**Economic applications in disaster research, mitigation, and planning**

Terry L. Clower, Ph.D.

Associate Director

Center for Economic Development and Research

University of North Texas

P.O. Box 310469

Denton, Texas 76203-0469

tclower@unt.edu.

**Abstract**

This chapter examines the contributions of the economics discipline to
disaster research, mitigation, and planning. Economics offers modeling
techniques for assessing the impacts of disasters, theories of
development for understanding the choices that individuals and firms
make in selecting residential and business locations, approaches for
risk and vulnerability assessment in insurance and disaster planning,
and policy insights in each of these areas that are affected by the
political economy. The chapter gives particular attention to common and
emerging techniques for assessing the indirect economic impacts of
disaster events offering an assessment of the strengths and weaknesses
of each analytic approach.

**Introduction**

Economics as a specific discipline, its many sub- and closely related
disciplines, and research techniques pervade the systematic study of
disasters and their human, social, and monetary impacts. The goal of
this chapter is to provide the non-economist a look into how this
discipline shapes scholarly and public understanding of disaster
impacts, the roles that economic information can play in the
*realpolitik* of disaster management and response, and reviews methods
of analysis in assessing the impact of disasters.

To accomplish this goal, specific data analysis techniques used to
estimate the economic impacts of disasters are presented along with a
description of how this information is used to address issues of
resource allocation and disaster avoidance. Discussion is presented on
issues relating to disaster insurance and the contribution of economics
to risk analysis. Finally, the chapter offers suggestions for expanded
or new research approaches that economists should undertake to further
contribute to the discipline of disaster management. But first, a
historical perspective of the role of economics in disaster research.

**A Brief Time in History**

Assessing the economic impacts of disasters is a very recent systematic
field of study. Disasters have been, and continue to be, human
tragedies. History books tell us that more than 2,000 died in the
Johnstown, Pennsylvania flood in 1889, the eruption of Krakatoa in 1883
-- described as the first catastrophe of the communications age (USGS,
2005) -- and the resulting tsunami killed more than 30,000, the
Galveston hurricane of 1900 killed more than 6,000 of the island's
residents, and of course the 1,503 lives lost in icy North Atlantic
waters on that 'night to remember' in 1912. These numbers represent
horrific human tolls and each also represents economic losses in the
tens or hundreds of millions of dollars. But, it is the loss of life
that catches our attention.[^1]

In addition, the provision of public monies to help those affected by
disasters has been a comparatively recent occurrence. Historically,
government policy and/or public sentiment simply did not support
monetary aid to disaster victims. Barnett (1999) cites an example from
1887 where President Grover Cleveland in response to an emergency
request for \$10,000 in aid to Texas drought victims noted that there is
no constitutional basis for public funds to be used to offset individual
suffering as a result of a disaster. Barnett also observes that even
though public policy had changed by 1915 with the advent of federal
disaster relief grants and loans, it was many years before the public at
large found the receipt of these grants and loans socially acceptable.

Concentrating on the loss of life to describe the magnitude of
disasters, combined with public attitudes about the costs of disasters
being borne by individuals, there was little demand for comprehensive
economic assessments of disasters. One of the few early assessments of
the economic impacts of a disaster was published in 1920 estimating the
impacts of the Halifax ship explosion of December 1917 (Scanlon, 1988).
Little else appears in the academic literature for more than 40 years,
but during those years, public policy and public attitudes about
disaster relief changed. With these changes came demand for information
about the size (impact) of disasters from an economic perspective. If
there were to be programs to provide aid to victims of disasters, then
the impacts must be quantified.

The development of warning systems broadcast over radio networks and
later television gave vital information that has saved innumerable
lives. In addition, investments in infrastructure, enhanced construction
techniques required by modern building codes, and other physical capital
have with one notable exception resulted in fewer deaths due to
disasters. For example, following the 1900 hurricane, Galveston Island
and almost every structure on the island were raised several feet.
Hurricanes have hit Galveston since 1900 but never with anything near
the human losses of the 1900 event. As this chapter is being completed,
recovery is underway for hurricane Katrina. In the largest disaster to
hit a US city since the San Francisco fire, New Orleans, a city of
450,000, was inundated by flood waters after sections of the Mississippi
River levee system failed due to storm-related flooding. (The City of
New Orleans sits several feet below sea-level and has been a high-risk
area for flooding since its founding in the late 18^th^ century.)
Inefficient and ineffective government response is being blamed for many
of the city's low-income population not being evacuated. Whether through
inability to evacuate, or unwillingness by individuals to evacuate, over
200,000 people were still in the city when the levees broke. Still, less
than one-half of one percent of the population perished.

Even with record numbers of people moving into relatively hazardous
areas, such as the Florida coast or mud-slide prone hills in central and
southern California, until Katrina we have seldom seen more than a few
deaths in the US related to natural disasters since the early parts of
the 20^th^ century. To justify on-going public aid to victims and
expenditures for disaster preparedness and management, efforts turned to
estimating the economic impacts of disasters.

**Political Economy of Disasters**

Prior to the 20^th^ century political economy was the proper name for
the discipline of economics. In today's context it means the convergence
of politics and economics. In the previous section changing public
policy in the US is illustrated by comparing President Cleveland's
strict interpretation of the constitution with the later advent of
federally funded grants and loans to aid victims of disasters. The
economic considerations were, in many respects, the same, but our policy
(political) approach had changed. Economic analysis is at the heart, but
is far from the whole, of the *realpolitik*[^2] of disasters. From the
time of the nation's founding through 1950, the US government enacted
128 pieces of legislation providing relief, mostly in the form of
in-kind donations, for victims of disasters (Barnett, 1999). By the
1950s, the US had gone through a fundamental shift in the expected role
of government. From the New Deal policies of the 1930s through the G.I.
Bill providing for a college education to veterans of World War II,
liberal ideas of government responsibility to the nation's citizens was
in its ascendancy. The Disaster Relief Act of 1950 and the Small
Business Administration Act of 1953 both offered standing programs for
disaster relief (Barnett) requiring economic analyses to support budget
projections.

Programs of the Great Society of the 1960s and afterwards also included
elements of disaster relief and mitigation in housing and introduced
formal civil rights considerations in disaster management and planning.
In 1953, the federal government provided just 1% of total disaster
relief spending. By the mid-1970s that percentage had risen to more than
70% (Barnett, 1999).

