- Email: [email protected]
A compensation mechanism for siting noxious facilities: Theory and experimental design
JOURNAL
OF ENVIRONMENTAL
ECONOMICS
AND
MANAGEMENT
14, 371-383 (1987)
A Compensation Mechanism for Siting Noxious Facilities: Theory and Experimental Design ’ HOWARDKUNREUTHER Deportment of Decision Sciences and Center for Risk and Decision Processes, University of Pennsylvunia, Philadelphia, Pennsylvania 19104
PAULKLEINDORFERANDPETER J. KNEZ Depurtment of Decision Sciences, University of Pennsylvania, Philadelphia, Pennsylvania I91 04
AND RUDYYAKSICK Depurtment of Regional Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Received June 17,1985; revised March 11,1986 In locating noxious facilities, such as a trash disposal plant or a hazardous waste disposal incinerator, the host community frequently incurs all the costs while other communities in the region receive the benefits. W e propose a mechanism for sharing the benefits with the potential loser. Each community submits a sealed bid indicating the minimum amount it would require to host the facility. The site providing the lowest bid obtains the facility and receives its bid as compensation. This compensation is financed by the other communities. If there are N candidate communities, then each of the other communities would pay l/(N - 1) of their acceptance bid. A series of controlled laboratory experiments show that the outcomes of this low-bid auction come close to predictions from a theoretical model based on maxi-mm rules. Equity and efficiency considerations also are discussed in the context of the noxious facility problem. *’ 19117 Academic Press. Inc.
The siting of noxious industrial facilities is an especially conflict ridden issue. G ladwin [lo] has indicated the m a g n itude of this conflict, by identifying 366 disputes regarding industrial facility siting or expansion in the United States during the period 1970 to m id 1978. Almost one half of these involved chemical process facilities. There also has been considerable public opposition to establishing new off-site disposal facilities that receive wastes from a variety of different sources (Goetze [ll]). This opposition has been so effective that no new off-site hazardous materials disposal facilities (HMDF) were sited in the U.S. during the period 1980-1983 (Bacow and M ilkey [2]). Society faces a dilemma in resolving this conflict. O n the one hand, people demand the goods and serviceswhose production yields waste as by-products. There appears to be widespread agreement that there is a need for properly designed and ‘This research was partially supported by NSF Grant SES8312123. W e would like to thank Don Coursey, Pat Harker, Richard Kihlstrom, Jim Laing, Wesley Magat, Andrew Postlewaite, Jim Richardson, and Vernon Smith for useful discussions on the low-bid auction mechanism. Mike Selman designed the computer program for the controlled laboratory experiments and two referees provided constructive comments on the paper. An earlier version of this paper was presented at seminars at the University of Pennsylvania and University of Wyoming, where helpful comments were received. Correspondence should be mailed to: Dr. Howard Kunreuther, Department of Decision Sciences, University of Pennsylvania, Philadelphia, PA 19104. 371 0095-0696/87 $3.00 Copyri&t 8 1987 by Academic Press, Inc. All rights of reproduction in any form reserved.
372
KUNREUTHER
ET AL
managed disposal facilities since, in the aggregate, their presence would yield benefits in excess of their risks and costs. On the other hand, opposition is vehement when mention is made of locating a trash disposal or hazardous waste facility in a community’s “backyard.” For example, an opinion poll found that over 95 percent of respondents would actively protest against siting a hazardous materials facility near their home (U.S. Council on Environmental Quality [25]). Conflicts with respect to siting these noxious facilities arise because the external costs are spatially concentrated around the host community while the net benefits of the noxious facility tend to accrue to those communities that are sufficiently distant from the site to be immune from any external costs (Austin et al. [l]). Consequently, the host community may believe that it is bearing an inequitable share of the external costs. Our aim in this paper is to explore the potential of ex ante compensation for reducing local opposition to the siting of noxious facilities. By ex ante compensation we mean either monetary and/or in-kind payments provided to the host community prior to the construction and operation of a facility. These payments may be helpful in resolving the conflicts between the potential losers (the host community) and those who benefit from the siting of the facility, i.e., other residents of the region, industry and the developer (O’Hare et al. [21]). We propose a sealed-bid auction procedure that elicits from a community a compensation requirement for accepting a facility in its jurisdiction.* The problem context involves N communities that are interested as a group in choosing a site for a noxious facility to serve the needs of the whole group, and possibly other parties. Each one of the N communities is a possible candidate for the facility. The auction procedure is directed at determining where to site the facility, if at all, and what compensation to pay the host community. Our analysis treats communities as individual actors, neglecting intra-community dynamics which involve differences in preference and risk perception among community residents.3 It is important to note that the procedures discussed here can work only if coupled with appropriate public participation mechanisms (e.g., referenda) that allow citizens and the municipal government to evaluate alternatives and to determine required compensation payments. The next section explores approaches for inducing individuals to reveal their true preferences. A simple example demonstrates the inability of the Clarke mechanism to provide compensation for any siting problem in which the facility has negative impacts on the host community and positive impacts on others. Section II develops a sealed- (low) bid auction siting model for facilitating the siting process when individual preferences may be subject to misrepresentation. The model assumes that the facility’s impacts are known with certainty and that the community that hosts the facility is compensated by the other communities who benefit from it. Individuals are assumed to use maxi-min strategies to determine their bids. Prototype data from controlled laboratory experiments presented in Section III support this assumption. The concluding sections propose variations of the low-bid auction and suggest additional theoretical and empirical studies on siting noxious facilities. ‘The that arc [16] and pollution ‘For
related problem of developing an appropriate disposal tax or charging scheme for industries responsible for producing hazardous materials will not be treated here. See Kneese and Bower Nichols [20] for discussions of a tax and charging scheme for environmental regulation and abatement. more detail on this problem, see U.S. EPA, 1979, O’Hare et al. [21], and Fischhoff [9].
SITING NOXIOUS FACILITIES
313
I. COMPENSATION AND THE CLARKE MECHANISM The provision of compensation to the host community faces severe implementation problems. One of the most difficult is that communities may have an incentive not only to exaggerate their willingness to accept (WTA) requirements, but also to underreport their willingness to pay (WTP) other communities to take the facility. Consequently, one of the emerging noxious facility siting challenges is to design decision procedures that provide communities with economic incentives to reveal their true preferences.4 O’Hare [22] was one of the first to recognize the possibility of preference m isrepresentation in the form of exaggerated claims for compensation. He suggested that a Vickrey-type, second-price auction procedure m ight eliminate the incentive for preference m isrepresentation but did not develop a formal model.5 The literature on demand-revealing mechanisms initially appears to be appropriate for analyzing the noxious facility siting problem. Specifically, under certain assumptions, the mechanism developed by Clarke [5, 61 induces individuals to declare their true preferences for a pure public good by charging a tax the size of which depends partly on how their responses affect the final outcome. Even though the Clarke mechanism is incentive compatible, it has several weaknessesas pointed out by Groves and Ledyard [12]. For example, in order to induce truthful preference revelation, the mechanism generates surplus tax revenues that must be discarded rather than returned to the individuals. Thus, the outcome of this procedure is not fully Pareto optimal. On closer inspection, one finds that the Clarke preference revelation mechanism requires that the project being evaluated have a positive value to each of the individuals so that there is a net surplus after the tax is levied. However, each community has a negative value associated with having the noxious facility in its jurisdiction. This means the Clarke mechanism may create a negative level of welfare for all the communities, since the community harmed by the collective decision cannot be compensated without impairing the truth-telling incentive of the mechanism (Tideman and Tullock [24]). Furthermore, since surplus taxes cannot be returned to the communities, no community will want to host the facility, even though it may create a net social benefit to the entire region. We now present a simple example to illustrate the difficulties in using the Clarke mechanism for the noxious facility problem. The value matrix (Table I) indicates that each community will prefer to have the facility located elsewhere,rather than in its jurisdiction, unless it receives compensation. Thus, Community 1 has a WTA = - 8 if the facility is located in its backyard (site 1); and a WTP = 4 if the site is at 2 or 3. The Clarke procedure adds the WTP and WTA figures for each community and selects the one with the largest net benefits (Community 2) as the “wir~ner.“~ Each community is then taxed only if its stated WTA and WTP are decisive in changing 4There is a strand of literature in noxious facility location theory that addresses the importance of compensation in dealing with community opposition [Austin, er al. [l], and Wolpert, [28]]. These papers do not address the possibility of strategic misrepresentation of compensation requirements by communities. ‘We do not develop such a model here. The case of public information on values described in Section IV has properties that resembles a second price auction. 6See Mueller [19, Chap. 41 or Tideman and Tullock [24] for a detailed explanation of the rationale underlying the Clarke tax.
