Risk Analysis, Vol. 12, No. 4, 1992

Risk Perception and the Value of Safety Timothy L. M~Daniels,’.~ Mark S. Kamlet,* and Gregory W. FischerJ Received February 28, 1991; revised January 27, 1992

This paper examines the relationship between perceived risk and willingness-to-pay (WTP) for increased safety from technological hazards in both conceptual and empirical terms. A conceptual model is developed in which a given household’s WTP for risk reductions is a function of traditional socioeconomic variables (Le., income and base level of risk) and perceived characteristics of the hazards (i.e., dread, knowledge, and exposure). Data to estimate the model are obtained through a combined contingent valuation and risk perception survey that considers 10 technological hazards, five of which are well-defined (e.g., death rates are known and the risks are relatively common) and five are less well-defined. Econometric results, using TOBIT estimation procedures, support the importance of both types of variables in explaining WTP across all 10 hazards. When the risks are split into two groups, the results show that WTP for well-defined hazards is most influenced by perceived personal exposure, while WTP for less well-defined risks is most influenced by levels of dread and severity. KEY WORDS: Risk perception; value of safety; contingent valuation; risk management.

1. RISK PERCEPTION AND THE VALUE OF

ards,(’,*) the potential relationship between them has received little attention. The two areas have flourished as separate dimensions of risk management research. Economists and decision theorists have investigated the social value of health risk changes (i.e., the “value of life”) that should be employed in benefit-cost analyses of risk management options. Normative economic analyses have emphasized the importance of an individual’s wealth and base or initial level of risk as influences on WTP for reductions in risks.(34) At the same time, psychologists have investigated how people perceive and react to various haza r d ~ . (This ~ ) research has shown that diverse hazards can be understood in terms of their perceived underlying attributes, such as the extent of knowledge about a hazard, whether it is a source of dread, and the perceived exposure to the hazard. Subjects’ scaled perceptions of hazards, cast in terms of such attributes, have been used to clarify the great public fear for some low probability hazards (e.g., nuclear power) and public indifference toward some technologies that hold high statistical risk (e.g., automobiles). Yet aside from simple correlations,

SAFETY What factors influence a person’s willingness-topay (WTP) for improvements in safety? How is WTP to reduce risks from, say, a nearby nuclear electric generation plant influenced by the perceived likelihood of a fatal radiation emission? By knowledge about nuclear energy plants? By dread of nuclear radiation? Questions such as these draw together two crucial aspects of social responses to risk: public perception of hazards and social values for risk changes. Although these two research areas are closely related, and are major themes in much of the writing on societal responses to technological hazSchool of Planning, University of British Columbia, 6333 Memorial Road, Vancouver, BC, Canada, V6T 122. Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213. Fuqua School of Business, Duke University, Durham, North Carolina 27706. To whom all correspondence should be addressed.

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little analysis appears in the literature regarding the nature of links between public risk perceptions and the economic values that society places on risk changes. Any attempt at exploring the links is faced with integrating the behavioral and normative perspectives into one framework. A useful starting point is Lancaster’s work in microeconomic theory, demonstrating that consumers value attributes of goods rather than simply the goods themselves.(8) Psychological research suggests that attributes of interest for the nonmarket good “technological safety” could include the characteristics of hazards as perceived by Thus, perceived characteristics of hazards could enter into consumer utility functions when evaluating safety tradeoffs and become partial determinants of WTP for safety. The basic premise of this paper is that a household’s WTP for safety changes is in part a function of perceived characteristics of the particular hazard, as emphasized in the risk perception literature, and in part a function of more traditional socioeconomic variables, as emphasized in the normative literature. The paper begins by developing a conceptual framework for the analysis. It then estimates this model empirically to test hypotheses regarding these research questions. When estimating the model, the work considers two types of hazards: “well-defined,” for which death rates are known (e.g., automobiles) and “less welldefined,” for which death rates are uncertain or even ambiguous (e.g., electric and magnetic fields). A combined contingent valuation and risk perception survey is conducted, and econometric techniques are employed to examine the hypothesis. Then the empirical results are discussed and conclusions presented. 2. CONCEPTUAL FRAMEWORK

