Comparing contingent valuation and conjoint analysis: the effect of multiple. alternatives and question design on explaining divergences

Comparing contingent valuation and conjoint analysis: the effect of multiple alternatives and question design on explaining divergences Abstract A split-sample design is used to compare a contingent valuation (simple-dichotomous format) and a conjoint analysis (ranking format) experiment. Manipulating the designs of the experiments in two samples, we investigate if differences derive from question designs or from the presence of a third alternative in conjoint analysis. We compare models, welfare measures and the number of times different treatments shared by all formats are chosen over the status quo for simple dichotomous, ranking and recoded choice data sets. The results show differences between the ranking and the other formats (mainly due to the second rank), but not between contingent valuation and choice. Analysis of sets with dominated treatments shows that respondents choose more often do something alternatives in conjoint analysis than in contingent valuation, even when the third alternative is irrelevant in the decision. Thus, the differences between these formats can be explained by the presence of a third alternative in conjoint analysis techniques. In addition, we find that the practice of presenting several questions in conjoint analysis does not seem to have an undesired effect in the estimated models. Keywords: stated preferences, elicitation formats, status quo 1

1. Introduction Contingent valuation and conjoint analysis are techniques widely used for environmental valuation. Although they pursue the same objective (estimating the willingness to pay (WTP) for a good with no price), they present differences in the procedures and aspects involved in the valuation processes that could explain differences between hicksian surplus measures obtained with each technique. Since they are close cousins in the family of stated preferences techniques, it seems difficult to conclude which one is better for valuing environmental goods (Boyle et al., 2001). However, identifying the causes of different results could be useful to discern which one is more appropriate for different contexts and valuation scenarios. Previous studies have found differences between contingent valuation and conjoint analysis, generally obtaining higher values in conjoint analysis, especially in ranking models (Magat et al., 1988; Ready et al., 1995; Boxall et al., 1996; Stevens et al., 2000; Sikaamaki and Layton, 2007). Boxall et al. (1996) also found higher values for contingent valuation models compared to choice experiments, although they obtained convergent validity for one specific model. Magat et al. (1988), however, found higher values for choice tasks, although they used a paired comparison format. Sikaamaki and Layton (2007) performed a comparison between a contingent valuation and a conjoint analysis exercise (rating/ranking) designed for valuing a good with variation only in the levels of one attribute. They found similar results between contingent valuation and the models using only the first rank (choice) and significant differences when all rankings were taken into account. It has been pointed that the econometric model used for ranking models could be one cause of these differences (Ben-Akiva, Morikawa and Shiroishi 1991; Foster and Mourato, 2002). This model, the rank-ordered logit (Beggs et al., 1981), pools the several ranks made by respondents in the same function in spite of the potential presence of heterogeneity or 2

ranking inconsistency. Since Caparrós et al. (2008) demonstrated that making a ranking of two alternatives plus status quo we can use the first rank as if it were a choice, it would be interesting knowing if the second rank can be used as additional information to the first rank information (choice) in this case. While the divergence in the results offered by different formats has been demonstrated, there are still questions about the origin of these divergences. It would be interesting to analyze if contingent valuation and conjoint analysis elicitation formats reveal the same preferences about the same alternatives (same attribute description). This can be done analyzing the number of times an alternative is chosen over the status quo in each case (a ranking is needed for this). Another potential source of divergence in preferences can derive from the different designs of the questions/sets of alternatives used in contingent valuation and in conjoint analysis. While conjoint analysis present the environmental good being valued decomposed on its attributes and together with other alternatives that differ on the levels of these attributes, contingent valuation only provides one option for the environmental good whose attributes are not explicitly decomposed. The only common elements in the designs used in both techniques are the presence of a status quo alternative and the variation of the bid among questions and alternatives. Related with the presentation of the questions, there is also a procedure widely accepted in conjoint analysis but not in contingent valuation. Conjoint analysis exercises usually offer several choice/rank sets of alternatives in the same questionnaire, while this practice is not accepted in contingent valuation due to the presence of order effects (Clark and Fresen, 2008). There is no apparent reason to justify the repetition of sets in conjoint analysis, since the same order effect could appear when applying this technique. 3

The different formats and designs used in each technique could led to the respondents to focus on the trade-offs between attributes and alternatives in conjoint analysis, and on the trade-offs between the offered bid and the status quo in contingent valuation. Additionally, the presence of additional alternatives could generate asymmetry in the response if respondents focus more on the do something alternatives and pay less attention to the status quo. This could generate choices of do something alternatives as a consequence of this asymmetry and not deriving from true preferences. In the ranking format this can translate in an inertia effect that led respondents to choose a do something alternative in the second rank only because a do something alternative was chosen in the first rank. With these ideas in mind, we made a survey that used a split-sample design to compare two contingent valuation and two conjoint analysis experiments. In these exercises we included specific designs that allowed for testing the presence of differences in the preferences about a subset of alternatives (treatments) shared by all formats used and if these differences derive from the explicit attribute decomposition design or from the presence of a third alternative in conjoint analysis. We also test if these differences translate in different results and WTP estimations for the econometric models used in each case and if they remain when the same econometric model is used in analyzing each data set. Our comparison implies an additional step respect to the comparison made by Siikamäki and Layton (2007) since we analyze an environmental good characterized by two attributes in addition to the bid. Siikamäki and Layton (2007) also compared contingent valuation and conjoint analysis (rating/ranking) but presented an environmental good that differ only in one attribute among alternatives in addition to the bid. 2. Methodology 4

