A Recreational Demand Model for Jasmund

A Recreational Demand Model for Jasmund National Park:
Taking into Account Anchoring and Averaging Bias
David Wuepper
Prepared for the 20th Annual Conference of the European Association of Environmental
and Resource Economists (EAERE)
June 26-29, 2013, Toulouse, France
ABSTRACT
Combining a Choice Experiment (CE) and a Zonal Travel Cost Model (ZTCM), this study analyses
the recreational demand for Jasmund National Park in the northeast of Germany. Before results are
derived, the methods are tested for biases and convergent validity. The CE is found to be affected by
two biases: Anchoring - which lets responses be biased by the researcher specified scale of costs - and
Averaging - which lets respondents average the value of two park characteristics, instead of adding
them together. It seems that Averaging is not trivially correctable but it can be avoided by design.
Anchoring can be avoided by design and can also be corrected later. For the correction, the author
proposes a calibration with the results of a calibrated travel cost model (TCM). The calibration of the
TCM is necessary because for Jasmund, only a zonal TCM with researcher-defined costs can be
estimated, whose scale is unidentified at first. The costs can however be calibrated with the scale of
respondent-reported costs and once the scale of the TCM is corrected it can be used to calibrate the
results of the CE.
Empirically, Jasmund’s characteristics are valued and a recommendation is made: Introducing a low
entrance fee for the park and abolishing the entrance fee for the visitor center could raise visitor
satisfaction and increase the park management’s budget.
Keywords: Nature Recreation; National Park Valuation; Choice Experiment; Travel Cost Model
Acknowledgements: I thank U. Latacz-Lohmann, G. Breustedt, N. Schulz, R. Scarpa,
K. Rehdanz, D. Snower, K. Train and A. Hole for their suggestions and advice, I. Stodian and U.
Steiner for their cooperation at the national park and A. Gutierrez-Uribe and N. Wuepper for their
support with collecting the data.
1 1. Introduction
Most non-market valuation studies either rely on revealed, stated or combined preference data. As
both revealed and stated preference methods have characteristic advantages and drawbacks, there is
great value in using them together- to enhance strengths and mitigate limitations (see e.g.Cameron
1992; Adamowicz, Louviere and Williams 1994; Boxall, Englin and Adamowicz 2003; Whitehead,
Pattanayak, Houtven and Gelso 2007).
For instance, one of the caveats of (non-experimental-) revealed preference approaches is their being
bound to the status quo. If the researcher is interested in how the national park´s value would change
in response to changes in its characteristic, it is common practice to utilize stated preference
approaches (e.g. Hanley and Spash 1993). In turn, the main critique on using stated choice data is the
inherent risk that stated choices could differ from real choices due to cognitive biases (McFadden
1999 and 2000) or strategic behaviour (Alpizar, Carlsson and Martinsson 2001). Hence, the use of
revealed preference data might be used to ensure realism while stated preference data can be used to
estimate additional values. For this study, choice experiments (CE) are combined with a zonal travel
cost model (ZTCM) to estimate the recreational use value(s) of Jasmund National Park, in northeast
Germany. The methodological result is a better understanding of the internal and external validity of
the two non-market valuation techniques. The practical results are an estimate of Jasmund´s
recreational value (aggregated and individual characteristics) and a policy recommendation.
2 2. Jasmund National Park
Jasmund National Park is located on a peninsula adjacent to Germany´s largest island, Ruegen, in the
Baltic Sea of northeastern Germany. It comprises 3057 ha protected area, including 2168 ha of natural
beech forest, of which the oldest 500 ha are listed as natural World Heritage site (UNESCO 2012).
Besides its precious forest, the park also includes 632 ha in the Baltic Sea, 10 Km of chalky coast and
over 250 ha of wetlands, abandoned chalk quarries, streams and lakes, which all give habitat to a rare
and divers flora and fauna (Kutscher 2002). Especially popular amongst the majority of visitors are
Jasmund´s birds and mammals, such as cranes, sea eagles, housemartins and deer. Besides these
flagship species, Jasmund is also home to over 500 species of butterfly, including some endemic ones,
several Orchid species, black woodpeckers and many other rare flowers, insects, beetles, birds and
smaller mammals (Kutscher 2002, Stodian 2012).
A tourists-magnet are Jasmund´s up to 120m high chalk cliffs, which are the landmark of the region.
In front of the cliffs there is a large and modern visitor centre, which divides opinions: Some visitors
are happily willing to pay the entrance fee of 7,50 € and enjoy the centre´s exhibitions and others
offerings while others are quite upset about this service being so expensive, especially because the fee
must also be paid in order to pass through the centre to reach the major ledge of the chalk cliffs.
