Good Practice Guidelines for the Non-Market Valuation of Forest ...

Action E45 EUROpean FORest EXternalities (EUROFOREX)

Good Practice Guidelines for the Non-Market Valuation of Forest Goods and Services

Edited by

Pere Riera and

Giovanni Signorello

   

Good Practice Guidelines for the Non-Market Valuation of Forest Goods and Services

Edited by Pere Riera (Autonomous University of Barcelona) Giovanni Signorello (University of Catania)

Reproduction of this publication for educational or other noncommercial purposes is authorized without prior written permission from the copyright holder provided the source is fully acknowledged. Reproduction of this publication for resale or other commercial purposes is prohibited without prior written permission of the copyright holder.

 

Contents           II  

LIST OF CONTRIBUTORS LIST OF FIGURES

IV  

LIST OF TABLES

IV  

LIST OF BOXES

IV  

PREFACE

V  

BEST PRACTICE GUIDELINES IN STATED PREFERENCE METHODS OF FOREST EXTERNALITIES

0  

BEST PRACTICE GUIDELINES IN REVEALED PREFERENCE METHODS OF FOREST EXTERNALITIES

36  

BEST PRACTICE GUIDELINES IN BENEFIT TRANSFER OF FOREST EXTERNALITIES

62  

 

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List  of  contributors  

STATED PREFERENCE GUIDELINES Leading authors (in alphabetical order): Pamela KAVAL Livia MADUREIRA Pierre-Alexandre MAHIEU Jurgen MEYERHOFF Robert MAVSAR Pere RIERA Bénédicte RULLEAU Main contributors: Dulce Armonia BORREGO Raul BREY Peter ELSASSER Hamed DALY-HASSEN Simona DRAGOI Marek GIERGICZNY Paula HORNE Anze JAPEL J Miroslav KOVALCIK Alejandra LEITAO Sandra NOTARO Richard MOLONEY Roland OLSCHEWSKI Mordechai SHECHTER Jozef TUTKA

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REVEALED PREFERENCE GUIDELINES Leading authors (in alphabetical order): Maria DE SALVO Giovanni SIGNORELLO Mara THIENE Main contributors: Olvar BERGLAND David HOYOS Claire MONTAGNÉ Edward MOREY

BENEFIT TRANSFER GUIDELINES Leading author: Stale NAVRUD Main contributors: Tzipi ESHET Henrik LINDHJEM Randy ROSENBERGER Mette TERMANSEN

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List  of  Figures     Figure 1  ...............................................................................................................................................................................  3 Figure 2.  ..............................................................................................................................................................................  8

List  of  Tables     Table 1. Attributes and corresponding levels first example  ...........................................................................  22 Table 2. Complete factorial design for forest management  ............................................................................  24 Table 3. Coding of a two and a three level attribute  .........................................................................................  25 Table 4. Example of dataset for estimating a single-site travel cost model  ..............................................  44 Table 5. Example of dataset for estimating a three-sites travel cost modell  .............................................  45 Table 6. Example of dataset for the estimation of the hedonic function  ....................................................  53 Table 7 . Functional forms that can be used to estimate the hedonic function  .........................................  55

List  of  Boxes     Box 1.The nested structure for National rock climbing sites and the list of variables taken into account  ....................................................................................................................................................................  42 Box 2. Estimating the aggregate value of forest recreation in a regional context by means of a discrete-count linked model  .............................................................................................................................  48 Box 3. A tool to address confounding random scale effects site choice: utility in the preference space vs utility in the WTP space  ..................................................................................................................  49 Box 4. Estimating the demand for tree canopy: a second-stage hedonic price analysis  .......................  55

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Preface  

This volume contains good practice protocols for the economic valuation of non-market forest goods and services developed by participants to the European Cooperation in Science and Technology (COST) Action 45 “European forest externalities (EUROFOREX – COST E45).1 The starting point for the Action was that, although there is a considerable body of research in Europe concerning the valuation of forest externalities, the results from these valuation studies are often not comparable due to variations in the application of the valuation methods and the reporting of results. The aim of the protocols is to facilitate a better and more consistent application and reporting of non-market valuation projects. Action E45 involved 20 European countries and two non-European institutions, from New Zealand and Tunisia. It was organized into three working groups, each focusing on a family of valuation methods. One group was concerned with the stated preference methods. Two variants were studied in particular: contingent valuation and choice experiments. Another working group focused on the use of the revealed preference methods, such as the hedonic pricing approach and the travel cost models. The remaining group covered the benefit transfer approaches. We would like to thank all participants to the Action E45, and individuals who helped us organize and run all activities. We would also like to thank Arne Been, Günter Siegel, and Melae Langbein, Cost Science Officers, Kiril Sotirovsky, Action E45 Rapporteur, the authors of the papers presented at the meetings, and the instructors of training courses. Finally we would also like to acknowledge Michael Hanemann, Ted McConnell, Edward Morey and Randy Rosenberg for their precious contribution to better shape the contents of protocol in the working group discussions.

Pere Riera, Autonomous University of Barcelona, Spain Giovanni Signorello, University of Catania, Italy

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More information are available the web site http://www.cost.eu/domains_actions/fps/Actions/E45 v

Best  Practice  Guidelines  in  Stated   Preference  Methods  of  Forest  Externalities   In this set of guidelines, we present good practice techniques for Stated Preference (SP) studies to estimate the monetary value of forest externalities. In a SP study, the researcher wishes to determine the value a respondent places on a hypothetical good or hypothetical situation. This hypothetical good or hypothetical situation, such as harvesting trees in an area of a forest or establishing a walking trail through a forest, may or may not actually take place. There are several SP techniques available to determine the values for a hypothetical situation including the Contingent Valuation (CV) and Choice Modelling (CM) methods; these techniques will be discussed in more detail later in these guidelines. The CM label refers here to the family of attribute based stated preference methods.

1. BASIC STEPS As each individual will have their own unique value for a situation, a way to determine their value is to ask them. Value determination is typically conducted in the form of a survey. The creation of a good survey is a very complex process; however, there are several basic steps to follow: determining your aim/goal, conducting background work, determining the type of survey to use, determining how complicated the questions should be, writing the first draft of the survey, determining who to survey, determining when to survey and determining your sample size.

Determine your aim/goal Prior to constructing a questionnaire, you need to sit down and determine the precise aim/goal of your survey. Do you want to determine the value of an endangered species or do you just want to know how many people in a school have ever seen that endangered species there? The latter goal would not require a valuation study. Thus, it is important to determine exactly what you want to achieve with your survey. Be specific, as this will enable you to narrow your aim/goal down to write an appropriate valuation question and survey (Bateman et al. 2002; Champ et al. 2003; Fink 2008; Freeman 2003; Pearce and Turner 1990).

Conduct some background work Once your aim/goal has been determined, you should conduct some background work. Has anyone ever accomplished your aim/goal in the past? If they have, how close was what they did to your study? Were they successful in accomplishing their goal? What kind of survey did they use? What kinds of questions did they ask? The background work may involve a literature review, obtaining copies of old surveys with a similar goal, and sitting down with a person (or people) that has (have) conducted surveys of this type before. It may also involve conducting a focus group to narrow down important questions (not only your valuation question, but the other questions in your survey). After

the background work has been collected, you should also be able to determine which valuation question type (e.g., CV, CM) will be most appropriate for your valuation project (Bateman et al. 2002; Champ et al. 2003; Fink 2008; Freeman 2003; Pearce and Turner 1990).

What type of survey should you use? The type of survey you use will depend on the population of interest, the characteristics of the sample, the types of questions, the topic, the response rate, the cost and the time you have available to conduct your study. In general, surveys can be conducted in a variety of ways: in-person, on the phone, through the mail, via a website or email, through a central facility, or via a mixed mode. Mixed mode is defined as “mixing” several modes of administration, such as calling people on the phone, mailing them a survey and then calling them again to follow up. In-person surveys are typically the most costly and time consuming, phone surveys can be done quickly, while mail surveys take a longer period of time, as you have to wait for the respondents of the survey to fill it out and return it. In addition, while phone surveys will typically take a shorter period of time to conduct than mail surveys, inperson surveys can be more in-depth in terms of the responses you receive than mail surveys (Bateman et al. 2002; Champ et al. 2003; Cochran 1977; Lyberg and Kasprzyk 1991).

How complicated should your questions be? When you are writing the questions in your survey, you want all respondents that read your questions to be able to understand them. You do not want to talk down to your respondents, but you do want to adapt the wording of the questions so they are straightforward. Some things that you can do to make sure your questions are straightforward include: avoiding abbreviations, avoiding vague terms and slang, and not including too many things in one question. Avoiding abbreviations. Abbreviations can mean different things to different people, i.e., AA can mean Alcoholics Anonymous, American Airlines or the stock tick symbol for Alcoa International, as well as other things, so it is better to avoid them altogether. Avoiding vague terms and slang. For example: “wicked” could mean good (as in, that is a wicked pair of hiking shoes), but could also mean evil (as in the wicked witch); “older people” could mean 20 years olds to a 12 year olds, but 90 year olds to a 70 year olds; and “the last decade” could mean “1990 to 1999” to some, while it could mean “2000 to 2010” to others. So try to avoid these terms. Not including too many things in one question. For example, don’t ask “Would you like a visitor centre painted with a mural and/or one with a toilet (water-closet) that can be entered when the visitor centre is closed.” This question is too complicated. The respondents may want a visitor centre, but they may not want it painted with a mural; they may want a visitor centre, but may not mind if the toilet/water closet is located outside or inside of the visitor centre; or they may not want a visitor centre at all (Dillman 1978; Dillman 1991; Fink 2008; Salant and Dillman 1994; TaylorPowell 1998).

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Write the first draft of the survey, including the valuation question In relation to the valuation question within your survey, no matter which SP valuation technique you choose to use, when you present your valuation question to your respondents, the research question/valuation aim has to be clear. If the respondents interpret the question(s) differently, your results will not be of much use. Therefore, your valuation question needs to be very specific. It needs to be something that could actually take place and be therefore believable to the respondent. This is done to try to create an actual market type condition, which is what people are familiar with. Before the valuation question is asked, or within the question, however, an introductory section with background information needs to be provided to enable the respondent to answer the question. This information should include: 1) what the respondent is being asked to value, 2) how the value will be provided, and 3) how the project will be paid for. For example, it would be incorrect to ask a respondent “How much do you value an increase in the number of trees in the forest.” This question is just too vague for a respondent to conceptualize. More specifically, the way you present your valuation question will depend upon the valuation method you will be using. If you will be using the CV method, you will be determining the respondents’ value by asking a Willingness-To-Pay (WTP) or Willingness-To-Accept (WTA) question. In this way, you will present the background information and then present your question. A very simple example of a CV question to be given to respondents that have been to a forest is: “There are currently no picnic tables in this forest and no fee to hike. There are many areas of the forest that have been harvested, or will be harvested, in the future. Would you be willing-topay 2.0 € per day to picnic if the picnic tables were located in areas where there was no harvesting?” If you are conducting a CM study, you will be asking a series of questions about the respondent’s preferences for various management strategies. For each question, there will typically be three or four alternative strategies with similar attributes presented to the respondents. An example of one CM question is provided in Figure 1.

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  Figure 1 Which of the following three options (Current Situation, Option 1 or Option 2) do you prefer? Read the descriptions and tick the box that corresponds to your preferred option. Item of Interest

Current Situation (Status Quo)

Option 1 (with management)

Option 2 (with management)

Forest with trees cut down (where the tree stumps of the removed trees are visible)

Seeing tree stumps for

Seeing tree stumps for

Seeing no tree stumps

15 minutes during a 1 hour walk (25% of the time) through Example Forest

30 minutes during a 1 hour walk (50% of the time) through Example Forest

during a 1 hour walk (0% of the time) through Example Forest

Ibex (wild goat)*

Ibex seen

Ibex seen

Ibex seen

(Frequency of seeing the Ibex in Example Forest)

in 1 out of 50 visits to Example Forest

in 10 out of 50 visits to Example Forest

in 5 out of 50 visits to Example Forest

Musk Ox seen

Musk Ox seen

Musk Ox seen

(Frequency of seeing the Musk Ox in Example Forest)

in 1 out of 10 visits to Example Forest

in 5 out of 10 visits to Example Forest

in 3 out of 10 visits to Example Forest

Additional amount to be paid in your annual taxes for the years of 2011 and 2012

0€

20 €

10 €

(Frequency of seeing trees that have been cut down after a logging operation while you are walking through Example Forest)

Musk Ox*

I would choose (please mark your choice)

*The ibex and musk ox are currently listed on the endangered species list in Europe.

Question wording for CV and CM questions will be discussed in more detail in another section of the guidelines (Alberini and Kahn, 2009; Bateman et al., 2002; Carson et al., 2001; Champ et al., 2003; Haab and McConnell, 2003; Hanemann, 1994; Mitchell and Carson, 1989).

Survey testing to increase understanding and reduce bias When the initial survey draft is complete, you have probably read through it and edited it many times. You may think all the questions make perfect sense, but just because a question you wrote makes complete sense to you, it may be interpreted completely differently by someone else. 3

Therefore, substantial developmental work should be conducted before the survey is finalized, including focus groups and pre-tests. Have a series of focus groups. Development work typically includes a series of focus groups. A focus group is a group of people brought together at a specified location (central facility) that may have some knowledge of the good to be valued. The number of meetings and number of participants at each meeting will depend on funding and time restraints. At this meeting, everyone discusses each of the individual questions to make sure they are worded properly, make sense, and are asking the question the survey administrator is trying to answer. Once this step is complete, surveys should be updated accordingly. Conduct a pre-test with people in your target audience. Once the survey has been updated according to the comments from the focus groups, it is good to conduct a few pre-tests. Pre-tests typically include several one-on-one interviews in which the respondent will visit through each survey question with the administrator and provide any suggestions for wording and organization. Once these updates are added to the survey, the survey can be finalized and distributed (Alberini and Kahn 2009; Bateman et al. 2002; Champ et al. 2003; Fink 2008; Taylor-Powell 1998).

Other important survey writing considerations Not only is survey testing conducted so the respondent is able to understand the questions, it is also conducted to reduce several issues, such as scope, zero protest answers, and low response rates. Scope issues occur when the respondent’s value per unit of the good does not change when the quantity of the good changes, i.e., a respondent would value a forest with 10 musk oxen the same as they would value a forest with 30 musk oxen. This is an issue for researchers, as they would expect people to value 30 musk oxen more than 10 musk oxen. One explanation for this is the warm glow effect, or caring externality, where the respondent feels good about contributing to a good cause, and therefore, the amount she contributes may be the same for 1 musk ox or 5,000 musk oxen. However, it is more commonly believed that there will be no scope effect if the hypothetical scenario in question is clearly defined. Again, this stresses the importance of clearly defining your aim/goal. Protest answers occur when a respondent protests or refuses to provide an answer to a (valuation) question. This often occurs with WTA questions, in which the respondent is asked to give up something, such as access to a park, access to a particular trail in a park, or a change in environmental conditions. An example might be, ‘would you be willing-to-accept a payoff of 50 euro if the area where you hike frequently is clear-cut of all its trees.’ The respondent may not be willing-to-accept any amount for the cutting of trees in their hiking area. Protest answers also occur when people are not conditioned to paying for something. For instance, there may be a park that the respondent regularly frequents that currently does not charge an entrance fee. The park, on the other hand, wants to start charging an entrance fee to reduce the number of recreationists and lower the environmental impact from them. In this case, people may refuse to pay anything and protest the question. Therefore, in constructing your questions, keep in mind that people don’t like to give up things they currently get for free, and that it may be easier for people to be willing-to-pay a little more for something they already pay for, than to start paying for something they have not paid for in the past. For these reasons, it may be good to include an open-ended follow-up question afterwards, asking them to explain why they would pay or refuse to pay. Response rates differ according to the type of survey that is administered (e.g., telephone, in-person, mail, e-mail, central facility, or mixed method), however, low response rates typically occur when a 4

survey is too complicated or too long for a respondent to fill out. This can be mitigated if the survey is simply constructed and is kept short enough to obtain the information you need. Here, you need to pay special attention to who the respondents will be, how you word the questions, and how many questions you have. In addition, a survey that is 50 pages long will have a lower response rate than a 2 page survey, as people just do not have the time to fill out a 50 page survey. Another way to increase response rates is to provide reminders, i.e., if it is a survey at a central facility, the survey facilitator should call everyone the night before the session to remind them to attend. If it is a mail survey, then a second survey, or a reminder postcard, can be mailed to those people that did not respond to the first mailing. If there are still some people that have not responded to the first or second mailing, a third mailing could take place. Again, remember that this will depend on the time and funding you have. If you need to analyze your results quickly, conducting a mail survey with two follow up mailings for non-respondents may not be appropriate (Arrow et al. 1993; Bateman et al. 2002; Carson 2009; Carson et al. 2001; Portney 1994; Randall 1997).

Who do you survey? Now that you have written your survey, you should determine the most appropriate target audience to answer your questions. This can be accomplished by determining whose values are relevant, or, more specifically, who will benefit from the good in question and who will pay for the good in question. Perhaps you would like to conduct a general recreation survey of Spanish residents. In this case, a random selection of respondents living in Spain would be appropriate. This would include people that currently recreate (users), as well as people that do not (non-users and/or potential users). It is important to include the non-users and/or potential users, as they do have the opportunity to recreate if they wish. For those that do not wish to recreate, they may still have an existence or bequest value for recreation. However, defining the population to study for the case of determining the value of an endangered bird at a lake may be more difficult. In this case, you would want to investigate people that recreate at the lake and whether they are interested in the endangered bird or not, as well as non-users, because people that do not visit the area to see the birds may still have an existence or bequest value for it. These non-users can be locals, nationals, or from other countries, therefore, making the decision of whom to survey a little more difficult. Ultimately, when you are considering who to survey, you want your respondents to resemble the study population (Alberini and Kahn 2009; Bateman et al. 2002; Champ et al. 2003).

When should you survey? When is the best time to collect the data? When thinking about the time of year, if you are analyzing summer recreation at a beach, you will need to survey people in the summer, or at least shortly afterwards, so summertime recreation is fresh in their minds. If you are trying to determine annual recreation numbers, however, you may want to conduct your survey over a longer period of time, as different numbers of people visit places at different times. For example, perhaps a large proportion of the people visiting forests visit on weekends and school holidays, while others prefer to visit when there are less people and visit on non-holiday weekdays.

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If you will be calling your potential respondents on the telephone, the time of day, in conjunction with the phone number you are calling them on, is important to consider. Many times, it is easier to obtain potential respondents home phone numbers, rather than their work numbers. Therefore, if you try to contact potential respondents during the day (8:30 am to 4:30 pm) during the workweek (Monday through Friday) on their home phone numbers, you will find that few people answer the phone. The people that you may contact during those times are typically composed of retired persons, people that work at home, homemakers, and some students. If these are the respondents you are targeting, this is the ideal time for you to call. But if you are targeting the average working person and only have their home phone numbers, calling between 6:30 pm and 8:30 pm Monday through Thursday nights works best (Sunday during that time of day is also acceptable). If you call earlier (4:30 pm to 6:30 pm), people may not be home, may be making dinner, or may be eating and not want to be disturbed. If you call after 8:30 pm, people may be trying to relax, getting ready for bed, or putting their children to bed and not wanting to be disturbed (Kaval 2004; Kaval and Roskruge 2008; Yao and Kaval 2006). In addition, studies by Kaval (2004), Yao and Kaval (2006), and Kaval and Roskruge (2008) have indicated that fewer people answer the phone on Fridays and Saturdays, and those that do are not always very helpful, as they frequently rush through your questions to get you off the phone. As a result, it might be better to avoid calling people on Fridays and Saturdays. As can be seen, there is a small window of opportunity to make phone calls and obtain answers from your ideal number of respondents (Alberini and Kahn 2009; Carson 2000; Kaval 2004; Kaval and Roskruge 2008; Yao and Kaval 2006).

What should your sample size be? How many people should you survey? There are many things that can be considered in this decision, such as: 1) What your acceptable significance level is; 2) What your acceptable error is; 3) What the total population that can be considered for your study is; 4) What the variance of the pilot study was; 5) How many populations you will be comparing (i.e., Spanish residents that recreate vs. Spanish residents that do not); 6) Which survey method you will be using (e.g., CV or CM), and 7) Which survey type you are conducting (e.g., telephone, in-person, mail, e-mail, central facility, or mixed method). For more information, refer to: Mitchell and Carson (1989) and Hanemann and Kanninen (1999). Once you have determined the answers to the questions that you believe are pertinent to your situation, you can refer to a variety of statistical procedures to calculate the most appropriate number of respondents for your particular survey. Good references that detail some of these tests include Cochran (1977), Lydberg and Kasprzyk (1991), and Vaughan and Darling (2000). However, keep in mind that your funding and time restraints may be a limiting factor in obtaining the ideal number of respondents.

