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The present study aims at assessing dairy farmers’ preferences over different technological options related to the anaerobic digestion technology. A Choice Experiment study was conducted in Arborea, a NVZ dairy district located in Sardinia, Italy. The results show that profitability of the investment is a general driver of the choice. Heterogeneity of preferences is observed, especially as regards the options of investment on-farm or off-farm. Farmers who are especially interested in an investment on-farm are characterized by higher awareness of energy issues; while farmers with excess waste load would prefer an off-farm investment. Digestate treatment options have practically been ignored in our choice experiments: farmers do not seem aware of the opportunities offered by further processing of the digestate to improve management of the farm waste. New regulations associated with the Circular Economy EU package could increase the farmers’ perception of economic benefits associated with the adoption of anaerobic digestion technologies.

Observations with protest votes in contingent valuation surveys could result in biased estimates of the willingness to pay measure, if treated as expressions of genuine indifference to public good or simply removed from the analysis. We propose an elicitation design and a sample selection model that allow correction of the possible bias due to protest votes. An application shows that selection bias can sensibly affect the estimate of the value of the public good. Inference in sample selection models may sometimes be difficult due the flatness of the likelihood function that causes uncertainty about the presence of selection bias. Even so, we recommend use of the sample selection model, since it takes into account this uncertainty., Statistica; Vol 61, No 3 (2001)

This study adopts a discrete choice modelling methodology to evaluate individuals' preferences over planning alternatives for an urban site of environmental interest. Since such projects involve some uncertainty and irreversibility, a special attention is devoted to the estimation of the quasi-option values which are associated to project development. Two distinct measures for the quasi-option value are estimated, and both coefficients indicate that the public places a significant value on reduction of the possibility of adverse irreversible effects: a more prudent development strategy is valued about four times more than a procedure that provides a lesser hedge against undesired outcomes. Furthermore, the study involved elicitation of intertemporal preferences over projects with different time spans, and estimation of the implicit discount rates: the values obtained seem high if compared to standard discount rates applied to public projects, but not far from current interest rates on consumption.

The sample selection model is based upon a bivariate or a multivariate structure, and distributional assumptions are in this context more severe than in univariate settings, due to the limited availability of tractable multivariate distributions. While the standard FIML estimation of the selectivity model assumes normality of the joint distribution, alternative approaches require less stringent distributional hypotheses. As shown by Smith (2003), copulas allow great flexibility also in FIML models. The copula model is very useful in situations where the applied researcher has a prior on the distributional form of the margins, since it allows separating their modelling from that of the dependence structure. In the present paper the copula approach to sample selection is first compared to the semiparametric approach and to the standard FIML, bivariate normal model, in an illustrative application on female work data. Then its performance is analysed more thoroughly in an application to Contingent Valuation data on recreational values of forests.

Distributional assumptions are crucial in the estimation of the value of public projects assessed by means of contingent valuation analyses, and it would seem obvious that tests for model specification should play an important part in the statistical analysis. It can be observed, though, that when the competing hypotheses are non nested, the choice of the model is often based on heuristic grounds, or, at most, on deterministic selection model criteria such as Akaike's (1973). In this paper we study two alternative, probabilistic, approaches to checking model specification, that, like Akaike's, are based on the Kullback-Leibler Information Criterion (KLIC): the model selection testing proposed by Vuong (1989) and the non nested model test proposed by Cox, in the simulated approach of Pesaran and Pesaran (1993). The three approaches are confronted by comparing their performance in selecting among different models applied to simulated contingent valuation data. Our results seem to warrant the use of the Cox test for medium-large size samples, while for small size samples its performance is less satisfactory. When the data set is small, use of a model selection method may be preferred to model testing. In this case, the Vuong model selection testing is recommended as an alternative to the deterministic approach of the Akaike's criterion.

Selectivity 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.

When modelling data generated from a discrete choice contingent valuation question, the treatment of zero bids affects the welfare estimates. Zero bids may come from respondents who are not interested in the provision of the public good; alternatively, some zero-bidders may be protesting about the valuation exercise, but hold positive values for the good. In this paper we investigate the effect of different levels of information on zero-bidders on welfare estimates for the population. We find that different strategies of identification may have non-trivial effects. We recommend use of full debriefing questions for zero-bidders, and use of sample selection models to correct for bias caused by protest behaviour.

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, and no estimator can be said to be less biased than the other. The greater efficiency of the double bound is confirmed, although differences tend to reduce by increasing the sample size. Provided that a reliable pre-test is conducted, and the sample size is large, use of the single rather than the double bound model is warranted.