Modelling scale heterogeneity in the estimation of the
bene…ts of road tra¢ c risk reductions: the case of
moose-vehicle collisions in Newfoundland
Roberto Martínez-Espiñeira
and
Nikita Lyssenko
and
Justin Quinton
Department of Economics, Memorial University of Newfoundland, Canada
Canadian Resource and Environmental Economics
Study Group Annual Conference 2013
Brock University, St. Catharines
Friday-Sunday, September 27-29, 2013
June 14, 2013
Abstract
Keywords: moose vehicle collisions; risk reduction; contingent valuation; value of a statistical life; scale heterogeneity, heterogeneous anchoring
Newfoundland’s moose population density is about the highest in the world (Clevenger 2011)
and has led to con‡icts between humans and moose, mainly because of moose-vehicle collisions
(MVC). There are about 600-800 MVC with an average of two fatalities per year (Clevenger 2011)
and many consider moose as environmental bad. Some of the social costs of MVC must be valued
using non-market valuation techniques (concerns about safety, human su¤ering and reduction in
the quality of life of MVC victims, averting behavior costs, and, most importantly, the cost of
injuries and lives lost). For instance, according to Huijser et al (2009) the estimated cost for an
average moose collision is $30,760 (2007 US). However, this …gure is only a fraction of the full
social costs of MVC in NL, as some of the non-market damages are not taken into account.
Using contingent valuation, we will estimate the bene…ts of mitigation of MVC in Newfoundland. We will draw from the literature dealing with risk valuation in a variety of contexts (Huang
Department of Economics, Memorial University of Newfoundland, Canada Tel: 1-709-864-3676 Fax: 1-902-8673610. E-mail: rmartinezesp@mun.ca
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et al. 2004; Konishi and Adachi 2011), road mortality risk perception (e.g Andersson 2011, Andersson and Svensson 2008) and valuation studies of road safety (e.g. Strazzera et al. 2003; De
Blaeij et al 2003; Hultkrantz et al 2006; Svensson 2009).
A phone survey is being administered to a random sample (N =1200) of Newfoundlanders which
presents a brief description of the e¤ects of a MVC risk mitigation policy involving the erection of
fences along highways. Willingness to pay is elicited using a double-bounded dichotomous-choice
format (Hanemann Loomis and Kanninen 1991; Alberini et al. 2004) considering distortions of
the basic models and take into account issues of anchoring, starting point bias, yea-saying, and
framing (Aprahamian et al. 2008; Herriges and Shogren 1996; De Shazo 2002; Whitehead 2002;
Chien et al. 2005; Flachaire and Hollard 2007; Watson and Ryan 2007; Farmer and Belasco 2011).
Our data will allow to estimate the willingness to pay for di¤erent levels of risk reductions
and to calculate the value of avoiding a statistical fatality, controlling for the e¤ects of variables
commonly used in valuation studies of risk reductions. In particular, we will have either direct
information about or proxies for cognitive scales, educational attaintment levels, perceived health
status (as in ), age, income, gender, family status (Svensson 2009), experience of the risk (having
hit a moose or knowing about someone close who hit a moose), degree of exposure to the risk
(residence location, Km driven annually, whether the respondent’s job involves night-driving, and
type of car owned), perceived own level of risk (Andersson 2007), degree of risk aversion, scope of
the proposed risk reduction policy (mortality only versus mortality and morbidity), and degree of
certainty about the response to the the willingness to pay questions (Alberini, Cropper, Krupnick,
and Simon 2004). A subsample of our respondents received a question based on the provision
of a public good; some others received a question about a private good, and a third subsample
received both types. We will be able to compare external and internal tests of the e¤ect of the
type of good (Johannesson, Johansson, and O’Conor 1996; Hultkrantz, Lindberg, and Andersson
2006; Andersson and Lindberg 2009; Svensson and Vredin Johansson 2010; Dekker, Brouwer, and
Hofkes 2011). We will also be able to control for question ordering (Svensson and Vredin Johansson
2010), payment vehicle (income tax versus fee surcharge), provider of the public good (provincial
versus federal government), and the e¤ect of using a “provision condition” reminder about the
referendum on the public good (Hultkrantz, Lindberg, and Andersson 2006).
In this paper we will pay particular attention to the modelling of scale heterogeneity as a
function of observable characteristics of the respondents and of the proposed policy. We will
consider the in‡uence of sociodemographic characteristics of the respondents, their experience
with the risk considered, and the certainty wit which they respond to the valuation questions.
We also consider how accounting for scale heterogeneity impacts the analysis of double bound
dichotomous-choice questions, in particular the estimation of shift and anchoring e¤ects and how
considering both scale heterogeneity and heterogenous shift and anchoring e¤ects improves the
estimation (Aprahamian, Chanel, and Luchini 2008; Flachaire, Hollard, and Luchini 2007; Farmer
and Belasco 2011; McNair, Hensher, and Bennett 2012).
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