Publisher s notice. Citing this article

Transcription

1 Publisher s notice This Tourism Economics Fast Track paper has been peer-reviewed, revised and fully accepted for publication in Tourism Economics. However, this is an unedited manuscript and will undergo a rigorous editing process before its appearance in an issue of the journal. This means that this manuscript version of the paper may not conform to journal style in terms of presentation, spelling and other usages. It may also contain minor errors of typography, grammar, spelling, referencing, etc, all of which will be corrected in the processes of copy-editing and proofreading. Tourism Economics operates a Fast Track online publication system so that papers can be published and made available almost immediately on final acceptance by the journal. Citing this article Each Tourism Economics Fast Track article is given a DOI. When the paper is assigned to an issue, this DOI will automatically be transferred to the article in the journal issue. This version of the article may be cited using the DOI. Citations should include the author s or authors name(s), the title of the article, the title of the journal followed by the words Fast Track, the year of Fast Track publication and the DOI. For example: Smith, J. (2013), Article title, Tourism Economics Fast Track, DOI xxxxxxxx. Once the paper has been published in an issue of the journal, the DOI will automatically resolve to that final version and the article can be cited in accordance with normal bibliographical conventions. Article copyright 2013 IP Publishing Ltd. doi: /te

2 Research note Exploring the determinants of cruise passengers expenditure while at ports of call in Uruguay JUAN GABRIEL BRIDA Competence Centre in Tourism Management and Tourism Economics (TOMTE), School of Economics and Management, Free University of Bolzano Piazza Università 1/ Universitätsplatz 1, Bolzano, Italy. VINCENZO FASONE Kore University of Enna Cittadella Universitaria, Enna, Italy RAFFAELE SCUDERI (corresponding author) School of Economics and Management, Free University of Bolzano, Piazza Università 1/ Universitätsplatz 1, Bolzano, Italy. Tel Fax raffaele.scuderi@unibz.it. SANDRA ZAPATA-AGUIRRE Kore University of Enna Cittadella Universitaria, Enna, Italy sandra.zapataaguirre@unikore.it Passengers disembarking from cruise ships at ports of call generate significant economic impacts on the host destination. This paper studies the determinants of cruisers spending while visiting the two ports of Uruguay. Data from an official survey are used to investigate the likely determinants of expenditure in the cruise season. The Heckit model suggests the presence of a decision process of purchasing that might be more instinctive than suggested by the findings of other papers for general tourism expenditure. The improvement of the economic effects of a cruise requires the formulation of appropriate marketing strategies according to the nationality of passengers. In addition, several findings indicate that such enhancement is more required in the port of Punta del Este, whose logistics do not facilitate the direct disembarkation of tourists to the port. Keywords: cruises; passengers expenditure; ports of call; Heckit model; Uruguay JEL classification: O10, L83, C22

3 Our research was supported by the Autonomous Province of Bolzano (Research Funds 2009), project: Le attrazioni culturali e naturali come motore dello sviluppo turistico. Un analisi del loro impatto economico, sociale e culturale and by the Free University of Bolzano project Determinants of tourist expenditure: theory and microeconometric models.

4 In the last two decades the cruise industry has been experiencing a significant expansion and became the most dynamic growing segment of the travel and tourism industry (Lee and Ramdeen, 2013). Despite its fast and outstanding growth, it is still considered as a market that has not reached its maturity yet (Sun et al, 2011; Juan and Chen, 2012). From the evolution of the annual number of passengers (Figure 1) it emerges that the average yearly passengers growth rate since 1990 was of approximately 7.4%. South American markets together with the Mediterranean areas are leading this trend of growth, with an annual growth rate of about 10% (Hur and Adler, 2011; CLIA, 2011, 2012). The benefits of cruise tourism go beyond the mere profitability for market operators. Indeed the potential advantages for ports in which passengers disembark are several. (see Dwyer et al, 2004; Dwyer and Forsyth, 1998; Wanhill, 1982). Possibly, this is the reason why destinations may be interested in being part of the selected group of ports chosen by major cruise lines (Lekakou et al., 2009). A similar argument is raised by policy makers to justify millions of dollars spent building new cruise ship terminals and expanding their infrastructure (Brida et al, 2012a). Undoubtedly, destinations receive economic benefits from the stop of cruise ships, depending on whether it is a home or a secondary port. In general, a port of call is a place where passengers disembark temporarily, and then embark in order to continue the cruise. In this case, the generated tourist consumption is mainly related to shopping, guided tours and on-land excursions. On the other hand, in a homeport passengers begin and finish their cruise travel. In this type of ports, overnight stays in hotels are one of the main expenditure items. For both