374
KUNREUTHER
ET AL.
TABLE I Value Matrix for the Three Community
1 2 3
Potential sites -___ ~__ 2
1
Community
-8
Net benefits
Location Problem
4 4 I 3
4 4
-5 I 6
Clarke tax
3
-8
9 0 9
0
the outcome. For example, if one eliminated Community l’s WTP and WTA (i.e., if Row 1 were eliminated from Table I), then Community 1 would be chosen as the site and the net benefit of this facility would be $11 (i.e., $4 + $7; the WTP for communities 2 plus 3, respectively). Community 1 would be taxed an amount of $11 - $2 = $9, representing the difference between the net benefits from Community 2 (the best site) and Community 1 when its preferences were not considered. Similarly, if community 3 WTP and WTA were calculated then it would pay a tax of $8 - (-$1) = $9. C ommunity 2 would not have to pay a tax since it would still be chosen when its own preferences were eliminated from consideration. A little reflection on these outcomes reveals that all communities will be decidedly unhappy with their post-Clarke tax situation. By bidding their true preferences, they are taxed an amount which leaves them worse off than under the status quo. Hence, they will not want to site a facility using this procedure even though aggregate net benefits may be positive. One way to use the Clarke procedure for this problem is to provide each candidate site with a sufficiently large subsidy (payable upon completion of the low-bid process) so that the values in the matrix would all be positive. However, one would have to know in advance the approximate WTA amounts for each community in order to specify the level of the subsidy, and the determination of these approximate amounts might itself be subject to strategic misrepresentation. The siting of a noxious facility thus differs from the pure public good problem in a fundamental way. In siting a public good, such as a park, the entire region as well as the host community receives net benefits. The only question is where the public good should be located to achieve efficiency. 7 In the case of a noxious facility, however, the host community will tend to suffer a loss while the remaining communities within the region will tend to benefit. Hence, there is a need to develop a bidding procedure which will both yield efficient siting solutions and deal with the distribution of costs and benefits among communities. II. A LOW-BID
AUCTION
MECHANISM
This section presents a low-bid auction model for selecting and compensating the community that hosts a hazardous facility when N potential sites are competing with one another. The key idea is to create an incentive for communities not to misrepresent their preferences by making their payments for not having the facility ‘See Coursey and Smith [7] for a detailed analysis of market-based experimental results for the public goods problem.
SITING
NOXIOUS
375
FACILITIES
in their backyard be proportionate to their demand for compensation if they are selected as the site. The low-bid auction procedure requires each community to specify a bid indicating its willingness to accept (WTA) the facility in its backyard. The community making the lowest bid is selected as the host site. Each of the other N - 1 communities pays a tax which is l/(N - 1) of its WTA. The notation and assumptions of the model are as follows: i
vi ‘i
V, + Kj
tij
x,
tj
The communities which are potential sites i = 1,. . . , N. Community i’s value of having the facility in its own backyard (K < 0). Thus, community i’s true WTA is - l$ Possible transfer payment to community i for hosting the facility, Total value to community i if the facility is sited there.8 The value to community i of having the facility sited in community j. We assume yj > 0. Thus, community i’s WTP for siting the facility in community J is Vij. Community i’s transfer payment (a tax if tij < 0) if the site is located in community j. Community i’s stated WTA for agreeing to host the facility in its jurisdiction.
We assume that each community is perfectly informed about the facility’s consequences to them at each alternative location.’ The basic structure of the low-bid auction mechanism is as follows. A regional siting agency has the task of determining which one of the communities will be chosen. It elicits an acceptance bid Xi > 0 from each community i = 1,2,. . . , N. This bid represents the minimum compensation required by community i to site the facility within its jurisdiction. If the facility is eventually sited in community j, then community i must pay an amount, tij = -Xi/( N - l), to compensate the “winning” community j which receives compensation tj = Xi. We assume that the agency selects the lowest bid as the winning site. If Xi = Min/{ X,lj = 1,. . . , N} is the lowest bid, then total transfer payments resulting from this mechanism will be
ti + 1 tji = xi - c j#l
j+r
(N"'
1)
'
"
0)
where the inequality follows since, by assumption, Xj > Xi for all j # i. Thus, the low-bid auction does not give rise to deficits. The default option is critical in the decision by a community as to whether it will participate in this process. We assume that each community would like to be at least ‘We are assuming that each community’s preferences are quasi-linear in “site value” and income. This facilitates the analysis of compensation. In particular, if each community resident has preferences of a similar quasi-linear structure, one can view community’s WTA and WTP values as being simple aggregates of the WTA and WTP values of their residents. When preferences are not quasi-linear in money the theoretical foundations of compensation mechanisms remain a largely open research question (see Groves [ll]). ‘In practice, compensation procedures of the sort studied here would be coupled with community planning grants and other information generation procedures which enable communities to estimate the ” value” they place on alternative sites (see Kleindorfer [15] for a discussion of these points).
376
KUNREUTHER
ET AL.
as well off no matter where the site is located than if no site were chosen. Assuming a default option value of 0, this means that communities would only participate in the proposed low-bid auction if they can each determine a bid Xi such that
y+ti=
v,+x,>o
(2)
and I/ij + tij = I/ij - Xi/(N
- 1) > 0
for all j # i.
(3)
It can be verified that the participation constraints defined by (2) and (3) have at least one feasible solution Xi i = 1,. . . , iV if and only if the “sincere WTA bid” Xi = - 5 satisfies (2), (3). This is the case precisely when Min { vjlj i
# i} 2 - T/;./(N - l),
i=l
,-*-, N.
(4
If (4) does not hold, some community may not wish to participate because it could end up worse off than under the default option. We assume that (4) holds so that the benefits of the proposed facility siting process are guaranteed to be nomregative for each of the communities involved. Our proposed bidding procedure links the localized social costs of a facility to its social benefits by requiring that tij = -Xi/( N - 1). Thus, if a community submits a higher acceptance bid Xi it will have to pay a larger tax to the regional agency if the facility is sited in another community j # i. For this reason there is a limited incentive to misrepresent one’s preferences by bidding Xi # K although strategizing may still be present.‘O We now consider bidding strategies for communities participating in the low-bid auction procedure. When each community knows its own preferences, but has no information on others, then a maxi-mm bidding strategy is a prudent one to follow and is consistent with the elimination of dominated strategies.” As we argue below, this means that the community will choose Xi so that
It is easy to verify that the solution to (5) is an equilibrium maxi-min strategy. The first expression on the left hand side of (5) represents the payoff to community i if site i is selected, while Fj + tij is the payoff if some other site j # i is selected. As Xi is increased, Xi + y increases and yj + tij = vj - Xi/( N - 1) decreases. The maxi-mm payoff occurs when equality is achieved between the site i payoff, Xi + vi, and the worst possible alternative site j # i payoff, Min { qjlj i
# i} - Xi/( N - 1).
“‘For example, suppose community i knew that all other communities would bid $100,000 or more as an acceptance bid. If community i was willing to accept the facility for only $50,000, they would still want to bid up to $99,999. “See Moulin [18] for a formal proof of this statement.
377
SITING NOXIOUS FACILITIES
We can solve (5) for Xi to obtain N-l Xi = N
(6)
It is interesting to note that under maxi-min bidding the low-bid auction yields Pareto efficient outcomes when each community is indifferent as to site location as long as it is not in their backyard. Specifically, assume that qj=
&=
vei
for all j, k f i and all i,
(7)
where Vpi represents the indifference value. Assuming (7), the net benefits of locating the facility at i are Bi = v + c ci = J( + c Vwj.
63)
j#i
j#i
Under maxi-mm bidding, we have from (6) that N-l x; = ,-(v-i
(9
- Pg.
