Valuing Children’s Health: Parental Perspectives*

Mark Dickie

and 

Shelby Gerking

Department of Economics

University of Central Florida

Orlando, FL 32826

September 2003

* We thank Anna Alberini, Pascale Scapecchi, and participants in the Organisation for Economic Cooperation and Development “Workshop on Valuation of Environmental Health Risks for Children” for 

helpful comments on previous drafts. Support from the OECD Environment Directorate is gratefully

acknowledged. The US Environmental Protection Agency partially funded the research described here 

under R-82871701-0. The research has not been subjected to Agency review and therefore does not

necessarily reflect the views of the agency, and no official endorsement should be inferred. 
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Executive Summary

Protection of children's health is an important priority of U.S. environmental policy. Executive Order

#13045 issued by President Clinton directed federal agencies to identify and assess environmental health and

safety risks that may disproportionately affect children and the Bush administration has continued to

emphasize children's health in federal policy. Strategies for reducing environmental risks faced by children,

however, operate through parents and other adult caregivers who are responsible for children's behavior. Thus, 

effectiveness of this aspect of federal policy will depend on choices parents make between their children's

health, their own health and consumption of other goods. 

This report surveys the literature on parents’ willingness to pay for protection of their children's health and 

provides new empirical estimates from a study of skin cancer. The literature review begins by describing two 

types of theoretical models that have been used as the foundation for willingness to pay estimates. These are

unitary models, in which households are assumed to maximize a single utility function, and pluralistic models, 

which account for differences in preferences of individual adult family members. Previous empirical estimates 

of willingness to pay for improved children's health are discussed and several implications for future research

are drawn out. These include: (1) jointly estimating the value of reducing morbidity and mortality in a

consistent framework, (2) estimating these values for both parents and children in a consistent way so that

comparisons between adults and children become possible, (3) accounting for how parents' perceptions of risks 

to their children might differ from actual risks, and (4) using an appropriate experimental design to circumvent 

econometric problems that have limited the usefulness of previous studies. 

The report concludes by presenting new empirical results from a study of skin cancer risks to parents and

children. The unitary parental perspective is adopted to develop an expected utility model that focuses on

morbidity and mortality risks to a parent and one child. This model, for example, provides a basis for

computing parents' marginal rates of substitution between risk of death from skin cancer faced by both

themselves and their children. This calculation shows how parents value children's health relative to their own 

and may be useful for benefits transfer in situations where willingness to pay for reduced risk to adults have

been established but corresponding values for children are not available. Key features of the survey and

experimental design are described, including: (1) determining parents' perceptions of skin cancer risk to

themselves and their children, and (2) obtaining willingness to pay for skin cancer risk reductions. Risk

reductions were presented to parents using randomly assigned labels of a hypothetical sun lotion that offered

different amounts of protection to adults and children. Random assignments of risk reductions facilitate

estimation of marginal rates of substitution. For example, parents’ marginal rate of substitution between their

own lifetime chances of getting skin cancer and their children’s lifetime chances of getting skin cancer is about 

two. This indicates that parents value reductions in risk to their children’s health more than reductions in risk 

to their own health. Additionally, parents’ marginal rates of substitution between risk of getting skin cancer

and risk of dying from it are about 20. This indicates that parents value reductions in risk of death much more 

than reductions in risk of illness for both themselves and their children. 

Empirical results obtained suggest that the recent policy emphasis on protecting children’s health may be 

justified from an efficiency standpoint when a parental perspective is adopted. Simply transferring benefit

estimates from adults to children on a one-to-one basis is likely to understate benefits of protecting children’s

health. While results in this paper and other recent research seem to suggest that children’s health benefits

may be roughly approximated as two times adults’ benefits, little evidence yet exists on how this ratio may

vary across different health risks or between different demographic groups. Estimating children’s health

benefits as any constant multiple of adult benefits may be misleading. Primary research directed at valuing

children’s health is likely to yield more reliable estimates for policy purposes. 
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Valuing Children’s Health: Parental Perspectives

1. Introduction 

Protection of children's health is an important priority of U. S. environmental policy because children are

at greater risk than adults from hazards such as pesticides, drinking water contaminants, and exposure to solar 

radiation (USEPA 1996). These added risks arise because children's immunological, neurological, and

digestive systems still are developing and because children spend more time outdoors than adults and eat more 

food and drink more fluids relative to their body weight than adults. For example, studies of pesticide

exposure indicate increased risks of leukemia, the most common form of childhood cancer, for children whose 

parents used pesticides at home or at work as well as for children exposed to pesticides in the home (Zahm and 

Ward 1998, Buckley, et al. 1989). Also, Needleman et al. (1979) demonstrate that environmental lead

exposure can lead to neurofunctional deficits in young children. Moreover, links between environmental

exposure to ultraviolet radiation and skin cancer are well established (Finkel 1998, MacKie, Freudenberger,

and Aitchison 1989, Rhodes et al. 1987, Scotto, Fears, and Fraumeni 1982) and exposure during childhood is

an important determinant of lifetime skin cancer risk (Reynolds, et al.1996, Robinson, Rigel, and Amonette

1997, Creech and Mayer 1998). In fact, the American Academy of Dermatology (1997) estimates that up to

80% of a person's lifetime exposure to ultraviolet light occurs before the age of 18 and even a few severe 

sunburns between the ages of 10 and 15 can triple the chances of developing malignant melanoma later in life. 

President Clinton's Executive Order #13045 (Federal Register 1997) directed federal agencies to identify

and assess environmental health and safety risks that may disproportionately affect children and the Bush

administration has continued to emphasize protection of children’s health in federal environmental policy. 

Appropriate strategies for reducing environmental risks to children, however, operate at least partly through

parents and other adult caregivers who are responsible for children’s behavior. For example, a key aspect of

reducing children’s environmental health risks is to encourage parents to take protective actions that will

reduce exposure. Effectiveness of this approach, however, will depend on parents’ beliefs about risks to their

children’s health, as well as how they make choices between their children’s health, their own health and other 

goods. 

This report critically surveys the economics literature on what parents are willing to pay for greater

protection of their children's health and provides new empirical estimates from a study of skin cancer. The

next three sections provide an overview of the parental perspective for valuing children’s health. Section 2

outlines the theoretical foundations of the parental perspective approach to valuation. Section 3 focuses on

difficulties in using this approach to estimate social benefits. Section 4 summarizes prior empirical results. 

The subsequent three sections present a detailed illustration of the parental perspective to valuing children's

health using results from the skin cancer study. Section 5 presents a specific theoretical model. Section 6

discusses the experimental design and survey used to collect the data. Section 7 presents empirical analysis

that looks at tradeoffs between parents' health and children' health. The final section of the report provides

concluding comments. 

2. Theoretical Foundations of the Parental Perspective 

Theoretical foundations for estimating parents’ willingness to pay (WTP) for children’s health are drawn

from the literature on the economics of the family. This literature addresses a variety of issues including

household formation and dissolution through marriage and divorce, labor supply and consumption decisions of 

husbands and wives, and allocation of resources to and among children. Behrman, Pollack and Taubman

(1995) classify models of family decision-making according to two criteria: (1) whether children are treated as 

independent decision-makers, and (2) whether the family is assumed to maximize a single or “unitary” family 
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utility function. This report considers both unitary (assuming the family maximizes a single utility function)

and “pluralistic” (recognizing conflicting as well as common interests among family members) models, but

follows the lead of most theoretical and empirical work in family economics and assumes that children are not 

active participants in family decision-making. 

A simple way to illustrate models of family decision-making is to compare the utility functions used in

each approach. Consider a three-member family where each person consumes a composite good ( Xi) and

where a measure of each person’s health is denoted , H i i = m (mother), f (father), and c (child). Total

consumption of the composite good is . X =++ XXX mfc

In the unitary model, family members seek to maximize a common utility function such as

( , , , ). F X HHH mfc Of course, the list of arguments in the utility function could be changed to consider more 

or fewer family members, more goods, separate consumption of goods by family members or other features of 

a specific valuation problem. The existence of a common preference function for the household usually is

taken as given, although Becker (1993) and Samuelson (1956) provide alternative ways of deriving such a

function from the individual preferences of family members. 

A “pluralistic” (Bergstrom 1997) model, in contrast, accounts directly for individual preferences of

household members, or at least of adult household members. The two spouses are allowed to have distinct 

preferences represented by individual utility functions such as ( , , , ) U X m m XHH cmc for the mother and

( , , , ) U X f fcfc XHH for the father. In this specification, each parent cares about his or her own consumption 

and health, as well as about the consumption and health of the child. In other words, the parents’ consumption 

and health are private goods, while the child’s consumption and health are public or non-rival goods within the 

household. Alternative specifications of the parents’ utility functions could be used to allow for other features 

pertinent to a particular application. Various pluralistic models have been proposed that differ in how the

family is assumed to coordinate resource allocation in the presence of the competing interests of the spouses. 

The distinction between unitary and pluralistic approaches is important to consider in two-parent families, 

because estimation and interpretation of WTP occurs within the context of a model of family decision-making. 

Welfare measures derived from unitary and pluralistic models are not in general identical, although there are

specific cases where the two approaches give the same WTP expressions (Mount et al. 2001). The

mathematical form of the WTP expression, its interpretation, and the question of whether WTP for children’s

health may be estimated based on the behavior or survey responses of only one of two parents all depend on

the choice between unitary and pluralistic approaches. Welfare measurement is simpler in the unitary

framework because benefit estimation methods developed for individuals easily extend to the family setting,

but some empirical evidence discussed below casts doubt on the validity of the unitary approach. 

