1. Chapter 12 Valuing the environment In this chapter you will  learn about the categories of economic value assigned to the natural environment, and the distinction between use and non-use values work through the utility theory on which environmental valuation techniques are based learn how Contingent Valuation uses individuals’ responses to hypothetical questions to infer use and non-use values  find out about the technique of Choice Modelling  find out how the Travel Cost method uses data on actual behaviour to infer use value  learn about Hedonic Price method  be introduced to valuation methods that are based on production function analysis 12.1 Categories of environmental benefits 12.2 The theory of environmental valuation 12.3 Contingent valuation 12.4 Choice experiments 12.5 The travel cost method 12.6 Hedonic pricing 12.7 Production function-based techniques

2. The travel cost method The TC method is a revealed preference technique for estimating use values, the recreational benefits of environmental resources – national parks, forests, reserves, fishing and hunting sites Travel costs incurred visiting a site vary across visitors It is assumed that visitors react to variations in travel costs as they would to admission fee variation TC suggested in 1947 by Hotelling First application in 1959 by Clawson TC assumes weak complementarity – if site too expensive for an individual and no visits, changes in the condition and availability of the site do not affect the individual’s utility. An individual may have non-use values regarding a site not visited. TC cannot elicit such. TC can be used to estimate value of changes in quality of a site

3. TC – theoretical basis u – utility x – consumption of composite commodity r – visits to a recreational site q – site quality m – non-labour income w – wage rate t w – time spent working c – round trip cost of visit Maximise 12.23 subject to and 12.24 12.25 Substituting 12.25 in 12.24 gives Define the price of a trip as The first order condition is where λ is the marginal utility of money 12.26 12.27 12.28 12.29 is the demand function for visits NOTE - the theory does not imply any particular functional form for this demand function

4. TC – zonal model 1. Define a set of i concentric zones surrounding the recreational site of interest. 2. Collect information on the annual number of visitors from each zone by means of a survey. 3. Calculate the visitation rate by dividing the number of visits arising from each zone by the population of that zone. 4. Using a standard value per unit distance travelled and a standard value per unit of time calculate the return trip travel cost from each zone. 5. Estimate a regression equation linking the visitation rate (v) to travel costs (c) i.e. 12.30 6. Use the equation to predict visitation rates with different hypothetical entrance fees e.g. starting with £10 12.31 7. Calculate total visitor numbers by multiplying the predicted visitation rate by the zonal population and then sum across all i zones yielding a point on a demand curve. 8. Employ the same procedure to evaluate the effect of imposing various other hypothetical admission charges e.g. £20, £30, £40 and £50 etc to identify additional points on the demand curve. 9. The final step is to estimate the total economic benefit of the site by calculating the area under the demand curve.

5. TC –Box 12.5 An illustrative ZTC model example: 1 ZoneVisitsPopulation (thousands) Round-trip distance (miles) 115000 200010 248000 800015 311250 250020 4450001500025 5340002266030 Estimate the parameters of the trip generating function 12.33 where V i is ith zone trips per thousand population and C i is the cost of a trip from zone i. OLS gives 12.34 Assuming the same reaction to an admission price P, 12.34 can be written as 12.35 and used to simulate the effects of changes in P. Travel cost per mile £1

6. TC – Box 12.5 An illustrative ZTC model example: 2 Basic visitor data for a national park with no admission charge are as follows: ZoneVisitsPopulation (thousands) Round-trip distance (miles) 115000 200010 248000 800015 311250 250020 4450001500025 5340002266030 115612000 2204.536000 3253 7500 4301.522500 5350 0 ZoneC i + PViVi V i x Population (thousands) Total 78000 The third step is to obtain from this data an estimate of consumers’ surplus for the year. Given that in fact P = 0, consumers’ surplus is the total area under this demand function, which is: + = £1,061,875. For P = £5, estimated trip generating function gives Working through P = £10 and so on P (£) Total Visits 0153250 578000 1036750 1518000 203000 250

7. TC – Box 12.5 An illustrative ZTC model example: 3 The third step is to obtain from this data an estimate of consumers’ surplus for the year. Given that in fact P = 0, consumers’ surplus is the total area under this surrogate demand function, which is: + + + + = £1 061 875

8. TC – generic shortcomings In most TC applications travel costs are ‘researcher assigned travel cost estimates’ and so ‘welfare estimates remain artefacts of the travel cost accounting and specification conventions selected for imposition’. (Randall 1994 – who argues for the use of costs as perceived by visitors) The determination of the opportunity cost of time spent travelling – the theoretical TC model assumes that work and travel time confer negative utility and that individuals can choose how much they work. This would value travel time at the wage rate. But these assumptions are not valid. In much TC work arbitrary rules are used to value travel time. The assignment of costs to the components of multi-purpose trips is problematic. Substitute sites – a properly specified demand function for site i would include as arguments the prices of all substitute sites. This is rarely done.

9. TC – the individual travel cost model, ITC ITC model uses data generated by a survey of individuals either at a site or in their home. GIS software is used to determine distance to site, which can be done for substitute sites. Respondents asked about number of visits, socio-economic characteristics. 12.36 where n = {0, 1, 2.....} and r is the number of trips made by i. With λ as an exponential function of travel costs, c 12.37 the demand curve for the site is 12.38 Because number of trips must be a non- negative integer ITC model is estimated using a Poisson model for which Probability Density Function is Consumer surplus for i is given by 12.39 with definite integral 12.40 which for β 1 negative is 12.41 Average CS per respondent is multiplied by the number of individuals living in the area surveyed to determine the site’s currrent recreational value.

10. TC – Box 12.6 The value of recreational fishing VariableCoefficientStandard Error TCOST-0.001850.00047 TTIME-0.03030.00916 CONSTANT1.520.454 Pseudo R 2 0.57 Shrestha et al (2002) conduct an ITC analysis to estimate the value of recreational fishing in the Brazilian Pantanal. A survey was conducted of recreational anglers whilst they were weighing their catches at the mandatory weighing stations. Individuals were asked about the number of trips TRIPS taken in the past 12 months along with their round trip financial cost TCOST in US dollars and round trip travel time TTIME in hours. Note that this study is unusual in that following the advice of Randall the authors rely on respondents’ perceptions. 286 questionnaires were completed. Because the interviews were conducted onsite the number of visits in the last twelve months is a strictly positive integer and the regression results displayed in the Table here were obtained using a truncated Poisson model. Both TCOST and TTIME are negative and statistically significant. The,unusual, use of perceived travel cost and perceived travel time enables the authors to estimate the perceived value of time. Comparing the coefficients on TCOST and TTIME the implied value of travel time is $16.39 per hour. The CS per trip is given by the inverse of the coefficient on the travel cost variable. This evaluates to $540.54 per trip or $86.35 per day given the average trip length. Multiplying the number of recreational anglers by the mean number of trips gives 64,860 trips per year. The recreational value of the site to anglers is therefore $35,059,424 per annum.

