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on Tourism Economics |
| By: | Filippo Randelli (Dipartimento di Scienze per l'Economia e l'Impresa); Federico Martellozzo (Dipartimento di Scienze per l'Economia e l'Impresa) |
| Abstract: | Rural tourism (RT) has grown in many rural regions worldwide and today it is a stable driver of rural development. In this paper we argue that the growth of RT has to be totally divergent from seaside tourism development that tends to create holiday resorts and artificial villages with no identity. To built-up new houses in order to increase accommodation facilities in rural areas could have a twofold negative effect: compromise the beauty of the landscape, a basic local resource, and develop a rural mass tourism. In order to monitor the impact of RT on land use we propose to analyse the development of new building areas in the countryside using a GIS (Geographical Information System) approach. The main source of data for this analysis are the Global Human Settlement Layer (GHSL) of the European Union. The analytical model will be applied to the case of Tuscany. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:frz:wpaper:wp2018_02.rdf&r=tur |
| By: | Batabyal, Amitrajeet; Yoo, Seung Jick |
| Abstract: | We provide the first theoretical analysis of the working of an agency that provides guided tours in multiple foreign languages to tourists. We begin by delineating a simple model of the functioning of such an agency. Our model accounts for the fact that this agency is operating in an environment of uncertainty because the demand for guided tours in foreign languages is probabilistic. Next, we compute the probability that in the next day, the agency under study will receive a total of n∈N requests for guided tours. Finally, we ascertain the probability density function of the amount of time it takes to complete a requested guided tour in an arbitrary foreign language. |
| Keywords: | Foreign Language Guided Tour, Tourist Agency, Uncertainty |
| JEL: | D81 L83 |
| Date: | 2017–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:84378&r=tur |
| By: | Susan Athey; David Blei; Robert Donnelly; Francisco Ruiz; Tobias Schmidt |
| Abstract: | This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location. |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1801.07826&r=tur |