The main idea of this paper is that conjoint analysis is more focused on the trade-offs between alternatives and attributes, while contingent valuation is more focused on the price (the cost). Although we will not directly test this hypothesis, we will test hypotheses that could point out in this direction. Two samples faced a contingent valuation exercise, one where respondents faced a standard contingent valuation (CV); the other where respondents faced a contingent valuation exercise that reproduced the format commonly used in CA (CV FCA ). For both, the CV and the CV FCA questionnaires, respondents answered four questions. Each question was double bounded and had thus a follow-up (for the design we followed Alberini (1995)) The third sample faced a contingent ranking (CA 1 ) following a standard experimental design (see below). Respondents had to answer four rank sets. The fourth sample faced a contingent ranking (CA 2 ) where the experimental design was modified in order to include sets with dominant treatments, and sets where the only variation between alternatives occurred in one attribute (single-attribute variation). This CA 2 included eight rank sets in each questionnaire. Appendix 1 shows an example of the questions/sets used in each valuation exercises. Although we employed a ranking elicitation format, the design was done as if it were a choice experiment, since Caparrós et al. (2008) showed that if a ranking is designed as a choice and the first rank is analyzed using choice techniques the results are indistinguishable from those obtained with a classical choice experiment (at least in the case of pair-wise comparisons plus status quo). In the CV and the CV FCA experiment, the attributes of the environmental good were the same in the questions sharing the same position in the questionnaires (1 st, 2 nd, 3 rd or 4 th ), but differ within the different positions (the bid was randomly assigned to each question). In the CA 1 the blocking in the treatments was done so that the rank sets included as one of the 5

alternatives the same environmental good corresponding to the position of the question in CV and CV FCA. The reason of this particular design is that it will allow for making an analysis of the WTP based on the position of the valuation question. Given the designs explained above, we will analyze and compare the data from the CV and the CV FCA using the binary responses to these formats, and the data of the CA 1 and CA 2 using the whole ranking information (CR) and the information of the first rank as if it were a choice (CH). 2.1 Single-alternative analysis The first level of comparison between formats is at alternative level. The hypothesis to be tested is that the same treatment (alternative of the environmental good) is chosen over the status quo differently when respondents face the CV exercise than when they face the CA 1 exercise. This will test if these formats imply different statement of preferences about the good being valued, regardless of the different econometric techniques applied in each case. We want to make sure that different results are derived from the different stated preferences. Additionally, we will compare the CV with the subsample of the CA 2 where the only difference between treatments in the same set is the level of one attribute (CA FIX ) (the experimental design of CA 1 format excludes these cases) aside from the bid, which always differs among alternatives in both contingent valuation and conjoint analysis. This represents the most basic difference between a contingent valuation exercise and a conjoint analysis exercise with two alternatives plus status quo. Since in conjoint analysis respondents have to process more information than in contingent valuation, we want to tests if the minimum additional information required by conjoint analysis also generates different results. 6

If we find differences in these comparisons, we will analyze if they are caused either by the specific format used in CA, which may emphasize the trade-offs between attributes reducing the attention to the price, or by the presence of a third alternative in the CA, which may emphasize the trade-offs between alternatives reducing the attention to the price. To test if the particular format used in CA is causing differences, we will compare the CV and the CV FCA. To test if differences are caused by the presence of a third alternative, we will compare the CV and the CV FCA results with the subsample of CA 2 with dominance between treatments. The presence of a dominated alternative should be irrelevant in the rank set and the results for the shared treatments should be equal to the results in CV for these treatments. For one subset of the rank sets, the dominance is included in the bid (CA D-BID ). For other subset of the rank sets, the dominance is included in one of the attributes (CA D-SUR ). Particularly troubling would be the result that the shared treatments are preferred more often in the CA experiment when they are compared with a dominated alternative. This comparison will be even more homogeneous for the CV FCA since this format replicates the CA one, and if the dominated alternative were removed, the question would be exactly equal. 2.2 Model and welfare measures analysis The second level of analysis will be comparing the models estimated for CV, CV FCA, and CA 1 (CR and CH), and the corresponding welfare measures, to see if differences go in the same direction than the ones obtained in the single-alternative level analysis. We will try to recover the WTP values obtained with the CV (focused on only one specific alternative) using the parameters obtained with the CR and the CH. If we obtain divergent WTP values, the reason may be the different statistical methods used. Then, the next step is recoding to the CV format the responses to the shared treatments 7