Since N - l/N is a constant, this means that the site i selected in the low-bid auction will satisfy V-;-
V;.= M in{(Vjj
V_,)lj=
l,...,
N}
00)
But we can rewrite (8) as B,=y-V-;+
(11)
where the last term in (11) is a constant. Thus, the site i maximizing Bi will be the one with maximum (v - V-,), or equivalently the one with m inimum (Vei - y). From (10) we see that this is precisely the site which the low-bid auction will select if communities follow a maxi-mm strategy and are indifferent as to site locations outside of their own boundaries. In sum, the one-shot, sealed- (low) bid auction is an individually rational and coalition-free mechanism with some efficiency properties. It is not incentive compatible, but communities are dissuaded from greatly exaggerating their compensation requirements. Also, it generates a tax surplus (net of compensation). The procedure is coalition free since each community’s transfer function is independent of any other community’s acceptance bid. Thus, two or more communities cannot strategically link their bids in order to extract mutual gain from the procedure.i2 This latter characteristic contrasts with demand revealing mechanisms, none of which have coalition incentive compatibility features (Groves and Ledyard [12]). “Communities may have an incentive to collude if they are permitted to make side payments. The transactions costs of negotiating such side payments are likely to be high, however.
378
KUNREUTHER
ET AL
TABLE II Value Matrix and Maxi-min Bids for 5 Participant Low-Bid Auction Potential sites 1 2 3 4 5 Maxi-min hid
1
2
Participants 3
4
5
-loo0 575 575 575 575
175 -1500 175 175 775
1275 1275 -2400 1275 1275
375 375 375 -600 375
475 475 475 475 -800
1260
1820
2940
780
1020
Net benefits of each site 1900 1200 -200 2500 2200
To illustrate the low-bid auction, consider the sample value matrix depicted in Table II. If a participant was following a maxi-min strategy, then they would want to offer a WTA value indicated by the relevant figure in the bottom row. Community 1, for example, would bid WTA = 1260 thus guaranteeing that it would make a net profit of 260 whether or not it was the lowest bid. In the case of community 1, if yj -z 250 then it would prefer not to participate in the auction. If each community followed a maxi-min strategy then community 4 would have the lowest bid and the facility would be located there. Since (7) is satisfied for this value matrix, site 4 is guaranteed to be an efficient location, as shown by the last column in Table II. III. EXPERIMENTAL
RESULTS
A set of controlled laboratory experiments have been conducted to determine whether participants follow a maxi-min strategy for siting problems analogous to the one described above. All information about the decision process including instructions, values, and rules are provided via microcomputer network to the participants. Before making a final decision, each participant is able to explore his options by entering trial bids into the computer. Payoffs are in a fictitious currency (francs) with each franc worth one-half cent. All participants receive private information on their own Vi and Vij for value matrices such as the one depicted in Table II. Each experiment consisted of a series of 10 one-period replications of a low-bid auction mechanism with five participants using a different matrix each period.13 Figure 1 shows the average absolute percentage (AAP) deviations from maxi&n bids for the combined five subjects in each period for two experiments. (Experiment 2 involved inexperienced subjects and 3 involved experienced ones).14 The deviations from maxi-min bids are relatively small for later periods for both experienced and inexperienced subjects. The average absolute percentage (AAP) deviation is 6 percent or less in periods 7-10 in both experiments. In contrast, during periods l-6 the AAP deviation is as high as 25 percent and is below 6 percent only 3 out of 12 13The matrices utilized for the experiments and individual subject behavior are available from Howard Kunreuther, Center for Risk and Decision Processes, Wharton School, University of Pennsylvania, Philadelphia, PA 19104. I4 Inexperienced means subjects had no previous experience with the auction mechanism. Experienced means participants had been in at least one compensation experiment.
379
SITING NOXIOUS FACILITIES
FIG. 1. Average absolute percent-deviations from maxi-min bids (experiments 2 and 3).
times. These results suggest two important features of learning in auction-markets of this type. First, evaluating the merits of an auction-market mechanism of this type on the basis of a single one period trial can be very m isleading. Second, most learning in an experiment takes place within the first 6 periods so that the last 4 periods of an inexperienced group are not significantly different from all 10 periods of an experienced group. This learning phenomena is further demonstrated at the individual level. In periods 7-10 in all three experiments approximately three quarters of the 60 different bids were within five percent of the maxi-min values and none deviated by more than 18 percent. In 80 percent of the bids from all three experiments, the participant who specified the lowest bid was the same as the one predicted by assuming a maxi-min decision process. It is encouraging that the outcomes from the experiments are consistent with maxi-min theoretical predictions after learning has taken place.
IV. EXTENSIONS
OF THE LOW-BID
AUCTION
MECHANISM
The low-bid auction mechanism can be examined under alternative institutional and information conditions to determine whether participants follow strategies predicted by theoretical analyses. Several variations are discussed below. Field Experiments A field experiment testing the low-bid auction mechanism procedure recently was undertaken in Laramie, Wyoming (Brookshire et al. [3]). Individuals were asked to specify a WTP value to have flowers delivered to their home and a WTA value to perform this service for N other households. The flower delivery service is thus conceptually analogous to hosting a noxious facility even though it does not produce the same negative reactions by potential “winners.” The low-bid auction procedure elicited a comparative set of WTA and WTP values. An econometric analysis revealed that individuals with higher opportunity costs of time reflected by higher wage rates require more compensation. As the value of N increases so does the required WTA, as one would expect. Field experiments such as this one can
380
KUNREUTHER
ET AL.
identify important economic and demographic variables that can ease the transition from a controlled laboratory setting to a real world problem. Public Information on Values When the Kj and V, of all participants are made public, then a stable equilibrium will be one where all bids converge to the second lowest WTA, denoted as X**. Specifically, if each participant calculates its maxi-min bid under no information then the one with the lowest WTA will have an incentive to bid 6 units below X**, knowing that it can increase its profits while still being the lowest bid. Other participants will want to lower their bids to X**, knowing that they will not be the lowest WTA while at the same time reducing the tax they have to pay. If all participants follow this strategy the resulting bids will be a stable Nash equilibrium that has the attractive characteristic of being budget balancing. The participant who has made the lowest bid will obtain a profit identical to the amount he or she would have obtained had a Vickrey second price auction been administered. To illustrate an optimal bidding strategy for the perfect information case, consider the value matrix depicted in Table II. In contrast to the “no information” case, participant 4 would now want to raise her bid to 1019, participant 5 (the second lowest bidder) will not change her bid while 1, 2, and 3 will all want to reduce their bids to 1020. Controlled laboratory studies can determine whether participants, in fact, follow these strategies. Lottery cum Auction This institutional variation on the low-bid auction consists of two stages. In Stage 1 each individual submits a bid Xi* and one participant is chosen at random using a lottery procedure. This bid is publicly posted. Stage 2 consists of the low-bid auction procedure among the N - 1 individuals who were not chosen by the lottery. If one or more of these participants submits a bid below the posted price then the lowest bid is the “winner”; otherwise, the individual chosen by lottery is the “winner.” If participant i is not the “winner” then he is taxed ti = -XJ(N - l), where Xi is the bid entered in Stage 1. One reason for proposing the lottery cum auction mechanism is to address the equity issues associated with siting noxious facilities (Kasperson [14]). If the poorest communities are the ones submitting the lowest bids then under the strict low-bid auction procedure, they will always be the “winner.” In a lottery cum auction each potential site has an equal chance of being selected initially. The poorest communities might bid very low in Stage 1 but if they are not chosen by the lottery they can raise their bid above the posted price and still only have to pay a relatively small tax. From a theoretical standpoint one would not expect any difference in Stage 1 and Stage 2 bids if the initial bids truly reflected individuals’ preferences. If, on the other hand, there was some gaming by participants, hoping that they would not be selected by the lottery, then Stage 1 bids will be lower than maxi&n values and in Stage 2 bids will be higher than the posted price. In this case the lottery cum auction may yield a budget deficit on any one trial. In expected value terms the mechanism
SITING NOXIOUS FACILITIES
381
is budget balancing, since a community making a low bid initially and planning to raise it in the second stage may be selected by the lottery and actually host the facility. Stochastic Externalities The low-bid auction can be extended to cover the case where there are stochastic losses. In this environment, two types of compensation arrangements can be used for siting a facility: ex ante and ex post compensation. Ex ante compensation provides individual i with an amount Xi if he were the lowest bidder. In this case, if a loss Li occurs (with probability p), then the final outcome is Xi - Li. Fix post compensation arrangements are those that become operative only after the facility has produced an identifiable adverse consequence. One way to establish such an arrangement is to create, for example, a regional or community-wide self-insurance program. In this program each participant (i.e., those deemed liable for accident damages) contributes a premium each year to a central compensation fund. After an accident has occurred, victims receive payments from the fund. In the context of the noxious facility problem where there is a potential loss Li with probability p, the low bidder receives Xi if there is a loss and 0 otherwise. All other participants must pay Xi/(N - 1) to the regional authority-in effect, a premium based on the expected loss and the number of individuals benefitting from having the facility outside of their backyard.” V. CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH We have analyzed a collective choice problem where one individual (community) can be harmed by hosting a site that benefits a number of other individuals. We adopted a strategic approach to the problem since individuals have an incentive to m isrepresent their preferences. We modeled this strategic decision by inducing an incomplete information game via a direct revelation mechanism: the low-bid auction. A unique feature is that we constrained the design to have a mechanism that requires a m inimum of computational tasks by the participants and an easily understood incentive scheme. This concern for the bounded rationality of participants is a major departure from the usual approach to mechanism design and evaluation. Our research goal has been to determine the properties of the low-bid auction mechanism. To do so, we analyzed the noncooperative game induced by the mechanism under the behavioral assumption that the bidders choose maxi-min bidding strategies. Although the mechanism does not induce individuals to report their true preferences, an efficient location is chosen if each community is indifferent to where the facility is sited so long as it is not in their backyard. Tax revenues also exceed the host community’s compensation requirements. In general, the low-bid auction procedure offers the possibility of clarifying the relative costs and “The implicit assumption in this payment rule is that the N - 1 nonhost communities are liable for accident damages. To the extent that the plant owner and/or facility customers are held liable, then they have to cover the losses. For a detailed analysis of the efficiency of alternative liability-insurance arrangements, see Shave11[23] and Yaksick [29].
