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# OAR Box 1846 Prepped by Ollie Stewart



### Document

Number: 

# 81 ) IV-A- 3
Docket Number: 

# A-90-0 7

Printed 11/18/2008 7:30:17

AM Sheet 81 of 93P. 2

AP R 0 6 '9 4 04:06P M AB T ASSOC . BETHESDf l P.2/2 1 

## INDUSTRIA L KPNOVUC S INCJORPORATE D 206 7 Massachusett s Avenu a Cambridg e Massachusett s 0214 0 Tstophpne : 817/354-007 4 Teiecopta : s i 7/354-046 3

MEMORANDU M

6 November 199 2 

# 9 8111 1 

# t

s 

# U J MA Y 2 6199 4 

# \3 EP A AI R DOCKE T

TO :

FROM : Ji mDeMocke r

Offic e o f Polic y Analysi s an d Revie w Offic e of Ai r an d Radiatio n U.S
. Environmenta l Protectio n Agenc y Bo b Unsworth , Ji mNeumann , an d W
. Eri c Brown e Industria l Economics , Incorporate d SUBJECT : Revie w o

f Existin g Valu e o f

Lif e Estimates : Valuatio n Documen t

INTRODUCTIO N Thi s memorandu msummarize s ou r revie w o f existin g estimate s fo r

th e valu e of

astatistica

l life
. Thi s memorandu mi s intende d t o serv e a s a n initia l ste p i n generatin g valu e o f lif e estimate s fo r applicatio n t o th e dea n Ai r Ac t Sectio n 81 2 analysis
. Additiona l researc h wil l b e performe d an d tb e conclusion s modifie d in respons e t o comment s received
. Th e goal s o f thi s effor t are : (1 ) t o summariz e existin g benefi t estimate s which ma

y b e appropriat e for

placin g value s o n th e type s o f mortalit y risk s generall y attribute d t o ai r pollution ; (2 ) t o identif

y factor s tha t

coul d caus e thes e existin g estimate s t o b e biased ; and , (3 ) wher e possible , t o identif y quantitativ e estimate s of th e magnitud e o f

thes e biases
. Summar y o f Rsarit t W e reviewe d thre e existin g survey s of th e valu e of

lif e literature : Viscus i (1992) , Miller (1990) , an d Fishe r

e t a L (1989)
. Eac h of

thes e survey s evaluate s thre e types; , o f valu e of lif e estimates : wage-ris

k studies , whic h estimat e th e additiona l compensatio n individual s deman d i n th e labo r marke t for takin g riskier

jobs ; contingen t

valuatio n studies , i n whic h individual s ar e aske d t o stat e thei r

willingness-to-pa

y t o avoi d additiona l increment s o f statistical mortalit

y risk; an d consume r behavio r studies , whic h examin e marke t situation s othe r

tha n th e labo r

marke t tha t involv e arisk-dollar

tradeof f (e.g. , th e marke t

fo r smok e detectors)
. Ou r

analysi s an d conclusion s ar e base d primaril y o n arevie w o f

tiiese summaries
. Not e tha t n o effor t ha s bee n mad e t o obtai n o r analyz e primar y dat afro mth e underlyin g studies
. 

### 1

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We bas e mos

to f ou r conclusions an

d recommendation

s o n th e result

s o f Viscusi . This surve y represent

s th e mos trecen

teffor tt o summariz

e tb e valu e o f

lif e literatur e an d proposes a rang e o f broadl y applicable value s rathe

r tha n a poin testimate . Base

d on a n analysi s o f Viscusi' s results , w e conclud e tha

tth e valu e of

a statistica

l lif e ca n b e roughl

y characterize

d b y a lognorma

l distribution . W

e us e Viscusi' s surve

y result s t o determin

e th e parameter

s o f thi s distribution . Th e 2 6 estimate s h e finds mos tapplicabl e t o polic y analysi s hav e a mea n valu e of $4. 8 million , a geometri c mea

n o f $3. 6 million , a media

n o f $3. 8 million , a standar

d deviatio n o f

$3. 2 million , an

d a geometri c standar

d deviatio n o f

$2 3 million (199 0 dollars) . Th e geometri c mea n an d geometri c standar d deviatio n ar

e sufficien tt o characteriz

e ou r suggeste

d lognorma

l distribution . Thes e result s ar e summarize

d i n Exhibi

t1 . Th e underlyin

g valuatio n studie s used t o construc tthi s proposed rang

e reflec ta variet y of risk s t o huma

n healt h othe r tha

n thos e pose d b

y air pollution . Mos

tar e base d o n risk-dolla

r tradeoff s observe d in th e labo

r marketSinc

e th e characteristic

s an d incidenc e o

f mortalit y risk considere d i n thes e studie s ma y diffe r significantl

y fro m thos e pose d b

y ai r pollution , thes e existin

g studie s ma

y provide a biase

d estimat e o f th e tru e willingness-to-pa

y t o contro l mortalit y risks associated wit

h air pollution . Base

d o n a revie

w o f th e literature , w e hav

e identifie d thre

e majo r sources of bias tha tshoul d b e considere

d i n assignin

g a distributio

n t o th e valu e o f

a statistica

l lif e fo r us e i n th e Sectio n 81 2 stud y risk perception , age , an

d income . W

e recommen

d tha tth e mortalit

y ris k benefi tdistributio

n give n abov e b e teste d for sensitivit y t o tiiese biases . W

e propos e quantitativ

e method s t o conduc

tsensitivit y test s fo r difference

s i n ag e an d income , an

d provide a

n illustrativ e calculation base

d o n a hypothetica

l mortalit y risk scenario . Thes e propose d adjustmen

t method s ar

e sufficientl

y uncertain , however , tha tw e d o notpropos e thei r us e i n adjustin g th e primar y benefit s estimat e withoutfurthe

r refinemen t
Nef f Steps . Thi s memorandu

m provides a

n importan

tstartin g pointfo r valuin

g mortalit y risk reduction fo r th e Sectio n 81 2 analysis . W e suggesttha

tou r propose

d methodology , includin

g the primar

y valu e o f lif e estimat e an d quantitativ

e sensitivit

y analyses , b e applie

d t o th e actua

l mortalit y risk estimate s generate d as par to f th e Sectio n 81 2 study

. Th e result

s o f thi s applicatio

n should the

n b e criticall y assesse d t o determine i f

additiona l researc h o r refinemen

to f adjustmen

tmethod s i s called for . Outlin e o f

th e Memorandu

m Th e remainder o f

thi s memorandu

m i s organize

d into three parts . First

, w e summariz

e issue s raised i n th e recen tliteratur e regardin g th e value o

f a statistica

l life . Second ; w e summariz e th e result s o f thre e recen tsurvey

s o f this literature , an

d base d o n thi s literatur

e review , propos e a

n estimat e o f th e valu e o f

hfe , expresse

d as a probabilit

y distribution . Third , w e presen ta discussion of biase s tha

tma y resultfro

m th e applicatio

n of thes e values t o estimat

e th e economi c benefit

s of reductions i n ai r pollutio n induced mortalit y risks an d presen ta n illustrativ

e sensitivit

y analysi s showin g th e potentia

l impac to f thes e biases .

MX5TOA L KENOMIC

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IO ID J ? 

