Contigent Valuation of the Benefits of Recreation and Biodiversity Protection Programs

Identifying the warm glow effect in contingent valuation

Paulo A. L. D. Nunes a,b,*

Erik Schokkaert a

a Katholieke Universiteit Leuven, Center for Economic Studies

69 Naamsestraat, 3000 Leuven

Belgium

b Vrije Universiteit, Department of Spatial Economics

De Boelelaan 1105, 1081 HV Amsterdam

Netherlands

* Corresponding author. Tel. +31-20-4446029; Fax. +31-20-4446004

E-mail address:

1

Abstract

This paper reports the results from a contingent valuation study designed to investigate the influence of warm glow in willingness-to-pay responses. Interindividual differences in warm glow motivation are measured through a factor analysis, performed on a list of attitudinal items. The reported willingness to pay measures fail to pass the scope test. Both socioeconomic variables and motivational factor scores are significant in the explanation of the individual WTP measures. We compute “cold” WTP measures by taking out the effect of the warm glow motivation. These “cold” measures satisfy both the scope test and Hausman’s adding-up property.

JEL Codes

C12, C13, C14, Q26

Key words

Contingent valuation, recreation, wildlife, willingness to pay, warm glow.

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1. Introduction

One of the main points in the ongoing debate about the use of contingent valuation (CV) studies is the so-called embedding phenomenon. The embedding problem may be present whenever reported willingness to pay (WTP) responses fail to meet the scope test, i.e., when the WTP for two environmental goods taken together, is about the same as the WTP for one of the individual goods, considered separately.[1] It has been suggested that this valuation pattern reflects that CV respondents derive moral satisfaction or a warm glow from the act of giving per se [11]. Prominent critics of CV [10] hold that the embedding problem shows that CV answers do not reflect real economic preferences and should therefore not be used in cost benefit analysis. This may be true if the embedding problem followed from incoherent responses. However, if it is indeed possible to explain it by the existence of a warm glow component, this negative position is debatable. After all, at least since Arrow [3] the modern theory of social choice has emphasized that it is immaterial whether individual’s preferences reflect selfish interest or moral judgment: “The individual may order all social states by whatever standards he deems relevant”. Following this tradition, the warm glow may be seen as a perfectly legitimate component or source of WTP.

In this paper we present some further empirical evidence on the significance of the warm glow effect. Our data refer to a CV survey designed to measure the economic benefits from preventing commercial tourism development in the Alentejo Natural Park in Portugal. To investigate the warm glow effect we included in the questionnaire a list of attitudinal items. Factor analytic techniques are used to reduce the individual items to three underlying factors that can be related to the use value, the existence value and the warm glow of giving. We then test whether interindividual variation in the factor scores for these different motivations can explain differences in the WTP answers. We also investigate the relationship between the warm glow and the embedding phenomenon.

The organization of the paper is as follows. In Section 2 we present a simple testing strategy for the scope and the adding-up test and we propose a methodology to correct the WTP answers for the warm glow component, i.e. to compute what we call a “cold” WTP measure. In Section 3 we describe the survey and introduce the attitudinal items and the factor analysis. In Section 4 we perform a traditional CV analysis. It turns out that the resulting WTPs do not pass the scope test. In Section 5 we refine the estimation procedure by specifying the sources of interindividual variation in the WTP answers. The psychological motivation factors are statistically significant and this holds also for the warm glow effect. After taking out this warm glow effect, the resulting “cold” WTP measures are lower and satisfy both the scope and the adding-up test. Section 6 concludes.

2. A simple strategy to operationalize the warm glow effect

In order to focus our discussion, we consider the case of a Natural Park, consisting of a wilderness area with restricted visitor’s access and a recreation area where visitors may enjoy recreational activities in a natural environment. Three different protection programs are considered. In the first one, the wilderness area is protected while the recreation area is further developed for commercial tourism. In the second one, the wilderness area is given up but the recreation area is kept intact, i.e. remains reserved for activities that are non-destructive for the natural environment. The last one protects both the wilderness area and the recreational area. We call these protection programs WA, RA and (WA+RA) respectively.

Since the first two programs are embedded in the last one, it is interesting to test whether the reported values of willingness-to-pay satisfy the scope test. Using the notation to refer to the reported willingness-to-pay for protection program j, we can formulate the two null-hypotheses:

and

Non-rejection of these hypotheses suggests that there is a problem of incoherence of the reported willingness-to-pay values, unless one accepts the assumption that WA and RA are perfect substitutes. Critics like Diamond et al. [5] go further and argue that “if the answers reflect economic preferences”, they should satisfy an adding up-hypothesis, formulated as follows: . This position reflects a narrow interpretation of the concept of “economic preferences”. Moreover, the existence of complementarity and substitution relationships between WA and RA can easily lead to a rejection of the adding-up test even for a fully coherent respondent with narrow economic preferences.

