Using canonical correlation analysis to identify environmental attitude groups: considerations for national forest planning in the southwestern U.S.

As public land management agencies pursue region-specific resource management plans, with meaningful consideration of public attitudes and values, there is a need to characterize the complex mix of environmental attitudes in a diverse population. The contribution of this investigation is to make use of a unique household, mail/internet survey data set collected in 2007 in the Southwestern United States (Region 3 of the U.S. Forest Service). With over 5,800 survey responses to a set of 25 Public Land Value statements, canonical correlation analysis is able to identify 7 statistically distinct environmental attitudinal groups. We also examine the effect of expected changes in regional demographics on overall environmental attitudes, which may help guide in the development of socially acceptable long-term forest management policies. Results show significant support for conservationist management policies and passive environmental values, as well as a greater role for stakeholder groups in generating consensus for current and future forest management policies.

A two-agent dynamic model with an invasive weed diffusion externality: an application to Yellow Starthistle (Centaurea solstitialis L.) in New Mexico.

This paper presents a bioeconomic model for two agents, a cattle rancher and a non-rancher, with interdependencies between their individual effort of invasive weed management and profitability. Dynamic simulations allow us to find numerically the optimal effort of weed control over a 5-year time horizon under a variety of beginning infestation levels. In a base-case scenario without governmental cost-share of control costs, we find that efforts to control the weed are not profitable. The base-case scenarios also indicate that grazing contributes to giving the invasive weed a competitive edge. A second series of simulations include incentive payments for weed control which are set at the minimum level required to have a net positive impact on the rancher's profitability. From these simulations, we find that the level of infestation impacts the size of the incentive necessary to get the agent to control the weeds and that the incentive payments impact the level of effort of the rancher. In addition, results reveal that the higher incentive payments for lower levels of weed infestations reduce the total cumulative incentive payments over time. Efficient policies directed towards management of invasive weeds may need to be adjusted for each individual case.

Patient preferences for depression treatment programs and willingness to pay for treatment.

BACKGROUND: Current estimates of the societal costs of depression do not include estimates of how much individuals diagnosed with Major Depressive Disorder (MDD) would be willing to pay to eliminate their depression or how much they would have to be paid in order to accept continued depression. Choice-question data and discrete-choice random-utility models provide a useful method for valuing changes in mental health and mental-health treatment programs. AIMS OF THE STUDY: (i) To demonstrate how choice questions and discrete-choice random-utility models can be used to estimate preferences over treatment programs for depression and willingness-to-pay (WTP) to eliminate depression. (ii) To investigate whether consumption of goods provides less utility when one is depressed (an anhedonia effect) and, if so, the magnitude of the effect. (iii) To model and estimate the extent of heterogeneity in preferences for treatment programs for depression. (iv) To derive preliminary estimates of WTP and willingness-to-accept (WTA) for eliminating depression, both with, and without side effects. METHODS: The data are from a choice-question survey of 104 individuals diagnosed with a new episode of MDD. Individuals indicated their preferred treatment from options that varied in effectiveness, hours of psychotherapy per month, use of anti-depressants, money costs, and side effects (weight gain, little or no interest in sex, inability to orgasm). Choices over treatment alternatives, including no treatment, were modeled using a discrete-choice random-utility model. Preference parameters were estimated using maximum likelihood estimation. RESULTS AND DISCUSSION: Estimated WTP to eliminate MDD is large but side effects can substantially reduce WTP. Preferences over treatment programs, and WTP, vary as a function of the individual's age, gender, income category, body-mass-index, and family composition. Some depressed individuals seeking treatment have a high estimated probability of choosing no treatment. Depression is found to have a direct, negative impact on utility, as expected. It also has an indirect effect: utility from consumption is found to decrease the more severe one's level of depression. The magnitude of this indirect effect is estimated. This indirect effect manifests itself by driving a wedge between estimated WTP to eliminate depression and WTA to accept continued depression. Preferences for treatment are only being estimated for those individuals who are referred to or directly seek treatment at a mental-health clinic, not for the general population of depressed. The estimates are plausible but the sample size is small, so caution is warranted. IMPLICATIONS: The WTP estimates suggest that depression imposes a high cost on society beyond the cost of treatment and the cost of lost output. WTP should be included in any benefit-cost analysis of whether additional societal resources should be allocated to the treatment of depression. Side effects from anti-depressants also impose a large cost on society. Estimates such as the ones reported here could provide a mechanism for better matching treatment programs to the patient and thus potentially reduce non-adherence. The WTP estimates suggest that the pharmaceutical industry could increase revenues by making anti-depressants more effective or reducing their side effects.

Using patient characteristics and attitudinal data to identify depression treatment preference groups: a latent-class model.

A latent-class model is used to identify and characterize groups of patients who share similar attitudes towards treating depression. The results predict the probability of preference-group membership on the basis of observable characteristics and answers to attitudinal questions. Understanding the types of preference groups that exist and a patient's probability of membership in each of the groups can help clinicians tailor the treatment to the patient and may increase patient adherence. One hundred four depressed patients completed a survey on attitudes towards treatment of Major Depressive Disorder. Analysis shows that treatment preferences vary among depressed patients. Three classes are identified that differ in their sensitivity to treatment costs and side effects. One class cares primarily about treatment effectiveness; side effects and the cost of treatment have little impact on this class's treatment decisions. Another class is highly sensitive to cost and side effects. A third class is somewhat sensitive to cost and side effects. Younger and male patients are more likely to be sensitive to treatment costs and side effects.