In addition to the specification of preferences, theoretical models can be distinguished in two other ways

when considering children’s health. First, it is useful if the model accommodates household production

relatively easily. Household production functions often are used to analyze individual household resource

allocations affecting health and to account for protective or defensive behavior. “Protective behavior” refers to 

actions taken to defend against some health risk, for example by reducing exposure to pollution or by

mitigating the effects of exposure. Examples of protective behavior include asthmatic children’s greater

reliance on medication when air pollution levels are high (Roemer, Hoek and Brunekreef 1993) and a family’s 

use of bottled water or a water filtration system at home to reduce exposure to contaminated drinking water. 
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Sections 4 and 6 of this report provide additional examples of defensive behavior in the context of studies

estimating parental WTP to protect children’s health. 

A second point to consider is how easily the model accommodates non-rival goods and intra-family 

externalities. A good is non-rival if one family member’s use of it does not reduce the amount available to

other family members. Thus, a family that installs a water filtration system at home, or that changes

residential location to avoid pollution, is employing a non-rival protective action in that all family members

benefit. Conversely, increased use of asthma medication when air pollution levels are high would be a rival

protective action. Intra-family externalities arise when the health of one family member affects the resources

available to others. A common example occurs when a parent misses work or diverts time from other

activities to care for a sick child. 

Unitary Models

The unitary approach has been adopted in most economic studies of children’s health, including those

focused on estimating parental WTP for children’s health. The existence of a single utility function means that 

household WTP (as opposed to individual WTP) is a relevant welfare measure, and existing benefit estimation 

methods are applicable. The approach readily accounts for household production functions, non-rival 

consumption, and intra-family externalities, as shown by Jacobson’s (2000), extension of Grossman’s (1972)

model of health investment to a family setting. 

Empirical applications of unitary models to children’s health issues provide two results that are of

particular interest. First, family structure and composition affect resource allocations and health outcomes

experienced (Case and Paxson 2001, Rosenzweig and Schultz 1982). The “quantity-quality tradeoff”

represents one example in which fewer resources are invested per child when more children are present

(Hanushek 1992, Haveman and Wolfe 1995). Researchers previously have not emphasized factors like family 

size or marital status when estimating an adult’s WTP to protect his or her own health, but these factors may

be important determinants of parental WTP for children’s health in a family setting. Second, parents do not

necessarily treat the health of all family members equally, but instead may allocate resources differently

depending on health status, gender or age (Pitt, Rosenzweig and Hassan 1990, Pitt and Rosenzweig 1990). 

These results suggest that parental WTP also may vary between family members, and in particular that WTP

for adults’ and children’s health need not be identical. 

Despite successful empirical applications of the unitary approach, a key implication of the model often is

been rejected when tested. The model implies that household decisions depend on the pooled income of

family members, not on the individual amounts accruing to individuals. The hypothesis of pooling has been

rejected in tests involving demands for private goods (Browning et al. 1992, Lundburg, Pollack and Wales

1997), as well as in tests focused on decisions involving children. Schultz (1990) found that an increase in

unearned income accruing to husbands and wives in Thailand had opposite effects on fertility; in turn, fertility 

is a key determinant of allocations of resources to children according to the quantity-quality tradeoff. Thomas 

(1990) found that positive impacts of unearned income on child health were greater when the income accrued 

to mothers than to fathers in Brazil. Haddad and Hoddinott (1991) report that an important indicator of child

health in Cote d’Ivoire was positively related to the share of family resources controlled by the mother. 

Furthermore, men and women tend to perceive risk differently (Davidson and Freudenburg 1996). These

results, which contradict the income pooling implication of the unitary model, as well as the inability to make 

inferences about individual welfare in the unitary approach, have stimulated additional interest in pluralistic

models. 
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Pluralistic Models

Several models have been developed that view household decisions in terms of adults with individual

preferences making collective decisions. Two of the more prominent models, the Nash cooperative bargaining 

model of Manser and Brown (1980) and McElroy and Horney (1981) and the collective approach of Chiappori 

(1988), have been used or proposed for nonmarket valuation. 

In the cooperative bargaining model, a couple divides the potential gains from marriage by acting as if

they maximize the Nash product [ ( ) ][ ( ) ], Um − − Tm U T f f g g where , Ti

i m= (mother), f (father) denote the

threat points. Somewhat different implications are obtained depending on how the threat point is specified

(Bergstrom 1995), but the most common approach is to view each spouse’s threat point as the maximum utility 

he or she could obtain outside of the marriage. Bolin, Jacobson and Lindgren (2001) have extended the

Grossman (1972) model to a family setting using the cooperative bargaining framework. 

Mount et al. (2001) develop a model of the value of reduced fatality risk for adults and children in a family 

using a Nash cooperative bargaining model. They show that the value of statistical life (VSL) in their model

has the same form as the VSL from a unitary model. However, this result hinges on the assumption that the

threat points are independent of risk and exogenous factors in the model. Bergstrom (1997) discusses how

independence of the threat points implies that household decisions are consistent with maximization of a

unitary utility function. While the assumption that threat points are independent of exogenous factors may or

may not be reasonable in the Mount et al. model, the assumption is unlikely to hold in general. For example,

an environmental change that improved a child’s health or safety could easily increase the utility of the

custodial parent following a divorce. The resulting impact on the threat point would be reflected in the

spouse’s WTP for the environmental change. In general, WTP expressions obtained from cooperative

bargaining models will differ from, and be more complex than, WTP expressions obtained from the unitary

approach. 

An alternative and more general pluralistic model of family decision-making is the collective approach of

Chiappori (1988, 1992). Rather than assume a specific bargaining solution for the household allocation

problem, the collective approach assumes that the household reaches a Pareto efficient allocation of resources

by some unspecified decision process. (The cooperative bargaining model implies a Pareto efficient solution

and so could be viewed as one example of a decision process underlying the collective approach.) Pareto

efficiency implies that the household acts as if to maximize the utility of one spouse, subject to a constraint on 

the other spouse’s utility. The model envisions this equilibrium occurring through a two-step process in which 

the spouses allocate resources after first agreeing on a sharing rule that divides income between them. 

Although empirical applications of the collective approach generally have focused on estimating labor

supply or consumption demand, Smith and van Houtven (2002) extend the collective model to consider

measurement of Hicksian consumer surplus. They show how two measures of compensating variation can be 

derived from the model, depending on whether the spouses’ shares of income are held constant. However,

they do not specifically consider WTP for children’s health or safety. 

In the collective approach, each spouse’s WTP depends on his or her share of family income. But the

sharing rule is an analytical device that is not observed. Consequently, identification of the model and

recovery of individual preferences requires some way of distinguishing the individual utility functions and

sharing rule from one another. This is accomplished by assuming that some variables affect the sharing rule

but not preferences and by assuming that a good is “assignable,” so that each spouse’s separate consumption
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can be observed. Thus household production, non-rival goods or intra-family externalities complicate the

approach by reducing the separability of decisions between spouses. For example, the assumptions proposed

for identification in the presence of household production may not be plausible (Apps and Rees 1996, 1997,

Chiappori 1997). In view of the potential importance of household production and intra-family externalities in 

models of children’s health, the difficulties these factors pose for the collective approach may partly explain

why most attempts to estimate parental WTP for child health have used unitary models. 

3. Difficulties Hindering the Use of Parental Preferences to Estimate Social Benefits

In the presence of competing models of parental preferences, there is some uncertainty concerning the

appropriate framework to use when estimating parents’ WTP to protect children’s health. More broadly, at

least two other difficulties complicate the use of any model of parents’ preferences to estimate social benefits

of children’s health. These difficulties reflect concern about whether application of the parental perspective

would promote sound social decisions. 

One concern is that parental WTP may be based on inaccurate perceptions of the health risks faced by

children. Misperception of risk raises questions about whether social decisions should be based on individual 

WTP (Portney 1992). This problem potentially arises whenever environmental health risks are valued, but it

may be compounded when risks to children are considered. Jacobson (2000) argues that it is easier to judge

whether the benefit of an incremental health investment justifies the cost when considering one’s own health

as opposed to the health of another person. While individuals have difficulty accurately assessing their own

risks, parents may find it even more difficult to assess risks facing their children. Similarly, parents may be

less certain about future health status or medical technology when considering children’s lives than when

considering their own, relatively shorter, remaining lifetimes. 

A second concern is that society may be more willing to respect an adult’s preferences concerning her own 

health than to respect a parent’s preferences concerning her child’s health. For example, respect for individual 

preferences would imply accepting an adult’s decision to smoke cigarettes so long as the adult believed the

satisfaction of smoking justified the risk to health. Respect for parental preferences would imply accepting a

parent’s smoking, so long as she believed her satisfaction justified her own health risk as well as the risk

imposed on her child. Society may not be willing to respect parental preferences if it seems that at least some 

parents are inadequately attentive to the health of their children. Additionally, empirical evidence mentioned

in the previous section suggests that parents do not treat the health of all children equally. But any differences 

in parental WTP between children of different age, gender or health status may be inconsistent with social

standards. In these cases, parental WTP for children’s health may not reflect social preferences. 

A related way in which social values may diverge from parental WTP would occur if members of society

feel paternalistic altruism toward children outside their own households. As discussed by Jones-Lee (1991,

1992), social WTP coincides with aggregate private WTP under pure altruism (caring for the general wellbeing of others while respecting their preferences), but not under paternalistic altruism (caring for a specific

component of others’ well-being, such as their health). If society were less willing to respect parental

preferences for children than to respect individual preferences, then paternalistic rather than pure altruism 

would be more likely to occur for children than for adults. 

Empirical evidence can shed some light on the nature of these difficulties. Tests between competing

models of parental preferences, evidence on parents’ perceptions of children’s health risks, measures of

altruistic WTP toward children in other households, and comparisons parents’ WTP for their own and their

children’s health would be particularly useful in evaluating the difficulties considered here. But there have

been relatively few studies of parents’ WTP for children’s health and some of these issues have not been
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addressed fully in the existing literature. The next section examines selected empirical applications of the

parental perspective. 

