Comparing CV and Unkown

8/6/2019 Comparing CV and Unkown

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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


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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


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ranking inconsistency. Since Caparrs 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


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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


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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 (CVFCA). For both, the CV and the

CVFCA 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 (CA1) following a standard experimental

design (see below). Respondents had to answer four rank sets. The fourth sample faced a

contingent ranking (CA2) where the experimental design was modified in order to include

sets with dominant treatments, and sets where the only variation between alternatives


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alternatives the same environmental good corresponding to the position of the question in CV

and CVFCA. 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 CVFCA using the binary responses to these formats, and the data of the CA1 and

CA2 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 CA1


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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 CVFCA. To test if differences are caused by the presence of a third alternative, we

will compare the CV and the CVFCA results with the subsample of CA2 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 (CAD-BID).

For other subset of the rank sets, the dominance is included in one of the attributes (CAD-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


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in the CR (CRCV 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 CVFCA 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


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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.


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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 000s (20, 40, 60, 80) and BID stands for the bid value (5, 20,35, 50).

[Table 1]

For the CV and the CVFCA 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 CVFCA questions because it would have

implied four more CV questions. We opted for taking an intermediate level (40,000) and the


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quo levels were coded as 0. We also include in the model an alternative specific constant

(ASCREF) 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 CVFCA, 294 (1,176 observations) for

the CA1 and 146 (1,168 observations) for the CA2. After removing invalid and protest

responses, we have 556 useable observations for the CV, 548 for the CVFCA, 1,036 for the

CA1 and 1,040 for the CA2.

4.1 Single alternative analysis


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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 CA2 sets where there is variation only in the level of one attribute between

treatments (CAFIX), 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 CVFCA 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


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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


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Table 6 shows the results of the regressions models for each of the data set analyzed (CV,

CVFCA, and CH and CR for CA1). 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 Caparrs 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 Caparrs 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/ASCREF.

[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


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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 (Siikamki 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 CRCV 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, CVFCA and CA1 (CR and CH). This table reinforces the idea that


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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

(Siikamki and Layton, 2007).

Working with the manipulated designs in each experiment, we find that differences


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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 Begoa Alvarz-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


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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 Management15:355-379.

Cameron, T. A. 1991. Interval estimates for non-market resource values from referendum

contingent valuation surveys.Land Economics 67(4):413-421.

Caparrs, 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.

Caparrs, A., E. Cerd, P. Ovando, and P. Campos. 2009. Carbon Sequestration with

reforestations and biodiversity-scenic values.Environmental and Resources Economics,


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Layton, D.F. 2000. Random Coefficient Models for Stated Preference Surveys. Journal of

Environmental Economics and Management40:21-36.

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 Management15:395-411.

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


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Table 1. Attributes of the Experiment and Levels

Attributes Levels

Vegetation removed by thereforestation (VEG)

Shrubland; Eucalyptus grove

Surface covered by thereforestation (SUR)

20,000 hectares (base level); 40.000 hectares;60,000 hectares; 80.000 hectares

Increase in taxes for this year(BID)

5; 20;35;450


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Table 2. Percentage of choices over the status quo of shared treatments in CV, CVFCA, and CA1 (CR

and CH) formats. Test results for the difference between proportions.

z-test for difference between proportionsAlternative CV CVFCA

CA1

(CR)

CA1(CH)

CV vs. CVFCA 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.

CVFCA: contingent valuation using the format of conjoint analysis sample.

CA1: conjoint analysis sample.

CR: ranking data analysis.

CH: choice data analysis.


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Table 3. Percentage of choice over the status quo of shared treatments in CA2 (CRFIX and CHFIX).

Subset of treatments with single attribute variation. Comparison test for the differencebetween proportions.

z-test for difference between proportions

Alternative CRFIX CHFIX

CRFIX vs CV CHFIX vs CVCRFIX vs

CVFCA

CHFIX vs

CVFCA

[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***



CA2: conjoint analysis sample using a special design for testing the effect of single attribute variation and dominance.

CRFIX: ranking data analysis for the subset of CA2 with single attribute variation.

CHFIX: choice data analysis for the subset of CA2 with single attribute variation.


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Table 4. Percentage of choice over the status quo of shared treatments in CA2 (CRD-BID and

CHD-BID). Subset of treatments with dominance in the bid. Comparison test for thedifference between proportions.

z-test for difference between proportionsAlternative CHD-BID

CHD-BID vs CV CHD-BID vs CVFCA

Dominant

[Shrub-40-20] 74% 0.5487 0.5312

[Eucal-40-20] 76% 0.6335 0.5088

CRD-BID CRD-BID vs CV CRD-BID vs CVFCA

Dominated

[Shrub-80-50] 59% 0.0599* 0.5719

[Eucal-80-50] 56% 0.0001*** 0.0001***



CA2: conjoint analysis sample using a special design for testing the effect of single attribute variation and dominance.

CRD-BID: ranking data analysis for the subset of CA2 with dominance in the bid.

CHD-BID: choice data analysis for the subset of CA2 with dominance in the bid.


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Table 5. Percentage of choice over the status quo of shared treatments in CA2 (CRD-SUR and

CHD-SUR). Subset of treatments with dominance in the attribute SUR. Comparisontest for the difference between proportions.

z-test for difference between proportionsAlternative CHD-SUR

CHD-SUR vs CV CHD-SUR vs CVFCA

Dominant

[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***



CA2: conjoint analysis sample using a special design for testing the effect of single-attribute variation and dominance.CRD-SUR: ranking data analysis for the subset of CA2 with dominance in the attribute SUR.

