Suchergebnisse

Valuing Climate Change Mitigation discusses the role of uncertainty in valuing the benefits of climate change mitigation policies using contingent valuation and choice experiment techniques. It treats climate change using three dimensions of uncertainty: scenario, policy and preference. Conceptual frameworks are advanced to account simultaneously for these various dimensions of uncertainty. The authors then explore the impact of introducing these uncertainties into benefit estimates for the Australian Carbon Pollution Reduction Scheme. -- 'Written in a clear and mostly nontechnical manner, while offering the right amount of details, this book would be certainly useful to academics and practitioners aiming at understanding peoples' attitudes towards different climate change mitigation options. Ultimately, different sources of uncertainty are important not only for climate change policies, but also for many other environmental issues. This book provides a useful road map, together with numerous practical details, that would be very helpful for anybody wanting to encompass uncertainty in SP surveys.' (Carlo Fezzi, Journal of Economic Literature Reviews).

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

Abstract
Evidence-based policies require well-established research and reliable data. One of the major difficulties in delivering such data for cultural policies lies in measuring culture, expressing its ephemeral nature in numbers and indicators, as research used to do in other sectors. Stated-preference based non-market valuation is one of the few tools able to reveal the benefits that cultural goods deliver to the society for cost–benefit analysis. The prevalent problem in this kind of studies is poorly defined, ambiguous goods. When attempting to define them, we cannot forget that the essence of art and culture, which is in itself worth measuring, is quality. The goal of the article was to review previous uses of quality measurements of arts and culture in stated-preference based non-market valuation research, most of which are dedicated to performing arts and cultural heritage.

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.