4. Empirical Applications of the Parental Perspective

The parental perspective has been used to estimate the value of statistical life (VSL) for children as well as 

to estimate values of reduced pollution and reduced morbidity using health production and stated preference

approaches. These studies are reviewed briefly below, and implications of the existing research are drawn out 

at the end of this section. 

Protective Behavior and the Value of Statistical Life for Children 

Carlin and Sandy (1991) used a defensive behavior approach in a unitary model to estimate the value of a

statistical life of a child under five years old. In the model, a mother makes tradeoffs between the benefits of

reduced risk and the money and time costs of using automobile child-safety seats. The authors estimate a VSL 

for a child under age five of approximately $420,000 ($1985), net of costs of child raising. This value is

substantially below the estimate obtained by Blomquist (1979) in his study of adult seat belt usage. 

Blomquist, Miller and Levy (1996) also examine motorists’ use of child safety seats to estimate a VSL for 

children. Adjusting for misperception of risk and addressing the impact on utility of restraining children in car 

seats, they obtain a VSL for children under age five of $3.7 million ($2000). They also estimate a VSL for

adults, based on seat-belt use, of $2.8 million. 

Jenkins, Owens and Wiggins (2001) estimate a VSL for children and adults based on purchases of bicycle 

helmets. They compute the ratio of the annualized cost of a bicycle helmet to the reduction in fatality risk

using aggregate data. When accounting for the fact that helmets are not always worn, the authors estimate a

VSL that is approximately equal across two age groups of children (five to nine years and 10 to 14 years), but 

larger for adults (20 to 59 years). The methods used preclude testing whether these differences are statistically 

significant. The estimates for adults and the older children exceed the median, however, while the estimate for 

the youngest age group is less than the median, making it difficult to assess how median VSL might vary

across age groups. 

Safety seats and bicycle helmets are rival protective actions, in that only one person may use the good at

any given time. In contrast, Mount et al. (2001) treat automobile safety as a non-rival good within the family. 

They use a modified hedonic approach to estimate a VSL for children, adults (younger than 65 years), and

seniors (older than 65 years) in the context of a Nash cooperative bargaining model. According to this model, 

the family acts to equate the marginal benefit of safety to the marginal cost of purchasing and operating a safer 

car. The marginal benefit equals the sum of the automobile-usage-weighted VSL of each family member. 

Data limitations force the authors to restrict their analysis to single-car families, to assume how many

occupants ride in a vehicle and where they sit, and to assume an income elasticity of the VSL in order to adjust 

for income differences between types of families. Relative valuations obtained are quite sensitive to the two

values of income elasticity used. The VSL for children is 32 percent less than the adult value if the income

elasticity is 0.33, but 38 percent more than the adult value if the income elasticity is unity. 

Health Production Function Estimates of the Value of Reduced Pollution or Reduced Morbidity 

Joyce, Grossman and Goldman (1989) apply a health production function approach in a unitary model to

estimate economic benefits of reductions in infant mortality associated with ambient air quality. Parents are

assumed to choose inputs such as smoking and prenatal care to influence the child’s birth weight and

probability of survival. The model is estimated separately by race using county-level data. Although
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qualitatively similar results were obtained in white and black subsamples, the estimated WTP for reduced

sulfur dioxide pollution was four to seven times larger for blacks than for whites. 

Agee and Crocker (1996) estimate parents' WTP for reduced lead body burdens in a child, using a health

production function approach in the context of a unitary model. Data were obtained from an earlier study by

Needleman et al. (1979). Agee and Crocker employ a methodology similar to the prior work of Dickie and

Gerking (1991) on adults’ health benefits from ozone control. This method requires that the change to be

valued causes an exogenous shift in the demand for an input that is necessary to produce better health. In the

Agee and Crocker (1996) study, changes in a child’s body lead burden shift the demand for chelation. Mean

WTP for a one percent reduction in body lead burden ranged from $11 among parents who do not chelate to

$104 among parents who do, with an overall mean of $16 per child for a one percent reduction in body lead

burden. Implied aggregate benefits were two to 20 times larger than previous estimates of illness costs. 

Agee and Crocker (2001) use data from the 1991 National Maternal and Infant Health Survey to estimate

smoking mothers’ valuations of their own and their children’s health in the context of a unitary model. By

computing ratios of the child and own valuations, the authors estimate the marginal rate of substitution (MRS). 

Health is measured on the “EVGFP” scale (excellent, very good, good, fair or poor) for the current state of

health. The impact of maternal smoking on own and child future health is not considered directly. The

estimated MRS implies that the average mother values her child’s health about twice as highly as her own. 

Dickie (2000) estimated parents’ WTP to avoid acute illness in their children using a health production

approach with a unitary model. Data were taken from the Panel Study of Income Dynamics, Child

Development Supplement, and acute illness was measured by the annual number of days of school absence

due to illness. He found that children with greater stocks of health capital, whose parents limit family size and 

invest in preventive and remedial pediatric care, experience fewer days of school loss. Estimated WTP to

avoid one school loss day of about $500 is comparable to previously estimated valuations of workplace and

household injuries. 

Stated Preference Studies of Reduced Acute Morbidity 

Viscusi, Magat and Huber (1987) and Viscusi, Magat and Forrest (1988) presented evidence on the value

of reducing health risks to children from misuse of household chemicals. These studies did not have as their

primary objective the estimation of WTP to reduce children's health risks, but rather were designed to test

more general hypotheses about risk valuations. Adult subjects familiar with the type of household chemical

considered were recruited by mall intercept and were asked their maximum willingness to pay, in the form of

higher product prices, for reductions in risks of relatively minor injuries including child poisonings. In

Viscusi, Magat and Huber (1987), reduced risks of child poisonings were valued more highly than other risks. 

But child poisonings were the most severe injuries considered, and therefore no inference can be drawn about

the relative magnitudes of willingness to pay for equally severe injuries to adults and children. 

Viscusi, Magat and Forrest (1988) used data from the same survey to estimate private and altruistic

valuations of health risks. Respondents were asked their willingness to pay for an advertising campaign that

would reduce risks to persons in other households in their home state and in the rest of the U.S. The majority

of respondents indicated that they would pay something for the program in their home state, but fewer than

one-quarter would pay any additional amount for a nationwide program. As discussed by Viscusi, Magat and

Forrest, the resulting reduction in contributions probably reflects both a declining marginal valuation of

altruism and a more remote relationship to persons outside of the state. Individuals’ mean altruistic valuations 

were on the order of one cent per statistical injury to persons outside the individual’s household. But implied

aggregate altruistic valuations are quite large, because the individual values would be summed over a large

population. 
-8

Liu et al. (2000) estimate Taiwanese mothers’ WTP to prevent a cold for themselves and for their children 

using a stated preference survey. Respondents were asked to describe the symptoms and duration of the colds 

most recently experienced by themselves and their children, and were asked their willingness to pay for

preventive medicine that would prevent recurrence of an identical illness. After accounting for the greater

severity and duration of the mother’s cold, WTP for the child was about 1.9 to 2.5 times greater than for the

mother. Mothers were willing to pay more for their sons than for their daughters to avoid a cold. 

Dickie and Ulery (2003) estimate parents’ WTP to relieve acute illness affecting themselves or their

children in a unitary model. Data are obtained from a stated preference survey conducted in Mississippi. 

Dickie and Ulery estimate marginal rates of substitution between child and parent illness of about two,

indicating that parents value avoiding an illness affecting their children about twice as highly as an equivalent 

illness affecting themselves. The estimated MRS is larger for younger children and falls toward unity as the

child approaches adulthood. Parents’ WTP to avoid own or child illness declines with fertility, increases with 

presence of asthma, and is higher in African-American than in white families. 

Implications for Further Research 

Previous research estimating parents’ WTP for children’s health highlights eight key issues that should be 

addressed in future empirical work. First, it would be useful to estimate jointly the value of reduced risk of

morbidity and mortality in a consistent framework. Many environmental hazards increase risks of both illness 

and premature death, yet these two risks usually are treated separately in valuation studies. Similarly, the

safety goods considered in each of the four studies reviewed here would reduce risk of both nonfatal and fatal 

injuries. But researchers often ignore the morbidity risk reduction (Carlin and Sandy) or else adjust for it by

assuming some arbitrary division of the value of a safety good between risks of fatal and nonfatal injuries

(Jenkins, Owens and Wiggins). 

Second, when valuing an outcome that may be experienced by both adults and children, it is useful if a

study estimates values for both adults and children in a consistent way. Comparing values for adults and

children is helpful in understanding parents’ preferences for children’s health and in shedding light on issues

of benefits transfer. For example, if parental valuations of children’s health are approximately equal to adults’ 

valuations of their own health, then existing benefit estimates based on transferring adult values to children

may be reasonably accurate. Conversely, if values for children’s health outcomes generally exceed values for 

comparable outcomes experienced by adults, as suggested by the three morbidity studies that estimate values

for parents and children, then benefit estimates computed by applying adult valuations to children substantially 

understate benefits. Of course, this implication would apply only to health risks affecting children and adults. 

Third, additional research is needed to estimate parental WTP for children’s morbidity endpoints that have 

not been valued in the existing literature, as well as to provide additional evidence in situations where the

relative magnitude of adult and child valuations is uncertain. For example, little evidence is available on

parents’ WTP to reduce risks of chronic or latent morbidity outcomes for their children. Likewise, three of the 

four mortality valuation studies discussed above estimate a VSL for adults and children, but they do not point

to a consistent conclusion about the relative magnitudes of these values. Further research is needed to assess

the relative magnitude of VSLs for adults and children. 

