ML12338A400
| ML12338A400 | |
| Person / Time | |
|---|---|
| Site: | Indian Point  |
| Issue date: | 03/28/2012 |
| From: | Boardman A, Greenberg D, Vining A, Weimer D Prentice-Hall, Simon Fraser Univ, Univ of British Columbia, Vancouver, Canada, Univ of Maryland - Baltimore, Univ of Wisconsin - Madison |
| To: | Atomic Safety and Licensing Board Panel |
| SECY RAS | |
| References | |
| RAS 22098, 50-247-LR, 50-286-LR, ASLBP 07-858-03-LR-BD01 | |
| Download: ML12338A400 (7) | |
Text
United States Nuclear Regulatory Commission Official Hearing Exhibit ENT000154 Entergy Nuclear Operations, Inc.
In the Matter of: Submitted: March 28, 2012 (Indian Point Nuclear Generating Units 2 and 3)
ASLBP #: 07-858-03-LR-BD01 Docket #: 05000247 l 05000286 Exhibit #: ENT000154-00-BD01 Identified: 10/15/2012 Admitted: 10/15/2012 Withdrawn:
Rejected: Stricken:
Other:
THIRD EDITION COST-BENEFIT ANALYSIS Concepts and Practice Anthony E. Boardman University of British Columbia David H. Greenberg University of Maryland Baltimore County Aidan R. Vining SinIan Fraser University David L. Weimer University of Wisconsin- Madison PEARSON Prentice Hall Upper Saddle River, New Jersey 07458
r ,III I'll!")' 01' (:Hngrcss Cataloging-ill-Publicati on Data Cost-benefit an alysis: concepts (l nd pr:1cli,,;c / A ntho ll Y E. Boardman ... fe t aJ.J. - 3 rd cd .
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348 PART III Vaill ation o(lmpacts announcement of a new program or policy. T he main advantage of using stock prices is tha t new information concerning policy changes is qu ickly and efficientl y capitaliz,;"
into stock prices. Changes in stock prices provide an un biased estimate of the value of a policy change to share holders. A lso, stock price da ta are readily accessible ill computer-reada bl e form.
In an evenl st udy, researchers estimate the abnormal return lO a security, which i ~
the difference between the return to a secu rity in the presence of an event and t!ll\
re turn to th c security in the absence of the event. U suall y, researchers estimate dai ly abnorm al returns during an event window, that is, for th e period during which the eveli!
is ass umed to affect stock prices - oft en a few days. Because the return to the securil y in the absence of the event is unobservable, it is inferred from changes in the price, of other stocks in the ma rkct, such as the D ow Jones Index or the FTSE lOO.J1 '1'111; esti ma ted daily abnormal returns during the event window can be aggregated to oblain the cum ulative abnormal return, which measures th e to tal retu rn to shareholders lhal can be attributed to the evenl. Cumulative abno rmal returns provide an estimate 01' Ih o change in prod ucer surplus due to some new policy.
The va luation methods discussed earlier in this chapter have several poten tial lim ita -
tions, many of which were discussed earli er. This section focuses on the mnitted v(/ri ,
able problem and self-selection bi as.
The Omitted Variable Problem All of the me thods discussed thus fa r in this chapter implicitly assu lllc that all olher explanatory va riables are he ld constant, but this* is unli kely in practice. Considcr, for example, using the intermediate good method to va lue irrigation. Ideall y, ana lysts wou ld compare the incomes of fa rmers if the irrigation project were buil t with the incomes of the same farmers if the project were not built. In practice, if the project is built, analys[,
cannot dirccliy obse rve what th e fa rm ers' incomes would have been if it had not been buil t. One way to infer what their incomes wou ld have been without the project is to liS!.'
the incomes of the same fanners bcfore the project was built (a before and after design) or the incomes of similar fanners who did not benefit from an irrigation project (a n CHl '
experimental compa rison group design). lllC before and afler design is rcasonable only if all other variables th at affect farme rs' incomes remain consta nt , such 8S weath er conditions, crop choices, taxes, and subsidies. If these variables change then the i ncomc~i observed before the project arc not good estimates of what incomes would have bee n if the proj ect had not been imple mented. Si mila rl y, the comparison gro up design is approp riate only if the comparison grou p is similar in all important respects to th e fa rmers with irrigation, except fo r the presence of irriga tion.