Political considerations also influenced the distribution of private
relief money. Prior to Hurricane Camille in 1969, the American Red Cross
distributed disaster assistance based on economic need. After being
heavily criticized in the press and in some political circles, the Red
Cross standardized their rules for funds eligibility and removed
economic need as a criterion. This can be seen as a reflection of the
growing size and political influence of the middle class in the US after
World War II. As observed in surveys conducted by Leitko et al. (1980),
middle class victims of disasters view relief as "a corrective to a
naturally induced injustice" (page 735) and tend to demand larger
amounts of relief regardless of their own resources. This liberal
approach to the distribution of disaster relief has survived the
increasingly conservative nature of other public assistance in the US
since the early 1980s. Leitko, et. al. observed that the public does not
see disaster relief as welfare. How else can one politically account for
general acceptance at the national level for potentially providing
grants and low interest loans to wealthy families whose homes in gated
Florida communities are damaged by hurricane events?

The good news about the surprisingly liberal attitudes of the US
electorate towards disaster relief and mitigation is that we have had a
steady, if not sufficient, stream of economic resources for disaster
mitigation, preparation, and management. Of course, this has also been
influenced by the *realpolitik* of 9-11 and there will certainly be a
shift in federal government spending policy due to failures and
perceived failures that led to the New Orleans/Katrina disaster -- at
least in the short run. The bad news is that federal intervention is
increasingly distorting economic decisions at the local level.
Kunreuther (1998) notes local governments are not seeking own-source
solutions to disaster response needs, such as private sector insurance,
because of perceived certainty of federal resource availability. These
distortions are also apparent in residential real estate markets.

One of the clear reasons the costs of disasters have escalated rapidly
in recent years is a function of the level of development in high risk
areas. For example, in 2003, 153 million people, 53 percent of the US
total population, resided in coastal counties -- an area that comprises
just 17 percent of the mainland US land mass (Crossett, et al, 2004).
The coastal population has increased by 33 million in 23 years
representing a rapid increase in population density and significant
development intrusions into barrier islands and marsh lands that offer
natural protection from storm events. Moreover, this population growth
does not reflect the growth in the number of second and vacation homes,
hotels, and resorts that increasingly fill the coastal landscape. The
political reality is that local and state governments are willing to
trade the potential for more expensive disaster events, which is offset
by federal assistance, for tax base growth.

There are three other ways that political economy approaches can help
explain the level and distribution of disaster mitigation and planning
funding. The first is the political dimension of who qualifies for
post-disaster assistance. The release of federal grants and loans for
disaster relief is based on the declaration by the President that a
specified region, most often a county, is a "disaster area." The general
public largely thinks this designation is about damage to buildings,
homes, and infrastructure along the lines of the Fujita Scale of
tornadic damage. However, it is the impact the disaster event has on
local government that forms the basis for a disaster declaration. In
theory, local government or state government are supposed to provide
disaster assistance. If demand for assistance and services exceeds local
capacities or if local government revenues are substantially threatened,
then Federal resources are engaged. If a hotel is damaged by a tornado,
as in the case of Fort Worth, Texas, in 2000 (see McEntire, 2002 for a
description), local government experiences losses in revenue from sales
taxes, hotel occupancy taxes, and property taxes and thus local
government's ability to provide services and recovery aid is
diminished.[^3] This interesting quirk of US disaster policy is keenly
felt by victims of certain types of disasters. Tornadoes can cause
widespread damage qualifying the area for Federal disaster assistance.
However, if the tornado destroys only houses located along one block, it
is doubtful that the revenue of local government would be severely
impacted and those victims will not qualify for federal assistance --
even though their individual loss is as great as any individual in a
much larger disaster.

In practice, presidential disaster declarations can be overt acts of
political largess or electioneering. Sylves (1996) noted that in the
winter of 1995, President Clinton waived qualification rules repeatedly
in making federal funds available to residents and businesses in
California as a result of two flood events. The fact that California had
a Democratic governor at the time and holds the largest number of
electoral votes in presidential races is assumed to have played a role
in Mr. Clinton's decision. In the spring of 1996, widespread flooding
causing substantial damage occurred across Pennsylvania, yet only six
counties were declared eligible for federal disaster assistance.
Governor Tom Ridge publicly threatened consequences in the fall
elections for federal officials "playing games with Pennsylvania." Very
quickly, 58 of 67 Pennsylvania counties received federal disaster area
status (Platt, 1999). Platt describes the political influence in federal
assistance as "disaster gerrymandering."

Public policy also affects private insurance approaches to economic
mitigation of disaster impacts. The insurance industry remains one of
the most heavily regulated industries in the US with many states having
oversight bodies approving rates based on allowable underwriting
profitability.[^4] The problem is that the event horizons for disasters
are often long, meaning that the insurer's premiums should account for
building risk event reserves over several years. However, accumulating
risk event reserves can appear as profits in the short run and are thus
the targets of regulators looking to deliver politically popular rate
decisions. Moreover, accounting rules and taxing policies on retained
earnings hurt insurers' ability to build risk reserves (Andersen, 2004).
Together, these policy factors result in wide fluctuations in disaster
insurance availability[^5] and premiums with the lowest
availability/highest rates following disaster events. These higher rates
discourage private sector adoption of own-source risk mitigation
increasing the dependence on federal level solutions (Klein, 1998). In
addition, closely timed disaster events, such as this year's early
season hurricane in Florida just 11 months after the last major storm
event, place further strains on insurance provider resources that are
reflected in subsequent premiums.

The final political economy dimension to disaster research covered here
is the potential for overt political considerations in the distribution
of disaster relief. Though they specifically studied an Australian case,
Butler and Doessel (1980) claim that politics can influence disaster
relief in a federalist system of governance. Sverny and Marcal (2002),
Scanlon (1988), and McEntire and Dawson (forthcoming) also discuss the
politics of disaster relief and preparedness. Some disaster mitigation
projects could be considered little more than pork-barrel politics.
Moreover, as suggested earlier, there is more than a little of the
political economy of wealth redistribution in some disaster policies in
the US.

Several techniques and approaches will be presented in the remainder of
this chapter for estimating the economic impacts of disasters and
disaster planning and management. However, the application of the
findings of these analyses remains an exercise in political economy.