As common for a German national park, there is no general entrance fee to the park.
3 3. Choice Experiments and Analysis
The main method which shall be used to analyse Jasmund´s recreational demand is a CE, which is a
stated discrete choice approach to generate data.
The method consists of two steps: First, the design of choice sets (see 3.1) and second, letting
sampled visitors chose repeatedly from different choice sets (see 3.2), which allows to infer their
preferences in the following analysis (3.3).
Every choice set consists of three hypothetical alternatives - two nature recreation destinations and the
option not to choose either one. The alternatives are described by their characteristics and the costs to
visit, so that visitors must make a trade-off between getting a characteristic and paying its “price” which indicates their willingness to pay (WTP).
3.1 Choice Set Design
Revealed preference data often exhibits multicollinearity – the correlation of two or more predictor
variables - so that the value of the individual variables cannot be precisely estimated. To avoid this
problem, CE consist of a large number of choice sets, which all differ in their combination of
characteristics (later predictor variables for the probability of choice). Using statistical design theory,
the characteristics are mixed in a way to minimize multicollinearity. The success of doing so can be
measured e.g. by the D-efficiency criterion (Kuhfeld, Tobias and Garrat 1994), which is defined
as: 𝐷 − 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = [|Ω|!/! ]!! , where Ω denotes the covariance matrix of the K parameters.
Because the choice sets should not contain alternatives that cannot rationally be chosen (lowest valued
characteristics with highest possible price), these “dominated” alternatives are better improved, which
comes at the expense of a slightly decreased D-efficiency value. For this study, the obtained value
decreased marginally, from originally 98.3 to 97.4 out of 100.
When defining the characteristics of the choice sets, it is important to exhaustively describe all
attributes that could describe recreation at Jasmund National Park (Train 2009). Furthermore, all
combinations of characteristics must be plausible – e.g. no combination of a national park with
commercial forestry (Alpizar et al. 2001).
4 The chosen characteristics for the choice sets of Jasmund National Park are shown in table 1. The
result is 64 choice sets, with 128 unique nature recreation destinations.
As can be seen in the last row of table 1, the choice sets are divided into three subsamples, which differ
only in their costs. This enables the test whether differences in specified costs lead to differences in
estimated values, which has been termed “Anchoring” by Ariely, Loewenstein and Prelec (2003) and
describes a situation where respondent´s choices are influenced by cues contained in the experimental
setting (e.g. the researcher chosen scale of costs gives an idea of what are normal/reasonable costs,
which “anchors” WTP estimates).
Table 1: Choice Sets for Jasmund National Park
Category
nature
attraction
animals
diversity
service
costs
Characteristic
Planted forest for forestry use / 200 meters of protected area along the coast, the rest is planted
forest / The whole forest is a national park
Jasmund’s chalky cliffs / a part of the forest is a World Heritage site / Jasmund’s chalky
cliffs and a part of the forest is a World Heritage site
No special ones / mouflon and fallow deer / mouflon, fallow deer and lynxes
Little diversity (forest and coast) /
large diversity (forest, wetlands, meadows, streams, lakes and coast)
A small information centre / a small information centre and gastronomy /a large information
centre with multimedia exhibitions, guided tours and gastronomy
Differ across three subsamples: entrance fees between 2,50 and 9,50 € / travel costs between
10 and 40 € / travel costs between 40 and 100 €
If Anchoring affects this study´s estimates, it is to be expected that estimated WTP is significantly
higher in the subsample with high travel costs compared to the subsample with low travel costs.
A second test included in the choice sets tests for “Averaging”. In the context of some marketing
studies, a puzzling anomaly has occurred: Contrary microeconomic theory, respondents have repeatedly
and consistently shown to value the combination of two differently valuable products less than the one
with higher value alone (Brough and Chernev 2011; Weaver, Garcia and Schwarz 2011). Because
respondents sometimes seem to average the value of two goods instead of adding their value together,
5 this bias has been termed “Averaging Bias” (Chernev and Gal 2010). Averaging can be understood as
being part of a whole class of “simplifying-biases”, which all stem from respondents using
oversimplified decision rules (“flawed heuristics”). Averaging is an example, where respondents do not
calculate costs and benefits for the characteristics of the alternatives but value the alternatives all at
once and categorical (Brough and Chernev 2011; Weaver et al. 2011) which clearly simplifies this
complex task while possibly introducing logical inconsistencies (see Hensher 2010 for a general
discussion of heuristics in CE).
This paradox could be of importance in the context of environmental valuation, since it would violate a
fundamental assumption of most CE: The additivity of the partial WTP estimates.
