Conducting a cost-benefit analysis involves a comparison of a project’s total cost and the aggregate benefits of the beneficiary population. In the aggregation process of benefits estimates, individual-level estimates – such as the sample mean or median willingness to pay WTP – are extrapolated to those population (Morrison, 2000; Bateman et al., 2006). This is a common practice, especially within choice experiments’ literature (CE) (Kopp et al., 1997; Breidert et al., 2006). The issue raised by this procedure is the potential bias in the estimates of aggregate values calculated based on estimates from an unrepresentative and/or a small sample. According to Bateman et al. (2006), this bias may be traced to two related sources: 1- the average
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2006). In the empirical valuation literature, more focus has been devoted to the spatial impact or the effect of distance from the resource on its valuation than the socioeconomic or demographic characteristics. That is based on the notion that the average value is expected to decay with distance from the resource in question. Within the stated preference approach, there is a large body of research – dominated by contingent valuation studies (CV) – confirming the sensitivity of WTP measures to spatial aspects; distance precisely (e.g. Sotherland and Walsh, 1985; Pate and Loomis, 1997; Loomis and Mueller, 2013; Mueller, 2014). An example that illustrates the potential bias in aggregate estimates when not considering WTP sensitivity to spatial aspects is derived from Mueller’s (2014) study in the context of forested watershed restoration. The study found that failure to account for the impact of distance to restoration area results in an upwardly biased aggregate restoration benefits estimate of $218,000 per month as compared to $42,000 when controlling for distance in the …show more content…
(2006, P.3) argue that “ the issue of spatial representativeness is also important”, hence, drawing a line between the effects of spatial and population’s characteristics . In a broader sense, one could argue that spatial aspects - like distance from the resource- do not affect individuals’ preferences and valuation of a commodity in a direct manner. Rather, it operates – along with other factors like socioeconomic and demographic variables- to form and enhance individuals’ perception of risk(1) associated with the environmental hazard intended to be alleviated by the policy under valuation(2). Moreover, the influence of spatial aspects on the perceived risk of a specific environmental hazard is unlikely to be linear. Rather, it may be augmented or attenuated depending on socioeconomic and demographic characteristics as well as the experience or familiarity with that hazard. Variation in the level of risk perception of environmental hazards, on the other hand, is substantiated to have a significant impact on the WTP for reductions or increased safety from those hazards. For example, in the context of technological hazards, McDaniels et al. (1992) found that WTP to reduce risks associated with familiar hazards is mostly influenced by individuals’ perceived risk; Sukharomann
2006). In the empirical valuation literature, more focus has been devoted to the spatial impact or the effect of distance from the resource on its valuation than the socioeconomic or demographic characteristics. That is based on the notion that the average value is expected to decay with distance from the resource in question. Within the stated preference approach, there is a large body of research – dominated by contingent valuation studies (CV) – confirming the sensitivity of WTP measures to spatial aspects; distance precisely (e.g. Sotherland and Walsh, 1985; Pate and Loomis, 1997; Loomis and Mueller, 2013; Mueller, 2014). An example that illustrates the potential bias in aggregate estimates when not considering WTP sensitivity to spatial aspects is derived from Mueller’s (2014) study in the context of forested watershed restoration. The study found that failure to account for the impact of distance to restoration area results in an upwardly biased aggregate restoration benefits estimate of $218,000 per month as compared to $42,000 when controlling for distance in the …show more content…
(2006, P.3) argue that “ the issue of spatial representativeness is also important”, hence, drawing a line between the effects of spatial and population’s characteristics . In a broader sense, one could argue that spatial aspects - like distance from the resource- do not affect individuals’ preferences and valuation of a commodity in a direct manner. Rather, it operates – along with other factors like socioeconomic and demographic variables- to form and enhance individuals’ perception of risk(1) associated with the environmental hazard intended to be alleviated by the policy under valuation(2). Moreover, the influence of spatial aspects on the perceived risk of a specific environmental hazard is unlikely to be linear. Rather, it may be augmented or attenuated depending on socioeconomic and demographic characteristics as well as the experience or familiarity with that hazard. Variation in the level of risk perception of environmental hazards, on the other hand, is substantiated to have a significant impact on the WTP for reductions or increased safety from those hazards. For example, in the context of technological hazards, McDaniels et al. (1992) found that WTP to reduce risks associated with familiar hazards is mostly influenced by individuals’ perceived risk; Sukharomann