## Exhibi t1

c n c n
0 1

o * . I o c n I

o * .
c n t ) s
o o 

# Observe d Distributio n o f Estimate s

Derive d fro m Viscus i (1992 )

Mea n Value : $4. 8 millio n Standar d Deviation : $3. 2 millio n Geometri c Mean : $3. 6 millio n

« 

# Geom . Std . Dev. : $2. 3 millio n n = 2 6 ( 5 CV , 2 1 labo r market

)

. , , , , 

### 7- 8 8- 9 9-1 0 10-1111-1 2 12-1 3 13-1 4 "T J 

# Rang e o f Estimate

s (millio n 1990$)P. 5

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RISK-DOLLA R TRADEOF F ESTIMATIO N ISSUE S ADDRESSE D I N EXISTIN G SURVEY S O F TH E LITERATUR E Mos tstudie s tha

testimat e th e value o f

a statistica

l lif e acknowledg

e tha tth e difficultie

s i n measuring a risk-dollar tradeof f ar e fonnidable . Th e estimation of th e value of

a lif e involves inferenc e abou

tindividuals ' implicittradeof f o f mortalit y risk fo r monetar

y compensation . Analyst

s encounte r uncertaintie s notonl y in isolating th e amoun

to f compensation a perso

n receive s fo r assumin g mortalit y risk, bu tals o in estimating th e actua l an d perceived incremen

ti n mortalit

y risk tha ti s implie d b

y th e transaction. 1 Thi s sectio n provides a discussio n o f thes e issue s an

d uncertainties . I n addition , w e describ e ho

w thes e issue s ar

e addresse d in thre e recen

tsurvey s of th e valu e o f lif e literatur e (Fishe r e ta

L 1989 , Mille

r 1990 , an d Viscus i 1992) . Thre e method s ar

e use d t o estimat

e th e valu e o f

a statistica

l life : (1 ) thos e based o n analyse s o f

tabo r marke tdat a (Le , wag

e risk studies) ; (2 ) thos e base d o n analyse

s o f produc t market s an d co^&ume

r behavior, an

d (3 ) thos e base

d o n direc

tquestionin g o f

individual s t o determin e thei r willingnes s t o pa y t o avoi d mortalit y risks (i.e. , contingen

tvaluation studies) . Som e o f th e factor

s tha tintroduc e uncertaint

y into thes e valu

e o f lif e estimate s ar

e specifi c t o wage-ris

k studies . Others involv

e risk estimatio n assumption s o r difficultie

s in contingen

tvaluation . Finally , bia s ma y b e introduce d b

y limitin g th e valu e o f

lif e estimat e t o a n individual' s value o

f ow n life , withou tconsiderin g tha

tpeopl e ar

e willing t o pa

y t o reduc e risk s pose d t o family, friends , an

d othe r person s (Le. , altruism) . ftiVF ffimjpe to WapsRis k Studi o Mostvalue of

lif e studie s reporte

d i n th e literatur

e appl y a wage-ris

k framework

. Thes e studie s estimat

e th e additiona

l compensation a

n individua

l receive s t o accep ta riskier job . Ther e ar e thre e issue s t o conside r whe n evaluatin

g wage-ris k studies . First

, i ti s imperativ

e tha tth e stud y differentiat

e betwee n job-relate d risks, othe r unpleasan tjo b characteristics , an

d risks associated wit

h lifestyl e in orde r t o obtain a n unbiase d wage-ris

k estimate . Isolation o f

job-related risk i s on e criteri a use d t o evaluat

e studie s i n th e thre e literatur e survey s w e reviewe d

1 Som e analyst

s hav e chose n t o accoun

tfo r thes e inheren

tbiase s throug

h quantitativ

e adjustment

. Fo r example , Mille r (1990 ) divide s labo r marke testimate s based o n Nationa l Institut e o f Occupationa

l Safet y an d Healt h (NIOSH ) occupationa

l ris k dat a b y a factor of

thre e t o adjus

t fo r difference s i n estimate s base d o n NIOS H an d U.S . Burea

u o f Labo r Statistic s (BLS ) data . Viscus i (1992) , however , argue s tha tther e i s greate r rando m measuremen

terro r associate d wit

h th e BL S data , whic

h ma y explain thes e differences . Viscusi furthe r argue s tha tsystematic difference s i n th e reportin g o f

th e tw o set s o f

dat a impl y i ti s incorrec

tt o transfor

m NIOS H dat

a int o BL S dat

a throug h th e us e o f

a constan

tmultiplicativ

e scal e facto r (Viscusi , 1992 , p.57) . Given th e lac k of consensu s o n adjustmen

tfactor s for thes e types o

f potentia l biases , w e conclud e tha tusin g adjustmen

tfactor s o f th e typ e MiHe

r advocate

s i s les s defensibl e tha

n applyin g a broa d rang e o f value s t o incorporat

e th e uncertaint

y inherenti

n the underlyin

g estimatio n procedures int o th e Sectio n 81 2 analysis .

• MXKTWALErXTOAlCSWaORPORATE D

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Second , wage-ris

k studie s assum

e a perfec tlabo r market ; thi

s approac h wil l notproduc

e accurat e wage-risk estimate s unde r condition s o f

a n imperfec

tmarke tin whic h worker s ar e no tfree t o move betwee n job s o r i n whic h onl y unio n member

s hav e sufficien tmarke tpowe r t o receiv e higher wage s Co

r highe r ris

k jobs . Al l thre

e survey s conside r thi s second factor , bu td o no tadjus

t fo r i L Third , mostwage-ris

k studie s rel

y on wag e dat a fo r manua

l labo r t o obtai n a significan

trisk coefficient . Gega

x e tal . (198S ) argu

e tha tlac k of variatio n i n th e risks associate

d wit h white-collar job s coul d caus e estimate s o f non-manua

l workers ' risk premium

s t o b e les s robus tUsin g contingentvaluatio n methods , Gega x e ta i obtain a value o f

lif e for white-collar worker

s simila r t o tha tobtaine d b

y th e bul k o f wage-ris k studie s base

d o n manua

l laborers ' data . Becaus e of thi s similarity , Fishe r e tal . (1989 ) do no tadjus twage-ris k estimate s fo r a manuai/noo-manua l labo r factor . Althoug

h h e doe s no taddress thi s issue directly , Viscusi (1992 ) propose s tha tan y bias whic

h ma y b e create d b

y thi s facto r wil

l b e eliminate

d throug h adjustmen

to f th e result

s base d o o comparison o f

th e incom e of

th e sampl

e grou p relativ e t o tha to f th e genera

l population . Althoug

h i ti s likely tha

tsom e degre e o f

bia s i s introduce

d b y thes e thre e factors , th e overal l directio n an

d magnitude of

th e bias i s unknown.
Pid f F^rimatin n AMimppon a Bot h wage-risk an

d contingen

tvalue methods assum

e tha tmember s o f th e stud y populatio n understan d th e natur

e of th e risk bein g faced o r considered . Thus , th e degree o f ris k communication i s on e facto r tha tmus tb e considered . I

f a labor marke

tstud y i s t o obtai

n a n unbiase d wage-ris k premium, workers ' perception o f risk level s across job s mus tmatc h actua l ris k levels . Otherwise , workers ' risk compensation requirement s wil l no tproperl y reflec trelative ris

k levels . Fishe r e tal . (1989 ) believ e thi s potentia l bia s t o b e small , citin g a stud y b y Viscus i an

d O'Connor (1984 ) tha

tfound chemica

l workers ' risk perceptions t o b e simila r t o objectiv

e measure s of on-the-jo b risk. 1 In contras tMille r (1990 ) cite s a stud

y b y Lichtenstein e tal . (1978) , whic h foun d tha tworker s underestimat

e th e risk o f

on-the-jo b injurie s b y abou t2 0 percen tMille r use s thi s estimat

e as a basis t o adjus

tvalue of lif e estimates compute

d fro m actua l risk dat a upwards. Problem s i n risk communicatio

n ca n als o b e a potentia

l sourc e o f bia s in contingentvaluation surveys . Viscusi an

d Maga t(1987 ) found evidence tha trespondent

s ten d t o misunderstan

d low probabilit y risks . I f a responden

tt o a contingen

tvaluatio n surve y cannotdifferentiat

e between change s i n th e relativ

e degre e o f

risk (e.& , a

n increase d ris k o f 1 deat

h pe r 1,000,00

0 exposed populatio n versu s 1 deat

h pe r 100,00 0 exposed population) , th e resultin g value o f lif e estimat e ma y b e distorte d b

y a s muc h a s a n order o f

magnitude . Fisher etal , Miller, an d Viscusi al

l conside r th e us e o f extensiv e risk characterizatio n an d consistenc

y check s i n contingentvaluatio n survey s as a qualitative criteri a fo r th e degre e of credibilit y assigne d t o th e reported estimates .

