The Relationship between the Size of Risk Change Presented ...

研究論文The Relationship between the Size of Risk Change Presented in a Contingent Valuation Method and

the Estimated Value of Statistical Life

Makoto Tamura, Ph.D.

Takashi Fukuda, Ph.D.

Aki Tsuchiya, M.A.

Abstract

Background. There is a large variation amongst the values of statistical life

estimated in the past. The authors have focused their attention on the relationship

between the size of risk change presented in a contingent valuation method and the

estimated value of statistical life.

Methods. A survey was performed on 600 community residents. The subjects

were presented with various WTP scenarios, which included a different size of risk

change.

Results. Regression analysis of the value of statistical life with the size of

risk change shows that R2 was high, indicating that the relationship is strong.

Though this relationship was expected, it is quite interesting that the relationship

is almost linear.

Conclusion. The range of risk change should be fairly narrow when the value

of statistical life is universally discussed. For economic evaluation of health care

programs, which reduce the risk of death, it may be desirable to measure WTP

each time for each specific program.

Keywords : The values of statistical life, Willingness-to-pay (WTP),

Risk, Contingent valuation method (CVM), Economic evaluation,

Cost-benefit analysis

*Department of Health Sociology, Graduate School of Medicine,

The University of Tokyo

Department of Health Economics, Graduate School of Medicine,

The University of Tokyo

# Postdoctoral Fellow for Research Abroad, Japan Society for the Promotion of Science

Visiting Research Fellow, Centre for Health Economics, University of York

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医療と社会VoL8　 No.31998

1.INTRODUCTION

In order to perform cost benefit analyses of health care programs, which involve changes

in risk of death, it is necessary to estimate the monetary value of human lives, There are

three ways to accomplish this task, of which pros and cons will be briefly described below in

turn:

1) the human capital approach,

2) the revealed preference method, and

3) the contingent valuation method.

Under the human capital approach, value of a human life is represented by the present

value of the stream of expected future income. For example, this approach is commonly

applied in calculating the amount of monetary compensation for loss of productivity due to

traffic accidents. Since expected future income is to represent the value of human life, this

approach has difficulties recognizing the economic value of the lives of retirees, homemakers,

and those unable to work (Pauly, 1995) . Further, there is an argument that the approach

estimates externalities of life-saving health care programs rather than the actual value of the

lives saved (Johannesson, 1996). In any case, there is a growing consensus (Johannesson,

Jonsson, and Karlsson, 1996) that the human capital approach is not the most desirable way to

estimate the monetary value of human life, and therefore, we will not discuss this approach

any further in this paper.

The revealed preference method employs observed market behavior in order to estimate

the value of human life. For example, wage-risk studies compare wages of jobs which involve

different risks of death, other things being equal; i.e. the difference in wages between cleaning

windows on the ground level and cleaning windows on the upper levels of skyscrapers is

assumed to come from the different risks of death involved. Since data are collected from

actual markets, they are expected to represent the actual preferences of the people. Yet on the

other hand, there are two limitations (Fisher, Chestnut, and Violette, 1989) . One is that,

wage-risk premiums in most cases reflect not only the increased risks of death, but also

increased risks of non-fatal injuries, and the relationship between larger wages and larger risks

of death may not be straightforward. The other is that, the data will reflect existing market

distortions : this includes imperfect information implying the workers not knowing the full

extent of the increased risks, and wages being disproportionately low for the disabled, women

and ethnic minorities.

In stead of relying on observed market behavior, the contingent valuation method employs

hypothetical market situations, where respondents will be asked for either the maximum

monetary amount they are willing to pay or the minimum they are willing to accept in

exchange for alternative scenarios.

