GAINING FROM LOSSES: USING DISASTER LOSS DATA AS A TOOL FOR APPRAISING NATURAL DISASTER POLICY

by

SHALINI MOHLEJI B.A., University of Virginia, 2000 M.S., Purdue University, 2002

A thesis submitted to the

Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirement for the degree of

Doctor of Philosophy Environmental Studies Program 2011

This thesis entitled:

Gaining from Losses: Using Disaster Loss Data as a Tool for Appraising Natural Disaster Policy

written by Shalini Mohleji

has been approved for the Environmental Studies Program

Roger Pielke Jr.

Sam Fitch

Date 5/26/11

The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline.

IRB protocol #: 11-0029

iii

Mohleji, Shalini (Ph.D., Environmental Studies)

Gaining from Losses: Using Disaster Loss Data as a Tool for Appraising Natural Disaster Policy

Thesis directed by Dr. Roger Pielke Jr.

ABSTRACT

This dissertation capitalizes on an opportunity, untapped until now, to utilize data on disaster losses to appraise natural disaster policy. Through a set of three distinct studies, I use data on economic losses caused by natural disasters in order to analyze trends in disaster severity and answer important disaster policy questions.

The first study reconciles the apparent disconnect between (a) claims that global disaster losses are increasing due to anthropogenic climate change and (b) studies that find regional losses are increasing due to socioeconomic factors. I assess climate change and global disaster severity through regional analyses derived by disaggregating global loss data into their regional components. Economic losses from North American, Asian, European, and Australian storms and floods contribute to 97% of the increase in global economic losses with each region‟s increasing losses attributed to socioeconomic factors.

The second study evaluates the National Flood Insurance Program and the National

Earthquake Hazards Reduction Program with respect to their legislated mandates to reduce economic losses. I evaluate these policies by utilizing a new metric which compares the trend in actual losses exhibited after the enactment of policy, to a projected trend based on losses from the pre-policy era. The trends in actual losses are either increasing at the same rate or a slightly

iv larger rate as the projections from the period prior to the enactment of policy. This suggests there is no discernible evidence that the policies have an impact on reducing losses.

The third study compares the degree to which U.S. federal funding levels for natural disaster research and development (R&D) correspond with the level of documented impact from individual disaster types. Storms cause the greatest human and economic losses in the U.S. however earthquake R&D receives the largest federal funding allocation with storm R&D receiving the second highest level of funding of all disaster types. This suggests there is some correspondence although not complete correspondence between federal funding levels and level of impact from individual disaster types.

v TABLE OF CONTENTS CHAPTER 1: INTRODUCTION ...... 1 1.1 WHY NATURAL DISASTERS MATTER ...... 7 1.2 DISASTER DATA ...... 12 1.3 CHALLENGES ASSOCIATED WITH DISASTER DATABASES ...... 14 1.4 DETERMINING DATABASE ROBUSTNESS ...... 20 1.5 THE DATABASES ...... 24 1.6 SIGNIFICANCE OF DISASTER DATA ...... 31

CHAPTER 2: RECONCILING THE APPARENT DISCONNECT BETWEEN CLAIMS ON GLOBAL DISASTER-CAUSED ECONOMIC LOSSES AND REGIONAL LOSSES ...... 34 2.1 INTRODUCTION ...... 34 2.2 CONFLICTING CLAIMS ...... 35 2.3 DETECTION AND ATTRIBUTION ...... 40 2.4 DATA AND METHODOLOGIES ...... 44 2.4.1 DATA OVERVIEW...... 44 2.4.2 METHODOLOGIES ...... 48 2.4.2 .1 STEP 1 - ANALYZING THE TREND IN GLOBAL LOSSES...... 50 2.4.2.2 STEP 2 - DISAGGREGATING AND QUANTIFYING THE GLOBAL TREND INTO REGIONAL COMPONENTS ..……………………… 51 2.4.2.3 STEP 3 - ATTRIBUTING REGIONAL LOSSES TO DOCUMENTED FACTORS ..…………………………………………………....55 2.5 RESULTS ...... 55 2.5.1 STEP 1 - ANALYZING THE TREND IN GLOBAL LOSSES ...... 55 2.5.2 STEP 2 - DISAGGREGATING AND QUANTIFYING THE GLOBAL TREND INTO REGIONAL COMPONENTS……………………………56 2.5.2.1 NORTH AMERICA ……………………………………………60 2.5.2.2 ASIA …………………………………………………………66 2.5.2.3 EUROPE ……………………………………………………...71 2.5.2.4 AUSTRALIA ………………………………………………… 76 2.5.2.5 SOUTH AMERICA …………………………………………… 79 2.5.2.6 AFRICA ………………………………………………………83 2.6 DISCUSSION ...... 86 2.6.1 STEP 3 - ATTRIBUTING REGIONAL LOSSES TO DOCUMENTED FACTORS ………………………………………………………….89 2.7 CONCLUSION ………………………………………………………… 92

CHAPTER 3: A NEW METRIC FOR GAUGING SUCCESS OF THE NFIP AND NEHRP: COMPARING PROJECTIONS OF PRE-POLICY LOSSES TO ACTUAL LOSSES ...... 98 3.1 INTRODUCTION ...... 98 3.2 METHODOLOGIES ...... 101

vi 3.3 NFIP ...... 111 3.3.1 BACKGROUND ...... 111 3.3.2 DATA ...... 120 3.3.3 RESULTS ……………………………………………………….124 3.4 NEHRP ...... 134 3.4.1 BACKGROUND ………………………………………………….. 134 3.4.2 DATA …………………………………………………………… 140 3.4.3 RESULTS ………………………………………………………... 142 3.5 DISCUSSION ...... 152 3.6 CONCLUSION ...... 156

CHAPTER 4: AN ASSESSMENT OF FEDERAL R&D FOR NATURAL DISASTERS ...... 159 4.1 INTRODUCTION ...... 159 4.2 DATA AND METHODOLOGIES ...... 165 4.2.1 DATA ...... 165 4.2.2 METHODOLOGIES ...... 170 4.3 RESULTS ...... 175 4.3.1 ANALYSES OF ECONOMIC AND HUMAN LOSSES ...... 175 4.3.2 BUDGET SNAPSHOT ...... 184 4.3.3 INTERVIEWS ...... 185 4.4 DISCUSSION ...... 195 4.5 CONCLUSION ...... 198 CHAPTER 4 APPENDIX ……………………………………………...201

CHAPTER 5: CONCLUSION ...... 204 5.1 INTRODUCTION ...... 204 5.2 STUDY #1: THE RECONCILIATION STUDY ...... 207 5.2.1 SIGNIFICANCE OF STUDY ...... 208 5.2.2 CHALLENGES TO THE STUDY ...... 211 5.2.3 FUTURE WORK ...... 212 5.3 STUDY #2: THE EVALUATION STUDY ...... 213 5.3.1 SIGNIFICANCE OF STUDY ...... 215 5.3.2 CHALLENGES TO THE STUDY ...... 217 5.3.3 FUTURE WORK ...... 218 5.4 STUDY #3: THE CORRESPONDENCE STUDY ...... 219 5.4.1 SIGNIFICANCE OF STUDY ...... 219 5.4.2 CHALLENGES TO THE STUDY ...... 221 5.4.3 FUTURE WORK ...... 223 5.5 CONCLUSION ...... 224

REFERENCES ..……… ...... 225

vii LIST OF FIGURES

FIGURE 2.1: EXAMPLE SOURCES AND CAUSAL FACTORS ...... 43 FIGURE 2.2: SCOPE OF DISASTER TYPES ...... 46 FIGURE 2.3: DATA CATEGORIZATION FOR A GIVEN YEAR'S DISASTER EVENTS ...... 52 FIGURE 2.4: GLOBAL LOSSES ...... 56 FIGURE 2.5: PERCENT OF GLOBAL LOSSES BY DISASTER TYPE ...... 57 FIGURE 2.6: GLOBAL LOSSES BY CONTINENT ...... 58 FIGURE 2.7: CONTINENTAL LOSSES AS A % OF GLOBAL LOSSES ...... 59 FIGURE 2.8: CONTINENTAL LOSSES FOR NORTH AMERICA ...... 61 FIGURE 2.9: GLOBAL LOSSES EXCLUDING NORTH AMERICAN STORM LOSSES ...... 64 FIGURE 2.10: GLOBAL LOSSES EXCLUDING NORTH AMERICAN FLOOD LOSSES ...... 65 FIGURE 2.11: GLOBAL LOSSES EXCLUDING NORTH AMERICAN OTHER LOSSES ...... 65 FIGURE 2.12: CONTINENTAL LOSSES FOR ASIA ...... 66 FIGURE 2.13: GLOBAL LOSSES EXCLUDING ASIAN STORM LOSSES ...... 69 FIGURE 2.14: GLOBAL LOSSES EXCLUDING ASIAN FLOOD LOSSES ...... 70 FIGURE 2.15: GLOBAL LOSSES EXCLUDING ASIAN OTHER LOSSES ...... 70 FIGURE 2.16: CONTINENTAL LOSSES FOR EUROPE ...... 72 FIGURE 2.17: GLOBAL LOSSES EXCLUDING EUROPEAN STORM LOSSES ...... 74 FIGURE 2.18: GLOBAL LOSSES EXCLUDING EUROPEAN FLOOD LOSSES ...... 75 FIGURE 2.19: GLOBAL LOSSES EXCLUDING EUROPEAN OTHER LOSSES ...... 76 FIGURE 2.20: CONTINENTAL LOSSES FOR AUSTRALIA...... 77 FIGURE 2.21: GLOBAL LOSSES EXCLUDING AUSTRALIAN STORM LOSSES .... 78 FIGURE 2.22: GLOBAL LOSSES EXCLUDING AUSTRALIAN FLOOD LOSSES ..... 79 FIGURE 2.23: CONTINENTAL LOSSES FOR SOUTH AMERICA ...... 80 FIGURE 2.24: GLOBAL LOSSES EXCLUDING SOUTH AMERICAN STORM LOSSES ...... 82 FIGURE 2.25: GLOBAL LOSSES EXCLUDING SOUTH AMERICAN FLOOD LOSSES ...... 82 FIGURE 2.26: CONTINENTAL LOSSES FOR AFRICA ...... 84 FIGURE 2.27: GLOBAL LOSSES EXCLUDING AFRICAN LOSSES ...... 85 FIGURE 2.28: PERCENTAGE OF GLOBAL INCREASE ATTRIBUTABLE TO REGIONAL LOSSES ...... 87 FIGURE 2.29: GDP OF EACH CONTINENT AS A PERCENTAGE OF GLOBAL GDP ...... 88 FIGURE 3.1: PRE-NFIP LOSSES VERSUS ACTUAL LOSSES ...... 104 FIGURE 3.2: DETERMINING THE TREND LINE AND EQUATION FOR PRE-NFIP LOSSES AND ACTUAL LOSSES ...... 105 FIGURE 3.3: CREATING THE PROJECTION ...... 106 FIGURE 3.4: YEAR-TO-YEAR DIFFERENCE (BLUE) AND RATE (RED) FOR INFLATION-ADJUSTED FLOOD LOSSES ...... 107

viii FIGURE 3.5: YEAR-TO-YEAR DIFFERENCE (BLUE) AND RATE (RED) FOR FLOOD LOSSES PER CAPITA ...... 107 FIGURE 3.6: YEAR-TO-YEAR DIFFERENCE (BLUE) AND RATE (RED) FOR FLOOD LOSSES AS A % OF GDP ...... 107 FIGURE 3.7: YEAR-TO-YEAR DIFFERENCE (BLUE) AND RATE (RED) FOR INFLATION-ADJUSTED EARTHQUAKE LOSSES ...... 108 FIGURE 3.8: YEAR-TO-YEAR DIFFERENCE (BLUE) AND RATE (RED) FOR EARTHQUAKE LOSSES PER CAPITA ...... 108 FIGURE 3.9: YEAR-TO-YEAR DIFFERENCE (BLUE) AND RATE (RED) FOR EARTHQUAKE LOSSES AS A % GDP ...... 108 FIGURE 3.10: COMPARISON OF THE PROJECTION AND ACTUAL TREND ...... 109 FIGURE 3.11: COMPARISON OF INFLATION-ADJUSTED FLOOD LOSSES ...... 127 FIGURE 3.12: COMPARISON OF INFLATION-ADJUSTED FLOOD LOSSES PER CAPITA ...... 128 FIGURE 3.13: COMPARISON OF INFLATION-ADJUSTED FLOOD LOSSES AS A % OF GDP ...... 130 FIGURE 3.14: COMPARISON OF FLOOD LOSSES AFTER THE ENACTMENT OF THE NFIP IN 1968 ...... 131 FIGURE 3.15: COMPARISON OF FLOOD LOSSES AFTER THE FLOOD INSURANCE REFORM ACT OF 2004 ...... 132 FIGURE 3.16: COMPARISON OF FLOOD LOSSES AFTER THE 1993 GREAT FLOOD IN THE MIDWEST ...... 133 FIGURE 3.17: COMPARISON OF FLOOD LOSSES AFTER THE 2005 FLOODS.... 134 FIGURE 3.18: COMPARISON OF INFLATION-ADJUSTED EARTHQUAKE LOSSES (LOGARITHMIC SCALE) ...... 145 FIGURE 3.19: COMPARISON OF INFLATION-ADJUSTED EARTHQUAKE LOSSES (LINEAR SCALE) ...... 145 FIGURE 3.20: COMPARISON OF INFLATION-ADJUSTED EARTHQUAKE LOSSES PER CAPITA (LOGARITHMIC SCALE)...... 147 FIGURE 3.21: COMPARISON OF INFLATION-ADJUSTED EARTHQUAKE LOSSES PER CAPITA (LINEAR SCALE) ...... 147 FIGURE 3.22: COMPARISON OF INFLATION-ADJUSTED EARTHQUAKE LOSSES AS A % OF GDP (LOGARITHMIC SCALE) ...... 149 FIGURE 3.23: COMPARISON OF INFLATION-ADJUSTED EARTHQUAKE LOSSES AS A % OF GDP (LINEAR SCALE) ...... 149 FIGURE 3.24: COMPARISON OF EARTHQUAKE LOSSES AFTER THE NEHRP REAUTHORIZATION ACT OF 2004 ...... 150 FIGURE 3.25: COMPARISON OF EARTHQUAKE LOSSES AFTER THE 1994 NORTHRIDGE EARTHQUAKE ...... 151 FIGURE 4.1: U.S. ECONOMIC LOSSES BY DISASTER TYPE BASED ON © NATCATSERVICE DATA ...... 176 FIGURE 4.2: U.S. ECONOMIC LOSSES BY DISASTER TYPE BASED ON SHELDUS DATA ...... 176 FIGURE 4.3: % ECONOMIC LOSSES BY DISASTER TYPE BASED ON © NATCATSERVICE DATA ...... 178

ix FIGURE 4.4: % ECONOMIC LOSSES BY DISASTER TYPE BASED ON SHELDUS DATA ...... 178 FIGURE 4.5: U.S. HUMAN LOSSES BY DISASTER TYPE BASED ON © NATCATSERVICE DATA ...... 179 FIGURE 4.6: U.S. HUMAN LOSSES BY DISASTER TYPE BASED ON SHELDUS DATA ...... 180 FIGURE 4.7: U.S. HUMAN LOSSES BY DISASTER TYPE BASED ON NWS/USGS DATA ...... 181 FIGURE 4.8: % HUMAN LOSSES BY DISASTER TYPE BASED ON © NATCATSERVICE DATA ...... 183 FIGURE 4.9: % HUMAN LOSSES BY DISASTER TYPE BASED ON NWS/USGS DATA ...... 183

x LIST OF TABLES

TABLE 1.1: DISSERTATION SUMMARY ...... 6 TABLE 1.2: DATABASES THAT MEET CRITERIA ...... 30 TABLE 2.1: CONFLICTING CLAIMS ...... 39 TABLE 2.2: LOSSES AS A % OF CONTINENTAL GDP FOR NORTH AMERICA IN 1991 ...... 54 TABLE 2.3: LOSS RATES FOR EACH REGIONAL SOURCE ...... 86 TABLE 3.1: POSSIBLE OUTCOMES ...... 100 TABLE 3.2: RATIO OF NWS-HIC LOSSES TO PIELKE ET AL. LOSSES ...... 124 TABLE 4.1: COMPARISON OF HUMAN LOSSES ...... 172 TABLE 4.2: 2010 FUNDING LEVELS BY DISASTER TYPE ...... 185

1 CHAPTER 1: INTRODUCTION

On March 11, 2011, a severe earthquake of 9.0 magnitude and subsequent tsunami struck

Japan. The 2011 Tohoku Earthquake is the largest to strike Japan in 130 years and the fourth largest earthquake recorded in the world since 1900 (USGS, March 14, 2011). The impact of this disaster event is far-reaching with scores of human and economic losses, an unstable situation involving a damaged nuclear facility, and a number of obliterated prefectures resulting in thousands of affected residents. While it will take time to finalize the magnitude of losses, as of April 2011 the Japanese National Police Agency confirmed 12,087 fatalities, 15,552 missing individuals, and economic losses estimated between $190 - $309 billion with the upper limit equal to almost three times the cost of Hurricane Katrina (Reuters, April 3, 2011; Ujikane, March

23, 2011). The tsunami caused damage to Fukushima Dai-ichi nuclear complex which is leaking radiation into the surrounding air and water with each day. As the tsunami swept inland, it washed away a number of towns, called prefectures, in northern Japan causing 161,600 residents to evacuate. Of the houses that were not destroyed 167,000 homes do not have electricity and

200,000 homes lack running water (Ujikane, March 23, 2011).

The 2011 Tohoku Earthquake raises a number of questions about Japan‟s policies on natural disasters. Japan is prone to earthquakes with a history of 50 earthquakes striking the nation since 1900.1 How has the country dealt with this chronic earthquake problem? Japan‟s main disaster policy is the Disaster Countermeasure Basics Act which the government enacted in

1961 with the mission to reduce risk to all disasters (Government of Japan, 2005). Through this act, the Japanese government prioritizes earthquake countermeasures, specifically against tsunamis. In addition, another Japanese law – the revised Building Standard Law - focuses on

1 U.S Geological Survey. Historic world earthquakes. Earthquake Hazards Program. http://earthquake.usgs.gov/earthquakes/world/historical_country.php#japan http://earthquake.usgs.gov/earthquakes/world/historical_country.php#japan

2 constructing all buildings built after 1981 to be earthquake-resistant although older buildings remain untouched (Government of Japan, 2005). The immense destruction of the earthquake and tsunami suggest that both policies failed since the countermeasures and buildings could not withstand the disaster‟s impact. The Japanese government also invests in earthquake research and development (R&D) through a national earthquake prediction program (Hirata, 2004). The

2011 Tohoku Earthquake will require the Japanese government to reassess its federal R&D policy which currently centers on earthquake prediction with little focus on communicating with the public, a factor that is as important as prediction for minimizing human losses.

This dissertation explores the same questions for U.S natural disaster policy. However, instead of assessing one disaster event, I assess hundreds of disaster events, focusing specifically on the economic losses caused by natural disasters over decades. The impacts of natural disasters extend beyond economic losses to include losses such as fatalities and ecosystem damage. However this dissertation focuses on economic impact only which is well-suited for appraising U.S. disaster policies which largely focus on reducing economic losses since the U.S. deals with financial losses more than it experiences fatalities or ecosystem damage. Studying economic impact is also well-suited to the availability of research-quality data as several robust databases provide data on economic losses more so than fatality or natural habitat data.

This dissertation addresses the broader question of how society manages risk and vulnerability in light of natural disasters, particularly severe disaster events causing increasingly larger economic losses with time. It also answers questions on how patterns in natural disaster severity have changed over time and how effective the policies are which focus on disaster severity. By using disaster data on economic losses, I analyze trends in losses which indicate the severity of natural disasters over time. Analyzing trends in disaster severity allows me to

3 evaluate the impact of disaster policies focused on reducing natural disaster severity. Therefore this dissertation utilizes data on disaster losses in order to appraise disaster policy. While researchers analyze disaster data to answer research questions and disaster experts appraise disaster policy, this dissertation combines the two and introduces a novel approach of using disaster data as a tool for appraising natural disaster policy. It thereby capitalizes on an opportunity, untapped until now, to utilize robust datasets of disaster losses in order to answer important policy questions and improve decision making for disaster policy.

Through a set of three distinct studies, I utilize disaster economic loss data to analyze trends in disaster severity which enable me to appraise disaster policy. The first study reconciles the apparent disconnect between (a) claims, based on global disaster loss data, that anthropogenic climate change is causing natural disasters to become more severe and (b) studies focused on regional losses which find no evidence of such causality. In the insurance arena, experts claim that global disaster-caused losses are increasing due to anthropogenic climate change influencing natural disasters to become more severe. However the disaster research community has produced a number of studies concluding that increasing regional disaster-caused losses can be explained entirely by socioeconomic factors. This study reconciles the apparent disconnect by conducting an independent assessment of climate change and global disaster severity through regional analyses derived by disaggregating global loss data. I disaggregate global losses into their regional components and quantify the percentage of the global increase in disaster losses attributable to each region‟s losses. Then I compare this disaggregation to the findings from the existing literature which attributes individual regions‟ losses to socioeconomic factors.

4 I conclude that losses from North American, Asian, European, and Australian storms and floods contribute to 97% of the increase in global losses; due to socioeconomic factors in their region such as increasing wealth, population growth, and increasing development in vulnerable areas. Of the 97% of the increase in global losses, 57% is attributed to losses caused by North

American storms, 15% to losses from Asian storms, 10% to Asian flood losses, 8% to losses caused by European floods, 4% attributed to losses caused by European storms, 2% to losses from North American floods, 0.81% attributed to Australian storm losses, and 0.24% to losses caused by Australian floods. At present the literature on regional trends has not conducted research addressing the causality of the remaining 3% of the increase in global losses. By quantifying the global increase and linking the regional percentages to regional socioeconomic factors found in the literature, this study finds no additional factors beyond those that can be explained by socioeconomic change to explain 97% of the increase in global losses. Thus, the apparent disconnect is reconciled, and there is no disconnect at all.

The second study appraises the U.S. National Flood Insurance Program and the National

Earthquake Hazards Reduction Program with respect to their legislated mandates to reduce economic losses. I appraise these policies by utilizing a new metric to evaluate their impact on reducing losses. I evaluate the policies‟ success or failure in reducing economic losses by comparing the trend in the actual losses exhibited after the enactment of the policy, to a projected trend based on losses from the pre-policy era. Comparing the trend of post-policy losses to the projection of pre-policy losses indicates whether the policy has had an impact on reducing economic losses below the level that might have reasonably been expected by policy makers and the public upon passage of the initial legislation. The findings from this study indicate that disaster loss trends are either increasing at the same rate or a slightly increased rate as compared

5 to the period prior to the enactment of policy. This suggests that while the policies may have had demonstrable effects with respect to disasters, there is no discernible evidence of an impact on reducing losses.

In the third study, I evaluate the degree to which U.S. federal spending levels for natural disasters correspond to the documented impacts of disaster events on society. Here impacts are defined in terms of losses and I compare the magnitudes of human and economic losses caused by different disaster types in order to assess the impact from each. The basis of my comparison is the normative assumption that federal disaster policy should appropriate federal resources in proportion to the level of impact posed by each disaster, with the greatest amount of resources allocated to the disaster type causing the greatest impact. This study investigates the degree to which this assumption holds true by identifying the relative level of impact from each disaster type and then comparing the relative level of federal funding allocated to each type. The findings indicate that storms cause the largest human and economic losses and while the largest federal funding level is allocated to earthquake R&D, the second largest funding level is allocated to storm R&D thus indicating some but not complete correspondence.

All three of these studies utilize data on disaster-caused economic losses and analyze trends in disaster severity in order to appraise the science for disaster policy, the efficacy of U.S. natural disaster policy, and the policy for U.S. disaster R&D (see Table 1.1).

6 Policy Chapter Purpose Methodology Results Relevance To reconcile the North American, apparent Asian, European, and disconnect Disaggregate and Australian flood and between claims quantify global storm losses contribute that global losses losses into to 97% of the increase 2: are increasing due regional losses, in global losses. The The Science to anthropogenic then associate existing literature reconciliation for policy climate change results with the attributes regional study and studies that existing disaster socioeconomic factors find regional literature on for this 97%. Research losses are regional loss has not been conducted increasing due to trends. yet to attribute the socioeconomic remaining 3%. factors. To evaluate the Actual disaster losses National Flood exhibit trends that are Insurance either increasing at the Program and the Compare the trend same rate or a slightly National in actual post- increased rate as 3: Earthquake Policy policy losses to a compared to the period The evaluation Hazards evaluation projected trend of prior to the enactment study Reduction the losses from of policy which Program with the pre-policy era. suggests the policies respect to their have had no discernible legal mandate to impact on reducing reduce economic losses. losses. Storms cause the Identify the To determine the greatest human and disaster types degree to which economic losses in the causing the federal funding U.S. Earthquake R&D greatest impact in levels for natural receives the largest 4: human and disaster R&D funding allocation The Policy for economic losses, correspond to however storm R&D correspondence science then compile levels of receives the second study annual budget to documented largest level of funding determine which impact caused by therefore some disaster types individual natural correspondence exists receive the disaster types. between impact and greatest funding. funding level. Table 1.1: Dissertation summary

7 The 2011 Tohoku Earthquake is a recent example of the impact that natural disasters can have across sectors of society and regions of the world and the tsunami had a ripple effect felt around the world, both literally and figuratively. The U.S. National Weather Service warned of the Japanese tsunami waves traveling across the Pacific Ocean to the U.S. by issuing a tsunami warning to Oregon and California (Lin, March 11, 2011). Meanwhile, the entire world has been captivated by the news of the earthquake and tsunami‟s impact on the Fukushima Dai-ichi nuclear complex, with concerns of radiation leaking and spreading globally. The impact of the

2011 Tohoku Earthquake has also shaken up the global insurance industry. Until now, the insurance industry allocated only a small share of the global risk from disasters to Japan which had translated into lower insurance premium rates for the country. However, the extent of impact from this disaster event has forced the insurance industry to reconsider and likely recalculate global insurance premiums with an increased price for Japanese insurance (Ito &

Yamazaki, March 25, 2011). As severe as this disaster event was, it was just one of many severe natural disasters to have occurred recently.

1.1. WHY NATURAL DISASTERS MATTER

Natural disasters pose a formidable global threat to society. Since 2000 alone, natural disaster events have severely impacted areas around the world and taken a great societal toll with significant human and economic losses. In early February 2011, a series of winter storms dominated the U.S. and wreaked havoc across states, sectors, and society. These storms impacted 26 states from every region of the nation with 16 states receiving more than a foot of snow. The transportation sector suffered with the flight industry cancelling 18,357 flights. The storms even disrupted Super Bowl 2011. The Midwestern portion of storms trapped Chicago

8 commuters on a snowbanked Lake Shore Drive for up to eight hours with 544 vehicles ultimately towed from the road that serves as a main artery in and out of downtown. Across eight states, 82 shelters served 1100 people (Cooper, February 4, 2011). The severity of these storms impacted many aspects of society and the disruption extended far and wide presenting many challenges to bear.

One month prior, in January 2011, Queensland Australia experienced devastating floods across the state affecting an estimated 200,000 people (BBC News, January 10, 2011) and estimated billions of dollars of damage (Queensland Government, January 10, 2011). As Anna

Bligh, the Premier of Queensland stated, "Queensland is reeling this morning from the worst natural disaster in our history and possibly in the history of our nation…We've seen three- quarters of our state having experienced the devastation of raging floodwaters and we now face a reconstruction task of postwar proportions." (Glendinning, January 13, 2011).

The previous year, in March 2010, the Icelandic volcano Eyjafjallajökull erupted and shut down European airspace costing the European airline industry £165 million/day (Wilson, 2010).

One month prior in February 2010, a 9.5 magnitude earthquake struck Chile causing $30 billion of losses (The Munich Reinsurance Company, 2011). The Chilean earthquake caused high economic losses but low fatalities as opposed to the 7.0 magnitude earthquake in Haiti which occurred six weeks prior in January 2010 and caused 222,570 fatalities (The Munich

Reinsurance Company, 2011). It was the strongest earthquake to occur in the region in 200 years and it leveled the capital city of Port-au-Prince (Than, 2010). Also in 2010, devastating floods struck Pakistan while Russia experienced its hottest summer in history (The Munich Reinsurance

Company, 2011) with a heat wave and forest fires that caused 56,000 fatalities (The Munich

Reinsurance Company, 2011).

9 Several severe disasters occurred in 2008 with Cyclone Nargis killing 138,373 people in

Myanmar; a 7.9 magnitude earthquake in Sichuan, China causing 87,449 fatalities – the population size of Miami Beach, Florida; and Hurricane Ike creating $40 billion in economic losses to the U.S. and the Caribbean (Swiss Reinsurance Company, 2009). In 2007 Cyclone Sidr hit Bangladesh and India, a 6.3 magnitude earthquake struck Indonesia in 2006, Hurricanes

Katrina, Rita, and Wilma caused a staggering total of more than $177 billion2 (2008 USD) in economic losses while a 7.6 magnitude earthquake struck Pakistan and caused 73,300 fatalities.

Three severe earthquakes preceded the Pakistani earthquake: the unprecedented 2004 Asian tsunami and 9.0 magnitude earthquake that caused 280,000 fatalities in Indonesia and Thailand - nearly the population size of Boulder County in Colorado; the 2003 6.5 magnitude Bam earthquake in Iran causing 41,000 fatalities; and the 2001 6.7 magnitude earthquake in India that caused 15,000 fatalities. These disaster events are just the largest events to occur since 2000.

They caused significant human and economic losses taking a dramatic toll on society across the globe. With repeat offenses and severe impacts, natural disasters are clearly a threat to society and they have disrupted if not destroyed the way of life for people around the world and the impacts are far-reaching.

Beyond the human and economic losses caused by disasters, natural disasters are also linked to impacting food availability, economic vitality, and global security. Natural disaster events such as floods, droughts, and extreme temperatures can ruin crops on scales large enough to disrupt the global food system. The 2010 heat wave and forest fires in Russia ruined the

Russian wheat supply while flooding in Canada and Pakistan ruined other global grain supplies

(Krugman, 2011). The depletion of these grain supplies sharply escalated the price for grains

2 This calculation is based on an independent analysis of The Munich Reinsurance Company‟s NatCatSERVICE© data.

10 globally and caused food shortages for African countries and Afghanistan which rely mainly on the supplies from the impacted regions (Allen, 2010).3 Small country-states are particularly vulnerable to shocks to the food system caused by disaster events (Skees, 2000). Although 2011 has just begun, Sri Lanka faces major food shortages due to massive flooding that has already occurred this year. The country has lost food staples such as rice because the flooding destroyed crops and “thousands of hectares of farmland” (Hoffman, 2011). The damaged farmland has depleted the bulk of available produce and what is available sells for inflated prices. The flooding also killed livestock including 75,000 cattle and thousands of chickens (Hoffman,

2011). For further evidence of shocks in small countries, the 1998 Bangladeshi flood is a historic example as it “caused a shortfall of 2.2 million tons in the rice production and threatened the food security of tens of millions of households” (Del Ninno & Dorosh, 2001) and forced the country to import “substantial amounts of foodgrain” (Shah, 1999) consequently putting pressure on an already stressed national budget.

Disruptive disaster events such as winter storms and hurricanes often cause municipal and state shutdowns which affect economic vitality. The winter weather that struck England in

2010 caused a temporary halt of much of the country including the shutdown of the economic and political hub of London. Britain‟s Office of National Statistics held “bad weather” as accountable for the national GDP decreasing by 0.5% in the fourth quarter of 2010 (Jolis, 2011).

Some even blamed winter weather for the low numbers of the January 2011 U.S. job market statistics by suggesting that the winter storms forced job losses in the construction, transportation, and warehousing sectors (Rich, 2011). In fact, by the end of the first quarter of

3 Maplecroft issued a report entitled Climate Change Vulnerability Index 2011 linking anthropogenic climate change to food shortages via intensifying natural disaster events. While the research community has not proven this link as of yet, extreme weather events such as floods, droughts, and extreme temperatures have been proven to damage food supplies. This link underlies the explanation of 2010 weather events and grain shortages.

11 2011, some attributed the full quarter‟s slow economic growth in part to severe winter weather

(Irwin, 2011; Aversa, 2011).

Some scholars link natural disasters to global security threats under the aegis of climate change although disaster events independently pose a threat to global security. In 2003, the

Department of Defense issued a report that explored the environmental scenarios possible under future climate change and suggested global instability could arise from resource shortages caused by natural disasters. For instance, climate-induced droughts and soil erosion from windstorms could affect agricultural productivity and thus food supply, while floods and droughts could alter the availability of fresh water (Schwartz & Randall, 2003). These resource shortages could lead to international conflict and possibly even wars as deprived areas struggle for resources. The

U.S. Army War College also suggests that disaster events could lead to instability in Central

America and the Caribbean due to the high costs that may be incurred by natural disaster damage and consequent economic collapse. This is based on the region‟s vulnerability to storms of high intensity such as hurricanes, as well as massive floods, landslides, and droughts (Ramirez, 2010).

In the past decade, natural disaster events around the world have made headlines repeatedly as they have severely impacted areas and caused significant human and economic losses. Why is it that we are hearing so much about natural disasters recently? Has the behavior of natural disasters changed over time? Have disaster events intensified in strength? Have they become more frequent? Are their impacts more severe to society? Has society become less capable of dealing with natural disasters? Analyzing disaster data offers a methodological approach to understanding natural disaster behavior. Disaster data include information on the number of disaster events occurring, the intensity of these events, and the severity of their impact among other things. Databases around the world collect this type of information over decades

12 thus allowing for the analysis of trends in disaster behavior. In order to deal with the threat of natural disasters, we need to understand them and enable policies to effectively counter the threat and disaster data assists us to do so.

1.2. DISASTER DATA

Disaster data include information on individual disaster events such as the disaster type, dates and locations of occurrence, intensity, and the severity in terms of human and economic losses. Trends are an important product of compiled disaster data and they provide information on disaster behavior in the past, at present, and they can suggest future behavior through trend projections and extrapolations. As disaster data experts state, “Historical data allow analysts to track disaster trends and causal factors both over time and geographically.” (Guha-Sapir &

Below).

There are many types of natural disasters. Geophysical disasters include earthquakes, tsunamis, landslides, subsidence, and volcanic activity. Hydrological disasters include floods, flash floods, and some storm surges. Weather-related disasters include hurricanes, severe storms, tornadoes, hailstorms, avalanches, winter storms, snowstorms, blizzards, frost, storm surges, wildfires, cold spells and heat waves. Climatic disasters include droughts. Disaster data report the type of natural disaster involved in an event and disaster-specific trends.

Natural disaster events occur on different timescales. Mesoscale weather events such as tornadoes can last a few minutes. Similarly, the snap in seismic plates causing an earthquake lasts only minutes. On the other hand, a synoptic scale weather event such as a hurricane can last days as it travels across the ocean. Floods can also last for several days if the influx of water is a steady rainfall from a weather system stalled over an area. Disaster data record the timescale of

13 events as well as the dates when they occur and temporal data provide trends on the frequency of disaster events.

The spatial scale of natural disasters varies as well. Tornadoes can cover one block or an entire city while tsunamis can span an ocean. Also, spatial data describe the extent of natural disasters which can span across counties, states, regions, and even countries. Disaster data specify the locations and the span of disaster events and spatial data provide trends on the size of disaster events and geographic areas vulnerable to repeated disasters.

The intensity refers to the physical strength of a disaster event. For example hurricane intensity data include peak wind speeds, heights of storm surges, lowest central pressure, and greatest translational velocity. For floods, data include peak amount of rainfall and water level crests. For earthquakes, data include the greatest magnitude of released seismic energy and waves. The trend in intensity data identifies whether natural disasters have broadly evolved into stronger or weaker events over time.

The severity of disaster events refers to the impact that natural disasters have on society, specifically in human and economic impact. Human impact refers to the number of people injured, killed, requiring immediate assistance, or left homeless. Economic loss data describe the insured and uninsured monetary losses from direct and indirect damage. Trends in severity data indicate whether the impact from natural disaster events has increased or decreased over time.

With disaster data providing an abundance of useful information, disaster databases exist across the U.S. and the world, maintained by institutions that benefit from the information.

Governmental organizations maintain databases to provide useful information to decision makers involved with natural disaster policy. Insurance and reinsurance companies maintain disaster

14 databases as the premise for their business and research institutions maintain databases in order to study natural disaster behavior.

1.3. CHALLENGES ASSOCIATED WITH DISASTER DATABASES

The irony with disaster data is that while the characteristics of disaster events are seemingly objective, disaster data vary across different databases. There is no single leading provider of data or leading institution to standardize data (Guha-Sapir, Hargitt, & Hoyois, 2004;

Guha-Sapir & Below). Subsequently there is no standardized protocol for what disaster data should reflect. In the absence of any standards, different databases vary in their methods for collecting, processing, and reporting data and this causes disaster data to differ and ultimately poses problems to data users.

Without one nationally centralized database for all disaster data in the U.S., multiple databases operate concurrently with varying methodologies resulting in the variance of disaster data: “…there is no single data-collection system for mutually consistent, uniform estimates of property damages directly caused by droughts, earthquakes, floods, hurricanes, and tornadoes.

Responsibility is shared among a variety of public and private agencies…” (White, Kates &

Burton, 2001). Several federal agencies maintain databases, however these databases likely differ according to agencies‟ specific missions (Gall, Borden, & Cutter, 2009). For instance, the

National Oceanic and Atmospheric Administration (NOAA) collects data on storms but focuses on the intensity of storm events whereas the Federal Emergency Management Agency (FEMA) collects data on storms with a focus on recovery costs. In addition, because agencies maintain databases to align with their missions, they likely exhibit hazard bias with a focus on a particular disaster type (Gall, Borden, & Cutter, 2009). For example, the U.S. Geological Survey collects

15 information mainly on earthquakes while the National Weather Service maintains an exclusive flood database. Multiple databases lead to piecemeal information as suggested by the manager of one U.S. disaster database: “…the United States has no central repository where comprehensive information on direct, indirect, insured, and/or uninsured losses caused by natural hazards is stored.” (Cutter, Gall, & Emrich, 2008). The absence of any such database creates a void and poses a problem for data users.

