Hurricane fatalities and hurricane damages: are safer hurricanes more damaging?

• Author: Sadowski, Nicole Cornell

1. Introduction

   Hurricanes have long threatened the coastal areas of the United States. The nation has invested millions of dollars to understand and forecast hurricanes. Research efforts led by the National Hurricane Center (Simpson 1998) have succeeded in making land-falling hurricanes less deadly. In the 1990s, the modernization of the National Weather Service, featuring the installation of the Advanced Weather Interactive Processing System to process data from radar, satellites, and surface observations at high speeds and a nationwide network of Doppler weather radars, contributed to improved forecasts of weather hazards (Friday 1994). Annual hurricane fatalities have fallen from 0.5 per million residents nationally during the 1950s to 0.05 per million residents during the 1980s and 1990s. Kunkel, Pielke, and Changnon (1999) attribute the decline to improved hurricane forecasts. (1)

   Although hurricanes have become less deadly over time, hurricane damages have increased, particularly in recent years. By 1995, hurricane damage in the 1990s had already exceeded total damage in the 1970s and 1980s combined. This escalation has led to interest among policy makers and researchers regarding the causes of increasing hurricane damages. Some observers attribute rising damages to an increase in the number and severity of hurricanes; for instance, a 1995 congressional report asserts that hurricanes "have become increasingly frequent and severe over the last four decades as climatic conditions have changed in the tropics" (cited in Pielke and Landsea 1998, p. 623). This explanation, however, is simply false. Katz (2002) for instance finds no statistically significant increase in the number of land-falling hurricanes over time. (2) And the period from 1991 to 1994 had the fewest tropical storms of any four-year period in the last 50 years.

   Increasing societal vulnerability, that is, more people and wealth along hurricane-prone coasts, seems to explain increasing hurricane damages. Figure 1 illustrates the increase in coastal county populations during the 20th century. The figure graphs the population growth rates by decade for 130 Atlantic and Gulf coast counties and for the United States overall. As illustrated, the coastal counties grew faster than the nation in each decade. A wealthier population will also have more property vulnerable to destruction by a hurricane. Pielke and Landsea (1998), Changnon and Hewings (2001), and Katz (2002) find no time trend for hurricane damages after normalizing for changes in population and wealth in addition to inflation.

   An understanding of increasing hurricane losses requires an explanation for the increase in coastal county populations, and several have been advanced. One is the rising standard of living in the United States: wealthier people will spend more on luxuries, such as living near the ocean. Another possibility involves low-probability event bias. Considerable evidence suggests that people do not behave according to expected utility theory with respect to low-probability, high-consequence events like hurricanes. Instead of considering the expected cost of these events, which is considerable, people act as if such events "couldn't happen to me" and treat the low probability as a zero probability (Kunreuther et al. 1978; Camerer and Kunreuther 1989). Finally, a number of government policies, including subsidized insurance, disaster assistance, and structural restoration measures (e.g., rebuilding roads and restoring beaches after storms) contribute to overbuilding on hurricane-prone coasts (Platt 1999). (3)

   We consider an alternative explanation, one which, to our knowledge, has not been widely discussed, namely the very reduction in hurricane lethality. Through improved hurricane warnings, better evacuation, and engineering advances, the probability of fatalities has been reduced, thereby decreasing the expected cost of living along hurricane-exposed coasts. The law of increasing demand consequently explains at least a part of the increase in coastal populations. (4) We provide evidence of the impact of reduced hurricane fatalities on damages using a database of land-falling hurricanes in the United States between 1940 and 1999. We do not argue that reduced lethality is the exclusive cause of increasing hurricane damages, only that it is a contributing and overlooked factor. Our explanation utilizes the concept of offsetting behavior in response to an exogenous change in the riskiness of an activity, first proposed by Peltzmaal (1975) for automobile safety.

   The remainder of this paper is organized as follows: Section 2 presents an expected utility model of a household's location choice and shows how a reduction in the probability of deaths from a hurricane makes a household more likely to live along a hurricane-prone coast. In particular, the effect of reduced fatalities will be greatest when the probability of a hurricane is highest. Section 3 explains our econometric model. We first estimate a time-varying measure of hurricane lethality in a Poisson model of hurricane fatalities. We then use this measure of lethality with a lag to explain hurricane damages. We also interact this measure with the probability of a hurricane. Section 4 presents the empirical results, and Section 5 offers a brief conclusion.

2. Hurricane Forecasts and Locational Choice

   In this section we examine a simple model of household location choice to derive testable predictions concerning hurricane lethality and damages. Consider a representative household's choice to live on a hurricane-exposed coast. Let [pi] be the probability of a hurricane and let [sigma] be the probability that the household suffers a casualty given that a hurricane strikes the household's residence on the coast. Let I be the household's income, which we assume does not depend on location decision, and let L be the dollar value of property losses that occur if the household lives on the coast and their residence is struck by a hurricane. The household can purchase insurance against property damage. Let x be the dollar value of coverage purchased and let p be the price per dollar of coverage. The household's total premium is [p.sup.*]x, and the household receives a payment of x if a hurricane loss occurs. Let y denote the disposable income spent on consumption goods.

   Household utility is a function of disposable income y, the household's location, and the household's state of health. Let [theta] denote the household's state of health, with [[theta].sup.h] indicating full health and [[theta].sup.i] indicating that the household has suffered a hurricane casualty. (5) We assume that utility is lower (and the marginal utility of income higher) when the household suffers a hurricane casualty. Let a superscript on the utility function designate the household's location choice, with c representing the hurricane-vulnerable coast and o the location away from the coast. Let [U.sup.c] (y,[theta]) be the household's expected utility if they choose to live on the coast, which can be written

   (1) [U.sup.c](y, [theta]) = (1 - [pi]) x [U.sup.c](I - px, [[theta].sup.h]) + [pi] x (1 - [sigma] x [U.sup.c](I - L - px + x, [[theta].sup.h]) + [pi] x [sigma] x [U.sup.c](I - L - px + x, [[theta].sup.i])

   We assume that x is the household's expected utility-maximizing insurance purchase. Utility if the household chooses to live inland is [U.sup.o](y,[[theta].sup.h]), which is the household's reservation utility level. The household will live on the coast if [U.sup.c](y,[theta]) [greater than or equal to] [U.sup.o](y,[[theta].sup.h]).

   We examine the comparative statics of the household's location decision. Consider first the effect of a change in the probability of a casualty, [sigma]. Forecasts allow residents to evacuate in advance of an approaching hurricane, so improved warnings will reduce [sigma], but not the probability of a hurricane, [pi]. A change in [sigma] does not affect the reservation level of utility, [U.sup.o](y,[[theta].sup.h]). Thus, the effect on [U.sup.c](y,[theta]) is

   (2) [differential][U.sup.c]/[differential][sigma] = [pi] x [[U.sup.c](I - L - px +...