facilities. For other 

components, such as the estimation of building and lifeline component damage, existing 

models were adopted and modified for use within a GIS environment. 

The methodology was illustrated through a case study for the city of Palo Alto, 

California. Damage and loss estimates were made for four earthquake scenarios. Then 

peak ground acceleration, shaking intensity (interaction with soil types), and liquifaction 

probabilities were estimated. Then they estimated amount of damage, lives lost, and 

'lifeline' damage. 

They came up with a list of 'critical facilities' based on the expected harm done. This used 

a weighting method to weight physical damage, lives lost, injuries, indirect effects etc. to 

determine the expected cost to society as a result of the earthquake. Indirect effects 

included things like fire damage resulting from a failure of the water supply system (fires 

not put out). 

This in turn fed into a benefit cost analysis to estimate the benefit of seismic retrofit. 

Kunreuther, H., R. Meyer, and C. Van den Bulte. 2004. Risk Analysis for Extreme Events: 

Economic Incentives for Reducing Future Losses. NIST. 

They present a conceptual framework of how risk assessment, risk perception and risk 

management are linked with each other. Risk assessment evaluates the likelihood and 

consequences of prospective risks. Risk perception is concerned with the psychological 

and emotional aspects of risks. Risk management involves developing strategies for 

reducing the likelihood and/or consequences of extreme events. 

Regarding risk assessment, they describe how to generate an exceedance probability 

curve, which summarizes the risk and provides valuable input for different stakeholders 

to develop strategies for managing risk. 

Regarding risk perception they discuss how individual decisions on whether or not to 

adopt protective measures are influenced by psychological and emotional factors. They 

argue that individuals under-invest in mitigation and their explanations focus primarily 

on bounded rationality type explanations. 

Regarding risk management strategies, they focus on insurance and mitigation as two 

complementary strategies for reducing future losses and providing funds for recovery. 
75 

A major theme of the report is the need for public-private partnerships in managing 

disaster risk. 

M. Lindell, R. Perry, 2000. “Household Adjustment to Earthquake Hazard: A Review of 

Research” Environment and Behavior 32 (4): 461-501. 

They do a literature survey of 23 studies trying to understand what factors contribute to 

the adoption of earthquake mitigation measures by households. They find that 

households’ adoption of earthquake hazard adjustments is correlated with: 

• Perceptions of the hazard. That is, the greater the perceived hazard, the more likely it 

was that people would adopt mitigation measures. 

• Alternative adjustments. That is, the less costly a mitigation measure was, the more 

alternative uses it had, and the more effective it was perceived to be, the more likely it 

is to be adopted. 

• Social influences. Mitigation measures are more likely to be adopted if others around 

are adopting them. 

• Demographic characteristics. Demographic factors do not correlate well with 

mitigation. 

M. Lindell, C. Prater, 2000. “Household adoption of seismic hazard adjustments: A comparison 

of residents in two states” International Journal of Mass Emergencies and Disasters 18 (2): 317-

338. 

Residents of a high seismic hazard area were compared with those in a moderate seismic 

hazard area in terms of demographic characteristics, personal hazard experience, risk 

perception, hazard intrusiveness, and self-reported adoption of 16 hazard adjustments. 

The results show that the two locations differed substantially in hazard experience, 

somewhat less so in risk perceptions and hazard intrusiveness, and little in hazard 

adjustment. Multiple regression analyses supported a causal chain in which location and 

demographic characteristics cause hazard experience, hazard experience causes hazard 

intrusiveness, perceived risk causes hazard intrusiveness, and hazard intrusiveness causes 

the adoption of hazard adjustments. 

M. Lindell, D. Whitney, 2000. “Correlates of Household Seismic Hazard Adjustment Adoption” 

Risk Analysis 20 (1): 13-26. 

This study examined the relationships of self-reported adoption of 12 seismic hazard 

adjustments with respondents’ demographic characteristics, perceived risk, perceived 

hazard knowledge, perceived protection responsibility, and perceived attributes of the 

hazard adjustments. 
76 

Perceived attributes of the hazard adjustments differentiated among the adjustments and 

had stronger correlations with adoption than any of the other predictors. These results 

identify the adjustments and attributes that emergency managers should address to have 

the greatest impact on improving household adjustment to earthquake hazard. 

Liu, Haibin, Rachel Davidson, and T. Apanasovich. 2007. “Statistical Forecasting of Electric 

Power Restoration Times in Hurricanes and Ice Storms.” Power Systems, IEEE Transactions on

22:2270-2279. 

The paper develops an empirical method for estimating the number of outages and the 

amount of time required to restore power after ice storms and hurricanes. 

S. Miles, S. Chang, 2003. Urban disaster recovery: a framework and simulation model. 

MCEER. 

This paper concerns the modeling of urban recovery from earthquake disasters with the 

emphasis on simulating recovery processes rather than on estimating dollar losses. A 

conceptual framework of disaster recovery is proposed that is then implemented in a 

prototype simulation model. The model emphasizes the dynamic or temporal processes of 

recovery, simulates impacts at the individual agent level of analysis, and relates recovery 

across business, household, and lifeline infrastructure sectors. 

The prototype model is then applied to an actual event, the 1995 Kobe earthquake. The 

Kobe prototype successfully modeled some aspects of the Kobe recovery, but showed 

some serious deficiencies in other areas. 

D. Mileti, 1999. Disasters by Design: A Reassessment of Natural Hazards in the United States. 

Washington D.C.: Joseph Henry Press. 

He argues that U.S. Disaster losses are increasing at an unsustainable rate, and will soon 

become unsupportable. These losses, and the fact that there seems to be an inability to 

reduce such losses, are the consequences of narrow and short-sighted development 

patterns, cultural premises, and attitudes toward the natural environment, science, and 

technology. This is a problem that can be fixed by the application of sustainable 

community planning. 

His principles for sustainable hazard mitigation are: (1) maintain and if possible enhance 

environmental quality; (2) maintain and if possible enhance people’s quality of life; (3) 

foster local resiliency to and responsibility for disasters; (4) recognize that sustainable, 

vital local economies are essential; (5) identify and ensure inter- and intra-generational 

equity; and (6) adopt a consensus-building approach, starting with the local level. 

The main solutions he advocates are: 
77 

• Get out of the way: don’t build in the flood plain, or on the beach in hurricane 

country. 

• Protect the environment. 

• Good Building codes properly enforced. 

• Hazard prediction and warning (in particular, some sort of national warning system). 

D. Mileti, J. Sorensen, 1990. Communication of emergency public warnings: a social science 

perspective and state-of-the-art assessment. Oak Ridge National Laboratory. 

More than 200 studies of warning systems and warning response were reviewed for this 

social-science perspective and state-of-the-art assessment of communication of 

emergency public warnings. 

First, they discuss many current myths about public response to emergency warning that 

are at odds with knowledge derived from field investigations. Some of these myths 

include the “keep it simple” notion, the “cry wolf” syndrome, public panic and hysteria, 

and those concerning public willingness to respond to warnings. 

Variations in the nature and content of warnings have a large impact on whether or not 

the public heeds the warning. Relevant factors include the warning source; the warning 

channel; the consistency, credibility, accuracy, and understandability of the message; and 

the warning frequency. 

People want information from multiple sources, and respond to warnings by seeking 

more information. They don’t blindly do what they are told. However, they may be 

persuadable. As a result, they recommend providing plenty of detail about the hazard. 

This helps people understand the risks, and the reasons for the recommended responses. 

An important point they make is that the response to the warning needs to be monitored. 