in the CR (CR CV model). We will recode the CR answers treating each rank as a binary response to a CV question where the ranked alternative is compared to the status quo. If the rank assigned to the alternative is higher than the rank assigned to the status quo we code the answer as 1; otherwise we code it as 0. Model comparison can be extended to the recovering of WTP values for each position of the question in the questionnaires. We will test if the including several questions in the valuation exercise, and the position of the questions, have a significant effect in the WTP. For the CV and CV FCA samples, we will perform binary logit models to analyze the dichotomous-simple question following Cameron (1988, 1991). For the CA sample, we use the rank-ordered logit model (Beggs et al., 1981) to analyze the CR data set, and the Conditional Logit (CL) (McFadden, 1974), the Nested Logit (NL) (McFadden, 1981) and the Random Paremeter Logit (RPL) (Layton, 2000) to analyze the CH data set. For welfare measures, we generate an empirical distribution of the parameters associated to each attribute through the Krinsky and Robb (1986) bootstraping technique with 1,000 replacements. Using the mean values of the empirical distribution of the parameters, we estimate the mean WTP for a marginal increase in the level of an attribute (mwtp) dividing the mean β associated to the attribute (β k ) by the mean β associated to the bid (β BID ), with negative sign. Then we estimate the WTP for a concrete reforestation alternative adding the WTP values for the values of the attributes that defined the alternative. For this WTP values generated through bootstrapping, we obtain the standard deviation from the empirical distribution and the 95% confidence interval through the percentile approach (Efron and Tibshriani 1993). For testing the equality of the WTP for the same reforestation alternatives we employ the complete combinatorial test (Poe et al., 2005). 3. Case study and survey logistics 8

In the last two decades, stone pine reforestations have been subsidized in Spain within the framework of the European Union Common Agricultural Policy. We decided to investigate whether social preferences, expressed through willingness to pay (WTP), are in alignment with conserving and increasing stone pine forest extent in the southwest of Spain. Thus, the stated preference exercises presented in this article were applied to the valuation by Spanish households of a reforestation program with stone pine trees in the southwest of Spain. In this region, stone pine forests extend over 237,000 hectares. The survey was made by a professional surveying company. The sample consisted of Spanish households from provinces located in southwestern Spain. The provinces were selected taking into account the proximity of stone pine forest so that respondents could have certain knowledge and familiarity with them. The sample was stratified by provinces, considering the population of each province, and randomly selected within each province. The selected provinces were Cádiz, Málaga, Sevilla, Córdoba, Huelva, Badajoz, Cáceres, Valladolid, Madrid, Segovia, Toledo, Salamanca, Zamora and Ávila. The interviews were face-to-face and were performed with Spanish households from April to July 2008. Respondents were provided with an informative booklet with basic information about stone pine forests in Spain and the implications of the different reforestation options. Previously, two focus groups were used to identify the main attributes of a reforestation program for the general public, and to evaluate the extent to which the information presented in the survey was understood. A preliminary design for the valuation exercises was tested as well. We used the focus group information to create a pre-test, which was used to obtain the vector of monetary values to be offered in the main survey. In addition to the focus group and the pre-test, interviews with experts were held. The pre-test was presented to 50 households. 9

Given the information obtained in the focus-group and the pre-test, the attributes presented in table 1 were chosen for the analysis. In the following, we will refer to the reforestation alternatives (treatments) characterized by these attributes as [VEG-SUR-BID], where VEG stands for the vegetation that would be removed by the reforestation program, either shrub (SHR) or eucalyptus (EUC), SUR stands for the hectares covered by the reforestation in 000 s (20, 40, 60, 80) and BID stands for the bid value ( 5, 20, 35, 50). [Table 1] For the CV and the CV FCA questionnaires, the alternative [SHR-40-BID] was located in the first question, the alternative [EUC-40-BID] in the second question, the alternative [SHR-80-BID] in the third question and the alternative [EUC-80-BID] in the fourth question. The BID was assigned randomly to each question. The levels 20,000 and 60,000 hectares for the SUR attribute were not included in the CV and CV FCA questions because it would have implied four more CV questions. We opted for taking an intermediate level (40,000) and the maximum level (80,000) so that they were equally spaced. For the CA 1 experiment, we made a main effects experimental design with 16 treatments from the universe of 32 possible combinations of attributes, placing them in pairwise combinations and obtaining 32 choice sets. We then blocked these 32 sets into 8 questionnaires making sure that all the sets including the treatment [SHR-40-BID] were located in the first set presented, those including [EUC-40-BID] in the second set, those including [SHR-80-BID] in the third set, and those including [EUC-80-BID] in the fourth set. Thus, the comparison of treatments based on the position of the question could be done also for the CA 1 sample. For the econometric models, we include the attribute SUR as effect codes (20,000 was the base level), as well as the attribute VEG (SHR was coded as 1 and EUC as -1). All status 10

quo levels were coded as 0. We also include in the model an alternative specific constant (ASC REF ) taking value 1 for the reforestation alternatives and value 0 for the status quo. 4. Results Refusals to answer the survey represent 30% of total attempts, until obtaining a final number of 750 completed questionnaires. The total number of respondents was 159 (636 observations) for the CV, 151 (604 observations) for the CV FCA, 294 (1,176 observations) for the CA 1 and 146 (1,168 observations) for the CA 2. After removing invalid and protest responses, we have 556 useable observations for the CV, 548 for the CV FCA, 1,036 for the CA 1 and 1,040 for the CA 2. 4.1 Single alternative analysis Table 2 shows the number of times that the shared treatments were preferred over the status quo for CV, CV FCA and CA 1 (CR and CH). In the CV and CV FCA format, this information comes from the answer to the dichotomous simple question. In the CR, we take into account the choices of shared treatment either as first rank or as second rank. For the CH, we take into account only the choices of the shared treatments as first rank. [Table 2] We find that in seven out of eight cases the shared treatments are chosen over the status quo more times in CR, and that these differences are significant for the higher bid values. For the CH, however, the percentage of choices of the shared treatments is lower than in CV and these differences are significant in four out of eight cases. The difference of percentages between CR and CH is the percentage of choices of the shared treatments as 11