To test for Averaging Bias, two characteristics – Jasmund´s chalk cliffs and its World Heritage site - are
both included individually and as a combination. According to consumer theory, the value of the
combination should not be smaller than that of either characteristic individually if both characteristics
are valued with the same sign (+ or -) and the value of the combination should be between the value of
the two individual characteristics if the two characteristics are valued with different signs (one + and
one -). Assuming the characteristics are independent from each other, a violation would indicate a bias.
Specifically, Averaging occurs, if the combination is valued as an average of the individual values.
3.2 Choice Experiment Data Collection
In June 2012, about 250 visitors were randomly sampled at different points of Jasmund National Park.
They were asked to imagine the choice sets to be real travel brochures and indicate on 8 different
choice sets which alternative they would like to visit at the described prices. The second part of the
questionnaires asked questions on socio-demographic covariates, such as travel budget, age,
education, etc., and data for a travel cost model, such as area codes, number of visits per year, travel
expenses and local expenditures, amongst others.
Of 230 returned questionnaires, 206 were finally used.
6 3.3 Choice Data Analysis
To analyze the choice data, a generalized multinomial logit (GMNL) is used (Fiebig, Keane, Louviere
and Wasi 2009), which can e.g. be done in Stata (Gu, Hole and Knox, forthcoming) or Matlab
(Czajkowsi and Hanley 2012). In the following, the Stata model is used.
The utility from the choice of an alternative is given by the following utility function:
𝑈!" = 𝛽! ! 𝑥!" − 𝜙! 𝑐!" + 𝜀!" /𝜎! ,
(1)
where 𝑈!" is the utility of alternative j for person i, 𝛽! is a vector of marginal utilities for the observed
non-monetary characteristics 𝑥!" and𝜙! is a vector of marginal disutility for the cost vector 𝑐!" . The
error term 𝜀!" , picks up unobserved utility and is scaled by 𝜎! . As in every logit, the error term is
assumed to be extreme value type I distributed.
Normalizing the scale (for identification), we obtain Mcfadden´s (1974) conditional logit, with the
following conditional probability that individual i choses alternative j:
𝑃𝑟!" =
!
!"#(!! !!" !!! !!" )
!!!"#(!! ! !!" !!! !!" )
.
(2)
To take into account individual tastes, the 𝛽! vector can be decomposed into an average taste 𝛽 and
individual deviations thereof 𝜂! , which makes the model a mixed logit, where the parameters have a
distribution of random preferences (Train 1998 and 2009).
The GMNL further extends the model and allows for individual scaling, which especially improves
data fit if some respondents show lexicographic preferences (low error variances and high scaled
coefficients) whereas others choose almost at random (high error variances and low scaled
coefficients).
The GMNL augments utility function (1) as follows:
𝑈!" = 𝜎! 𝛽 + 𝛾𝜂! + 1 − 𝛾 𝜎! 𝜂! 𝑥!" − 𝜎! 𝜙 + 𝛾𝜂!" + 1 − 𝛾 𝜎! 𝜂!" 𝑐!" + 𝜀!" ,
(3)
7 where 𝛽 is a vector of mean marginal utilities and 𝜂! are respondents’ individual deviations thereof.
Scale heterogeneity is taken into account by 𝜎! (assumed log-normal with mean 1 and standard
deviation 𝜏) while 𝛾! governs the co-variation of scale and residual taste heterogeneity.
As already well known from the mixed logit, also the GMNL does not have a closed form solution, so
the function must be approximated using simulation instead (Train 2009).
An important issue is the identification of the model, which demands that one parameter in equation
(1) needs to be normalized. The two commonest normalizations are to either set the scale parameter to
one, which gives a model in “preference space” or to set the costs parameter to one, which gives a
model in “WTP space” (Train and Weeks 2005). In the following, all models will are parameterized in
WTP space (for discussion on this topic, see e.g. Scarpa and Willis 2010; Sonnier; Ainsle and Otter
2007; Scarpa, Thiene and Train 2008). Besides several advantages on statistical grounds, the WTPspecification of the model gives a very convenient interpretation to the estimated coefficients: They
directly express the estimated WTP for each characteristic.
A last point worth mentioning is the advantage of allowing the model´s coefficients to be correlated.
The gain in model fit is due to the fact that preferences for certain characteristics might correlate, such
as people who like their nature experience adventurous might neither like a large info-centre, nor
restaurants, nor comfortable paths (see Scarpa, Thiene and Train 2008).
An issue with the GMNL is that it demands relatively large sample sizes to achieve convergence. This
is an issue because in the next section, three relatively small subsample models must be estimated, to
test for differences due to costs-specification.