As mentione d ill Exhi bit 13-2, salary diffe re nces be tween those with a co llcge degree a nd those with a high school degree may d epe nd 0 11 ability, intelligence, soeio..
eeollomic background and other factors in addition to college attendance. Similarly, in labor market studies of thc value of life, differe nces in wages among jobs may depend on variations in status a mong jobs and the bargain ing power of differe nt unions in
CHAPTER 13 Valuing Impacts fram Observed Behavior: Indirect Market Methods 349 g stock prk(1)j l~ addition to fatality risk, In simple asset price studies, the price of a house typically ently capilali "! depends on factors such as its distance from the centra l business district and sil.e, as e of the vahh: of well as whether it has a view, Analysts should take account of all important explana-ly accessi!>k iii tory variables, If a relevant explanatory variable is omitted from the model and if it is correlated with the included variable(s) of interest, then the estimated coefficients will ecurity, whidl lij be biased, as discussed in Chapter 12.
n event nlld liuj rs estimate dail Y Self-Selection Bias which the Cl't\iil Another potential problem is self-selection bias. Risk -seeking people tend to self-n to the seculit y select themselves for dangerous jobs. Because they like to take risks they may be will-ges in the priuj~ ing to accept low salaries in quite risky jobs. Consequently, we may observe only a very FTSE 100. 11 '1M small wage premium for dangerous jobs. Because risk seekers are not representative of rcgated to obI",,] society as a whole, the observed wage differential may underestimate the amount that hareholders Ihilt average members of society would be willing to pay to reduce risks and, hence, may n estimate of tlui lead to underestimation of the value of a statistical life.
The self-selection problem arises whenever different people attach diffe rent val-ues to particular attributes, As another example, suppose we want to use differences in house prices to estimate a shadow price for noise. People who are not adverse to noise, possibly because of hearing disabilities, naturally tend to move into noisy neighbor-hoods. As a result, the price differential between quiet houses and noisy houses may be quite small, which would lead to an underestimation of the shadow price of noise for potentiallimila the "average)) person.
th e omitted \lad HEDONIC PRICING METHOD me that all olhcl ice. Consider, for The hedonic pricing me thod , sometimes called the h edon ic regressioN melhod, offers a lly,analys(s would way to overcome the omitted variables problem and self-selection bias that arise in the
'ith the incomes nl relatively simple valuation methods discussed earlier. Most rece nt wage-risk ct is built, analy~!~; studies for valuing a statistical life (alSO called labor marke t studies) apply the hedonic if it had not bel_'ll regression method.
he project is to us\~
Hedonic Regression e and after design )
on project (a nOll Suppose, for example, that scenic views can be scaled from 1. to 10 and that we want to is reasonable on ly estimate the benefits of improving the (quality) "level" of scenic view in an area by one t, such as weaUll..'r unit. We could estimate the relationship between individual house prices and the level e then the incom ~s of their scenic views. But we know that the market value of houses depends on other would have bc"" factors, such as the si7.e of the lot, which is probably correlated with the quality of on group design is scenic view. We also suspect that people who live in houses with good scenic views tend nt respects to tIll; to value scenic views more than other people, Consequently, we would have an omitted variables problem and self-selection bias.
ose with a coilege. 111e hedonic pricing method attempts to overcome both of these types of prob-intelligence, socio e lems 12 It consists of two steps. The first estimates the effect of a marginally better Idance. Similarly, in scenic view on the value (price) of houses, a slope parameter in a regression model, g jobs may depend while controlling for other variables that affect house prices. The second step estimates different unions in the willingness-la-pay for scenic views, after controlling for "tastes," which arc proxied
350 PART III Valuation o(lmpacts by income and other socioeconomic factors. From this information, we can calculate the change in consumer surplus resulting from projects that improve or worsen the views from some houses.
The hedonic pricing method can be used to value an attribute, or a change in an attribute, whenever its value is capitalized into the price of an asset, such as houses or salaries. The first step estimates the relationship between the price of an asset and all of the attributes (characteristics) that affect its valueD The price of a house, P, for exam-ple, depends on such attributes as the quality of its scenic view, VIEW, its distance from the central business district, CBD, its lot size, SIZE, and various characteristics of its neighborhood, NBHD, such as school quality. A model of the factors affecting house prices can be written as follows:
P = f(CBD, SIZE, VIEW, NBHD) (13.2)
This equation is called a hedonic price function or implicit price function. t4 The change in the price of a house that results from a unit change in a particular attribute (i.e., the slope) is called the hedonic price, implicit price, or rent differential of the at tribute. In a well-functioning market, the hedon ic price can naturally be interpreted as the addi-tional cost of purchasing a ho use that is marginally better in terms of a particular attribute. For example, the hedonic price of scenic views, which we denote as r" mea-sures the additional cost of buying a house with a slightly better (higher-level) scenic view. IS Sometimes hedonic prices are referred to as marginal hedonic prices or marginal implicit prices. Although these terms are technically more correct, we will not use them in order to make the explanation as easy to follow as possible.
Usually analysts assume the hedonic price function has a multiplicative functional form, which implies that house prices increase as the level of scenic view increases but at a decreasing rate. Assumin g the hedonic pricing model represented in equation (13.2) has a multiplicative functional form, we can write:
(13.3)
The parameters,{:J P{:J2' {:J3' and {:J4' arc elasticities: TIlCY measure the proportional change in house prices that results from a proportional change in the associated altribute. 16 We expect {:Jl < 0 because house prices decline with distance to the CBD, but {:J2' {:J3' and
{:J4> 0 because house prices increase as SIZE, VIEW, and NBHD increase.
