Using stated preference methods to inform public health decision making

Croatia has recognised the importance of prevention programmes in the field of public health, although their effectiveness is not satisfactory due to the low population response, which has a negative impact on the rationalisation of public spending. One of the possible solutions is to consider the stated preferences of the target population. Therefore, the main objective of this study is to evaluate the use of stated preference methods in improving public health prevention programmes. For the purpose of the study, a questionnaire was designed using three different methods - Discrete Choice Experiment (DCE), Contingent Rating method (CR) and Best-Worst Scaling method (BWS). This study shows that the attributes of the Croatian cervical cancer screening programme are significantly associated with the respondents' utility level, which in turn is related to women's response. Since BWS, DCE, and CR measure the same construct - utility - we can say that convergent validity partially confirms the external validity of the methods. The author concludes that it is necessary to implement market principles, i.e. the demand-side analysis using stated preference methods, in the planning, implementation and re-evaluation of public health programmes.

Economics and the Endangered Species Act

Economics plays strong roles in the design, implementation, and evaluation of the Endangered Species Act (ESA). First, the ESA’s language allows for economic analysis of critical habitat designations, recovery plan implementations, listing postponements, and the design of habitat-conservation plans. Extensive administrative changes to the ESA in the 1990s were designed to reduce economic costs and to elicit landowners’ cooperation. These reforms were partly motivated and guided by economic analysis. Second, economic analysis plays a role in providing credible estimates of the economic costs of ESA implementation. Cost estimates are highly variable and likely to depend on species’ characteristics and the effectiveness of recovery programs. Emerging evidence suggests that the 1990 reforms are reducing costs and increasing effectiveness. Third, economic science contributes to estimation of benefits. Because of the “public goods” nature of nearly all ecosystem and species conservation efforts, estimates must be based on stated preference methods. This use leads to difficulties in establishing the authenticity of benefits estimates. Also, research suggests that benefits estimates are highly sensitive to the spatial nature of the market (beneficiaries’ geographic locations). Future research needs to tackle both authenticity and spatial issues. Fourth, benefit–cost analysis (BCA) is required by law to inform many resource decisions affecting ecosystem and species conservation. Four illustrative BCAs show that whether benefits exceed costs is highly dependent on the authenticity of benefits based on stated preference methods and assumptions about the spatial nature of the market. Substantial uncertainty accompanies both benefit and cost estimates.

State of the Art of Contingent Valuation

The contingent valuation method (CVM) is a stated preference approach to the valuation of non-market goods. It has a 50+-year history beginning with a clever suggestion to simply ask people for their consumer surplus. The first study was conducted in the 1960s and over 10,000 studies have been conducted to date. The CVM is used to estimate the use and non-use values of changes in the environment. It is one of the more flexible valuation methods, having been applied in a large number of contexts and policies. The CVM requires construction of a hypothetical scenario that makes clear what will be received in exchange for payment. The scenario must be realistic and consequential. Economists prefer revealed preference methods for environmental valuation due to their reliance on actual behavior data. In unguarded moments, economists are quick to condemn stated preference methods due to their reliance on hypothetical behavior data. Stated preference methods should be seen as approaches to providing estimates of the value of certain changes in the allocation of environmental and natural resources for which no other method can be used. The CVM has a tortured history, having suffered slings and arrows from industry-funded critics following the Exxon Valdez and British Petroleum (BP)–Deepwater Horizon oil spills. The critics have harped on studies that fail certain tests of hypothetical bias and scope, among others. Nonetheless, CVM proponents have found that it produces similar value estimates to those estimated from revealed preference methods such as the travel cost and hedonic methods. The CVM has produced willingness to pay (WTP) estimates that exhibit internal validity. CVM research teams must have a range of capabilities. A CVM study involves survey design so that the elicited WTP estimates have face validity. Questionnaire development and data collection are skills that must be mastered. Welfare economic theory is used to guide empirical tests of theory such as the scope test. Limited dependent variable econometric methods are often used with panel data to test value models and develop estimates of WTP. The popularity of the CVM is on the wane; indeed, another name for this article could be “the rise and fall of CVM,” not because the CVM is any less useful than other valuation methods. It is because the best practice in the CVM is merging with discrete choice experiments, and researchers seem to prefer to call their approach discrete choice experiments. Nevertheless, the problems that plague discrete choice experiments are the same as those that plague contingent valuation. Discrete choice experiment–contingent valuation–stated preference researchers should continue down the same familiar path of methods development.

Comparing treatments to reduce hypothetical bias in choice experiments regarding organic food

Hypothetical bias is one of the strongest criticisms brought to stated preference methods. We evaluate and compare the use of Cheap Talk and Honesty Priming as methods to mitigate such bias. Our study analyses the demand for organic food products in the UK, and the results reveal a core of consumers with positive willingness to pay (WTP) for organic. However, when correcting for hypothetical bias, consumers appear to be willing to pay even more for other attributes. Most importantly, the results show that implementing mechanisms to correct for hypothetical bias are efficient to reduce WTP, with Cheap Talk having a higher overall significance than Honesty Priming.

A query theory account of a discrete choice experiment under oath

One limitation of stated-preference methods is the formation of hypothetical bias. To address this, the honesty oath has been used as an ex ante technique to reduce hypothetical bias. Our study provides a query account of the honesty oath in a discrete-choice experiment setting by using Query Theory to examine the mechanism behind the effectiveness of the honesty oath. Our results show that the honesty oath can change the content and order of queries; potentially reducing hypothetical bias in discrete choice experiments. The study suggests the potential usefulness of Query Theory in examining thought processes of respondents in valuation studies.

Econometrics of Stated Preferences

Stated preference methods are used to collect individual-level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article is data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.

Valuation of Health Risks

Many public policies and individual actions have consequences for population health. To understand whether a (costly) policy undertaken to improve population health is a wise use of resources, analysts can use economic evaluation methods to assess the costs and benefits. To do this, it is necessary to evaluate the costs and benefits using the same metric, and for convenience, a monetary measure is commonly used. It is well established that money measures of a reduction in health risks can be theoretically derived using the willingness-to-pay concept. However, because a market price for health risks is not available, analysts have to rely on analytical techniques to estimate the willingness to pay using revealed- or stated-preference methods. Revealed-preference methods infer willingness to pay based on individuals’ actual behavior in markets related to health risks, and they include such approaches as hedonic pricing techniques. Stated-preference methods use a hypothetical market scenario in which respondents make trade-offs between wealth and health risks. Using, for example, a random utility framework, it is possible to directly estimate individuals’ willingness to pay by analyzing the trade-offs they make in the hypothetical scenario. Stated-preference methods are commonly applied using contingent valuation or discrete choice experiment techniques. Despite criticism and the shortcomings of both the revealed- and stated-preference methods, substantial progress has been made since the 1990s in using both approaches to estimate the willingness to pay for health-risk reductions.