Because we are only interested in the systematic, non-personal, difference between the subsamples
however, McFadden´s (1974) conditional logit can be used here, without important loss of information
(also, Hausman test shows no violation of the assumption of independence of irrelevant alternatives;
8 see Hausman and McFadden 1984). Then, in section 3.3.2, the GMNL is estimated on the complete
travel costs sample.
3.3 Result Choice Data
3.3.1 Split Samples: Testing for Anchoring
The WTP estimates from the three subsamples clearly identify “Anchoring” (see table 2, especially the
last two rows. For brevity, only the characteristics that describe Jasmund are shown). A likelihood
ratio test confirms significant differences between the subsample, which are obvious: The estimated
recreational value of Jasmund National Park is estimated to lie between 40.44 and 43.92 € when the
low travel cost subsample is used and between 102.57 and 104.94 € in case the high travel cost
subsample is used. The estimated WTP based on the entrance fee subsample is harder to interpret as
this cost is additional to any incurred travel costs (but will be of interest in the last section).
Table 2: Estimated WTP based on the Subsamples
Fee
Low TC
High TC
Constant
6€
13. 78 €
36.89 €
National park
2.45 €
9.81 €
20.77 €
Chalk cliffs (CC)
1.01 €
7.03 €
14.98 €
World Heritage (WH)
1.63 €
5.62 €
11.41 €
CC and WH
1.90 €
15.57 €
23.41
Deer and mouflonobservabl.
0.55 €
3€
11.77 €
Large Info centre
-2.05 €
Not significant
8.29 €
Biotope diversity
0.65 €
Not significant
2.67 €
Aggregate Jasmund NP
9.51 €
42.18 €
103.80 €
Confidence Interval
[8.27 € - 10.74 €]
[40.44 € - 43.92 €]
[102.57 € - 104.94 €]
Previous studies that found the Anchoring effect in a stated choice context include van Exel, Brouwer,
van den Berg and Koopmanshap (2006), Carlsson and Martinsson (2007), Bateman, Day, Loomes and
Sugden (2007), Ladenburg and Olsen (2008) and Luisetti, Bateman and Turner (2008).
9 The finding of Anchoring implies that the estimates are only interpretable at an ordinal scale and
might need to be scaled externally. However, this must be tested as real-life anchoring and
experimental-anchoring could by chance be sufficiently similar.
In the following section, a GMNL is estimated based on the whole travel costs sample, to make
estimates as detailed and precise as possible. Then, to calibrate the overall scale of the estimates, a
zonal travel cost model is estimated in section 4, which perhaps is anchored too, but to the costs of the
real alternatives to Jasmund, so that the derived estimates reflect the current demand at the real park.
3.3.2 Pooled Sample
To obtain starting values for the estimation of the GMNL, a conditional logit is estimated on the full
travel costs sample (3348 observations). Afterwards, the final GMNL converged at a log likelihood
value of -925.71 and a Wald Chi2 value of 110.72.
Table 3: WTP Estimates from the GMNL (*** = Sign. at 0,01; **= Sign. at 0,05; *= Sign. at 0,10)
WTP (€) / Std.Error
SD
Z-Value
Biosphere Reserve
36.73 ***/ 7.36
1.52***
4.99
National Park
54.10 ***/ 7.42
1.64***
7.29
Chalk Cliffs
22.65 ***/ 6.42
1.67***
3.53
World Heritage Site
26.57 ***/ 7.04
1.62***
3.77
World
30.75 ***/ 6.22
1.21***
4.94
Deer and Mouflon observable
22.00 ***/ 6.38
1.24***
3.45
Deer. Mouflon and Lynxes
24.35 ***/ 6.52
1.22***
3.73
Biotope Diversity
9.49 * / 5.36
1.50***
1.77
Medium Info centre
7.61
1.52***
1.16
Large Info centre
-6.38
2.28***
-0.85
Aggregate Jasmund NP
142.44 *** / 15.52
Confidence Interval
[112.02 – 172.87]
Chalk
Cliffs
and
Heritage Site
/ 6.54
/ 7.46
9.32
The result is shown in table 3. Considerable preference heterogeneity is indicated by highly significant
standard deviations of the WTP coefficients. Not shown in the table is the scaling parameter, which
10 has a mean of 3.14 and a standard deviation of 0.93. This shows that there is indeed a significant
amount of response variation that is different from preference heterogeneity, which is also shown by
the comparison to mixed logit estimations: A classical mixed logit converged at -1109.91 when
estimated with maximum simulated likelihood and uncorrelated coefficients and at -952.83 when
estimated with Hierarchical Bayes and correlated coefficients. Both values are clearly inferior to the 925.71 of the GMNL.