3 I ti s possibl e tha

tworker s in th e chemicals industr y ar e mor e wel l informe

d abou tth e magnitud e an d natur e o f th e risks the

y face . Note tha

tnon-fatal , non-cance

r occupationa

l risks fo r SI C 2 8 ar e slightl y higher tha

n th e averag

e for al l industr y (5 7 versu

s 3 7 annua

l incident s pe r 10,00 0 workers ) (U.S . Departmen to f Labo r 1991) .

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I n addition t o error

s associate d wit

h poo r communicatio

n o f risk, th e baseline leve

l o f risk i n th e sample population s i s a potentia l sourc e o f bia s i n al l value o f lif e studies . Blomquist(1981 ) found tha tpeopl e ata highe r leve l of baseline risk plac

e greate r valu

e o n th e sam

e degree o f

risk reduction . I

f thi s i s th e case , a stud

y base d on a sampl

e o f individual

s exposed t o a smalle

r baseline ris k wil l produc e a lowe r valu e o f

lite estimat

e tha n a stud y base d o n a sampl

e o f individual

s expose d t o highe r level s o f

baselin e risk. Fishe r e ta l appea r t o ascribe t o thi s theory . The y pointou ttha

t thei r surve

y include s studie s tha

tconside r risks between 1 an d 1 0 annua

l deaths pe r 10,000 expose d population . Fishe r e tal . stat e tha tthei r selecte d rang

e fo r th e statistica

l value o f lif e i s probabl

y no t applicabl e for evaluating th e benefit s o f reducin g risks wit h baselin e level s wel

l outsid e thi s range , especiall y categorie s o f

risk tha tar e muc h larger. Miller, o n th e othe r hand , found n o relationship betwee n th e valu e o f

risk reduction an

d th e baseline leve l o f

risk. Althoug

h curren tvalue of lif e estimate s ma

y contai n som e systemati c bia s related t

o baselin e risk level , there i s insufficien t information i n th e literatur e t o adjus tfo r thi s bias .

Issue s relate d t o economi c valuatio n coul d affec

tth e magnitud

e o f th e value o f

lif e estimate s reporte d i n th e literature . Fo r example , Mille r (1990 ) consider s th e inconsistenttreatmen tof taxe s in th e literature . Man

y labor marke

tstudies analyz

e before-ta

x wages , whil e othe r market-base d an d contingen tvalu e studie s analyz e expenditures o f

after-ta x dollars . After-ta

x wages ar

e considere d b y mos teconomists t o b e th e appropriat e measure , i.e. , tha

tindividuals derive n o utilit y fro m margina l taxe s paid . Mille r multiplie s estimates base d on before-ta

x wag e rates b y a n adjustmen

t facto r o f 1.35 . Fishe r e tal . (1989 ) conside r th e us e o f wag e rates a s oppose d t o tota

l compensation . The y conclud e that , whil e wag e rates ignor e th e effec

to f taxe s o n take-home pay , th e us e o f wage differential s an

d a sample o f

individuals in simila

r ta x bracket s ensures tha

tan y upwar d bias introduced b

y thi s facto r i s small . A thir d facto r involves ex post

benefi taccounting . Ex

post benefi t accountin g take s int

o accoun tworker' s compensation an d othe r benefit

s tha tmigh taffec tworke r jo b choice . Viscus i (1992 ) note s tha tsevera l studie s includ e a worker' s compensatio n measur e as supplementar

y compensatio n for jo b risk s t o accoun

tfor th e rol e tha

tthi s facto r ma

y hav e as a jo b choic e criteria . Fo r example , lif e insuranc

e provide d as par to f a compensation packag

e could reflec ta risk premium . Incorporatio n o f thi s facto r int o al l o f th e value o

f lif e estimate s reported i n th e literatur

e woul d requir

e effort s beyon d th e scop e o f

thi s repor

t(i.e. , suc h accountin

g woul d requir e analysi s o f th e primar

y dat a fro m thes e studies) . A particularl

y difficul tquestio n concern s th e extentt o whic h curren tvalue o f lif e estimate s includ e compensatio n fo r non-fata l risks. Fishe r e tal . observ e tha tmost - studie s d o no ttrea

tnon fata l risks a s par

tofth e wage-ris

k premium . Althoug

h workers ' compensation probabl y cover s som e portio n o f

non-fata l risk, som e erro r i n th e estimation o f fata l risk ma y stil l resultfro m thi s factor . Mille r state s tha

t5 5 t o 8 0 percen

to f tb e wagcris k premium

s pai d in five studie s h e surveye d wer e t o reduc e non-fata

l risks , H e goe s o n t o propos e an d appl y a n adjustmen

tfacton-fo

r thi s effec t Miller , however , di d no tconside r th e effecto f worker s compensation , healt

h insurance , o r lif

e insuranc e a s benefits . Viscusi include s fatalit y risk , workers ' compensation , and/or th e implicitvalu e o f injur y as categorie s i n hi s summar y iable , bu

tdoc s notattemp tt o adjus tfo r thes e factors . Treatmento f

non-fata l risks i n eac h stud

y serves as a qualitativ

e criteri a i n Viscusi' s selectio n o f estimate s for us e i n developin g hi s recommended rang e for th e statistica

l value o f a life . Th e overal

l directio n an

d magnitude o f

bias introduced b

y thes e factor

s i s unknown.

^^DUSTRIALEaO^CrMlCSNQaRJXXATI D
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Altruism A fina l facto r t o conside r whe

n estimating th e statistica l valu e o f a lif e i s th e presence o f altruism . Th e existin g literatur e focuse s o n individua

l risk tradeoffs , bu

tther e i s substantia

l evidenc e tha tpeopl e ar

e willin g t o pa

y t o reduc e risks pose d t o other

s (i.e. , peopl e los e utilit

y whe n someon e els e i s pu ta

trisk o f dying) . In a stud

y tha tincluded willingnes s t o pa y t o reduce others ' risk o f illnes s fro m insecticides , Viscusi , Magat , an d Forrest(1988 ) foun

d evidenc e o f

significan taltruistic values . I n addition , Jones-Le e e tal . (1985 ) adde d to a mea

n willingness-to-pa

y valu e t o reduc

e one' s ow n risk a n adjustmen

tt o tak e accoun to f people' s concer

n fo r tb e safet

y o f others . Th e Jones-Le e valu e wa s 500,00 0 (about$650,00 0 a tth e 1985 exchang

e rat e o f $1.30/) , adde

d t o th e initia l estimat e of 1. 5 million (abou

t$2. 0 million) for th e statistica l valu e o f

life . However , Bergstro m (1982 ) argue

s tha ta n altruisti c add-o n ma y notappl

y i n al l circumstances . Viscusi (1992 ) advocate s furthe

r stud y i n thi s are

a befor e attemptin

g t o inflat e valu e o f

lif e estimate s t

o reflec t altruism . VALU E O F LIF E ESTIMATE S Significan testimatio n uncertaintie s ar e presen tin existin

g value o f

lif e estimates . W e propos e t o accoun

tfor thes e uncertainties throug h consideration o f

a broa d rang e o f value s reporte d i n th e literature . Not e thatthes e uncertainties appl y t o al l application

s o f th e value o f

lif e literatur e for policy purposes ; tha

tis , the y ar e no tspecifi c t o th e ai

r pollutio n cas e o f

specifi c interes there . W e examined three recentsurvey s o f th e value o f

lif e literature : Viscus i (1992) ; Fishe r e t al . (1989) ; an

d Miller (1990) . Exhibi

t2 summarize

s th e result

s o f thes e thre e surveys . W

e describ e th e methodologie s use d i n an d result s obtaine d fro

m eac h o f thes e survey

s below . VJscuafi992 ) Viscusi' s surve

y o f th e literatur

e concerned wit

h value s o f

risks t o lif

e an d healt h i s par to f a large r effor texaminin

g publi c an

d privat e responsibilitie s fo r risk reduction . Viscusi examine d 3 9 wage-risk , consumer market , an d contingentvaluation estimate s o f th e valu e o f