If people react to the warning in ways that increase the hazard, adjustments to the 

warning need to be made. 

Characteristics of the population receiving the warning affect warning response. These 

include social characteristics such as gender, ethnicity and age, social setting 

characteristics such as stage of life or family context, psychological characteristics such 

as fatalism or risk perception, and knowledge characteristics such as experience or 

training. 

Finally, different methods of warning the public are not equally effective at providing an 

alert and notification in different physical and social settings. Most systems can provide a 

warning given three or more hours of available warning time. Special systems such as 

tone-alert radios are needed to provide rapid warning. 
78 

S. Miller, R. Muir-Wood, A. Boissonnade, 2008. “An exploration of trends in normalized 

weather-related catastrophe losses.” pp. 225-247. In Diaz, Henry, and Richard Murnane, eds. 

Climate extremes and society. Cambridge University Press. 

They attempt to evaluate potential trends in global natural catastrophe losses, and 

estimate the impact of climate change on those trends. In doing so, they compensate for 

changes in asset values and exposures over time. They create a Global Normalized 

Catastrophe Catalogue covering weather-related catastrophe losses in the principal 

developed (Australia, Canada, Europe, Japan, South Korea, United States) and 

developing (Caribbean, Central America, China, India, the Philippines) regions of the 

world. They survey losses from 1950 through 2005, although data availability means that 

for many regions the record is incomplete for the period before the 1970’s even for the 

largest events. 

After 1970, when the global record becomes more comprehensive, they find evidence of 

an annual upward trend for normalized losses of 2 % per year. Conclusions are heavily 

weighted by U.S. losses, and their removal eliminates any statistically significant trend. 

Large events, such as Hurricane Katrina and China flood losses in the 1990s, also exert a 

strong impact on trend results. In addition, once national losses are further normalized 

relative to per capita wealth, the significance of the post-1970 global trend disappears. 

They find insufficient evidence to claim a statistical relationship between global 

temperature increase and normalized catastrophe losses. 

A. Muermann, H. Kunreuther, 2008. “Self-protection and insurance with interdependencies” 

Journal of Risk and Uncertainty 36 (2): 103-123. 

They study optimal investment in self-protection of insured individuals when they face 

interdependencies in the form of potential externalities from others. If individuals cannot 

coordinate their actions, then the positive externality of investing in self-protection 

implies that, in equilibrium, individuals under-invest in self-protection. Limiting 

insurance coverage through deductibles or selling “at-fault” insurance can partially 

internalize this externality and thereby improve individual and social welfare. 

National Research Council, 1999. The Impacts of Natural Disasters: A Framework for Loss 

Estimation Washington DC: National Academies Press. 

The committee compiled a list of sources for disaster losses. They recommend that a 

national database of losses be compiled, and recommend Bureau of Economic Analysis 

as the lead agency. Data should be collected for all “major” disasters, which they define 

as anything with total damage over $25 Million or if so declared by the President under 

the Stafford Act. 
79 

They note that there will be demand for early estimates, but that final totals will not be 

known for many months (or even years). So they suggest that the estimates be subject to 

revisions as new data come in, much like routine economic data. 

They suggest that the database include (among other things): 

• Physical damage: 

• Event 

• Type of disaster (hurricane, flood, earthquake, etc.) 

• Location - ideally to county or ZIP-code level 

F. Norris, M. Friedman, P. Watson, C. Byrne, E. Diaz, K. Kaniasty, 2002. “60,000 Disaster 

Victims Speak: Part I. An Empirical Review of the Empirical Literature, 1981–2001” 

Psychiatry: Interpersonal & Biological Processes 65 (3): 207-239. 

Results for 160 samples of disaster victims were coded as to sample type, disaster type, 

disaster location, outcomes and risk factors observed, and overall severity of impairment. 

Regression analyses showed that samples were more likely to have high impairment if 

they were composed of youth rather than adults, were from developing rather than 

developed countries, or experienced mass violence (e.g., terrorism, shooting sprees) 

rather than natural or technological disasters. Most samples of rescue and recovery 

workers showed remarkable resilience. Within adult samples, more severe exposure, 

female gender, middle age, ethnic minority status, secondary stressors, prior psychiatric 

problems, and weak or deteriorating psychosocial resources most consistently increased 

the likelihood of adverse outcomes. 

It seems likely that a substantial portion of the differences between first and third world 

effects is a product of differential sampling. Sampling issues pervade the paper. This is 

not a random sample of disasters, even within the U.S.. 

F. Norris, S. Stevens, B. Pfefferbaum, K. Wyche, R. Pfefferbaum, 2008. “Community Resilience 

as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness” American 

Journal of Community Psychology 41 (1-2): 127-150. 

They draw upon literatures in several disciplines, to present a theory of resilience that 

encompasses contemporary understandings of stress, adaptation, wellness, and resource 

dynamics. Community resilience is a process linking a network of adaptive capacities 

(resources with dynamic attributes) to adaptation after a disturbance or adversity. 

They say “across these definitions, there is general consensus on two important points: 

first, resilience is better conceptualized as an ability or process than as an outcome...; and 
80 

second, resilience is better conceptualized as adaptability than as stability.... In fact, in 

some circumstances, stability (or failure to change) could point to lack of resilience.” 

Community resilience emerges from four primary sets of adaptive capacities—Economic 

Development, Social Capital, Information and Communication, and Community 

Competence—that together provide a strategy for disaster readiness. 

To build collective resilience, communities must reduce risk and resource inequities, 

engage local people in mitigation, create organizational linkages, boost and protect social 

supports, and plan for not having a plan, which requires flexibility, decision-making 

skills, and trusted sources of information that function in the face of unknowns. 

R. Pielke, M. Downton, 2000. “Precipitation and Damaging Floods: Trends in the United States, 

1932–97” Journal of Climate 13 (20): 3625-3637. 

They examine the time trend for flooding. As usual, damage from floods is increasing 

exponentially. After accounting for population growth it is still increasing exponentially, 

but after accounting for increase in National wealth, flood damage is decreasing. 

Flood damage is positively correlated with extreme precipitation. They also show that 

extreme precipitation is increasing over time. So climate change is showing up in the 

damage record. However damage per unit of wealth is decreasing with time. So we must 

be becoming less vulnerable to flood damage with time per unit of wealth. 

R. Pielke, C. Landsea, 1998. “Normalized Hurricane Damages in the United States: 1925–95” 

Weather and Forecasting 13 (3): 621-631. 

Previous research into long-term trends in hurricane-caused damage along the U.S. coast 

has suggested that damage has been quickly increasing within the last two decades, even 

after considering inflation. However, to best capture the year-to-year variability in 

tropical cyclone damage, consideration must also be given toward two additional factors: 

coastal population changes and changes in wealth. 

Both population and wealth have increased dramatically over the last several decades and 

act to enhance the recent hurricane damages preferentially over those occurring 

previously. More appropriate trends in the United States hurricane damages can be 

calculated when a normalization of the damages are done to take into account inflation 

and changes in coastal population and wealth. 

With this normalization, the trend of increasing damage amounts in recent decades 

disappears. Instead, substantial multi-decadal variations in normalized damages are 

observed: the 1970s and 1980s actually incurred less damages than in the preceding few 

decades. Only during the early 1990s does damage approach the high level of impact 

seen back in the 1940s through the 1960s, showing that what has been observed recently 

is not unprecedented. 
81 

Over the long term, the average annual impact of damages in the continental United 

States is about $4.8 billion (1995 $), substantially more than previous estimates. Of these 

damages, over 83 % are accounted for by the intense hurricanes (Saffir–Simpson 

categories 3, 4, and 5), yet these make up only 21 % of the U.S.-landfalling tropical 

cyclones. 