Even more problematic than the absence of a centralized database is the lack of a standardized protocol defining what disaster data should reflect. The National Research Council addressed the need of a standard protocol for estimating losses and stated, “The monitoring and collection of loss data from natural hazards is a piecemeal approach lacking in standardized procedures, leadership, resources, and political commitment.” (National Research Council,

1999). Therefore each database can report economic loss values reflecting different variables and resulting in differing values. Some variables that economic losses may reflect include insured losses, uninsured losses, losses from direct damage to tangible assets, indirect damages of revenue losses from industries impacted by disasters, replacement costs for damaged goods where the price of raw materials and labor are included, etc. As disaster scholars Gilbert White and others mention, “…there are no agreed criteria for taking into account the indirect losses, the market and depreciated value of the property and the cost of emergency measures taken to avoid direct damage.” (White, Kates, & Burton, 2001). They continue by comparing these different factors, “The most consistent measure of losses is probably in reported insurance claims, but this misses many costs not covered by insurance. There is no generally accepted method of computing indirect losses to property owners and non-property owners, or of estimating losses to public agencies beyond replacement of damaged property.” (White, Kates, & Burton, 2001).

16 Variable loss estimates are of concern because decision makers rely on them: “When risk assessments and the allocation of resources are based on such loss estimates, the outcome can be inadvertently flawed by the propagation of systemic and other biases inherent in databases.”

(Gall, Borden, & Cutter, 2009).

The lack of standard definitions extends to other definitions beyond economic losses as well. Databases differ in how they define disaster types. For example, storm surges are elevated levels of water moving across the ground and driven either by winds associated with a storm or by excess influx of water resulting from a flood. Some databases define storm surge as an independent disaster type categorized as a type of storm, others define storm surge as an independent disaster type categorized as a type of flood, while others define storm surge not as an independent disaster type but as a causal factor of either storms or floods.

The use of thresholds also complicates data definitions. If a database creates a threshold, it is defining disaster events through a manufactured classification system. For example, a database might define a minimum threshold of $50,000 while another database defines a

$500,000 minimum threshold which would result in a large discrepancy between the two databases for events less than $500,000. In an analysis of disaster databases, researchers suggested, “A major methodological problem is the inconsistent threshold criteria found across different loss databases. Discrepancies between inclusion criteria contribute to wide disparities in disaster information, including the total number of events included. Clearly, the filtering process for inclusion plays a major role in the relative size of disaster loss databases.” (Gall,

Borden, & Cutter, 2009). Since thresholds concern lower limits of disaster losses only, thresholds create a bias favoring larger events that cause higher human and economic losses as opposed to lower severity events (Gall, Borden, & Cutter, 2009). As database experts state,

17 “Along with the complexity of collecting information in disasters due to the constraints of time, funding, and the complexity of the situation there also remains huge variability in definitions, methodologies, sources, and data points collected.” (Tschoegl, Below, & Guha-Sapir, 2006).

Just as federal agency databases differ from each other due to agency missions, database- managing institutions compile different information based on the aspects of data they value and excel in based on their differing purposes. For example, the World Health Organization‟s collaborating Centre for Research on the Epidemiology of Disasters (CRED) maintains the

Emergency Events Database to assist with international humanitarian efforts. Therefore it values and excels in the data on human impacts. Meanwhile, The Munich Reinsurance Company manages the NatCatSERVICE© natural catastrophe database as a tool for reinsurance underwriting purposes, and consequently this database provides more robust data on economic losses than other databases. As a result, disaster databases vary in the types of data they collect; their methodologies for collection, processing, and reporting data; and thus, the robustness of data.

These differences among databases create a challenge for users of disaster data, many of whom are unaware that databases are incompatible (Gall, Borden, & Cutter, 2009). They tend to use data across databases which lead to flawed methods and incorrect results, unbeknownst to the user. Furthermore, managing institutions often do not outwardly disclose their methods so users cannot interpret disaster data at face value since they do not know exactly what the data represent or the variables used in the calculations. For example, The Munich Reinsurance

Company and the Swiss Reinsurance Company both present the same variable of economic losses that reflect different information. For The Munich Reinsurance Company, economic loss

18 data reflect rebuilding costs4 while the Swiss Reinsurance Company economic loss data reflect property value losses. However the user is likely to assume both variables are identical. In an assessment of disaster databases, several database experts addressed the shortcomings for data users, “…without full integration of standard methods between loss data producers as well as loss databases, disaster data will continue to lack comparability, limiting users‟ abilities to draw meaningful conclusions about the nature of disaster losses over time and across spatial scales

(Gall, Borden, & Cutter, 2009). Several database managers agree on the difficulty for users,

“Inconsistencies, data gaps, and ambiguity of terminology make comparisons and use of the different data sets difficult. This leads to a fair amount of confusion in the evaluation of a disaster situation.” (Asian Disaster Preparedness Center, 2006).

Along with database users, the institutions that maintain databases also face challenges.

All disaster databases face the same challenges in data collection, processing, and reporting.

Institutions must decide which on-the-ground sources they deem reliable to provide information for their database. Potential sources include government officials, scientific organizations, national associations, news agencies, international diplomacy institutions, aid groups on location, media outlets, local residents, etc. (The Munich Reinsurance Company, 2003). These sources either witness disaster events firsthand or collect local information from disaster victims. Some of these sources are more reliable than others. For instance, media outlets might overestimate losses for sensationalism while local residents might underestimate losses since they only have a narrow view of the impact on their local surroundings. Furthermore, database-managing institutions must decide how to reconcile if multiple sources report differing loss estimates (Gall,

Borden, & Cutter, 2009). If each source reports a different value, which loss value should the

4 Personal communication with Peter Höppe of The Munich Reinsurance Company. June 26, 2009. Boulder, CO.

19 database use – the average value of the estimates, the median value, the lowest or highest value, or a range of values?

Disasters, by their very nature, are chaotic and often create circumstances that make it difficult for database institutions to maintain their standards. For example, in remote areas, there are a limited number of sources available and information from any source is still preferable to an absence of information but then databases ultimately include data of lower quality. These types of circumstances create challenges for managing institutions in maintaining consistency in data collection, processing, and reporting.

Institutions must make many decisions related to managing their databases including the types of data, categorization of data, and range of data. For example, databases include sudden onset events such as hurricanes and earthquakes but should they also include gradual onset events such as droughts? Databases categorize data based on primary events such as severe storms so if the storm spawns a tornado, does the database list that as a separate event or part of the severe storm event? The decision will affect the overall statistics on number of events for that given year and geographic area. In addition, “disasters have direct costs, such as the destruction of a building, but also indirect costs.” (Downton & Pielke, 2005). To what extent should databases record losses? In fact, the extent of causal relationships poses a challenge for databases in numerous contexts. For example, heavy rains often cause flooding which causes standing water which can slowly infest with bacteria and causes illnesses in people who could ultimately die from disease. In this range of causal relationships, what extent of impact should be tied to the disaster event? The decision will affect the overall statistics on impacts for that disaster event.

20 Another challenge all databases face involves the vetting process for data. Most institutions rely on in-house vetting, particularly the insurance companies that limit the accessibility of their data for proprietary purposes. While this is understandable from a business perspective, it poses a challenge to the robustness of data when it has not undergone external examination.

Despite the number of challenges posed, many databases do exist and collect data at the international, regional, national, and state level. These databases provide information to decision makers, emergency responders, humanitarian aid groups, and researchers. Therefore they must prove to be of a high level of quality relying on credible sources, remaining consistent in methodologies, and constantly being updated to keep up with the dynamic nature of disaster events and their impact.

1.4. DETERMINING DATABASE ROBUSTNESS

Since databases employ different methodologies for collecting, processing, and reporting data, data quality varies but data users should only rely on robust databases for their needs; whether for policy making, research, or decision making for disaster response. This dissertation presents an original set of criteria to evaluate the robustness of data by assessing individual databases in their procedures for data collection, processing, and reporting. The criteria require that databases rely on credible sources for information, maintain consistent methodologies throughout time for data processing, and vet their data routinely. After applying these criteria to disaster databases, I only utilize those that pass the criteria in this dissertation‟s three studies.

In the data collection procedure, the central issue is the reliability of data sources.

Sources either witness disaster events firsthand or collect local information from disaster victims

21 and then pass the information on for databases to record. However, as one study found,

“information is not specifically gathered for statistical purposes and so, inevitably, even where the compiling organization applies strict definitions for disaster events and parameters, the original suppliers of the information may not.” (Asian Disaster Preparedness Center, 2006). The credibility of the source affects the quality of data therefore credible sources provide credible information – that which users perceive to be valid, accurate, and reflect technical quality (Cash

& Buizer, 2005). In the supply and demand for information, credible information is more effective and useable to end-users (Cash et al., 2003). In fact, end-users are expecting credible information: “Faced with catastrophic economic and human losses accumulating in each passing decade, development policy makers are demanding credible and complete data on disasters.”

(Guha-Sapir & Below). An example of a credible source can be found in the very recent Tohoku

Earthquake in Japan. After the earthquake and tsunami struck, impacted towns - called prefectures - reported the number of people missing based on detailed lists that they maintained of all of their residents. Therefore they were able to estimate the number of residents, as well as the details of each individual who were missing post-disaster.

Credible sources include those that systematically assess disaster impact through established methods such as population lists similar to the lists maintained by the Japanese prefectures. For example local, state, and national governments maintain census information and can estimate human impact with accuracy. Emergency responders have expertise in gauging severity because they repeatedly assess disaster impact firsthand. On the other hand, media outlets may sensationalize estimates of disaster impact in order to produce a more interesting story and therefore databases may not consider them as credible sources. In order for databases to selectively compile information from credible sources only, managing institutions must

22 determine which existing sources they deem as credible and then uphold to rely solely on those sources.

Data processing refers to the procedures for calculating loss values and categorizing data. Data users analyze disaster data in longitudinal studies therefore to ensure accuracy in analyses, data must stay compatible over time by reflecting consistent calculation and categorization methods. As database managers explain, “Requests for accurate data, which are comparable across countries and consistent over time, are required for priority setting…”

(Guha-Sapir & Below).

Disaster data include actual values such as the date of a disaster event or the number of fatalities, as well as derived values that involve calculations, such as economic losses.

Economic loss values reflect a number of variables including losses from individuals‟ damaged assets, damaged infrastructure, damaged crops, etc. Databases utilize specific calculations which vary from one database to another but should be used consistently over time to ensure comparable data. The Swiss Reinsurance Company provides an example of inconsistency in that it maintains a database of disaster economic losses; however the calculation for economic losses varies over time with some losses reflecting the total damage and other losses reflecting material damage only.5 Also, databases organize disaster events into categories whether by disaster type (e.g. all storms or all earthquakes), disaster severity (e.g. all disaster events causing $500 million or more of damage), or other categories and they need to maintain consistent categories throughout time.

Data vetting is an important aspect of the data reporting procedure and databases should routinely vet their data to keep up with the dynamic nature of disaster information.

5 This observation is based on an independent analysis of The Swiss Reinsurance Company‟s disaster data provided in the annual Sigma publications from 1974-2008.

23 After a disaster event occurs, the estimates for the event follow a typical pattern of initial high estimates later followed by low estimates which are then increased again for final values. This corresponds to the availability of information. In the immediate aftermath of a disaster event, it is customary to report initial estimates for the purposes of gauging the intensity, size, and severity of the disaster. However in the first few hours of the aftermath, it is difficult to gauge the extent and impact of a disaster event and sources typically overestimate initially providing high estimates for the intensity, spatial extent, and human and economic losses caused by a disaster event. Over time as emergency responders conduct response missions on the scene, more information becomes available and initial values tend to reflect overestimates which databases accordingly lower in their reporting.

With additional months, the bulk of information on an event becomes available as sources explore the full scope of the impact. For instance, emergency responders complete response missions and finalize the number of living victims, missing people, and fatalities. Typically these final values are higher than the prior adjustment and databases increase the estimates once again. This cycle was evident with the 2010 Haiti earthquake where the Haitian government released initial estimates of 212,000 fatalities within several weeks of the event.

One month later, the Red Cross provided fatality estimates of less than 100,000. Five months later, The Rand Corporation estimated fatalities of 300,000. 6 Databases need to routinely vet their data and update the information in order to complete records and provide best estimates and optimal accuracy. As the database managers for the Emergency Events

Database (EM-DAT) state, “Completeness of records is a useful indicator of quality.”

(Guha-Sapir & Below).

6 Bilham, Roger. Global earthquake fatalities: nature versus human nature. University of Colorado‟s Center for Science and Technology Policy Research noontime seminar series. March 3, 2011.

24 1.5. THE DATABASES

Of the many existing disaster databases, four databases meet the criteria presented here of relying solely on credible sources, maintaining consistent data processing methodologies throughout time, and routinely vetting data. These four databases are: The

Munich Reinsurance Company‟s NatCatSERVICE© database, the University of South

Carolina‟s Spatial Hazard Events and Losses Database for the United States (SHELDUS), the

Pielke et al. Flood Damage dataset,7 and the Vranes and Pielke earthquake dataset.8

NatCatSERVICE©

The Munich Reinsurance Company maintains the NatCatSERVICE© database which reports data on disaster events and human and economic losses, with a focus on disaster-caused economic losses for insurance and reinsurance purposes. The NatCatSERVICE© database provides robust economic loss data on a global scale since 1980. It reports individual natural disaster events occurring anywhere in the world with details on the disaster type (e.g. storms, floods), the dates of occurrence, countries of occurrence, number of fatalities, and the associated dollar losses from damage.

The Munich Reinsurance Company relies on a number of sources it deems as credible including news agencies (Factiva/Dow Jones, the Associated Press) for which The Munich

Reinsurance Company assigns rankings based on the agency‟s record over time; a ranking of one

7 Pielke, Jr., R.A., M.W. Downton, and J.Z. Barnard Miller. (2002). Flood Damage in the United States, 1926- 2000: A Reanalysis of National Weather Service Estimates. UCAR. Boulder, Colorado. http://flooddamagedata.org/full_report.html

8 Vranes, K., and R.A. Pielke, Jr. (2009, August). Normalized Earthquake Damage and Fatalities in the United States: 1900 - 2005. Natural Hazards Review. Pp. 84-101.

25 equaling the most credible and a ranking of six translating to the least credible source.9 Other sources include national insurance associations, trade press and information services catering to the insurance industry (Lloyd‟s List, World Insurance Report, Property Claims Service), press and media reports, international government institutions (United Nations, European Union,

World Health Organization), humanitarian institutions (Red Cross), scientific institutes (National

Hurricane Center, Tsunami Warning Center, Meteo France, Deutscher Wetterdienst, Japan

Meteorological Agency, World Meteorological Organization), and academic sources

(universities) (The Munich Reinsurance Company, 2003).

The Munich Reinsurance Company routinely uses several data processing procedures in a consistent and systematic manner. For economic losses, it first collects loss values at the time of the disaster event. At the end of each month, it adjusts all loss values from disaster events occurring in that month for current market rates. It also collects original loss values in the currency of the country where the disaster event occurred and then converts the losses to euros for business purposes since the company is located in Germany, and finally it converts the loss values from euros to U.S. dollars.10

The NatCatSERVICE© database goes through vetting every three to six months. The vetting process involves first checking the quality of data by noting the ranking of the data source, then evaluating the accuracy of the loss values by comparing them to insurance loss payments. The Munich Reinsurance Company checks all loss values and investigates any

9 Personal communication with Angelika Wirtz at The Munich Reinsurance Company. May 25, 2010. Munich, Germany.

10 Personal communication with Angelika Wirtz at The Munich Reinsurance Company. May 25, 2010. Munich, Germany.

26 questionable values to the extent of contacting local sources if needed.11 The NatCatSERVICE© database proves to be robust since The Munich Reinsurance Company employs thorough and systematic methods to rely on credible sources only, uses consistent data processing procedures, and routinely vets its data.

SHELDUS

The University of South Carolina maintains the Spatial Hazard Events and Losses

Database for the United States (SHELDUS) which provides information on disaster events and human and economic losses in the U.S. from 1960-present. Specifically, it reports the disaster type, dates of occurrence, counties and states of occurrence, number of injured, number of fatalities, and the economic losses from damage.

The SHELDUS obtains its raw data from three sources: the National Climatic Data

Center (NCDC), the National Geophysical Data Center (NGDC), and the Storm Prediction

Center (SPC) with the NCDC serving as the main source of raw data. All three sources are federal scientific institutions and therefore credible sources.

The NCDC has changed its data processing procedure over time which caused the

SHELDUS data processing procedure to be inconsistent over time. Originally, the NCDC maintained a minimum threshold of $50,000 current U.S. dollars for economic losses; however since 1996, the NCDC has dropped the threshold and reports all disaster events causing any economic losses. Accordingly, SHELDUS only reports historic disaster events causing losses above the threshold and all disaster events causing any economic losses from 1996-present. The

SHELDUS is in the process of revisiting all of its historic data to make them compatible by

11 Personal communication with Angelika Wirtz at The Munich Reinsurance Company. May 25, 2010. Munich, Germany.

27 including disaster events that were previously excluded. The SHELDUS has revisited the losses reported from 1960-1979 and corrected the data to report all disaster events that caused any economic loss or fatality. Over time, the database managing institution plans to reprocess the remaining data from 1980-1995 to report all disaster events causing any economic losses thus making the entire dataset consistent. For the purposes of the study in this dissertation, I apply a minimum threshold of $580 million (2008 U.S. dollars) to create consistency across the entire

SHELDUS dataset as well as to match the NatCatSERVICE© database which I also use with

SHELDUS in one of the studies.

The database managing institution vets SHELDUS data annually and offers an updated version of the database each year which includes new disaster events that occur throughout that year and also reprocesses historic data in increments.12 This dissertation utilizes SHELDUS data because they prove to be robust since they meet the criteria for both data sources and vetting, and largely meet the criteria for robust data processing (and is in the process of wholly meeting the criteria). In order to ensure robust analyses in this dissertation, I compensate for the inconsistency in data processing by imposing a minimum threshold for economic losses which ensures consistency for all SHELDUS data used.

Both the Pielke et al. flood damage dataset and the Vranes and Pielke earthquake dataset are research datasets rather than operational databases like the NatCatSERVICE© database and

SHELDUS. This means both datasets are static, covering a fixed period of data instead of ongoing data; therefore they are referred to as datasets rather than databases. As research datasets, this also means that both are presented in the academic literature and therefore have been peer-reviewed and prove to be of research quality.

12 Hazards & Vulnerability Research Institute. (2009). The Spatial Hazard Events and Losses Database for the United States, Version 7.0 [Online Database]. University of South Carolina. Columbia, South Carolina. http://webra.cas.sc.edu/hvri/products/sheldusmetadata.aspx#5

28 Pielke et al. Flood Damage Dataset

The Pielke et al. Flood Damage dataset relies on flood loss data from the National

Weather Service Hydrologic Information Center (NWS-HIC), along with data collected from other federal and state agencies. Since all sources are government institutions, they are credible sources. This dataset is a reanalysis of the NWS-HIC flood loss data so, while the original database contained several inconsistencies, the Pielke et al. dataset resolves those inconsistencies to provide a dataset of uniform flood loss estimates reflecting consistent data processing methodologies, time periods, and geographic regions.

In the reanalysis, the Pielke et al. dataset compared the NWS-HIC estimates against other loss estimates from federal and state agencies. Through this process, it validated the loss values which were in agreement among agencies and investigated the estimates that varied substantially, often resulting in corrections of loss values. Also, the Pielke et al. dataset filled a data void of several years when the National Weather Service had stopped publishing flood losses. It created temporal consistency by calculating annual losses by calendar or fiscal year to match the original data and it imposed spatial consistency by compiling loss data from the 50 states only, excluding the U.S. territories which the original database included in some years and excluded in other years.13 Since the dataset is static, it does not require ongoing vetting therefore the dataset is robust since the sources are credible and the data reflect consistent methodologies throughout.

13 Pielke, Jr., R.A., M.W. Downton, and J.Z. Barnard Miller. (2002). Flood Damage in the United States, 1926- 2000: A Reanalysis of National Weather Service Estimates. UCAR. Boulder, Colorado. http://flooddamagedata.org/full_report.html

29 Vranes and Pielke Earthquake Dataset

This dataset compiles earthquake losses from the National Geophysical Data Center‟s

Significant Earthquake Database (NGDC-s), the University of South Carolina Hazards Research

Lab‟s Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the

Centre for Research on the Epidemiology of Disasters‟ Emergency Events Database (EM-DAT).

These sources are databases maintained by a government institution, a university, and a humanitarian institution, respectively, all of which are credible.

Similar to the Pielke et al. Flood Damage dataset, the Vranes and Pielke Earthquake dataset also serves as a reanalysis dataset, comparing the NGDC-s, SHELDUS, and EM-DAT data against other earthquake databases to validate loss estimates. If multiple loss estimates exist for the same earthquake event, it selects the highest and lowest values and reports them in respective categories in addition to a middle category that includes any loss estimate found in the academic literature that lies in between the highest and lowest values. In the absence of a middle value, the dataset provides an average value.14 The dataset applies this methodology across all of the data thus creating consistency throughout the full dataset. It also fills any data voids by including earthquake events that the three data sources do not include. Since it is a static dataset, there is no vetting procedure needed therefore the Vranes and Pielke Earthquake dataset proves to be robust since it relies on credible sources and maintains consistent data processing procedures (see Table 1.2).

14 Vranes, K., and R.A. Pielke, Jr. (2009, August). Normalized Earthquake Damage and Fatalities in the United States: 1900 - 2005. Natural Hazards Review. Pp. 84-101.

Routine Database Credible Sources Consistent Methodologies Vetting  News agencies  Adjusts economic losses to  Every 3-6  National insurance associations reflect current market rates months  Trade press and information  Converts currency for services for the insurance economic losses industry NatCatSERVICE©  Press and media reports  Government institution reports  Humanitarian institution reports  Scientific institutions  Academic institutions  National Climatic Data Center  Includes all disaster events  Annually  National Geophysical Data causing any economic losses SHELDUS Center (in process)  Storm Prediction Center  National Weather Service  Validates economic loss  Static Hydrologic Information Center values dataset  Federal agencies  Corrects inconsistent values Pielke et al. Flood  State agencies  Fills in missing loss values Damage dataset  Calculates annual losses to match original data  Calculates losses for 50 states  National Geophysical Data  Includes low, middle, and  Static Center‟s Significant Earthquake high estimates of losses dataset Database  Fills in missing loss values Vranes and Pielke  SHELDUS Earthquake dataset  Centre for Research on the Epidemiology of Disasters‟ Emergency Events Database Table 1.2: Databases that meet criteria

30

31

1.6. SIGNIFICANCE OF DISASTER DATA

This dissertation is driven by the underlying notion that data play a very important role in decision making for natural disasters. As disaster researchers state, “Policy makers need accurate disaster loss data for decisions about disaster assistance, policy evaluation, and scientific research priorities.” (Downton & Pielke, 2005). Loss data in particular, offer information on the human and economic losses providing policy makers with evidence to justify disaster prevention and preparedness policy (Guha-Sapir and Below; Guha-Sapir, Hargitt, &

Hoyois, 2004).

Loss data also indicate the extent of damage from a disaster event and therefore assist decision makers with response and recovery decisions as mentioned in a study on disaster loss data: “Accounting for disaster losses does matter because decision makers use loss information as input to a range of important decisions. Among the most important of these are federal government decisions about the provision of disaster relief assistance, e.g., how much, when, and in what form.” (Downton & Pielke, 2005).

In fact, since the goal of disaster policy is to reduce human and economic losses, loss data are of particular importance for appraising current policies and for designing new disaster policies as well. If loss data did not exist or when the quality of loss data is poor, we do not know the magnitude of the problem (Brown Gaddis, Miles, Morse, & Lewis, 2007) or the ability of disaster policies to reduce losses. The database managers of the Centre for Research on the

Epidemiology of Disasters‟ Emergency Events Database (EM-DAT) produced a study which states, “Accurate accounting for disaster impacts is a critical aspect of improving disaster risk management.” (Guha-Sapir & Below).

32

All disaster data, economic losses and other, contribute to informed decision making by providing critical information on the temporal and spatial extent of disaster events, the physical strength of disasters (intensity), and the human and economic losses caused by natural disasters

(severity). These types of information inform decision makers of the magnitude of the problem.

Data compiled over time provide trends in disaster behavior across time and space and trends serve as a tool for policy appraisal. Trends reveal any patterns that occur in natural or social behavior and even portray changes in patterns. For instance, if severe storms increasingly bring more precipitation over the years, trends will capture such pattern changes.

Natural disaster policies aim to reduce losses. Therefore, since trends portray changes in patterns, they reflect whether losses actually decrease over time and consequently whether disaster policy is successful. The National Research Council addresses the use of trends for appraising policy: “Decision makers also use trends and spatial patterns in losses as measures of policy successes and failures and consequently shape thinking about a wide range of policies such as flood insurance and climate policy.” (National Research Council, 1999).

Decision and policy makers rely heavily on loss data which is why the data need to be robust. For instance, risk assessments and cost-benefit analyses are two methods used in policy making and poor-quality data can lead to poor policy decisions: “[a]n inability to provide adequate, useful information …can lead to incorrect estimates of risk, which then affect cost- benefit analyses of proposed development and mitigation projects” (U.S. Commission on Ocean

Policy, 2004). On the other hand, good-quality data can lead to good policy decisions:

“…accounting for disaster losses matters a great deal. To the extent that loss information is used in decision making related to disaster assistance, policy evaluation, and science policy, having

33 accurate and reliable data has potential to improve the information base on which such decisions are made.” (Downton & Pielke, 2005).

This dissertation acknowledges the important role data play in decision making for natural disasters and includes three distinct studies that appraise natural disaster policy through analyses of data on disaster-caused economic losses. Chapter 2 covers the first study which reconciles claims of global losses increasing due to anthropogenic climate change, and regional studies attributing increasing regional losses to socioeconomic factors. Chapter 3 covers the second study which evaluates the level of success of the U.S. National Flood Insurance Program and National Earthquake Hazards Reduction Program in reducing economic losses caused by floods and earthquakes, respectively. Chapter 4 covers the third study which determines the degree to which federal funding for disaster research and development corresponds with the level of impact caused by specific disaster types. Finally, Chapter 5 covers conclusions. After all, this dissertation follows the words of Albert Einstein, “Know where to find the information and how to use it - that's the secret of success.”

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CHAPTER 2: RECONCILING THE APPARENT DISCONNECT BETWEEN CLAIMS ON GLOBAL DISASTER-CAUSED ECONOMIC LOSSES AND REGIONAL LOSSES

2.1 INTRODUCTION

This dissertation utilizes data on disaster-caused economic losses in order to appraise natural disaster policies. This first study appraises the science for disaster policy by investigating why disaster-caused economic losses are increasing. Currently there is an apparent disconnect between (a) claims, based on global disaster loss data, that losses are increasing due to anthropogenic climate change and (b) studies focused on regional losses which find no evidence of such causality. In institutions such as the insurance arena, experts claim that global disaster-caused economic losses are increasing because anthropogenic climate change is causing natural disasters to become more severe. However, disaster literature includes a number of studies on regional disaster-caused economic losses that find increasing regional losses can be explained entirely by socioeconomic factors.

This study reconciles the apparent disconnect by conducting an independent assessment of climate change and global disaster severity through regional analyses derived by disaggregating global loss data. I disaggregate global losses into their regional components and quantify the percentage of the global increase in disaster losses attributable to each region‟s losses. Then I associate this disaggregation to the findings from the existing literature which conclude that each region‟s losses can be explained by socioeconomic factors. I rely solely on existing disaster literature findings to explain each region‟s losses and I apply these findings to this study at face value without concern for the methodologies or any uncertainty associated with each study. I conclude that losses from North American, Asian, European, and Australian storms and floods contribute to 97% of the increase in global losses; due to socioeconomic factors in

35 their region such as increasing wealth, population growth, and increasing development in vulnerable areas. The remaining 3% of the global increase is collectively caused by losses from other natural disasters in Europe and South American storms. The disaggregation is able to fully account for 100% of the global increase in losses. Existing studies in the disaster literature explain the regions contributing to 97% of the global increase but studies have not currently assessed the regions contributing to the remaining 3%. By quantifying the global increase and linking the regional percentages to regional socioeconomic factors documented in the literature, this study finds no additional factors beyond those that can be explained by socioeconomic change to explain 97% of the increase in global losses. Thus, the apparent disconnect is reconciled, and there is no disconnect at all.

2.2 CONFLICTING CLAIMS

Many in the climate community make claims as to whether anthropogenic climate change is affecting the frequency, intensity, and severity of natural disasters.15 The frequency, intensity, and severity of disaster events can be related to each other but can also be mutually exclusive of each other. For instance, disaster severity can increase due to an increasing number of events or more intense events however severity can also increase independent of these factors. This study isolates disaster severity from frequency and intensity and considers it as a mutually exclusive factor. Anthropogenic climate change refers to the shift in climate due to the increase in atmospheric greenhouse gases caused by human activity. The debatable issue links this shift to worsening natural disaster behavior such as more frequently occurring, stronger, and more

15Disaster dialogue uses the term severity with multiple meanings. Some refer to the intensity of the natural phenomenon (e.g. strength of winds, rate of precipitation) while others use severity to mean the impact in damages caused by natural disasters. This dissertation uses the term severity focused on the impact in damages caused by natural disasters; referring to the socioeconomic impact in human and economic losses.

36 damaging disaster events than in the past. The issue has garnered significant attention especially within the political arena, the insurance industry, as well as the research realm. The following excerpts exemplify the debate over the effects of anthropogenic climate change on natural disaster severity specifically:

In the political arena, The Global Humanitarian Forum, a non-profit organization created by the Swiss government and headed by former United Nations Secretary General Kofi Annan, affirms the link in its report on the impact of climate change on society:

…The outlook for the future is not encouraging, with more frequent, more severe and more prolonged weather-related disasters on the horizon. Linear projections suggest that by 2030, the number of weather-related disasters recorded in a single year will be approximately three times higher than the average occurrence rate during the 1975-2008 time span…If these projections prove correct, weather-related disasters due to climate change could affect about 350 million.16

In the insurance industry, the Munich Reinsurance Company links an increase in loss-causing disaster events to climate change:

…loss-related floods have more than tripled since 1980, and windstorm natural catastrophes more than doubled, with particularly heavy losses from Atlantic hurricanes. This rise cannot be explained without global warming.17

The insurance company Allianz issued a report on natural catastrophes where it attributes much of the increase in disaster-caused insured losses to socioeconomic factors but also projects the influence of anthropogenic climate change on disaster severity:

16 Global Humanitarian Forum. (2009). Human Impact Report: Climate Change – The Anatomy of a Silent Crisis. P.13

17 The Munich Reinsurance Company. (2010, November 8). Number of weather extremes a strong indicator of climate change. http://www.munichre.com/en/media_relations/company_news/2010/2010-11-08_company_news.aspx

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The main factor behind increasing insured losses is, very simply, economic growth. Property values rise and areas of population density expand, often in highly at-risk areas. At the same time, the insurance density in these areas is increasing. There is also a link between human activity and climate change, which will have an impact on storm volatility, flood activity and on water levels and coastal regions.18

In strategizing for the insurance industry‟s future, the Association of British Insurers offers projections of the influence of climate change and disaster severity for Great Britain:

The changing climate will directly increase the risk of damage to buildings by flood, storm and subsidence…Climate change is expected to increase the probability of flood events in the future, and the average annual damages arising from them.19

The Financial Times newspaper reported on the link and includes quotes from Axel Lehmann,

Chief Risk Officer of the insurance company Zurich Financial Services who discusses both the current uncertainty in linking climate change to natural disasters and the certainty in linking socioeconomic factors to disaster severity:

It is still proving extremely difficult for scientists to extract a clear sign of the effects of climate change from the normal long-term historic cycles of weather and climate activity… „In terms of severity and frequency, is this type of event happening in a more systematic way? We do not yet have an answer on that‟ he [Lehmann] says. „But on a systematic basis we do know that a growing population puts pressure on the earth and its resources.‟20

Through academic literature, the research realm has contributed claims that support both sides of the debate. In the debate over the link between anthropogenic climate change and the growing

18 Allianz. (2011, March). Allianz risk pulse – Focus: natural catastrophes. https://www.allianz.com/static- resources/en/press/media/documents/v_1300883864000/allianz_risk_pulse_focus_natural_catastrophes_1103.pdf

19 Association of British Insurers. Climate Adaptation – Guidance on insurance issues for new developments. London, England. www.abi.org.uk/Information/48390.pdf

20 Davies, Paul J. (2011, May 2). Climate change: Food and water supplies show strain. Financial Times. http://www.ft.com/cms/s/0/e47c265a-7215-11e0-9adf-00144feabdc0.html#axzz1RLav1nB9

38 severity of U.S. hurricanes, one researcher conclusively attributes increasing economic losses to socioeconomic factors while speculating on the role of anthropogenic climate change:

…economic losses caused by tropical cyclones have increased dramatically. Historical changes in losses are a result of meteorological factors (changes in the incidence of severe cyclones, whether due to natural climate variability or as a result of human activity) and socio-economic factors (increased prosperity and a greater tendency for people to settle in exposed areas) … this increase must therefore be at least due to the impact of natural climate variability but, more likely than not, also due to anthropogenic forcings.21

Some researchers dispute linking anthropogenic climate change to increasingly severe disasters:

…because of issues related to data quality, the low frequency of extreme event impacts, limited length of the time series, and various societal factors present in the disaster loss record, it is still not possible to determine the portion of the increase in damages that might be attributed to climate change brought about by greenhouse gas emissions. This conclusion is likely to remain unchanged in the near future.22

21 Schmidt, S. et al. (2009). Tropical cyclone losses in the USA and the impact of climate change – A trend analysis based on data from a new approach to adjusting storm losses. Environmental Impact Assessment Review, Vol. 29. 359-369.

22 Bouwer, L.M., R.P. Crompton, E. Faust, P. Höppe, and R.A. Pielke, Jr. (2007, November 2). Confronting Disaster Losses. Science, Vol. 318. 753.

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Arena Affirms the link Does not affirm the link Political: The Global Humanitarian Forum X Insurance: The Munich Reinsurance Company X Insurance: Allianz X Insurance: Association of British Insurers X Insurance: Zurich Financial Services X Research: Schmidt et al. X

Research: Bouwer et al. X Research: Pielke and Landsea X Research: Pielke et al. X Research: Kunkel et al. X Research: Changnon X Research: Pielke et al. X Research: Raghavan and Rajesh X Research: Zhang et al. X Research: Miller et al. X Research: Barredo X Research: Barredo X Research: Pielke and Downton X Table 2.1: Conflicting claims

These conflicting claims focus on whether or not anthropogenic climate change can be linked to increasing disaster severity in that the claims either affirm or do not affirm the link (see

Table 2.123). Such claims are part of a larger debate that extends beyond simply affirming the link or not, to actually attributing increasing disaster severity to specific factors, whether anthropogenic climate change or other factors. As seen above, some institutions such as the insurance industry claim that global disaster-caused losses are increasing due to anthropogenic climate change causing natural disasters to become more severe. However an entire subset of disaster literature includes studies that fully explain the increases in regional disaster-caused losses by socioeconomic factors.

Since global and regional losses are inherently connected, these conflicting claims reflect a disconnect by attributing global losses to one cause and regional losses to a different cause.

23 Table 2.1 includes additional studies that serve as the regional studies discussed in Section 2.4.

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Global losses equal the aggregate sum of all regional losses therefore global losses are caused by the aggregate of factors causing regional losses; and these factors should match at both levels.

Similarly, the global loss trend can be explained by the aggregate of regional trends. This study reconciles the apparent disconnect by independently investigating climate change and global disaster severity through regional analyses using an approach that assesses global disaster losses by disaggregating and analyzing them as regional losses. Regional losses do not contribute equally to the composition of global losses or to the increase of global losses over time. Some regions lose more than others because they experience more disaster events; some lose more because they possess more wealth at risk than others. This study therefore quantifies the percentage of the global increase in losses attributable to each region.

2.3 DETECTION AND ATTRIBUTION

Disaster severity refers to natural disasters‟ impacts on society such as the number of deaths, injuries, victims left homeless, and economic losses from damage to property and infrastructure. While the impact on human life and the natural habitat is equally if not more important than economic impact, severity research largely focuses on the influence of anthropogenic climate change on economic losses (also referred to as an anthropogenic climate signal here.) This is likely due to the greater difficulty in directly linking anthropogenic climate change to human and environmental losses where many more factors play a role than with economic losses. In accordance with the majority of severity research, this study also focuses solely on an anthropogenic climate signal in economic losses.

If anthropogenic climate change is influencing disaster severity, disasters should become more severe and therefore economic losses should increase. However losses may increase for a

41 number of reasons other than anthropogenic climate change such as increasing wealth at risk to loss, increasing number of assets owned by a growing population in vulnerable areas, increasing costs due to inflation, or increasing insurance coverage at risk to loss. Therefore severity studies assess whether economic losses are increasing and if so, they attribute the increases to appropriate causes, whether anthropogenic climate change or other factors.

Since this study relates to anthropogenic climate change and disaster losses, it follows the standard protocol for identifying a climate signal. The leading international group for assessing climate change - the United Nations‟ Intergovernmental Panel on Climate Change (IPCC) has established an approach called Detection and Attribution for identifying a possible anthropogenic climate signal in disaster losses (Intergovernmental Panel on Climate Change; Solomon et al.,

2007). Detection “is the process of demonstrating that climate has changed in some defined statistical sense, without providing a reason for that change” (Baede, 2007). For economic loss data, detection of a climate signal requires a trend of increasing losses over at least 30 years - the minimum time period for long-term climate variability24 - which makes the increasing trend statistically significant and indicates phenomena beyond natural variability as the cause for increased economic losses from disasters. If an increasing trend is detected, the next step of attribution “is the process of establishing the most likely causes for the detected change with some defined level of confidence.” (Baede, 2007). Attribution searches for the factors causing the increase. If increased losses cannot be justified by any hypothesized factors, the alternative hypothesis suggests that anthropogenic climate change may be influencing disaster losses.

This dissertation uses a three-step process that aligns with the Detection and Attribution approach by analyzing the global loss trend, disaggregating the global loss trend into its regional

24 National Aeronautics and Space Administration. (2005, February 1). What‟s the difference between weather and climate? http://www.nasa.gov/mission_pages/noaa-n/climate/climate_weather.html

42 components, and attributing regional losses to documented factors. The first step analyzes the trend in annual global economic losses from 1980-2008, with an expectation of an increasing trend if anthropogenic climate change is an influence.25 This corresponds to the Detection phase which requires a trend analysis in order to detect whether losses have consistently increased.

Since the trend in global economic losses is in fact increasing, (see Section 2.3) this study proceeds to the second and third steps of disaggregating global losses into regional losses and attributing the regional losses to documented factors. These steps correspond with the

Attribution phase which seeks to attribute the increasing trend to appropriate factors.