As a matter of fact least since Kahneman and Knetsch [11], the idea that respondents express only “narrow economic preferences” in their WTP answers has been questioned. Inspired by the work of Andreoni [1, 2] and others on impure altruism, they put forward the idea that respondents purchase moral satisfaction through their CV answers. In this approach the individual consumer contributes to the provision of a public good for two reasons. First, because she wants more of the public good and, secondly, because she derives some private benefit from contributing to its provision. The latter effect may be related to social pressure, to feelings of guilt and sympathy, or simply to the desire for a “warm glow”. It implies that the individual’s contribution to the public good enters into her utility function twice: firstly, as a contribution to public good provision; secondly, as a private good. It is then plausible to argue that the reported WTP also consists of different components: one relating to the warm glow, the second to the value of the public good itself. We call these components and respectively. It is easy to show that the adding-up condition can be rejected for the reported measures, while holding perfectly at the level of the “cold” measure. As a simple example, consider the additive case

with the warm glow effect subject to rapidly declining marginal utility, such that for each respondent . Even if the adding-up condition holds for the “cold” measures, it will be rejected for the reported willingness-to-pay measures.

If this description of reality makes sense, it is obvious that much progress could be made if we were able to distinguish empirically the different components in WTP. One possible approach to this problem (already proposed in another context by Schokkaert and Van Ootegem [18]) is to exploit the interindividual variation in the willingness-to-pay and in the importance attached to the warm glow-effect.[2] This interindividual variation will be related to differences in socioeconomic characteristics such as income, education, gender, etc. Different individuals will also differ in their sensitivity to the warm glow effect and in the importance they attach to use and existence values. We can therefore write , where ai refers to a vector of socioeconomic characteristics and refers to the psychological characteristics of respondent i: the satisfaction generated by the act of giving (warm glow motivation), the importance attached to the use or recreational value (use motivation), the utility with respect to the protection of nature independently of recreational use (existence motivation) respectively. We will return in the next section to the operationalization of the vector m.

In our empirical work we will work with the following semilogarithmic form[3]:

where e is a normally distributed error term and the ’s are the coefficients to be estimated. These coefficients relate to the amount of warm glow obtained from contributions for project j and to the use and existence value of that same project respectively. They are therefore specific to the project considered and assumed equal for all individuals. The estimate of in equation (1) will allow us to test directly whether the warm glow effect plays a role in the reported willingness-to-pay measures and, more specifically, whether individual respondents with different values for the warm glow component (i.e. different values for ) indeed report different values for their WTP.

We can go one step further and assess what would be the WTP of the respondents if they were immune for the warm glow effect. Define by the minimal value of , i.e. the value of the warm glow motivation for an individual who does not get any warm feeling from giving. We can then compute for each respondent a “cold” WTP, i.e. the value of her willingness-to-pay if she had this (minimal) warm glow motivation:

If the rejection of the scope test and the adding up test for the reported values of willingness-to-pay can be fully explained by the presence of the warm glow effect, then the “cold” measures should satisfy the scope test. In our empirical work we will formally test this hypothesis.

3. The data: willingness to pay and consumer motivations

In Section 3.1 we will first describe the general features of the survey design. In Section 3.2 we go deeper into the calculation of the indices for the psychological motivations.

3.1. Survey design and data collection

Our empirical data are taken from a large-scale contingent valuation study with a representative sample of the Portuguese population. The good being valued is the protection from commercial tourism development of the Recreation Areas and Wilderness Areas in the Alentejo Natural Park, covering about 180 miles along the southwest coastline of Portugal. To structure the willingness-to-pay question we used the double bounded dichotomous choice elicitation question format described by Hanemann et al. [8].

We used a split-sample design with different versions of the questionnaire. First, as described in the previous section, there were three different versions, focusing on the Recreation Areas protection program (RA), the Wilderness Areas protection program (WA), and the joint Wilderness and Recreation Areas protection program (WA+RA) respectively. The survey formulation of each policy protection program combined the use of narrative and visual material, including maps, photos of animals and computer generated photos of landscapes (before and after tourist development), in order to help describing the scenarios. The narrative material was based on multidisciplinary work, involving the participation of biologists with solid experience in the field, and making use of all available scientific information.[4] In the second place, we varied the payment vehicle to test for free riding incentives. For part of the sample (in each of the three variants WA, RA and (WA+RA)), the questions referred to a voluntary contribution in the form of a one-time lump-sum payment to a trust fund, for another part of the sample reference was made to a tax. In both cases it was explained that the money collected would only be used to financing the protection efforts of the Natural Park’s management agency. Statistical analysis (Nunes [16]) shows that the hypothesis of an equal distribution of the WTP’s for the two payment vehicles cannot be rejected for the WA and WA+RA-versions, but that there is some indication of free riding in the RA-scenario. We will therefore include the payment vehicle as an explanatory variable in the multivariate analysis of Section 5. We use the pooled data for the univariate analysis in Section 4.