Poland, Chris, David Bonowitz, Joe Maffei, and Christopher Barkley. 2009. “The Resilient 

City.” Urbanist 4-21. 

This is a plan for disaster mitigation, response and recovery for the City of San Francisco. 

The report includes targets for service levels, recommendations for building code 

modifications and retrofit requirements, recommendations for upgrading lifeline systems, 

emergency response and recovery. 

E. Quarantelli, 1983. Delivery of emergency medical services in disasters: assumptions and 

realities. New York N.Y.: Irvington Publishers. 

Emergency response in disasters is poorly coordinated (if at all) with very little 

communication between components. Plans rarely work as intended. Partially, this is 

because much of the response is by people who are normally outside the EMS system. 

However, even within the normal EMS system there is little coordination between (say) 

police and fire departments. 

Fire and police are usually in control of the site and in charge of search and rescue. 

However, in search and rescue, much is done by bystanders, and there is very little real 

control or coordination. A lot of people and resources self-respond. That can overwhelm 

some aspects of response. 

There is little communications between the site and the hospital(s). Hospitals get most of 

their information from patients and ambulance drivers. Initial guesses as to casualties are 

generally wildly high. 

Very little triage at the site occurs. In general there is a tendency to transport victims as 

quickly as possible to the hospital. As a practical matter, that means the less injured get 

transported before the critically injured. Transportation broke down by approximately 

60 % ambulance, 24 % private vehicle (including foot), 16 % police car. In large 

disasters, a significant percentage of transporters are from outside the area. 

Sometimes there are more patients than ambulances. However, this will only apply to 

very large scale disasters. The usual problem is that there are more ambulances than 

patients. 

The number of patients can strain emergency rooms. This is especially true since patients 

are not distributed evenly to hospitals in the area. In mass disasters, hospitals just do not 

keep good records of patients, injuries, and treatments. Especially for those with minor 

injuries. 
82 

The few suggestions of inadequate treatment mostly come from overresponse. That is 

resources are pulled to the disaster away from other critical needs. So, ambulance 

response leaves other areas temporarily unserved (or underserved), and hospital 

personnel may be pulled away from critical care areas leaving other patients uncared for. 

E. Quarantelli, 1982. Sheltering and housing after major community disasters: case studies and 

general observations. Disaster Research Center: University of Delaware. 

He divides shelter into four categories: 

• Emergency Sheltering: Victims seeking shelter outside their own homes for short 

periods of time (hours). 

• Temporary Sheltering: Victims seeking shelter outside their own homes for the 

duration of the emergency (usually a few days). Household routines are not 

reestablished. Here, the issue of how the sheltered will be fed becomes an issue, 

where in the former case it usually does not. 

• Temporary Housing: Household routines are reestablished. 

• Permanent Housing 

A. Rose, S. Liao, 2005. “Modeling Regional Economic Resilience to Disasters: A Computable 

General Equilibrium Analysis of Water Service Disruptions” Journal of Regional Science 45 (1): 

75-112. 

Recent natural and manmade disasters have had significant regional economic impacts. 

These effects have been muted, however, by what they term ‘resilience,’ which is the 

inherent ability and adaptive response that enable firms and regions to avoid potential 

losses. In this category of ‘resilience’ they includes things like input and import 

substitution, use of inventories, conservation, the rationing feature of markets, and the 

ability to import critical inputs from other regions. 

Computable general equilibrium (CGE) analysis is a promising approach to disaster 

impact analysis because it is able to model the behavioral response to input shortages and 

changing market conditions. However, without further refinement, CGE models, as well 

as nearly all other economic models, reflect only “business‐as‐usual” conditions, when 

they are based on historical data. This paper advances the CGE analysis of major supply 

disruptions of critical inputs by: specifying operational definitions of individual business 

and regional macroeconomic resilience, linking production function parameters to 

various types of producer adaptations in emergencies, developing algorithms for 

recalibrating production functions to empirical or simulation data, and decomposing 

partial and general equilibrium responses. They illustrate some of these contributions in a 

case study of the sectoral and regional economic impacts of a disruption to the Portland 

Metropolitan Water System in the aftermath of a major earthquake. 
83 

A. Rose, D. Lim, 2002. “Business interruption losses from natural hazards: conceptual and 

methodological issues in the case of the Northridge earthquake” Global Environmental Change 

Part B: Environmental Hazards 4 (1): 1-14. 

The paper contains a valuable discussion on the problems involved in counting business 

interruption costs. In particular, since the value of a business asset is the discounted value 

of its income, counting both replacement value and business interruption costs involves 

some double-counting.

They estimate the business interruption losses during the Northridge Earthquake due to 

power outages in the LADWP service area. They adjust for ‘resiliency:’ that is the ability 

of firms to adjust so as to minimize losses. 

What they do not do is adjust for other interruptions. Just because power is restored does 

not mean a firm is operating. One way to correct for that would be to use power 

deliveries to measure business operation. 

A. Rose, G. Oladosu, S. Liao, 2007. “Business Interruption Impacts of a Terrorist Attack on the 

Electric Power System of Los Angeles: Customer Resilience to a Total Blackout” Risk Analysis

27 (3): 513-531. 

They estimate the largest category of economic losses from electricity outages—business 

interruption—in the context of a total blackout of electricity in Los Angeles. In particular, 

they do so while taking into account indirect effects and resilience. 

When a disaster occurs effects occur at three different degrees of separation: 

• First, you have those businesses, structures, and utilities that are directly damaged. 

• Second, there are businesses that cannot operate because a critical utility is off. Firms 

that cannot operate because the power (or water) is out, or because their workers 

cannot get to work due to road outages fall into this category. 

• Third, there are businesses that cannot operate because a key supplier or customer is 

not operating. So, a factory 200 miles away from the disaster that cannot operate 

because its parts supplier is down falls into this category. 

They consider only the third category to be ‘indirect costs.’ The third category is similar 

to the ‘multiplier effect’ from macroeconomics. 

The results indicate that indirect effects in the context of general equilibrium analysis are 

moderate in size. The stronger factor, and one that pushes in the opposite direction, is 

resilience. Their analysis indicates that electricity customers have the ability to mute the 

potential shock to their business operations by as much as 86 %. Moreover, market 

resilience lowers the losses, in part through the dampening of general equilibrium effects. 
84 

A. Rose, 2004. “Economic Principles, Issues, and Research Priorities in Hazard Loss 

Estimation.” pp. 13-36. In Okuyama, Yasuhide, and Stephanie Chang, eds. Modeling Spatial and 

Economic Impacts of Disasters. Springer. 

He reviews the relative strengths and weaknesses of Input-Output analysis and 

Computable General Equilibrium analysis for the estimation of business interruption 

losses in disasters. 

Input-Output analysis is inflexible and linear. So firms in an IO model do not find 

alternate sources of inputs when a supplier goes down. There are no increasing or 

decreasing returns to scale. 

Computable General Equilibrium analysis assumes equilibrium and instantaneous 

adjustment. So, firms find new suppliers instantly, prices adjust to the new supplydemand schedule instantly, etc. 

He recommends that “I-O models, with an adjustment for inventories, are probably better 

suited to recovery periods of less than one week, but CGE models are better suited to all 

other cases....” 

He talks briefly about econometric estimation of macroeconomic models. He says this 

has been only rarely used due to the data requirements of such a model. 

