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Risk Analysis, Vol. 31, No. 10, 2011 DOI: 10.1111/j.1539-6924.2011.01606.x

Florida Households Expected Responses to Hurricane

Hazard Mitigation Incentives

Yue Ge,1,

Walter Gillis Peacock,1 and Michael K. Lindell1

This study tested a series of models predicting household expectations of participating in hur-ricane hazard mitigation incentive programs. Data from 599 households in Florida revealedthat mitigation incentive adoption expectations were most strongly and consistently relatedto hazard intrusiveness and risk perception and, to a lesser extent, worry. Demographic andhazard exposure had indirect effects on mitigation incentive adoption expectations that weremediated by the psychological variables. The results also revealed differences in the factorsaffecting mitigation incentive adoption expectations for each of five specific incentive pro-grams. Overall, the results suggest that hazard managers are more likely to increase par-ticipation in mitigation incentive programs if they provide messages that repeatedly (thusincreasing hazard intrusiveness) remind people of the likelihood of severe negative conse-quences of hurricane impact (thus increasing risk perception).

KEY WORDS: Florida; hazard mitigation; hurricanes; mitigation incentive programs

1. INTRODUCTION

Research on environmental hazards has ad-dressed a range of economic and noneconomicmitigation incentives that can be employed in envi-ronmental hazard management.(14) This researchhas raised questions about the extent to which house-holds mitigate environmental hazards and why somemitigation tools fail to work as expected. Evidencefrom this research has shown, for instance, that theNational Flood Insurance Program (NFIP) has hadonly modest success. Although it was designed to

encourage property owners in flood prone areas toreduce their property losses by increasing hazardmitigation, only 13% of the eligible property ownersactually purchase flood insurance policies.(5) This

1Hazard Reduction & Recovery Center, Texas A&M University,College Station, TX, USA.Address correspondence to Yue Ge, Hazard Reduction & Re-

covery Center, Texas A&M University, College Station, TX778433137, USA; tel: +1 979 845 1010; [email protected].

can be problematic because a majority of householdswithin flood hazard zones are eligible to receivefederal postdisaster assistance for presidentially de-clared disasters. These post-disaster benefitswhichinclude relocation benefits, tax credits, and low-interest loansresult in taxpayers subsidizing someof the losses that are incurred. The most immediateobjective of the NFIP is to encourage floodplainresidents to insure themselves rather than rely onpostdisaster recovery assistance. However, due tovariations in attitudes toward incentive programs,

perceptions of risks, and the continued demandfor waterfront and other environmental amenities,floodplains and coastal zones continue to havehigh property values(6) and attract new residents.(7)

Consequently, the objectives of hazard mitigationprograms often cannot be achieved.

After Hurricane Andrew, one of the mostcostly hurricane disasters in U.S. history, the FloridaDepartment of Community Affairs(810) collabo-rated with county and municipal governments to

1676 0272-4332/11/0100-1676$22.00/1 C 2011 Society for Risk Analysis

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Expected Response to Hurricane Hazard Mitigation Incentives 1677

implement a variety of hurricane hazard mitigationprograms for Floridas property owners. Counties insoutheastern Florida (Miami-Dade, Broward, andMonroe counties), and later throughout the state,strengthened building codes in specially designated

high hazard areas in order to provide additionalprotection from hurricane wind. In addition, hur-ricane mitigation programs throughout the statepromoted the installation of window shutters andprotective coverings. The majority of these programswere educational, but some counties also undertookspecial programs to provide shutters for low-incomeelderly homeowners. The state also considered avariety of incentive programs that might motivatehomeowners of existing housing to install shuttersor impact-resistant windows. This study examinesFlorida households responses to these incentiveprograms and the factors that significantly influencehouseholds expectations of participating in hazardmitigation incentive programs.

2. HAZARD MITIGATION INCENTIVES

Environmental hazard managers, planners, andpolicymakers oftentimes seek to encourage theircommunities to adopt hazard adjustments,(11) whichare defined as those actions that intentionally orunintentionally reduce risk from extreme events inthe natural environment (Ref. 12, p. 328). Emer-

gency response and disaster recovery are post-impactadjustments whereas emergency preparedness andhazard mitigation are pre-impact adjustments. Thelatter two categories of hazard adjustments, espe-cially hazard mitigation, have substantial potentialto reduce property losses and casualties if house-holds adopt them. However, according to the resultsfrom the nations Second Assessment of Research onNatural Hazards,(3,13) even simple hazard mitigationactions such as strapping water heaters, tall furni-ture, and heavy objects to building walls, and in-stalling cupboard latches in earthquake prone areas

are infrequently implemented. Even if shutter sys-tems or protective window coverings might be morefrequently adopted in hurricane risk areas of south-eastern Florida than comparable earthquake miti-gation actions are adopted in California, there arestill difficulties in ensuring that these systems areretrofitted and code compliant in most or all homeslocated in high-hazard areas. Many state and localgovernments have used incentives, as well as man-dates such as building codes, to promote the adoption

and implementation of household hazard mitigationactions.

From a general perspective, Lindell and his col-leagues(2,14) place the concept of incentives in thebroader context of the local emergency management

system.Such changes require one of three types of motivationaltacticsincentives, sanctions, or risk communication.Incentives provide extrinsic rewards for compliance withcommunity policies. That is, they offer positive induce-ments that add to the inherent positive consequences ofa hazard adjustment or offset the inherent negative con-sequences of that hazard adjustment. (Ref. 2, p. 17)

This definition systematically identifies the threemajor types of policy tools that are performedthrough all the four phases of emergency manage-ment. Unlike regulations such as zoning and build-ing codes that punish noncompliance, incentives

provide rewards for adopting and implementing haz-ard adjustments. In turn, incentives can be classifiedas economic or noneconomic.(3)

2.1. Economic Incentives

One common aim of economic incentives is tooffset the cost of implementing hazard mitigationmeasures. An incentive might be provided directly,as when a homeowner receives a rebate for thecost of materials used to construct storm shutters.Alternatively, an incentive might be provided

indirectlysuch as the reduction in flood insurancepremiums that is implemented through the NFIPsCommunity Rating System (CRS).(15) The CRSawards communities points in exchange for engagingin 18 different flood hazard reduction activities in thecategories of public information, damage reduction,flood preparedness, and local coordination. Themore points a community earns, the higher its classrating. In turn, communities with higher class ratingsreceive higher discounts on their residents floodinsurance premiums. These premium rate discountsrange as high as 45%.

