Impact of Socio-demographic Factors on Willingness to Pay for the Reduction of a Future Health Risk

Knowledge of socio-demographic factors affecting attitudes to and perception of risk is an important instrument in enhancing efficiencies of interventions. The authors evaluated whether socio-demographic variables affected attitudes to an environmental issue (securing future drinking water). An important aspect was the delay between time of environmental pollution and time of human exposure and thereby potential health risk. Gender, education, place of residence and age all influenced the extent to which individuals were willing to allocate present resources to alleviate a future problem. Specifically, people above the age of 50 appeared more reluctant to pay for an intervention against a future potential health threat. The authors found a significant correlation between attitude and willingness to pay (WTP). In the authors' scenarios, the WTP variable worked more as a dichotomous variable than as a continuous variable, stressing the importance and relevance of the WTP=0 answers.     

Statistical inference using the g or K point pattern spatial statistics

Spatial point pattern analysis provides a statistical method to compare an observed spatial pattern against a hypothesized spatial process model. The G statistic, which considers the distribution of nearest neighbor distances, and the K statistic, which evaluates the distribution of all neighbor distances, are commonly used in such analyses. One method of employing these statistics involves building a simulation envelope from the result of many simulated patterns of the hypothesized model. Specifically, a simulation envelope is created by calculating, at every distance, the minimum and maximum results computed across the simulated patterns. A statistical test is performed by evaluating where the results from an observed pattern fall with respect to the simulation envelope. However, this method, which differs from P. Diggle's suggested approach, is invalid for inference because it violates the assumptions of Monte Carlo methods and results in incorrect type I error rate performance. Similarly, using the simulation envelope to estimate the range of distances over which an observed pattern deviates from the hypothesized model is also suspect. The technical details of why the simulation envelope provides incorrect type I error rate performance are described. A valid test is then proposed, and details about how the number of simulated patterns impacts the statistical significance are explained. Finally, an example of using the proposed test within an exploratory data analysis framework is provided.