Fourth, it is important to account for parents’ individual perceptions of risks faced by their children. Each 

of the studies estimating a VSL for children acknowledges that subjective risk perceptions often diverge

substantially from objective risk estimates. Using objective risks to estimate WTP may bias results. But the

studies either assume that parents perceive small changes in objective risks (on the order of 4

10−in Carlin and

Sandy and 610−in Jenkins, Owens and Wiggins) or adjust for risk misperception using a constant

multiplicative scaling factor (Mount et al., Blomquist, Levy and Miller). 
-9

Fifth, as emphasized by the health production approach, the health outcomes an individual experiences

partly reflect personal or familial choices. Some valuation efforts ignore the potential endogeneity of health

outcomes, potentially leading to biased statistical estimation. In contrast, health production studies usually

correct for endogeneity using instrumental variable methods. This approach hinges on the use of instruments

that are truly exogenous variables correlated with an endogenous explanatory variable but uncorrelated with

unobserved influences on the outcome of interest. In practice the selection of appropriate instruments usually

is subject to considerable uncertainty, and results obtained can be quite sensitive to the choice of instruments. 

However, the method applied by Dickie and Gerking (1991) and Agee and Crocker (1996) does not require

instrumental variables, provided that the change to be valued causes an exogenous shift in the demand for a

necessary input. The empirical work presented in Section 7 presents an alternative approach to control for

endogeneity through experimental design. 

Sixth, some of the existing empirical evidence points to possible racial differences in WTP for children’s

health and illustrates the potential importance of family structure and composition. Further research should

investigate how parental WTP varies with age, race, gender or health status, even though policy makers may

prefer to use the same WTP value for all children. 

Seventh, in many cases parents’ investments in the health or safety of their children jointly affect other

family members or parental utility. Although these problems do not arise in every case, they are common. 

Existing research reveals uneven handling of these issues. Mount et al. explicitly account for the non-rival 

consumption of automobile safety, while Blomquist, Miller and Levy attempt to assess the utility impact of

using safety seats. However, Agee and Crocker (1996) assume that chelation has no joint costs or benefits,

when in fact it is a painful treatment with potentially dangerous side effects, and Dickie (2000) assumes that

pediatric care is used exclusively to reduce acute illness. 

Eighth, previous research illustrates how difficult it is to estimate WTP from secondary data. The inability 

to account for morbidity risks, subjective risk perceptions and joint production problems in the VSL studies

often can be traced to the use of secondary data. Secondary data used to value morbidity often do not include 

measures of the occurrence or severity of specific morbidity endpoints, forcing researchers to focus instead on 

behavioral reactions to morbidity changes like school loss days, or on coarse summary measures like the

EVGFP scale for overall health. The data often lack other key variables as well, such as the money price of

medical care, or the time required to consume it. Additionally, secondary data often force researchers to make 

untested assumptions about key parameters, such as the size of the income elasticity of VSL, the length of

useful life of a bicycle helmet, or the seating patterns in a family automobile. Collection of primary data

allows many factors such as these to be measured and possibly controlled in an experimental design. 

While stated preference studies avoid the problems associated with use of secondary data, the hypothetical 

nature of the valuation introduces potential for several well-known problems including insensitivity to scope

and hypothetical bias. These problems may be mitigated in studies that focus on estimating ratios of values. 

For example, hypothetical bias would influence estimates of the MRS only to the extent that valuations of

parent and child health were differentially contaminated by the bias. Variation in the extent of hypothetical

bias between parent and child certainly is possible, particularly in designs like those used by Liu et al. and

Dickie and Ulery where parents answer separate valuation questions for themselves and their children. This

possibility would be mitigated in an approach that inferred both parent and child values from the same

questions. 

Remaining sections of this report discuss a new empirical study designed in light of the implications just

discussed. The study estimates values of avoided morbidity and mortality for both adults and children in a

consistent framework, while accounting for parents’ subjective perceptions of risks that they and their children 
-10

face. Primary data are collected according to an experimental design that randomly assigns exogenous

changes in risk to survey respondents. 

5. A Model of Perceived Morbidity and Mortality Risks in the Family

This section develops a specific parental perspective model that is subsequently used to value reductions in 

the risk of skin cancer to both parents and their children. From an environmental policy viewpoint, skin cancer 

is particularly interesting and important to consider for several reasons. Skin cancers are the most common

cancers in the U.S., accounting for about half of all incidence (American Cancer Society 1998b). Also, links

between environmental exposure to ultraviolet radiation and skin cancer are well established and chances of

getting skin cancer, for a given amount of solar radiation exposure, depend partly on observable genetic

characteristics such as skin type and complexion. Solar radiation exposure has been increasing in recent years 

due to stratospheric ozone depletion, and exposure during childhood is an important determinant of lifetime

skin cancer risk (e.g., Reynolds, et al. 1996, Robinson, Rigel and Amonette 1997, and Creech and Mayer

1998). In fact, as stated in Section 1, the American Academy of Dermatology (1997) estimates that up to 80% 

of a person’s lifetime exposure to ultraviolet light occurs before the age of 18 and even a few severe sunburns 

experienced by children between the ages of 10 and 15 years can triple the chances of developing malignant

melanoma later in life. 

The model adopts the unitary approach in which parental decisions are guided by one expected utility

function and one set of perceived risks. This assumption is justified if expenditures on goods related to skin

cancer risks represent a small fraction of family budgets. Protective actions considered in the model may be

rival or non-rival. Intra-family externalities are ignored because skin cancer typically occurs later in life, so

that the resources available to the parent or child are unlikely to be affected significantly by whether the other 

party contracts skin cancer. By the time a parent or child develops skin cancer, the other party, if surviving, is 

likely to live in a separate household. 

The theoretical approach supports empirical estimation of morbidity and mortality risk reduction in a

consistent framework. As mentioned in Section 4, prior empirical studies have valued either morbidity or

mortality risk reduction, yet these two health endpoints are closely related. The model also has the advantage 

of considering a risk faced by both parents and children. This means that parents' values of reducing the risk

to their children and to themselves can be compared. In fact, the model is useful in showing how to compute

the marginal rate of substitution between risk reduction to the parent and risk reduction to the child. Estimates 

of marginal rates of substitution may assist in benefits transfer in cases where willingness to pay to reduce a

risk has been established for adults but not for children. 

The model to be applied has at least broad similarities to other prominent approaches taken in the literature 

on environmental risks to health. In their analysis of health consequences of exposure to hazardous wastes,

Smith and Desvousges (1986, 1987) split the unconditional risk of death from exposure into the probability of 

exposure and the conditional probability of premature death given exposure. Their model, however, envisions 

only two health states (alive and dead), so morbidity is not explicitly treated. Eeckhoudt and Hammitt (2001) 

look at a model with two health states (alive and dead) and demonstrate how a specific risk to an individual’s

health (such as a possible fatal job accident) should be valued when that individual faces a set of independent 

life-threatening background risks. The model developed here generalizes this framework in that background

risks incorporated may pertain to another family member. The value of reduced child morbidity, for example, 

is computed with parents' morbidity and mortality risks in the background. Shogren and Crocker (1991,1999) 

consider a model with endogenous risk and examine conditions under which willingness to pay to avoid risk

can be expressed without terms from the utility function. Risk also is treated as endogenous in the model

developed here; in fact, it is measured as risk perceived by survey respondents. The method for estimating

willingness to pay, however, rests on directly estimating an indifference relation showing utility-constant 
-11

trade-offs between morbidity risks, mortality risks, and consumption goods. It does not rest on a particular

model specification that happens to result in elimination of utility terms. 

In the one-period version of the model presented here, a parent’s expected utility E(U) is a probability

weighted sum of utilities in 32=9 possible states of the world that depend on whether a parent and one child are 

healthy, sick, or dead. Extension to the case of a larger “family” (more than one child, for example) is not

conceptually difficult, but would geometrically expand the number of health states and may be unmanageable

when applied in a survey context. 

The model focuses on four probabilities: (1) the probability that the parent will get skin cancer (

p

S ), (2)

the conditional probability that the parent will die from skin cancer given that the disease is contracted ( Dp), 

(3) the probability that the child will get skin cancer ( Sc), and (4) the conditional probability that the child will 

die from skin cancer given that the disease is contracted ( Dc). Probabilities are influenced by predetermined 

factors such as genetic endowments ( , , j Ω =j p c ), but nevertheless are treated as endogenously determined

because parents may choose to engage in self- and child-protection behavior ( , , Z j

j = p c ). For example,

parents may use sun protection products to reduce the chance that they or their children will get skin cancer

and may seek regular medical examinations to detect skin cancer at an early stage so that treatment might

lower conditional death risk if this disease is contracted. Additionally, probabilities can be viewed as certain

(known) or uncertain (stochastic). The assumption of uncertainty is attractive because parents may not form

risk perceptions with complete precision, but the discussion below sticks to the easier case of certainty of

probabilities. In any case, morbidity and conditional mortality probabilities are determined according to 

( , , ) ( , , ), , , j j j j j j j j j j S = S Z Ω l d D = D Z Ω =j p c (1)

where ljand jd are experimental design parameters representing (hypothetical) treatments that reduce risk. 

Perceived skin cancer risks are incorporated into the expected utility model as shown in equation (2).

0

( ) (1 )(1 ) ( )

(1 ) [ ( ) ( )] (1 ) [ ( ) ( )]

[(1 )(1 ) ( ) (1 ) ( ) (1 ) ( ) ( )],

p c

c p p p p p c c c c

p c p c pc c p p p c c p c pc

E U S SUY

S S U Y D V Y S S U Y D V Y

S S D D U Y D D W Y D D W Y D D W Y

= − −

+ − + + − +

+ − − + − + − +

(2)

where U0denotes utility in the state where both parent and child are healthy, U j denotes utility in a state in

which either the parent or child ( j = p c, ) contracts skin cancer and the other does not and neither dies, Vj

denotes utility in a state in which either the parent or child ( j = p c, ) dies from skin cancer but the other does 

not get it, U pc denotes utility in the state where both parent and child get skin cancer but neither dies, Wj

denotes utility in the state in which both parent and child contract skin cancer and one of the two dies

( j = p c, ) but the other does not, and Wpc denotes utility in the state in which both parent and child die from

skin cancer. In states in which the parent and/or child die, parents’ utility is taken to be positive; for example, 

if the child dies, the parent’s life may still go on and if the parent dies utility may be obtained from a bequest. 