2.2. Noneconomic Incentives

Noneconomic incentives have long been recog-nized by emergency managers as ways to encouragecompliance with evacuation warnings.(16) In this con-text, incentives are measures that local emergencymanagers can take to overcome common psycholog-ical or logistical barriers to the adoption of adap-tive behavior. For example, community emergency

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1678 Ge, Peacock, and Lindell

managers can reduce evacuation implementationbarriers by providing telephone hotlines to accom-modate peoples known patterns of warning confir-mation behavior and by publicizing the presence ofpolice patrols in evacuated areas to allay concerns

about losing property to looters.(16)More recently, interest has developed in con-

ducting research on noneconomic incentives for thepreimpact phaseshazard mitigation and emergencypreparedness. Lindell and Perry(17) found scatteredevidence that households decided whether to adoptseismic hazard adjustments on the basis of noneco-nomic factors such as an adjustments requirementsfor knowledge and skill, time and effort, knowledgeand tools, and social cooperation. Accordingly,Lindell and Whitney,(18) Lindell and Prater,(19)

and Lindell, Arlikatti, and Prater(20) systematicallyexamined the influence of these noneconomic fac-tors on seismic hazard adjustment. This researchfound that risk area residents did indeed differen-tiate among 16 hazard adjustments with respect tothree noneconomic attributes (knowledge and skill,time and effort, and social cooperation). In otherwords, education programs designed to increasehouseholds knowledge and skill about how toundertake specific mitigation actions could increasethe probability of households actually undertakingthese actions. However, cost was also an importantdifferentiating attribute.

3. HOUSEHOLD RESPONSES TO HAZARDMITIGATION INCENTIVES

The research cited in the previous sectionsuggests that the provision of economic and noneco-nomic incentives will increase households adoptionof hazard adjustments. However, none of this re-search has examined peoples expectations of takingadvantage of such incentives to mitigate damage totheir homes. This research suggests that people willtake advantage of mitigation incentive programs ifthey believe there is a significant hazard and that

hazard mitigation measures will protect themselvesand their property. Participation in mitigation incen-tive programs is especially likely if people have notalready adopted hazard mitigation measures becauseof implementation barriers such as excessive cost,knowledge/skill, time/effort, tools/equipment, andsocial cooperation. Such people would be expectedto have attitudes toward mitigation incentives thatare quite similar to their attitudes toward hazard

mitigation itself. Accordingly, one would expectvariables that have been shown to predict the in-tentions and actual adoption of hazard adjustmentswould also predict expectations of participatingin incentive programs that support adopting those

hazard adjustments.Many conceptual frameworks can be utilized to

explain why households take protective actions.(14)

Among these models, a modified form of the protec-tive action decision model (PADM)(14,21) identifiesmany variables that might promote or thwart house-hold participation in hazard mitigation incentive pro-grams (see Fig. 1). The model includes four psy-chological factors (risk perception, perceived hazardknowledge, worry, and hazard intrusiveness), an ex-periential factor (hazard experience), exposure fac-tors (hazard proximity and past tenure), and de-mographic factors (gender, ethnicity, age, education,and income) that might explain mitigation incentiveadoption expectations.

Specifically, the research cited in the previousparagraph suggests that households expectationsof participation in hazard mitigation incentiveprograms will be positively correlated with psycho-logical factors (risk perception, perceived hazardknowledge, worry, and hazard intrusiveness), hazardexperience, and household income. The psycho-logical factors will in turn be positively correlatedwith hurricane hazard experience, hazard proximity,female gender, ethnic minority status, age, presence

of children under 12, and household members allage 65 or over. Moreover, the psychological factorswill be negatively correlated with household incomeand education. Further, hurricane hazard experiencewill be positively correlated with past tenure, hazardproximity, and ethnic minority status, but negativelyrelated to income, education, age, presence of chil-dren under 12, and household members all age 65or over. A critical implication of the PADM is thatthe psychological variables will act as interveningvariables shaping whether or not households expectto participate in mitigation incentive programs. By

contrast, demographic factors are seen as having aninfluence on participation in mitigation incentiveprograms primarily through their effects on the psy-chological variables. The model in Fig. 1 is similar toone that has been tested in a multistage analysis onthe adoption of mitigation actions(22) but the presentarticle has a different objectiveexamining thefactors that influence expectations of participating inmitigation incentive programs.

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Fig. 1. A model of mitigation incentiveadoption expectations.

3.1. Psychological Factors

Psychological variables such as risk perception,perceived hazard knowledge, worry, and hazardintrusiveness have been studied by hazard re-searchers in a number of projects that have dealtwith household mitigation adoption for a varietyof environmental hazards. First, the relationshipbetween risk perception and hazard adjustmentshas been explored by an extensive body of em-pirical research.(17,23) Although a few studies havefound nonsignificant relationship between thesevariables,(24) most studies report a positive effect ofrisk perception on hazard adjustments.(25) Second,perceived hazard knowledge generally means infor-mation about a hazards genesis, its mechanisms

of exposure, and the types of hazard adjustmentsthat can avoid its impacts(Ref. 14, p. 153). Existingresearch has indicated that higher levels of perceivedhazard knowledge increase the likelihood that ahousehold will adopt hazard adjustments.(25,26) How-ever, overconfidence in skills and knowledge maymake people with higher levels of knowledge unwill-ing to undertake hazard adjustments.(18,26) Third, riskanalysis researchers have examined the relationshipbetween worry and risk perception(27) and the roleof worry (or dread) in risk reduction,(28) yet little hasbeen done to look at the relationship between worry