The second step quantifies the percentage of the global increase attributable to each region. I disaggregate global losses into losses by continent and disaster type (e.g. Asian storms,

European floods). Then I calculate each regional source‟s rate of losses as a percentage of the global rate of losses. Rates of economic losses vary among continents because natural disasters affect geographic regions disparately due to differing geographies, topographies, resiliencies, and demographics. Similarly, rates of economic losses vary among disaster types because disasters cause different levels of impact on different timescales. The economic losses caused by hurricanes can differ in orders of magnitude and more frequently than the losses caused by wildfires. For the regional sources with rates comprising the largest percentages of the global rate of losses, I label them as significant contributors to the global increase in losses.

The third step associates the disaggregation of regional sources to existing studies in the disaster literature focused on individual regional economic loss trends. These studies attribute each regional source‟s increasing economic losses to socioeconomic factors in that region. As a

25 Since robust disaster data is only available since 1980, the datasets for this study cover a 28 year time period. The ideal 30+ year time period will be available for future studies using these datasets.

43 result, this study accordingly attributes increasing regional losses to socioeconomic factors thus ultimately attributing increasing global losses to socioeconomic factors.

Figure 2.1: Example sources and causal factors

This dissertation uses original data analyses in the first and second step when analyzing the global trend and disaggregating it into and calculating the percentage of its regional components. The third step associates the quantified disaggregation to existing disaster literature in order to identify the documented factors for increasing regional losses. I conclude that global economic losses are increasing with time, largely dominated by increasing losses from four specific regions and two disaster types: North American, Asian, European, and Australian storms and floods. Combined, these regional sources contribute to 97% of the increase in global losses, all of which can be explained by socioeconomic factors. The remaining 3% of the global loss trend is collectively attributed to the following regional sources: European other (including wildfires, coldwaves, frost, and heatwaves) and South American storms. While this study identifies these regional sources, researchers have not yet conducted studies addressing the

44 causality of this 3% and so the factors causing this portion of the increase in global losses currently remain unexplained.

This chapter proceeds as follows: Section 2.2 explains this study‟s data scope, the database utilized for disaster loss data, and methodologies. Section 2.3 provides the results of the global trend analysis, each regional source analysis, and the calculations of the percentage of the global trend attributed to each regional source. Section 2.4 provides a discussion of the results and the attribution to causal factors. Section 2.5 provides conclusions.

2.4 DATA AND METHODOLOGIES:

2.4.1. Data Overview

Detecting a climate signal requires global loss data. Here loss data consist of economic losses from insured and uninsured dollar losses caused by material damage to property and infrastructure, costs of direct losses such as destroyed agriculture, and indirect economic losses such as loss of revenue from affected businesses temporarily closing.

As this study centers on climate change, I only include weather-related disasters since existing peer-reviewed studies have not affirmed an effect of climate change on geophysical disasters such as earthquakes. As a result, this study includes the following three categories of weather-related disaster types: Storms – hurricanes, cyclones, typhoons, hailstorms, winter storms, snowstorms, blizzards, severe storms, and tornadoes; Floods – flash floods, surges, and regular floods; and Other – wildfires, brush fires, forest fires, cold spells, frost, and heat waves. I exclude geophysical disaster types of earthquakes, tsunamis, volcanic activity, subsidence, and landslides (see Figure 2.2). Furthermore, since this study focuses on economic losses, I only

45 include discrete weather-related disaster events since losses from long-term continuous events cannot be accurately accounted for at present.

Database managing institutions continue ongoing efforts to improve their methodologies for long-term events such as droughts however accurate methods for calculating long-term losses do not currently exist. For instance, the database used in this study reports global drought losses as only 6% of total losses caused by natural disasters worldwide.26 This percentage is likely a significant underestimate as the Federal Emergency Management Agency (FEMA) estimates that annual drought losses in the U.S. alone equal $6-8 billion and the aggregate losses from

North/Central America/Caribbean equal $37.5 billion (2010 USD) thus representing at least 16% of continental losses (Hayes et al.; The Munich Reinsurance Company, 2011). The underestimates of drought losses are due to the difficulty in identifying drought conditions because of their elusive nature. Since economic losses from droughts cannot be systematically assessed or accurately quantified, I exclude droughts from this study. Even with the exclusion of droughts, the results are likely accurate because the database used in this study reports the U.S. as the country experiencing the most drought events worldwide from 1980-2008 and as this study finds, storm losses dominate North American losses by many orders of magnitude. As a result, whatever actual drought losses are, they are likely relatively small compared to other losses such as storm losses.

26 Based on data provided by The Munich Reinsurance Company‟s NatCatSERVICE© database Categories 5 and 6.

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Figure 2.2: Scope of disaster types

Two institutions operate databases with robust disaster economic loss data at the global level. The Munich Reinsurance Company maintains the NatCatSERVICE© database and the

Centre for Research on the Epidemiology of Disasters (CRED) maintains the Emergency Events

Database (EM-DAT). The EM-DAT reports a range of disaster data of which human impact information is its main focus rather than economic loss values. Therefore while the EM-DAT reports economic losses, it receives these data from the NatCatSERVICE© database to supplement its own human impact data. As a result, this study focuses on The Munich

Reinsurance Company‟s NatCatSERVICE© database only. This database is robust in that The

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Munich Reinsurance Company relies solely on credible sources for loss data, upholds consistent methodologies over time for calculating loss values, and regularly vets the data.27

The NatCatSERVICE© database provides robust disaster economic loss data on a global scale since 1980. Specifically, it reports individual natural disaster events occurring anywhere in the world, the disaster type (e.g. storms, floods), the dates of occurrence, countries of occurrence, and the associated dollar losses from damage. The resulting global dataset consists of the compilation of insured and uninsured dollar losses from each disaster event in all countries from

1980-present.

NatCatSERVICE© organizes disaster data into seven categories based on the severity of economic and humanitarian losses caused by the disaster event. This study uses

NatCatSERVICE© data from both Category 5– Devastating catastrophes and Category 6 –

Great natural catastrophes; the categories involving the largest economic losses. In terms of economic losses, Category 5 includes all disaster events that caused > $580 million (2008 USD) of damage; this threshold is the 2008 inflation-adjusted value of the original threshold. The

Munich Reinsurance Company created all loss category thresholds concurrently based on the historic distribution of losses.28 Category 6 includes all disaster events that caused economic losses equal to 5% of national GDP/capita of the country where the event occurred. The

Category 6 economic loss threshold is the Munich Reinsurance Company‟s interpretation for an

27 Credible sources include scientific, government, and non-governmental organizations as well as insurance companies.

28 Personal communication with Angelika Wirtz. The Munich Reinsurance Company. January 12, 2010.

48 economic threshold related to the United Nations definition of a “great disaster” (thousands of fatalities, economy severely affected, extreme insured losses).29

2.4.2. Methodologies

NatCatSERVICE© collects economic loss data from countries around the world. It performs a data processing procedure of collecting economic loss data in the original currency and ultimately converting the losses to U.S. dollar values for the year of the disaster event.

Having the economic losses reported in one currency of U.S. dollars allows me to apply adjustments and calculations on the entire dataset.

The NatCatSERVICE© dataset reports economic loss data as raw unadjusted data meaning the losses are reported in current-dollar values which reflect the cost of the losses in the year that the disaster event occurred. The values do not reflect inflation over time therefore one can only consider current-dollar values in the context of the original year. In order to compare losses across different years, I adjust the economic loss data for inflation to 2008 constant-dollar values. The inflation adjustment involves multiplying the current-dollar loss values by the

Office of Management and Budget‟s (OMB) Gross Domestic Product (GDP) Implicit Price

Deflator.30 This produces a dataset with a consistent unit which then allows for longitudinal comparisons across years.

The adjustment method used in this study starts with losses in foreign currency values, converts the losses to U.S. current-dollar values, and then adjusts for U.S. inflation to constant-

29 United Nations International Decade for Natural Disaster Reduction Department of Humanitarian Affairs. (1992). IDNDR/DHA 1992.

30 OMB GDP Implicit Price Deflator = Office of Management and Budget Gross Domestic Product Implicit Price Deflator.

49 dollar values. A more accurate adjustment method would start with original losses as values in their foreign currency and adjust for inflation of the foreign country‟s currency which would then be converted to U.S. dollars. This method would reflect the true value of foreign losses based on inflation rates and currency values of the foreign country. However, information on foreign inflation rates is not available for all countries. Since this study covers a global scope and a uniform method must be applied to all countries, the methodology used here adjusts for U.S. inflation instead, for which the information is readily available.

This study adjusts the economic loss data for inflation only. For improved accuracy, these data should ideally undergo additional socioeconomic and geospatial adjustments. For example, socioeconomic factors such as increases in population size and population wealth contribute significantly to the magnitude of economic losses. Adjusting the data to reflect these socioeconomic factors alters the trend in losses and depicts a more accurate portrayal of disaster severity. For example, analyses of hurricane damages in the U.S. adjust economic losses for inflation, changes in wealth and population size (Pielke & Landsea, 1998; Pielke et al., 2008).

Vranes and Pielke normalize U.S. earthquake economic losses for inflation, wealth, and population size as well (Vranes & Pielke, 2009). Miller et al. also adjust global disaster losses accordingly (Miller, Muir-Wood, & Boissonnade, 2008). Pielke and Downton adjust flood losses for population size and national wealth (Pielke & Downton, 2000). Changnon adjusts insured losses from weather extremes for population size (Changnon, 1999). Schmidt et al. adjust for the stock of material assets in their assessment of U.S. tropical cyclone losses (Schmidt et al., 2009). Collins and Lowe adjust U.S. hurricane losses for inflation, stock of properties and its contents, and the insurance system (Collins & Lowe). Crompton and McAneney adjust

Australian weather disaster losses for the number and value of dwelling units (Crompton &

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McAneney, 2008). Raghavan and Rajesh normalize tropical cyclone losses in Andhra Pradesh,

India for inflation, population growth, and income increases (Raghavan & Rajesh, 2003).

Similarly, disasters span over large areas causing different impacts in different affected regions. Adjusting data to reflect disaster impact at the regional scale instead of a national scale also depicts a more accurate portrayal of disaster impact. For example, Nordhaus conducts a regional-scale analysis of the U.S. Atlantic coast‟s vulnerability to hurricanes and finds that the most vulnerable areas of the Atlantic coastline are areas with low elevations and high economic activity and capital stock (Nordhaus, 2006).

This study is unable to apply the aforementioned socioeconomic and geospatial adjustments due to lack of availability of data. Due to the global scope, any adjustments must be applied uniformly for all countries and the information needed for socioeconomic and regional adjustments does not exist for all countries and years covered in this study. The lack of available data at the ideal extent or resolution thus limits the adjustment of data for inflation only.

2.4.2.1. Step 1 – Analyzing the trend in global losses

In order to detect an anthropogenic climate signal in disaster-caused economic losses, I first assess the trend in annual global economic losses from 1980-2008 with an expectation of an increasing trend if anthropogenic climate change is influencing disaster severity. In this step, I am interested in the trend over time in economic losses caused by all weather-related disaster types in all countries. Since the database reports individual disaster events, their dates of occurrence, and losses, I calculate annual losses by summing all losses together from disaster events occurring in the same year. I use the following equation:

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∑( ) ( )

where Index i is each individual disaster event that occurred within the year y

This results in a dataset of 28 annual loss values which I plot and apply a best-fit linear regression. The regression provides a trendline which portrays the trend in annual global losses over the 1980-2008 time period.

2.4.3.2. STEP 2 – DISAGGREGATING AND QUANTIFYING THE GLOBAL TREND INTO

REGIONAL COMPONENTS

The second step disaggregates global losses into its regional components in order to determine the percentage of the global trend attributable to each regional trend. After adjusting the economic losses for inflation, I disaggregate the global dataset into six continental subsets:

Africa, Asia (including the Middle East), Australia (including Oceania), Europe, North America

(including the Caribbean), and South America (including Central America). I categorize each disaster event by the continent in which it occurred and those occurring in multiple continents are categorized in the continent with the greatest number of countries affected.

Within each of these continental subsets, I then arrange the disaster events chronologically starting with 1980 and ending with 2008. For each year, I sort the disaster events that occurred in that year into one of three categories (see Figure 2.2): Storms, Floods, or

Other (see Figure 2.3). Often disaster events involve a chain reaction with a first-order disaster spawning second-order disasters (e.g. floods producing landslides). Here, I categorize disaster events into one of three categories based on the first-order disaster type.

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Africa's STORM events AFRICA'S Africa's FLOOD disaster events events Africa's OTHER events

Asia's STORM events ASIA'S Asia's FLOOD disaster events events Asia's OTHER events

Australia's STORM events AUSTRALIA'S disaster events Australia's All discrete FLOOD events weather- Australia's related OTHER events disaster Europe's events STORM events EUROPE'S Europe's disaster events FLOOD events

Europe's OTHER events

North America's NORTH STORM events AMERICA'S North America's disaster events FLOOD events North America's OTHER events

South America's STORM events SOUTH AMERICA'S South America's disaster events FLOOD events

South America's OTHER events

Figure 2.3 Data categorization for a given year’s disaster events

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In a given year, I sum the inflation-adjusted losses from each disaster event in the same category to get the annual continental economic losses by disaster category. The equation for

Asian storm losses is provided below as an example:

∑( ) ( )

where Index i is each individual storm event that occurred within Asia in year y

Once I calculate the continent‟s losses for each of the three disaster categories (storms, floods, other) for each year, I compare them to the Gross Domestic Product (GDP) for that continent and year by calculating losses as a percentage of continental GDP (see Table 2.2 for an example). The annual GDP values come from the International Monetary Fund‟s (IMF) data on national GDP‟s for all countries. The IMF data provide GDP values in U.S. current-dollar values.31,32 Therefore I multiply the GDP values by the OMB Implicit Price Deflator in order to adjust for inflation. The product of the calculation provides GDP values in 2008 U.S. constant- dollar values. I organize the inflation-adjusted national GDP values into the same six continental subsets as the economic loss data. Then I sum together the annual GDP‟s of all countries in one continent which results in the annual continental GDP. The equation is provided below:

31 International Monetary Fund. World Economic Outlook Dataset: Nominal GDP. http://imf.org/external/datamapper/index.php

32 Several countries and their GDP‟s are not included in the IMF database and therefore had to be excluded from the continental GDP calculations. These include Anguilla, Aruba, Borneo Islands, Guadalupe, Guam, Macau, Marshall Islands, Martinique, Micronesia, Montserrat, Northern Mariana Islands, Netherland Antilles, Somalia, St. Maarten, Sumatra, Virgin Islands, and Zaire.

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∑( ) ( )

where Index i is each country within the continent for year y

Annual Annual STORM Annual FLOOD Annual OTHER continental STORM losses as a FLOOD losses as a OTHER losses as a GDP losses % of losses % of losses % of Year (in 2008 ( in 2008 Annual (in 2008 Annual (in 2008 Annual $US $US continental $US continental $US continental billions) billions) GDP billions) GDP billions) GDP

1991 $10,078.65 $5.28 0.05% $1.24 0.01% $3.63 0.04%

Table 2.2: Losses as a % of continental GDP for North America in 1991

With six continents and three disaster types (storm, flood, and other), the full categorization process results in 18 regional sources. Using the calculations described, I calculate the annual losses from each of these 18 sources over the time period 1980-2008. Using the same methodology as in Step 1 for the global loss trend, I plot the annual loss values and apply a best-fit linear regression which provides a trendline of annual continental losses for storms, floods, and other disasters over the years 1980-2008.

Each of the 18 regional trendlines possesses a slope which corresponds to the rate of losses for that regional source; the same holds true for the global trendline and rate of losses. By calculating the slope of each regional source as a percentage of the global slope, I quantify the percentage of the global rate of losses attributable to each regional rate. I label the regional sources with the largest percentages as significant contributors to the global rate of losses.

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2.4.2.3. STEP 3 – ATTRIBUTING REGIONAL LOSSES TO DOCUMENTED FACTORS

Once I identify the regional significant contributors, I refer to the existing peer-reviewed disaster literature in order to determine the factors causing each region‟s losses. The literature includes a number of studies focused on the regional losses and causal factors for losses of each significant contributor and I include all factors that the studies document as causal factors for regional losses.

2.5 RESULTS:

As described earlier, it should be noted that the NatCatSERVICE© data used in this study only include disaster events causing economic losses > $580 million (2008 USD) or equal to 5% of national GDP/capita of the country in which the disaster occurred. This is why some of the following analyses report low values of economic losses in given years. Even if several small disaster events occurred in a year which when summed together would total a significant loss value, no single disaster event is included if it caused losses lower than the stated threshold. As a result, the dataset reports low loss values in years without any large disaster event.

2.5.1. STEP 1 – ANALYZING THE TREND IN GLOBAL LOSSES

In accordance with the IPCC Detection step, I analyze global losses to determine whether the trend in losses is increasing over time. Using global loss data that are adjusted for inflation and calculated as the percentage of global GDP (inflation-adjusted) on an annual basis, I indeed identify an increasing trend (see Figure 2.4) over the study‟s time period of 1980-2008.

Two noticeable peaks occur in 1998 and 2005. The disaster events contributing to the large losses in 1998 occur in two different continents from two different disaster types. In Asia,

56 three separate flood events causing large losses occurred in North Korea,

Bangladesh/India/Nepal, and China. In South America (which also includes Central America in this study), Hurricane Mitch caused even larger losses across Central America and the United

States in 1998. The major peak for losses in 2005 reflects three North American storms; specifically Hurricane Wilma, Hurricane Rita, and Hurricane Katrina.

Global Losses as % of Global GDP 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05

0

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1980

Figure 2.4: Global losses

2.5.2. STEP 2 – DISAGGREGATING AND QUANTIFYING THE GLOBAL TREND INTO

REGIONAL COMPONENTS

Disaggregating the global trend

Figure 2.5 shows the percentage of global losses attributable to each disaster category.

This study categorizes all disaster events and their losses into three disaster categories: Storms – hurricanes, cyclones, typhoons, hailstorms, winter storms and damage, snowstorms, blizzards,

57 severe storms, and tornadoes; Floods – flash floods, surges, and regular floods; and Other – wildfires, brush fires, forest fires, cold spells, frost, and heat waves (see Figure 2.2). More than half of all global losses are attributed to storms while flood losses constitute about one-third of global losses and other losses total less than ten percent of global losses.

Percent of Global Losses Caused by Each Disaster Type

Other 8%

Floods 32% Storms 60%

Figure 2.5: Percent of global losses by disaster type

Figure 2.6 shows each of the six continent‟s annual total losses in actual dollar amount.

It reveals the negligible role of losses from Africa, Australia, and South America. Europe‟s losses rank larger but they do not match the level of losses from Asia and North America. The losses from both of these continents dominate throughout the entire time period.

58

Annual Global Losses by Continent 250000

200000

150000

100000 2008 $US2008millions 50000

0

1987 1980 1981 1982 1983 1984 1985 1986 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Australia/Oceania South America/Central America North America/Caribbean Europe Asia Africa

Figure 2.6: Global losses by continent

Quantifying the global trend

In order to quantify the global trend, I disaggregate global losses by continent and disaster type. Figure 2.7 depicts the calculated percentage of global losses attributable to each continent annually. In certain years a single continent‟s losses dominate over other continental losses; a result of solely including disaster events causing losses above the monetary threshold.

A year when only one continent is depicted with losses means that only one continent experienced disaster events causing losses above the threshold. Similar to Figure 2.6, Figure 2.7 reveals the negligible role of losses from Africa, Australia, and South America. Figure 2.7 shows South America‟s losses in years that do not correspond to years of the continent‟s peak losses. Rather, South America‟s losses are noticeable in years when global losses are low, thus allowing the continent to hold a more dominant percentage of global losses. Europe‟s losses are larger than negligible amounts but not as significant as losses from Asia and North America.

59

Similar to South America, Figure 2.7 depicts European losses as the largest percentage of global losses in years when global losses are relatively low although the six years depicted correspond to the years of the continent‟s peak losses. For the majority of the time period however, Asia and

North America hold the dominant percentages of global losses.

Continental Total Losses as % of Global Losses

100

80

60

40 % % Global of Losses 20

0

1986 2006 1980 1981 1982 1983 1984 1985 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 Australia/Oceania South America/Central America North America/Caribbean Europe Asia Africa

Figure 2.7: Continental losses as a % of global losses

The first set of analyses in this section includes eighteen analyses of losses by regional source based on the three disaster types for each of the six continents. The second set of analyses in this section includes eighteen analyses that determine the rate of losses for each regional source and compares these rates to the global rate of losses. For each of these analyses,

I start with the dataset of global losses. Then I remove the losses of an individual regional source which results in a second dataset of global losses in exclusion of that particular regional source‟s losses. I apply a linear regression to each dataset producing two best-fit linear

60 trendlines. Based on a visual comparison of the trendlines, the greater the difference is between the two, the greater the portion of global losses is from that regional source. I employ this method of excluding regional sources because it provides useful visual representations since a significant contribution from a regional source is more visibly noticeable when removed from total global losses.

Beyond the visual comparison, the trendlines also provide useful information through their slopes which quantify the rate of losses or average amount of losses per year. The slope of the global trendline reflects the global rate of losses and similarly, the slope of each regional trendline reflects the rate of losses for that regional source. The global rate equals the aggregate of all regional rates but these regional rates vary in magnitude and therefore compose the global rate in varying magnitudes. I quantify the percentage of the global rate attributable to each regional rate by calculating the difference between the two slopes and then the percentage.

Both sets of analyses are presented below and organized by continent in order from the largest to smallest percentage of the global rate of losses.

2.5.2.1. NORTH AMERICA

Overall, the values shown in Figure 2.8 seem fairly small because the unit of analysis is losses as a percentage of continental GDP. Since North America has a high GDP, the percentages are consequently lower. However, the losses in actual dollar amounts are often larger from North American events that appear smaller than many of the events causing major losses in other continents.

North America has consistently experienced major storms with every year but one reporting storm losses. Figure 2.8 portrays large storm losses of which most are from United

61

States hurricanes. Hurricane Andrew caused major losses in the Caribbean/United States in

1992. The largest peak found in 2005 reflects losses from Hurricane Wilma, Hurricane Rita, and

Hurricane Katrina. Hurricane Wilma affected the Bahamas/Cuba/Haiti/Jamaica/Mexico/United

States while Hurricanes Rita and Katrina affected the United States. At present, the data report economic losses caused by Hurricane Katrina equal to $135,936,113,600 (2008 USD). The losses caused by these three U.S. hurricanes largely constitute total 2005 losses and create the massive peak seen in losses which is visible not only in North American losses (see Figure 2.8) but also in global losses (see Figure 2.4).

North America - Continental Losses as a % of Annual Continental GDP 1.2 1 0.8 0.6 0.4 0.2

0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Adjusted Direct overall OTHER losses as a % of Annual continental GDP Adjusted Direct overall FLOOD losses as a % of Annual continental GDP Adjusted Direct overall STORM losses as a % of Annual continental GDP

Figure 2.8: Continental losses for North America

After excluding North American storm losses, the resulting dataset differs significantly from global losses as can be seen by their noticeably different trendlines (see Figure 2.9). This means that North American storm losses compose a large portion of global losses. Removing

62

North American flood losses results in a dataset with a slightly different trendline than the trendline for global losses (see Figure 2.10). The trendline for global losses excluding North

American other losses is almost identical to the trendline for global losses (see Figure 2.11).

This suggests that North American storm losses constitute a significant portion of global losses while North American flood losses constitute some portion and North American other losses constitute very little of global losses.

The difference is very large between the slope magnitudes for global losses and global losses that exclude North American storm losses. This means that the rate of North American storm losses is large therefore storm losses are increasing greatly each year. Since the difference in slopes is a large and positive value, it also means that the rate of North American storm losses is a large portion of the global rate of losses and plays a large role in the global increase in losses. On the other hand, the difference in slope magnitudes is much smaller between global losses and global losses that exclude North American flood losses. The same is true for the difference between global losses and the exclusion of North American other losses. Thus the rates of both North American flood losses and North American other losses are small and they do not affect the global increase in losses significantly.

I determine the actual rates of North American storm, flood, and other losses by calculating the difference between the slope magnitudes for global losses and for global losses excluding each of the North American category losses. The results indicate that North American storm losses are increasing at a rate of $1.8 billion (2008 USD)/year. North American flood losses are increasing at a rate of $79.2 million (2008 USD)/year. North American other losses are actually decreasing at a rate of $2.2 million (2008 USD)/year. The rate of North American storm losses constitutes 58.44% of the global rate of losses. The rate of North American flood

63 losses constitutes 2.54% of the global loss rate, and the rate of North American other losses constitutes -0.07% of the global loss rate meaning 0.07% of the global rate is actually decreasing

(see Table 2.2).

Figure 2.9 portrays the dataset of global losses excluding North American storm losses.

The losses in most of the years from 1980-2008 change noticeably after removing storm losses.

One of the more noticeable changes is in 1992 when Hurricane Andrew caused major losses of

$37.6 billion (2008 USD) to the Caribbean and the United States. The most noticeable change is in 2005 when Hurricane Wilma, Hurricane Rita, and Hurricane Katrina occurred. These hurricanes caused losses of $23.9 billion; $17.4 billion; and $135.9 billion (2008 USD) respectively for a staggering total loss of $177.3 billion (2008 USD). This total does not include other storm events that also occurred in 2005 but caused smaller losses such as Hurricane

Dennis. Lastly, in 2008 Hurricane Ike caused $38.0 billion (2008 USD) of losses to

Cuba/Haiti/Dominican Republic/Turks and Caicos Islands/Bahamas/United States. After removing these losses, the resulting global losses are noticeably lower thus reflecting the contribution of North American storm losses to global losses.

64

Global Losses Excluding North American Storm Losses 250000 y = 3116.6x + 5392.9 200000 y = 1295.2x + 11679 150000

100000

50000

0

Losses Losses in $US2008million

1981 1987 1993 1980 1982 1983 1984 1985 1986 1988 1989 1990 1991 1992 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Global losses Global losses - North American storm losses Linear (Global losses) Linear (Global losses - North American storm losses)

Figure 2.9: Global losses excluding North American storm losses

After removing North American flood losses from global losses, the one year with a noticeable change is 1993 (see Figure 2.10). In 1993, the United States experienced the

Midwestern flood which caused $29.1 billion (2008 USD) of losses. After removing North

American other losses, two years show a slight change: 1989 and 1998 (see Figure 2.11). Two disaster events occurred in 1989; a cold wave in the United States and forest fires in Canada. In

1998, a heat wave affected the United States causing $5.4 billion (2008 USD) of losses.

65

Global Losses Excluding North American Flood Losses 250000 y = 3116.6x + 5392.9 200000 y = 3037.4x + 4500.1 150000

100000

50000 Losses Losses in $US2008millions

0

1981 1986 1991 1996 1980 1982 1983 1984 1985 1987 1988 1989 1990 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Global losses Global losses - North American flood losses Linear (Global losses) Linear (Global losses - North American flood losses)

Figure 2.10: Global losses excluding North American flood losses

Global Losses Excluding North American Other Losses

250000

y = 3116.6x + 5392.9 200000 y = 3118.8x + 3964.8 150000

100000

50000 Losses in Losses millions $US 2008

0

1984 2005 2006 1980 1981 1982 1983 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2007 2008

Global losses Global losses - North American other losses Linear (Global losses) Linear (Global losses - North American other losses)

Figure 2.11: Global losses excluding North American other losses

66

2.5.2.2. ASIA

Asia has experienced many natural disaster events such as storms and flood events causing large losses (see Figure 2.12). Forest fires in Indonesia caused the peak in losses in

1982. Two events in 1991 contributed large losses: floods in China and Typhoon Mireille in

Japan. The 1993 peak in losses reflects Typhoon Robin across much of east and southeast Asia.

In the same year, losses also resulted from flash floods in Japan, floods in

Bangladesh/India/Nepal, as well as floods in China. Floods in China also contributed to the large losses in 1996. The largest peak in 1998 reflects losses from multiple flood events. A number of floods and storms (specifically heavy rainstorms) affected North Korea and were likely associated with Typhoon Penny. Additional floods occurred in Bangladesh/India/Nepal as well as in China in 1998.

Asia - Continental Losses as a % of Annual Continental GDP 0.7 0.6 0.5 0.4 0.3 0.2 0.1

0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Adjusted Direct overall OTHER losses as a % of Annual continental GDP Adjusted Direct overall FLOOD losses as a % of Annual continental GDP Adjusted Direct overall STORM losses as a % of Annual continental GDP

Figure 2.12: Continental losses for Asia

67

Of Asian storm, flood, and other losses, the removal of Asian storm losses results in the most noticeably different dataset. Based on the trendlines, the difference is greatest between global losses and the exclusion of Asian storm losses (see Figure 2.13). The next largest difference is between the trendlines for global losses and the exclusion of Asian flood losses (see

Figure 2.14). The trendlines for global losses and the exclusion of Asian other losses appear very similar (see Figure 2.15).

Based on the slopes of the trendlines, the difference is greatest between the slope magnitudes for global losses and global losses excluding storm losses (see Figure 2.13). This indicates that the rate of Asian storm losses is the largest amongst the three Asian disaster categories and these losses are increasing the most per year. Since the difference in slopes is a relatively large and positive value, it also means that the rate of Asian storm losses is a large portion of the global rate of losses and plays a role in the global increase in losses. The next largest difference is between the slopes of global losses and the exclusion of Asian flood losses

(see Figure 2.14) and the smallest difference is between the slopes for global losses and global losses excluding other losses (see Figure 2.15). Thus the rate of Asian flood losses composes the second largest portion of the global rate amongst the three Asian disaster categories. The rate of

Asian other losses composes a very small portion of the global rate and plays a negligible role in the global increase in losses.

I determine the actual rates of Asian storm, flood, and other losses by calculating the difference between the slope magnitudes for global losses and global losses excluding each of the Asian category losses. The results indicate that Asian storm losses are increasing at the rate of $483.0 million (2008 USD)/year. Asian flood losses are increasing at the rate of $315.1 million (2008 USD)/year. Asian other losses are actually decreasing at a rate of $83.8 million

68

(2008 USD)/year. The rate of Asian storm losses constitutes 15.50% of the global rate, the rate of Asian flood losses constitutes 10.11% of the global rate, and the rate of Asian other losses constitutes -2.69% of the global rate of losses meaning 2.69% of the global rate is actually decreasing (see Table 2.2).

After removing Asian storm losses, the noticeable changes in the dataset occur in 1990-

1991 and from 2006-2008 (see Figure 2.13). From 1990-1991, a number of typhoons and tropical cyclones affected Asia including Typhoon Flo, Typhoon Mike, Typhoon Mireille, and

Tropical Cyclone Thelma. More storms affected Asia between 2006-2008 including Typhoon

Bilis, Typhoon Chanchu, Typhoon Durian, Typhoon Kaemi, Typhoon Prapiroon, Typhoon

Saomai, Typhoon Shanshan, Typhoon Xangsane, Cyclone Yemyin, Typhoon Fitow, Typhoon

Krosa, Typhoon Sepat, Typhoon Wipha, Cyclone Sidr, Tropical Cyclone Gonu, Typhoon

Fengshen, Typhoon Fung Wong, Typhoon Hagupit, and Cyclone Nargis. Removing the losses from these events results in a dataset with significantly lower values and reflects the large portion of global losses that Asian storm losses compose.

69

Global Losses Excluding Asian Storm Losses

250000

y = 3116.6x + 5392.9 200000 y = 2633.6x + 6396.9 150000

100000

50000 Losses Losses in $US2008millions

0

1986 1991 1981 1982 1983 1984 1985 1987 1988 1989 1990 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1980 Global losses Global losses - Asian storm losses Linear (Global losses) Linear (Global losses - Asian storm losses)

Figure 2.13: Global losses excluding Asian storm losses

After removing Asian flood losses from global losses, noticeable changes occur in 1987,

1991, 1993, 1995-1996, 1998, and 2003 (see Figure 2.14). While there were a number of flood events, the events with the largest losses include the Chinese flood in 1991 which caused losses of $19.8 billion (2008 USD), the South Korean flood in 1995 that caused losses of $19.9 billion

(2008 USD), the Chinese flood in 1996 that caused losses of $31.3 billion (2008 USD), and the

Chinese flood of 1998 that caused losses of $38.9 billion (2008 USD). After removing these and other flood losses, the resulting dataset shows noticeably lower losses. This reflects the large portion of global losses contributed by Asian flood losses. After removing Asian other losses, the two noticeable changes can be seen in 1982 and 1997 due to two separate forest fires in Asia

(see Figure 2.15). In 1982 Indonesian forest fires caused losses of $17.3 billion (2008 USD) while the 1997 forest fire caused losses of $10.6 billion (2008 USD).

70

Global Losses Excluding Asian Flood Losses

250000

200000 y = 3116.6x + 5392.9 y = 2801.5x + 269.17 150000

100000

50000 Losses Losses in $US2008millions

0

1986 1991 1981 1982 1983 1984 1985 1987 1988 1989 1990 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1980 Global losses Global losses - Asian flood losses Linear (Global losses) Linear (Global losses - Asian flood losses)

Figure 2.14: Global losses excluding Asian flood losses

Global Losses Excluding Asian Other Losses

250000

y = 3116.6x + 5392.9 200000 y = 3200.4x + 3000.3 150000

100000

50000 Losses Losses in $US2008millions

0

1988 2005 1980 1981 1982 1983 1984 1985 1986 1987 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2006 2007 2008 Global losses Global losses - Asian other losses Linear (Global losses) Linear (Global losses - Asian other losses)

Figure 2.15: Global losses excluding Asian other losses

71

2.5.2.3. EUROPE

Europe has consistently experienced severe disaster events from storms and floods during the time period 1980-2008. Figure 2.16 depicts 26 years that report either/both storm and flood losses. In 1982, a heat wave in Italy caused the largest losses. In the same year, a winter storm also caused large losses to France/Switzerland/Spain/Portugal/Austria/Italy. Another winter storm caused losses to France/Norway/Spain/United Kingdom in 1987. In addition, a number of flood events occurred in 1987. One event caused losses in

France/Belgium/Switzerland/Austria/Italy. Another flood event occurred across

Switzerland/Austria/Italy. Flash floods swept through Spain and another flood event caused losses in Italy. In 1990, Winter Storm Daria caused major losses across many countries including Belgium/Denmark/Finland/France/Germany/Ireland/Luxembourg/the Netherlands/

Norway/Poland/Sweden. Floods once again occurred in Italy in 1994. Two major winter storms affected Europe in 1999; Winter Storm Martin caused losses in France/Spain/Switzerland while

Winter Storm Lothar affected France/Germany/Switzerland/Belgium/Austria. Two flood events caused large losses in 2000; one occurred in Italy/Switzerland/France and the other in the United

Kingdom. Additionally, floods and severe storms caused losses in the United Kingdom/Ireland as well as the United Kingdom/France/Belgium/Netherlands. Two flood events causing major losses occurred in 2002. One event caused significant losses to Germany/Austria/Italy/Czech

Republic/Hungary/Moldova/Switzerland/Slovakia while the other caused less but still large losses to Germany/Austria/Italy/Czech Republic/Romania/Bulgaria/Ukraine/United Kingdom/

Russia. It is unclear whether these two flood events were related. Finally a heat wave caused major losses to Austria/Belgium/Bosnia Herzegovina/Bulgaria/Croatia/Estonia/France/

Germany/Hungary/Italy/Netherlands/Norway/ Poland/Portugal/Romania/Slovakia in 2003.

72

Europe - Continental Losses as a % of Annual Continental GDP 0.3

0.25

0.2

0.15

0.1

0.05

0

1999 2006 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2007 2008

Adjusted Direct overall OTHER losses as a % of Annual continental GDP Adjusted Direct overall FLOOD losses as a % of Annual continental GDP Adjusted Direct overall STORM losses as a % of Annual continental GDP

Figure 2.16: Continental losses for Europe

European storm and flood losses constitute a substantive portion of global losses while

European other losses compose only a negligible portion. The trendlines for global losses and global losses that exclude European storm losses show some difference (see Figure 2.17).

Similarly, the trendlines for global losses and the exclusion of European flood losses show some difference (see Figure 2.18). However, the trendlines for global losses and global losses that exclude European other losses appear nearly the same (see Figure 2.19).

Of the three rates of losses, the difference is largest between the slope magnitude for global losses and global losses that exclude European flood losses (see Figure 2.18). As a result, the rate of European flood losses is the largest of the three European rates and thus flood losses are increasing more per year than losses from storms or other events. Since the difference in slopes is a relatively large and positive value, it also means that the rate of European flood losses is a significant portion of the global rate of losses and plays a role in the increase in global losses.

73

The difference is second largest between the slope magnitudes for global losses and the exclusion of European storm losses (see Figure 2.17) and the slope magnitudes are similar between global losses and the exclusion of European other losses (see Figure 2.19). This means that the rate of European storm losses has little influence on the global increase in losses while the rate of European other losses has an even smaller and negligible role.

I determine the actual rates of European storm, flood, and other losses by calculating the difference between the slope magnitude for global losses and the slope magnitude for global losses that exclude each of the European category losses. The value of the difference equals the rate of loss. The results indicate that European storm losses are increasing at a rate of $120.2 million (2008 USD)/year. European flood losses are increasing at a rate of $248.1 million (2008

USD)/year. European other losses are increasing at a rate of $67.2 million (2008 USD)/year.

The rate of European storm losses constitutes 3.86% of the global rate of losses. The rate of

European flood losses constitutes 7.96% of the global rate of losses and the rate of European other losses constitutes 2.16% of the global rate of losses (see Table 2.2).

European storm losses were significantly large in the years 1987, 1990, 1999, and 2007

(see Figure 2.17). A winter storm caused major losses to France/Spain/Norway/United Kingdom in 1987. In 1990 Winter Storm Daria caused losses of $10.3 billion (2008 USD) to

Belgium/Denmark/Finland/France/Germany/Ireland/Luxembourg/the Netherlands/Norway/

Poland/Sweden/United Kingdom. In 1999, three winter storms caused major losses: Winter

Storm Martin, Winter Storm Lothar, and Winter Storm Anatol. In 2007, Winter Storm Kryll caused widespread and significant losses of $10.2 billion (2008 USD) to United

Kingdom/Germany/France/the Netherlands/ Belgium/Denmark/Austria/Switzerland/Czech

Republic/Poland/Slovenia/Belarus/Ukraine.