**Measuring Disaster Losses**

Economists are rarely called on to estimate the direct physical damage
caused by disasters. This is a job for engineers, architects,
construction specialists, and others. These damages include property
damage to buildings and infrastructure, debris removal, and the cost of
emergency protective services (McEntire and Cope, 2004). It is the
losses associated with employment income and indirect losses that occupy
the efforts of economists in the field of disaster research. Though
there is some disagreement among scholars as to exactly what counts as
indirect costs, they include the loss of business activity due to
reduced activities at damaged firms, loss of income in secondary and
tertiary employment, and business disruptions not directly attributable
to damage. For example, if a manufacturing firm is damaged sufficiently
to disrupt production, then they will not require trucking services to
deliver raw materials or pick up finished goods, which may impact the
employment of drivers. Rose (2004) illustrates indirect effects with the
example of a utility plant being damaged resulting in utility customers
(businesses) not being able to operate. Cochrane (2004) uses the
comparatively simple definitions that direct damage is property damage
plus lost income, and indirect damage is anything else. Rose, along with
other researchers cited in his study, find that direct and indirect
business interruption losses can be as large as physical losses. Of
course, the degree of impact of a disaster depends in large part on the
scale of the analysis.

*Macroeconomic Analyses*

Macroeconomic analysis considers economic events and activities at a
national or at least state scale. Dacy and Kunreuther (1969) held that
the total national cost of a disaster is the replacement value of the
property damaged, regardless of the presence of a relief program. Even
when other costs are included, it is a matter of simple division to see
that disaster impacts rarely have a meaningful impact on a national
economy. Whatever the damage, the divisor is very large. As noted by
Mileti (1999), capital markets are simply too large to be disturbed
beyond a short period of time by natural disasters.[^6] The notable
exception would be sustained droughts in countries with an
agrarian-based economy (Albala-Bertrand, cited in Horwich, 2000). Nobel
Laureate Gary Becker (2005) has noted that even the pandemic flu of
1918-1919 had no major effect on the world economy. To illustrate how
this can be, Horwich (2000) offers an example based on the Kobe
earthquake.

The Great Hanshin earthquake struck Kobe, Japan on January 17, 1995. In
the earthquake and subsequent fires, more than 100,000 businesses were
destroyed, 300,000 individuals became homeless, and 6,500 people were
killed with total damages estimated at \$114 billion (Horwich, 2000).
The damage estimate represented about 2.5% of Japanese gross domestic
product (GDP) in 1995. Yet within 15 months manufacturing was operating
at 98% of the pre-earthquake trend, all department stores and 78% of
small shops had reopened within 18 months, and trade at the port was
operating close to pre-earthquake levels within 1 year (Horwich;
Landers, 2001). That is a remarkable recovery based on GDP impacts.
Similarly, Hurricane Katrina destroyed a sizable proportion of the
economic capacity of Louisiana and Mississippi, but these states combine
to represent less than 2% of US GDP. The most recent data from the US
Department of Labor estimates that Katrina took 230,000 jobs from
directly affected areas, but that total national employment for the
month of September declined by only 35,000 -- little more that a
statistical blip on the economic map (Balls and Swann, 2005). Horwich
suggests it is more telling to consider the impact of a disaster on
economic potential as opposed to economic activity.

Economic potential can be measured by the level of capital stock
including unused capacity in the economy. For example, other Japanese
ports took on much of the trade activity while Kobe was under repair. In
addition, Horwich (2000) suggests that human capital is the dominant
economic resource and that, horrible as the losses were, 99.8% of the
population in the earthquake impact zone survived. Horwich includes the
economic value of life at \$2 million per person, plus the \$114 billion
damage to capital stock, to estimate the capitalized value of the
Hanshin earthquake on Japan at \$127 billion (\$114 billion +(6500\*\$2
million)). Horwich calculates Japan's total resource value by
capitalizing GDP (about \$5 trillion in 1995) at a real interest rate of
3% for a total of \$167 trillion (\$5 trillion/0.03), which includes the
value of a highly skilled workforce. Using this approach, the Great
Hanshin earthquake had a total impact of 0.08% of the economic potential
of the Japanese economy. Much of the economic activity lost due to the
physical damage was regained in the form of rebuilding and repair. While
Horwich makes some heroic assumptions in these calculations, they offer
a clear indication of the resilience of the economy of large
industrialized nations. However, even smaller nations, in terms of
economic output, appear to possess economic resilience to disaster
events.

One week after the Sumatra tsunami of 2004, the Indonesian and Malaysian
stock markets had gained value from the pre-disaster level, the Thai
stock market declined only slightly, and the Sri Lankan markets were off
a few percent (Becker, 2005). Tavares (2004) using an ordinary least
squares regression analysis calculates that natural disasters lower US
GDP by 0.052% per year.[^7] Of course, the same may not hold true for
smaller nations with more specialized economies.

In addition to the previously mentioned agrarian-based economies,
Auffret (2003) finds that natural disasters are an important determinant
of economic volatility in Caribbean economies, which is attributed, in
part, to consumption shocks due to underdeveloped or ineffective risk
management mechanisms. Of course, tourism-based economies are subject to
market responses to disaster events -- or predictions of disaster events
-- over which they have little control.

The other factor that minimizes the impacts of most disasters is their
short duration. Waters recede, storms pass, and eventually droughts
break. But for some types of disaster, the threat of an event can have a
long-term effect on macroeconomic performance -- specifically the threat
of terrorism. Tavares (2004) estimates that the continuous threat of
terrorist attacks reduces gross domestic product in Israel by 4%. The
Basque region of Spain, which has seen decades of separatist terrorist
activities, looses about 10% of its potential economic activity due to
the threat of terrorism. Terrorism impacts national economies in 3
ways: 1) increased risk decreases business insurability meaning that
risk is not spread across a greater number of economic actors, 2) trade
costs are increased leading to lower levels of international
transactions, and 3) increased public and private spending for security
and defense decreases capital available for investment (Tavares). Hobijn
(2002) estimates that increased security costs incurred after 9-11 has
reduced US economic activity by 0.66%.

One area of national level impacts that has received press coverage, but
little academic analysis to date, is the impact of disasters on the US
energy industry. In 2004, hurricanes in the Gulf of Mexico substantially
damaged that region's oil and gas production and transmission capacity.
Winds and high waves toppled or dislodged the moorings for offshore
rigs, and hurricane-spawned underwater mudslides destroyed sections of
transmission pipelines. This damage resulted in lower domestic energy
supplies that increased the market price for oil and gas and was
reflected in the cost of gasoline, diesel, and fuel oil that rippled
throughout the US economy.[^8] Damage sustained by refineries located in
the New Orleans region along with off-shore oil production losses as a
result of Katrina and subsequent flooding is currently blamed for adding
as much as 40 cents to the price of a gallon of gasoline at the pump.
These impacts, though temporary, should be formally assessed.