382
KUNREUTHER
ET AL
benefits of alternative locations. By introducing competitive bidding, each community can determine whether it is worth being a candidate for hosting the facility by examining its default option in relation to the potential outcomes associated with specific WTA bids. The mechanism also complements the large body of controlled experimental and empirical work designed to measure willingness to pay and willingness to accept values for public and private goods as well as externalities (See Brookshire et al. [4] and Cummings, et al. [8], for a summary). In evaluating this approach relative to other procedures, one should examine a set of process and outcome measures, which would include data collection costs and computational ease, allocative efficiency, equity and distributional considerations, and budget balancing features. The low-bid auction procedure must be viewed as only one of a set of policy tools for dealing with the noxious facility location process. For example, health and safety standards may have to be imposed by regional or state governmental agencies so that residents at all potential sites are convinced that they are sufficiently protected against adverse environmental effects, such as air an water pollution. In addition, public participation by all residents needs to be a part of the siting process. This requires some voting mechanism by the citizens or the town governnment for finalizing a bid as well as determining the acceptability of proposed design and control procedures (Kunreuther and Kleindorfer [17]). Finally, the low-bid auction mechanism is compatible with other arrangements between interested parties in the siting process. For example, a developer may be legally required to reveal construction costs at each potential location. The community’s bid can then be redefined as the sum of its WTA bid plus the construction costs. Hence, those communities which have relatively lower construction costs will have a comparative advantage in the bidding process. The implication of this advantage needs to be examined in future research.
REFERENCES 1. M. Austin, T. E. Smith, and J. Wolpert, The implementation of controversial facility-complex programs, Geogruphic. Awl. 2. 315-329 (1970). 2. L. Bacow and J. Milkey, “Responding to Local Opposition to Hazardous Waste Facilities: The Massachusetts Approach,” Resolve, Winter/Spring 1983,1,4, 8, Published by the Conservation Foundation, Washington, D.C. (1983). 3. D. S. Brookshire. D. L. Coursey, and H. Kunreuther, Compensation schemes in the presence of negative externalities, October, mimeo (1985). 4. D. S. Brookshire. D. L. Coursey, and W. D. Schulze, Experiments in the solicitation of private and public values: An overview, in “Advances in Behavioral Economics” (L. Green and J. Kagel, Eds.), JAI Press, Greenwich (1986). 5. E. H. Clarke, “Multipart pricing of public goods, Public Choice 11, 17-33. 6. E. H. Clarke, Multipart pricing of public goods: An example, in “Public Prices for Public Products” (S. Mushkin, Ed.), pp. 125-130, The Urban Institute, Washington (1972). 7. D. Coursey and V. Smith, Experimental tests of an allocation mechanism for private, public or externality goods, Scund. J. Econom. 86. 468-484 (1984). 8. R. G. Cummings, D. S. Brookshire and W. D, Schulze, “Valuing Public Goods,” Rowman & Littlefield, Totowa (1986). 9. B. Fischhoff, S. Lichtenstein, P. Slavic, R. Keeney, and S. Derby “Acceptable Risk,” Cambridge Univ. Press, Cambridge (1981). 10. T. Gladwin. Patterns of environmental conflict over industrial facilities in the United States, 1970-1978. Nururul Res. J. 20, 243-274 (1980).
SITING NOXIOUS FACILITIES
383
11. D. Goetze, A decentralized mechanism for siting hazardous waste disposal facilities, Public Choice 39, 361-370 (1982). 12. T. Groves and J. 0. Ledyard, Some limitations of demand revealing processes, Public Choice XXIX-2, 107-124 (1977). 13. T. Groves, Efficient collective choice with compensation, Reu. Econom. Stud. 45, 227-241 (1979). 14. R. Kasperson (Ed.), “Equity Issues in Radioactive Waste Management” Oelgeschlager, Bunn & Hair Cambridge, MA (1983). 15. P. R. Kleindorfer, “Compensation and Negotiation in the Siting of Hazardous Waste Facilities.” In The Science of the Total Environment (1986) in press. 16. A. Kneese and B. Bower, Causing offsite costs to be reflected in waste disposal decisions, in “Economics of the Environment” (R. Dorfman and N. Dorfman, Eds.), pp. 135-154, W. W. Norton and Co., New York (1972). 17. H. Kunreuther and P. R. Kleindorfer, A sealed-bid auction mechanism for siting noxious facilities, “American Economic Review: Papers and Proceedings,” 76, 295-299, May (1986). 1X. H. Moulin, “Game Theory for the Social Sciences,”New York Univ. Press, New York (1981). 19. D. C. Mueller. “Public Choice,” Chap. 4, pp. 68-89, Cambridge University Press, Cambridge (1979). 20. A. L. Nichols, “Targeting Economic Incentives for Environmental Protection,” MIT Press, Cambridge, (1984). 21. M. O’Hare, L. Bacow, and D. Sanderson, “Facility Siting and Public Opposition,” Van Nostrand-Reinholt, New York (1983). 22. M. O’Hare, Not on my block you don’t: Facility siting and the strategic importance of compensation,” Public Policy 25, 409-458 (1977). 23. S. Shavell, On liability and insurance, Bell J. Econom. 13, No. 1, 120-132 (1982). 24. J. N. Tideman and G. Tullock, A new and superior process for making social choices, J. Polit. Ecorrom. 84, 1145-1159 (1976). 25. U.S. Council on Environmental Quality, “Public Opinion on Environmental Issues: Results of a National Public Opinion Survey,” Washington, D.C. (1980). 26. U.S. Environmental Protection Agency, “Hazardous Waste Generation and Commercial Hazardous Waste Management Capacity,” Report SW-894, Washington, D.C. (1980). 27. U.S. Environmental Protection Agency, “Siting Hazardous Waste Management Facilities and Public Opposition,” Final Report SW-809, Prepared by Centaur Associates, Inc., Washington, D.C. (1979). 2X. J. Wolpert, Regressive Siting of Public Facilities Natuml Res. J. 16, 103-115. 29. R. Yaksick. Strict liability and insurance under loss misestimation, in “Insurance and Risk Theory” (M. Goovaerts et a/., Eds.), Reidel, Dordrecht (1986).