and adoption of hazard adjustments. Experimentalstudies suggest worry would be an important psycho-logical factor influencing peoples adoption of pro-tective actions. Fourth, hazard intrusiveness, whichis thoughts generated by the distinctive hazard-relevant associations that people have with everydayevents, informal hazard-relevant discussions withpeers, and hazard-relevant information received pas-sively from the media (Ref. 14, p. 125), also affectspeoples adoption of hazard adjustments.(18,24) In

general, the expectation is that intrusiveness has apositive effect on whether households will undertakea hazard adjustment, or in this case, will expect to

participate in a mitigation incentive program.Last, previous personal experience with a haz-

ard is an oft-cited causal factor in hazard adjust-ment adoption. Empirical research on experiencewith environmental hazards demonstrates both sig-nificant and nonsignificant effects of experience ona variety of hazard adjustments.(26) The disparitymay be explained by the large number of potentialintervening variables lying between hazard experi-ence and hazard adjustments in the protective actiondecision-making process.(14,22) Specifically, the ma-jority of household hazard adjustment studies con-

ducted over the past 30 years that have dealt withhazard experience have found that hazard experi-ence is positively correlated with perceived hazardknowledge, risk perception, and adoption of hazardadjustments.(13) However, another issue is related tothe nature of experience itself and how it has beenmeasured. Oftentimes, individuals indicate that theyhave experienced a hazard event, when in fact theyhave not actually experienced damage to their homesor injury to themselves or their loved ones. Thus,individuals who may have experienced a disasterevent, in that it struck some areas near their homes,may gain false confidence that they have survived

the event and therefore need not undertake hazardmitigation.

3.2. Demographic Factors

In hazards research, demographic variables havetypically been included as potential exploratory fac-tors in hazard adjustment adoption. These vari-ables include gender, race/ethnicity, age, presence of

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dependents (school-age children or the aged), educa-tion, income, occupation, and marital status.(23) Theynot only contribute to the explanation of social vul-nerability to natural and technological hazards, butalso imply the ways in which demographic groups

vary in their perceptions, knowledge, and proximityto hazard prone areas.

First, there is some evidence that peoples adop-tion of hazard adjustments is related to household in-come.(22,26,29) In the case of shutters, these systemsoften require significant outlays of money; hence,income may well be related to participation in mit-igation incentive programs. The exact nature of therelationship with likely participation in incentive pro-grams is difficult to gauge. Overall, it might be ex-pected that lower-income households would viewattitudes toward incentive programs more favorably,whereas higher-income households might not feelthe necessity of these programs. Indeed, they mightview likely restrictions associated with such programsas limiting their own mitigation options such as thestyle and type of window protection. Second, manystudies indicate that household demographic char-acteristics are significantly associated with risk per-ception. These characteristics include lower educa-tion and income,(6,29,30) age,(22) female gender,(31)

and ethnic minority status.(30,32,33) These studies sug-gest that these demographic factors will be relatedto expectations of participation in mitigation incen-tive programs but, at least partially if not completely,

mediated by the psychological variables identified inFig. 1.

It is, however, also possible that these demo-graphic factors might have effects beyond their re-lationship to psychological variables. For example,race and ethnicity can have consequences for accessto loans, which may well be critical for financingrather expensive hazard adjustments such as shuttersystems and impact-resistant windows.(25) The liter-ature has shown that ethnic minorities have histori-cally been denied access to loans and mortgages andwhen they do gain access, it is often at higher inter-

est rates.(34,35)

As a consequence, ethnic minoritiesmay well show greater interest in some mitigation in-centive programs such as low-interest and forgivableloan programs. Similarly, households at differentstages in their life-cycle are likely to view incentiveprograms differently because of their ability to as-sume the financial risk associated with some of theseprograms, as well as their ability to fully actualizethe potential benefits of hazard adjustments over a

longer period of time. For example, older householdsare more likely to be on fixed income and not as will-ing or able to take on additional financial risk. Conse-quently, they might well be more negatively inclinedtoward low-interest loan programs, regardless of

their discounted interest rates. This may even applyto forgivable loan programs, which generally requiresome period of time before the loan is fully forgiven.Some forgivable loans require a household to remainin a residence for 510 years before a loan is fully for-given. Older individuals and elderly households maynot be willing to pass on this financial risk to theirfamilies or estates should something happen to themduring the required period of home occupancy. Con-versely, younger households may well view incen-tive programs more positively because they are morelikely to experience the benefits of such a programfor a longer period of time, may be more likely to re-main in their homes for the forgivable period of time,or simply are more capable of assuming the financialrisks associated with low-interest loan programs.

3.3. Exposure Factors

Two principal exposure factors are hazard prox-imity and tenure, both of which are determinedby peoples continuing choiceor necessityto liveand work in areas that are exposed to environmen-tal hazards. Some people have little or no controlover whether they are pushed by their low incomes

or minority status into hazard prone areas or, con-versely, when hazardous facilities are forced upontheir communities and they are simply not able toescape the hazardous area.(32,36) In other cases,people voluntarily occupy hazard prone areas be-cause of environmental amenities such as access toor views of water and mountains. When people thinkthe environmental amenities outweigh the environ-mental disamenities, they are willing to move intohazard prone areas and pay higher prices for homesin those areas.(6,7) However, only a limited amountof research has examined the mechanisms by which

hazard proximity affects hazard adjustment adoptionand none has studied the effects of hazard proxim-ity on expectations of participation in mitigation in-centive programs. Empirical studies of seismic haz-ard proximity show complex effects of fault prox-imity, hazard experience, and social influences onpeoples hazard adjustment.(17,23) Peacock and hiscolleagues conducted some of the very few stud-ies that have dealt with hurricane hazard proximity,

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examining Florida coastal households mitigation ad-justments of installing shutters and envelope cover-age to reduce wind damage to their homes.(25,26) Al-though these researchers found that homes in coastalcounties have significantly better shutter and enve-

lope coverage than those in inland counties, theyalso found that homes located within the evacua-tion zones of these coastal counties do not have sig-nificantly better shutter or envelope coverage thanhomes in inland areas of these counties. Thesefindings indicate that hazard proximity can shapepeoples risk perception and their expectations ofparticipation in mitigation incentive programs. Whilethe finds have been inconsistent, the overall litera-ture suggests that proximity to a hazard should havea positive effect on expectations of participation inmitigation incentive programs.