2. THE NULL ALTERNATIVE When creating a SP study, an important element to consider is what will happen if the project is not implemented. In other words, the “null alternative” is crucial. This element helps clarify the stakes of the valuation study. To successfully complete this, the researcher must explicitly identify the ins and outs of the situation, making the scenarios presented to the respondents easier to construct. For instance, an afforestation/deforestation program may have environmental consequences for the 6

landscape, but also for the water quality, soil quality and the biodiversity. These implications must be identified. In addition, the null alternative has implications as to the type of welfare measures that can be estimated. Indeed, the economic values of the environmental good can be calculated as the WTP or WTA compensation of the target population. One must, in consequence, decide upon the type of values necessary and define the null alternative accordingly, that is to say, as the reference situation. The null alternative must be defined clearly. Its definition will depend on the context, but typically refers to the do-nothing situation explaining “what will happen if the project is not implemented”. The researchers thus label the null alternative as the “status quo” or “business as usual” situation. In the case of CV, a study may estimate the WTP for an improvement in environmental quality or quantity, or the WTA if this hypothetical improvement does not occur. However, the null alternative may also imply a change. A change might be the deterioration in environmental quality, i.e., a worsening reference situation. A WTP to avoid an unwanted change in the environmental good, or the WTA if this change occurs, could then be elicited. Finally, the reference situation may represent an improvement in the quality of the environmental good. In CV, the null alternative might be stated implicitly or explicitly. If explicit, the description of what will happen if the project is not implemented is part of the “valuation section” of the questionnaire. It has to be presented before the value elicitation questions. It is typically included in the presentation of the valuation scenario. If implicit, an example of the wording of the questionnaire may be: “What is your max you would pay to picnic in this forest if the picnic tables were located in areas where there was no harvesting?” . In CM, the null alternative is an alternative that the respondent can select from a “choice set” of options. Each choice set, or valuation question, typically consists of the reference situation and at least one alternative scenario, while the former remains identical to the valuation sequence. The “what will happen if the project is not implemented” situation is thus one proposed alternative in the choice set. Respondents are instructed to choose the best scenario or rank/rate the scenarios in each choice set from a set of alternatives. Theoretically, respondents choose the alternative that provides them the highest level of utility. Since the null alternative is commonly included in the alternatives of the choice sets, it can be explicitly described and has several advantages. Firstly, if the possibility of not choosing is not offered, the WTP/WTA estimates may be biased. Secondly, the explicit presentation of the null alternative mitigates yea-saying bias. This bias is the tendency of respondents to agree, regardless of their preferences and of the question (Bateman et al. (2002, p. 302). In contingent ranking as well as in choice experiment, a null alternative is usually included to ensure welfare consistent estimations. If picked first, this option may then be excluded from the choice set (Hanley et al. 2001). By definition and contrary to the contingent rating methodology, a null alternative must always be present in the pairs for statistical and econometric analysis of paired comparisons (Pearce et al. 2006). In all the methods, the null alternative may be subject to response bias. Responses are said to suffer from the so-called response, or “status quo,” bias when an unreasonably high number of people choose the reference situation. The latter situation may be seen as an “easy way out” if choice tasks prove too difficult. Status-quo bias might also arise when people are averse to change (Holmes and Adamowicz, 2003).

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Generally speaking, a clear presentation to the respondents of what will happen in the future if the project is not implemented must be privileged. Indeed, it guarantees that respondents know exactly what this baseline situation refers to. They, in consequence, should not necessarily need to build their own interpretation of the scenarios that are presented during the valuation study. This procedure enables to enhance the level of objectivity. This explicit description can take several different forms. In addition to the text itself, adequate details may be provided through photographs, drawings, or graphs. These visual aids are particularly useful when the situation one wishes to describe is complex, such as multiple changes in the provision of the attributes of the good, or when this situation is hypothetical (Arrow et al. 1993). Visual aids have for example been provided by Meyerhoff and Liebe (2008).

Figure 2. Example of a choice set with visual aids

3. PAYMENT AND DELIVERY CONDITIONS The questionnaire ought to specify the market conditions under which the payment would be made and the good delivered. This implies being clear about the payment vehicle, the payment conditions, the decision rules and the delivery condition.

Payment vehicles Taxes in the form of income, sales, property, local, and earmarked, as well as entrance fees, donations, and higher prices, are some of the payment vehicles used in SP questionnaires. The payment vehicle used should be realistic and relevant. For instance, it would not be realistic to establish an entrance fee to a forest, if there is currently an open access policy in place. It would not be relevant to ask about an increase in income tax, if the respondent does not pay income taxes, due to a lack of income. To help detect the best payment vehicle to use in a SP, focus group and debriefing questions can be asked.

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The payment conditions Depending on the intended use of the value estimates, payments could be made periodical, say for five years, or indefinitely, or on a one-time only basis. If periodical, or if the one-time payment is to happen in the future, it must be made clear to the respondent whether the value is expressed in nominal or real prices. In addition, the discounting of the payments could constitute an issue. It should be clear to respondents whether the payment refers to the household, or the individual who answers the questionnaire. Payments could be expressed as total nominal amounts, as proportions of income, or of taxes already paid by the respondents, for instance. Sometimes, the individual is only required to pay part of the cost –say, half of it–. If so, the questionnaire should specify this. Respondents should be aware of the payment conditions for the rest of the population, not only their own.

The decision rules The sampled population ought to be aware of the purpose of the valuation exercise, the consequences it is likely to have, and under what rule the decision will be made. It could be, for example, an exercise aiming at an investment decision in a public forest, to be undertaken if the majority of people would pay the cost amount. Or the rule could be that the total WTP exceeds the costs, as in a cost-benefit analysis framework. The specification of the decision rule should be taken jointly with the incentive compatibility problem.

The delivery conditions The WTP for the provision of a given good also depends on the delivery conditions, and therefore they should be specified; typically, who, how, where and when the good would be delivered matter. Partly, this problem is treated in another section, under biases related to the sponsor of the project. But conditions like being able to enjoy an afforestation program right away, or being carried out in the next 10 years, influence the amount of welfare gain an individual would derive. Also, planting the same amount of forest each year, planting more now, or planting more later, might have a noticeable impact. Afforesting near one’s home is not the same as afforesting farther away; usually there is distance decay function that penalizes the provision of local public goods according to the distance and the costs to enjoy them. Finally, the judgment of a forest program may depend on how it is delivered. One type of silvicultural practice could be seen as being more desirable to respondents, than another one. Most issues related to payment and delivery conditions could be checked during focus group sessions, one-on-one interviews, and pilot tests. The use of debriefing questions, or a detailed analysis of the protest answers, could also be of help.

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4. WILLINGNESS TO PAY OR WILLINGNESS TO ACCEPT COMPENSATION? SP methods use two measures of economic value. One is the WTP, which reflects the maximum amount of money that an individual would pay to obtain a good. The other is the WTA compensation, which reflects the minimum monetary amount required to cede a good. In general it is assumed, if the value of the exchange in question is not representing a significant share of the respondent’s income, or when the associated transaction costs are not very high, these two measures should approximately yield equal estimates of value (Brown and Gregory 1999). Nevertheless, the empirical studies have demonstrated a disparity between these two measures, where the WTA estimates are typically two or more times higher than the WTP estimates (Brown and Gregory 1999; Horowitz and McConnell 2002; Neilson et al. 2008). This evidence was observed in field studies and laboratory experiments, using consumer and/or environmental goods. Economic and psychological reasons could be potential explanations for this disparity. The economic reasons include income effect, transaction cost, implied value, and the profit motive; while, the psychological reasons can be summarized into the endowment effect, legitimacy, ambiguity, and (moral) responsibility (Brown and Gregory 1999; Neilson et al. 2008). Income and substitution effects are observed when the payment for a good is constrained by income, but the compensation demand to give up the good is not. The magnitude of the income effect depends on the availability and the price of a substitute; because, in theory, the WTA should not exceed the price at which a perfect substitute can be purchased. Thus, the lack of perfect substitutes could increase the disparity between the WTA and WTP (Hanemann 1991). The income effect is unlikely to play a role in the WTA-WTP disparity for inexpensive market goods with existing substitutes. It is likely to be an important factor in the disparity observed for unique and more valuable non-market goods (e.g., environmental services). Transaction costs are costs incurred in making an economic exchange. If the extent of transaction costs borne by the buyer and seller are different, they may contribute to the WTA-WTP disparity. Implied value can be caused by the attempt to sell or buy a good or service. For example, if something is offered for sale it might give the impression of being unwanted, which can decrease the value of the good. On the other hand, a good that someone is attempting to purchase, may be considered as more desirable, causing an increase of its price. This effect could be a source of WTA-WTP disparity in unanticipated trade, as it is the case for many environmental goods, but it is not as relevant for ordinary consumer goods (Brown and Gregory 1999). Profit motive is the main driving force of many real market exchanges. Many goods do not have a well defined price, but rather a range of possible values. Therefore, it is likely that buyers will intend to pay a price at the lower bound of this range, whereas the sellers will attempt to sell a good at the higher bound value. This behaviour can lead to a difference in WTA and WTP for a good. Endowment effect is the effect that is explained by the asymmetry of the valuation of gains and losses. Namely, it is believed that buying a good creates gains, whereas the selling of the same good generates losses. This results in reluctance to sell, because a good that is owned is worth more, just because it is at hand. The endowment effect can be expected to be stronger in situations where the proposed sale is involuntary, as it is often the case with environmental goods.

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Legitimacy is an issue in cases where some proposed exchanges may be rejected because they provoke an ethical dilemma. This effect arises in particular when trading with human safety or health, and environmental goods. For example, an individual might not be willing-to-pay a significant amount to preserve one additional specimen of an endangered species, but might be reluctant to sell a part of the population of this same species. Ambiguity may occur, because almost each transaction involves uncertainty about some of the key factors, such as the market price of a good or its substitutes, the characteristics of a good, the enjoyment that a good will bring if purchased or the regret that it might provoke if sold. This is even more pronounced in the case of transactions involving goods unfamiliar to the buyer or seller, like environmental goods. Thus, under conditions of high ambiguity, the buyers will tend to underestimate the value of a good, while the sellers will overestimate its value, which may lead to a disparity. Moral responsibility is another issue that can be the origin for the disparity between WTA and WTP values. In the case when individuals feel moral responsible and the proposed action can provoke a harmful outcome, people will prefer doing nothing. This behaviour can be explained by the fact that people feel less bad if the damage just happens, then if they acted in a way that caused it. Therefore, the premium required to compensate for this potential harmful outcome can cause the disparity, by decreasing the WTP and increasing the WTA. Which of these reasons contribute to the disparity in a given situation is unclear. Brown and Gregory (1999) suggested that in the case of ordinary (inexpensive) market goods profit motive, endowment effect, ambiguity and responsibility might provoke the disparity; while, when valuing environmental goods, the differences between the WTA and WTP might be caused by all the listed reasons, except the profit motive. Furthermore, Horowitz and McConnell (2002) observed the highest disparities between the two measures for public and non-market goods, and health and safety (the mean ration of WTA/WTP was approximately 10). Whereas, disparities for ordinary market goods, or money, were about fivetimes lower. However, they did not find evidence that a higher WTA/WTP disparity was caused by hypothetical payoffs or elicitation techniques, or that the difference between WTA and WTP became lower when individuals were more familiar with an experiment. The disparity between the WTA and WTP was the main reason that WTA has seldom been employed as a measure of value. For example, the NOAA report (Arrow et al. 1993) observed that “respondents are more likely to exaggerate the compensation they would require than their WTP” and thus suggested that the WTP format “should be used instead of compensation required because the former is the conservative choice.” Nevertheless, it is recommended that WTP should be used in cases of positive changes (gains or improvements); whereas, for damages or losses (negative changes), the WTA measure is more appropriate

5. VALUATION FUNCTION A valuation function can be generically defined as a statistical way to relate respondent’s WTP (WTA) with a set of explanatory variables. The attributes of the good and the respondent socioeconomic characteristics are usually used as covariates of the individual WTP. Whereas other covariates might be included when it is expected them to explain the WTP, including variables such as the experience and familiarity with the good under valuation, and/or the respondent perceptions, 11

preferences and attitudes towards the good or related goods or even more general environmental issues (Carson et al. 2003; Mitchell and Carson 1989; SEPA 2006). The valuation function is a very useful tool to summarize SP data, allowing you to understand how the WTP responds to changes in the attributes of the good and on the socioeconomic characteristics of the respondents. Therefore, in addition of providing WTP estimates, the valuation function provides valuable information on the data validity and reliability. An additional application of the valuation function arises within the context of benefit transfer. When estimates of a valuation function relating the WTP for a particular good with its attributes and the socioeconomic characteristics of the respondents are available that allows for adjusted benefit transfer through the benefit function transfer approach (see the benefit transfer guidelines). If one adopts a broad definition of the valuation function it can be used to address both alternative ways of modelling SP data to obtain the individual WTP estimates: (1) specifying the indirect utility function and derive the WTP from its estimates (for a detailed description see Hanemann 1984); (2) direct modelling the WTP (Hanemann and Kanninen 1999). Nonetheless, only the direct modelling of WTP provides actually a valuation function (also named value function or bid function), in the sense that it delivers directly estimates for the individual WTP for a given valuation alternative as a function of its attributes and other explanatory variables. For the CV applications, direct valuation function can be specified and estimated, both for openended and close-ended elicitation formats. Within the latter format, the dependent variable is the probability of a “yes” answer to a certain bid and the bid is one of the explanatory variables. However, Cameron and James (1987) and Cameron (1988) developed a procedure to reparameterise discrete choice probability models, such as the probit and logit specifications, into a direct valuation function that provides the individual WTP for a given valuation alternative. CM elicitation formats, entailing the comparison of two or more valuation alternatives are usually conducted as indirect valuations, obtained by specifying an indirect utility function, which estimates are used to derive the marginal WTP for the non monetary attributes. Nevertheless, models parameterized in terms of WTP can also be obtained with CM data when modelling is built on multinomial logit (MNL) model. However if you choose to model data through random parameters econometric specifications, such as the mixed logit model, then it is usually more complicated to derive convenient distributions for preferences from the “utility space” to the “WTP space” and vice-versa (for a detailed discussion see Haab and McConnell 2003; Train and Weeks 2005). Given that at the present time, the use of valuation functions is only a current practice for CV applications, the guidance for practitioners on how to specify, estimate and to use a valuation function builds on CV data.

Specifying and estimating a valuation function If you are planning to specify a valuation function there are two start point questions: Why you want to estimate it, and which is the type of data you are going to use. These questions are determinant to respond to the main decisions placed by the specification of a valuation function, which are: (a) to define the set of explanatory variables to include as the WTP covariates; (b) to choose a statistical distribution for the WTP; (c) to define the functional form of the function. Regarding the purpose of the estimation, there are two basic types of valuation functions: Empirical and theoretical constrained valuation functions. Estimates for the empirical valuation functions are usually obtained selecting the significant covariates of the WTP (often through a stepwise 12

procedure) among the set of variables expected to influence it included in the questionnaires applied on the SP surveys. Thus empirical valuation functions provide best fit of the empirical data and can be used both to explore the data and to assess its quality, as well to obtain individual WTP estimates. Theoretical constrained valuation functions have been used to describe WTP for marginal changes in the non-market goods, or its attributes, in accordance with the requirements of the economic theory. The valuation function has proven to be a particularly flexible modelling tool in the context of the multi-attribute valuation, providing estimates for marginal value of different attributes, and allowing for testing theoretical issues, such as the substitution or complementary effects between attributes (e.g. Hoehn 1991; Madureira 2001; Santos 1998). The main difference between specifying an empirical and theoretical constrained valuation functions addresses the choices about the number, the type and the format of explanatory variables to include in the functions specification. Estimating both, empirical and theoretical constrained valuation functions is a common practice among CV practitioners. The estimation of empirical valuation functions allows for identifying the WTP main covariates and for assessing the quality of the data and the WTP estimates. Therefore its estimates are an important tool to assist the specification of theoretical constrained valuation functions. Furthermore it might me a good practice to keep a few variables like income, regardless of whether they are significant or not, as explained in Bateman et al. (2009). The type of CV data (dichotomous choice or referendum) open-ended versus close-ended determines the statistical options underlying the specification of the valuation function. CV openend data allows for direct specification of the valuation function, while close-ended format imply the re-parameterization of the utility modelling as proposed by Cameron and James (1987) and Cameron (1988).

Valuation functions for open-ended data The specification of a valuation function using CV open-ended data is quite straightforward. Data obtained for individual maximum WTP for the change in the good quality or in its attributes (e.g., a change in the forest management or valuing the forest preservation) are directly regressed across a set of covariates, which includes the individual’s socio-demographic characteristics, but might include as well, the attributes of the good and variables used to describe attitudes, perceptions and/or preferences regarding the good or other aspects of the valuation scenario. The econometric model to be estimated is clear-cut: WTPi = f ( z j , si , ! i )

where zj are the characteristics of the good which change is being valued; si are the sociodemographic characteristics of the individual (e.g., age, gender, education level, income), her experience and familiarity with the good and eventually variables used to describe attitudes, perceptions and/or preferences regarding the good under valuation and respective context (e.g., personal interest in environmental issues, concern with environment preservation, believes about the role of forest regarding soil protection). The ε is a random error component accounting for not observed aspects. The econometric issues with CV open-ended data modelling are relatively simple. To prevent negative estimates of the WTP, which are likely to appear due to zero answers to WTP question, censored regression models are usually used. The tobit model can be used (e.g. Halstead et al. 1991). 13

Yet other models have been used, such as the multivariate binomial – lognormal mixture model, used by Langford et al. (1998) to develop a bid function (valuation function) including explanatory variables such as income, sex, age and education. Why to preclude negative values for WTP? This procedure is usually adopted based upon the assumption that if we are valuing a good, then it is expected the respondents to present a nonnegative value for it. However, negative values might arise within the context of empirical applications and needed to be accounted for. Negative values for WTP can be easily incorporated within the modelling of open-ended CV data, for this you can use a Tobit model which assumes a normal distribution. Linear forms are usually adopted allowing for estimates of the parameters for the various covariates included in the regression. Yet, more complicated functions can be specified if the practitioner were looking for testing the conformity of WTP estimates with theoretical or empirical hypothesis. A recent application related to forest valuation is provided by Adams et al. (2008), that present and discuss estimates for valuation functions obtained with data from CV using open-ended format, conducted to value the conservation of a rainforest protected area in Brazil. Hoehn (1991) provides a standard example of a theoretical constrained valuation function estimated with open-ended CV data. This function builds on a quadratic functional form with parameters for interactions between attributes and quadratic terms, aiming to estimate the substitution relationships between attributes of air quality (in some cities of US), and the concavity of the valuation function. Statistical guidance for dealing with censored regression models, namely with the tobit model, is given by Greene (1997) and Haab and McConnell (2003). Estimates for the parameters of the valuation function can be obtained using common econometric software packages, where maximum likelihood method is available. The Ordinary Least Squares (OLS) is not a suitable estimation method in this case, given that the dependent variable (WTP) must be censored.

Valuation functions for closed-ended data There is, as aforementioned, procedures to re-parameterise the discrete choice probability model, obtained from the “yes” and “no” answers to the bid assigned to a valuation alternative, into a direct valuation function. Cameron and James (1987) and Cameron (1988) developed a re-parameterize procedure for converting, respectively, the probit and logit specifications of discrete choice probability model, into a direct valuation function, which provides the individual WTP for a given alternative. Detailed description of these procedures can be found in Cameron and James (1987) and Cameron (1988). Haab and McConnell (2003) offer also useful guidance in this respect, considering alternative statistical distributions for the WTP. Again, econometric software packages, where maximum likelihood method is available, can be used to estimate the valuation function through the re-parameterization of the discrete choice models, such as the probit and logit models. Applications related to forest valuation are provided by Santos (1998), whom presents empirical and theoretical constrained valuation functions obtained with CV DC data.