The resilience a given economy has to disaster events is, of course,
largely dependent on national resources committed to mitigation,
planning, and response. Horwich (2000) reports comments by noted
disaster researcher Fred Cuny stating that if the earthquake that hit
San Salvador had instead hit San Francisco, it would have rattled the
china, not killed 1,500 people. As national income rises, disaster costs
tend to rise, but relative costs as well as the number of lives lost
decrease (Dacy and Kunreuther, 1969; Freeman et al., 2003).

However, aggregated analyses at the macroeconomic level miss the
intensity of regional and local impacts that create comparative winners
and losers when disaster strikes. In addition, macro level analysis
often fails to identify and address disaster impacts and vulnerability
across populations at differing income levels. As an overall economy
gains wealth, it is often the case that low income populations are
forced to reside in lower-cost / higher-risk areas compounded by their
inability to afford insurance (Barnett, 1999; Scanlon, 1988; Vatsa,
2004). The stark, often horrific, images of the low-income victims of
Hurricane Katrina and their disproportionate death rate and loss of most
all worldly goods has brought into focus how disaster events can
disproportionately affect the poorest segments of our population. Even
when the national or regional economy recovers in terms of production
and employment, specific localities, groups, and individuals may still
be paying the price of disasters.

It is said that all politics are local. Given the earlier assertion that
politics intertwines disaster economics and policies, it is reasonable
to assume that the politics of disaster are often local. This is one
reason why the preponderance of studies examining the economic impacts
of disasters are conducted at the local or regional level.

# Microeconomic Analysis of Disaster Impacts

It is well documented that the cost of disasters are rising, though care
must be taken when making comparisons across time and when translating
impacts across different currencies. Mileti (1999) reports the following
disaster cost estimates based on a review of several studies:

-   Loma Prieta Earthquake 1981 \$10 Billion

-   Hurricane Hugo 1989 \$6 Billion

-   Hurricane Andrew 1992 \$20 Billion

-   Northridge Earthquake 1994 \$25 Billion

Mileti also cites an analysis that looked back at the 1923 Tokyo
earthquake and estimated total damages at \$1 Trillion in 1995 US
Dollars (USD). That estimate stretches credulity considering that just a
few years after the earthquake Japan had the excess economic capacity to
begin a massive military buildup. As noted, the damage estimate of the
Kobe earthquake in 1995 was \$114 billion USD based on simple currency
exchange rates. However, if purchasing parity adjustments are made to
the damage estimate, Horwich (2000) reports the cost estimate is \$64
billion -- a 44% reduction. Nonetheless, there is pervasive evidence
that disasters are becoming more economically costly. Current
assessments of private and public liabilities for rebuilding New Orleans
in the wake of Hurricane Katrina exceed \$200 billion. Yet, there are
mitigating factors and evidence to suggest that the impacts are not
always as large as advertised.

Tomsho (1999) reports on one of the most common factors that complicates
the assessment of damage costs of disaster events -- the Jacuzzi effect.
The Jacuzzi effect occurs specifically when homeowners add new or
improved features to their dwellings during disaster repairs. Of course,
this ability to rebuild and restructure is one of the primary reasons
that post disaster regional economies often improve their performance in
the long term. As noted by Horwich (2000): "Restored economies will not
be a replica of the pre-disaster economy. Destruction of physical assets
is a form of accelerated depreciation that hastens adoption of new
technologies and varieties of investment" (page 530). In addition,
federal grants and low-interest loans act as economic stimuli with
effects similar to transfer payments. The Charleston, South Carolina
economy received \$370 million in unexpected income after Hurricane Hugo
in 1989 that helped the local economy to perform better than expected in
7 of the 10 quarters following the disaster event (Tomsho). But, as
suggested earlier, the overall effect masked a great deal of disruption
and volatility. Some businesses were permanently destroyed while new
businesses opened. New Orleans, a city that had been suffering economic
decline for decades prior to Katrina, may never recover its economic
base beyond tourism and petro-chemicals according to some forecasters.

The main contribution that micro-economic analysis can bring is an
understanding of the dynamics of the economic churn that is sparked by a
disaster event. Which industries are most heavily impacted? Which are
most likely to gain? A few years ago while riding in a taxi in Derry,
Northern Ireland (Londonderry if you are of loyalist persuasion), the
driver observed to me that the first people on the scene of a terrorist
bombing in his city are often the construction contractors preparing
their repair bids. Even if this is a bit of an Irish yarn, it clearly
points out that some industries and businesses will see potentially huge
increases in their business activities resulting from disasters. By
understanding the dynamics of the total economic impacts of disasters,
we can more efficiently allocate disaster response resources so that
those in need are the ones that are served. In addition, through
predictive models using this information, we can make better decisions
regarding disaster preparedness and pre-event mitigation strategies
(Mileti, 1999; Gordon et. al., 2005).

There are several data analysis techniques used to assess the indirect
and income effects of disasters. These techniques include surveys,
econometric models, Box-Jenkins time series analyses, input-output
models, general equilibrium models, and economic accounting models
(Cochrane, 2004; Chang, 2003; Zimmerman et. al., 2005).

Surveys provide direct information from those impacted or in close
association with those directly affected by disasters. They can be
flexible in design to accomplish simple data gathering (How much will
it/did it cost to rebuild your facility?) to more in-depth approaches
(How did you finance your rebuilding? Have you lost customers because of
down time? Are you looking to relocate your business?). Tierney (cited
in Rose and Liao, 2005) uses surveys to assess impacts on businesses of
the 1993 mid-west floods and the Northridge earthquake. The largest
problem with survey approaches is non-response bias. The researcher
cannot know if the respondents are truly representative of the broader
population of disaster victims. Given the psychological trauma
associated with disasters, the researcher would have to be diligent in
assessing response reliability -- respondents' answers may change if
questioned immediately after the event versus 6 months later. There
could be issues of strategic behavior in the responses such as
exaggerating losses in the hope of attracting additional aid. There are
also potential logistics problems with surveys. Researchers may not have
access to the disaster area immediately and may be unable to locate
victims later. Moreover, the most appropriate survey medium would likely
be in-person interviews, which are expensive and time consuming. Still,
surveys offer the best opportunity for obtaining direct, relevant data.