Rose, Adam et al. 2007. “Benefit-Cost Analysis of FEMA Hazard Mitigation Grants.” Natural 

Hazards Review 8:97-111. 

This paper reports on a study that applied Benefit-Cost Analysis methodologies to a 

statistical sample of the nearly 5,500 Federal Emergency Management Agency (FEMA) 

mitigation grants between 1993 and 2003 for earthquake, flood, and wind hazards. 

HAZUS-MH was employed to assess the benefits, with and without FEMA mitigation in 

regions across the country, for a variety of hazards with different probabilities and 

severities. The results indicate that the overall benefit-cost ratio for FEMA mitigation 

grants is about 4:1, though the ratio varies from 1.5 for earthquake mitigation to 5.1 for 

flood mitigation. Sensitivity analysis was conducted and shows these estimates to be 

quite robust. 

C. Rubin, 1985. Community Recovery from a Major Natural Disaster. Institute of Behavioral 

Science, University of Colorado. 

She conducts a series of case studies of disasters to see how they handled recovery. None 

of her disasters were particularly large. In terms of response, it seems unlikely that any of 

them seriously strained the resources available to the local communities. 

She suggests three factors that matter to recovery: Leadership, Ability to Act, and 

Knowledge. She also suggests a number of propositions leading to good recovery. 
85 

One implicit lesson on these comments is either have someone with a clear vision who 

can carry through their vision quickly, or have a preexisting community consensus as to 

what the recovered community should look like. 

D. Stephenson, 2008. “Definition, diagnosis, and origin of extreme weather and climate events.” 

pp. 11-23. In Diaz, Henry, and Richard Murnane, eds. Climate extremes and society. Cambridge 

University Press. 

Extreme weather and climate events are a major source of risk for all human societies. 

Various societal changes, such as increased populations in coastal and urban areas and 

increasingly complex infrastructure, have made us potentially more vulnerable to such 

events than we were in the past. In addition, the properties of extreme weather and 

climate events are likely to change in the twenty-first century owing to anthropogenic 

climate change. 

The definition, classification, and diagnosis of extreme events are far from simple. There 

is no universal unique definition of what is an extreme event. He notes that an ‘extreme’ 

weather event (defined as one that occurs rarely) is not the same as a ‘high-impact event.’ 

He develops a working definitions of an extreme weather event and evaluates their rate of 

occurrence through time. He finds that ‘rare’ flood events have been increasing in 

frequency in the UK. 

H. Tatano, K. Yamaguchi, N. Okada, 2004. “Risk Perception, Location Choice and Land-use 

Patterns under Disaster Risk: Long-term Consequences of Information Provision in a Spatial 

Economy.” pp. 163-177. In Okuyama, Yasuhide, and Stephanie Chang, eds. Modeling Spatial 

and Economic Impacts of Disasters. Springer. 

They develop a model of choice where people hear what experts have to say about a risk, 

and act in ways different from what the experts recommend. In their information model, 

people have (uninformative) beliefs about a hazard, and are given expert information 

about location hazard that they consider noisy (but which is assumed in this paper to be 

perfectly reliable). So how far their belief is from the ‘true’ value of the hazard depends 

on how reliable they consider the expert information relative to their initial beliefs. 

They then derives housing prices as a function of commuting distance, exposure to 

hazard, and beliefs about the reliability of the expert information. 

The Infrastructure Security Partnership, 2006. Regional disaster resilience: a guide for 

developing an action plan. Reston Va.: American Society of Civil Engineers. 

They define disaster resilience as the capability to prevent or protect against significant 

multihazard threats and incidents, including terrorist attacks, and to expeditiously recover 

and reconstitute critical services with minimum damage to public safety and health, the 

economy, and national security. 
86 

They say that “comprehensive regional preparedness is key to ensuring that communities, 

states, and the nation can expeditiously respond to and recover from disasters of all types, 

particularly extreme events,” and develop a list of resources and planning guidelines for 

preparing for a disaster. 

Thomas, Douglas, and Robert Chapman. 2008. A Guide to Printed and Electronic Resources for 

Developing a Cost-Effective Risk Mitigation Plan for New and Existing Constructed Facilities. 

NIST SP 1082. Gaithersburg, MD: NIST. 

This paper is provides a list of resources useful for implementing the ASTM 2506 

protocol for evaluating the cost effectiveness of proposed disaster mitigation measures. 

This document provides an annotated bibliography of printed and electronic resources 

that serves as that central source of data and tools to help the owners, managers, and 

designers of constructed facilities develop a cost-effective risk mitigation plan. 

K. Tierney, M. Lindell, R. Perry, 2001. Facing the unexpected: disaster preparedness and 

response in the United States. Washington D.C.: Joseph Henry Press. 

This is a summary of the existing literature. Among other findings, they observe that 

there is little or no empirical research on adoption of mitigation measures by 

governments. ‘Preparedness’ is also poorly correlated (or perhaps even uncorrelated) 

with response effectiveness. In any particular disaster it is difficult to determine whether 

results are attributable to planning and preparation, luck, or good improvisation. 

K. Tierney, 2006. “Social Inequality, Hazards, and Disasters.” pp. 109-128. In Daniels, Ronald, 

Donald Kettl, and Howard Kunreuther, eds. On risk and disaster: lessons from Hurricane 

Katrina. Philadelphia: University of Pennsylvania Press. 

Poverty, lack of education, disability, race, etc. increase vulnerability. Poverty primarily 

works through budget constraints, and can constrain (among other things) the ability to 

evacuate. It can also affect the cost of evacuation, in the sense that poor, hourly workers 

are more likely to lose income, and larger percentages of their income, if they evacuate. 

In the U.S., the assumption is that people have transportation to manage their own 

evacuation. In New Orleans, that was not the case, and she implies that that often holds 

for the poor. 

In general, she suggests that disaster preparedness and assistance programs are not all that 

well tailored for the poor and disadvantaged. This includes, among other things, that 

richer victims are also better able to search for and find assistance. 

One of her themes is that several of these characteristics impact behavior and needs more 

or less independent of vulnerability. So the disabled are specifically impaired from 

evacuating when necessary. Some ethnic groups preferentially have multi-family living 

arrangements or extended family living arrangements, and that impacts the kind of aid 

they need. 
87 

K. Tierney, 1997. “Impacts of Recent Disasters on Businesses: The 1993 Midwest Floods and 

the 1994 Northridge Earthquake.” pp. 189-222. In Jones, Barclay, ed. Economic consequences of 

earthquakes: Preparing for the unexpected. NCEER. 

This paper presents findings from two Disaster Research Center surveys on disasterrelated business impacts. The first study, conducted in 1994, focuses on the ways in 

which the 1993 Midwest floods affected the operations of businesses in Des Moines / 

Polk County, Iowa. The second project uses a similar methodological approach to assess 

the impacts of the 1994 Northridge earthquake on businesses in Los Angeles and Santa 

Monica, CA. Both studies utilize large representative samples that include both large and 

small firms and a range of business types. 

Previous studies found that marginal businesses were hardest hit, as well as those that 

leased (rather than owned) their location, lost expensive inventory, or were heavily 

dependent of foot traffic and had to relocate. Smaller businesses and those in services 

were more vulnerable. Indirect losses resulted from damage to transportation system, 

problems with employee or customer access, and shipping delays. 

She found that most businesses self-insured. Most businesses were about as well off as 

before the disaster. Of the rest, they were about equally split between those better off and 

those worse off. Indirect effects were a big part of losses, especially in Des Moines where 

most businesses were not directly affected by flooding. ‘Lifeline’ interruptions were a 

major cause of losses. 