second rank. This second rank is the one causing the change of the sign from the differences found between CH and CV to the differences found between CR and CV. For those CA 2 sets where there is variation only in the level of one attribute between treatments (CA FIX ), Table 3 shows results similar to those of Table 2. Thus, even for the most basic difference between contingent valuation and conjoint analysis (two alternatives differentiated only in the level of one attribute), these techniques offer different results that go in the same direction than the ones obtained when using a standard CA exercise (variations on the levels of all attributes). [Table 3] Since the comparison between CV and CV FCA offers no significant differences (Table 2), the differences found between CV and CR and between CV and CH cannot be explained by the specific design of the questions used in conjoint analysis experiments. Table 4 shows the results of the comparison considering the CA exercise where the shared treatments were dominant or dominated respect to the BID. The shared treatment dominates the other treatment when both treatments present the same good and the bid value is lower in the shared treatment. The shared treatment is dominated by the other treatment when the opposite happens. In the first case, the relevant information is given by the CH since choosing the dominant treatment as second option would be inconsistent. In the second case, the relevant information is given by the second rank because choosing the dominated treatment as first option is inconsistent. Inconsistent answers are less than 10% in all cases and do not have a significant impact in the results reported in Table 4. [Table 4] The results show that the percentages of choices over status quo are now higher in both CR and CH respect to CV. In addition, this difference is significantly higher for the treatments with the higher bids, as it happened in Table 2. 12

Table 5 shows the results when the dominance comes from the attribute SUR. Here, the shared treatments are always dominant since they have the same BID and VEG attributes than the other treatment but the level of the attribute SUR is always higher for the shared treatments. For this type of dominance we also find that the percentage of choices of shared treatments over the status quo is higher in CH (and consequently in CR) 1 than in CV, and that this difference is significant for the treatments with higher bids. [Table 5] Thus, we find evidence that in conjoint analysis formats, and especially in ranking data, there is an effect that led respondents to choose do something alternatives over status quo more times than in contingent valuation when there is dominance between treatments in the sets of alternatives. This is a surprising result since the presence of dominance should imply that the choices of the dominant alternative as first rank in conjoint analysis should be equivalent to the choices of this same alternative in contingent valuation; while the choices of a dominated alternative as second rank in conjoint analysis should be equivalent to the choices of this same alternative in contingent valuation. These results hold both for dominance coming from the bid and for dominance coming from the attribute SUR (an attribute related with the quantity of the good being valued) and remain similar when the comparison is made either to the CV or to the CV FCA. Thus, there are evidences that the presence of an additional alternative (a third one in our applied exercise) affects to the results of the CA formats, being the most relevant effect the one found in the choice made by respondents in the second rank. 4.2 Models and welfare measure analysis 1 Theoretically, there should not be difference between the CH and the CR because the shared treatment is always better than the other treatment and thus should be chosen. 13

Table 6 shows the results of the regressions models for each of the data set analyzed (CV, CV FCA, and CH and CR for CA 1 ). The models takes into account all treatments included in each design not just the shared treatments. From these models, we expect to find a negative sign associated to the parameter VEG since there is a social preference for the removal of eucalyptus groves in the south of Spain, as shown by Caparrós et al. (2009) and by the results obtained in the focus group and pre-test. Regarding the attribute SUR, we expect to find a positive sign in the hectares of surface of the reforestation, as shown by Caparrós et al. (2008) and by the focus group and pre-test. We expect a negative sign from the BID and a positive sign from the intercept/asc REF. [Table 6] The CV model shows no significance in the attributes SUR and VEG. Thus, the estimated WTP for each question (Table 7), which corresponds to a different reforestation alternative, are not statistically different. This suggests that using several CV questions in the same questionnaire, and varying the attributes of the good being valued in each question, is not a good strategy for CV if we want to estimate the WTP disaggregated by attributes. The results of the CV FCA model reinforce this finding. In this case, SUR and VEG are significant but with the opposite signs to the expected ones. The significance of the attributes here could be present due to the particular format design which emphasizes the trade-off between attributes. However, looking at the WTP values (Table 7), we find that they are not statistically different from the ones obtained with the CV model. Thus, from the perspective of hicksian surplus estimation, the same results obtained for CV apply to CV FCA. This, however, does not happen to the CA 1 formats. In the CR and CH models, which take into account the four rank sets presented to the respondents, the parameters are significant and offer the expected signs. The WTP values increase when changing from 14

shrubland to eucalyptus and from 40,000 hectares to 80,000 hectares (Table 7). In Table 7, the WTP values offered for CH corresponds to the RPL model, which is a more flexible method (Siikamäki and Layton, 2007) and can capture heterogeneity among respondents and within the ranks made by the same respondents. This WTP values are statically higher for CR compared to CV. For the comparison between CV and CH we find significant differences only in one case, which points that the WTP obtained with CV is higher than the one obtained with CH. Recoding the CR responses to CV, we obtain the CR CV model. The WTP estimations of this model showed no statistical difference with the WTP values of the CR model. Thus, the difference between CR and CV does not derive from the different econometric models used but from different preferences stated by respondents. Appendix 2 and 3 show the models and WTP values for each one of the positions of the questions/sets in CV, CV FCA and CA 1 (CR and CH). This table reinforces the idea that contingent valuation should not use several questions in the same questionnaires. Comparison test offered no significant differences for most of the WTP values estimated with the CV and CV FCA models. In the case of the CR and CH models, we find some significant differences that go in the expected direction. 5. Conclusions We present a comparison between a standard contingent valuation and a standard conjoint analysis experiment with a contingent valuation and a conjoint analysis experiment where the designs have been manipulated in order to test for the origin of divergent results. We analyze a simple dichotomous question for the contingent valuation, and the ranking (whole rank) and the choice (first rank) for the conjoint analysis. 15