74

Global Losses Excluding European Storm Losses

250000 y = 3116.6x + 5392.9 200000 y = 2996.4x + 3939.3 150000

100000

50000

Losses Losses in $US2008millions 0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Global losses Global losses - European storm losses Linear (Global losses) Linear (Global losses - European storm losses)

Figure 2.17: Global losses excluding European storm losses

After removing European flood losses from global losses, noticeable changes occur in

1994 and the period 2000-2002 (see Figure 2.18). A number of flood events caused higher losses in these years with two events causing significantly large losses. In 1994, floods in Italy caused losses of $12.6 billion (2008 USD). In 2000, another flood event caused large losses in

Italy/Switzerland/France. A widespread and severe flood event occurred in 2002 across

Germany/Austria/Italy/Czech Republic/Hungary/Moldova/Switzerland/Slovakia causing $19.4 billion (2008 USD) of losses.

75

Global Losses Excluding European Flood Losses

250000

y = 3116.6x + 5392.9 200000 y = 2868.5x + 5344.8 150000

100000

50000 Losses Losses in $US2008millions

0

1986 2002 1980 1981 1982 1983 1984 1985 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2003 2004 2005 2006 2007 2008

Global losses Global losses - European flood losses Linear (Global losses) Linear (Global losses - European flood losses)

Figure 2.18: Global losses excluding European flood losses

The only noticeable change after removing European other losses can be seen in 2003 when two events caused large losses (see Figure 2.19). First, a widespread forest fire affected much of Europe including Austria/Bosnia Herzegovina/Bulgaria/Croatia/

France/Germany/Italy/Portugal/ Slovenia/Spain/Switzerland. Second, a widespread heatwave caused significant losses to Austria/Belgium/Bosnia Herzegovina/Bulgaria/

Croatia/Estonia/France/Germany/Hungary/Italy/ the Netherlands/Norway/Poland/Portugal/

Romania/Slovakia/Slovenia/Spain/Switzerland/United Kingdom.

76

Global Losses Excluding European Other Losses

250000

y = 3116.6x + 5392.9 200000 y = 3049.4x + 5167 150000

100000

50000 Losses Losses in $US2008millions

0

1991 2008 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Global losses Global losses - European other losses Linear (Global losses) Linear (Global losses - European other losses)

Figure 2.19: Global losses excluding European other losses

2.5.2.4. AUSTRALIA

As Figure 2.6 depicts, Australia‟s losses are negligible compared to other continent‟s losses because Australia experienced few disaster events during the time period of this study (see

Figure 2.20). Of those disaster events that occurred, even fewer caused losses above the monetary threshold. The events shown in Figure 2.20 include floods in New South

Wales/Queensland in 1983. Severe storms occurred in New South Wales in 1990. Typhoon

Paka affected Guam in 1997. The largest peak in losses occurred in 1999 due to hailstorms.

Tropical Cyclone Larry caused losses in Queensland in 2006. Hailstorms as well as severe storms affected New South Wales in 2007 and two flood events in Queensland caused losses in

2008.

77

Australia - Continental Losses as a % of Annual Continental GDP

0.35 0.3 0.25 0.2 0.15 0.1 0.05

0

1988 1995 1980 1981 1982 1983 1984 1985 1986 1987 1989 1990 1991 1992 1993 1994 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Adjusted Direct overall OTHER losses as a % of Annual continental GDP Adjusted Direct overall FLOOD losses as a % of Annual continental GDP Adjusted Direct overall STORM losses as a % of Annual continental GDP

Figure 2.20: Continental losses for Australia

The loss data do not report any Australian other disaster events so this analysis includes storm and flood losses only. In addition, the loss data only report two years with Australian flood losses therefore the linear regression trendline and corresponding slope for Australian flood losses are not highly accurate.

The Australian rates of storm and flood losses do not constitute a noticeable portion of global losses. After removing either Australian storm or flood losses from the global loss data, the resulting datasets indicate no noticeable change and the trendlines appear nearly identical for global losses and global losses that exclude either category of Australian losses (see Figure 2.21,

Figure 2.22). This is in agreement with the loss data that indicate Australia did not experience disaster events causing major losses during the time period of this study.

The slope magnitude for the rate of global losses is fairly similar to the slope magnitudes for both global losses that exclude Australian storm losses and global losses that exclude

78

Australian flood losses. Of the two rates, the rate for Australian storm losses composes a slightly larger portion of the global rate. Since the differences in slope magnitudes are very small positive values, the rates for both Australian storm losses and flood losses are accordingly a very small portion of the global rate and neither plays a significant role in the increase in global losses

(see Figure 2.21, Figure 2.22).

I determine the actual rates of Australian storm and flood losses by calculating the difference between the slope magnitude for global losses and the slope magnitude for global losses in exclusion of each of the Australian category losses. The value of the difference equals the rate of loss. The results indicate that Australian storm losses are increasing at a rate of $25.9 million (2008 USD)/year. Australian flood losses are increasing at a rate of $7.7 million (2008

USD)/year. The rate of Australian storm losses constitutes 0.83% of the global rate of losses and the rate of Australian flood losses constitutes 0.25% of the global rate of losses (see Table 2.2).

Global Losses Excluding Australian Storm Losses

250000

y = 3116.6x + 5392.9 200000 y = 3090.7x + 5548.8 150000

100000

50000 Losses Losses in $US2008millions

0

1990 2003 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2004 2005 2006 2007 2008

Global losses Global losses - Australian storm losses Linear (Global losses) Linear (Global losses - Australian storm losses)

Figure 2.21: Global losses excluding Australian storm losses

79

Global losses Excluding Australian Flood Losses

250000

y = 3116.6x + 5392.9 200000 y = 3108.9x + 5422.9 150000

100000

50000 Losses Losses in $US2008millions

0

1983 2006 1980 1981 1982 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 Global losses Global losses - Australian flood losses Linear (Global losses) Linear (Global losses - Australian flood losses)

Figure 2.22: Global losses excluding Australian flood losses

2.5.2.5. SOUTH AMERICA

Over the time period of this study, South America experienced an interesting pattern of losses with periods of negligible losses interspersed with peaks of large losses. Figure 2.23 identifies that the peaks in losses are fairly split between those caused by storms and floods. In

1982, floods in Peru/Ecuador caused major losses. Hurricane Olivia also occurred in 1982 causing major losses in El Salvador/Guatemala/Mexico. Severe storms caused major losses in

Argentina in 1985. Three disaster events contributed to the losses in 1988: Brazil experienced two separate flood events while Hurricane Joan-Miriam affected

Venezuela/Colombia/Panama/Costa Rica/Nicaragua/Honduras/Guatemala/El Salvador/Mexico.

The largest losses occurred in 1998 due to Hurricane Mitch which caused losses of $8.9 billion

(2008 USD) in Honduras/Nicaragua/El Salvador/Guatemala/Mexico/Costa Rica/Panama/United

States.

80

South America - Continental Losses as a % of Annual Continental GDP 0.6 0.5 0.4 0.3 0.2 0.1

0

1980 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2004 2005 2006 2007 2008

Adjusted Direct overall OTHER losses as a % of Annual continental GDP Adjusted Direct overall FLOOD losses as a % of Annual continental GDP Adjusted Direct overall STORM losses as a % of Annual continental GDP

Figure 2.23: Continental losses for South America

During the time period of this study, South America did not experience many disaster events causing major economic losses and the loss data do not report any South American other disaster events so the analyses for South American include storm and flood losses only. After removing either South American storm or flood losses from the global loss data, the resulting dataset depicts no noticeable change. Accordingly, the trendlines are very similar for global losses and global losses that exclude either South American storm or flood losses (see Figure

2.24, Figure 2.25). Thus both storm and flood losses are fairly small and neither South American storm nor flood losses constitute a significant portion of global losses.

The slope magnitudes are nearly the same for global losses, global losses that exclude

South American storm losses, and global losses that exclude South American flood losses. This means the rates of both South American storm losses and flood losses are very small and equate to a low average amount of losses per year. Since the difference in slope magnitudes is a very

81 small and positive value between global losses and the exclusion of South American storm losses, the rate for storm losses plays a negligible role in the global rate. Since the difference in slope magnitudes is a very small and negative value between global losses and the exclusion of

South American flood losses, the rate for flood losses is decreasing and plays a negligible role in the global rate.

I determine the actual rates of South American storm and flood losses by calculating the difference between the slope magnitude for global losses and the slope magnitude for global losses that exclude each of the South American losses. The value of the difference equals the rate of loss. The results indicate that South American storm losses are increasing at a rate of

$14.1 million (2008 USD)/year. South American flood losses are actually decreasing at a rate of

$13.7 million (2008 USD)/year. The rate of South American storm losses constitutes 0.45% of the global rate while the rate of South American flood losses constitutes -0.44% of the global rate of losses meaning 0.44% of the global rate is actually decreasing.

After removing storm losses, 1998 is the only year with a noticeable difference from the global dataset; due to Hurricane Mitch which caused losses of $8.9 billion (2008 USD) to

Honduras/Nicaragua/El Salvador/Guatemala/Costa Rica/Panama/Mexico/United States. While other storm and flood events have affected South America during the time period of this study, no other single event caused significant losses that are noticeable (see Figure 2.24, Figure 2.25).

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Global Losses Excluding South American Storm Losses

250000 y = 3116.6x + 5392.9 200000 y = 3102.5x + 4952.9 150000

100000

50000

Losses Losses in $US2008millions 0

1993 2002 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1994 1995 1996 1997 1998 1999 2000 2001 2003 2004 2005 2006 2007 2008

Global losses Global losses - South American storm losses Linear (Global losses) Linear (Global losses - South American storm losses)

Figure 2.24: Global losses excluding South American storm losses

Global Losses Excluding South American Flood Losses

250000 y = 3116.6x + 5392.9 200000 y = 3130.3x + 4597.1 150000

100000

50000

Losses Losses in $US2008millions 0

1995 2003 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1996 1997 1998 1999 2000 2001 2002 2004 2005 2006 2007 2008

Global losses Global losses - South American flood losses Linear (Global losses) Linear (Global losses - South American flood losses)

Figure 2.25: Global losses excluding South American flood losses

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2.5.2.6. AFRICA

While severe natural disasters impact Africa, this study does not truly reflect the severity for two reasons. First, this study focuses on severity in economic losses. Africa experiences severe humanitarian losses but in the context of economic losses, the continent‟s losses are smaller due to the poverty of various African nations and any economic losses that occur are usually lower than the monetary thresholds upheld in this study. Second, of all disaster types, droughts have a major impact on Africa but this study excludes long-term disaster types such as droughts (see Figure 2.2). As a result, Africa‟s economic loss record includes only two floods causing eligible losses.

Figure 2.26 portrays an increase from the first to second flood, however the data indicate that flood losses were not significantly higher in the second flood. The flood losses from the

1987 flood totaled $871.7 million (2008 USD) while the flood losses from the 2000 flood totaled nearly the same amount at $876.3 million (2008 USD). Rather the GDP of the continent decreased from that in 1987 to the 2000 GDP. Since the unit of analysis is losses as a percentage of GDP, the increase shown in Figure 2.26 reflects a percentage increase due to decreasing GDP.

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Africa - Continental Losses as a % of Annual Continental GDP 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02

0

1984 1985 1986 1987 1980 1981 1982 1983 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Adjusted Direct overall FLOOD losses as a % of Annual continental GDP

Figure 2.26: Continental losses for Africa

While the analysis of African flood losses follows, one cannot consider the linear regression trendline and corresponding slope as highly accurate because the data only report two years with losses. The best-fit trendline for global losses is nearly identical to the best-fit trendline for losses that exclude African flood losses (see Figure 2.27). This concludes that

African losses constitute a negligible portion of global losses. Similarly, the slope magnitudes are essentially the same between global losses and the exclusion of African losses. Since the difference is a very small and negative value, the rate of African losses plays a negligible role in the global rate. The rate of African losses constitutes -.00013% of the global rate of losses which is insignificant enough to conclude that the African rate of losses does not influence the rate of global losses.

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Global Losses Excluding African Losses

250000

y = 3116.6x + 5392.9 200000 y = 3117x + 5326.4 150000

100000

50000 Losses Losses in $US2008millions

0

1986 1991 1981 1982 1983 1984 1985 1987 1988 1989 1990 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1980 Global losses Global losses - African total losses Linear (Global losses) Linear (Global losses - African total losses)

Figure 2.27: Global losses excluding African losses

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The following tables summarize the rates of losses for each regional source and the percentage of the global rate of losses attributable to each regional rate.33

Rate of Rate of Global Regional Losses Excluding Source’s Rate of Global Regional Source’s Losses Regional Losses Losses (in 2008 Rate as a % (in 2008 USD (in 2008 USD USD of Global million/year) million/year) million/year) Rate 3116.6 North American storms 1295.2 1821.4 58% Asian storms 2633.6 483 15% Asian floods 2801.5 315.1 10% European floods 2868.5 248.1 8% European storms 2996.4 120.2 4% North American floods 3037.4 79.2 3% European other 3049.4 67.2 2% Australian storms 3090.7 25.9 0.83% South American storms 3102.5 14.1 0.45% Australian floods 3108.9 7.7 0.25% North American other 3118.8 -2.2 -0.07% South American floods 3130.3 -13.7 -0.44% Asian other 3200.4 -83.8 -3% Table 2.3 : Loss rates for each regional source

2.6 DISCUSSION

Table 2.3 summarizes the percentage of the global rate in losses attributable to each regional source. Since the global rate is increasing overall, I consider only the regional sources with increasing rates of losses and I recalculate the percentage of the global rate increase

33 Several regional sources are not included if they had insignificant/no economic losses reported for the time period of this study.

87 attributable to each regional rate; the recalculations resulting largely in the same percentages (see

Figure 2.28).

------Other------

European flood losses 8%

Asian flood losses North 10% American storm losses 57% Asian storm losses 15% Other: European storm losses: 4% North American flood losses: 2% European other losses: 2% Australian storm losses: 0.81% South American storm losses: 0.44% Australian flood losses: 0.24%

Figure 2.28: Percentage of global increase attributable to regional losses

Figure 2.28 indicates that North American storm losses contribute to more than half of the global increase in losses at 57%. This is a disproportionate contribution of economic losses considering that the GDP for North America only constitutes 33% of global GDP (see Figure

2.29). On the contrary, the GDP for Europe constitutes a similar 34% of global GDP yet

European losses (which reflect the collective sum of European storm, flood, and other losses) only contribute to 14% of the global increase in losses. The 25% contribution from Asian losses

88 matches proportionally to the Asian GDP constituting 25% of global GDP. For South America,

Australia, and Africa, all three continents contribute a percentage of losses much lower than their

GDP is a percentage of global GDP.

Africa 2% Australia 2%

North Asia America 25% 33%

Europe South 34% America 4%

Figure 2.29: GDP of each continent as a percentage of global GDP

Figure 2.28 indicates that 96% of the increase in global losses can be attributed to losses from the following regional sources listed from largest percentage to least: North American storms, Asian storms, Asian floods, European floods, European storms, and North American floods. Since these sources contribute the largest percentages, I label them as the significant contributors to the increase in global losses.

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After disaggregating and quantifying the global increase in losses to regional components, I associate the disaggregation to the disaster literature. A number of different factors can cause increasing economic losses including anthropogenic climate change, increasing wealth at risk to loss, increasing number of assets owned by a growing population in vulnerable areas, increasing costs due to inflation, and increasing insurance coverage at risk to loss. The disaster literature includes a number of studies that determine the causal factors for the increase in losses from each regional source identified as a significant contributor in addition to

Australian storm and flood losses, thus accounting for the regional components contributing to

97% of the global increase. In the third step I synthesize the relevant literature in order to attribute the increase in global losses to documented causal factors.

2.6.1. STEP 3 – ATTRIBUTING REGIONAL LOSSES TO DOCUMENTED FACTORS

Several studies focus on North American storm losses and the factors causing the large increase in losses over time. Two studies on U.S. hurricane losses both determine that economic losses are increasing due to inflation, population growth in vulnerable coastal areas, and increasing wealth (Pielke & Landsea, 1998; Pielke et al., 2008). When these studies adjust the economic loss data to reflect these causes, losses no longer show an increasing trend. Schmidt et al. attribute the majority of the increase in U.S. hurricane losses to population growth in vulnerable areas and increased wealth. However they attribute a small portion of the increase to climate variability, although inconclusively between natural and anthropogenic climate variability (Schmidt, Kemfert, & Höppe, 2010; Schmidt et al., 2009).

Kunkel et al. attribute the increase in U.S. storm losses (thunderstorms and hailstorms) to inflation, population growth, increasing property at risk, increasing property value, and

90 increasing liability coverage (Kunkel, Pielke, & Changnon, 1999). Changnon attributes increasing U.S. thunderstorm losses to inflation, shifts in insurance coverage, increased development, increased wealth, and population growth in vulnerable areas (Changnon, 2001).

Pielke et al. completed a severity study for Cuban hurricanes where they determine that increasing economic losses are wholly attributed to inflation, population growth, and increasing wealth (Pielke et al., 2003). Thus for North American storm losses, all studies conclusively explain the increase in losses entirely by socioeconomic changes with no attribution to anthropogenic climate change. Since this analysis identified North American storm losses as the source for 57% of the increase in global losses, it accordingly attributes 57% of the increase to socioeconomic factors.

Two existing studies focus on Asian storm losses. Raghavan and Rajesh attribute increased economic losses from Indian tropical cyclones to inflation, population growth, and increasing per capita domestic product (Raghavan & Rajesh, 2003). Similarly Zhang et al. determine that economic losses from Chinese tropical cyclones are increasing due to inflation, growing population, and increased wealth (Zhang, Wu, & Liu, 2009). For Asian flood losses, a study by Miller et al. attributes a portion of increasing Asian flood losses to improved flood data since the 1980‟s for China and Japan. This same study further acknowledges large losses from

Chinese floods due to increasing economic development (Miller, Muir-Wood, & Boissonnade,

2008). These three studies attribute increasing Asian storm and flood losses to several socioeconomic factors but not anthropogenic climate change. Since this analysis identified

Asian storm losses as the source for 15% of the increase in global losses, it accordingly attributes

15% of the increase to socioeconomic factors. Similarly, since this analysis identified Asian

91 flood losses as the source for 10% of the increase in global losses, it accordingly attributes 10% of the increase to socioeconomic factors.

In the study on European flood losses, Barredo explains increasing European flood losses by population growth in vulnerable areas, increasing development in vulnerable areas, increase in exposed values, increasing vulnerability of development and assets, failure of flood protection systems, and changes in environmental conditions (Barredo, 2007). The range of causes refers to socioeconomic factors including political and environmental factors. However Barredo does not cite anthropogenic climate change as a cause for European flood losses. Since this analysis identified European flood losses as the source for 8% of the increase in global losses, it accordingly attributes 8% of the increase to socioeconomic factors.

Barredo also assesses European storm losses in a separate study on European windstorms. While the study covers only one of the many disaster types categorized as storms, it serves as a starting point for attributing European storm losses to appropriate causal factors.

Additional studies on European storms will improve the robustness of European severity studies, particularly if new studies focus on European winter storms which are the most frequently occurring storm type to cause significant losses in Europe.34 Barredo attributes European windstorm losses to increasing standard of living, real per capita wealth, and improved disaster data collection (Barredo, 2010). Thus he finds socioeconomic factors responsible for European storm losses; not anthropogenic climate change. As this analysis identified European storm losses as the source for 4% of the increase in global losses, it accordingly attributes 4% of the increase to socioeconomic factors.

Pielke and Downton assess the economic losses caused by U.S. floods. They attribute increasing flood losses to increased wealth, population growth, and inflation. By performing

34 Based on the data presented in The Munich Reinsurance Company‟s NatCatSERVICE© database.

92 sensitivity studies with each of these factors, they conclude that increasing wealth is the largest cause of increasing U.S. flood losses followed by inflation and then population growth (Pielke &

Downton, 2000). They find socioeconomic factors responsible for increasing losses and not anthropogenic climate change. Therefore, the 2% of the increase in global losses attributed to

North American flood losses correlates to 2% attributed to socioeconomic factors.

Crompton and McAneney assess economic losses caused by Australian tropical cyclones, thunderstorms, hailstorms, floods, and bushfires (Crompton & McAneney, 2008). Tropical cyclones, thunderstorms, and hailstorms fit in this study‟s category of Storms, floods correspond directly with the category Floods, and bushfires fit in the category of Other. They normalize the data for the number and value of dwellings over time; these adjustments are similar to adjusting losses for inflation, population, and wealth. They conclude that these socioeconomic factors are causing Australian storm, flood, and other losses, not anthropogenic climate change. Therefore the 1.05% of the increase in global losses attributed to Australian storm and flood losses correlates to 1.05% attributed to socioeconomic factors.

2.7. CONCLUSION

This study reconciles the apparent disconnect between claims that global disaster-caused losses are increasing due to anthropogenic climate change, and studies finding that regional losses are increasing due to socioeconomic factors. It disaggregates global losses, quantifies the percentage of the global increase attributable to each regional component, and associates the disaggregation to the disaster literature in order to determine the causal factors for increasing losses.

93

Global economic losses are increasing largely due to losses from four specific regions and two disaster types: North American storms and floods, Asian storms and floods, European storms and floods, and Australian storms and floods. These regional sources contribute to 97% of the increase in global losses of which 57% is attributed to losses caused by North American storms, 15% to losses from Asian storms, 10% to Asian flood losses, 8% to losses caused by

European floods, 4% attributed to losses caused by European storms, 2% to losses from North

American floods, 0.81% to Australian storm losses, and 0.24% to Australian flood losses. Based on the findings of existing studies in the literature, this study finds no additional factors beyond those that can be explained by socioeconomic change to explain 97% of the increase in global losses thus the apparent disconnect is reconciled and there is no disconnect at all. These socioeconomic factors include inflation, growing populations in vulnerable areas, increasing assets of a growing population, increasing wealth at risk, increasing liability coverage, increasing property at risk, increasing property value, increasing development, failure of protection systems, and improved data collection. The remaining 3% of the increase in global losses is attributed to the following regional sources: European other losses (including losses from wildfires, coldwaves, frost, and heatwaves) and South American storm losses. Currently the disaster literature does not include any studies that have assessed these regional sources so the factors causing 3% of the increase in global losses remain unexplained at present.

I rely solely on existing disaster literature findings to explain each region‟s losses and currently all studies conclusively attribute each region‟s increasing losses to socioeconomic factors. At present, if any uncertainty exists in these studies, researchers are only speculating on possible explanations. Future research may overturn these results if new findings can

94 conclusively explain any uncertainty or if with time, disaster severity patterns show correlation to anthropogenic climate change conditions.

The findings of this study should remain valid in the near future despite predictions of new climate change impacts on natural disasters. Some disaster experts predict new impacts of anthropogenic climate change materializing in upcoming years that would seemingly alter this study‟s findings. Examples of new impacts include an increase in precipitation causing an increasing number of flood events or more severe hurricanes due to warming sea surface temperatures. While some researchers dispute such predictions, even if new impacts materialize, they will not alter this study‟s findings for at least three decades since alternate findings should be supported by new data over a minimum of 30 years in order to be a robust and accurate climate analysis. In addition, the percentages quantified by this study will remain accurate for many years since they are long-term calculations derived from 28 years of data. Long-term calculations will not be altered with the inclusion of a few new data points from the next few years. Rather, long-term calculations need many more data points (many more years‟ worth of data) in order for values to change.

Crompton et al. conclude that an anthropogenic climate signal will not be identifiable in

U.S. tropical cyclone losses for another 120-550 years with even longer timescales expected for other global weather-related natural disasters (Crompton, Pielke, & McAneney, 2011). As a result, they “urge extreme caution in attributing short term trends (i.e., over many decades and longer) in normalized US tropical cyclone losses to anthropogenic climate change. The same conclusion applies to global weather-related natural disaster losses in the near future.”

(Crompton, Pielke, & McAneney, 2011). As quoted earlier, Pielke et al. similarly suggest that it is unlikely to identify a climate signal in disaster losses in the near future:

95

…because of issues related to data quality, the low frequency of extreme event impacts, limited length of the time series, and various societal factors present in the disaster loss record, it is still not possible to determine the portion of the increase in damages that might be attributed to climate change brought about by greenhouse gas emissions. This conclusion is likely to remain unchanged in the near future.”35

It becomes even more complicated to identify a climate signal in losses other than economic losses. While natural disasters impact human lives and the natural habitat, it is very difficult to detect a climate signal in fatality patterns or ecosystem damage because so many additional factors play a role and conflate the impact, making it difficult to isolate individual factors. For instance, fatalities associated with natural disasters can be caused by many different factors. If a severe flood causes large fatalities in a town‟s population, it is difficult to pinpoint whether victims drowned because the flood was more intense due to climate change, the flooding was the outcome of a single bad storm, the victims‟ homes were built in an area vulnerable to flooding, lack of warnings failed to evacuate the population, or the victims did not heed the warnings. Perhaps the fatalities were not caused by drowning but by post-disaster conditions such as dehydration or cholera due to a lack of clean drinking water. These are all common causes for fatalities related to natural disasters, particularly in the developing world. The challenges in detecting a climate signal in fatalities or ecosystem damage do not relate to economic losses. Economic losses provide a more straightforward scenario and as a result, this study only focuses on economic losses which ultimately provides a perspective biased toward the impact on the developed world over the developing world, but also allows for more conclusive findings.

Several factors could improve the robustness of this study. First, extending the time period of analysis would improve the quality since the robustness of climate studies increases as the time period of analysis increases. Identifying a climate signal requires a minimum time

35 Bouwer, L.M., R.P. Crompton, E. Faust, P. Höppe, and R.A. Pielke, Jr. (2007, November 2). Confronting Disaster Losses. Science, Vol. 318. 753.

96 period of 30 years. This study nearly meets the minimum requirement and could improve robustness with additional decades‟ worth of data. Also, the inclusion of droughts in some capacity would improve the comprehensiveness of this study since the omission skews the results slightly, particularly in underestimating African losses. However, methodologies to identify droughts and gauge their impacts remain undefined currently and therefore I exclude droughts from the scope of this study.

The comprehensiveness of this study would also improve by adding new literature to the third step of this study which involves the synthesis of existing literature. The extent of the current literature is limited at present but as new peer-reviewed literature arises, the breadth would improve as new studies are added to the current collection of regional studies. Finally, the rigor of this study would improve with two changes to methodology. Increasing the sample size of datasets would increase the rigor of results, although other existing databases do not currently collect global disaster-caused economic loss data of comparable quality to the NatCatSERVICE© database. The rigor would also improve if this study adjusted data for regional socioeconomic factors such as regional inflation or GDP. Adjustment methodologies such as these however, are not possible for a global study due to lack of data for most regions around the world; even if the data exist for some regions, the data need to be available for all regions to maintain consistency in a global study. Each of the factors listed would improve the rigor of this study but are currently not possible. However they are more likely in the future therefore they are worth noting for any future continuation of this study.

This study provides multiple findings that could improve decision making for natural disaster policy. It determines that global losses are increasing and then quantifies and attributes the increase to regional socioeconomic factors including increasing wealth, growing populations

97 in disaster prone areas, and increasing vulnerability caused by the growing population and its growing assets at risk. Since one purpose of disaster policy is to limit economic losses, decision makers can look to the socioeconomic factors presented here in order to create effective policy solutions. However, the specific socioeconomic factors identified here pose some challenges to developing a pragmatic policy solution. For example, increasing wealth is a sign of societal progress and therefore hampering wealth is not desirable. Reducing the population growth in vulnerable areas is an effective but politically infeasible solution. The optimal solution lies in decreasing societal vulnerability through measures such as improving the resilience of buildings and infrastructure in disaster-prone areas, increasing disaster protection measures such as flood diversion channels and storm surge barrier wetlands, and building redundancy in systems to avoid shutdown in a disaster event. These types of adaptive measures increase protection for society and decrease potential for disaster losses in the face of any and all circumstances, whether it is more frequently occurring, intense, or severe natural disasters caused by socioeconomic factors or anthropogenic climate change.

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CHAPTER 3: A NEW METRIC FOR GAUGING SUCCESS OF THE NFIP AND NEHRP: COMPARING PROJECTIONS OF PRE-POLICY LOSSES TO ACTUAL LOSSES

3.1. INTRODUCTION

The theme of this dissertation involves using data on disaster losses in order to appraise natural disaster policy. In this second study, I analyze trends in U.S. flood losses and earthquake losses as a metric for gauging whether the national flood and earthquake policies (the National

Flood Insurance Program and the National Earthquake Hazards Reduction Program, respectively) have been successful in reducing economic losses as their legislated mandates require.

I create two distinct subsets of disaster loss data: losses before and after the enactment of the policy. The losses prior to the enactment of policy reflect no influence of that policy and provide a baseline for comparison when determining whether the policy has had any influence on losses. I create the trend in these pre-policy losses and I project it into the post-policy era; this projection reflects the trend expected in losses had no policy been enacted. The losses after the enactment of policy reflect any influence that the policy has imparted on losses and I similarly create a trend in these post-policy losses. In comparing the projection to the trend in actual losses, any difference between the two lines reflects the policy‟s impact on losses. I only consider the policy to be successful if the outcome meets a baseline level where post-policy losses are less than the projected losses and therefore the trend from the post-policy era is smaller than the projection. This is the only outcome that shows evidence that the policy is able to reduce losses to a level lower than would be expected had there been no policy at all.

The comparison can result in three possible outcomes: the trend in actual losses is larger than the projection, the same as the projection, or smaller than the projection (see Table 3.1). If

99 the trend in actual losses is larger than the projection, then actual losses are larger than projected losses suggesting that actual losses proved to be larger than the level of losses we would have expected without the policy. This outcome shows no evidence that the policy has had an influence in reducing losses to the baseline level - a level lower than losses would be had no policy been enacted. If the trend in actual losses is the same as the projection, then actual losses prove no different than the level of losses we would have expected had no policy been enacted.

This outcome also shows no evidence that the policy has had any influence in reducing losses to the baseline level. However, if the trend in actual losses is smaller than the projection, then actual losses are less than projected losses suggesting that actual losses are lower than the level of losses we would have expected had no policy been enacted. This outcome suggests that the policy has played a role in reducing losses and it shows evidence that the policy has successfully had an influence in reducing losses to the baseline level.

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Possible Illustration Conclusion Outcomes

Actual Shows no evidence that trend is the policy has had an #1 larger than influence in reducing the losses to the baseline projection level.

Shows no evidence that Actual the policy has had an trend is the #2 influence in reducing same as the losses to the baseline projection level.

Actual Shows evidence that the trend is policy has successfully #3 smaller had an influence in than the reducing losses to the projection baseline level.

Table 3.1: Possible outcomes

The United States maintains two federal policies focused on natural disasters at a national scale: the National Flood Insurance Program (NFIP) and the National Earthquake Hazards

Reduction Program (NEHRP). These policies have legislative mandates to reduce losses caused by floods and earthquakes, respectively. Therefore to determine the success of these policies in some part, one must gauge their ability to reduce losses. The mandates for both policies are very

101 broad and simply state that losses should be reduced but without details on the specific types of losses to reduce. Economic losses encompass losses from frequently occurring standard- intensity events, losses from infrequently occurring extraordinary-intensity events, direct damage to infrastructure, indirect losses from service interruption, etc. These losses differ greatly from each other and require very different reduction approaches. The policies also lack prescriptive conditions as they do not describe any approaches that should be used to reduce losses. In the absence of any specifics, the policies are mandated simply to reduce losses and this study accordingly gauges simply whether the policies successfully reduce losses to a baseline level.

Disaster researchers and policy analysts alike have appraised the impact of these policies for many decades now, with conflicting conclusions of policy success in reducing economic losses caused by floods and earthquakes. This study provides a new assessment of the NFIP and

NEHRP‟s success or failure in reducing economic losses by comparing the actual trend in losses after the enactment of policies, to a projection of the trend exhibited by losses prior to the enactment of policies. Comparing the post-policy trend to the pre-policy trend projection reveals whether the policy has had an impact on reducing economic losses.

3.2. METHODOLOGIES

This study involves three analyses of disaster losses that adjust for changing socioeconomic factors: inflation-adjusted economic losses, economic losses per capita, and economic losses as a % of GDP. In addition, a number of sensitivity studies explore both the impact on disaster losses from major policy amendments to the NFIP and NEHRP, as well as the impact on the trend in losses from extreme disaster events that cause losses on larger orders of magnitude than other events. The first analysis examines inflation-adjusted economic losses

102 over time. Both the flood loss dataset and the earthquake loss dataset report raw unadjusted data meaning the losses are reported in current-dollar values reflecting the cost of the losses in the year that the disaster event occurred. The values do not reflect inflation over time therefore current-dollar values can only be considered in the context of the original year.

In order to compare losses across different years, I adjust the losses from both datasets for inflation to 2009 constant-dollar values. This adjustment involves multiplying the current-dollar loss values by the Bureau of Economic Analysis (BEA) Implicit Price Deflators for Gross

Domestic Product.36 This produces a dataset with a consistent unit which then allows for longitudinal comparisons across years. The BEA provides Implicit Price Deflators on a quarterly and annual basis for most years; the inflation adjustment in this study uses annual BEA Implicit

Price Deflators. Since the BEA Implicit Price Deflator values are available starting in 1929, the time period for all analyses and sensitivity studies starts in 1929 accordingly.

The second analysis assesses economic losses per capita in order to determine the socioeconomic influence of population size on economic losses. As the population size grows, more individuals may be experiencing losses causing the trend in economic losses to increase over time. This analysis determines whether the growing population is influencing the trend in losses by analyzing economic losses per capita; calculated by dividing inflation-adjusted economic losses in a given year by the population size for that year. The U.S. Census Bureau

36 U.S. Department of Commerce Bureau of Economic Analysis. Table 1.1.9. Implicit Price Deflators for Gross Domestic Product. Retrieved from http://www.bea.gov/national/nipaweb/DownSS2.asp?3Place=N#XLS

103 provides readily accessible U.S. population data for the contiguous United States starting in 1900 and the continental United States starting in 1950.37

The third analysis compares economic losses to the U.S. Gross Domestic Product (GDP) in order to determine the socioeconomic influence of growing wealth on economic losses. As the nation grows wealthier, individuals may own more assets that are vulnerable to loss. This analysis determines whether increased wealth is influencing the trend in losses by calculating inflation-adjusted economic losses in a given year as a percent of that year‟s GDP. The Bureau of Economic Analysis provides annual current-dollar U.S. GDP data starting in 1929 and quarterly current-dollar U.S. GDP data starting in 1947. This analysis uses BEA annual current- dollar U.S. GDP data which I adjust for inflation to 2009 dollars using the same BEA Implicit

Price Deflators for GDP as those used to adjust the loss data in the first analysis. For continuity purposes, this analysis starts in the same year of 1929 as the first and second analysis.

After completing the data analyses, I create the projections for each one. The following explanation describes the methodology for the NFIP and flood loss analyses. I separately plot the results of each of the three data analyses described above: inflation-adjusted flood losses, flood losses per capita, and flood losses as a % of GDP. These datasets include losses over the entire time period of the study which I label as actual losses. I compare them to a subset of losses over the time period prior to the enactment of the policy; labeled as Pre-(Policy x) losses.

For the NFIP, while Congress enacted the policy in 1968, it was only on a voluntary basis until the 1973 amendment made it mandatory and state and local governments began noticeable implementation. Therefore the data subset includes flood losses prior to 1973, from

37 U.S. Census Bureau. Historical National Population Estimates: July 1, 1900 to July 1, 1999, Table T1: Population estimates. Population Estimates Program. http://www.census.gov/popest/archives/1990s/popclockest.txt http://factfinder.census.gov/servlet/DTTable?_bm=y&-geo_id=01000US&-ds_name=PEP_2009_EST&- _lang=en&-mt_name=PEP_2009_EST_G2009_T001&-format=&-CONTEXT=dt

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1929-1972, which I label as Pre-NFIP losses. In addition to this subset, I include the dataset of actual losses which includes flood losses for the entire time period of 1929-2009 (see Figure

3.1).

Inflation-adjusted Flood Losses

50000

40000

30000

20000

10000 2009 U.S.2009 Dollars (millions)

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Pre-NFIP losses

Figure 3.1: Pre-NFIP losses versus Actual losses

Then I determine the best-fit linear trend line for both datasets. I use a linear trend line as opposed to other trend lines (e.g. exponential or polynomial) because I am interested in the long- term average of losses without concern for annual variability. When determining the linear trend line, I also determine the linear equation for each trend line (see Figure 3.2).

105

Inflation-adjusted Flood Losses

50000

40000 y = 76.277x + 298.95 y = 103.37x - 440.07 30000

20000

10000 2009 U.S.2009 Dollars (millions)

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Pre-NFIP losses Linear (Actual losses) Linear (Pre-NFIP losses)

Figure 3.2: Determining the trend line and equation for Pre-NFIP losses and Actual losses

Using the equation for the Pre-NFIP trend line, I calculate future loss values and create a projection of flood losses from 1973-2009 that extends the trend exhibited by the Pre-NFIP loss data in 1929-1972 (see Figure 3.3). By using this calculation, I assume future loss values will continue in the same pattern and continue to increase (as opposed to decreasing or reaching a plateau) due to the socioeconomic factors of inflation, population growth, and growing wealth that had influenced the losses from the pre-policy era.

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Inflation-adjusted Flood Losses

50000 y = 76.277x + 298.95 40000

30000

20000

10000

2009 U.S.2009 Dollars (millions) 0

1983 2005 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2007 2009

Actual losses Pre-NFIP losses Projection Linear (Actual losses) Linear (Pre-NFIP losses)

Figure 3.3: Creating the projection

Labeled as projection, it continues linearly (as opposed to an exponential or logarithmic trend) because this study assumes losses will continue to increase at the same steady historical rate.

Figures 3.4-3.9 include a series of analyses for the NFIP and NEHRP pre-policy periods that calculate the difference in losses from year to year in order to validate that the rate remained steady historically.

Annual Difference in Inflation-adjusted Annual Difference in Flood Losses Per Flood Losses Capita 20000 100 15000 80 60 10000 40 5000 20 0 0 -5000 -20 -40 -10000 -60

-15000 -80

1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1929 Figure 3.4: Year-to-year difference (blue) and rate (red) Figure 3.5: Year-to-year difference (blue) and rate (red) for inflation-adjusted flood losses for flood losses per capita

Annual Difference in Flood Losses as a % of GDP 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4

1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1929 Figure 3.6: Year-to-year difference (blue) and rate (red)

for flood losses as a % of GDP 1

07

Figure 3.9: Year-to-year difference (blue) and rate (red) for earthquake losses as a % GDP Annual Difference in Inflation-adjusted Earthquake Losses Annual Difference in Earthquake 4000 Losses Per Capita 3000 20 2000 15 1000 10 0 5 -1000 0 -2000 -5 -3000 -10 -4000 -15

-20

1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975

1929

1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 Figure 3.7: Year-to-year difference (blue) and rate (red) 1929 for inflation-adjusted earthquake losses Figure 3.8: Year-to-year difference (blue) and rate (red) for earthquake losses per capita

Annual Difference in Earthquake Losses as % GDP

0.1

0.05

0

-0.05

-0.1

1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1929

108

109

Finally, I compare the projection to the trend line for actual losses, labeled as actual trend

(see Figure 3.10).

