insuring public buildings, contents, vehicles, and ... · an ffrdc operated by the rand corporation...

An FFRDC operated by the RAND Corporation under contract with DHS

HS ACHOMELAND SECURITYOPERATIONAL ANALYSIS CENTER

Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters Current Practices of State and Local Government and Options for Closing the Insurance Gap

LLOYD DIXON, JASON THOMAS BARNOSKY, NOREEN CLANCY

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https://www.rand.org/pubs/research_reports/RRA332-1.html

https://www.rand.org/hsrd/hsoac.html

Published in 2020

iii

Preface

In the United States, federal, state, and local governments—as well as the private and nonprofit sectors—share responsibility for managing disaster risk. Traditionally a state and local function, more responsibility for the cost of recovering from disasters has shifted to the federal government. This has happened over time as programs funding disaster response and recovery have proliferated and provided support to state and local entities. How best to manage this risk is much debated. In its 2018–2022 strategic plan, for example, the Federal Emergency Management Agency (FEMA), part of the U.S. Department of Homeland Security (DHS), called for the greater use of insurance and identified as a goal closing the insurance gap—that is, the difference between what is currently insured and what is insurable.

These issues have grown even more important as the cost and severity of disasters have increased, and, with this study, we aimed to contribute to this debate by improv-ing our understanding of state and local financial risk-management practices and the role that FEMA’s Public Assistance grant program plays in these decisions. Drawing on a variety of data sources, our study addressed three research questions:

1. What insurance and financial risk-management practices do state and local governments employ for buildings, contents, vehicles, and equipment?

2. What role does FEMA assistance play in these decisions?3. What are options for closing the insurance gap for buildings, contents, vehicles,

and equipment?

This research was sponsored by the DHS Science and Technology Directorate and conducted within the Strategy, Policy, and Operations Program of the Home-land Security Operational Analysis Center (HSOAC), a federally funded research and development center (FFRDC).

iv Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

About the Homeland Security Operational Analysis Center

The Homeland Security Act of 2002 (Section 305 of Public Law 107-296, as codified at 6 U.S.C. § 185) authorizes the Secretary of Homeland Security, acting through the Under Secretary for Science and Technology, to establish one or more FFRDCs to pro-vide independent analysis of homeland security issues. The RAND Corporation oper-ates HSOAC as an FFRDC for DHS under contract HSHQDC-16-D-00007.

The HSOAC FFRDC provides the government with independent and objective analyses and advice in core areas important to the department in support of policy development, decision making, alternative approaches, and new ideas on issues of sig-nificance. The HSOAC FFRDC also works with and supports other federal, state, local, tribal, and public- and private-sector organizations that make up the homeland security enterprise. The HSOAC FFRDC’s research is undertaken by mutual consent with DHS and is organized as a set of discrete tasks. This report presents the results of research and analysis conducted under task order 70FA4018F00000128, Requirements Analysis and Specification for FEMA.

The results presented in this report do not necessarily reflect official DHS opin-ion or policy.

For more information on HSOAC, see www.rand.org/hsoac. For more informa-tion on this publication, see www.rand.org/t/RRA332-1.

http://www.rand.org/hsoac

http://www.rand.org/t/RRA332-1

v

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xixAbbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

CHAPTER ONE

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Research Questions and Study Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Report Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

CHAPTER TWO

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Risk Management in the Public Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7The Public Assistance Program and the Allocation of Risk Between Federal, State, and

Local Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Definition and Measurement of Insurance Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

CHAPTER THREE

Insurance on Public Buildings, Contents, Vehicles, and Equipment . . . . . . . . . . . . . . . . . . . . . . 21Risk-Management Tools Used by State and Local Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Insurance Take-Up Rate and Insurance Share for Buildings, Contents, Vehicles, and

Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

CHAPTER FOUR

Drivers of Insurance Share for Buildings, Contents, Vehicles, and Equipment . . . . . . . . . 29Relationship Between Insurance Share and Size of Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Difference in Insurance Share, by Incident Type and Peril Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Difference in Insurance Shares Inside and Outside Special Flood Hazard Areas . . . . . . . . . . . . . 37

vi Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

Variation in Insurance Share, by Property Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39The Effect That Jurisdiction Size Has on Insurance Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40The Effect That Prior Disaster Declarations Have on Insurance Share . . . . . . . . . . . . . . . . . . . . . . . 44Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

CHAPTER FIVE

The Role That FEMA Assistance Programs Play in State and Local Risk- Management Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Previous Research on the Effect That Disaster Assistance Has on Insurance Purchasing Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Expert Perspectives on the Effect That the Public Assistance Program Has on State and Local Risk-Management Practices for Buildings, Contents, Vehicles, and Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

CHAPTER SIX

Options for Closing the Insurance Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Encourage Credit Rating Agencies to Consider Financial Preparedness for Disasters in

Their Assessments of State and Local Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Raise Communities’ Awareness of Their Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Require State and Local Governments to Cover the First Layer of Loss . . . . . . . . . . . . . . . . . . . . . . . 62Eliminate Public Assistance Funding for Buildings and Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

CHAPTER SEVEN

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65State and Local Government Insurance Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65The Effect That FEMA Assistance Has on Risk-Management Decisions . . . . . . . . . . . . . . . . . . . . 66Options for Closing the Insurance Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

APPENDIXES

A. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71B. Statistical Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75C. Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95D. Parametric Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

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

vii

Figures

1.1. A Risk Allocation Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1. Typical Relationship Between Insurance Share and Repair Costs . . . . . . . . . . . . . . . . . 14 2.2. Locations of Buildings, Contents, Vehicles, and Equipment for Which

State and Local Governments Have Applied for Public Assistance . . . . . . . . . . . . . . . . 19 3.1. Distribution of Insurance Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1. Predicted Insurance Share, by Repair Cost per Capita, Holding Other

Applicant and Incident Attributes Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2. Predicted Insurance Share, by Incident Type, Holding Other Applicant

and Incident Attributes Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3. Predicted Insurance Share, by Population, Holding Other Applicant and

Incident Attributes Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.4. Predicted Insurance Share, by Population and Repair Cost Per Capita . . . . . . . . . . . 41 4.5. Median Repair Cost Per Capita and Average Insurance Share, by

Jurisdiction Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 B.1. Confidence Intervals for Predicted Insurance Shares, by Population and

Repair Cost Per Capita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

ix

Tables

S.1. Summary of the Relationship Between Incident and Applicant Attributes and Insurance Share for Buildings, Contents, Vehicles, and Equipment . . . . . . . . xiv

S.2. Overview of Options for Encouraging State and Local Governments to Purchase Insurance for Public Buildings, Contents, Vehicles, and Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

1.1. Our Methodology, by Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1. Categories of Public Assistance Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2. Public Assistance Grant Program Obligations, by Category of Work, Fiscal

Year 1999 Through the Third Quarter of 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3. Overview of Public Assistance to State and Local Government for

Buildings, Contents Vehicles, and Equipment (for Disaster Declaration Dates January 1, 2008, to June 30, 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1. Insurance Take-Up Rate for Disaster Declarations Between January 2008 and June 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2. Average and Aggregate Insurance Shares for Disaster Declarations Made Between January 2008 and June 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3. Average Insurance Share, by Jurisdiction Type and Size and Repair Cost . . . . . . . 28 4.1. Incident and Peril Types for Public Assistance Applicants for Disaster

Declarations Made Between January 2008 and June 2018 . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2. Predicted Insurance Share, by Proportion of Repair Costs, Inside a Special

Flood Hazard Area, Holding Other Applicant and Incident Attributes Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.3. Predicted Insurance Share, by Proportion of Repair Costs Attributable to Equipment, Holding Other Applicant and Incident Characteristics Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.4. Predicted Insurance Share, by Number of Prior Declarations Holding Other Applicant and Incident Characteristics Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.5. The Relationship Between Insurance Share for Buildings, Contents, Vehicles, and Equipment and Incident and Applicant Attributes . . . . . . . . . . . . . . . . 48

6.1. Overview of Options for Encouraging State and Local Governments to Purchase Insurance for Public Buildings, Contents, Vehicles, and Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

x Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

B.1. Public Assistance Costs for Buildings, Contents, Vehicles, and Equipment for Disaster Declarations Made Between January 2008 and June 2018 . . . . . . . . . 77

B.2. Insurance Share, by Applicant and Incident Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 B.3. Tobit Model of Insurance Share (Derivatives of Responses) . . . . . . . . . . . . . . . . . . . . . . . 88 B.4. Tobit Model of Insurance Share with Interaction of Flood Zone and

Incident Type (Tobit Coefficients) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 B.5. Tobit Model of Insurance Share with Variables That Distinguish More-

Recent from More-Distant Declarations (Derivatives of Responses) . . . . . . . . . . . . . . 93

xi

Summary

Federal, state, and local governments in the United States—as well as the private and nonprofit sectors—share responsibility for managing disaster risk. How best to address this risk is much debated, and the issue has grown even more important as the cost and severity of disasters have increased. In its 2018–2022 strategic plan, for example, the Federal Emergency Management Agency (FEMA) calls for greater use of insurance, identifying as a goal the closing of the insurance gap—that is, the difference between what is currently insured and what is insurable.1 This study contributes to this debate by improving our understanding of what state and local governments do to manage risk to their facilities—specifically, their buildings, contents, vehicles, and equipment (BCVE)—and how funding from FEMA’s Public Assistance (PA) grant program con-tributes to governments’ decisions about whether or not to purchase insurance. In this process, we addressed three research questions:

1. What insurance and financial risk-management practices do state and local governments employ for BCVE?

2. What role does FEMA assistance play in these decisions?3. What are options for closing the insurance gap for BCVE?

Approach

To answer each question, we interviewed FEMA staff and conferred with insurance brokers who served as part of the research team. More than 40 FEMA staff and 23 staff at insurance brokers Aon, Guy Carpenter, and Marsh contributed their expertise to the project. We also conducted a literature review and analyzed data from FEMA’s Emer-gency Management Mission Integrated Environment (EMMIE) database. Finally, we developed, executed, and assessed the results of a request for information on state and

1 Broadly defined, insurance is a financial contract that transfers risk from one party to another in exchange for advance payments, known as premiums. Note that this definition does not include self-insurance.

xii Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

local government financial risk-management practices, as well as the insurance prod-ucts available to them.

EMMIE provides detail on the cost of repairs funded through FEMA’s PA pro-gram. States, local governments, other public entities, and certain nonprofit organi-zations can apply to the program for assistance to repair damaged BCVE and other facilities following major disasters. We used this information to calculate repair costs; the amount of repair costs covered by the applicant’s insurance; and insurance share, which is the ratio of insurance payments to repair costs. Insurance share is calculated for each applicant in a given disaster declaration (applicant–declaration pair), and the data cover disaster declarations between January 1, 2008, and June 30, 2019.

The definition of insurance share we used for this study includes some repair costs for which the applicant does not receive an insurance payment but for which FEMA will not pay. For example, insurance share also includes required reductions for flood-related damage in the Special Flood Hazard Area (SFHA) up to the coverage limits provided by the National Flood Insurance Program regardless of whether the applicant purchased flood coverage. The magnitude of these types of reductions in EMMIE is not large rela-tive to insurance payments, although the coding of them may not be complete.

Insurance share calculated using EMMIE data provides insight into state and local government insurance practices, but several caveats should be kept in mind when interpreting the findings. First, EMMIE provides data on BCVE only in a community for which a major disaster has been declared. It would be appropriate to characterize findings from the EMMIE data as describing the situation in the higher-risk parts of the country to the extent that communities that have been included in disaster declara-tion are at higher risk than those that have not. Second, EMMIE also does not neces-sarily provide a random sample of BCVE in communities that have been affected by major disasters. FEMA generates EMMIE data only for damaged BCVE for which PA applications have been submitted. Applicants have incentives to submit all damaged BCVE, but the insurance shares reported in this study will be low to the extent that applicants do not submit damaged buildings to PA with high levels of insurance.

Findings

What Insurance and Financial Risk-Management Practices Do State and Local Governments Employ for Buildings, Contents, Vehicles, and Equipment?

We found that a significant portion of state and local governments that apply for PA purchase some type of insurance for their BCVE. However, data limitations prevent precise estimates of the take-up rate. Although insurance appears to be common over-all, the average insurance share for applicant–declaration pairs is 46 percent. And, because the insurance share is much lower for larger losses, the proportion of overall repair costs covered by insurance is only 28 percent, which suggests that insurance is

Summary xiii

not covering a substantial portion of BCVE repairs. When we look closely at these figures, we see differences by jurisdiction type and size.2 At 44 percent, the insurance share for cities is much higher than the average for states (27 percent). And the average for smaller jurisdictions is much higher than for larger ones, falling from 48 percent for jurisdictions with populations of less than 50,000 to 26 percent for jurisdictions with populations greater than or equal to 1 million.

To help us understand what drives this variation in insurance share, we used regression analysis to isolate the effects of different incident and public-entity attri-butes. Our findings were generally consistent with the observations of experts with whom we conferred. Exceptions are noted in the following summary of the findings.

Repair Cost Per Capita

An applicant’s total repair cost per capita in a particular declaration is a measure of incident severity that allows comparison across jurisdictions of different sizes. As shown in the first row of Table S.1, we found that insurance share increases consider-ably as repair cost per capita increases. This increase is consistent with FEMA covering the applicant’s insurance deductible and the applicant’s insurance covering remaining losses up to policy cap. Deductibles can be large, resulting in substantial federal pay-ment for this first level of loss.

Type of Peril

Insurance share varies importantly by peril type. The very low insurance share for earthquake combined with the high value of assets exposed to earthquake means that earthquakes present a major financial exposure for the federal government. Limited flood coverage remains an ongoing challenge, with insurance share for flood losses being substantially lower than for losses not involving flood.

Location in a Special Flood Hazard Area

Our analysis suggests that insurance share for flood losses is lower outside SFHAs, which are FEMA-designated areas with at least a 1-percent annual chance of flooding, essentially the riskiest areas for flooding. The results are not particularly convincing, however. The standard errors of the estimates are large, driven by the relatively small number of applicant–declaration pairs for flood-only incidents.

In contrast to this finding, the experts with whom we conferred generally indi-cated, flood coverage is higher outside SFHAs. However, this inconsistency could be, in part, due to a feature of the PA program that increases our measure of insurance share inside an SFHA. Specifically, PA deducts the amount of insurance that an appli-cant could have purchased from the National Flood Insurance Program regardless of whether flood insurance was actually purchased for structures in an SFHA. Our

2 Jurisdiction here refers to a city, county, state, or other public entity, such as a school district, that is eligible to apply for PA funding.

xiv Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

measure of insurance share includes these reductions and could, in principle, cause measured insurance share to be higher inside than outside an SFHA. Further work on the difference in insurance share for flood and other peril types inside versus outside an SFHA is warranted.

Property Type

Insurance share appears somewhat lower for equipment than for buildings, contents, and vehicles. However, equipment repair costs do not account for a substantial share of overall repair costs.

Jurisdiction Size

Insurance share is highest for the smallest jurisdictions and declines considerably as population increases. Insurance share is approximately 48 percent for jurisdictions with populations less than 5,000; 5,000 to 10,000; and 10,000 to 50,000. It then falls

Table S.1Summary of the Relationship Between Incident and Applicant Attributes and Insurance Share for Buildings, Contents, Vehicles, and Equipment

AttributeRelationship Between Insurance Share

and Attribute Range of Predicted Insurance Share

Incident

Size of loss per capita

Increases as repair cost per capita increases

16–59% over the range of per capita repair cost observed

Type of peril Lowest for earthquake and flood 15% for earthquake and 25% for flood; 50% for storms without flood

Location in an SFHA

Lower for flood outside SFHAs 22–38% for flood-only events, depending on the percentage of damage outside SFHAs

Property type Lower for equipment than for buildings, contents, and vehicles

34–42%, depending on the percentage of damage attributable to equipment

Public entity

Jurisdiction size

Highest for midsized jurisdictions, lower for smaller and larger jurisdictions. Higher repair cost per capita in small jurisdictions brings up the observed insurance share for the smallest jurisdictions.

33% for smallest jurisdictions, rising to 47% for midsized jurisdictions, then dropping to 31% for the largest jurisdictionsa

Frequency of prior disaster declarations

Higher in jurisdictions with a substantial number of disaster declarations in the prior five years, but declines as the number of declarations more than five years prior to current declaration grows

40–46%, depending on the number of declarations in the prior five years; 34–43%, depending on the number of declarations more than five years prior to the incident

a The 31% for the largest jurisdictions is the average of the predicted shares (details in Chapter Four), excluding the prediction for repair costs lower than $0.25 per capita.

Summary xv

steadily to 26 percent for jurisdictions with population greater than or equal to 5 mil-lion. This steady decline, however, appears due, in part, to variations in other factors besides population. When other applicant and incident attributes are held constant, insurance share peaks for jurisdictions with populations between 10,000 and 50,000 and is lower for both smaller and larger jurisdictions.

The lower insurance share for larger communities might reflect increased use of risk-management tools that are less readily available to smaller communities. For example, larger communities can more readily set up rainy-day funds to repair disas-ter damage, borrow postevent to repair uninsured damage, or rely on postevent tax increases to cover losses. Our measure of insurance share does not capture these and other types of risk-management approaches. This unobserved or hidden insurance, which is sometimes referred to as self-insurance, is not easy for FEMA to document and is not deducted from PA awards—even though it can be called on when FEMA PA funding is not available. The result is a lower insurance share for large communities but not necessarily less capacity than smaller communities to address disaster losses. The availability of these types of self-insurance means that larger communities are ben-efiting more from FEMA PA because they can strategically adopt a risk-management approach that maximizes FEMA assistance. An important priority for policymakers is understanding how to address these types of self-insurance when determining eligibil-ity for federal assistance dollars.

Financial constraints in small communities might be responsible for the fact that insurance shares are lower in small communities than in midsized ones when other incident and applicant attributes are held constant. One reason that the observed insurance share (the insurance share before correcting for other incident and applicant characteristics) is higher for small communities than midsized ones is that smaller communities tend to have higher repair costs per capita than larger communities. As discussed previously, higher repair costs per capita are associated with higher insurance shares, raising the observed insurance share for small communities. Further work is needed to better understand the causes for and implications of the higher repair cost per capita of smaller communities in declared disasters.

Frequency of Prior Disaster Declarations

Finally, we found that applicants who have experienced a substantial number of disas-ter declarations in the five years prior to the current incident have higher insurance share than those with fewer declarations. What is surprising is that insurance share falls as the number of declarations more than five years prior to the current incident increases. The differences are not particularly large, however, and are not estimated with a great deal of precision. The pattern suggests that insurance share is lowest in the high-risk areas where FEMA spends the most money. The findings suggest that discus-sion of insurance practices in repetitive loss areas should be a priority.

xvi Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

What Role Does FEMA Assistance Play in the Risk-Management Decisions of State and Local Government?

Scholars have explored the relationship between disaster assistance programs and the purchase of insurance, but their work has not provided firm conclusions as to whether postdisaster assistance discourages public-sector entities from purchasing insurance.

The predominant view among the FEMA staff and insurance brokers with whom we consulted was that the potential for FEMA assistance reduces insurance purchases by state and local government. At one extreme, experts pointed to public entities that do not buy any catastrophic coverage. These entities view public buildings as essential facilities and believe that it is FEMA’s responsibility to get essential facilities up and running after a disaster.

Experts familiar with the risk-management thinking and decisions of state and local government also noted less extreme responses. Some public entities buy insur-ance for only the facilities that they believe are critical to repair quickly. Examples include police and fire stations and critical administrative buildings. These public enti-ties rely on the PA program to repair or replace the rest. It was also commonly believed that state and local governments that do purchase coverage buy less than they would absent the PA program. For example, a county might buy coverage adequate to cover a 1-in-100-year event and rely on FEMA to cover larger events. The county realizes that FEMA assistance would not be available for events that do not rise to the level of a presidential declaration but would be available for the largest events. Such entities recognize that they are underinsured but conclude that the gap is acceptable because FEMA assistance will likely be available for large events. Experts also observed that some state and local governments select larger deductibles than they would otherwise, knowing that PA will cover the deductible for first-time losses.

The circumstances and risk-management practices of state and local government are diverse, and some experts also saw no evidence that some bought less insurance because they expected that FEMA would bail them out, and several noted that some state and local governments are reluctant to rely on FEMA.

Were FEMA to eliminate PA for BCVE, experts with whom we spoke stated con-sistently that the extent to which communities believe that such a policy change was permanent would be a key factor in determining how they respond. But experts offered different perspectives on how, were such a policy change successful, it would affect state and local behavior. Experts argued that it would have little impact on public entities in some cases, but a more common view was that state and local governments would take more responsibility for managing their disaster risks.

What Are Options for Closing the Insurance Gap for Buildings, Contents, Vehicles, and Equipment?

Throughout our research, we asked subject-matter experts to identify approaches that could encourage state and local governments to purchase insurance for their BCVE.

Summary xvii

Four approaches emerged, which we have summarized in Table S.2. First, experts rec-ommended requiring credit rating agencies (CRAs) to evaluate communities’ financial preparedness for disasters. They noted that, in assessing a community’s creditwor-thiness, CRAs do not generally evaluate this and that encouraging CRAs to do so could create an incentive for communities to adopt better financial risk-management practices. Second, experts suggested requiring state and local governments to analyze and disclose information on their vulnerabilities to the hazards they face, noting that they often do not have a good understanding of their economic consequences. Such a requirement could increase their awareness of their risks, promoting better mitiga-tion and enabling the private sector to develop appropriate solutions. Third, experts

Table S.2Overview of Options for Encouraging State and Local Governments to Purchase Insurance for Public Buildings, Contents, Vehicles, and Equipment

Option Description Advantages Disadvantages

Encourage CRAs to consider disaster vulnerability.

Encourages CRAs to consider financial preparedness for disasters in their assessments of communities’ creditworthiness

Creates incentives for sound financial risk-management practices; is relatively inexpensive

CRAs may conclude that credit ratings do not require revision, given the existence of the FEMA backstop.

Raise state and local awareness of risk.

Requires communities to identify major gaps and vulnerabilities and evaluate economic impacts of disasters

Increases awareness of vulnerabilities and creates a sense of ownership; helps the private sector develop solutions

Might not result in any changes in risk-management practices; might require technical and financial support from FEMA

Require state and local governments to cover the first layer of loss.

Requires communities to obtain coverage up to the 1-in-100 or 1-in-200-year event

Federal government no longer covers the first layer of loss but is still there for large, hard-to-insure incidents; removes discussions about amount of self-insurance; improves community incentives to insure against and mitigate risk

Makes disagreements likely on the appropriate level of insurance; could make costs burdensome for some communities

Eliminate PA for BCVE.

Ends the eligibility of BCVE for the PA program

Federal government no longer covers the first layer of loss. No need to specify what part of loss distribution should be covered by the federal government; removes discussions about amount of self-insurance; creates incentives to insure and mitigate risk

Communities responsible for costs incurred from difficult-to-insure large incidents; might not result in changes to risk-management practices if state and local governments do not view program elimination as politically sustainable

xviii Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

argued for holding state and local governments responsible for the first layer of loss to BCVE, suggesting that communities obtain coverage for up to 1-in-100 or 1-in-200-year events. PA funding would be available only for losses over this threshold, creating an incentive for state and local governments to mitigate their risks. Finally, the President’s fiscal year 2021 budget proposes ending eligibility for BCVE to receive PA funding. State and local governments would become responsible for bearing all the costs of repairing or restoring BCVE, which could encourage mitigation and poten-tially lower the overall risk that communities face. It would also render moot discus-sions about whether larger entities have self-insurance that FEMA should deduct from PA awards. However, eliminating PA would leave state and local governments respon-sible for managing the largest incidents, which can be difficult and expensive to insure.

Each of these policy alternatives would result in a different allocation of risk between federal, state, and local governments. Policymakers should consider the advantages and disadvantages of different approaches and consider what point on the risk allocation spectrum makes the most sense to target. There are drawbacks of attempting to shift the entire risk for BCVE to state and local government, but there are also clearly drawbacks of the current approach—in which the federal government frequently covers the first layer of loss and provides assistance for a substantial portion of overall loss.

Further Research

Throughout the course of our work, we identified several areas for additional research that would help further our understanding of state and local government insurance practices and how to encourage communities to purchase insurance. These areas include the following:

• evaluating the insurance practices of large jurisdictions• evaluating flood insurance practices outside SFHAs• analyzing the relationship between the purchase of insurance and past disaster

frequency• evaluating insurance share by property type• assessing the relationship between mitigation funding and insurance share• analyzing other PA categories of permanent work• evaluating approaches for closing the insurance gap for individuals• evaluating approaches for facilitating the transfer of risk.

xix

Acknowledgments

We wish to thank our Federal Emergency Management Agency (FEMA) and U.S. Department of Homeland Security Science and Technology Directorate sponsors and staff for their support and encouragement throughout this effort. From FEMA’s Office of Policy and Program Analysis, these include Lindsey Anderson, Katherine Peterlin, Mario J. Weber, Angelica Saavedra, and Sheanna Paine. At the Science and Tech-nology Directorate, these include David Alexander and Angela Blair. We would also like to offer our thanks to Esten Rudolph and Daniel Abraham in FEMA’s Recovery Reporting Analytics Division for their assistance in providing us with data essential to this project and their patience in answering our questions. We would like to acknowl-edge the important contributions that Aon, Guy Carpenter, and Marsh made as part of the research team.

At the Homeland Security Operational Analysis Center, we would like to thank Henry H. Willis for his support and encouragement throughout this effort and Erica Robles for shepherding the report through the review and editorial processes. We would also like to thank Yousuf Abdelfatah, Andrew Beck, Bruce Bender, Justin Lee, Alexis Levedahl, and Sean Mann for their assistance with various components of this project and Lisa Bernard for her skillful editing.