Econometric modeling approaches can be used when there is substantial
data readily available for the affected region. Using a variety of
regression techniques, the fully-partialed[^9] effects of a disaster
event can be modeled as an intrusion on a series of data. However, data
availability can be a problem. Much of the economic data that would be
used are gathered and published with substantial lags, this approach may
not be practical until 2 or 3 years after the event. Of course,
predictive models can help us understand post-disaster dynamics, but
most econometric approaches do not easily account for product
substitution, immediate changes in the imports of goods, or the
non-linear nature of production functions inevitable when an economy
receives a significant shock. Still, several researchers have offered
credible analyses using regression techniques including Ellison, et al
(1984), Cochrane (1974), and Guimaraes, et al (1993), among many others.

One econometric modeling approach is to use variations of hedonic
pricing models. Hedonic models (derived from the term hedonism) account
for preferences in purchasing decisions. These models are most commonly
used in real estate research to described why some homes are more
desirable (higher priced) even when other factors such as size and
features are the same. MacDonald et. al., (1987) use a hedonic model to
assess housing value impacts of being located in a flood-risk area.
Brookshire et. al., (1985) examined hedonic price gradients based on
earthquake safety attributes for housing. This modeling approach could
add valuable insights into consumer behavior, especially if standard
housing price models are adapted for longitudinal studies to examine
changing hedonic factors in cases of recurring disaster events, such as
housing prices in Florida after multiple major hurricanes.

A variation on the intrusion model method is an Auto-Regressive
Integrated Moving Average (ARIMA) model. This analytic technique takes a
Box-Jenkins approach to time series analysis. A Box-Jenkins analysis
uses previous values of the study variable to predict the next value.
Data analysis software packages use complex algorithms to account for
secular trends in the data (are overall prices rising or falling?), the
correlation between current and previous observations, seasonal
variations, and other factors. For example, in examining the impacts of
a tornado event on local retail sales, the analyst considers trends and
patterns in a series of relevant data. The ARIMA model would control for
a trend that total retail sales have generally risen over several years,
the seasonal variations for Christmas, back-to-school, and other
especially busy times, and the fact that if a retailer is successful one
month, they will likely be successful the following month. The ARIMA
model provides a prediction for what retail sales should be, which can
then be compared to what actually happened after the disaster. The
difference is an estimate of the disaster's impact on retail sales. The
biggest weakness of this approach is being able to account for
confounding concomitant events -- such as a large retailer closing about
the same time as a disaster for unrelated reasons. Because ARIMA models
do not require the gathering of data for large numbers of relevant
variables, the approach is very cost effective. Enders et al., (1992)
uses an ARIMA model to assess losses in the tourism industry due to
terrorist events while Worthington and Valakhani (2004) use this
modeling technique to estimate the impacts of disasters on the
Australian All Ordinaries averages. Due to its relative simplicity but
powerful analytical strengths, this data analysis methodology should be
more widely used in disaster research.

Input/output (I/O) models are based originally on the work of Wassily
Leontief in the 1930s in which the flow of goods across industries are
captured using transaction matrices. For any given commodity there are
raw materials, goods, and services purchased as inputs in the production
process. Based on economic surveys, we know, on average, which
industries produce which commodities and services. These models then
provide a description of how demand-satisfying production creates
upstream and downstream economic activities. For example, a writing pad
is made of backing, paper, ink for the lines, and glue to bind the
pages. There are firms that produce each of these inputs. In addition,
the paper converter (manufacturer of goods converted from raw paper)
hires accountants, computer services firms, and trucking companies, buys
advertising space in trade publications, and purchases a host of other
goods and services to support its business operations. The I/O models
then use data from government organizations such as the Bureau of Labor
Statistics to reflect relationships between labor demand for production
activities and prevailing salaries, wages, and benefits to estimate not
only the value of economic activity associated with a given level of
production for a commodity, but how many jobs are supported and how much
is paid in labor earnings.

National-level I/O models can be adjusted for regional economies by
allowing for some activities to "leak" out of the economy. If the ink
used to print lines on a tablet is not produced locally, then spending
for that good does not impact the local economy and the related jobs and
income are created elsewhere. However, being more precise, in a large
regional economy there is likely to be at least one company that makes
the ink, but that does not mean that company gets 100% of local market
ink sales. Therefore, the regional I/O models estimate the proportion of
total spending for intermediate goods that stay in the regional economy
(expressed as regional purchasing coefficients). An I/O model may or may
not include the economic activities (purchases) of households, though
most do. The models produce three types of impact assessments: direct,
indirect, and induced. Direct effects can be thought of as direct
purchases by the industry being described. Indirect effects include
purchases by related companies in the supply chain, such as the ink
manufacturer buying office supplies from a local retailer. The induced
effects capture the economic activity created by employees spending a
portion of their earnings in the local economy for goods and services.
When you add the direct, indirect, and induced impacts, expressed as
coefficients, you can get a total effect greater than 1.0, which is the
economic multiplier. For example, demand for \$100 worth of writing pads
in the Houston economy could create a total of \$160 worth of local
economic activity when all three types of impacts are summed.

Unfortunately, the multiplier effect works when production is added and
when production is lost. If the paper converter's plant is damaged or
destroyed, the related indirect and induced impacts spread across the
regional economy.

The popularity of I/O modeling approaches has grown with the use and
affordability of personal computers. There are two major off-the-shelf
I/O models available on the market. One is produced by the Bureau of
Economic Analysis of the US Department of Commerce, and the other is
called an IMPLAN model developed by the Minnesota IMPLAN Group. Both
models are cost effective and offer modeling capability at the county
level. The IMPLAN model allows the user more flexibility in adjusting
regional purchasing coefficients and offers estimates of economic
activity at a highly disaggregated level -- as many as 528 different
industry categorizations. In addition, at the basic level, I/O models
are relatively easy to use and can be used to quickly obtain an initial
impact estimate.

The greatest weaknesses of I/O models are that they are static
(measuring economic relationships at a particular point in time), the
highly disaggregated impacts sometimes require heroic assumptions, and
they are linear. If a new firm has come to town, or an existing firm has
departed since the data base year, the regional purchasing coefficients
may be wrong. Because detailed data for individual firms is masked in
economic surveys, calculating very detailed industry estimates requires
using national level data that may not accurately reflect local economic
relationships. Finally, I/O models do not easily account for product
substitutions, and the coefficients are fixed, which likely will not
reflect reality in the aftermath of a disaster. Nonetheless, if used
appropriately I/O models can provide reasonable estimates, not exact
calculations, and are a valuable addition to the disaster researcher's
toolkit. For an example of I/O modeling in disaster research see Rose,
et al (1997) in which the indirect regional economic effects are
simulated for an earthquake event that damages electricity generating
infrastructure.