This section develops a conceptual model of household WTP for safety improvements cast as a function of traditional normative variables as well as perceived characteristics of the hazards under consideration. Here, we define WTP for safety in terms of ex ante payments that are not contingent on the health outcome that is realized. This definition of WTP has been referred to as the “option price” associated with a risk reduction.(9)One motivation for this model is the observation that previous normative work has not usually differentiated between the perceived characteristics of hazards when considering the determinants of WTP.(’s6) Rather, “hazard” has generally been defined as unidimensional with the nature of the risk described only in terms of probability of harm (e.g., Howard’s “white pilIs” and “black pills”). A sec-

ond motivation is the observation that many North Americans have apparently demonstrated, through actions in their political and regulatory processes, a high revealed WTP to increase safety from risks such as hazardous waste or nuclear power. These hazards are perceived to be highly dreaded and unknown,(’) but are estimated by experts to hold relatively low statistical risks. Thus, the model provides a framework to explain a prevalent empirical phenomenon that appears to diverge from prescriptions regarding efficiency in risk management. We begin by positing a household “value” function, V, for a given risk that a household faces. This value function is assumed to be additively separable into two parts, one concerning household income and the other concerning aspects of risks to life faced by the household:

6,= f(4,FSj) + g(Aj, FS,,

FEU,PE,, C,) (1) Here the subscript refers to family and the subscript refers to hazard i. 4 is the family’s income; FSj is the family size; Aj is the base level of risk faced by the household, apart from that due to hazard i; FE,, is the perceived exposure of family to hazard i; PE, is the perceived exposure of the overall public to hazard (as perceived by family); and C, represents a vector of perceived characteristics of hazard i. We discuss each of the components of Eq. (1). Consider first the far right term, the perceived characteristics of a given hazard. Research summarized by Slavic(') identifies three of attributes that describe how people perceive various technological hazards and risky activities. The first cluster of attributes concerns the extent to which a given risk is a source of dread. In general, “dread” risks are those perceived as potentially severe, uncontrollable, and catastrophic. The second cluster involves attributes related to the perceived level of knowledge about a hazard. In general, risks are perceived as “unknown” when they are new or unfamiliar, are involuntarily imposed, and have delayed effects. The third cluster of attributes concerns level of perceived exposure to the risk, and encompasses both personal and societal levels of exposure. C,, which captures the dread and knowledge clusters of perceived risk characteristics, is specified as follows:

C, = h(Vol,, Sev,, Knw,, Drd,, Con,) (2) Volv, represents the voluntariness of hazard as perceived by household j . Similarly, Sev represents severity; Knw represents knowledge; Drd represents dread; and Con represents controllability. We assume the variables in Eq. (2) interact multiplicatively. Exposure, the

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third cluster of perceived attributes, is included in g ( - ) in Eq. (l), where family exposure FEij, and public ex-

exposed. A priori, one could imagine the sign of B3 being in either dire~tion.~ posure, PE,, are found. To derive the household’s WTP for greater safety from hazard i, let Kj(I‘,., FE,, PE,,) represent the value The two remaining variables in Eq. (1) are income of the household as a function of a given level of houseand base level of risk, both of which have been shown hold income (Z’,.,) and a given value of perceived family to be important in normative models of WTP for risk and public exposure, holding all other variables in Eq. change~.(~y~) These models imply that older people (who (3) constant. Let greater safety be expressed in terms of have higher levels of base risk) should have higher WTP a reduction in family and public exposure from FE,O and for a given increment of risk reduction, all else equal. PE,O to FE,’ and PE,‘. The maximum WTP, or option We capture this in Aj in terms of the age of household price, of householdj for this reduction in risk is equal members. Later, in the empirical analysis, we use as a to Io-Il,where I, is the household’s initial level of inproxy the age of the adult household member who comcome, and I,, is defined as the level of household income pleted our s u ~ v e y . ~ that leaves V, equivalent before and after the risk change. To formulate a household’s WTP for reductions in Then, after algebraic manipulation, and the use of a Tayrisk, we assume that c, f, g, and h are multiplicative. lor’s series as detailed in a longer version of this paOmitting the subscripts for convenience, the specificaper,(lO)the following equation can be derived: tion we adopt is: W‘. ZJ = 1, - II = aF,”B3-B2FEB“pEB5 V = alIB1FSB2+ a&P3FEB4PEBS V ~ ~ B ~ S ~ ~ B ~ K ~ ~ B B D ~ 11(1~ B 01 ~ 1C )Op ~ B ’ (4) V~l~~Sev~~Knw~~Dr lem d ~ (3) ~Con*~~A~~ where a = ( -a2(l -r)”4F5)/a,Bl).Taking logarithms of All the variables are defined previously except rn, both sides of (4) yields the specification to be estimated: which is an error term representing other factors influIn WP, = In@) (B3 - B2)lnFS encing the valuation of the household of situations in+ B4lnFE + BSlnPE + B6lnVol + B7lnSev ( 5 ) volving risk apart from those explicitly represented. It + B8lnKnw + B9lnDrd + BlOlnCon is assumed that m is normally distributed and indepen+ BlllnA + (1 - Bl)lnZo + rn dent of the variables in Eq. (3).6

