Aim of this paper is to analyze the determinants of social preferences toward measures to improve water ecosystem services, that European countries should implement in compliance with the Water Framework Directive 2000(60). A Choice Experiment study has been conducted to evaluate social costs and benefits associated with measures to improve water ecosystem services in Sardinia, Italy. Socio-psychological factors complement usual socio-demographic and economic factors to analyze the determinants of individuals’ preferences. After fitting the choice data by means of a Random Parameter Logit model, we apply a set of Posterior Analysis methods to examine the conditional distributions for identification of socioeconomic, demographic and psychological factors influencing individual preferences and WTP. This approach proves useful to uncover a variety of causal effects that could not be estimated efficiently by Mixed Logit models with heterogeneity in mean and/or variance.

The Socio-Psychological Determinants of Preferences toward Ecosystem Services: a Choice Experiment study with Posterior Analysis of Conditional Preference Distributions

Strazzera, Elisabetta;Meleddu, Daniela;Mura, Marina;Statzu, Vania
2018-01-01

Abstract

Aim of this paper is to analyze the determinants of social preferences toward measures to improve water ecosystem services, that European countries should implement in compliance with the Water Framework Directive 2000(60). A Choice Experiment study has been conducted to evaluate social costs and benefits associated with measures to improve water ecosystem services in Sardinia, Italy. Socio-psychological factors complement usual socio-demographic and economic factors to analyze the determinants of individuals’ preferences. After fitting the choice data by means of a Random Parameter Logit model, we apply a set of Posterior Analysis methods to examine the conditional distributions for identification of socioeconomic, demographic and psychological factors influencing individual preferences and WTP. This approach proves useful to uncover a variety of causal effects that could not be estimated efficiently by Mixed Logit models with heterogeneity in mean and/or variance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/256254
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