Finally, we would like to thank Paul Brenner and Benjamin M. Miller at the RAND Corporation and Adam J. Rose at the University of Southern California Sol Price School of Public Policy for their constructive reviews of the draft report.

xxi

Abbreviations

BCVE buildings, contents, vehicles, and equipment

CRA credit rating agency

DHS U.S. Department of Homeland Security

EMMIE Emergency Management Mission Integrated Environment

FEMA Federal Emergency Management Agency

FFRDC federally funded research and development center

FY fiscal year

GAO U.S. Government Accountability Office

HMGP Hazard Mitigation Grant Program

HSOAC Homeland Security Operational Analysis Center

IA Individual Assistance

NFIP National Flood Insurance Program

OIG Office of Inspector General

O&M obtain and maintain

PA Public Assistance

PNP private nonprofit

RRAD Recovery Reporting Analytics Division

RFI request for information

SFHA special flood hazard area

SLTT state, local, tribal, and territorial

1

CHAPTER ONE

Introduction

In the United States, federal, state, and local governments share responsibility for man-aging disaster risk. Traditionally a state and local function, more responsibility for the cost of recovery has shifted to the federal government over time, and federal programs providing support to state and local entities for disaster response and recovery have proliferated.1 How best to manage this risk and whether the current allocation of risk between the different levels of government are appropriate is much debated, and, as the cost and severity of disasters have increased, these issues have become even more important. The National Oceanic and Atmospheric Administration has found that the number of billion-dollar disasters has more than doubled since 1980. The nation aver-aged about six billion-dollar disasters a year between 1980 and 2013—and more than 12 a year from 2014 to 2018.2 These trends will likely continue as rising natural hazard risk, deteriorating infrastructure, the construction of new infrastructure, and budget-ary pressures that limit investment in resilience contribute to increasing disaster costs.3

These costs pose significant challenges to the public sector. For example, the U.S. Government Accountability Office (GAO) has pointed to disaster costs as a key source of federal fiscal exposure, noting that federal funding for disaster assistance has approached half a trillion dollars since 2005. GAO has further stated that disas-ter costs are projected to increase as extreme weather events become more common.4 At the federal level, these costs fall to several agencies. For instance, the U.S. Small Business Administration assists businesses postdisaster through its disaster loan pro-gram, and the U.S. Department of Housing and Urban Development provides fund-ing for housing, infrastructure, and economic revitalization through its Community Development Block Grant Disaster Recovery program.5 However, a significant portion

1 For an overview of the evolution the federal government’s role in emergency management, see Roberts, 2006, and Roberts, 2010.2 National Centers for Environmental Information, undated.3 See, for example, Federal Emergency Management Agency (FEMA), 2018a, p. 10.4 Frank, 2019; GAO, 2019b, pp. 110, 113.5 For more on these programs, see, for example, GAO, 2019c, and GAO, 2020.

2 Insuring Public Buildings, Contents, Vehicles, and Equipment Against Disasters

of disaster costs falls on the Federal Emergency Management Agency (FEMA), the agency responsible for leading federal disaster response and recovery. One of FEMA’s largest programs is the Public Assistance (PA) grant program, which provides funding to support debris removal; emergency protective measures; and the repair, replacement, or restoration of facilities that are publicly owned or owned or operated by certain private nonprofit (PNP) organizations. One category eligible for PA funding includes buildings, contents, vehicles, and equipment (BCVE).

In recent years, FEMA has sought to shift risk away from the federal government. In 2016, for example, FEMA proposed creating a disaster deductible for the PA pro-gram. Under this proposal, FEMA would have required a PA recipient (or applicant)—such as a state, territory, or tribe—to provide a predetermined level of financial or other commitment before receiving assistance from the PA program. FEMA proposed deter-mining the annual deductible amount based on an index of state risk and fiscal capac-ity. To offset the deductible requirement, the applicant would have the opportunity to apply for credits that it could receive for engaging in activities promoting prepared-ness, mitigation, or resilience.6 However, FEMA withdrew the proposal amid concerns about its complexity and pushback from state and local governments.7 More recently, FEMA has prioritized transferring more risk to the private sector. In its 2018–2022 strategic plan, FEMA identified as a goal the closing of the insurance gap—that is, the difference between what is currently insured and what is insurable—for communi-ties, individuals, and businesses.8 Across the United States, a significant insurance gap exists. Residences are often uninsured, for example. A 2018 FEMA analysis showed that 53 percent of homeowners in high-risk flood zones lacked flood insurance.9 And only about 10 percent of homes in California have earthquake insurance.10

Managing public-sector disaster risk involves considering a variety of trade-offs. Shifting a greater portion of disaster costs to state and local entities could encourage them to adopt mitigation measures or transfer risk to the insurance market, which—when claims are expeditiously resolved—would enable survivors to get back on their feet quickly and promote fast recoveries for affected communities. However, private insurance markets do not always function well, especially for extreme events, and leav-ing the responsibility with the federal government ensures that these risks are spread

6 FEMA, 2016, p. 3082; FEMA, 2017a, pp. 4064–4065.7 GAO, 2019f, p. 14.8 FEMA, 2018a, p. 15; Kaniewski, 2018, p. 5. Note that it is not always appropriate for a property owner to insure a property. For example, in some cases, insurance might not be available at a rate that is appropriate to the risk.9 FEMA, 2018b, p. 14. The percentage with flood insurance can be calculated from the numbers presented in FEMA, 2018b, Table 2.6. The 53-percent estimate does not include private flood policies, which somewhat lower the percentage without insurance.10 Glenn Pomeroy, chief executive officer, California Earthquake Authority, as quoted in Westervelt, 2018.

Introduction 3

widely.11 Figure 1.1 provides a framework for conceptualizing the allocation of risk between the different levels of government and identifies some of the advantages and disadvantages of different allocations.

With this study, we aimed to contribute to this debate by improving our under-standing of state and local financial risk-management practices and the role that FEMA’s PA program plays in these decisions. Little systematic information exists on insurance practices for public facilities.12 Consequently, it is difficult to characterize how state and local governments manage their risk and what can be done to encourage better risk-management practices. Our analysis provides insight into what state and local governments are actually doing to manage risk to their facilities—specifically, their BCVE—and how FEMA PA funding contributes to these decisions. In this report, we also discuss different approaches for sharing risk between federal, state, and local governments and the trade-offs associated with them. In doing so, we hope to shed light on what can be done to address public-sector disaster risks and the costs associated with them.

11 For example, the need to establish programs, such as the National Flood Insurance Program (NFIP), the Cali-fornia Earthquake Authority, and the Terrorism Risk Insurance Program, illustrates the difficulties that extreme events create for private insurance markets.12 For discussion of this point, see Rose et al., 2020, p. 5.

Figure 1.1A Risk Allocation Framework

Increasing risk-mitigation incentives for state and local governments

State and local governmentsbear the full risk

Fed

eral

sh

are

of

risk

• Creates incentives to mitigate and transfer via insurance and capital markets

• Localizes costs and avoids cross-subsidies

• Leaves state and local entities with limited options when extreme risks challenge the insurance market

• Avoids requiring state and local entities to cover the most-expensive and hardest-to-insure incidents

• Provides state and local entities with some incentives to avoid and mitigate risks

• Broadly spreads risk for the largest incidents

• Spreads risk widely for all incidents

• Provides fewer incentives to state and local entities to avoid and mitigate risks

• Can create cross subsidies between low- and high-risk areas

Federal government bearsthe full risk

State and local governments bear the first layer of risk


Research Questions and Study Approach

In developing this report, our team conducted research and analysis on the insurance and financial risk-management practices of state and local governments. This section provides an overview of the methods we used to address three research questions:


2. What role does FEMA assistance play in these decisions?3. What are options for closing the insurance gap for BCVE?

For each research question, we drew on interviews with FEMA staff and con-ferred with insurance brokers who served as part of the research team. Brokers often work with state and local governments to help them determine the right level of insur-ance, and, through these relationships, they gain insight into state and local risks and needs. The brokers on our team had worked extensively with public entities and devel-oped expertise in state and local government risk-management practices. More than 40 FEMA staff and 23 staff at insurance brokers Aon, Guy Carpenter, and Marsh contributed their expertise to the project. (Appendix C provides additional informa-tion on the interviews.) We conducted a literature review, including key policy docu-ments and analyzed data from FEMA’s Emergency Management Mission Integrated Environment (EMMIE) database (Appendix B provides more detail on this analysis). Because the Paperwork Reduction Act limited our ability to interview state and local officials,13 we also developed, executed, and assessed the results of a request for infor-mation (RFI) on state and local government financial risk-management practices, as well as the insurance products available to them. We received six responses to the RFI. Table 1.1 provides our methodology for each research question in more detail.

It is important to note several limitations to the scope of our analysis. Although state and local governments own and operate a variety of types of facilities, we have restricted our analysis to BCVE—what FEMA refers to as category E in its PA pro-gram. (Chapter Two discusses the PA categories in detail.) We have done this for two reasons: First, the other categories of permanent work for the PA program include types of facilities—such as roads, bridges, and water control facilities—for which obtaining insurance is much more difficult or might not exist. In contrast, insurance policies for BCVE are readily available today from the private insurance market. Second, the fiscal year (FY) 2021 President’s budget proposes ending category E eligibility for PA fund-ing, explaining, “Eliminating this assistance will encourage State and local govern-ments to more responsibly manage their risk, including better land management and

13 44 U.S.C. Ch. 35.

Introduction 5

planning, purchasing insurance, and/or investing in mitigation.”14 As the federal gov-ernment considers this change, understanding state and local risk-management prac-tices for BCVE and impact of the PA program becomes even more important.

Another reason for limiting the scope of our analysis has to do with the pool of PA applicants we considered. Although the PA program is open to tribes, territories, PNPs, states, and localities, we evaluated only the latter two types of entities. Tribes and territories accounted for too small a portion of the observations in our data set—approximately 200 of the 15,000 observations we analyzed—for us to be able to con-duct robust analysis. PNPs composed about 19 percent of the observations. But given the diversity of the nonprofit pool—which includes organizations ranging from hos-pitals and universities to churches and day care centers—PNPs posed some methodi-cal challenges beyond the scope of this study. Further work on each of these applicant pools is warranted.

Report Organization

The remainder of this report is divided into five chapters, accompanied by three appendixes:

• Chapter Two. Background: We provide an overview of risk management in the public sector, the PA program, and how we measured state and local insurance share.

14 Office of Management and Budget, 2020, p. 112.

Table 1.1Our Methodology, by Research Question

Research Question Methodology


• Conduct a literature review on state and local risk-management practices.

• Interview FEMA headquarters and regional staff.• Confer with insurance brokers.• Review the EMMIE database for information on state and local

insurance and risk-management practices.• Develop an RFI and analyze the results.

2. What role does FEMA assistance play in these decisions?

• Conduct a literature review on the impact that disaster assistance has on insurance decisions.

• Interview FEMA headquarters and regional staff.• Confer with insurance brokers.• Develop an RFI and analyze the results.

3. What are options for closing the insurance gap for BCVE?

• Conduct a literature review on policies and practices for encourag-ing the purchase of insurance.

• Interview FEMA headquarters and regional staff.• Confer with insurance brokers.


• Chapter Three. Insurance on Public Buildings, Contents, Vehicles, and Equipment: This chapter presents findings on the insurance take-up rate for public entities and share of BCVE repair costs covered by the insurance.

• Chapter Four. Drivers of Insurance Share for Buildings, Contents, Vehicles, and Equipment: This chapter provides insight into the factors that drive the substantial variation across public entities in the share of repair costs covered by insurance.

• Chapter Five. The Role That FEMA Assistance Programs Play in State and Local Risk-Management Decisions: This chapter reviews past literature on the role that federal assistance programs play in risk-management decisions and sum-marizes the views of the experts with whom we conferred during the course of this study on the PA program’s impact on public entities’ risk-management deci-sions.

• Chapter Six. Options for Closing the Insurance Gap: This chapter outlines approaches for increasing insurance coverage among state and local governments and the trade-offs associated with them.

• Chapter Seven. Conclusion: In this chapter, we provide final observations.• Appendix A. Glossary: This appendix defines key terms used throughout the

report.• Appendix B. Statistical Methodology: We provide a detailed description of the

statistical methods used to analyze the data pulled from the EMMIE database.• Appendix C. Interviews: In this appendix, we discuss the interviews we con-

ducted and provide a selection of our interview protocols.• Appendix D. Parametric Insurance: This appendix provides an overview on

parametric insurance and its relationship to state and local risk management.

7

CHAPTER TWO

Background

Public-sector organizations manage their risk from natural disasters in a variety of ways—including through insurance, mitigation, and support from government pro-grams. One such program is FEMA’s PA grant program, which provides funding to state, local, tribal, and territorial (SLTT) governments and certain PNP organizations to support debris removal; emergency protective measures; and the repair, replacement, or restoration of facilities.1 As a condition of receiving funding, applicants must obtain and maintain (O&M) insurance for damaged facilities to protect against future losses, and the amount of insurance required is based on the project amount costs.2

In this chapter, we provide a brief overview of risk management in the public sector and relevant key concepts, focusing on insurance in particular. We then describe how the PA program works and how it allocates risk between different levels of govern-ment. Finally, we conclude with a methodological discussion of our measurement of the share of damage to BCVE covered by insurance (insurance share) at the state and local levels.

Risk Management in the Public Sector

Public-sector organizations have certain tools on which they can rely to manage their risk from natural disasters. Disaster assistance programs, for example, can serve as a form of risk sharing, spreading the costs of recovery between different levels of govern-ment. FEMA’s PA program, which we discuss in detail in the next section, works in this fashion, generally requiring states and localities to retain responsibility for paying for a portion of eligible damage, typically a 25-percent share, while FEMA funds the rest. Some states have established similar programs for supporting local governments, and states also often provide disaster assistance to localities on a case-by-case basis.3

1 Public Law 93-288, 1974, as amended; 42 U.S.C. § 5121 et seq.2 42 U.S.C. § 5154. The state insurance commissioner can reduce the amount of insurance required to the amount the commissioner deems reasonably available, adequate, or necessary.3 GAO, 2015, p. 17; Pew Charitable Trusts, 2018, p. 11.


Public-sector organizations can also opt to retain their risk by relying on tax increases or budget reallocations to fund disaster recovery.4

Risk transfer mechanisms, such as traditional insurance, offer federal, state, and local governments another tool for managing their disaster risk. Broadly defined, insur-ance is a financial contract that transfers risk from one party to another in exchange for advance payments, known as premiums.5 Generally, insurance firms seek to spread risk by entering into contracts with large numbers of policyholders—using the premiums to cover losses and expenses, as well as to make a profit.6 Firms offer insurance prod-ucts directly to customers or through brokers who sell and service insurance policies. (Appendix A contains a glossary of terminology used in this report.)

Insurance products can provide coverage for health, liability, casualty, disabil-ity, and property, among other things. Property insurance generally takes one of two forms: residential (also known as homeowner) or commercial. Residential property insurance typically provides compensation for damage due to specified hazards to a home, including the structure and contents on the property. Commercial property insurance generally provides compensation for damage due to specified hazards to a building, furniture and equipment, and inventory.7 Insurers can, in turn, transfer risk to other insurers, in a practice known as reinsurance. This reinsurance allows insurers to moderate the volatility in their outlays.

Some organizations use self-insurance—a term with varied meanings. For exam-ple, in some cases, entities set up formal self-insurance plans that include setting aside funds in a special account to be accessed when disaster strikes. For entities without traditional insurance coverage, these funds would cover losses that occur, and sepa-rate funds might be set up for different categories of peril. For entities with traditional insurance, the fund might cover the insurance premium, the deductible, or losses over the policy cap. Self-insurance can also refer to instances in which a community in effect has no ex ante mechanism to finance losses. In these cases, the entity has, in effect, no insurance and relies on some type of ex post funding to cover the damage, such as taxing residents or requesting assistance from the state or federal government.

Responsibility for regulating insurance falls largely on the states. Each state has its own insurance laws and regulations, as well as agencies devoted to regulating and licensing insurance companies and brokers.8 State insurance commissioners typically head these agencies. The agencies’ responsibilities include approving insurance prod-ucts and rates, as well as examining firms’ finances and market behavior, for the admit-

4 Clarke and Dercon, 2016, p. 83.5 Michel-Kerjan, 2006, p. 282.6 GAO, 2019d, p. 4.7 Federal Insurance Office, 2015, p. 13.8 Congressional Research Service, 2018, p. 1.

Background 9

ted markets in their states. To protect insurance policyholders, states finance guaranty funds through assessments on insurance firms. These funds provide continued cover-age to a policyholder should the insurer no longer be able to meet its obligations.9

The Public Assistance Program and the Allocation of Risk Between Federal, State, and Local Governments

Disaster assistance programs offer public-sector organizations a tool for sharing risk with different levels of government. At the federal level, FEMA serves as the agency responsible for leading disaster response and recovery efforts, and the agency adminis-ters multiple programs providing disaster assistance to state and local governments—including the PA grant program. FEMA’s Public Assistance Program and Policy Guide describes the mission of the program as to “provide assistance to State, local, Territo-rial, or Tribal, and local [sic] (SLTT) governments, and certain types of private non-profit (PNP) organizations so that communities can quickly respond to and recover from major disasters or emergencies declared by the President.”10 Established through the Robert T. Stafford Disaster Relief and Emergency Assistance Act, the PA program is one of the most important federal programs supporting disaster response and recov-ery and FEMA’s largest.11 Between FY 1999 and the third quarter of 2019, the agency obligated approximately $89 billion through the program.12

Two broad types of activities receive funding through the PA program: emergency work and permanent work. Emergency work is an activity that addresses an immedi-ate threat, while permanent work is an activity that restores a damaged facility. FEMA separates these two types of work into seven categories—categories A through G—two of which fall under emergency work and five of which fall under permanent work. Table 2.1 identifies these seven categories.

In the categories of permanent work, FEMA obligates the largest share of fund-ing to PA category E, BCVE. Between FY 1999 and the third quarter of 2019, PA category E received $19 billion in total obligations—22 percent of the total. Table 2.2 provides the percentage of PA obligations by category of work.

FEMA awards PA funding directly to state, tribal, and territorial governments (applicants). These entities may then pass this funding on to subapplicants—which include the approximately 90,000 local governments in the United States.13 According

9 GAO, 2019e, p. 5.10 FEMA, 2020a, p. 17.11 GAO, 2017, p. 2; Pub. L. 93-288, 1974, as amended; 42 U.S.C. § 5121 et seq.12 FEMA, undated.13 The local government figure comes from Hogue, 2013, p. 1.


Table 2.1Categories of Public Assistance Work

Category Description

Emergency work

A Debris removal

B Emergency protective measures

Permanent work

C Roads and bridges

D Water control facilities

E BCVE

F Utilities

G Parks, recreational, and other facilities

Table 2.2Public Assistance Grant Program Obligations, by Category of Work, Fiscal Year 1999 Through the Third Quarter of 2019

CategoryTotal Obligations,

in Billions of Dollars Percentage of Total

Emergency work

A. Debris removal 14 16

B. Emergency protective measures 24 27

Permanent work

C. Roads and bridges 9 10

D. Water control facilities 1 1

E. BCVE 19 22

F. Utilities 12 14

G. Parks, recreational, and other facilities 4 5

Recipient management costsa 5 6

Total 89 100

SOURCE: FEMA, undated.a FEMA allows recipients and subrecipients of PA funding to use a portion of their funding for indirect expenses, administrative expenses, and other expenses for managing a PA award that they cannot charge to a specific project. FEMA refers to these costs as category Z. See FEMA, 2020a, p. 147.

Background 11

to one estimate, these local governments together own more than 3 million buildings.14 PNPs that provide critical or essential social services are also eligible to be subappli-cants. These can include such entities as universities, hospitals, and houses of worship.15

Public Assistance Award Process

The issuance of PA funding begins with a presidential disaster declaration. Once the President has issued a declaration for a specific geographic area—such as a county, city, or Indian tribal government—state, tribal, and territorial governments work with FEMA to assess the damage and identify projects eligible for assistance through the PA program. FEMA conducts an extensive review of proposed projects to ensure that the applicant, facility, work, and costs are eligible for funding.16 For example, the Staf-ford Act generally prohibits the duplication of benefits.17 If an applicant has received funding for a project through another source, FEMA must deny, reduce, or deobligate funding for that project. In the case of insurance, for instance, if an applicant has received—or anticipates receiving—proceeds for losses, FEMA deducts that amount from the repair costs, and the remainder is the project amount.18 After FEMA approves a project, PA essentially functions as a reimbursement program: The agency obligates funding to the applicant, which is a state, tribal, or territorial government. The appli-cant can then provide funding to the subapplicant—local government or certain PNP organizations—for projects they have completed.19

Risk sharing is a key element of the program. As a condition of receiving PA, the recipient is responsible for a cost share under which the recipient pays 25 percent of the project amount and FEMA pays the remaining 75 percent. The President, through FEMA, has the discretion to increase the federal cost share to as much as 90 percent for permanent work and more for emergency work.20 States administer this cost share in a variety of ways, differing in the extent to which localities are responsible for part

14 Littman, 2017.15 FEMA, 2020a, pp. 10–13. The risk-management behaviors of PNPs are not addressed in this report.16 As discussed later in this chapter, a project can involve repairs at multiple, separately identified facilities (FEMA, 2020a, p. 9).17 See 42 U.S.C. § 5155. Under some conditions, the President may waive this prohibition.18 FEMA, 2020a, p. 39-40; 44 C.F.R. § 206.253(a).19 FEMA uses applicant to refer to the states that formally apply for PA assistance and subapplicant for the coun-ties, cities, and other public entities that apply through the state. For simplicity, we use applicant to refer to all these entities in the remainder of this report. In 2013, the Sandy Recovery Improvement Act (Pub. L. 113-2) amended the Stafford Act to create an additional process for providing PA funding. Under these alternative pro-cedures, recipients can receive PA funding based on fixed-cost estimates rather than the actual costs of completed projects. If the project costs exceed the estimate, the recipient is responsible for them. But if the costs fall below the estimate, the recipient can use the remaining funds for mitigation efforts. See GAO, 2019a, pp. 9–10.20 FEMA, 2020a, pp. 3–4. For example, in the wake of 2017 hurricane season, FEMA extended a 90-percent cost share to Puerto Rico for projects doing permanent work.


of it. According to the National Emergency Management Association, six states fund the entire 25 percent, while 42 states pay a portion of the match.21 In cases in which the cost share is split between states and localities, the split takes different forms. For example, in New Jersey, responsibility for the cost share is determined on a case-by-case basis, while, in Pennsylvania, localities are responsible for the cost share for snow-related events but the state takes on the full cost share in all other cases.22

Requirement to Obtain and Maintain Insurance

The Stafford Act states that it is Congress’s intent to encourage, through the act, “indi-viduals, States, and local governments to protect themselves by obtaining insurance coverage to supplement or replace governmental assistance.”23 In support of this, the Stafford Act requires that, as a condition of receiving PA funding, an applicant must obtain and maintain insurance for a damaged facility for the life of the facility to protect against future losses.24 Specifically, the applicant must insure such a facility with the “types and extent” of insurance that are “reasonably available, adequate, and necessary.”25 If an applicant does not meet the O&M requirement, FEMA denies or deobligates funds for the immediate disaster, and the facility will be permanently ineli-gible for PA funding in a future disaster.26 FEMA applies the O&M requirement only to BCVE because it has concluded that insurance is not readily available for other types of public facilities.

FEMA administers the O&M requirement through insurance reviews through-out the project approval process. Through these reviews, FEMA determines whether an applicant previously received PA for a damaged facility and, if so, whether the applicant has complied with its insurance requirements. For applicants that have not received PA previously, FEMA provides a notification outlining the type and extent of insurance required. Concerning insurance type, FEMA requires an applicant to obtain insurance against the hazard that caused the damage—an earthquake, for example. If multiple hazards caused the damage, the applicant must insure against each of them.27 To determine the extent of insurance required, FEMA calculates the estimated or actual costs eligible for PA funding as a result of that disaster prior to any reductions, such as from insurance proceeds.28

21 National Emergency Management Association, 2016, p. 10.22 Pew Charitable Trusts, 2018, p. 13.23 Pub. L. 93-288, 1974, as amended; 42 U.S.C. § 5121 et seq.24 42 U.S.C. § 5154.25 42 U.S.C. § 5154.26 FEMA, 2020a, p. 87.27 FEMA, 2020a, p. 86; FEMA, 2015, p. 4.28 FEMA, 2015, p. 3.

Background 13

Once FEMA has determined the type and extent of insurance an applicant requires, the applicant has discretion to choose the policy it finds most appropriate. An applicant has broad discretion in choosing the size of the deductible, and the policy can cover losses that PA does not. Further, applicants have the ability to choose the form of coverage they want—such as a blanket policy or an insurance pool.29 In most cases, an applicant can also opt to employ a self-insurance plan to meet the required insurance type and extent. These self-insurance plans must meet several criteria. For example, they must describe the financial arrangements used to fund the plan and include a formalized system for collecting contributions and paying losses. FEMA reviews these plans to ensure compliance.30

Under some circumstances, FEMA can modify insurance requirements for appli-cants. For example, an applicant may request that FEMA modify its requirements if the applicant has tried to comply but found that such insurance is unavailable, an alternative provides adequate protection, or the required insurance is not necessary.31 Similarly, FEMA will not require greater types or extent of insurance than the relevant state insurance commissioner deems reasonably available, adequate, or necessary, and the agency will modify an applicant’s insurance requirement based on a state insurance commissioner’s certification of these factors.32 This policy provides insurance com-missioners with an important role in shaping O&M requirements because they can significantly reduce or even eliminate such requirements based on this certification. In Chapter Five, we discuss instances in which this has occurred.