+

The specification in (3) is flexible and allows for a wide range of household preferences. The multiplicative specification for I and FS in f(.) allows for various patterns of interaction between household income and household size. Additionally, the inclusion of FS in the g ( . ) term allows for complementarities or substitutabilities of risk across family members. Holding the income measure constant, the tradeoff between income and safety may vary, depending on how many family members are Other variables regarding characeristics of individuals could also have been includcd, such as the individual’s experience with the risk in question, the person’s sex, and perhaps whether the person has technical knowledge or training regarding the risk. As discussed later, the estimation procedure adopted for this analysis addresses these concerns by incorporating a dummy variable for each subject that captures other characteristics not specified in the equation. The equation (value function) is additive bchveen the income component on the left and characteristics of the hazards on the right, and multiplicative within the two components. There are a number of rationales for this form. Empirical analysis has shown that additive functions are excellent predictors in judgment contexts,(’*) and either additive or multiplicative functions are widely employed for multiattribute utility and value elicitation.(13)A multiplicative form essentially means that there are additive terms, as well as weighted interaction between the additive which is conceptually similar to how risk perceptions have been analytically represented.”)

3. SURVEY DESIGN AND IMPLEMENTATION

Data to estimate the model were obtained through a combined contingent valuation and risk perception survey. Contingent valuation is an approach widely applied by economists to place dollar values on nonmarket goods, including risk changes.(”) It involves establishing a hyFor certain classes of preferences, Eq. (1) and (3) are sufficiently general to be consistent with von Neumann and Morganstern expected utility maximization, in which case they become indirect expected utility functions as detailed in a longer version of this paper.(’”) Yet the equations allow a broader interpretation as well, which is why we refer to V(’)as a value function rather than a utility function. In particular, we do not insist that the perceived level of exposure enter into the value function in the precise function required for expected utility maximization. In order for Eq. (3) to be consistent with expected utility, the subjective probability of household exposure, p , must be equal to FEW. But conversely, the reader must feel comfortable that FEW (where FE is measured subsequently on a seven-point psychometric scale reflecting exposure) serves as a measure of the household’s subjective p, in order for Eq. (3) to be consistent with expected utility. While this does not seem unrealistic, we stress that in this analysis we are neither interested in nor able to test the “correctness” of households’ assessment of their level of exposure to the “true” risk that they face from given hazards.

O A B ’

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pothetical market context and then asking subjects direct questions regarding the dollar values they place on specific goods. The survey instrument for this study began with a brief explanation of the survey’s context and purpose. The second section provided a series of instructions and points to bear in mind when answering questions, suggesting various ways that respondents might think about the questions, urging careful consideration before answering. Next the contingent valuation questions were asked, followed by the risk perception questions. To minimize the unfamiliarity of the survey context, the value questions were posed in terms of subjects’ WTP increased amounts for relevant goods and services (e.g., air tickets) in exchange for reduced deaths. Brief, neutral information about the nature of the various risks was provided to ensure consumers were adequately informed. The questions were framed as uniformly as possible. Finally, the survey instrument was pretested and slightly modified to minimize any confusion or difficulties when answering questions.

3.1. W T P Questions

WTP questions were posed for 10 hazards which were divided into two groups of five; each group had a slightly different form for the evaluation questions. One group included five risks that are relatively familiar, or well-defined, in that the deaths they cause are identifiable and the numbers are documented. The well-defined group included automobiles, commercial aviation, power tools, liquefied natural gasjiquefied petroleum gas (LNG/ LPG) and a workplace chemical, vinyl chloride monomer (VCM). Consistent with the theoretical framework presented above, we represented the reduction in risk in terms of a given proportional reduction in deaths per year. We chose the proportion 20%. Questions for the well-defined risks were cast in terms of the household’s WTP to reduce the annual deaths from the particular hazard by a given number, next year, where the deaths to be avoided amounted to a 20% reduction in estimated annual fatalities.8 The number of deaths to be avoided varied for each of the five risks in the well-defined group. The assumed number of deaths to be avoided next year for each risk was: Automobiles 10,000 Commercial aviation 40 VCM 1 Power tools 80 LNG/LPG 2

An example of one these questions is presented below: Aviation

On average about 200 people die each ycar in the U.S. from aviation accidents on commercial flights. This number could be reduced by improvements in air traffic control systems, airport safety equipment, and other factors. How much would your household be willing to pay next year in terms of increased air rmvel costs in order to help pay for safety measures that would reduce the number of aviation deaths by 40 next ycar?