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Assessing data quality with valuation functions Valuation functions, because they relate the WTP with good quantity/quality levels (or attributes levels) and the socioeconomic characteristics of the respondents are also useful in assessing the validity and reliability of data and estimates. SP questionnaires usually include a set of debriefing questions, alongside with questions about the individual’s socioeconomic characteristics. Thus, a considerable amount of relevant information for valuation is collected about the individual’s familiarity and experience with the good, or attributes being valued, their attitudes, perceptions and preferences regarding the good, or other relevant features of valuation scenario (e.g., the valuation scope, the payment vehicle or the motivations for not paying). The gathering of this information is explicitly, or implicitly, recommended by good practices guidelines (Bateman et al. 2002; EPA 2000; Haab and McConnell 2003; Mitchell and Carson 1989; SEPA 2006) because it allows for a partial assessment of the validity and reliability of valuation data and WTP estimates. Empirical valuation functions comprise the tool to deal with all that information, when one aims to assess the quality of data respective estimates. Therefore, its estimation is recommended to the practitioners as a first stage, when estimating WTP models and/or deriving its estimates. Carson et al. (2003), having CV in mind, recommend estimating empirical valuation functions in several steps. The first step consists in “cleaning” the data, dealing with the missing values, and deciding which variables to include in the regression. Then, a second step is running a stepwise procedure to select among the variables expected to influence WTP, by only retaining the significant ones (according to a significance level previously established). A third step is using the valuation function to make adjustments to WTP estimates for issues such as protest answers. The contribution of the valuation function to assess validity primarily considers theoretical validity, which is usually included within the construct validity (for a detailed description see Mitchell and Carson 1989). Theoretical validity is supported when the coefficients estimated for the covariates expected to be predictors of WTP, such as the amount of the good (or attribute) and the income, are statistical significant and reveal the correct sign. The total explanatory power of the function is often used as a measure of reliability. It captures the true variance of WTP in the population and also of the variability introduced by the measurement tool (the questionnaire and its administration). The R2 statistic is proposed by Mitchell and Carson (1989) as being a measure of reliability, when estimates are obtained through an OLS regression. For close-ended elicitation formats, similar goodness-of-fit measures are usually used such as the pseudo-R2 or the adjusted pseudo- R 2 , namely in the format proposed by McFadden (1974), referred as the rho-square statistic ! 2 (or adjusted rho-square, ! 2 ), while other options commonly reported in CV studies are the likelihood ratio (LR) and the percentage of correct predictions. Therefore, estimating a valuation function and reporting the respective estimates should be a routine procedure in SP studies. Its presentation and the analysis of its estimates in respect to the validity and reliability issues are recommended as a good practice by valuation guidelines. The presentation of a valuation function is one of the criteria to evaluate the quality of SP studies (e.g. SEPA 2006; Söderqvist and Soutukorva 2009). Furthermore, its estimates might also provide information on the respondent’s heterogeneity, helping to identify relevant segmentations on the population. This latter information might be also useful to benefit transfer exercises that attempt to use results of a particular valuation study in other settings. 15

The validity assessment of data and WTP estimates is based on the analysis of the statistical significance and the sign of the estimated coefficients for the covariates expected to influence WTP in a certain way. The statistical significance is usually evaluated though the analysis of the values of t-ratios (or asymptotic t-ratios for CV DC and CM). Currently, accepted significance levels are used as thresholds. The analysis of the validity of SP data and estimates comprises the comparison of the signs of estimated coefficients with the expected ones. These expectations can be derived from the economic theory or be empirically driven. Theory predicts a positive sign for the good quality (or attribute presence), and a positive one for the income variable. Regarding the signs for other coefficients there are also some reasonable expectations, such as a positive influence of familiarity and experience with the good, or a pro-environmental attitude or behaviour on the individual’s WTP. Protest behaviour is expected to influence it negatively. Yet, more often unexpected, signs can be explained due to the population or the good specificities. Therefore, the recommendation is to check theoretical conformity, namely through the inclusion of a variable for individual, or household, income within the questionnaire, and to analyse the influence of other covariates according to the expectations of the inquired population and the valuation setting characteristics. The analysis of reliability, based on the total explanatory power relies on the value obtained for goodness-of-fit measures, such as LR, some pseudo-R2 or the percentage of correct predictions. A common problem with the SP studies is that the estimated valuation functions display quite different values for the same goodness-of-fit measures, making it difficult to settle a threshold to classify a study as acceptable, in terms of the reliability of its data and WTP estimates. Therefore, higher values for the goodness-of-fit indicators aforementioned is the best, yet when the values are beyond the minimum threshold (e.g., pseudo R² larger than zero) it is not possible to rule out studies presenting goodness-of-indicators bellow the suggestions of literature (e.g. Bateman et al. 2002; Louviere et al. 2000; Mitchell and Carson 1989). For additional guidance regarding this topic the resort to “grey literature” is strongly recommended. This is so because, while the procedures of checking for missing values and for the WTP (or its indicators) covariates are trivial among valuation practitioners, its outcomes are often omitted in the results displayed in the scientific articles or working papers which are focused in specific subjects. Detailed analysis of these issues can be more easily accessed within PhD thesis or technical reports. The orientations given in this section, regarding the assessment of the validity and reliability of CV data and estimates build on the valuation function framework, are also useful when data (CV DC or CM data) are modelled through the indirect utility function. Within this situation, the probability of choosing an alternative obtained by this elicitation formats can be envisaged as an indicator of the individual WTP, thus allowing to interpret the estimates of the regression of this probability as a function of covariates in the same sense as the estimates for the “true” valuation function (obtained from CV data), i.e., as a way to check the validity of data and WTP estimates.

The use of valuation function for benefit transfer The estimates of a valuation function relating the WTP for a particular good (or site) with its attributes and the socioeconomic characteristics of the respondents might be quite useful for benefit transfer. Transferring benefit functions is one of the three basic approaches available for benefit 16

transfer (Bateman et al., 2000). When estimates for a valuation function are available adjusted benefit transfer is allowed, because it is possible to adjust the transferred values for the characteristics of the good (or site) and of the population who valued it. The questions of how this can be done and the advantages and limitations of this approach for benefit transfer in comparison to others are discussed on the benefit transfer guidelines.

6. SCOPE EFFECT AND HYPOTHETICAL BIAS SP survey results can be biased. People may tend to overestimate their WTP (hypothetical bias), or be insensitive to the provision of the good (scope effect). Furthermore, they may try to manipulate the outcome of the survey (strategic bias), or be influenced by the first amount assigned when responding to the follow-up question in a double-bounded dichotomous choice (DBDC) survey (anchoring effect). Also, they may be influenced by the interviewer (interviewer bias). For a more comprehensive review of existing biases, see Mitchell and Carson (1989) and Carson and Hanemann (2005). Of these biases, hypothetical bias and scope effect have received the most attention in the literature (Harrison 2006). According to Bonnieux and Desaigues (1998), they are the main threat to the validity of WTP estimates.

Scope effect Scope effect occurs when the “willingness-to-pay varies inadequately with changes in the scale or scope of the item value” (Hanemann 1994 page 34), which is the case when an individual states the same WTP for one lake, two lakes or ten lakes. A way to detect it in CV studies is to check whether varying the quality or quantity of the good between individuals affect the answers. Testing for scope in CM can be done by testing whether the parameters of the good attributes are significantly different from zero (Hanley et al. 2003). Great care should be taken in the scenario design, since well-designed studies, bolstered by the use of focus group and clear definition of the good, may limit the scope effect. In this perspective, Carson (1997 page 148) argue that the respondent must “(i) clearly understand the characteristics of the good (...) (ii) find the CV scenario elements related to the good’s provision plausible, and (iii) answer the CV questions in a deliberate and meaningful manner”. Furthermore, recent studies suggest that using label in CM may also help to mitigate the scope effect (Czajkowski and Hanley 2009) and that emotional state of mind of the individual may also contribute to scope effect (Arana and Leon 2008).

Hypothetical bias Hypothetical bias is a different issue. When participants of a valuation survey do not face the consequences of their decisions, which are to pay the amount stated, they tend to respond differently: they typically overestimate their WTP (Murphy et al. 2005). In CV surveys, participants declare amounts they are not actually willing to pay; while in CM surveys, they underestimate the importance they attached to the money attribute, and favour alternatives they would discard in reality.

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Detecting hypothetical bias The two main approaches to detect hypothetical bias are the within and between sample approaches. The former approach consists of assigning two valuation tasks to each of the participants, one task involving a hypothetical payment and the other a real payment, and making intra-individual comparisons (Cummings and Taylor 1999). The bias is demonstrated when responses diverge; as it is the case of an individual first stating to be willing to pay 5 euro for an environmental program and then declaring 2 euro when informed that the payment becomes actual. However, individuals may refuse to revise their answers to remain consistent, as pointed out by Johansson-Stenman and Svedsater (2008). “People (...) seem to prefer to do what they say they would do, although this may not reflect their true preferences regarding the good being valued” This issue, which corresponds to “cognitive dissonance” (Festinger 1962), has lead some practioners to favour the between sample approach. Each participant is either faced with a real or an actual payment, and statements are compared between the two subsamples. Hypothetical bias is demonstrated as follows. In CV, the mean WTP of the group facing a hypothetical payment exceeds the mean of the group facing the actual payment (Cummings et al. 1995). In CM, hypothetical total WTP for the good exceeds real WTP (Jayson and Schroeder 2004) and hypothetical marginal WTP exceeds real marginal WTP. Comparisons of within and between sample approaches have been carried out in both CM (Johansson-Stenman and Svedsater 2008) and CV (Blumenschein et al. 1998) surveys. People appears to be more sensitive to changes in the scope of the good when a within sample test is performed rather than a between sample test.

Mitigating hypothetical bias Different instruments have been developed to mitigate hypothetical bias. They can be classified into two categories. The ex ante instruments aim at mitigating the bias in the survey design stage while the ex post instrument aims at mitigating it through the use of debriefing questions. Cheap talk and consequentialism are certainly the most well known ex ante procedures. Cheap talk (Cummings and Taylor 1999) is a script introduced just before the elicitation question to inform participants about hypothetical bias. Participants are explicitly warned about the tendency to overestimate the WTP and are encouraged to consider their income. Bulte et al. (2005) include the following script in a DC survey related to seal conservation: In general, people experience difficulties answering hypothetical questions. People typically bid more money then they are really WTP. One reason why people might be tempted to bid too much is as follows. People try to accept or reject a bid based on their evaluation of the ‘‘true value’’ of the commodity (...). But if people should actually make the payment, they also consider that they can spend their money only once and that money spent on seal conservation is not available for other purchases. When answering the bid question below, try to think whether you are really WTP this amount for the conservation of seals. Try to imagine that this amount of money is no longer available to finance other purchases. An alternative approach to downward WTP statements is to make participants believe that their answer will have real economic consequences. For instance, participants are told that “the results of the study will be made available to policy makers, and could serve as a guide for future decisions with respect to taxation for this purpose. It is important that you think before answering the question” (Bulte et al. 2005). This approach, labelled consequentialism, may decrease the degree of hypotheticalness of the survey, and thus, mitigate hypothetical bias.

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An ex post instrument, the uncertainty adjustment, have also been developed with this aim. In DC surveys, “yes” answers are treated as “no” answer when participants are insufficiently sure of their response, thus decreasing the mean WTP automatically. The degree of uncertainty is assessed by a qualitative or quantitative procedure. Champ et al. (1997) rely on a scale ranging from 1 (“very uncertain”) to 10 (“very certain”), while Blumenschein et al. (1998) ask the “yes” respondents whether they are “fairly sure” or “absolutely sure” that they would buy the good. Statistical bias function represents another way to calibrate answers (Blackburn et al. 1994). This function allows estimation of the probability that a given individual would say “yes” in a real treatment, given that she has responded “yes” in a hypothetical treatment. To do so, the database of another survey is used and serves as a reference for calibration. A risk with this approach is the function to be context-specific (Fox et al. 1998) or individual-specific (List and Shogren 2002).

7. SPECIFIC FACTORS FOR THE CONTINGENT VALUATION METHOD Elicitation formats One of the most crucial decisions in designing a CV study is the choice of the elicitation or response format. Various formats have been developed and tested extensively, but no unambiguous recommendations can be provided for one of them. The formats vary with respect to their incentives for strategic behaviour, how much information they convey to respondents, and how much information they collect from respondents. The information that is given to a respondent through the elicitation format may influence their responses, while on the other hand, the information collected affects, for instance, the sample size and the econometric approach necessary to analyse the responses. Overall, it is important to be aware that all elicitation formats influence the determined WTP in some way and could have a strong influence on the estimated welfare. In general, the open-ended format is straightforward. Here, respondents are directly asked about their maximum WTP for changes in the provision of the good in question. Thus, it is very informative and does not provide respondents with clues concerning their potential WTP. Drawbacks to the open-ended format are that they are not incentive compatible and may evoke a large number of zero answers and outliers. In a bidding game, primarily used in the 1970s and 1980s, respondents faced a series of discrete choice questions. Depending on the responses, subsequent questions presented a lower or higher bid, until the respondents agreed to pay the offered amount or until she was not willing-to-pay the higher amount. The major drawback of the bidding game is that the final estimate may strongly depend on the starting point, i.e., the initial monetary amount presented to the respondent. When a payment card method is used, respondents are presented with a number of preselected bids on the payment card and are asked to select the amount of money that is the most they would be willing-to-pay. Respondents are therefore provided with a broad range of potential amounts of money they might be willing-to-pay. While the payment card method does avoid anchoring effect, the range of monetary amounts can still bias the results. For example, cutting monetary amounts from the lower of the upper end of the bid distribution affects welfare estimates (Rowe et al. 1996). Responses may also be affected by presenting the bids on the payment card in ascending or descending order (Alberini et al. 2003). DC questions ask respondents whether they are willing-to-pay a certain amount of money for a specified change in the provision of a good in question. Respondents answer with “yes” or “no”. 19

This equals the first round in the bidding-game. The bid amount is varied over different respondents. The main advantage of this format is that the DC format is incentive compatible, if further conditions are imposed. For example, the question has to be posed as a vote on a referendum (Carson and Hanemann 2005). One drawback of the format is that it only provides limited information about the respondent’s WTP. If only one DC question is asked during an interview, all that is known is whether her WTP is greater, or less, than the amount presented in the question. Accordingly, sample sizes have to be much larger, compared to open-ended or payment card questions. As the DC question only provides limited information on respondents’ WTP, the DBDC choice format was invented (Hanemann and Kanninen 1999). Here, as in a bidding game, the respondents are presented with a second bid depending on their responses to the first bid. The second bid is lowered when respondents answered “no” and increased when respondents answered “yes”. Thus, an interval of the WTP is elicited and the DBDC format requires fewer interviews than the singlebounded format. A problem arising with the DBDC format is that the two responses do not correspond to the same underlying WTP distribution (Hanemann and Kanninen (1999); Carson and Hanemann (2005)). In response to the DBDC format, the one and a half bounded dichotomous choice was proposed by Cooper et al. (2002). In this case, respondents are informed that the costs for providing the good in question will be between two monetary amounts. If the lower bid is presented first and the respondent neglects to pay this amount, no further question is asked, if she responds positively, the second higher bid is presented. Conversely, respondents may be presented with the higher bid first. Another recently introduced format is multiple-bounded questions. In their simplest form, respondents are presented with a series of bids on a payment card and asked whether they are willing-to-pay for each bid amount or not. More advanced forms of the multiple-bounded format ask respondents to state the probability (“definitely no” to “definitely yes”) that they would pay for each amount (Alberini et al. 2003; Welsh and Poe 1998). Which format should you choose? Without providing a definite answer, as one does not exist, here are some recommendations from the literature. Bateman et al. (2002) recommends payment cards and DC formats. They argue that payment cards are more informative and cheaper to implement than DC, and are, at the same time, superior to both open-ended and bidding games. On the other hand, DC formats may be incentive compatible and facilitate respondents’ valuation task. Boyle (2003) presented a similar conclusion. While he argued that DC questions, framed as a referendum approach, are the safest approach, one should also consider the payment card or multiple bounded questions, because they avoid problems, such as anchoring and yea-saying. Apart from the open-ended format, all other formats require bids that are preselected. As the literature has shown, preselected bids can affect the welfare estimates, which suggests careful consideration in the selection of bid amounts. In summary, studies on optimal bid design have shown that a small number of bid amounts (5 to 8) are preferable and that the bids should be clustered near the median WTP and not be placed in the tails of the distribution (Hanemann and Kanninen 1999). A reasonable way to get information on the central tendency and dispersion of the WTP distribution is to have a field pretest (Boyle 2003). Payment cards, on the other hand, should not cluster bid amounts near the median, but might include high and low bid amounts (Boyle 2003). Based on the information the elicitation format has collected from respondents, various econometric approaches are suitable to analyse the data. Responses to open-ended questions can be analysed by calculating the arithmetic mean and estimating functions that explain WTP as a function of independent variables. Often, OLS regressions are used although a tobit model may be more 20

appropriate, because negative values are not allowed and there is a probability spike at zero (Boyle 2003). All other elicitation formats require more advanced approaches, as they do not provide as much information about respondents’ WTP as the open-ended format. Instead of resulting in a continuous stream of data, they provide binary or interval data. In general, random utility models are used for estimating the WTP distribution, because the true WTP is not actually observed, e.g., it lies in an interval between two bid amounts, and not all influences that affect WTP are known. A crucial assumption concerns the nature of the random terms when using these models. Given that this term is independently and identically distributed with a mean of zero, two widely used distributions are the normal and the logistic ones. Based on these distributions, the probit and logit model are derived and maximum likelihood routines are used for estimation (Haab and McConnell 2003). Readings and software: The manual by Bateman et al. (2002) provides an overview of both the question formats and the econometric approaches used in data analysis. The chapter on CV in practise by Boyle (2003) is also a good starting point for collecting the pro and cons of elicitation formats. A detailed presentation of the statistical issues raised by DC elicitation formats is Hanemann and Kanninen (1999). Another standard with respect to econometric analysis is the textbook by Haab and McConnell (2003). This textbook also provides data and code for estimating the presented examples using STATA or LIMDEP.

8. SPECIFIC FACTORS FOR CHOICE EXPERIMENT Choice experiments belong to the class of SP techniques assembled under the headline “CM”. Additionally, CM includes the techniques contingent ranking, contingent rating, and paired comparison. All techniques are based around the idea that any good can be described in terms of its attributes, or characteristics, and the levels these can take. In a nutshell, in a choice experiment, respondents are presented with a series of choice sets comprising at least two alternatives and asked to choose their most preferred. Contingent ranking requires respondents to rank a set of alternative options. Contingent rating requires them to rate alternatives separately on a semantic or numeric scale, while paired comparison asks respondents to choose among two alternatives by indicating the strength of their preference in a numeric or semantic scale (Bateman et al. 2002; Hanley et al. 2001). However, not all these techniques have a strong behavioural theoretical foundation that is consistent with economic theory. Choice experiments and contingent ranking are said to have this foundation. In this section, only choice experiment will be considered, as it has recently been used increasingly in valuing a wide range of multiple environmental resources, among them forests (e.g. Holmes and Adamowicz 2003; Lehtonen et al. 2003). Major steps in designing and analysing a choice experiment on forest management are considered, i.e., a) identifying and describing attributes, b) designing the choice sets and developing an experimental design, c) estimation of the model, and, d) interpretation of the results for policy analysis. Figure 1 displays an example of a choice set that might be used in a choice experiment concerning forest management options, where a choice set is defined as a single set of alternatives presented to respondents during a choice experiment asking them to indicate their preferred choice. A choice alternative is a choice option described by a number of attributes. These attributes can take on different values, or levels. In general, respondents face a couple of choice sets during an interview.

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Identifying and describing attributes and their levels Choosing attributes often has many requirements. The attributes have to inform the respondents about the environmental change in question and provide decision-makers with the required information. On the other hand, the complexity of the choice situation increases, among other things, with the number of attributes used. Thus, it is important to carefully balance both aspects. Another aspect of choosing attributes is that the combinations of the attributes and their levels, as they occur in the choice sets, form plausible alternatives. Holmes and Adamowicz (2003) pointed out that attributes in environmental valuation problems, such as forest management, may be highly correlated with natural processes. For example, the attributes species diversity and area not harvested may be so highly correlated, that it is not possible to reach a high level of species diversity when only a small forest area is not harvested. Respondents who are aware of this correlation might become confused and reject to answer the choice tasks when they are presented such implausible or even impossible combinations. Holmes and Adamowicz thus recommend to use only feasible combinations of attributes, i.e., selecting attributes that represent separable dimensions of the valuation problem. Once the attributes are selected, the number of levels has to be determined for each attribute. Table 1 shows four attributes of the choice experiment on forest management and their levels. For the sake of simplicity, each attribute only has two levels. This implies that only linear effects between the two attribute levels can be measured. In the example, an expansion of the forest area not harvested would always have the same value, regardless of whether it is the 501st hectare or the 2001st hectare. If it is likely that respondents would value both differently, more levels should be introduced. For example, a third level with the value, 1500 hectares, could be added. It would enable the researcher to investigate whether respondents value changes from 500 to 1500 hectares differently, than it changes from 1500 to 2500 hectares. However, more levels mean more possible combinations between attribute levels and leads to, everything else equal, a larger experimental design. Finally, choosing the bid levels is crucial, because this can strongly impact on the welfare estimates. Assistance is, however, not very easy to obtain. Stewart and Kahn (2006) pointed out that the guidelines are generally the same as those for CV with referendum-style question formats (Alberini 1995; Cooper 1993).