FEMA does not approve assistance until an applicant has documented its com-pliance with the O&M requirement through proof of insurance. In a case in which the applicant has not obtained insurance before PA approval, the applicant can pro-vide FEMA with a letter of commitment stating its intention to meet the requirement and submit proof of insurance once the applicant has it.33 Prior to grant closeout—the point at which recipients and subrecipients have completed all grant work and FEMA has completed all administrative actions—FEMA and the recipient verify compliance with the O&M requirement.34

In making its PA award, FEMA deducts any insurance proceeds from the fund-ing issued. The first time a facility has been submitted to the PA program, FEMA deducts the anticipated insurance proceeds (which means that it, in effect, pays the deductible) and then makes a final reduction to match the actual amount of insur-

29 FEMA, 2017b, pp. 5-103–5-104.30 FEMA, 2015, p. 5.31 FEMA, 2015, p. 5.32 FEMA, 2015, pp. 6–7.33 FEMA, 2015, p. 7.34 FEMA, 2013, p. 3; FEMA, 2015, p. 5.


ance.35 For a facility subject to an O&M requirement because of PA in a previous disaster, FEMA deducts either the amount of insurance in the O&M requirement or the actual insurance proceeds for eligible losses, whichever is higher.36 FEMA also does not pay the deductible in these instances.37 FEMA thus does not pay a second time for losses at a facility up to the O&M requirement from the previous event, regardless of whether the applicant actually purchased insurance.

Definition and Measurement of Insurance Share

The concept of insurance share is a key part of our analysis throughout this report. In this section, we define insurance share and discuss the implications of our interpreta-tion. We then discuss issues that arise when evaluating insurance share using FEMA’s data on PA expenditures. The section concludes with an overview of data that underlie the analysis in Chapters Three and Four.

Definition of Insurance Share

Insurance policies are typically structured with a deductible and policy limit. As shown in the left panel of Figure 2.1, insurance payment is 0 until the deductible is reached

35 FEMA, 2017b, p. 5-107.36 FEMA, 2013, p. 87; FEMA, 2017b, p. 5-107.37 FEMA, 2015, p. 9.

Figure 2.1Typical Relationship Between Insurance Share and Repair Costs

NOTE: Our definition of insurance share includes payments by insurers and other insurance-related reductions that FEMA makes.

Repair cost, in dollars Repair cost, in dollars

Insu

ran

ce p

aym

ent,

in d

olla

rs

Insu

ran

ce s

har

e, a

s a

per

cen

tag

e

Share paidby PA

PolicylimitDeductible

PolicylimitDeductible

100

0

Background 15

and then increases one-to-one with repair costs until repair costs reach the policy limit. The resulting ratio of insurance payments to repair cost—the insurance share—is shown in the right panel of Figure 2.1. The share begins as 0, increases once repair costs exceed the deductible, and then declines once repair costs exceed the policy limit. For example, if repair costs were $500,000 and the public entity received $200,000 from the insurer, the insurance share would be 40 percent. The insurance share for a given repair cost can vary from 0 (when no insurance is available) to close to 100 per-cent (when the deductible is small and repair costs do not exceed the policy limit).

So far, we have presented insurance from the point of view of a public entity holding the insurance. We now turn to FEMA’s perspective. When the PA program pays the deductible (as it does for first-time losses), the portion of repair cost covered by PA is the difference between the blue curve in the right panel of Figure 2.1 and the dotted line at 100 percent. If FEMA pays losses over the policy limit but does not pay the deductible (as would be the case for insurance bought in order to comply with the O&M requirement), the portion of loss paid by FEMA would start at 0 and remain at 0 until the policy limit is reached. It would then increase at a decreasing rate as repair cost increases above the policy limit.

The definition of insurance share we use in this study includes some repair costs for which the applicant does not receive an insurance payment but for which FEMA will not pay. For example, insurance share includes the deductible when coverage is mandated by the O&M requirement because the PA program will not cover the deductible in these circumstances. Insurance share also includes required reductions for flood-related damage in a special flood hazard area (SFHA) up to the coverage limits provided by the NFIP regardless of whether the applicant purchased flood cov-erage or how much was purchased. However, as shown in Appendix B, the magnitude of these adjustments recorded in EMMIE is small.38 Insurance share as used here in effect identifies the portion of the repair costs that PA will not pay, prior to the appli-cation of the state cost share. It reflects the amount of insurance the applicant had or should have had on the damaged property according to PA policy.

Our definition of insurance captures only the formal or observed insurance held by public agencies—which becomes manifest in insurance reimbursement for loss. It does not include rainy-day funds or other types of resources that some agencies, par-ticularly larger ones, may bring to bear in a disaster. Such a definition is relevant to projecting PA program outlays and assumes, in effect, that the PA applicant has no unobserved or hidden insurance. However, it may be the case that some entities have additional resources that function very much like insurance. We return later to the potential for unobserved insurance.

38 The extent to which such reductions are identified in EMMIE (as opposed to being lumped together with actual insurance payments) depends both on the extent to which such reductions are first enforced and on the extent to which they are entered with the correct cost codes in EMMIE. It could be that these types of reductions are larger than can be detected in EMMIE.


Measurement of Insurance Share

FEMA uses the EMMIE system primarily to process PA grant applications from state, local, and tribal governments and certain PNP entities.39 In August 2019, the Recovery Reporting Analytics Division (RRAD) in FEMA’s Office of Response and Recovery provided the Homeland Security Operational Analysis Center with detailed EMMIE data on PA category E projects. The data cover PA projects for which federal dollars have been obligated and include projects for disaster declarations between late 2007 through April 2019. Because the number of entries in 2007 was so small, we restricted our attention to PA projects for disaster declarations that occurred on or after Janu-ary 1, 2008. To reduce the number of projects with very preliminary or incomplete cost estimates, we excluded projects for declarations occurring after June 30, 2018.

We used EMMIE to provide insight into the type and amount of insurance on BCVE owned by public entities eligible for PA. Some caveats are needed in using the EMMIE data for this purpose. To characterize insurance practices, one would ideally want data on a random sample of communities and structures across the country. The EMMIE data differ from this ideal in several ways:

• First, EMMIE provides data on BCVE only in communities for which major disasters were declared during the study period. These communities are presum-ably at higher risk of natural disaster than a random sample of all communities across the country. Further work is needed to compare the risk profile of commu-nities that have been included in a disaster declaration with those that have not. To the extent that a community that has been included in a disaster declaration is at higher risk than one that has not, it would be appropriate to characterize find-ings from the EMMIE data as describing the situation in the higher-risk parts of the United States.

• Second, EMMIE does not necessarily provide a random sample of BCVE in com-munities that have been affected by major disasters. FEMA generates EMMIE data only for damaged BCVE for which PA applications have been submitted. For example, it could be that applicants do not submit PA applications for properties that have substantial insurance. Our interviews with PA regional staff indicated that FEMA encourages applicants to submit all damaged buildings to the PA program regardless of insurance, mitigating this potential bias. Applicants have an incentive to do so because FEMA uses the total amount of damage, before reduction due to any insurance payments, to determine the amount of mitigation assistance for which a community is eligible. FEMA staff believe that applicants typically include all damaged BCVE in PA applications, but we heard of excep-tions during the course of our investigations. Further investigation into the sub-mission practices of PA applicants is warranted. The insurance shares reported

39 U.S. Department of Homeland Security (DHS), 2018, p. 5.

Background 17

here are low to the extent that applicants do not submit damaged buildings with high levels of insurance to PA.

• Finally, BCVE damage (and thus the likelihood that the BCVE damage appears in the EMMIE database) may be correlated with the amount of insurance in place on the building. For example, an applicant might buy more insurance on a building in a riskier part of the community. But because insurance is more expen-sive on buildings at greater risk, an applicant might alternatively buy less insur-ance on a risky building. Thus, the direction of a potential bias in estimates of the amount of insurance is not obvious. In addition, as discussed in Chapter Three, communities frequently buy blanket policies that provide insurance coverage on all their structures, thus mitigating concern that only the more (or less) risky buildings are insured.

Repairs at multiple separate facilities can be bundled together into a single PA project, but FEMA reports cost and insurance information in EMMIE only at the project level. Even though costs are available at the project level, we chose applicant–declaration pair as our unit of analysis. An applicant–declaration pair combines all the PA category E projects for a particular applicant in a given disaster declaration. We chose applicant–declaration pair as the unit of analysis because applicants often have blanket insurance policies that cover all buildings, vehicles, or equipment (or some combination of these) in their jurisdictions. It therefore does not make sense to apply the insurance deductible or the policy cap to an individual project or facility. In fact, one FEMA regional staff member in the PA program indicated that FEMA typically applies the deductible to the largest loss for a particular disaster in order to avoid the time and expense that would be involved in allocating it across projects or facilities.

As shown in Table 2.3, we have identified 11,561 applicant–declaration pairs for disasters declared between January 1, 2008, and June 30, 2018. Although most appli-cants applied for PA assistance in only one disaster declaration during this period, some applied for assistance in multiple declarations. Consequently, just over 8,900 unique applicants appear in the database, with applications submitted in 500 unique disaster declarations.40 There are nearly 40,000 projects in the database, amounting to 3.4 proj-ects per applicant–declaration pair on average.41 Projects, in turn, can involve repair or replacement of one or more facilities, and there are 1.6 facilities per project on average. A facility might be a city hall, a university building, a vehicle, or any of various types of equipment. Figure 2.2 plots the approximately 64,500 project–facility pairs appearing

40 A single disaster can result in multiple disaster declarations. A separate declaration is typically issued for each state affected by an event. For example, Hurricane Sandy resulted in separate declarations in ten states (Connecti-cut, Delaware, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and West Virginia) and the District of Columbia that resulted in category E applications.41 Each project-level record is also referred to as a project worksheet.


in the database. Definitions of the cost components in the bottom part of Table 2.3 and the statistical techniques used to analyze the data can be found in Appendix B.

Table 2.3Overview of Public Assistance to State and Local Government for Buildings, Contents Vehicles, and Equipment (for Disaster Declaration Dates January 1, 2008, to June 30, 2018)

Feature Value

Applicants, declarations, and facilities

Applicant–declaration pairs 11,561

Unique applicants 8,909

Unique disaster declarations 500

Projects 39,168

Facility–project pairs 64,573

Cost component, in billions of dollars

Repair costs 9.39

Insurance reductions 2.65

Mitigation costs 2.40

Administrative costs 0.21

Project amount 9.36

Background 19

Figure 2.2Locations of Buildings, Contents, Vehicles, and Equipment for Which State and Local Governments Have Applied for Public Assistance

21

CHAPTER THREE

Insurance on Public Buildings, Contents, Vehicles, and Equipment

We begin this chapter by describing the risk-management tools that state and local governments use for BCVE. We then present findings on the insurance take-up rate and the share of BCVE repair costs covered by public entities’ insurance. In this chap-ter, we document the large variation in take-up rates and insurance share across public entities but do not examine the reasons for that variation. We present our analysis of the effects of incident and public-entity attributes on insurance share in Chapter Four.

Risk-Management Tools Used by State and Local Governments

State and local governments employ a wide variety of risk-management tools for BCVE. Pew Charitable Trusts, for example, found that at least 42 states and the Dis-trict of Columbia use insurance to protect real estate, infrastructure, and other assets from disasters.1 In this section, we discuss several types of risk-management tools that states and localities employ, such as the following:

• insurance pools• commercial indemnity policies• NFIP policies• self-insurance• mitigation.

An insurance pool is an intergovernmental local membership group, joint power authority, or mutual or reciprocal insurance company owned by the qualifying public entities that agree to join the pool. More than 400 such pools exist in North America, and, of the 90,000 public entities in the region, approximately 80 percent of them rely on pools for risk management.2 Examples include the Iowa Communities Assurance

1 Pew Charitable Trusts, 2020, p. 22.2 Guy Carpenter, 2019. Similarly, one industry magazine reported that “approximately 80% of cities, counties, schools and special districts in the U.S. access insurance coverage or services through pools” (Connelly, 2015).


Pool and the California Joint Powers Insurance Authority. Pools typically consist of entities of similar types (e.g., counties or school districts) and of similar size. According to one of the experts with whom we conferred, approximately 60 percent of Iowa com-munities rely on the pool to insure their public buildings.

Pool members pay premiums to the pool and typically have some latitude in selecting the deductible and policy limit that best fits their individual circumstances. Pools typically retain part of the risk themselves and buy commercial insurance poli-cies in excess of the pool retention. For example, a pool might retain the first $5 mil-lion in losses above the member deductible and buy a commercial insurance policy for losses in excess of $5 million up to a cap. An insurance broker will typically advise the pool and assist in placing the pool’s coverage.

Larger public entities will frequently buy indemnity policies directly from private-sector insurers with the support of an insurance agent or broker.3 A larger entity might do so because it prefers to more directly manage its risk or because pools often do not want to include a member whose risk exposures will dominate the pool.

Insurance is also available for particular peril types from various public and quasi-public agencies. For example, the NFIP provides coverage for flood-related losses. As we discuss in Chapter Four, private insurers typically provide coverage for commercial and public properties in high-risk flood areas in excess of the amount available from the NFIP. The NFIP provides up to $500,000 in structure coverage and $500,000 in content coverage per structure for commercial and public properties. The NFIP does not provide coverage for vehicles or for equipment that is not considered building contents.

Property coverage provided by pools or the private market often takes the form of a blanket policy. A blanket policy provides coverage for a specified set of proper-ties, usually with a cap on the aggregate payment across all properties per event. For example, Boston, Massachusetts, has obtained an all-risk blanket property insurance policy for its buildings. The covered peril types include fire, terrorism, earthquake, flood, and wind.4 A blanket policy can include a schedule of values for the properties that the policy covers. A pool policy might cap payments for an individual structure at the scheduled value of the structure (subject to the overall policy limit) or pay up to the policy cap regardless of the scheduled value.

Self-insurance can come in many flavors, from arrangements that look similar to traditional insurance contracts to what amounts to no insurance at all. Accord-ing to the Pew Charitable Trusts, at least 36 states and the District of Columbia use self-insurance for state-owned properties.5 Larger entities sometimes set up formal

3 An indemnity policy insures a policyholder against the occurrence of an event by paying for a portion of the actual losses.4 City of Boston, 2019.5 Pew Charitable Trusts, 2020, p. 23.

Insurance on Public Buildings, Contents, Vehicles, and Equipment 23

self-insurance programs. These formal self-insurance arrangements require regular contributions to the plan and have guidelines for paying losses for a specified set of properties.6 Some public entities also set up more-informal forms of self-insurance, such as dedicated accounts or rainy-day funds. These funds would cover losses that occur, and separate funds might be set up for different types of perils.7 As discussed in Chapter Two, self-insurance can also refer to situations in which the community has no ex ante mechanism to finance losses, and some entities—more frequently, the largest states and local governments—take this approach. FEMA is not well equipped to reduce PA to account for self-insurance. It might be able to detect and account for proceeds from formal self-insurance plans, but it currently does not have a way to adjust PA expenditures to account for informal self-insurance strategies. As dis-cussed in Chapter Two, our measure of insurance share does not capture these types of self-insurance.

Parametric insurance has become increasingly available to state and local govern-ments as the insurance industry has expanded its offerings of parametric products.8 In contrast to traditional indemnity insurance, which provides a policyholder compensa-tion for a portion of their losses, parametric insurance insures a policyholder against an event by providing compensation based on an event’s magnitude, which is known as the trigger. From our discussions with insurance brokers, it is clear that there is much interest from state and local governments in parametric policies. However, few para-metric policies have been placed with state or local governments to date. Appendix D provides more detail on parametric policies.

Finally, another risk-management tool that state and local governments employ is mitigation—that is, actions taken before a disaster designed to reduce the impact of that disaster. Mitigation includes a broad array of activities—from adopting stricter building codes to creating tornado-safe rooms to managing vegetation to decrease wildfire risk. Public-sector organizations often adopt mitigation measures not only to reduce property damage and loss of life but also to save money. In a 2017 study, the National Institute of Building Sciences found that, for every $1 the federal government invests in mitigation, taxpayers save approximately $6 in future spending.9

6 FEMA, 2015, p. 5. Self-insurance is used in a variety of ways by the various parties with whom we spoke. Formal self-insurance should be distinguished from less formal arrangements that can also be referred to as self-insurance.7 Entities that purchase insurance might use such funds to cover the insurance premium, the deductible, or losses over the policy cap.8 See, for example, Flavelle and Chiglinsky, 2019, or Ward, 2019.9 National Institute of Building Sciences, 2017, p. 1.


Insurance Take-Up Rate and Insurance Share for Buildings, Contents, Vehicles, and Equipment

The data available in EMMIE do not allow precise measurement of the percentage of state and local government entities with property insurance, but they at least suggest that the proportion is substantial. One indicator of insurance is the presence of insur-ance reductions—that is, instances in which FEMA deducts insurance proceeds from the overall cost of repair. As shown in the first row of Table 3.1, 68 percent of applicant–declaration pairs contain insurance reductions.10 A limitation of this indicator is that an applicant might have insurance but incur a loss below the deductible amount, so the applicant receives no insurance payment. This indicator may thus understate the per-centage of applicants that have insurance. Conversely, insurance reductions may reflect reductions for insurance that the applicant should have had but did not. For example, for a building in an SFHA, assistance will be reduced by the amount of coverage the entity could have purchased from the NFIP, regardless of whether it has insurance or not. Consequently, this indicator of insurance may overstate the percentage of appli-cants that have insurance.

The PA data also provide some information on the insurance policies held by applicants, indicating that 59 percent of state and local governments applying to repair or replace BCVE reported having some type of policy potentially relevant to the loss (Table 3.1).11

10 An applicant–declaration pair combines all the category E projects for a particular applicant in a given disas-ter declaration. As explained in Chapter Two, we chose applicant–declaration pair as the unit of analysis because applicants often have blanket insurance policies that cover all the facilities in their jurisdictions. There is thus no good way to allocate deductibles and policy caps to individual projects.11 Presumably, an applicant will report an insurance policy that is relevant to the loss. For example, an applicant would not report a liability policy for a property loss. But it could turn out that some of the policies reported do not provide coverage for the particular loss.

Table 3.1Insurance Take-Up Rate for Disaster Declarations Between January 2008 and June 2018

Indicator of Insurance Take-UpPercentage of PA

Applicants with Insurance

Insurance reductions 68

Reported insurance policies 59

Either insurance reductions or reported insurance policies 72

NOTE: N = 11,114 applicant–declaration pairs with $100 or more in repair costs.


This measure also has drawbacks. Insurance information might not be entered if the costs incurred fall below the deductible.12 This measure of insurance take-up will thus understate take-up rates to the extent that this is the case.

As shown in the last row of Table 3.1, the percentage of applicant–declaration pairs rises to 72 percent when either insurance reductions or the reported insurance policies are used to indicate that the applicant has insurance. Given the limitations of the data, it is not possible to develop a precise estimate of the percentage of state and local governments with property coverage for BCVE. The percentage with coverage is likely at least 59 percent but may well be considerably higher.

Even if a state or local government has insurance, the payout may be minimal—meaning that it does not cover a substantial proportion of BCVE losses. In this sense, the “quality” of insurance, as termed by one expert, is low. The policy, for example, might not cover the peril that caused the loss, or the amount of coverage might be lim-ited. For applicant–declaration pairs between January 2008 and June 2018, the average insurance share was 46 percent (Table 3.2). As we discuss next, the insurance share is much lower for the larger losses. Consequently, the aggregate insurance share for all BCVE repair costs (28 percent) is considerably lower than the simple average.

As discussed in Appendix B, it is possible that PA applicants do not submit dam-aged facilities to PA for which they have insurance coverage or that they can cover with internal funds. FEMA PA staff encourage applicants to submit all damaged facilities to PA and believe that applicants typically do so. However, we know that there are cases in which this does not occur. The share of damage covered by insurance will thus be higher than found here to the extent that this is the case. Data are not currently avail-able to estimate the potential magnitude of this effect.

The overall averages in Table 3.2 hide considerable variation in insurance share across applicant–declaration pairs. As can be seen in Figure 3.1, just over 30 percent

12 There is some evidence that information on insurance policies is not entered when the cost incurred falls below the deductible amount. We found that the percentage of applicant–declaration pairs for which information on insurance policies has been entered rises steadily from 46 percent (when repair costs are less than $10,000) to 94 percent (when repair costs are greater than or equal to $10 million).

Table 3.2Average and Aggregate Insurance Shares for Disaster Declarations Made Between January 2008 and June 2018

Insurance Share Insurance Share, as a Percentage

Average 46

Aggregatea 28

a N = 11,114 observations with repair costs of $100 or more.


of the applicant–declaration pairs have insurance shares of 0, meaning that there were no insurance reductions made from the PA award. At the other end of the spectrum, the insurance share is 80 percent or higher for approximately 30 percent of the pairs. The roughly 40 percent that remains fall somewhere in between. The wide variation in insurance share underscores the diversity of risk-management strategies across state and local governments—and the challenges of comprehensively characterizing them.

There are several potential explanations for the substantial proportion of applicant–declaration pairs with low insurance shares:

• The state or local government may choose a large deductible. The PA program pays this deductible unless the structure is subject to an O&M requirement, thus reducing the insurance share. Our investigation found that deductibles can be very large. In some cases (such as for wind and earthquake coverage), the deduct-ible can be 5 to 15 percent of insured value. The deductible can be specified as a dollar amount and can range from $5,000 to $1 million, and $100,000 to $250,000 deductibles are not uncommon.

• The state or local government may choose a policy limit that does not provide coverage adequate to repair damage from large events. The entity may be unwill-ing to pay the costs of purchasing coverage adequate to cover repairs in the largest events or, as discussed in Chapter Five, may expect FEMA to cover losses from large events.

Figure 3.1Distribution of Insurance Share

NOTE: N = 11,114 observations with repair costs of $100 or more.

0

5

10

15

20

25

30

35

0 1–19 20–39 40–59 60–79 80–99 100

Insurance share, as a percentage

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• The state or local government may not keep estimates of building replacement costs up to date, thus selecting a policy limit that is inadequate for covering the damage. State and local governments may avoid updating building values to keep insurance premiums lower than they would be otherwise.

• The insurance pool or self-insurance facility providing coverage can become insolvent in large events, resulting in prorated claim payments. Such insolvencies, however, appear to be rare.

Insurance share differs substantially by the type and size of a jurisdiction and the magnitude of repair costs. Insurance share is markedly lower for states than for other jurisdiction types and is considerably lower for large jurisdictions (see Table 3.3). Insur-ance share is also much lower when the damage is large. As shown in the bottom part of Table 3.3, insurance share averages 51 percent for the large number of applicant–declaration pairs with between $10,000 and $100,000 in repair costs and 29 percent for the small number of pairs with repair costs greater than or equal to $100 million. In Chapter Four, we investigate the factors that drive these differences.

Discussion

Expert experience and the data on PA applicants suggest that insurance for BCVE appears to be common among public entities. However, available data do not allow precise esti-mates of the take-up rate. Even though insurance take-up appears to be widespread, approximately 30 percent of public entities that applied to the PA program for damaged BCVE had no insurance reductions made. The absence of reductions for insurance in these cases may be because the applicant had no coverage for the peril that caused the loss or for the type of BCVE damaged or because the loss did not exceed the deductible. At 28 percent, the overall percentage of PA BCVE repair costs covered by insurance is low. This low percentage is driven by a low insurance share for the largest losses.

There is considerable variation across entities in insurance share, underscoring the diversity of state and local risk-management practices, and, as we see in Chapter Four, considerable differences in risk-management practices across perils. Insurance share is much lower for larger jurisdictions, but the tabulations in this chapter do not isolate the effect of particular incident or public-entity attributes while holding others con-stant. We turn to this topic in Chapter Four.


Table 3.3Average Insurance Share, by Jurisdiction Type and Size and Repair Cost

Jurisdiction Type and Size Observations Percentage of TotalAverage Insurance Share,

as a Percentagea

Jurisdiction type

City or township 5,248 47 44

County 1,494 13 47

State 238 2 27

Public school 1,939 17 52

Public higher education 676 6 41

Special districtb 1,519 13 45

Total 11,114 100 46

Jurisdiction size (population)

<5,000 2,111 30 47

5,000 to 10,000 903 13 48

10,000 to 50,000 2,246 32 48

50,000 to 100,000 601 9 38

100,000 to 1 million 706 10 31

1 million to 5 million 159 2 27

≥5 million 126 2 26

Missing 128 2 39

Total 6,980 100 46

Repair cost, in dollars

<10,000 4,120 37 34

10,000 to 100,000 4,625 42 51

100,000 to 1 million 1,803 16 56



>100 million 8 <0.5 29

Total 11,114 100 46

NOTE: Insurance share here is the simple average of insurance share for the applicant–declaration pairs in each category. In contrast, the predicted insurance shares in Chapter Four are calculated by setting one incident or applicant attribute to a particular value and holding the other attributes constant at values observed in the data. Because of rounding, totals might not sum precisely.a Applicant–declaration pairs with repair costs of $100 or more.b Examples of special districts include water districts, library districts, housing authorities, fire districts, drainage districts, regional airports, hospital districts, and utility authorities.

29

CHAPTER FOUR

Drivers of Insurance Share for Buildings, Contents, Vehicles, and Equipment

Chapter Three highlighted the substantial differences in insurance share across public entities that apply for PA to repair or replace damaged BCVE. In this chapter, we investigate the factors that drive this variation. Observations from the experts with whom we conferred, responses to a federal RFI (posted by the DHS Science and Tech-nology Directorate for FEMA), and statistical analysis of EMMIE data on PA expendi-tures inform our conclusions. In contrast to Chapter Three—which reported observed insurance share—for this chapter, we used regression analysis to isolate the effect of one factor from others. The construction of the incident and public-entity attributes and the regression models are detailed in Appendix B. In those models, insurance share is the outcome that is being explained (dependent variable). The explanatory, or inde-pendent, variables are the incident and public-entity attributes.