I/O models can also include social accounting matrices (SAM) that expand
the I/O model calculations to include transfer payments, value-added
accounting, and the ability to examine distributional impacts across
households at various income levels. Cole (2004) uses a SAM I/O model to
project potential impacts of damage to the electric industry in upstate
New York to aid regional disaster planning.

Another adaptation of I/O modeling uses econometric techniques to
address some of the weaknesses noted above. The improvements include
better coefficients that more accurately reflect local economic
conditions and the ability to alter those coefficients to adjust for the
structural economic changes that would attend a major disaster. This
approach iteratively feeds back and forth from the I/O to the
econometric portions of the model. Of course, the increased complexity
and accuracy come with a price. The base models are more sophisticated
than typical I/O models and thus are substantially more expensive. In
addition, operating and adjusting the parameters is not typically
accomplished by the end-user without extensive training and experience.
Greater input data requirements and sophisticated user input mean that
this model requires more time to complete an impact analysis. Therefore,
these hybrid models usually do not offer details for as many industries
as covered by I/O models.

The most widely used commercially available econometric-I/O hybrid model
is REMI. However, a review of the disaster literature did not find any
published articles using this model. Nonetheless, many state economic
planning bodies have contracted access to the REMI model that could be
used for disaster planning and impact analysis. For example, a REMI
model could assess the regional and state level economic impacts of a
tornado where repair services are being performed by a combination of
firms previously located in the local economy, firms that open a
permanent office in the region, and firms that send in 'guest workers'
for as long as there is sufficient demand.

Another recent adaptation of an I/O model was developed by the Center
for Risk and Economic Analysis of Terrorism Events at the University of
Southern California. This model begins with an IMPLAN model of the Los
Angeles area (multiple counties), then applies a regional disaggregation
model to allocate induced impacts across the region at the municipal
level. The disaggregation model uses journey-to-work and
journey-to-non-work (shop) transportation matrices that also account for
intraregional freight flows (Gordon et al., 2005). However, because the
base data of IMPLAN does not reach the sub-county level, this model
aggregates the 509 IMPLAN industry sectors into 17 sectors. Still, this
modeling approach could improve our ability to forecast or estimate how
the economic impacts of a disaster event affect individual
municipalities in a large metropolitan area. For example, Gordon et al.
use the model to assess where the greatest economic disruptions would
occur within the Greater Los Angeles area if there were terrorist
attacks on the ports of Long Beach and Los Angeles.

The methodology being increasingly used in disaster research over the
past few years has been computable general equilibrium models (CGE).
Advocates of this modeling approach assert that CGE models are much more
accurate than I/O models because they can incorporate a range of input
substitutions and different elasticities of supply and demand can be
applied across different tiers of economic activity (Rose & Liao, 2005).
If a given input in a production process is no longer available in a
post disaster environment, but can be easily imported from another
region, then the CGE model more accurately estimates the direct,
indirect, and induced effects of this change. However, this level of
flexibility is very data intensive. Therefore, CGE models rarely cover
more than a few industrial sectors. In addition, CGE models emphasize
equilibrium states -- a situation not likely to be the case in the
aftermath of a significant disaster.[^10] Among recent disaster-related
research, Wittner et al. (2005) use a dynamic regional CGE model in a
simulation modeling exercise on the effects of a disease or pest
outbreak, while Rose and Liao (2005) demonstrate how CGE models can be
used to value pre-event mitigation. Rose (2004) reviews at least three
other studies that use CGE models for analyzing disaster impacts and
policy responses. Because of its intensive data requirements and
practical limitation on the number of industries that can be effectively
analyzed at one time, CGE approaches to disaster impact modeling are
better suited to *a-priori* assessments of potential impacts for
planning purposes.

FEMA offers an impact assessment software that uses a combination of
I/O, hybrid-I/O, and CGE modeling approaches to estimate direct and
indirect economic impacts of disasters. The HAZUS-MH model is available
for download from the FEMA website, but does require a geographic
information system (GIS) model for input and output operations (FEMA,
2005). The HAZUS model is highly flexible allowing users to do a
relatively quick and simple analysis using preprogrammed assumptions
about the local economy (not recommended), to having to engage in
detailed data gathering that would likely require the services of
subject matter experts. The portion of the model that estimates indirect
economic disaster impacts starts with IMPLAN data matrices and then
employs adjustment algorithms similar to those described for hybrid-IO
and CGE models. While the HAZUS model does offer many solutions to the
problems of I/O impact analysis, it does not offer much in the way of
industry detail aggregating the total regional economy into 10 basic
industrial sectors that correspond to 1-digit Standard Industrial Code
classifications. The HAZUS technical manual, available by request from
FEMA, offers a case study based on the Northridge earthquake as well as
simulation studies showing applications of the HAZUS model.

Finally, the economic accounting approach to estimating the impacts of
disaster events differ from other approaches covered in this section in
that it explicitly includes the valuation of human life and injuries.
The economic accounting approach also draws from other methodologies to
estimate business losses using case based analysis (surveys), GDP
estimates (econometric), or I/O models. These two elements are then
added to estimates of physical losses to estimate the total economic
impacts of a disaster (Zimmerman et al., 2005). The greatest challenge
for the economic accounting method is valuing human life. The US
National Safety Council uses a loss of life value of \$20,000 compared
to the Environmental Protection Agency that calculates the value of lost
lives at \$5.8 million each. The Special Master for the Department of
Justice overseeing claims related to the terrorist attacks on the World
Trade Centers has used life values ranging from \$250,000 to \$7 million
(Zimmerman et al.).

There are a number of weaknesses in the study of the economic impacts of
disasters pointed to by many of the researchers cited above. Mileti
(1997) and Cochrane (2004) both lament that most disaster impact studies
only include losses that can be measured in transactions. The loss of
historic monuments, memorabilia, cultural assets, and the hidden cost of
trauma are rarely quantified (Mileti, 1997). In addition, Cochrane
cautions against confusion over causality of a post-event loss, using
too limited a time frame, and double counting losses among others.
McEntire and Dawson (forthcoming) have called for formalizing an
approach to document volunteer disaster responders' efforts. These
researchers note that volunteer time can be used in federal grant
matching requirements. Standardized methods of valuing volunteer time
should be used in calculating the total economic impacts of a disaster
event. While volunteers do not draw compensation, the time they spend in
disaster response does have an opportunity cost.