11. TC – The pooled travel cost model Some projects requiring evaluation concern changes in quality – cleaning up a fishing lake for example. Given strong assumptions, the pooled travel cost model can be used to estimate the value of changes in site quality It combines travel cost data from sites with differing quality levels – angling success rates, water turbidity, water area etc With r ij for trips to site j by i c ij for travel cost q j for site quality the PTC model assumes 12.42 Problems – the generic shortcomings, plus neglects substitute sites assumes number visiting given site unaffected by changes at any other site

12. TC - Random utility models: 1 The RUM considers choices on particular occasions and can value site characteristics and substitution between sites If the ith individual visits site j = g, then 12.43 where v is utility. If the error term follows a type 1 extreme value distribution, using the Conditional Logit framework the probability of i choosing g is 12.44 With a linear functional form for the indirect utility function so that 12.45 12.46 where m is income, c is travel cost, and q is site quality

13. TC – Random utility models: 2 The consumer surplus for a change in site quality is given by The consumer surplus for adding a site is given by 12.47 12.48 The Independence of Irrelevant Alternatives assumption – for the Conditional Logit model, eliminating a site would cause visitors to re-distribute across the remaining sites so as to leave relative probabilities of visiting those sites unchanged. This is often an unreasonable assumption.

14. TC – Box 12.7 Valuing deer-hunting ecosystem services Kuchi and Lupi (2007) use RUM to value ecosystem services to deer hunting in Michigan. 1955 deer hunting licence holders responded to a survey which asked about trips to most regularly visited site within 50 miles of home, and more than 50 miles away.Trips during the firearm and archery seasons were distinguished. Travel costs were assigned by the researchers using the same cost per mile across all respondents. Time was costed at 1/3 rd of respondent’s hourly wage, and time taken calculated by assuming average trip speed of 40 mph. FirearmsArchery PRICE-0.033<0.001-0.039<0.001 DEER0.130<0.0010.068<0.001 ACCESS0.056<0.0010.072<0.001 PEOPLE-0.102<0.001-0.063<0.001 SIZE-0.449<0.001-0.2840.014 UP2.329<0.0011.664<0.001 NLP0.490<0.0010.490<0.001 No. Obs.1416 685 VariableCoefficien t P-ValueCoefficien t P-Value Pseudo-R 2 0.347 0.405 Firearm$1.91$8,754,000 Archery$2.23$10,250,000 Per Trip Benefit Aggregate Welfare Total $19,004,000 Table 12.11 Results for the Conditional Logit modelTable 12.12 Welfare benefits of a 10 percent change in and accessible to hunters The 10% increase in hunting land would yield $39 per acre, whereas the Michigan Hunter Access Program pays farmers $5.55 per acre, and the value of agricultural output is $443 per acre.

15. Hedonic pricing The hedonic price - HP - method is widely-used revealed preference valuation technique. HP is usually, but not exclusively, applied to the property market within which many environmental goods are implicitly traded. Households reveal their preferences for these goods through their decision about where to locate. HP has been widely used to value household preferences for noise nuisance, air quality, physical separation from locally-undesirable land uses and the value of a statistical life. The first formal characterisation of the ‘hedonic price function’ was provided by Rosen (1974) building on Lancaster’s characteristics theory of value (Lancaster, 1966). The hedonic price function describes the price of a quality-differentiated commodity in terms of its quality attributes

16. HP – Theoretical model 1 The hedonic price function describes the price of a quality-differentiated commodity in terms of its quality attributes. In fact the hedonic price function is the double envelope of buyers’ bid functions and sellers’ offer curves. The precise shape of the hedonic price function displayed in figure 12.2 is determined by aggregate supply and demand for differing levels of the quality attributes within the market. Bid curves show all those combinations of prices and levels of the quality attribute that leave the buyer at the same level of utility. The slope of the bid function represents the maximum amount of money that the individual is WTP for an extra unit of the quality attribute. Offer curves show all those combinations of prices and levels of the quality attribute that leave sellers with the same profit.

17. HP – Theoretical model 2 Let h be the price of housing, q 1, q 2,...,q n its characteristics and ε a random error term to represent the non-quantifiable aspects. The hedonic price function is Taking the derivative of the hedonic price function with respect to the j th characteristic yields the implicit price function for that characteristic 12.49 12.50 Let the household maximise its utility u, which depends upon the household’s consumption of a composite good x, and the level of the housing attributes q. This maximisation problem is subject to a constraint linking the household’s income y, consumption of the composite good (whose price is normalised at unity) and the price of housing h, which is a function of housing quality. The Lagrangian associated with the maximisation problem is 12.51

18. HP – Theoretical model 3 The first order conditions indicate that the household’s marginal willingness to pay, MWTP, for the environmental attribute is equal to the derivative of the hedonic price function evaluated at the household’s chosen location. Households can be thought of as moving along their demand curves for the environmental commodity until the unit price p j of environmental quality is just equal to the MWTP. This is illustrated in Figure 12.3. For the Lagrangian 12.51 12.52 Evaluating this function at the attribute levels defining the household’s chosen location yields the household’s MWTP for this environmental characteristic.

19. HP – Theoretical model 4 Since we are frequently interested in evaluating non- marginal changes, there is a second stage to the HP technique that attempts to identify the household’s demand curve for housing quality. The second stage involves relating the quantity of the j th characteristic consumed by household i to the implicit price of the amenities, the household’s income, y, and other socioeconomic characteristics of the household 12.53 Early researchers erroneously believed that they could estimate this demand curve from the information provided by the implicit price functions taken from one market. In fact, the only way we can identify the demand curve is to have observations on households with the same socio-economic characteristics facing different implicit prices, such as might prevail in different cities or time periods as displayed in Figure 12.4. For an example of a study that attempts the second stage regression see Day et al (2007).

20. HP – Empirical implementation The first step in conducting a hedonic house price analysis involves collecting information on the sale price of individual properties Whenever the dependent variable refers to sale prices rather than rental prices the MWTP for housing attributes corresponds to the present value of future benefits. When sale prices are used rather than rental values the implicit price of environmental quality immediately reflects any anticipated change in environmental quality. For example, one would not expect to see a compensating price differential for a house situated in an area at risk of flooding if it had already been announced that a new flood relief project would soon entirely eliminate the risk of flooding. One good reason for analysing house price data rather than data on rental values is that in some countries (e.g. Germany) rents are highly regulated and may not reflect market conditions. A hedonic house price study will have to control for the characteristics of property. This would be unnecessary if data on the price of bare land were available. In addition to house prices therefore, the researcher also needs to record all relevant characteristics of the property itself e.g. the age of the property, type of property, length of the lease, presence of central heating, availability of off-street parking etc. Another important set of variables relates to accessibility e.g. distance to the nearest bus stop or train station, town centre, school, shopping centre etc. Also included would be the characteristics of the neighbourhood e.g. the rate of unemployment and the crime rate. Last but not least one would include the environmental characteristics of the property such as night time noise levels, ambient air quality, distance to the nearest landfill site etc. Despite the inclusion of a large number of regressors hedonic regressions are frequently able to explain only a small fraction of the variation in house prices implying that important yet difficult to quantify characteristics have been omitted. Examples include the physical attractiveness of the building, the odour arising from nearby industrial activities and the quality of the view.