We restricted our attention to cities, counties, and states because we found that jurisdiction size is an important driver of behavior, and we have not yet developed size metrics for public schools, public higher education, or special districts. Specifically, we examined the effect that the following incident and public-entity attributes have on insurance share:

• incident attributes – size of loss per capita – type of incident (e.g., hurricane) and peril causing loss (e.g., wind, flood) – proportion of loss inside the SFHA – type of property damaged in event (BCVE)

• public-entity attributes – jurisdiction size – frequency of prior disaster declarations.

Relationship Between Insurance Share and Size of Loss

As demonstrated in Chapter Two, deductibles and policy caps produce an insurance share that is expected to initially increase as the size of the loss increases and then grad-


ually fall. To investigate whether this pattern exists in the data on PA expenditures for BCVE, we separated repair cost per capita into six bins and report on the relationship between insurance share and repair cost per capita, holding other applicant and inci-dent attributes constant. We used repair cost per capita to compare repair costs across jurisdictions that vary greatly in size.

Figure 4.1 shows the predicted insurance share for each repair cost category when other applicant and incident attributes are held at their observed values.1 As can be seen, insurance share is predicted to be less than 20 percent when repair costs are less than $0.25 per capita but nearly 60 percent for the relatively small number of applicant–declaration pairs with repair costs greater than or equal to $200 per capita.2 These large increases are consistent with a deductible that FEMA covers. We did not observe a subsequent drop-off in insurance share, as appears in Figure 2.1 in Chap-ter Two. Such a decline might exist for higher repair costs, but the sample sizes are not adequate to isolate even larger repair costs per capita.)

1 For example, to calculate the predicted insurance share when repair cost is between $1 and $10 per capita, the repair cost category for each applicant–declaration pair in the analysis data set was set to $1 to $10 per capita, and the other variables remained at the values recorded for that observation. The regression model was then used to predict insurance share for each applicant–declaration pair and the results averaged.2 See Table B.2 in Appendix B for the number of applicant–declaration pairs in each repair cost category.

Figure 4.1Predicted Insurance Share, by Repair Cost per Capita, Holding Other Applicant and Incident Attributes Constant

0

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70

<0.25 0.25–1 1–10 10–50 50–200 ≥200

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Repair cost per capita, in dollars

NOTE: For cities, counties, and states.

95% confidence intervalfor the estimates

Drivers of Insurance Share for Buildings, Contents, Vehicles, and Equipment 31

Difference in Insurance Share, by Incident Type and Peril Type

The EMMIE database captures the incident that precipitated the damage, such as a tropical storm, and the peril type, which could be wind or flood. The information we collected and the experts with whom we conferred indicate that insurance coverage for states and localities varies importantly by the type of peril. Coverage for damage caused by earthquake or flood is typically more limited than that for other peril types. Coverage for named storms (which covers wind and sometimes the associated flood damage) can be less than for the base “all-risk” policy (which covers fire and other perils that are not explicitly excluded). The City of Boston’s description of its insurance coverage illustrates such a situation:

The all-risk policy provides aggregate policy limits of $300 million for Fire and Terrorism, $125 million for Earthquake, and $100 million for Wind. Flood is capped at $125 million for all buildings with a $20 million sublimit for all exposed locations in FEMA 100 and 500 year zones, and [a] $5 million cap on one specific building located close to the coastline. The all-risk policy has a $10 million aggre-gate self-retention; once total losses reach $10 million in a single year, the retention subsequently drops to $1 million for the remainder of the year.3

In this section, we first summarize the views of experts familiar with the risk-management practices of public entities on the market conditions and risk-management practices for fire, wind, flood, earthquake, and convective storms. We then turn to the variation in insurance share among PA applicants across peril types.

Expert Perspectives on Insurance Practices, by Peril TypeFire

With few exceptions, property policies cover losses due to fire regardless of cause, and public entities that buy coverage will typically be insured for close to replacement value. Deductibles, also referred to as retentions, can be very low (down to several thousand dollars for some insured entities). Given the recent uptick in wildfire losses, insurers are paying attention to their fire exposures. There are no sublimits on fire poli-cies yet, but such limits might develop for areas exposed to wildfire risk.4

Wind

Property policies typically cover losses due to wind, although coverage may be more limited for named storms. Coverage for named storms is widespread. But the sublimit can be substantially less than the overall policy limit, and deductibles typically range

3 City of Boston, 2019.4 A sublimit is a limitation in an insurance policy on the coverage available for a particular type of loss.


from 5 to 10 percent of insured value. Named-storm deductibles and sublimits provide public entities with options for reducing costs.

Even in areas subject to hurricanes and tropical storms, wind coverage appears readily available—in part, because insurers have confidence in the models developed to predict expected and probable maximum losses due to wind events. Finding cover-age at premiums that public entities are willing to pay, however, can be difficult. This challenge likely grows after the largest events because insurers often try to reduce their exposure and raise prices after such events.5 Modification of O&M requirements after large events also provides insight into market conditions following such events. Experts observed that the modification of O&M requirements is more common after very large events. For example, following Hurricane Katrina, the Louisiana insurance commis-sioner provided a disasterwide certification that a PA applicant would not be required to pay more than 3 percent of its operating budget on property insurance. Although an applicant may be able to absorb the cost of insurance for a modest number of damaged buildings in a typical disaster, purchasing insurance for a large number of buildings can create hardship. FEMA PA staff indicate that the most common reason for O&M appeals is cost rather than lack of availability.

Flood

Flood is the most difficult peril for a public entity to find coverage for. Outside SFHAs, policies often cover flood with the same coverage limits as the overall policy.6 Thus, deductibles can be low and limits close to replacement value outside SFHAs. Of course, a high deductible chosen for an all-peril policy would also apply to flood losses outside an SFHA. However, the situation outside SFHAs appears to be changing. Insurers are seeing increasing flood losses outside SFHAs, and policies with flood sublimits outside SFHAs are beginning to appear.

The situation is much different in SFHAs. Private insurers usually provide cov-erage only in excess of the $500,000 in structure coverage and $500,000 in content coverage available from the NFIP. And as illustrated by Boston’s property policy, the sublimit for flood can be considerably lower than for the overall policy.7 For public entities that buy coverage through pools, a pool also usually provides coverage only in excess of the NFIP limits. Public entities often do not take advantage of the coverage

5 These insurer responses can be driven by factors that include an upward adjustment of expected loss, increased uncertainty regarding the risk, or a relaxation of underwriting standards during a period with few events. See, for example, Gron, 1994.6 There might not be any separate sublimit for flood. The overall policy covers flood and other perils, and one coverage limit applies to both.7 Flood might be expected to affect only the lower floors of a structure, so it might not make sense to buy cov-erage up to the replacement costs of an entire building. However, insurers also understand this, and the cost of coverage for a sublimit that is, say, 50 percent of replacement value might not be that much less than that for full coverage.


available from the NFIP. This may be, in part, due to a decision to retain some risk to reduce insurance costs, but it is also in part due to the drawbacks of NFIP coverage, such as the following:

• NFIP policies provide actual cash value of the damaged property as opposed to replacement cost.

• Coverage is limited for the parts of the building below grade.• Coverage is available only for buildings and their contents, not for equipment or

vehicles.• Separate policies must be purchased for each building, which can create a sub-

stantial administrative burden for public entities with large numbers of structures.

The reduced coverage for flood is due to challenges on the supply and demand sides of the market. On the supply side, flood models are not nearly as well devel-oped and validated as wind models, causing private insurers to increase premiums or limit availability because of the substantial uncertainty over projected losses during the policy period. The limitations of the coverage offered by the NFIP also reduce the attractiveness of flood coverage for potential purchasers. On the demand side, a common refrain is that, similar to residential property owners, the individuals who make risk-management decisions for public entities underestimate the frequency and severity of flood. And, as discussed in Chapter Five, some public-sector risk managers consider the potential for FEMA PA and consequently reduce flood insurance pur-chases from what they would be without the PA program or forgo insurance entirely.

Earthquake

Earthquake insurance in the western United States is expensive, and the amount of coverage in place on public BCVE is limited. When coverage is purchased for BCVE, deductibles can be large and the sublimits low relative to insurable value. In some cases, entities buy coverage on essential facilities (e.g., police and fire stations or city hall) but not on other buildings. In other cases, entities buy coverage only when loan agreements or bond covenants require it.

Earthquake risk is reasonably well modeled, although the relative infrequency of events has offered less opportunity for validation than wind models. In spite of sophis-ticated modeling, capacity (i.e., the amount of coverage available) seems to be limited. It can be very difficult to find insurance to cover, for example, a $100 million building up to replacement cost. Part of the problem on the supply side is that, in contrast to take-up of wind coverage, low take-up reduces the ability to diversify risk, thus rais-ing premiums and reducing the number of insurers willing to offer coverage. Experts pointed to the frequent reduction or elimination of O&M requirements for earthquake-related loss as an indicator of the limited supply. Although capacity currently is limited, some experts believed that capacity would gradually respond to increased premiums and broaden take-up. The low frequency of significant earthquakes is a challenge on


the demand side of the market. Public entities along the Gulf Coast, for example, have faced major hurricanes in the past five or ten years that demonstrate the consequences of not obtaining insurance. The lack of frequent reminders of earthquake risk allows public entities in the western United States to focus on other risks or budget priorities.

Convective Storm

Coverage for tornadoes and hail associated with convective storms is often purchased with a separate deductible and sublimit similar to the situation for named windstorms. Some public entities find it increasingly difficult to obtain convective-storm coverage.

Insurance Share by Peril Type

The variation of insurance share among PA applicants by peril type is generally consis-tent with the above characterization of state and local practices for the different peril types. We first summarize our ability to characterize the cause of loss by peril type, given the data available. We then present findings on the difference in insurance share by incident type while holding applicant attributes constant.

Approach for Characterizing the Peril Causing the Loss

To analyze the insurance share by peril type, we divided incidents into seven initial categories based on the descriptions in FEMA’s disaster declaration database:

• named storms (hurricanes, typhoons, tropical storms)• storms• winter storms• fire• earthquake• flooding only• other without flooding.8

We then broke storms and winter storms into flood and nonflood events based on a search for the text string “flood” in FEMA’s incident description (see Appendix B for further detail).

This incident-type breakdown provides insight into the cause of loss for the dif-ferent declarations in the data set. The relevant peril types for hurricanes are wind and flood (see Table 4.1). For storms without flooding, the peril types are severe storms, straight-line winds, and tornadoes. Storms with flooding add flooding to the relevant peril types. Peril types in winter storms include wind, snow, and ice. The peril types for fire and earthquake are apparent. The flooding-only category includes events in which the only cause of loss identified is flooding, including tsunami waves and ice jams. Examples include riverine flooding not associated with a storm in the immedi-

8 FEMA, 2019a. The incident title in this data set was used to categorize incidents.


ate area. Few applicant–declaration pairs fall into the other-without-flooding incident-type category. Incidents in this category include explosions, volcanic eruptions and lava flows, and freezes.

Findings for Difference in Insurance Share, by Peril Type

Figure 4.2 reports predicted insurance share by incident type, holding applicant and incident attributes other than applicant size constant at their observed values.

Wind is a primary cause of loss in storms without flooding. At 50 percent, the insurance share for this incident type is the highest. In contrast, the insurance shares for earthquake and flooding are much lower. The insurance share for earthquake reaches only 15 percent, and the share for flood-only incidents is 28 percent. Com-paring the blue and orange bars in Figure 4.2 (incidents with a flooding component versus those without one) indicates that combining flood with wind damage results in an insurance share that falls between the share for incidents that involve wind but not flooding and the share for incidents with only flood. At 39 percent, the insurance share for storms with flooding is 11 percentage points lower than the 50-percent share for storms without flooding. The share for hurricanes—which involve both wind and flood—is very close to that for storms with flooding. For winter storms, there is little

Table 4.1Incident and Peril Types for Public Assistance Applicants for Disaster Declarations Made Between January 2008 and June 2018

Incident Type Peril TypeApplicant–Declaration

Pairs Percentage of Total

Named storm Wind, flood 4,734 41

Storm, no flood Severe storms, straight-line wind, tornadoes

770 7

Storm, flood Severe storms, straight-line wind, tornadoes, flood

3,383 29

Winter storm, no flood Wind, snow, ice 1,536 13

Winter storm, flood Wind, snow, ice, flood 903 8

Fire Wildfire 83 1

Earthquake Earthquake 43 <0.5

Flooding only Flood, tsunami, ice jam 105 1

Other, no flood Explosions, volcanic eruptions, lava flow, freezes

4 <0.5

Total — 11,561 100

NOTE: Percentages have been rounded.


difference between the insurance share for incidents with flooding and those without flooding. It could be that, even when present, flooding is not a cause of damage for a substantial share of the structures damaged in a winter storm with flood.9 The insur-ance share for fire is 10 percentage points less than for storms without flooding, which is surprising given that property insurance typically provides at least as much coverage for wildfire as wind (not including named storms). One possible explanation is that wildfire damage more frequently affects property in the equipment category, which, as discussed later in this chapter, has a lower insurance share than building contents and vehicles.10 Further work on the relatively low insurance share for fire is warranted.

9 More-detailed analysis of information in EMMIE on the cause of loss at the facility level provides initial sup-port for the hypothesis. For state-owned properties with incidents categorized as “winter storm, with flood,” 59 out of 126 project worksheets (47 percent) did not include a facility where the cause of loss included flooding. Further investigation of this and other possible explanations is warranted.10 We estimate that equipment accounts for more than 75 percent of repair costs for 28 percent of applicant–declaration pairs with fire as the cause of loss. This is higher than the 19 percent for the sample as a whole.

Figure 4.2Predicted Insurance Share, by Incident Type, Holding Other Applicant and Incident Attributes Constant


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Flooding only

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flooding

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without flooding

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flooding

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Storm without flooding


Difference in Insurance Shares Inside and Outside Special Flood Hazard Areas

As described in the previous section, flood coverage can be different inside and outside an SFHA. To investigate how insurance share differs in an SFHA, we geocoded each of the facilities in the EMMIE database. However, because EMMIE provides cost data at the project level but not the facility level (i.e., not at the individual structure level), we could construct only rough estimates of the insurance share of repair costs inside the SFHA.11 With this information, we grouped applicant–declaration pairs into the following categories:

• less than 25 percent of repair costs inside an SFHA• 25 to 75 percent of repair costs inside an SFHA• 75 percent or more of repair costs inside an SFHA.

Although this method does not precisely characterize the percentage of repair costs in the SFHA covered by insurance, it does allow an initial look into how insurance share may differ inside SFHAs.

To investigate whether flood coverage differs inside SFHAs, we sorted applicant–declaration pairs into three categories based on a rough estimate of the proportion of repair costs for flood damage (see the description in Appendix B):

• incidents not involving flood (earthquake, fire, storm without flood, winter storms without flood, and other without flood)

• incidents involving flood and other hazards (hurricane, storms with flood, and winter storms with flood)

• incidents involving flood only.

We then analyzed the relationship between insurance share and the percentage of repair costs covered by insurance inside an SFHA for each incident category, holding other applicant and incident attributes constant.

Findings on Difference in Insurance Share Inside Special Flood Hazard Areas

Our findings suggest that insurance does differ for properties inside SFHAs. As seen in Table 4.2, insurance share appears to be lower inside SFHAs when flooding is not involved. Projected insurance share drops from 45 percent when repair costs are for property primarily outside SFHAs to 36 percent when repair costs are primarily for property inside SFHAs. In contrast, insurance share is higher for flood-only events

11 In particular, we determined the proportion of facilities in each project that were inside an SFHA and then apportioned repair costs accordingly. As discussed in more detail in Appendix B, this approach in effect assumes that the repair costs for all facilities in a particular project are equal—which may be far from the case.


inside SFHAs, rising from 22 percent to 38 percent. Although the results suggest that the insurance share for flood could be higher inside an SFHA, the results are not particularly convincing. The standard errors of the estimates are large, driven by the relatively small number of applicant–declaration pairs for flood-only incidents (72 out of 6,852).

The expert views summarized previously suggest that flood coverage is lower inside SFHAs, which is inconsistent with the relationship observed for insurance share. However, this inconsistency could result, in part, from a feature of the PA program that will increase the measure of insurance share inside SFHAs: Specifically, PA will deduct the amount of insurance that an applicant could have purchased from the NFIP regardless of whether flood insurance was actually purchased for structures in an SFHA and regardless of whether the structure was subject to an O&M require-ment from a previous loss.12 Our measure of insurance share includes these reductions

12 This policy is consistent with federal requirements that flood insurance be purchased for structures in SHFAs built with direct federal investment, as well as for private structures in SFHAs that secure loans from federally regulated lenders.

Table 4.2Predicted Insurance Share, by Proportion of Repair Costs, Inside a Special Flood Hazard Area, Holding Other Applicant and Incident Attributes Constant

Proportion of Repair Cost in an SFHA Observations

Predicted Insurance Share, as a Percentage 95% Confidence Interval

Incidents with no flooding

1,408 — —

<25 1,306 45 [43, 48]

25–75 35 42 [33, 50]

≥75 67 36 [28, 44]

Incidents with some flooding

5,372 — —

<25 4,278 40 [38, 41]

25–75 352 38 [35, 42]

≥75 742 37 [34, 39]

Incidents with only flooding

72 — —

<25 50 22 [14, 31]

25–75 2 41 [37, 45]

≥75 20 38 [21, 55]

NOTE: The 95% confidence interval roughly estimates plus or minus twice the standard error.


and could, in principle, cause insurance share to be higher inside SFHAs. The higher insurance share for flood losses inside an SFHA could, in principle, occur even if the percentage of properties with flood insurance inside the SFHA were the same as out-side the SFHA. Further work on the difference in insurance share for flood and other peril types inside SFHAs is warranted.

Variation in Insurance Share, by Property Type

Buildings, contents, vehicles, and equipment are different property types that may have separate insuring agreements with different terms. What is included in the build-ing and content category and vehicle category is fairly intuitive, but the equipment category considered here is eclectic.13 Examples of PA work that falls into this cat-egory include the repair and replacement of fences, tractors, athletic facilities, landfills, shooting ranges, swimming pools, cranes, outdoor stages, and weather alert sirens. To understand how insurance coverage varies by property type, we used the application title and facility name recorded in EMMIE to sort property into different categories. To calculate the share of repair cost that falls into each category, we used the same approach as we did in the SFHA analysis, and we grouped applicant–declaration pairs into three categories based on a rough estimate of the percentage of repair costs that was for equipment. Appendix B provides detail on the percentage of applicant–declara-tion pairs and repair costs that falls into each category.

Table 4.3 suggests that insurance shares for equipment may be somewhat lower than those for buildings, contents, and vehicles. The insurance share falls from 42 per-cent when less than 25 percent of repair costs are for work that involves equipment to

13 For the purposes of the PA program, FEMA defines a building as “a walled and roofed structure, other than a gas or liquid storage tank, that is principally above ground and affixed to a permanent site, as well as a manu-factured home or a permanent foundation.” Contents is the “portable property located within a building.” Vehicle refers to a “mechanized device used for carrying passengers, goods, or equipment.” Equipment is “tangible per-sonal property having a useful life of more than one year and an acquisition cost of $5,000 or more per unit.” See FEMA, 2015, pp. 1–3.

Table 4.3Predicted Insurance Share, by Proportion of Repair Costs Attributable to Equipment, Holding Other Applicant and Incident Characteristics Constant

Proportion of Repair Costs Attributable to Equipment, as a Percentage

Predicted Insurance Share, as a Percentage 95% Confidence Interval

<25 42 [41, 43]

25–75 39 [37, 41]

≥75 34 [32, 36]


34 percent when the share for equipment is greater than or equal to 75 percent. The actual difference between buildings, contents, and vehicles on the one hand and equip-ment on the other would presumably be greater than in Table 4.3 because the three categories contain varying mixtures of the four products.

Although the insurance share for equipment appears lower, equipment does not appear to account for a large share of overall repair costs. Table B.2 in Appendix B provides support for this, showing that applicant–declaration pairs that involve pre-dominantly equipment damage account for less than 10 percent of overall repair costs. Writing computer code to accurately classify the different facilities proved challenging, and, even when a facility description in EMMIE indicated that the repair costs were for a building or vehicle, a facility could also include repair costs for other property types. Further work is warranted on how insurance share varies by the type of property damaged.

The Effect That Jurisdiction Size Has on Insurance Share

Analysis of the Relationship Between Insurance Share and Jurisdiction Size

Analysis of the PA expenditure data indicates that insurance share is lowest for the smallest jurisdictions, rises for midsized jurisdictions, and then falls again for the largest jurisdictions. Figure 4.3 shows the relationship between insurance share and jurisdiction population, holding other applicant and incident attributes constant. Figure 4.3 reports the relationship considering all incident sizes (repair cost per capita) together, while Figure 4.4 allows the relationship to vary across incident-size category. If different-sized jurisdictions select the same deductibles and policy limits on a per capita basis, the lines in each of the figures would be horizontal. An upward slope in the line when moving from a smaller jurisdiction to a larger one means that the larger jurisdiction has more-complete coverage adjusted for size (lower deductibles or higher policy limit, or both) than the smaller jurisdiction. The 95-percent confidence inter-vals are large for jurisdictions with populations greater than 1 million because of the relatively small number of such jurisdictions in our sample (see Figure 4.3). Confidence intervals for Figure 4.4 are shown in Figure B.1 in Appendix B.14

Two broad themes emerge for Figures 4.3 and 4.4. First, Figure 4.4 indicates that public entities receive lower insurance payments for minor losses (the blue and red lines) than for larger losses (green, yellow, and black lines). The one major exception is the largest jurisdictions (those with population 1 million or more), which appear to

14 See Table 3.3 in Chapter Three for the number of observations by jurisdiction size. The PA applicants that fall into the largest population category (at least 5 million) are Los Angeles County, New York City, Cook County, and several of the 23 states with populations of more than 5 million. The populations of Los Angeles County and New York City are approximately 10.1 million and 8.4 million, respectively (Census Reporter, 2018a; Census Reporter, 2018b). As of 2019, nine states had populations exceeding 10 million (U.S. Census Bureau, 2019).


Figure 4.3Predicted Insurance Share, by Population, Holding Other Applicant and Incident Attributes Constant

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≤5,000 5,000–10,000

10,000–50,000

50,000–100,000

100,000–1 million

1 million–5 million

≥5 million

NOTE: For cities, counties, and states. Combining population categories 1 million to 5 million and≥5 million produced similar results: Predicted insurance share is 41% with a confidence interval of 32 to 50%.

95% confidence intervalfor the estimates

Figure 4.4Predicted Insurance Share, by Population and Repair Cost Per Capita

≤0.25

0.25–1

1–10

10–50

≥50

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≤5,000 5,000–10,000

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50,000–100,000

100,000–1 million

≥1 million


50 Repair cost per capita,in dollars


have less coverage for the largest losses (the black line is below the other four lines for jurisdictions of 1 million or more).

Second, Figures 4.3 and 4.4 provide evidence that insurance share is lower for the smallest communities and rises as population increases to between 10,000 to 50,000. Insurance share then falls as population increases above 50,000. Figure 4.3 does not show a continued decline in insurance share once population reaches 1 million. But when we examine incidents of different sizes separately in Figure 4.4, the insurance share does continue to decline for all but one of the categories of repair cost per capita. The exception is the smallest incident-size category (repair costs of less than $0.25 per capita), but a lack of a relationship should be expected if these small events tend to fall below the deductibles for all the larger jurisdiction categories. On the whole, the results suggest lower insurance share for the largest jurisdictions. However, the confidence intervals are large for the jurisdictions with populations greater than 1 million, so the evidence is not particularly strong.

This hump-shaped pattern contrasts with the lack of relationship between mea-sured insurance share and jurisdiction size for jurisdictions with populations of less than 50,000. The insurance shares reported in Table 3.3 in Chapter Three are reproduced in Figure 4.5, showing an insurance share that hovers around 48 percent for jurisdiction-size categories of less than 50,000. How do we reconcile the two findings? At least part of the answer is that repair cost per capita correlates with jurisdiction size, which masks the lower insurance share for smallest communities. To see this, first refer to the blue

Figure 4.5Median Repair Cost Per Capita and Average Insurance Share, by Jurisdiction Size

Median repair cost per capitaAverage insurance share

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≤5,000 5,000–10,000

10,000–50,000

50,000–100,000

100,000–1 million

1 million–5 million

≥5 million


2

1


bars in Figure 4.5, which show the average repair cost per capita by jurisdiction size. The median repair cost per capita equals $6.77 per capita for the smallest jurisdictions, falls to $0.73 per capita for jurisdictions with populations between 10,000 and 50,000, and then ranges between $0.02 and $0.38 for larger jurisdictions. Then recall from Figure 4.1 that applicant–declaration pairs with the highest repair costs per capita have higher insurance shares. The high per capita repair cost for small communities thus pulls up the measured insurance share for the smallest communities—masking the relationship between risk-management practices and size that appears when incident size is controlled for.15

Further work is needed to better understand the reasons for and the implications of the much higher repair costs per capita for smaller communities. Relevant questions include whether the higher repair costs per capita in small communities are due to

• less attention paid to mitigation and disaster preparedness• lower frequency but high severity of events16

• greater amounts of BCVE per capita• a tendency not to apply for PA when losses per capita are low because nonreim-

bursed administrative costs could be large relative to the assistance (a repair cost of $4 per capita would amount to only $10,000 for a community with a popula-tion of 2,500).

Expert Perspectives on the Relationship Between Risk-Management Practices and Jurisdiction Size

Consistent with the decline in insurance share in the right portions of Figures 4.3 and 4.4, many of the experts with whom we conferred observed that large entities tend to purchase less insurance in proportion to their size than smaller ones. They offered a few potential reasons for this behavior.