These amounts are referred to as the Willingness To Pay (WTP) and the Willingness To

Accept (WTA), respectively. When the objective of the study is to evaluate human lives,

different scenarios usually represent different levels of safety. The advantage of this

contingent valuation method is that it can, in theory, be applied to estimate any kind and

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The Relationship between the Size of Risk Change Presented in a Contingent Valuation Method an d the Estimated Value of Statistical Life

degree of risk, including those that are not marketed. An issue re lated to this method is the

possible existence of biases, such as hypothetical bias, strategic bias, starting point bias, etc.

(Johansson, 1995 ; Tolley, Kenkel, and Fabian, 1994) , which arise from the hypothetical

characteristic of the method. Nevertheless, there are several techniques suggested and

examined (Johansson, 1987) to avoid or to minimize the effects of such biases.

The common practice is to employ either the revealed preference method or the contingent

valuation method to obtain the "statistical" value of life, which is done by extrapolating the

difference in monetary value between a small change in the risk of death to the difference in

monetary value between death for certain and life for certain. That the revealed preference

method should deal with statistical values of life is obvious from the fact that there are no

markets where lives or deaths for certain are traded. The reason_ for contingent valuations

aiming for statistical values of life has to do with the fact that, faced with WTP and/or WTA

questions on certain death (i.e., "How much will you pay in order to avoid a certain death now?" or, "How much will you accept in exchange for a certain death now?") there will be respondents

who will refuse to settle with any finite amount (Drummond et al. , 1997) . This becomes a

practical difficulty in cost benefit analysis, since programs with the slightest risk of death will be assigned an infinite cost, which will not be compensated for by any finite benefit. Despite

the mathematical expected value of an infinite amount of non-z ero probability still being

infinite, WTP and WTA questions on risks of death are known to yield finite amounts, and

thus, contingent valuations deal with risks of death, and analyz e statistical values of life

(Broome, 1978 ; Ulph, 1982).

There is a large variation amongst the values of statistical life estimated by the above

methods (Fisher, Chestnut, and Violette, 1989 ; Viscusi, 1992). For example, Fisher, Chestnut,

and Violette (1989) estimated $ 1.6 million to $ 8.5 million (1986 dollars) as an appropriate

value of statistical life. There has been only one estimation in Japan (Yamamoto, and Oka,

1994), which showed a huge amount, with a range from k 2.5 bil lion to 3.6 billion ( $ 21

million to $ 30 million') ) . It is recommended for any economic evaluation to perform

appropriate sensitivity analyses to explore the extent and effects of variance in data (Tolley,

Kenkel, and Fabian, 1994 ; Drummond et al., 1997). Although one purpose of estimating the

value of statistical life is to apply the value, estimated before-hand, to a cost-benefit analysis,

the variation in the value is too large even if sensitivity analyses are applied. Therefore, the

main purpose of this study is to clarify the factors responsible for the variation in the value of

statistical life.

Johansson (1995) pointed out several factors that may cause this variation : age, income,

the type of risk, the initial risk level, and the size of risk change. In this study, in order to

empirically examine factors that cause the variation, we will focus attention on the initial risk

level and the rate of risk change for the reasons indicated below. First, although possible

effects due to the initial risk level and the rate of risk change have been previously pointed

out (Fisher, Chestnut, and Violette, 1989 ; Johansson 1995 ; McGuire, Henderson, and Mooney,

1988), they have not been examined empirically. Second, Fisher's data indicates a negative

1) One dollar is equivalent to about 120 yen on average in 1997.

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医療と社会Vo韮.8　 Nα31998

correlation　 between　 the　 estimated　 value　 of　statistical　 Iife　and　 the　 mean　 risk　 level　 of　the　 sample

(Fisher,　 Chestnut,　 and　 Violette,1989),　 We　 calculated　 a　correlation　 coefficient　 of-0、506　 for　 the

relationship　 between　 the　 estimated　 value　 of$tatistical　 life　and　 the　 mean　 risk　 leve1,　 Although

the　calculation　 is　quite　 rough,　 it　should　 be　sufficient　 to　focus　 attention　 on　theτisk　 issue,

