Measuring the Effect of Revealed Cultural Preferences on Tourism Exports

Abstract

The aim of this article is to propose a novel method for measuring the effect of cultural preference on bilateral tourism receipts. The method applied is inspired from Disdier et al. (2010). Using the UNESCO classification and data on bilateral trade in cultural product, a proxy for cultural preferences is constructed. The variable is used in a gravity model for tourism export, which is estimated using a two-step procedure to avoid issues related to endogeneity. The data set used is a panel of 12 OECD countries for a period of 11 years. The variable for cultural preferences eliminates the problems with traditional methods, which by using dummy variables to account for cultural preferences, assume that the latter are time-invariant and symmetrical. The cultural variable constructed is found to be significant in explaining bilateral tourism exports with an elasticity of 0.39.

European cultural heritage and tourism flows: The magnetic role of superstar World Heritage Sites

“…The relationship between cultural heritage and tourism has been extensively discussed within the academic literature and it has been studied from different perspectives and with different aims of the analyses. Several studies have explored the determinants of tourism demand in the attempt of modelling and forecasting it focusing on the attributes of tourist origins and destinations (see, for instance, Faber & Gaubert, 2019; Petit & Seetaram, 2018). Likewise, several other studies have investigated the determinants of tourists' choices based on micro data related to, for instance, socio‐economic conditions of the traveller or psychographic characteristics (see, for instance, Brida & Scuderi, 2013; Marrocu, Paci, & Zara, 2015; Park, Woo, & Nicolau, 2019).…”

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“…In some studies, researchers focus on a single country to determine the components of tourism demand (Akis, 1998; Algieri, 2006; Lorde et al, 2015; Pintassilgo et al, 2016; Samitas et al, 2018; Velasquez and Oh, 2013), while some other researchers center upon country groups or country states (Bento, 2014; Dogru et al, 2017; Eilat and Einav, 2004; Petit and Seetaram, 2018; Shafiullah et al, 2018). In these studies, the researchers who focus on single country follow time series analysis methods such as cointegration, vector autoregression model, autoregressive distributed lag model, and Granger causality (Dritsakis, 2004; Santos and Cincera, 2018; Shafiullah et al, 2018).…”

“…In other studies, researchers who study country groups use panel data methodologies. (a) The first group employs traditional panel data methods such as panel least squares (ordinary least squares (OLS)), first-generation panel unit root, cointegration and causality analysis, and generalized method of moments (Balli et al, 2016; De Vita, 2014; Khadaroo and Seetanah, 2008; Lorde et al, 2015; Massidda and Etzo, 2012; Petit and Seetaram, 2018; Shafiullah et al, 2018). (b) The second group employs second-generation panel data analysis methods and multinomial logit estimation (Eilat and Einav, 2004; Shafiullah et al, 2018).…”

Tourism Carbon Kuznets-Curve Hypothesis: A Systematic Literature Review and a Paradigm Shift to a Corporation-Performance Perspective

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