The results of the comparison show significant differences between the ranking and the choice data, and between the ranking and the contingent valuation data. We do not find significant differences between the choice and the contingent valuation. Thus, the found differences appear mainly due to the second rank made by respondents in the conjoint analysis exercise. These results are obtained when comparing the choice of single alternatives over the status quo, which involves no different econometric techniques for each format, and when comparing the estimated models and welfare measures. We also find these results for the case where the variation between alternatives in the same rank set is the minimum one and thus the extra cognitive effort made by the respondents when facing the conjoint analysis is reduced. The obtained results are consistent with previous research that compared the same formats, but that were applied to an environmental good describe solely by one attribute (Siikamäki and Layton, 2007). Working with the manipulated designs in each experiment, we find that differences are not explained by the specific format used in conjoint analysis, where the attributes are explicitly decomposed. We also find that the practice of presenting several questions in conjoint analysis does not seem to have an undesired effect in the estimations, while this practice does seem to be inappropriate for contingent valuation. When analyzing the subset of treatments that are accompanied by dominant or dominated alternatives, we obtain the same differences for the ranking compared to contingent valuation. In this case, however, we also obtain significant differences for the choice, which offers higher choices of the shared treatments than in contingent valuation. Thus, the presence of a third alternative makes that respondents choose more often do something alternatives in conjoint analysis and this would estimate higher WTP values through this technique. This result also provides evidence of the presence of some sort of asymmetry in the choices in conjoint analysis, since even when a treatment is accompanied 16

by a dominated alternative the choices of this treatment over the status quo are higher than in an equivalent contingent valuation. Acknowledgements We thank Pablo Campos and Begoña Alvaréz-Farizo for their helpful comments and suggestions in the initial stages of the design of the survey. We gratefully acknowledge funding provided by the National Institute of Alimentary and Agrarian Technology Research (INIA). (Proyect: CPE03-001-C5) and by the I + D National Plan of the Ministry of Science and Education (project DYNOPAGROF). The usual disclaimer applies. References Alberini, A. 1995. Optimal design for discrete choice contingent valuation surveys: singlebound, double-bound and bivariate models. Journal of Environmental Economics and Management 28(3):287-306. Beggs, S., S. Cardell, and J. Hausman. 1981. Assessing the Potential Demand for Electric Cars. Journal of Econometrics 17(1):1-19. Ben-Akiva, M., T. Morikawa, and F. Shiroishi. 1991. Analysis of the Reliability of Preference Rank Data. Journal of Business Research 23(3):253-268. Boxall, P.C., W.L. Adamowicz, J. Swait, M. Williams, and J. Louviere. 1996. A Comparison of Stated Preference Methods for Environmental Valuation. Ecological Economics 18:243-253 17

Boyle, K.J., T.P. Holmes, M.F. Teisl, and B. Roe. 2001. A Comparison of Conjoint Analysis Response Formats. American Journal of Agricultural Economics 83(2):441-454. Cameron, T. A. 1988. A new paradigm for valuing non-market goods using referendum data: maximum likelihood estimation by censored logistic regression. Journal of Environmental Economics and Management 15:355-379. Cameron, T. A. 1991. Interval estimates for non-market resource values from referendum contingent valuation surveys. Land Economics 67(4):413-421. Caparrós, A., J.L. Oviedo, P. Campos. 2008. Would you Choose your Preferred Option? Comparing Choice and Recoded Ranking Experiments. American Journal of Agricultural Economics 90(3):843-855. Caparrós, A., E. Cerdá, P. Ovando, and P. Campos. 2009. Carbon Sequestration with reforestations and biodiversity-scenic values. Environmental and Resources Economics, in press. Clark, J., and L. Friesen. 2008. The Causes of Order Effects in Contingent Valuation Surveys: An Experimental Investigation. Journal of Environmental Economics and Management 56:195-206. Efron, B., and R.J. Tibshirani. 1993. An introduction to the bootstrap. New York: Chapman & Hall ed. Foster, V., and S. Mourato. 2002. Testing for Consistency in Contingent Ranking Experiments. Journal of Environmental Economics and Management 44(2):309-328. Krinsky, I., and A.L. Robb.1986. On Approximating the Statistical Properties of Elasticities. Review of Economics and Statistics 68(4):715-719. 18