　　　　To　 examine　 the　relationship　 between　 the　initial　 risk　 leve1,　 the　 rate　 of　risk　 change　 and　 the

estimated　 value　 of　statistical　 life,　we　 used　 the　 contingent　 valuation　 rnethod　 because　 the　 initial

risk　 level　 and　 the　 rate　 of　risk　 change　 can　 be　 set　without　 restriction.　 When　 respondents　 are

asked　 for　their　 maximum　 WTP　 with　 a　certain　 initial　 risk　 level　 and　 a　rate　 of　risk　 change,　 the

relationship　 between　 the　 size　of　risk　change　 presented　 and　 the　 estimated　 value　 of　statistical　 life

will　be　clarified.

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 2.METHODS

(1)THE　 SAMPLE

　　　　The　 sample,600　 people,　 was　 randomly　 drawn　 from　 adult　 males,　 aged　 40　to　69,　 living　 in

the　 Tokyo　 metropolitan　 area,　 The　 subjects　 were　 limited　 to　this　 group　 in　 order　 to　minimize

gender　 and　 age　 variance.　 Self-reported　 questionnaires　 were　 sent　 to　all　sublects.　 We　 visited

the　sublects'homes　 to　collect　 the　 questionnaires　 if　they　 were　 not　 returned　 within　 a　couple　 of

weeks。

(2)QUESTIONNAIRE

　　　　We　 presented　 the　 subjects　 with　 two　 WTP　 scenarios　 for　 the　 estimation　 of　 the　 value　 of

statistical　 life。　 One　 WTP　 scenario　 was　 for　 a　 vaccination　 which　 can　 prevent　 a　prevalent

infectious　 disease(the　 mortality　 is　100%).　 The　 other　 scenario　 was　 for　 a　safer　 flight,　 assuming

there　 are　two　 airline　 companies.　　　　　　 -

　　　 With　 the　vaccination　 WTP,　 a　baseline　 risk　and　 a　risk　 reduction　 rate　 were　 presented.　 For

this　questionnaire　 we　 set　the　 baseline　 risk,　 which　 represents　 the　 possibility　 to　be　 infected,　 at

O.01%and　 1%.　 To　 help　 the　subjects　 easily　 understand　 the　magnitude　 of　the　 risk,　 the　following

statement　 was　 included:"One　 out　 of　X　people(e。g,10,000)will　 be　infected!'The　 risk　reduction

rate　 was　 the　 rate　 at　which　 the　 vaccination　 can　 prevent　 the　 infectious　 disease.　 We　 prepared

three　 different　 questionnaires　 with　 risk　 reduction　 rates　 of:80%,50%,　 and　 20%.　 Respondents

were　 randomly　 divided　 to　three　 groups　 and　 each　 group　 answered、a　 different　 questionnaire,

Ultimately,　 the　 number　 of　combinations　 of　risk　 became　 six(2　 baseline　 risks　 and　 3　risk　reduction

rates),　 although　 each　 respondent　 was　 presented　 with　 only　 two　 combinations(baseline　 risks).

　　　 Gafni(1991)insisted　 that　 WTP　 questions　 should　 be　 asked　 in　the　 context　 of　a　hypothetical

insurance　 purchase　 because　 the　 payment　 mechanism　 for　most　 health　 care　 services　 is　through

an　 insurance　 system.　 In　 Japan,　 most　 medical　 care　 is　provided　 through　 the　 Social　 Health

Insurance　 System,　 but　 some　 preventive　 care,　 including　 vaccinations　 and　 medical　 check-ups,

are　 out℃f-pocket　 expenses,　 Hence,　 we　 believe　 the　 hypothetical　 setting　 de$cribed　 above　 is

apPropriately　 realistic.