Also, Y denotes the parent’s wealth net of: (1) expenditures for self- and child-protection goods ( Z j

) and (2)
-12

bids for treatments presented in the experimental design (ljand jd ). The parent’s gross wealth is denoted as 

y and for simplicity here is assumed to be the same in all health states. Of course, parents may have different 

amounts of net wealth available because self- and child-protection expenditures may vary by health status. 

These differences are recognized, but are not made explicit here in order to economize on notation (see

Shogren and Crocker (1991) for discussion of a model reflecting these aspects.)

The model can be manipulated to obtain values of reduced risk of morbidity and mortality. The starting

point is equation (2), assuming that: (1) the parent already has chosen expected utility maximizing values of

self- and child-protection expenditures in each health state, and (2) lj

and jd are initially zero. Morbidity and 

mortality values are obtained by calculating the sacrifice in y needed to hold E U( ) constant in the face of

changes in treatment effects that increase safety. Prices of treatments and magnitudes of risk reduction are

randomized across sample members; thus parents are asked to value parametric risk changes. Assume that

/ / 1. j j j j ∂S D ∂l d = ∂ ∂ = − Then, willingness to pay for a reduction in the probability of the child getting

skin cancer (morbidity risk) can be obtained from equation (2) by setting ( ) 0 p p c dE U = === ddd ldd , 

assuming that re-optimization leads to negligible changes in expenditures for other self-and child-protective 

goods, and computing

0

/ [(1 )( ) (1 ) ( ) (1 )( ) (1 )( )

( ) ( )]/

c p c p c c c p p p pc p c p pc c

p p p p p c p p pc

y S U U S D U V S D U U S D D U W

S D V W S D D W W

∂ ∂l = − − − + − − + − − + − −

+ − + − Δ

(3)

where Δ denotes the expected marginal utility of income, which is positive if the marginal utility of income is 

positive in each state. Also, the numerator of the right hand side of equation (3) is negative using the fact that 

/ 1 j j ∂S ∂l = − together with the plausible assumptions that utility is positive in all states and that the utility

differences in each term of the sum are positive (i.e., healthy is preferred to sick, sick is preferred to dead, one 

person sick is preferred to two people sick, etc.). Thus, / 0, c

∂∂< y l indicating that wealth must fall to hold

expected utility constant when the child’s morbidity risk is reduced. 

Willingness to pay for a small reduction in perceived conditional death risk faced by the child, holding all 

other perceived health risks constant, is 

/ {(1 ) ( ) [(1 )( ) ( )]} c p c c c p c p pc c p c pc ∂y ∂d = − − S S U −V + S S − D U W− + D W W− Δ (4)

Thus / 0, c

∂y ∂ < d if / 0. c∂∂< y l 

Equations (3) and (4), together with corresponding results for parents ( / 0, p∂y ∂ < l / 0 p∂y ∂ < d ), 

support three types of empirical estimates that are of direct interest. First, parents’ marginal rate of

substitution between reduced skin cancer risk to their children and reduced skin cancer risk to themselves is

( / ) ( / ) c p ∂y y ∂l l ∂ ∂ . This ratio shows the extent to which parents value morbidity risks to their children

differently than morbidity risks to themselves, holding conditional mortality risks (

c

d and pd ) constant. It

would be of interest to test whether ( / ) ( / ) 1. c p ∂y y ∂l l ∂ ∂ = Second, because perceived unconditional risk

of death from skin cancer is , , , Rj j j = = S D j p c parent's willingness to pay for a reduction in unconditional 

death risk for a child seen by parents, is
-13

( ) ( )

0

/ {(1 )(1 )(1 ) (1 )[ (1 ) ]

(1 )[ ] [ ]}/

1 1 / (1/ ) / ,

1 1

c c p p c p p p p p

p p c pc c p c p pc

c c

c c c

c c

y R P P R U P P R U R V

P R PU W R PW W

S D

y S y

R R

l d

−∂ ∂ = − − − + − − −

+ − − + − Δ

 − −      −∂ ∂ +   −∂ ∂

 − −   

 (5)

where ( )/(1 ), , Pj j j j = S − R − = R j p c denotes the conditional risk of a nonfatal case of skin cancer. This 

expression is positive as long as the marginal utility of wealth is positive and expected utility is higher when

the child is alive than dead. Third, notice that the model permits a corresponding calculation of willingness to 

pay for a reduction in unconditional death risk for parents. This estimate is useful in its own right and as a

benchmark in assessing the magnitude of /

c −∂ ∂ y R as might be done by looking at parents’ marginal rate of

substitution between their children's unconditional risk of death and their own unconditional risk of death 

( / )/( / ). c p ∂y ∂R ∂ ∂ y R It would be of interest to test whether ( / )/( / ) 1. c p ∂y ∂R ∂y R∂ = However, equation (5) 

cannot be used to estimate the value of a statistical life, if the risks considered occur in the future, because the 

value of a statistical life refers to willingness to pay today to save a life today (or in the very near future). 

The next section discusses survey data to be used to estimate the model, and estimation results are presented in 

Section 7. 

6. Sampling, Survey Methods and Data

Sample Selection 

Data were collected during summer of 2002 in a survey of 610 parents of children aged 3-12. All survey 

respondents were residents of the Hattiesburg, MS metropolitan statistical area. Hattiesburg is located in the

southern part of Mississippi, has a mean annual high temperature reading of 77.5 degrees Fahrenheit, a

subtropical climate, and a large number of sunshine days each year. Thus, residents have experience with

exposure to ultraviolet radiation from sunlight, which has been linked to development of both melanoma and

non-melanoma skin cancer. The Hattiesburg population of approximately 112,000 persons is comprised of

71.7% whites and 26.3% African-Americans. Melanoma incidence among whites is about 16 times the

incidence of melanoma among African-Americans (Ries et al. 1999). In consequence, the Hattiesburg survey

provides an opportunity to compare risk beliefs and willingness to pay for groups with quite different skin

cancer incidence rates. 

The sample was drawn by first generating all possible telephone numbers in the Hattiesburg area. 

This list was screened to remove business, government, and cellular numbers. Interviewers then began dialing 

the remaining telephone numbers at random. If the call reached a non-working telephone line, the number was 

removed from the list. If the call reached a working line, but no one answered or an adult was unavailable, the 

number was redialed up to two more times over the next 7 days. Dialing was scheduled so that if a number

was dialed 3 times, contact attempts fell on a weekday, a weekday evening, and a Saturday. When a call

reached an adult, interviewers described the general purpose of the survey (federally funded research on health 

risks to parents and their children) and asked whether the prospective sample member had at least one

biological child between the ages of 3-12 living at the same address. If the adult did not meet this eligibility

requirement, the interviewer ended the call and the number was not tried again. Otherwise, interviewers asked 

whether the person was willing come to the University of Southern Mississippi to participate in the survey, and 

stated that respondents would be paid $25 for completing the 30-minute questionnaire. Interviewers reached

agreements with people to participate in the survey in about 1% of the total number of calls placed. 
-14

Survey Instrument 

The survey, which was administered by computer using interactive software, began by ascertaining the

race, age and gender of the respondent and the number of children living at home. Table 6.1 shows that of the 

610 sample members, 75.4% were white, 20.0% were African-American, 23.4% were male, and 76.9% were

under the age of 40. The average household size was 4.01 persons and an average of 2.08 children lived in

each household. The responding parent had an average of 1.54 biological children between the ages of 3-12 

living in their household. Biological children are considered in this study in order to look at characteristics

governing skin cancer risk (e.g., fairness of skin and sensitivity of skin to sunlight) that are inherited from

parents. The upper age limit of 12 years was chosen because parents have more knowledge and control of

younger children than teenagers. 

From among the biological children aged 3-12, one child was randomly selected (if there was more than

one) and designated as the sample child. Because of random selection, about half (50.5%) of the sample

children were male. The average age of sample children was 7.07 years. The remainder of the survey

obtained information about the parent/respondent and the sample child. Information was not obtained about

other children in the household to limit the length of the interview, to avoid repetitive questioning, and because 

the model presented in Section 3 assumes that parents treat each child equally. 

The survey then asked preliminary questions about respondents' knowledge about skin cancer. As shown 

in Table 6.2, 95.4% or respondents had heard of skin cancer, 83.8% knew of someone (public figures, friends, 

or relatives) who had been diagnosed with this disease, 22.1% knew of someone who had died from skin

cancer, 3.4% had been diagnosed with this disease themselves, and 71.1% had considered the possibility that

one of their children might get skin cancer. Additionally, 80.3% of respondents had thought about the

possibility that they might get skin cancer and 71.1% had considered the possibility that their children might

get this disease. Thus, respondents appear to be generally aware of skin cancer, that their children could get it, 

and the possibility that this disease can be fatal.

After these preliminary questions, respondents' attention was directed to the chances that they or their

sample children might get skin cancer in the future. Chances of getting skin cancer were assessed using an

interactive risk scale that closely resembled the grid squares used by Krupnick et al. (2002). This approach

was used because risk information appears to be better understood using this type of visual aid (Corso,

Hammitt, and Graham 2001). The scale depicted a large square divided into 20 rows and 20 columns showing 

400 equal-sized smaller squares. Initially, all 400 of these squares were green. Respondents changed green

squares into red ones to represent the chance that an event might occur. By pressing a button at the bottom of 

each column of squares, they could recolor a column of 20 squares from green to red (or from red back to

green). Additionally, they could change the color of any individual square by clicking on it with their mouse. 

A box beneath the scale showed the percentage of squares out of 400 that had been colored red. This

calculation was updated each time the respondent made an alteration in risk scale. 