In addition, the number of years or tenure in ahome is related to how long a household that is livingin a coastal area has been exposed to hurricane haz-ard and it also is related to hazard experience.(22,32,36)

Peacock hypothesized that years in residence wouldhave a positive effect on the nature and quality of ashutter system a household has, in part because thesesystems are often installed piecemeal over time.(25)

However, if a household has lived in a home for anextended period of time without installing hurricaneshutters, its members may well believe that it is un-necessary to install a system now.

3.4. Research Objectives

In sum, the literature suggests a number of fac-tors should be related to expectations of participationin mitigation incentive programs. Following Kanget al.(37) the term expectation is used rather than in-tention because respondents might not have formedspecific behavioral intentions based on salient beliefsas defined in prevailing theories of attitude/behaviorrelations such as the theory of reasoned action.(38)

More specifically, Fig. 1 implies six hypotheses.

H1: The four psychological variables (risk percep-tion, perceived hazard knowledge, worry, andhazard intrusiveness) are expected to have pos-itive effects on expectations of participation inmitigation incentive programs.

H2: Hazard experience is expected to have a posi-tive effect on all four psychological variables andalso a direct effect on expectations of participa-tion in mitigation incentive programs.

H3: Hazard exposure (proximity to coastal hazardareas and tenure at the households current lo-cation) is expected to have a positive effect onhazard experience.

H4: Income is expected to have a negative effect

on expectations of participation in mitigation in-centive programs.

H5: The remaining demographic variables are ex-pected to have significant correlations withthe psychological variablesAnglos, males, andhigher education, higher income, and older re-spondents having lower risk perceptions, hazardintrusiveness, and worry, as well as higher per-ceived hazard knowledge.

H6: Ethnic majority status, age, education, and in-come are expected to have positive correlationswith hazard proximity.

4. METHOD

This study will test the expectations about the ef-fects of psychological, experiential, hazard exposure,and demographic variables by examining house-holds expectations of participation in five differ-ent economic and noneconomic incentive programs.Two of the four economic incentive programs of-fered direct inducements in the form of low-interestor 5-year forgivable loans. The latter is a loan thathas the possibility of being completely forgiven (i.e.,requiring no payback), if the household maintains

the home as its primary residence for 5 years. Ineach case, the loan would be used directly to payfor the installation of hurricane shutters. Two indi-rect economic incentive programs were discountedhomeowners insurance premiums or reduced prop-erty taxes. In each of these cases, homeownerswho install shutter systems would indirectly benefitfrom shutter installation. The last incentive programprovided a free hurricane safety home audit. Thisaudit would provide homeowners with detailed infor-mation about how to increase their homes hurricanesafety.

4.1. Sample Selection

The data for this study were collected as partof Floridas statewide Hurricane Loss MitigationBaseline Survey.(39) The Institute for Public Opin-ion Research at Florida International Universityconducted the survey during February and March2003. The interviews were conducted on an equal-probability random digit dialed sample of 1,260

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households residing in single-family owner occupieddetached homes. These households are the majorcontributors to the Floridas catastrophic insurancefund and are the primary target for many statewidehurricane mitigation initiatives.(26) However, many

of these households already had shutter systems in-stalled on their homes at the time the study was con-ducted. Since the objective of the study is to examineattitudes toward participating in incentive programsby those who currently lack shutters, only that por-tion of the sample not having shutters is included inthe analyses below. Thus, only 599 (47.5%) of the1,260 households in the original sample are includedin these analyses. Although those who already hadshutters would be able to provide some very clearanswers about why they purchased shutters, we areaddressing incentives for purchasing shutters and wewant to generalize our conclusions to the populationof those who would be eligible for the incentive pro-grams. Since those who already had shutters werenot eligible for incentive programs when they madetheir purchases (because the programs did not ex-ist at that time), and they would not be eligible inthe future (because they already had shutters), theirpreferences for different shutter programs would beirrelevant.

4.2. Measures

The survey asked respondents a series of ques-

tions regarding incentives that might encourageFloridas single-family homeowners to make theirhomes safer from wind damage. There were 587 res-idents who answered the questions about the likeli-hood of each economic incentive motivating them toundertake hurricane mitigation measures. For eachincentive, informants were asked how likely it wasthat their household would participate in each incen-tive program with the possible response categoriesbeing: very likely, somewhat likely, or not very likely(respondents who said they were unsure, they dontknow, or it would depend were coded as missing

for these analyses). For this analysis, responses ofvery likely and somewhat likely were coded as1 and not very likely was coded as 0. Overall,31.1% of the informants responded that their house-holds would likely be motivated to install shutters inresponse to a low-interest loan program, 60.4% aslikely given a 5-year forgivable loan program, 69.4%as likely given a reduction in their homeowners in-surance, and 69.2% in response to property tax re-duction. Additionally, 368 residents reported how

interested they were in a hurricane preparednessinspection program.(39,40) Specifically, homeownerswere asked how interested (very, somewhat, or not)they would be to participate in a home inspectionprogram in which their home would be evaluated for

hurricane safety. Response categories were dummycoded with very interested and somewhat inter-ested responses coded as 1 and not interestedcoded as 0. In all, 67.5% suggested that they wouldbe interested in such a program.

In addition to considering each of these incentiveprograms separately, the five measures were com-bined into an index indicating the overall expec-tations of participation in the mitigation incentivesprograms. The correlations among these five dichoto-mous responses to the hurricane mitigation incen-tives (see the upper left corner of Table I) wererelatively strong, yielding an average correlation ofri j = 0.46 (all correlations significant at p < 0.01)and an internal consistency reliability of = 0.81(see Ref. 41 for a discussion of Cronbachs andother reliability coefficients). The combined measurewas generated by calculating a weighted average re-sponse over the items, where the weights were de-termined from factor loadings in a principal compo-nent analysis. Utilizing an average, rather than a sum,allows a score to be calculated for households thatdid not respond to one of the five measures, withoutlosing observations.