　　　 In　the　flight　 WTP,　 we　 asked　 WTP　 questions　 for　Company　 B's　air　fare,　 assuming　 that　 the

crash　 possibility　 of　Company　 A's　flight　 is　5,0×10一6,　 while　 Company　 B's　crash　 possibility　 is　20%

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The Relationship between the Size of Risk Change Presented in a Contingent Valuation Method and the Estimated Value of Statistical Life

of A's, and that Company A's air fare is 100 thousand yen. Here, everyone was asked the

same question.

We used the bidding game method for both WTP. An algorithm, which indicated a certain

amount of money, was described to respondents beforehand. The advantage of the bidding

game is that it requires only a yes/no response to each bid and thus has better resemblance to the market than single open-ended questions (O'Brien, and Viramontes, 1994). Recently, the

binary contingent valuation method has been proposed as being more appropriate for WTP

(NOAA, 1993) • However, we used the bidding game method for two reasons. First, we

would like to examine the relationship between WTP and the respondent's socioeconomic

status (this is almost impossible by the binary contingent valuation method) ; and second, the

absolute value of statistical life is not a major concern in this study.

We took notice of three points in preparing the questionnaire. First, previous studies

have shown the existence of a starting point bias in bidding games (Johansson, 1995 ; Tolley,

Kenkel, and Fabian, 1994). Although a particular empirical study (O'Brien, and Viramontes,

1994) did not prove the existence of such a bias, we thought it was an important issue to be

addressed. Therefore, in this study we set different starting points for each combination of

risks, so that if respondents choose the starting point, the value of statistical life would be

almost the same, regardless of the combination of risks. Second, we tried to exclude health

status, which were neither death nor perfect health, in the contingent case as much as possible.

Although some studies (Jones-Lee, Hammerton, and Philips, 1985) include health status to

estimate the value of statistical life, it may result in an overestimated value for contingent

cases with low levels of quality of life (QOL), since the value includes both avoidance of death

and of decline in QOL. Thus, for the vaccination WTP, the convalescence of the infectious

disease was set to be either perfect health or death. For an aircraft crash, the result is

normally death. Third, we tried to exclude bias due to risk perception. The risks were

presented in the form of probabilities. We did not suggest names of specific diseases to avoid a risk perception bias. There were some studies (Yamamoto, and Oka, 1994 ; Lindholm, Rosen,

and Hellsten, 1994) in which WTP seemed to be overestimated due to risk perception bias.

Furthermore, we asked questions on six variables, which may affect the value of statistical

life : age, residency status, occupation, income, self-rated health, and risk preference.

(3) STATISTICAL ANALYSIS

The value of statistical life for each person was calculated using the following formula :

[The value of statistical life] [WTP] / ([baseline risk] X [risk reduction rate] )

The estimated value of statistical life was analyzed in two ways. First, mean (and median)

WTP for different baseline risks with the same risk reduction rate was compared with each

other. For this analysis, the difference in distribution was tested by the use of the Wilcoxon

sign-rank test, Second, mean (and median) WTP for different risk reduction rates with the

same baseline risk was compared with each other. For this, the difference in distribution was

tested by the use of the Mann-Whitney U-test and the difference in median was tested by the

median test.

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医療と社会Vol.8　 Nα31998

Next, using ordinary least squares, the dependent variable, value of statistical life, was

regressed to the six variables, described above. The raw data were used to analyze age. With

a residential status, two categories, own house and others, were set. Occupation had two

categories : "manager or specialist" and "others". For income, we set nine categories with 2.5

million yen intervals. Self-rated health had five ranks : very good, good, normal, bad, and

very bad. Risk attitudes of each respondent was elicited and classified into five ranks, from

risk averse to risk loving, by asking for their preference over a risky lottery and a less risky

one. For the purpose of controlling variables, the risk reduction rates were analyzed as

dummy variables.

3 . RESULTS

Of the 600 people, 321 returned their questionnaires resulting in a response rate of 53.5%.