Inflation-adjusted Flood Losses

50000

40000

30000

20000

10000 2009 U.S.2009 Dollars (millions)

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.10: Comparison of the Projection and Actual trend

The comparison leads to one of three possible outcomes from which I conclude whether the NFIP has been successful or not in reducing flood losses. If the actual trend is larger than the projection, this outcome shows no evidence that the policy has had an influence in reducing losses to the baseline level - a level lower than losses would have expected to be had no policy been enacted. If the actual trend is the same as the projection, this outcome also shows no evidence that the policy has had any influence in reducing losses to the baseline level. However, if the actual trend is smaller than the projection, this outcome suggests that the policy has played a role in reducing losses and it shows evidence that the policy has successfully reduced losses to the baseline level.

110

I follow the same methodology for the NEHRP as I do for the NFIP by completing the same three data analyses described above: inflation-adjusted earthquake losses, earthquake losses per capita, and earthquake losses as a % of GDP. In plotting the data, the earthquake losses vary from each other in orders of magnitude so I calculate the logarithmic value of losses in order to plot the data in a more useful format. These datasets include losses over the entire time period of the study of 1929-2005, and I label them as actual losses. I compare them to a subset of losses over the time period prior to the enactment of the policy. Since the NEHRP was enacted in 1977, the subset includes earthquake losses from 1929-1976, labeled as Pre-NEHRP losses.

I determine the best-fit linear trends and corresponding linear equations for each trend line. Based on the linear equation for the Pre-NEHRP trend line, I calculate future loss values and create a linear projection of earthquake losses from 1977-2005 that extends the trend exhibited by the Pre-NEHRP losses in 1929-1976. Labeled as projection, it depicts the expected trend for losses had the NEHRP never been enacted. The projection is then compared to the trend line for actual losses, labeled as actual trend. I conclude whether the NEHRP has been successful or unsuccessful in reducing earthquake losses based on the comparison‟s outcome which will be one of the three described above for the NFIP.

In addition to the three analyses, I perform several sensitivity studies for both the NFIP and NEHRP in order to determine whether significant policy amendments influenced the trend in losses. I also perform sensitivity studies to determine whether individual flood or earthquake events influenced the trend in flood and earthquake losses, respectively. Any individual flood or earthquake events used in a sensitivity study are those that caused extraordinary dollar losses on larger orders of magnitude than other events throughout the study‟s time period. I use the same

111 methodology for these sensitivity studies; comparing the actual trend in losses to a projection that is an extension of the trend in losses exhibited in the period prior to the year of the policy amendment or major disaster event.

3.3. NFIP

3.3.1. Background

In 1968, the U.S. Congress enacted the National Flood Insurance Program “as a protection against flood losses in exchange for State and community floodplain management regulations that reduce future flood damages.” (FEMA, 2002). The NFIP concurrently provides insurance coverage against economic losses to property and assets caused by flood damage, while also requiring state and local governments to implement flood protection measures. In corresponding with the program‟s goal, insurance coverage provides protection against losses while both insurance and flood protection should reduce future flood damages.

Appraisals of the NFIP largely conclude that the policy has failed to reduce flood losses.

One study notes, “In the 1960s, a number of scholars in the U.S. argued that flood insurance could stem the tide of increasing floodplain development and limit the rising costs of flood disasters. Obviously, with flood losses steadily rising, the flood insurance program has not had the full effects intended.” (Burby, 2001). Ironically, most appraisals blame the NFIP not just for failing to reduce losses but for actually increasing flood losses instead. Four fundamental problems with the NFIP have led to increasing losses: 1) non-actuarial pricing 2) lack of enforcement 3) weak standards and 4) public misperception.

Non-actuarial pricing is the most culpable for causing increased flood losses. In non- actuarial pricing, the NFIP assigns prices for flood insurance premiums that do not reflect the

112 true risk of loss. The insurance prices are disproportionately low either due to federal subsidies that reduce true prices or the omission of relevant risks in price calculations. Through cost reductions to the insurance purchaser, the subsidies and risk omissions provide incentives for vulnerable development in vulnerable areas and promote risky and ultimately costly behavior.

On the perverse incentive of subsidies, “Subsidized insurance allows landholders to develop areas that the market alone might otherwise deem too risky for construction” (Bagstad,

Stapleton, & D‟Agostino, 2007). Regarding development, Pinter states, “…the optimum strategy for reducing flood losses is to limit or even reduce infrastructure on floodplains.” (Pinter, 2005).

However, over time vulnerability has grown leading to increasing damage thus causing increasing losses.

Non-actuarial pricing has also skewed the financing of the NFIP since the cost discounts reduce incoming funds while payouts increase from growing losses. Subsidized premium rates cost only 35-40% of the full premium price and total about $1.3 billion annually (GAO, 2009;

GAO, 2007). The Government Accountability Office stated, “In general, the program was not designed to collect sufficient premium income to cover flood losses.” (GAO, 2009).

Federal subsidies are the outcome of the grandfather clause that the NFIP created for properties built in regions susceptible to flood hazard (referred to as floodplains) prior to the enactment of the policy in 1968. The NFIP implementation process starts first with mapping floodplains to delineate the region into zones based on risk level. This produces the flood insurance rate map (FIRM) which the NFIP relies upon to set insurance premium rates based on the level of risk. When the NFIP was enacted in 1968, properties had already begun developing in floodplains without the knowledge of future insurance requirements. So as not to punish property owners who had purchased property without this knowledge in prior years, the NFIP

113 subsidized the insurance premium rates for pre-FIRM property owners and exempted them from many of the NFIP requirements as well. About 23% of NFIP policies remain subsidized today

(GAO, 2009).

The subsidization of pre-FIRM property insurance premiums evolved into a chronic problem of the NFIP, and it remains so even today. First, pre-FIRM properties developed mostly in the floodplain zones with the highest risk and so they experience the largest flood damage and thus flood losses. Furthermore, they are exempt from NFIP rules that require rebuilding flood- damaged property to current building standards. Therefore pre-FIRM properties can rebuild to less expensive, older, less resilient standards. Thus creates the vicious cycle: allowing the properties at greatest risk to maintain less resilient building standards leads to large amounts of damage and losses, which are compensated by insurance and then allows for the rebuilding of these properties in high-risk zones with less resilient building standards again, and so on. The subsidies and exemptions also promote a moral hazard by supporting actions that encourage settlement in high-risk zones. This encouragement is the single largest problem exemplifying how the NFIP causes increased flood losses. As early as 1975, the well-respected disaster scholar Gilbert White stated the following about the importance of floodplain development regulation, “Floodplain regulations are seen as the „single adjustment most likely to lead to a decline in national flood losses.‟” (Arnell, 1984; White, 1975).

Pre-FIRM properties also contribute to increasing losses because of their consistent repeated losses. Because of their immense vulnerability, pre-FIRM properties repeatedly experience losses, accounting for 30% of all flood claims in the NFIP (Bagstad, Stapleton, &

D‟Agostino, 2007). In subsidizing pre-FIRM property insurance premiums, followed by repeated payments on losses, the NFIP has been severely hemorrhaging for most of its existence.

114

In 2004 Congress passed The Flood Insurance Reform Act of 2004 “[t]o amend the National

Flood Insurance Act of 1968 to reduce losses to properties for which repetitive flood insurance claim payments have been made.” (U.S. Congress, 2004). The amendment aims to rectify the problems with pre-FIRM properties and repeated losses. Time will tell whether these amendments do indeed resolve the largest financial burden of the NFIP.

Non-actuarial pricing is also seen with NFIP premium rates that are disproportionately low due to the omission of relevant risks in the rate calculation. Insurance premium rates do not reflect the risk of flood protection structure failure such as levee breaches or dam breaks, and since floodplains are likely to have flood protection structures, structure failure is highly possible. The NFIP rates also omit the risk of coastal erosion as a contributor to flooding

(Burby, 2001). The risk of coastal erosion continues to increase with developers building a growing number of luxury properties on the coasts. Coastal erosion increases expensive properties‟ vulnerability to flooding, consequently resulting in larger damage and higher loss values. These high losses are a component of increasing flood losses.

The second problem with the NFIP relates to the lack of enforcement (Kunreuther &

White, 1994). While the NFIP is a federal policy, state governments implement the policy and local governments enforce it. However many local governments fail to enforce the requirements of the NFIP (Bagstad, Stapleton, & D‟Agostino, 2007; Burby, 2001). The NFIP charges local governments to enforce mitigation efforts when pre-FIRM properties are rebuilding, for height standards of building elevation above ground, and most importantly for land management with consideration of present and future hazards. Many local governments across the country, however, have failed to enforce any of these responsibilities either because of their low priority or due to politics. Local budgets are usually very limited so a chronic long-term problem such as

115 flood hazard is less of a priority than more pressing short-term problems such as roads or school overcrowding. Also, local officials often underestimate the flood risk which also contributes to it being a low priority. In addition, it is thought that NFIP enforcement is often unpopular with the public and can hurt a local government member‟s chance for re-election. As a result, local government officials often avoid enforcement issues altogether.

Federally regulated lending institutions are also responsible for enforcement of the NFIP and have long failed to fulfill this task (Kunreuther & White, 1994). In 1968, the NFIP was first enacted on a voluntary basis and so few people participated in the program. To increase participation, policy makers amended the NFIP in 1973 to require property owners to purchase flood insurance if they were receiving a federal loan (i.e. business or mortgage). The NFIP tasked lending institutions with enforcing this requirement. However these institutions failed to do so because of the lack of any incentive for enforcement. As Kunreuther explains, “The commission on a flood insurance policy is 15% of the price and an average policy normally costs less than $300 annually. Hence the average commission is less than $45.” (Kunreuther & White,

1994). This is a low commission relative to other insurance sales and furthermore, it is only earned if the insurance agent makes an insurance sale. As a result, less than one-third of property owners who should purchase flood insurance actually purchase it (Kunreuther & White,

1994). One study found that only 25-30% of buildings at risk had sufficient insurance coverage in 1995 (Myers, 1995). Insurance agents are aware of the low probability of a flood insurance sale therefore they are unlikely to spend their time selling it over more lucrative insurances

(Kunreuther & White, 1994). Property owners who should purchase flood insurance remain uninsured; only about half of all floodplain residents purchase flood insurance (GAO, 2007).

116

Ultimately, flood losses continue to increase due to lack of enforcement of specific NFIP requirements intended to reduce losses.

Weak standards for the NFIP contribute less to the increase in losses than the first two problems, but still play a role. Disaster experts believe that current standards are too lax and incapable of countering flood losses sufficiently, thus contributing to increasing flood losses.

These standards have significance in that they dictate the implementation for the NFIP, which ultimately determines the extent of flood losses. Disaster experts suggest that new standards would lead to greater flood protection and thus fewer losses.

First, the basis for the NFIP centers on the 100-year flood. This is defined as a flood event of a magnitude large enough that there is only a 1% likelihood of it occurring in any given year. The NFIP risk assessments, rate calculations, land management, and property development are all based on the characteristics expected from the 100-year flood including flood heights and the spatial extent of inundated land. Some disaster experts suggest that the NFIP should replace the standard event with a more conservative 500-year flood which is a larger-scaled flood event with only a 0.2% likelihood of occurring (Myers, 1995; Burby, 2001). This would cause all

NFIP decisions to take a more cautious approach and raise protection to a higher level.

Similarly, the characteristics expected from the 100-year flood are based on predictions with a standard 50% confidence interval whereas the 500-year flood would replace the standard confidence interval to a more conservative and certain interval of 90-95% (Burby, 2001). More conservative standards would lead to more cautious implementation decisions for the NFIP.

The public misperception of flood risk also causes increasing flood losses. Communities that settle in floodplains and are subject to flood hazards underestimate the level of risk they face often assuming the “likelihood of catastrophe expected is much lower than insurance company

117 estimates” as well as “an overly optimistic view of expected losses” (GAO, 2007). This underestimation of risk prevents many at-risk community members from taking proper precautionary actions. “The failure of most floodplain dwellers to purchase insurance voluntarily when not required to do so is due primarily to their belief that a damaging flood will not occur during their occupancy of the structure.” (Kunreuther & White, 1994). The public also possesses a false sense of security due to “short-term memory of disasters” (Majmudar, 2008) and the public‟s growing expectation and reliance on federal disaster relief aid to compensate for any losses. These public misperceptions discourage people from taking precautionary actions and also promote risky behavior, all of which contribute to increasing flood losses.

While the NFIP is a deeply flawed policy as described above, it perpetuates both good and bad outcomes for floodplain development which can be seen in a concept introduced here as the Development Paradox. On the negative side, the NFIP leads to the bad outcome of settlement in high-risk areas due to the reasons described earlier. On the positive side, the NFIP leads to the good outcome of increasingly resilient buildings on floodplains. If new settlements

(other than pre-FIRM) are going to continue to develop on floodplains, which they are, the NFIP mandates building standards that promote greater resiliency. Consider the following assessments:

A 1984 study found:

The NFIP regulations have not curbed floodplain encroachment but have resulted in new structures being protected from the 100-year flood. NFIP claims data show that new development … is less prone to flooding than earlier development.38

38 Arnell, Nigel W. (1984). Flood hazard management in the United States and the National Flood Insurance Program. Geoforum, Vol. 15, No. 4. 525-542.

118

An appraisal in 2001 stated:

Although the flood insurance program has had limited impact in steering development to locations outside of flood-hazard areas, for new construction that does occur in floodplains, NFIP mitigation standards clearly have reduced susceptibility to loss in floods.39

An assessment comparing structures built before and after the enactment of the NFIP concluded:

In general, structures built before 1975, which were subject to no NFIP standards, suffered about six times more damage from flooding than those built after NFIP mitigation requirements became effective.40

The NFIP has promoted greater resiliency for new construction in floodplains. This reflects progressive improvement and a good outcome of the policy, assuming that floodplain development is inevitable. Critics argue that the lowest-risk solution is zero development in a floodplain. Therefore even though the NFIP promotes greater resilience, any new construction is a bad outcome. True, if comparing floodplain development to no development, then clearly it is not the best alternative since zero development is the least risky. However, zero development is not a realistic option particularly considering that property has been developing in floodplains even prior to the enactment of the NFIP. As mentioned above, the NFIP encourages floodplain development and the market further promotes such development with discounted prices such as floodplain land selling at a fraction of the price of regular land (Chivers & Flores, 2002). The annual increase in floodplain development is twice the rate of development in areas not at risk to floods (Johnston and Associates, 1992). Thus, floodplain development is in fact occurring and

39 Burby, Raymond J. (2001). Flood insurance and floodplain management: the U.S. experience. Environmental Hazards. Vol. 3. 111-122.

40 Pasterick, E.T. The national flood insurance program. In H. Kunreuther & R.J. Roth Sr. (Eds.), Paying the price: The status and role of insurance against natural disasters in the United States (pp. 125-154). Washington, D.C.: Joseph Henry/National Academy Press.

119 more realistic than zero floodplain development. It is with this consideration that some experts support the NFIP as a successful policy.

While most assessments of the NFIP suggest that the policy has failed to reduce losses, some institutions involved in federal flood protection and insurance do credit the policy with flood loss reduction. The U.S. Army Corps of Engineers concurs with the Federal Flood

Insurance Administrator‟s statement: “Flood damage is reduced by nearly $1 billion a year through partnerships with communities, the insurance industry and the lending industry.”41,42

Assessments that support the NFIP consider floodplain development as inevitable and suggest that the policy has improved a situation that would be worse without the policy. An example of such an assessment states “buildings constructed in compliance with NFIP building standards suffer approximately 80 percent less damage annually than those not built in compliance” (U.S.

Army Corps of Engineers). Another assessment explains, “FEMA [Federal Emergency

Management Agency] estimates … 2,070,000 new buildings have been constructed out of the reach of one-percent floods [100-year flood] and represent an average annual reduction of about

$569 million in damages that would have been experienced if they had been built at low elevations.” (Kunreuther & White, 1994).

Assessments such as those described above compare the present situation with the policy, to a hypothetical situation without the policy. Using the same assessment tool, this study therefore compares the present situation with the NFIP, to a situation without the policy; the

41 U.S. Army Corps of Engineers. Nonstructural flood damage reduction measures. U.S. Army Corps of Engineers National economic development manuals. Retrieved from http://www.corpsnedmanuals.us/FloodDamageReduction/FDRID094NonstrucFldDmgMeas.asp

42 Statement of Anthony Lowe, Mitigation Division Director and Federal Flood Insurance Administrator, Emergency Preparedness and Response Directorate, Department of Homeland Security. (2003, April 1). The National Flood Insurance Program: Review and Reauthorization. Hearing before the U.S. House of Representatives, Committee on Financial Services, Subcommittee on Housing and Community Opportunity.

120 former portrayed by the actual trend in flood losses and the latter portrayed by the projection.

The actual trend is based on the series of U.S. flood losses over the full time period of this study of 1929-2009 which includes the enactment of the NFIP.43 The projection is based on both the trend in U.S. flood losses prior to the enactment of the NFIP from 1929-1972, and an extrapolation of that trend from 1973-2009. In comparing these two trends, any difference between the two reveals the impact that the policy has had on losses and thus indicates whether the program is succeeding or failing to reduce losses.

The outcome of this comparison can reveal the NFIP‟s impact on flood losses and ultimately determine whether it has successfully fulfilled its mandate and reduced losses below the level expected had the NFIP not been enacted. If the two trends are similar, one can conclude that the NFIP‟s impact on losses is minimal and that the policy has not been successful in reducing flood losses per its mandate. If the two trends differ significantly, the success or failure of the NFIP depends on which trend is greater. If the actual trend is larger than the projection, this shows no evidence that the policy has had an influence in reducing losses to the baseline level. However, if the trend in actual losses is smaller than the projection, then the policy has played a role in reducing losses and it shows evidence that the policy has successfully reduced losses to the baseline level.

3.3.2. Data

Since the NFIP aims to reduce economic losses, an assessment of the policy‟s success must accordingly analyze economic losses. I analyze U.S. economic losses caused by floods during the time period 1929-2009 using the dataset presented in the study Flood Damage in the

43 All figures in this study depict only a portion of the actual trend from the time period 1973-2009 which corresponds to the time period after the enactment of the NFIP.

121

United States, 1926-2003: A Reanalysis of National Weather Service Estimates.44 This dataset compiles flood loss data from the National Weather Service Hydrologic Information Center

(NWS-HIC) along with data collected from other federal and state agencies.

The NWS-HIC data compile economic loss estimates from each flood event reported by any of the National Weather Service (NWS) field offices located throughout the country; covering losses from significant flood events where flooding results from hydrometeorological causes of rainfall or snowmelt.45 The data exclude flooding caused by winds (storm surges) and flooding caused by geophysical disasters such as tsunamis or mudslides.46 The NWS-HIC reports data based on water-years which start in October and end in September.47 The data cover direct damages caused by flooding such as the costs of damaged buildings, infrastructure, land, or crops but excludes indirect damages such as the loss of revenue due to a damaged restaurant or other business shutting down.

The dataset used in this study reflects best-estimates of the actual costs of flood damage because the elusiveness of actual cost data presents difficulties for databases. First, best- estimates are necessary due to timing. The NWS-HIC compiles data from NWS reports, and the

NWS collects flood loss data “soon after each flood event, before the actual costs of repair and

44 Pielke, Jr., R.A., M.W. Downton, and J.Z. Barnard Miller. (2002). Flood damage in the United States, 1926- 2000: A reanalysis of National Weather Service estimates. UCAR. Boulder, Colorado. http://flooddamagedata.org/full_report.html

45 National Weather Service Hydrologic Information Center. Flood losses: Compilation of flood loss statistics. http://nws.noaa.gov/oh/hic/flood_stats/Flood_loss_time_series.shtml

46 Pielke, Jr., R.A., M.W. Downton, and J.Z. Barnard Miller. (2002). Flood damage in the United States, 1926- 2000: A reanalysis of National Weather Service estimates. UCAR. Boulder, Colorado. http://flooddamagedata.org/full_report.html

47 National Weather Service Hydrologic Information Center. Flood losses: Compilation of flood loss statistics. http://nws.noaa.gov/oh/hic/flood_stats/Flood_loss_time_series.shtml

122 replacement can be known.”48 Furthermore, the NWS does not vet its data against actual costs when such information is available later.

Second, the compiled data reflect varying data quality because the NWS-HIC data compile loss data from a number of diverse sources such as insurance companies and media reports, each with their own methodologies for calculating losses which vary in accuracy. Since the level of accuracy is inconsistent and often unknown across the dataset, the resulting loss values of the NWS-HIC dataset can only be considered best-estimates.49

Third, the extent of available data for each flood event rarely covers the full scope of damage therefore loss data reflect best-estimates based on the data that are available. After a disaster event, institutions report losses relevant to them which results in a piecemeal estimate and reflects portions of full losses since some losses fall outside the purview of any institution.

For example, for one flood event the Department of Agriculture might report damage to crops and the Federal Emergency Management Agency might report damage to infrastructure so the resulting loss value in the dataset will reflect the sum of these two values but exclude property losses and reflect only a portion of the full losses. The next flood event may only include an

NWS estimate of losses and so the dataset will reflect that loss only.

The Pielke et al. dataset provides a reanalysis of the NWS-HIC data and resolves the inconsistencies of the original data to provide a robust dataset that meets the criteria for use presented in this dissertation. Details of the criteria and the data treatments applied by the Pielke et al. dataset can be found in Chapter 1.

48 Pielke, Jr., R.A., M.W. Downton, and J.Z. Barnard Miller. (2002). Flood damage in the United States, 1926- 2000: A reanalysis of National Weather Service estimates. UCAR. Boulder, Colorado. Retrieved from http://flooddamagedata.org/full_report.html

49 Ibid.

123

Since the dataset from the Pielke et al. study only includes flood losses through 2003, I extend the dataset used in my study to also include flood losses from 2004-2009. I rely on the loss data reported by the NWS-HIC which has continued to report flood losses in the years following the completion of the Pielke et al. study. My dataset maintains continuity throughout its entirety even though it includes the Pielke et al. data (a composite of both NWS-HIC data and other agency reported losses) from 1929-2003 and solely NWS-HIC data in the extended years.

Comparing the NWS-HIC data to the Pielke et al. data reveals that the two datasets agree (see

Table 3.2) from 1983-2003 which spans over the era when U.S. flood damage data improved to research-quality robustness. While the table shows comparisons through 2003 when the Pielke et al. dataset ends, one can assume that the 2004-2009 data keep the same continuity because the

NWS-HIC has maintained the same data collection, processing, and reporting practices as before.

124

NWS-HIC Pielke et NWS-HIC Losses al. Losses Losses as Year (Thousands (Thousands Proportion of Current of Current of Pielke et Dollars) Dollars) al. Losses 1983 4000000 3693572 1.08 1984 3750000 3540770 1.06 1985 500000 379303 1.32 1986 6000000 5939994 1.01 1987 1444199 1442349 1.00 1988 225298 214297 1.05 1989 1080814 1080814 1.00 1990 1636431 1636366 1.00 1991 1698781 1698765 1.00 1992 762762 672635 1.13 1993 16370010 16364710 1.00 1994 1120309 1120149 1.00 1995 5110829 5110714 1.00 1996 6121884 6121753 1.00 1997 8730407 8934923 0.98 1998 2496960 2465048 1.01 1999 5455263 5450375 1.00 2000 1338735 1336744 1.00 2001 7309308 7158700 1.02 2002 1211339 1116959 1.08 2003 2482230 2405685 1.03 Average Proportion from 1983- 1.04 2003 Table 3.2: Ratio of NWS-HIC losses to Pielke et al. losses

3.3.3. Results

For the NFIP, I completed three analyses of flood losses: inflation-adjusted flood losses, flood losses per capita, and flood losses as a percent of GDP, as well as four sensitivity studies to determine the impact on the trend in flood losses from NFIP policy amendments and extreme flood events. The sensitivity study years include 1968 and 2004 as significant policy amendment years and 1993 and 2005 for extreme flood events. Although the analyses in this study use 1973 as the year for NFIP enactment, Congress officially enacted the NFIP in 1968. Thus 1968 is a

125 year of interest for whether one can see any impact on flood losses from the actual enactment.

The 2004 sensitivity study focuses on the Flood Insurance Reform Act of 2004 which amended the NFIP with aim to reduce losses incurred by repeated claims. For extreme flood events, the

1993 sensitivity study evaluates the impact of The Great Flood in the Midwest on the long-term trend in flood losses. The 2005 sensitivity study evaluates the impact of Hurricanes Dennis and

Wilma on the long-term trend.

It should be noted that the 2005 flood loss value in this study‟s dataset does not reflect losses caused by Hurricanes Katrina or Rita because of the NWS‟s current inability to accurately determine the extent of economic losses, the portion of losses for which flooding is the cause, or the portion of losses attributed to flooding caused by precipitation but not winds or structure failure.50 However, even when excluding Hurricanes Katrina and Rita, the 2005 flood loss value is still significantly large and therefore included as a sensitivity study.

Analyses

The first analysis involves flood losses that have been adjusted for inflation to 2009 U.S. dollars and compares the actual trend - a trend in losses after the enactment of the NFIP - to the projection - an extension of the trend exhibited by losses prior to the enactment of the NFIP.

Figure 3.11 shows the actual trend as larger than the projection, although only slightly. There are three possible explanations for this outcome. First, the NFIP could be ineffectual in reducing flood losses and instead is causing larger losses due to ongoing floodplain development and other reasons explained above. This could be supported by the fact that the projection is smaller since it is reflects the scenario without the NFIP as a factor contributing to losses. Second,

50 National Weather Service Hydrologic Information Center. Flood losses: Compilation of flood loss statistics. http://nws.noaa.gov/oh/hic/flood_stats/Flood_loss_time_series.shtml

126 socioeconomic factors such as population size and wealth are larger in absolute numbers in the post-NFIP period than in the pre-NFIP period. With more to lose today, losses are accordingly larger. Third, exceptionally large flood events have occurred in the post-NFIP period; events large enough to skew the actual trend in losses upwards. In this case the projection is smaller because it reflects only ordinary flood events without any extreme events.

The first explanation is possible while the second and third explanations are probable and supported by Figure 3.11. Referring to the second explanation, with more wealth in existence, more wealth is at risk. As a result, society is losing more in absolute dollars although the rate of loss has remained the same over time. Therefore as Figure 3.11 depicts, the actual trend is a larger line than the projection while both lines maintain a similar slope and thus rate. Beyond socioeconomics, the third explanation is most likely responsible for the outcome of this analysis.

As Figure 3.11 indicates, the two years with the largest flood losses occurred in the post-NFIP time period with the first in 1993 and the second in 2005. Both of these years experienced significantly large losses which biased the actual trend line to skew upwards. This can be seen with the gap between the actual trend and projection lines. The gap between the two lines is fairly small from 1973-1992 but the major flood year of 1993 increases the gap. The gap is largest by 2005, the year with the largest flood losses of the entire period of this study, and thus the largest skew increasing the actual trend line.

This explanation suggests that the NFIP has showed no evidence of reducing losses from ordinary flood events and is incapable of countering extreme flood events. It cannot reduce extreme losses nor even provide a buffer to keep losses steady. Instead extreme disaster events cause increasing losses which skew the trend line and indicate that the NFIP is futile against them.

127

Inflation-adjusted Flood Losses 50000

45000

40000 35000 30000 25000 20000 15000

10000 2009 U.S.2009 Dollars (millions) 5000

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.11: Comparison of inflation-adjusted flood losses

The second analysis involves per capita inflation-adjusted flood losses for the U.S., adjusted to 2009 U.S. dollars. This analysis uses the same comparison as the first analysis; between the projection of losses from the pre-NFIP time period of 1929-1972, and the actual trend in losses from 1929-2009.

Figure 3.12 shows identical projection and actual trend lines which suggests that the

NFIP has no influence on flood losses after they are adjusted for population size. The NFIP seems inconsequential since the projection – the trend from the pre-NFIP period without the policy, is the same as the actual trend – the trend from the post-NFIP period with the policy.

Additionally, this analysis also shows that population size and wealth are both growing and larger in absolute numbers in the post-NFIP period than in the pre-NFIP period. These factors likewise contribute to increasing flood losses which are also larger in absolute numbers in the post-NFIP period. Since Figure 3.12 portrays the projection and actual trend lines to be the

128 same, this suggests that the proportion of flood losses to population size has remained the same over time. While population size and flood losses are both increasing in the post-NFIP period, they are increasing at the same rate and thus maintaining the same proportion of flood losses per capita as in the pre-NFIP period.

Inflation-adjusted Flood Losses Per Capita 160

140

120

100

80

60 2009 U.S.2009 Dollars 40

20

0

1941 1977 1929 1931 1933 1935 1937 1939 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.12: Comparison of inflation-adjusted flood losses per capita

The third analysis calculates inflation-adjusted flood losses as a percent of U.S. GDP, adjusted to 2009 U.S. dollars. This analysis uses the same comparison as the first analysis; between the projection of losses from the pre-NFIP time period of 1929-1972, and the actual trend in losses from 1929-2009.

Figure 3.13 shows matching projection and actual trend lines suggesting that the NFIP has no influence on flood losses after they are adjusted for national wealth. The NFIP seems futile since the losses without the policy (the projection) are the same as losses with the policy

129

(the actual trend). Additionally, this analysis provides insight on the socioeconomic factor of national wealth through the variable of GDP. As mentioned, U.S. wealth is growing and GDP is larger in absolute numbers in the post-NFIP period than in the pre-NFIP period. Increasing wealth contributes to increasing flood losses, which are also larger in absolute numbers in the post-NFIP period. Figure 3.13 shows the projection and actual trend lines to be the same, suggesting that the proportion of flood losses to GDP has remained the same over time. While

GDP and flood losses are both increasing in the post-NFIP period, they are increasing at the same rate and thus maintaining the same percentage of flood losses to GDP as in the pre-NFIP period.

For the four years in which Figure 3.13 depicts the highest percentages, one cannot automatically conclude that the highest flood losses occurred. Percentage values reflect the ratio of flood losses to GDP. The four years depicted with the largest percentages did not involve the highest flood losses but rather the ratio of relatively large flood losses to relatively lower GDP values. In 1936, 1937, 1951, and 1972, flood losses were larger than losses in most years but not as extraordinarily high as those caused by floods in 1993 and 2005. However, the 1993 and

2005 percentages are smaller because the GDP values in these years were much larger than the

GDP in 1936, 1937, 1951, and 1972. Between 1930 and 1993, the GDP increased nearly tenfold

(2009 dollars). The significant growth in GDP over time also explains the decreasing trend portrayed in Figure 3.13.

130

Inflation-adjusted Flood Losses as % of GDP 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05

0

1937 1947 1957 1967 1977 1987 1929 1931 1933 1935 1939 1941 1943 1945 1949 1951 1953 1955 1959 1961 1963 1965 1969 1971 1973 1975 1979 1981 1983 1985 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.13: Comparison of inflation-adjusted flood losses as a % of GDP

Sensitivity studies

The first sensitivity study evaluates whether the official enactment of the NFIP in 1968 had any influence on flood losses. I use inflation-adjusted flood losses and compare the projection from the trend in losses during the time period 1929-1967, to the actual trend in losses from 1968-2009. Figure 3.14 portrays that actual flood losses are larger than projected losses.

The three explanations suggested for the first analysis are valid here as well. First, a failed NFIP could be responsible for the actual flood losses being larger than the projection. It is possible that the NFIP is ineffectual in reducing flood losses and is instead causing losses to increase through perverse policy incentives. This would support the fact that projected losses are smaller, since they represent a scenario without the NFIP as a factor contributing to losses.

Second, socioeconomic factors that contribute to losses such as population size and wealth are larger in absolute numbers in the post-NFIP period than in the pre-NFIP period so

131 losses are accordingly larger although the rate remains the same. Figure 3.14 supports this explanation by showing the actual trend as a larger line than the projection but with the same slope. Third, extreme flood events with significant losses occur in the post-NFIP period and skew the actual loss trend upwards. In this case the projection is smaller because it reflects ordinary flood events in the absence of any extreme events. Of the three explanations, this last one is most likely because the three extreme flood events all occur in the 1968-2009 period and in each of the three years (1972, 1993, 2005), the difference between the projection and actual trend line increase noticeably. Figure 3.14 depicts the gap increase in these three years which suggests the flood losses in these years are large enough to bias the actual trend line to skew upwards.

Inflation-adjusted Flood Losses 50000

45000

40000 35000 30000 25000 20000 15000

10000 2009 U.S.2009 Dollars (millions) 5000

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.14: Comparison of flood losses after the enactment of the NFIP in 1968

132

The 2004 sensitivity study assesses whether the Flood Insurance Reform Act of 2004, an amendment to the NFIP, has any influence on flood losses. The projection is based on the trend exhibited by losses from 1929-2003, prior to the enactment of the Flood Insurance Reform Act.

The actual trend is based on flood losses during the full time period of 1929-2009 and therefore with the Flood Insurance Reform Act. It is difficult to gauge much from this sensitivity study since the cutoff year leaves only five data points from 2005-2009. Nonetheless, a comparison of the projection and actual trend reveals the actual trend to be larger than the projection (see Figure

3.15). Similar to the other analyses, the difference is likely due to the extreme floods in 2005 creating large losses and skewing the actual trend line causing it to be larger than the projection which does not include 2005 flood losses. If it plays any role, the Flood Insurance Reform Act has less influence on the trend in losses than the extreme flood events of 2005.

Inflation-adjusted Flood Losses 50000

45000

40000 35000 30000 25000 20000 15000

10000 2009 U.S.2009 Dollars (millions) 5000

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.15: Comparison of flood losses after the Flood Insurance Reform Act of 2004

133

The 1993 sensitivity study evaluates the impact on the trend in flood losses from the 1993

Great Flood in the Midwest. The projection is based on the trend exhibited by losses from 1929-

1992, prior to the flood event. The actual trend is based on losses during the full time period of

1929-2009 and will reflect any impact made by the 1993 flood. Figure 3.16 shows the actual trend line as larger than the projection which is smaller because it does not reflect the losses from

1993 or 2005. Therefore this sensitivity study concludes that the 1993 Great Flood in the

Midwest did have an impact on the trend for flood losses.

Inflation-adjusted Flood Losses 50000

45000

40000 35000 30000 25000 20000 15000

10000 2009 U.S.2009 Dollars (millions) 5000

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.16: Comparison of flood losses after the 1993 Great Flood in the Midwest

The 2005 sensitivity study determines the impact of the 2005 floods on the trend in flood losses. The projection is based on the trend exhibited by losses in the time period of 1929-2004, prior to the 2005 floods. The actual trend is based on flood losses during the full time period of

1929-2009 and will reflect any impact of the 2005 floods. It is difficult to gauge much from this sensitivity study since the cutoff year leaves only four data points from 2006-2009. Nonetheless,

134 a comparison of the projection and actual trend reveals the actual trend to be only slightly larger than the projection (see Figure 3.17). The actual trend is likely larger due to the impact of the

2005 floods, which the projection does not include. Therefore this sensitivity study proves that the 2005 floods did increase the actual trend in flood losses.

Inflation-adjusted Flood Losses 50000

45000

40000 35000 30000 25000 20000 15000

10000 2009 U.S.2009 Dollars (millions) 5000

0

1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Actual losses Projection Actual trend

Figure 3.17: Comparison of flood losses after the 2005 floods

3.4. NEHRP

3.4.1. BACKGROUND

Congress passed the Earthquake Hazards Reduction Act of 1977 which created the

National Earthquake Hazards Reduction Program (NEHRP). The mission of NEHRP is “to reduce the risks of life and property from future earthquakes in the United States through the

135 establishment and maintenance of an effective earthquake hazards reduction program.”51 Since

NEHRP strives to reduce the risk to property, and thus economic losses caused by earthquakes, its success or failure must be in some part determined by its ability to reduce economic losses.

Appraisals of NEHRP provide mixed conclusions on whether it is a successful policy due to the program‟s split success among its dual activities of research and implementation. These two activities form the backbone of NEHRP as can be seen in the program‟s four goals which reflect desired outcomes from both research and implementation activities:

1) Develop effective practices and policies for earthquake loss reduction and accelerate their implementation. 2) Improve techniques for reducing earthquake vulnerabilities of facilities and systems. 3) Improve earthquake hazards identification and risk assessment methods, and their use. 4) Improve the understanding of earthquakes and their effects.52

Most appraisals of NEHRP agree that the policy is successful in its research pursuits but unsuccessful in its implementation activities. In the 1995 assessment report Reducing

Earthquake Losses, the Office of Technology Assessment referred to NEHRP‟s dichotomous success: “[NEHRP] has made significant contributions toward improving our understanding of earthquakes and strategies to reduce their impact. Implementing action based on this understanding, however, has been quite difficult.” (U.S. Congress Office of Technology

Assessment, 1995). Disaster and policy experts that have assessed NEHRP largely conclude that the policy has been successful in producing useful research to enhance our understanding of earthquake matters however it has failed to implement those research findings into useful practices that could reduce losses. Thus, since NEHRP‟s mission is to reduce risks to property

51 National Earthquake Hazards Reduction Program. www.nehrp.gov/about/history.htm

52 Ibid.

136 and therefore reduce losses, the policy has ultimately failed despite its success in producing useful research.

NEHRP‟s mixed performance is attributed to the management of the program as a predominantly research entity since its inception. In 1976 – one year prior to NEHRP‟s creation, an expert panel released a report to the federal government assessing three issues: the state of earthquake research at the time, the potential for future research, and the issues relevant to earthquake mitigation (National Science Foundation, 1976). In response to the report, the federal government appropriated $20 million supplementals each to the U.S. Geological Survey (USGS) and the National Science Foundation (NSF) for increased earthquake research; the USGS focused on geological studies while the NSF concentrated on earthquake engineering (Hamilton,

2003). One year later the government created NEHRP as a multiagency program with four member agencies: the U.S. Geological Survey (USGS), National Science Foundation (NSF),

National Institute of Standards and Technology (NIST), and the Federal Emergency

Management Agency (FEMA). Since the USGS and NSF had already established themselves as earthquake agencies, they held precedence for NEHRP funding and focus over NIST and FEMA.

