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Perman_etal - Valuing the environment u on visitors and...

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Unformatted text preview: Valuing the environment u on visitors and nonwvisitors by means of a, ._ ly postal, survey of a sample of the popula- '-considered to be relevant as potential visitors. respondents could be asked about their travel it but this is rare and typically travel costs are '_ u -- to respondents by the analyst on the basis distance travelled, which itself is usually estim- by the analyst by assigning respondents, on the of information supplied by them such as a , . . or zip code, to a number of zones and meas- in" g distance from the centre of each zone using a | p. The regression is then. usually. of the number visits per unit population from zone r' on travel from zone t'. For some sites and some surveys it possible to have 1' index individuals, when the . ndent variable is the number of visits in a period -. time by individual i and the explanatory variable it'suavel costs per visit for individual i. Where the fitlata are such that either approach could be followed, there is some dispute as to whether it is better to use the individual data or to average costs over indi- E'yiduals in given zones and regress total zonal visits per unit population on average zonal travel costs. Most TCM applications employ the zonal average approach, often simply because of data limitations. The MCS figure that is produced by the TCM 'I-as described here is the total MCS for the sample ;_of visitors included in the survey. Unless the survey has been such that the sample is the population, Box 12.1 An Illustrative zonal average TOM example The basic data for a national park with no admission charge are: _______._____——————— Visits Population Distance {thousands} [miles] ‘15 000 2 000 10 48 000 8 000 15 11 25!} 2 500 20 45 000 ‘15 000 25 34 [100 22 560 30 where distance is measured from the centre of the zone, and we are assuming, in the interest of keeping the story simple, that we know the total number-of visits in the year from each zone. We will also assume that we know the travel cost per- there remains the question of how to go from this figure to the MCS figure to be used in ECBA. This can be quite complicated and the answer depends on the nature and timing of the survey in relation to the characteristics of the site concerned. One fairly standard procedure is to divide the MCS figure produced as described here by the total number of visits covered by the survey to get a figure for MCS per visit, which is then multiplied by the (usually estimated) number of visits per year to get a figure for MCS per year for use in the ECBA. In Willis and Garrod (1991a) MCS per visit is estimated using both individual data on visits per year and zonal data on visits per unit population as dependent variable, with explanatory variables defined appropriately in each case. Across six forest sites in the UK MCS per visit ranged from £1.43 to £2.60 (average £2.03) using the zonal averaging approach. and from £0.06 to £0.96 (average £0.48) using individual data. The two methods did not even rank the sites in the same order. Clearly, given annual total visitor numbers of the order of one million, one could draw quite different conclusions as to the use value of one of these sites according to which of these approaches was used. Boa l2.l works through an illustrative, zonal aver— age, TCM application where the numbers have been constructed so as to make the calculations simple and bring out the basic ideas as clearly as possible. mile to be £1. The first step is to estimate the parameters 'of the trip generating function vi=ot+fltTt+PJ +2, where ii, is visits per thousand population from the ith zone, T, is travel cost from the it]: zone, P is the admission price which. is zero. and e, is the error term. We get ordinary least squares estimates for a and b using; . Zapata-r1 B: Eta—rt 414 Project appraisal __g'dtscuss fou ' . .. _ . Edie discuss ”13 _ - _ ' _ - :1 do occurs 2B ' 25 30 “10.0 20. we get the. estimated trip- generating equation 'as [[153 250 — 23 000] .x 5 55:50.5] + [78 0003.5] 91‘" Jilly—5 D3[T+P} plus The seeond step is to use this estimate to [[78 000 - 35 750] x_-5._><_0.51+ [36' 750 X 5'] derive the rolationinip between visits and the price of admission. which is often referred to in _ - - the literature as the surrogate demand function. [(36 750 +- _-18 goo] X5 x- 0.5] + [1.8 000 x _5.l.-_ ' 'Wa mil eonaider =P varying in. steps.- of £5. For P: £5 predicted visits from each Zone V, and _ _ _ _ _ total predicted visits are. calculated. using the [[18 000 — 30001 x 5 x115] +' [3000 x 5] - estimated trip generating- function as follows: ' ' " " ' ' ' - plus plus plus [3000 x5 x 0.5] which is £1 081 875.. q=hsss=1ds "730‘ 191' ' " ”s‘. 2. Laos 12000 4.5 - a m. at, 36 000 '3 _ 7500 1.5 22 500 0 o ' 73 0.00 Proceeding in- the same way for P- =_ £10 and so _ . - . __ -' forest sil on, we get the following simulated prioehrisits ' - ' of the TI data forithe'l'smsHtB'-damsnd bastion:- . . _ not prov _ _ . .' ' , . . _ . _ I reportin, ..V" _ 3 ' ' ' ' ' ' _ II ' t;- -- -- form. : 12.4. .10 - - .' ' -. ‘ . .- " - - - - 15 ' . " ., .. - .' -' - . -. -. .. '_ . .- '. In many - 20. ' . .' ' _ ; ' . . . _ . _ -. _ _. ; : - -- . . . . .. . -- genera“ .—'———-——-—-—-—'25 ' ' I ' . ; ' - 3 '_ . f :1 ' ' ' ' . .-'.'.:” method . Figure 1'2. _8 shows the surrogate. demand function. The third step is- to get fiom- this the . estimate of eonsuiners' -surplus for the year. Given thatiii feetP; g.tot_a_1e0nsumets somlusisthe ' _- '-_ .-' - _ __ _ _J- demand function which' is Figure 12;8._.f.\n Hlus'trative "surroga'te‘déh'lahti function J - L'J ...
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