a statistica

l life . H e conclude s tha

teconomist

s hav e bee n mostsuccessfu l i n usin g labo r marke tdat a t o asses s fata

l riskdolla r tradeoffs ! In discussin

g th e qualit

y o f th e estimate s h e reports , Viscus i list s four criteria tha

t h e find s importan

tin detenmnin g th e valu e o f lif e fo r polic y applications ,

• Us e o f

appropriat

e ris k measures . Us e o f

actuaria l dat a t o determin e th e ris k level s face d b y worker s makin g a risk-dollar tradeof

f employmen

t decisio n wi

n bias th e valu e o f

statistica l lif e downward

. Thi s occur

s becaus e thes e actuaria

l dat a ar e no tlimited t

o occupationa

l risks, bu tinclude d types o f fatalit y risk face d b y th e individual . Thi s overl y broa d risk measur

e biase s th e valu e o f

statistica l lif e estimat e downward

, bu tb y a n unknow n amoun t

R-99 % lNDUSTWALK30r<rV«C S INCORPORATE )
30 1 65 2 753 0 04-06-9 4 04:25P M P00 3 3! 2I

C D o a

Ettibtt. 2 SUMMAR

Y O F RECEN T VALUBOIMJP B SURVEY S

Surve y Autho r and FUbttcatio o Dat e Viscus i (1992 )

Fisher. Chestnut

, aa d Viotett e (1989 )

- o I

o

o o UJ Mille r (1990 ) Numbe r aa d Typ e of f
3 9 from 197 4 t o 199 1 (2 6 wage-mi l studies , 7 consume r marke

t studies , 6 amtinfen t valu e ttudles )

2 1 studie s published from 1974 t o 1988 (1 5 wage-risk ttudles , 4 consume r marke

t studies , 2 contingen

t valuatio n studies )

6 7 studie s publishe

d from 1973 t o 199 0 (3 7

studies , 1 5 studies , an

d I S coatingenMahistio a studies ) :e f Reporte d o f Valu e a f Li u (modottan ) $70,00 0 t o $16. 2 raiUian (originally repotted i n 199 0 dottats )

SSaOjQO O l o $10. 1 million (o r $450,00 0 t o $8. 5 «ullio « a s originall y reported 1986doBars )

$40,00 0 to $167.

7 miliu m (o r $36,00 0 t o $15. 1 nWo a a s originall y reporte d 1988duJUrs ) (199 0 dollars ) Mos tmaaoaabl

a estimate s o f

th e valu e o f lif e a m dusten d i n tb e $ 3 t o $ 7 millio n

Th e mea tdefeasibl e empirica

l result s indicat e a rang e o f estimate s fro

m $1. 9 t o $10. 1 ailton . • Concludes tha tmarke tstudie s othe r tha

n risk studie s a

m no tappropriat

e Co r polit y application .

• Concludes tha

tstructura

l models d o no tyiel d robustestimates .

• Place s les s emphasis o n 'exploratory " pilot-scal e contingen tvaluatio n studies . 

## £



## S

Th e snggtsir d valu e o f

a statistica

l lif e for us e i n oast-benefitanalysi s an d lega l damag e case s i s i a th

e rang e (ra m $1. 7 mflHo

a to $3. 1 minion , wit

h a centra l tifdem y o f

$2 4 saillion . • Conclude s ttwtwage-ris k estimate s belo w $1. 9 millio n resul

tfrom biase s i n di e measure d risk variabl e an d thus shoul d no tb e use d for policy purposes .

• Concludes dia

l result s from consume r marke

t studie s reflec

tpotentiall

y significan

tdownwar

d

• State s tha

tmostestimate s reported i n th e literatur e understat

e th e valu e pe r statistica

l lif e appropriat

e for environmenta

l polic y analysis .

• Adjust s studie s fon us e o f

beforc-ta x earnings ; erro r i n risk perception ; error I n specificatio n o f risk variables ; exclusio

n o f non-fata l injur y risks ; ag e o f sampl e population ; an

d applie s n staodardue d 2 3 percen

tdiscoun trate .

• Excludes 21

1 studie s from consideratio n base d o n concerns ove

r stud y quality . Note : Ai l values converted t o 199 0 dolar s usin g th e CP l deflator . T J I D m
8P.1 0

GP R 0 6 '9 4 04:10P

M ABT ASSOC . BEHTCSDP . P.10/2 1

Mode l type . Labo r marke tstudie s tha tar e primaril y designe d t o determin e th e valu e o f

a lif e year , an d th e implici tdiscoun trat e person s appl y t o thi s value , ar e les s reliabl e fo r estimatin g th e valu e o f a statistica l life , Althoug h dies e studie s ar e usefu l i n determinin g th e appropriat e discoun trate , Viscus i argue s tha tgreate r emphasi s shoul d b e place d o n studie s usin g conventiona l wage-ris k estimatio n procedures , becaus e th e complexit y o f th e underlyin g structural model s use d t o estimat e discoun trate s lead s t o les s robus t estimate s o f

th e valu e o f

a statistica l life . Tw o o f th e existin g pilot-scal e contingen tvaluatio n survey s ai e based o n muc h smalle r sample s (u=3 0 an d 36 ) tha n th e othe r fou r contingen tvalu e estimates . Viscus i consider s thes e tw o estimate s (on e o f $0. 1 millio n an d on e o f

$15. 6 million ) t o b e les s reliabl e tha n th e fou r large scal e surve y estimates , l_f « o f appropriat e wilHi>gn««

B fo p^ v proades . Viscus i finds tha tal l seve n estimate s fro m consume r marke tstudie s fai l t o provid e a n unbiase d estimat e o f th e dolla r sid e o f

th e risk-dollar tradeoff , a s observe d i n consume r preference s for safety . Fishe r e tal . agre e wit h thi s assessment , citin g simila r limitation s tha tcaus e consume r marke tstudie s t o produc e estimate s o f th e valu e o f a statistica l lif

e tha tar e likel y t o b e biase d downward .
Viscus i conclude s tha tlabo r marke tstudie s wit h carefull y constructe d risk variables , an d contingen tvalu e studie s with relativel y larg e sampl e sizes , ar e mos tsuitabl e fo r polic y applications . Applyin g hi s exper tjudgement , h e finds tha tmos treasonabl e estimate s o f th e valu e o f

lif e ar e clustere d i n th e $ 3 millio n t o $ 7 millio n rang e (199 0 dollars) . However , Viscus i caution s thatthes e estimate s ar e based o n sample s o f worker s "wh

o generall y hav e lowe r income s tha n di e individual s bein g protecte d b y broadly-base d risk regulations " (p p 73-74) . Base d o n hi s earlie r wor k (Viscus i an d Evan s 1990) , h e conclude s tha tth e valu e o f a statistica l lif

e i s positivel y correlate d with income . H e als o state s tha t"recognitio n o f th e incom e elasticity o f th e valu e o f

lif e wil l lea d t o th e us e o f differen tvalue s o f

lif e dependin g o n th e populatio n bein g protected " (pp . 74) . T o illustrat e thi s conclusion , Viscus i use s th e empiricall y derive d estimat e o f incom e elasticit y o f

on e from Viscus i an d Evan s (1990 ) t o sho w ho w di e result s o f eac h stud y coul d b e adjuste d fo r applicatio n t o airlin e travelers , wh o i n genera l hav e highe r incom e level s tha n th e genera l population . Viscus i conclude s tha ta majo r ga p i n th e applicatio n o f valu e o f lif e estimate s for polic y purpose s i s th e lac k o f

a n appropriat e schedul e o f estimate s applicabl e fo r differen tpopulation s a t risk. Fo r example , h e cite s th e importanc e o f distinguishin g group s tha tincu r risks voluntarily , hav e hig h risk tolerances , o r accep tcompensatio n t o bea r risk s fro m thos e group s wh o bea r risk s involuntarily , wh o hav e a hig h risk-aversion, o r wh o ar e no tcompensate d for bearin g a healt h risk. Viscus i doe s not , however , sugges ta framework fo r makin g thes e type s o f adjustments .