As of today, the USGS and NSF combined receive about 75% of NEHRP funding and influence the main focus of the program to be research (U.S. Congress Office of Technology Assessment,

1995). In addition, NIST also contributes to the NEHRP research effort since it is tasked with the applied engineering research role to enhance knowledge of the built environment and its resilience to earthquakes.53

As a result, NEHRP continues to maintain a strong research focus and appraisers of the program largely conclude that the resulting research is successful in improving our

53 Based on personal interview with a decision maker. March 16, 2011.

137 understanding of earthquakes and their effects. Some holes remain in our earthquake knowledge such as how to reduce structural and non-structural damage to buildings and how to calculate costs and benefits of mitigation (U.S. Congress Office of Technology Assessment, 1995).

However, most agree that “NEHRP has led to significant advances in our knowledge of both earth science and engineering aspects of earthquake risk reduction” including a better understanding of the seismic risk of the Pacific Northwest, how to build structures that will not collapse in an earthquake, and enhanced IT capability for building design (U.S. Congress Office of Technology Assessment, 1995).

In his 2010 testimony to the U.S. Senate, Dr. Desroches – an earthquake researcher and professor, spoke for the success of NEHRP research yet also suggested that the program needed to improve on implementing its research findings:

With the leadership of the NEHRP agencies, namely USGS, NSF, NIST, and FEMA, significant progress has been made in our understanding of the earthquake hazards in the various parts of the United States, as well as the vulnerabilities associated with different types of structural systems. New design codes and guidelines have incorporated lessons learned from recent earthquakes, as well as new knowledge developed from researchers and practicing engineers in cooperation with the NEHRP agencies. The transfer of scientific research successes from the NEHRP efforts to building and design codes is one important step towards earthquake preparedness in the United States. Still, there is more work to be done.54

The quote mentions building codes which are a symbolic example reflecting the larger

NEHRP problem between research and implementation practices. Through NIST research,

NEHRP has produced new findings on structural design, material strength, etc. that provide useful information for improved building codes. Based on NIST research findings, experts have developed national building codes that offer guidelines for more resilient construction of new buildings in earthquake hazard areas. However, while the building codes suggest sound guidelines, they still only provide guidance because they are not enforceable. While national

54 U.S. Senate. (2010, September 30). Testimony of Dr. Reginald Desroches. Committee on Homeland Security and Government Affairs, Ad Hoc Subcommittee on State, Local, and Private Sector Preparedness and Integration.

138 building codes reflect successful research and even a rare example of the successful transfer of research, the codes are not enforceable and thus fail to be implemented.

In fact, lack of enforcement is the prime culprit behind NEHRP‟s failure to implement useful practices. Appraisers of NEHRP largely agree on the program‟s failure to transfer its research findings into implementation practices; even coining the term the “implementation gap” to describe the policy‟s shortcoming. The implementation gap is the outcome of lack of enforcement, caused by the program‟s own lack of regulatory power as well as local governments‟ lack of enforcement of implementation practices.

Since NEHRP was created mainly as a research entity, Congress did not prescribe any regulatory powers for the program. At the time of NEHRP‟s creation, researchers were discovering a number of new findings about earthquakes. This led to the expectation that researchers would soon be able to routinely predict earthquakes and thus redefine earthquakes as a common threat in people‟s minds. Such a paradigm shift for earthquakes would motivate society to willingly engage in mitigation practices as common behavior (U.S. Congress Office of

Technology Assessment, 1995). Therefore, regulations for mitigation practices seemed unnecessary. A 1995 report by the Office of Technology Assessment stated the following:

“…NEHRP was given neither regulatory teeth nor significant financial incentives to promote mitigation. Instead, the program aimed to develop a body of knowledge from which local and state authorities and the private sector would draw. Since then, however, prediction has proved more elusive than originally thought, and the original role of NEHRP as a source of knowledge from which decisionmakers would eagerly draw is now seen by many as insufficient, due to the lack of regulations or incentives to implement the knowledge.”55

As of today, researchers are still unable to routinely predict earthquakes in a precise manner although they continue to improve their capabilities. Although several factors beyond this have limited the number of mitigation practices across the U.S., mitigation practices are less prevalent

55 U.S. Congress Office of Technology Assessment. (1995, September). Reducing Earthquake Losses. OTA- ETI-623. U.S. Government Printing Office. Washington, D.C.

139 than originally anticipated on a national scale. A 1996 report by the National Science and

Technology Council explained, “The initial NEHRP legislation envisioned the federal role as that of a provider of information. Subsequent amendments to the legislation added the roles of stimulating and promoting risk reduction actions. However, the actual level of such actions as evidenced by the adoption of earthquake resistant building codes by local or state governments has not kept pace with expectations.” (National Science and Technology Council, 1996). This is a particular challenge faced by FEMA, the NEHRP agency tasked with implementation. With no regulatory powers, NEHRP is unable to enforce any mitigation practices, even when its research offers useful findings for implementation.

Furthermore, since NEHRP is a federal program it has no jurisdiction at the state and local level and cannot require the implementation of mitigation practices at these levels. Local governments hold the power and responsibility to enforce mitigation practices and with the exception of some California localities, they rarely enforce any such practices. The report by the

National Science and Technology Council noted, “The mitigation practices developed through research and development must be voluntarily adopted by bodies largely outside the control of the federal government. As a consequence, the degree of national earthquake risk reduction envisioned by many has not been achieved.”56

Since earthquakes are infrequent and occur over timescales longer than a political term, they are of low priority for a local government official. Local officials are juggling a myriad of issues, many of which are more urgent such as school overcrowding or shortage of police and they must prioritize which issues to tend to with their limited funding and time. Furthermore, mitigation practices commonly involve structural design and since most at-risk communities are

56 National Science and Technology Council. (1996, April). Strategy for national earthquake loss reduction. http://clinton4.nara.gov/textonly/WH/EOP/OSTP/NSTC/html/USGS/index.html#anchor299544

140 already developed, they require retrofitting of buildings as opposed to new construction.

Retrofitting existing structures is more expensive than new construction, thus adding a disincentive for using limited local budgets on mitigation practices. As a result, earthquakes are a low priority for local government officials who do not enforce mitigation practices and thus, never implement research findings into practice.

3.4.2. DATA

For the NEHRP, I analyze U.S. economic losses caused by earthquakes during the time period 1929-2005. The earthquake loss data come from the dataset presented in the Vranes and

Pielke study Normalized Earthquake Damage and Fatalities in the United States: 1900-2005.57

This dataset compiles earthquake losses from the National Geophysical Data Center‟s Significant

Earthquake Database (NGDC-s), the University of South Carolina Hazards Research Lab‟s

Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the Centre for Research on the Epidemiology of Disasters‟ Emergency Events Database (EM-DAT).

The NGDC-s database relies heavily on the earthquake loss values from the dataset presented in the study Seismicity of the United States, 1568-1989,58 while also relying on the study Earthquake History of the United States,59 the U.S. Geological Survey (USGS) reports, and occasionally the EM-DAT (Vranes & Pielke, 2009). The SHELDUS obtains its earthquake

57 Vranes, K. and R.A. Pielke, Jr. (2009, August). Normalized earthquake damage and fatalities in the United States: 1900 - 2005. Natural Hazards Review. Pp. 84-101.

58 Stover, C.W. and J.L. Coffman. (1993). Seismicity of the United States, 1568-1989 (revised). USGS Professional Paper 1527, 427. U.S. Geological Survey, Washington, D.C.

59 Coffman, J.L., C.A. von Hake, and C.W. Stover. (1982). Earthquake history of the United States, Revised Edition (through 1970), Reprinted 1982 with supplement (1971-80). Pub. 41-1, 258. U.S. Department of Commerce, Boulder, Colorado.

141 loss data largely from the NGDC-s but sometimes relies on other published reports as well

(Vranes & Pielke, 2009). The EM-DAT relies on a number of sources for earthquake losses including U.N reports, federal agencies of national governments across the world, non- governmental organizations, the World Bank, reinsurance companies, and the AFP.60

As no methodological protocol exists for the calculation of earthquake losses, databases often report varying loss values for the same earthquake event. Since they often lack transparency, it is unclear how some databases calculate earthquake loss values and the role of direct and/or indirect damages in calculations (Vranes & Pielke, 2009). The NGDC-s calculation methodology is unknown. However if a numerical loss value is not reported for an earthquake event, the database uses the qualitative damage description to assign a rough estimate of monetary loss (based on pre-determined categories of damage).61 The SHELDUS reports losses when a single earthquake event causes $50,000 or more loss in direct damages to property and crops.62 The EM-DAT‟s reported losses reflect both direct and indirect damage.63 The Vranes and Pielke dataset reconciles this variance by maintaining three different subsets: one subset reporting the highest estimates, the next reports the lowest estimates, and the third subset reports

60 Centre for Research on the Epidemiology of Disasters. EM-DAT: The international disaster database – Source entry. http://www.emdat.be/source-entry

61 National Oceanic and Atmospheric Administration. Significant earthquakes database. National Geophysical Data Center. http://www.ngdc.noaa.gov/nndc/struts/form?t=101650&s=1&d=1

62 Hazards & Vulnerability Research Institute. SHELDUS - Spatial Hazard Events and Losses Database for the United States. University of South Carolina. http://webra.cas.sc.edu/hvri/products/sheldus.aspx

63 Centre for Research on the Epidemiology of Disasters. EM-DAT: The international disaster database – Glossary. http://www.emdat.be/glossary/9#term89

142 the middle or average estimate. I use the middle dataset as the source for earthquake loss data in this study.

3.4.3. RESULTS

For the NEHRP, I completed three analyses of earthquake losses similar to those for the

NFIP, and two sensitivity studies. The three analyses evaluate inflation-adjusted earthquake losses, earthquake losses per capita, and earthquake losses as a percent of GDP. The sensitivity studies include 2004 as the significant policy year due to the NEHRP Reauthorization Act of

2004, and 1994 as the year for a significant earthquake event due to the 1994 Northridge earthquake.

Similar to the logarithmic scale of the Richter scale, I calculated the logarithmic values

(to the base 10) for earthquake losses since the values differ from each other in orders of magnitude. However, due to the wide range of values, the calculations caused many of the smaller loss values to become negative numbers or fractions. In order to plot the values in an effective visual manner, I multiplied the logarithmic values by a factor of 1000. These calculations do not affect the trend patterns which remain the same as they would with the smaller values. However the calculations do affect the proximity between the projection and actual trend lines with the lines appearing closer to each other on the logarithmic scale. For this reason, I present the three analyses in a logarithmic plot as well as a linear plot since the visual comparison of the two lines is the crux of the analysis.

143

Analyses

In the first analysis, I adjust earthquake losses for inflation to 2009 U.S. dollars. Then I create a projection based on the trend exhibited by earthquake losses from 1929-1976, the period prior to the enactment of the NEHRP. The projection represents the expected trend in earthquake losses had the NEHRP not been enacted. I compare the projection to the actual trend exhibited by earthquake losses over the entire time period of 1929-2005 which includes the years with the NEHRP.

While Figure 3.18 shows identical projection and actual trend lines, Figure 3.19 has a linear scale which reveals a small but noticeable difference between the two lines. The actual trend is larger than the projection and similar to the NFIP, two possible explanations exist for this outcome. First, socioeconomic factors such as population size and wealth are larger in absolute numbers in the post-NEHRP period than in the pre-NEHRP period. With more existing wealth that also happens to be at risk to disaster impact, more wealth is vulnerable and leads society to lose more in absolute dollars. This is particularly relevant to earthquake losses since in the U.S., California is the most vulnerable region to earthquakes, where the majority of at-risk communities are highly populated and relatively affluent.

Second, two extreme earthquake events occurred in the post-NEHRP period; events large enough to skew the actual loss trend to increase. This can be seen with the gap between the two lines (see Figure 3.19). Initially the difference is fairly small but the gap increases slightly in

1989 when the 1989 Loma Prieta earthquake occurred. The gap is largest by 1994 when the

Northridge earthquake caused the largest economic losses of any recorded earthquake, and consequently the largest bias to skew the actual trend line upwards. This explanation suggests that the NEHRP is incapable of countering extreme earthquake events. The analysis shows no

144 evidence that the policy has reduced losses from ordinary earthquake events and it seems that the

NEHRP is incapable of countering the impacts from extreme events. It cannot reduce extreme losses nor even provide a buffer to keep losses steady. Instead extreme disaster events cause the trend to increase which suggests that the NEHRP is futile against them.

145

Inflation-adjusted Earthquake Losses 8

7

6

5

4

of Damages

10 3 Log 2

1

0

1937 1949 1961 1973 1985 1997 1929 1931 1933 1935 1939 1941 1943 1945 1947 1951 1953 1955 1957 1959 1963 1965 1967 1969 1971 1975 1977 1979 1981 1983 1987 1989 1991 1993 1995 1999 2001 2003 2005

Actual losses Projection Actual trend

Figure 3.18: Comparison of inflation-adjusted earthquake losses (logarithmic scale)

Inflation-adjusted Earthquake Losses 70000

60000

50000

40000

30000

20000 2009 U.S.2009 dollars (millions) 10000

0

1953 1975 1997 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1999 2001 2003 2005

Actual losses Projection Actual trend

Figure 3.19: Comparison of inflation-adjusted earthquake losses (linear scale)

146

The second analysis involves U.S. per capita inflation-adjusted earthquake losses adjusted to 2009 U.S. dollars. This analysis uses the same comparison as the first analysis; between the projection of losses from the pre-NEHRP time period of 1929-1976, and the actual trend in losses from 1929-2005 which includes the years with the policy.

Figure 3.20 depicts both trend lines as the same while Figure 3.21 reveals a small difference between the two, with the actual trend line showing slightly larger than the projection.

The outcome of this analysis looks nearly identical to the outcome from the first analysis. Since the first analysis showed earthquake losses increasing, the outcome of this second analysis means that the population size and earthquake losses are increasing at the same rate. Therefore the proportion of losses to population size stays constant and exhibits a nearly identical trend line.

As in the first analysis, the actual trend line is larger than the projection line due to the influence of the extreme losses from the 1989 and 1994 earthquakes. This explanation suggests that the

NEHRP is incapable of countering extreme earthquake events.

147

Inflation-adjusted Earthquake Losses Per Capita

6

5

4

3

of Damages

10 2 Log 1

0

1937 1949 1961 1973 1985 1997 1929 1931 1933 1935 1939 1941 1943 1945 1947 1951 1953 1955 1957 1959 1963 1965 1967 1969 1971 1975 1977 1979 1981 1983 1987 1989 1991 1993 1995 1999 2001 2003 2005

Actual losses Projection Actual trendline

Figure 3.20: Comparison of inflation-adjusted earthquake losses per capita (logarithmic scale)

Inflation-adjusted Earthquake Losses Per Capita 300

250

200

150

100 2009 U.S.2009 Dollars 50

0

1989 1991 1993 1995 1997 1999 2001 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 2003 2005

Actual losses Projection Actual trend

Figure 3.21: Comparison of inflation-adjusted earthquake losses per capita (linear scale)

148

The third analysis calculates inflation-adjusted earthquake losses as a percent of U.S.

GDP, adjusted to 2009 U.S. dollars. This analysis uses the same comparison as the first analysis; between the projection of losses from the pre-NEHRP time period of 1929-1976, and the actual trend in losses from 1929-2005 which includes the years with the policy.

Figure 3.22 shows matching trend lines while Figure 3.23 reveals a small difference between the two, with the actual trend line slightly larger than the projection line. The outcome of this analysis looks nearly identical to the outcome from the first two analyses regarding the pattern of the trends and their distance from each other. Since the first analysis determined that earthquake losses are increasing, the outcome of this analysis means that the GDP is likewise increasing at the same rate. Therefore the proportion of losses to GDP stays constant and exhibits a nearly identical trend line. This also suggests that the explanations for the first analysis hold true in this analysis as well. The actual trend line is larger than the projection line due to the influence of the extreme losses from the 1989 and 1994 earthquakes. This explanation suggests that the NEHRP is incapable of countering extreme earthquake events.

149

Earthquake Losses as a % of GDP 7

6

5

4

of Damages 3 10 10

Log 2

1

0

1937 1949 1961 1973 1985 1997 1929 1931 1933 1935 1939 1941 1943 1945 1947 1951 1953 1955 1957 1959 1963 1965 1967 1969 1971 1975 1977 1979 1981 1983 1987 1989 1991 1993 1995 1999 2001 2003 2005

Actual losses Projection Actual trendline

Figure 3.22: Comparison of inflation-adjusted earthquake losses as a % of GDP (logarithmic scale)

Earthquake Losses as % of GDP 0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

1959 2001 1929 1931 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2003 2005

Actual losses Projection Actual trend

Figure 3.23: Comparison of inflation-adjusted earthquake losses as a % of GDP (linear scale)

150

Sensitivity studies

The 2004 sensitivity study determines whether the NEHRP Reauthorization Act of 2004 had an impact on earthquake losses. The projection is based on the trend exhibited by losses in the time period of 1929-2003, prior to the enactment of the amendment. The actual trend is based on the trend exhibited by earthquake losses during the full time period of 1929-2005 which includes the years after the enactment of the amendment. It is difficult to gauge anything from this sensitivity study since the cutoff year leaves only two data points for 2004-2005.

Nonetheless, a comparison of the projection and actual trend reveals the projection as larger than the actual trend, although only slightly (see Figure 3.24). The actual trend is likely smaller than the projection line due to the significantly low losses in 2004 and 2005 which decreased the actual trend line but are excluded by the projection.

Inflation-adjusted Earthquake Losses 6

5

4

3

of Damages of 0 0

2 Log1

1

0

1937 1949 1961 1973 1985 1997 1929 1931 1933 1935 1939 1941 1943 1945 1947 1951 1953 1955 1957 1959 1963 1965 1967 1969 1971 1975 1977 1979 1981 1983 1987 1989 1991 1993 1995 1999 2001 2003 2005

Actual losses Projection Actual trend

Figure 3.24: Comparison of earthquake losses after the NEHRP Reauthorization Act of 2004

151

The 1994 sensitivity study evaluates a major earthquake event year by determining the impact of the 1994 Northridge earthquake on the trend in earthquake losses. The projection is based on the trend exhibited by losses in the time period of 1929-1993, prior to the Northridge earthquake. The actual trend is based on the trend exhibited by earthquake losses during the full time period of 1929-2005 and it reflects any impact of the Northridge earthquake. A comparison of the projection and actual trend reveals the projection as noticeably larger than the actual trend

(see Figure 3.25). For the actual trend, even though the losses from the 1994 Northridge earthquake were extraordinarily high, the years that follow include four years without any losses and the final two years showing some of the lowest losses of the entire time period. Together, these factors counter the Northridge earthquake losses and result in a lower actual trend. Since the cutoff year for this study is 1994, the projection line does not consider these factors that ultimately countered the increasing trend.

Inflation-adjusted Earthquake Losses 8

7

6

5

4

of Damages

10 3 Log 2

1

0

1937 1949 1961 1973 1985 1997 1929 1931 1933 1935 1939 1941 1943 1945 1947 1951 1953 1955 1957 1959 1963 1965 1967 1969 1971 1975 1977 1979 1981 1983 1987 1989 1991 1993 1995 1999 2001 2003 2005

Actual losses Projection Actual trend

Figure 3.25: Comparison of earthquake losses after the 1994 Northridge earthquake

152

3.5. DISCUSSION

The strength of this study lies in the metric presented as a new measure to gauge the impact of policies. However one limitation with the methodology is the short time periods of the trends that I analyze. In general, trend analyses improve in robustness as time periods increase, particularly when natural phenomena with long timescales are involved. As Chapter 2 mentioned, a 30 year time period is necessary for statistically significant trends in climate analyses. Although this study does not concern climate, with 30 years as a standard, the trend analyses in this study fall short and therefore the findings must be considered with this in mind.

At best, the findings can serve as possible indications of results but a large level of uncertainty accompanies these indications until more time passes and data can be collected. Fortunately, the level of uncertainty will be resolved and this study will only improve in robustness with time.

Also, the metric stands as a useful new measure that can be adopted for any policy appraisal.

The mandates for both policies are very broad and simply state that losses should be reduced, but with no mention of the specific types of losses to reduce or any prescriptive conditions for approaches to use in reducing losses. In the absence of any specifics, the policies are mandated simply to reduce losses and this study accordingly gauges simply whether the policies successfully reduce losses to a baseline level. This study does not further investigate the types of losses contributing to increasing flood and earthquake losses nor the mechanisms driving the increasing losses. Such factors lend themselves well to future sensitivity studies such as an analysis of whether losses are increasing due to a growing number of disaster events. The sensitivity studies included here, however, did inadvertently reveal that the less frequently- occurring severe disaster events causing extraordinary losses are largely shaping the increasing trend in losses over time. Nonetheless, this study appraises the NFIP and NEHRP in accordance

153 with their mandates by conducting a broad analysis of whether the policies are reducing losses to a baseline level.

One potential argument against this study‟s conclusion could suggest that while actual losses are larger than the projected losses, they might still reflect a reduction from an originally larger level. This would suggest that the policy (NFIP or NEHRP) has successfully reduced an originally larger level of losses to the level of actual losses. This is not likely to be the case because the factors responsible for increasing losses are socioeconomic changes and this study adjusts the data for inflation, population growth, and national wealth. Therefore the factors that would cause higher losses are accounted for so one can expect that actual losses are at their true level rather than potentially being a larger level.

However, an amount of uncertainty does exist in whether the policies impart some level of impact in influencing vulnerable societies to adopt favorable behavior which ultimately results in avoiding potential losses. For example, could the NFIP influence residents who are living in floodplains to build more resilient homes and therefore avoid potential losses? Could the

NEHRP promote more resilient construction of buildings that can resist shaking and thereby avoid potential losses? It is difficult to quantify these potential impacts and so it is difficult to gauge whether either policy has imparted such an impact; therefore this uncertainty remains unresolved.

Nonetheless, even if the policies have imparted this type of impact, it is not to the baseline level required to consider the policies successful in this study in which the trend in actual losses must be smaller than the projection. The projection reflects the trend in losses expected had the policy not been enacted. Therefore the policy must impart an impact large enough to reduce losses below the projection to prove that it does play a role and is able to

154 reduce losses to a level lower than would be expected had there been no policy at all. Otherwise even though the policy does have an impact in reducing losses, it is not considered successful since it cannot counter the effects of the socioeconomic factors that are increasing losses.

The findings from this study show no evidence that the NFIP is reducing losses and suggests that the NFIP is incapable of countering increasing losses sufficiently. Regardless of whether the policy has partially reduced losses or not, this study determines that flood losses are nonetheless increasing and the NFIP has been unable to stop that increase. Therefore while the

NFIP‟s goal explicitly mentions reducing flood damage, countering increasing losses is inherent to this statement and the NFIP has been unable to do so.

While the findings of this study provide insight into the NFIP‟s performance, they must be considered with the caveat that they reflect part of the scope of the policy and one piece of the larger network of the national flood protection system. This study assesses the NFIP on a national scale by determining the policy‟s impact on the U.S. as a whole. A macro-assessment such as this provides a sound first step for an appraisal of the NFIP as it depicts the larger picture. However, since state governments manage the NFIP and local governments enforce it, the influence of the policy varies regionally, sometimes in a significant manner. To capture the regional scope of the NFIP, a continuation of this study could extend the appraisal to assess the policy at the state and local levels which would subsequently highlight regions‟ differing levels and methods of NFIP implementation (e.g. floodplain development, land management, enforcement). Ultimately, appraising the NFIP at the state and local level could provide alternate findings that identify the policy as successful in reducing flood losses on a regional level.

This appraisal of the NFIP also provides a first step in assessing the national flood protection system. However the NFIP represents only one piece of a larger U.S flood protection

155 system. Flood management, specifically protection against flood-caused economic losses, can be categorized into structural and non-structural measures. Structural measures include all of the devices built to control floods such as levees, dikes, sea walls, reservoirs, and channels (Arnell,

1984). Non-structural measures include policies and actions designed to mitigate flood losses such as land management, land acquisition, zoning, building codes, subsidies, and tax incentives

(Arnell, 1984). The federal government favors structural measures therefore these devices dominate the U.S. flood protection system. As the NFIP incorporates all of the non-structural measures but none of the structural measures, it does not represent the full network of the U.S flood protection system. Therefore the findings from this study suggest something about the

NFIP‟s performance but cannot conclude as much about the national flood protection system. A more complete assessment of the U.S. flood protection system would encompass this study coupled with other studies focused on the ability of structural measures to reduce flood losses.

Similar to the NFIP, the results from all studies but one, show no evidence that the

NEHRP has reduced earthquake losses to the baseline level. Post-policy earthquake losses exhibit actual trends that are larger than the trends in losses had there been no policy

(projections). In fact, earthquake losses have increased even with the NEHRP in force. The findings suggest that this is largely due to the fact that the NEHRP is unable to counter the impact from extreme earthquake events. A single extreme earthquake such as the 1994

Northridge earthquake exceeded the capabilities of the policy and caused economic losses in larger orders of magnitude than all other earthquakes.

This study appraises the NEHRP on a national scale by assessing the policy‟s influence on the country‟s earthquake losses as a whole. A macro-assessment such as this provides a sound first step for an appraisal of the NEHRP as it depicts the larger picture. However the

156 success of the policy varies regionally since the enforcement of NEHRP varies dramatically by region. Moreover, earthquakes are not national disaster events; they are specific only to certain regions. Therefore analyses comparing earthquake losses to national population size and wealth would be more useful if they compared losses to regional population sizes and wealth instead. A continuation of this study could assess the NEHRP and earthquake losses in specific regions.

While this study gauges policy success through trend line analyses, the trend lines actually reflect more about the earthquake events than they reflect the policy‟s success or failure.

Unlike flood losses which have a regular frequency, earthquakes occur intermittently with multi- year gaps of zero losses in between. In long-term trends such as those used in this study, the fewer number of disaster events that occur, the greater the impact from each event. Since the flood record includes a large number of floods, each individual flood only carries a certain weight in the long-term trend; however the earthquake record contains far a fewer number of earthquakes therefore each event carries a greater weight for the long-term trend. Individual earthquake events influence the trend for earthquake economic losses and they largely shape the trend lines. Therefore, the findings from the NEHRP analyses should be interpreted with caution as they mainly support larger conclusions.

3.6. CONCLUSION

The NFIP and NEHRP are both policies with legislated mandates to reduce economic losses caused by floods and earthquakes, respectively. Therefore to assess the success of these policies, one must gauge their ability to reduce losses. Many disaster experts have appraised these two policies but assessments are mixed as to whether the NFIP or NEHRP have been successful in reducing economic losses. This study offers a new metric for gauging success of

157 the NFIP and NEHRP by comparing losses from two situations: with and without the policy. I portray the situation with the policy by using the actual trend exhibited by losses over the full time period of this study which includes the years after the enactment of the policy. I portray the situation without the policy by using a projection of the trend exhibited by losses during the time period prior to the enactment of the policy. A comparison of the trends with and without the policy (i.e. after and before the policy) thus reveals if the policy has had an impact on losses, specifically in reducing losses.

The findings of this study reveal that the trends in economic losses show little to no difference between situations with and without the policies. This suggests that the policies have had little to no impact on losses, an outcome contrary to their mandates for reducing economic losses. For most of this study‟s analyses, the trends are similar although the actual trend is slightly larger than the projection. Rather than the policy influencing losses, extreme flood and earthquake events are the influential factors. Both policies show no evidence of reducing losses from ordinary disaster events and more so, they seem unable to counter the impacts from extreme events. Neither can reduce extreme losses nor even provide a buffer to keep losses steady. Instead extreme disaster events cause increasing losses and portray the policies as futile against them. Even if losses were originally higher and both policies were able to reduce them to the resulting losses portrayed in this study, the resulting losses are still increasing which suggests that both the NFIP and NEHRP are unable to reduce losses sufficiently.

There is cause for concern that both policies are unable to counter the impacts of extreme events. This is troubling for the nation‟s future resiliency if extreme flood events increase in frequency and severity as some scientists predict happening. More so, as societal vulnerability increases, even ordinary disaster events can cause extreme impacts, thus heightening the concern

158 for the country‟s resiliency. While flood losses are currently increasing in a linear manner, extreme flood events will transform the linear increase into an exponential increase. In response, the NFIP should redefine its methods to replace the 100-year flood with a new standard that is high enough to encompass extreme flood events. For earthquakes, since scientists predict an inevitable extreme earthquake from the San Andreas Fault (Fialko, 2006), it is highly problematic if NEHRP remains incapable of countering extreme earthquake events. The

NEHRP requires a much stronger enforcement effort to implement a number of promising strategies it has developed that remain unenforced at present. Strengthening both the NFIP and

NEHRP, whether through tougher standards or greater enforcement, resolves the policies‟ limitations while concurrently improving the nation‟s resiliency to natural disasters; ultimately creating the optimal solution.

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CHAPTER 4: AN ASSESSMENT OF FEDERAL R&D FOR NATURAL DISASTERS

4.1. INTRODUCTION

The theme of this dissertation centers on analyzing disaster loss data in order to appraise natural disaster policy. In this third study, I appraise the policy for science aspect of natural disaster policy by assessing the research and development (R&D) efforts related to natural disasters. As disaster researchers explain, “Science policy decisions represent a particularly important area for the application of disaster loss data, both to set priorities about what science to fund and to evaluate the contributions of science to real-world outcomes.” (Downton & Pielke,

2005).

I evaluate whether federal funding levels for natural disaster R&D correspond with the level of documented impact from individual disaster types; essentially whether the disaster types that cause the greatest impact on society receive the greatest amount of funding for their research. I define impact to society in terms of human and economic losses so this study determines the level of human and economic losses caused by each disaster type. Then I determine the level of federal funding allocated for R&D focused on each disaster type and I compare the relative level of losses to funding. The normative assertion suggests that federal disaster policy should appropriate federal funding in proportion to the level of impact caused by natural disasters with the greatest amount of funding allocated to the largest impact. This study investigates whether this assertion holds true by identifying the relative level of impact created by each disaster type and then comparing the relative level of federal funding allocated to each type.

Here, funding refers to federal spending specifically for R&D efforts on natural disasters.

While federal disaster spending encompasses efforts beyond R&D, the full scope presents

160 complexities that make it difficult to track all spending in any coherent manner. Many federal disaster programs split responsibilities between federal, state, and local roles and the spending on such programs are split accordingly. This poses significant challenges to tracking the full amount of spending. Federal disaster spending also covers a broad scope of activities from insurance to emergency responder training where many activities cater to all natural disasters instead of being delineated to a specific disaster type. This also presents a difficulty for tracking, particularly tracking by disaster type as this study pursues. However, R&D programs present a more clearly demarcated class of federal disaster activities that are largely aligned with specific disaster types and of which federal spending can be tracked in a somewhat coherent manner.

Here R&D efforts include research on prediction, prevention, response, mitigation, and adaptation for natural disasters including multidisciplinary research spanning across the physical sciences, social sciences, and engineering. It encompasses research on topics such as the science of specific disaster phenomenon, environmental impact, public behavior, and engineering design related to natural disasters. R&D also includes technology development such as new instruments with increased sophistication for predicting disasters or product development such as gadgets needed by emergency responders in the field.

The role of R&D varies with the operating institution such that industry R&D differs from government R&D. Industry R&D focuses on growing markets whether through new science or familiar science to create either new markets or improve familiar markets (Thursby &

Thursby, 2006). To exemplify these terms - the science of telecommunications was new to the public in the 1990‟s and created a new market for communication devices such as the cell phone.

In the past decade, telecommunications has become a familiar science that has continued to improve the familiar communication market with devices involving improved technology such

161 as smartphones. One study found that of 235 new corporate R&D facilities built around the world, 39% of the R&D conducted is new science and 61% is familiar science. In fact, about

41% of R&D is familiar science to improve familiar markets that have already been established

(Thursby & Thursby, 2006).

On the other hand, researchers have long promoted government sponsored R&D as a means to meet societal needs. In Vannevar Bush‟s 1945 seminal science policy report Science – the Endless Frontier, he endorsed government sponsored R&D as a necessity for society‟s well- being as he stated, “But without scientific progress no amount of achievement in other directions can insure our health, prosperity, and security as a nation in the modern world.” (Bush, 1945).

Bush‟s report created a “social contract” (Pielke & Byerly, 1998) between society and the scientific enterprise; a contract which asks society to fund science through government sponsored R&D in exchange for benefits to society‟s well-being as produced by such R&D.

Today, this social contract remains an underlying foundation for government sponsored R&D.

The Clinton Administration issued the 1994 report Science in the National Interest which expanded the Vannevar Bush report and required science to contribute to national goals while also aligning with the Administration‟s larger strategic goals for science as follows:

1. Long term economic growth that creates jobs and protects the environment 2. A government that is more productive and more responsive to the needs of its citizens 3. World leadership in basic science, mathematics, and engineering64

In the report “Rising Above the Gathering Storm”, the National Academy of Science provided ten recommendations to enhance the U.S. scientific enterprise; the second greatest priority being a national commitment to research “to maintain the flow of new ideas that fuel the

64 Clinton, William J. and Albert Gore, Jr. (1994, August). Science in the National Interest. Executive Office of the President, Office of Science and Technology Policy. Washington, D.C. http://clinton1.nara.gov/White_House/EOP/OSTP/Science/html/Sitni_Home.html

162 economy, provide security, and enhance the quality of life.” (National Academies, 2007)

Bozeman and Sarewitz suggest that science should reflect public values (Bozeman & Sarewitz,

2005) and Pielke and Byerly recommend, “…science is consciously guided by society‟s goals rather than scientific serendipity. Good science is necessary but not sufficient; association with a societal goal is required.” (Byerly & Pielke, 1995).

However even if all government sponsored R&D meets societal needs, government resources are limited and can only fund a portion of the large collection of potential R&D projects. So how does the federal government decide which R&D projects to fund? One intuitive method is to prioritize R&D projects based on the extent of expected outcomes; the projects that can meet more or greater societal needs would be a higher priority. This is the metric established by the Government Performance and Results Act (GPRA) as one measure of performance of science agencies. In implementing GPRA, it proved quite difficult to link R&D to outcomes. From the onset of an R&D project through completion, many factors emerge that can alter the course of a project and result in different outcomes than originally anticipated

(GAO, 1996). Also, there is typically a time lapse between the R&D project and resulting outcomes which further challenges linking R&D to outcomes. Therefore while linking R&D to outcomes is a sound theoretical method for prioritization, it proves difficult to actually implement.

According to a 2000 RAND study on federal prioritization for R&D, the Office of

Technology Assessment recommended prioritization criteria for R&D projects of 1) scientific merit 2) social benefits and 3) programmatic concerns (Popper, Wagner, Fossum, & Stiles,

2000). Meanwhile, a 1992 report by the Carnegie Commission on Science, Technology, and

Government offered an alternative prioritization method which valued R&D that fills scientific

163 needs as determined by the scientific community (Carnegie Commission on Science,

Technology, and Government, 1992). A 1993 National Research Council report suggests a prioritization method based on filling the voids where national capabilities currently lapse

(National Research Council, 1993). The House of Representatives Committee on Science issued a report in 1998 entitled “Unlocking our future: Toward a new national science policy” which recommended prioritizing the R&D projects which fill the requirements of agency missions

(U.S. House of Representatives, 1998). These prioritization methods differ from each other but each offers an approach well-suited to specific contexts whether it is the research, agency, or national setting.

There is no standard prioritization protocol used by all decision makers in the federal government. Rather, decision makers practice different prioritization processes in different settings where these processes are driven by the values held by the institution. Ultimately, however, federal funding for any program reflects the compilation of decision processes of multiple institutions starting from the federal agency, through the Executive Branch‟s Office of

Management and Budget, House of Representatives, and Senate. Since the process results in one final appropriation decision, differing prioritization processes must ultimately reconcile whether through a consensus on prioritization or downstream institutions‟ priorities overriding the priorities of the institutions upstream.

The case for natural disaster R&D should be more straightforward than most R&D. If government sponsored R&D should contribute to society‟s well-being, then R&D on natural disasters should focus on protecting society from the impacts of natural disasters. In a 2005 report, the National Science and Technology Council reinforced this idea by stating, “To reduce future escalation of these costs [of losses], the United States invests significant Federal funds in

164 disaster-related science and technology to reduce the loss of life and property damage from hazards.” (National Science and Technology Council, 2005). Thus all disaster R&D programs should aim to protect society against human and economic losses caused by natural disasters.

The impact of disasters varies by the type of natural disaster. For instance windstorms cause large economic losses in the U.S. but less human losses whereas earthquakes cause great human losses. If disaster R&D should protect society from economic and human losses, then its highest priority should be focused on the disaster types causing the greatest economic and human losses.

Despite differing institutional values and prioritization processes, the case for disaster R&D is straightforward in that the societal need and values driving prioritization are largely unequivocal.

This study investigates the degree to which the actual prioritization and funding decisions for disaster R&D correspond with the level of impact documented for individual natural disaster types. Using data on economic and human losses caused by different types of natural disasters, I determine the level of impact caused by each disaster type. Then I use a budget snapshot of the

2010 federal budget from which I track R&D programs focused on natural disasters. The budget indicates the level of federal funding allocated to each disaster type and allows me to determine whether the disasters causing the greatest impact are receiving the greatest federal funding.

However a snapshot of one year‟s budget can be somewhat misleading since budget decisions reflect multiyear plans and annual appropriations are only portions of total multiyear allocations.

Therefore, in order to portray a more accurate picture of funding decisions, I supplement the budget snapshot with a number of interviews where I ask decision makers to explain the values, priorities, institutional dynamics, and decision making processes for disaster R&D prioritization and funding. Combined, the budget snapshot and interviews provide insight into the prioritization and funding processes and either validate the normative assertion by finding that

165 federal funding is allocated to the disasters causing the greatest impacts, or they explain what other factors are motivating funding decisions.

4.2. DATA AND METHODOLOGIES

4.2.1. DATA

Databases for natural disaster data vary in the quality of data. This dissertation defines databases with research-quality robustness as any which meet the following criteria: 1) relies solely on credible sources for loss data65 2) maintains consistent methodologies over time for data processing and 3) vets the data regularly. Two databases meet this criteria and provide useful data for this study – the Munich Reinsurance Company‟s NatCatSERVICE© database and the University of South Carolina‟s Spatial Hazard Events and Losses Database for the United

States (SHELDUS).