21. HP – underlying assumptions Numerous assumptions underpin the HP method. The first is that there should be perfect information regarding the price and attributes of different properties. Without such information it is difficult to argue that the household has positioned itself at the point where household MWTP for every environmental amenity of interest is equal to the implicit price. But since a house is usually the single largest acquisition a household makes it has every reason to become well informed prior to the purchase decision. If the household is out of equilibrium HP assumes that it can without impediment move to another, more preferred location. However, if transaction costs are sufficiently high then they may outweigh the benefits of moving. Transaction costs include the time spent searching for properties, expenses on estate agents, lawyers and surveyors, stamp duty and removal costs. It is necessary that the levels of all attributes vary continuously otherwise there might be a discontinuity in the implicit price function, and the location of the household might not reveal the household’s true MWTP for that amenity. Problems could arise if house price data are drawn from geographically or temporally distinct markets. One could potentially obtain very different hedonic price functions, as in Figure 12.5. The resulting regression could yield biased estimates of the household’s MWTP for key housing attributes. At the same, time market segmentation could aid identification of household demand functions.

22. HP- Econometric issues Numerous researchers have attempted to measure households’ preferences for air quality and the avoidance of noise pollution using the HP technique. Unfortunately both air pollution and noise nuisance are both generated by proximity to traffic. Consequently it can be hard to attribute correctly the observed variation in house prices to one disamenity rather than the other. Equally one should be suspicious of hedonic house price studies claiming to value marginal changes in noise nuisance whilst omitting ambient air pollution from the hedonic price regression. The problem here is one of either multicollinearity or omitted variable bias. A tension exists between the tractability of the functional form selected for the hedonic price equation and the ability of the regression equation to approximate the ‘true’ hedonic price function. It would be a mistake to view the appropriate functional form as a purely empirical matter. For example, if the price of property was anything other than proportional to plot size this would create an incentive for property owners to ‘repackage’ their properties into larger or smaller units. In fact such repackaging is commonplace in the property market and researchers often select price per square metre as the dependent variable in their hedonic price regression on the assumption that any opportunities for repackaging have been exhausted. One of the implications of a nonlinear functional form for the hedonic price function is that the implicit price of housing quality depends on the quantity consumed. Put another way, the prices of key housing attributes are no longer parametric to the individual. This causes the problem of endogeneity in the second stage hedonic regression leading to possible bias and inconsistency. Not all amenities of interest to households can be captured by the analyst and are instead consigned to the error term. Insofar as these omitted amenities are more similar for neighbouring properties there is likely to be ‘spatial autocorrelation’ in the residuals. Researchers can obtain more efficient estimates of the parameters of the hedonic price function by adopting an estimator that anticipates the correlation between the residuals of observations corresponding to neighbouring properties.

23. Box 12.8 Valuing improvements in air quality in Los Angeles 1 ( The dependent variable is the log of sale price. The independent variables are Housing structure Accessibility Sale date +ve Distance to beach -ve Age -ve Distance to employment -ve Living area +ve Bathrooms +ve Air pollution Pool +ve Total suspended particulates -ve Fireplaces +ve Nitrogen Oxide -ve Neighbourhood in separate regressions – coefficient signs same in both Crime, Log of -ve Except for crime, all estimated coefficients significant at 1% School Quality +ve Ethnic composition(% white) +ve R 2 = 0.89 in both regressions Housing density -ve For an improvement from ‘poor’ to ‘fair’ air quality, rent Public safety expenditures +ve differential $15.44 to $45.93 per month, from ‘fair’ to ‘good’ $33.17 to $128.46 (1978 prices). Higher differential associated with higher income community Brookshire et al estimated property price differentials associated with air quality improvements. Used sample of 634 sales of family homes Jan 77 to March 78. From Table 12.13 Results from hedonic house price regression

24. HP – the value of statistical life 1 Many environmental projects involve small changes in the probability of premature mortality for unidentified members of the community. Consider a scheme intended to cut emissions of harmful particulate matter in urban areas. In order to conduct an environmental CBA of such schemes a monetary value must be attached to the resulting change in the risk of premature mortality. The value of statistical life (VOSL) is the willingness to pay to avoid a 1 in N chance of premature mortality aggregated over N individuals, and where N is a very large number. The VOSL indicates how an individual would behave when confronted by an infinitesimal change in the probability of their premature death. It does not mean that the individual would accept an amount equal to the VOSL in exchange for immediate death. The VOSL moreover, is for the death of an anonymous individual. Originally the VOSL was calculated from the expected lifetime earnings of individuals. Theory suggests that the VOSL should exceed expected lifetime earnings so such estimates provide only a lower bound. The VOSL can also be determined using a variety of approaches to non-market valuation, most obviously CV. A sample of individuals might be asked about their WTP to reduce the risk of premature mortality by 1 in 10,000 and the average response multiplied by 10,000 to obtain the VOSL. Individuals find it very difficult to answer questions involving (a) very small probabilities and (b) their own mortality. The hedonic technique can be extended to the labour market to determine the implicit value that individuals place on marginal changes in the risk of premature mortality. The technique takes advantage of the fact that the benefits and disbenefits of particular occupations, including the risk of work-related mortality, are capitalised into wage rates.

25. HP – the value of statistical life 2 Empirical implementation of the hedonic wage technique entails analysing wages paid to a sample of workers engaged in different occupations across which the risk of a work-related fatality varies. The wage associated with each occupation is a function of both the characteristics of the worker and the characteristics of the job. This explains why occupations offering the highest remuneration often involve working in a comfortable office environment rather than down a coal mine or on a building site. The characteristics of the worker include their human capital proxied by years of education, years of work experience, ethnicity, age, and gender. The key job characteristic is the occupation-specific risk of death through a work-related accident. Given that fatal accidents at work are rare the risk of mortality should be estimated from multiple years of health and safety data. One ought also to include the risk of a serious yet non-fatal accident. Researchers however sometimes express the view that what matters more than the actuarial risk is workers’ own perceptions of the risk of a fatal accident. Furthermore, some occupations present latent risks arising from exposure to chemicals in the work place. Assuming that the periods over which remuneration is measured and the risk of death is calculated are identical differentiating the hedonic wage function and evaluating the derivative at the chosen risk level of the individual worker reveals that worker’s implicit MWTP for risk reduction (i.e. the VOSL).

