Processing Data from Social Dilemma Experiments: A Bayesian Comparison of Parametric Estimators

Observed choices in Social Dilemma Games usually take the form of bounded integers. We propose a doubly-truncated count data framework to process such data. We compare this framework to past approaches based on ordered outcomes and truncated continuous densities using Bayesian estimation and model selection techniques. We find that all three frameworks (i) support the presence of unobserved heterogeneity in individual decision-making, and (ii) agree on the ranking of regulatory treatment effects. The count data framework exhibits superior efficiency and produces more informative predictive distributions for outcomes of interest. The continuous framework fails to allocate adequate probability mass to boundary outcomes, which are often of pivotal importance in these games.Social dilemma games; Hierarchical modeling; Bayesian simulation; Common property resource

The effect of risk context on the Value of Statistical Life: a Bayesian meta-model

This paper presents the first value of statistical life (VSL) meta-analysis that empirically estimates correction factors for 'out of context' benefits transfer (BT) purposes. In the field of mortality risk reductions elicited willingness to pay values in one risk context, say road safety, are frequently applied in other risk contexts like air pollution. However, differences in risk perception and the population at risk across contexts are likely to result in diverging VSL estimates. In a meta-analysis of 26 international stated preference studies, a Bayesian model is estimated regressing contingent values for mortality risk reductions, originating from three different risk contexts, on the characteristics of the risk reduction itself and additional variables characterizing the underlying studies. A willingness to pay (WTP) premium for mortality risk reductions in the air pollution and general mortality risk context relative to improving road safety is observed. Evaluated at the mean, road safety VSL estimates should be multiplied by a factor 1. 8 before being applicable in the air pollution context. Moreover, in an illustrative BT exercise we find limited overlap in the set of context specific predictive VSL distributions. Consequently, 'out of context' BT results in a substantial over- or underestimation of the VSL. © 2011 The Author(s)

Protecting Electricity Networks from Natural Hazards

This handbook supports OSCE participating States in better protecting critical electrical energy infrastructure from natural hazards. By providing risk management options, tools and case studies, it is designed as a guide for policy-makers, state authorities, transmission networks operators and regulators in charge of protecting energy networks. In recent years the risk of supra-national power blackouts in the OSCE area causing significant economic losses has increased. One contributing factor is that extreme weather conditions occur more frequently. Another is an increased connectivity of power and telecommunication infrastructures and a higher technical complexity of the grid due to a changing energy mix, leaving industrial and commercial companies, the public and the private sector at risk

Meta-analysis of nature conservation values in Asia & Oceania: Data heterogeneity and benefit transfer issues

We conduct a meta-analysis (MA) of around 100 studies valuing nature conservation in Asia and Oceania. Dividing our dataset into two levels of heterogeneity in terms of good characteristics (endangered species vs. nature conservation more generally) and valuation methods, we show that the degree of regularity and conformity with theory and empirical expectations is higher for the more homogenous dataset of contingent valuation of endangered species. For example, we find that willingness to pay (WTP) for preservation of mammals tends to be higher than other species and that WTP for species preservation increases with income. Increasing the degree of heterogeneity in the valuation data, however, preserves much of the regularity, and the explanatory power of some of our models is in the range of other MA studies of goods typically assumed to be more homogenous (such as water quality). Subjecting our best MA models to a simple test forecasting values for out-of-sample observations, shows median (mean) forecasting errors of 24 (46) percent for endangered species and 46 (89) percent for nature conservation more generally, approaching levels that may be acceptable in benefit transfer for policy use. We recommend that the most prudent MA practice is to control for heterogeneity in regressions and sensitivity analysis, rather than to limit datasets by non-transparent criteria to a level of heterogeneity deemed acceptable to the individual analyst. However, the trade-off will always be present and the issue of acceptable level of heterogeneity in MA is far from settle

The Effect of Consumer Risk Perceptions on the Propensity to Purchase GM Foods in Romania

This study evaluates consumer purchase propensity for genetically modified (GM) food products in Romania, shedding light on consumer preferences in developing Eastern European nations. Results based on a bivariate probit model of purchase propensity for GM sunflower oil and table potatoes show that consumers in Romania are generally opposed to GM food consumption, similar to consumers in Western Europe, but contrary to consumers in many developing nations. Purchase probabilities carry across GM foods, indicating homogeneity in consumer risk perceptions. Additionally, income levels and risk perceptions are the primary drivers of purchase decisions, whereas positive or beneficiary product attributes (e.g., health, environmental, economic) are currently not considered by consumers. Negative consumer risk perceptions of GM goods in Romania will likely make the implementation of these crops for economic development purposes difficult