CHD-SUR: choice data analysis for the subset of CA2 with dominance in the attribute SUR.


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Table 6. Models for the contingent valuation (CV and CVFCA) and the conjoint analysis (CA1)

experiments.

Model

CHCV CVFCA CR

CL NL RPLVariable

Coefficient

(s.d.)

Coefficient

(s.d.)

Coefficient

(s.d.)

Coefficient

(s.d.)

Coefficient

(s.d.)

Coefficient

(s.d.)

1.8920*** 1.6233*** 2.0481*** 1.8189*** 1.8774*** 2.1466***Intercept / ASCREF

(0.1970) (0.1950) (0.0894) (0.1067) (0.1544) (0.1975)

0.0950 0.2057** -0.0654** -0.0971*** -0.1915*** -0.2116**Vegetation (=1 shrub; =-1

eucalyptus) (0.0952) (0.0931) (0.0314) (0.0376) (0.0675) (0.0881)

-0.1091 -0.1543*Surface (=1 if 80,000 ha;

=-1 if 40,000 ha) (0.0952) (0.0931)

-0.0294 -0.0309 -0.0024 0.0071SUR40

(0.0633) (0.0777) (0.1270) (0.1346)

-0.0111 0.0304 -0.0043 -0.0312SUR60

(0.0641) (0.0794) (0.1289) (0.1418)

0.1018 0.1593** 0.2252* 0.2289*SUR80

(0.0634) (0.0770) (0.1215) (0.1351)

-0.0570*** -0.0455*** -0.0355*** -0.0337*** -0.0582*** -0.0628***Bid

(0.0061) (0.0059) (0.0022) (0.0027) (0.0044) (0.0112)

1.8762***Inclusive value for

reforestations (0.0840)

Standard deviation parameters

1.5221***Vegetation


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Table 7. Willingness to pay values and confidence intervals for the contingent valuation (CV and

CVFCA) and the conjoint analysis (CA1) experiments. Complete combinatorial test for thecomparison of mean WTP values for the alternatives presented.

Complete combinatorial test

Alternative CV CVFCA CR CH CV vs

CVFCACV vs CR

CV vs

CH

36.82 43.77 55.01 31.48[SHR-40]

[31.14 , 43.30] [36.44 , 53.17] [49.53 , 61.22] [23.82 , 42.57]0.075

*0.001

***0.160

32.83 36.72 58.79 35.21

[EUC-40] [27.36 , 38.86] [29.97 , 44.64] [53.04 , 65.27] [26.09 , 48.17] 0.210 0.001***

0.368

33.55 34.70 58.79 38.34[SHR-80]

[27.75 , 39.52] [27.64 , 42.04] [52.94 , 65.49] [30.91 , 48.81]0.405 0.001

***0.185

29.55 27.65 62.58 42.06[EUC-80]

[24.16 , 35.28] [20.89 , 34.66] [56.49 , 69.56] [33.91 , 54.62]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.


CVFCA: contingent valuation using the format of conjoint analysis sample.CA1: conjoint analysis sample.

CR: ranking data analysis using the rank ordered-logit.



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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 areforestation on land currently occupied by SHRUBLAND that will increase in 40,000

hectares the surface ofSTONE 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 (IfYes to question 10a) Would you willing to pay 20 euros? Yes No

10a.2 (IfNo to question 10a) Would you willing to pay 2 euros? Yes No

Contingent valuation with the format of conjoint analysis (CVFCA)

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 beemployed for other things.

SET 1 (code 52)10a.

OPTION A OPTION B


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Rank the following alternatives from the MOST PREFERRED (1) to the LESSPREFERRED (3). Keep in mind that the payment would be real and that the money could not

be employed for other things.

Conjoint analysis (CA1)

SET 1 (code 1)10a.

Option A Option B Option C

Increase in the STONE PINE surface in

southwestern Spain in the next 5 years20,000 hectares 40,000 hectares

Land use over which the reforestation would be

made 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

1 2 3

OPTION B

1 2 3

OPTION C

1 2 3

Conjoint analysis (CA2) Single-attribute variation (CAFIX)

SET 3 (code 33)10c.Option A Option B Option C

Increase in the STONE PINE surface in

southwestern Spain in the next 5 years40,000 hectares 40,000 hectares

Land use over which the reforestation would be

madeShrubland Eucalyptus grove

No reforestation

Additional taxes ONLY this year 35 euros 20 euros 0 euros

OPTION A OPTION B OPTION C


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Appendix 2. Econometric models and willingness to pay values for the CV and CVFCA questions presented as 1st, 2

nd, 3

rdand 4

th

question in the questionnaire.

CV 1st CV 2nd CV 3rd CV 4th CVFCA 1st CVFCA 2

nd CVFCA 3rd CVFCA 4

th

Variable Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

2.0773*** 1.7641*** 1.5715*** 2.2955*** 1.7778*** 1.6043*** 1.3417*** 1.8331***Intercept

(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***Bid

(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 () -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]




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Appendix 3. Econometric models and willingness to pay values for the CR and CH questions (from the CA1 sample) presented as 1st,

2nd

, 3rd

and 4th

question in the questionnaire.

CR 1st CR 2nd CR 3rd CR 4th CH 1st CH 2nd CH 3rd CH 4th

Variable Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

Coefficient(s.d.)

2.2047*** 2.3862*** 1.1638*** 2.3479*** 1.4866*** 1.8495*** 1.1259** 2.4036***Intercept

(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*Veg

(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 0.0000Sur

(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***Bid

(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 () -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.


Documents

Comparing CV and Unkown