OF ENVIRONMENTAL
ECONOMICS
AND
MANAGEMENT
14, 371-383 (1987)
A Compensation Mechanism for Siting Noxious Facilities: Theory and Experimental Design ’ HOWARDKUNREUTHER Deportment of Decision Sciences and Center for Risk and Decision Processes, University of Pennsylvunia, Philadelphia, Pennsylvania 19104
PAULKLEINDORFERANDPETER J. KNEZ Depurtment of Decision Sciences, University of Pennsylvania, Philadelphia, Pennsylvania I91 04
AND RUDYYAKSICK Depurtment of Regional Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Received June 17,1985; revised March 11,1986 In locating noxious facilities, such as a trash disposal plant or a hazardous waste disposal incinerator, the host community frequently incurs all the costs while other communities in the region receive the benefits. W e propose a mechanism for sharing the benefits with the potential loser. Each community submits a sealed bid indicating the minimum amount it would require to host the facility. The site providing the lowest bid obtains the facility and receives its bid as compensation. This compensation is financed by the other communities. If there are N candidate communities, then each of the other communities would pay l/(N - 1) of their acceptance bid. A series of controlled laboratory experiments show that the outcomes of this low-bid auction come close to predictions from a theoretical model based on maxi-mm rules. Equity and efficiency considerations also are discussed in the context of the noxious facility problem. *’ 19117 Academic Press. Inc.
The siting of noxious industrial facilities is an especially conflict ridden issue. G ladwin [lo] has indicated the m a g n itude of this conflict, by identifying 366 disputes regarding industrial facility siting or expansion in the United States during the period 1970 to m id 1978. Almost one half of these involved chemical process facilities. There also has been considerable public opposition to establishing new off-site disposal facilities that receive wastes from a variety of different sources (Goetze [ll]). This opposition has been so effective that no new off-site hazardous materials disposal facilities (HMDF) were sited in the U.S. during the period 1980-1983 (Bacow and M ilkey [2]). Society faces a dilemma in resolving this conflict. O n the one hand, people demand the goods and serviceswhose production yields waste as by-products. There appears to be widespread agreement that there is a need for properly designed and ‘This research was partially supported by NSF Grant SES8312123. W e would like to thank Don Coursey, Pat Harker, Richard Kihlstrom, Jim Laing, Wesley Magat, Andrew Postlewaite, Jim Richardson, and Vernon Smith for useful discussions on the low-bid auction mechanism. Mike Selman designed the computer program for the controlled laboratory experiments and two referees provided constructive comments on the paper. An earlier version of this paper was presented at seminars at the University of Pennsylvania and University of Wyoming, where helpful comments were received. Correspondence should be mailed to: Dr. Howard Kunreuther, Department of Decision Sciences, University of Pennsylvania, Philadelphia, PA 19104. 371 0095-0696/87 $3.00 Copyri&t 8 1987 by Academic Press, Inc. All rights of reproduction in any form reserved.
372
KUNREUTHER
ET AL
managed disposal facilities since, in the aggregate, their presence would yield benefits in excess of their risks and costs. On the other hand, opposition is vehement when mention is made of locating a trash disposal or hazardous waste facility in a community’s “backyard.” For example, an opinion poll found that over 95 percent of respondents would actively protest against siting a hazardous materials facility near their home (U.S. Council on Environmental Quality [25]). Conflicts with respect to siting these noxious facilities arise because the external costs are spatially concentrated around the host community while the net benefits of the noxious facility tend to accrue to those communities that are sufficiently distant from the site to be immune from any external costs (Austin et al. [l]). Consequently, the host community may believe that it is bearing an inequitable share of the external costs. Our aim in this paper is to explore the potential of ex ante compensation for reducing local opposition to the siting of noxious facilities. By ex ante compensation we mean either monetary and/or in-kind payments provided to the host community prior to the construction and operation of a facility. These payments may be helpful in resolving the conflicts between the potential losers (the host community) and those who benefit from the siting of the facility, i.e., other residents of the region, industry and the developer (O’Hare et al. [21]). We propose a sealed-bid auction procedure that elicits from a community a compensation requirement for accepting a facility in its jurisdiction.* The problem context involves N communities that are interested as a group in choosing a site for a noxious facility to serve the needs of the whole group, and possibly other parties. Each one of the N communities is a possible candidate for the facility. The auction procedure is directed at determining where to site the facility, if at all, and what compensation to pay the host community. Our analysis treats communities as individual actors, neglecting intra-community dynamics which involve differences in preference and risk perception among community residents.3 It is important to note that the procedures discussed here can work only if coupled with appropriate public participation mechanisms (e.g., referenda) that allow citizens and the municipal government to evaluate alternatives and to determine required compensation payments. The next section explores approaches for inducing individuals to reveal their true preferences. A simple example demonstrates the inability of the Clarke mechanism to provide compensation for any siting problem in which the facility has negative impacts on the host community and positive impacts on others. Section II develops a sealed- (low) bid auction siting model for facilitating the siting process when individual preferences may be subject to misrepresentation. The model assumes that the facility’s impacts are known with certainty and that the community that hosts the facility is compensated by the other communities who benefit from it. Individuals are assumed to use maxi-min strategies to determine their bids. Prototype data from controlled laboratory experiments presented in Section III support this assumption. The concluding sections propose variations of the low-bid auction and suggest additional theoretical and empirical studies on siting noxious facilities. ‘The that arc [16] and pollution ‘For
related problem of developing an appropriate disposal tax or charging scheme for industries responsible for producing hazardous materials will not be treated here. See Kneese and Bower Nichols [20] for discussions of a tax and charging scheme for environmental regulation and abatement. more detail on this problem, see U.S. EPA, 1979, O’Hare et al. [21], and Fischhoff [9].
SITING NOXIOUS FACILITIES
313
I. COMPENSATION AND THE CLARKE MECHANISM The provision of compensation to the host community faces severe implementation problems. One of the most difficult is that communities may have an incentive not only to exaggerate their willingness to accept (WTA) requirements, but also to underreport their willingness to pay (WTP) other communities to take the facility. Consequently, one of the emerging noxious facility siting challenges is to design decision procedures that provide communities with economic incentives to reveal their true preferences.4 O’Hare [22] was one of the first to recognize the possibility of preference m isrepresentation in the form of exaggerated claims for compensation. He suggested that a Vickrey-type, second-price auction procedure m ight eliminate the incentive for preference m isrepresentation but did not develop a formal model.5 The literature on demand-revealing mechanisms initially appears to be appropriate for analyzing the noxious facility siting problem. Specifically, under certain assumptions, the mechanism developed by Clarke [5, 61 induces individuals to declare their true preferences for a pure public good by charging a tax the size of which depends partly on how their responses affect the final outcome. Even though the Clarke mechanism is incentive compatible, it has several weaknessesas pointed out by Groves and Ledyard [12]. For example, in order to induce truthful preference revelation, the mechanism generates surplus tax revenues that must be discarded rather than returned to the individuals. Thus, the outcome of this procedure is not fully Pareto optimal. On closer inspection, one finds that the Clarke preference revelation mechanism requires that the project being evaluated have a positive value to each of the individuals so that there is a net surplus after the tax is levied. However, each community has a negative value associated with having the noxious facility in its jurisdiction. This means the Clarke mechanism may create a negative level of welfare for all the communities, since the community harmed by the collective decision cannot be compensated without impairing the truth-telling incentive of the mechanism (Tideman and Tullock [24]). Furthermore, since surplus taxes cannot be returned to the communities, no community will want to host the facility, even though it may create a net social benefit to the entire region. We now present a simple example to illustrate the difficulties in using the Clarke mechanism for the noxious facility problem. The value matrix (Table I) indicates that each community will prefer to have the facility located elsewhere,rather than in its jurisdiction, unless it receives compensation. Thus, Community 1 has a WTA = - 8 if the facility is located in its backyard (site 1); and a WTP = 4 if the site is at 2 or 3. The Clarke procedure adds the WTP and WTA figures for each community and selects the one with the largest net benefits (Community 2) as the “wir~ner.“~ Each community is then taxed only if its stated WTA and WTP are decisive in changing 4There is a strand of literature in noxious facility location theory that addresses the importance of compensation in dealing with community opposition [Austin, er al. [l], and Wolpert, [28]]. These papers do not address the possibility of strategic misrepresentation of compensation requirements by communities. ‘We do not develop such a model here. The case of public information on values described in Section IV has properties that resembles a second price auction. 6See Mueller [19, Chap. 41 or Tideman and Tullock [24] for a detailed explanation of the rationale underlying the Clarke tax.