Regarding the uncollected questionnaires, 188 addressees were absent at the time (including

long-term absences from home) , 68 refused to answer, and 23 had moved. Of the 321

questionnaires collected, one was not filled in by the intended subject, so the effective collection number was 320.

Generally, it is important to ascertain in contingent valuation surveys whether or not

respondents understand the contingent question appropriately and they intend to cooperate.

First, we dropped from subsequent analysis those subjects who made economically irrational

decisions. If the choice of WTP for a given baseline risk was larger (smaller) than that for a

larger (smaller) baseline risk given to the same respondent despite risk reduction rates being

equal, we identified these responses as being economically irrational. We dropped 49 subjects

that we found to have made irrational economic decisions. Viscusi (1992) observed that some

studies were carried out in the same way. Second, we determined how to deal with those

subjects who answered zero for WTP questions. These subjects might have indicated their

refusal to cooperate with the research. Therefore, we made all subsequent analysis "with" and "without" those subjects who answered zero . Both "with" and "without" analyses were quite

similar. Therefore, we will show results for the "with" analysis only.

The characteristics of the respondents analyzed in this study are shown in Table 1.

(1) ESTIMATED VALUE OF STATISTICAL LIFE

The average of the estimated values of statistical life varied widely from 147 million yen to

6204 million yen ( $ 1.2 million to $ 51.7 million) , depending on the combination of risks

( Table 2). With the same risk reduction rate, the average increased significantly as the baseline risk decreased. On the contrary, with a similar baseline , risk, the average tended to

increase as the risk reduction rate decreased (some were statistically significant, and some

were not).

Similarly, the median value of statistical life varied widely from 15 million yen to 1875

million yen, depending on the combination of risks (Table 3). The relationship between the

combination of risks and the median was almost the same as that of the average; indeed, the

relationship was stronger in the case of medians than with averages. The medians were

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Table 1 Characteristics of the Respondents in the Study (n=220)

* Numbers do not add up to total due to missing data.

Table 2 Average of the value of statistical life

(million yen)

* Significant, p<0.10, after Bonferroni correction

** Significant, p<0.05, after Bonferroni correction

* * * Significant, p<0.01, after Bonferroni correction

consistently much higher than the averages, indicating that the distributions are skewed to the

right.

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医療と社会Vol.8競o.31998

Table 3 Median of the value of statistical life

(million yen)

* Significant, p<0.10, after Bonferroni correction

** Significant, p<0.05, after Bonferroni correction * * * Significant, p<0.01, after Bonferroni correction

Figure 1 The relationship between value of statistical life and size risk change

Size of risk change (logio X)

(2) THE SIZE OF RISK CHANGE AND THE ESTIMATED VALUE OF STATISTICAL LIFE

Since the relationship between the estimated value of statistical life and the combination of

risks have been verified, we will now examine the relationship between the estimated value of

statistical life and the size of risk change. The size of risk change is represented by multiplying

the baseline risk by the risk reduction rate. Figure 1 showed the relationship between the

median values of statistical life and the size of risk change (the horizontal axis is logarithmic).

60


Table 4 Results of Multiple Regression Analysis for the Value of Statistical Life

The figure showed the seven median values obtained from different combinations of risks and

also includes the value of statistical life estimated by Yamamoto, and Oka (1994). As far as we

know, the estimated value of statistical life, by Yamamoto, and Oka (1994), is the only one

that has been published so far in Japan",

Figure 1 showed a clear correlation. Regression analysis gave the following results:

[The value of statistical life] = Logic [Size of risk change] x (-5,19) - 13.01

The multiple correlation coefficient, R2, for this relationship was 0.85. The same regression

analysis was performed using average values. Nevertheless, since the fit of parameters was

better with medians than with averages, only the results of the former are presented here.

(3) RELATED FACTORS OF THE VALUE OF STATISTICAL LIFE

Regression analysis of the value of statistical life with each baseline risk is shown in Table 4,

2) We have not adjusted the amount of Yamamoto's estimation by inflation rates, because only

three years have passed since their report was published and the level of this indicator has been

very small in Japan.