Not shown for brevity, several socio-demographic covariates were tested in the model. Most
significantly, a respondent´s attitude towards nature partially explains the results: Respondents who
report to have a strong nature bond significantly prefer a large biotope diversity and dislike a large
info centre. This can be interpreted as a conflict of interests amongst Jasmund´s visitors, as there are
those who prefer the park as wild and untouched as possible and those who do not mind some degree
of human intervention. Due to this preference heterogeneity, the visitor centre is not estimated to have
a significant economic value, even though for some visitors it clearly does. While the before
mentioned can be accommodated in the estimation, another problem is more challenging: A share of
the visitors is angry about having to pay for the visitor centre, so the insignificant but negative WTP
for a large info centre (as found in Jasmund) is perhaps not a true valuation but a protest-statement. To
correct for this, the insignificant but positive WTP for a medium visitor centre is used as a proxy for
the value of Jasmund’s (large) visitor centre. Then, a statistically and theoretically informed overall
estimate for Jasmund is 142.44 € WTP per person (with a standard error of 15.52 €).
As approximate welfare measure, the consumer surplus (CS) can easily be obtained. The CS is defined
as difference between WTP and actual costs (e.g. Ward and Beal 2000), hence, subtracting the average
reported costs of 61 € from the WTP estimate, the estimated CS is about 81.44 €. This measure
makes the result comparable to those of the ZTCM.
Analyzing table 3, note the variables that are included to test for the Averaging Bias:
Chalk cliffs, World Heritage site and both together. At first sight, we have a normal case of
subadditivity, as the pooled characteristic “Chalk Cliffs and World Heritage Site” is valued higher
11 than “Chalk Cliffs” and “World Heritage Site” alone but not much higher. This could i.e. be
interpreted as WTP for characteristics decreases when the overall scale of WTP approaches the budget
limit. However, this is not what actually happens. Revelt and Train (2000) show how to obtain
individual-level parameters, a feature which is already incorporated in Stata’s GMNL command.
Analyzing the individual-level WTP of the respondent’s, we clearly find Averaging Bias, meaning that
a large share of respondents has a lower WTP for the pooled characteristic than for the higher valued
individual characteristic alone. This is similar to what has been found in the eralier mentioned
marketing studies (Weaver et al. 2011; Brough and Chernev 2011; Chernev and Gal 2010) but differs
in that the marketing studies found the effect for two very differently valued products, while this
study´s result suggest that often, for both individual “products” there is a significant and positive WTP
and still their combination is inconsistently low valued. Perhaps, not recognizing the bias could lead to
the interpretation that Jasmund’s World Heritage site is in conflict with its other attractions - thereby
lowering (!) its overall value. Also, if the values of Jasmund’s characteristics are summed to get an
overall value estimate, not recognizing the bias could significantly bias Jasmund’s value downwards.
Graphic 1: Anchoring Bias
Graphic 2: Averaging Bias
Trying to explain the bias, a logical starting point is the complexity of the choice task. There is much
research on the effect of the complexity of a CE on the ability of the respondents to express their
unbiased WTP (e.g. Mazotta and Opaluch 1995; Adamowicz and Swait 1996; Alpizar, et al. 2001;
Adamowicz, Louviere and Swait 1998 and Boxall, Adamowicz and Moon 2009; Hensher 2010;
12 Carlsson and Martinsson 2007 and Chintakayala, Hess, Rose and Wardman 2009). Most findings
suggest that respondents are likely to simplify complex choices by (partially) using heuristics, which
possibly distort estimates.
Weaver et al. (2011) find that the Averaging Bias disappears if respondents do not value the whole
product bundle at once but sequentially, while Brough and Chernev (2011) find that respondents
miscalculate due to categorical thinking (high value vs. low value instead of using a continuous value
scale).
Besides task-complexity, there are two other variables that could affect the bias: Education and costsspecification.
First, we might expect respondents with more formal education (i.e. University degree) to be less
affected. However, the data suggests the opposite as respondents with a University degree were more
likely to be affected. Assuming that the difference is that academics try to solve problems more
analytical while non-academics are more likely to answer intuitively, the results could indicate that the
latter is superior to failing in the first. However, this clearly needs more research.
Secondly, analysing the split samples separately, it seems that respondents are slightly more likely to
be biased when costs are defined as entrance fee than when costs are defined as travel costs. Brough
and Chernev 2011 have found the salience of costs to be positively correlated with Averaging. It is
plausible that an entrance fee is more salient than travel costs as (1) entrance costs are additional to all
travel costs and (2) they likely require more justification, because they are perceived to be avoidable.
3.4 Discussion
The finding of Averaging implies that if a CE design includes pooled characteristics (e.g. to save
space or to simplify the choice sets) it must be considered that expressed WTP could be lowered. In
the context of this study, the affected characteristic can be corrected without loss of information (as
both characteristics are included individually and under the assumption that this effect does only occur
in the experiment and not in real life).