374
KUNREUTHER
ET AL.
TABLE I Value Matrix for the Three Community
1 2 3
Potential sites -___ ~__ 2
1
Community
-8
Net benefits
Location Problem
4 4 I 3
4 4
-5 I 6
Clarke tax
3
-8
9 0 9
0
the outcome. For example, if one eliminated Community l’s WTP and WTA (i.e., if Row 1 were eliminated from Table I), then Community 1 would be chosen as the site and the net benefit of this facility would be $11 (i.e., $4 + $7; the WTP for communities 2 plus 3, respectively). Community 1 would be taxed an amount of $11 - $2 = $9, representing the difference between the net benefits from Community 2 (the best site) and Community 1 when its preferences were not considered. Similarly, if community 3 WTP and WTA were calculated then it would pay a tax of $8 - (-$1) = $9. C ommunity 2 would not have to pay a tax since it would still be chosen when its own preferences were eliminated from consideration. A little reflection on these outcomes reveals that all communities will be decidedly unhappy with their post-Clarke tax situation. By bidding their true preferences, they are taxed an amount which leaves them worse off than under the status quo. Hence, they will not want to site a facility using this procedure even though aggregate net benefits may be positive. One way to use the Clarke procedure for this problem is to provide each candidate site with a sufficiently large subsidy (payable upon completion of the low-bid process) so that the values in the matrix would all be positive. However, one would have to know in advance the approximate WTA amounts for each community in order to specify the level of the subsidy, and the determination of these approximate amounts might itself be subject to strategic misrepresentation. The siting of a noxious facility thus differs from the pure public good problem in a fundamental way. In siting a public good, such as a park, the entire region as well as the host community receives net benefits. The only question is where the public good should be located to achieve efficiency. 7 In the case of a noxious facility, however, the host community will tend to suffer a loss while the remaining communities within the region will tend to benefit. Hence, there is a need to develop a bidding procedure which will both yield efficient siting solutions and deal with the distribution of costs and benefits among communities. II. A LOW-BID
AUCTION
MECHANISM
This section presents a low-bid auction model for selecting and compensating the community that hosts a hazardous facility when N potential sites are competing with one another. The key idea is to create an incentive for communities not to misrepresent their preferences by making their payments for not having the facility ‘See Coursey and Smith [7] for a detailed analysis of market-based experimental results for the public goods problem.
SITING
NOXIOUS
375
FACILITIES
in their backyard be proportionate to their demand for compensation if they are selected as the site. The low-bid auction procedure requires each community to specify a bid indicating its willingness to accept (WTA) the facility in its backyard. The community making the lowest bid is selected as the host site. Each of the other N - 1 communities pays a tax which is l/(N - 1) of its WTA. The notation and assumptions of the model are as follows: i
vi ‘i
V, + Kj
tij
x,
tj
The communities which are potential sites i = 1,. . . , N. Community i’s value of having the facility in its own backyard (K < 0). Thus, community i’s true WTA is - l$ Possible transfer payment to community i for hosting the facility, Total value to community i if the facility is sited there.8 The value to community i of having the facility sited in community j. We assume yj > 0. Thus, community i’s WTP for siting the facility in community J is Vij. Community i’s transfer payment (a tax if tij < 0) if the site is located in community j. Community i’s stated WTA for agreeing to host the facility in its jurisdiction.
We assume that each community is perfectly informed about the facility’s consequences to them at each alternative location.’ The basic structure of the low-bid auction mechanism is as follows. A regional siting agency has the task of determining which one of the communities will be chosen. It elicits an acceptance bid Xi > 0 from each community i = 1,2,. . . , N. This bid represents the minimum compensation required by community i to site the facility within its jurisdiction. If the facility is eventually sited in community j, then community i must pay an amount, tij = -Xi/( N - l), to compensate the “winning” community j which receives compensation tj = Xi. We assume that the agency selects the lowest bid as the winning site. If Xi = Min/{ X,lj = 1,. . . , N} is the lowest bid, then total transfer payments resulting from this mechanism will be
ti + 1 tji = xi - c j#l
j+r
(N"'
1)
'
"
0)
where the inequality follows since, by assumption, Xj > Xi for all j # i. Thus, the low-bid auction does not give rise to deficits. The default option is critical in the decision by a community as to whether it will participate in this process. We assume that each community would like to be at least ‘We are assuming that each community’s preferences are quasi-linear in “site value” and income. This facilitates the analysis of compensation. In particular, if each community resident has preferences of a similar quasi-linear structure, one can view community’s WTA and WTP values as being simple aggregates of the WTA and WTP values of their residents. When preferences are not quasi-linear in money the theoretical foundations of compensation mechanisms remain a largely open research question (see Groves [ll]). ‘In practice, compensation procedures of the sort studied here would be coupled with community planning grants and other information generation procedures which enable communities to estimate the ” value” they place on alternative sites (see Kleindorfer [15] for a discussion of these points).
376
KUNREUTHER
ET AL.
as well off no matter where the site is located than if no site were chosen. Assuming a default option value of 0, this means that communities would only participate in the proposed low-bid auction if they can each determine a bid Xi such that
y+ti=
v,+x,>o
(2)
and I/ij + tij = I/ij - Xi/(N
- 1) > 0
for all j # i.
(3)
It can be verified that the participation constraints defined by (2) and (3) have at least one feasible solution Xi i = 1,. . . , iV if and only if the “sincere WTA bid” Xi = - 5 satisfies (2), (3). This is the case precisely when Min { vjlj i
# i} 2 - T/;./(N - l),
i=l
,-*-, N.
(4
If (4) does not hold, some community may not wish to participate because it could end up worse off than under the default option. We assume that (4) holds so that the benefits of the proposed facility siting process are guaranteed to be nomregative for each of the communities involved. Our proposed bidding procedure links the localized social costs of a facility to its social benefits by requiring that tij = -Xi/( N - 1). Thus, if a community submits a higher acceptance bid Xi it will have to pay a larger tax to the regional agency if the facility is sited in another community j # i. For this reason there is a limited incentive to misrepresent one’s preferences by bidding Xi # K although strategizing may still be present.‘O We now consider bidding strategies for communities participating in the low-bid auction procedure. When each community knows its own preferences, but has no information on others, then a maxi-mm bidding strategy is a prudent one to follow and is consistent with the elimination of dominated strategies.” As we argue below, this means that the community will choose Xi so that
It is easy to verify that the solution to (5) is an equilibrium maxi-min strategy. The first expression on the left hand side of (5) represents the payoff to community i if site i is selected, while Fj + tij is the payoff if some other site j # i is selected. As Xi is increased, Xi + y increases and yj + tij = vj - Xi/( N - 1) decreases. The maxi-mm payoff occurs when equality is achieved between the site i payoff, Xi + vi, and the worst possible alternative site j # i payoff, Min { qjlj i
# i} - Xi/( N - 1).
“‘For example, suppose community i knew that all other communities would bid $100,000 or more as an acceptance bid. If community i was willing to accept the facility for only $50,000, they would still want to bid up to $99,999. “See Moulin [18] for a formal proof of this statement.
377
SITING NOXIOUS FACILITIES
We can solve (5) for Xi to obtain N-l Xi = N
(6)
It is interesting to note that under maxi-min bidding the low-bid auction yields Pareto efficient outcomes when each community is indifferent as to site location as long as it is not in their backyard. Specifically, assume that qj=
&=
vei
for all j, k f i and all i,
(7)
where Vpi represents the indifference value. Assuming (7), the net benefits of locating the facility at i are Bi = v + c ci = J( + c Vwj.
63)
j#i
j#i
Under maxi-mm bidding, we have from (6) that N-l x; = ,-(v-i
(9
- Pg.