61


Income had a positive significant correlation with the value of statistical life with all baseline

risks. When the baseline risk was 0.0005%, occupation had a significant correlation; the value

of statistical life of managers and specialists tended to be lower than that of other occupations.

A similar relationship could be found with other baseline risks, though they were not

statistically significant.

4 . DISCUSSION

(1) BASELINE RISK, RISK REDUCTION RATE AND THE VALUE OF STATISTICAL LIFE

One purpose of this study is to clarify the relationship between baseline risk, risk reduction

rate, and the value of statistical life. We have shown that the relationship between baseline

risk and the value of statistical life is quite clear. Although correlation between risk reduction

rate and the value of statistical life was also found, especially when the value was expressed as

a median, it is somewhat obscure compared to the former relationship.

This result, however, may not mean that the former relationship was stronger than the

latter one for the following reasons: first, the magnitude of risk change was obviously different

between both relationships. Risk reduction rates varied from 20 % to 80 %; the difference

between the top and bottom is only four times. On the other hand, baseline risks varied from

0.0005% to 1% with the difference being 2000 times. This difference seemed to affect both

relationships, Secondly, there was a difference depending on whether the comparison was

made within samples or between samples, Although the comparison of different baseline risks

with the same risk reduction rate was made within a sample, the comparison of different risk

reduction rates with the same baseline risk was between samples. If a different risk change is

presented to the same respondents, it is natural that they should answer higher WTP for a

higher risk change. However, between different respondents, there is no guarantee that they

answer higher WTP for a higher risk change. Johanneson (1995) reported that there is a

possibility that the size of a small risk change is not correlated to WTP between samples.

(2) THE SIZE OF RISK CHANGE AND THE VALUE OF STATISTICAL LIFE

Regression analysis of the value of statistical life with the size of risk change showed that

R2 was high, meaning that the relationship was strong. Though this relationship was

expected, it is quite interesting that the relationship is almost linear.

Neumann, and Johannesson (1994) made a similar analysis of WTP studies of in vitro

fertilization. The relationship between the probability of successful fertilization and WTP per

baby was estimated, but the relationship was not linear.

Fisher, Chestnut, and Violette (1989) reviewed existing studies and concluded that if the

baseline risk is between 10-4 and lir, the appropriate range of the value of statistical life is from

$ 1.6 million to $ 8.5 million, This seems to be discussed under the condition that the value of

statistical life can converge when the range of risk is limited within the above range. However,

from our data, the above risk range may be too large. If two baseline risks, 10 and 10-5, are

substituted for the regression analysis, the difference of the value of statistical life becomes 30

million-yen ($2.5 million) assuming the risk reduction rate is 50%.

62


Note that for this regression the number of data was very small and this contributed to a

high R2. However, this seems to be enough to conclude that the range of risk change should

be fairly narrow when the value of statistical life is discussed universally. In other words, it is

very difficult to discuss the value of statistical life without reference to specific contexts, For

economic evaluation of health care programs, which reduce the risk of death, it may be

desirable to measure WTP each time for each specific program.

(3) RELATED FACTORS FOR THE VALUE OF STATISTICAL LIFE

The result of regression analysis of the value of statistical life indicated that the

independent variables did not explain the dependent variable very well. Jones performed

similar analysis and could not obtain many significant independent variables either (Jones-Lee,

Hammerton, and Philips, 1985).

The only independent variable, which had a significant relationship to the dependent

variable, was income. It is said that the existence of this relationship proves the validity of

the research (Neumann, and Johannesson, 1994 ; Donaldson, and Shackley, 1997) • From this

point of view, we can say the validity of our WTP questions was proved. Contrary to our expectation, risk preference was not found to be a significant variable,

The question for risk preference was the same as the one, which was significantly related to a

standard gamble question in our past study ( Tamura, Nozaki, and Fukuda, 1996) , so the

validity of the question might not be an issue. The correlation coefficients were negative,

although none of them were significantly different, so this may indicate a possible relationship

between risk preference and the value of statistical life.

