No abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in SUMMARYSelectivity bias caused by protest responses in Contingent Valuation studies can be detected and corrected by means of sample selection models. This paper compares two methods: the Heckman 2-steps method and the full ML, applied to data on forest recreation -where WTP is elicited as a continuous variable. Either method has its own drawback: computational complexity for the ML method, susceptibility to collinearity problems for the 2-steps method. The latter problem is observed in our best fitting specification, with the ML estimator outperforming the 2-steps. In this application, overlooking the effect of protest responses would cause an upwards bias of the final estimates of WTP.Keywords: Contingent valuation, protest responses, sample selection, MLE, two-steps method JEL: C35, C51, C81, D60, H41, Q26 NON TECHNICAL SUMMARYContingent Valuation studies are often characterized by a considerable amount of protest responses, which may have an important effect on the final estimates if the protest responses are not randomly distributed across the sample. If the standard procedure of censoring protest responses is adopted, the estimates may be biased. Sample selection models can detect and -if necessary-correct selectivity bias. We apply a sample selection model to data on valuation of forest resources for recreational use, where WTP responses are obtained through a mixed dichotomous choice-open ended elicitation method. Dealing with continuous data for WTP allows us to apply the Heckman 2-steps method, and compare it to the full ML estimator. Either method has its own drawback: computational complexity for the ML method, susceptibility to collinearity problems for the 2-steps method. The latter is observed in our model. The results show that censoring protest responses in this study would lead to overestimates of the willingness to pay. CONTENTS

In contingent valuation surveys the category of zero bidders refers to individuals that are not willing to pay anything for the programme under analysis. Specific questions can help to identify true zero values, coming from people that are indifferent to the programme, separately from protest responses: the latter are generally excluded from the analysis. This paper introduces a mixture-sample selection model that takes into account both zero values and protest responses in the estimates. The model is applied to the valuation of a traffic calming scheme aimed at reducing risks for residents in three villages in the north-east of England.

No abstract

The dichotomous choice contingent valuation method can be used either in the single or double bound formulation. The former is easier to implement, while the latter is known to be more efficient. We analyse the bias of the ML estimates produced by either model, and the gain in efficiency associated to the double bound model, in different experimental settings. We find that there are no relevant differences in point estimates given by the two models, even for small sample size. The greater efficiency of the double bound is confirmed, although differences tend to reduce by increasing the sample size. Provided that a reliable pretest is conducted, and the sample size is large, use of the single rather than the double bound model is warranted.