Table 1. Attributes and corresponding levels first example  

Levels

Species diversity (SP) High Low

Attributes Recreational Area not harvested facilities (RF) (ANH) Yes 500 ha No 2500 ha

Tax (TAX) 0.5 € 2.0 €

Choice set design and experimental design Once the attributes and the corresponding levels have been selected, it has to be decided how many alternatives will be present on a choice set and how the attribute levels will be combined into alternatives. Figure 1 shows the choice set with three alternatives. They comprise a status quo, i.e., an alternative where the attribute levels describe the current situation, and two hypothetical 22

alternatives describing different forests that might be obtained in the future by forest management. This three alternative format currently dominates in applied choice experiments. Rolfe and Bennett (2009) compare choice sets with two and three alternatives and conclude that choice sets with three alternatives are preferable. Another issue that is important for design choice sets is whether the choice set comprises a status-quo alternative, which is sometimes also called the non-buy or opt-out alternative. The idea is to give respondents the option not to choose one of the designed alternatives that describe changes in environmental management. As in a shop, respondents are given the alternative not to buy anything. If the choice set does not comprise a status-quo alternative, respondents are forced to choose one of the alternatives. This may negatively influence the results, because respondents will choose an alternative that they would not choose if they were not forced. Additionally, the status-quo option functions as a baseline for calculating welfare changes, i.e., the welfare measures express how beneficial a move away from the status quo would be. Finally, another important decision is whether the alternatives on the choice set are generic or labelled. The alternatives in Figure 1 are generic, providing no additional information about the alternatives via their names (labels). The alternatives are simply named “Option 1” and “Option 2”. In contrast, Option 1 could be named “recreation” and Option 2 “nature conservation”. Thus, the names would convey additional information about the alternatives to respondents. Blamey et al. (2000) stated that it depends on the objective of the study in which approach is preferable. When the objective is to estimate attribute values, or marginal rates of substitution, they prefer the generic. This approach has the potential to elicit more discerning trade-off information. When the objective is to predict the amount of money people would actually pay to obtain a given policy alternative, and meaningful labels for the alternatives are apparent, Blamey et al. (2000) prefer the labelled approach, because it is expected to result in greater predictive validity. The experimental design enables the researcher to combine the attribute levels, such that she can investigate the effects she is interested in. A complete factorial design, as it is presented in Table 2, contains all possible combinations of the attribute levels. Using a complete factorial design not only allows the researcher to investigate parameter estimates for the main effects on utility, but also for all the possible interactions between them. In the case of main effects, there will be a zero correlation between the attribute levels. A main effect provides changes in utility separately for each attribute, for example, how a change from recreational facilities “no” to “yes” will affect respondents choices, and thus, their utility without regarding changes in other attribute levels. Interactions enable researchers to investigate how the influence of joint changes of species diversity and recreational facilities, for example, will impact on respondents’ choices. However, with an increasing number of attributes, and/or attribute levels, a complete factorial quickly becomes very large. Thus, researchers often use fractional factorial designs. Generally, they only comprise main effects and, in some cases, several or all two-way interactions (for an example, see Mogas et al. 2006).

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Table 2. Complete factorial design for forest management   Management type

Species diversity

Picnicking facilities

Area not harvested

Tax

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

High High High High Low Low Low Low High High High High Low Low Low Low

Yes Yes No No Yes Yes No No Yes Yes No No Yes Yes No No

2500 ha 1000 ha 2500 ha 1000 ha 2500 ha 1000 ha 2500 ha 1000 ha 2500 ha 1000 ha 2500 ha 1000 ha 2500 ha 1000 ha 2500 ha 1000 ha

0.5 € 0.5 € 0.5 € 0.5 € 0.5 € 0.5 € 0.5 € 0.5 € 2.0 € 2.0 € 2.0 € 2.0 € 2.0 € 2.0 € 2.0 € 2.0 €

Directly related to the experimental design is the question of how many choice sets each respondent should be presented. This depends on the number of interviews that are conducted and the amount of choices respondents are expected to handle, without losing attention or becoming fatigued. Unfortunately, the literature does not provide clear guidelines on this. In environmental valuation, many CE’s present each respondent with between four to eight choice sets. In marketing studies, this number is often much larger. One strategy to reduce the number of choice sets per respondents is to block the sets, so that each respondent is only presented with a subsample of choice sets. For example, a fractional factorial design with 36 choice sets may be blocked into six blocks with each respondent facing six choice sets. To ensure that the effects are estimable, each choice set should be presented to a minimum number of respondents. However, in this case, recommendations are rare. Bennett and Adamowicz (2001) recommend that each block be presented to approximately 50 respondents. Readings and software: A good introduction into experimental design for choice experiments was provided by Johnson et al. (2006). The authors present examples using design catalogues and the macros for SAS, provided by Kuhfeld (2005). Another good introduction is provided by Hensher et al. (2005). They demonstrate how to generate an experimental design with interactions, using SPSS tools in combination with Microsoft Excel. The book by Burgess and Street (2007) provides comprehensive information about optimal designs and is accompanied by a webpage that enables the generation of experimental choice designs. A recently released software package, especially for generating experimental designs used in stated choice experiments, is NGene. It allows for the generation of orthogonal designs, optimal orthogonal designs and efficient stated choice designs (http://www.choice-metrics.com/features.html).

Estimation The statistical analysis of the choice data informs researchers of the likelihood that an individual will choose an alternative change when one or more attribute levels change. For example, does an increase in the forest area not harvested, increase the likelihood that respondents choose an 24

alternative instead of the status quo option? Based on this information, welfare measures can be calculated. Prior to estimating any model, the qualitative attributes have to be coded. In determining whether a forest area should be harvested, attributes are comprised of quantitative and qualitative levels. The attributes “area not harvested” and “tax” are quantitative variables. Their coding is straightforward, because the attribute levels are quantities. Thus, the levels can be directly incorporated in the estimation. On the other hand, species diversity (high/low) and availability of picnicking facilities (yes/no) have qualitative levels. To represent the qualitative levels in the estimation, some coding is required (quantitative variable coding may be advisable when nonlinear effects are expected). The two different coding approaches available are dummy variable coding and effect variable coding. Table 3 illustrates both for a variable with two levels and for one with three levels. As we only have variables with two levels, the latter is simply for demonstration purposes. In general, the number of variables necessary to represent a qualitative variable is the number of levels minus 1, i.e., one level (variable) is excluded. If we have two levels, we need one variable, if we have three levels, we need two variables, and so on. The difference between both types of coding is that in a dummy treatment, the coefficient of the excluded variable is correlated with any information contained in the intercept term. The coefficient for the excluded variable under effects coding is the negative of the sum of the included variable coefficients. In the latter, the coefficient of the excluded variable is directly comparable with the coefficients of the included variables (Stewart and Kahn 2006). Thus, effects coding provides more information about respondents’ preferences, and is, thus, preferable.

Table 3. Coding of a two and a three level attribute   Attribute Levels

Effects coding High

Dummy coding Medium

High

Medium

High

+1

+1

Low

-1

0

High

+1

0

+1

0

Medium

0

+1

0

+1

Low

-1

-1

0

0

When individuals are presented with choice sets for more than two alternatives, as it is the case in Figure 1, models for binary choices, e.g., with “yes” or “no” responses, such as the logit or probit model are no longer suitable. Thus, the multinomial logit (MNL) has become the workhorse used to analyse multinomial choices. Using an MNL model, the individual choice probability that individual n will choose alternative i out of j alternatives is Pin =

exp(ì Vin) ! exp(ì Vjn) j"C

25

where Vin = β ' X, is the deterministic part of utility, β' are the estimated coefficients and X are the attributes. As it is not possible to separately identify the impact of the deterministic part separately from the scale factor µ, the latter one is traditionally set to 1. Table 4 shows the (invented) results of the MNL for the choice experiment on forest management. All parameters, except for picnicking facilities, were significant and reveal the expected sign. Increasing species diversity and the forest area not harvested would positively affect respondents’ utility. In contrast, an increasing price decreases utility from the alternatives Option 1 or Option 2. Provision of picnicking areas in the forest does not significantly influence respondents choices among alternatives, and thus, has, on average, no effect on their utility. The positive value of the alternative specific constant indicates that, on average, respondents would not gain utility from changing the current form of forest management.

Table 4 Parameter estimates for the forest management example (conditional logit) Variable

Coefficient

T-statistic

p-value

ASC_status quo Species diversity (SP) Picnicking facilities (PF) Area not harvested (ANH) Tax (TAX)

3.3176 0.3538 0.0157 0.1931 -0.0401

3.722 3.520 0.293 2.848 -7.067

0.001 0.001 0.769 0.004 0.001

While the MNL model is very popular for its computational simplicity, at the same time, it relies on a couple of assumptions, such as the independence of irrelevant alternatives (IIA), or that taste does not vary across individuals (Swait 2007; Train 2003). The former assumption requires that substitution patterns across alternatives are proportional. This assumption, however, might not apply when the utility of the two alternatives, in this example, Option 1 and Option 2, are more similar to each other then the utility of the status quo alternative. One way to circumvent this is to estimate a nested logit model characterised by grouping subsets of alternatives which are more similar. Another model that allows accounting for the similarity of alternatives is the error component logit model (Scarpa et al. 2007). In addition to taking the similarity of alternatives into account, this model can recognise the panel character of most choice data, i.e., that each individual answers more than one choice set. Heterogeneity in taste can be investigated by introducing interactions between socio-demographics and alternative specific constants, for instance. This approach requires, however, that the researcher has an idea about the sources of heterogeneity, such as age, gender, income, and number of forest visits. Many studies find that typical socio-demographics do not explain much taste heterogeneity. Two approaches to investigate unobserved heterogeneity are the random parameter logit model and the latent class model. While in the former, individuality of preferences is reflected in individual specific departures from the mean values of utility parameters, in the latter, the population is segmented into groups of individuals with identical preferences. Both require a decent knowledge about choice models with the random parameter logit model the much more demanding approach, as a couple of decisions, such as choosing the distribution of the random parameters, have to be made. Any analysis of choice data should thus start with the MNL model and then move to a more complex model, if necessary. 26

Readings and software: A very helpful introduction into the analysis of CM data is provided by Hensher et al. (2005). They present numerous examples of how to use LIMDEP/NLOGIT to estimate various models, beginning with the MNL and proceeding to the nested logit and the random parameter logit. For those using STATA, a good starting point is the book by Cameron and Trivedi (2009), who demonstrate how to use STATA for microeconometric analysis models, among others, the MNL, the nested logit and the random parameter logit. A free software programme for analysing choice data is BIOGEME by Michel Bliemer (http://transp-or.epfl.ch/page63023.html). It estimates a broad range of models, including those named previously and, as an exception, allows the researcher to easily set up different scale parameters for various sub-samples. Finally, if latent class analysis is the main objective, Latent Gold is an easy to use software program, but NLOGIT also allows the estimation of latent class models for up to five classes.

Welfare analysis using choice experiments Choice experiments provide more information about the welfare impacts of the changes under consideration. In general, CV provides a welfare estimate for a single environmental change, whereas the results from choice experiment can supply estimates for all changes that are covered by the attributes and their levels. The general specification is:

WTP = "

1

! Tax

( utility of new policy - status quo utility).

Using the attributes with significant coefficients from the forest management example, the specification becomes: WTP(Choice)=-[(ASC+β SP+β ANH) -(ASC+β SP+β ANH) ]/β . This formula applies to so called “states of the world” situations where each alternative describes how the “world” could look like. For example, it compares the status quo situation with an alternative that could be present, instead of the status quo. Only one of the states of the world can be present at the same time. In the case of multiple alternatives that could be present at the same time as is the case with different recreation sites, the formula has to be extended in order to take into account the multiple alternatives (Holmes and Adamowicz 2003). Other information that choice experiment can provide is the implicit price of an attribute. It shows the WTP for a change in any of the attributes and is useful to assess the relative importance of the attributes. The Implicit Price (IP) is calculated as the ratio between any coefficients for a non-monetary attribute, over the negative of the estimated coefficient for the price attribute:

IP = "

! Attribute ! Tax

27

REFERENCES Adams, C., Seroa da Motta, R., Ortiz, R. A., Reid, J., Ebersbach Aznar, C., and de Almeida Sinisgalli, P. A. (2008) “The Use of Contingent Valuation for Evaluating Protected Areas in the Developing World: Economic Valuation of Morro Do Diabo State Park, Atlantic Rainforest, São Paulo State (Brazil)”. Ecological Economics 66 (2-3): 359-370. Alberini, A. (1995) “Optimal Designs for Discrete-Choice Contingent Valuation Surveys: SingleBound, Double-Bound and Bivariate Models”. Journal of Environmental Economics and Management 28 (3): 287-306. Alberini, A., Boyle, K., and Welsh, M. (2003) “Analysis of Contingent Valuation Data with Multiple Bids and Response Options Allowing Respondents to Express Uncertainty”. Journal of Environmental Economics and Management 45 (1): 40-62. Alberini, A., and Kahn, J. R. (2009) Handbook on Contingent Valuation. Edward Elgar. Cheltenham. Arana, J. E., and Leon, C. J. (2008) “Do Emotions Matter? Coherent Preferences under Anchoring and Emotional Effects ”. Ecological Economics 66 (4): 700-711. Arrow, K., Solow, R., Portney, P., Leamer, E., Radner, R., and Schuman, H. (1993) “Report of the Noaa Panel on Contingent Valuation”. Federal Register 58 (10): 4602-4614. Bateman, I. J., Brouwer, R., Ferrini, S., Schaafsma, M., Barton, D. N., Dubgaard, A., Hasler, B., Hime, S., Liekens, I., Navrud, S., De Nocker, L., Sceponaviciute, R., and Semėnienė, D. (2009) “Making Benefit Transfers Work: Deriving and Testing Principles for Value Transfers for Similar and Disimilar Sites Using a Case Study of the Non-Market Benefits of Water Quality Improvements Accross Europe”. Working paper, University of East Anglia, Norwich. Bateman, I. J., Carson, R. T., Day, B., Hanemann, M., Hanley, N., Hett, T., Jones-Lee, M. W., Loomes, G., Mourato, S., Ozdemiroglu, E., Pearce, D. W., Sugden, R., and Swanson, J. (2002) Economic Valuation with Stated Preference Techniques: A Manual. Edward Elgar. Cheltenham. Bennett, J. W., and Adamowicz, W. L. (2001) Some Fundamentals of Environmental Choice Modeling. In The Choice Modeling Approach to Environmental Valuation, ed. J. W. Bennett, and R. K. Blamey. Northampton: Edward Elgar. Blackburn, M., Harrison, G. W., and Rutström, E. E. (1994) “Statistical Bias Functions and Informative Hypothetical Surveys”. American Journal of Agricultural Economics 76 (5): 1084-1088. Blamey, R. K., Bennett, J. W., Louviere, J. J., Morrison, M. D., and Rolfe, J. (2000) “A Test of Policy Labels in Environmental Choice Modelling Studies”. Ecological Economics 32 (2): 269-286.

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Welsh, M. P., and Poe, G. L. (1998) “Elicitation Effects in Contingent Valuation: Comparisons to a Multiple Bounded Discrete Choice Approach”. Journal of Environmental Economics and Management 36 (2): 170-185. Yao, R., and Kaval, P. (2006) Trees and Plants. A Willingness-to-Pay Survey: A Report of the Refinement of the Survey Design and the Second Focus Group Meeting Initial Survey Creation Process and First Focus Group Discussion. A Report Prepared for Agresearch as Part of a Frst Funded Project Entitled “Improved Policy Interventions for Encouraging the Voluntary Use by Landowners of Practices Protecting and Enhancing Biodiversity”, pp. 44.

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ABBREVIATIONS

- DC

Dichotomous choice

- DBDC

Double-bounded dichotomous choice

- CM

Choice modelling

- CV

Contingent valuation

- MNL

Multinomial logit

- SP:

Stated preferences

- WTA:

Willingness-to-accept

- WTP:

Willingness-to-pay

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Best Practice Guidelines in Revealed Preference Methods of Forest Externalities

1. FOREST SERVICES AND REVEALED PREFERENCE The main aim of this report is to develop good practise guidelines to estimate the monetary value of forest services by means of revealed preference methods. These methods can only estimate some of the services provided by forest, as for example, air quality, outdoor recreation, cultural values and landscape quality. They rely on data reporting actual choices of people, since they derive from the observation of people behaviour. Individuals reveal, or partially reveal, their use values for certain forest services by their behaviour. For example, one does not visit a recreational site unless the expected benefits of the trip are greater than the cost, and one does not pay the extra x Euro to live closer to a site-specific amenity unless the increased benefit is greater than the increased cost of housing. As a consequence, revealed preference methods can only be used to estimate use values. Commonly-known and used revealed preference valuation methods include2: a) travel cost method (TC) b) hedonic method (HP) The travel cost method uses travel costs, observed site characteristics and observed trip patterns to value the characteristics or existence of a site-specific environmental amenities. In the hedonic method the value of the characteristics of a resource are inferred from observable market transactions (for example sale prices of land or building).

2. TRAVEL COST METHOD The travel-cost method estimates use values for site-specific amenities, specifically the use values that can only be obtained by visiting a site. Its use requires that travel costs are significant and that they vary across users. The method is used to value recreational sites (forests, fishing sites, hiking and climbing trails), scenic destinations and cultural destinations. Forests provide a lot of non-timber products, namely a wide range of goods and services, such as recreation, biodiversity and landscape (Starbuck et al., 2004). Zandersen and Tol (2009) conducted a meta-analysis of studies on forest recreation in Europe based on travel cost models since 1979. They found that the consumer surplus per trip varies from €0.66 to €112. The basic method proceeds in two steps: (1) one estimates demand functions for trips to a site or group of sites and then (2) uses these estimated demand functions to derive willingness-to-pay                                                                                                                 2

Other Revealed preference methods are the defensive expenditure and the household production function. 36

estimates of changes in the characteristics of a site or sites, or for the existence of a site. The travel cost method can be used to estimate the use value of a forest: the benefits to visitors. Typically an individual’s demand for trips to a site is modelled as a function of the cost of a trip to the site, the costs of visiting substitute sites, the characteristics of the site, the characteristics of its substitutes, income, and other characteristics of the individual. Trips decrease as the cost of a visit increases and increase as the quality of the site increases. The approach was first suggested by Hotelling in 1947, later implemented by Clawson (1959) and Clawson and Knetsch (1966) and hundreds studies have since been done. The literature presents several travel cost approaches. They can differ quite a lot depending on the way the variables are defined and measured, on the specification of the model and the estimation procedures. When the focus of the research is to value some changes of characteristics in different sites simultaneously, we need to specify an appropriate model which handle multiple-sites. Hence, some preliminary decisions need to be taken into account in advance.

Which travel cost approach? Some strategic decisions for a valuation study Travel cost models are useful tools allowing a value estimation of forest externalities. However, they can be implemented with different purposes, so it is important to choose carefully on the basis of the goal of the investigation. Major shortcoming associated with the TC method are highlighted by Randall (1994). Travel cost models can be used to value the access to sites, i.e. the welfare effects of the elimination of a recreational site as a forest or to value the characteristics of a site, i.e. a change in the level of quality of the site. One might be interested in estimating the loss of welfare due, for example, to a change in land use from a public to a commercial use or closure of the site to public access. Because recreation typically takes place outdoors, it can be highly affected by the environmental quality of sites. Travel cost models are more commonly used to estimate benefit changes due to the implementation of an environmental policy which aims, for example, at increasing the level of biodiversity or extending the net of hiking trails in a forest. The method is also often used to estimate damages from lost services. More generally, one might be interested to value changes in site attributes considering a set of recreation sites. Furthermore: how does the variation of sites quality associated to forest services affect the probability of choosing one site rather then another? At least four critical issues are involved in the decision of the model approach. First, one starts from the premise that the individual has preferences over recreational amenities and other goods, and that he/she maximizes his/her utility subject to individual constraints. One can either start by assuming a utility function and then deriving the demand functions for the site-specific activities of interest, or one can directly specify the system of demand functions making sure their functional forms are consistent with an underlying utility function (Haab and McConnell, 2002). The utility function approach usually deals with the discrete-choice models based on the random utility maximisation. They allow the welfare change to be estimated due to modification of characteristics of the site(s) or the implementation of environmental policy (Hanemann, 1999). Discrete choice models incorporate the full set of choices and can also include the choice of whether to visit each site, as well as the number of times to visit each site. On the other hand, if one starts with the system of demand functions one has to integrate backwards to get the estimated preference function from the estimated demand functions. The second important decision deals with the fact that one may want to model the choice among all sites in the study area, a few or just one site (single-site or multi-site 37

models). The third relevant issue relates to the fact that one would want to model participation or not. The fourth issue deals with the fact that a travel-cost model varies whether one is modelling the behaviour of individuals or just the aggregate behaviour of people who live in zones. A single-site travel-cost model models the number of trips to a specific site, so is a participation model for the site in question, where everything else (not taking a trip or visiting another site) is aggregated into the numeraire, doing "something else". The demand function approach is typically used in this case. In contrast, a site choice only model, models which sites are chosen given the decision to take a trip; the participation decision is not modelled. In this case, a discrete-choice random utility model is typically estimated3. This is probably the most common modelling strategy, although not the only one, when the goal is to analyze choices among many alternatives; it has the ability to capture sites substitutes and quality changes 4 . The most complete model is the participation and site-choice model, which takes into account both the choice of whether to visit a site and the number or visit5. Zonal travel-cost models model aggregate (by zone) rather than individual behaviour. They are seldom used.Most current applications are multiple- site choice models with or without participation modelled, and most model the behaviour of the individual6. By estimating a site-choice model rather than a participation and site-choice model, one is limited in what one can say about welfare measure for a site or its characteristics (Morey, Rowe and Watson, 1993; Morey and Waldman, 1998; Morey 1999). In a model of trips to a single site it is generally not possible to infer economic values of quality changes, due to the fact that all visitors experience the same site quality. This implies that, unless making strong assumptions, the single site models are generally used to estimate the value of access to a site, but do not measure the value of changes in the quality of the site7.