A potential danger that is not analysed here is whether respondents might pool other characteristics
into broader characteristics too, such as “National Park and “Biotope Diversity” into “nature”. If this
13 actually happens, it is easy to be overlooked as usually only aggregate coefficients are analysed, which
represent average preferences (and possibly a distribution of individual preferences around this
average), while Averaging occurs at the level of individual respondents. Furthermore, different biases
can potentially equal each other out in the aggregate, so that all coefficients “look plausible” while
some are devalued by a cognitive illusion of a share of the respondents.
The Anchoring effect is well understood by now: Often, respondents to a CE do not express a
previously determined WTP but their expressed WTP is formed during the attempted elicitation
(Payne, Bettman and Schkade 1999). Several studies found Anchoring to affect the estimates of CE
(see section 3.3.1) and many authors have concluded, that CE estimates generally benefit from
calibration with revealed preference data (for instance Ben Akiva, Bradley, Morikawa, Benjamin,
Novak, Oppewal and Rao 1994). The estimation of a revealed preference model is the subject of the
following section.
4. Zonal Travel Cost Model
There are different Travel Cost Models, of which only the zonal travel cost model (ZTCM) is feasible
for Jasmund- given the data and visitation pattern.
The ZTCM is the oldest non-marked valuation technique (Hotelling 1947; Wood and Trice 1958;
Clawson 1959) and its main idea is that visitors to a National Park do not pay a price for their visit but
do have to incur other costs, namely travel costs, which are complementary to the National Park visit.
Under certain assumptions, travel costs are a surrogate market on which it is possible to observe a
negative relationship between costs of visit and rate of visits per zone, enabling the estimation of a
demand curve (Hanley and Spash 1993; Ward and Beal 2000).
4.1 Travel Costs Specification
For the ZTCM, geographic zones are created around the park. Naturally, the further away a zone, the
higher the average travel costs for visitors and hence, the fewer visitors. For Jasmund, the zones were
14 defined as Germany´s federal states plus a few neighbouring countries (Switzerland, Austria, Slowenia
and Netherlands).
An important and hotly debated topic is the specification of the travel costs.
It is common practice to use the costs of driving plus the opportunity costs of travel time (e.g. Phaneuf
and Smith 2005) but the main alternatives are to either use researcher estimated costs or respondent
reported costs. For the former, it is common to multiply travel distance and fuel price and add a share
of the average zonal wage-rate times travel time (e.g. Ward and Loomis 1986). For the latter,
respondents are asked about their incurred costs directly and how much they would be willing to pay
for a reduction of travel time (e.g. English and Bowker 1996 or Ovaskainen, Neuvonen and Pouta
2012).
Graphic 3 Distributions of the cost-specifications
Using researcher estimated costs has often been criticized on the ground that these costs might be
different from those costs that are perceived by the visitors (especially Randall 1994 and Common,
Bull and Stoeckl 1999), however respondent reported costs are not necessarily superior in the case of
the ZTCM, as this depends on the assumption of costs perceptions varying systematically more
between the zones than between individuals. English and Bowker (1996), for instance, found that their
ZTCM had less explanatory power using respondent reported costs than when using researcher
specified costs and in the coefficient of the reported costs was not significant.
15 For this study, researcher estimated costs (EC), respondent reported costs (SC) and a mixture (MC)
were tested. In the EC specification, the scale of the time costs was varied (specified as hourly wage
rate divided by 2, 4, 6 or 8). Graph 3 shows the different costs specifications.
Deciding which is the best specification is not trivial. On purely statistical grounds, the EC
specifications fare best and the SC worse, with MC in the middle. However, this only shows which
specification fits best to the models assumptions (namely the importance of the travel costs) and not
which model best describes visitor behaviour. However, under the fairly weak assumption of visitors
adjusting their behaviour to their costs, the SC specification can be sorted out because the costs
coefficient is not statistically significant and the MC specification has an implausibly low travel cost
coefficient. Amongst the EC specifications, the choice is between different scales of time costs, where
EC4 would correspond most closely to the common choice, EC6 and EC8 to what visitors report and
EC2 fits the model best to the data. Using the different EC specifications serves as robustness check
below but EC6 and EC8 might be finally used.
Interestingly, the relationship between costs and visits is different for East Germans and other visitors,
most likely for historic reasons. In East Germany, costs do not play a significant role and visitation is
generally higher. To pick up this effect while still estimation one model, a dummy variable needs to be
included, which might be interpreted as the “tradition value” that many East Germans attach to
Jasmund because Analysing the stated preference data shows that East Germans do not have a higher
valuation for Jasmund’s generic characteristics.
