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2019 | OriginalPaper | Chapter

12. Non-constant Reputation Effect at Spanish Mediterranean Destinations

Authors : Isabel P. Albaladejo, María Isabel González-Martínez

Published in: Tourist Destination Management

Publisher: Springer International Publishing

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Abstract

This chapter analyzes the international tourism demand at Spanish Mediterranean area. This destination receives the highest number of international arrivals in Spain. A dynamic econometric model is built following the Tourism Area Life Cycle (TALC) theory. Unlike other dynamic tourism demand models, our specification allows that the reputation and persistence effect (the effect of the lagged demand on current tourism demand) not to be constant. We estimate the model using panel data consisting of the 11 provinces which make up the Spanish Mediterranean area, and the 7 European countries which are the main origin markets, for the period 2001–2015. The results show a strong persistence in tourism demand. Furthermore, the reputation and persistence effect is positive and decreasing with the ratio between tourists and carrying capacity of the destination. Thus, this effect is not constant but varies across provinces and over time.

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Footnotes
1
Although tourism carrying capacity has traditionally been considered as a static value, several authors argue that it can be subject to change (Saveriades 2000; Cole 2012; Albaladejo and Martínez-García 2015). Carrying capacity could evolve over time due to changes in tourists’ preferences, tourism supply, or the evolution of environmental or social restrictions. Moreover, destinations can expand their capacity simply by rejuvenating the products and services, by investing in developing new ones, opening up to new markets or improving the communication infrastructures.
 
2
Note that the most common dynamic specification set β2 = 0, omitting the quadratic term \( \frac{T_{t-1}^2}{CC_{t-1}} \). In the resulting linear model, the marginal effect is constant.
 
3
The nominal exchange rate between Spain and Eurozone countries is equal to 1. Therefore, we only need to multiply the CPI of the origin country by the nominal exchange rate in the case of United Kingdom.
 
4
The data of hotel beds correspond to beds available in the month of August of each year.
 
5
One-step GMM estimator is based on the assumption that the εij,t are i.i.d. In this chapter, we use one-step robust estimators, where the resulting standard errors are consistent with panel-specific autocorrelation and heteroscedasticity.
 
6
The Hansen statistics is a chi-squared test to determine if the residuals are correlated with the instrument variables. If non-sphericity is suspected in the errors, the Hansen over identification test is theoretically superior to the Sargan (1958) test.
 
7
The value of reputation and persistence effect has been calculated using the estimated coefficients shown in the third column of Table 12.3. For each province, the reputation effect is the average of the calculated effects for tourists arriving from different origin countries.
 