Your household’s value: $

next year.

The second group of hazards comprised risks that were, by nature, less readily quantifiable, less well-defined. Because of uncertainty in exposure and effects, these risks could not be discussed in terms of the specific number of deaths now caused or to be avoided. This less well-defined group included chlorinated water, hazardous waste, nuclear energy, sulphur air pollution, and electromagnetic fields. For these hazards, the valuation questions were cast in terms of the household’s WTP for a 20% reduction in the potential for death from each risk. Somewhat more description of the nature of these risks was deemed necessary. An example follows: Electromagnetic Fields Electromagnetic fields from certain home appliances (such as dishwashers and electric blankets) or from high voltage power lines may pose long-term health hazards. Possible effccts include increases in rates of cancer or birth dcfects. Experts arc unsure of the extent of these risks, but most believe the risks are very low. Better appliance shielding or rerouting of power lines could reduce this low risk still further. Suppose the potential risk could be reduced 20%. How much would your household be willing to pay next year in increased cosis of consumer goods to help pay for safety measures that would reduce the risks from electromagnetic fields by 20% next year?

Your household’s value:

$

next year.

The decision to incorporate these two types of risks (and thus two types of questions) within one survey was in part a practical one. We wanted to analyze hazards that were well-distributed in the Slovic ef al.(’) risk perception “factor space.” That is, we sought risks that differ greatly in terms of their perceived characteristics. Both well-defined and less well-defined risks were required in order to have a variety of risk types. In addition, we wished to be able to examine possible differences across types of hazards in the factors influencing WTP for safety across types of hazards.

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3.2. Risk Perception Questions

Following the WTP questions, subjects were asked to rate these hazards on eight risk perception psychometric scales, nearly identical to scales employed in various studies by Slovic et ~ z l . (Eight ~ ) scales were used; seven involved perceived risk characteristics, while the eight considered overall perceived risk. The perceived characteristics addressed in this study included: voluntariness, severity, knowledge, control, dread, personal exposure, public exposure, and overall risk. An example follows:

The socioeconomic characteristics of survey subjects were mixed. All subjects were over age 25, with the modal age category being between 25 and 35. The modal household income category was $30,000-$45,000 annually. It seems likely that the subjects were, on average, highly educated, although no data were collected. Three of the groups contained at least one professional with active interest in risk analysis or economic evaluation. Clearly, the survey sample should not be viewed as representative of a larger population, but rather as a sample of convenience to test the approach.

Not Dread-Dread

1s this a risk that people have learned to live with and can think about reasonably calmly, or is it one that people have great dread for-on the level of a gut reaction? Not Dread

1

2

3

4

5

6

7

Dread

Each subject rated all 10 risks on a given scale before preceding to the next scale. Only eight scales were employed in order to minimize the number of judgments required of each subject. The last section of the survey asked questions about subjects’ socioeconomic characteristics. A complete text of the questionnaire is provided in an earlier, lengthier version of this paper,(I0)which is available upon request. 3.3. Sample

The survey was administered to four groups of adults in Pittsburgh, Pennsylvania. In total, 55 complete, usable questionnaires were obtained; all subjects were given the same questionnaire. The four groups included: parents of children attending a daycare center and workers at the center (n = 20); professionals and clerical staff in the office of an economics consulting firm (n = 10); residents of a middle-class neighborhood (n = 6 ) ; and students in a graduate mid-career public management program (n = 19). With the first three groups, questionnaireswere distributed, completed by those who agreed to participate, and returned. In the group of mid-career students, the questionnaires were completed in class and nearly all parti~ipated.~ The questionnaires distributed were as follows: daycare, 84; consulting firm, 11; neighborhood, 10; mid-career students, 20. Thus, 24% of the questionnaires distributed at the daycare, 91% of the questionnaires distributed at the consulting firm, 60% of the questionnaires distributed in the neighborhood, and 95% of the questionnaires distributed to the mid-career students were actually employed in the analysis.