5 the total amount of spending in the port will depend on the ship size, passengers income level and length of the cruise itinerary. According to the last available report of the cruise industry (FCCA, 2012), cruise stops are quite lucrative for the local community, with an average cruise passenger spending of US$100 (US$90 in the case of crew members spending). This is a surprising result, given that for several destinations this amount is higher than the average expenditure of stay-over tourists that spend in accommodation. Literature on cruise passengers' expenditure during the visit to a particular destination is very recent. Seidl et al. (2006 and 2007) provide a descriptive analysis of cruise tourism economics in Costa Rica, showing that the average cruise tourist is older, less educated and more American, and spends less per person day than its overnight tourists. Morrison et al (2003) compares the expenditure patterns of cruise passengers and stayover tourists, showing that cruisers have a high tendency to buy all-inclusive package vacations but less total expenditure. Douglas and Douglas (2004) estimate the expenditure patterns of cruise ship passengers in several Pacific island ports of call, showing that passenger contribution is invaluable to destinations that have few exploitable resources. In addition to these contributions, which are based on descriptive analysis, there are few papers using econometric tools to model the factors influencing cruisers expenditure. In a study of the port of Cartagena, Brida et al (2012b) estimate two cross-section regression models for the cruising expenditure, showing that heavy spenders are distinguishable from the other segments in terms of age, hours spent off the ship, nationality, income levels and spending patterns. Similarly, Brida and Risso

6 (2010) show that those tourists spending higher amounts of money than the remainder while at the port are also the ones that stay off the ship for a longer time. Cruise activity in Uruguay Uruguay is a relatively small South American country located between Argentina and Brazil. More than 3 million tourists chose Uruguay, a country of 3.2 million inhabitants, as their vacation destination in Their total contribution to the economy was estimated in excess of U.S. $2.2 billion in 2011 (MINTURD, 2012a - see also Brida et al, 2010b). The first cruise ships arrived in the 90s at the ports of Montevideo and Punta del Este. In 2011 the number of cruise passengers represented approximately 10% of the total number of international arrivals. Cruises season in Uruguay coincides with the peak of other forms of tourism, thus generating a competition between cruise passengers and overnight tourists for visiting the same attractions. According to the official data (MINTURD, 2012a), during the season , 225 cruise ships arrived in the country (53% at Montevideo, 47% at Punta del Este). This produced an increase of 32% compared to the previous season, and it was the most outstanding outcome for both ports since 2004 as it can be observed in Table 1. The increase in the average of number of passengers per ship comes in addition to a rise in the capacity of ships, which during the season was about four times higher than in Likewise, the growth rate in the number of cruisers disembarked at Punta del Este and Montevideo in the last eight seasons was 74% and 22%, respectively. The total expenditure of cruise visitors in Uruguay during

7 was of USD.20,884,091, 47.1% of which was spent in Montevideo and the reminder 52.9% in Punta del Este. Methodology In presence of zero-censored variables, such as expenditure, OLS regression is proven to produce inconsistent estimates. Despite this it has been widely used in the literature (Brida and Scuderi, 2013). To overcome this limitation, this paper employs Heckman (1979) approach, also called Heckit see application in tourism by Brida et al (2013), Jang and Ham (2009), Jang et al (2007), Hong et al (2005), Nicolau and Más (2005). It models the decision stage (whether to spend or not), as separate from the one of the amount to allocate on the purchase ( outcome ). It can be resumed by. (1) In Formula (1), is a set of regressors that might even differ between the two stages, and an error term; is a latent variable, proxied by an observed decision indicator, with for those who made a purchase, elsewhere. The outcome latent variable equals to an observed variable, reporting individual expenditure when. In what follows, are the estimates of via a Probit model. Correlation between and produces inconsistent OLS estimates of. For this reason Heckman (1979) augments Stage 2 by the Inverse Mill s Ratio (IMR henceforth),,, (2)

8 where is an error term. The IMR is calculated as, where is a density function and is a cumulative density function. The two estimates reported in this paper are both performed via a procedure for obtaining efficient estimates of the parameters, standard errors, and covariance matrix. The first estimate is of an unrestricted model (UM) that utilizes the same set of regressors for both selection and outcome stages. This means to hypothesize that both stages of deciding whether to spend and its amount are influenced by the same elements. To the end of avoiding multicollinearity, this was tested and rejected. The second approach estimates a model with exclusion restrictions (RM). Specifically, we hypothesize that satisfaction influences only the decision whether to purchase or not, whereas occupation as proxy of income is considered only in the outcome stage. Empirical findings Data are taken from the yearly official survey of the Ministry for Tourism and Sport of Uruguay (MINTURD, 2012b). Table 2 reports the list of regressors and descriptive statistics. Table 3 reports the results from Heckit model. The IMR of the UM is not significant. It indicates that the same set of variables produces no selection effect between spenders and non-spenders. Thus a Tobit regression was applied, which unlike OLS models the level of expenditure by accounting for the censoring at zero. Results indicate the lack of significance in age and gender. Tourists from Brazil and the remainder countries of Latin America show a significant positive attitude towards spending, with respect to