Some background to get started The choice of the travel cost model depends on the aim of the research. If the focus is on a single site, then the model which most likely is going to be used is based on a demand function:

tnj = f(ct, cs, y, z)

(1)

where t is the number of visits made by an individual n to a site j during a season, ct is the trip cost to the site, cs is a vector of trip costs to reach other substitute sites, y is income and z is a vector of socio-economic variables8. Equation (1) can take several forms. Parameters of the estimated models are used to derive the consumer surplus or access value.                                                                                                                 3

The first application of random utility model to recreation is by Bockstael, Hanemann and Kling (1987). See also Morey (1981). 4 Since the Random Utility Model is the most widely used multiple-site approach in what follow we will briefly mention it as a multi-site model. 5 The generalized corner solution approach (Phaneuf, Kling and Herriges, 2000; von Hafen, Phaneuf and Parsons, 2004). 6 For a detailed description of travel cost methods see Haab and McConnell, 2002; Bockstael and McConnel, 2007. 7 Efforts along this line are pooled models and systems of single-site demand equations, see section 2.3.6.1. 8 To be less restrictive, one should also take into account the characteristics of other sites. 38

Alternatively, when the aim of the research is on multiple-sites, the discrete-choice random utility model (RUM) is the most widely-used (McFadden, 1974; Hanemann, 1999; Train, 2003). Its main aim is to value changes in site characteristics (and possibly access value simultaneously) and the substitution pattern among sites. The RUM model considers a person’s choice for a recreation trip from a set of many possible sites with regard to a single choice occasion. The basic assumption is that the choice depends on the trip costs and attributes of the sites. The latter are generally environmental characteristics or a quality index. In a forest it may be tree species, total extension of the area, hectares of broadleaves, landscape quality index and type of access (easy or not). A person chooses the forest offering him the highest utility among all the others in the choice set. The utility of the individual visiting forest j is: Uj = Vj + εj

(2)

where sites C are denoted as j=0, 1, 2…J, and j=0 is the stay-at-home or doing something else alternative, which may be taken into account or not. The term Vj is the deterministic part of the overall utility, since it is known to both researcher and the individual whereas εj is a error term accounting for the unobserved factors. The utility of the selected forest, which is a function of trip cost and site characteristics, takes a linear form9:

V j = " cc j + " p p j + ! j

(3)

where cj is the trip cost to reach forest j, pj is a vector of site characteristics, the βs are the parameters. The basic idea is that site utility increases with appreciated attributes of the site, as for example, nice trails in a forest10. Utility perceived by a visitor can vary depending on changes in the attributes of one or more sites. Let’s assume a fire destroys the whole forest or a part of it, for example site 1, the utility of the person who visited the area before the fire, would be affected and decrease. For example, considering J forest sites, it is possible to compute the utility loss due to the complete destruction of a forest by fire. Back to the Zonal travel cost (ZTC), this is used rather seldom as being a pioneer approach. Nevertheless, basic data for the estimation can be collected quite easily, by avoiding expensive questionnaire survey to be undertaken. Typically, concentric circles are drawn around the site so that everyone residing in a circle (zones) lives approximately the same distance from the site. The number of trips to the site from a zone is assumed a function of the travel cost from the zone to the site, the population of the zone, and average socioeconomic levels in the zone (income, education, etc). A shortcoming is that the estimated demand is generated by a “representative consumer” whose behaviour reflects the average behaviour in the population (Haab and McConnell, 2002).

Basic steps in estimating travel cost models11

                                                                                                                9

Equation (3) does not take into account income effect, hence it is quite restrictive.

10  To  take  into  account  a  person  may  choose  to  stay  at  home  or  do  something  else,  the  no-­‐trip  utility  function  is  

V0 = " 0 + " 0 k + ! n

11

,  where  k  takes  into  account  attitudes  affecting  recreation.  

This chapter draws upon Parsons, 2003. 39

Travel cost models mostly share a similar pattern of estimation in terms of steps, as illustrated in what follows: 1. 2. 3. 4. 5. 6.

How to define the site(s) Visitors and their purpose Which strategy for the sampling and the model specification? How to implement the survey? Travel costs and other costs Model estimation and welfare estimates

How to define the site(s) One issue is the definition of the boundaries of the site to be valued. Boundaries can be are easy to identify (a lake, a park), but in some other cases may not be (a river for fishing, an area for hunting). A solution can be to include most of the area on the basis of the policy investigated (Parsons, 2003). Forests might not have particular problems in defining the boundaries, depending on the area where they are located. The closure of one or more forest sites for wildlife protection can be investigated, as well as the extension for hunting. Both would affect visits. One may be interested to analysing the loss of welfare due to a fire or a contamination that prevent the access to a forest. Some policy actions may be simulated to estimate the gain or loss of welfare associated to an extension of the net of trails. Some people may prefer hiking in a forest with easy trails, others may find difficult trails more challenging. Alternatively, the focus might be on welfare changes due to an increase of facilities, such as picnic areas or huts. Another major issue is the choice of site attributes, that can be broadly divided into environmental quality (i.e. biodiversity level, water or landscape quality index) and physical measures (size, land use, elevation). How do we measure environmental quality? Objective or perceived measures can be used (Adamowicz et al. 1997). The change in quality may be hectares of burnt or contaminated forest, or the change may be based on some rating provided by respondents. For example, interviewees may be asked to give a rating to better describe the environmental quality of a maple forest, instead of using a quality index provided by the local authority. It is important to collect information based on the expectations of respondents about environmental quality and on how such expectations affect their choices of sites. Make sure to ensure enough variation in quality across sites in order to estimate how it affects site choice. Large number of well-defined sites rather than a small number of larger ones is better. If you need to aggregate, do that on the basis of the similarities of characteristics, including trip cost (Parsons, 2003). Welfare estimates vary considerably depending on the aggregation scheme Parsons et al. (2000). In a recent study focusing on the potential of aggregation bias in recreation site choice models, Haener et al. (2004) find that accounting for the size of the aggregate sites in the estimation improve model fit and alleviate aggregate parameter bias. Substitutes patterns of outdoor recreation are relevant (Thiene and Scarpa, 2008). The following studies might be good examples. Parsons and Kealy (1992), who analyzed lake recreation in Wisconsin, considered all lakes larger than 100 acres within the area, up to over 1,000 lakes. The choice set varied among respondents on the basis of a specific distance from their home to the lake (maximum distance 150 miles). Morey et al. (1993) investigated salmon fishing within a choice set of eight Atlantic rivers. Angler recreation was also investigated by considering a choice set of twelve alternatives nested into three main groups: three saltwater sites, four lakes and five rivers (Morey, 1999). 40

Grijalva et al. (2002) decided to study rock climbing at national level in the U.S., rather then regional level, because climbing sites can be dispersed and climbers from all over the nation can be interested in reaching far-flung climbing resources. They identified sixty U.S. climbing sites, although many climbers may have only visited one or two. They used a regional nesting structure to group similar sites on the basis of geographical composition and seasonal similarities. The authors point out that other nesting structure may be used, such as by type of climbing, but the adopted one was particularly useful for modelling substitution among sites (see Box 1). Should choice set be defined by researcher or respondents? Hicks and Strand (2000) suggest people in the survey determine their own choice set. Parsons et al. (1999) instead support the researcherdefined choice set and underline the importance of considering the full choice set. They speculate on the importance of non-missing information by specifying familiar and unfamiliar sites with different utility functions. See Haab and Hicks (2000) and Phanheuf and Smith (2005) for a detailed discussion on choice set definition. Scrogin et al. (2010) suggest an efficiency approach to choice set formation to overcome limitations of deterministic and probabilistic approaches. What about sources of site attributes? Besides local management authorities, the use of geographical information systems is quite common. In some cases, different sources of information are needed. Consider, for example, the collection of characteristics of different forest sites. Information can probably quite easily be gathered on land use, as hectares of broadleaves or conifers, but it might be more difficult to obtain information about the presence or density of rare species of flowers within the areas. This kind of information may instead be more easily provided by a local expert or keen botanist who has an in-depth knowledge of the area. Within zonal travel cost model, a key issue is the definition of the zones of origin. These may be defined by concentric circles around the site, or by geographical divisions that make sense, such as metropolitan areas, geographical units, political jurisdictions, or population areas. See advances in multiple-site zonal travel cost model to value forest recreation access by spatial allocation (Barenklau et al., 2010).

41

Box 1.The nested structure for National rock climbing sites and the list of variables taken into account (Grijalva et al., 2002)   The nested structure for National rock climbing sites

List of variables YRSCLIMB SPORT TRAD BOULD MTN GYM STB SPORTLEAD TRADLEAD SEASON INCOME2 INCOME3 FLEX TOTHOURS MALE HH HHCLIMB TEMP FEDLAND% ACRES TC1, TC2, TC3 BOULD CLIMBS

Partecipation stage Number of years climbing experience. 1 indicates whether the person primarily is a sport climber 1 indicates whether the person primarily is a traditional climber 1 indicates whether the person primarily is a boulderer 1 indicates whether the person primarily is a mountain climber 1 indicates whether the person primarily is a gym climber 1 indicates whether the person equally is a sport climber, a traditional climber, and a boulderer A climber’s best lead for a sport climb A climber’s best lead for a traditional climb 1 indicates a climber is willing to climb during multiple seasons indicates a person’s income level is between $35,000–69,999 1 indicates a person’s income level is greater than $70,000 Using a scale of 1 to 7, measures the degree of flexibility in a person’s work schedule Total hours an individual worked during the year Male Number of household members Number of climbers in a household Regional stage Median annual temperature for a region The percentage of federally owned land in a region The size of a region in acres Site choice stage Travel cost computed at different levels Number of boulder problems located at a site Number of climbs located at a site

(Modified from Grijalva et al., 2002)

42

Visitors and their purpose The population of interest are the users and potential users of the sites. People visiting the site may take a day-trip or stay overnight. This is one of the main issues with travel cost, because trips to the site should be of the same length for each person. It is preferable not to mix one day and two or more day trips in the same analysis (Haab and McConnell, 2002). Most common is to use day-trip data, overnight trips can be used, but while less common, it is more complicated (Shaw and Ozog, 1999). For geographical area of relevance, Parsons (2003) suggests to define boundaries in terms of one day’s drive. Criteria are of course very case specific. A related issue is sites having multiple recreation uses. This might be the case of a forest, where people would go for short walks, hiking, picnicking, mushroom picking, bird-watching, hunting, just resting, etc. One solution might be to aggregate similar recreation activities, in order to simplify data collection and analysis, even if great care must be taken. Another solution is to identify the primary purpose of a recreation trip and ask respondents to report the number of trips taken according to several purposes. Recent studies investigate how to specifically account for multiple activities in different sites by specifying utility functions that allow activities to enter in a separable way. This is particularly useful when sites with similar attributes support a similar set of activities (Bowman et al., 2007). How can multipurpose trips be handled? People may visit other recreation sites nearby or visit friends or do something else besides the main purpose of the trip. The problem is the treatment of expenses, since they appear to be no longer attributable only to the main recreation experience. Note that, the cost allocation to different purposes is generally not feasible, or at least quite complicated. Day-trip data are usually reasonably assumed to be single-purpose, whereas it is more difficult with overnight data. A general solution is to drop multipurpose trips from the dataset or to treat them separately. Mendelshon et al. (1992) and Parsons and Wilson (1997) suggest how to accommodate multipurpose trips. See Martinez-Espineira and Amoako-Tuffour (2009) for a review of how to handle multipurpose trips and Loomis (2006) for multisite destination trips.

Which strategy for the sampling and the model specification? Sampling strategies depend on the aim of the study, the population of interest and the type of model. Both users and potential users should be taken into account. Most people do not participate and this is a critical part of any model of participation and site choice. Within on-site sampling, commonly used in single-site models, visitors are surveyed at the site. The advantage is that the sample size can be smaller. Nonetheless, respondents with zero trips will not be considered, partly biasing the demand function, and, if not corrected, the welfare estimates (Parsons, 2003). It is easier to sample sites with clear entry points and this may not be the case with forests. Try to survey people at the end of their experience. Be aware of the problem of endogenous stratification and one trip truncation (see Haab and McConnell, 2002). See also Englin and Shonkwiler (1995) and Hesseln et al. (2004). In off-site sampling respondents are sampled from the general population, thus by allowing both participants and non-participants to be contacted. Most of the biases described above can be avoided. On the other hand, off-site samples can be costly due to the low participation rate. To correctly specify a model one needs to identify relevant determinants to explain recreation demand. For example, families with many children may be less interested in visiting a forest for hiking or mushroom picking. On the other hand, club membership can be helpful in explaining 43

rock-climbing in a well-known area, i.e. the Alps. Many rock-climbers belong to a mountaineering association (i.e. Alpine Club), either because they have taken training courses or because it might be helpful in case of accident rescue. In recreation studies demographic characteristics are important; for example because people retired and students are generally likely to spend more time than others on outdoor activities. Behavioural and attitudinal questions are becoming more important in providing additional information to describe the profile of respondents. These questions allow information to be collected such as “How many years have you been hiking or climbing?”(Scarpa et al., 2007). The use of the Likert scale can be a similar approach with questions like: “On a scale from 1 to 5, where 1 means I strongly agree and 5 means I strongly disagree, the activity you do at this campground could as easily be done elsewhere?” (Hailu et al., 2005). There is no clear indication for selecting the appropriate number or type of variables, this is left to the decision of the analyst. Recreation in a forest is likely be influenced by the age of the forest, whether it is natural or cultivated, size of the area, extent of the network of trails, species of trees, biodiversity in terms of wildlife, accessibility of the area, environmental quality, whether or not it is a protected area, and facilities (camping, refuges, picnic services…).

How to implement the survey? Adequate survey methodology is crucial. The introduction is particularly relevant to explain the purpose of the study. Some easy questions make the respondent familiar with the issue of the survey. Relevant issues in the questionnaire are: a) a detailed description the area under investigation, b) potential problems affecting the place (i.e. congestion, lack of services), c) a short list of amenities (i.e. the presence of secular trees in a forest). Potential effects of environmental policies should also be described. A specific section should focus on costs and trips, by explicitly asking questions on the number of trips taken to the site(s) over a specific period of time. Ideally, information should be collected for each trip taken to the sites, in practice this is impossible, so information concern the last trips to the sites. Examples of datasets are reported in table 4 and 5.   Table 4. Example of dataset for estimating a single-site travel cost model   Respondent code

Visits made to the site

Cost of the visit to site

Age

Times per week training

Family size

(€)

Cost of the visit to substitute site (€)

(n.)

(n.)

(yrs)

(n.)

(n.)

1 2 3 4 … n

4 5 8 3 … 2

34 56 75 21 … 34

45 100 21 10 … 57

45 18 32 40 … 55

4 1 2 1 … 2

0 1 4 1 … 3

44

Table 5. Example of dataset for estimating a three-sites travel cost modell   Respondent code

1 2 3 … n

Trips to forest sites (number)

Travel cost to forest sites (€)

Trail extension (km)

Income

Forest 1

Forest 2

Forest 3

Forest 1

Forest 2

Forest 3

Forest 1

Forest 2

Forest 3

(€) (.000)

2 3 1 … 2

0 1 0 … 14

12 3 7 … 4

22 34 20 … 23

70 120 35 … 35

56 74 80 … 98

345 345 345 … 345

170 170 170 … 170

68 68 88 … 68

35 42 80 … 56

Gender

0 1 1 … 0

Travel costs and other costs What kind of costs have to be considered? Although there is no fixed answer, the general agreement is to sum the day trip expenditures, typically transportation cost, access fee and time cost. Costs can be explicitly asked to respondents or computed by the researcher. The latter case, which is the most common, would ensure more uniform data and avoid missing data of respondents. Costs of travel usually include all transit expenses. Specifically, when most of the visits are made by car, round-trip costs include fuel, upkeep and tolls. Software package can be of help in calculating the distance and the related figures of cost. It is important to know the number of people sharing a car in order to correctly compute the travel cost per person. Entrance fees have to be included in the trip cost if a fee is paid to visit a recreation area. This might be the case in a protected area or a park. Equipment costs might be also considered. They may differ quite a lot depending on the type of recreation activity practiced. For some of these, the cost can be estimated by considering the market price, but for durable goods one needs to consider a rent (Parson, 2003). Consider for example, activities that can be practiced in a forest and the related equipment, such as the hunter’s gun or the skiing equipment for cross-country. Equipment costs are difficult to estimate and are usually not computed. One of the most discussed and crucial issue is probably the estimation of the opportunity cost of time associated to the trip (see Phanheuf and Smith, 2005). If the value of travel time is not considered it may affect the travel costs and subsequently the welfare measures, which may be underestimated. As a basic assumption, travel time does not provide any utility or disutility on its own, i.e. a person does not choose a site because the travel itself to the site provides utility (Haab and McConnell, 2002). On the other hand, one needs to deal with the value of opportunities lost while spending time reaching the site, staying there and returning home. Since the cost of time is generally related to a person’s income (Cesario, 1976), the estimation of the time cost is commonly derived by multiplying the hourly wage by the travel time to the site. A fraction of the imputed wage is sometimes used in travel cost studies (McConnell and Strand, 1981), ranging from onethird to the full wage. Alternative approaches to estimate the value of time were proposed (McConnell, 1992; Shaw and Feather, 1999).Hynes et al. (2009) suggest an extension of the Smith et al. (1983) approach that makes use of secondary data source to estimate a wage equation which is then used to compute individual wage values. In a very recent paper Palmquist et al. (2010) claim that the traditional approaches based on labor market miss relevant issues and propose a new approach to value time which makes use of non-employment time commitments. This model allows to derive the value of time devoted to recreation.

45

Model estimation and welfare estimates12 The choice of the model is strictly associated to the data and the aim of the estimation. In what follows basic estimation suggestions of a single and multi-site model will be provided (see the huge literature on modelling , for example Phaneuf and Smith, 2005; Bockstael and McConnell, 2007).

Single-site travel cost models Count models are mostly used to analyse the data within single-site travel cost for two reasons: first, the dependent variable (number of trips) is nonnegative and integer and second, there is a major fraction of people taking zero or small number of trips (Hellerstein and Mendelshon, 1993; Haab and McConnell, 2002).The Poisson regression, which is the basic specification, estimates the probability of observing an individual take t trips during a selected period of time: Pr (t ) =

exp( # ! ) " !t

(4)

t!

where the parameter λ is the expected number of trips and is assumed to be a function of the variables specified in the demand model. When the focus is also in modelling participation or not, off-site random samples should be taken into account. The Hurdle Poisson models the probability of not participating as well as the number of trips taken. The Poisson model is characterized by a strong assumption, the mean and the variance of rn are constrained to be equal. In other words, the Poisson assumes that the chance of making a trip is randomly distributed and all individuals have an equal chance of making the same number of trips. However, this assumption may be incorrect: you may observe relatively more people making a higher number of trips than the Poisson predicts. More frequently, in the outdoor recreation data the observations are over dispersed, i.e. the variance exceeds the mean. The Negative Binomial Model and the Generalized Negative Binomial are variations of the Poisson relaxing this constraint. See Cameron and Trivedi (1998) for specific details and Scarpa et al. (2007) provide an application. Systems of recreation demand equations can also be used (see Phanheuf and Smith, 2005). Signorello et al., (2009) provide advances in demand system approaches based on the compound Poisson distribution, that allows for cross-site correlation and dispersion. Consumer surplus estimates can be expressed in several ways: as a mean seasonal value per person, as a total seasonal value for the population and as an average per trip per person. The average per trip per person value through the Poisson regression is: CS t =

("ˆ

n

)

/ # !ˆcr = 1 / # !ˆcr ˆ "

(5)

n

                                                                                                                12

Examples of software programmes for estimation are LIMDEP\NLOGIT, BIOGEME and LatentGold. 46

Random utility travel cost models The basic random utility model is the Multinomial Logit (ML) which states that the probability of visiting a selected site j depends on the attributes of that site and the attributes of all the other sites within the choice set:

pr ( j ) =

(

exp " c c j + " p p j C

)

(

exp (# 0 + # 1 k )+ ! exp " c c i + " p pi

(6)

)

j =1

As an example, the welfare loss expressed as per choice occasion due to the closure of the first three forests is:

(

)

(

)

C C ) & ) & ln (exp ("ˆ 0 + "ˆ1 k ) + * exp !ˆc c j + !ˆ p p j % # ln (exp ("ˆ 0 + "ˆ1 k ) + * exp !ˆc c j + !ˆ p p j % j= 4 j =1 $ ' $ CS n = ' # !ˆ c

(7)

(Source: modified from Parsons, 2003)

Similarly as in the single-site travel cost, several models increasingly demanding in terms of econometrics can be used to estimate a multi-site model. Again, the goal here is not to discuss technicalities of different specifications, nevertheless one needs to be aware of some crucial key issues to be taken into account when deciding the type of model. Different model specifications have been used recently, because of their ability to overcome some strong restrictions associated with the MNL model. The most common restriction is the independence of irrelevant alternative (IIA), which implies that the odds of choosing between two sites is not influenced by modifications involving other sites of the choice set. That is to say, a decrease in a quality attribute of a forest site would induce a proportional increase in the probability of visiting all the other sites, by suggesting that all other forests are good substitutes. Model specifications that relax this assumption are the nested logit (NL) and mixed logit (ML). Nested logit allows for patterns of substitution among sites grouped in the same nest (group) and the mixed logit, which allows the parameters to be random and provides individual estimates. Recently, the issue of preference heterogeneity has been investigated more in depth, by means of latent class models (LCM). The framework of this modelling relies on finite distributions instead of continuous-mixture distributions as in the case of mixed logit. Latent class models allow segregation into groups or classes sharing a similar structure in terms of preference and behaviour (see Provencher et al., 2002; Scarpa and Thiene, 2005). A very recent application to forest makes use of a discrete-cont linked model, linking a site choice model to a trip demand model to estimate the aggregate recreational value of all forests in Mallorca, Spain (Bujosa Bestard and Riera Font, 2010). See case study 3 that report some results. Advances in modelling involves Kuhn-Tucker models, which analyse recreational behaviour by incorporating participation and site choice within a single optimization (see Phaneuf, Kling and Herriges, 2000; von Haefen, Phanheuf and Parsons, 2004). Frontiers in estimation through RU models suggest to use a modelling approach which allows to directly estimate the willingness to pay estimates for site attributes (Utility in WTP space), rather than first estimating the coefficients of the utility function and than deriving the WTP estimates as a ratio between the latter and the cost coefficient (Scarpa et al., 2008; Thiene et al., 2009). See Box 3.