K. Tierney, 1997. “Business impacts of the Northridge earthquake” Journal of Contingencies 

and Crisis Management 5 (2): 87-97. 

This paper focuses on the immediate and longer-term impacts the earthquake had on 

businesses in the Greater Los Angeles region after the Northridge earthquake. The data 

reported here are based on a survey that the Disaster Research Center, at the University of 

Delaware, conducted with a representative, randomly-selected sample of businesses in 

the cities of Los Angeles and Santa Monica, two jurisdictions that were particularly hardhit by the earthquake. 

The paper addresses the following research questions: (1) What direct impacts and losses 

did businesses experience in the earthquake? (2) In what ways did the earthquake affect 

the operations of the businesses studied? If they experienced business interruption, why 

were they forced to close? What other kinds of problems did business have to cope with 

following the earthquake? (3) What earthquake preparedness measures had businesses 

undertaken prior to the disaster, and what have they done subsequently to prepare? and 

(4) To what extent have business operations returned to pre-earthquake levels, and which 

businesses appear to be experiencing the most difficulty with recovery? 

She found that about 2/3 of businesses reporting damage reported non-structural damage. 

The median loss was about $5,000, but the mean loss was about $156,000. As usual, the 
88 

damage is highly skewed. About 13 % of businesses reported that their building had been 

declared ‘unsafe’. Average closure time was approximately 2 days. Reasons include 

‘lifeline’ interruptions and cleanup. 

U.S. Government Accountability Office. 2007. NATURAL DISASTERS: Public Policy Options 

for Changing the Federal Role in Natural Catastrophe Insurance. Washington DC: U.S. GAO. 

The purpose of the document is to explore ways of restructuring the ‘Catastrophe’ 

Insurance market for the following objectives: 

• Charging premium rates that fully reflect actual risks, 

• Encouraging private markets to provide natural catastrophe insurance, 

• Encouraging broad participation in natural catastrophe insurance programs, and 

• Limiting costs to taxpayers before and after a disaster. 

The short version is that these are probably incompatible, especially after taking into 

account the Public Choice aspects of the market. 

J. Vigdor, 2007. “The Katrina Effect: Was There a Bright Side to the Evacuation of Greater New 

Orleans?” The B.E. Journal of Economic Analysis & Policy: Advances 7 (1): Article 64. 

He uses longitudinal data from Current Population Surveys conducted between 2004 and 

2006 to estimate the net impact of Hurricane Katrina-related evacuation on various 

indicators of well-being. While evacuees who have returned to the affected region show 

evidence of returning to normalcy in terms of labor supply and earnings, those who 

persisted in other locations exhibit large and persistent gaps, even relative to the poor 

outcomes of individuals destined to become evacuees observed prior to Katrina. 

Overall, there is little evidence to support the notion that poor underemployed residents 

of the New Orleans area were disadvantaged by their location in a relatively depressed 

region. Evacuees started out worse than people who never left. People who never 

returned started out worse than people who left and came back. People who never 

returned did worse (on average, of course) than they did before they left. 

J. Vigdor, 2008. “The economic aftermath of Hurricane Katrina” The Journal of Economic 

Perspectives 22 (4): 135–154. 

He notes that it is the norm for cities to recovery. The only Pre-New Orleans, modern 

example he can find where a city did not was Dresden. That includes a number of 

bombed-out cities in WWII, including Hiroshima, Nagasaki and Warsaw. 

He argues (paralleling Haas) that the only cities that do not recover from disaster are 

those already in decline. So, as with Haas, the disaster serves to accelerate a process 
89 

already ongoing. Since (he argues) New Orleans has been in a long-term economic 

decline, he does not expect it to fully bounce back. 

Viscusi, W. K. 2009. “Valuing risks of death from terrorism and natural disasters.” Journal of 

Risk and Uncertainty 38:191-213. 

He conducts a survey where he essentially asks people whether they value disaster deaths 

more or less than traffic deaths. The basic question he asks of survey participants can be 

paraphrased as “Would you rather spend your money to prevent x traffic deaths or y 

natural disaster deaths.” 

He found that natural disaster deaths are worth only half of what traffic deaths are worth. 

Terrorism deaths are worth about the same as traffic deaths. 

Risk exposure is (very) weakly related to preferences on disasters, via income. It is 

strongly related in regard to terrorism. Risk aversion is weakly related to several of these 

via Seat-Belt Usage. 

Waugh, W., and K. Tierney, eds. 2007. Emergency management: principles and practice for 

local government. 2nd ed. Washington D.C.: ICMA Press. 

This is a textbook on emergency management and response to disasters. Its intended 

audience is local government officials who are tasked with developing and implementing 

emergency response plans and managing the local response to disasters. 

G. Webb, K. Tierney, J. Dahlhamer, 2000. “Businesses and Disasters: Empirical Patterns and 

Unanswered Questions” Natural Hazards Review 1 (2): 83-90. 

Through five systematic, large-scale mail surveys conducted since 1993, the Disaster 

Research Center obtained data on hazard awareness, preparedness, disaster impacts, and 

short- and long-term recovery among 5,000 private-sector firms in communities across 

the United States (Memphis/Shelby County, Tenn.; Des Moines, Iowa; Los Angeles, 

Calif.; Santa Cruz County, Calif.; and South Dade County, Fla.). 

This paper summarizes findings from those studies in three major areas: (1) factors 

influencing business disaster preparedness; (2) disaster-related sources of business 

disruption and financial loss; and (3) factors that affect the ability of businesses to 

recover following major disaster events. 

Business ‘Preparedness’ is operationally defined as fulfilling a checklist prepared by the 

researchers. By that definition, Business Preparedness is pretty thin. Bigger businesses 

are more likely to adopt mitigation measures. Measures that enhance life-safety are more 

likely to be adopted than measures that enhance business continuity and survival. As 

usual, cost and benefit matter to the choices in implementation. So, the cheaper a measure 

is, the more likely to be adopted; and the more alternative uses it has the more likely to be 

adopted. 
90 

Business interruption is most often (in the disasters reviewed) a product of ‘lifeline’ 

damage—electricity, water, communication, and transportation interruptions. 

Bigger businesses do better in recovery. Preparedness has no relation to recovery. 

Construction firms are better off. Businesses in declining industries are less likely to 

recover than businesses in growing industries. Aid does not seem to have any impact on 

recovery. 

They suggest that the reason why preparedness has so little to do with recovery is that 

(among other reasons) the preparedness measures recommended and implemented are 

aimed at enhancing response, not recovery. 

D. Wenger, E. Quarantelli, R. Dynes, 1989. Disaster analysis: Police and fire departments. 

Disaster Research Center: University of Delaware. 

Both police and fire departments tended to avoid tasks different than those related to their 

traditional roles. So, fire departments engaged in search and rescue, put out the fires, and 

then left. Police departments engaged in limited search and rescue, and did traffic, site, 

and crowd control. 

Traffic and Crowd Control were difficult and not very successful in any of the cases. 

Congestion at the site was a problem in most of the cases they looked at. In some cases, 

needed emergency personnel and equipment could not be gotten to the site due to 

congestion. 

Interagency coordination is always (and not just in these case studies) an issue. In effect, 

though, the lack of formal coordination between police and fire was rarely a problem. 

There was usually an implicit informal coordination taking place, where they ceded to 

each other their respective expertises. 