INDUSTRIA

L BOONOVUC

S faXRPORATE

D

R-98 % 30 1 65 2 753 0 04-06-9 4 04:25P M P01 0 31 2P.1 1

PP R 0 6 '9 4 04:10P

M flBT ASSOC

. BET1-ESD

A P.11/2 1

Flstier . Chestnu tan d Violat e f198f l Fishe r e ta l (1989 ) examine d 2 1 studie s o f

willingnes s t

o pa y fo r reduction

s i n risk . They conclud e th e mos tdefensibl e estimate s li e i n a rang e fro m S1. 9 million t o $10. 1 millio n (199 0 dollars , o r $1. 6 t o $8. 5 million a s originall y reported 198 6 dollars) . Thi s rang e include s al

l of th e estimate s judge d t o b e reasonabl e an d reliabl e b

y thes e authors . Fishe r e tal . g o o n t o expres s greate r confidenc e i n th e lo w end o f thi s range . The

y determined thei r recommended rang e b y excluding : result

s of earl y wage-ris k studie s tha

tma y exhibitbiase s i n th e measure d ris k variable ; a stud y o f polic e officers ' wage-ris

k tradeof f tha tthe y believ e traces a labor suppl y curv e rathe r tha n showin g wage differentials ; an d consumer marke

tstudie s tha

treflec tpotentially significan

t downwar d biases . I

n addition , th e estimate s measur e a n individual' s valu e t o reduce bi s o r he r ow n risk, an d do no taccoun

tfor th e valuatio n o f

others (family , friends , o r strangers ) t o reduce tha t person' s risk. A s a result , i ti s likel y tha tth e lo w en d o f thi s rang e i s lower tha

n society' s willingnes s t o pa y t o reduc e fata

l risk s o f thi s type . Fishe r e ta

L conclud e tha

tthei r recommende

d rang e fo r th e valu e o f

lif e provides a basis for "evaluatin

g policie s o r regulation s expecte d t o affec trisks o f fata l injur y i n th e wor

k place " (pp . 96-97) . However , the

y als o believ e tha

tthi s rang

e understate

s th e valu e o f

a statistica

l lif e appropriat

e fo r environmenta

l polic y assessmen tI

n addition , the

y sugges ttha

tthi s recommended rang e i s mos tappropriat

e for valuing reduction

s i n annua l risks o f deat h tha tar e betwee n 1 an

d 1 0 pe r 10,00 0 expose d persons . The y als o sugges ttha

ta n adjustmen

tfo r tb e ag

e o f affecte d persons i s appropriate , an

d thu s tha ta mor e appropriat

e measur e woul d conside r di e year s o f lif e saved . Thi s adjustment

, however , requires informatio n o n tb e impac

to f policie s o n th e lif

e table s of expose d individuals , i n orde r t o calculat e tb e year

s of lif e potentially save d b

y th e policy . The

y conclud e tha

tsuc h informatio

n i s rarel y availabl e o r reliable . MjHe r (1990 1 Mille r (1990 ) examine

d 6 7 estimate s o f

th e value o f

life . Hi s goal wa s t o narro w th e rang

e o f credibl e willingnes s t o pa y values b y weedin g ou

tstudie s wit

h seriou s flaws an

d adjustin g fo r source s o f

inconsistency . Mille r judge d 47 o f th e 6 7 estimate s t o b e "reasonabl y sound* . Unlik e Viscus i an d Fishe r e tal , Mille r conclude s tha tth e consume r marke

testimate s o f

value o i lif

e ar e appropriat

e fo r applicatio n i n cast-benefi tanalysis . Adjustment

s t o th e 4 7 estimate s no tdiscarde d included correctio n fo r systematic bias relate d to : ag e o f populatio n under study ; income ; th e accurac y o f ris k perception ; an

d th e baselin e leve

l o f risk . Fo r fTfrppfr

, Mille r standardize

d th e value of

lif e t o reflec tth e valuation o f

a 3 8 yea r ol d perso n wit

h 3 9 yea n o f

remainin g lif e expectancy . This adjustmen tinvolve d multiplyin

g the valu e o f lif e estimat e b y th e rati o o f

discounted lif

e year s remainin

g a tag e 3 8 t o discounte d lif e year s remainin g atth e sampl e mea n age . Mille r als o adjuste

d for risk irnsperceptio

n i n wage-risk studie s b y multiplyin

g th e result

s b y 1233 . Th e precis e derivatio n of thi s valu e i s notdescribed , bu ti trelie s o n dat a from a Lichtenstei n e tal . (1978 ) stud y o f risk perceptio n an

d a procedur

e described i n



### 1 0
^CtJFTRlALBC0^OV^lCS^JCDRP0ftATH )
R-99 K 30 1 65 2 753 0 04-06-9 4 04:25P M P01 I »1 2_„__
_P.1 2

AP R 0 6 '9 4 04:11P M AB T ASSOC . BETI-ESD A P.12/2 1
Blomquis t(1981). 3 I n addition , Mille r re-estimate d som e result s usin g standardize d value s fo r trave l tim e (6 0 percen to f

th e wag e rat e pe r perso n o r 12 0 percen tpe r vehicle 4 ) an d th e rea l discoun trat e ( 2 J percent) . Afte r thes e adjustments , Mille r calculate s th e mea n valu e o f th e 4 7 soun d estimate s t o b e $2. 4 millio n (199 0 dollars , o r $1 2 millio n i n th e originall y reporte d 198 8 dollars) . H e calculate s th e standar d deviatio n o f

th e adjuste d estimate s t o b e $0.7 2 millio n (199 0 dollars , o r 0.6 5 millio n i n 198 8 dollars) , bu tconclude s tha tth e value s ar e to o uniforml y sprea d t o b e normall y distribute d (fe w ar e withi n 0 i standar d deviation s o f th e mean) . Mille r state s hi s conclusion s i n th e for m o f th e mea n valu e an d a rang e o f

± 3 0 percen to f

th e mea n (i.e. , $1. 7 t o $3. 1 million) , RECOMMENDATION S O f th e thre e surve y paper s w e reviewed , w e believ e tha tth e Viscus i an d Fishe r e tal . studie s represen tth e bes tstartin g point s fo r valu e o f lif e estimate s fo r tw o primar y reasons . First , Miller' s result s incorporat e estimate s fro m consume r behavio r studies , whic h bot h Fishe r e tal . an d Viscus i argu e forcefull y ar e no tappropriat e for environmenta l polic y purposes . Second , Miller' s result s ar e dependen to n adjustment s mad e t o wage-ris k data . Thes e adjustments , describe d earlier , ar e th e subjec to f debat e amon g economist s i n th e field . W e believ e tha tthei r inclusio n i n ou r analysi s woul d mak e ou r result s les s defensible . W e furthe r sugges tusin g th e Viscus i surve y rathe r tha n th e Fishe r e ta L review , Althoug h th e Fishe r e ta

L revie w ha s bee n use d b y EP A i n th e pas ta s th e basi s for a valu e o f lif e (Dec k 1989) , th e mor e recen tViscus i revie w include s studie s publishe d sinc e th e Fishe r e ta L surve y wa s complete d I n addition , Viscus i include s mor e recen tversion s o f studie s tha tFishe r e tal . judge d t o b e sound . Thre e valu e o f lif e estimate s fro m tw o studie s ar e include d b y Fishe r e tal . bu tno tb y Viscus i - Gega x e ta

L (1985 ) an d V.K . Smit h (1976) / Viscusi' s mor e recen tsurvey , soo n t o b e publishe d i n th e Journa l o f Economi c Literature , include s thes e tw o studies . Afte r consultatio n wit h Viscusi , w e hav e adde d th e Gega x e tal . contingen tvalu e estimat e an d th e V.K . Smit h wage-ris k estimat e t o ou r analysis . Viscus i reject s th e othe r Gega x estimates , whic h wa s base d o n a labo r marke tmodel , becaus e th e author s di d no tconside r di e ful l rang e o f mortality risk s i n constructin g thei r risk variable .

i Lichtenstei n foun d tha tindividual s underestimate thei r risk o f on-the-jo b injurie s by . abou t2 0 percent , th e ris k o f

injury i n moto r vehicl e crashe s b y abou t4 0 percent , an d th e ris k o f fire-related injurie s b y abou t5 5 percen t 4 Value s derive d fro m Deaco n an d Sonsteli e (1985) , Winsto n (1985) , Chu i an d McFarlan d (1985) , an d Mille r (1989) .

