Abstract
This paper combines the travel cost method (TCM) with contingent behavior questions to estimate domestic visitors’ use values for cultural heritage sites in Armenia, a transition economy in which conservation of cultural monuments is hampered by limited resources. Respondents intercepted at four cultural monuments provided information on their visitation patterns, experience at the site, perception of the state of conservation of the monuments, and rating of the quality of the services and infrastructure. We combine actual trips with stated trips under hypothetical programs that would enhance the conservation of the monuments and improve one of (i) the cultural experience at the site, (ii) the quality of the infrastructure, or (iii) the quality of the services, and use the combined actual and stated trips to fit a panel data model. Our study is one of the few applications of the TCM to value cultural heritage sites. Our investigation shows that (i) significant use values are associated with the four study monuments, and (ii) conservation programs and initiatives that improve the cultural experience, or simply make it easier for the respondent to reach and spend time at the monument, are valued by domestic visitors and would encourage higher visitation rates. Actual and intended trips reported by the respondents exhibit good construct validity, in the sense that they are well predicted by price, location, hypothetical scenario and other individual characteristics of the respondents.
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Notes
The number of foreign tourists in Armenia has grown from 31,800 in 1998 to 206,000 in 2003, according to the Ministry of Trade and Economic Development. The reopening of the cultural monuments that were repaired in 2002–03 is thought to have played a significant role in the growth of tourist flows to Armenia (http://www.minted.am/en/tourism.html). Thirty percent of these foreign visitors are from the European Union, 20% from the United States, and 22% from former Soviet Republics.
The TCM is only capable of measuring use values, and thus cannot capture non-use values. (Non-use values are those of people that do not visit the monuments, but wish to conserve them in their own right, for future generations, and in the event they should wish to visit them in the future.) Evidence that Armenian nationals are willing to pay for the protection of cultural heritage sites, even if they do not currently visit them nor plan to do so in the future, comes from a companion contingent valuation survey (Alberini, 2004).
Khor Virap is famous as the place where King Tiridates (Trdat) III imprisoned St. Gregory the Illuminator (the founder of Christianity in Armenia) for 13 years in the late 3rd century. Legend has it that, after ordering the execution of a group of Christian virgins led by Hripsime and Gayane, the King experienced a metamorphosis whereby his head turned into the head of a boar. Upon the release of St. Gregory and the conversion of the King to Christianity, he resumed his human aspect. This led to the adoption of Christianity as the country’s official religion in 301 AD, which makes Armenia the first Christian nation in the world. It is still possible to visit the subterranean cell where St. Gregory was imprisoned.
If the respondent mentioned other destinations visited or to be visited on this trip, we urged him to consider, to the best of his ability, only the costs associating with visiting this site and town/village.
ARMSTAT (2003) reports an average annual income of 1,045 US$ per household for the population of Armenia in year 2001. Moreover, the 2001 Republic of Armenian Population Census indicates that women account for 51.8% of the Armenian population, that 62.1% of the Armenians are married, and that the average age is 38. This suggests that our interviewees tend to be wealthier and more educated than the average Armenian, and are slightly more likely to be male and married, but are roughly of the same age as the average Armenian.
In addition to “visiting the monument” (40.6% of the respondents) and “religious purposes” (19%), many respondents (23.3% of the sample) mentioned that the reason for their visit was to “take foreign guests.”
At the time of the survey, one US dollar was equivalent to 515 AMD.
We note that while everyone reported information about the total cost of the present trip, missing values for the number of people for whom the cost is incurred result in only 469 valid observations for the price of the trip per person.
We conjecture that this is because Tatev is far from the capital, Yerevan, where most of our respondents come from. It is difficult to visit Tatev on a daily trip from the capital because of the time it takes to reach it. Matters are further complicated by the lack of accommodations. We may reasonably expect that an improvement in the quality of the roads and of the services at the site might enhance the enjoyment of the visit.
This model further assumes that travel time and time spent at the site are exogenous, that there is no utility or disutility from traveling to the site, and that each trip to the site is undertaken for no other purpose than visiting the site. It also assumes that individuals perceive and respond to changes in travel costs in the same way they would to changes in a fee for being admitted to the site (Freeman, 2003). Finally, the model assumes that work hours are flexible.
We include dummies for Tatev, Garni, Haghardzin, and Khor Virap. The model does not, therefore, contain the intercept.
Most theoretical models assume that the opportunity cost of time is the wage rate. Much of the empirical literature (since Cesario, 1976) imputes a fraction (usually, about one-third) of the market wage rate as the opportunity cost of time, but Azevedo, Herriges, and Kling (2003) point out that doing so is likely to introduce measurement error into the price variable, which in turn biases the coefficient on the price downward. As in Hanley, Bell, and Alvarez-Farizo (2003) and Alberini, Zanatta, and Rosato (in press), we prefer to enter the out-of-pocket cost of a trip and income separately.