Since N - l/N is a constant, this means that the site i selected in the low-bid auction will satisfy V-;-
V;.= M in{(Vjj
V_,)lj=
l,...,
N}
00)
But we can rewrite (8) as B,=y-V-;+
(11)
where the last term in (11) is a constant. Thus, the site i maximizing Bi will be the one with maximum (v - V-,), or equivalently the one with m inimum (Vei - y). From (10) we see that this is precisely the site which the low-bid auction will select if communities follow a maxi-mm strategy and are indifferent as to site locations outside of their own boundaries. In sum, the one-shot, sealed- (low) bid auction is an individually rational and coalition-free mechanism with some efficiency properties. It is not incentive compatible, but communities are dissuaded from greatly exaggerating their compensation requirements. Also, it generates a tax surplus (net of compensation). The procedure is coalition free since each community’s transfer function is independent of any other community’s acceptance bid. Thus, two or more communities cannot strategically link their bids in order to extract mutual gain from the procedure.i2 This latter characteristic contrasts with demand revealing mechanisms, none of which have coalition incentive compatibility features (Groves and Ledyard [12]). “Communities may have an incentive to collude if they are permitted to make side payments. The transactions costs of negotiating such side payments are likely to be high, however.
378
KUNREUTHER
ET AL
TABLE II Value Matrix and Maxi-min Bids for 5 Participant Low-Bid Auction Potential sites 1 2 3 4 5 Maxi-min hid
1
2
Participants 3
4
5
-loo0 575 575 575 575
175 -1500 175 175 775
1275 1275 -2400 1275 1275
375 375 375 -600 375
475 475 475 475 -800
1260
1820
2940
780
1020
Net benefits of each site 1900 1200 -200 2500 2200
To illustrate the low-bid auction, consider the sample value matrix depicted in Table II. If a participant was following a maxi-min strategy, then they would want to offer a WTA value indicated by the relevant figure in the bottom row. Community 1, for example, would bid WTA = 1260 thus guaranteeing that it would make a net profit of 260 whether or not it was the lowest bid. In the case of community 1, if yj -z 250 then it would prefer not to participate in the auction. If each community followed a maxi-min strategy then community 4 would have the lowest bid and the facility would be located there. Since (7) is satisfied for this value matrix, site 4 is guaranteed to be an efficient location, as shown by the last column in Table II. III. EXPERIMENTAL
RESULTS
A set of controlled laboratory experiments have been conducted to determine whether participants follow a maxi-min strategy for siting problems analogous to the one described above. All information about the decision process including instructions, values, and rules are provided via microcomputer network to the participants. Before making a final decision, each participant is able to explore his options by entering trial bids into the computer. Payoffs are in a fictitious currency (francs) with each franc worth one-half cent. All participants receive private information on their own Vi and Vij for value matrices such as the one depicted in Table II. Each experiment consisted of a series of 10 one-period replications of a low-bid auction mechanism with five participants using a different matrix each period.13 Figure 1 shows the average absolute percentage (AAP) deviations from maxi&n bids for the combined five subjects in each period for two experiments. (Experiment 2 involved inexperienced subjects and 3 involved experienced ones).14 The deviations from maxi-min bids are relatively small for later periods for both experienced and inexperienced subjects. The average absolute percentage (AAP) deviation is 6 percent or less in periods 7-10 in both experiments. In contrast, during periods l-6 the AAP deviation is as high as 25 percent and is below 6 percent only 3 out of 12 13The matrices utilized for the experiments and individual subject behavior are available from Howard Kunreuther, Center for Risk and Decision Processes, Wharton School, University of Pennsylvania, Philadelphia, PA 19104. I4 Inexperienced means subjects had no previous experience with the auction mechanism. Experienced means participants had been in at least one compensation experiment.
379
SITING NOXIOUS FACILITIES
FIG. 1. Average absolute percent-deviations from maxi-min bids (experiments 2 and 3).
times. These results suggest two important features of learning in auction-markets of this type. First, evaluating the merits of an auction-market mechanism of this type on the basis of a single one period trial can be very m isleading. Second, most learning in an experiment takes place within the first 6 periods so that the last 4 periods of an inexperienced group are not significantly different from all 10 periods of an experienced group. This learning phenomena is further demonstrated at the individual level. In periods 7-10 in all three experiments approximately three quarters of the 60 different bids were within five percent of the maxi-min values and none deviated by more than 18 percent. In 80 percent of the bids from all three experiments, the participant who specified the lowest bid was the same as the one predicted by assuming a maxi-min decision process. It is encouraging that the outcomes from the experiments are consistent with maxi-min theoretical predictions after learning has taken place.
IV. EXTENSIONS
OF THE LOW-BID
AUCTION
MECHANISM
The low-bid auction mechanism can be examined under alternative institutional and information conditions to determine whether participants follow strategies predicted by theoretical analyses. Several variations are discussed below. Field Experiments A field experiment testing the low-bid auction mechanism procedure recently was undertaken in Laramie, Wyoming (Brookshire et al. [3]). Individuals were asked to specify a WTP value to have flowers delivered to their home and a WTA value to perform this service for N other households. The flower delivery service is thus conceptually analogous to hosting a noxious facility even though it does not produce the same negative reactions by potential “winners.” The low-bid auction procedure elicited a comparative set of WTA and WTP values. An econometric analysis revealed that individuals with higher opportunity costs of time reflected by higher wage rates require more compensation. As the value of N increases so does the required WTA, as one would expect. Field experiments such as this one can
380
KUNREUTHER
ET AL.
identify important economic and demographic variables that can ease the transition from a controlled laboratory setting to a real world problem. Public Information on Values When the Kj and V, of all participants are made public, then a stable equilibrium will be one where all bids converge to the second lowest WTA, denoted as X**. Specifically, if each participant calculates its maxi-min bid under no information then the one with the lowest WTA will have an incentive to bid 6 units below X**, knowing that it can increase its profits while still being the lowest bid. Other participants will want to lower their bids to X**, knowing that they will not be the lowest WTA while at the same time reducing the tax they have to pay. If all participants follow this strategy the resulting bids will be a stable Nash equilibrium that has the attractive characteristic of being budget balancing. The participant who has made the lowest bid will obtain a profit identical to the amount he or she would have obtained had a Vickrey second price auction been administered. To illustrate an optimal bidding strategy for the perfect information case, consider the value matrix depicted in Table II. In contrast to the “no information” case, participant 4 would now want to raise her bid to 1019, participant 5 (the second lowest bidder) will not change her bid while 1, 2, and 3 will all want to reduce their bids to 1020. Controlled laboratory studies can determine whether participants, in fact, follow these strategies. Lottery cum Auction This institutional variation on the low-bid auction consists of two stages. In Stage 1 each individual submits a bid Xi* and one participant is chosen at random using a lottery procedure. This bid is publicly posted. Stage 2 consists of the low-bid auction procedure among the N - 1 individuals who were not chosen by the lottery. If one or more of these participants submits a bid below the posted price then the lowest bid is the “winner”; otherwise, the individual chosen by lottery is the “winner.” If participant i is not the “winner” then he is taxed ti = -XJ(N - l), where Xi is the bid entered in Stage 1. One reason for proposing the lottery cum auction mechanism is to address the equity issues associated with siting noxious facilities (Kasperson [14]). If the poorest communities are the ones submitting the lowest bids then under the strict low-bid auction procedure, they will always be the “winner.” In a lottery cum auction each potential site has an equal chance of being selected initially. The poorest communities might bid very low in Stage 1 but if they are not chosen by the lottery they can raise their bid above the posted price and still only have to pay a relatively small tax. From a theoretical standpoint one would not expect any difference in Stage 1 and Stage 2 bids if the initial bids truly reflected individuals’ preferences. If, on the other hand, there was some gaming by participants, hoping that they would not be selected by the lottery, then Stage 1 bids will be lower than maxi&n values and in Stage 2 bids will be higher than the posted price. In this case the lottery cum auction may yield a budget deficit on any one trial. In expected value terms the mechanism
SITING NOXIOUS FACILITIES
381
is budget balancing, since a community making a low bid initially and planning to raise it in the second stage may be selected by the lottery and actually host the facility. Stochastic Externalities The low-bid auction can be extended to cover the case where there are stochastic losses. In this environment, two types of compensation arrangements can be used for siting a facility: ex ante and ex post compensation. Ex ante compensation provides individual i with an amount Xi if he were the lowest bidder. In this case, if a loss Li occurs (with probability p), then the final outcome is Xi - Li. Fix post compensation arrangements are those that become operative only after the facility has produced an identifiable adverse consequence. One way to establish such an arrangement is to create, for example, a regional or community-wide self-insurance program. In this program each participant (i.e., those deemed liable for accident damages) contributes a premium each year to a central compensation fund. After an accident has occurred, victims receive payments from the fund. In the context of the noxious facility problem where there is a potential loss Li with probability p, the low bidder receives Xi if there is a loss and 0 otherwise. All other participants must pay Xi/(N - 1) to the regional authority-in effect, a premium based on the expected loss and the number of individuals benefitting from having the facility outside of their backyard.” V. CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH We have analyzed a collective choice problem where one individual (community) can be harmed by hosting a site that benefits a number of other individuals. We adopted a strategic approach to the problem since individuals have an incentive to m isrepresent their preferences. We modeled this strategic decision by inducing an incomplete information game via a direct revelation mechanism: the low-bid auction. A unique feature is that we constrained the design to have a mechanism that requires a m inimum of computational tasks by the participants and an easily understood incentive scheme. This concern for the bounded rationality of participants is a major departure from the usual approach to mechanism design and evaluation. Our research goal has been to determine the properties of the low-bid auction mechanism. To do so, we analyzed the noncooperative game induced by the mechanism under the behavioral assumption that the bidders choose maxi-min bidding strategies. Although the mechanism does not induce individuals to report their true preferences, an efficient location is chosen if each community is indifferent to where the facility is sited so long as it is not in their backyard. Tax revenues also exceed the host community’s compensation requirements. In general, the low-bid auction procedure offers the possibility of clarifying the relative costs and “The implicit assumption in this payment rule is that the N - 1 nonhost communities are liable for accident damages. To the extent that the plant owner and/or facility customers are held liable, then they have to cover the losses. For a detailed analysis of the efficiency of alternative liability-insurance arrangements, see Shave11[23] and Yaksick [29].
