Efficient and robust willingness-to-pay designs for choice experiments: some evidence from simulations.

We apply a design efficiency criterion to construct conjoint choice experiments specifically focused on the accuracy of marginal estimates. In a simulation study and a numerical example, the resulting optimal designs are compared to alternative designs suggested in the literature. It turns out that optimal designs not only improve the estimation accuracy of the marginal, as expected on the basis of the nature of the efficiency criterion, but they also considerably reduce the occurrence of extreme estimates, which also exhibit smaller deviations from the real values. The proposed criterion is there for evaluable for non-market valuation studies as it reduces the sample size required for a given degree of accuracy and it produces estimates with fewer outliers.Willingness-to-pay; Optimal design; Choice experiments; Conditional logit model; Robust;

The importance of attribute interactions in conjoint choice design and modeling.

Within the context of choice experimental designs, most authors have proposed designs for the multinomial logit model under the assumption that only the main effects matter. Very little attention has been paid to designs for the attribute interaction models. In this paper, we present Bayesian D-optimal interaction-effects designs for the multinomial logit models and compare their predictive performances with those of main-effects designs. We show that in situations where a researcher is not sure whether or not the attribute interaction effects are present, incorporating interaction effects into both design stage and model estimation stage is most robust against misspecification of the underlying model for making precise predictions.Bayesian; Choice; Interaction effects; Experimental design; Predictions; Multinomial logit;

A comparison of different Bayesian design criteria to compute efficient conjoint choice experiments.

Bayesian design theory applied to nonlinear models is a promising route to cope with the problem of design dependence on the unknown parameters. The traditional Bayesian design criterion which is often used in the literature is derived from the second derivatives of the loglikelihood function. However, other design criteria are possible. Examples are design criteria based on the second derivative of the log posterior density, the expected posterior covariance matrix, or on the amount of information provided by the experiment. Not much is known in general about how well these criteria perform in constructing efficient designs and which criterion yields robust designs that are efficient for various parameter values. In this study, we apply these Bayesian design criteria to conjoint choice experimental designs and investigate how robust the resulting Bayesian optimal designs are with respect to other design criteria for which they were not optimized. We also examine the sensitivity of each design criterion to the prior distribution. Finally, we try to find out which design criterion is most appealing in a non-Bayesian framework where it is accepted that prior information must be used for design but should not be used in the analysis, and which one is most appealing in a Bayesian framework when the prior distribution is taken into account both for design and for analysis.Bayesian design criterion; Posterior density; Expected posterior covariance matrix; Conjoint choice design; Laplace approximation; Fisher information;

Individually adapted sequential Bayesian designs for conjoint choice experiments.

In this paper, we propose an efficient individually adapted sequential Bayesian approach for constructing conjoint choice experiments. It uses Bayesian updating, a Bayesian analysis and a Bayesian design criterion for generating choice-set-designs for each individual respondent based on previous answers of that particular respondent. The proposed design approach is compared with two non-adaptive design approaches (the average customization design proposed by Arora and Huber 2001 and the nearly orthogonal design constructed with Sawtooth software) under various degree of response error and respondent heterogeneity. The simulation study shows that the individually adapted sequential Bayesian approach leads to designs which are robust not only to respondent heterogeneity but also to response error. It turns out that the proposed method outperforms the benchmark methods in all scenarios that we have looked at. In particular, for conditions with high response error (the responses from a respondent can hardly provide proper information about the individual-level parameter and is therefore very challenging for individually adapted choice designs), our approach leads to substantially improvement not only in the precision of the parameter estimates but also in the predictive accuracy when the respondent heterogeneity is large. The new method therefore overcomes the limitation of the recently proposed adaptive polyhedral choice-based question design approach by Toubia et al. (2004), whose method performs well only when the response error is low. Furthermore, our study provides compelling evidence that adapting each respondent's choice sets based on the previous responses of that particular respondent in a Bayesian framework enables one to capture more information for the individual- level parameters and therefore also on the population-level parameters. It is shown that it is substantially better to employ the adaptive approach when the response heterogeneity is high.Adaptive Bayesian design; Conjoint choice experiments; Respondent heterogeneity; Response error;

Experimental designs for environmental valuation with choice-experiments: A Monte Carlo investigation

We review the practice of experimental design in the environmental economics literature concerned with choice experiments. We then contrast this with advances in the field of experimental design and present a comparison of statistical efficiency across four different experimental designs evaluated by Monte Carlo experiments. Two different situations are envisaged. First, a correct a priori knowledge of the multinomial logit specification used to derive the design and then an incorrect one. The data generating process is based on estimates from data of a real choice experiment with which preference for rural landscape attributes were studied. Results indicate the D-optimal designs are promising, especially those based on Bayesian algorithms with informative prior. However, if good a priori information is lacking, and if there is strong uncertainty about the real data generating process - conditions which are quite common in environmental valuation - then practitioners might be better off with conventional fractional designs from linear models. Under misspecification, a design of this type produces less biased estimates than its competitors

Preferences for European unemployment insurance : a question of economic ideology or EU support?