Before using the scale to represent skin cancer risk, respondents were told about the meaning of "chances

in 400" and given an opportunity to practice using the risk scale in the context of an unrelated event (a possible 

auto accident). Respondents then used the risk scale to represent their own chance of getting skin cancer in the 

future (or of getting it again if they had already had it). Before using the scale, they were told not to consider 

how serious the case of skin cancer might be; rather they were to consider only the chances that they would get 

this disease. Then, they were given an opportunity to answer using the risk scale and were allowed to make as 

many changes as desired before moving on. After completing this task, respondents were shown a new risk

scale and asked to estimate the sample child's lifetime chance of getting skin cancer, again disregarding the

issue of severity. Table 6.3 presents frequency distributions for these responses. Risk estimates tended to

pile up at the 5, 10, 15, etc. percent marks perhaps because respondents had the ability to recolor 20 squares at 
-15

a time. In any case, risk estimates for both the parent/respondent and the sample child exhibit considerable

variation, with some respondents believing that skin cancer is unlikely to occur (e.g., 25.3% of respondents

believed that their sample child had less than a 10% chance of getting it) to occur, while others believed that

skin cancer is inevitable (e.g., 8 of the 610 respondents thought that their child's chances of getting skin cancer 

exceeded 90%). 

Table 6.4 shows mean risk estimates for white and African-American parents and children. White parents, 

on average, estimated that their own lifetime risk of getting skin cancer exceeded that for their sample child

(27.6% vs. 22.8%), whereas the opposite was true among African-Americans (11.8% vs. 12.9%). Estimates of 

both white and African-American parents appear to exceed corresponding actual lifetime risk estimates

reported in epidemiological studies. Ries et al. (1999) found that whites have about a 21% chance of getting

either melanoma or non-melanoma skin cancer at some point in their life, while African-Americans have less

than a 1% lifetime chance of contracting this disease. Thus, African-Americans appear to have overestimated 

risk by the greatest amount, an outcome consistent with literature suggesting that people tend to overestimate

small risks. Additionally, the fact that the survey introduced the possibility of getting skin cancer again if the

respondent had already had it does not appear to be an important complicating factor because sample members 

are relatively young and only 3.4% reported having been previously diagnosed with this disease. 

The null hypothesis that perceived lifetime skin cancer risks are equal for parents and children can be

efficiently tested by noting that respondents made pairs of risk estimates for themselves and for the sample

children. Thus, an appropriate test statistic, asymptotically distributed as a unit normal variate, is the average

of the individual parent/child risk differences divided by its standard error. For whites, this statistic is

4.85/0.65=7.46 so the null hypothesis is rejected. For African-Americans, on the other hand, the statistic is –

1.08/0.76=-1.43, so the null hypothesis is not rejected. The outcome for whites may reflect parents' beliefs

that they take greater precautions to protect their children from skin cancer risk with their own children than

their parents did in an earlier period when less was known about the hazards of solar radiation exposure. Also, 

it may reflect a belief that skin cancer will take longer to develop in children than in parents together with the 

idea that delayed risks are perceived as smaller. African-Americans, on the other hand, perceive lower risk

levels, and therefore may have fewer incentives to think about precautions against solar radiation exposure and 

how their own risk might differ from that of their children. 

After estimating skin cancer risks, respondents were told that, according to the National Cancer Institute,

the average person in the United States has a lifetime risk of getting skin cancer of 18%. Additionally, they

were asked a series of questions about themselves and the sample child that drew attention to reasons why an 

individual’s risk may differ from this average. These questions gathered information about: (1) skin color and 

sensitivity to sunlight, (2) family history of skin cancer, (3) amount of time spent in direct sunlight, (4)

experience with sunburns, and (5) use of sun protection products. Brief narratives provided information about 

how these aspects have been related to skin cancer risks in epidemiological studies. Table 6.5 shows skin

characteristics by race with blacks, of course, reporting darker skin with less sensitivity to sunlight than

whites. Table 6.6 presents information about sunlight exposure parents and children together retrospective

information on sunburns experienced and use of sun protection products. Respondents reported greater levels 

of solar radiation exposure and more skin damage for themselves than for their children. Parents also tended

to apply sun lotions with higher SPF levels to their children than they applied to themselves. 

Then, respondents again were shown the risk scales with the initial risk estimates for themselves and for

their sample children and asked whether they wished to change their estimate in light of the information that

just had been presented. Surprisingly, respondents made virtually no revisions in their original risk estimates

for themselves, but instead made significant downward revisions in risk estimates for their children. For

parents, the average difference between original and revised risk estimates divided by its standard error is
-16

0.0057/0.388=0.015 and for children, this statistic is 1.57/0.402=3.913. Thus, the null hypothesis of no

revision is rejected for children, but it is not rejected for parents. In fact, the mean of revised risk estimates for 

parents differed from the mean of the original risk estimates by about one-hundredth of a percentage point,

whereas the mean of revised risk estimates for children was about 1.5 percentage points lower than the mean

of the original risk estimates. 

Once respondents had revised their lifetime skin cancer risk estimates, they were asked for their beliefs

about age at onset and severity of this disease both for themselves and for their sample children. Regarding

age at onset, respondents were asked, "If you do get skin cancer...at what age do you think that you would get 

it for the first time (or get it again)?" A parallel question asked for an estimate of age at onset of skin cancer

for the sample child. Responses, tabulated in Table 6.7, were obtained for five-year age intervals between the 

ages of 40-79 with the oldest age category top-coded at age 80 or later. These responses were used to test the 

null hypothesis that parents believed they would get skin cancer at the same age as their sample children. 

Averaging the respondent-specific differences in age at onset estimates for parents and children shows that

parents believe the their children would be 5.70 years older than they would be if skin cancer develops. This

age difference, which has a standard error of 2.07, is statistically significant at the 1% level indicating that the 

null hypothesis of equal parent/child age at onset is rejected. 

To assess perceived severity of skin cancer, respondents were asked, "Suppose that a doctor tells you that

you have skin cancer and you begin treatment. What do you think is the chance that you would die within five 

years of this diagnosis?" In this way the perceived risk of contracting skin cancer was fixed before

respondents considered the conditional risk of dying from the disease. Respondents answered using the

previously described risk scale by estimating the chance of death in 400 (given the diagnosis of skin cancer)

for both themselves and their sample child. Table 6.8 presents the frequency distribution of these responses. 

On average, respondents perceived higher conditional death risks for themselves (12.2%) than for their sample 

children (9.4%), a difference of 2.8 percentage points. Dividing this difference by its standard error yields

2.8/0.291=9.62. Thus, the null hypothesis of equal perceived conditional death risks for parents and children

is rejected at the 1% level. Also, about two-thirds of respondents believed that their conditional risk of death

given a diagnosis of skin cancer is 10% or less and about three-fourths of respondents believed that if similarly 

diagnosed, their sample child's conditional risk of death is 10% or less. Many respondents felt that the

conditional risk of death is virtually zero both for themselves and for their children. This outcome suggests

that respondents were aware that skin cancer, particularly non-melanoma skin cancer, is seldom fatal. 

The next section of the survey assessed willingness to pay for a hypothetical sun protection product that

would reduce skin cancer risk if the product were used as directed. The survey employed a private good rather 

than a public policy to reduce risk, because as shown in Table 6.6, over 5/6 of respondents had at least some 

experience purchasing sunscreen lotion. As a consequence, however, willingness-to-pay estimates do not

account for any potential altruism for the children of others, an important issue to address in future research. 

Respondents became familiar with the hypothetical sunscreen lotion product by reading a label that was

designed to look like those used on bottles of over-the-counter sun lotions (see Figure 1). The label indicated

that the hypothetical sunscreen would be similar in most respects to currently marketed products (available in a 

variety of SPFs, non-comedogeneic, oil-free, and unscented), but that it would be more water-resistant and

offer greater levels of skin cancer protection. Similarity of the new sunscreen to current products in all

respects other than risk reduction and water resistance implies that joint benefits of sunscreen use (such as

protection against sunburn or against premature aging of skin) do not affect willingness to pay for the new

sunscreen. See Dickie and Gerking (1996) for analysis of how joint products affect demand for sunscreen. 

Eight labels were used in the study. Labels were identical in every respect, except for differences in the

amount of skin cancer protection they afforded. Four labels varied reductions in risk of getting skin cancer,
-17

while four other labels varied reductions in conditional death risk of this disease. Table 6.9 shows the

reductions in risk stated on each of label. Labels A, D, E, and H offered equal percentage reductions in skin

cancer risk (either 10% or 50%) for both adults and children. Labels B and F offered relatively greater skin

cancer protection for children, while Labels C and G offered protection for adults. Each respondent was

shown two randomly assigned labels. One of these offered reduced risk of getting skin cancer and the other

offered reduced conditional death risk from skin cancer. The order in which these labels were presented was 

randomized. 

After respondents were given time to read the label presented as if they were buying a product for the first 

time, they were referred back to the risk scale and shown the amount by which use of the hypothetical

sunscreen would alter skin cancer risks for themselves and their children. Then, they were asked, "Now please 

think about whether you would buy the new sun protection lotion for yourself or your child. Please do not

consider buying it for anyone else. Suppose that buying enough of the lotion to last you and your child for one 

year would cost $X. Of course, if you did buy it, you would have less money for all of the other things that

your family needs. Would you be willing to pay $X for enough of the sunscreen to last you and your child for 

one year?" The value of X was varied between $20 and $125. When responses were affirmative, respondents 

were asked if they would pay a higher price; when responses were negative, they were asked whether a lower

price would be paid. Initial and follow-up prices are shown in Tables 6.10 and 6.11 along with a frequency

distribution of responses. As expected, more respondents expressed an intention to buy the new sunscreen at

lower prices than at higher prices.

The survey concluded by ascertaining marital status, schooling completed, household income, and

occupation for each respondent. Means of these variables are presented in Table 6.12. Mean household

income was $53,000 per year. Only 16.5% of respondents had not attended college. Also, 75.9% of

respondents were married and 59.0% worker full-time. The disproportionate number of college graduates may 

reflect that many respondents were graduates of University of Southern Mississippi and were willing to return 

to a familiar location to participate in the survey. The relatively small percentage of respondents that were

employed full-time may reflect that most respondents were women with child-care responsibilities. 