Layton, D.F. 2000. Random Models for Stated Preference Surveys. Journal of Environmental Economics and Management 40:21-36. Magat, W.A., W.K. Viscusi, and J. Huber. 1998. Paired Comparison and Contingent Valuation Approaches to Morbidity Risk Valuation. Journal of Environmental Economics and Management 15:395-411. McFadden, D. 1974. Conditional Logit Analysis of Qualitative Choice Behaviour. In P. Zarembka, ed., Frontier in Econometrics. New Cork: Academia Press. McFadden, D. 1981. Econometric models of probabilistic choice. In C. Manski, and D. McFadden, eds. Structural analysis of discrete data with econometric applications. Cambridge, Mass.: MIT Press, pp. 198-272. Poe, G.L., K.L. Giraud, and J.B. Loomis. 2005. Computational Methods for Measuring the Difference of Empirical Distributions. American Journal of Agricultural Economics 87(2):353-365. Ready, R., J. Wwhitehead, G. Blomquist. 1995. Contingent Valuation When Respondets Are Ambivalent. Journal of Environmental Economics and Management 29:181-197. Siikamäki, J., and D.F. Layton. 2007. Discrete Choice Survey Experiments: A Comparison Using Flexible Methods. Journal of Environmental Economics and Management 53(1):122-139. Stevens, T.H., R. Belkner, D. Dennis, D. Kittredge, and C. Willis. 2000. Comparison of Contingent Valuation and Conjoint Analysis in Ecosystem Management. Ecological Economics 32:63-74. 19

Table 1. Attributes of the Experiment and Levels Attributes Vegetation removed by the reforestation (VEG) Surface covered by the reforestation (SUR) Increase in taxes for this year (BID) Levels Shrubland; Eucalyptus grove 20,000 hectares (base level); 40.000 hectares; 60,000 hectares; 80.000 hectares 5; 20; 35; 450 20

Table 2. Percentage of choices over the status quo of shared treatments in CV, CV FCA, and CA 1 (CR and CH) formats. Test results for the difference between proportions. Alternative CV CV FCA CA 1 (CR) CA 1 (CH) z-test for difference between proportions CV vs. CV FCA CV vs. CR CV vs. CH [Shrub-40-35] 52% 69% 68% 30% 0.1703 0.0893* 0.0251** [Shrub -40-20] 67% 67% 76% 45% 1.0000 0.3556 0.0529* [Eucal-40-50] 32% 38% 55% 14% 0.6452 0.0401** 0.0376** [Eucal-40-20] 71% 69% 83% 61% 0.8547 0.1871 0.2879 [Shrub-80-5] 86% 86% 86% 68% 0.9617 0.9645 0.0420** [Shrub-80-50] 35% 52% 58% 28% 0.2046 0.0413** 0.4652 [Eucal-80-20] 63% 47% 76% 58% 0.1653 0.1505 0.6269 [Eucal-80-50] 10% 17% 53% 27% 0.4320 0.0001*** 0.0674* CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. CA 1 : conjoint analysis sample. CR: ranking data analysis. CH: choice data analysis. 21

Table 3. Percentage of choice over the status quo of shared treatments in CA 2 (CR FIX and CH FIX ). Subset of treatments with single attribute variation. Comparison test for the difference between proportions. Alternative CR FIX CH FIX CR FIX vs CV z-test for difference between proportions CH FIX vs CV CR FIX vs CV FCA CH FIX vs CV FCA [Shrub-40-35] 88% 40% 0.0002*** 0.3097 0.0346** 0.0121** [Eucal-40-50] 77% 26% 0.0001*** 0.5549 0.0003*** 0.2675 [Shrub-80-5] 91% 54% 0.4094 0.0020*** 0.4378 0.0016*** [Eucal-80-20] 91% 74% 0.0005*** 0.2436 0.0001*** 0.0099*** CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. CA 2 : conjoint analysis sample using a special design for testing the effect of single attribute variation and dominance. CR FIX : ranking data analysis for the subset of CA 2 with single attribute variation. CH FIX : choice data analysis for the subset of CA 2 with single attribute variation. 22

Table 4. Percentage of choice over the status quo of shared treatments in CA 2 (CR D-BID and CH D-BID ). Subset of treatments with dominance in the bid. Comparison test for the difference between proportions. Alternative Dominant CH D-BID z-test for difference between proportions CH D-BID vs CV CH D-BID vs CV FCA [Shrub-40-20] 74% 0.5487 0.5312 [Eucal-40-20] 76% 0.6335 0.5088 CR D-BID CR D-BID vs CV CR D-BID vs CV FCA Dominated [Shrub-80-50] 59% 0.0599* 0.5719 [Eucal-80-50] 56% 0.0001*** 0.0001*** CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. CA 2 : conjoint analysis sample using a special design for testing the effect of single attribute variation and dominance. CR D-BID : ranking data analysis for the subset of CA 2 with dominance in the bid. CH D-BID : choice data analysis for the subset of CA 2 with dominance in the bid. 23

Table 5. Percentage of choice over the status quo of shared treatments in CA 2 (CR D-SUR and CH D-SUR ). Subset of treatments with dominance in the attribute SUR. Comparison test for the difference between proportions. Alternative Dominant CH D-SUR z-test for difference between proportions CH D-SUR vs CV CH D-SUR vs CV FCA [Shrub-40-20] 77% 0.3444 0.3230 [Eucal-40-20] 77% 0.5891 0.4643 [Shrub-80-50] 64% 0.0173** 0.3050 [Eucal-80-50] 59% 0.0001*** 0.0002*** CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. CA 2 : conjoint analysis sample using a special design for testing the effect of single-attribute variation and dominance. CR D-SUR : ranking data analysis for the subset of CA 2 with dominance in the attribute SUR. CH D-SUR : choice data analysis for the subset of CA 2 with dominance in the attribute SUR. 24