The authors were generally not impressed with the Incident Command System where it 

appeared in these cases. It in effect ended up being a Fire Management System, and did a 

poor job of coordinating multiple agencies and the numerous volunteers who were 

present for several of the disasters. It did not even do a very good job of coordinating 

different fire organizations. The tendency to bump the Incident Commander task up the 

chain as soon as a new officer came on the scene was something they considered a 

potential problem. 

D. Wenger, E. Quarantelli, R. Dynes, 1986. Disaster Analysis: Emergency Management Offices 

And Arrangements. Disaster Research Center: University of Delaware. 

Numerous previous cases studies had found that planning and preparedness were 

deficient and that communications were a major issue in disaster response. Adequate 

resources generally were available; the problem was one of distributing them. They found 

in these case studies that planning and preparedness had improved. There was some 

evidence that it had brought some improvement to disaster response. However, planning 

did not guarantee effective response. 
91 

The key to effective Local Emergency Management Agencies really was extensive 

previous experience. The areas of most difficulty in their case studies were 

communication, task assignment and coordination, and authority relationships. 

Essentially, you had independent units working, not talking to each other, and no one 

(that everyone agreed upon) in charge. Having a Local Emergency Management Agency 

did not eliminate this problem. However, planning does appear to ease these problems. 

C. West, D. Lenze, 1994. “Modeling the Regional Impact of Natural Disaster and Recovery: A 

General Framework and an Application to Hurricane Andrew” International Regional Science 

Review 17 (2): 121-150. 

There are a number of approaches to modeling and estimating the impacts of natural 

disasters. However, many of those approaches require a great deal of specific detailed 

information to answer important questions regarding the impacts. This is a careful 

evaluation of the specific impacts of Hurricane Andrew on south Florida, intended for use 

in impact modeling and estimation. 

They use a wide variety of data sources, and pick the impacts apart piece by piece. They 

estimate physical damage by aggregating information from a variety of sources (all of 

which are incomplete and of inconsistent quality), labor market adjustments, and 

attempted to estimate demand and population impacts. So, for example, they estimate the 

number of people to leave the area based on specific known job losses (like the closure of 

Homestead AFB) and other factors. 

They also discuss challenges facing most regional models in estimating disaster impacts. 

In particular, most regional models do not explicitly account for capital stock (which is 

where the major direct impact of a disaster falls), and do not have a fully specified 

housing market. Level of aggregation for modeled sectors can be an issue, especially 

when the demand induced by the disaster within a sector has different characteristics 

from average demand absent a disaster. 

J. Wright, P. Rossi, S. Wright, E. Weber-Burdin, 1979. After the clean-up: Long-range effects of 

natural disasters. Beverly Hills, Calif: Sage. 

They are interested in the long-range effects of disasters on communities. They analyze 

all disasters to hit the United States in the 1960’s, by county and census tract. Based on 

their database, they could find no evidence of long-term impact from a disaster on either 

the county or census tract it was in. 

Note that the overwhelming majority of the disasters are small: the median tornado, for 

example, killed no one and caused less than $500,000 in damage. So, the impact of really 

large events may be obscured by a forest of small events. 

 
92 
93 

Appendix B Selected Databases, Software Tools, and Web Portals 

The data sources listed below fall into three broad categories. There are several data sources that 

provide data on disaster damages. These include CRED, flooddamagedata.org, SHELDUS, 

Swiss Re, and the Property Claims Service. Second, there are data sources on hazards, including 

NOAA, NIFC and the U.S.GS. The remaining data sources provide macroeconomic, 

demographic or geographic data that are useful for analyzing disaster loss data. 

The disaster loss data sources are not independent. They all draw from the same raw sources for 

their estimates of damages. However, they have different conventions for including the data in 

the database, and different scope. In addition, scope, accuracy, and methodologies have changed 

over time, so results on time trends may not be reliable. 

Software includes several “Catastrophe” models (including HAZUS-MH, RMS Inc., and 

EQECAT), and a number of software packages designed for the development of regional 

macroeconomic models. 

Data Sources 

American Chamber of Commerce Research Association, 

ACCRA Cost Of Living Index 

http://www.coli.org/. 

They maintain regional Cost of Living Indices. 

BEA, U.S. Department of Commerce 

The Bureau of Economic Analysis collects and publishes data for the U.S. Economy. In 

particular, the following data source is of interest for this research effort. 

Fixed Asset Tables 

http://www.bea.gov/national/FA2004/index.asp. 

The Fixed Asset tables record the constructed building stock, and other fixed and durable assets, 

by year. The data goes back to 1925. Data are annual, and are broken down by asset category, 

industry, and legal form of organization. 

Center for Research on the Epidemiology of Disasters, 

CRED Website 

http://www.cred.be/. 

They are a division of the World Health Organization, and collect data on disaster losses (and 

deaths) world-wide. A preliminary review of the data suggests that it is not consistent with 

similar data collected for the U.S. from other sources. “The database is compiled from various 

sources, including UN agencies, non-governmental organizations, insurance companies, research 
94 

institutes and press agencies. Priority is given to data from UN agencies, governments and the 

International Federation of Red Cross and Red Crescent Societies.” 

T. Chandler, 1987. Four thousand years of urban growth: an historical census. Lewiston N.Y. 

U.S.A.: St. David’s University Press. 

A compilation of population estimates for the world’s largest cities over a period of 

approximately 4,000 years. The data are fairly low-resolution. So for the earlier periods, there 

may be population estimates only every century at best. The data get better as we move closer to 

the present. 

Flood Damage in the United States 

http://www.flooddamagedata.org/. 

Flood damage data have been collected and published by the National Weather Bureau since 

1933, with a hiatus between 1980 and 1982. Downton et al. (2005) compiled and published that 

data, and reanalyzed it for scope accuracy and consistency. The data set has problems, some of 

which are discussed below, but they argue that it still represents a good, long-term data set on 

flood damage. 

Some of the characteristics of the data they observe are: 

• The original data for the most part were estimated to the level of order of magnitude (e.g., 

between $500,000 and $5 Million), were collected by personnel untrained in estimating 

damages, were not checked for accuracy, and were incomplete. 

• In general, however, the larger the event, the more accurate (in log terms) the estimate. 

• The data are bottom censored. That means the totals are probably biased down. 

• However, most of the damage occurs in a few very large events. So, for example, in 

Pennsylvania (which has the highest total damage in the database), something like 75 % 

of the total damage (over 75 years) occurred in a single flood event. 

• With those caveats, they consider the data to be unbiased. 

Hazards and Vulnerability Research Institute 

Department of Geography 

University of South Carolina 

Spatial Hazard Events and Losses Database for the United States (SHELDUS)

http://webra.cas.sc.edu/hvri/products/sheldus.aspx. 

This is an effort at a comprehensive national loss inventory of natural hazards. The database is 

described in detail in Cutter et al. (2008).The data starts in (approximately) 1960 and continues 

to the present. The data are drawn from a variety of mostly governmental sources. As such the 
95 

data are not independent of the flood data at www.flooddamagedata.org. Several observations 

about the data are worth mentioning: 

• Their loss estimates are conservative. That is, when a range of values are reported in their 

original sources they enter the lowest available estimate of damages in their database. 

That biases the damage data down. It also biases trends in the data. Since older data are 

less accurate and less precise, the downward bias becomes greater for the older data. So 

any attempt to find a trend will find one based solely on the biases in the dataset. 

• Some types of disasters are underreported. They note in particular that drought is under 

reported. 

• Of course, the data are bottom censored. The bottom censoring gets worse the older the 

data. 