7. Empirical Results

Data described in Section 6 are used to estimate parents’ marginal rates of substitution between their own

risk of skin cancer risk and their children’s risk of skin cancer. They also are used to estimate parents’

marginal rates of substitution between the risk of getting skin cancer and the risk of dying from this disease

given that it occurs both for themselves and their children. These estimates are obtained by regressing the

bids for the hypothetical sunscreen on the risk changes shown on the labels. The equation estimated was

obtained from the model presented in Section 5 by totally differentiating equation (2), setting dE U( ) = 0, and 

interpreting the bid for the sunscreen as the change in wealth, dy. 

Estimates make use of a double-bound maximum likelihood procedure in which the latent dependent

variable, willingness to pay for a year’s supply of sun protection lotion, is assumed log-normally distributed. 

Estimates are obtained in a random effects framework by incorporating a respondent-specific error term to

reflect that each parent expressed purchase intentions for two types of the hypothetical sun protection lotion. 

Also, estimation uses the fact that risk changes presented by the sun protection product labels were

randomized across respondents. Thus, variables measuring risk change can be treated as exogenous in the

regression and are orthogonal to other variables (e.g., family income and number of children in the household) 

that may be used as controls. This feature is an advantage over related studies reviewed in Section 4 because 
-18

in econometrically estimating the desired marginal rates of substitution, it is not necessary to instrument for

risk change and estimates are unaffected by the choice of controls. 

Table 7.1 presents covariate definitions in column 1 and regression results in columns 2 and 3. Five

covariates are dummy variables and interactions of dummy variables that reflect skin cancer risk reductions

shown on the labels. GET shows whether the label presented a reduction in the chance of getting skin cancer 

or a reduction in the conditional risk of dying from it. Thus, GET=1 for Labels A-D and GET=0 for Labels EH. Also, PCHG=1 if the label offered parents a 50% reduction in risk for themselves and KCHG=1 if the

label offered a 50% risk reduction for their children. This setup means that willingness to pay for Label E,

offering a 10% reduction in the conditional risk of dying from skin cancer for both parents and children, is

estimated by the constant term in the regression. The willingness to pay for Label A, offering a 10% reduction 

in the risk of getting skin cancer for both parents and children is estimated by the constant term plus the

coefficient of GET. The willingness to pay for Label B, then, would be estimated by the willingness to pay

for Label A plus the coefficient of KCHG*GET. Willingness to pay for the risk reductions indicated by the

other labels can be estimated in a corresponding manner. Remaining covariates measure household income

and number of children per household. 

For the estimates presented, likelihood ratio tests at the 1% level reject the null hypotheses that: (1) the

variance of the parent-specific error is zero and (2) all slope parameters are jointly or individually zero. The

positive coefficients of GET*PCHG, GET*KCHG, (1-GET)*PCHG, and (1-GET)*KCHG indicate that parents 

are willing to pay more for larger risk reductions than for smaller risk reductions (see Hammitt and Graham

1999 for further discussion of this issue). Also, because risk changes were randomly assigned to sample

members, variables used to distinguish between the eight labels are orthogonal to respondent characteristics. 

Thus, when additional covariates are included in the column 3 regression, coefficients of the label variables are 

almost identical to those presented in the column 2 regression. Moreover, in column 3, the coefficient of

household income is positive and significant indicating that respondents with higher incomes are willing to

pay more for the new sunscreen. The coefficient of the number of children, on the other hand, is negative

suggesting that respondents with more children are less inclined to pay as much for protection for themselves

and just one of their children. This outcome is consistent with the idea discussed in Section 2 that fewer

resources are invested per child when more children are present. 

Additionally, coefficient estimates of the label variables can be used to estimate values of willingness to

pay and marginal rates of substitution discussed in connection with the model in Section 5. Estimates from the 

column 3 regression are used for this purpose, although the choice of which regression to use matters little

because coefficients of the label variables are virtually identical in each. Estimates of WTP in Table 7.2 are

interpreted as values of a statistical case of skin cancer or a statistical life in the future and are computed by

first calculating marginal WTP using equations (3) and (5), and then inflating to the value of a statistical case. 

Marginal WTP was estimated using the regression to compute willingness to pay for 1% risk changes (recall

that the dependent variable is in natural logarithms) and then using the sample mean of the baseline risk to

translate the value of a percentage risk reduction to the value of an absolute risk reduction. The resulting

values reflect parents’ WTP in the first year only and cannot be used to estimate the value of statistical life. 

The value of a statistical life refers to willingness to pay today to save a life today (or in the very near future), 

whereas the willingness to pay values that can be computed from the Table 7.1 regressions refer to reducing

risk of events in the distant future, as is clear from the discussion of perceived skin cancer latency in Section 6. 

Also, marginal rates of substitution shown in Table 7.3 can be computed as ratios of WTP values given in

Table 7.2. The child-to-parent WTP ratios reflect parents’ relative valuation today of future risks to

themselves and their children, given the longer latency period associated with the children’s risk, and thus

represent one type of tradeoff relevant for policy purposes. 
-19

As shown in Table 7.3, parents’ marginal rate of substitution between their children’s risk of getting skin

cancer (Sc) and their own risk of getting skin cancer (Sp) is 2.05. This value, which is a tradeoff between

unconditional morbidity risks, is significantly different from unity at the 1% level and suggests that parents are 

willing to pay about twice as much to reduce lifetime skin cancer risk for their children as for themselves. 

Correspondingly, parents’ marginal rate of substitution between their children’s unconditional risk of dying 

from skin cancer (Rc=ScDc) and their own unconditional risk of dying from skin cancer (Rp=SpDp) is 2.33. 

Again, this value is significantly greater than unity at the 1% level and indicates that parents are willing to pay 

more than twice as much to reduce the unconditional chance of dying from skin cancer for their children as for 

themselves. That parents are willing to pay more to reduce risks to their children’s health than they are willing 

to pay to reduce risks to their own health is of particular interest because age at onset of skin cancer is in the

more distant future for children than for parents. Obviously, parents are older than their children and parents’ 

estimates of age at onset (see Section 6) are about 6 years higher for their children than for themselves. If the 

time to onset of illness were the same both for parents and children, the marginal rate of substitution values

may well be larger. 

Table 7.3 also reports calculations of parents’ marginal rates of substitution between the unconditional risk 

of dying from skin cancer and the unconditional risk of getting skin cancer for themselves and for their

children. Whereas the marginal rates of substitution discussed above reflect tradeoffs between the same risk

faced by different people, these calculations reflect tradeoffs between different types of risk faced by the same 

person. As shown in Table 7.3, parents’ marginal rate of substitution between unconditional death risk and

unconditional morbidity risk for themselves is 19.16 and the corresponding value for their children is 21.78. 

These estimates indicate that parents are willing to pay approximately 20 times more to reduce unconditional

death risk by one unit than to reduce unconditional morbidity by one unit. This outcome supports the idea that 

public policies aimed at reducing death are much more important to people than policies aimed at reducing

morbidity.

8. Conclusions

This report has critically reviewed the economics literature on what parents are willing to pay (WTP) for

greater protection of their children's health and provided new empirical estimates from a study of skin cancer. 

Theoretical foundations of the parental perspective approach to valuation, including both unitary and

pluralistic models of family decision-making were reviewed. The report discussed difficulties arising when

the parental perspective is used to estimate social benefits, and examined prior empirical research to assess

issues that should be considered in future research. 

A detailed illustration of application of the parental perspective to value morbidity and mortality risks

affecting children was presented. The application focused on skin cancer, the most common form of cancer in 

the U.S. Links between environmental exposure to ultraviolet radiation and skin cancer are well established,

and chances of getting skin cancer, for a given amount of exposure to solar radiation, depend partly on

observable genetic characteristics such as skin type and complexion. The theoretical model adopts the unitary 

approach and supports empirical valuation of morbidity and mortality risks faced by both parents and children 

in a consistent framework. Risk is treated as endogenous and is measured as the risk perceived by survey

respondents. The method for estimating WTP rests on directly estimating an indifference relation showing

utility-constant trade-offs between morbidity risks, mortality risks, and consumption goods. 

The model provides a basis for computing parents' marginal rates of substitution between risk of death 

from skin cancer faced by both themselves and their children. This calculation shows how parents value

children's health relative to their own and may be useful benefits transfer in situations where willingness to pay 

for reduced risk to adults have been established but corresponding values for children are not available. The

model is estimated using data collected by an interactive computerized questionnaire administered on the
-20

University of Southern Mississippi campus during summer of 2002. Key aspects of the experimental design

were to: (1) determine parents' perceptions of skin cancer risk to themselves and their children, and (2) obtain

willingness to pay for skin cancer risk reductions. Risk reductions were presented to parents using randomly

assigned labels of a hypothetical sun lotion that offered different amounts of protection to adults and children. 

Random assignments of risk reductions facilitate estimation of marginal rates of substitution between parent's 

health and children's health. For example, parents’ marginal rate of substitution between their own lifetime

chances of getting skin cancer and their children’s lifetime chances of getting skin cancer is about two. This

indicates that parents value reductions in risk to their children’s health more than reductions in risk to their

own health. Additionally, parents’ marginal rates of substitution between risk of getting skin cancer and risk

of dying from it are about for 20. This indicates that parents value reductions in mortality risk much more than 

reductions in morbidity risk, for both themselves and their children. Of course, the rate of substitution

between morbidity and mortality risk may differ for other health effects and might even be less than unity for

illnesses or injuries resulting in permanent disability. 