' A s cite d i n Fishe r e tal . (1989) . 1 1
MXSTWA L ECONOMIC

S INO0RPORATE

)

R-98 % 3° 1 65 2 753 0 04-06-9 4 04:25P M P01 2 t

f 1 2P.1 3

AP R 0 6 '9 4 04:11P

M RBT ASSOC . BETKESD

A P.13/2 1 We applie d th e fou

r criteri a Viscusi suggest

s (described above ) t o limi tou r revie w t o the 2 6 estimate s h e considere d applicable t o polic y analysis. 6 Th e distributio n of thes e 2 6 estimate s i s given i n Exhibit1 . Eleve n o f di e estimate s ar

e in Viscusi' s recommende d rang e o f $ 3 millio n t o $ 7 million , an

d th e mea n o f al l 2 6 studie s i s $4. 8 million . Th e geometri c mea n i s $3, 6 million . Th e five estimate s base

d o n larg e scal e contingen

tvaluatio n survey s ar e al l belo w th e mea

n value , rangin

g fro m $1 2 millio n t o $3 4 million . Labo r marke tbase d estimate s demonstrat

e a wide r range , fro

m $0. 6 millio n t o $13. 5 million , Fishe r e tal.' s recommende d rang e o f $2. 0 million t o $10. 4 millio n include s 1 8 o f

di e 26 value s w e selecte d fro m Viscusi . Frv

e o f th e 2 6 values ar

e belo w $2. 0 million , an d on e i s abov

e $10. 4 million . Fo r th e purpose s o f

th e Sectio n 81 2 study , w e nee d t o determine th e shap e o f th e distribution implie d b y th e costin g valu e o f lif e estimates aa d th e parameter s o f tha tdistribution . W e were unabl e t o analyz e dat a fro m th e underlyin

g studie s t

o gain insight

s int o th e distributio

n o f individua l risk-dollar trade-offs . Therefore , w e rel y o n a compariso

n o f th e shap e o f

simulate d distributions t o th e observe d distributio

n o f authors ' bes testimate s fro m thes e studies . Exhibit

s 3 , 4 , an d 5 presen tthre e simulate d candidat

e distributions . Exhibi

t3 i s a lognorma

l distributio

n define d b y a geometri c mea

n o f $3. 6 million an

d a geometri c standar

d d^-viatio

n o f $2. 3 millio n calculated fro

m th e soun

d studies. 7 Exhibit

s 4 an d 5 ar e triangula

r distribution

s wit h mos tlikel y value s (Le. , peaks ) correspondin g t o th e median , $3 4 million , o f the se to f 2 6 soun d studies* . Th e distribution i n Exhibi

t4 show s a rang e o f values , o r bas e o f

th e triangle , correspondin g t o th e rang e recommende d b

y Fishe r e ta

L Exhibi t5 show s a rang e o f value s correspondin

g t o tb e rang

e o f th e estimate s i n th e 2 6 soun

d studies . Comparin g thes e thre e simulate

d distribution

s wit h th e observe

d distributio

n illustrate

d i n Exhibit1 , w e conclud e tha tth e lognorma

l distributio

n in Exhibi

t3 appear s t o mos tclosely reflec

t th e actua l distributio

n o f estimates . Bot

h o f the simulate

d triangula

r distribution

s appea r t o hav e to o muc h probabilit

y i n th e righttail . I

n addition , th e expecte d value s associated wit h th e tw o simulated triangula

r distribution

s ar e $0,7 an d $0. 9 million higher, respectively , tha n th e mea n o f th e 2 6 soun d estimates . T o furthe r confir m ou r hypothesi s tha

ta lognorma

l distributio

n ca n b e use d t o represen

tth e value o f life , w e conducted a statistica l goodncss-of-fittes tcomparin g th e observe

d wit h th e hypothesized lognorma

l distribution . Using a Kolmogorov-Smimof f test , w e foun

d w e coul d no t rejectth e nul

l hypothesi s o f

a lognorma

l distribution. ' Base

d o n thi s visua

l an d statistica

l evidence , w e conclud e a lognorma l distributio

n suc h a s tha tsimulate d i n Exhibi

t3 wil l provide a goo d approximatio

n o f th e rang e o f valu e o f lif e estimate s foun

d in th e literature .

R-98 % ' A lis to f thes e 2 6 estimate s ca

n b e found in Exhibi

tA- 1 a tth e en d o f thi s memorandum. Exhibi tA- 2 list s th e estimate s tha twer e droppe d fro m th e analysis . 7 Not e tha

tth e ©RIS K softwar

e packag e tha twil l b e use d i n th e Sectio n 81 2 Partitio n Mode l use s th e arithmetic mea

n an d standaid deviatio n t

o simulat e a lognorma

l distribution .

' Triangula

r distributions ar

e ofte n used a s approximation s t o norma

l an d lognorma

l distributions for modelin g purposes . * W e teste d th e log-transforme d dat a fo r goodness-of-fi

tt o a norma

l distribution . Se e Exhibi t A- 3 for th e dat

a use d i n thi s tes tan

d more ' detaile d results .

1 2 r^CUSTTUALeOONCWGI^CDRPOR ^

30 1 65 2 753 0 04-06-9 4 04:25P M P01 3 31 2

# Expecte d Result

5 5 

# 4.8071 7 20 % '

o

C D

C O o

o4-06-

o4 :25PMPO 

# P R O B A B I L 1 T Y 16 %
12 %
8 %
4 %
0 % Exhibi t3 

# DISTRIBUTIO N O F VALU E O F LIF E ESTIMATE S Simulate d Lognorma l Distributio n

jt..... . 

# Mea n Value : $4. 8 millio n

Standar d Deviation : $3. 2 millio n a ID 

# 5
rv )

tn



# 1.7 5 3. 5 5.2 5 7 8.7 5 10. 5 12.2 5

Valu e i n Million s o f Dollar sID

ID 

# Expecte d Resutt = 5.45490 8 Exhibi t4 

# DISTRIBUTIO N O F VALU E O F LIF E ESTIMATE S Simulate d Triangula r Distributio n

O ) U l tv )

~ ) U l

o
* » • I o o > I

U l K 

# P R O B A B I L I T Y 20 %
16 %
12 %
8 %
4 %
0 % 70 

# i 8



# Mos t Likel y Value : $3. 8 millio n Rang e o f Values : $2. 0 t o $10. 4 millio n £



# 3. 5 5.2 5 7 8.7 5 Valu e i n Million s o f Dollar s 

# i — — 10. 5 12.2 5 

# s

1 4

T Jw a C D 

# Expecte d Result 

# 5.73229 2

i o CT) I ID

•T J s

r o 

# P R O B A B I L I T Y 20 %
16 %
12 %
8 %
4 %
0 % Exhibit s 

# DISTRIBUTIO N O F VALU E O F LIF E ESTIMATE S

Triangula r Distributio n

. . . — . ...i . 

