Valuation of a Moose*Vehicle Accident Mitigation Policy in Newfoundland1

410 Pages·2017·2.16 MB·English

Valuation of a Moose-Vehicle Accident
Mitigation Policy in Newfoundland1
Roberto Mart(cid:237)nez-Espiæeira
Nikita Lyssenko
Department of Economics, Memorial University of Newfoundland2
May 8, 2017
1Subject to revision, please do not quote without permission
2This project was funded by the Collaborative Applied Research in Economics
(CARE) initiative.
Abstract
WhilemooseareaniconicCanadiansymbol,theyarealsoconsideredbymanyinNewfound-
land and Labrador as an environmental disamenity, due to the rising number of negative
moose-peopleinteractions. Thereareabout600-800moose-vehiclecollisionseachyear,which
result on average in two fatalities per year. There is also an ongoing public debate regard-
ing moose control options in the province. Economic theory suggests that only policies that
balance bene(cid:133)ts and costs result in a maximization of net social bene(cid:133)ts. When it comes to
the removal or abatement of an environmental bad (such as wildlife-vehicle accidents), it is
more often than not the case that, while it is relatively easy to compute the cost of a policy,
itsbene(cid:133)tsare di¢ cultto monetize,because such policiesoften dealwith goodsand services
without a market price. Economists have, however, developed non-market valuation tech-
niques that aim at estimating the economic bene(cid:133)ts of these types of policies. In this case,
we apply the Contingent Valuation Method to estimate the bene(cid:133)ts that Newfoundlanders
would derive from a moose control program, including the estimation of a key ingredient of
thepolicydecisioninthisinstance,thevalueofastatisticallife. Thevaluationexerciseyields
estimates ofthe bene(cid:133)ts that respondents would derive from the mitigation ofmoose related
road accidents in Newfoundland. These values could then be used to inform cost-bene(cid:133)t
analyses of risk reduction policies related to collisions with wildlife in this province but also,
withappropriateadjustments,ofpoliciesrelatedtothesacri(cid:133)ceofresourcesinanyotherway
in orderto protect the public.
Contents
I Introduction 13
II Background 21
1 Moose in Newfoundland and Labrador 22
2 Moose-vehicle collisions in Newfoundland 27
2.1 The issue of moose-vehicle collisions . . . . . . . . . . . . . . . . 27
2.1.1 Other jusrisdictions . . . . . . . . . . . . . . . . . . . . . 28
2.1.2 The issue in Newfoundland . . . . . . . . . . . . . . . . . 30
2.1.3 Temporal and spatial distribution of accidents . . . . . . 31
2.2 Measures to mitigate MVCs . . . . . . . . . . . . . . . . . . . . . 34
2.2.1 Hunting as a density reduction measure . . . . . . . . . . 35
2.2.2 Road signs and detection systems. . . . . . . . . . . . . . 36
2.2.3 Vegetation removal . . . . . . . . . . . . . . . . . . . . . . 37
2.2.4 Fencing strategies . . . . . . . . . . . . . . . . . . . . . . 39
2.2.5 Safe crossing . . . . . . . . . . . . . . . . . . . . . . . . . 40
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2.2.6 What has been done to prevent MVCs in NL? . . . . . . 42
2.3 Options for a mitigation strategy in Newfoundland . . . . . . . . 45
III The theory and praxis of valuing road tra¢ c risks 47
3 Valuing tra¢ c risk reductions: The value of a statistical life 48
3.1 The standard VSL model . . . . . . . . . . . . . . . . . . . . . . 56
3.1.1 The dead-anyway e⁄ect and the wealth e⁄ect . . . . . . . 59
3.1.2 Risk aversion and background risks . . . . . . . . . . . . . 60
3.1.3 Altruism. . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4 Valuation of road risk reductions and VSL in practice 65
4.1 Empirical estimation of VSL . . . . . . . . . . . . . . . . . . . . 65
4.2 What a⁄ects the size of the VSL . . . . . . . . . . . . . . . . . . 67
4.2.1 Wealth/income . . . . . . . . . . . . . . . . . . . . . . . . 68
4.2.2 Baseline risk . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.2.3 Background risk . . . . . . . . . . . . . . . . . . . . . . . 69
4.2.4 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2.5 Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2.6 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2.7 Health status . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2.8 Altruism (private versus public policies) . . . . . . . . . . 73
4.2.9 Magnitude (scope or scale) of the risk reduction . . . . . 75
4.2.10 Other factors . . . . . . . . . . . . . . . . . . . . . . . . . 77
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IV Methodology 79
5 Non-market valuation methods 80
6 The contingent valuation method 86
6.1 Payment vehicle and elicitation format . . . . . . . . . . . . . . . 88
6.2 Respondent uncertainty and hypothetical bias . . . . . . . . . . 91
6.3 Treatment of (cid:145)don(cid:146)t know(cid:146)responses . . . . . . . . . . . . . . . . 93
6.4 Protest responses . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.5 Scope e⁄ects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.6 Analysis of the drivers of willingness to pay . . . . . . . . . . . . 109
6.7 Potential endogeneity of predictors of willingness to pay . . . . . 111
7 Analysis of double-bounded dichotomous choice questions 113
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.2 Estimating willingness to pay . . . . . . . . . . . . . . . . . . . . 116
7.3 Testing the Validity of the Survey Results . . . . . . . . . . . . . 126
7.3.1 Analysis of question e⁄ects with the DBDC format . . . . 127
7.3.2 Behavioral explanations for question e⁄ects . . . . . . . . 129
7.3.3 Analysis of question e⁄ects . . . . . . . . . . . . . . . . . 133
V Further issues in the valuation of risk reductions 138
8 Risk perception 139