47

Box 2. Estimating the aggregate value of forest recreation in a regional context by means of a discrete-count linked model (Bujosa Bestard and Riera Font, 2010) Estimated coefficients of the site-choice model (RPL model). Variable   Travel  cost   Picnic  site   Playground   Parking   Camping  area   Hiking  trails   Climbing  area   Kilometers  of  roads   Distance  to  coast   Reservoir   Citrus-­‐farming  area   Scrubland   Broad-­‐leaved  forests   Mixed  forests   Juniperus  Phoenicia  area   Burned  area   Urban  area   Cliffs   Total  edge   Coefficient  of  patches  variation   Visibility  index   Landscape  quality  index   Standard  deviations   Citrus-­‐farming  area   Urban  area   Visibility  index     Log-­‐likelihood  function   Restricted  log-­‐likelihood   McFadden-­‐R2   Adjusted  McFadden-­‐R2   Number  of  observations  

Coefficient   -­‐0.2363   0.0345   0.0801   0.0391   1.0012   0.0322   0.0040   0.3047   0.0195   0.0007   -­‐0.0462   -­‐0.0009   0.0003   -­‐0.0003   0.0017   -­‐0.0099   -­‐0.0068   -­‐0.3388   0.0005   0.4179   0.1222   0.4083     0.0250   0.0047   0.1585   -­‐2974.628   -­‐3429.209   0.1326   0.1321   841  

    b/St.Er.     -­‐19.850     3.051     5.348     2.669     3.939     7.545   (*)   2.191     13.876   (*)   -­‐2.218   (*)   2.235     -­‐3.742     -­‐7.046     4.438     -­‐5.580     5.613     -­‐3.045     -­‐3.674     -­‐2.998     5.291     7.740     4.530     8.307         3.315     3.804     3.091              

           

Annual consumer surplus estimates from the trip demand model Estimate   Minimum   Median   Mean   Maximum   Median's  95%  confidence  interval  

Annual  value  (euros  per  person)   0   55.90   68.60   478.25   55.76-­‐56.04   48

Box 3. A tool to address confounding random scale effects site choice: utility in the preference space vs utility in the WTP space (Scarpa et al., 2008) Site-specific data          

         

1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18  

Vette  Feltrine  

0.7  

Piccole  Dolomiti  -­‐  Pas.  

2.1  

Cansiglio-­‐Alpago  

0.5  

1.7  

Asiago    

1.5  

Grappa  

0.9  

2.1  

Baldo-­‐Lessini  

   

   

   

   

Site  Attributes  

Descriptive  Statistics  of  Trips  

Degree  of    

Easy    

Hard  

Mean   Std.Dev.   Visits   Percentage   difficulty   Ferratas   trails   Shelters   Trails   1.5  

642  

7  

3  

3   0.61  

25   0.07  

4   1808  

19.6  

1  

4   0.54  

13   0.17  

414  

4.5  

3  

4   0.86  

10   0.08  

2.8   1318  

14.3  

1  

0   1.00  

13   0.00  

757  

8.2  

1  

1   0.99  

5   0.01  

1.2  

3.6   1045  

11.3  

1  

2   0.76  

18   0.02  

Antelao  

0.3  

0.7  

244  

2.6  

3  

0   0.68  

6   0.08  

Pelmo  

0.3  

0.6  

243  

2.6  

3  

0   0.66  

9   0.04  

Cortina  

0.3  

0.8  

220  

204  

2  

22   0.53  

32   0.11  

Duranno  –  Cima  Preti  

0.1  

0.3  

44  

0.5  

3  

0   0.33  

4   0.09  

Sorapis  

0.1  

0.5  

128  

1.4  

3  

4   0.36  

9   0.23  

Agner-­‐Pale  S.Lucano  

0.1  

0.5  

112  

1.2  

3  

2   0.51  

7   0.14  

Tamer-­‐Bosconero  

0.2  

0.6  

188  

2  

3  

0   0.30  

6   0.06  

Marmarole  

0.2  

0.7  

161  

1.7  

2  

1   0.51  

9   0.07  

Tre  Cime-­‐Cadini  

0.6  

1.2  

547  

5.9  

2  

4   0.60  

9   0.08  

Civetta-­‐Moiazza  

0.7  

1.3  

561  

6.1  

2  

4   0.34  

16   0.11  

Pale  S.Martino  

0.7  

1.3  

564  

6.1  

2  

11   0.46  

14   0.14  

Marmolada  

0.3  

0.7  

225  

2.4  

3  

2   0.21  

13   0.25  

Distributions of WTP for one additional alpine shelter

49

3. THE HEDONIC METHOD13 The hedonic pricing method is based on market transactions for differentiated goods in order to estimate the value of specific environmental characteristics.14. It is most commonly applied to variations in residential properties (house) prices that reflect the value of local environmental attributes. In his seminal paper Rosen (1974) developed the theoretic framework. It can be used to estimate economic benefits or costs associated with environmental quality, including air pollution, water pollution, or noise and environmental amenities, such as visual amenities, aesthetic views or access to recreational facilities or sites (Bastian et al., 2002; Patterson and Boyle, 2002; Loomis, 2004; Waltert and Schläpfer, 2010). For example, the price of a house reflects its characteristics either in terms of proximity to services (school, hospital, etc.) or proximity to environmental amenities (forest, park, lake, etc.). The individual characteristics of a house or other good can therefore be valued by looking at how people are willing to pay for its changes. Hence, this method seems to be particularly suitable to investigate how a forest view or a forest proximity affect the price of a house. Hedonic analysis of the market of differentiated goods is usually divided into two steps, commonly known as the first and second stage. The first stage aims at estimating the hedonic price function by using the prices of the differentiated commodity and its characteristics. Implicit prices of the characteristics and the structure of preferences can be obtained. The second stage deals with the estimation of the demand functions for the characteristics of the commodity by using the implicit prices obtained in the first stage (Taylor, 2003; Palmquist 2005, Bockstael and McConnell, 2007). A recent review of hedonic price studies on environmental amenities can be found in Waltert and Schläpfer, 2010. Hedonic literature on forest/urban tree include among others Garrod and Willis, 1992a; Garrod and Willis, 1992b; Tyrvanen, 1997; Tyrvanen and Miettinen, 2000, Scarpa et al., 2000; Yan et al., 2004; Mansfield et al., 2005, Sander et al., 2010.

Some background to get started The underlying idea is to estimate a hedonic price function where the prices of properties (i.e. houses) are a function of some characteristics of the structure and some relevant environmental attributes of the houses, such as proximity to a nice forest, quality of the air or quality of water of the nearby lakes:

P = " 1 + !1 x1 + ! 2 x 2 ...! n x n + ! 1 z1 + ! 2 z 2 ...! n z n

(8)

where the dependent variable (P) is the sales price of a property and the independent variables ( x = x1 , x 2 ,...x n ) are the structural characteristics and ( z = z1 , z 2 ,...z n ) are the environmental characteristics of the commodity.

                                                                                                                13 14

This part draws upon Taylor (2003). Hedonic wage studies are not considered here. 50

A detailed exposition of the theoretical aspects of the hedonic approach is given in Palmquist (1991, 2005), Freeman (2003) and Bockstael and McConnell (2007). Basic steps in estimating the hedonic price function The following steps can be followed to estimate the hedonic price function: 1. data to be collected: property values and property attributes 2. sampling 3. model estimation and welfare measures Data to be collected The goal is to estimate a hedonic price function in order to calculate implicit prices, that is the marginal willingness to pay for the attributes of the house. We need property values for the dependent variable and property attributes and environmental quality characteristics for the independent variables. In collecting the data the following issues should be taken into account: sources, selection and potential biases. Property values Source and selection of data Sources of property values are typically (Taylor, 2003): -

market prices

-

estimates of value from homeowner or tax authorities

-

rental prices

The most common dependent variable is market prices, but values assessed by homeowners or tax authorities can also be cautiously taken into account. In many countries transfers, and therefore the sales prices and other characteristics, are recorded by local government or tax offices. Availability and reliability of data within the country is a crucial issue. In some countries, the data may be not entirely reliable. The sales price recorded in transfers might be underestimated and the figure used to calculate property taxes. Direct surveys are preferred. Data collected by private firms are sometimes available upon sale. Homeowners can be asked within specific surveys to provide the price they paid to purchase their property along with the year of the sale and attributes of the house. Although these data may differ from the market sales, they can be considered a reasonably good approximation. Rental prices are mainly used with holiday homes (Taylor and Smith, 2000). Observations on individual transactions on parcels or housing units are preferable when the research aims at investigating the effect of a change in the environmental quality, as in the case of a forest. Aggregate data may also be considered, but be aware of shortcomings (Schultz and King, 2001). Individual transactions become particularly relevant. If the property data are aggregated it might be difficult to understand the effect on prices of the quality change if the area affected by the quality changes is, for example, too small compared to the property area. Potential bias The analyst needs to know the market very well in order to identify and perhaps discard low or implausible sales values. A common shortcoming of sales prices is with sample selection. Properties with less known characteristics may not be sampled, thus affecting estimates. Compare marginal estimates with those obtained from a complete set of property values. 51

Measurement error in property values estimates may not be a relevant issue if not correlated with the independent variables. Although there would be a loss of efficiency, derived implicit prices would be unbiased (Taylor, 2003). Check for correlation across values from homeowners or tax authorities and the characteristics of the properties.

Property attributes and environmental quality attributes Broadly speaking, data that can be used as independent variables are: -

property characteristics; neighbourhood characteristics; environmental quality characteristics.

Source and selection of data There is no straight way to select the variables for the regression, knowledge of the market is one of the main tools. Data on property characteristics may include the size of the plot (typically measured in hectares or acres), size of the building, the number of bedrooms and bathrooms (typically measured in squared metres or feet), age of the structures. For apartments, other components such as floor level, the presence of a lift, the presence of a garage or parking place may play a crucial role. Data are generally gathered from tax authorities records or homeowner surveys. Neighbourhood characteristics deal with quality and location. The first group often includes the quality of local school, level of crime, average income, average age and ethnic composition. These data are usually available from the government census. Location characteristics deal with distance to the town centre, nearest shopping centres, distance to train station, nearest light rail or bus stop, nearest motorway exit. Environmental characteristics also deal with quality and location. Environmental amenities include the quality of the air (pollution level) or the clarity of the water of the lake close to the property. Scientific measures of these variables are typically used. Information about the location include: distance to an open space, a forest, a public park, a lake, a landfill, a quarry. These are usually measured in kilometres and obtained through a GIS software package. Environmental and neighbourhood characteristics may conflict: the proximity to a motorway might positively influence the sale prices but display a negative impact due to the noise effect. Palmquist and Fulcher (2006) describe in details the structure of the date and suggest specifications for the property and neighbourhood variables. Asabere and Huffman (2009) find that proximity to view, cul-de-sac, golf courses, playground, tennis courts and pool add significantly to home value. Different types of forest cover provide specific amenities to homeowner and to the population. Several definitions of forests cover and greenness are explored by Mansfield et al. (2005) and associated value estimates are provided. An example of dataset for estimating a hedonic price function is reported in table 6. Relevant extensions in data collection involve the investigation of spatial relationships among properties (spatial dependence and spatial heterogeneity) by means of specific econometric tools (Anselin and Florax, 1995; Legget and Bockstael, 2000, Anselin and Le Gallo, 2006, Bin et al., 2009). Based on a unique dataset accounting for both temporal and spatial variation in land use, Kleiber and Phaneuf (2010) investigate the influence of many types of open space on household location choices using an advanced hedonic approach. See case study 5 as an example for spatial issues in hedonic studies. 52

Phaneuf et al. (2008) provide a new approach by simultaneously integrating data on private locational choices and amenities provided by ecosystem services. They allow residential and recreational choices to be combined. Table 6. Example of dataset for the estimation of the hedonic function   Observation code

Market price

Area

Bedrooms

Distance to train station

Distance to the forest

Air pollution

(n.) 1 2 3 4 … n

(€ .000) 250 200 380 190 … 80

(sqm) 200 180 250 100 … 80

(n) 3 2 4 2 … 1

(km) 2 3 5 1 … 7

(km) 12 8 2 10 … 25

(PM10) 26.5 25.7 20.2 37.4 … 32.5

Potential bias Check for outliers, that is control for non conflicting values between variables (i.e. size of the house and number of rooms). One relevant concern is how the variables are measured (Palmquist, 2005). With quality characteristics there may be a discrepancy between the effective measurement of the researcher and the way it is perceived by buyers and sellers. For example Poor et al., (2001) illustrate how buyers may perceive the quality level of water differently to what is reported by the authorities. This bias might be even stronger in the case of a proxy of the variable. In both cases it may lead to biased coefficients. How many variables should be included in the hedonic regression? Too many variables may increase the probability of multi-collinearity, whereas too few may lead to biased coefficient estimates. Multi-collinearity should be test. Sampling Two main issues need to be carefully defined: -

geographic area; length of time.

The size of the area is crucial because one needs to allow for enough variation of environmental characteristic. It seems easier to allow for variation in location (for example the distance to a nice forest) rather then quality variation (for example index of water quality). The geographical area of the properties could involve more than one market, which might in turn affect the assumption of the hedonic approach based on an equilibrium function (Taylor, 2003). Although each urban area can be considered as a single market, there may be market segmentation within the same area, due to social aspects or product heterogeneity. Test for market segmentation by using, for example, the Ftest. Enough variation in the period of time has also to be ensured. The sample size may be too small because of the market size. Keep in mind to account for prices to be deflated. Use preferably a price index or include some dummy variables in the regression to account for the year of sale of the houses. Moreover, does the valuation of an environmental variable change over time, therefore differently affecting the sale prices? For example the creation of an open space might have a higher effect within the first years and then tend to decline because the major effect would already be capitalized in the price.

53

Model estimation and welfare estimates The choice of the functional form is a crucial issue as it can substantially impact results (Haab and McConnell, 2002; Bockstael and McConnel, 2007, Palmquist, 2005). As the price is determined at market equilibrium by buyers and sellers, there is no specific rule to follow in choosing the functional form. The most common functional forms are (Table 4): -

linear; semi-log; double-log; quadratic quadratic Box-Cox.

In the linear functional form the implicit price for any specific attribute (structural or property characteristics, neighbourhood issues and environmental attributes) is simply the estimated coefficient. Implicit prices differ across attributes, but are constant within each attribute. Linear hedonic approaches should be avoided, because of their inability to allow for incremental changes (Taylor, 2003). The impact of each functional form on the results is very different and it is related to the specific dataset. Implicit prices provide information about the possible capitalization of an environmental characteristic on the market. Be aware that they do not say about the value placed by consumers on a discrete change in the amenity, that is the willingness to pay associated to a specific variation. Bockstael and McConnel (2007, p.160) state “…but marginal changes do not typically characterize proposed policies or natural resource damages cases”. Investigation of how purchaser perceptions and intention influence price for forest land is also an interesting issue (Snyder et al., 2008). Asabere and Huffman (2009) find that greenbelts and trails are generally associated with 2% -5% price premiums. The challenge is to move from the estimation of a hedonic price function to the demand for amenities by households (the so-called second stage). This generally requires more efforts in terms of modelling and data requirements (Palmquist, 2005). Some results of a study reporting estimates obtained from the second stage (Netusil et al., 2010) are reported in Box 4.

54

Functional   form     Linear  

Equation      

Implicit  prices  

"P / "x i = ! i   P = # 0 + ! " i xi   Table 7. Functional forms that can be used to estimate the hedonic function  

Semi-­‐log  

ln P = # 0 + ! " i x i  

"P / "x i = # i ! P  

ln P = # 0 + ! " i ln x i  

"P / "x i = # i ! P / x i  

  Double-­‐log  

  Quadratic  

N

P = $ + ! # i xi + i =1

1 N ! 2 i =1

N

!" j =1

ij

N

$P / $x i = & i # P / x + ! % ij x j + % ii x i  

xi x j  

j" 1

  Quadratic     Box-­‐Cox  

N

P(# ) = & + ! %i xi(" ) + i =1

  (Source:  modified  from  Taylor,  2003)  

N 1 N + ( $ ij xi(" )x (j" )   ,P / , x i = ) $ i x i(" ) + - # ij x i(" %1) x (j" ) & P 1% !   ! ) & 2 i , j= 1 j =1 * '

Box 4. Estimating the demand for tree canopy: a second-stage hedonic price analysis (Netusil et al., 2010). Estimated  Coefficients:  Quadratic  First  Stage,   Linear  Second  Stage  (St.  Errors  in  parenthesis)   %TREE_CANOPY  

-­‐20.35   ***  

INCOME     AGE   EDUCATION     EDUCATION_SQUARED   SITETIME  

0.0001071  

 

(0.00047)  

2.49  

 

(2.05)  

-­‐192.55   **  

(81.47)  

5.85   **  

(2.41)  

-­‐0.12  

Constant  

(4.58)  

 

 (2.41)  

2,150.48   ***  

2

 (2.41)  

R  

0.27    

 

Observations  

440      

   

**Significant  at  5%,  ***  Significant  at  1%  

 

                       

Estimated  Coefficients:  Quadratic  First  Stage,   Double-­‐Log  Second  Stage  (Robust  St.  Err.  in  par.)   LN_%TREE_CANOPY    

-­‐0.1682   ***     (0.0423)  

LN_INCOME  

0.1720   *  

 (0.0972)  

LN_AGE  

0.1927  

 

(0.1825)  

LN_EDUCATION  

-­‐0.5539  

 

(0.3455)  

LN_SITETIME  

-­‐0.0937   **    

(0.0455)  

5.4090   ***    

(1.569)  

Constant     2

R  

0.2161    

 

377      

   

Observations  

*Significant  at  10%,  **Significant  at  5%,     ***  Significant  at  1%  

 

 

Per  Property  Benefit  Estimates  For  Alternative  Canopy  Cover      

and  95%  Confidence  Intervals  

Percentage  of  

   

 

Quadratic  First  Stage   Quadratic  First  Stage   Linear  Second  Stage   ($)   Log  Second  Stage    ($)  

Tree  Canopy   2.53  

1671   ±  

15  

548   ±  

2  

7.21  

4416   ±  

42  

1310   ±  

5  

8.21  

4944   ±  

47  

1459   ±  

5  

15  

7988   ±  

87  

2409   ±  

8  

25  

10747   ±  

144  

3684   ±  

14  

35  

11453   ±  

202  

4874   ±  

18  

40  

11037   ±  

231  

5447   ±  

20  

55

 