Some experts observed that large communities have risk-management tools that might not be available to smaller communities. For example, they can set up rainy-day funds and can more easily borrow postevent to repair uninsured damage. Our mea-sure of insurance share does not capture these and other types of risk-management approaches that FEMA cannot easily document. This unobserved or hidden insur-ance, which can sometimes be referred to as self-insurance, is not deducted from PA

15 The measured insurance share is the share calculated from the raw data. The predicted insurance share is from a regression analysis of the effect of changing one attribute while holding others constant. So Figure 4.5 and all the figures in Chapter Three are measured insurance share. The rest of the figures use predicted share.16 Lower disaster declaration frequency might be anticipated given the small geographic footprint one might expect for smaller communities. The measurement of declaration frequency developed for this project does pro-vide some initial evidence that frequency is lower in smaller jurisdictions. Jurisdictions with populations of less than 5,000, 5,000 to 10,000, 10,000 to 50,000, 50,000 to 100,000, and 100,000 to 1 million each average about 15 declarations prior to the indicated declaration (back to 1965). Those with populations between 1 million and 5 million and at least 5 million average 22 and 25 declarations, respectively. However, this measure of frequency is crude, and further investigation of event frequency and severity in small jurisdictions is warranted.


awards—even though it can be called on when FEMA PA funding is not available.17 The result is a low insurance share as measured here, but not necessarily a reduced capacity to address disaster losses relative to smaller communities.

Experts pointed to a few other explanations for the different behavior between large and less-large communities, including the following:

• Larger communities are more confident that they have the political visibility needed to draw FEMA’s attention following an event.

• A larger community will likely have buildings dispersed over a wider area than a smaller one. A smaller community might not want to take the risk that closely clustered buildings damaged in an isolated incident will go uncovered should the incident fail to trigger a presidential disaster declaration.

• Larger communities are able to hire risk managers who can take the time to under-stand how they can leverage FEMA assistance in an overall risk-management strategy. A risk manager in a smaller community can have multiple responsibili-ties and might think about insurance and risk-management strategies only once a year, when policies are up for renewal.

On the other hand, some experts observed that insurance shares for small com-munities were often lower than for larger ones—which is consistent with the rise in insurance share in the left parts of Figure 4.3 and 4.4. They noted that some small communities are so financially strapped that they buy little or no insurance. They also argued that, because disaster declarations are based on per capita losses, some large communities are not willing to take the risk that a disaster would not be declared, resulting in higher insurance purchases by larger communities than smaller ones.

The Effect That Prior Disaster Declarations Have on Insurance Share

The frequency of prior disaster declarations might be expected to affect state and local risk-management practices. Prior disaster declarations might increase the demand for insurance for several reasons. For example, demand for insurance is likely greater in higher-risk areas, and the number of previous disaster declarations is presumably an indicator of risk. Prior declarations would also, in principle, increase the number of structures subject to the PA O&M requirement, and the incidents triggering prior dec-larations may increase awareness of the risk. On the supply side, however, insurance premiums might be higher in areas with more declarations in prior years—putting downward pressure on insurance purchases. The overall expected effect of prior dec-

17 A potential measure of unobserved or hidden insurance in large jurisdictions might be the difference between observed insurance amounts in small and large jurisdictions. FEMA might then consider deducting this measure of unobserved insurance from PA awards.


larations on insurance share is thus uncertain. Prior research has found a relationship between insurance take-up and the frequency of prior events. For example, Dixon et al. found evidence that recent flood events increase homeowners’ take-up of flood insurance, as did Petrolia, Landry, and Coble.18 These findings might not be transfer-able to public entities, however.

Expert Perspectives on the Relationship Between Insurance Share and Prior Disaster Declarations

The experts with whom we conferred for the most part believed that, when one holds underlying risk constant, public entities that experience disasters less frequently have less insurance coverage.19 The discussion of the insurance share for earthquake earlier in the chapter illustrates this point. Experts believed that one of the challenges for the earthquake insurance market is the low frequency of substantial seismic events and contrasted that with hurricane risk along the Gulf Coast, where hurricanes occur fre-quently and are a recent memory for most public entities. In the experts’ view, it is dif-ficult to appreciate the consequence of a severe event when the event occurs only rarely or has not occurred for a substantial period of time. It is also harder to divert resources from the most-pressing problems currently facing a public entity to the purchase of insurance for an event that has not happened recently.

Observations on this matter were not uniform, however. Some experts had seen examples in which public entities did not increase insurance after an event or increased it a small amount relative to the risk facing the entity. Experts pointed to set budget allocations for insurance purchases as a reason for this lack of response.

Analysis of the Relationship Between Insurance Share and Event Frequency

We used a database of federal disaster declarations to determine the number of disaster declarations that occurred prior to the disaster declaration date for each applicant–declaration pair.20 We refer to the declaration for each applicant–declaration pair as the indicated declaration and determined the number of declarations prior to the indicated declaration. We counted declarations in the county in which the damage occurred back to 1965 (the first date for which declarations were recorded by county). Because the effect of more-recent declarations may differ from those further back in time, we also calculated the number of declarations during the five years prior to the indicated declaration and during the period more than five years prior to the indicated declara-

18 Dixon, Clancy, et al., 2006, p. 47. Petrolia, Landry, and Coble found that “an additional past experience with flood (i.e., an increase of one past flood event) increases the probability of holding flood insurance by 11.4%” (Petrolia, Landry, and Coble, 2013, p. 236).19 Lower event frequency could signify lower risk, and one might expect lower insurance coverage if the underly-ing risk were lower. However, this is not the point being made here.20 FEMA, 2018d. The data capture all federally declared disasters, which consist of major disaster declarations, fire management declarations, and emergency declarations (special declarations).


tion (back to 1965). The number of previous disaster declarations is quite large. More than 40 percent of the applicant–declaration pairs had experienced three or more dec-larations in the five years prior to the indicated declaration, and approximately 70 per-cent had experienced ten or more declarations between 1965 and five years prior to the event (see Table B.2 in Appendix B).

Overall, we found evidence that insurance share is higher in jurisdictions with substantial numbers of recent events and lower in jurisdictions with larger numbers of more-distant events. Table 4.4 reports on the predicted insurance share first as the number of more-recent declarations varies holding the number of more-distant decla-rations constant. As shown, jurisdictions with more than six declarations in the prior five years had higher predicted insurance shares than other groups, although there was no statistical difference in insurance share among jurisdictions with fewer than six dec-larations in the prior five years. Recent memory of a disaster and O&M requirements could be driving this pattern. In contrast, the bottom set of rows in Table 4.4 shows that insurance share falls as the number of declarations five or more years prior to the declaration increases. One possible explanation is higher insurance cost or reduced insurance availability in areas with frequent declarations. There could also be a feed-

Table 4.4Predicted Insurance Share, by Number of Prior Declarations Holding Other Applicant and Incident Characteristics Constant

Prior DeclarationsPredicted Insurance Share,

as a Percentage 95% Confidence Interval

In the five years prior to the indicated declarationa

0 40 [38, 43]

1–2 39 [38, 41]

3–5 41 [39, 42]

≥6 46 [43, 48]

Between 1965 and five years prior to the indicated declarationb

0–9 43 [42, 45]

10–14 41 [39, 42]

16–19 40 [39, 42]

≥20 34 [31, 36]

NOTE: Excludes states and other observations for which the number of prior disasters is missing.a Holding the number of declarations more than five years before the indicated declaration constant.b Holding the number of declarations less than five years before the indicated declaration constant.


back loop between insurance share and disaster declarations. Lower insurance shares could lead to more-frequent disaster declarations because estimates of the amount of losses covered by insurance are considered in disaster declarations.

Additional work is warranted to more fully explore the relationship between insur-ance share and prior declarations. The measure we used to capture applicant exposure to past disasters has some limitations. FEMA reports disaster declarations at the county level, so a prior declaration in the same county might not have affected the applicant in the indicated declaration.21 This measure also does not distinguish between the various perils. For example, the number of previous earthquakes may have little impact on the uptake of flood insurance. Finally, our analysis of prior declarations does not consider declarations in neighboring counties, which may influence behavior.22

Discussion

Table 4.5 summarizes our findings on the drivers of insurance share for BCVE. The findings provide insight into areas in which the federal government bears more of the financial risk for BCVE and suggest areas in which discussions on the appropriate division of risk between federal, state, and local government should focus. The second column shows the direction of each effect, and the third column provides a sense of the magnitude of the effects. In assessing the magnitude of each effect, the difference in predicted insurance shares is most relevant; however, we report the range of the pre-dicted levels in order to make the results more concrete.

The increase in insurance share as repair cost per capita rises is consistent with FEMA covering the insurance deductible. Deductibles can be high, and FEMA does not put a limit on the size of a deductible PA will cover, resulting in substantial federal payments for this first level of loss.

Insurance share varies importantly by peril type. The very low insurance share for earthquakes combined with the high value of assets exposed to earthquakes means that earthquakes present a major financial exposure for the federal government. Limited flood coverage remains an ongoing challenge. Our analysis suggests that insurance share for flood losses is lower outside SFHAs, indicating that policymakers should not ignore insurance practices for properties outside SFHAs. Further work, however, is needed to confirm the lower insurance share for flood outside SFHAs. Given that insurance policies typically cover fire losses regardless of cause, it is initially surprising that the insurance share is not higher for wildfire incidents. However, some of the larg-

21 A measure of the number of declarations within a particular distance from the county in which the damage occurred might be better.22 See Gallagher, 2014, for analysis of the impact that previous floods have had on the take-up of flood insurance provided and the role of media markets.


est jurisdictions do not buy insurance coverage at all, and policies may exclude certain types of equipment.

Insurance share appears somewhat lower for equipment than for buildings, con-tents, and vehicles. However, equipment repair costs do not account for a substantial share of overall repair costs.

Insurance share peaks for jurisdictions with populations between 10,000 and 50,000 and is considerably lower for both smaller and larger jurisdictions when other factors, such as the size of repair cost per capita, are controlled for. The lower insurance share for larger communities might reflect increased use of risk-management strategies that are more readily available to larger communities and result in a lower measured insurance share but not necessarily a reduced capacity to cover losses. These types of

Table 4.5The Relationship Between Insurance Share for Buildings, Contents, Vehicles, and Equipment and Incident and Applicant Attributes

AttributeRelationship Between

Insurance Share and AttributeRange of Predicted Insurance

Share

Incident

Size of loss per capita Increases as repair cost per capita increases

16–59% for the range of per capita repair costs observed

Type of peril Lowest for earthquake and flood

15% for earthquake and 25% for flood; 50% for storms without flood

Location in an SFHA Lower for flood outside SFHAs 22–38% for flood-only events, based on percentage of repair costs outside SFHAs

Type of property Lower for equipment than for buildings, contents, and vehicles

34–42%, depending on the percentage of repair costs attributable to equipment

Public entity

Jurisdiction size Highest for midsized jurisdictions, lower for smaller and larger jurisdictions. Higher repair cost per capita in small jurisdictions brings up insurance share for the smallest jurisdictions.

33% for smallest jurisdictions, 47% for midsized jurisdictions, and 31% for the largest jurisdictionsa

Frequency of prior disaster declarations

Higher in jurisdictions with a substantial number of disaster declarations in the prior five years; declines as the number of declarations more than five years prior to the declaration grows

40–46%, depending on the number of declarations in the prior five years; 34–43% depending on the number of declarations more than five years prior to incident

a The 31% for the largest jurisdictions is the average of the predicted shares in Figure 4.4, excluding the prediction for repair costs less than $0.25 per capita.


self-insurance mean that larger communities are benefiting more from FEMA’s PA program because they can strategically adopt risk-management approaches that maxi-mize FEMA assistance. An important priority for policymakers is understanding how to address these types of self-insurance when determining federal assistance.

Small communities have lower insurance shares when one controls for other fac-tors, such as event size, but the per capita losses in small communities in incidents trig-gering disaster declarations are much greater than those for larger communities. The higher per capita losses raise the measured insurance share for small communities. Fur-ther work is needed to better understand the causes for and implications of the higher repair cost per capita of smaller communities in declared disasters.

Finally, we found that insurance share is higher for jurisdictions with a larger number of recent events. What is surprising is that insurance falls as the number of more-distant events increases. The differences are not particularly large, however, and are not estimated with a great deal of precision. This pattern suggests that insurance share is lowest in the high-risk areas where FEMA spends the most money.23 The find-ings suggest that discussion of insurance practices in repetitive-loss areas should be a priority.

23 See Table B.2 in Appendix B for tabulations of repair costs by the number of previous declarations.

51

CHAPTER FIVE

The Role That FEMA Assistance Programs Play in State and Local Risk-Management Decisions

The extent to which PA and other disaster assistance programs influence the state and local government risk-management behaviors documented in the previous two chapters has been a longstanding topic of debate. For example, FEMA and the DHS Office of Inspector General (OIG) have raised concerns that the PA program as cur-rently structured may discourage applicants from obtaining insurance before a disaster. In 2000, FEMA published an advance notice of proposed rulemaking in the Federal Register outlining potential reforms to the PA program. In the notice, FEMA argued that, by paying for facility repair costs regardless of whether the facility had insurance, the PA program creates a disincentive for the owner to obtain insurance before a disas-ter occurs.1 OIG echoed these concerns several years later, stating, “[T]he PA program pays for building repair costs following a first disaster. This effectively eliminates any incentive to purchase insurance before a disaster occurs.”2 More recently, the FY 2021 President’s budget made an even stronger claim, arguing, “FEMA’s current program is a no-limit, no-premium insurance policy for State and local governments, which disincentivizes self- protection and burdens taxpayers with the risky decisions made by State and local governments.”3

In this chapter, we first review previous research on the effect that disaster assis-tance has on insurance purchasing decisions. We then summarize the observations of experts who work closely with state and local government on the effect that federal assistance has had on their risk-management decisions. Our summaries focus on PA’s effect on current state and local risk-management decisions for BCVE, the expected state and local response to reduction or elimination of PA for BCVE, and the effect that the O&M requirement has on state and local insurance practices.

1 FEMA, 2000, p. 8928.2 OIG, 2011, pp. 11–12.3 Office of Management and Budget, 2020, p. 112.


Previous Research on the Effect That Disaster Assistance Has on Insurance Purchasing Decisions

Scholars have explored the relationship between disaster assistance programs—including PA—and the purchase of insurance. In particular, these writers have sought to evaluate the effect of what they refer to as “moral hazard”—that is, the theory that individuals might underinsure (or not insure) because they expect postdisaster govern-ment assistance.4 In its most detailed form, proponents of this theory posit that post-disaster government assistance essentially functions as premium-free insurance against natural hazards—which discourages individuals from turning to the insurance market for coverage. This can lead to a vicious cycle in which demand for natural disaster insurance decreases, insurers leave the market as a result, and the remaining insurers must charge higher premiums. Consequently, more individuals will shun insurance coverage and turn to government assistance instead.5

Although the logic of this theory is compelling, the evidence that postdisaster assistance discourages the purchase of insurance in practice is mixed—and, in fact, in some studies, researchers have reached the opposite conclusion. For example, Brown and Hoyt studied NFIP purchases—both the amount of flood insurance in force and the number of policies bought—and found that they increased with federal disaster expenditures.6 Petrolia, Landry, and Coble reached similar conclusions, finding that expectations for receiving disaster assistance increased the likelihood of flood insur-ance purchases. They noted, “[O]ur results suggest that the expectation of disaster assistance does not infringe, and may even increase, NFIP participation.”7 In a separate study, Petrolia, Hwang, et al. found a similar dynamic in wind insurance purchases among coastal U.S. homeowners, concluding that the expectation of federal disaster assistance did not affect the likelihood of holding a wind insurance policy.8

In contrast, several studies do provide modest support for the moral hazard theory. For example, Kousky, Michel-Kerjan, and Raschky found that a $1,000 increase in the average FEMA Individual Assistance (IA) grant to an individual for uninsured property damage decreased average NFIP insurance coverage by between $5,322 and $6,350. However, they also found that insurance coverage increased for households in the bottom quarter of the FEMA IA distribution. They suggested that this may occur because those who receive low amounts of aid may realize the limits of govern-ment assistance and the importance of insurance. They also found that Small Business

4 For further discussion of this concept, see Browne and Hoyt, 2000; Raschky and Weck-Hannemann, 2007; and Raschky, Schwarze, et al., 2013.5 Raschky, Schwarze, et al., 2013, p. 181.6 Browne and Hoyt, 2000.7 Petrolia, Landry, and Coble, 2013, p. 241.8 Petrolia, Hwang, et al., 2015.

The Role That FEMA Assistance Programs Play in State and Local Risk-Management Decisions 53

Administration disaster loans had no impact on the amount of insurance coverage pur-chased.9 In an analysis of German homeowners, Andor, Osberghaus, and Simora simi-larly reached mixed results, finding that the relationship between disaster assistance and flood insurance coverage depends on risk level. Homeowners in areas with a high risk of flooding were likely to avoid coverage if they expected government assistance. For households facing a low risk of flooding, there was no significant relationship between the two.10 In contrast, Davlasheridze and Miao offered a stronger case, using U.S. data, in favor of the moral hazard theory. On the effect that PA funding has on NFIP purchases, they found that, for each 10-percent increase in PA funding to a county, the number of policyholders declined by 1.5 percent, total insurance coverage by 1.4 percent, and premiums by 1.2 percent.11

Overall, the evidence that postdisaster assistance discourages the purchase of insurance in practice is inconsistent. However, it is worth noting that some of these studies apply to insurance purchasing decisions made by individuals and the effects of FEMA’s IA program—which is a much more limited program than PA. Consequently, it is unknown whether these results apply to the behavior of public entities.

Expert Perspectives on the Effect That the Public Assistance Program Has on State and Local Risk-Management Practices for Buildings, Contents, Vehicles, and Equipment

Its Effect on Current Risk-Management Practices

The predominant view among the FEMA staff and the insurance brokers we consulted is that the potential for FEMA assistance reduces state and local insurance purchases. At one extreme, experts pointed to state and local governments that do not buy any catastrophic coverage (for example, for wind, flood, or earthquake losses). These enti-ties view public buildings as essential facilities and believe that it is FEMA’s responsi-bility to get essential facilities up and running after a disaster. One expert pointed to a large city that has no insurance and commented that “FEMA is their risk-management strategy.” In the view of another, the “material” exposure of uninsured BCVE is made “immaterial” by FEMA assistance programs.12

Less extreme responses were also noted by experts familiar with state and local risk-management thinking and decisions. Some state and local governments buy insur-ance only for facilities that they believe are critical to repair quickly. Examples include

9 Kousky, Michel-Kerjan, and Raschky, 2013.10 Andor, Osberghaus, and Simora, 2017.11 Davlasheridze and Miao, 2019.12 In financial and accounting terminology, material events or information are those that would affect the judg-ment of an informed investor (Business Literacy Institute, undated).


police and fire stations and critical administrative buildings. These state and local governments rely on PA to repair or replace the rest. It was also commonly believed that state and local governments that do purchase coverage buy less than they would absent the PA program. For example, a county might buy coverage adequate to cover a 1-in-100-year event and rely on FEMA to cover larger events. The county realizes that FEMA assistance would not be available for events that do not rise to the level of a presidential declaration but would be available for the largest events. Such entities recognize that they are underinsured but conclude that the gap is acceptable because FEMA assistance will likely be available for large events. Experts also observed that some state and local governments select larger deductibles than they would otherwise, knowing that PA will cover the deductible for first-time losses.

Although predominant, the view that FEMA PA affects state and local risk-management decisions was not universal. This difference of opinion reflects both the diversity of experience across experts and the tremendous diversity in the hazards facing state and local governments and their risk-management approaches. Some experts saw no indication that state and local governments buy less insurance expect-ing that FEMA will bail them out. Others thought that risk-management decisions in some jurisdictions are driven by credit rating agencies (CRAs) that provide rat-ings for state and local debt offerings, not FEMA. Another observed that some state and local risk managers are reluctant to rely on FEMA for reasons that include that (1) risk managers realize that PA is contingent on a presidential disaster declaration and (2) some risk managers are concerned that elected officials will conclude that the need to seek FEMA assistance means that the risk manager is not doing their job. Finally, according to one expert, some risk managers argue that they are doing their best to protect the communities given their budgets and that FEMA assistance does not enter their calculus. That may be true, but the availability of assistance may still put downward pressure on the budget allocated for insurance.

The Expected Response to Eliminating or Significantly Reducing Public Assistance

Expert predictions of how state and local governments would respond to the reduction or elimination of PA for BCVE provide additional insight into the effect that FEMA assistance has on state and local risk-management practices. Experts frequently pre-dicted that state and local governments would take more responsibility for managing natural disaster risk if PA were substantially reduced or eliminated. One said that the eliminating PA would “change everything” for risk managers and would induce enti-ties to buy more coverage. Many argued that communities would not want to go bare (i.e., go without insurance) for the large catastrophic losses, which would lead them to devise ways to replace the catastrophic coverage provided by the PA program. Another predicted that some state and local governments would better insure those buildings that they could not afford to lose and increase insurance in order to avoid a community that is “torn-up for 10 to 20 years.” A third expert argued that some state and local gov-


ernments would purchase more insurance rather than fund repairs with postevent tax increases because they had seen examples in which tax increases had induced people to move away. Underscoring its importance to public entities, the experts with whom we spoke overwhelmingly predicted that state and local government would vigorously oppose the reduction or elimination of PA for BCVE.

Experts also identified other potential consequences of reducing or eliminating PA for BCVE. For example, some experts argued that, once a community realized that it was on the hook for losses, it would alter building code and zoning decisions to avoid building in risky areas and construct structures that are better able to withstand wind, flood, earthquake, and other natural hazards.

The belief that reduced PA would induce greater insurance purchases was not universal. Experts offered several rationales for this:

• State and local governments would look for other sources of assistance to offset the lack of access to PA dollars for BCVE. For example, state and local governments might turn to the Department of Housing and Urban Development’s Commu-nity Development Block Grant Disaster Recovery program for assistance.

• Cognitive biases would hinder state and local governments from increasing insur-ance purchases. These cognitive biases would make it easy for decisionmakers who have not seen substantial losses in recent years to think that losses will not happen to them. They also would make it easy for those who have experienced recent losses to think that they would not be hit again anytime soon.

• Some communities are not taking into account the longer-term consequences of being underinsured, such as a prolonged drop in economic activity that can lead to out-migration and further reduction in economic activity.

• Some communities may have little awareness of the PA program and consequently would not be likely to change their behavior if FEMA were to modify it.

• Some state and local governments might also not initially respond to a scaling back of the PA program because they would wait to see whether program benefits would be restored once a large event occurred. In the opinion of one expert, state and local governments will change their behavior only once they have large unin-sured losses that are not covered by FEMA.13

Some experts hypothesized that the reasons communities might not significantly adjust risk-management practices in response to changes in the PA program would vary by community size. For example, small and midsized communities might see little need to adjust their practices in response to a PA cutback or may find it difficult to obtain afford additional coverage. Large communities might continue to rely on rainy-day funds and draw on their knowledge and experience with federal programs to

13 Paraphrasing one interviewee, “what type of response will depend on how influential their congressman is.”


look for other sources of compensation. Larger communities might also make the case to their constituents that it makes more sense to rely on postdisaster tax increases to rebuild public buildings than to increase taxes to pay for policies that cover the types of catastrophic events for which PA is available.

Effect of the Obtain and Maintain Requirement

In principle, the O&M requirement offsets, to some extent, any downward pres-sure on state and local insurance purchases exerted by the availability of PA. Recall from Chapter Two that PA applicants must obtain and maintain insurance for the amount of repair costs prior to the reduction of insurance proceeds. FEMA does not pay for subsequent damage to the structure up to the O&M requirement, regardless of whether the applicant purchases the coverage and the size of the policy’s deductible. In addition, FEMA can deny future assistance to a facility that failed to comply with the O&M requirement and can claw back the funding for the first repair. The O&M requirement will thus presumably affect the risk-management decisions of at least some entities that have previously received PA.

Experts noted that many state and local governments buy insurance to satisfy the O&M requirement. Earthquake coverage is rare in California, and one expert observed that a particular public entity in California had earthquake coverage in order to comply with the O&M requirement. Another observed that many public entities in Florida have substantial amounts of coverage, which he believed was in part due to the many facilities subject to the O&M requirement. Ohio has set up a property insur-ance program that is mandatory for all state-owned buildings, excluding state colleges and universities, and stated in its response to the RFI for this project that “our strat-egy for making insurance mandatory is largely due to the FEMA Public Assistance Program.”14

There is also some indication that the O&M requirement might reduce reliance on the PA program in the first place. The O&M requirement may offset the PA pro-gram’s downward pressure on insurance purchases for properties that have yet to expe-rience loss because it reduces the attractiveness of the PA program to some extent. One expert with whom we conferred relayed the experience of a public entity that regretted receiving PA because the ongoing cost of the O&M requirement meant that it had paid out in insurance premiums many times what it had received in PA.

Although the O&M requirement appears to provide powerful incentives to pur-chase coverage, several factors tend to mute its impact. Reduction or elimination of the O&M requirement by state insurance commissioners or FEMA has not been common according to the experts, but there are notable exceptions. One expert reported that O&M requirements have been frequently waived for earthquake assistance in the West, and another noted the broad reduction of the O&M requirement by the Louisiana

14 State of Ohio, 2019.


insurance commissioner following Hurricane Katrina.15 More recently, the New York state insurance commissioner determined that New York City does not need to buy any wind coverage to the comply with O&M requirements for PA received subsequent to Hurricane Sandy and must buy flood insurance only up to the limits offered by the NFIP ($500,000 of the structure and $500,000 for contents).16 Another factor limiting the impact that the O&M requirement has on insurance purchase is the lack of limits on the size of the deductible. One expert has observed some very large deductibles in policies used to comply with an O&M requirement. FEMA does not have authority to push back on a large deductible, and, according to a strict reading of O&M policy, it has no financial reason to do so because it will not cover the deductible in subsequent losses.17 These factors limit the impact of the O&M requirement, and the negative relationship reported in Chapter Four between insurance share and the number of prior declarations raises further questions about its effectiveness.