The NatCatSERVICE© database provides information on economic and human losses caused by natural disaster events occurring globally. Specifically, it reports individual natural disaster events occurring anywhere in the world, the disaster type (e.g. storms, floods), the dates of occurrence, countries of occurrence, the associated dollar losses from damage, and number of fatalities. NatCatSERVICE© organizes disaster data into seven categories based on the severity of economic and human losses caused by the disaster event. This study uses NatCatSERVICE© data from both Category 5– Devastating catastrophes and Category 6 – Great natural catastrophes; the categories involving the largest economic losses and likely largest human losses since these events are the most severe on record. In terms of economic losses, Category 5 includes all disaster events that caused > $580 million (2008 USD) of damage. The Category 5

65 Credible sources include scientific, government, and non-governmental organizations as well as insurance companies.

166 threshold of $580 million is the 2008 inflation-adjusted value of the original threshold. The

Munich Reinsurance Company initially created all loss category thresholds concurrently based on the historical distribution of losses.66 Category 6 includes all disaster events that caused economic losses equal to 5% of national GDP/capita of the country where the event occurred.

The Category 6 economic loss threshold is the Munich Reinsurance Company‟s interpretation for an economic threshold related to the United Nations definition of a “great disaster” (thousands of fatalities, economy severely affected, extreme insured losses).67

The data are of research-quality robustness starting in 1980 and for this study, the data include economic and human losses caused by natural disasters in the U.S. from 1980-2008.

Economic losses encompass both insured and uninsured monetary losses caused by direct losses such as material damage to property and infrastructure or the price of destroyed agriculture, and indirect losses such as loss of revenue from affected businesses temporarily closing. Human losses refer to the number of fatalities resulting from natural disaster events. This study covers disaster events causing human and economic losses of the following types of natural disasters: hurricanes, severe storms, tornadoes, hailstorms, winter storms, snowstorms, blizzards, floods, flash floods, storm surges, droughts, earthquakes, tsunamis, landslides, volcanic activity, wildfires, cold spells, frost, and heat waves.

The SHELDUS reports individual disaster events occurring in the U.S., with specific information on the state and county of occurrence, start and end date, disaster type, dollar losses

66 Personal communication with Angelika Wirtz. The Munich Reinsurance Company. January 12, 2010.

67 United Nations International Decade for Natural Disaster Reduction Department of Humanitarian Affairs. (1992). IDNDR/DHA 1992.

167 from damage, number of injured victims, and number of fatalities. The SHELDUS68 provides information on economic and human losses caused by natural disasters in the U.S. from 1960 - present. It covers the 50 states plus Washington, D.C. and the scope of natural disasters includes avalanches, storms, fog, heat waves, floods, earthquakes, tsunamis, landslides, wildfires, drought, and volcanic activity. Economic losses encompass monetary losses resulting from direct damage to property and crops. Human losses refer to the number of fatalities resulting from natural disaster events.

The SHELDUS obtains its raw data from several sources including the National Climatic

Data Center (NCDC) – the main source of raw data, the National Geophysical Data Center

(NGDC), and the Storm Prediction Center (SPC). During the periods 1960-1979 and 1996- present, the database reports every disaster event causing any economic loss or fatality in the

U.S. However during the period 1960-1995, the database originally maintained a threshold and only reported disaster events causing at least $50,000 current U.S. dollars of loss or any fatalities which corresponded to the NCDC‟s threshold of $50,000-$500,000 for its Category 5 disasters.

In fact, the NCDC used to report economic losses by categories rather than actual dollar amounts. Since 1996 the NCDC has eliminated thresholds and categories and reports all losses, regardless of value, in actual dollar amounts, as does the SHELDUS accordingly. Since then, the

SHELDUS has revisited the losses reported from 1960-1979 and corrected the data to report all disaster events that caused any economic loss or fatality. Over time, the database managing institution plans to reprocess the remaining data from 1980-1995 to report all disaster events without any threshold and ultimately impart consistency throughout the data.

68 Hazards & Vulnerability Research Institute. (2009). The Spatial Hazard Events and Losses Database for the United States. Version 7.0 [Online Database]. University of South Carolina. Columbia, South Carolina. http://webra.cas.sc.edu/hvri/products/sheldusmetadata.aspx#5

168

The NCDC also improved the spatial resolution of its data as it now reports economic and human losses by county as opposed to region. Previously the NCDC reported total economic losses for the affected region and averaged the total over the affected counties to calculate the economic losses per county, as did SHELDUS accordingly. Since 1996 the NCDC reports actual losses for specific counties and thus SHELDUS reports so accordingly. Therefore from 1960-1995, SHELDUS reports average losses per county but from 1996-present, it reports losses specific to affected counties.

While SHELDUS lacks consistency across its range of data, it continues to improve with ongoing vetting and reprocessing of historic data. This study imposes research-quality robustness of SHELDUS data by applying a $580 million (2008 U.S. dollars) threshold across the entire period of data in order to achieve consistency throughout the data as well as consistency with the NatCatSERVICE© data. The SHELDUS data used in this study spans from

1980-2008 to correspond to the time period of the data from the NatCatSERVICE© database.

For one analysis in this study, I also use a third dataset which I create from National

Weather Service and U.S. Geological Survey data. I mainly rely on NatCatSERVICE© and

SHELDUS data for this study, particularly for economic loss analyses because both databases provide robust quality economic loss data. However, both databases appear to provide incorrect fatality data due to a data processing complication with the NatCatSERVICE© data and unknown reasons for the SHELDUS data. Therefore I compile a third dataset consisting of fatality data for the human loss analysis.

The National Oceanic and Atmospheric Administration (NOAA) National Weather

Service (NWS) provides data on weather-related disaster events that cause “loss of life, injuries,

169 significant property damage, and/or disruption to commerce”69 in addition to significant record weather events such as extreme temperature or precipitation events. This data include human and economic losses caused by lightning, tornadoes, floods, hurricanes, heat waves, cold waves, winter storms, rip currents, and windstorms. The NOAA publication Storm Data reports data provided by NWS field offices in all 50 states and U.S. territories, other government agencies, law enforcement, private companies, the media, and individuals and compiled by the NOAA

NWS Office of Climate, Water, and Weather Services and the National Climatic Data Center.70

In accordance with the criteria for data robustness presented in this dissertation, these sources are considered credible. For data collection, the Office of the Federal Coordinator for Meteorology sends out quick response teams to collect data 12-24 hours after an extreme disaster event occurs. These quick response teams collect information on damage to structures and extent of flooded area from which the magnitude and classification of disaster events are determined.71

This is the standard procedure for data collection and processing thus indicating that the data processing method remains consistent over time, as required by the criteria for data robustness.

69 National Climatic Data Center. Storm Data Reference Notes. National Oceanic and Atmospheric Administration. http://www.ncdc.noaa.gov/oa/climate/sd/referencenotes.pdf

70 Office of Climate, Water, and Weather Services. U.S. Natural Hazard Statistics. National Weather Service, National Oceanic and Atmospheric Administration. http://www.weather.gov/os/hazstats.shtml

71 Office of Climate, Water, and Weather Services. Post Storm Data Acquisition. National Weather Service, National Oceanic and Atmospheric Administration. http://www.weather.gov/om/data/stormdata.shtml

170

The NOAA NWS makes its best effort to vet the data although it may report information unverified by the NWS because of time and resource constraints.72

In the third dataset I use for the human loss analysis, I supplement the fatality data from weather-related disaster events by also including data on human losses caused by earthquakes.

This data is provided by a U.S. Geological Survey (USGS) dataset consisting of earthquake fatalities in the U.S. from 1811-2003. To maintain consistency with the other data used in this study, I only include the losses from events starting in 1980. The source of this dataset is the study Seismicity of the United States, 1568-1989 which is peer-reviewed and therefore credible.73

Since it is a dataset, it is static and only reports information from the fixed time period of 1568-

1989 therefore it does not require ongoing vetting.

4.2.2. METHODOLOGIES

The analyses for this study involve three steps. In the first step, I analyze the loss data in order to determine the natural disaster types causing the greatest losses. Both the

NatCatSERVICE© and SHELDUS databases report raw data in current dollars. Therefore I adjust the economic loss values for inflation to 2009 U.S. constant dollars using the Bureau of

Economic Analysis (BEA) Implicit Price Deflators for Gross Domestic Product.74 The BEA provides Implicit Price Deflators on a quarterly and annual basis for most years; the inflation

72 National Climatic Data Center. Storm Data Reference Notes. National Oceanic and Atmospheric Administration. http://www.ncdc.noaa.gov/oa/climate/sd/referencenotes.pdf

73 Stover, C.W. and J.L. Coffman. (1993). Seismicity of the United States, 1568-1989 (revised). USGS Professional Paper 1527, 427. U.S. Geological Survey, Washington, D.C.

74 U.S. Department of Commerce Bureau of Economic Analysis. Table 1.1.9. Implicit Price Deflators for Gross Domestic Product. http://www.bea.gov/national/nipaweb/DownSS2.asp?3Place=N#XLS

171 adjustment in this study uses annual BEA Implicit Price Deflators for each year of this study‟s time period of 1980-2008. Once all economic losses are adjusted for inflation, I then categorize losses by disaster type based on the natural disaster event causing the losses. Often with natural disasters, a primary disaster event can spawn secondary disaster events. For instance, a severe storm can spawn tornadoes. In this analysis, I categorize losses by the primary disaster event.

Once categorized, I sum the losses from all disaster events of the same disaster type (e.g. losses from all floods) over the time period of this study, 1980-2008. Then I determine total losses by summing all losses from all disaster types over the time period. I calculate the losses of each disaster type as a percentage of total losses. The following is an example equation summarizing the methodology of this first step for floods:

∑ ( ) ( ) { } ∑ ( ) ( )

where Index i is each individual year between 1980-2008 and y is each disaster type

For human losses, I sum the fatalities from each disaster event of the same disaster type over the time period 1980-2008. To normalize the data for population growth, I use a supplemental second analysis that calculates the fatalities from each disaster event as a percentage of total population in that year. I use total population values provided by the U.S.

Census Bureau which provides readily accessible U.S. population data for the contiguous United

States starting in 1900 and the continental United States starting in 1950.75 However, the

75 U.S. Census Bureau. Historical National Population Estimates: July 1, 1900 to July 1, 1999, Table T1: Population estimates. Population Estimates Program. http://www.census.gov/popest/archives/1990s/popclockest.txt http://factfinder.census.gov/servlet/DTTable?_bm=y&-geo_id=01000US&-ds_name=PEP_2009_EST&- _lang=en&-mt_name=PEP_2009_EST_G2009_T001&-format=&-CONTEXT=dt

172 supplemental analysis results in negligible differences (see Table 4.1) so I only rely on the first analysis of unadjusted fatalities. Both of the calculations for economic and human losses result in the percentage of total economic and human losses attributable to each disaster type and these values describe the severity of natural disasters relative to each other.

% human losses % adjusted human by disaster type losses by disaster type Storms 87.8 86.8 Floods 0.9 0.9 Drought 1.0 1.0 Earthquakes 0.5 0.5 Wildfires 0.8 0.8 Volcanoes 0.2 0.2 Extreme temps 8.8 9.7 Table 4.1: Comparison of human losses The second step involves a snapshot of the federal budget created by compiling the natural disaster-focused R&D programs existing in 2010 across the federal agencies based on budget resources provided by the agencies as well as the Congressional appropriations bills. The budgets allow me to compare the relative levels of funding which indicate the government‟s level of priority for each program; the greater the appropriation, the greater the priority.

I identify the federal agencies known to conduct natural disaster-related R&D. These include the Federal Emergency Management Agency (FEMA), the National Institute of

Standards and Technology (NIST), the National Oceanic and Atmospheric Administration

(NOAA), the National Science Foundation (NSF), the U.S. Forest Service, the U.S. Geological

Survey (USGS), and the Department of the Interior‟s Office of Wildland Fire. Then I refer to the agency‟s 2010 request known as the President‟s Budget and I record the proposed funding levels for any natural disaster-related R&D programs. To find these programs, I search through the agency budget request in any agency offices and programs that appear to be involved with

173 research. For instance, NOAA maintains an Office of Oceanic and Atmospheric Research within which the Operations/Research/Facilities program exists. Once within the program, I research individual projects to identify those focused on natural disasters. For interagency programs such as the National Earthquake Hazards Reduction Program (NEHRP), I am able to find explicit program budgets. For Congressional appropriations, I search the 2010 conference report

Appropriations bill for the same agencies listed above. However, because the 2010 budget was a continuing resolution, many agencies do not have a conference report Appropriations bill in which case I rely on the final version of the bill from either house (e.g. Senate appropriations bill for NOAA, House of Representatives bill for FEMA.)

After identifying the existing programs and their funding levels, I categorize the programs by disaster type and total the appropriations for all programs of the same disaster type.

With these sums, I compare the appropriation of federal funding among different disaster types to infer the federal government‟s greatest priority. While this snapshot of the budget conveys the funding decisions, it does not offer any information on the processes leading to the funding decisions. Funding decisions are the outcome of complex processes involving a number of factors that lead up to the appropriation decisions. In order to better understand the decisions, I conduct interviews with decision makers to learn more about the processes.

The third step involves interviewing decision makers who are currently or were previously involved in the prioritization and funding processes for natural disaster R&D programs. I was able to interview sixteen decision makers whom I selected using a snowball sampling approach where the initial interviewees recommended subsequent decision makers to interview. These decision makers are non-political career staff across the federal government in the Executive and Legislative branches whose professional responsibilities include prioritizing

174 and funding natural disaster R&D programs. Through a series of interview questions, I asked these decision makers to identify disaster R&D programs, discuss the character of R&D programs compared to non-R&D programs, and to explain the factors they considered when prioritizing and funding disaster R&D programs. The interview questions are as follows:

1) What programs are you aware of across all federal agencies, which involve R&D on any type of natural disaster? Here R&D can be associated to prediction, prevention, response, mitigation, or adaptation. Research encompasses studies on topics such as the science of specific disaster phenomenon, environmental impact of disasters, public behavior, and engineering design. Development refers to technology development in relation to natural disasters.

2) Do you know what the 2010 funding level was for any of those R&D programs or where I might find the values?

3) When you consider how to fund and manage programs, are you concerned with different factors for R&D programs than those for operational programs? If so, what factors differ between the two types of programs?

4) What factors do Executive Branch appropriators consider when determining funding levels for natural disaster R&D projects? What factors do Congressional appropriators consider?

5) In the funding process (for the agencies, OMB, and Congress), does the level of threat posed by disaster types play a role in decision making? If so, what type of role?

6) How do decision makers (in agencies, OMB, and Congress) prioritize natural disaster R&D projects among all disaster R&D projects? How do decision makers prioritize disaster R&D programs among all agency programs?

My three-step analysis compares the results of the data analyses to funding decisions. If funding levels correspond to the disaster types causing the greatest impact, then the interviews provide supplemental information on additional factors that decision makers consider in the prioritization and funding process. This is useful to provide a more complete picture of these processes since they are more complex than a simple linear relationship of greatest impact equals more money. If funding levels are not aligned with level of impact, then the interviews explain what other factors are relevant to the prioritization and funding processes.

175

4.3. RESULTS

4.3.1. ANALYSES OF ECONOMIC AND HUMAN LOSSES

Both the Munich Reinsurance Company‟s NatCatSERVICE© and SHELDUS data report storms as the disaster type causing the greatest economic losses (see Figures 4.1, 4.2). The

NatCatSERVICE© data report losses at a magnitude ten times greater than the SHELDUS data because the Munich Reinsurance Company‟s database calculates economic losses from damage to property, infrastructure, agriculture, and business interruption among other things while the

SHELDUS economic losses only reflect damage to property and crops. Nonetheless, both databases report the greatest losses from storms, mainly caused by hurricanes. The

NatCatSERVICE© data report that of the ten costliest disaster events in the time period of this study, seven are hurricanes including (from greatest to least losses): Hurricanes Katrina, Ike,

Andrew, Ivan, Wilma, Charley, and Rita. SHELDUS attributes the largest storm losses to

Hurricanes Katrina and Wilma.

The SHELDUS data report floods as the disaster type causing the second greatest economic losses however this is not reflected by the NatCatSERVICE© data. This is because

SHELDUS categorizes losses caused by storm surge in the flood category while the Munich

Reinsurance Company categorizes storm surge losses in the storm category. The SHELDUS‟s flood loss category is high because it reflects the storm surges caused by Hurricanes Katrina and

Wilma. Both databases agree on earthquakes as the other disaster category with noticeably large economic losses, due to the 1989 Loma Prieta earthquake and the 1994 Northridge earthquake in

California causing large economic losses.

176

NatCatSERVICE©: U.S. Economic Losses by Disaster Type from 1980-2008

800000

600000

400000

200000 millions millions of$2009 0

Figure 4.1: U.S. economic losses by disaster type based on NatCatSERVICE© data

SHELDUS: U.S. Economic Losses by Disaster Type from 1980-2008

80000

60000

40000

20000 millions millions of$2009 0

Figure 4.2: U.S. economic losses by disaster type based on SHELDUS data

177

Since the two databases vary in the variables they incorporate in their calculations for economic losses, the disaster events included in the databases also vary. For instance, since the

SHELDUS calculates economic losses as property and crop damage, it may not include the eruption of Mt. St. Helens if the event did not cause any property or crop damage. Meanwhile

NatCatSERVICE© may include the volcanic eruption because it caused infrastructure damage.

Due to the variations between the two databases‟ data collection and processing methodologies, the percentage of total economic losses caused by each disaster type also differs. Both databases indicate that storms are causing the largest losses but the NatCatSERVICE© calculates a predominant 74% of total losses while the SHELDUS calculates a slight majority of 38% of total losses (see Figures 4.3, 4.4). Due to the SHELDUS categorizing storm surge as floods, the

SHELDUS reports 33% of total losses as caused by floods. Because the NatCatSERVICE© data report many additional disaster types than the SHELDUS, the remaining disasters other than storms compose smaller percentages of total losses for NatCatSERVICE© data.

178

NatCatSERVICE©: % Economic Losses by Disaster Type Other Earthquakes 10% Drought 5% Floods 7% Storms 74%

OTHER Extreme Temperatures: 2% Wildfires: 2%

Figure 4.3: % Economic losses by disaster type based on NatCatSERVICE© data

SHELDUS: % Economic Losses by Disaster Type

Other

Storms Earthquakes 38% 23%

Floods 33%

OTHER Wildfires: 4% Slides: 2%

Figure 4.4: % Economic losses by disaster type based on SHELDUS data

179

For human losses, the NatCatSERVICE© data provide a skewed perspective because of its geographic classification. The database reports global disaster events and often an individual event can affect multiple countries. In this case, the database sums the total economic and human losses caused across all countries and reports one total value. During the time period of this study, two hurricanes caused major losses to multiple countries of which the U.S. was one country: Hurricane Mitch affected the United States but mostly caused losses in many Central and South American countries while Hurricane Georges affected the United States and more so, the Caribbean. Both of these hurricanes caused excessively large numbers of human losses which are less likely to have occurred in the U.S. as in the other affected developing countries.

Unfortunately, due to the method in which the NatCatSERVICE© data report losses, it is not possible to separate out the U.S. human losses. Therefore Figure 4.5 portrays a disproportionately large number of U.S. fatalities from storms.

NatCatSERVICE©: U.S. Human Losses by Disaster Type from 1980-2008

25000

20000 15000 10000

# Fatalities# 5000 0

Figure 4.5: U.S. human losses by disaster type based on NatCatSERVICE© data

180

Figure 4.6 portrays the human losses reported by the SHELDUS data. These data reflect total fatalities caused by each disaster type over the time period 1980-2008. These results cannot be accurate because the number of fatalities are unrealistically small; smaller by orders of magnitude compared to the NatCatSERVICE© data. The fatality numbers also raise suspicion based on the fact that Hurricane Katrina alone caused at least 1836 fatalities76 which are not reflected in the storm data. While the managing institution of SHELDUS does not note any errors with its fatality data, these data appear to be erroneous. As a result, I disregard the

SHELDUS data and Figure 4.6 in this analysis and I use a third dataset that I compile from NWS and USGS data.

SHELDUS: U.S. Human Losses by Disaster Type from 1980-2008

200

150

100

# Fatalities# 50

0

Figure 4.6: U.S. human losses by disaster type based on SHELDUS data

Figure 4.7 portrays the results from the third dataset and the number of fatalities seems more reasonable than the NatCatSERVICE© or SHELDUS data. The USGS data provides the number of fatalities caused by earthquakes. The NWS data reports human losses by the

76 HurricaneKatrinaRelief.com FAQs. http://www.hurricanekatrinarelief.com/faqs.html

181 following disaster types: lightning, tornadoes, floods, hurricanes, heat waves, cold waves, winter storms, rip currents, and windstorms. To remain consistent with the data categorization used throughout this dissertation, I add the number of fatalities from lightning, tornadoes, hurricanes, winter storms, and windstorms to equal the number of fatalities for the category Storms. Flood losses align with this dissertation‟s category of Floods, and I add the number of fatalities from heat and cold waves for the category Extreme Temperatures. Figure 4.7 indicates that storms are causing the largest losses and extreme temperatures are causing the second largest losses. Storm losses are nearly three times the magnitude of flood losses and almost double the magnitude of losses caused by extreme temperatures. The large magnitude of storm losses are attributed to hurricane losses in 2005 when Hurricanes Katrina, Rita, and Wilma occurred; followed by three years of large tornado losses in 1984, 1998, and 2008.

NWS/USGS: U.S. Human Losses by Disaster Type from 1980-2008

7000

6000

5000 4000 3000

# of# Fatalities 2000 1000 0 Earthquake Storm Flood Extreme Temperatures

Figure 4.7: U.S. human losses by disaster type based on NWS/USGS data

The results from both the NatCatSERVICE© data and the NWS/USGS data indicate that storms are causing the greatest level of human losses (see Figures 4.8, 4.9). According to

182

NatCatSERVICE© data, storms are causing an overwhelming 88% of fatalities among all disaster types and the NWS/USGS data indicate that storms are causing almost half of all fatalities. Both datasets report extreme temperatures (i.e. heat waves and cold waves) as the disaster causing the second largest level of human losses. The NWS/USGS data report a larger percentage of losses caused by extreme temperatures than NatCatSERVICE©. This is likely due to the fact that

NatCatSERVICE© excels in economic loss data but not human loss data therefore it may not capture events causing fatalities as well as other datasets. Also, the NatCatSERVICE© covers more disaster types than the NWS/USGS dataset so the percentages will all vary.

183

NatCatSERVICE©: % Human Losses by Disaster Type

Extreme temps 9%

Storms 88%

OTHER Drought: 1.02% Floods: 0.88% Wildfires: 0.80% Earthquakes: 0.49% Volcanoes: 0.20%

Figure 4.8: % Human losses by disaster type based on NatCatSERVICE© data

NWS/USGS: % Human Losses by Disaster Type

Earthquakes 1%

Extreme Temperatures 30% Storms 48%

Floods 21%

Figure 4.9: % Human losses by disaster type based on NWS/USGS data

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4.3.2. BUDGET SNAPSHOT

The budget snapshot tracks disaster related R&D programs across the federal agencies. It is not a comprehensive summary of disaster R&D and in fact, it provides a slightly skewed picture of federal funding for existing programs. The snapshot is not comprehensive because disaster related R&D programs can be embedded within many agency programs, often within programs that are not explicitly focused on natural disasters. For instance, NOAA maintains ongoing projects focused on weather prediction and some of these projects likely involve research capabilities to improve severe weather forecasting. However, it is difficult to track specific efforts such as this within programs and therefore the snapshot is not comprehensive in that it does not reflect such efforts.

Furthermore, an annual snapshot is slightly skewed because budget allocations are multiyear decisions where funding levels can be a lump sum allocated in one budget year but to be spent over many upcoming years, or annual funding levels can represent a portion of a multiyear allocation that has been divided up into annual amounts. With the latter, the annual allocations are rarely the same amount each year because the lifetime of an R&D project involves phases with early phases demanding a heavier investment than later phases. When evaluating a single year‟s budget, it is difficult to determine the phase of the R&D project being observed.

Therefore this snapshot provides a general indicator of budget allocations among disaster types. Also, it only reflects R&D projects, not implementation projects such as the National

Flood Insurance Program or construction projects for flood protection structures. Table 4.3 includes all disaster related R&D programs that I identified through agency budgets and

Congressional appropriations bills. The results are slightly skewed in that the funding level for

185 wildfires is excessively large for an annual appropriation. This is likely due to the Department of

Interior‟s lump sum appropriation for the Wildland Fire Management program in which

Congress appropriated nearly $795 million to be spent over many years. Beyond this outlier, however, Table 4.2 indicates that earthquake R&D receives the most federal funding relative to other disasters. This could be an accurate depiction or it could be due to my ability to track the complete NEHRP budget as opposed to other programs that I am unable to find. While I am able to separately track programs for hurricanes, tornadoes, and storms, I added them together to get an aggregate funding level that matches with the disaster category of storms used in the loss analyses in the first step of this study. The separate programs and funding levels can be found in

Table 4.3 while the aggregate is listed in Table 4.2.

2010 Funding (in $ millions) Wildfires 848.5 Earthquakes 200.5 Storms 91.2 Volcanoes 24.4 Tsunamis 23.3 Droughts 6.5 Landslides 3.4 Floods 2.8 Table 4.2: 2010 funding levels by disaster type

4.3.3. INTERVIEWS

I conducted interviews with sixteen current or former decision makers located in federal agencies, White House offices, and Congress; all of whom have professional responsibilities that include prioritizing and funding federal natural disaster programs. I asked six questions concerning natural disaster R&D programs, the processes for federal funding, the values and

186 priorities of decision makers, and how these issues differ between institutions. The questions can be found in Section 4.2 under Methodologies.

The first step in the federal R&D process is determining the research topics that natural disaster R&D should address. Decision makers from both the White House offices and Congress say they largely leave decisions on research focus to the federal agencies. A number of agencies conduct natural disaster R&D. The National Science Foundation (NSF) is responsible for basic research on natural disasters - research that enhances our understanding of disaster-related issues.

The National Oceanic and Atmospheric Administration (NOAA) is involved with basic and applied science research for hydrometeorological natural disasters. The U.S. Geological Survey

(USGS) conducts applied science research and monitors and reports earthquakes and volcanoes.

The National Institute for Standards (NIST) performs applied engineering research to create building standards and codes for resilience against natural disasters. The Federal Emergency

Management Agency (FEMA) does not conduct R&D but is a primary user of disaster R&D through emergency responders using technology in the field. Decision makers in these agencies

(all but FEMA) decide on R&D topics by listening to what their research community identifies as research needs.

One decision maker in a White House office stated that they rely on agency staff to determine research needs because agencies are in close contact with researchers and research users and therefore they are knowledgeable on the pressing research needs over time. This decision maker mentioned that they listen to agency perspectives knowing that the National

Science Foundation communicates with the academic community to determine needs, the

Federal Emergency Management Agency stays informed of the needs of its R&D users, and the

187

U.S. Geological Survey maintains dialogue with land managers about their needs.77 These agencies, particularly the NSF and NIST, maintain strong contacts with the research community, both in academia and industry, by holding workshops, sitting on committees, and staying informed of current research literature from which they identify the gaps in knowledge and thus research needs.78 In fact, NIST relies on National Academies studies, Applied Technology

Council reports, and industry roadmaps that identify current and future R&D needs.79

Occasionally in the aftermath of a catastrophic disaster event, Congress will bypass the agencies and make a decision to pursue R&D related to the specific disaster type that just transpired. This can be a politically motivated decision to promote a proactive image of the government and calm societal concerns that often arise in the aftermath of a catastrophic event.

Other than this situation however, both the White House offices and Congress give great latitude to agencies to make decisions on the topics for R&D. As one decision maker stated, “Unless there is a specific political issue in the aftermath of a disaster, Congress leaves R&D decisions and topical focus to the agencies.” 80

However agencies consider the concept of needs in different ways depending on their mission. For instance, NSF research is often not driven by the purpose of meeting national needs such as reducing losses, as much as it is concerned with gaining knowledge in accordance with its agency mission of promoting the progress of science.81 In contrast, FEMA considers

77 Based on personal interview with a decision maker. March 11, 2011.

78 Based on personal interview with a decision maker. March 9, 2011.

79 Based on personal interview with a decision maker. March 16, 2011.

80 Based on personal interview with a decision maker. March 22, 2011.

188 implementation needs. For instance, one decision maker at FEMA described how the agency retrospectively evaluates each disaster event, how it transpired, and if differently than expected, the reasons for the difference. FEMA will evaluate what elements were successful and unsuccessful from an emergency management perspective, and whether the unsuccessful elements were caused by a lack of planning or by a gap in knowledge. If the latter, FEMA will define research needs to fill that gap. FEMA also maintains strong contacts with its users and researchers and will request R&D to meet its user needs. For instance, if emergency responders are saying that their equipment is too heavy, FEMA will turn to the researchers and request R&D that will produce lighter equipment.82

Even after agencies make decisions on research topics, they are constrained by budgetary limitations and cannot pursue all R&D projects that they would like. This is why prioritization processes become necessary. All three institutions: the agencies, White House offices, and

Congress use prioritization processes to select the programs they want to pursue over other programs. However the processes vary between the three institutions because of different institutional dynamics.

Since agencies differ in their missions and the types of programs they fund, their decisions on prioritization are motivated by different factors. For instance, the NSF relies on its expert panels to recommend proposals and the agency prioritizes programs from proposals that rank highly in the NSF merit review process and with agency guidelines such as having

81 The National Science Foundation. http://nsf.gov/about/

82 Based on personal interview with a decision maker. March 25, 2011.

189 diversity.83 However all agencies do share a common factor for prioritization in that they rely on

Presidential priorities when proposing their R&D and other programs. Every year the

President‟s Administration sets overall priorities for all facets of the federal government, including priorities for R&D. One White House office, the Office of Management and Budget

(OMB) informs agencies of the annual Presidential priorities which then direct the agencies as to what priorities they should consider when selecting their R&D programs. Presidential priorities play a significant role in guiding agency prioritization as well as prioritization in the White

House offices.

The White House offices consider a number of factors when prioritizing R&D programs.

They consider whether states, cities, or even the private sector can fund a program instead of funding it through federal money. For example, if private industry is motivated to conduct a given type of R&D, White House decision makers are less likely to support these programs. In fact, the OMB considers this factor in its prioritization process and also advises agencies to do the same when they create their budget estimates, as stated in this guidance from OMB to agencies: “Also consider the appropriate roles for Federal, State, and local governments, as well as the private sector, in conducting the covered activities [budget estimates].” (Office of

Management and Budget, 2010).

When prioritizing programs, decision makers in the White House offices consider qualitative and quantitative factors. One decision maker listed three factors considered in decision making: whether R&D programs align with Presidential priorities, meet the needs of the larger community, and create a good research opportunity as defined by research experts such as the National Research Council or National Science and Technology Council, the latter being the Executive Branch group that coordinates science and technology policy for the federal

83 Based on personal interview with a decision maker. March 9, 2011.

190

R&D enterprise.84,85 Multiple decision makers discussed the use of cost-benefit analyses, risk analyses,86 performance evaluations; and all echoed that they assess whether programs fit with

Presidential priorities or agency missions.87,88,89 In fact, in 2010 when the federal government implemented a spending freeze, the OMB instructed agencies to reduce their spending by eliminating low priority programs defined as programs having the lowest impact on agency missions, with additional consideration of “whether the program has an unclear or duplicative purpose, uncertain Federal role, completed mission, or lack of demonstrated effectiveness.”

(Office of Management and Budget, 2010). The OMB further recommended that high priority programs should promote Presidential goals (Office of Management and Budget, 2010).

Decision makers in White House offices evaluate existing programs on performance which, based on interview responses, typically means an evaluation of whether the program is efficient, successful, and how well it meets national needs. For example, decision makers evaluate NIST on the number of citations for each of its publications. For NOAA a performance evaluation could assess the accuracy of a weather forecasting model. However these performance metrics are difficult to gauge for most R&D other than technology development

84 National Science and Technology Council. http://www.whitehouse.gov/administration/eop/ostp/nstc

85 Based on personal interview with a decision maker. March 11, 2011.

86 Although risk analyses are ineffective in a political environment because policy makers do not want to exclude a program if any level of risk is determined even if they deem it as less of a priority than another program.

87 Based on personal interview with a decision maker. March 8, 2011.

88 Based on personal interview with a decision maker. March 15, 2011.

89 Based on personal interview with a decision maker. March 22, 2011.

191 because the success of science is difficult to evaluate, as explained in Section 4.1. This is particularly true for basic science programs which are assigned with performance metrics that decision makers themselves often find irrelevant. Instead of performance, decision makers often use metrics evaluating process for R&D instead such as whether the program is well managed or resources are allocated efficiently and in a way that ensures quality research. For natural disaster

R&D in particular, decision makers tend to prioritize R&D for widespread threats over R&D for localized disasters.

Congressional decision makers prioritize programs with consideration to different factors than those used by agencies or the White House offices, with three main differences. First,

Congressional decision makers are more likely to be motivated by the needs of the specific regions they represent. Second, earmarking is more prevalent among Congressional members and sometimes the programs that decision makers prioritize reflect a personal interest or motivation of a member. Third, the institutional framework in Congress does not correspond to the institutional framework of the Executive Branch. In Congress, agencies are aggregated in authorization and appropriations bills in different forms than in the Executive Branch and

Congressional decision makers often consider agency budgets collectively rather than individually.

When Congressional decision makers are focusing on the more detailed level, agency programs often compete with each other and Congressional decision makers must prioritize among numerous programs within the agency as well as across agencies. As one decision maker mentioned, this often poses challenges for R&D programs which tend to be less of a priority, a

“weaker player”, than competing operational programs.90

90 Based on personal interview with a decision maker. March 4, 2011.

192

When deciding on funding levels for programs, decision makers in all three institutions tend to base funding decisions on past budgets and extend funding levels of the previous year with minor increases or decreases in funding. In the guidance that the OMB offers to agencies on how to determine budget estimates, it recommends, “Absent more specific guidance, the outyear estimates included in the previous budget serve as a starting point for the next budget.”

(Office of Management and Budget, 2010). There are exceptions to the rule but often decisions maintain the status quo with slight modifications. For interagency programs like the National

Earthquake Hazards Reduction Program (NEHRP), agencies such as NIST decide on funding levels based on their role in the program. For example, NIST had been involved with earthquake research since the early 1970‟s. Throughout time, NIST played a small role in earthquake research and accordingly allocated a small amount of funding for earthquake R&D programs.

However in 2004, NIST became the lead agency for the NEHRP and its funding increased by four times the previous levels.91

One decision maker noted that decision makers in White House offices sometimes decide on funding levels by starting with the previous year and then considering the marginal costs and benefits of increasing or decreasing the level of funding.92 In the process, they may assess tradeoffs between investments. For example, if they are deciding on the funding level for a hurricane R&D program in NOAA, they would consider what an increase of $2 million would provide to that program. Then they might consider what an increase of $2 million would provide for a NOAA flood R&D program and the opportunity cost for funding one program instead of the other. The program in which the $2 million would have a greater impact would receive the

91 Based on personal interview with a decision maker. March 16, 2011.

92 Based on personal interview with a decision maker. March 15, 2011.

193 funding increase.93 Decision makers also consider the potential for growth in programs.

However it can be challenging for both the White House offices and Congress to make decisions involving long-term planning because of shifts in policy that derive from election cycles.

Decision makers in Congress receive the budget proposed by the Executive Branch, called the President‟s Budget, and then adjust the proposed funding levels. The Congressional funding process starts with Congressional committees “submit[ting] their views and estimates of spending and revenues within their respective jurisdictions to the House and Senate Budget

Committees.” (Saturno, 2004). The Budget Committees consider these perspectives in addition to “budget and economic projections, programmatic information, and budget priorities” supplied by groups such as the Congressional Budget Office (CBO), OMB, the Federal Reserve, federal agencies, and congressional leadership (Saturno, 2004). The Budget Committees are responsible for drafting concurrent Budget Resolutions in each house which determine “the overall size of the federal budget, and the general composition of the budget in terms of functional categories.”

(Saturno, 2004). The Budget Committees send their Budget Resolutions to the Appropriations

Committee in each house where the committee decides on the total funding level allocated to each of its 13 subcommittees. Finally each of the 13 subcommittees prepares their appropriation bill in which they are sometimes constrained in funding decisions by authorized legislation or spending caps that may exist (Saturno, 2004).

The purpose of these interviews was to learn about the prioritization and funding processes used by agencies, White House offices, and Congress and to investigate whether these institutions prioritize and fund disaster R&D programs with consideration of the level of impact caused by individual types of disasters. None of the decision makers whom I interviewed

93 Based on personal interview with a decision maker. March 15, 2011.

194 indicated that they consider the level of impact when making decisions on natural disaster R&D.

The interviews suggest that agencies may consider natural disaster R&D programs individually; this is more likely if natural disasters play a prominent role in the agency mission such as earthquakes do for the USGS. However beyond the agencies, neither the White House offices nor Congress are likely to consider natural disaster R&D programs explicitly, much less specific issues such as level of impact caused by disasters. Natural disaster R&D programs are usually small in comparison to other programs. The White House offices and Congress are involved with macroscale prioritization and funding decisions that are often too large to be concerned with programs on the scale of natural disaster R&D.

The role of identifying research needs falls to the agencies which rely on their research community to recognize R&D opportunities. However, no agency decision maker whom I interviewed suggested that research communities focus on the level of impact caused by disasters when they determine research needs. This may or may not be the case in reality since I was only able to interview decision makers from some agencies. It is likely that the earthquake engineering research community, a group concerned with applied research solely focused on resilience, are very focused on earthquake impact but this is not reflected in my study because I did not have the opportunity to interview anyone from this community or the USGS.

Agencies work at a detailed level where they focus on the substantive matter for individual programs. Based on existing institutional dynamics, it is at the agency level where decision makers should consider the level of impact caused by individual disaster types and align disaster R&D accordingly. The White House offices and Congress work on larger scales and are focused at the macrolevel. The White House offices focus on managing agencies and aligning them with Presidential priorities; their focus on programs tends to focus on the management of

195 programs regarding costs and performance. Congressional decision makers make prioritization and funding decisions that have less to do with individual programs than they do with larger level Congressional bill and agency budgets.

4.4. DISCUSSION

The results indicate that storms are causing the greatest economic and human losses in the U.S. According to the 2010 federal budget for disaster R&D, earthquakes received the highest level of funding (when the lump sum is removed from the wildfires account) and storms received the second largest funding allocation (see Table 4.2). These allocations do not completely correspond but have some correspondence with the natural disasters causing the greatest impacts on society.

Of the partial correspondence that exists, it is likely that these outcomes happen to align rather than the results of a concerted effort on the part of decision makers. The disaster programs that do exist inherently reflect that these disaster types are a priority for decision makers. For instance, hurricanes have multiple programs which can be interpreted as being a large priority for decision makers. However, the level of impact caused by individual disaster types does not appear to be a consideration of decision makers when prioritizing and funding natural disaster

R&D programs.