Even with some weaknesses, there have been great strides in the analytic
approaches to estimating the economic impacts of disaster events at the
macro- and micro-economic levels. The challenge is to continue to
improve the accuracy of our impact models, while keeping the methods
computationally reasonable and having the ability to provide timely
information to disaster management planners, political leaders, and
responders.

# 

# **Insurance**

While insurance has its own academic and professional research
literature, economics provides data, modeling techniques, and research
methodologies to the study of disaster-related insurance markets. Most
obviously are the techniques described above for estimating damage,
especially indirect damage, following a disaster event. For example, of
the \$32.5 billion in insurance payouts as a result of the 9-11
terrorist attacks, \$11 billion was for business interruption claims
(Kunreuther & Michel-Kerjan, 2005). On the cutting edge of research
techniques, Chen et al. (2004) employ neural network modeling to help
predict house survival in Australian bushfires. In addition, simulation
modeling for risk and economic losses is being used to establish premium
levels, the degree to which risk spread is required, and the viability
of insurance related derivative instruments (Andersen, 2004). One of
these derivative instruments is an interesting market-based approach for
addressing insurer exposures to the rising costs of disasters.

Catastrophe bonds (cat-bonds) are investments meant to spread the risk
of insurance loss due to disaster events. As explained by Andersen
(2004), these bonds are issued (sold) to investors. The proceeds of the
bond sales are placed in high-grade investments that are relatively
liquid (can be sold quickly) and have low interest rate sensitivity to
serve as collateral for debt service payments. The holding entity issues
insurance contracts and receives income from the policy premiums.
Insurance claims are paid from policy proceeds as well as the investment
portfolio resources. At maturity, the investors receive the full
principal of the bond only if insurance payouts have not been made. For
example, cat-bonds have been issued to spread the risk of insuring
against FIFA's potential losses if the 2006 World Cup (soccer)
tournament in Germany is cancelled due to terrorism. Unfortunately,
Kunreuther and Michel-Kerjan (2005) note that cat-bonds have not been
broadly accepted by the market. Similarly, the Chicago Board of Trade
and the Bermuda Commodity Exchange both tried issuing disaster related
financial derivatives through options and futures contracts but saw
little market interest and have subsequently stopped trade in these
financial instruments.

Political economy elements can also be seen in the disaster insurance
market. Given huge losses and uncertainty about further attacks, the
terrorism reinsurance market effectively stopped functioning in the
months immediately after 9-11. Recognizing the connection between
business growth and availability of insurance, Congress passed the
Terrorism Risk Insurance Act (TRIA) of 2002 that provides up to \$100
billion of reinsurance coverage for international terrorism events in
the US (Kunreuther & Michel-Kerjan, 2005).[^11] However, once it became
clear that no further attacks were imminent, the insurance market
re-established itself. Brown et al. (2004) judge that TRIA has been, at
best, value neutral for insurers and is seen as an impediment to
market-based solutions by companies in the banking, construction,
transportation, and other industries. Policymakers' concern about
insurance market responses in the immediate aftermath of disasters has
been a subject for discussion since the early 1990s, which saw huge
industry losses in consecutive years as a result of hurricane Andrew
(1992), the mid-west floods (1993), and the Northridge earthquake (1994)
(Barnett, 1999). But, aside from TRIA, there has been no meaningful
congressional action on these concerns.[^12] At the state level,
California and Florida have created risk pools to promote insurance
availability in their disaster-prone areas (Barnett).

## 

## **Regional Development Theory**

There is a small but growing literature drawing connections between
regional development and disaster planning, though the efforts are far
from concerted. McEntire (2004) calls on disaster researchers to
integrate development theory into their own research. As an example, he
draws on the works of Max Weber and Karl Marx to show potential insights
into disaster studies. Perhaps one of the greatest opportunities is to
use current regional development thought to help explain consumer
behavior in the face of disaster risk.

For many of us who do not live in the great state of Florida, we wonder
why the state continues to have a fast growing real estate market in
light of repeated disaster events over the past several years. A
preliminary attempt at providing an explanation for this phenomenon
requires multiple research disciplines. First is the acknowledgement
from the social-psychology field that researchers do not understand
peoples' responses to low probability events (Ganderton et al., 2000).
However, it can be reasonably hypothesized that individuals expect
either government or insurance resources to make them effectively whole
in the case of disaster. This is supported by Kleindorfer and
Kunreuther's (2000) finding that even with low costs and reasonable time
periods for investment recovery, most consumers will not spend money for
risk mitigation measures. Moreover, the probability of sustaining
life-threatening injuries is likely perceived as virtually nil -- at
least when considering loss of life incident rates resulting directly
from hurricanes. So that may explain why individuals are willing to risk
hurricane damage to gain the environmental and recreational amenities of
the Florida peninsula.[^13] But that begs the question of why do
businesses locate where there is a greater risk of physical damage and
activity loss to go along with higher costs for insurance coverage?

Richard Florida offers a potential explanation in his writing about the
"creative class." Florida (2002) asserts that business site location
decisions are increasingly driven by the presence of cultural,
recreational, and environmental amenities. In other words, site
locations used to be based on proximity to raw materials and/or markets,
now it is more about being in a location where potential employees want
to live. Therefore, businesses locate where they have the greatest
advantage in attracting the most talented workers, even if it is in an
area with a higher probability of a disaster event. While Florida's
theories are not universally accepted by regional economists, there is
supporting evidence in the behavior of some firms. This suggests that
the level of economic exposure to disasters will continue to rise until
individuals perceive greater disincentives for moving to disaster prone
areas. Berz (1994) and other researchers have called for greater use of
building restrictions in coastal and riparian zones and other market
interventions to slow growth in disaster prone areas; however, there is
little political support for these suggestions.

The danger of wildfire losses from increasing development encroachment
into forested lands are another notable disaster risk. The social,
political, and economic conditions that can lead to greater exposure to
catastrophe in areas that attract residential development because of
environmental amenities are illustrated by Diamond (2005) for the
Bitterroot River Valley of Montana.