26. Box 12.9 The reward for risk in the labour market Parameter (T-statistic) Parameter (T-statistic) CONSTANT1.9865 (26.09) 1.9433 (25.44) S0.0572 (23.63) 0.0583 (24.14) EX0.0466 (26.09) 0.0462 (34.42) EX 2 -0.0008 (30.79) -0.0008 (30.58) Log WEEKS1.1296 (61.43) 1.1307 (61.69) GENRISK0.0128 (2.10) ACCRISK 0.3663 (3.95) UNION0.0186 (7.58) 0.0020 (8.18) OCC0.0076 (21.44) 0.0081 (22.12) UNION x GENRISK -0.0004 (2.52) UNION x ACCRISK -0.0046 (1.77) R-squared0.57 No. Obs.5,4645,509 Table 12.14 Results from the hedonic wage rate regression 12.59 12.60 Marin and Psacharopoulos 1982 look at 233 occupational groups. Regression dependent variable is the log of annual earnings. 12.61 Using the results from the ACCRISK regression

27. Production function based techniques We now consider valuation techniques in which the level of environmental quality is an argument in firms’ production functions. We consider the welfare impact of both marginal and non-marginal changes in the level of environmental quality. The firm minimises wz where w is the price of inputs and z is the quantity of inputs. This is subject to the production function constraint q is output and e is environmental quality, and dq/de>0. This results in a series of conditional input demands for z. The optimised value of the function is the cost function c(w,q,e). The contribution to social welfare arising from the production and consumption of the good is given by Taking the derivative of this expression with respect to the level of environmental quality gives This says that the value of a marginal improvement in environmental quality is equal to the negative of the change in production costs. This is equivalent to multiplying the change in production by the price of output In the case of non-marginal changes in the level of environmental quality the change in welfare is given by e 0 is the initial level of environmental quality and e 1 is the final level. q 0 is initial output and q 1 is final output. Equation 12.66 makes it clear that in order to compute the welfare impact of non-marginal changes it is necessary to know the effect of the change in production costs on the equilibrium level of output. 12.62 12.63 12.64 12.65 12.66

28. Box 12.10 The value of wetlands to the blue crab fishery of the Gulf Coast ElasticityOptimal Management ($) Open Access ($) 0.5248,009269,436 10116,46447,898 The estuarine of the Gulf Coast of Florida provide blue crabs with spawning grounds habitat and food. Ellis and Fisher(1987) investigate the value of the wetland to the blue crab industry 1972-75. With an iso-elastic demand function and a Cobb Douglas production function the industry cost function is with marginal cost of effort where Q for quantity, P for price, T for effort (traps), L for wetland area, C for cost. This system can be solved for P and Q given L, and so changes in total surplus conditional on L found. 12.67 12.68 12.69 12.70 Freeman (1981) pointed out that the Ellis and Fisher analysis assumes an optimally managed fishery, whereas it was open access.In that case effort expands until 12.71 The change in total surplus is given by 12.72 For assumed elasticities, the optimal and open access results are Table 12.15 The value to the blue crab fishery of changes in wetland area

29. Purposes of environmental valuation Inclusion of environmental impacts in cost benefit analysis of projects/policies Determination of targets for environmental quality standards Accounting for environment impacts in measuring national economic performance In the USA, fixing compensation by those the courts hold responsible for environmental damage

30. Categories of environmental benefits UseNon-use Consumptive Non-consumptive Existence Altruistic Bequest Revealed preference methodsStated preference methods Indirect use – carbon fixation, micro-climate regulation Total economic value

31. Environmental valuation theory 1 Assume that quantity/quality of environmental good e can be treated as an argument in a well-behaved utility function. The individual cannot choose level of e. y is income. u = u(y, e) e0e0 e1e1 At A, WTP for e improvement = BC is Compensating Surplus, CS At A, WTA in lieu of e improvement = DA is Equivalent Surplus, ES e1e1 e0e0 At B, WTP to avoid deterioration = BC is Equivalent Surplus, ES At B, WTA compensation for deterioration = DA, Compensating Surplus, CS

32. Environmental valuation theory 2 CSES ImprovementWTP for the change occurring WTA compensation for the change not occurring DeteriorationWTA compensation for the change occurring WTP for the change not to occur Table 12.1 Monetary measures for environmental quality changes WTP Willingness to pay WTA Willingness to accept CS Compensating surplus ES Equivalent surplus

33. Contingent valuation 1 CV is a survey stated preference technique CV can measure both Use and Non-use values CV provides theoretically correct WTP and WTA measures of utility change CV is the most widely used valuation technique Hundreds of applications – air and water quality improvement preservation benefits of wilderness benefits of outdoor recreation opportunities benefits of reduced transport risks benefits of improvements in public utility reliability environmental damages

34. Contingent valuation 2 The steps involved in conducting a CV study can be stated as follows: 1.Creating a survey instrument (i.e. questionnaire). This can itself be broken down into a number of tasks including (a)identifying possible uses of and attitudes towards the environmental good in question, (b) constructing the hypothetical scenario, (c) deciding whether to ask about WTP or WTA, (d) determining an appropriate payment vehicle, (e) selecting an appropriate elicitation method (f) collecting auxiliary information about the respondent. 2. Choosing an appropriate survey technique. 3. Identifying the population of interest and developing a sampling strategy. 4. Analysing the responses to the survey. 5. Aggregating the WTP or WTA responses over the population of interest. 6. Evaluating ex-post the success (or otherwise) of the CV exercise.

35. CV - Creating the survey instrument A typical CV survey would include the following parts 1.Explanation of the purpose of the exercise 2. Questions about respondent’s knowledge and attitudes 3. Description of problem 4. Description of project to address problem 5. Statement of payment vehicle 6.Reminders about substitutes and income constraints 7. Ask about WTP (usually) via one of elicitation methods Open ended question Bidding game Payment ladder Referendum – single bounded dichotomous choice Referendum – double bounded dichotomous choice 8. Follow-up questions ( to identify protest bids etc) 9. Questions about respondent characteristics

36. CV – elicitation methods Open ended : ask ‘what is your maximum WTP for...?’ Avoids giving respondents cues, but difficult for them Bidding game : the respondent is asked if WTP a sequence of increasing amounts until says ‘no’. Anchoring? Payment ladder : the respondent is asked to tick amount would be WTP, cross amounts not WTP. Anchoring? Single bounded dichotomous choice : tell respondent if referendum supports project it goes ahead and costs each $x, which is varied across respondents. Incentive compatible. Easy to understand. Large sample needed. Double bounded dichotomous choice : if respondent says ‘yes’ to $x, $x + $y? If ‘no’ to $x, $x - $y?. Is statistically more efficient than single bounded, which is important given costs of survey. Different methods produce different results Pre-testing

37. CV - Choice of survey method There are three broad options for distributing the survey questionnaire. Conducting face to face interviews is extremely expensive but offers several potential advantages, notably a high response rate. Face to face surveys can be conducted either in the respondent’s home or in some other location such as the site being valued. Mail surveys are much more economical, but get far lower response rates and tend to limit the amount of information that can be provided and the number of questions that can be asked. There is no control over the identity of the respondent or over the sequencing of questions. There is also the problem of self-selection bias (only those with strong feelings tend to reply to unsolicited mail-shots). Telephone interviewing is cheap, but restricts the information that can be provided. Nevertheless these techniques are not mutually exclusive and questionnaires can be mailed out to respondents who are subsequently contacted by telephone. Which of these methods is to be used will clearly influence the design of the survey questionnaire. And in some circumstances one or more of these methods might be infeasible. For example, in CV experiments where the provision of photographic material is necessary telephone surveys cannot be used.