382
KUNREUTHER
ET AL
benefits of alternative locations. By introducing competitive bidding, each community can determine whether it is worth being a candidate for hosting the facility by examining its default option in relation to the potential outcomes associated with specific WTA bids. The mechanism also complements the large body of controlled experimental and empirical work designed to measure willingness to pay and willingness to accept values for public and private goods as well as externalities (See Brookshire et al. [4] and Cummings, et al. [8], for a summary). In evaluating this approach relative to other procedures, one should examine a set of process and outcome measures, which would include data collection costs and computational ease, allocative efficiency, equity and distributional considerations, and budget balancing features. The low-bid auction procedure must be viewed as only one of a set of policy tools for dealing with the noxious facility location process. For example, health and safety standards may have to be imposed by regional or state governmental agencies so that residents at all potential sites are convinced that they are sufficiently protected against adverse environmental effects, such as air an water pollution. In addition, public participation by all residents needs to be a part of the siting process. This requires some voting mechanism by the citizens or the town governnment for finalizing a bid as well as determining the acceptability of proposed design and control procedures (Kunreuther and Kleindorfer [17]). Finally, the low-bid auction mechanism is compatible with other arrangements between interested parties in the siting process. For example, a developer may be legally required to reveal construction costs at each potential location. The community’s bid can then be redefined as the sum of its WTA bid plus the construction costs. Hence, those communities which have relatively lower construction costs will have a comparative advantage in the bidding process. The implication of this advantage needs to be examined in future research.
REFERENCES 1. M. Austin, T. E. Smith, and J. Wolpert, The implementation of controversial facility-complex programs, Geogruphic. Awl. 2. 315-329 (1970). 2. L. Bacow and J. Milkey, “Responding to Local Opposition to Hazardous Waste Facilities: The Massachusetts Approach,” Resolve, Winter/Spring 1983,1,4, 8, Published by the Conservation Foundation, Washington, D.C. (1983). 3. D. S. Brookshire. D. L. Coursey, and H. Kunreuther, Compensation schemes in the presence of negative externalities, October, mimeo (1985). 4. D. S. Brookshire. D. L. Coursey, and W. D. Schulze, Experiments in the solicitation of private and public values: An overview, in “Advances in Behavioral Economics” (L. Green and J. Kagel, Eds.), JAI Press, Greenwich (1986). 5. E. H. Clarke, “Multipart pricing of public goods, Public Choice 11, 17-33. 6. E. H. Clarke, Multipart pricing of public goods: An example, in “Public Prices for Public Products” (S. Mushkin, Ed.), pp. 125-130, The Urban Institute, Washington (1972). 7. D. Coursey and V. Smith, Experimental tests of an allocation mechanism for private, public or externality goods, Scund. J. Econom. 86. 468-484 (1984). 8. R. G. Cummings, D. S. Brookshire and W. D, Schulze, “Valuing Public Goods,” Rowman & Littlefield, Totowa (1986). 9. B. Fischhoff, S. Lichtenstein, P. Slavic, R. Keeney, and S. Derby “Acceptable Risk,” Cambridge Univ. Press, Cambridge (1981). 10. T. Gladwin. Patterns of environmental conflict over industrial facilities in the United States, 1970-1978. Nururul Res. J. 20, 243-274 (1980).
SITING NOXIOUS FACILITIES
383
11. D. Goetze, A decentralized mechanism for siting hazardous waste disposal facilities, Public Choice 39, 361-370 (1982). 12. T. Groves and J. 0. Ledyard, Some limitations of demand revealing processes, Public Choice XXIX-2, 107-124 (1977). 13. T. Groves, Efficient collective choice with compensation, Reu. Econom. Stud. 45, 227-241 (1979). 14. R. Kasperson (Ed.), “Equity Issues in Radioactive Waste Management” Oelgeschlager, Bunn & Hair Cambridge, MA (1983). 15. P. R. Kleindorfer, “Compensation and Negotiation in the Siting of Hazardous Waste Facilities.” In The Science of the Total Environment (1986) in press. 16. A. Kneese and B. Bower, Causing offsite costs to be reflected in waste disposal decisions, in “Economics of the Environment” (R. Dorfman and N. Dorfman, Eds.), pp. 135-154, W. W. Norton and Co., New York (1972). 17. H. Kunreuther and P. R. Kleindorfer, A sealed-bid auction mechanism for siting noxious facilities, “American Economic Review: Papers and Proceedings,” 76, 295-299, May (1986). 1X. H. Moulin, “Game Theory for the Social Sciences,”New York Univ. Press, New York (1981). 19. D. C. Mueller. “Public Choice,” Chap. 4, pp. 68-89, Cambridge University Press, Cambridge (1979). 20. A. L. Nichols, “Targeting Economic Incentives for Environmental Protection,” MIT Press, Cambridge, (1984). 21. M. O’Hare, L. Bacow, and D. Sanderson, “Facility Siting and Public Opposition,” Van Nostrand-Reinholt, New York (1983). 22. M. O’Hare, Not on my block you don’t: Facility siting and the strategic importance of compensation,” Public Policy 25, 409-458 (1977). 23. S. Shavell, On liability and insurance, Bell J. Econom. 13, No. 1, 120-132 (1982). 24. J. N. Tideman and G. Tullock, A new and superior process for making social choices, J. Polit. Ecorrom. 84, 1145-1159 (1976). 25. U.S. Council on Environmental Quality, “Public Opinion on Environmental Issues: Results of a National Public Opinion Survey,” Washington, D.C. (1980). 26. U.S. Environmental Protection Agency, “Hazardous Waste Generation and Commercial Hazardous Waste Management Capacity,” Report SW-894, Washington, D.C. (1980). 27. U.S. Environmental Protection Agency, “Siting Hazardous Waste Management Facilities and Public Opposition,” Final Report SW-809, Prepared by Centaur Associates, Inc., Washington, D.C. (1979). 2X. J. Wolpert, Regressive Siting of Public Facilities Natuml Res. J. 16, 103-115. 29. R. Yaksick. Strict liability and insurance under loss misestimation, in “Insurance and Risk Theory” (M. Goovaerts et a/., Eds.), Reidel, Dordrecht (1986).