The measures of the independent variables in-

cluded in these analyses are as follows.Perceived hurricane risk was assessed by re-

sponses to three questions: (1) how likely is it thata major hurricane, Category 3 or higher, would pre-vent them or their households members from beingable to work or to do their jobs this coming season;(2) how likely is it that a major hurricane would dis-rupt their daily lives this season; (3) how likely is itthat a major hurricane would damage their homesthis season. Each item was rated on a scale from notvery likely ( = 1) to very likely ( = 3). The re-sulting measure has a reliability of = 0.73. Per-

ceived hurricane knowledge was assessed by addingthe responses to three questions: (1) how knowledge-able did the respondent feel their household wasabout the chances of being impacted by a hurricane;(2) how knowledgeable did the respondent feel theirhousehold was about the types of damage their homemight sustain from a hurricane; and (3) how knowl-edgeable did the respondent feel their householdwas about what they might do to reduce hurricanedamage. Each item was rated on a scale from not

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TableI

.IntercorrelationsAmongVariables

Notes:Samplesizes(N)rangefrom

280to599dependingonmissingdata.

Allcorrelationsthatarestatisticallysignificantatp $100,000).

Race/ethnicity was converted to a set of three di-chotomous variables indicating whether the house-hold is non-Hispanic black (1 if yes; 0 otherwise),Hispanic (1 if yes; 0 otherwise), or Anglo (non-Hispanic nonblack) (1 if yes 0 otherwise). To avoid

multicollinearity in the multivariate analyses, Anglo,the comparison group, was omitted from all regres-sion analyses. Elder household was measured by adichotomous variable indicating that all householdmembers are 65 years of age or older (= 1) or not(= 0). Households with young children was a dummyvariable indicating the presence of children under theage of 12 in the home (= 1) or not (= 0). Years in res-idence was the total number of years that the house-hold has resided in their current home. Proximity to

hazard was indicated by whether the home is locatedin a coastal county (= 1) or not (= 0), gender wascoded as male= 1 and female= 0, and education wasmeasured by the number of years of schooling.

4.3. Analyses

The analysis proceeded in two steps. First, thecorrelations among the dependent and independentvariables were calculated. Of particular interest iswhether the obtained correlations are consistent withthe expectations derived from the literature and thepropositions of the PADM as illustrated in Fig. 1.Following an examination of these correlations, theanalysis turned to a multivariate analysis of the com-posite mitigation incentive expectations index. Thiswas followed by a series of logistic regressions pre-dicting expectations of participating in each of the in-

dividual incentive programs.

5. RESULTS

Table I presents the intercorrelations amongall variables, both dependent and independent, dis-cussed above.2 The shadowed correlations are allstatistically significant. First, the expectations of par-ticipating in the individual incentive programs aresignificantly positively correlated with each other,but the magnitude of the correlations ranges from0.31 r 0.80. As will be discussed later, this varia-

tion in the correlations might have arisen because themitigation incentive programs vary systematically interms of respondents perceptions of their character-istics.

Consistent with H1, the psychological variablesare significantly correlated with mitigation incentiveexpectations, both with respect to the overall mit-igation incentive expectations index and with re-spect to the expectations of participating in each ofthe individual incentive programs. Specifically, haz-ard intrusiveness (r = 0.32), risk perception (r =0.27), worry (r = 0.25), and hazard experience (r= 0.17) are significantly and positively related to

the mitigation incentive expectations index. Indeed,the psychological measures are all significantly re-lated to each of the individual incentive programsas well. Overall, hazard intrusiveness and perceived

2Among the total 231 [k (k 1)/2 = (22 21)/2 = 231] correlationcoefficients, 60.2% of them (139/231) are statistically significantat p < 0.05, indicating that experiment-wise error rate is not aplausible explanation for the models empirical support discussedlater.

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risk have slightly higher correlations with the miti-gation incentive expectations index than does worry.Perceived hazard knowledge is not significantly cor-related with the overall mitigation incentive expecta-tions index or any of the individual mitigation incen-

tive programspossibly because it was a single itemof unknown reliability.

Consistent with H2, hazard experience has sig-nificant positive correlations with the psychologicalvariables, although these correlations are small (r=0.13) and the correlation with risk perception was notstatistically significant. Although not hypothesized,coastal proximity had correlations with the psycho-logical variables (r= 0.11) that were just about asstrong as hazard experience. However, the otherexposure variable, community tenure, had gener-ally nonsignificant correlations with the psychologi-cal variables (r= 0.06).

Partially consistent with H3, hazard proximitywas significantly correlated with hazard experience(r= 0.14) but community tenure was not. Contraryto H4, income was not significantly correlated withthe mitigation incentive expectations index. How-ever, partially consistent with H5, ethnic minorities,females, and lower education, lower income, andyounger respondents tended to have higher risk per-ceptions, hazard intrusiveness, and worry, as wellas lower perceived hazard knowledge. Specifically,racial/ethnic minorities have higher risk perceptionsas do residents with lower income, education, and

age, as well as females. Older, Anglo, higher in-come, and more highly educated respondents re-port higher levels of perceived hazard knowledge.However, there are surprisingly few relationships ofdemographic factors with hazard intrusiveness andworry. Indeed, the only significant relationships areassociated with race/ethnicity, where Hispanic andnon-Hispanic black households have higher levelsof worry and Hispanics reported higher levels ofhazard intrusiveness. Hurricane experience is onlypositively related to income and male gender and,perhaps surprisingly, negatively related to age. The

latter is probably due to the very low frequencies ofhurricanes in the period from the mid 1970s throughthe 1990s.

Finally, there was little support for H6. Hispan-ics were more likely to live close to the coast, whichis consistent with the hypothesis. However, age andincome were uncorrelated with residence in coastalcounties and education was positively correlated withproximity to the coast, all of which are contrary to thehypothesis.