38. CV - Sampling Identify the population from which the sample is to be drawn - the group liable to be affected by a change in the level of provision of the environmental good. For use values the population from which one should draw a sample is already well defined. For example, if we wished to assess the use value of improved water quality in a lake an obvious user group would be the population of anglers using the lake. Sampling from a much larger population e.g. all households within a certain radius of the site, would permit researchers to assess non-use as well as use values. For some non-use values e.g. those relating to tropical forests, the candidate population is potentially the whole global population. The required sample size will be determined by a number of considerations including the statistical precision required and the financial cost of the surveys. Some elicitation methods can generate a high number of refusals whilst others like the double bounded dichotomous choice garner more information than others. Frequently the sample of respondents is split in order to examine whether a differing treatment, e.g. in terms of the information provided or the scope of the project, affects the WTP estimates that emerge increasing further the minimum sample size. It is vital that the sample is representative of the target population. All members of the relevant population should have an equal probability of being included in the sample.

39. CV – analysing responses The statistics of interest are the mean and the median WTP/WTA. Usually estimated after elimination of protest, and other problematic, bids Appropriate method for analysis depends on how responses generated – elicitation method For open-ended questions use a spread sheet For referendum dichotomous choice questions Parametric Estimation Random Utility Model RUM Random Expenditure Function REF Non-parametric Estimation The Turnbull estimator

40. CV – Random utility model Assumes ‘yes’ vote if utility from environmental improvement plus tax payment is greater than from ‘no’ and status quo. If 12.1 Where v is the individual’s (linear) indirect utility function, y is the respondent’s income, t is the tax level, x is a dummy variable which takes the value unity if the environmental good is provided and is zero otherwise, and ε is an independent and identically distributed random error term. If ε follows a Gumbel distribution then the probability of an individual saying “Yes” is 12.2 which is the Logit model. Assuming ε normally distributed leads to the Probit model. In either case α and β can be estimated using a standard statistical package The parameter α represents the extra utility generated by the provision of the environmental good whereas the parameter β represents the (negative of the) marginal utility of income. The coefficient on the bid variable t ought to be negative indicating that as the implied tax liability increases the probability of saying “Yes” will decline whereas the constant term parameter α should be positive. Irrespective of whether the data has been analysed using the Logit or the Probit model mean (and median) WTP is given by 12.3

41. CV - Random expenditure function 1 Assumes respondent votes ‘yes’ when their WTP exceeds the tax level. That is votes ‘yes’ when 12.4 Selecting a particular functional form for the WTP function we might choose 12.5 Where α is a parameter whose value we wish to estimate. The calculations in the random expenditure function approach can be undertaken using either the Logit or the Probit model. The probability that this function exceeds the tax level is given by 12.6 Assuming ε is normally distributed with zero mean we can transform the model by dividing through by the standard error 12.7 where F is the standard normal cumulative distribution function. Note that the coefficient on ln(t) is 1/σ. The objective is to estimate the parameters α and σ.

42. CV – Random expenditure function 2 12.7 The objective is to estimate the parameters α and σ. The first of these can be obtained by dividing the coefficient on the constant term in the Probit regression by the coefficient on ln(t). At this point the similarity with the RUM approach should become apparent. Calculating mean WTP is slightly more involved in the case where ln(t) rather than t is used as a regressor 12.8 This is the mean of a lognormal distribution whereas 12.9 when ln(t) rather than t is used in the Probit regression the mean and median are no longer identical Although the Logit and Probit models are widely used to estimate mean and median from referendum dichotomous choice response data, there is no prior reason to suppose WTP follows a particular distribution. An alternative is to use a non-parametric estimator which makes no distributional assumption Such as the Turnbull estimator

43. CV – Turnbull estimator The Turnbull estimator assumes only that as the tax rises the proportion of respondents WTP declines monotonically 1. For bids indexed j=1,…,M calculate F j =N j /R j where N j is the number of “No” responses to bid level t j and R j is the total number of responses. 2. Beginning with j=1, compare F j and F j+1. The proportion of “No” responses should increase as the bid level increases. 3. If indeed F j+1 >F j then continue. 4. If F j+1 ≤F j then pool cells j and j+1 into one cell with boundaries (t j,t j+2 ) and calculate F j *=(N j +N j+1 )/(R j +R j+1 )=N j */R j *. 5. Continue to pool adjacent cells until the Cumulative Density Function is monotonically increasing. 6. Set F M+1 *=1 and F 0 *=0. 7. Calculate the Probability Density Function as the step difference in the final Cumulative Density Function: f j *=F j+1 *-F j * for each bid level. 8. Multiply each bid level (t j ) by the probability f j *. 9. Sum the quantities from the preceding step over all bid levels to get an estimate of the lower bound on mean WTP 10. The highest bid level at which the F j *≤0.5 provides a lower bound on median WTP

44. CV – Box 12.1 An example using the Turnbull estimator £0 0.0 0.1£0.00 £25050.1 0.15£0.30 £450150.30.250.15£0.60 £1050100.2Pooled-- £2050200.4 0.3£6.00 £5050350.7 0.2£10.00 £10050450.9 0.1£10.00 Infinity 1.0 -£0.00 Bid (t)Respondents (R) Refusals (N) Prob. (F)Smoothed CDF (F*) PDF (f*) t x f* Total £26.90 Table 12.2 An example of the Turnbull estimator The lower bound to median WTP is £20.00. The next highest bid level of £50 is rejected by 70 percent of individuals. The true median lies between £20.00 and £50.00. The lower bound on mean WTP is £26.90 and this is calculated by multiplying the PDF with the bid levels and then summing the final column. In other words we have £0.00 x 0.1 + £2.00 x 0.15 + £4.00 x 0.15 + £20.00 x 0.3 + £50.00 x 0.2 + £100.00 x 0.1 = £26.90.

45. CV - Mean or median? It is possible to estimate both mean and median WTP. Most research papers provide estimates of both. Where the mean and the median are allowed to differ it is often observed that the mean value is higher, sometimes by a considerable margin. Which is more appropriate for policy purposes? Although economic theory suggests that mean WTP is more appropriate there are two practical arguments in favour of using the median. First - political acceptance. We are not suggesting that researchers should in any sense tailor their results to make them more acceptable, merely pointing out that in order to gain democratic support it may be necessary to base any decision on a value for the environmental good which at least 50 percent of households would be WTP. Second - the median may be more robust to outlying observations. In many CV exercises there are a handful of individuals who submit very high bids failing perhaps, to take into account their budget constraint. These should, but might not always, be flagged up as problematic bids and excluded from the analysis. Although few in number such individuals can exert considerable influence on estimates of mean WTP.