The hedonic price of a particular attribute is the slope of equation (13.2) with respect to that attribute. In general, the hedonic price of an att ribute may be a function of all of the variables in the hedonic price equation 17 For the multiplicative model in equation (13.3) , the hedonic price of scenic views, r v ' is: 18 p
r, = f33 VIEW> 0 (13.4)
In this model, the hedonic price of scenic views depends on the value of the parameter i3 3, the price of the house, and the view from the house. Thus, it varies from one obser-vation (house) to another. Note that plotting this hedonic price against the level of
CHAPTER 13 Valuing Impacts from Observed Behavior: Indirect Morket Methods 35 I can caiculnlc scenic view provides a downward-sloping curve, which implies that the implicit price of
)f worsen l1li' scenic views declines as the level of the view increases.
The prcceding points are illustrated in Figure 13-3. The top panel shows an illus-1 change in HII trative hedonic price function with housc priccs increasing as the level of scenic view h as houst'*; !i( increases, but at a decreasing rate. The slopc of this curve, which equals thc hedonic isset and ;111 ,,( price of scenic vicws, decreases as the level of the scenic view increases. The bottom
~, P, for exam panel shows marc prccisely the relationship between the hedonic price of scenic views distance 1'1<11\1 (the slope of the curvc in the top panel) and thc level of scenic view.
- teristics Ill' jh In a well-functioning market, utility-maximizing households will purchase ffecting 1\(11'" houses so that their willingness-to-pay for a marginal increase in a particular House
!.14 The cll,H1W: price (PI I
~
ribute (i.e., 1lIi'
~ attribute. III I!
,d as the addl of a parliculH! Hedonic price tote as r v' InUi! function er-Ievel) sccllIl
~onic prh:I's ti!
'cet, we wi!1lld!
Hive functiOlwi w increasc~ hlU cd in equ<llhHi v, V3 Level of scenic view (V)
Hedonic price of tortional Cil;lII!:\i" scenic 1attribute,lll Locus of household views (rv)
W1 equilibrium but {32' P. I. "lid /illingn:ses-to-pay 1se.
tion (13.2) wil h (V1 ----------
ay be a fUllcli( lll I I
icative model In I I
I (v2 -----.--------}---------
I I I I
%-----------~---------~-- rv I I I I I I I I
)f the paranwltH I I from one nbs!;! V3 Level of scenic linst the level ill view (V)
352 PART III Va/u"'ioll o(lmpact' all ribule equals its hedonic price. Consequently, in equilibrium, the hedonic pricc 01 an att ribute can be interpreted as the willingness of housebolds to pay for a mar -
ginal increase in that attribute. The graph of the hedonic price of scenic views, f, .,
against the level of scenic view is shown in the lower panel of Figure 13-3. Assuming Dean all households have identical incomes and tastes, this curve can be interpreted as a Biggs 1 household inverse demand curve for scenic views. mate t Yet, households differ in their incomes and taste. Some are willing to pay a consid* Canad.
crable amou nt of money for a scenic view; others are not. This brings us to the second price e step of the hedonic pricing method. To account for different incomes and tastes, ana -
lysts should estimate thc following willingness-to-pay function (inverse demand curve) for scenic views;1 9 lni r" = W(V1EW, 1'; Z) (13.5) where I erty val where fv is estimated from equation (13.4), Y is household income, and Z is a vector 01" ent noi household characteristics that reflects tastes (e.g., socioeconomic background, race, "some" age, and family size). Three willingness-to-pay functions, denoted W 1, W2 , and W3, for 25-40 three different types of households are drawn in the lower panel of Figure 13_3. 20 occurs ~
Equilibria occur where these functions intersect the r" function_ Thus, when incomes actcristi and socioeconomic characteristics differ, the r, function is the locus of household equi- Thei librium willingnesses-to-pay for scenic views, Intenu Using the methods described in Chapter 4, it is straightforward to use equation SOllrc;e: P (13.5) to calculate the change in consumer surplus to a household due (0 a change in and the I:
the level of scenic view. These changes in individual househo ld consumer surplus can be aggregated across all households to obtain the total change in consumer surplus.
Using Hedonic Models to Determine the VSL 111e simple forms of consumer purchase and labor market studies (0 value life that we described previously may result in biased estimates due ( 0 omitted variables or self-selection problems. For example, labor market studies to value life that examine fatality risk (the risk of death) often omit potentially relevant variables sueh as injury risk (the risk of nonfatal injury). This problem may be reduced by using the hedonic pricing method. For example, a researcher might estimate the following no nlinear regression model to find the hedonic price of fatality risk: 21 In(wage rate) = f3 0 + f31In(fat ality risk) + f32In(injury risk) + f33In(job tenure)
+ (3)n(cducation) + f3sln(age) + (13,6) 111e inclusion of injury risk,job tenure, education, and age in the regression model controls for variables that affect wages and would bias the estimated coefficient of f3 1 if they were excluded. Using the procedure demonstrated in the preceding section, the analyst can convert the estimate of f3 1 to a hedonic price of fatality risk and can then estimate individuals' willingness- to-pay to avoid fatal risks. Most of the empirical esti-mates of the value of life that are reported in Chapter 15 are o btain ed from labor market and consumer product studies that employ mocIels similar to the one presented in equation (13.6).