Abstract
We use conjoint choice questions to investigate the preferences of people in four cities in Italy for income and future/permanent mortality risk reductions delivered by contaminated site remediation policies. The VSL is €5.6 million for an immediate risk reduction. If the risk reduction takes place 20 years from now, the implied VSL is €1.26 million. Respondents’ implicit discount rate is 7%. The VSL depends on respondent characteristics, familiarity with contaminated sites, concern about the health effects of exposure to toxicants, having a family member with cancer, perceived usefulness of public programs and beliefs about the goals of government remediation programs.
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Notes
The estimated cleanup costs for the sites on the Italian NPL are €3,149 million, but the available public funding tops off at €541 million.
In choosing delays of 2 and 10 years, we were hoping to strike a compromise between what participants in focus groups and one-on-one surveys judged reasonable, and lag ranges used in actual policy analyses. The EPA Science Advisory Board assumed a 20-year lag when examining the maximum contaminant limit allowable for arsenic in drinking water (see www.house.gov/science/ets/oct04/ets_charter_100401.htm, accessed 22 January 2006), and the model used by the U.S. Environmental Protection Agency for arsenic in water, which is adapted from a smoking cessation lag model, predicts that the majority of the reduction in the risk of cancer is incurred within the first five years following cessation (U.S. Environmental Protection Agency 2003).
At the time of the survey (May 2005), one Euro exchanged on average for $1.25 U.S.
If the discount rate is assumed to be zero, our bids correspond (depending on the duration and delay) to VSL values ranging from €37,000 to €4.750 million. If the discount rate is assumed to be 2%, the bids correspond to VSL amounts ranging from €56,000 to €5.76 million. For a discount rate of 5%, the VSL range is €93,000 to €7.5 million, and for a discount rate of 10%, it is €169,000 to €11 million.
A pair has a dominated alternative if one of them is obviously better (e.g., saves more lives over a longer period of time) and no more expensive than the other.
As shown below, although we find that the marginal utility of risk reduction is different for different population sizes, in practice the VSL is constant with respect to population size. For this reason, we incorporate covariates only in the simpler specification of the indirect utility function, allowing α and β to vary across respondents but not across the size of population in the conjoint choice questions.
We were unable to find estimates of the risks and population at risk for the sites on the Italian NPL or other government-compiled list. We calculated an estimate of the baseline risks before cleanup by transferring estimates of risks in other contaminated areas in Italy. Specifically, we relied on a World Health Organization study which identifies highly industrialized and polluted areas in Italy, computes mortality rates for men and women in these areas in 1990–1994, and compares them with those of the surrounding regions. This study concludes that in those years the highly industrialized areas experienced about 800 excess deaths per year (Martuzzi et al. 2002; Mitis et al. 2005). When this figure is divided by the exposed population (3,295,380 people), we obtain an excess risk of about 243 per million, which we posit to be our baseline risk.
These cities were selected to ensure geographic representativeness and because each has one or more sites on the National Priorities List. The chemical and oil refining complex of Porto Marghera in the Venice hinterland is probably the most egregious contaminated site on the NPL, with soils, groundwater and Lagoon sediments contaminated by polycyclic aromatic hydrocarbons (PAHs), heavy metals and many other pollutants. The former Fibronit complex, an asbestos-processing facility, is located in downtown Bari, while the NPL site in Naples is a closed steel mill. The Milan area has several NPL sites. The four cities also have a number of non-NPL sites. Ideally, to ensure surveying the beneficiaries of a cleanup program that matches as closely as possible the one in the questionnaire, we would have liked to draw our respondents from the population living within a short distance from one or more orphan sites (whether or not such sites are on the NPL, since the language in the questionnaire does not restrict the program to NPL sites). Unfortunately, this was not possible for two reasons. First, the universe of contaminated sites in the four cities is simply not made available to the general public by the National and Regional environmental protection agencies (the Regions are jurisdictions roughly comparable for lawmaking power and environmental programs to the States in the U.S. or the Provinces in Canada). Second, even if we had such a comprehensive list of sites, the agencies do not disclose information about the sites’ current ownership or orphan status. We therefore opted for the general population of the four cities on the assumption that there are numerous sites needing remediation in those cities and that the population density in those cities is sufficiently high so that most people live near one such site. We did, however, ask respondents whether they were aware of a contaminated site in the neighborhood or near work, and which parties they considered to be the beneficiaries of the program in the survey—themselves, their families, or other people.