• There is no indexing by event. That can to some extent be remedied ex post, but only to 

some extent. 

• Puerto Rico, Guam, and other U.S. territories are not included. 

• Damage is distributed by county, often by simply dividing the estimated total damage by 

number of counties. So the spatial heterogeneity of damage is not reflected in the 

database. 

ISO 

Property Claims Services

http://www.iso.com/Products/Property-Claim-Services/Property-Claim-Services-PCS-info-onlosses-from-catastrophes.html. 

PCS investigates reported disasters and determines the extent and type of damage, dates of 

occurrence, and geographic areas affected. To gather the information needed for its services, 

PCS has developed a national network of insurer claim departments, insurance adjusters, 

emergency managers, insurance agents, meteorologists, and fire and police officials. PCS also 

maintains contact with industry representatives in many countries. 

Minnesota IMPLAN Group, Inc.

http://implan.com/v3/. 

They are publishers of software, regional Social Accounting Matrices, and consulting services. 

The regional Social Accounting Matrices in particular are frequently used in the estimation of 

disaster impacts. An IMPLAN Social Accounting Matrix is at the heart of the FEMA hazard loss 

model, HAZUS-MH . 

National Interagency Fire Center, 

Fire Information - Wildland Fire Statistics 

http://www.nifc.gov/fire_info/fire_stats.htm. 
96 

Statistics on the number and size of wildland fires in the U.S., including some estimates of 

losses. 

National Oceanic and Atmospheric Administration 

Atlantic basin hurricane database (HURDAT)

http://www.aoml.noaa.gov/hrd/hurdat/

This is a list of all known hurricanes in the Atlantic since 1851, with estimates of their 

magnitude. The data are described in Landsea et al. (2003). Naturally the earlier data are 

incomplete Storms that never made landfall before 1910 are likely not in the database. They 

estimate that 0-6 storms were missed per year before 1875 and 0-4 before 1910. 

Some areas were sparsely populated, and so storms may have been missed even if they made 

landfall. For example, they figure that records for south Florida are unreliable before 1900, and 

for south Texas before 1880. 

Oak Ridge National Laboratory, 

LandScan Global Population Database 

http://www.ornl.gov/sci/landscan/. 

LandScan provides estimates of the population distribution worldwide down to the 1 km2

 scale. 

Risk Management Solutions, Inc., RMS Software 

http://www.rms.com/. 

They have hazard and risk data for primarily the U.S., including flood zones, distance to coast, 

hurricane risk scores and profiles, distance to faults, earthquake risk scores, predicted ground 

shaking intensities for index quakes, soil types, liquefaction susceptibility, landslide 

susceptibility, Alquist-Priolo zones, convective storm risk scores, slope and elevation files. 

They also have current insurance exposure data, and estimated insured loss data for predicted 

hazards. 

Swiss Re, Inc. 

Swiss Re is a reinsurance company that also collects and publishes data on insured losses from 

natural disasters. 

U.S. Department of Housing and Urban Development, 

Fair Market Rents 

http://www.huduser.org/portal/datasets/fmr.html. 

U.S. Geological Survey 
97 

The U.S.GS collects a variety of data regarding natural hazards and losses that would be useful 

in any evaluation of disasters and resilience. An incomplete list includes: 

PAGER - Prompt Assessment of Global Earthquakes for Response

http://earthquake.usgs.gov/earthquakes/pager/. 

Quick estimates of shaking intensities from earthquakes, including estimates of population 

impacted by the various intensity levels. 

Gap Analysis Program

http://gapanalysis.nbii.gov/portal/server.pt?open=512&objID=1482&mode=2&in_hi_userid=2&

cached=true. 

Ecological data, consisting mostly of land use/land-cover maps and ecological zones. 

Software 

EQECAT Inc. Catastrophe Models 

Described in Eguchi, et al. (1997), they sell software for the estimation of losses due to natural 

disasters. 

FEMA, 

HAZUS-MH 

http://www.fema.gov/plan/prevent/hazus/index.shtm. 

Hazards U.S. (HAZUS) is a software tool designed to provide individuals, businesses, and 

communities with information and tools to mitigate natural hazards. HAZUS is a natural hazard 

loss estimation software program developed by the National Institute of Building Sciences 

(NIBS) with funding from FEMA. It allows users to compute estimates of damage and losses 

from natural hazards using geographical information systems (GIS) technology. Originally 

designed to address earthquake hazards, HAZUS has been expanded into HAZUS Multi-Hazard 

(HAZUS-MH), a multi-hazard methodology with new modules for estimating potential losses 

from wind (including hurricane) and flood hazards. NIBS maintains committees of wind, flood, 

earthquake, and software experts to provide technical oversight and guidance to HAZUS-MH 

development. HAZUS-MH uses ArcGIS software to map and display hazard data and the results 

of damage and economic loss estimates for buildings and infrastructure. The ArcGIS software, 

developed by ESRI, is required to run HAZUS and is sold separately. Three data input tools have 

been developed to support data collection: the Inventory Collection Tool, Building Inventory 

Tool, and Flood Information Tool. The Inventory Collection Tool helps users collect and manage 

local building data for more refined analyses than are possible with the national level data sets 

that come with HAZUS. The Building Inventory Tool allows users to import building data from 
98 

large datasets, such as tax assessor records. The Flood Information Tool helps users manipulate 

flood data into the format required by the HAZUS flood model. FEMA has also developed a 

companion software tool called the HAZUS-MH Risk Assessment Tool to produce risk 

assessment outputs for earthquakes, floods, and hurricanes. The Risk Assessment Tool pulls 

natural hazard data, inventory data, and loss estimate data into pre-formatted summary tables and 

text. HAZUS-MH can provide a probability of minor damage, moderate damage, and the 

destruction of constructed facilities. This information is viewable by occupancy type, type of 

building, essential facilities, and by user defined facilities. 

GAMS 

http://www.gams.com/. 

A software package for creating and solving Computable General Equilibrium (CGE) models. 

GEMPACK 

http://www.monash.edu.au/policy/gempack.htm. 

Another software package for solving CGE models. 

IMPLAN

http://implan.com/v3/. 

They are publishers of software, regional Social Accounting Matrices, and consulting services. 

Their products and services are geared toward regional economic modeling, and the estimation 

of the economic impacts of government policies and other shocks. 

NIST 

Cost-Effectiveness Tool for Capital Asset Protection, Version 4 

http://www.nist.gov/bfrl/economics/CETSoftware.cfm

This paper describes the CET 4.0 software tool. It is intended to implement the ASTM E 2506 

protocol for evaluating the cost effectiveness of proposed disaster mitigation measures. It takes 

as inputs costs of the various proposed alternatives, including O&M costs; costs associated with 

relevant disaster events, and the probabilities associated with the events. It is capable of handling 

uncertainty in costs of likelihood of adverse events using Monte Carlo methods. 

Risk Management Solutions, Inc., 

http://www.rms.com/. 

They provide data, software, and consulting services primarily to the insurance industry. Their 

software is designed to provide real-time estimates of losses due to natural disasters, both 

predicted and actual. 
99 

Appendix C Mathematical Appendix 

C.1 What is the Optimal Recovery Path from Disaster20

Assume a single representative consumer who maximizes utility as: 

ܷ ൌ max஼ ׬ ݁ିఘ௧ ஼భష഑ିଵ

ଵିఙ

∞ ݐ݀

଴ (1) 

Subject to the budget constraint: 

ܥ൅ܭሶ ൑ ሺ݁௫௧ܮሻଵିఈܭఈ െ ܭߜ) 2 (

Where: 

C is a function representing consumption at any given time. 