Table 6. Models for the contingent valuation (CV and CV FCA ) and the conjoint analysis (CA 1 ) experiments. Variable Intercept / ASC REF CV CV FCA CR Model CH CL NL RPL 1.8920*** 1.6233*** 2.0481*** 1.8189*** 1.8774*** 2.1466*** (0.1970) (0.1950) (0.0894) (0.1067) (0.1544) (0.1975) Vegetation (=1 shrub; =-1 0.0950 0.2057** -0.0654** -0.0971*** -0.1915*** -0.2116** eucalyptus) (0.0952) (0.0931) (0.0314) (0.0376) (0.0675) (0.0881) Surface (=1 if 80,000 ha; -0.1091-0.1543* =-1 if 40,000 ha) (0.0952) (0.0931) SUR40 SUR60 SUR80 Bid -0.0294-0.0309-0.0024 0.0071 (0.0633) (0.0777) (0.1270) (0.1346) -0.0111 0.0304-0.0043-0.0312 (0.0641) (0.0794) (0.1289) (0.1418) 0.1018 0.1593** 0.2252* 0.2289* (0.0634) (0.0770) (0.1215) (0.1351) -0.0570*** -0.0455*** -0.0355*** -0.0337*** -0.0582*** -0.0628*** (0.0061) (0.0059) (0.0022) (0.0027) (0.0044) (0.0112) Inclusive value for 1.8762*** reforestations (0.0840) Standard deviation parameters Vegetation 1.5221*** (0.4292) n 556 548 1,036 1,036 1,036 1,036 L (β) -324.77-334.6511-1,535.064-962.4455-958.6764-956.8034 CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. CA 1 : conjoint analysis sample. CR: ranking data analysis using the rank ordered-logit. CH: choice data analysis. CL: conditional logit model. NL: nested logit model. RPL: random parameter logit model. 25

Table 7. Willingness to pay values and confidence intervals for the contingent valuation (CV and CV FCA ) and the conjoint analysis (CA 1 ) experiments. Complete combinatorial test for the comparison of mean WTP values for the alternatives presented. Alternative CV CV FCA CR CH CV vs CV FCA [SHR-40] [EUC-40] [SHR-80] [EUC-80] 36.82 43.77 55.01 31.48 [31.14, 43.30] [36.44, 53.17] [49.53, 61.22] [23.82, 42.57] 32.83 36.72 58.79 35.21 [27.36, 38.86] [29.97, 44.64] [53.04, 65.27] [26.09, 48.17] 33.55 34.70 58.79 38.34 [27.75, 39.52] [27.64, 42.04] [52.94, 65.49] [30.91, 48.81] 29.55 27.65 62.58 42.06 [24.16, 35.28] [20.89, 34.66] [56.49, 69.56] [33.91, 54.62] Complete combinatorial test CV vs CR CV vs CH 0.075 * 0.001 *** 0.160 0.210 0.001 *** 0.368 0.405 0.001 *** 0.185 0.338 0.001 *** 0.001 *** Note: the WTP values presented for CH data analysis corresponds to the random parameter logit (RPL) model from Table 6. CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. CA 1 : conjoint analysis sample. CR: ranking data analysis using the rank ordered-logit. CH: choice data analysis. 26

Appendix 1. Contingent valuation and conjoint analysis questions Contingent valuation (CV) 10a (code 52). Would you willing to pay 5 euros (ONLY this year) for funding a reforestation on land currently occupied by SHRUBLAND that will increase in 40,000 hectares the surface of STONE PINES in the southwest of Spain in the next 5 years? Keep in mind that the payment would be real and that the money could not be employed for other things. Yes (p. 10a.1) No (p. 10a.2) 10a.1 (If Yes to question 10a) Would you willing to pay 20 euros? Yes No 10a.2 (If No to question 10a) Would you willing to pay 2 euros? Yes No Contingent valuation with the format of conjoint analysis (CV FCA ) From the following two alternatives, please mark the ONE THAT YOU WOULD CHOOSE (ONLY ONE). Keep in mind that the payment would be real and that the money could not be employed for other things. 10a. Increase in the STONE PINE surface in southwestern Spain in the next 5 years Land use over which the reforestation would be made SET 1 (code 52) OPTION A OPTION B 40,000 hectares No reforestation Shrubland Additional taxes ONLY this year 5 euros 0 euros Please, mark ONLY ONE OPTION OPTION A (q. 10a.1) OPTION B (q. 10a.2) 10a.1 (If OPTION A was marked in q. 10a) which option would you choose if the amount to be paid were 20 euros? Additional taxes ONLY this year 20 euros 0 euros Please, mark ONLY ONE OPTION OPTION A OPTION B 10a.2 (If OPTION B was marked in q. 10a) which option would you choose if the amount to be paid were 2 euros?: Additional taxes ONLY this year 2 euros 0 euros Please, mark ONLY ONE OPTION OPTION A OPTION B 27