A total of 804 respondents completed the questionnaire, but we discarded the choice responses of the 22 individuals who were shown a conjoint choice question screen with a typographical error in the risk reduction.
The VSL is here estimated as (α/β) × 1 million. The multiplication by one million is necessary because in our dataset for estimation purposes the risk reduction was coded as 10, 20, or 30, instead of 10, 20, or 30 × 10−6. The standard errors were computed using the delta method (described in Alberini et al. 2006b, Appendix D).
We cross-validated these estimates by re-estimating the model after dropping one observation at the time. The “jackknife” mean VSL over these replications was €5.583 million and the standard deviation of the distribution of the VSL estimates, which serve as robust standard error around the estimate of the VSL, is €1.08 million. The estimates of the VSL and the discount rate are robust to, among other things, (1) restricting the sample to the responses to the questions where the choice set includes the status quo (VSL = €6.065 mill., s.e. 1.136 mill., δ = 6.24%); (2) omitting the responses to the choice questions about the first pair of programs (VSL = €5.000 mill., s.e. 0.810 mill., δ = 7.0%); (3) using only the four responses to the questions about the third and fourth pair of programs (VSL = €5.250 mill., s.e. 1.030 mill., δ = 7.1%), (4) using only the two responses to the last pair of programs (VSL = €5.780 mill., s.e. 1.390 mill., δ = 8.4%), (5) dropping the responses to questions about plans with cost equal to €50 or €100 (VSL = 5.141 mill., s.e. 1.371 mill., δ = 6.6%), (6) excluding people who exhibited preference reversals (5,736 obs., VSL = €5.160 mill., s.e. 0.745 mill., δ = 7.4%), (7) excluding people who failed the mathematical/cognitive quiz (5792 obs, VSL = €5.250 mill., s.e. 0.750 mill., δ = 7.2%, and (8) excluding those who exhibited a “reversal” and those who failed the cognitive quiz (5,296 obs, VSL = €4.870 mill., s.e. 0.740 mill., δ = 7.2%). The VSL does fall a bit if we (9) exclude the responses to questions where one or both of the plan cost €950, the largest bid used in this survey. The purpose of (2), (3), (4), (6), (7) and (8) is to check for possible learning, fatigue or other survey response effects that might alter the preferences for income and risk reduction over the course of the survey, and possible “anchoring” effects to the cost of the program (see Carlsson and Martinsson 2006, for a discussion of the potential of such effects in conjoint choice experiments, and Ladenburg and Olsen 2006, for an empirical investigation).
We experimented with different ways of constructing the low income dummies (for example, a low-income person is one with annual household income less than €15,000, which corresponds to about a quarter of the sample), and found that the results are qualitatively robust to these changes.
We conjectured that such a negative coefficient might reflect the negative correlation between importance given to the health effects of exposure and the educational attainment of the respondent, but found that the correlation coefficient between the former variable and having a college degree is very low (−0.07). We conclude that this is an unlikely explanation.
This rate is for the 1980s, the most recent period for which estimates are available (see http://www.istitutotumori.mi.it/menuistituto/diparclinici/epidemiologia, and Verdecchia et al. 2001).
These calculations assume that the risk reductions would last 45 years. Patassini et al. suggest that excavation and removal of the contaminated soil affords a 95% risk reduction. This would imply benefits for €49 million, whereas the cost of the remedy is €45 million.
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Acknowledgments
This research was supported by funding from CO.RI.LA and MIUR PRIN Grant 2005134530_002. We wish to thank seminar participants at the U.S. Environmental Protection Agency, FEEM Venice, the 3rd World Congress of Environmental and Resource Economists, Douglas Noonan and two anonymous reviewers for their comments and suggestions on earlier drafts of this paper.
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Appendix
Appendix
1.1 Example of a conjoint choice question
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Alberini, A., Tonin, S., Turvani, M. et al. Paying for permanence: Public preferences for contaminated site cleanup. J Risk Uncertainty 34, 155–178 (2007). https://doi.org/10.1007/s11166-007-9007-8
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DOI: https://doi.org/10.1007/s11166-007-9007-8
Keywords
- Value of a statistical life
- Latent risk reductions
- Individual discount rates
- Conjoint choice questions
- Contaminated sites
- Remediation