8.1 Heuristics and risk perception . . . . . . . . . . . . . . . . . . . . 141
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8.2 Cultural theory and risk perception. . . . . . . . . . . . . . . . . 142
8.3 The psychometric paradigm and risk perception . . . . . . . . . . 145
8.4 Limitations of the cultural theory and psychometric paradigms . 148
8.5 Risk perception and road tra¢ c risks . . . . . . . . . . . . . . . . 150
9 Cognitive skills e⁄ects in the valuation of risk reductions 152
VI Data description 156
10 The survey 157
10.1 Survey versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
10.2 Socioeconomic characteristics of Newfoundlanders in the sample. 163
10.3 Descriptive statistics on risk pro(cid:133)les . . . . . . . . . . . . . . . . 169
10.4 Measures of scope and scale . . . . . . . . . . . . . . . . . . . . . 178
10.5 Willingness to pay and di⁄erent proposed policies . . . . . . . . . 180
10.6 Additional variables about the payment scenario characteristics 188
10.7 Additional variables about respondent characteristics . . . . . . . 189
11 Data screening and manipulations 193
11.1 Treatment of (cid:145)don(cid:146)t know(cid:146)responses . . . . . . . . . . . . . . . . 193
11.2 Baseline risk outliers . . . . . . . . . . . . . . . . . . . . . . . . . 197
11.3 Protest responses . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
11.4 Sampling weights . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
11.5 Uncertain responses . . . . . . . . . . . . . . . . . . . . . . . . . 203
11.6 Imputation of missing values . . . . . . . . . . . . . . . . . . . . 204
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11.7 Stacking of multiple response observations . . . . . . . . . . . . . 205
11.8 Clustering by respondent . . . . . . . . . . . . . . . . . . . . . . 205
VII Results 207
12 Willingness to pay 208
12.1 Univariate and bivariate probit analysis . . . . . . . . . . . . . . 214
12.2 Random-e⁄ects models . . . . . . . . . . . . . . . . . . . . . . . . 221
12.3 SBDC model and DBDC interval and question e⁄ects models . . 224
12.4 Models with covariates . . . . . . . . . . . . . . . . . . . . . . . . 235
13 Welfare estimates 251
13.1 Estimates of aggregate WTP . . . . . . . . . . . . . . . . . . . . 251
13.2 Calculation of values of a statistical life . . . . . . . . . . . . . . 259
13.3 How do our VSL estimates compare with earlier estimates? . . . 265
13.4 VSL in other transport safety studies . . . . . . . . . . . . . . . . 267
14 Discussion 272
VIII Conclusions and suggestions for further research275
A Abbreviations 340
B Modeling of risk perception 342
C Modeling of respondent uncertainty 345
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D Modeling of mathscore 353
E Survey questionnaire text 358
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List of Tables
2.1 Moose-Vehicle-Collisions in Newfoundland and Labrador. . . . . 31
7.1 Formulae for calculating mean and median WTP . . . . . . . . . 122
10.1 Frequency distribution of respondents by survey version. . . . . . 162
10.2 Average respondent characteristics. . . . . . . . . . . . . . . . . . 163
10.3 Gender distribution. . . . . . . . . . . . . . . . . . . . . . . . . . 164
10.4 Highest levels of education. . . . . . . . . . . . . . . . . . . . . . 167
10.5 College-educated respondents. . . . . . . . . . . . . . . . . . . . . 167
10.6 Responses to What best describes you? . . . . . . . . . . . . . . . 168
10.7 Responses to How long have you now lived in Newfoundland? . . 168
10.8 Frequent drivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
10.9 Type of vehicle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
10.10Vehicle year. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
10.11Average number of Km. driven annually.. . . . . . . . . . . . . . 171
10.12Responses to Do you drive more than 30 Km to work? . . . . . . 171
10.13Frequency distribution of variable job12to6am. . . . . . . . . . . 172
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10.14Frequency distribution of variable seenmoosecross. . . . . . . . . 172
10.15Frequency distribution of variable hitmoose. . . . . . . . . . . . . 173
10.16Frequency distribution of variable knowelse. . . . . . . . . . . . . 173
10.17Summary of risk related variables (%). . . . . . . . . . . . . . . . 174
10.18Frequency distribution of "Objective" mortality rates from MVCs.175
10.19Self-perceived baseline risks and proportions and absolute mag-
nitudes of risk reductions. . . . . . . . . . . . . . . . . . . . . . . 178
10.20Self-perceived baseline risks and proportions and absolute mag-
nitudes of risk reductions. Trimmed sample. . . . . . . . . . . . . 179
10.21Frequency distribution of variable COST. . . . . . . . . . . . . . 186
10.22Willingness to pay initial bid. . . . . . . . . . . . . . . . . . . . . 187
10.23Frequency distribution of response patterns by initial bid. . . . . 188
10.24Frequency distribution of numerical certainty scale. . . . . . . . . 189
10.25Summary descriptives for additional variables.. . . . . . . . . . . 192
11.1 Protest responses. . . . . . . . . . . . . . . . . . . . . . . . . . . 201
11.2 "Clear" protest responses. . . . . . . . . . . . . . . . . . . . . . . 201
11.3 Frequency distribution of reasons to reject payment question. . . 202
12.1 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . 209
12.2 Univariate and Bivariate Probit Analyses. . . . . . . . . . . . . . 216
12.3 Likelihood-ratio test comparing bivariate probit models. . . . . . 220
12.4 Random-e⁄ects models of WTP. . . . . . . . . . . . . . . . . . . 222
12.5 Single bounded, interval, and interval models with question e⁄ects.227
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