It is important to recognize that these correla-tions provide only a partial test of the predictionfrom Fig. 1 and the six hypotheses that, other thanincome and hazard experience, only the psycholog-ical variables directly affect expected participation

in mitigation incentive programs. Indeed, contraryto H1, Table I indicates that Hispanic ethnicity,the three age-related variables, and the two haz-ard exposure variables are significantly correlatedwith expected participation in mitigation incentiveprograms. Thus, regression analyses are needed todetermine if any of the demographic or exposurevariables have statistical significant coefficients whenentered into a regression analysis along with thepsychological variables. Table II presents a series ofordinary least squares (OLS) regression equationspredicting mitigation incentive expectations indexscores. The first model includes only psychologicalmeasures (excluding worry, which was too highlycorrelated with risk perception and hazard intrusive-ness), the second model includes only demographicvariables (excluding education, which was too highlycorrelated with income, and also excluding allmembers 65 or over and children under 12, whichwere too highly correlated with age), the third modelincludes only hazard exposure measures, and thefinal model includes all three sets of measures. Allfour models are statistically significant but, of thefirst three models, the psychological model accountsfor the highest proportion of the variance (R2 =

0.124), followed by the demographic model (R2 =0.090) and the exposure model (R2 = 0.037). Thefinal, full model, accounts for 15.7% of the variancein the mitigation incentive expectations index.

Focusing first on the psychological model, it canbe seen that, with the exception of perceived haz-ard knowledge, all measures have positive effectson the mitigation incentives expectations index. Inother words, higher levels of risk perception, haz-ard intrusiveness, and hazard experience generallylead to heightened expectations of participating inthe mitigation incentive programs. Of the significant

measures, hazard intrusiveness has the strongest ef-fect, followed by risk perception, and then hazardexperience.

With respect to the demographic model,Hispanic and age are the only significant predictors,with Hispanics having higher and older house-holds having lower expectations of participating inmitigation incentive programs. With respect to thehazard exposure model, both variables are signifi-cant, with residential tenure having a negative effect

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Table II. Models of Households Hurricane Mitigation Incentive Adoption Expectations

Psychological Model Demographic Model Exposure Model Full Model

b b b b

Constant 1.913

1.778

0.106 2.844

PsychologicalRisk perception 0.201

0.159 0.172

0.140

Perceived hazard knowledge 0.046 0.036 0.027 0.022Hazard intrusiveness 0.279

0.254 0.238

0.223

Hazard experience 0.144

0.097 0.113 0.079

DemographicHispanic 0.604

0.098 0.332 0.053

Black 0.140 0.020 0.055 0.008Income 0.001 0.024 0.000 0.000Male 0.140 0.042 0.068 0.021Age 0.033

0.291 0.021

0.182

ExposureYears in residence 0.131

0.170 0.054 0.070

Coastal county 0.382

0.105 0.178 0.050

R2 0.136 0.107 0.043 0.194Adj-R2 0.124 0.090 0.037 0.157

N 289 276 318 255

p 0.01;

p 0.05 (all significance tests are one tailed because the available research clearly indicated the direction of effect for the

independent variables).b: unstandardized coefficient; : standardized coefficient. Worry was excluded due to collinearity with risk perception and hazard intrusive-ness. Anglo was excluded due to collinearity with other race/ethnicity variables. Education was excluded due to collinearity with income.All members 65 or over and children under 12 in home were excluded due to collinearity with age. In all cases, the excluded variables hadlower zero-order correlations with the dependent variable than the variables that were retained.

and coastal residence having a positive effect. Ofthe two, tenure appears to be the more important.

Despite the statistical significance of tenure andcoastal residence in the exposure model, theseeffects disappear when all variables are entered intothe full model. Of the two significant variables inthe demographic model (Hispanic and age), onlyage retains its significance in the full model. Insummary, hazard intrusiveness, risk perception, andage are the most plausible direct causes of mitigationincentive expectations. Because the other variablesin the demographic model and both variables inthe exposure model have nonsignificant regressioncoefficients in the full model but have significant

correlations with the psychological variables, theyappear to have indirect effects on mitigation in-centive program participation expectations that arecompletely mediated by the psychological variables.For example, Hispanic ethnicity (significant in thedemographic model but not the full model) hassignificant correlations with risk perception andhazard intrusiveness, both of which are significant inthe full model. Similarly, years in residence on the

coast is strongly correlated with age, which is alsosignificant in the full model.

The results of the second step of the analyses,examining the responses to individual incentive pro-grams, are presented in Table III. This table showsthe results of five logistic regression models predict-ing expectations of participating in each of the foureconomic incentive programs and the (noneconomic)inspection program. Rather than presenting all in-dividual models as in Table II, only the full modelsare presented. Each full model is statistically signifi-cant, but the models vary considerably with respectto their prediction of the dependent variables. Onthe whole, the models predicting expected participa-

tion in the low-interest loan (pseudo-R2

= 0.128) andinspection (pseudo-R2 = 0.105) programs appear toperform slightly better than the other three. Inter-estingly, one of the best predicted programs involvesan economic incentive, whereas the other involves anoneconomic incentive.

When examining how the individual sets ofvariables perform in these models, the results forthe psychological variables are consistent with the

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Table III. Binary Logistic Regression Models of Households Hurricane Mitigation Incentive Adoption Expectations

HurricaneLow-Interest Forgiveness Lower Insurance Property Tax Preparedness

Loans Loans Premium Reduction Inspection

b b b b b

Constant 2.406

3.115

3.247

3.164

2.906

Psychological

Risk perception 0.316

1.372 0.219

1.244 0.216

1.242 0.054 1.056 0.194 1.214Perceived hazard knowledge 0.012 1.012 0.006 0.994 0.015 1.015 0.050 1.051 0.000 1.000Hazard intrusiveness 0.167