Discussion

There does not appear to be any systematic empirical work on how FEMA disaster assistance programs affect state and local risk-management practices. The work to date has largely focused on insurance purchasing decisions made by individuals and the effects of FEMA’s IA program—which is a much more limited program than PA. Con-sequently, the results of these studies might not apply to the behavior of public entities.

The prevailing sentiment among experts who regularly interact with the risk managers of state and local governments is that the PA program significantly depresses the insurance purchases of state and local governments both in terms of insurance take-up and the amount of insurance purchased. This perspective was not universal, however, and there are undoubtedly examples in which state and local governments did not consider the role of federal assistance in risk-management decisions. It is also impossible to quantify the magnitude of the effect based on the data collected.

Predictions that many state and local governments would increase insurance pur-chases if PA for BCVE were eliminated highlight the importance of federal assistance in state and local financial risk-management decisions. There were exceptions to this view, but, in some cases, the reason that state and local governments would not respond to a change to the PA program was not that the program was not considered in risk-management decisions but rather that they believed that they would be able to find

15 See Governor of Louisiana’s Office of Homeland Security and Emergency Preparedness, undated.16 New York City Office of Management and Budget, personal communication, April 2, 2020.17 FEMA written policy is to not cover the deductible in subsequent losses, but that policy could be overridden by congressional action or FEMA’s own decisionmaking.


other sources of federal assistance or that the cuts would be eliminated and payments restored once an incident producing large uninsured losses occurred.

59

CHAPTER SIX

Options for Closing the Insurance Gap

Throughout our research, we asked subject-matter experts to identify approaches that could encourage state and local governments to purchase insurance for their BCVE. In this chapter, we describe the approaches they shared, as well as a proposal from the President’s FY 2021 budget, and the trade-offs associated with the approaches.1 We do not intend for this list to be exhaustive or even prioritized, but it does represent feasible approaches proposed during the course of this study:

• Encourage CRAs to consider financial preparedness for disasters in their assess-ments of state and local governments.

• Raise communities’ awareness of their risks.• Require state and local governments to cover the first layer of loss.• Eliminate PA funding for BCVE.

Encourage Credit Rating Agencies to Consider Financial Preparedness for Disasters in Their Assessments of State and Local Governments

CRAs play an important role in state and local finance by providing assessments of the creditworthiness of these entities and the bonds they issue.2 More specifically, CRAs collect and analyze data on state and local governments and their bonds and assess the likelihood of repayment.3 These assessments can have a significant impact on a gov-ernment’s ability to raise funds through bonds. Lower ratings signify to investors that bonds are higher risk, which, in turn, may raise borrowing costs for a government.

In our discussions with subject-matter experts, they noted that, in assessing a community’s creditworthiness, CRAs do not typically evaluate its financial prepared-ness for disasters or the potential impact of that preparedness on the community’s

1 We do not discuss mitigation measures directly in this section. However, the proposed approaches may indi-rectly encourage mitigation by creating incentives for communities to adopt such measures.2 Mărgulescu and Mărgulescu, 2013, p. 1.3 White, 2013, p. 95.


ability to pay bondholders.4 This could include, for example, the consideration of the potential decline in a poorly prepared community’s tax base due to out-migration after a disaster. Encouraging—or requiring—CRAs to include this type of criteria in their assessment methodologies could create a powerful incentive for communities to adopt sound financial risk-management practices for disasters. Specifically, the possibility of facing higher borrowing costs, as well as the reputational risk associated with a down-grade, could lead state and local governments to look for ways to assure CRAs that they could meet their financial obligations in the wake of a disaster—such as through the purchase of insurance.5

A major benefit of this approach is that it is relatively inexpensive, with the costs largely carried by CRAs that will need to revise their methodologies to include assess-ments of financial preparedness for disasters (see Table 6.1). One challenge is that, even if they update their methodologies, CRAs may conclude that, given the backstop FEMA provides to state and local governments through the PA program, communi-ties’ credit ratings do not require revision. Consequently, the extent to which such changes would affect state and local government behavior is unclear, especially when PA continues to be an option for them. Another concern is that this approach could potentially introduce factors that have little effect on a community’s ability to repay debt and thus result in an unwarranted downgrading of a community’s credit rating.

Raise Communities’ Awareness of Their Risks

In our discussions with subject-matter experts, they often noted that—in their view—state and local governments did not have a systematic understanding of the major gaps, vulnerabilities, or potential economic impact of the hazards they face. As a con-sequence, state and local governments lack critical information and analysis that could encourage them to engage in better financial risk management—such as the purchase of insurance. To address this challenge, subject-matter experts with whom we spoke proposed requiring state and local governments to analyze and disclose information on the vulnerabilities to the hazards they face (see third row of Table 6.1) and suggested that FEMA provide financial or in-kind support to small and medium-sized com-munities to assist with this effort. FEMA could encourage SLTTs to do this as part of their mitigation planning. FEMA’s Threat and Hazard Identification and Risk Assess-ment and Stakeholder Preparedness Review processes may also offer a model for this. Through these processes, states, territories, tribes, and high-risk urban areas identify the major threats and hazards they face and the capabilities needed to address them. They then assess their actual capabilities, note any gaps that exist, and identify strate-

4 If CRAs expect the federal government to pay for the costs of disasters, this could be a sensible approach.5 On this point, see also Miller, 2018.

Options for Closing the Insurance Gap 61

gies for addressing them.6 FEMA could also consider offering technical assistance and increased engagement with SLTTs to support these efforts.

Requiring a wider variety of localities to engage in this type of analysis could increase their awareness of their risks and potential mitigation strategies—and induce changes in their financial risk management, such as purchasing insurance. Moreover, as subject-matter experts noted, when communities go through this type of analysis and disclose it, it creates a sense of ownership in terms of that exposure and how they handle it. The private sector could, in turn, use these analyses to develop solutions

6 For more detail on these processes, see FEMA, 2018c.

Table 6.1Overview of Options for Encouraging State and Local Governments to Purchase Insurance for Public Buildings, Contents, Vehicles, and Equipment

Option Description Advantages Disadvantages

Encourage CRAs to consider disaster vulnerability.

Encourages CRAs to consider financial preparedness for disasters in their assessments of communities’ creditworthiness

Creates incentives for sound financial risk-management practices; is relatively inexpensive

CRAs may conclude that credit ratings do not require revision, given the existence of the FEMA backstop.

Raise state and local awareness of risk.

Requires communities to identify major gaps and vulnerabilities and evaluate economic impacts of disasters

Increases awareness of vulnerabilities and creates a sense of ownership; helps the private sector develop solutions

Might not result in any changes in risk-management practices; might require technical and financial support from FEMA

Require state and local governments to cover the first layer of loss.

Requires communities to obtain coverage up to the 1-in-100 or 1-in-200-year event

Federal government no longer covers the first layer of loss but is still there for large, hard-to-insure incidents; removes discussions about amount of self-insurance; improves community incentives to insure against and mitigate risk

Makes disagreements likely on the appropriate level of insurance; could make costs burdensome for some communities

Eliminate PA for BCVE. Ends the eligibility of public buildings and their contents for the PA program

Federal government no longer covers the first layer of loss. No need to specify what part of loss distribution should be covered by the federal government; removes discussions about amount of self-insurance; creates incentives to insure and mitigate risk

Communities responsible for costs incurred from difficult-to-insure large incidents; might not result in changes to risk-management practices if state and local governments do not view program elimination as politically sustainable


appropriate to these state and local governments. Of course, many communities may lack the resources to conduct this type of analysis and consequently require techni-cal and financial support from FEMA. More fundamentally, however, analysis does not always lead to action. State and local governments may evaluate their risks, deter-mine that FEMA will address their needs, and conclude that no changes to their risk-management practices are necessary. As a result, supplementary efforts to close the insurance gap may still be necessary, such as requiring minimal levels of insurance coverage, which we discuss next.

Require State and Local Governments to Cover the First Layer of Loss

Subject-matter experts with whom we spoke also recommended requiring state and local governments to obtain a minimum level of insurance coverage for the types of perils their communities face (see the fourth row of Table 6.1). In their view, the cov-erage requirements should be significantly higher than those under FEMA’s O&M requirement—mandating that communities obtain coverage for up to 1-in-100 or 1-in-200-year events. Moreover, this requirement should not be tied to whether a com-munity received funding through the PA program for a previous event but rather be a blanket requirement for state and local governments across the country before it receives any PA funding.

This type of requirement would significantly shift responsibility for disaster response and recovery away from the federal government, leaving state and local gov-ernments and the private sector responsible for all but the largest, hardest-to-insure events. This would create a strong incentive for state and local governments to miti-gate their risk because they (and their insurers) would bear greater responsibility for response and recovery costs. This, in turn, could help buy down the overall risk that communities face from natural hazards and promote greater community resilience. This option would also render moot any discussions of whether larger entities have self-insurance that should be deducted from PA awards. A major drawback of this approach, of course, is that coverage mandates at this level would likely be burdensome for some communities. To ameliorate this, FEMA might want to consider applying this requirement only to larger jurisdictions, which have less incentive to purchase insurance because rainy-day funds and loans are available. FEMA could also consider addressing this through temporary premium assistance for truly needy communities. It allows government assistance to target poorer communities. Currently, PA is avail-able to all communities regardless of whether they are able to afford their own disaster response or not. This approach will also require a mechanism to determine how much risk each community would be expected to retain.

Options for Closing the Insurance Gap 63

Eliminate Public Assistance Funding for Buildings and Equipment

The FY 2021 President’s budget proposes another approach for requiring state and local governments to retain more risk: ending category E eligibility for PA funding. As explained in the budget,

FEMA’s current program is a no-limit, no-premium insurance policy for State and local governments, which disincentivizes self-protection and burdens taxpay-ers with the risky decisions made by State and local governments. Eliminating this assistance will encourage State and local governments to more responsibly manage their risk, including better land management and planning, purchasing insurance, and/or investing in mitigation.7

Eliminating this type of assistance over a specified transition period would significantly shift risk away from the federal government, requiring state and local governments to retain more risk than they currently do. For the reasons discussed in Chapter Five, many state and local governments would be expected to purchase additional cover-age in response to elimination of PA. Another benefit of this approach is that it would create a strong incentive for these governments to mitigate their risks because they would bear all the costs for rebuilding and restoring of public buildings. This could help lower the overall risk faced by these communities and encourage the adoption of risk transfer methods, such as the purchase of insurance. This approach would elimi-nate the administrative burden of determining how much risk state and local govern-ments would retain—they would retain all risk for BCVE. It would also make moot any discussions of whether larger entities have self-insurance that should be deducted from PA awards.

However, such an approach also has potential drawbacks. Eliminating PA would mean that the states would become responsible for the largest incidents—which are those that create the greatest challenges for private insurers. Insuring some of these risks would likely be very expensive and burdensome for many communities. As a result—as nearly all of our interviewees pointed out—state and local governments would likely strongly oppose the elimination of PA category E. The response to proposed rulemak-ing in 2000 provided evidence to support this view. In 2000, FEMA put forward an advance notice of proposed rulemaking that would make a public building eligible for PA only if it had been insured at the time of the disaster.8 According to interviewees, opposition to the proposal was strong, with pushback in particular from communities facing earthquake risk. FEMA subsequently dropped the initiative.

Eliminating PA might also not necessarily induce some entities to change their risk-management practices, as discussed in Chapter Five. Chief among the reasons for

7 Office of Management and Budget, 2020, p. 112.8 FEMA, 2000.


this was a belief that eliminating PA for BCVE is not politically sustainable. If it were eliminated and an event happened, there may be strong political pressure for FEMA to come in and help anyway. Program elimination may consequently result in com-munities that are mostly uncovered for large losses, at least until the policy is tested in practice and not subsequently reversed.

Discussion

Each of the policy alternatives discussed here will result in a different allocation of risk between federal, state, and local governments. Policymakers should consider the advantages and disadvantages of different approaches and consider what point on the risk allocation spectrum presented in Figure 1.1 in Chapter One makes the most sense to target. There are drawbacks of attempting to shift the entire risk for BCVE to state and local governments, but there are also clear drawbacks of the current approach—in which the federal government frequently covers the first layer of loss and provides assis-tance for a substantial portion of overall loss.

65

CHAPTER SEVEN

Conclusion

As the debate about how to best allocate risk between federal, state, and local govern-ments continues, understanding the financial risk-management practices of state and local governments will be critical. Our research offers a first step in this direction—detailing insurance practices state and local governments employ for their BCVE, the role FEMA assistance plays in these decisions, and options for closing the insurance gap for BCVE. In this chapter, we highlight some of our key findings and their impli-cations, as well as areas for future research.

State and Local Government Insurance Practices

Our analysis shows that a significant portion of state and local governments that are eligible for PA purchase some type of insurance for their BCVE, although current data do not allow a precise estimate. However, although insurance appears to be common, in the aggregate, the average insurance share (i.e., insurance payments as a share of all repair costs) is only 28 percent—meaning that the insurance is not covering a substan-tial portion of BCVE repairs. When we drill down into these numbers, we see diversity by jurisdiction type and size, as well as by the magnitude of repair costs. For example, insurance share is much lower for states than for other jurisdiction types, and it is sig-nificantly lower for large jurisdictions. Insurance share is substantially lower when the damage is significant.

To help us understand what drives this variation in insurance share, we used regression analysis to isolate the correlations between insurance share and different incident and public-entity attributes. For incident attributes, we found that insurance share varies by peril type. Earthquakes and floods, for example, had the lowest insur-ance shares. This suggests that these types of peril represent important financial expo-sures for the federal government. Moreover, insurance share for flood losses is lower outside SFHAs than inside them. We also found that insurance share is lower for equipment than for buildings, contents, and vehicles.

Regarding public-sector entity attributes, our analysis showed that insurance share is highest for jurisdictions with populations between 10,000 and 50,000 and lower for


both smaller and larger jurisdictions when repair cost per capita and other incident and applicant attributes are held constant. For larger communities, this might reflect their use of particular risk-management strategies that are not available to smaller commu-nities. Finally, our analysis also shows that the number of previous declarations influ-ences insurance share: Insurance share is higher when there are more declarations in recent years but lower when there are more declarations five or more years prior to the current declaration.

The Effect That FEMA Assistance Has on Risk-Management Decisions

Scholars have explored the relationship between disaster assistance programs and the purchase of insurance. The evidence that postdisaster assistance discourages the pur-chase of insurance in practice is mixed—and, in fact, researchers on some studies have reached the opposite conclusion. However, it is worth noting that some of these studies apply to insurance purchasing decisions made by individuals and the effects of FEMA’s IA program—which is a much more limited program than PA. Consequently, it is unknown whether these results apply to the behavior of public entities.

In our discussions with subject-matter experts, many stated that the potential for PA reduces the purchase of insurance by state and local governments. Some noted, for example, that public entities often view public buildings as essential facilities that FEMA is responsible for repairing and restoring. Others pointed to less extreme responses, stating that state and local governments will limit their insurance purchases to facilities they believe are critical in a postdisaster environment, such as police and fire stations, and would count on FEMA to fund the repair of the rest. Interviewees also argued that the amount of insurance that state and local governments purchase is less than it would have been in the absence of PA and that some choose larger deduct-ibles than they would otherwise have because PA covers the deductible for first-time PA recipients.

Although most of the experts with whom we spoke felt that PA had a negative impact on the purchase of insurance, this view was not universal. For example, some saw no evidence that state and local governments bought less insurance because they expected that FEMA would bail them out. Several noted that they are often reluctant to rely on FEMA, and one argued that CRAs are a bigger driver for risk-management decisions. Moreover, some experts said that state and local governments purchase insurance to meet an O&M requirement, which offsets, to some extent, any down-ward pressure the PA program exerts on insurance purchases. However, several factors lessen its effect—such as the reduction of O&M requirements by state insurance com-missioners and the leeway a PA applicant has in choosing the size of its deductible.

Experts with whom we spoke consistently stated that, should FEMA eliminate PA for BCVE, the extent to which communities believe that such a policy change was

Conclusion 67

permanent would be a key factor in determining how they respond. But experts offered different perspectives as to how, were such a policy change implemented, it would affect state and local behavior. Some argued that it would have little impact on some public entities—pointing out, for example, that some state and local governments have little awareness of the intricacies of the PA program. A more common view was that state and local governments would take more responsibility for managing their disaster risks. The experts noted that emergency managers were problem-solvers who would not simply hope that the program would be restored once a large incident occurred.

Options for Closing the Insurance Gap

During the course of our research, several approaches emerged for increasing insur-ance coverage among state and local governments for BCVE. For example, several experts with whom we spoke recommended encouraging CRAs to evaluate communi-ties’ financial preparedness for disasters. They noted that, in assessing a community’s creditworthiness, CRAs do not typically evaluate this and that pushing CRAs to do so could create an incentive for communities to adopt sound financial risk-management practices for disasters. An advantage of this approach is that it more accurately reflects a community’s financial position and is relatively inexpensive, but the extent to which such changes would affect state and local government behavior is unclear, especially when PA continues to be an option for them.

Experts also stated that state and local governments often do not have a good understanding of the potential economic consequences of the hazards they face. Con-sequently, they lack information that could promote better financial risk management. To address this, experts recommended requiring state and local governments to ana-lyze and disclose information on the vulnerabilities to the hazards they face and sug-gested that FEMA provide financial or in-kind support to small and medium-sized communities to assist with this effort. This type of requirement could increase aware-ness of their risks and promote better mitigation. It could also enable the private sector to develop appropriate solutions. But many communities lack the resources to conduct this type of analysis and would require support from FEMA.

A more direct approach is holding state and local governments responsible for the first layer of loss to BCVE. Experts with whom we spoke recommended that commu-nities obtain coverage for up to 1-in-100 or 1-in-200-year events. PA funding would then be available only for losses over this threshold. Such a requirement would elimi-nate federal funding for the first dollar of loss, which is now frequently the case, and shifts more responsibility for disaster response and recovery to the state and local levels. Doing so would create an incentive for state and local governments to mitigate their risks. However, it would also place a significant burden on many communities.


Finally, the FY 2021 President’s budget proposes ending eligibility for BCVE to receive PA funding, arguing that this would encourage state and local governments to better manage their risk and promote the purchase of insurance. Because state and local governments would become responsible for bearing all the costs of repairing or restoring BCVE, this approach would create an incentive for mitigation and poten-tially lower the overall risk communities face. However, eliminating PA would leave these entities responsible for managing the largest incidents, which are the most dif-ficult and expensive to insure against, and consequently place a large burden on them. Moreover, as several experts with whom we spoke noted, eliminating PA might not result in changes to state and local risk-management practices. They explained that many communities will not see the elimination of PA for BCVE as politically sustain-able. Consequently, they may go uncovered for large losses until they face a large unin-sured loss that FEMA and other federal programs do not cover.

Further Research

Throughout the course of our work, we identified several areas for additional research that would help further the collective understanding of state and local government insurance practices and how to encourage communities to purchase insurance, includ-ing the following:

• Evaluate the insurance practices of large jurisdictions. One of our most-striking findings is that large jurisdictions—which are thought to have more resources—tend to have less insurance than midsized ones. However, more work is needed to confirm this conclusion. For example, it is possible that the relation-ship between insurance share and jurisdiction size could be exaggerated if larger jurisdictions avoid submitting claims for damaged facilities that they can cover with insurance or other sources of funds proportionally more often than smaller jurisdictions—which our data did not allow us to determine. Further work, such as through surveys, is also needed to understand what other types of risk-manage-ment strategies large entities use.

• Evaluate flood insurance practices outside SFHAs. Our analysis provides some evidence that insurance share for flood damage is lower outside SFHAs, but additional work to support this finding is necessary. Data limitations allowed us to construct only rough measures of the share of repair costs that were outside SFHAs and of the share of damage that was flood related.

• Analyze the relationship between the purchase of insurance and past disas-ter frequency. Although our work found that the frequency of past disaster dec-larations appears to affect insurance share, more research is needed in this area. Our measure for capturing past disasters, for example, did not distinguish type of

Conclusion 69

peril causing the past disaster or whether a damaged facility received PA in a prior event—factors that might affect insurance purchasing decisions.

• Evaluate insurance share by property type. Characterizing the different types of property in a PA application proved difficult. Even in instances in which a facility description indicated that its repair costs were for a building or vehicle, the facility could also include repair costs for other property types. Additional work is needed that explores how insurance share varies by the type of property damaged.

• Assess the relationship between mitigation funding and insurance share. Our work could easily be extended to examine the relationship between FEMA mitigation funding and insurance share. Such an analysis, for example, could evaluate whether mitigation funding is higher when insurance share is lower—to assess whether applicants are more likely to mitigate when they are underinsured.

• Analyze other PA categories of permanent work. Our analysis focused solely on category E—the category of permanent work receiving the largest percentage of PA funding and the one for which insurance is most readily available. Expand-ing this analysis to include the other categories would provide insight into state and local government insurance practices for other types of facilities and steps that could be taken to encourage the purchase of insurance for them.

• Evaluate approaches for closing the insurance gap for individuals. Low insur-ance take-up rates by homeowners and renters results in individuals turning to government for assistance postevent. These requests strain government budgets and slow other government expenditures that could speed the recovery process. Relevant insurance gaps include not only those for flood and earthquake property risks but also lack of medical insurance—as has been apparent in the coronavirus pandemic crisis.

• Evaluate approaches for facilitating the transfer of risk. Should the federal government eliminate the eligibility of BCVE for PA, responsibility for address-ing natural disaster losses will fall entirely to state and local governments. One challenge for them will be finding insurance coverage for large incidents, and the Terrorism Risk Insurance Act offers a potential model for addressing this challenge.1 To price insurance, firms need to know the frequency and the sever-ity of an event, which is particularly challenging for both terrorist acts and some natural disasters. With the Terrorism Risk Insurance Act, the federal government removes some of that uncertainty by stepping in if an event reaches a specified threshold, which essentially provides a backstop for the private insurance market. This threshold amount essentially acts as a ceiling for potential losses for the private insurers, allowing them to efficiently price coverage below that ceiling because their losses are capped. The federal government would step in to cover losses once the threshold has been reached. A similar program may be appropri-

1 Pub. L. 107-297, 2002.


ate for natural disaster risk. Evaluating approaches such as these will help identify innovative ways to close the insurance gap.

71

APPENDIX A

Glossary

Unless otherwise noted, all definitions in this glossary come from the Insurance Infor-mation Institute.1

all-risk policy: An insurance policy that provides coverage against several differ-ent risk or peril types—such as earthquakes, fires, and hurricanes.

applicant–declaration pair: The combination of all the Public Assistance cat-egory E projects for a particular applicant in a given disaster declaration.2

blanket policy: A policy that provides coverage for a specified set of properties, usually with a cap on the aggregate payment across all properties per event.3

broker: “An intermediary between a customer and an insurance company. Bro-kers typically search the market for coverage appropriate to their clients.”

commercial insurance: Insurance designed for businesses. “Among the major coverages are boiler and machinery, business income, commercial auto, comprehensive general liability, directors and officers liability, fire and allied lines, inland marine, medical malpractice liability, product liability, professional liability, surety and fidelity, and workers compensation.”

deductible: “The amount of loss paid by the policyholder [whether] a specified dollar amount, a percentage of the claim amount, or a specified amount of time that must elapse before benefits are paid. The bigger the deductible, the lower the premium charged for the same coverage.”

EMMIE: The Emergency Management Mission Integrated Environment, a Fed-eral Emergency Management Agency system used to process Public Assistance grant applications from state, local, and tribal governments and certain private nonprofit entities.

hard market: An insurance market in which insurance prices increase and the amount of insurance capacity available decreases.4

1 Insurance Information Institute, undated.2 Our definition.3 Also see National Association of Insurance Commissioners, undated.4 International Risk Management Institute, undated.


homeowner’s insurance: See residential insurance.indemnity policy: A policy that insures a policyholder against the occurrence of

an event by paying for a portion of the actual losses.insurance pool: An arrangement in which multiple communities contribute

funds to a central pool that is used to pay claims. If the pool runs a surplus, it may return some of the contributions to its members.5

insurance gap: The difference between what is currently insured and what is insurable.6

insurable risk: Risk for which it is relatively easy to get insurance and that meet certain criteria. These include being definable, accidental in nature, and part of a group of similar risks large enough to make losses predictable. The insurance company also must be able to derive a reasonable price for the insurance. (Also see uninsurable risk.)

insurance share: Insurance payments as a share of an applicant’s repair costs for a given disaster declaration.7 As used in this report, insurance share includes some repair costs for which the applicant does not have insurance but for which the Federal Emergency Management Agency will not pay. For example, insurance share includes the deductible when coverage is included in the obtain and maintain requirement because the Public Assistance program will not cover the deductible in these circum-stances. (The Federal Emergency Management Agency covers the deductible for prop-erty that is not subject to obtain and maintain requirements.) Insurance share also includes required reductions for flood-related damage in special flood hazard areas up to the coverage limits provided by the National Flood Insurance Program regardless of whether the applicant purchased flood coverage. Current data on the Public Assis-tance costs suggest that these additional elements of insurance share are not large, but the extent to which these costs are underidentified because data coding is incomplete is unknown.

moral hazard: The theory that individuals may underinsure (or not insure) because they expect postdisaster government assistance.8

multiple-peril policy: An insurance policy that provides coverage against several different risk or peril types—such as earthquakes, fires, and hurricanes. Also called an all-risk policy.

parametric insurance: A type of policy that insures a policyholder against the occurrence of an event by paying a set amount based on the magnitude and location of the event, which is known as the trigger.9

5 Also see National Association of Insurance Commissioners, undated.6 Kaniewski, 2018, p. 5.7 Our definition.8 Browne and Hoyt, 2000; Raschky and Weck-Hannemann, 2007; Raschky, Schwarze, et al., 2013.9 National Association of Insurance Commissioners, 2020.

