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Combining the travel cost and contingent behavior methods to value cultural heritage sites: Evidence from Armenia

机译:结合旅行费用和偶然性方法评估文化遗产的价值:来自亚美尼亚的证据

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摘要

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.
机译:本文将旅行成本法(TCM)与偶然性行为问题相结合,以估计国内游客对亚美尼亚文化遗产的使用价值,亚美尼亚是转型经济中资源有限的文化古迹保护。在四个文化古迹处被拦截的受访者提供了有关其访问方式,现场经历,对古迹保护状况的看法以及对服务和基础设施质量的评级的信息。我们将假想行程中的实际行程与既定行程相结合,以加强对古迹的保护并改善(i)现场的文化体验,(ii)基础设施的质量或(iii)服务的质量,并使用实际和陈述的行程组合来拟合面板数据模型。我们的研究是中医药重视文化遗产的少数应用之一。我们的调查表明,(i)与四个研究古迹相关的重要使用价值,以及(ii)改善文化体验,或者仅仅是使受访者更容易到达和花费在古迹上的保护计划和举措,都是受到国内游客的重视,并会鼓励更高的访问率。受访者报告的实际旅行和预期旅行具有良好的结构效度,从价格,位置,假设情景和受访者的其他个人特征可以很好地预测它们。

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