46. CV - Aggregation The final task of any CV exercise is to aggregate mean or median WTP values over the target population. Sometimes the target population will be the national population but as discussed it might be a smaller population such as the number of people using a particular environmental resource or the number of households living within a particular radius of the site or living in a particular political region. If the sample is representative of the target population then aggregate WTP is simply mean or median WTP multiplied by N, the number of observational units (typically the number of households) in the target population 12.10 If the sample is not representative of the target population then the appropriate procedure is either to stratify the sample and calculate a mean WTP appropriate for each household type or to predict an appropriate value using statistical regression in order to achieve the same goal. Mean WTP for each household type is then multiplied by N i the number of observational units of each type present in the target population 12.11 This procedure is especially important if the sample is geographically unrepresentative and WTP for the project depends on distance to the site.

47. CV – reliability and validity Establishing the success or failure of a CV study involves weighing evidence of various kinds but there is usually no entirely satisfactory way of validating the results. CV surveys ought to be reliable in the sense that administering the same survey to a different sample of respondents or the same sample of respondents at a later date should yield similar results. Surveys designed and undertaken by different researchers but purporting to measure the same thing should also produce similar results. Face validity – does the survey ask the right questions about the right scenario? Criterion validity – compare with actual referendum outcome Convergent validity – compare with results from another valuation method Theoretical validity – estimate a bid function ( a regression equation) with arguments that explain the bid. Do the coefficient signs agree with theory/expectations? Is, eg, WTP positively related to income?

48. CV – types of bias and problems Potential biases have been identified in the CV literature Part – whole bias. The value for a specific good the same as for a more inclusive good. A particular species and all endangered species. Embedding Hypothetical bias. Insensitivity to scope. A manifestation of part – whole bias, or reflecting that the bid responses are largely symbolic in nature – ‘warm glow’ effects Interviewer bias - Yea saying - to please the interviewer Prominence bias Temporal embedding – WTP insensitive to frequency of payment Strategic bias – respondents believe answers will affect their tax liability. Does not apply to dichotomous choice. Starting point bias – anchoring. Sequence effects Information bias – individual’s WTP reflects inadequacy of their knowledge

49. CV – the WTP/WTA disparity 1 Theory predicts that WTA should exceed WTP by a small amount. In Figure 12.1 consider an individual who starts with private consumption of y 1. WTP for an increase in the environmental good from e 0 to e­ 1 is DA which is greater than BC. If the individual is richer, he is typically willing and able to spend more in order to increase the level of the environmental good. The difference between y 1 and y 0 is the same as WTA for a reduction from e 1 to e 0. Large divergences between WTA and WTP are therefore possible only if an individual’s WTP would change significantly if his income were augmented by an amount equal to WTA. Such an effect is likely to occur only if (a) WTA accounts for a significant share of the individual’s income and (b) WTP is highly income elastic.

50. CV – the WTP/WTA disparity 2 As an example of the orders of magnitude involved suppose that each 1 per cent increase in income leads to a 1 per cent increase in WTP. Then if WTA is equal to 1 per cent of an individual’s income, the ratio of WTA to WTP is 1.01. But in most applications WTA will be much less than 1 per cent of income. And most empirical evidence appears to suggest that the income elasticity of WTP for environmental goods is quite low as well. Together this suggests that the difference between WTP and WTA should be small. But in rather many studies WTA frequently exceeds WTP by an order of magnitude or more. Hanemann (1991) showed that utility theory actually predicts that for commodities where there are limited possibilities for substitution, WTA could be much larger than WTP. In the limiting case where there is perfect substitution between the composite commodity the environmental good the indifference curves become straight lines and the difference between WTP and WTA disappears. Other explanations of the WTP / WTA disparity include the possibility that this is a consequence of individual’s lack of familiarity with WTP / WTA questions.

51. CV – the WTP/WTA disparity – Prospect theory Another possible explanation for the WTP/WTA disparity is provided by Prospect theory As outlined in Kahneman and Tversky (1979) it has three elements 1. An individual views things in terms of changes from a reference level, usually the status quo 2. Gains and losses are subject to diminishing returns 3. Loss aversion – the value function is steeper for losses than gains Hence an endowment effect. People value a good or service more once their property right to it has been established, place a higher value on a thing that they own than on the same thing that they do not own. Many observed violations of expected utility theory are predicted by prospect theory If WTP and WTA answers can be very different then it matters which one is used, since it could make the difference between approving and rejecting a project. The choice between WTP and WTA is really a decision about property rights. If property rights are considered to be defined by the current level of environmental quality then improvements in environmental quality should be valued using WTP and reductions in environmental quality should be valued using WTA.

52. CV – Box 12.2 the NOAA panel - judgement In the USA the courts have decided that CV based evidence may be admissible in determining the compensation payments to be made where actual damage has occurred. The US government agency responsible for setting the rules for the assessment of damages from oil spills, the National Oceanic and Atmospheric Administration, NOAA, of the US Department of Commerce convened a panel of experts. It reported in 1993, giving CV for passive use ( i.e non-use ) qualified approval. “The Panel starts from the premise that passive-use loss – interim or permanent – is a meaningful component of the total damage resulting from environmental accidents”. “It has been argued in the literature and in comments addressed to the Panel that the results of CV studies are variable, sensitive to the details of the survey instrument used, and vulnerable to upward bias. These arguments are plausible. However, some antagonists of the CV approach go so far as to suggest that there can be no useful information content to CV results. The Panel is not persuaded by these extreme arguments”. “The simplest way to approach the problem is to consider the CV survey as essentially a self-contained (sample) referendum in which respondents vote on whether to tax themselves or not for a particular purpose.” The Panel identified a number of stringent guidelines for the conduct of CV studies, concluding that: “...under those conditions...CV studies convey useful information. We think it is fair to describe such information as reliable by the standards that seem to be implicit in similar contexts, like market analysis for new and innovative products and the assessment of other damages normally allowed in court proceedings”. “CV studies can produce estimates reliable enough to be the starting of a judicial process of damage assessment, including lost passive-use values”.