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Morey, E. R., and Waldman, D. M. (1998) “Measurement Error in Recreation Demand Models: The Joint Estimation of Participation, Site Choice, and Site Characteristics”. Journal of environmental economics and management 35 (3): 262-276. Netusil, N. R., Chattopadhyay, S., and Kovacs, K. F. (2010) “Estimating the Demand for Tree Canopy: A Second-Stage Hedonic Price Analysis in Portland, Oregon”. Land Economics 86 (2): 281-293. Palmquist, R. B. (1991) Hedonic Methods. In Measuring the Demand for Environmental Quality, ed. J. B. Braden, and C. D. Kolstad. Amsterdam: North-Holland. Palmquist, R. B. (2005) Property Value Models. In Handbook of Environmental Economics, ed. K.G. Mäler, and J. Vincent. Amsterdam: North-Holland. Palmquist, R. B., and Fulcher, C. M. (2006) The Economic Valuation of Shoreline: 30 Years Later. In Explorations in Environmental and Natural Resource Economics: Essays in Honor of Gardner M. Brown, Jr, ed. Northampton: Edward Elgar. Palmquist, R. B., Phaneuf, D. J., and Smith, V. K. (2010) “Short Run Constraints and the Increasing Marginal Value of Time in Recreation”. Environmental and Resource Economics 46 (1): 1941. Parsons, G. R. (2003) The Travel Cost Model. In A Primer on Nonmarket Valuation, ed. P. A. Champ, K. J. Boyle, and T. C. Brown. London: Mass.: Kluwer Academic Publishing. Parsons, G. R., and Kealy, M. J. (1992) “Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation”. Land Economics 68 (1): 93-106. Parsons, G. R., Massey, D. M., and Tomasi, T. (1999) “Familiar and Favorite Sites in a Random Utility Model of Beach Recreation”. Marine Resource Economics 14: 299-315. Parsons, G. R., Plantinga, A. J., and Boyle, K. J. (2000) “Narrow Choice Sets in a Random Utility Model of Recreation Demand”. Land Economics: 86-99. Parsons, G. R., and Wilson, A. J. (1997) “Incidental and Joint Consumption in Recreation Demand”. Agricultural and Resource Economics Review 26 (1). Paterson, R. W., and Boyle, K. J. (2002) “Out of Sight, out of Mind? Using Gis to Incorporate Visibility in Hedonic Property Value Models”. Land Economics 78 (3): 417-425. Phaneuf, D. J., Kling, C. L., and Herriges, J. A. (2000) “Estimation and Welfare Calculations in a Generalized Corner Solution Model with an Application to Recreation Demand”. Review of Economics and Statistics 82 (1): 83-92. Phaneuf, D. J., and Smith, K. V. (2005) Recreation Demand Models. In Handbook of Environmental Economics, ed. K.-G. Mäler, and J. Vincent. Amsterdam: North-Holland. Phaneuf, D. J., Smith, V. K., Palmquist, R. B., and Pope, J. C. (2008) “Integrating Property Value and Local Recreation Models to Value Ecosystem Services in Urban Watersheds”. Land Economics 84 (3): 361-381. 59

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Smith, V. K., Desvousges, W. H., and McGivney, M. P. (1983) “The Opportunity Cost of Travel Time in Recreation Demand Models”. Land Economics 59 (3): 259-278. Snyder, S. A., Kilgore, M. A., Hudson, R., and Donnay, J. (2008) “Influence of Purchaser Perceptions and Intentions on Price for Forest Land Parcels: A Hedonic Pricing Approach”. Journal of Forest Economics 14 (1): 47-72. Starbuck, C., Alexander, S. J., Berrens, R. P., and Bohara, A. K. (2004) “Valuing Special Forest Products Harvesting: A Two-Step Travel Cost Recreation Demand Analysis”. Journal of Forest Economics 10 (1): 37-53. Taylor, L. (2003) The Hedonic Method. In A Primer on Nonmarket Valuation, ed. P. A. Champ, K. J. Boyle, and T. C. Brown. London: Mass.: Kluwer Academic Publishing. Taylor, L. O., and Smith, V. K. (2000) “Environmental Amenities as a Source of Market Power”. Land Economics: 550-568. Thiene, M., and Scarpa, R. (2008) “Hiking in the Alps: Exploring Substitution Patterns of Hiking Destinations”. Tourism Economics 14 (2): 263-282. Thiene, M., and Scarpa, R. (2009) “Deriving and Testing Efficient Estimates of WTP Distributions in Destination Choice Models”. Environmental and Resource Economics 44 (3): 379-395. Train, K. (2003) Discrete Choice Methods with Simulation. Cambridge Univ Pr. Tyrväinen, L. (1997) “The Amenity Value of the Urban Forest: An Application of the Hedonic Pricing Method”. Landscape and Urban planning 37 (3-4): 211-222. Tyrväinen, L., and Miettinen, A. (2000) “Property Prices and Urban Forest Amenities”. Journal of environmental economics and management 39 (2): 205-223. von Haefen, R. H., Phaneuf, D. J., and Parsons, G. R. (2004) “Estimation and Welfare Analysis with Large Demand Systems”. Journal of Business and Economic Statistics 22 (2): 194-205. Waltert, F., and Schläpfer, F. (2010) “Landscape Amenities and Local Development: A Review of Migration, Regional Economic and Hedonic Pricing Studies”. Ecological Economics 70 (2): 141-152. Zandersen, M., and Tol, R. S. J. (2009) “A Meta-Analysis of Forest Recreation Values in Europe”. Journal of Forest Economics 15 (1): 109-130.

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Best Practice Guidelines in Benefit Transfer of Forest Externalities 15

1. INTRODUCTION The main aim of this report is to develop good practice guidelines for benefit transfer of forest externalities; i.e. show how use and non-use values of non-timber environmental goods in forests can be estimated when there is no time and/or money to perform a new primary valuation studies for a specific project or policy analysis. Thus, the demand for benefit transfer arises from increased policy use of economic estimates of these non-timber benefits. The use of these guidelines requires a good basic knowledge of the environmental valuation techniques; see best practice guidelines on revealed preference (RP) and stated preference (SP) techniques applied to forest externalities; produced as part of the COST E45 Action. Benefit transfer involves transferring economic estimates from previous studies (often termed study sites) of similar changes in environmental quality to value the environmental change at the policy site. This procedure is most often termed benefit transfer, but damage estimates can also be transferred and a more general term would be value transfer. However, the term benefit transfer seems to be dominating the literature in this area.

The policy uses of these transferred economic estimates include: i)

Cost-benefit analysis (CBA) of investment projects and policies aimed at managing forest (e.g. preservation of forests, restrictions on cutting practises, and forest fire prevention measures) , or projects that affect forests (e.g. road construction)

ii)

Environmental accounting at the national level, i.e. including externalities of forests in green national accounts

iii)

Environmental costing, i.e calculating marginal external costs as a basis for optimal economic management of the forests and design of optimal regulatory instruments (e.g. environmental charges)

iv)

Natural Resource Damage Assessment (NRDA) according to the Environmental Liability Directive; i.e. calculate compensation payments for acute injuries to forest ecosystems

                                                                                                                15  This  document  is  based  on  Navrud  (2006):  Database  and  Benefit  Transfer  Protocol  for  Forest  Environmental  

Valuation.   Report   to   LEF,   Laboratoire   d'Economie   Forestière   at   INRA-­‐ENGREF,   Nancy,   France;   as   part   of   their   project   “A   protocol   and   database   for   environmental   valuation   of   French   /   European   Forests”.   It   also   draws   upon   Navrud,   S.   (2007):   Practical   tools   for   benefit   transfer   in   Denmark   –   Guidelines   and   examples.   Report   to   the   Danish   Environmental   Protection   Agency,   Copenhagen.   I   have   also   benefitted   from   comments   by   Henrik   Lindhjem,   Randy   Rosenberger,   and   Mette   Termansen;   and   of   course   from   discussions   with   the   COST   E45   participants  during  the  Working  Group  meetings  on  Benefit  Transfer.  The  usual  disclaimer  applies.   62

The demand for accuracy increase when you move down this list of policy uses (Navrud 2005), and the most frequent use of benefit transfer will probably be in CBAs of projects and policies, e.g. forest preservation plans and infrastructure projects impacting forest areas. In order to perform benefit transfer, we need: (i)

Best practice guidelines for valuation methods/surveys including criteria for assessment of the quality of primary valuation studies,

(ii)

Benefit transfer techniques

(iii)

Benefit transfer guidelines

(iv)

A database of primary valuation studies (to transfer from)

Best practice guidelines (i) for Stated Preference (SP) and Revealed Preference (RP) methods applied to forest externalities have been produced as part of the COST Action E45. In addition, Söderqvist and Soutukorva (2006)

provide criteria for assessment of the quality of RP and SP studies in general. Benefit transfer techniques (ii) are described in chapter 2 below, and chapter 3 presents the benefit transfer guidelines (iii) applied to forests. Chapter 4 discusses an important prerequisite for benefit transfer – a database for primary valuation studies of forest externalities with enough detail to judge similarity between the primary studies and the policies we are using benefit transfer to evaluate. The aim has been that the guidelines should be practical and simple to use, and show in a transparent and step-by-step manner how we can arrive at economic values for use and non-use benefits of forest ecosystems. For other practical guides to value transfer for environmental goods in general, we would like to refer to the Danish EPA Guidelines (Navrud 2006) and the recent UK Defra Guidelines (Bateman et al 2009a).

2. BENEFIT TRANSFER TECHNIQUES There are two main groups of benefit transfer techniques (Navrud 2004): 1. Unit Value Transfer i) Simple (naïve) unit value transfer ii) Unit value transfer with income adjustments 2. Function Transfer i) Benefit Function Transfer ii) Meta analysis Simple (naïve) unit transfer (from one study or as a mean value estimate from several studies) is the simplest approach to transferring benefit estimates from a study site (or as a mean from several study sites) to the policy site. This approach assumes that the wellbeing experienced by an average individual at the study site is the same as will be experienced by the average individual at the policy site, and that the change in the ecosystem service being valued is the same at the two sites. Thus, we can directly transfer the benefit estimate, often expressed as mean willingness-to-pay (WTP)/household/year (or as consumer surplus per visitor day or per visitor (per year) for recreational use values), from the study site to the policy site.

63

For the past few decades this procedure has routinely been used in the United States to estimate the recreational use benefits associated with multipurpose reservoir developments and forest management (USDA Forest Service). The selection of these unit values could be based on estimates from only one or a few valuation studies considered being close to the policy site (both geographically, and in terms of similarity of the characteristics of the good valued). The obvious problem with simple unit value transfer for recreational activities is that individuals at the policy site may not value recreational activities the same as the average individual at the study sites. There are two principal reasons for this difference. First, people at the policy site might be different from individuals at the study sites in terms of income, education, religion, ethnic group or other socio-economic characteristics that affect their demand for recreation. Second, even if individuals´ preferences for recreation at the policy and study sites were the same, the recreational opportunities (i.e., substitute sites and activities) and the change in the good valued might not be. Unit values for non-use values of e.g. ecosystem services from CV studies might be even more difficult to transfer than recreational (use) values for at least two reasons. First, the unit of transfer is more difficult to define. While the obvious choice of unit for use values are consumer surplus (CS) per activity day, there is greater variability in reporting non-use values from CV surveys, both in terms of WTP for whom, and for what time period. WTP is reported both per household or per individual, and as a one-time payment, annually for a limited time period, annually for an indefinite time, or even monthly payments. Second, the WTP is reported for one or more specified discrete changes in e.g. forest ecosystem services, and not on a marginal (e.g per ha) basis. We recommend using WTP/household/year as the transfer unit, and then aggregate over the total number of affected households to get an estimate of total benefits. Using WTP/individual/year might lead to overestimation of total benefits when aggregated over individuals, as shown by Lindhjem and Navrud (2009). WTP as a one-time amount might lead to underestimation of annual WTP, as reported WTP will be the present value of a flow of annual WTP amounts and will be constrained by their income in the year they report their one-time amount WTP. Using this transfer unit for a specified change in ecosystem services also avoids the procedure of scaling up and down reported WTP to the size of the area at the policy site. Such scaling assumes a constant value pr ha. (and no non-linerarities in valuation); which does not seem to be the case in practice (See e.g. Lindhjem 2007; Lindhjem and Navrud 2008). The simple unit value transfer approach should not be used for transfer between countries with different income levels and costs of living (or between regions with very different income levels within a country). Therefore, unit transfer with income adjustments has been applied. The adjusted WTP estimate Bp' at the policy site can be calculated as

(1)

WTPp' = WTPs (Yp / Ys)ß

where WTPs is the original WTP estimate from the study site, Ys and Yp are the income levels at the study and policy site, respectively, and ß is the income elasticity of demand for the environmental good in question. The income elasticity of WTP ß for different environmental goods is typically smaller than 1, and often in the 0.4 - 0.7 range (Kriström and Riera 1996; Hökby and Söderqvist 2003; Desaigues et al 2006) Note that this is the income elasticity of WTP, and not of demand; and that there is no simple relationship between the two measures). When we lack data on the income levels of the affected populations at the policy and study sites, Gross Domestic Product (GDP) per capita figures have been used as proxies for income in international benefit transfers. However, this approach could give wrong results in international benefit transfers when income levels at the local study and/or policy site deviates from the average income level in the countries.

64

Using the official exchange rates to convert transferred estimates in U.S. dollars to the national currencies does not reflect the true purchasing power of currencies, since the official exchange rates reflect political and macroeconomic risk factors. If a currency is weak on the international market (partly because it is not fully convertible), people tend to buy domestically produced goods and services that are readily available locally. This enhances the purchasing powers of such currencies on local markets. To reflect the true underlying purchasing power of international currencies, the U.S. International Comparison Program (ICP) has developed measures of real GDP on an internationally comparable scale. The transformation factors are called Purchasing Power Parities (PPPs). Even if PPP adjusted GDP figures and exchange rates can be used to adjust for differences in income and cost of living in different countries, it will not be able to correct for differences in individual preferences, baseline levels of recreational opportunities and ecosystem services, and cultural and institutional conditions between countries (or even within different parts of a country). Thus, population characteristics should be as similar as possible between the study and policy sites.

Transferring the entire benefit function is conceptually /theoretically more appealing than just transferring unit values because more information is effectively taken into account in the transfer. The evidence is mixed with regards to whether function transfers performs better than unit value transfer (see e.g. Ready et al 1997 and Bateman et al 2009b), but in many instances benefit function transfer does not seem to reduce transfer errors significantly compared to simple (naïve) unit value transfer. The benefit relationship to be transferred from the study site(s) to the policy site could be estimated using either revealed preference (RP) approaches like TC and HP methods or stated preferences (SP) approaches like the CV method and Choice modelling (CM). For a CV study, the benefit function can be written as: WTPij = b0 + b1Gj + b2 Hij + e

(2)

where WTPij = the willingness-to-pay of household i at site j, Gj = the set of characteristics of the environmental good at site j, and Hij = the set of characteristics of household i at site j, and b0 , b1 and b2 are sets of parameters and e is the random error. To implement this approach the analyst would have to find a study in the existing literature with estimates of the constant b0 and the sets of parameters, b1 and b2. Then the analyst would have to collect data on the two groups of independent variables, G and H, at the policy site, insert them in equation (2), and calculate households´ WTP at the policy site. The main problem with the benefit function approach is due to the exclusion of relevant variables in the WTP (or bid) function estimated in a single study. When the estimation is based on observations from a single study of one or a small number of recreational sites or a particular change in environmental quality, a lack of variation in some of the independent variables usually prohibits inclusion of these variables. For domestic benefit transfers researchers tackle this problem by choosing the study site to be as similar as possible to the policy site. Instead of transferring the benefit function from one selected valuation study, results from several valuation studies could be combined in a meta-analysis to estimate one common benefit function. Meta-analysis has been used to synthesize research findings and improve the quality of literature reviews of valuation studies in order to come up with adjusted unit values. In a meta-analysis, several original studies are analysed as a group, where the result from each study is treated as a single observation in a regression analysis. If multiple results from each study are used, various meta-regression specifications can be used to account for such panel effects.

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The meta analysis allows us to evaluate the influence of a wider range in characteristics of the environmental good, the features of the samples used in each analysis (including characteristics of the population affected by the change in environmental quality), and the modelling assumptions. In practice, however, detailed characteristics of the good/study site and the population are often not reported in the primary studies (especially not if they are are published journal papers, which often focus on methodological tests of valuation methods rather than reporting monetary estimates and the data needed in a meta regression analysis), and it requires a large effort to find them (if at all possible).The resulting regression equations explaining variations in unit values can then be used together with data collected on the independent variables in the model that describes the policy site to construct an adjusted unit value. The regression from a meta-analysis would look similar to equation (2), but a set of variables reflecting differences in the environmental valuation method applied need to be added; i.e. Cs = characteristics of the methodology applied in study s; as meta analyses typically find that differences in valuation methodologies account for a significant part of the variation in mean willingness-to-pay across studies s; WTPs . Bartczak, Lindhjem and Stenger (2008) provides an overview of the studies in Europe and the USA which have tested the validity and transfer errors of transferring use and non-use values of forests. They found 12 studies dealing with benefit transfer between forest sites and a few others in which forest sites were among other analyzed environmental resources. The majority of these studies transferred recreation benefits using the benefit function based either on contingent valuation or travel costs estimations. They mainly focused on four areas: physical attributes of forests, time aspects, and methodological improvements to increase the estimated accuracy and reduce surveys costs.

3. GUIDELINES FOR BENEFIT TRANSFER There are few detailed guidelines on value transfer. In the US there exist guides that cover the key aspects of conducting a value transfer, notably Desvouges et al (1998) aimed at transfer for valuing environmental and health impacts of air pollution from electricity production and recently Bateman et al (2009b) guidelines for value transfer of environmental goods in general in a CBA context. Adapted to the economic valuation of non-timber environmental goods of forests for CBA and other policy uses, the following 8-steps guidelines is proposed: 1)

Identify the change in the environmental good to be valued at policy site

2)

Identify the affected population at the policy site (size and socioeconomic characteristics)

3)

Conduct a literature review to identify relevant primary studies (preferably based on a database, but supplemented by journal and general web search)

4)

Assessing the relevance/similarity and quality of study site values for transfer

5)

Select and summarize the data available from the study site(s)

6)

Transfer value estimate from study site(s) to policy site

7)

Calculating total benefits or costs

8)

Assessment of uncertainty and transfer error / Sensitivity analysis 66

STEP 1 - Identify the change in the environmental good to be valued at policy site (i)

Type of environmental good The Total Economic Value (TEV) of non-timber goods in forests can be broadly classified in three groups: (i) Direct Use Value (i.e. recreational activities like walking, fishing, hunting; and non-timber commercial forest products (berries, mushrooms etc)) (ii) Indirect Use Values (i.e. ecosystem services of forests like biological diversity, climate regulation and carbon sequestration, watershed services (water quality and quantity), soil stabilization and erosion control, aesthetic value of forest scenery) and Non-use Values (Existence and preservation/bequest values including historic/cultural heritage values of forests, and endangered species habitat).

(ii)

Describe (expected) change in environmental quality a) baseline level (e.g. frequency of recreational use at the policy site and availability and quality of substitute sites; quality and amount of ecosystem services), b) magnitude and direction of change (Gain vs. loss; and prevention16 vs. restoration)

STEP 2 – Identify the affected population at the policy site Desvousges et al. (1998) use this as the last step in their Value transfer guide. However, it is important to identify the size of the affected population at the policy site before we review the valuation literature and evaluate the relevance of selected studies. The transferred value should come from the same type of affected individuals in terms of spatial scale. Population characteristics also need to be similar in order to ensure they share the same type and level of welfare determinants If we just want to establish the use value of some activity, the relevant, affected population is the recreationists. If we would like to estimate both use and non-use values, and the policy site is only of local importance (e.g. a small forest area with many substitute sites regionally), we should use only the population of the municipality. If there are few substitutes for the sites at the regional level, the population in several communities, or even the county population, should be used. If the good is of national importance, e.g. a national park, or the single site of an endangered species in the country, the national population should be used.

For use values, the number of individual recreationists should be estimated (before and after the change), while for non-use values (or use and non-use values combined) the number of households                                                                                                                 16

A distinction should be made between prevention (which preserves the original/undisturbed environmental good) and restoration. People have been found to put a higher value on keeping the original (i.e. prevention) than restoration. 67

should be the unit of aggregation at the relevant geographical scale (community, regional/county or national level).

