Abstract
The value of statistical life is an essential parameter used in ascribing monetary values to the mortality costs of air pollution in health risk analyses. However, this willingness to pay estimate is virtually non-existent for most developing countries. In the absence of local estimates, two major benefit transfer approaches lend themselves to the estimation of the value of statistical life: the value transfer method and the meta-regression analysis. Using Nigeria as a sample country, we find that the latter method is better tailored than the former for incorporating many characteristics that vary between study sites and policy sites into its benefit transfer application. It is therefore likely to provide more accurate value of statistical life predictions for very low-income countries. Employing the meta-regression method, we find Nigeria's value of statistical life estimate to be $489,000. Combining this estimate with dose response functions from the epidemiological literature, it follows that if Nigeria had mitigated its 2006 particulate air pollution to the World Health Organisation standards, it could have avoided at least 58,000 premature deaths and recorded an avoided mortality related welfare loss of about $28 billion or 19 % of the nation's GDP for that year. © 2012 Springer Science+Business Media B.V.