Against the backdrop of proposals to introduce a European unemployment insurance scheme, we study public support for such schemes by conducting a conjoint experiment on support for European unemployment insurance in 13 EU member states. We argue that European-level social policy initiatives and the underlying notions of solidarity cannot be reduced to a one-dimensional concept, but rather include various dimensions. Unemployment schemes vary in their generosity, the conditions for support, their impact on taxation, the extent to which they preclude permanent redistribution between countries, and the EU's role in their administration. Generosity, conditions and taxation are 'domestic' dimensions, since they mainly resonate with domestic policy debates; between-country redistribution and administration are 'cross-border' dimensions, referring to relationships between countries. We expect economic ideology to interact predominantly with domestic dimensions and EU support to interact predominantly with cross-border dimensions. Findings confirm these expectations, with the exception of between-country redistribution and country-level conditionality

Efficient conjoint choice designs in the presence of respondent heterogeneity.

The authors propose a fast and efficient algorithm for constructing D-optimal conjoint choice designs for mixed logit models in the presence of respondent heterogeneity. With this new algorithm, the construction of semi-Bayesian D-optimal mixed logit designs with large numbers of attributes and attribute levels becomes practically feasible. The results from the comparison of eight designs (ranging from the simple locally D-optimal design for the multinomial logit model and the nearly orthogonal design generated by Sawtooth (CBC) to the complex semi-Bayesian mixed logit design) across wide ranges of parameter values show that the semi-Bayesian mixed logit approach outperforms the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it was found that semi-Bayesian mixed logit designs constructed with large heterogeneity parameters are most robust against the misspecification of the values for the mean of the individual level coefficients for making precise estimations and predictions.Keywords:semi-Bayesianmixedlogitdesign,heterogeneity,predictionaccuracy,multinomiallogitdesign,model-robustdesign,D-optimality,algorithmAlgorithm; D-Optimality; Heterogeneity; Model-robust design; Multinomial logit design; Prediction accuracy; Semi-Bayesian mixed logit design;

Venture Capitalists' Evaluations of Start-up Teams: Trade-offs, Knock-out Criteria, and the Impact of VC Experience

The start-up team plays a key role in venture capitalists' evaluations of venture proposals. Our findings go beyond existing research, first by providing a detailed exploration of VCs' team evaluation criteria, and second by investigating the moderator variable of VC experience. Our results reveal utility trade-offs between team characteristics and thus provide answers to questions such as "What strength does it take to compensate for a weakness in characteristic A?" Moreover, our analysis reveals that novice VCs tend to focus on the qualifications of individual team members, while experienced VCs focus more on team cohesion. Data was obtained in a conjoint experiment with 51 professionals in VC firms and analyzed using discrete choice econometric models. (author's abstract

Comparing algorithms and criteria for designing Bayesian conjoint choice experiments.

The recent algorithm to find efficient conjoint choice designs, the RSC-algorithm developed by Sándor and Wedel (2001), uses Bayesian design methods that integrate the D-optimality criterion over a prior distribution of likely parameter values. Characteristic for this algorithm is that the designs satisfy the minimal level overlap property provided the starting design complies with it. Another, more embedded, algorithm in the literature, developed by Zwerina et al. (1996), involves an adaptation of the modified Fedorov exchange algorithm to the multinomial logit choice model. However, it does not take into account the uncertainty about the assumed parameter values. In this paper, we adjust the modified Fedorov choice algorithm in a Bayesian fashion and compare its designs to those produced by the RSC-algorithm. Additionally, we introduce a measure to investigate the utility balances of the designs. Besides the widely used D-optimality criterion, we also implement the A-, G- and V-optimality criteria and look for the criterion that is most suitable for prediction purposes and that offers the best quality in terms of computational effectiveness. The comparison study reveals that the Bayesian modified Fedorov choice algorithm provides more efficient designs than the RSC-algorithm and that the Dand V-optimality criteria are the best criteria for prediction, but the computation time with the V-optimality criterion is longer.A-Optimality; Algorithms; Bayesian design; Bayesian modified Fedorov choice algorithm; Choice; Conjoint choice experiments; Criteria; D-Optimality; Design; Discrete choice experiments; Distribution; Effectiveness; Fashion; G-optimality; Logit; Methods; Model; Multinomial logit; Predictive validity; Quality; Research; RSC-algorithm; Studies; Time; Uncertainty; V-optimality; Value;

A De-biased Direct Question Approach to Measuring Consumers' Willingness to Pay

Knowledge of consumers' willingness to pay (WTP) is a prerequisite to profitable price-setting. To gauge consumers' WTP, practitioners often rely on a direct single question approach in which consumers are asked to explicitly state their WTP for a product. Despite its popularity among practitioners, this approach has been found to suffer from hypothetical bias. In this paper, we propose a rigorous method that improves the accuracy of the direct single question approach. Specifically, we systematically assess the hypothetical biases associated with the direct single question approach and explore ways to de-bias it. Our results show that by using the de-biasing procedures we propose, we can generate a de-biased direct single question approach that is accu-rate enough to be useful for managerial decision-making. We validate this approach with two studies in this paper.Comment: Market Research, Pricing, Demand Estimation, Direct Estimation, Single Question Approach, Choice Experiments, Willingness to Pay, Hypothetical Bia