1.181 0.177

1.194 0.157

1.170 0.193

1.213 0.326

1.386

Hazard experience 0.261

1.298 0.023 0.978 0.049 0.953 0.082 0.921 0.168 1.183

DemographicHispanic 0.564 1.758 0.711 2.037 0.543 1.721 0.875

2.398 0.897 2.453

Black 0.453 0.636 0.007 0.993 0.243 1.275 0.057 1.059 0.626 1.871Income 0.012

0.988 0.003 0.997 0.002 1.002 0.009

1.009 0.009 1.009

Male 0.179 1.196 0.482

1.620 0.114 1.121 0.256 0.774 0.137 0.872Age 0.025

0.975 0.030

0.970 0.025

0.975 0.028

0.972 0.006 0.995

ExposureYears in residence 0.067 0.935 0.003 1.003 0.078 0.925 0.015 0.985 0.064 0.938

Coastal county 0.145 1.156 0.386 1.471 0.313 1.368 0.130 1.139 0.212 1.236

Pseudo R2 0.128 0.090 0.083 0.083 0.105N 415 415 406 410 288

p 0.01;

p 0.05 (all significance tests are one tailed because the available research clearly indicated the direction of effect for the

independent variables).b: unstandardized coefficient; : odds ratio (the ratio of the probability of a yes response divided by the probability of a no response.Worry was excluded due to collinearity with risk perception and hazard intrusiveness. Anglo was excluded from prediction models dueto collinearity with other race/ethnicity variables. Education was excluded due to collinearity with income. All members 65 or over andchildren under 12 in home were excluded due to collinearity with age.

analysis of the overall index because hazard in-

trusiveness is statistically significant for all fiveprograms, risk perception is significant for three ofthem (low-interest loans, forgiveness loans, lowerinsurance premiums), and hazard experience issignificant for one (low-interest loans). The ef-fects of the demographic factors on the individ-ual programs are also consistent with the resultsfor the overall index. Age was significantly nega-tive for four programs (low-interest loans, forgive-ness loans, lower insurance premiums, and prop-erty tax reduction). Household income is signif-icantly negative only for low-interest loans and

property tax reduction; male gender is signifi-cant only for forgiveness loans. Hispanic is signif-icant for property tax reduction but black is notsignificant for any of the five individual incentive pro-grams. Finally, the effects of the exposure variableson the individual programs are also consistent withthe results of the overall index. Coastal residence andcoastal tenure have no significant effects at all.

Overall, the analyses of expected participation ineach of the individual incentive programs confirm the

results from the full model of the mitigation incen-

tives expectations index. The demographic variablesother than age are definitely not significant predictorsof the mitigation incentives expectations index and,although they have a moderate percentage (4/20 =20%) of statistically significant coefficients in predict-ing expectations of participating in each of the fiveindividual mitigation incentive programs, the patternof significant coefficients is inconsistent. Finally, thehazard exposure variables have no statistically signif-icant coefficients in predicting expectations of partic-ipating in the five individual mitigation incentive pro-grams.

6. DISCUSSION

This study appears to be the first to systemat-ically examine peoples expectations of participat-ing in incentive programs that seek to motivatetheir adoption of mitigation measures for hurricanewind hazard. Although some work has examinedparticipation in flood insurance programs and thepotential consequences of community participation

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in the CRS program for household adoption, nostudy has examined the potential consequences ofincentive program for likely household adjustment.In undertaking this study, the hazard adjustmentliteratureparticularly the literature addressing pro-

tective action decisionswas critical. That literaturehas generally found that psychological factors areof paramount importance in shaping whether or nothouseholds and individuals undertake and imple-ment mitigation adjustments. At times, some demo-graphic factors have been found to be important, butthe overall findings on the demographic variables areinconsistent.(17,23)

The findings of this study are supportive of thegeneral findings associated with the PADM. Specifi-cally, two psychological variableshazard intrusive-ness and risk perceptionwere found to be the mostconsistently significant predictors of expectations ofparticipating in mitigation incentive programs. Inparticular, the results of this study are consistent withthose of previous studies that have reported posi-tive effects of hazard intrusiveness on peoples adop-tion of hazard adjustments.(18,24) The positive effectof hazard intrusiveness suggests that the frequency ofthought and discussion about a hazard reminds peo-ple of their vulnerability and, thus, the need to takeaction. Similarly, the positive effect of risk perceptionon hazard adjustments is also consistent with previ-ous studies (see Ref. 25 for hurricanes and Ref. 17for earthquakes). The significant effect of risk per-

ception suggests that the perception of a high likeli-hood of severe consequences also is important in mo-tivating people to take action.

By contrast, the finding that perceived hazardknowledge had a nonsignificant effect is more diffi-cult to interpret. It might be that the nonsignificantresult is due to the fact that people had inaccurateperceptions of their knowledge about hurricane haz-ard and that this methodological difference accountsfor the difference from the findings of previous stud-ies.(25,26) Indeed, it might be that overconfidence inskills and knowledge makes people with higher levels

of knowledge unwilling to undertake hazard adjust-ments.(18,26) Finally, the weak effect of worry on mit-igation incentive expectations might seem to conflictwith the findings of previous studies supporting theimportance of this variable.(27,28) Indeed, a nonsignif-icant effect for worry would be especially surprisinggiven Slovic and his colleagues research on the affectheuristic.(42,43) However, careful inspection of TableI indicates that worry failed to emerge as a significantpredictor of hazard mitigation incentive expectations

not because it was uncorrelated with the dependentvariable but because it had a lower correlation thanhazard intrusiveness and risk perception, both ofwhich correlated strongly with worry. Thus, the re-sults seem to indicate that worry accounts for little

variance in hazard mitigation incentive expectationsover and above the effects of hazard intrusiveness

and risk perception. Consequently, the theoreticaland empirical interrelationships among hazard intru-siveness, risk perception, and worry require furtherstudy.

It is also important to note that age had a statisti-cally significant negative effect on the overall hazardmitigation incentive expectations, as well as on fourof the five individual incentive programs. Althoughthe effect is consistent, the reason for it is not entirelyclear. Gender, education, income, and ethnicity wereall controlled in the regression analyses, so age can-not be a proxy for any of these other variables. Onepossible explanation is that older residents do not ex-pect to live long enough to benefit from an invest-ment that might take decades to pay off. However,this explanation needs to be addressed in furtherresearch.