Decision makers, particularly in the White House offices and Congress, do not often distinguish natural disaster R&D programs explicitly. Natural disaster R&D programs are often embedded within larger programs and so they get shuffled into a number of contexts whether with weather programs in NOAA or security programs in DHS. Therefore there are not many programs exclusively focused on R&D for individual disaster types. This could be the reason

196 why decision makers do not think in terms of individual disaster types and the level of their impact. Furthermore, the current trend for natural disaster policy has moved toward an all- hazards approach which strategizes an umbrella management approach for all disaster types by focusing on their commonalities and moves away from the distinction of natural disasters by individual disaster types. This new approach is also likely to contribute to the fact that decision makers are not considering the level of impact for individual disaster types.

As for impact, earthquakes receive more than twice the amount of funding as storms and the discrepancy between the magnitudes of funding has several possible explanations. First, the budget snapshot may be skewed based on my ability to track disaster R&D programs. The

National Earthquake Hazards Reduction Program (NEHRP) compiles detailed and easily accessible budget information which I was able to obtain. The R&D efforts for storms exist in a fragmented manner by storm type (e.g. severe storms, tornadoes, hurricanes, winter storms, etc.) and are likely embedded within broader programs posing difficulties for tracking. Therefore the federal allocation for storms is likely larger than the $91 million cited in this study however, the true total allocation is likely still not as large at the $200 million appropriated to earthquakes; any storm programs that I have not been able to track still do not likely have a collective budget as large as $109 million.

A second possible explanation for the discrepancy could relate to the institutional dynamics involved in the federal budget process. Individual agencies start the budget process by prioritizing among their own competing programs for funding. In the case of an agency like

NOAA which handles most storm R&D efforts, it must choose among a number of storm R&D programs that are competing with each other. The internal competition within the agency ultimately decreases the number of programs with storm R&D efforts. Meanwhile, NEHRP is an

197 interagency program where several agencies contribute a portion of the total program funding.

This setup benefits NEHRP financially since it is not dependent or constrained by any single agency‟s budget and perhaps this lends to a relatively high level of funding for the program.

NEHRP is also authorized in statute which helps NEHRP maintain funding from Congress amidst competing programs.

The discrepancy between funding for earthquake and storm R&D could also stem from decision makers‟ perceptions of the role and benefit of R&D for each of these disaster types.

Decision makers may perceive that R&D can contribute more to alleviating earthquake problems than to storm problems. If decision makers define the earthquake problem through issues that can be resolved through gains in knowledge or technology development, such as improved earthquake prediction, then an investment in earthquake R&D seems appropriate. In this same logic, if decision makers attribute storm problems to issues unrelated to gains in knowledge or technology development, such as perverse financial incentives and human behavior, then an investment in storm R&D seems inappropriate for resolving the problem.

Another possible explanation for the discrepancy could be that funding levels may reflect anticipated impact more than it reflects proven impact. This study analyzes proven impact by utilizing data on the documented economic and human losses caused by natural disasters historically. While this explains disaster behavior in the past, decision makers may be equally as concerned with the anticipated impact in the future. Several of the decision makers in White

House offices mentioned that they use risk analyses in making prioritization decisions. Since risk analyses consider the level of threat and consequence, decision makers may find earthquakes pose a larger threat or consequence than storms and therefore invest more in earthquake R&D.

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4.5. CONCLUSION

The U.S. disaster R&D enterprise is robust with more than $500 million of federal funding for 2010 alone (see Figure 4.3). Natural disaster-related R&D covers research and technology development for prediction, prevention, response, mitigation, and adaptation for natural disasters including storms, floods, earthquakes, tsunamis, volcanoes, landslides, wildfires, and droughts. Under the current social contract model, society expects R&D to meet societal needs and promote its well-being. For natural disaster-related R&D, this means that government sponsored R&D should protect society from the impacts of natural disasters, specifically against economic and human losses. Limited resources constrain the federal government to fund only a portion of disaster R&D therefore the projects that do receive funding should focus on the natural disasters causing the greatest economic and human losses. This study somewhat validates this normative assertion by determining that while the largest federal funding appropriations are allocated to earthquakes, the second largest appropriation is allocated to storms which cause the greatest human and economic losses among all natural disaster types.

Most people in the disaster community endorse disaster R&D efforts as highly beneficial.

For earthquakes in particular, “an investment in research at this stage on the order of millions will help to reduce national economic losses on the order of billions for a single earthquake of significant intensity in the future.” (Martin, 2004). These projections take future earthquake events into account; an important factor since they have estimated future losses of $100 billion in the U.S. (Comerio, 1998; Earthquake Engineering Research Institute, 2003; U.S. Congress

Office of Technology Assessment, 1995). FEMA states,

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If research can lead to a reduction of economic loss from a single future earthquake by as little as 10%, the payoff on the research investment will be as much as a thousand times the annual research budget for earthquake engineering research in the U.S.!94

However, the disaster community also cautions that disaster R&D alone cannot provide many of the societal needs; without implementation, the benefits of R&D cannot be realized.

The lack of implementation continues to be the chronic difficulty preventing much of natural disaster policy from being effective. Regarding disaster R&D, the Office of Technology

Assessment stated in a 1995 report: “Information alone will not result in widespread implementation.” (U.S. Congress Office of Technology Assessment, 1995). FEMA reinforces this view by concluding,

…research is part of a long-term mitigation effort that must not only be continued, but accelerated. At the same time, the discussion must emphasize that research alone does not reduce vulnerability. …it is the successful implementation of that knowledge and resultant change in behavior and practices that result in hazard reduction.95

Disaster R&D enhances our understanding of disaster issues and improves our ability to better predict, prepare for, respond, and recover from natural disasters. Our disaster R&D enterprise is thriving and partially corresponds to our greatest societal needs. Yet the real benefits from R&D can only be realized with proper and sufficient implementation of such enhanced understanding.

As the esteemed disaster scholar Gilbert White concluded, “…a lack of knowledge is not a major contributory factor to the growth of disaster losses…There is perhaps more reason to attribute rising losses on the failure to act.” (White, Kates, & Burton, 2001). Whether through greater

94 Martin II, James R. (2004, October). Earthquake hazard and emergency management. Instructor Guide. Emergency Management Institute. Federal Emergency Management Agency. http://training.fema.gov/EMIWeb/downloads/EarthquakeEM/01%20Instructor%20Guide%20Final%20Cover%20Pa ge.pdf

95 Ibid.

200 enforcement of land zoning, a more resilient built environment, or more cautious public behavior, society will only reap the benefits of R&D once it implements the findings.

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Appendix: EARTHQUAKES 2010 Funding Agency Agency Office Program Project (in millions $) Federal Insurance and FEMA Mitigation (DHS) Administration NEHRP 8.9 Scientific and NIST Technical Research (DOC) and Services NEHRP 4.1 NSF NEHRP 55.3 USGS (DOI) NEHRP 62.8 Incorporated Directorate for Research Geosciences, Division GEO Funding for Centers Institutions for NSF of Earth Sciences and Facilities Seismology 12.36 USGS (DOI) Earthquake projects 57.021 TOTAL: $200.481 FLOODS 2010 Funding Agency Agency Office Program Project (in millions $) Observing, Modeling, Office of Oceanic and and Visualizing NOAA Atmospheric Storm Surge (DOC) Research "A partnership program" Inundation, FL 0.1 NOAA National Weather Susquehanna River (DOC) Service Operations and Research Basin Flood System 2.4 Upper Spring River NOAA National Weather Flood Warning (DOC) Service Operations and Research System 0.125 Delaware River NOAA National Weather Weather Radio Enhanced Flood (DOC) Service Transmitters Warning System 0.2 TOTAL: $2.825 STORMS 2010 Funding Agency Agency Office Program Project (in millions $) Ocean Resources Conservation and NOAA National Ocean Assessment Ocean (DOC) Service Assessment Program (OAP) Coastal Storms 2.8 TOTAL: $2.8

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HURRICANES 2010 Funding Agency Agency Office Program Project (in millions $)

Aviation and Hurricane Office of Oceanic and Research Utilizing NOAA Atmospheric "A partnership Unmanned Aerial (DOC) Research program" Systems, FL 0.3

Weather Research and Office of Oceanic and Forecasting NOAA Atmospheric Weather and Air Developmental Testbed (DOC) Research Quality Research Center 2.0

Hurricane Forecast System Improvements + NOAA National Weather Operations, Research, Joint Center for (DOC) Service and Facilities Hurricane Research, FL 79.525 TOTAL: $81.825 TORNADOES 2010 Funding Agency Agency Office Program Project (in millions $)

Office of Oceanic and Tornado Severe NOAA Atmospheric Weather & Air Quality Storm Research/ (DOC) Research Research Programs Phased Array Radar 3.972 Office of Oceanic and Severe Weather NOAA Atmospheric Weather & Air Quality Forecast (DOC) Research Research Programs Improvements 2.592 TOTAL: $6.564 TSUNAMIS 2010 Funding Agency Agency Office Program Project (in millions $) Strengthen U.S. NOAA National Weather Tsunami Warning (DOC) Service Operations and Research Network 23.264 TOTAL: $23.264

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WILDFIRES 2010 Funding Agency Agency Office Program Project (in millions $) FEMA U.S. Fire Federal Fire Prevention (DHS) Administration and Control Act of 1974 45.588 Office of Wildland Fire Wildland Fire (DOI) Management 794.897 U.S. Forest Service Joint Fire Science Fire Science (USDA) Program Research 8.0 TOTAL: $848.485 VOLCANOES 2010 Funding Agency Agency Office Program Project (in millions $) USGS (DOI) Volcano projects 24.421 TOTAL: $24.421 LANDSLIDES 2010 Funding Agency Agency Office Program Project (in millions $) USGS (DOI) Landslide projects 3.405 TOTAL: $3.405 DROUGHTS 2010 Funding Agency Agency Office Program Project (in millions $) Office of Oceanic and National Integrated NOAA Atmospheric Competitive Research Drought Information (DOC) Research Program System 6.5 TOTAL: $6.5 Table 4.3: Funding levels for federal natural disaster R&D programs

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CHAPTER 5: CONCLUSION

5.1. INTRODUCTION

This dissertation capitalizes on an opportunity, untapped until now, to utilize robust disaster data to appraise natural disaster policy. Through three distinct studies, I utilize data on economic losses caused by natural disasters to analyze trends in disaster severity which enable me to appraise policies designed to reduce disaster losses. This dissertation addresses the broader question of how society manages risk and vulnerability in light of natural disasters, particularly severe disaster events causing increasingly larger economic losses with time. It also answers questions on how patterns in natural disaster severity have changed over time and how effective the policies are which focus on disaster severity. The findings from these studies answer important policy questions and can improve decision making for natural disasters.

The first study reconciles the apparent disconnect between (a) claims that global disaster losses are increasing due to anthropogenic climate change and (b) studies that find regional losses are increasing due to socioeconomic factors. I assess climate change and global disaster severity through regional analyses derived by disaggregating global loss data into their regional components. This study finds that economic losses from North American, Asian, European, and

Australian storms and floods contribute to 97% of the increase in global economic losses, with each region‟s increasing losses attributed to socioeconomic factors.

The second study evaluates the National Flood Insurance Program and the National

Earthquake Hazards Reduction Program with respect to their legislated mandates to reduce economic losses. I evaluate these policies by utilizing a new metric which compares the trend in actual losses exhibited after the enactment of policy, to a projected trend based on losses from the pre-policy era. Any difference between these two trends indicates the impact of the policy on

205 the losses. The trends in actual losses are either increasing at the same rate or a slightly larger rate as the projections from the period prior to the enactment of policy. This suggests there is no discernible evidence that the policies have an impact on reducing losses.

The third study compares the degree to which U.S. federal funding levels for natural disaster research and development (R&D) correspond with the level of documented impact from individual disaster types. Storms cause the greatest human and economic losses in the U.S. and federal funding levels correspond somewhat with storm R&D receiving the second largest level of funding of all disaster types, behind earthquake R&D. The following table from Chapter 1 summarizes the three studies.

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Policy Chapter Purpose Methodology Results Relevance To reconcile the North American, apparent Asian, European, and disconnect Disaggregate and Australian flood and between claims quantify global storm losses contribute that global losses losses into to 97% of the increase 2: are increasing due regional losses, in global losses. The The Science to anthropogenic then associate existing literature reconciliation for policy climate change results with the attributes regional study and studies that existing disaster socioeconomic factors find regional literature on for this 97%. Research losses are regional loss has not been conducted increasing due to trends. yet to attribute the socioeconomic remaining 3%. factors. To evaluate the Actual disaster losses National Flood exhibit trends that are Insurance either increasing at the Program and the Compare the trend same rate or a slightly National in actual post- increased rate as 3: Earthquake Policy policy losses to a compared to the period The evaluation Hazards evaluation projected trend of prior to the enactment study Reduction the losses from of policy which Program with the pre-policy era. suggests the policies respect to their have had no discernible legal mandate to impact on reducing reduce economic losses. losses. Storms cause the Identify the To determine the greatest human and disaster types degree to which economic losses in the causing the federal funding U.S. Earthquake R&D greatest impact in levels for natural receives the largest 4: human and disaster R&D funding allocation The Policy for economic losses, correspond to however storm R&D correspondence science then compile levels of receives the second study annual budget to documented largest level of funding determine which impact caused by therefore some disaster types individual natural correspondence exists receive the disaster types. between impact and greatest funding. funding level.

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5.2. STUDY #1: THE RECONCILIATION STUDY

From Chapter 2, the first study reconciles the apparent disconnect between claims that global losses are increasing due to anthropogenic climate change and studies concluding that regional losses are increasing due to socioeconomic factors. I connect global and regional losses together by conducting an independent study of climate change and global disaster severity through regional analyses derived by disaggregating global losses into their regional components. I disaggregate global losses into regional losses and quantify the percentage of the increase in global losses attributable to each regional component. Then I associate the disaggregation to the disaster literature which consists of a number of studies focused on regional trends in losses.

I conclude that losses from North American, Asian, European, and Australian storms and floods contribute to 97% of the increase in global losses; due to socioeconomic factors in their region. Of the 97% of the increase in global losses, 57% is attributed to losses caused by North

American storms, 15% to losses from Asian storms, 10% to Asian flood losses, 8% to losses caused by European floods, 4% attributed to losses caused by European storms, 2% to losses from North American floods, 0.81% attributed to Australian storm losses, and 0.24% to losses caused by Australian floods. At present the literature on regional trends has not conducted research addressing the causality of the remaining 3% of the increasing global trend in losses.

By quantifying the global increase and linking the regional percentages to regional socioeconomic factors found in the literature, this study finds no additional factors beyond those that can be explained by socioeconomic change to explain 97% of the increase in global losses.

Thus, this study finds no evidence to support claims of anthropogenic climate change causing

208 global losses to increase. As a result, the apparent disconnect is reconciled, and there is no disconnect at all.

5.2.1. SIGNIFICANCE OF STUDY

These findings provide useful science for policy by answering the question of why disaster-caused economic losses are increasing. They can improve decision making for natural disasters by appropriately centering the focus on socioeconomic changes as the root of the problem rather than anthropogenic climate change. Since policy makers create disaster policy with the purpose of minimizing losses, it is important for them to recognize the causal factors contributing to the problem in order to develop effective solutions.

Addressing the importance of socioeconomic changes is particularly necessary given its current trends around the world. As Uitto explains, the impact of a disaster event depends on 1) the hazard (e.g. hurricane, earthquake) 2) exposure and 3) vulnerability (Uitto, 1998). Current trends in socioeconomics are leading to increased exposure and vulnerability thus increasing the potential impact of a disaster event.

The main socioeconomic change leading to increased exposure and vulnerability is the increased settlement of U.S. populations in coastal areas vulnerable to hurricanes, floods, and earthquakes. In a study of the coastal settlement trend in the U.S., the National Oceanic and

Atmospheric Administration (NOAA) found:

The narrow coastal fringe that makes up 17 percent of the nation's contiguous land area is home to more than half of its population. In 2003, approximately 153 million people (53 percent of the nation‟s population) lived in the 673 U.S. coastal counties, an increase of 33 million people since 1980.96

96 Crossett, Kristen M., Thomas J. Culliton, Peter C. Wiley, Timothy R. Goodspeed. (2004, September). Population Trends Along the Coastal United States: 1980-2008. National Oceanic and Atmospheric Administration.

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The current situation is troubling since more than half of the nation‟s population lives in at-risk areas. From 1980-2003, California - the state most vulnerable to a large earthquake in the

U.S. - experienced the largest population growth of coastal states with an increase of 9.9 million residents. During the same time period, Florida - a state very vulnerable to hurricanes - experienced the second largest population growth of coastal states with an increase of 7.1 million residents (Crossett, Culliton, Wiley, & Goodspeed, 2004). More so, the projection of future coastal settlement expects that U.S. coastal populations will continue this trend with more people settling on the coasts of the country (Woods and Poole Economics, Inc., 2003).

In the U.S., coastal populations are often highly affluent which adds another element of vulnerability since increased wealth at risk generally leads to increasing losses. Affluent individuals are likely to purchase insurance since they own more assets of greater value which may explain why insured coastal property values increased from 100% to 300% during the time period 1980-2003 when significant migration took place (Ross & Lott, 2003). NOAA provides further evidence of the affluence of coastal populations by reporting, “On average, coastal counties have higher median household incomes than noncoastal counties, differing by almost

17%.” (Crossett, Culliton, Wiley, & Goodspeed, 2004). In fact, California, which was found to have the largest population growth of coastal states, is a largely affluent state with many counties reporting a median household income in the highest range across the country (Crossett, Culliton,

Wiley, & Goodspeed, 2004).

As more individuals move and settle on the coasts, these populations require increased infrastructure (National Safety Council, 1998). They require new homes which contribute the most to increased infrastructure. However new populations become communities that require roads, bridges, utilities, schools, and hospitals. Eventually retail development follows and

210 ultimately communities become towns and cities encompassing large networks of infrastructure, all of which fall vulnerable to natural disasters. This phenomenon is rapidly becoming a trend across the U.S. coasts. The American Planning Association projects that “[m]ore than half of the built environment of the United States we will see in 2025 did not exist in 2000.” (American

Planning Association, 2006). The insurance industry estimates that U.S. losses due to hurricanes will double every ten years because of inflation and increasing number of structures (Insurance

Journal, 2006).

The trend of growing populations in vulnerable areas is not unique to the U.S., it is occurring across the globe. Currently Japan and the U.S. are the countries with the large coastal cities (Martinez et al., 2007). However coastal populations are growing worldwide and their growth is larger than the growth of the global population. “From 1990-2002, coastal populations worldwide grew 56% while the overall global population grew 14%.” (Martinez et al., 2007). In a study on global cities and their vulnerability to natural disasters, one researcher found, “More than 50% of the coastal countries have from 80 to 100% of their total population within 100 km of the coastline.” (Martinez et al., 2007). In fact, “worldwide, settlements are concentrated within 5km of the coastline” with most of those settlements at elevations below 20 meters (Small

& Nicholls, 2003).

Global megacities are a specific type of population growth of concern for vulnerability to natural disasters. Megacities are extremely large urban population centers that are springing up around the world. Typically, megacities arise when poor individuals from across their country migrate to urban areas in search for employment and improved living. Megacities tend to develop very rapidly but poorly since these urban centers are populated by poor individuals with a lack of finances for proper development. In a study of coasts worldwide, one researcher found

211 that “21 of the world‟s 33 megacities (with more than 8 million people) are located within 100 km of the coast.” (Martinez, 2007). These megacities are not just vulnerable to coastal disasters; many major global cities are located in earthquake zones as well (Sanderson, 2000).

These trends in socioeconomic changes suggest an increasing vulnerability to natural disasters worldwide. With these trends projected to continue in the future, global vulnerability will grow as will the impact of disaster events. Disaster severity is expected to take an increasing toll on human and economic losses with time unless policy makers can interrupt the process with disaster policies that reduce societal vulnerability.

5.2.2. CHALLENGES TO THE STUDY

The lack of information was the greatest challenge to achieving robustness in this study.

First, the only database with robust economic loss data at the global level was the Munich

Reinsurance Company‟s NatCatSERVICE© data. The robustness of this study would improve if

I could analyze multiple datasets of economic loss data however this is not possible until a new robust global dataset emerges. Furthermore, the robustness of methods would improve if I could adjust the economic loss data for regional socioeconomic factors such as national inflation rates and GDP‟s for individual countries. However, this is also not possible at this point because the information is not available for all countries and this study needs to adjust for all countries in order to maintain a consistent global study.

Most importantly, the limited number of regional studies in the disaster literature presented the greatest challenge to achieving robustness in this study. The third step of this study associates the results from my analyses to disaster literature of studies focused on regional trends in losses. This study disaggregates global losses into regional components such as

European storms and Asian floods. Existing literature includes very few studies for some of

212 these components; often only one study for a component. Furthermore, these components are large and diverse categories that are not comprehensively represented with the one or few studies. For instance, European storms include severe storms, windstorms, tornadoes, winter storms, hailstorms, snowstorms, and blizzards. However the only study available at the moment focuses on European windstorms. While this is a good first step, it does not represent European storms in an adequate manner. Another example is the regional component of Asian storms which covers a vast geographic span of many countries. However, the two studies on Asian storms include one focused on Indian tropical cyclones and another on Chinese tropical cyclones.

The lack of available studies overlooks huge contributors to Asian storms such as Japanese typhoons and Bangladeshi cyclones. These gaps in information are unavoidable at the moment but do present challenges to the robustness of this study.

5.2.3. FUTURE WORK

A continuation of this study can fill in the current gaps as new information becomes available. If new datasets of economic loss data emerge, the analyses can be repeated using the new data. If regional socioeconomic data become available, the adjustment methodologies can be duplicated with the new information. And as new studies focused on regional loss trends arise, they should be included in the third step of this study. In particular, the remaining 3% of the increase in global losses is attributed to losses from European other events and South

American storms and currently there are no studies that assess these losses. Should these studies ever materialize, they need to be included.

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5.3. STUDY #2: THE EVALUATION STUDY

From Chapter 3, the second study evaluates the two national disaster policies: the

National Flood Insurance Program (NFIP) and the National Earthquake Hazards Reduction

Program (NEHRP). I appraise these policies with respect to their legislated mandates to reduce losses. I utilize a new metric to evaluate their impact on reducing economic losses by comparing the trend in the actual losses exhibited after the enactment of the policy, to a projected trend based on losses from the pre-policy era. Comparing the trend of post-policy losses to the projection of pre-policy losses indicates whether the policy has had an impact on reducing economic losses below the level that might have reasonably been expected by policy makers upon passage of the initial legislation.

The findings from this study indicate that the trends in actual losses are either increasing at the same rate or a slightly increased rate as compared to the projections based on losses from the period prior to the enactment of policy. This suggests that while the policies may have had demonstrable effects with respect to disasters, there is no discernible evidence of an impact on reducing losses.

This study provides policy appraisals that evaluate the ability of national disaster policies to meet their mandates. The findings indicate no evidence that the policies have had an impact in reducing losses to a baseline level. The metric used in this study would only indicate that the policy successfully had an impact on reducing losses if the trend in actual losses proved smaller than the projection in losses from the pre-policy period. One potential argument against this study‟s conclusion could suggest that while actual losses are larger than the projected losses, they might still reflect a reduction from an originally larger level. This would suggest that the policy (NFIP or NEHRP) has successfully reduced an originally larger level of losses to the level

214 of actual losses. This is not likely to be the case because the factors responsible for increasing losses are socioeconomic changes and this study adjusts the data for inflation, population growth, and national wealth. Therefore the factors that would cause higher losses are accounted for so one can expect that actual losses are at their true level rather than potentially being a larger level.

However, an amount of uncertainty does exist in whether the policies impart some level of impact in influencing vulnerable societies to adopt favorable behavior which ultimately results in avoiding potential losses. For example, could the NFIP influence residents living in floodplains to build more resilient homes and therefore avoid potential losses? Could the

NEHRP promote more resilient construction of buildings that can resist shaking and thereby avoid potential losses? It is difficult to quantify these potential impacts and so it is difficult to gauge whether either policy has imparted such an impact; therefore this uncertainty remains unresolved.

Nonetheless, even if the policies have imparted this type of impact, it is at an inadequate level. The trend in actual losses must be smaller than the projection in order for the level of impact to prove adequate. The projection reflects the trend in losses expected had the policy not been enacted. Therefore the policy must impart an impact large enough to reduce losses below the projection to prove that it does play a role and is able to reduce losses to a level lower than would be expected had there been no policy at all. Otherwise even though the policy does have an impact in reducing losses, it is not performing adequately since it cannot counter the effects of the socioeconomic factors that are increasing losses.

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5.3.1. SIGNIFICANCE OF STUDY

The findings from this study provide policy evaluations by appraising the policies‟ abilities to reduce economic losses, and indicating to policy makers that both the NFIP and

NEHRP can improve in efficacy in part by extending their scope to encompass extreme disaster events. For the successful longevity of these policies, it is important for them to be adaptable to future conditions.

This study‟s findings show no evidence that the policies are reducing losses to the baseline level and suggest that they are incapable of countering increasing losses sufficiently.

Regardless of whether the policies have partially reduced losses or not, this study determines that flood and earthquake losses are nonetheless increasing and the policies have been unable to stop that increase. Therefore while the policy goals explicitly mention reducing losses, the ability to counter increasing losses is inherent to this statement and neither policy has been able to do so.

This study also concludes that extreme events are significant in shaping the trend in losses; a finding which became evident even though it was not explicitly investigated. The significance of extreme events cannot be underestimated because they can impact society drastically and are more likely to occur in the future (Sarewitz & Pielke, 2001; Etkin, 1999).

Therefore this finding offers useful information to policy makers concerned with designing effective disaster policies for the future. Disaster literature uses the term extreme events for disaster events with physical characteristics that are more intense than most events. Statistically speaking, in a normal distribution of the frequency for any physical characteristic, extreme events have characteristics of low frequency; the frequency found in the tails of the normal distribution (Meehl, 2000). For example, heatwaves or coldwaves are extreme temperature events where the temperatures are significantly and uncommonly high or low. Extreme events

216 conventionally refer to disaster frequency and intensity however this study uses the term analogously for disaster severity where I define extreme events as individual disaster events causing significantly and uncommonly high economic losses, in larger orders of magnitude than other disaster events.

Such events causing significantly high economic losses are more likely in the future as socioeconomic changes continue. With increases expected for societal wealth, inflation, insurance coverage, population growth, and the number of assets at risk to disasters, societal vulnerability is also expected to increase. As Meehl et al. state, “…the vulnerability of the human and natural systems contributes to how severe the impact will be” (Meehl, 2000) therefore even if disaster events continue at the same frequency and intensity, their severity will worsen since societal vulnerability continues to increase. As a result, what might be a standard disaster event today could be an extreme event in the future due to the larger societal impact. As climatologists at the National Climatic Data Center explain, “The general increase in population since 1900 has placed more people at risk when an extreme weather event occurs.” (Ross & Lott,

2003).

Policy makers need to understand how disaster events are evolving in this manner and design disaster policies with a range wide enough to address ordinary as well as extreme events.

Societal vulnerability is increasing; disaster impact will increase, therefore policy makers need

“[t]he ability to determine the scale of disaster and develop appropriately matched institutional responses” (Baker & Refsgaard, 2007). For instance, evacuation policies should be able to evacuate more people in a quicker manner, and development policies need to enforce strict construction codes for new buildings and infrastructure as the number and density of the built environment increases.

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There is already evidence of the rise of extreme events. Of the ten costliest disaster events occurring worldwide from 1980-2010, five have occurred since 2004. All have occurred in the last twenty years with the earliest being Hurricane Andrew in 1992. Geographically, four were in the U.S., three of them being hurricanes, five were in Japan or China, and one was the

2010 earthquake in Chile.97 Most of these countries represent developed nations with affluent populations that likely experienced significant economic losses thus lending the events to the ten costliest events worldwide. Another study reports that from 1980-2003, 58 weather events caused $1 billion or greater of losses (in current dollars) and 49 of those occurred since 1988.98

Currently disaster policy focuses on ordinary disaster events which are the norm at this time with extreme events occurring occasionally. However, even an occasional extreme event causes “enormous damage” (Takeuchi, 2001); an impact on larger orders of magnitude than any ordinary disaster event. Beyond the losses that extreme events can cause, they can significantly impact society to a level from which it is difficult to recover. This is particularly true for populations that are not affluent or in developing nations where the impact of an extreme event can destabilize already marginal lives and “their resilience to bounce back to pre-disaster level of normality is highly limited” (Kesavan & Swaminathan, 2006).

5.3.2. CHALLENGES TO THE STUDY

The one challenge with this study was the limited length of time for the trends that I analyzed. The trends from the main analyses were adequate with 30+ years of data but could

97 The Munich Reinsurance Company. (2011, March). Significant natural catastrophes 1980-2010. NatCatSERVICE, Geo Risks Research.

98 Ross, Tom and Neal Lott. (2003, December). A climatology of 1980-2003 extreme weather and climate events. National Climatic Data Center Technical Report No. 2003-01.

218 become more robust with more time. However many of the sensitivity studies involved disaster events or policy amendments in the very recent past resulting in trends of 3-10 years which were too short for concluding anything substantial. While I interpreted these results in the same manner I used for the longer trends, it is difficult to state any findings and should be determined as inconclusive at the moment.

5.3.3. FUTURE WORK

Fortunately, the challenges to this study can be resolved easily; simply with time. The robustness of trends increases with time and so this study can be continued after adding more years of data to the trends. One potential continuation of this study could focus on the current uncertainty in the NFIP findings. For the NFIP, a level of uncertainty exists in whether the policy has reduced losses from a potentially larger level through influencing vulnerable societies to adopt favorable behavior. Favorable behavior such as building more resilient homes in floodplains could avoid potential losses. Currently it is difficult to quantify such actions and correlate them to outcomes such as changes in economic losses. However, continuation of this study could attempt to quantify the level of favorable behavior that has already occurred and then assess the level of insured losses over the same time period to determine whether there is any correlation. Any conclusive results from such a study would fill in the gap of uncertainty currently associated with the NFIP‟s performance. For the NEHRP, a continuation of this study could perform the same analyses but on a regional scale for the state of California where the most significant earthquake activity occurs in the U.S. and a large portion of NEHRP measures do exist. A future study could analyze earthquake losses for California only and adjust the data for population growth and median income of affected counties in the California rather than national statistics.

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5.4. STUDY #3: THE CORRESPONDENCE STUDY

From Chapter 4, the third study assesses whether federal funding levels for natural disaster R&D correspond with the level of documented impact from individual disaster types; essentially whether the disaster types that cause the greatest impact on society receive the greatest amount of funding for their research. I define impact to society in terms of human and economic losses and I determine the level of these losses caused by each disaster type. Then I determine the level of federal funding allocated for R&D on each disaster type and I compare whether the level of funding corresponds to the level of impact. The normative assertion suggests that federal disaster policy should appropriate federal funding in proportion to the level of impact created by natural disasters with the greatest amount of funding allocated to the largest impact. This study investigates whether this assertion holds true and the findings indicate that storms are causing the largest human and economic losses in the U.S. and funding levels correspond somewhat with disaster R&D on storms receiving the second largest level of funding behind earthquake R&D.

5.4.1. SIGNIFICANCE OF STUDY

This study appraises policy for science in assessing the R&D effort for natural disasters.

The findings can enhance program efficiency by recommending that decision makers consider the level of impact caused by disasters in their prioritization decisions and fund R&D focused on the disasters causing the greatest impact.

This study discusses the role of R&D which raises questions on how we can best utilize

R&D for natural disaster policy. On the role of research for natural disaster policy, science policy experts state, “research seeks to provide knowledge and tools that can contribute to the

220 effectiveness of policy” (Sarewitz & Pielke, 2001). For natural disasters, effectiveness means that R&D should focus on reducing the impact of disasters on society. However, there are many approaches that R&D efforts can pursue to reduce the impact from natural disasters and we do not know which efforts are more effective than others.

This study associates impact to disaster type and suggests that R&D focused on the disaster types causing the greatest impacts can result in reduced impact. However in a 2003 study, the RAND Science and Technology Policy Institute suggested an alternative focus for

R&D. Instead of associating impact to disaster type, the RAND study considered associating impact with the following factors: societal vulnerability, individual decisions on acceptable risks, efforts made by the hazard community, financial burden on the government and insurance industry, future uncertainties, and mitigation measures (Meade & Abbott, 2003). Would disaster policy be more effective in reducing losses if decision makers focused R&D efforts on these factors rather than on disaster type?

A large portion of current R&D efforts focus on improving prediction of natural disasters.

Both the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological

Survey (USGS) fund R&D for improved hurricane and earthquake prediction, respectively.

Improved predictions would likely reduce the impact on human losses and possibly reduce some impact on economic losses. However, one potential argument against R&D efforts for prediction could be that our predictive capabilities for storms are already fairly robust and any further research would bring marginal improvements thereby proving to be a less effective approach for

R&D. Furthermore, predictive capabilities for earthquakes are not fully developed yet and some scientists claim that earthquakes cannot be predicted at all (Geller et al., 1996). These arguments suggest that perhaps prediction is not the most effective approach for R&D.

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Recently, disaster experts have hailed technology development for reducing human losses. Advances in information technology, made possible by R&D, have improved information dissemination used for preparedness, early warnings, and relief efforts (International

Federation of Red Cross and Red Crescent Societies, 2005). These uses can translate into lives saved. R&D has also developed satellite capabilities to assist with storm warnings and even search-and-rescue efforts that are also useful for saving lives (Walter, 1990). Therefore this

R&D effort certainly seems effective in reducing impact. With a number of potential approaches and budgetary constraints that limit funding, decision makers must decide which approach for

R&D is most effective for reducing the impact of natural disasters on society.

5.4.2. CHALLENGES TO THE STUDY

The discrepancies between the Munich Reinsurance Company‟s NatCatSERVICE© data and the SHELDUS data created one of the largest challenges to this study. As discussed in

Chapter 1, databases use different methodologies for data processing and these two databases differ in their processing procedures for data calculations and categorization. This caused many of the results from this study‟s analyses to be incompatible with each other. First, the difference in each database‟s calculation for economic losses caused differing results. The

NatCatSERVICE© calculates economic losses from damage to property, infrastructure, agriculture, and business interruption among other things while the SHELDUS economic losses only reflect damage to property and crops. While both sets of results determined that storms cause the largest economic losses, the resulting losses varied by a magnitude of ten.

Then, the discrepancy in categorizing storm surge created differing results for the disaster type causing the second greatest economic losses. NatCatSERVICE© categorizes storm surge as

222 a storm event and SHELDUS categorizes storm surge as a flood event. Therefore the

NatCatSERVICE© reported economic losses for storms to be very large and it did not determine that floods caused the second largest level of economic losses as SHELDUS determined due to its categorization of storm surge. Since SHELDUS calculates economic losses with only two variables, it does not report disaster events that cause losses beyond property and crop losses.

Therefore SHELDUS does not report all of the disaster events that NatCatSERVICE© reports.

As a result, there is also a discrepancy in the loss percentages per disaster types. In addition, a geographic categorization glitch caused the NatCatSERVICE© to overestimate human losses that actually occurred in Central America but were categorized in a manner that incorrectly reflected

U.S. losses.

Identifying R&D programs and their funding levels was another challenge and ultimately an impediment to this study. An analysis of one year‟s budget cannot provide many conclusive findings beyond indicators of federal activity. It was difficult to track natural disaster R&D programs because disaster R&D does not always exist as explicit items and instead they are often combined into larger programs, many of which give no indication to a layperson that they include the R&D element. Sometimes disaster R&D is at the project level rather than the program level and these projects are embedded within programs and are rarely listed explicitly in the budget. Often, each of the agencies involved in an interagency R&D program does not report their portion of the budget and only a fraction of the funding can be tracked. Funding levels for multiyear programs are not consistently reported for each year of the program so whatever budget can be tracked for these programs is piecemeal. Sometimes decision makers do not differentiate R&D programs from operational programs and this also poses difficulty to tracking

R&D funding. Originally I had wanted to track disaster R&D program funding over several

223 decades however I was not able to do so due to all of these reasons. Furthermore, the one year snapshot that I tracked is by no means comprehensive since I was not able to find many of the projects that are embedded within larger programs.

The interviews also presented a challenge to this study since I did not have access to all of the decision makers that I would have liked to interview. A number of agencies conduct natural disaster R&D however I did not have any contacts for some of the agencies and therefore some gaps exist in the information I received from the interviews.

5.4.3. FUTURE WORK

This study has many possibilities for continuation based on the challenges that I encountered. The robustness of this study would improve if the analyses were duplicated with another research-quality database that was more compatible with either one of the two databases used in this study. Currently another database with robust data on U.S. economic losses does not exist. A continuation of this study could include more extensive tracking of program funding. I do not know how this could be accomplished since the information might not exist but the current annual snapshot needs to be extended for any substantive type of analysis. Similarly, more interviews could be added, particularly with agency decision makers not included in this study.

Another potential study could explore the different R&D approaches discussed earlier.

Instead of comparing disaster impact to R&D by disaster type, it could compare impact to an alternative R&D approach. For example a future study could compare human losses over time to the funding for prediction R&D over time.

224

5.5. CONCLUSION

This dissertation relates data on disaster-caused losses to natural disaster policy and in doing so it creates a new user need for data and fills an information gap for disaster policy. In a study on metrics for R&D, the National Research Council said, “Data help illuminate the paths that public policy should take to solve public issues.” (National Research Council, 2005). The three studies in this dissertation appraise natural disaster policy by investigating science for policy, evaluating national policies, and assessing policy for science. The findings from this dissertation explain that global economic losses are increasing because of regional socioeconomic factors. The findings show no evidence that the NFIP or NEHRP have an impact on reducing economic losses to a baseline level. Nor do these policies appear to have the ability to counter increasing losses or losses from extreme events. The findings partially validate the efficiency of disaster R&D policy since the disaster types causing the greatest impacts on society receive one of the largest levels of funding for its R&D.

These findings are useful to decision makers, policy makers, database managers, data users, researchers, and the insurance industry. The original criteria presented here can assist data managers with improving the quality of their data and it can guide data users and researchers on the robust databases to use. The analyses can inform the insurance industry on trends in disaster losses and can improve decisions and policies for natural disasters. If the words of Albert

Einstein are true: “Know where to find the information and how to use it - that's the secret of success”, then this dissertation begins to uncover the secret of success.

225

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