4. STATISTICAL, ANALYSIS

After rejecting two questionnaires because of missing data for WTP questions, responses from the remaining 55 questionnaires were coded.1° Table I summarizes the descriptive statistics for responses to the WTP questions. A key concern for contingent valuation studies is the treatment of outliers in the response data. Various procedures are often used to obtain a “core of serviceable data” for empirical results, because of possible “protest responses” (extremely high values or zero values). Two common approaches are to omit subjects with responses above some given level (say, a specific number of standard deviations about the mean), and to omit subjects with zero responses. Neither of these approaches was employed in this study. Responses of zero were not omitted because, although zero was the most frequent response in terms of WTP across all hazards (27% of the total responses were zero), no subject indicated zero responses for every risk. Thus, subjects indicated an ability to discriminate between those hazards they were willing to pay to reduce, and those they were not, lending credence to the zero responses. At the other extreme, two subjects gave high values (over $1000) for reduction of a few of the hazards. These subjects were not omitted because the estimation procedure discussed subsequently corrects for these individual effects by estimating a dummy variable for each subject. In that case, the relative, and not the absolute, magnitude of a sub-

’” Missing data also occurred for some of the risk perception scales. There were 4400 perception scale responses in total (80 for each of 55 subjects), and of these 66 were missing. No question had more than three missing values. In order to avoid rejecting a large number of questionnaires, these missing values were estimated as the mean response (rounded to the nearest integer) for that scale question for the whole sample.

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ject’s responses across risks is more important in this analysis.” 4.1. Econometric Structure Data from the survey instrument were used to estimate the influence of socioeconomic and perceived risk variables on households’ WTP for reductions in risk. The specification derived in Section 2 was employed in the analysis [Eq. (5)]. This log-log specification is also justified by the high degree of skewness of the WTP data, which for the nonzero responses roughly resembled a lognormal distribution. The dependent variable was (the logarithm 09 subjects’ stated WTP changes in the 10 risks of the survey. The independent variables included the logarithm of the responses from the survey’s seven risk perception scales and three socioeconomic variables. l 2 These are listed in Table 11, along with the expected signs for the 10 independent variables and a brief explanation. We had prior expectations regarding the relative importance of certain independent variables. The findings of previous risk perception studies indicate that characteristics measured by the knowledge, dread, and exposure scales are very important in explaining perceived risk across a wide range of hazards.(7) As in the theoretical discussion of Section 2, we hypothesized that these scales (X,,X,,X,) may also be important determinants of willingness-to-pay for risk reduction. Thus, we expected that these coefficients would be larger than the others. In addition, following previous normative theAs a check on the influence of these high values on the results, we

I2

estimated the equation with and without them; omitting the high values had no significant effect on the signs and magnitudes of the coefficient estimates presented later. The scaling of the independent variables deserves comment. All the independcnt variables were ordinal scales: the risk perception variables were psychometric scales from 1-7, while the socioeconomic variables were categorical data scaled from 15. Two potential criticisms of analyzing these data with standard econometric techniques are: (1) that the results are sensitive to the range of the scale selected; and (2) the estimator employed implicitly treats these ordinal numbers as cardinal values. A number of responses are in order. First, all the risk perception variables are scalcd the same. This should minimize the degree to which comparisons between them are affected by the spread of the scale. Also, we assume that a given individual would treat the intervals of all perception scales equivalently. The categorical data for the socioeconomic variables was mandated by the survey format: people are loath to give out precise personal information. Our confidence in the treatment of the independent variables and the estimation procedure is strengthened by the fact that the results are robust under a number of different specifications.

ory, we also expected that age (as a measure of base X,) should be level of risk) and household income (X8, important explanatory variables. We ran the anlaysis for all the risks pooled together, as well as separately for the two groups of hazards: welldefined and less well-defined. We expected that perceived personal exposure would be more important for the well-defined group of risks, for which the current number of deaths is relatively well known. Conversely, we expected the risk perception variables, particularly dread, to be more important for less well-defined risks (e.g., hazardous waste, nuclear power). The reasoning was that personal exposure is more difficult to gauge for less well-defined hazards, and also that risks of that nature rank highly in terms of dread.