(4) THE LIMITATIONS OF THIS STUDY AND FUTURE RESEARCH TOPICS

There were two major limitations in this study. One was the low response rate, 53.5%.

One reason for the low response rate might be that respondents were limited to males, whose

response rates are usually low. The other reason might be the difficulty of answering the

questions, especially those on WTP. We performed pretests several times to help respondents answer the questions, However, it was possible that a considerable number of people did not

like to answer such types of questions. Because of these considerations, the representativeness

of this research might be a problem. Therefore, the absolute value of statistical life estimated

in this study should be interpreted with caution (it was certainly not the purpose of this study).

The second limitation was a possibility that respondents might have difficulties in understanding

the contingent case of WTP questions. As described above, to avoid a risk perception bias,

we did not indicate specific names of diseases, and alternatively indicated the probability of the

disease. Although the validity was proved at a certain level as previously mentioned, the

absolute value of statistical life should be carefully interpreted.

One future research goal is to acquire more data to empirically examine the relationship

between the size of risk change and the value of statistical life, To generalize the result of this

study, a wider range of risk or various types of risk should be examined. A second research

goal is to expand the scope of subjects, In this study, all the respondents answered the WTP

to reduce their own risk. However, some people may also be willing to pay something for

63


altruistic reasons, to reduce the risk to others (O'Brien, and Gafni, 1996). For example, people

who are not living in the area where certain infectious diseases are prevalent may still be

willing to pay some money to eradicate these infectious diseases. If those people were

included in the respondents, the relationships between baseline risk, risk reduction rate, and

the value of statistical life might well change.

5 . CONCLUSION

The range of risk change should be fairly narrow when the value of statistical life is

universally discussed. For economic evaluation of health care programs, which reduce the

risk of death, it may be desirable to measure WTP each time for each specific program.

Acknowledgment

Financial support for this study was provided entirely by a grant from Institute for Health

Economics and Policy, Tokyo, Japan.

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Survey on the Willingness to Pay for Reducing a Health Risk of a Drinking Water," Environmental

Sciences. 7(4) : 89-301.

(Received July 7, 1998 ; Accepted Sep 1, 1998)

65

医療と社会VoL8Nα31998

仮想市場法において提示するリスク変化の

大きさと統計的生命価値額の関係

田村誠

福田敬

土屋有紀

1.はじめに

従来推計された統計的生命価値額には非常に大きなバラッキがある。筆者らは,仮想市場法にお

いて提示されるリスク変化の大きさと統計的生命価値額の大きさの関係に焦点をあてた。

2.方法

600人の一般市民を対象とした調査を行った。リスク変化の大きさの異なる,さまざまなWTPの

質問を行なった。

3.結果

統計的生命価値額を目的変数とし,リスク変化の大きさを説明変数とした重回帰分析を行ったと

ころ,R2が非常に高くなった。すなわち,両者の関係は非常に強かった。こうした関係のあること

は予期されていたが,両者の関係がほぼ線形であることは興味深いものであった。

4.結論

統計的生命価値額を普遍的に論じようとする場合,リスク変化の大きさの幅は非常に小さくすべ

きであると考えられた。死亡率の低下を伴う保健医療プログラムの経済的評価のためには,それぞ

れのプログラムのためにWTPを測定することが望ましいと考えられた。

キーワード:統計的生命価値額,WTP,リスク,仮想市場法,経済的評価,費用-便益分析

東京大学大学院医学系研究科健康社会学分野

東京大学大学院医学系研究科保健経済学分野

日本学術振興会海外特別研究員

VisitingResearchFellow,CentreforHealthEcono皿ics,UniversityofYork

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