Empirical results obtained suggest that the recent policy emphasis on protecting children’s health may be 

justified from an efficiency standpoint when a parental perspective is adopted. Estimating willingness to pay

for children’s health by transferring estimates computed for adults on a one-to-one basis would appear likely to 

substantially understate children’s health benefits. Although results in this and other recent research suggest

that parents are willing to pay about twice as much to protect their children’s health as to protect their own, it

is important to note that little evidence yet exists on how the marginal rate of substitution may vary across

different health risks or between different demographic groups. If the value of child health relative to parental 

health varies significantly, for example with the age, gender or health status of the parent or child, the

estimating children’s health benefits as any constant multiple of adult benefits may be misleading. Primary

research directed at valuing children’s health protection is likely to yield more reliable benefit estimates for

policy purposes. 
-21

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-25

Table 6.1

Respondent and Household Characteristics

n=610

Gender

Male 23.4%

Female 76.6%

Age

Less than 25 6.9%

25 to 29 18.0%

30 to 34 26.9%

35 to 39 25.1%

40 to 44 17.0%

45 to 49 3.3%

50 and up 2.8%

Race / Ethnicity

White, not Hispanic 75.4%

Black, African American 20.0%

Hispanic 2.0%

Asian / Pacific Islander 0.7%

Native American / Alaskan Native 0.5%

Other 1.5%

Household Information

Household Size 4.01

Children 2.08

 Biological Children Aged 3-12 1.54
-26

Table 6.2

Respondents' Familiarity with Skin Cancer

n=610

Have heard of Skin Cancer 95.4%

Know of Someone Diagnosed 83.8%

Known of Someone Who Died 22.1%

Have Been Diagnosed 3.4%

Relative Diagnosed 20.3%

Thought about Chance of Skin Cancer 80.3%

Thought about Child's Chance Skin 

Cancer 71.1%

Table 6.3

Frequency Distribution of Initial Risk 

Responses: Parents and Children

n=610

Risk Range (%) Parents Children

0 - 4.75 85 75

5 - 9.75 57 79

10 - 14.75 70 94

15 - 19.75 65 69

20 - 24.75 65 74

25 - 29.75 66 73

30 - 34.75 45 35

35 - 39.75 23 19

40 - 44.75 36 25

45 - 49.75 6 5

50 - 54.75 53 32

55 - 59.75 4 2

60 - 64.75 5 7

65 - 69.75 0 1

70 - 74.75 5 2

75 - 79.75 6 5

80 - 84.75 2 3

85 - 89.75 3 2

90 - 94.75 8 5

95 – 100 6 3
-27

Table 6.4

Mean Initial Risks: Parents and 

Children By Race

White, n=460

Parent 27.6%

Child 22.8%

African American, n=122

Parent 11.8%

Child 12.9%

Table 6.5

Skin Characteristics By Race

Respondents

White, 

not 

Hispanic

Black / 

African 

American

n=460 n=122

Natural Skin Color

Very Fair 23.5% 3.3%

Moderately Fair 36.7% 5.7%

Medium 37.6% 58.2%

Dark 2.2% 32.8%

Skin Type

Always Burns 30.9% 2.5%

Burns then Tans 49.6% 12.3%

Tans 9.6% 36.1%

Tans Easily 8.9% 14.8%

Neither Tans nor Burns 1.1% 34.4%
-28

Table 6.6

Exposure History and Protective Actions: Parents and Children

Respondent Child

Exposure

Direct Sunlight as Child/Teenager

More than Others 33.3% 12.0%

Same as Others 53.3% 61.4%

Less than Others 13.4% 26.6%

Lifetime Exposure

Greater than Others 28.5% 11.3%

Same as Others 53.5% 66.4%

Less than Others 18.0% 22.3%

Sun Protection Product Use - Total 486 530

Use During Summer Outdoors

Less than half the time 28.0% 19.0%

About half the time 24.7% 24.7%

More than half the time 19.1% 20.6%

Always or almost always 28.2% 35.7%

SPF Normally Used

Less than 15 16.0% 6.4%

15 to less than 30 41.8% 23.4%

30 or higher 42.2% 70.2%

Skin Damage

Sunburn 85.6% 59.8%

Bad Sunburn 76.4% 23.1%

3 or more Bad Sunburns 54.9% 5.1%

Bad Sunburn as a Child / Teenager 70.0% --
-29

Table 6.7

Distribution of Age at Skin Cancer Diagnosis: Parents 

and Children

n=610

Age at Diagnosis Parents Children

Before Age 40 61 99

Between Ages 40 and 44 81 61

Between Ages 45 and 49 79 66

Between Ages 50 and 54 127 97

Between Ages 55 and 59 72 70

Between Ages 60 and 64 99 65

Between Ages 65 and 69 49 54

Between Ages 70 and 74 24 59

Between Ages 75 and 79 6 16

Age 80 or later 12 23

 Table 6.8

Frequency Distribution of Conditional Death 

Risk Estimates: Parents and Children

n=610

Risk Range (%) Parents Children

0 - 4.75 103 142

5 - 9.75 163 194

10 - 14.75 122 111

15 - 19.75 67 44

20 - 24.75 42 31

25 - 29.75 26 23

30 - 34.75 13 8

35 - 39.75 8 8

40 - 44.75 7 7

45 - 49.75 5 2

50 - 54.75 19 11

55 - 59.75 2 1

60 - 64.75 3 0

65 - 69.75 0 0

70 - 74.75 2 0

75 - 79.75 0 0

80 – 100 0 0
-30

Table 6.9

Hypothetical Sun Protection Product Labels

Percent Change in 

Morbidity Risk

Percent Change in 

Mortality Risk

Label Parent Child Parent Child

A 10 10 0 0

B 10 50 0 0

C 50 10 0 0

D 50 50 0 0

E 0 0 10 10

F 0 0 10 50

G 0 0 50 10

H 0 0 50 50

Table 6.10

WTP Responses for Hypothetical Sun Protection Product with Reduced 

Skin Cancer Risk Labels

n=610

WTP Responses 

(Initial / Follow-Up)

Initial 

Price

If "YES" to 

Initial, 

Higher 

Follow-Up 

Prices

If "NO" to 

Initial, 

Lower 

Follow-Up 

Prices

Yes / 

No

Yes / 

Yes

No / 

Yes

No / 

No

20 40 15 10 26 1 6

25 45 10 24 48 6 7

30 50 20 12 20 1 4

40 75 25 18 13 4 5

50 90 20 28 35 15 15

50 100 35 13 11 6 9

60 120 45 12 8 2 14

75 120 35 24 23 15 15

100 150 45 21 22 12 28

125 200 60 14 29 6 28
-31

Table 6.11

WTP Responses for Hypothetical Sun Protection Product with Reduced 

Conditional Death Risk from Skin Cancer Labels

n=610

WTP Responses 

(Initial / Follow-Up)

Initial 

Price

If "YES" to 

Initial, 

Higher 

Follow-Up 

Prices

If "NO" to 

Initial,

Lower 

Follow-Up 

Prices

Yes / 

No

Yes / 

Yes

No / 

Yes

No / 

No

20 40 15 15 20 0 3

25 45 10 20 48 6 9

30 50 20 9 23 2 11

40 75 25 10 15 4 6

50 90 20 17 28 18 11

50 100 35 12 10 3 11

60 120 45 13 13 4 11

75 120 35 30 33 21 12

100 150 45 16 33 17 16

125 200 60 14 18 16 32
-32

Table 6.12

Demographic and Socioeconomic Characteristics

n=610

Household Income

Less than $20,000 18.0%

$20,000 to less than $50,000 38.0%

$50,000 to less than $75,000 20.7%

$75,000 to less than $100,000 14.4%

$100,000 and up 8.9%

Mean Household Income $53,000

Educational Attainment

< High School 1.8%

High School, including Equiv 14.7%

Some College (incl. 2-yr degree) 32.3%

4-yr Degree 28.7%

Graduate / Professional Degree 22.5%

Marital Status

Married 75.9%

Divorced 9.3%

Separated 3.4%

Widowed 0.5%

Never Married 9.3%

Living with a Partner 1.5%

Employment Status

Full-time 59.0%

Part-time 16.3%

Not employed 24.7%
-33

Table 7.1 

Willingness to Pay for Reduced Risk of 

Skin Cancer

Variable Coefficient

(t-statistic)

Coefficient

(t-statistic)

Constant 4.028

(130.32)

4.023

(86.63)

GET=1 if label changes risk of getting skin 

cancer; 

0 if label changes conditional risk of dying 

from skin cancer

-0.089

(-1.992)

-0.092

(-2.079)

PCHG=1 if parent risk change = 50%; 

0 if risk change = 10%

---

a

---

a

KCHG=1 if child risk change = 50%; 

0 if parent risk change = 10%.

---

a

---

a

GET*PCHG 0.251

(6.82)

0.252

(6.86)

GET*KCHG 0.436

(11.84)

0.437

(11.85)

(1-GET)*PCHG 0.309

(8.38)

0.306

(8.30)

(1-GET)*KCHG 0.340

(9.23)

0.339

(9.22)

FAMILY INCOME 

($10,000/year)

--- 0.031

(7.66)

NUMBER OF CHILDREN 

IN HOUSEHOLD

--- -0.076

(-5.23)

a Denotes omitted dummy variable.
-34

Table 7.2 

Willingness to Pay in the First Year to Prevent a Future Case of Skin Cancer 

Or a Future Fatality

Unconditional 

Morbidity

Unconditional 

Mortality

Parent $526.42 $10,086

Child $1080.79 $23,544

Table 7.3

Estimated Marginal Rates of Substitution

Marginal Rate of Substitution Estimate

(standard error)

Child vs. Parent Unconditional 

Morbidity

2.05

(0.35)

Child vs. Parent Unconditional 

Mortality

2.33

(0.32)

Unconditional Mortality vs. 

Unconditional Morbidity 

(Parent)

19.16

(3.15)

Unconditional Mortality vs. 

Unconditional Morbidity 

(Child)

21.78

(2.59)