K is a function representing amount of capital at any given time. 

L Labor, which is normalized to 1 for this problem. 

α Cobb-Douglass production parameter; related to capital intensity. 

δ depreciation rate of capital. 

ρ discount rate. 

σ preferences parameter; related to the intertemporal elasticity of substitution. 

x exogenous economic growth rate. 

The Hamiltonian for the problem is: 

ܪൌ݁ିఘ௧ ஼భష഑ିଵ

ଵିఙ ൅ ߤሺሺ݁௫௧ܮሻଵିఈܭఈ െ ܭߜ െ ܥሻ (3) 

The solution requires the following conditions be met: 

߲

߲ܥ ܪൌ݁ିఘ௧ܥିఙ െߤൌ0

and 

߲

߲ܭ ܪൌߤሺߙሺ݁௫௧ܮሻଵିఈܭఈିଵ െ ߜሻ ൌ െߤሶ

                                                           

20 This discussion is based on Barro and Sala-i-Martin (2003). 
100 

The following change of variables effectively normalizes for economic growth: 

ܥൌ݁௫௧ܿ and ܭൌ݁௫௧݇

Which imply: 

ܥሶ ൌ ݔ݁௫௧ܿ൅݁௫௧ܿሶ and ܭሶ ൌ ݔ݁௫௧݇൅݁௫௧݇ሶ

So, after the change of variables, our solution must meet the following three equations: 

݇ሶ ൌ ݇ఈ െ ሺߜ൅ݔሻ݇െܿ (4) 

ߤൌ݁ିሺఘାఙ௫ሻ௧ܿିఙ (5) 

െ ఓሶ

ఓ

ൌ ߙ݇ఈିଵ െ ߜ) 6 (

Taking the derivative of the log of equation 5 gives: 

݀

ൌ ߤ logݐ݀

݀

ሻܿ log ߪ െ ݐሻݔߪ ൅ ߩሺെሺ ݐ݀

or 

ሶߤ

ߪ െ ሻݔߪ ൅ ߩെሺ ൌ ߤ

ܿሶ

ܿ

Substituting into equation 6 gives: 

ሶ ௖ߪ

௖ ൌ ߙ݇ఈିଵ െ ሺߜ ൅ ߩ ൅ ݔߪሻ (7) 

On the balanced-growth path both ݇ሶ and ܿሶ are zero. Solving for c and k then gives: 

ߙ ൬ ൌ כ݇

൰ݔߪ ൅ ߩ ൅ ߜ

ଵ

ଵିఈ

ߙ ൬ ൌ כܿ

൰ݔߪ ൅ ߩ ൅ ߜ

ఈ

ଵିఈ െ ሺߜ൅ݔሻ ൬ ߙ

൰ݔߪ ൅ ߩ ൅ ߜ

ଵ

ଵିఈ

The optimal recovery path was solved using numerical methods to jointly solve equations 4 and 

7, and using the standard21 parameter values of α = 0.75, δ = 0.05, ρ = 0.02, σ = 3, and x = 0.02. 

                                                           

21 According to Barro and Sala-i-Martin (2003). 
101 

C.2 A Rationalization of the Multiplicative Resilience Index 

Note that throughout this appendix we will suppress expression of t. 

Let I be the set of individuals in a community. 

Let J be the set of services provided. 

Let J* be the set of all subsets of J. 

௜ݍ Let

௝ א ሾ0,1ሿ be a quality of service variable such that ݍ௜௝

 is the normalized quality of service 

individual i receives for service j. 

The expression | · | returns the number of elements in the set. 

Then the Quality of Service indicator for Service j is 

௜ݍ ∑ ൌ௝ ܳ

௝

ூ

|ܫ|

And the MCEER combined quality of service is 

ܳெ ൌ ∏ ܳ௝ ௃

כ௃ ൧ כ௃א௝ ௝ܳ ∏ ଵି|כ௃|ሻ1ሺെൣ∑ 

As discussed in the text, this is a social welfare function, and there is no particular reason to 

think it is the only one. 

To simplify our notation, we define the set function: 

௜ݍหܫ א ݅൛ ൌ ሻݔ ݆,ଵሺିݍ

ൟݔ ൒ ௝

That is, ݍିଵሺ݆, ݔ, ݐሻ is the set of all the people who have a quality of service of at least x for 

service j. 

And, for some S ؿ J, and vector x א Թ|S|

௜ݍหܫ א ݅൛ ሩ ൌ ሻݔ ܵ,ଵሺିݍ

ൟ௝ݔ ൒ ௝

஼א௝

In this notation, it can be readily shown that: 

௜ݍ ∑ ൌ௝ ܳ

௝

ூ

1 ൌ| ܫ|

|ܫ|

ߙ݀|ሻߙ ݆,ଵሺିݍ| න

ଵ

଴

The main result is this theorem: 
102 

Theorem: 

Given the following assumptions: 

• Independence of service outages: 

௜ݍ൛ݎܲ

௜ݍหݔ ൒ ௝

௜ݍ൛ݎܲ ൌ௃\௝ൟ 

௝ ൒ ݔ ൟfor all j. 

• Leontief Utility: 

Each individual i, has the utility function ݑ௜൫ݍ௜

௃

൯ ൌ min௃൛ݍ௜

௝

ൟ. 

• Additive welfare function U: 

ܷ൫ݍூ

௃

൯ ൌ ∑ ௨೔ሺ௤೔ሻ ಺

|ூ| . 

Then the aggregate welfare function is: 

ܷ൫ݍூ

௃

൯ൌܷሺܳ௃ሻ ൌෑܳ௝

௃

Proof: 

ܷ൫ݍூ

௃

൯ ൌ ∑ ݑ௜൫ݍ௜

௃

ூ ൯

1 ൌ| ܫ|

|ܫ|

ߙ݀|ሻߙ ,ܬଵሺିݍ| න

ଵ

଴

By independence we can rewrite this as: 

ܷ൫ݍூ

௃

൯ ൌ 1

|ܫ|

න |ܫ|ෑ |ݍିଵሺ݆, ߙሻ|

௃ |ܫ|

ߙ݀

ଵ

଴

ൌන ෑ |ݍିଵሺ݆, ߙሻ|

௃ |ܫ|

ߙ݀

ଵ

଴

A second application of independence gives us: 

ܷ൫ݍூ

௃

൯ൌෑ න |ݍିଵሺ݆, ߙሻ|ߙ݀ |ܫ|

ଵ

௃ ଴

ൌෑܳ௝

௃

∎

Aside from the implicit normalization, this is equivalent to the MCEER formulation. 

The MCEER formulation is additionally normalized as: 

ܷெሺܳ௃ሻ ൌ ∏ ܳ௝ ௃

כ௃ ൧ כ௃א௝ ௝ܳ ∏ ଵି|כ௃|ሻ1ሺെൣ∑ 
103 

If all services are either on or off then the ݍ௜

௝ א ሼ0,1ሽ. Maintaining the assumption of 

independence, we can rewrite the MCEER formulation as: 

ܷெ൫ݍூ

௃

௜ݍ :ܬ א ݆׊หܫ א ݅൛ห ൌ ൯

௝ ൌ 1ൟห

௜ݍ :ܬ א ݆׌หܫ א ݅൛ห

௝ ൌ 1ൟห

In words, it is the ratio of people who have suffered no service outage to the number of people 

with some service. 

 
104 
105 

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