The role of the dummy is hence an important one as the higher valuation of Jasmund by East Germans
is not picked up by the CE but only by the ZTCM. However, exact quantification is difficult because
the demand of East Germans is very price-inelastic and likely there is a step in the function. As a first
approximation, the data of the ZTCM can be interpreted by taking the product of the estimated costs
incurred for the park and every zone’s visitation rate. If this is done for East and West Germany
separately, it can be seen that visitors from East Germany are roughly willing to pay 17% more to visit
Jasmund than visitors from the West.
16 As a side note, a test whether estimated costs change in response to averaging the distance from the
reported area codes instead of using the distance from the centre of each zone showed that these two
alternatives give statistically equal results.
4.2 Travel Cost Analysis
The relationship between average zonal travel costs and zonal visitation rates can be estimated using
Tobit regression (as Smith and Desvouges (1986) show, truncation due to the impossibility of negative
visits must be taken into account to avoid biased estimates - which precludes the use of OLS
regression). Importantly, the estimated relationship and associated WTP is not for Jasmund alone but a
considerable share of the visitors´ expenses can be assumed to be for other benefits of the region
Ruegen. Most visitors to jasmund stay several days on the island and hence it would be an
overestimation of Jasmund´s value not to correct for the other benefits of the region. Is has been found
that optimally, the model is first estimated uncorrected and only the final outcome should be corrected
with the right share (e.g. Clough and Meister 1991). The correction share can either be percentage of
time spent on the site in relation to the overall holidays (Flemming and Cook 2008) or the stated
importance of the park in relation to the overall holidays (Hanley and Ruffel 1992).
The best fitting functional form for the estimated demand function turned out to be log-log. For
instance using the EC6 costs, the model obtains a Pseudo R2-value 0.24 and a LR Chi2 with two
degrees of freedom of 15.62. The estimated model then is:
𝐿𝑁 𝑉𝑅! = 8.13 − 1.26 ∗ 𝐿𝑁 𝑇𝐶! + 1.24 ∗ 𝐷!" ,
(1)
where 𝑉𝑅! is the visitation rate and 𝑇𝐶! are the trvel costs and with 𝐷!" = 1 if zone i lies in East
Germany and 0 otherwise.
Then, the aggregate demand curve is:
𝑄=
!"
!!! 𝑝𝑜𝑝!
𝑉𝑅! = !"
!!! 𝑝𝑜𝑝!
𝑓(𝑇𝐶! + 𝐶),
(2)
where 𝑝𝑜𝑝! is the Population of zone i , 𝑇𝐶! are the actual costs for visitors from zone i and C are
hypothetical increases in the travel costs to predict how the visitation rate from zone i would react to
17 costs-increases. This prediction is based on knowing the general relationship between costs and visits
for all zones, as estimated with the Tobit regression.
Then, taking into account the log-log functional form of the demand function, we have aggregate
demand as
𝑄 = 𝑒 !!
!" !"!!
!!! !""" (𝑇𝐶!
+ 𝐶)!! .
(3)
Following Chotikapanich and Griffith (1998), the consumer surplus can directly be estimated with the
following formula:
𝐶𝑆 = 𝑒 !! lim !" !"# →!
!" !"#
!"!
!"!!
! (!""")(𝑇𝐶!
+ 𝐶)!! 𝑑𝐶
(4)
and if 𝛽! is less than -1, (4) can be simplified to:
𝐶𝑆 = !! !!
!! !!
!"!!
!! !!
! (!""")(𝑇𝐶! )
(5)
4.3 Result Travel Cost Model
Depending on how to correct the CS of Jasmund for the share of the whole island, the per person
estimate lies between 104 € to 141 € (EC8) and 113€ to 153 € (EC6). Using EC4 instead would give
131 € to 178 € and EC2 would give 186 to 252 €. Furthermore, visitors from East Germany are
willing to pay 17% more than visitors from the West for a “tradition value” attached to Jasmund. This
value is not captured by the CE.
4.4 Discussion of Results
The estimated per person CS from the CE lies between 51 and 112 €, while the ZTCM estimates the
CS between 104 and 153 €. The somewhat lower estimate from the CE could stem from the difficulty
of estimating the value of Jasmund’s visitor center or from a slightly downwards-biasing Anchoring.
However, keeping in mind how different the approaches are (and hence the measured constructs) and
how much inherent uncertainty there is in the valuation of nature recreation, the values seem close
enough to classify both method’s results as converging. Also worth attention is the fact that the
estimates of the ZTCM are two point estimates, which are surrounded by confidence intervals (CI),
18 which however are not trivially estimated in this application. These CI, however, are likely to be large,
given that the ZTCM uses highly aggregated data.