4.2. Estimation Procedure

Because the data for the dependent variable (willingness-to-pay) were truncated at zero, we used a limited dependent variable model (Tobin’s probit, “Tobit”) for the e~timati0n.l~ Within that framework, two basic approaches to estimation were adopted. One approach involved pooling the data for the 10 risks into one large regression. The second approach split the risks into the two groups described previously (well-defined and less well-defined risks), and treated the groups as two regressions. The decision to pool the data into one or two large regressions (as opposed to estimating a separate equation for each risk) is consistent with the theoretical model presented above, which illustrates how responses to a given proportional reduction across different hazards can be incorporated into a single specification. Whether run as one data set or two, the pooled data were analyzed with a one-way “fixed effects” model, employing separate dummy variables for each subject. The fixed effects model was justified on theoretical grounds because it was recognized that the 10 responses given by each subject were not independent. A variety of subjective, unobserved variables could systematically affect a given subject’s expressed values. These omitted variables could include risk preference, familiarity with the risks, response to the survey environment, reference points, reliance on heuristics, conservatism, or many others. The fixed-effects approach estimates a separate l3

To estimate the Tobit model, the nonzero responses wcre analyzed relative to a “threshold” level that was $.01 below the lowest nonzero value response across all risks and subjects. The zero responses were coded as a small positive number below that threshold, to facilitate taking logarithms.

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Risk Perceptions and Values Table 1. Descriptive Statistics for Willingness-to-Pay Responses" Technological hazard Automobiles Commercial aviation Workplace chemical (VCM) Power tools LNG/LPG Water chlorination Hazardous chemical waste Nuclear energy Electromagnetic fields Sulphur air emissions from power plants

$385.44 55.30 138.60 70.30 26.50 37.90 78.30 69.30 15.30 37.30 $91.31

Combined answers

Geo. mean

Median

SE

$47.63 4.70 0.26 1.82 0.47 2.87 17.53 5.33 0.21 5.39

$100 10 7

216.0 12.6 127.0 54.0 18.0 18.0 27.0 28.0 9.2 7.2

5

1 10 25 20 0 14

1.12

~

Data shown are mean incremental willingness-to-pay for a 20% reduction in the number of deaths, or potential for deaths, next year from each risk (1986$). n = 55, except for combined answers, where n = 550.

intercept for each subject, adjusting for the effects of these individual omitted variables. Consequently, the relative values provided by a given respondent become more important than the absolute values in estimating coefficients for the 10 independent variables of interest.

5.

RESULTS

Table I11 summarizes the estimation results: Column 1 presents the estimated coefficients and standard errors when the data for all 10 hazards were pooled into one large regression. columns 2 and 3 report the results when the data are split into two groups: well-defined hazards and less well-defined hazards. When reviewing these results, we will be more concerned with the signs and relative values than the absolute values of the coefficient estimates. Note that because the specification is in log form, the coefficients can be viewed as elasticities, and the values of coefficients indicate their relative magnitude in influencing WTP.14 l4

It would be misleading to directly interpret the coefficients in Table 111 as estimates of elasticities. Due to the underlying assumptions of a Tobit model, Tobit coefficient estimates contain two kinds of information: (1) the change in the probability of being above the limit of the dependent variable (in this case, the change in the probability of giving a response above zero; i.e., willing to pay something); and (2) the change in the dependent variable given that it is already above zero. McDonald and the Moffitt(14)provide a useful rule for disaggregating a Tobit coefficient into its component parts. Their method was employed to disaggregate the coefficients in Table Ill, results are presentcd in Ref. 10, available upon request.

The first column of Table 111 presents the results of the pooled analysis in which the data for both types of risk were pooled into one large regression. Seven of the 10 coefficients are significantly different from zero at the 95% confidence level. All signs are as expected except for X , (knowledge) and X , (control). The pooled analysis suggests that socioeconomic variables, perceived risk characteristics, and perceived exposure levels all influence expressed values for safety in an integrated model, across a range of hazards. Moreover, perceived levels of public exposure appear to influence the value of safety, even controlling for perceived personal exposure. The second and third columns of Table I11 report the results of splitting the risks into two groups (welldefined and less well-defined), while retaining a complete set of dummy variables for each group. While the reduction in degrees of freedom reduces the statistical power of the coefficient estimates in these two columns, the results nonetheless indicate a sharp contrast between the influences on values for safety for the two types of hazards. For the less well-defined risks, two perception variables (dread and severity) are of larger magnitude and greater statistical significance than for either the more well-defined risks or for the pooled data. Moreover, personal exposure appears to play a much less important role than public exposure for these less well-defined risks. The opposite is true for the well-defined risks. There, perceived personal exposure is the only perception variable that has a coefficient statistically different from zero. It is important to note that the traditional socioeconomic variables, income and age (as a proxy for base