# Mos

t Likel y Value : $3. 8 millio n Rang e o f Values : $0. 0 t o $13. 5 millio n

Valu e i n Million s o

f Dollar s

T J 0 5P.1 7

AP R 0 6 '9 4 04:13P M AB T ASSOC . BETHESD AP.17/2 1



## SENsrnvrr Y ANALYSE S 

### FO R TOE VALU E O P 

### LIF E ESTIMAT E I n developin g the Sectio n 81 2 analysi s i ti s importan

tt o conside r differences i n th e natur e o f ai r pollutio n ris

k as oppose d t o th e risk

s face d b y person s whos e risk-dollar tradeof f decision s hav e bee n addressed i n th e literature . Fo r example , prematur e mortalit y risk s fro m ai r pollutio n ar e experience d o n a

n involuntar

y basi s an d ar e generall y uncompensated , whil e job-related risk s ar e assume d b y individual s wh

o presumabl

y hav e som e choic e as t o occupatio n an d ar e compensated fo r taking a riskie

r job . I n addition , th e demographic s o f th e population a

trisk fro m ai r pollution , particularl y i n term s o f ag e an d incom e status , ma

y diffe r fro m th e demographic

s o f individual s surveye d i n th e literature . A s a result , th e benefit

s transfe r proces s ma

y introduc

e bia s int o th e analysis . Th e valu e o f

lif e rang e proposed abov

e reflect s measuremen

tuncertaint

y i n th e estimatio n methods , bu

tdoe s no taccoun

tfor differences i n willingnes s t o pa y t o reduce air pollutio n mortalit y risk s as oppose d t o othe r type s o f mortalit y risk . W e suggestconducting sensitivit y analyse s o f results o f calculation

s using th e recommended valu e o f lif e range . There ar

e three majo

r sources o f

bias tha tshould b e considere d i n thes e sensitivit y analyses : risk perception , age , an d income . Ou

r conclusions ar

e summarize

d in Exhibi

t6 , alon g wit h a lis to f source s tha

tdiscus s th e estimated direction an d magnitud

e of thes e biases . In th e remainder o f

thi s sectio n w e discus s th e directio n an d potentia l magnitud

e o f bia s introduced b

y thes e thre e factors . Based on thi s review , w e propos e a detaile d framewor k for conductin g sensitivit

y analyse s fo r ag e an d incom e t o tes tfo

r th e effec tof thes e sources of

bias .

• 

### Ris k Perceptio n Th e valu e tha

tpeopl e plac e o n mortalit

y ris k reductio n depend s o n th e natur

e o f th e risk . However , ther e i s genera

l agreemen

ti n the literatur

e tha tcurren tvalue o f lif e estimates d o no t accoun tfo r a numbe

r o f importan tfactor s tha taffec trisk perception . I tseem s clea r tha tth e natur

e o f air pollutio n induced healt h risk s ar e differen ttha n job-related risks. Becaus e marr y valu e of lif e estimate s ax

e base d o n risk-dollar tradeoff s observe d i n th e labo r market

, thi s difference should b e considere d whe

n attempting t o transfe

r existin g valu e of lif e estimate s t o environmenta l polic y analyses .



### 1 6
^a5TWALE0>O^^^JCDRP0RA7H )

R-98 % 30 1 65 2 753 0 04-06-9 4 04:25P M P01 7 »1 2» I ID 0 0 J ?
BihnVi i 6 DIRECTIO N AN D MAGNITUD E O F BIA S T O VALU E O P UP E RANG E

PROPOSE D FO R US E I N TH E SECTIO N 8t 2 STUD Y DcmipUo a o f Ba n Degre e so d typ e o f

risk a s perceive d b y th e expose d population :

• Voluntar

y vs . involuntar

y risks

• Controllabl e vs . iracoairoUabl e risk s

• Ordinar

y vs . catastrophi c risks

• Ol d vs . ne w risks Ag e o f expose d populatio n

Incom e leve l o f

expose d populatio n

i i ID

s o f Bias

Cause s underestimate o f

valu e o f fife Unclea r
Unclea r Unclea r Depend s o n ag e o f

affecte d population . Lead s t o overestimat

e i f affected populatio n i s olde r o r younger tha n populatio n sample d b

y vakje-of-Uf

e studies . Cause s underestimat

e o f valu e o f lir e i n case s wher e averag e incom e o f populatio n eapose d t o ai

r pollutio n ris

k i s greate r tha n tha

to f person s surveye

d (latte r valu e h

i abou t$22,000) . Method s for Adjusting for Bia s

Unknown Unknow n
Unknown Unknow n (1 ) Adjus tusin g rati o o

f discounte d lif

e year s remaining

. (2 ) Us e empirica

l estimate s fro

m Jones-Le e e ta

L (1985 ) data . Workers ' implici

tvalue o f ris k o f job-relate d injuries has Incom e elasticit y o f approiimaiel

y 1. 0 * Reporte d incom e elasticitie s range fro m 0L2 5 t o 0.4 4 witi

i a bes t estimate o f

03 * Source s Begg s (1984) ; LUa

i (1980) ; Fishe r e tal . (1989) ; Weinstei n an d Quin n (1983 ) 

# £



# ?
s

Chestnu tan

d Row e (1988) ; Jones-Le e e tal . (1985) ; Cropper an d Sussma

n (1990) ; Moor e an d Viscus i (1988) ; Mille r (1990 ) Viscus i an d Evan s (1990) ; Viscus i (1992 )
Jones-Le e eta L (1985 )

Th e incom e elasticit

y o f th e valu e o f

statistica l lif e expresse s a

a individual' s rat

e o f chang e i n th e willingnes s t o pa

y t o reduc e mortalit

y risk s fo r give n rat e o f chang e in income (e.& , a n elasticit y o f 0 3 implies th e valu

e o f statistica l lif e increase s thre e percen

tfo r a 1 0 percentchang e in income) .
m r o
u r o 1 7 

# ^ JP.1 9

PP R 0 6 '9 4 04:14P M AB T ASSOC

. BCTTCSD A

Muc h wor k ha s bee n done t o describ e th e importan

tdimension

s i n whic h th e natur

e o f risks ar e perceive d as differen

tfro m on e another . Lita

i (1980 ) conducted a revie w o f literatur e in thi s area , including th e wor

k o f Star r (1969) , Lawrenc

e (1967) , Row

e (1977) , Otwa

y (1977) , an

d Fischhof f e tal . (1978) . Based o n thi s review , Lita i concludes tha

tth e following eigh tdimension s represente d a comprehensive lis to f th e factor

s affectin g huma n ris k perception : (1 ) voluntary/involuntary ; (2 ) ordinary/catastrophic ; (3 ) delayed/immediate ; (4 ) natural/man-made ; (5 ) old/new , (6 ) controllable

/ uncontrollable ; (7 ) necessary/unnecessary ; an d (8 ) occasional/continuous . Transfe r of value o f lif e estimate s betwee

n thes e categorie s coul d indicat e th e potentia

l fo r introduce d bias . Not e tha tther e dearl y ar

e othe r dimension s of

ris k perceptio n (eg. , compensated/uncompensated) , whic h migh tno tb e full

y encompasse

d b y Litai' s non-economi c categorizatio n approach. 1 0 O f thes e eigh tcategories , severa l ma y var y betwee n healt

h risks du e t o ai r pollutio n an d job relate d healt

h risks . Th e categor

y tha tbes tcapture

s th e difference betwee n job-relate d an d ai r pollution-induce d healt

h risk s i s voluntary/involuntary. I n general , job-relate d risks ar e considere d t o b e voluntary , whereas risk s du e t o air pollutio n ar

e not. 1 1 Viscusi state s tha tpolide s affecting peopl e wh o incur healt

h risk s i n job-relate d situations should b e treate d differentl y tha n polide s affectin g peopl e wh o incur healt h risk s involuntarily . Fishe r e tal . note tha t"psychologist

s hav e show n tha tpeopl e ar

e les s willin g t o accep t'Involuntary * risks , suc

h as nudear-powe r aeddem s an d ai r pollutio n exposure " (p . 97) . Thes e finding s indicat e tha tlabor market-base

d valu e o f

lif e estimate s ma

y understat

e th e tru e willingnes s t o pa

y t o reduc

e mortalit y risks associated wit

h ai r pollution . Othe r categorie s o f

risk perception ma

y als o b e relevan

tt o thi s analysis , iodudin g controuable/uncontroUable , ordinary/catastrophic , an d old/new . I

ti s likely tha

tsom e individual s view ai i pollutio n risk

s a s uncontrollabl e an

d job-related risk

s a s controllable . Weinstei n an d Quin n (1983 ) stat

e that"uncontrollabl

e risks ar e les s acceptable becaus e the y depriv e u s of th e emotiona

l comfor tafforde d b y a n unrealisticall

y ros y out-look " (p . 9) . Th e ordinary/catastrophi c categor y of ris k perceptio n define s differen tattitude s toward

s th e perceive d severit

y o f risk. Fo r example , fatalitie s tha

tar e expecte d (e.g. , du e t o ol d age ) ar e preferre d t o violent , unexpecte d death s (eg, , airplan e crash) . Som e type s o f

ai r pollutio n risk

s ma y b e viewe d as catastrophic , fo r example thos e du e t o accidenta

l chemica l releases , bu

tjob-relate d risk

s du e t o suc

h occurrences as min e cave-in s ma y als o b e viewe d a s catastrophi c Th e old/new categor y i s base d o n th e observation tha tpersons will tolerat

e a highe r leve l of

ris k fo r a familia

r hazar d suc h a s woo d smok

e tha n the y woul d accep t fo r a new , unknow

n source o f

risk , suc h a s tha tpose d b y radon . However , i ti s no tdea r tha tai r pollutio n healt

h risk s ar e "new " compare

d t o job-related healt

h risks .