Rank the following alternatives from the MOST PREFERRED (1) to the LESS PREFERRED (3). Keep in mind that the payment would be real and that the money could not be employed for other things. Conjoint analysis (CA 1 ) 10a. Increase in the STONE PINE surface in southwestern Spain in the next 5 years Land use over which the reforestation would be made SET 1 (code 1) Option A Option B Option C 20,000 hectares 40,000 hectares Eucalyptus grove Shrubland No reforestation Additional taxes ONLY this year 20 euros 35 euros 0 euros RANK THE THREE OPTIONS (A, B and C) OPTION A 1ª 2ª 3ª Conjoint analysis (CA 2 ) Single-attribute variation (CA FIX ) 10c. Increase in the STONE PINE surface in southwestern Spain in the next 5 years Land use over which the reforestation would be made OPTION B 1ª 2ª 3ª OPTION C 1ª 2ª 3ª SET 3 (code 33) Option A Option B Option C 40,000 hectares 40,000 hectares Shrubland Eucalyptus grove No reforestation Additional taxes ONLY this year 35 euros 20 euros 0 euros RANK THE THREE OPTIONS (A, B and C) OPTION A 1ª 2ª 3ª Conjoint analysis (CA 2 ) Dominance in bid (CA D-BID ) 10c. Increase in the STONE PINE surface in southwestern Spain in the next 5 years Land use over which the reforestation would be made OPTION B 1ª 2ª 3ª OPTION C 1ª 2ª 3ª SET 3 (code 35) Option A Option B Option C 40,000 hectares 40,000 hectares Shrubland Shrubland No reforestation Additional taxes ONLY this year 20 euros 50 euros 0 euros RANK THE THREE OPTIONS (A, B and C) OPTION A 1ª 2ª 3ª Conjoint analysis (CA 2 ) Dominance in attribute SUR (CA D-SUR ) 10c. Increase in the STONE PINE surface in southwestern Spain in the next 5 years Land use over which the reforestation would be made OPTION B 1ª 2ª 3ª OPTION C 1ª 2ª 3ª SET 3 (code 36) Option A Option B Option C 20,000 hectares 40,000 hectares Shrubland Shrubland No reforestation Additional taxes ONLY this year 20 euros 20 euros 0 euros RANK THE THREE OPTIONS (A, B and C) OPTION A 1ª 2ª 3ª OPTION B 1ª 2ª 3ª OPTION C 1ª 2ª 3ª 28

Appendix 2. Variable Intercept Bid Econometric models and willingness to pay values for the CV and CV FCA questions presented as 1 st, 2 nd, 3 rd and 4 th question in the questionnaire. CV 1 st CV 2 nd CV 3 rd CV 4 th CV FCA 1 st CV FCA 2 nd CV FCA 3 rd CV FCA 4 th 2.0773*** 1.7641*** 1.5715*** 2.2955*** 1.7778*** 1.6043*** 1.3417*** 1.8331*** (0.4074) (0.3707) (0.3718) (0.4437) (0.3994) (0.3813) (0.3654) (0.4183) -0.0516*** -0.0569*** -0.0507*** -0.0736*** -0.0367*** -0.0488*** -0.0358*** -0.0631*** (0.0117) (0.0119) (0.0119) (0.0139) (0.0115) (0.0119) (0.0114) (0.0128) n 139 139 139 139 137 137 137 137 L (β) -78.96-81.40-85.15-77.18-81.1844-84.1683-87.3453-80.0488 WTP 40.68 31.04 31.05 31.15 50.93 33.06 38.81 28.87 [32.65, 51.84] [24.06, 38.29] [23.21, 39.39] [25.64, 36.70] [37.33, 78.24] [25.00, 42.37] [27.33, 58.63] [22.06, 35.11] CV: contingent valuation sample. CV FCA : contingent valuation using the format of conjoint analysis sample. 29

Appendix 3. Econometric models and willingness to pay values for the CR and CH questions (from the CA 1 sample) presented as 1 st, 2 nd, 3 rd and 4 th question in the questionnaire. Variable Intercept Veg Sur Bid CR 1 st CR 2 nd CR 3 rd CR 4 th CH 1 st CH 2 nd CH 3 rd CH 4 th 2.2047*** 2.3862*** 1.1638*** 2.3479*** 1.4866*** 1.8495*** 1.1259** 2.4036*** (0.2630) (0.2702) (0.3476) (0.4644) (0.3170) (0.3272) (0.4414) (0.5580) -0.1017* -0.1696** -0.1924-0.1224-0.1325* -0.2084*** -0.1316-0.3157* (0.0614) (0.0676) (0.1276) (0.1289) (0.0754) (0.0800) (0.1520) (0.1620) 0.0000-0.0000** 0.0000** 0.0000 0.0000** 0.0000 0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) -0.0411*** -0.0341*** -0.0334*** -0.0417*** -0.0419*** -0.0316*** -0.0282*** -0.0418*** (0.0056) (0.0039) (0.0040) (0.0055) (0.0066) (0.0050) (0.0047) (0.0065) n 259 259 259 259 259 259 259 259 L (β) -386.4061-372.8363-375.8443-390.0486-244.8481-227.1044-235.9270-247.0192 WTP 51.77 34.20 50.57 56.13 46.54 52.56 40.45 65.15 [37.90, 69.77] [17.38, 51.49] [40.05, 63.43] [39.30, 72,57] [37.42, 58.75] [30.43, 80.57] [9.57, 79.90] [36.37, 95.72] CR: ranking data analysis using the rank ordered-logit. CH: choice data analysis. 30