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502 Table 11. Summary of Independent Variables ~

Variable Voluntariness (X,) Severity (X,) Knowledge (X,) Control (X,) Dread (X,)

Expected sign

+ + +

Large

-

Public exposure (X,) Age (X8) Income (X,)

+ + + + +

Family size (X,o)

+

Personal exposure (X,)

Expected magnitude

Large Large Large Large

level of risk), have larger coefficient estimates than any of the perception variables in all three sets of results.15 We must stress that the overall explanatory power of these models is moderate; much of the variation in willingness-to-pay remains unexplained. Although R2 figures are not available, the Tobit estimates presented earlier, R2 results are available for least-squares estimation of Eqs. (1)-(3). Those adjusted R2 ratios are .38 S O and .44,respectively. Clearly, there are other factors not captured by the variables considered here that influence the values placed on safety. In addition, the sample size, coupled with the degrees of freedom consumed by the dummy variables in a fixed-effect model, provide less statistical power than one would like. Nevertheless, we are able to draw important inferences from the data. Risk perception and socioeconomic variables both appear to play important roles in shaping expressed values for safety when many different kinds of risks are pooled into one model. The same underlying factors that provide a taxonomy for risk perception are important as explanatory variables in estimating willingness-to-pay. These patterns are altered and more pronounced when the risks are split into two groups: personal exposure is an important determinant of willingness-topay for risks that are well-defined and known to those exposed, while dread and severity are more important Is

Likelihood ratio tests were constructed to determine if the disaggregation of the pooled data into more and less well-defined hazards provides a significantly better fit of the data. Not surprisingly, given the qualitative differences in the results in Table 111, the likelihood ratio test indicates that the disaggregated data do provide a statistically better fit for the sample.

Rationale Higher value + less voluntary Higher value + more certain to be fatal Higher value -+ less known (important in risk perception analysis) Higher value + more controllable Higher value + more dread (important in risk perception analysis) Higher value + more exposure (important due to question structure) Higher value + more exposure Higher age + higher risk base rate Higher income -+ more available for risk reduction Higher family size + higher exposure; less disposal income

for less well-defined risks that are difficult or impossible to gauge in terms of exposure. 6. CONCLUSION

Since the work of Lancaster,@) economists have embraced the notion that consumers value characteristics of goods, rather than simply the goods themselves. Yet researchers have been slow to acknowledge that the value people place on changes in safety may be influenced by the perceived characteristics of different hazards and that perceived risk, and not “technical estimates” or “statistical” risk, will determine individual valuation.(15)This research is one of the first attempts to statistically examine the nexus between risk perceptions, socioeconomic factors, and risk evaluations across a range of technological hazards. The implications of these results for public policy regarding risk management depend in part on one’s view of the appropriate normative basis for risk management decisions. If one adheres to the view that subjectively defined welfare changes, reflecting personal views of well-being, are conceptually appropriate for valuing risk changes, then this model suggests that perceived characteristics will directly enter into evaluation decisions. On the other hand, if one’s view is that public policies toward hazards should be based on objective, statistical risk and not subjective assessments, then these results suggest the importance of risk communication efforts to alter the public’s perceptions and their resulting values for safety from different hazards.

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Table 111. TOBIT Estimation Results: Specifications 1, 2, and 3 Specification 1“ Independent variable Intercept

Specification Specification 26 3’

Coefficient Estimates (Standard errors in parentheses)” - 10.54

(3.77). 0.42 XI (voluntariness) (0.39) X, (severity) 0.74 (0.39) X, (knowledge) - 1.21* (0.42) X, (control) 0.59’ (0.29) X, (dread) 1.67** (0.34) X, (personal exposure) 1.30’. (0.30) X, (public exposure) 1.11” (0.44) 7.86** X8 (age) (2.88) X, (income) 7.82** (3.03) X,,(family size) - 2.66 (2.07) Log-likelihood function - 1185.0

-24.90 - 11.34* (643.90) (5.46) 0.53 1.05 (0.57) (0.64) 2.11-0.18 (0.57) (0.58) - 1.02 - 0.49 (0.84) (0.59) 0.55 0.28 (0.45) (0.46) 1.85** 0.52 (0.49) (0.58) - 0.003 2.46.’ (0.008) (0.46) 1.15 1.06 (0.64) (0.67) 32.18 4.44 (928.9) (4.18) 6.26 8.21 (3.26) (4.91) -5.33 - 0.07 (2.87) (2.41) -561.1 - 540.2