5. Methodology Discussion
In contrast to many other combined preference approaches, this study does not estimate a model with
pooled data but relies on two individual models, namely the discrete choice model (CE) and the travel
cost model (ZTCM). This is due to their methodological incompatibility – one relying on individual
visitor data and one relying on zonal aggregates. Still, they complement each other – one revealing the
value of marginal changes and how they can be explained by socio-demographic covariates while the
other can ensure realism of the estimated WTP. It seems that estimating the ZTCM with researcher
estimated costs, that are scaled by visitor reported costs, give an unbiased estimation of the consumer
surplus. To disaggregate the consumer surplus, the CE is feasible. However, due to the Anchoring
Bias the CE must usually be cross validated with a correctly calibrated second estimation.
The results of this study suggest that, generally, even such highly different methods as CE and ZTCM
can achieve (approximate) convergent validity. Louviere, Hensher and Swait (2000, ch.13) compile
sixteen other comparisons of different stated and revealed preference approaches in various fields and
find mixed results. According to them, convergence also has to do with the field of research, which
should motivate further research about the causes of this.
Regarding the discussion whether it is possible to improve the TCM with respondent reported costs,
this study finds empirical evidence that this works better with the individual version than with the
zonal one, most likely because reported costs are more individual. Most noteworthy is the fact that
reported costs can still play an important role in estimating ZTCM because they help to find the
appropriate “perceived scale” of the travel costs (compare Randall’s discussion in 1994). To estimate
the model itself however, classically estimated costs seem more useful than those based on reports. An
illustrative example is the following: Respondents from zones with lower wages report higher
19 opportunity costs of time. However, their travel behavior suggests lower opportunity costs of time,
which means that their actual behaviour fits the travel cost model but their statements do not.
A topic that affects all stated preference approaches is what to do when emotions turn the valuation
into an expression of those emotions. Jasmund´s large visitor centre is not found to produce significant
recreational value on average, which perhaps reflects that those visitors who are angry about having to
pay for the centre react very negative to, cancelling out the positive WTP of others who like it.
However, this emotional “valuation” is unlikely to reflect the true WTP of the visitors for the park (as
they could simply decide not to visit it) and hence might better be treated as protest. In this study, this
has been corrected for with a proxy value, using a second best estimate, which is the valuation of a
medium sized visitor centre, which is however not significant either.
A different topic for future studies could be whether Anchoring can be avoided directly in the CE by
means of experimental design. Instead of researcher specified costs, one could express the different
costs as some percent more (less) than a respondent´s current costs to visit Jasmund. This would
anchor the estimates to real costs. Problematic however, is that real costs are not readily available to
the respondents as they would need to estimate which share of total travel costs is actually incurred for
the national park. The researcher would need to first ask respondents about their costs and the
importance of the national park for their trip, correct the reported costs with this share and inform
respondents about their estimated actual costs in order for them to be able to respond adequately to
this design. A future study could use and compare both approaches.
6. Policy Implications and Conclusion
It is estimated that Jasmund National Park delivers a considerable recreation service each year.
Assuming1 1.1 million visitors, the total of Jasmund’s recreation characteristics is estimated to be
around 90 million € based on stated preferences and 140 million € based on revealed preferences.
There is also a significant “tradition value” for visitors from East Germany, which is estimated
1 the
usually cited visitor number is 1.5 million visitors annually, see e.g. BUND 2012, but the survey of this
study indicates less local visitors than implied by this estimate, so 1.1 million is more plausible 20 between 15 and 24 million €. All together, recreation at Jasmund National Park is then estimated to be
worth between 110 and 160 million Euros.
Regarding the values of Jasmund’s characteristics it is found that most important for Jasmund’s
visitors (in descending order) are its protection status as national park (39%), its World Heritage forest
(19%), its chalk cliffs (16%), the possibility to observe deer and mouflon (16%), its biotopes (6%) and
- rather roughly approximated - its visitor centre (5%).
At the same time of having much anger about the entrance fee of the visitor centre, 81% of this study’s
respondents state to be willing to pay an entrance fee for the whole national park; the average
estimated WTP for this is around 10 € (see entrance fee subsample in section 3.3.1). Hence, taking
e.g. Yellowstone National Park in the U.S. as a model (US National Park Service 2012), charging an
entrance fee for the whole park - instead of for the visitor centre - could bring more income and make
visitors happier at the same time (especially since the fee can be set low due to the increased number
of payers). To justify the fee, the additional income should directly be invested in the protection of
biodiversity - e.g. in more regular species and biotope censuses and the employment of highly
qualified staff and researchers.
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