53. CV – Box 12.2 the NOAA panel - guidelines The NOAA Panel recommended: 1. Probability sampling from the entire affected population 2. Minimise non-responses 3. Personal interviews 4. Careful pretesting for interviewer effects 5. Clear reporting, of defined population, sampling method, non-response rate and composition, wording of the questionnaire and communications 6. Careful pretesting of the CV questionnaire 7. Conservative design (prefer options that tend to underestimate, rather than overestimate, WTP) 8. WTP format instead of WTA 9. Referendum format 10. Accurate description of program of policy 11. Pretesting of any photographs to be used 12. Reminder of undamaged substitute commodities 13. Adequate time lapse from any incident to be valued 14. Temporal averaging of responses 15. “No-answer” option available 16. Yes-no follow ups to referendum question 17. Cross-tabulations with other questions such as attitudes toward site, environment etc. 18. Checks for understanding 19. Alternative expenditure possibilities provided 20. Present-value calculations made as clear as possible

54. CV – Box 12.3 the Exxon Valdez oil spill 1 The Exxon Valdez CV exercise can be considered as exemplifying compliance with the NOAA panel guidelines. In 1989 the Exxon Valdez ran into submerged rocks shortly after leaving the port of Valdez loaded with crude oil, and 11 million gallons of its cargo flowed from ruptured tanks into the waters of Prince William Sound off the coast of Alaska. This was the largest oil spill in US waters, and was widely regarded as a major environmental disaster, occurring as it did in a wilderness area of outstanding natural beauty. In anticipation of legal action against the ship’s owners, the Government of Alaska commissioned a team of economists to conduct a CV study to estimate the damages from the oil spill. The development of the survey instrument used took place over a period of 18 months, and involved initially focus groups, followed by trial interviews and pilot surveys. The form that it finally took was as follows. After being asked about their views on various kinds of public goods and knowledge of the Exxon Valdez incident, respondents were presented with information about Prince William Sound, the port of Valdez, the spill and its environmental effects, and a programme to prevent damage from another spill. The programme would involve two coastguard vessels escorting each loaded tanker on its passage through Prince William Sound. These vessels would have two functions: first, reducing the likelihood of a grounding or collision, and second, should an accident occur, keeping the spill from spreading beyond the tanker. The interviewer then stated that the programme would be funded by a one-off tax on oil companies using the port of Valdez and that all households would also pay a one-off tax levy. Before asking about willingness to pay this tax, the interviewer presented material about the reasons why a respondent might not want to pay such a tax, so as to make it clear that a ‘no’ vote was socially acceptable.

55. CV – Box 12.3 the Exxon Valdez oil spill 2 The WTP question was whether the respondent would vote for the programme, given that the one-off household tax would be an amount $x. The survey involved four different treatments in which the amount x varied as shown in Table 12.3 in the column headed A-15, which was the first WTP question number in the survey instrument. Depending on the answer to that question, a second WTP question was put to the interviewee. If the A-15 answer was ‘yes’, the respondent was asked whether he or she would vote for the programme if the tax cost were to be the higher amount shown in the column headed A-16. If the answer at A-15 was ‘no’, the interviewee was asked about voting given a tax cost at the lower amount shown in the column headed A-17. TreatmentA-15A-16A-17 A 10 30 5 B 6010 C 6012030 D12025060 Table 12.3 Monetary values used in WTP questionnaire for various treatments and various questions After the WTP questions, the interviewer asked a number of debriefing-type questions about motives for the responses given, about attitudes and beliefs relevant to the scenario, and about the respondent’s demographic and socio- economic characteristics. This information to be used for the bid function

56. CV – Box 12.3 the Exxon Valdez oil spill 3 he survey was conducted using a stratified random sample of dwelling units in the USA. Approximately 1600 units were selected. Respondents were randomly assigned to one of the four WTP treatments. The response rate, based on sample size after dropping non-English-speaking households, was 75.2%. T Th e second column of Table 12.4 gives the proportion of ‘yes’ responses to the first WTP question, A- 15, across the four treatments. The next four columns give the proportions for response patterns over the two WTP questions that all respondents were asked. Thus, for example, in the third column 45.08% of respondents asked initially about $10 said ‘yes’ to it and to the $30 that they were subsequently asked about. TreatmentYesYes–YesYes–NoNo–YesNo–No A ($10, 30, 5)67.4245.0822.35 3.0329.55 B ($30, 60, 10)51.6926.04 11.3236.60 C ($60, 120, 30)50.5921.2629.13 9.8439.76 D ($120, 250, 60)34.2413.6220.6211.6754.09 Table 12.4 Response proportions

57. CV - Box 12.3 the Exxon Valdez oil spill 4 The analysis of the response data assumed that WTP followed a Weibull distribution Estimating the parameters by maximum likelihood gave estimates of $30.30 (95% confidence interval $26.18 - $35.08) for median WTP $97.18 (95% confidence interval $85.82 - $108.54) for mean WTP Using the median $30.30 and multiplying by the number of english speaking US households gives total WTP for the escort ship programme of $2.75 billion. This was interpreted as an estimate of the lower bound for the TEV lost as a result of the oil spill. A valuation function, using data on respondents’ beliefs, attitudes and characteristics to construct explanatory variables for a regression with WTP as dependent variable, was estimated and the result was taken as demonstrating construct validity. WTP was found to be positively associated with income. The survey described here was conducted in 1991. Two years later the same survey instrument was used again with a national sample and ‘almost identical’ results were obtained. Sensitivity analysis was done using the estimated valuation function and it was concluded that the WTP results were reasonably robust

58. Choice experiments A choice experiments – CE – is a stated preference technique Respondents are presented with a number of discrete alternatives – in terms of a set of attributes - and asked to state which they prefer CE is growing in popularity because can deal with non-use values control of the experimental design is with the researcher avoids yea and nay-saying monetary values implicit – no WTP question can calculate WTP even if attribute levels change WTP values can be transferred across project analyses

59. CE – an example 1 It is proposed that a forested area currently subject to some timber harvesting and uncontrolled recreational access becomes a bird sanctuary with no logging and restricted recreational access An ECBA is to be done. Is PV of WTP for environmental enhancement greater than foregone logging profits and lost recreational opportunities? Attributes of project are number of bird species area of old growth forest permitted visitors each year cost to average tax payer Bird species51015 Old growth forest (hectares)150018002000 Visitors per year400030002000 Cost per taxpayer ($)01020 Table 12.5 Project attribute levels

60. CE – an example 2 Please consider carefully each of the following options. Given that only these options are available, which one would you choose? AttributeAlternative 1 Status Quo Alternative 2 Alternative 3 Bird Species51510 Hectares Old Growth 150018001500 Annual visitors200010002000 Cost to you ($)02010 Please circle your preferred option. I would choose the status quo at no cost to me...........................1 I would choose alternative 2 at a cost to me of $20....................2 I would choose alternative 3 at a cost to me of $10....................3 Table 12.6An example of a choice set and accompanying rubric Selecting a value for each attribute generates an ‘alternative’ Grouping several alternatives generates a ‘choice set’ The respondent is asked to select her most preferred alternative Alternatives must include the status quo Respondents can be presented with several choice sets

61. CE – compared with CV Compare the foregoing CE with an alternative CV dichotomous-choice based approach to valuing just one of the possible alternatives. The key CV question might run as follows: “Suppose that the opportunity arose to increase the number of endangered species from 5 to 10 whilst simultaneously increasing the areas of old growth forest from 1500 hectares to 1800 hectares and restricting visitor numbers to 2000 visits per annum. Would you be willing to pay a $X to secure such a change?” In this approach consistent with the dichotomous-choice approach $X would be varied across the sample of respondents from $10, $20, $50 and $100 and the results analysed in the manner outlined earlier in this chapter. Whilst the CV approach would provide a WTP estimate for a change from the status quo to 10 endangered species, 1800 hectares and 2000 visits per annum it would be impossible to then use the results to determine WTP for a change to say, 16 endangered species with 2100 hectares of old growth forest and a limit of 2500 visitors per annum. Instead, it would be necessary to conduct another CV experiment, which would be both time consuming and expensive. By contrast the CE approach enables WTP values to be constructed for any environmental project because the CE technique derives the implicit value of project attributes. This flexibility enables decision makers to investigate alternative options without the need to commission further valuation studies.