“ In Y = X,Ei + dummies (all 10 risks). In Yl-5= X,Ei + dummies (first five risks only). In Yhl0 = In X,Ei + dummies (second five risks only). Results are for the test statistic: H,:Ei = 0, H,:Bi PO, where Ei/Si has a T-distribution if Ho is true. * H,, is rejected at the 95% confidence level; ‘*H, is rejected at the 99% confidence level.

To put these points another way, the results show that, descriptively, perceived characteristics appear to matter for risk evaluations. Whether these characteristics should matter in terms of societal decisions is not a question that can be resolved here.(’6J7) Nevertheless, the conceptual framework outlined earlier demonstrates how and why the characteristics could matter for household decisions. Judging from recent environmental policy decisions, it seems clear that perceived risk does matter a great deal in terms of setting priorities of risk management.(’*) The descriptive and normative aspects of this issue are closely intertwined.

ACKNOWLEDGMENTS

The authors appreciate the financial support of the Andrew Mellon Foundation’s Program for Technology and Society at Camegie Mellon University. Robyn Dawes, Baruch Fischhoff, Robert Hahn, Granger Morgan, and Paul Slovic provided helpful advice at various stages in this research. Comments from two anonymous referees were valuable in sharpening the presentation. James Bennett, Steven Mart, and Jose Canela provided able research assistance.

REFERENCES 1. B. Fischhoff el al., Acceptable Risk, Cambridge, Cambridge University Press (1981). 2. M. G. Morgan, “Choosing and Managing Technology-Induced Risk,” IEEE Spectrum. December, 53-60 (1981). 3. M. W. Jones-Lee, The Value of Life: An Economic Analysis, Chicago, University of Chicago Press (1976). 4. J. Linnerooth, “The Value of Human Life: A Review of the Models,” Economic Inquiry 17, 52-74 (1979). 5. M. C. Weinstein, D. S. Shepard, and J. S. Pliskin, “The “Economic Value of Changing Mortality Probabilities: A DecisionTheoretic Approach,” Quarterly Journal of Economics 94, 373396 (1980). 6. R. A. Howard, “On Making Life and Death Decisons,” In R. Schwing and W. Albers, Jr. (eds.), Societal RiskAssessment: How Safe is Safe Enough? New York, Plenum (1980),pp. 89-107. 7. P. Slovic, “Perception of Risk,” Science 236, 280-286 (1987). 8. K. Lancaster, “A New Approach To Consumer Theory,” Journal of Political Economy 74, 132-157 (1966). 9. V. K. Smith and W. Demousges, “An Empirical Analysis of the Economic Value of Risk Changes,” J. Pol. Econ. 95, 1 (1987). 10. T. McDaniels, M. Kamlet, and G. Fischer, “Risk Perception and the Value of Safety,” Working Paper, School of Urban and Public Affairs, Carnegie Mellon University (1987). 11. R. Mitchell and R. T. Carson, Using Surveys To Value Public Gooh: The Contingent Valuation Method, Resources for the Future (1986). 12. R. M. Dawes and B. Corrigan, “Linear Models in Decision Making,’’ Psychological Bulletin 81, 95-106 (1974). 13. D. von Winterfeldt and W. Edwards, Decision Analysis and Eehavioral Research, Cambridge, New York (1986). 14. J. F. McDonald and R. A. Moffit, “The Uses of Tobit Analysis,” Review of Economics and Statistics 62, 318-321 (1980). 15. V. K. Smith, “Benefit Analysis for Natural Hazards,” Rkk Analysis 6, 325-334 (1986). 16. B. L. Cohen, “Criteria for Technology Acceptability,” Rkk Analysis 5, 1-3 (1985). 17. H. Otway, “Multidimensional Criteria for Technology Acceptability: A Response to Bernard L. Cohen,” Risk Analysis 5, 271273 (1985). 18. R. Morganstern and S. Sessions, “Weighing Environmental Risks: EPA’s Unfinished Business,” Environment 30, 14-39 (1988).

Risk perception and the value of safety.

This paper examines the relationship between perceived risk and willingness-to-pay (WTP) for increased safety from technological hazards in both conce...
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