Glossary 73

policy: An insurance contract between an insurance company and policyholder providing coverage details.

policy limit: The maximum amount of insurance that can be paid for a covered loss.

premium: Money charged for the insurance coverage reflecting expectation of loss.10

project amount: Amount of repair costs less insurance reductions. Applies to the Federal Emergency Management Agency’s Public Assistance program.

property and casualty insurance: Covers damage to or loss of policyholders’ property and legal liability for damage caused to other people or their property. This includes auto, homeowner’s, and commercial insurance.

reinsurance: Insurance bought by insurers.repair cost: The sum of all cost codes for an applicant–declaration pair minus

insurance payments, mitigation costs, and grantee or subgrantee costs for administer-ing the Public Assistance project.11

residential insurance: A policy type that typically provides compensation for damage from specified hazards to a home, including its contents and structures on the property (also known as homeowner’s insurance).12

risk management: Management of the varied risks to which a business firm or association might be subject. It includes analyzing the likelihood of loss and choosing options to better manage or minimize loss. These options typically include reducing and eliminating the risk with safety measures, buying insurance, and self-insurance.

self-insurance: A risk-management technique in which a company or individual sets aside a pool of money to be used to remedy an unexpected loss.13

single-peril policy: An insurance policy that provides coverage for a specific risk or peril type—such as earthquake, fire, or hurricane. Also called a named-peril policy.

soft insurance market: Opposite of a hard insurance market. An insurance market in which insurance prices decrease and the amount of insurance capacity avail-able increases.14

special flood hazard area: The land area covered by the floodwaters of a flood having a 1-percent chance of being equaled or exceeded in any given year.15

10 National Association of Insurance Commissioners, undated.11 Our definition.12 Federal Insurance Office, 2015, p. 13.13 Kenton, 2019.14 International Risk Management Institute, undated.15 FEMA, 2020b.


sublimit: A limit in an insurance policy on the coverage available for a particular type of loss.16

uninsurable risk: Risk for which it is difficult for an organization or individual to get insurance. (Also see insurable risk.)

16 See International Risk Management Institute, undated.

75

APPENDIX B

Statistical Methodology

This appendix first details the methods used to construct the cost and insurance mea-sures used in this analysis. We then describe the statistical techniques used to analyze the data and close by estimating a statistical model of insurance share. The higher-level discussion and interpretation of the model results are presented in Chapters Three and Four.

Calculation of Repair Costs and Insurance Share

The EMMIE database provides detailed cost information for each of the reported PA projects. Costs are categorized by cost codes, and we used these codes to calculate repair (or replacement) cost of the damaged buildings and equipment, the amount of insurance payments, and the resulting share of insurance in total repair costs for each applicant–declaration pair. Repair costs for applicant i in declaration j are calculated using Equation B.1, and insurance share is determined using Equation B.2.

repaircostij = projectamountij−insuranceij−mitigationcostsij−admincostsij , (B.1)

wherei = applicantj = disaster declaration.

insuranceshareij =insuranceijrepaircostij

×100. (B.2)

The calculation of repair costs starts with the sum of all cost codes for an applicant–declaration pair. The sum of all cost codes is termed the project amount in the EMMIE database. A key purpose of the EMMIE database is to determine the amount of federal funding FEMA will provide to the applicant, so insurance proceeds are entered as negative values in the EMMIE database so that they reduce the project


amount.1 To calculate repair cost, we consequently subtract insurance payments from the project amount. A facility that applies for PA can be eligible for improvements that reduce the facility’s vulnerability to future events. We were interested in the amount of insurance relative to the amount of damage to the facility and thus removed migra-tion costs from the project amount.2 We have also removed grantee or subgrantee costs for administering the PA grant from the project amount. Table B.1 shows the relative sizes of these different cost categories for all applicant–declaration pairs. As can be seen, mitigation costs total $2.4 billion. Administrative costs are small relative to the $9.39 billion in repair costs; consequently, including or removing administrative costs from the project amount would have little impact on our findings.

Insurance reductions on project worksheets include reductions in cases in which FEMA required an applicant to have insurance but the applicant did not have it. For example, federal law requires public buildings in SFHAs to carry flood insurance up to the $500,000 limit offered by the NFIP. FEMA consequently deducts the cost of flood-related repairs up to $500,000 from the overall damage when determining proj-ect amount—even if the applicant did not carry flood insurance. Similarly, FEMA deducts the amount of insurance specified in an O&M requirement from a previous event even if the applicant did not comply with the O&M requirement.3 FEMA also does not cover the deductible for facilities subject to an O&M requirement.

Reductions recorded in the EMMIE database for cases in which FEMA required the applicant to obtain insurance but the applicant did not are small. Table B.1 lists the totals across all applicant–declaration pairs for cost codes of interest. Codes 5903 and 5906 pertain to reductions for failure to obtain and maintain insurance. The combined total for these two codes is $0.04 billion, meaning that, in net, these codes increased rather than reduced project amount. FEMA staff were not confident that insurance reductions were recorded with sufficient fidelity to warrant meaningful distinctions between the different types of insurance reductions (codes 5900 through 5916). It is thus difficult to know whether the proportion of the overall insurance reduction that is due to a failure to purchase required insurance is as small as the EMMIE data indi-cate. However, it is important to keep in mind that the statistics on insurance reported here include some situations in which an applicant was required to have insurance but did not.

FEMA’s RRAD staff indicated that some insurance-related cost reductions might be included in cost code 9999, which is the cost code for “other” expenditures. Using

1 The state cost share is applied after the project amount is calculated. State cost shares are typically 25 percent of project amount but can vary across declarations.2 Mitigation beyond bringing the damaged parts of a building up to code is outside the scope of an insurance contract.3 PA can be denied entirely for a damaged facility if an applicant fails to comply with an O&M requirement from a previous disaster.

Statistical Methodology 77

search terms, such as insurance and insur, RRAD staff used previously developed algo-rithms to search the comment fields associated with this cost code to identify whether the entry was insurance-related. The magnitude of potential insurance-related costs coded as 9999 appears small. As shown in Table B.1, only $80 million was identified as such (see code 9999_ins). Insurance reductions total $2.65 billion across all applicant–declaration pairs (covering codes 5900 to 5916 and 9999_ins).

Table B.1Public Assistance Costs for Buildings, Contents, Vehicles, and Equipment for Disaster Declarations Made Between January 2008 and June 2018

Cost Type Cost CodeTotal Cost, in

Billions of Dollars

Insurance

Actual and anticipated insurance proceeds

Peril type unspecified 5900, 5901 –1.58

Flood 5904, 5905, 5908 –0.76

Wind 5911, 5912 –0.18

Earthquake 5913, 5914 <–0.01

Fire 5915, 5916 –0.02

O&M reductions 5903 –0.05

Adjustment in project amount due to failure to obtain and maintain

5906 0.09

Amount of general insurance deductible 5910 <–0.01

All other insurance codes 5902, 5907, 5909 –0.23

9999 code costs identified as insurance 9999_ins 0.08

Total insurance 5900 to 5916 and 9999_ins –2.65

Mitigation 0909 and costs embedded in noninsurance codes

2.40

Administration

Direct subgrantee 9901 0.21

Direct grantee 9902 <0.01

Total administration 9901 and 9902 0.21

Other

9999 code costs not identified as insurance 9999_not ins 1.51

Project amount (the sum of all cost codes) 0000 to 9999 9.36

Repair costs (see Equation B.1) — 9.39


Statistical Analysis of Insurance Share

In this section, we describe the statistical techniques that we used to analyze how insurance share varies with applicant and incident attributes. We begin by presenting a model of insurance share. We then turn to the empirical analysis of insurance share, beginning with a description of the variables used in the analysis. We conclude with the results from the regression analysis.

Insurance Share Model

The typical relationship between insurance payment, deductible, policy limit, and repair cost as discussed in Chapter Two (Figure 2.1) follows Equation B.3:

insurancepaymentij = f deductiblei ; policylimiti ;causeoflossij ;repaircostij( ). (B.3)

Insurance payment for applicant i in disaster declaration j is a function of the deduct-ible and policy limit of applicant i’s insurance policy and the repair cost for applicant i in disaster declaration j. Equation B.3 is based on the terms of the insurance contract. Deductibles and policy limits can vary by peril type (in the limit, there might be no coverage for a particular peril), so the cause of loss in a particular declaration is relevant to the ultimate insurance share for that event.4 Dividing both sides of Equation B.3 by repair cost, which is taken as an exogenous variable in this analysis, results in an equa-tion for insurance share.

The deductible, the overall policy limit, and peril type–specific sublimits reflect decisions made by the policyholder and are based on factors that include the overall value of an applicant’s assets, an assessment of losses that would occur in different inci-dent return periods, and the applicant’s risk tolerance.

An Empirical Analysis of Insurance ShareSpecification of the Insurance Model

EMMIE contains information on repair cost, and the cause of the loss can be roughly identified using FEMA’s disaster declaration databases. However, EMMIE contains little readily accessible information on deductibles and policy limits.5 We therefore look at applicant attributes that might be correlated with the deductible and policy

4 The deductible and policy limit variables in Equation B.3 can be viewed as vectors with distinct deductibles and policy limits for each peril type.5 In principle, the EMMIE insurance tables contain information on deductible and policy limit. However, these fields are often empty, and PA staff interviewed for this analysis indicated that there is often confusion on what information to enter into the various fields.


limits and examine the relationship between these attributes and insurance share. The following incident and applicant attributes were included in the model:

• applicant attributes – jurisdiction type (e.g., city, county, state) – jurisdiction population – number of disaster declarations prior to the event

• incident attributes – incident type (e.g., named storm, earthquake) – repair cost per capita – extent to which the repair work is complete – share of repair cost in an SFHA – share of repair cost for equipment – FEMA region.

To compare insurance behavior in jurisdictions of very different sizes, we normal-ized repair cost by population. By doing so, we in effect examined insurance purchase decision on a per capita basis—for example, considering deductible per capita, policy limit per capita, and insurance payments per capita.

Variables Constructed for the Analysis

Jurisdiction type. The applicant type we use in our analysis is based on the “Appli-cant Type” variable in the EMMIE database. The first part of Table B.2 breaks down applicant–declaration pairs by type and reports the distribution of repair costs across jurisdiction type. As can be seen, 47 percent of the applicant–declaration pairs are for cities and townships, and they together account for a similar percentage (42 percent) of the $9.39 billion in repair costs. States account for a small share of the applicant–declaration pairs (2 percent), but, because the repair costs are much larger per applicant–declaration pair, they amount to 11 percent of overall repair costs.

Applicant population. Insurance and risk-management practices might be expected to vary by applicant size. Larger applicants may have more risk-management tools at their disposal and thus rely less on traditional insurance arrangements. Larger applicants also have more BCVE at risk. We used population as a measure of size for the cities, counties, states, and territories in the sample. We took population figures from the 2010 census, which falls within the 2008–2018 period spanned by the data. We have not yet developed a measure of size for the other applicant types, such as public schools.

As seen in Table B.2, the database includes a large number of smaller jurisdictions. However, although 44 percent of the cities, counties, states, and territories had popu-lations less than 10,000, they account for only 5 percent of repair costs. In contrast, the largest jurisdictions—those with populations greater than or equal to 1 million—

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Table B.2Insurance Share, by Applicant and Incident Attributes

Attribute

Applicant–Declaration Pairs Repair Costs Average Insurance Sharea

NPercentage

of TotalAmount,

in Billions of DollarsPercentage

of Total N Percentage

All observations 11,561 100 9.39 100 11,114 46

Jurisdiction type

City or township 5,483 47 3.99 42 5,248 44

County 1,557 13 0.74 8 1,494 47

State 243 2 1.04 11 238 27

Public school 1,984 17 1.48 16 1,939 52

Public higher education 696 6 1.19 13 676 41

Special district 1,598 10 0.96 10 1,519 45

Applicant population (city, counties, and states only; N = 7,283 and 6,980 when limited to observations with repair cost ≥$100)

<5,000 2,228 31 0.17 3 2,111 47

5,000-10,000 940 13 0.14 2 903 48

10,000–50,000 2,325 32 0.50 9 2,246 48

50,000–100,000 625 9 0.12 2 601 38

100,000–1 million 732 10 0.53 9 706 31

1 million–5 million 162 2 0.65 11 159 27

≥5 million 130 2 3.61 63 126 26

Missing 141 2 0.02 0 128 39

Statistical Meth

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Attribute


NPercentage

of TotalAmount,



Incident type

Named storm 4,734 41 6.84 73 4,609 44

Storm, no flood 770 7 0.32 3 750 61

Storm, flood 3,383 29 1.81 19 3,249 47

Winter storm, no flood 1,536 13 0.06 1 1,429 45

Winter storm, flood 903 8 0.09 1 855 41

Fire 83 1 0.07 1 79 41

Earthquake 43 <0.5 0.10 1 41 8

Flooding only 105 1 0.06 1 98 32

Other, no flood 4 <0.5 0.06 1 4 40

Extent to which the work is complete

All projects open 2,166 19 2.01 21 2,084 42

Some open and some closed 631 5 5.86 62 631 42

All projects closed 8,764 76 1.52 16 8,399 47

Repair cost per capita (cities, counties, and states only; N = 7,283 and 6,980 when limited to observations with repair cost ≥$100), in dollars

<0.25 1,791 24 0.07 1 1,491 24

0.25–1.00 1,630 22 0.13 2 1,630 41

1–10 2,427 33 0.41 7 2,427 52

Table B.2—Continued

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Attribute


NPercentage

of TotalAmount,



10–50 857 12 0.86 15 857 56

50–200 284 4 0.62 11 284 56

≥200 163 2 3.66 63 163 51

Missing (due to missing population) 141 2 0.02 <0.5 128 39

Share of repair costs in SFHAs, as a percentage

25 9,459 82 3.70 39 9,326 46

25–75 568 5 4.15 44 564 43

≥75 1,251 11 1.54 16 1,224 45

Missingb 283 2 0 0 0 —

Share of repair costs that were for equipment, as a percentage

<25 7,927 69 7.69 82 7,805 48

25–75 953 8 1.06 11 951 42

≥75 2,398 21 0.65 7 2,358 38

Missingb 283 2 0 0 0 —

Number of disaster declarations prior to the event (back to 1965)

0–9 1,484 13 0.22 2 1,434 51

10–14 3,074 27 0.78 8 2,945 48

15–24 5,534 48 5.93 63 5,317 45


Statistical Meth

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Attribute


NPercentage

of TotalAmount,



≥25 927 8 0.62 7 891 38

Missingc 542 5 1.85 20 527 30

Number of disaster declarations in 5 years prior to event

0 1,177 10 0.30 3 1,129 44

1–2 3,826 33 5.26 56 3,681 45

3–5 4,625 40 1.43 15 4,461 47

≥6 1,391 12 0.56 6 1,316 48

Missingc 542 5 1.85 20 527 30

Number of disaster declarations more than 5 years prior to event (back to 1965)

0–9 3,008 26 0.70 7 2,885 51

10–14 3,425 30 4.52 48 3,281 45

15–19 3,309 29 1.43 15 3,197 47

≥20 1,277 11 0.90 10 1,224 36

Missingc 542 5 1.85 20 527 30

NOTE: The FEMA region factor variable is not reported.a For repair cost ≥$100.b Share is missing when repair cost equals $0.c Missing when county for the applicant–declaration observation is “statewide.” This occurs when the applicant type is “state” and for other applicant types that have damaged facilities in multiple counties.



account for just 4 percent of the applicant–declaration combinations but 74 percent of the repair costs.

Incident type. We used FEMA incident descriptions to characterize the cause of loss for a particular declaration. We extracted incident type from FEMA’s disaster declaration summary.6 The “Title” variable in that data set divides incidents into seven categories:

• named storms (including hurricanes, typhoons, and tropical storms)• storms• winter storms• fire • earthquake • flooding only• other without flooding.

Storms and winter storms are, in turn, broken down into flood and nonflood events based on a search for the word “flood” in the title. As shown in Table B.2, named storms account for a large majority of total repair costs (73 percent) and 41 percent of the applicant–declaration combinations.

This incident-type breakdown provides insight into the causes of loss for the dif-ferent declarations in the data set. For hurricanes, the relevant peril types are wind and flood. For storms without flood, the peril types are severe storms, straight-line winds, and tornadoes. Storms with flood add flood to the relevant peril types. Perils in winter storm include wind, snow, and ice. The perils for fire and earthquake are obvious. The flooding-only category includes events in which the only cause of loss identified is flooding, including tsunami waves and ice jams. Examples would include riverine flooding not associated with a storm in the immediate area. Few applicant–declaration pairs fall into other-without-flooding incident-type category. Examples of incidents in this category include explosions, volcanic eruptions and lava flows, and freezes.

Extent to which the repair work is complete. It is conceivable that the insur-ance information for open claims may systemically underrepresent (or overrepresent) the ultimate insurance share for the project. We addressed this issue by characterizing each applicant–declaration pair according to

• whether all the projects for the applicant–declaration pair remain open• some of projects remain open and some are closed• all the projects are closed.7

6 FEMA, 2019a.7 The repair work on the damaged facility may well have been completed a considerable time before the FEMA staff actually close the project. We also experimented with including a year fixed effect in the regression models


As shown in Table B.2, all projects were closed for more than 75 percent of the 11,561 applicant–declaration pairs, but these observations account for only 16 percent of the overall repair costs.

Repair cost per capita. As discussed in Chapter Two, the magnitude of repair cost for an applicant in a particular event is expected to have an important impact on the share of overall repair cost that insurance covered. To allow comparison between jurisdictions of different sizes, repair cost per capita is calculated. Repair cost per capita is divided into the six categories listed in Table B.2. As can be seen, repair costs are concentrated in a small number of observations. The 447 applicant–declaration obser-vations with repair costs higher than $50 per capita (6 percent of applicant–declaration combinations) account for 74 percent of repair costs. At the other extreme, the nearly 46 percent of applicant–declaration pairs with repair costs lower than $1 per capita account for less than 3 percent of total repair costs.

Share of repair costs in SFHAs. Both risk and insurance requirements for flood are different inside versus outside FEMA-designated SFHAs. An SFHA is an area with at least a 1-percent chance of flooding in any year. The EMMIE database provides the geocoordinates for each facility included in a PA project. We combined these coordi-nates with data in the National Flood Hazard Layer database to determine whether each facility was in an SFHA.8 Costs in EMMIE are reported at the project level, not the facility level, and our analysis rolls projects up into applicant–declaration pairs. To develop a rough measure of the share of repair costs in an SFHA for an applicant–declaration pair, we set the share of repair costs in the SFHA for each project based on the percentage of facilities for that project in the SFHA. Facilities for which the geocoordinates corresponded to areas that have not been mapped by the National Flood Hazard Layer were considered outside the SFHA.9 The main shortcoming of this approach is that the facilities can have very different repair costs in the same proj-ect. This method, in effect, weighs all facilities the same in estimating the overall share of repair costs in the SFHA. The same problem would exist if the analysis were done using project instead of applicant–declaration pair as the unit of analysis.

As shown in Table B.2, a large fraction of the applicant–declaration combina-tions (82 percent) have less than 25 percent of repair costs in SFHAs. However, these applicant–declaration pairs account for a disproportionately small share of overall repair costs (39 percent), suggesting that a considerable proportion of repair costs are inside SFHAs.

discussed below as another way to control for the gradual closure of projects, but the statistical significance of those year effects was marginal.8 See FEMA, 2019b.9 Geocoordinates could not be determined for 1.37 percent of the facilities. These facilities could not be geo-coded because the street or city was missing or because the address was unusable (e.g., “Hwy 87”). These facili-ties are assumed to be not in an SFHA. Given the small proportion of observations, it is highly unlikely that this assumption would meaningfully affect the results.


Share of repair cost for equipment. The PA program provides assistance for a wide variety of BCVE items that are classified as category E. Assistance is provided to repair buildings of all sorts, vehicles, as well as equipment. Examples that fall into this latter category include fences, tractors, athletic facilities, landfills, shooting ranges, swimming pools, cranes, outdoor stages, and weather alert sirens. To understand how insurance coverage might vary by property type, we used the application title and facility name recorded in EMMIE to group facilities into different categories. When the name indicated that a facility was building or contents, we tagged it as a building; when the name indicated that a facility was a vehicle, we tagged it as a vehicle.10 Writ-ing code to accurately classify the different facilities proved challenging, and further work is warranted on how best to group facilities. The same approach used to deter-mine the percentage of repair costs in SFHAs was used to calculate the share of repair cost that fell in each category.

We found that insurance shares for buildings, contents, and vehicles were simi-lar and combined them in order to isolate the proportion of repair costs for equip-ment. Equipment account for less than 25 percent of repair costs for a sizable majority (69 percent) of applicant–declaration pairs (see Table B.2). These applicant–declaration pairs also account for a large share (82 percent) of the repair costs between 2008 and June 2019.

Prior disaster declarations. The frequency of past disaster declarations might be expected to affect the risk-management practices of public entities and nonprofits. Demand for insurance might be greater in higher-risk areas, and the number of previ-ous disaster declarations would presumably be an indicator of risk. Previous declara-tions might result in more structures being subject to PA’s O&M requirement, and the incidents associated with the recent declarations may increase awareness of the risk. On the other hand, insurance premiums may be greater in areas that had more declara-tions in prior years, putting downward pressure on insurance purchases. The expected overall effect that prior declarations would have on insurance share is thus uncertain.

We used a database of federal disaster declarations to determine the number of disaster declarations prior to the incident begin date on each applicant–declaration pair.11 The FEMA disaster declaration database details the counties covered by each declaration, which we matched with the county where damage occurred as reported

10 For example, a record with an application title or facility name that included the word building, structure, courthouse, or offices would have been classified as a building. A record with an application title or facility name including the term vehicle, car, or Ford would have been classified as a vehicle. A facility could be identified as both a building and a vehicle.11 FEMA, 2018d. The data capture all federally declared disasters, which consist of major disaster declarations (those with the FEMA code DR), fire management declarations (those with the FEMA code FM or FS), and emergency declarations (those with the FEMA code EM). Of the 48,555 declaration–county pairs in the data set (before consolidation of multiple declarations in a county on the same day), 73 percent are type DR, 25 percent are type EM, and 2 percent are type FM or FS.


on the applicant–declaration record. In some cases, the county on the applicant–declaration record was recorded as “statewide” (most commonly, when the applicant was a state). When this occurred, the number of previous disasters was not calculated. Multiple declarations in a county on the same incident begin date were consolidated. This often occurred where there was both a disaster declaration (DR) and an emer-gency declaration (EM) for the same incident. The FEMA database allows determi-nation of previous declarations from 1965 (the first date for which declarations are recorded by county) to the date of the indicated declaration.

This measure of applicant experience with prior disasters has its limitations. Even if a prior declaration occurred in the same county, it might not have affected the prop-erty of the entity applying for PA in a current disaster. This measure also does not consider declarations in neighboring counties, which may influence risk-management behavior, and it does not distinguish between the various perils that can cause losses.

As shown in the last section of Table B.2, the number of prior declarations can be considerable. More than 80 percent of the applicant–declaration pairs had ten or more declarations in the same county as the one affected by the declaration in the entity’s application being analyzed from 1965 to the disaster date for that applicant–declaration pair. Because the effect of more-recent declarations may differ from those further back in time, we also calculated the number of declarations during the five years preced-ing the declaration in question and during the period more than five years prior to the declaration in question. The tabulations are reported at the end of Table B.2. Only 10 percent of the observations had not had a declaration in the five years preceding the date for the declaration in question.

FEMA region. In addition to the variables so far described, we also included a factor variable that identifies the FEMA region in which the declaration occurs. Such a variable can, in principle, pick up regional differences in implementation of the PA program that could affect the extent to which insurance is identified and deducted from repair costs.

Regression Results

We used a tobit model to estimate the relationship between insurance share and the variables included in Table B.2. The tobit model is appropriate because there is a clus-tering of insurance share at 0 and 1 (see Figure 3.1 in Chapter Three). We used the “tobit” command in the Stata software package to estimate the tobit model.12 The options we selected for the command account for potential correlation in the error term because the same applicant can appear for multiple declarations. The model is estimated for cities, counties, and states for which population could be determined. Excluded are public schools, public higher education, and special districts because size measures had not yet been developed for these jurisdiction types.

12 The following Stata command was used: tobit <ins_share> <explanatory variables> if repair_cost ≥ 100, ll(0) ul(1) vce(cluster <applicant identifier>).


The tobit procedure estimates the relationship between the explanatory variables and the underlying desired insurance share, which, in principle, can be less than 0 or greater than 1. We used Stata’s “margins” command to report the relationship between the explanatory variables and the observed insurance share, which falls between 0 and 1.13 The coefficients show the predicted change in insurance share if the value of one factor variable differs from that in a reference category. For example, insurance share for a jurisdiction with a population between 50,000 and 100,000 is predicted to be 2.9 percentage points lower than one with a population between 10,000 and 50,000. The Stata software calculates this by comparing (1) the average of the predicted insur-ance shares if each observation in the data set had a population between 50,000 and 100,000 with (2) the average predicted value if each observation had population between 10,000 and 50,000 (holding other variables at their observed values).

Because we had only a rough measure of an applicant’s past loss experience, we ran the regressions with and without the number of prior declarations. As can be seen by comparing the specification 1 and specification 2 columns in Table B.3, the results do not differ much.