The coefficient on INCMISS captures any systematic differences in the number of trips among those respondents who did and did not report income. The coefficient on PCAPPINC should be interpreted as the marginal effect of income on trips, conditional on information on income being available.
In our initial runs, we experimented with including age squared, household size, and other variables, but the models behaved poorly, so we decided to exclude these regressors from the specifications reported in this document.
As detailed in Englin and Shonkwiler (1995), the value of access for a visitor, or his consumer surplus at the current conditions, is thus the number of visits predicted by the model for this visitor (λ i + 1, where \({\lambda_{i}=\exp({\bf x}_{i}{\varvec{\beta}}_{1}+p_{i}\beta_{2}))}\), divided by the negative of the coefficient on price (−β2). This formula applies to our sample of visitors, who are likely to visit more frequently than the population of visitors at large.
We constructed the dependent variable for the hypothetical visits (j = 2, 3) as follows. For j > 1, we assigned the number of trips respondents said that they would take. If they said that they would visit the same number of times as during the previous year, then y j = y 1. Once again, correcting for the on-site intercept nature of the sample implies that we estimate a Poisson equation where the dependent variable is the number of visits minus 1.
This approach is in the spirit of Azevedo et al.’s point that revealed preference data (i.e., actual trips) should be viewed as complementary sources of values and information with stated preference data (i.e., hypothetical trips) (Azevedo et al., 2003). Revealed preference methods bring the “discipline of the market” to stated preference valuations, while the latter can shed light on consumer preferences for price and quality levels that are currently not observed.
Of course, we attempted to estimate the unrestricted model, but were dissatisfied with the fit of the model and with the implausible value of the unrestricted coefficient on price. All other coefficients, however, were very close to those of the restricted model. Accordingly, we opted for imposing the restriction and for reporting only the results of the restricted maximum likelihood estimation in this paper. We also explored a random effects Poisson model to allow for the possibility of correlation among the responses provided by the same person. In the unrestricted model we find some evidence of the presence of random effects, but the coefficients on all other variables are virtually the same as those of the model in which the observations are independent within respondents. The random effect model does not converge when we impose the restriction that β2 = −0.1263. Finally, we experimented with negative binomial models for the actual trip data, but encountered convergence difficulties, a problem probably caused by the functional form of the probability function for the negative binomial model with correction for endogenous truncation (Haab & McConnell, 2003). Monte Carlo simulations under controlled conditions suggest that these problems with the negative binomial occur frequently, and that Poisson models are well-behaved even when the true data generating process is a negative binomial (Alberini & Reppas, 2005).
Other researchers have investigated whether the slope of the demand function implied by the responses to the hypothetical questions is different from that implied by actual travel. Results are mixed. For example, Rosenberger and Loomis (1999) find that the slope of the demand function (i.e., the coefficient on price per trip) is the same across actual and hypothetical data, and Alberini, Zanatta, and Rosato (in press) report a similar result in a TCM study that examines fishing trips to the Lagoon of Venice by a sample of anglers in the Venice area. By contrast, Azevedo et al. (2003) find that individuals appear to be less sensitive to price in contingent behavior questions than we observe them to be in real life. They are, however, careful to point out that this could be due to the researcher’s poor measurement of the respondents’ travel costs. Finally, Grijalva, Berrens, Bohara, and Shaw (2002) observe rock climbers on multiple occasions before and after the implementation of a policy for the management of rock climbing routes in natural parks in Texas, finding that pre-policy responses (a combination of actual and stated trips) are less price-responsive than post-policy behaviors. Our interpretation of the literature is that stated and actual trips may or may not exhibit a different degree of sensitivity to changes in price and to changes in other conditions, depending on the study and the context.
A likelihood ratio test shows that adding individual characteristics of the respondents, as we do in specification II, improves the fit of the model significantly. The results of specification II are qualitatively similar to those of specification I.
To arrive at this estimate, since the average annual income of an Armenian household was 1,045 US$ dollars in 2001, we increased this figure by 10% to conservatively account for growth, divided it by 12, and further divided it by 4.5, the number of household members of the average Armenian households.
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This paper is based on a study conducted for the World Bank, Europe and Central Asia Department. The opinions expressed in this document are solely the authors’ and do not represent the official views of their respective institutions or the World Bank. We wish to thank Marina Djabbarzade, Gaiané Casnati, Maria Cristina Alberghini, Arpik Nairian, Gayane Shagoyan, Sasun and Eva Tsirunyan. We wish to thank two anonymous reviewers for their comments and suggestions.
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Alberini, A., Longo, A. Combining the travel cost and contingent behavior methods to value cultural heritage sites: Evidence from Armenia. J Cult Econ 30, 287–304 (2006). https://doi.org/10.1007/s10824-006-9020-9
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DOI: https://doi.org/10.1007/s10824-006-9020-9
Keywords
- Valuation of cultural heritage sites
- Non-market valuation
- Travel cost
- Consumer surplus
- Contingent behavior