R-98 % 10 Lita

i condude s tha

tther e ar e severa l othe r dimension

s o f ris k perceptio n mentione d i n th e literature which ar

e incorporated i n th e eigh tcategorie s o f ris k perceptio n liste d above . Lita i note s tha tFischhof f e ta L (1978 ) an

d Otwa y (1977 ) considered th e "dread " facto r i n som e detail . Thes e analyst s foun d tha tdrea

d (unexplained , instinctive fear ) wa s mos tstrongl

y associate d wit

h catastrophic ; involuntary , an d immediat e death . Anothe r factor considere d i n th e literatur e i s reversibilit

y o f effects . Lita

i assume d reversibilit

y o f effect s t o b e incorporate

d in th e "controllability " category . Therefore , w e do no tconside r thes e dimension s o f ris k perceptio n separately . 1 1 Thi s hypothesi s i s no tuniversall y accepted , sinc e som e analyst s sugges ttha

tjo b choic e i s no t entirel y voluntary . In th a case , estimate s of risk premium

s woul d b e underestimated . Although man y ofth e factor

s tha tcoul d limi tjo b choic e (e.g. , education , experience , marita l status , race , sex ) ar e controlle d fo r i n wage-ris k studies , othe r factors , suc h a s geographic limitation s o n labor mobility , hav e no talway

s bee n controlled for i

n suc h studies .
1 8 NDU5TNAirja0NCW ^

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AP R 0 6 '9 4 04:14P M AB T ASSOC

. BGTCSDA P.20/2 1

There i s othe r evidenc e i n th e literatur e tha texistin g valu e o f lif e estimate s underestimat

e th e valu e of

lif e appropriat

e for environmenta

l risks , becaus e the

y d o no taccoun tfo r differences i n ris k perception . Fishe r e tal . stat e tha tthe estimates dte d in thei r repor t"woul d b e expecte d t

o understat e th e value pe

r statistica

l lif e appropriat

e fo r environmenta

l polic y assessment " a

tleas t partl y becaus e the

y di d no tincorporat

e the effect

s o f difference

s i n risk perception . Beggs* (1984 ) surve y o f willingnes s t o pa

y t o avoi d th e risk o f

job-related an

d air poDution-induce

d cancer s provides additiona

l suppor tfo r a simila

r directiona

l adjustmen

tTherefore , w

e conclud e tha

tth e transfe r of existing labor-marke

tbase d valu e of lif e estimates t o a stud

y o f ai r pollution risk

s i s likely t o introduce a downwar

d bia s into th e results . Fe w attempt s hav e bee n mad

e in th e literatur

e t o correc

tfor thes e sources of

bias . Lita

i (1980 ) construct

s a mode l t o estimat

e "ris k conversio

n factors " fo r individua

l categories o f

ris k perception , bu

thi s estimate s ar

e bes tdescribe

d a s ratios of observe d risks , Litai' s factor

s d o no t contro l for compensated risks, an d thu s ar e no tapplicabl

e t o thi s effor tA tthi s tim e w e cannot recommend a quantitativ e adjustmen

tfacto r t o serv e as th e basis o f

sensitivit y test s fo r differences i n ris k perception .
AJ B Severa

l author s hav e attempte

d t o addres

s potentia l difference

s i n tb e valu e o f

lif e du e t o differences i n th e averag

e age of th e affecte

d populadon o r th e averag e ag e a twhic h a n effecti s experience d (Croppe

r an d Sussma n 1990 , Moor

e an d Viscusi 1988) . I

n th e cas e o f

mortalit y risks , h i s assume

d tha ta youn g perso n ha s th e opportunit

y t o experienc

e a greate r los s i n tota

l lifetim e utilit y tha n a n olde r person . Thi s intuitivel y attractiv e hypothesis ma

y b e confounde

d b y th e finding tha tolde r persons , wh o a s a grou p ten d t o avoi

d healt h risks assodate d wit

h drinking , smoking, an

d reckles s driving

, revea l a greate r deman d for reducing mortalit

y risk s an d hence hav

e a greate r implid tvalu e of

a lif e yea r (Ehrlic h an d Chum a 1990) . Considerin

g bot h remainin

g year s of lif e an d th e value o f

a lif e year, Jones-Lee e tal . (1985 ) fin d tha tth e value o f a lif e follows a consisten

t "invcrted-U " life-cycle , peakin g i n the regio

n of mea n age . Dat

a fro m Jones-Lee e taL

, illustrate d i n Exhibit7 , suppor

tthi s condusion . Theoretica l wor k b y Shepar d an d Zeckhauser (1982 ) als o suppor ta n inverte d U-shaped life-cycle . Therefore , i f th e age distributio

n o f person s affected b

y ai r pollution-induce d risk

s doe s notmatc

h th e ag e distributio

n o f individual

s considere d i n th e existin g valu e o f

lif e estimates , th e ac

tof transferrin

g thes e estimates ma

y introduc

e bias.into th e analysis . There i s som e evidenc e tha tTSP-induce

d mortalit y i s greate r fo r persons ove r 6

5 year s o f age (Chestnu

tan d Row e 1988). U l n general , considerin g th e ag e o f

person s mos tlikel y t o experienc e ai

r poDution-induce

d mortalit y risks, th e appropriat

e value o f lif e fo r ai r pollutio n risk

s i s probabl y lowe r tha

n th e mea n value use d in othe r polic y contexts . Preliminar

y result s fro m th e Sectio n 81 2 stud

y indicat e TS P wil l accoun tfo r th e majorit

y o f estimated air pollution-induced mortality . Therefore , th e direction o f th e adjustmen

tfor age wil

l probabl y b e downward

.

1 3 A n exhaustiv

e searc h t o determin

e th e ag e distributio

n o f individual

s expecte d t o experience huma n healt h effect s fo r othe r categorie s o f

pollutant s wa s beyon d th e scop e o f

thi s analysis , but suc h researc h wil l b e required prio r t o applyin g ag e adjustmen

tfactor s for mortalit

y endpoint

s fo r purpose s o f the Sectio n 81 2 study. 1 9

. MlJSTRlALB0CKy\\lCSt«)RPORATT O

R-98 % 30 1 65 2 753 0 04-06-9 4 04:25P M P02 0 81 2C O

- J 5 ? 

## Exhibi t7



# 1. 3301 O ) O l

O l o

o * - 1 o O ) 1 * . o < * 

### C

O Q ) 

# E

_ _ 

### ( 0 

## a > > 

## o o •- » 

## c 

# 3 E a . 3 O L . 

## o » < D 5 1 ( d •^ . 

### 0 0 * 3 

### ( 0 

## r r 

# f 

# 1. 2
1. 1

0.9^ 0. 8
0. 7 

# Rati o o

f Ag e Grou p Mea n Valu e o f Lif e t o Overal l Mea n Valu e o f Lif e
Source : Jones-Le e (1985 ) 8
I D

T J

K U l 

## 8 ro 

## 8 u



# <2 4 25-3 4 35-4 4 45-5 4

Ag e grou p o

f respondent s 55-6 4 >6 5 T J

h

o