13 The command is margins, dydx(<explanatory variables> predict(ystar(0,1)) vce(unconditional).

Table B.3Tobit Model of Insurance Share (Derivatives of Responses)

Variablea

Specification 1 Specification 2

Derivative Standard Error Derivative Standard Error

Jurisdiction type

City or township Reference Reference Reference Reference

County 0.091*** 0.012 0.098*** 0.012

Stateb –0.163** 0.067 –0.104* 0.054

Population (city, counties, states, and territories only)

<5,000 –0.102*** 0.011 –0.098*** 0.011

5,000–10,000 –0.040*** 0.013 –0.038*** 0.013

10,000–50,000 Reference Reference Reference Reference

50,000–100,000 –0.029* 0.018 –0.033* 0.018

100,000–1 million –0.070*** 0.016 –0.075*** 0.016

1 million–5 million –0.042 0.050 –0.058 0.048

≥5 million –0.063 0.059 –0.077 0.059


Variablea



Incident type

Named storm –0.120*** 0.017 –0.125*** 0.017

Storm, no flood Reference Reference Reference Reference

Storm, flood –0.116*** 0.016 –0.118*** 0.016

Winter storm, no flood –0.079*** 0.019 –0.077*** 0.019

Winter storm, flood –0.070*** 0.023 –0.080*** 0.023

Fire –0.108 0.066 –0.105 0.064

Earthquake –0.352*** 0.053 –0.345*** 0.055

Flooding only –0.228*** 0.043 –0.229*** 0.044

Other, no flood 0.052 0.399 0.054 0.402

Open–closed indicator

All open Reference Reference Reference Reference

Mixed 0.006 0.017 0.007 0.017

All closed 0.060*** 0.012 0.064*** 0.012

Repair cost per capita, in dollars

≤0.25 Reference Reference Reference Reference

0.25–1 0.167*** 0.012 0.169*** 0.012

1–10 0.307*** 0.012 0.308*** 0.012

10–50 0.378*** 0.015 0.380*** 0.015

50–200 0.407*** 0.021 0.406*** 0.022

≥200 0.412*** 0.026 0.410*** 0.025


<25 Reference Reference Reference Reference

25–75 –0.013 0.015 –0.012 0.017

≥75 –0.031** 0.013 –0.033*** 0.016

Share of repair costs that were for equipment, as a percentage

<25 Reference Reference Reference Reference

25–75 –0.029** 0.012 –0.028** 0.012

≥75 –0.077*** 0.011 –0.077*** 0.011



The model in Table B.3 does not allow the relationship between insurance share and jurisdiction size to vary by size of loss—changes in the repair cost per capita shift the relationship up or down. As discussed in Chapter Four, one might expect the relationship between insurance share and jurisdiction size to vary by size of loss. To investigate this possibility, we ran the tobit in Table B.3 separately for different catego-ries of repair cost per capita. The results were discussed in Chapter Four. Figure B.1 reproduces Figure 4.4 in Chapter Four with the 95-percent confidence intervals for the estimates.

The tobit model in Table B.3 suggests that the percentage of repair costs in SFHAs has a small effect on insurance: The largest effect observed is the 3.3-percentage-point reduction in insurance share when more than 75 percent of repair costs are in an SFHA versus when less than 25 percent of repair costs are in an SFHA. However, for a variety of reasons, SFHA status’s impact on insurance share may be greater for floods than for other hazards. First, the PA program reduces assistance by the amount of insurance available from the NFIP (for flood-related losses) when a structure is in an

Variablea



Number of prior disaster declarations (back to 1965)

0–9 Reference Reference Reference Reference

10–14 –0.008 0.013 — —

14–19 –0.037*** 0.014 — —

≥20 –0.082*** 0.021 — —

Missingc 0.241*** 0.067 — —

N 6,852 — 6,852 —

Left-censored observations in tobit (insurance share = 0)

2,260 — 2,260 —

Uncensored observations in tobit 4,275 — 4,275 —

Right-censored observations in tobit (insurance share =1)

317 — 317 —

NOTE: Derivatives are expressed as changes in the share of repair cost covered by insurance. To convert to the percentage-point change in insurance share, multiply by 100. * = Different from 0 at 90% probability. ** = Different from 0 at 95% probability. *** = Different from 0 at 99% probability.a Fixed effects for FEMA regions are included in each regression, but their marginal effects are not reported.b This category and the missing category for number of previous declarations are highly correlated. Thus, one should not conclude from this regression that states have lower insurance share, holding other factors constant.c Missing when applicant is a state or when the county where the damage occurred is “statewide.”



SFHA regardless of whether the property has flood insurance. Second, a property in an SFHA is at a higher risk for flood damage than one outside an SFHA,14 a fact that has potential consequences for insurance purchasing decisions.

To investigate the possibility that the effect that SFHA status has on insurance share varies by peril type, we compared insurance share with hazard. To make the results easier to interpret, we combined incident types into three categories:

• not involving flood (earthquake, fire, storm without flood, winter storm without flood, or other with no flood)

• involving flood and other hazards (hurricane, storm with flood, or winter storm with flood)

• involving flood only.

In Table B.4, we provide the coefficients from the tobit model (as opposed to the deriv-atives of responses).15 The results are interpreted in Chapter Four.

14 Exceptions could be made with two properties, one inside and one outside an SFHA, that are very close to the SFHA boundary.15 It does not make sense to calculate the derivatives of responses for interaction terms. Thus, coefficients from the tobit model are reported.

Figure B.1Confidence Intervals for Predicted Insurance Shares, by Population and Repair Cost Per Capita

≤0.25

0.25–1

1–10

10–50

≥50

0

10

20

30

40

60

70

80

Pred

icte

d in

sura

nce

sh

are,

as

a p

erce

nta

ge

Population

≤5,000 5,000–10,000

10,000–50,000

50,000–100,000

100,000–1 million

≥1 million

NOTE: For cities, counties, and states. Shaded areas show 95% confidence intervals for the predictions.

50

Repair cost per capita,in dollars


The tobit in Table B.3 indicates that insurance share is lower for applicants with more prior disaster declarations in the same county, holding other factors constant. However, this measure of prior declarations does not distinguish between more-recent and more-distant declarations. Table B.5 reports the marginal effects first for more-recent disasters, holding the number of more-distant disasters constant. The bottom set of rows shows the marginal effects of more-distant disasters, holding the number of more-recent disasters constant. As can be seen, the results suggest that the effects of disaster declarations in the two time periods is opposite. The existence of more-recent events tends to increase insurance share in the current event, while the existence of more-distant events tend to decrease it.

We have presented the results in Tables B.3, B.4, and B.5 as tobit coefficients or marginal effects. To make them more accessible to a nontechnical audience, we present the findings in Chapter Three in a different way—using the predicted level (or predic-

Table B.4Tobit Model of Insurance Share with Interaction of Flood Zone and Incident Type (Tobit Coefficients)

Variablea Coefficient Standard Error


<25 Reference Reference

25–75 –0.063 0.074

≥75 –0.160** 0.073

Incident type

No flood component Reference Reference

Flood with another hazard –0.100*** 0.019

Only flood –0.424*** 0.097

Interaction of share in SFHAs and incident type

Flood with another hazard and 25–75% in SFHAs 0.045 0.079

Flood with another hazard and ≥75% in SFHAs 0.111 0.076

Only flood and 25–75% in SFHAs 0.411*** 0.124

Only flood and ≥75% in SFHAs 0.455** 0.189

N 6,852 —

NOTE: Coefficients are expressed as changes in the share of repair cost covered by insurance. To convert to the percentage-point change in insurance share, multiply by 100. * = Different from 0 at 90% probability. ** = Different from 0 at 95% probability. *** = Different from 0 at 99% probability.a Coefficients for other variables included in Table B.3 regressions are not reported.


tive margin) for insurance share. We calculated the predicted level using a variant of the margins command.16 As before, we calculated the predicted shares by changing the value of the factor variable in question, holding the other variables at their observed values.

16 The command is margins <explanatory variables>, predict(ystar(0,1)) vce(unconditional).

Table B.5Tobit Model of Insurance Share with Variables That Distinguish More-Recent from More-Distant Declarations (Derivatives of Responses)

Variablea Derivative Standard Error

Number of declarations in the preceding 5 years

0 Reference Reference

1–2 –0.008 0.015

3–5 0.003 0.016

≥6 0.053*** 0.019

Number of declarations more than 5 years ago

0–9 Reference Reference

10–14 –0.029*** 0.011

15–19 –0.030** 0.014

≥20 –0.099*** 0.017

Nb 6,613

NOTE: Derivatives are expressed as changes in the share of repair cost that was covered by insurance. To convert to the percentage-point change in insurance share, multiply by 100. * = Different from 0 at 90% probability. ** = Different from 0 at 95% probability. *** = Different from 0 at 99% probability.a Coefficients for other variables included in the Table B.3 regressions are not reported.b Observations for which the number of prior disasters is missing are not included.

95

APPENDIX C

Interviews

Between March 2019 and March 2020, we conducted a series of semistructured, open-ended interviews with staff in FEMA headquarters and the regional offices to better understand state and local government risk-management practices and the PA pro-gram. For these interviews, we first selected FEMA offices with relevant responsibili-ties and then worked with FEMA to identify appropriate subject-matter experts within those offices to interview. In this appendix, we provide the protocols we used for these interviews. More than 40 FEMA staff contributed to this project.

We also conferred with insurance brokers from two firms who served as part of the research team. We selected these firms based on their experience working with public entities. Both worked extensively with state and local governments and conse-quently developed expertise in state and local government risk-management practices. More than ten insurance brokers contributed to this project.

Interview Protocol for FEMA Insurance Specialists

Type and Extent of Insurance for Buildings That Had Not Previously Received Public Assistance

This section explores the types and amount of insurance that SLTTs and PNP orga-nizations carry on buildings that apply for PA for the first time. These buildings have not been previously subjected to PA’s O&M requirements. These questions provide insight into the amount of insurance currently in place on facilities when insurance is voluntary (except for federal requirements to purchase flood insurance for buildings in SFHAs).

1. What kinds of financial risk-management practices do you tend to see in first-time recipients of category E PA (e.g., commercial indemnity policies, pools, self-insurance, no insurance)?a. How do risk-management practices vary by community size?b. How do the reductions in category E PA due to insurance compare to the

amount of damage that is eligible for PA (eligible cost)?


c. Do reductions tend to be higher in some states or for some types of build-ings or hazards than others?

Obtain and Maintain Requirements for the Public Assistance Program

This section explores how applicants comply with the insurance requirements of their PA applications. It examines the extent to which PA insurance requirements are modi-fied downward and the reasons for those reductions.

1. The PA program is required to provide applicants with an initial notification of their insurance requirements.a. How frequently do applicants request that the insurance requirement be

modified? How often do state insurance commissioners certify a different requirement?i. Are modifications requested more commonly for certain types or haz-

ards or in certain regions or states?ii. What are common reasons provided for the modifications?iii. What is the relation between FEMA’s initial estimate of the insurance

requirement and the modified requirement? How does the modified requirement compare with the project’s eligible cost?

2. As we understand it, there are three broad criteria that FEMA uses to evaluate whether an applicant is complying with the O&M policy: facility, type, and extent.a. Please walk us through the process of evaluating an applicant against these

criteria.b. What criteria are used to evaluate self-insurance proposals?c. We have reviewed the Stafford Act, the Code of Federal Regulations, the

PA Program and Policy Guide, and “Public Assistance Policy on Insurance.” Are there any other publications or standard operating procedures that out-line how these criteria should be applied?

3. When a structure subject to the O&M requirement subsequently applies for PA, how frequently do you find that it is not in compliance with its O&M require-ment?a. What are the reasons PA recipients have decided to no longer comply?b. How frequently does FEMA deny PA as a result?

Parametric Insurance

Puerto Rico has requested that FEMA evaluate whether its proposed approach to insuring public facilities damaged by Hurricanes Irma and Maria would meet the

Interviews 97

O&M requirement. Its proposal includes parametric insurance for losses from wind and seismic events.

1. What is FEMA’s process for evaluating whether a new or nontraditional finan-cial risk-management instrument satisfies the O&M requirements? What crite-ria does FEMA use in making this decision?a. What issues need to be considered in deciding whether and under what cir-

cumstances parametric policies should be allowed to satisfy O&M require-ments?

2. Do FEMA’s O&M policies need to be updated or revamped to address concerns or risks that may be associated with parametric insurance? In what ways?

The Effect That Eliminating Public Assistance Has on State, Local, Tribal, and Territorial Financial Risk-Management Practices

1. If PA were phased out over five years, what factors would enter into the deci-sions of how SLTT risk managers and PNP organizations would adapt?a. What types of responses are plausible?

2. Why do some SLTTs and PNP organizations decide to not insure their build-ings?a. In your view, which states already do a good job of managing their risk?

Which ones need the most improvement?b. What steps could encourage SLTTs and PNP organizations to insure their

buildings?

Concluding Questions

1. Are there any issues that we have not discussed that you would like to raise?2. Who else should we talk with?

a. In FEMA or DHSb. With other federal partnersc. From nonfederal stakeholders

Interview Protocol for FEMA Public Assistance Staff

Type and Extent of Insurance for Buildings That Have Not Previously Received Public Assistance

This section explores the types and amount of insurance that SLTTs and PNP organi-zations carry on buildings that apply for PA for the first time. These buildings have not been previously subjected to PA’s O&M requirements. Responses provide insight into


the amount of insurance currently in place on facilities when insurance is voluntary (except for federal requirements to purchase flood insurance for buildings in SFHAs).

1. What kinds of financial risk-management practices do you tend to see in first-time recipients of category E PA (e.g., commercial indemnity policies, pools, self-insurance, no insurance)?a. How do the reductions in category E PA due to insurance compare to the

amount of damage that is eligible for PA (eligible cost)?b. Do reductions tend to be higher in some states or for some types of build-

ings or hazards than others?

Obtain and Maintain Requirements for the Public Assistance Program

This section explores how applicants comply with the insurance requirements of their PA applications. It examines the extent to which PA insurance requirements are modi-fied downward and the reasons for those reductions.

1. The PA program is required to provide applicants with an initial notification of their insurance requirements.a. How frequently do applicants request that the insurance requirement be

modified? How often do state insurance commissioners certify a different requirement?i. Are modifications requested more commonly for certain types or haz-

ards or in certain regions or states?ii. What are common reasons provided for the modifications?

b. What is the relationship between FEMA’s initial estimate of the insur-ance requirement and the modified requirement? How does the modified requirement compare with the project’s eligible cost?

2. What is FEMA’s process for evaluating whether a new or nontraditional finan-cial risk-management instrument satisfies the O&M requirements? What crite-ria does FEMA use in making this decision?a. What issues need to be considered in deciding whether and under what cir-

cumstances parametric policies should be allowed to satisfy O&M require-ments?

3. When a structure subject to the O&M requirement subsequently applies for PA, how frequently do you find that it is not in compliance with its O&M require-ment?a. How frequently does FEMA deny PA as a result?

Interviews 99

The Effect That Eliminating Public Assistance Has on State, Local, Tribal, and Territorial Financial Risk-Management Practices

1. If PA were phased out over five years, what factors would enter into the deci-sions of how SLTT risk managers and PNP organizations would adapt?a. What types of responses are plausible?

2. Why do some SLTTs and PNP organizations decide to not insure their build-ings?a. In your view, which states already do a good job of managing their risk?

Which ones need the most improvement?b. What steps could encourage SLTTs and PNP organizations to insure their

buildings?



a. In FEMAb. In DHSc. With other federal partners

Interview Protocol for Federal Insurance and Mitigation Administration Staff

Current Risk-Management Practices for State, Local, Tribal, and Territorial Governments and Private Nonprofits

1. Based on your own experience and knowledge, what is your sense of the extent to which SLTTs and PNP organizations carry insurance on their buildings? (We are focusing on the types of buildings that would potentially be eligible for category E PA.)a. How does take-up vary by hazard (flood, wind, earthquake, fire)?b. Is flood insurance more common in SFHAs?c. Our understanding is that flood insurance requirements apply only once a

building in an SFHA receives direct federal assistance—is that correct?d. What type of financial risk-management strategies are common (e.g., pools,

indemnity policies, blanket policies, postevent funding)?e. Which states do a good job of managing their risk? Which ones need the

most improvement?f. Do PNPs’ practices differ markedly from those of SLTTs?


2. Please provide an overview of what information or data FEMA maintains con-cerning the insurance practices of SLTTs and PNP organizations (e.g., take-up).a. Any there any reporting requirements concerning insurance take-up for

public and PNP buildings through the Community Rating System pro-gram?

Obtain and Maintain Requirements for the Public Assistance and Hazard Mitigation Grant Programs

1. Is the Federal Insurance and Mitigation Administration involved in reviewing the extent to which PA applicants comply with the O&M requirements? If so,a. To what extent are O&M requirements reduced by state insurance com-

missioners?b. Has the Federal Insurance and Mitigation Administration considered the

circumstances under which parametric insurance should be allowed to sat-isfy O&M requirements?

c. Is there any information on the extent to which applicants remain in com-pliance over time (e.g., how often are subsequent PA applications denied)?

2. Does the Hazard Mitigation Grant Program (HMGP) have an O&M require-ment for flood? Are any other types of insurance required?

Risk-Management Products for Public and Private Nonprofits’ Buildings That Are Available from the Insurance Sector

1. Have there been any studies on the availability and cost of insurance for build-ings owned by PNPs or SLTTs? Please consider flood as well as other hazards.

2. What is your sense of the extent to which insurance is available for public build-ings and buildings owned by PNPs at costs that appropriately reflect risk?

3. What types of alternative risk-management instruments are available for build-ings owned by public or private nonprofits—or should be considered by SLTTs or PNPs?

The Effect That Changes to Category E Public Assistance Have on Financial Risk-Management Practices

1. If category E PA were phased out over five years, what factors would enter into SLTTs’ and PNP organizations’ decisions about how to adapt?a. What types of responses are plausible?b. What steps could encourage SLTTs and PNP organizations to insure their

buildings?

Interviews 101



a. In FEMAb. In DHSc. With other federal partners

Interview Protocol for FEMA Regional Staff

Current Risk-Management Practices for State, Local, Tribal, and Territorial Governments and Private Nonprofits

1. Based on your own experience and knowledge, what is your sense of the extent to which SLTTs carry insurance on their buildings and equipment?a. What type of financial risk-management strategies are common (e.g., pools,

blanket policies, self-insurance)?b. How does take-up vary by hazard (flood, wind, earthquake, fire) and loca-

tion (e.g., in versus out of SFHAs)?c. Why do some SLTTs decide not to insure their buildings?d. When PA becomes available, to what extent is it reduced because of insur-

ance?e. Which states do a good job of managing their risk? Which ones need the

most improvement?2. Do PNPs’ risk-management practices differ markedly from those of SLTTs?3. Does the regional office maintain any information or data concerning the insur-

ance practices of SLTTs and PNPs (e.g., take-up)?

Obtain and Maintain Requirements for the Public Assistance and Hazard Mitigation Grant Programs

1. In deciding whether to pursue Section 404 or 406 funds, to what extent do applicants consider the impact that mitigation would have on future insurance costs?1

2. How does mitigation enter into FEMA’s determination of the amount of insur-ance needed to satisfy the PA O&M requirement?

3. What considerations enter into FEMA decisions regarding whether an insur-ance arrangement proposed by an SLTT is sufficient to satisfy the O&M requirement?

1 Sections 404 and 406 are sections of the Stafford Act codified at 42 U.S.C. § 5170c and 5172, respectively.


a. To what extent do applicants use self-insurance to satisfy O&M require-ments? What particular issues come up in approving self-insurance plans?

b. Has your regional office ever considered the circumstances under which nontraditional insurance schemes, such as parametric insurance, should be allowed to satisfy O&M requirements? If yes, what factors should be con-sidered in deciding whether to allow parametric insurance?

4. To what extent are O&M requirements for PA or the HMGP reduced by state insurance commissioners or by appeal to FEMA?

5. Are you able to track whether applicants remain in compliance with PA or HMGP O&M requirements?a. Do you have a sense of how often PA or HMGP applications are denied for

failure to comply with O&M requirements?

The Effect That Changes to Category E Public Assistance Have on Financial Risk-Management Practices

1. If category E PA were phased out over five years, what factors would enter into SLTTs’ and PNP organizations’ decisions about how to adapt?a. What types of responses are plausible?b. What steps could encourage SLTTs and PNP organizations to insure their

buildings?



a. In the regionb. With other federal partnersc. With nonfederal stakeholders

103

APPENDIX D

Parametric Insurance

In contrast with traditional indemnity insurance, which compensates policyholders for a portion of their actual losses, parametric insurance insures a policyholder against an event by providing compensation based on the magnitude and location of an event, which is known as the trigger.1 Triggers are predetermined parameters or metrics, such as wind speed or earthquake magnitude.2 For example, a parametric policy might pay a specified amount when wind speeds exceed 130 miles per hour (category 4 hurricane). Parametric policies can pay out much more quickly than other policies because, in their simplest form, they do not consider the policyholder’s actual losses and consequently do not require damage verification or loss adjustment. They do, however, require sophisti-cated modeling to determine whether the policy is appropriately priced. Because para-metric policies consider only the trigger, there is a risk that the policyholder could experience significant damage even if the event does not satisfy the trigger, leaving the policyholder with uncompensated losses.3 Analogously, the policyholder could receive payment when there is no damage. Although parametric insurance has become more common in recent years, FEMA has not determined its applicability to the O&M requirement or risks that it may pose to the agency or PA applicants.

It is worth noting that parametric policies can take different forms. For exam-ple, in their simplest form, parametric policies provide compensation based purely on whether the trigger is satisfied. Those are considered “single-trigger” policies and are the type offered mainly outside the United States. However, within the United States, some policies may also require a second trigger, or a “double-trigger,” in which the policyholder needs to document actual losses that resulted from the event. For poli-cies such as these, to satisfy the second trigger, the insurer may audit the policyholder’s claims to verify losses. State regulators can require these double-trigger policies so that the parametric policy can be considered an insurance product rather than a financial derivative.

1 For parametric policies, the amount of payment can vary according to the magnitude of the trigger.2 World Bank Group, 2014, p. 37.3 Clarke and Dercon, 2016, p. 63.


Parametric insurance has become increasingly available to state and local govern-ments and nonprofit organizations as the insurance industry has expanded its offerings of parametric products.4 However, limited systematic research exists into the extent to which state and local governments and nonprofits have obtained parametric insurance. From our discussions with insurance brokers, it is clear that there is much interest from state and local governments in parametric policies. Brokers field many questions about parametric insurance and have even provided some bids for the cost of parametric coverage but have placed few actual policies. However, a couple of cases illustrate how these types of entities use parametric insurance to manage risk. For example, in 2010, Alabama became the first state in the country to obtain parametric insurance, pur-chasing a policy from Swiss Re for the Alabama State Insurance Fund—which insures state properties, including universities and public schools. The policy would provide compensation to the state insurance fund if a category 3 hurricane hit a designated part of the state’s coast.5 During the duration of the contract, the state did not receive a payout and declined to renew it in 2013 because the price increased.6 Similarly, the Miami-Dade County school board holds a parametric policy from Swiss Re that pro-vides $10 million if wind speeds exceed 87.5 miles per hour.7 However, the county’s experience highlights the risks of parametric insurance. When Hurricane Irma hit Miami-Dade in 2017, it caused $19 million in damage to the school district, but the school board did not receive any compensation because the storm did not reach the wind speed trigger.8 In FY 2019, the school board did not renew the parametric policy for storms because premiums increased significantly.9

The parametric insurance arrangement uses commercial insurance coverage for managing an insured’s high-risk exposure for catastrophic events. The design of this parametric arrangement requires the insurer to pay a higher percentage of the loss based on the increased severity from a specific event. Conversely, the insured’s partici-pation in the loss will diminish based on that same increased event severity.

The parametric plan design insures facilities up to their appraised value. How-ever, to receive the full value of a particular loss, the specific criteria of the triggering event need to be met. The severity of the event is used to determine both the insurer and the insured’s participation in the loss. From the insured’s perspective, this partici-pation could be defined as a diminishing deductible. From the insurance company’s perspective this could be defined as diminishing coinsurance.

4 See, for example, Flavelle and Chiglinsky, 2019, or Ward, 2019.5 Mărgulescu and Mărgulescu, 2013, p. 101.6 Flavelle and Chiglinsky, 2019.7 Financial Services, 2018, p. 18-GG; Flavelle and Chiglinsky, 2019.8 Flavelle and Chiglinsky, 2019.9 Office of the Controller, 2018, p. 65.

105

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$30.00

Cover image: Adobe Stock/Vastram.RR-A332-1-DHS

In the United States, federal, state, and local governments share responsibility

for managing disaster risk. Whether the current allocation of risk between

these different levels of government is appropriate is much debated. In the

study reported here, the authors contributed to this debate by improving the

understanding of state and local financial risk management practices and

the role that the Federal Emergency Management Agency’s (FEMA’s) Public

Assistance (PA) program plays in these decisions. They found that a significant

portion of state and local governments that are eligible for PA purchase some

type of insurance for their buildings, contents, vehicles, and equipment (BCVE)

but that the insurance share across all PA projects is low, meaning that FEMA

is paying for a substantial portion of the repairs. However, the insurance share

varies by the attributes of the incident causing the damage and of the public entity.

Regarding the role that the PA program plays in these decisions, the predominant

view of experts consulted for the study was that the potential for FEMA assistance

reduces state and local governments’ purchase of insurance. Finally, this report

discusses several approaches for increasing insurance coverage for state and local

governments and the trade-offs associated with them. These approaches include

encouraging credit rating agencies to evaluate communities’ financial preparedness

for disasters; requiring communities to analyze their risks in order to increase

their awareness of them; requiring communities to cover a substantial first layer

of loss; and eliminating PA for BCVE.

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ISBN-13 978-1-9774-0542-5ISBN-10 1-9774-0542-8

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