The demographic and hazard exposure variableswere significantly correlated with the psychologicalvariables. Specifically, 22 of the 45 (49%) corre-lations between demographic and psychologicalvariables in Table I were statistically significant.Similarly, 6 of the 10 (60%) correlations between

hazard exposure and psychological variables inTable I were statistically significant. When combinedwith the statistically significant effects of the psycho-logical variables on the behavioral expectations andthe results of the regression analyses, these resultsclearly imply that psychological variables mediatethe effects of the demographic and hazard exposurevariables on expectations of participating in a hazardmitigation incentive program.

Despite the consistency of this studys resultswith the propositions of the PADM and recent re-search on this model,(22,24) there are some inconsis-

tent results from one study to another that are noteasy to explain. For example, Lindell and Prater(24)

reported that risk perception was not a significantpredictor of earthquake hazard adjustments whereasthe present study found that both were significantpredictors of expected participation in hurricanemitigation incentive programs. Moreover, the rolesof community tenure, hazard proximity, and haz-ard experience are not well understood. It is un-clear whether the differences among studies are due

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to sample fluctuations, differences between hazards,differences between dependent variables, or some asyet unidentified other variables.

In addition to theoretical contributions, thefindings from this study also support some policy

recommendations. First, there does appear to besome interest among households without shutters ina variety of mitigation incentive programs. Althoughonly 31% of households reported that they would bemotivated to install shutters given their participationin a low-interest loan program, that percentage roseto over 60% in response to a 5-year forgivable loanprogram, and to over 69% in the case of reducingboth homeowners insurance premiums and propertytaxes. However, it must also be noted that somehouseholds will not be motivated to adopt hazardadjustments unless property taxes and insurance pre-miums too fall to unrealistically low levels. Indeed,the average expected reduction in property taxesand insurance premiums is approximately 28%, witha median value of 25%, which is unlikely to occur. Inaddition, nearly 68% of the respondents were alsointerested in hurricane safety inspections.

Moreover, the studys results suggest that educa-tion programs targeting risk perception and hazardintrusiveness are likely to promote participation inall mitigation incentive programs. Specifically, haz-ard awareness programs need to repeatedly (thus in-creasing hazard intrusiveness) remind people of thelikelihood of severe negative consequences of hurri-

cane impact (thus increasing risk perception). Unfor-tunately, it is also clear that long-term residents aregoing to be the most resistant to increasing the hurri-cane safety of their homes and the longer the time inresidence, the greater the inertia.

Of course, like all other empirical studies, thisstudy has its limitations. First, the mitigation incen-tives are significantly positively correlated but thecorrelations range from 0.31 r 0.80, so varia-tion in the (also positively correlated) predictors is tobe expected. As noted earlier, it is possible that thisvariation in the correlations arises from respondents

perceptions that the incentive programs vary in termsof unmeasured characteristics such as their profilesof investment and return over time. To address thisissue, future research should use free response ques-tionnaire items or focus groups to more fully exam-ine coastal residents perceptions of the differencesamong these mitigation incentives.

Second, telephone interviews were used to en-sure a demographically representative sample, butthe time constraints associated with telephone inter-

views limited the number of questions that could beasked and, consequently, the reliability of the scalesmeasuring the different variables. In turn, imperfectreliability would be expected to attenuate the mag-nitudes of the correlations among the variables. This

problem was especially significant for the measure-ment of worry (one item) but was also a problemfor hazard experience and hazard intrusiveness (twoitems each). In addition, the referent for the worryitem was the occurrence of an event, whereas riskperception referred to personal consequences. Thus,future research should develop improved multi-itemmeasures of the measures employed in this study andshould use personal consequences as the referent forhazard experience, perceived risk, and worry.

Third, these data are cross-sectional, whichmakes it difficult to determine the temporal or-dering of some of the variables and, thus, theircausal relationships.(44) Specifically, although onecan reasonably presume that the demographic andhazard exposure variables preceded the psychologi-cal and mitigation incentive expectation variables, itis not possible to determine the temporal ordering ofthese last two sets of variables.

Fourth, this study did not offer a full empiricalassessment of the PADM, so it is unsurprising thatTables II and III reveal relatively low R2 values. It isimportant to recognize that the omission of relevantcauses might not only produce low R2 values, but itcould also have biased the estimates of the regres-

sion coefficients. Future research should attempt toresolve these limitations by including a broader rangeof explanatory variables.

Fifth, this research examines expectations forparticipation in mitigation incentive programs, notactual participation in or effectiveness of these in-centive programs. Making inferences from these datato actual participation in specific incentive programsshould be undertaken with great caution. These data,at best, give preliminary expectations as to howhouseholds might act, when offered specific incen-tive programs, not definitive intentions about how

they will act. Kang et al.(37)

found a significant degreeof correspondence between evacuation expectationsand actual behavior 2 years later, but the degree towhich expectations about participation in mitigationincentive programs actually predict future behaviorremains an open question.

Finally, much of the literature on protective ac-tion decision making equates individual behaviorwith that of households. While there is bound tobe a degree of correspondence between the two,

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decision making (or the failure to make decisions)by a household is rarely the same as decisionmaking by a single individual. Household decisionmaking is fundamentally a social process where dif-ferentials in power, gender roles, and the complexi-

ties of differential risk assessments, knowledge, andinformation among the participants are bound tohave consequences for the outcome. Do we fix theroof, save for childrens education, fix the car, orput on hurricane shutters? Researchers addressingprotective action decision making have yet to fullygrapple with the potential problems associated withhousehold decision making. This is an important areawhere researchers should use both qualitative andquantitative approaches to better explain the fullcomplexities of this process.

ACKNOWLEDGMENTS

The data collection for this research was sup-ported by a grant from the Florida Department ofCommunity Affairs and this research was supportedin part by grants from the National Oceanic and At-mospheric Administration (NA06NOS4190219) ad-ministered by the Texas General Land Office to thesecond author and by the National Science Founda-tion under Grants SES 0838654 and CMMI 0927739to the third author. None of the conclusions ex-pressed here necessarily reflects views other thanthose of the authors.

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