It has been suggested that the threat of terrorism will impact urban
land forms. Glaeser and Shapiro (2002) note that there are three types
of effects that the threat of terror can have on urban design: promoting
density, promoting dispersion, and increasing costs of transportation.
Density in urban design is promoted through the psychology of safety in
numbers. A highly dense population center offers a safe harbor where
individuals enjoy mutual protection. Conversely, the same high
population density makes cities a more efficient target for terrorists,
which suggests that dispersing urban centers is appropriate. The third
factor considers average transportation costs that favor high density
urban designs. Glaeser and Shapiro conclude that, with a few exceptions,
these factors balance out, and the threat of terrorism does not
materially affect urban form. Rossi-Hansberg (2004) suggests that in
theory, bid-rents in areas with a higher probability of physical
destruction would decrease to account for increased risk and thus impact
property investment decisions and change the physical structure of a
city. However, this theoretical approach does not appear to fully
account for insurance and government assistance -- suggesting again that
current government disaster policy may be supporting increasingly
inefficient real estate markets in disaster prone areas.

**Disaster Planning**

In addition to the applications of economic theory and research
techniques described previously, there are a few other ways that the
discipline of economics contributes to disaster planning. For example,
Rose (2004) has offered measures of economic resilience -- the capacity
of an economy to absorb or diminish the effects of shocks -- that can
enhance the ability of planners and disaster responders to enable
individuals and communities to avoid some potential losses. The
distribution of mitigation funds could be made based on measures of
economic vulnerability and event risks (Adrianto and Matsuda, 2004;
Cole, 2004).

Of course, the threat of terrorism occupies much of the efforts of
disaster planners in our post-9/11 political environment. Data analysis
techniques from the economic discipline are being employed to assess the
risk and responses to the threat of terrorism such as spectral analysis
to examine cycles of events, vector autoregressive techniques for
quantifying patterns of attack, and game theory approaches for
predicting the likelihood of attacks and the effect of deterrence
strategies (Lapan and Sandler, 1988; Sandler et al., 1991; Arce and
Sandler, 2005; Averett, 2005).

Finally, with increases in funding for disaster planning in the past few
years, there is need for disaster planners to have access to the
knowledge of regional economists and economic development theory and
practice. For example, Dekle et. al. (2005) have developed a site
location tool to assess potential locations for disaster recovery
centers. While the physical location of disaster recovery centers and
centers for disaster research will continue to be influenced by the
political economy, we can hope that sound, practical reasons, such as
promoting the effectiveness of the delivery of disaster response
services, will remain the primary site location factor.

**Conclusions**

Offering the reader a reasonably brief overview of the use of economic
research methods and techniques for the study of disasters and disaster
management inevitably results in omissions, incomplete descriptions, and
failure to recognize the contributions of many talented and insightful
scholars. However, this chapter has presented an overview of the
contributions of the economic discipline to understanding the costs of
disasters, the analysis of private insurance markets, and theories and
research techniques used in various phases of disaster management
planning. In addition, it has illustrated how political considerations
affect disaster policy and the distribution of relief funds. Even so,
there is a great deal left in the field of disaster management that
could be aided through the application of economy theory and research
techniques.

Mileti (1999) specifically calls for the creation of a national database
of losses and vulnerability that would serve as a communications
feedback loop for communities, researchers, emergency managers, and
government. There have been some that have suggested standardizing the
approach used to estimate economic losses from disasters. However, from
a practical standpoint, it is better to allow for flexibility in
research technique for two reasons. First, the choice of cost estimation
technique should consider the information need -- how fast are the
estimates needed, on what scale, and to what depth? Second,
standardization will certainly serve to stifle innovation in new,
probably better, ways to assess the economic impacts of disasters.

We should continue to employ economic theory and modeling to address
issues of efficiency, equity, and consistency in disaster mitigation and
response. Under current policies the overall scope of a disaster has too
great of an influence in deciding the funds made available to
individuals in need. In addition, disaster costs are rising due to
rapidly growing populations in coastal and other high risk areas, local
zoning and building codes that do not adequately address disaster risks,
increasingly inefficient real estate markets that are distorted by
spreading the risk of locating in disaster prone areas to all taxpayers,
and spin-offs of a growing economy such as increases in the shipment and
use of hazardous materials.

Addressing critical information needs to disaster planners, policy
makers, and responders will continue to challenge economists. Working in
concert with researchers from disciplines such as sociology, geography,
anthropology, engineering and others, economists can address information
needs and offer guidance on maximizing our ability to mitigate disaster
impacts.

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[^1]: Of course, these human losses pale in comparison to many of the
    great disasters such as the 1976 Northeastern China earthquake that
    killed 240,000, the 40,000 killed in Northwestern Iran in 1990, the
    2004 Indonesian tsunami with a death toll exceeding 300,000 and the
    1918-1919 flu pandemic that claimed an estimated 30 million lives
    (Becker, 2005; World Book 2005).

[^2]: *Realpolitik* (literally the "politics of reality" in German)
    typically refers to a pragmatic, non-idealistic approach to
    international politics. In my usage it refers to the pragmatic
    application of politics, influenced by economic considerations, to
    disaster policy implementation.

[^3]: If the building is destroyed or substantially damaged, then
    taxable property values for improvements and business personal
    property decrease.

[^4]: See Klein, R. (1998) for an excellent introduction into insurance
    industry regulation in the US and regulatory impacts on disaster
    insurance.

[^5]: Payouts for claims associated with Hurricane Andrew put several
    insurance and reinsurance providers out of business.

[^6]: Worthington and Valakhani (2004) using an autoregressive moving
    average model found temporary shocks to the Australian All
    Ordinaries Index from brushfires, cyclones, and earthquakes, though
    the direction of the impacts (positive or negative) varies.

[^7]: This does not include the impacts of Hurricane Katrina. In
    comparison, Tavares (2002) found that currency crises decreased
    average economic output by 1.9% in the nations included in his
    model.

[^8]: Access to fuel became a problem for FEMA in getting supplies to
    victims of the Florida hurricanes of 2004.

[^9]: Fully-partialed means that other factors affecting the economy are
    controlled for statistically so that the estimates relate to only
    those costs associated with the disaster.

[^10]: Rose (2004) offers a partial solution to this weakness in CGE
    modeling.

[^11]: Unless reauthorized, TRIA expires in 2005.

[^12]: Interestingly, Kunreuther and Michel-Kerjan (2005) report that
    more companies are purchasing terrorism insurance because executives
    fear they could be sued under provisions of the Sarbanes-Oxley Act
    if their firm suffers an uninsured attack.

[^13]: The obvious question deals with the impact of Katrina on
    individuals' location decisions.