62. CE – conducting a CE A CE involves many of the same steps involved in conducting a CV identifying a target population whose values are to be measured developing the survey instrument using focus groups and a small-scale pilot survey. Additional issues specific to CEs are. enumeration of attributes – usually up to five selection of levels for attributes – at least two Full factorial design – all possible attribute levels – 81 in the example Fractional factorial design – a subset of all possible combinations of attribute levels Randomly select from designs to to create choice sets How many choice experiments in one interview?

63. CE – analysing the data 1 CE survey data is analysed using the Random Utility Model (RUM). This model assumes that respondents have consistently selected those alternatives conferring the highest level of utility. If individual i chooses choice g out of j=1,..,J alternatives it must be that: 12.12 Where u is the utility associated with the choice. If we further assume that utility comprises a deterministic component v and a stochastic component e then we can write: 12.13 The error term ε is to account for unobservable variations in taste. If it is also assumed that the error term follows the Gumbel distribution (also known as the Type I Extreme Value distribution) then the probability that individual i will select choice g out of the J alternatives is given by 12.14 This model of probability corresponds to the Conditional Logit model of choice]]

64. CE – analysing the data 2 The likelihood function of the Conditional Logit model of choice is given by the following expression in which i=1,...,N is the number of individuals and y ij is an indicator variable which takes the value unity if a particular choice was chosen and is zero otherwise. 12.15 For the example above, the deterministic part of i’s utility function would be 12.16 with β’s to be estimated from response data The marginal willingness to pay (MWTP) for attributes can be calculated as the negative of the ratio of the coefficient on the attribute divided by the coefficient on the cost attribute (which represents the marginal utility of money). In the current example the MWTP for an additional hectare of old growth forest is therefore given by 12.17 The WTP for a specific policy package is given by the utility of the package v 1 minus the utility of the status quo v 0 divided by the coefficient on the cost variable. It is in this way that the economic value of a variety of possible policy alternatives can be evaluated. 12.18

65. CE – problems and extensions The most widely recognised drawback of CEs compared to CV is that the former place greater strain on respondents’ cognitive abilities. The danger is that respondents then resort to simple rules of thumb as a way of ‘solving’ the CE puzzle confronting them. For example, individuals might choose on the basis of just one attribute. Someone who chooses on the basis of only one attribute is said to display lexicographic preferences. Respondents might even select one alternative over another regardless of its attributes. This frequently happens when a name or label naturally appends to a particular alternative e.g. “the status quo alternative” or “the environmentally friendly choice”. Asking individuals about only their most preferred option is potentially wasteful especially in view of the cost of conducting interviews. If individuals can rank the alternatives giving their first, second and subsequent choices it is possible to conduct a Contingent Ranking (CR) experiment. A CR experiment is like a sequence of CEs with the first choice being made from J alternatives; the second from the remaining J-1 alternatives; the third from the remaining J-2 alternatives etc. CR experiments increase further the cognitive effort required of the respondent and the choices may not always include the status quo.

66. CE – Box 12.4 Remnant vegetation and wetlands protection 1 The Macquarie Marshes was the largest area of wetland in New South Wales in Australia. Following the increased use of water for irrigation purposes the size of these wetlands has shrunk from 5000 to 1000 square kilometres. The frequency of water bird breeding-events has fallen from every year to once every four years, and the number of protected and endangered water bird species present has fallen from 34 to just 12. A CE questionnaire was developed to value a project to allocate more water to the marshes by purchasing water rights from farmers. The purchase of water rights would be funded by a one-off tax on all households New South Wales. A number of different projects were defined in terms of the following five attributes: water rates (RATES); irrigation related employment (JOBS); wetlands area (AREA); the frequency of water bird breeding events (BREED) and the number of protected and endangered species present (ENDSPECIES). Over a single weekend researchers distributed 416 questionnaires throughout Sydney on a drop-off and pick up basis, and eventually 318 usable questionnaires were obtained.. An example of a choice set for the Macquarie Marshes CE is given in Table 12.7 - next slide

67. CE – Box 12.4 Remnant vegetation and wetlands protection 2 Your water rates (one off increase) No change$20 increase $50 increase Irrigation related employment 4400 jobs4350 jobs Wetlands area1000 km 2 1250 km 2 1650 km 2 Waterbirds breedingEvery 4 years Every 3 years Every year Option 1 (Current Situation) Option 2Option 3 Protected species present 12 species25 species15 species I would choose option 1 □ I would choose option 2 □ I would choose option 3 □ I would not choose any of these because I prefer more water to be allocated to irrigation □ Table 12.7 Example of choice set for the Macquarie Marshes experiment

68. CE – Box 12.4 Remnant vegetation and wetlands protection 3 AttributeCoefficient (Standard Error) ASC-0.30* (0.19) RATES ($ per household)-0.12E-1*** (0.81E-3) JOBS (number)0.17E-2*** (0.65E-3) AREA (km 2 )0.56E-3*** (0.13E-3) BREED (years between breeding) -0.31*** (0.51E-1) ENDSPECIES (number)0.50E-1*** (0.97E-2) Log Likelihood-1756.947 * significance at the 10 percent level; ** significance at the 5 percent level; *** significance at the 1 percent level of confidence. Table 12.8 Results from the conditional logit model AttributeImplicit value per household ($) JOBS$0.14 per job AREA$0.05 per km 2 BREED (years between breeding) -$25.83 per year between breeding ENDSPECIES (number)$4.17 per protected species Table 12.9 The implicit values per household Note the inclusion of the alternative specific constant ASC. This variable takes the value zero when the current situation is selected and otherwise takes the value unity. Its negative value indicates that households have a preference for the status quo - a common finding in CEs. These implicit prices may be used to calculate the WTP for a project that increases the area of wetland from 1000 to 1400 km 2, reduces the number of years between waterbirds breeding from 4 to 3 years, and increases the number of protected and endangered species from 12 up to 16. There is no effect on the number of jobs. The WTP value of $36.50 is per household. There were 1.5 million households in Sydney 1.5 million in 2001.