The results were obtained by a nationwide survey conducted in mid September 1997 by the Survey Department of the Portuguese Catholic University. The survey was conducted in person by trained interviewers. A two-stage area probability sample was set up - see Thompson [19]. In the first stage, 37 parishes across Portugal were selected. In the second stage, a set of housing units was drawn. The interviewer teams paid visits to 3597 households but 21% of them could not be reached because the residents were not at home. From the households that were successfully contacted, we received a total of 1678completed interviews, corresponding to a participation response of approximately 60%. A comparison of the data of our survey with demographic statistics available from the last Census data for Portugal (1991) indicates that the different demographic clusters of the Portuguese population are well covered in our sample.

3.2. Consumer motivations

A crucial aspect of our survey is the attempt to measure consumer motivations towards the protection of nature in general, and towards the act of giving in particular. Therefore, we introduced into the questionnaire a list of 26 attitudinal questions[5] to be answered by the respondents on a five point Likert-scale, with values ranging from 1 (for “I disagree completely”) to 5 (for “I agree completely”). These items were formulated so as to capture the warm glow, use and existence motivations. Through the use of an attitudinal scale we deliberately have opted for a subjective measure of these motivations. An alternative would have been to use information on the actual behavior of the respondents, e.g. whether they use the resource or not or whether they spend a large proportion of their income on charitable giving. However, such behavior is also influenced by factors like the accessibility of Natural Parks or the number of times one is asked to contribute for charity – not to mention the income position of the respondent. We felt that for the purpose of explaining WTP answers, a direct measure of psychological motivations is preferable to behavioral indicators.

In order to get internally coherent measures of these motivations we used factor analysis as a variable reduction method - see Harman [9[Pan1]]. This technique is used, first to identify on the basis of the answers on the attitudinal questions a set of latent underlying motivations (the same for all individuals) and second, to estimate for each respondent his or her individual motivational profile, i.e. his or her position on these latent motivations.

In the first step the underlying latent motivations are identified on the basis of the correlations between the responses on the specific attitudinal items. The model assumes that these correlations can be explained by a linear relationship between the individual attitudinal items and a set of underlying latent factors. Highly correlated attitudinal items are assumed to be indicative of the same underlying factors. The so-called factor loadings then give the product-moment correlation between the responses on the attitudinal items and these underlying latent factors. The latter are scaled to have mean zero and unit variance. To get a clear picture we choose an orthogonal factor representation, implying that the basic consumer motivations do not overlap[6], and we opted for the varimax rotation procedure, which maximizes the variance of the squared loadings of the different items on the factors. The factor loadings after varimax rotation are shown in Table I. Printed results are multiplied by 100 and rounded to the nearest integer. The asterisks denote values above 0.45.

Table I. Factor loadings after varimax rotation

Interpretation of the factors resulting from a factor analysis is always a little subjective. Yet the overall pattern seems clear. The items loading on factor 1 relate to the direct consumption of the natural park for recreational use.[7] Therefore, this latent variable is interpreted as the consumer ‘use/recreation’ motivation. Factor 2 is associated with items that capture different “private good” motivations to contribute, such as the sensitivity to social pressure and campaigning efforts or the feeling of satisfaction generated by the act of giving.[8] Although this “private good” component is broader than exclusively the “warm glow” of giving, we designate it as the ‘warm glow’ motivation. Factor 3 is associated with items related to the conservation of nature, independent of its human use and we interpret it as the consumer ‘non-use/existence’ motivation.[9]

After having defined the content of the factors, the next step is to determine the position of the individuals on these factors, i.e. the vectors . These are given by the standardized factor scores, again with mean zero and unit variance. The factor score of individual i on factor k basically is a weighted mean of the answers of respondent i on the attitudinal items making up factor k. A higher value for (i.e. a larger factor score for Factor 1) indicates that the respondent attaches more importance to recreation and other use values. Higher values for (Factor 2) and (Factor 3) reveal that the respondent is more sensitive to the warm glow of giving and is more concerned with the protection of nature and no-extinction of wildlife respectively. Let us emphasize again that these factor scores are meant to reflect psychological dispositions, containing additional information that is not captured by other socioeconomic variables.[10] This finding will be tested (and confirmed) in the multivariate analysis of Section 5.