STEP 3 - Conduct a literature search to identify relevant primary studies

The next step is to conduct a literature search to identify relevant primary studie; preferably based on a database, but supplemented by journal and general web search. Databases like EVRI www.evri.ca, ENVALUE http://www.environment.nsw.gov.au/envalue/StudyCnt.asp and SWE ValueBase http://www.beijer.kva.se/valuebase.htm can be used to identify similar studies from the same country or other closely located countries (which share the same type institutional and cultural context). This recommendation is based on value transfer validity tests showing that studies closer spatially tend to have lower transfer errors (see e.g. Lindhjem and Navrud 2008). Studies closest in time should be selected for the same reason. The current practice of using the Consumer Price index (of the country where the policy site is located) is at best a crude approximation of how people’s preferences and values for non-timber benefits of forests change over time (as this good in not included in the basket of goods on which the CPI is calculated). While there are several studies testing transferability in space, only a few studies tests transferability over time; especially over long time periods typical of time horizons for management of forests. The few existing studies of how forest externalities transfer over time show that values are stable over periods of one year or less, but could change drastically over longer time spans of 20 years. This could be due both to changes in preferences, recreational use and availability and quality of substitutes, and last but not least due to the fact that forests mature over time and provide other use and non-use values compared to younger forests (Zandersen et al 2007b). However, one should note that this evidence is not conclusive. Meta-analyses (including also North American studies) could also be consulted, bearing in mind the limitations for value transfer of meta analyses with a broad scope (i.e. too large variation in definition of the environmental good). I am only aware of one meta-analysis of valuation studies of non-timber benefits of forests, covering both use and non-use values. Lindhjem (2007) constructed a spreadsheet database of all non-timber benefits valuation studies in Norway, Sweden and Finland, and used this to perform a meta analysis. Two important conclusions emerged from this study: (1) WTP is found to be insensitive to the size of the forest, casting doubt on the use of simplified WTP/area measures for complex environmental goods; and (2) WTP tends to be higher if people are asked as individuals rather than on behalf of their household. For recreational use values of forests, results from North American meta-analyses of recreational activities (Rosenberger & Loomis 2000, Shrestha & Loomis 2001) and European studies (Bateman & Jones 2003, Zandersen and Tol 2009) can be used. Journal articles and databases of valuation studies often do not have all the data needed for the relevance of the study site to be evaluated, and the full study report should be collected. Thus, existing databases for primary valuation studies can often only be used for screening potential candidate studies for transfer. Then, authors, forest management organisations etc at the study sites of the identified candidate primary studies have to be contacted in order to collect all information needed to judge the “similarity” of the site and population characteristics of these study sites versus the policy site. Due to the failure of existing databases to report results of primary studies in terms of all the determinants/variables needed to conduct a valid value transfer, COST E45 have started to develop a separate, detailed database for non-market environmental goods in forests, and populated it with studies from selected European countries. The determinants of the database have been developed 68

and the studies from Austria, France, Germany and Switzerland have been added; see Elsasser et al (2009) and the website; http://www.bfafh.de/DB_forestvalues.htm

STEP 4 – Assessing the relevance/similarity and quality of study site values for transfer

Here, the quality of the relevant valuation studies is assessed in terms of scientific soundness and richness of information. Desvousges et al. (1998) identify the following criteria for assessing the quality and relevance of candidate studies for transfer: i) Scientific soundness - The transfer estimates are only as good as the methodology and assumptions employed in the original studies -

Sound data collection procedures (for Stated Preference surveys this means either personal interviews, or mail/internet surveys with high response rate (>50 %), and questionnaires based on results from focus groups and pre-tests to test wording and scenarios

-

Sound empirical methodology (i.e. large sample size; adhere to “best practice”guidelines guidelines for SP and RP studies; e.g. Bateman et al (2002) for a manual in Stated Preference studies, and Söderquist and Soutokorva (2006) for a guideline in assessing the quality of both revealed and stated preference primary valuation studies). Specifically for non-timber forest environmental goods; see the best practice guidelines of SP and RP methods developed within COST E45.

-

Consistency with scientific or economic theory (e.g. links exists between endpoints of dose-response functions and the unit used for valuation, statistical techniques employed should be sound; and CV, CM, HP and TC functions should include variables predicted from economic theory to influence valuation)

ii) Relevance - the original studies should be similar and applicable to the “new” context -

Magnitude (and direction) of change in environmental quality should be similar

-

Baseline level of the environmental good should be similar

-

Affected eco-system services and environmental goods should be similar

-

The affected sites should be similar when relevant (e.g. when assessing recreational values)

-

Duration and timing of the impact should be similar

-

Socio-economic characteristics of the affected population should be similar

-

Property rights, culture, institutional setting should be similar

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iii) Richness in detail – the original studies should provide a detailed dataset and accompanying information -

Identify full specification of the primary valuation equations, including precise definitions and units of measurements of all variables, as well as their mean values

-

Explanation of how substitutes (and complementary) goods/sites were treated

-

Data on participation rates and extent of aggregation employed

-

Provision of standard errors and other statistical measures of dispersion

All three criteria and their components are equally important for assessing the relevance and quality of the study. Based on these three criteria, we have developed a check list for judging the similarity of characteristics of the good and population at the study sites versus policy site for forest externality valuation studies: I ) Characteristics of the good i)

Similar good? (i.e. similar type forest, similar use and/or non-use value components; similar recreational activities, similar ecosystem services)

ii)

Similar baseline, size and direction of change in the good valued? (To avoid scaling up and down values according to the size of the area, involving strict assumptions in terms of e.g. constant value per ha of use and/or non-use values; rather consider foreign study sites with nearly similar size than domestic study sites with a very different scale. The same applies to the baseline and the direction of the change. However, the general recommendation is to choose a domestic study site as close as possible geographically)

iii)

Similar availability of substitute sites? (For use values: recreational sites; For non-use values: National parks and other preserved areas and the ecosystem services they contain)

iv)

Similar forestry management regimes ? (Similar property rights, similar access rights to private forests for recreation etc.)

II) Population characteristics i)

Similar average income level (and income distribution)? (If not, income adjustments should be made when performing the value transfer)

ii)

Similar gender, age and educational composition?

iii)

Similar size of affected population? Expected similar distance decay, if any, in non-use values?

iv)

Similar attitudes to forest preservation? (attitudinal and cultural factors)

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STEP 5 – Select and summarize the data available from the study site(s)

Several parallel approaches should be applied, and the results from these should be used to present a range of values: Search the studies to provide low and high estimates, which can define a lower and upper bound for the transferred estimate, respectively. Collect data on the mean estimate and standard error, and specific spatial transfer errors if available. Consult relevant meta-analyses (e.g. Rosenberger et al 2001 for recreational activities, and Lindhjem 2006 for both recreational use and non-use values) to see if the scope of these are narrow enough to provide relevant information about the estimate to be transferred; as a check on the unit value transfer performed. The scope of the meta-analysis could be too wide to produce reliable estimates if the meta-analysis consists of studies which vary a lot in terms of methodology, and the environmental good considered. Compare the magnitude of the value from the meta-analyses, when methodological parameters in the meta-function is set according to the best practice guidelines and a context corresponding to the policy site. Methodological variables in meta-analyses (of CV studies) that reflect best practice guidelines include survey mode (preferable in-person interviews or mail surveys with high response rates), studies should be conducted after the NOAA Panel guidelines to CV (Arrow et al. 1993) (The year of study is often used as a proxy variable for quality in some meta-analyses), similar as possible in magnitude and direction of change, substitutes, characteristics of the population; and a realistic and fair payment vehicle (i.e. not voluntary contribution without a provision point mechanism, and not payment vehicles that create a large degree of protest behavior).

STEP 6 – Transfer value estimate from study site(s) to policy site

a) Determine the transfer unit The recommended units of transfer for use and non-use values are: i) use value: For recreation: Consumer surplus per activity day17 For ecosystem services: WTP/household/year

For recreation, consumer surplus per year (or per visit) per visitor could also be used, but then the average number of activity days (or visits) per year should be the same at the study and policy sites. For some ecosystem services, alternative estimates could be used, i.e. a unit cost per ton of carbon (with a sensitivity analysis) for carbon sequestration if this cost is based on abatement costs (in terms of market price of tradable permits for CO2) of modeled damages e.g. Richard Tol´s FUND – model (see e.g. Tol 2005).

                                                                                                                17

An activity day is defined as one individual performing recreation for a shorter or longer period during one day. 71

ii) non-use value: WTP/household/year18 The use of total WTP per ha ecosystem or landscape type assumes both the same size of the affected population and that the value pr. ha is constant. However, empirical evidence shows that WTP does not increase proportionally with the number of ha. of ecosystems or landscape types (for non-timber benefits of forests; see Lindhjem 2006). Since SP surveys clearly show that WTP per unit of area varies widely, I should caution against converting households´ stated mean WTP for a discrete change in environmental quality to marginal values like WTP pr km or ha per household. However, this unit is ”better” than total WTP per km or ha, because in the latter case one also has to assume similar population density at the policy and study sites.

b) Determine the transfer method for spatial transfer If the policy site is considered to be very close to the study sites in all respects, unit value transfer can be used. If we have got several equally suitable study sites to transfer from, they should all be evaluated and the transferred values calculated to form a value range. For unit transfers between countries, differences in currency, income and cost of living between countries can be corrected for by using Purchase Power Parity (PPP) corrected exchange rates; see e.g. http://www.oecd.org/dataoecd/61/56/1876133.xls. Within a country we could also use unit value transfer with an adjustment for differences in income level, and an income elasticity of WTP lower than 1. Function transfer can be used if value functions have sufficient explanatory power19 and contain variables for which data is readily available at the policy site. Most often the ”best” model is based on variables where new surveys have to be conducted at the policy site to collect data. Then one could just as well perform a full-blown primary valuation study. If models are constructed based on variables for which there exist data at the policy site, they very often have low explanatory power. In general, WTP functions based on Stated Preference surveys (especially Contingent Valuation) usually have much lower explanatory power than functions based on Travel Cost (TC) and Hedonic Price (HP) studies. Thus, it could be more relevant to use function transfer transferring estimates from these Revealed Preference methods.20 If relevant meta-analyses are identified (see previous step), estimates from these could also be used in a comparison of several transfer methods. Sensitivity analysis could be performed to see how much the transferred value estimate could vary. The constructed upper and lower values should be used to bound the transferred estimate.

To conclude, unit value transfer with income adjustment (where necessary) is recommended as the simplest and most transparent way of transfer both within and between countries. This transfer method has in general also been found to be just as reliable as the more complex procedures of value function transfers and meta-analysis. This is mainly due to the low explanatory power of willingness-to-pay (WTP) functions of Stated Preference studies, and the fact that methodological                                                                                                                 18

Some studies of use and non-use values have asked for individual WTP. However, we view the household as the smallest “economic” unit for none-use values of environmental goods in forests. Multiplying individual WTP with the mean number of adults per household would tend to overestimate household WTP. Therefore, we have conservatively assumed that the reported individual WTP is equivalent to household WTP. 19 Roughly said to be having a higher adjusted R2 than 0.5, i.e. explaining more than 50 % of the variation in value 20 This does, however, not mean that we should concentrate on RP studies when we perform new primary studies, as only SP methods are capable of valuing non-use values and future changes in environmental quality. 72

choice, rather than the characteristics of the site and the affected populations, has a large explanatory power in meta-analyses21.

c) Determine the transfer method for temporal transfer The standard approach to adjust the value estimate from the time of data collection to current currency is to use the Consumer Price Index (CPI) for the policy site country. If we transfer values from a study site outside the policy site country, we first convert to local currency in the year of data collection; using PPP corrected exchange rates in the year of data collection, and then use the national CPI to update to current-currency values. However, environmental goods could also increase more or less in value than the goods the CPI is based on, and the increase in value could be very site-specific. Results from Zandersen et al (2007a) shows in a comparison of that recreational value of Vestskoven Forest in Denmark increased 70 times 20 years from one of the least attractive to one of the most attractive sites in the region. The analysis of the other 51 forests shows that this sharp increase in benefits is unmatched in the region. This could be explained by the urban location of Vestskoven, and the fact that this was a newly established forest that have matured and improved in terms of design and shape. The research, however, also showed that behaviour had changed over the time period from far, less frequent and longer visits to closer vicinity visits, more frequent and less long ones. Zandersen et al (2007b) show how updating of benefit function transfer model with information about the transport mode and forest attributes like species diversity and age, will reduce the transfer error of recreational use values from an average of 282 % to 25 %. However, as there are very few studies testing temporal transferability, we recommend using the CPI as an approximation for shorter time periods (less than 10 years) and conduct more research in order to establish a more specific rule for adjustments of preferences for non-timber benefits over time. This temporal adjustment would of course come in addition to the spatial transfer which this 8-step benefit transfer procedure mostly concerns.

STEP 7 - Calculating total benefits or costs For non-use values, mean WTP/household/year is multiplied by the total number of affected households to derive the annual benefit or cost. If WTP at the study site is stated as annual WTP for e.g. 5 or 10 years, the total benefits or costs should be calculated as the Present Value (PV) over that same period. On the other hand, if WTP is stated as one-time amounts the amounts must be viewed as a present value (of all benefits from the environmental good in question).

The general equation for calculation the present value of the benefits PV (B) is: T PV (B) = Σ Bt / (1 + r)t

(3)

t=0                                                                                                                 21

This is partly due to the fact that meta-analyses often lack detailed data on the characteristics of the good, because the primary studies lack these data, 73

where Bt is the total benefits in year t, T is the time horizon (for the stated WTP amounts) and r is the social discount rate (r = 0.03 (3% p.a.) is the social discount rate currently used by the European Commission. Benefits and the discount rate are stated in real terms, i.e. 2006-euro and the discount rate is a real rate of return (i.e. corrected for inflation, and not a nominal rate). If the time horizon is not stated in the WTP question in SP surveys, we must assume that this is an annual payment over an infinite time horizon, i.e. t  ∞ . In this case, and if the annual benefits Bt are the same each year, equation (3) can be simplified to: PV (B) = Bt / r

(4)

Annual benefits Bt are equal to aggregated WTP over the affected population (WTPtot), which can be calculated as: WTPtot = n x WTPi

(5)

where n = number of affected households, and WTPi = mean Willingness–To–Pay for household i. Since WTP per household varies between different parts of the affected population (e.g. with distance from the site, whether users and/or non-users are considered etc.), the estimates from the study site(s) should be based on the same type of affected population as at the policy site. If this is not possible, distance decay in WTP (e.g. percentage reduction in WTP pr km increased distance from the environmental good) could be assumed, based on empirical evidence from relevant study sites (if such evidence does exist and suggests this). If we calculate use values, we just substitute households with individual recreationists in the equation above and use estimates for consumer surplus per activity day times the increase or decrease in number of activity days to calculate total use value of the project. For uses other than recreation, values are often elicited on a household basis, and the same procedure as for non-use values can be employed. When aggregating damages and costs of environmental goods, we also need to consider whether these goods are independent (meaning we can just add them up), or if they are substitutes or complementarities. In the first case we would overestimate aggregated damage or benefits, while in the latter case we would underestimate.

74

STEP 8: Assessment of uncertainty and transfer error / Sensitivity analysis Validity tests of benefit transfer (Navrud 2004) indicate that the transferred economic estimates should be presented with error bounds of + 40 %. However, if the sites are very similar, or the primary study was designed with transfer to sites similar to the policy site in mind, an error bound of + 20 % could be used. If the study and policy sites are not quite close, unit transfer could still be used, but arguments for over- and underestimation in the transfer should be listed and the unit value should be presented with error bounds of + 100 % (based on the observed large variation in individual estimates observed in validity tests. Ready and Navrud (2006) summarize the experience from international validity studies and find that these transfer errors are not different from those observed for transfers within a country. They find that the average transfer error for international benefit transfers tends to be in the range of 20% to 40%, but individual transfers have errors as high as 100–200%. Based on the above studies and the benefit transfer error test literature specifically for forest valuation studies (see Bartczak, Lindhjem and Stenger (2008) for an overview), we have developed 4 categories of how good the fit is between the study site and the policy site. The level of fit is based on the check list for judging the similarity between the study and policy sites in Step 4 of the Guidelines. Each category has a corresponding approximate transfer error that should be used to perform sensitivity analysis when conducting unit value transfer; see table 1 below. It is important to note that these transfer errors have to be added to the uncertainty in the primary studies due to sampling procedures, survey mode, valuation methods etc.

Table 7 Four categories of how similar the primary study (study site) is to the policy site (that we would like to transfer values to), and corresponding approximate transfer errors when performing unit value transfer. These indicative transfer errors are based on a re view of transfer errors from the benefit transfer validity test literature. The judgment of similarity should be based on the check list of site and population characteristics presented in Step 4 of the Guidelines.

Category

Level of fit between primary study and policy site

Percentage transfer error

1

Very good fit

+ 20

2

Good fit

+ 50

3

Poor fit

+ 100

4

Very poor fit

Discard primary study for unit value transfer (Meta analysis is the only option)

75

(%)

The transfer errors in the table above refer to the mean WTP estimate, and would come in addition to the inherent uncertainty of the valuation methods applied in the primary valuation study at the study site. The uncertainty about the size of the affected population would also have to be added to the estimate of total benefits. As the size of the affected population is equally important for calculating the total benefit estimate, sensitivity analyses should also be conducted for the size of the affected population. If there is evidence of distance decay in WTP in the primary study that one think could be transferred to the policy site, sensitivity analysis with WTP and population estimates for each distance zone should be performed (see Bateman et al 2009a; Box 21 for an example). When performing a Cost-benefit analysis of a new project or policy, the estimated Present Value (PV) of benefits (costs) should be compared with the corresponding PV of costs (benefits). The effect on total annual benefits (costs) of the expected transfer error (from table 1) should be evaluated in order to see if this reduces the PV of benefits (increases the costs) to a critical level; meaning that the PV of net benefits becomes negative (from positive). If this is the case, the transfer errors are large enough to change the outcome of our CBA, and we should try to increase the accuracy of the transferred estimate (either by conducting a full primary study or calibrating the transferred value by conducting a small scale primary study) When there is a need for estimates of environmental goods for policy purposes, a CBA of conducting a new environmental valuation study should be performed in order to determine whether the costs of a new primary study is worth the benefits in terms of lower probability of making the wrong decision. These decision rules could be used as a rough test of whether value transfer has acceptable transfer errors.

4. DATA BASES AND META-ANALYSES OF VALUATION STUDIES OF FOREST EXTERNALITIES The Environmental Valuation Reference Inventory (EVRI) www.evri.ca is the largest database for environmental valuation studies, and currently contains more than 2000 studies but not all are primary valuation studies). The EVRI Search Protocol could have been better adapted to search for environmental valuation studies related to forests. Also, EVRI does not require input of the detailed needed to do a complete judgment of “similarities” in the characteristics of the good, site and population which is needed in this 8-step procedure for benefit transfer. Since the instructions in EVRI on how the results should be reported is rather general, not all of the valuation studies on non-timber benefits contain all the data needed to conduct e.g. transfers based on benefit functions or to include the study in a meta analysis. Thus, to use EVRI for our purpose one would be to update all records of valuation studies of non-timber benefits in the database with the information considered necessary to perform transfers of these studies (and add new valuation studies). The main advantage of EVRI is that it is an existing, web-based database, that is being updated continuously (as opposed to e.g. other “sleeping” databases like ENVALUE). The disadvantage is that you have to subscribe to it (for a fee) to get access, unless your country subscribes to it and provides access free to all citizens (These countries currently include: Canada, USA, France, United Kingdom, Australia and New Zealand). The COST Action E45 could contact Environment Canada (which operates EVRI) and try to negotiate an agreement where we register/capture more forest externality valuation studies in EVRI (and more detailed information 76

about those already in EVRI), and in return the members of COST Action E45 would gain free access to the database. The other option is to develop a new database with a better search engine than EVRI and much more detailed information about each primary study forest valuation study; see Elsasser et al (2009) and the website: http://www.bfafh.de/DB_forestvalues.htm for a first attempt in this direction. There are relatively few meta-analyses of non-timber benefits studies, and most of them are on recreational use values, and contains mostly TC studies22. Rosenberger and Loomis (2003) and Shrestha and Loomis (2003) analyze US studies, Bateman and Jones (2003 consider UK studies, and Zandersen and Tol (2009) analyze 25 studies in 9 countries. Scarpa et al (2007), however, test the validity of transferring WTP values from similar CV studies in 26 recreational forests in Ireland (by conducting benefit function transfer from a meta analysis of 25 forests to predict WTP in the 26th forest, and comparing it to the original CV result). To our knowledge, Lindhjem (2007) is the only meta-analysis which considers both use and nonuse values of forests, and also contains the growing number of Contingent Valuation, and Choice modeling applied to forests. Lindhjem op. cit considers valuation studies in Norway, Sweden and Finland only. Lindhjem and Navrud (2008) test the validity of transfers based on this meta-analysis, and find that even under conditions of homogeneity in valuation method, and cultural and institutional conditions across countries, and a meta–analysis of high explanatory power, transfer errors could still be large. Further, Lindhjem and Navrud op. cit. find that international metaanalytical transfers for use and non-use values of forests do on average not perform better than simple unit transfers; averaging over domestic studies. This questions the validity of using metaanalysis for transfer of non-timber forest benefit estimates, but it still premature to discard metaanalysis for benefit transfer. Meta-analysis could be used to check whether the estimate from simple unit transfer falls within the range of values predicted from the meta-analysis. If it does, this would add to the reliability of the transferred estimate as two BT techniques then provide the same order of magnitude estimates. Benefit function transfer from the study site we transfer from in the simple unit value transfer could also be used as a validity check; but only in those cases where the benefit function has high explanatory power (see also Bateman et al 2009b). Based on the existing valuation studies of forests in a country (or groups of countries with similar type institutional, cultural, attitudes towards and use of forests) one could perform meta analyses e.g. using the same type of approach as Lindhjem (2007). If possible, one should try to limit the scope of the meta-analyses to studies using approximately the same type of methodology. This would avoid that characteristics of the valuation methods and models explain most of the variation in valuation estimates, which would then be dominated by characteristics of the site and the population, which is much more helpful in using the meta-analysis results for function transfers. As the primary studies often lacks data on site and population characteristics, a major improvement in BT of forest externalities would be to allocate resources to identify these characteristics which would greatly improve meta-analyses. It would also improve the recommended benefit transfer technique of unit value transfer by making it simpler to determine the degree of similarity between study sites and the policy site analyzed.

                                                                                                                22

Hedonic Price (HP) studies of forests also consider mainly use values in terms of recreational use and landscape aesthetic value; see e.g. Tyrväinen and Miettinen (2000) and Birr-Pedersen (2006), but no meta-analysis of the few existing HP studies of forest has been conducted. 77

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