Literature
Aguiló, E., Riera, A., & Roselló, J. (2005). The short-term price effect of a tourist tax through a dynamic demand model. Tourism Management, 26, 359–365.CrossRef
Albaladejo, I. P., González Martínez, M. I., & Martínez-García, M. P. (2016). Non-constant reputation effect in a dynamic tourism demand model for Spain. Tourism Management, 53, 132–139.CrossRef
Albaladejo, I. P., & Martínez-García, M. P. (2015). An R&D-based endogenous growth model of international tourism. Tourism Economics, 21(4), 701–719.CrossRef
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277–297.CrossRef
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51.CrossRef
Baltagi, B. (2008). Econometric analysis of panel data (4th ed.). Chichester: John Wiley.
Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15, 215–227.CrossRef
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143.CrossRef
Butler, R. W. (1980). The concept of a tourist area cycle of evolution: Implications for management of resources. Canadian Geographer, 24(1), 5–12.CrossRef
Capacci, S., Scorcu, A. E., & Vici, L. (2015). Seaside tourism and eco-labels: The economic impact of Blue Flags. Tourism Management, 47, 88–96.CrossRef
Cole, S. (2009). A logistic tourism model—Resort cycles, globalization and Chaos. Annals of Tourism Research, 36(4), 689–714.CrossRef
Cole, S. (2012). Synergy and congestion in the tourist destination life cycle. Tourism Management, 33(5), 1128–1140.CrossRef
Diedrich, A., & García-Buades, E. (2009). Local perceptions of tourism as indicators of destination decline. Tourism Management, 30, 512–521.CrossRef
Garín-Muñoz, T. (2006). Inbound international tourism to Canary Islands: A dynamic panel data model. Tourism Management, 27(2), 281–291.CrossRef
Garín-Muñoz, T. (2007). German demand for tourism in Spain. Tourism Management, 28(1), 12–22.CrossRef
Garín-Muñoz, T. (2009). Tourism in Galicia: Domestic and foreign demand. Tourism Economics, 15(4), 753–769.CrossRef
Garín-Muñoz, T., & Montero-Martín, L. F. (2007). Tourism in the Balearic Islands: A dynamic model for international demand using panel data. Tourism Management, 28(5), 1224–1235.CrossRef
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029–1054.CrossRef
Hsiao, C. (2003). Analysis of panel data (2nd ed.). Cambridge: Cambridge University Press.CrossRef
Hsu, L. C., & Wang, C. H. (2008). The development and testing of a modified diffusion model for predicting tourism demand. International Journal Management, 25(3), 439–445.
Lundtorp, S., & Wanhill, S. (2001). The resort life cycle theory. Generating processes and estimation. Annals of Tourism Research, 28(4), 947–964.CrossRef
Mahajan, V., Muller, E., & Bass, F. M. (1990). New product diffusion models in marketing: A review and directions for research. Journal of Marketing, 54(1), 1–26.CrossRef
Maloney, W. F., & Montes Rojas, G. V. (2005). How elastic are sea, sand and sun? Dynamic panel estimates of the demand for tourism. Applied Economics Letters, 12, 277–280.CrossRef
Massidda, C., & Etzo, I. (2012). The determinants of Italian domestic tourism: A panel data analysis. Tourism Management, 33(3), 603–610.CrossRef
Morley, C. L. (1998). A dynamic international model. Annals of Tourism Research, 25(1), 70–84.CrossRef
Morley, C. L. (2009). Dynamics in the specification of tourism demand models. Tourism Economics, 15(1), 23–39.CrossRef
Naudé, W., & Saayman, A. (2005). Determinants of tourist arrivals in Africa: A panel data regression analysis. Tourism Economics, 11, 365–391.CrossRef
Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49, 1417–1426.CrossRef
O’Reilly, A. M. (1986). Tourism carrying capacity—Concepts and issues. Tourism Management, 7(3), 154–167.
Poprawe, M. (2015). A panel data analysis of the effect of corruption on tourism. Applied Economics, 47(23), 2399–2412.CrossRef
Rodríguez, X. A., Martínez-Roget, F., & Pawlowska, E. (2012). Academic tourism demand in Galicia, Spain. Tourism Management, 33(6), 1583–1590.CrossRef
Roodman, D. M. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136.CrossRef
Roselló, J., Aguiló, E., & Riera, A. (2005). Un modelo dinámico de demanda turística para las Baleares. Revista de Economía Aplicada, XIII(39), 5–20.
Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26, 393–415.CrossRef
Saveriades, A. (2000). Establishing the social tourism carrying capacity for the tourist resorts of the east coast of the Republic of Cyprus. Tourism Management, 21, 147–156.CrossRef
Song, H., & Turner, L. (2006). Tourism demand forecasting. In L. Dwyer & P. Forsyth (Eds.), International handbook on the economics of tourism (pp. 89–114). Cheltenham: Edward Elgar.
Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.CrossRef
Witt, S. F., & Song, H. (2000). Forecasting future tourism flows. In S. Medlik & A. Lockwood (Eds.), Tourism and hospitality in the 21st century (pp. 106–118). Oxford: Butterworth-Heinemann.
Metadata
Title
Non-constant Reputation Effect at Spanish Mediterranean Destinations
Authors
Isabel P. Albaladejo
María Isabel González-Martínez
Copyright Year
2019
Publisher
Springer International Publishing
Book
Tourist Destination Management

Print ISBN: 978-3-030-16980-0

Electronic ISBN: 978-3-030-16981-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16981-7_12