9 Argentinians. Seasonality does not seem to be a prominent determinant. Punta del Este tourists exhibit a significantly lower amount of expenditure than Montevideo. Tranquility of the places and the enjoyment of food and drink influence expenditure positively, as well as hygiene to a lesser extent. Managers present the highest marginal effect among the occupations, with professionals and employees having also a significant effect. As it might be expected the party size has a positive influence on expenditure. The restricted model (RM) shows instead significant differences between the two stages. This time men tend to spend significantly more than women, and young cruisers show a negative attitude towards the decision of spending than middle-aged ones. Brazilians and other Latin Americans are likely to decide to spend more frequently than Argentinians. Brazilians expenditure level is high, whereas North Americans and Europeans show a negative coefficient. First timers spent instead a significantly lower amount of money than those who already visited Uruguay. There seems also to be a seasonal effect in the frequency tourists decided to purchase, rather than in the amount of expenditure. In Punta del Este tourists are likely to decide to purchase tourism goods less frequently, with respect to Montevideo. The satisfaction factors that influence the willingness to purchase are tranquility (see Henthorne, 2000), people and food and beverage, with the latter being the most important variable as in the unrestricted model. Managers and employees have a significant and similar positive attitude in doing purchases. Members of the crew show a likely attitude in influencing negatively on average purchases. Also here party size is in a positive relationship with spending.

10 General discussion and managerial implications A first and general indication is that modeling expenditure of disembarked cruise passengers via a two-step decision-outcome process is not significant, when both regressors are used in the two stages. This is not consistent with previous contributors that applied Heckit model to UMs (Jang and Ham, 2009; Jang et al, 2007; Hong et al, 2005). However these latter analyse the generic tourist spending process. Unlike retail purchase, buying a vacation requires time and is not driven by spontaneity or caprice (Gitelson and Crompton, 1984). Things instead seem to be different in the present case of the purchase of goods while stopping at a port of call. These differences can be ascribed to the shorter available time, which makes the purchase behavior relatively more instinctive. More robust evidence to this hypothesis would have been provided if other variables such as the individual time spent in the destination were available in the dataset. But the restrictions imposed to the RM seem to suggest that local tourist operators might be advised to increase the in-place satisfaction in order to promote the decision to spend. Such satisfaction latter is likely to be related with getting in touch with the tranquility of the places, food and drink, and local people. The positive attitude of passengers from Brazil and the rest of Latin America excluding Argentina towards spending suggests managers to focus on specific communication campaigns and pre-cruise marketing for these segments of customers. Table 4 provides a more detailed look at the average amount of expenditure per item. Percentage differences between overall sample (including non-spenders), and the subset of spenders, measures somehow the gap between average actual and potential expenditure. The highest amount per interviewee is given by shopping of Brazilians.

11 Marketing actions could be addressed to increase their expenditure in food and beverage, where the gap between actual and potential expenditure per tourist is by about 300%. Gaps become wider for tour and transportation expenditure, which is nevertheless related to the decision of self-organizing the visit while disembarked. Instead food and beverage constitute the item where other Latin Americans spend averagely more. In general the latter spend on average less than Brazilians, and they present the highest gaps between actual and potential expenditure also in tours and transportation. Expenditure behavior during the month of April deserves peculiar attention. This month corresponds to the end of the summer in Uruguay. Despite many of the cruise ships arrive during January and February, April reports the highest marginal effect in all models. Such an attitude can be an opportunity for destination authorities in order to encourage ships companies arrivals in this period. In addition, this is also encouraging to promote a better distribution of cruises between peak and off-peak months, in order to reduce the overcrowding in the coastal areas during the peak periods. The highest expenditure level reported by Montevideo can be easily explained by its long tradition and experience in managing cargo and passenger s traffic. Its high-leveled logistic support and cruise passenger s terminal allows visitor to easily walk from the ship directly to the commercial center and shopping areas. In contrast, Punta del Este has a great weakness in this sense. The port does not have a passenger ship terminal and, hence, cruise vessels do not dock adjacently to the city center. This implies that ship passengers spend a lot of time moving around but also in embarking and disembarking procedures. As the length of stay onshore highly influences the likelihood

12 to spend at destination (Brida et al., 2010a; 2012a) Punta del Este port authority should evaluate the building of a more functional infrastructure such as a cruise terminal, in order to benefit from the economic impact of consumers. Indeed the influence of cruise ship terminal s facilities on cruise passengers likelihood to spend onshore constitutes also an interesting field in which future research could investigate. References Brida, J.G., Disegna, M. and Scuderi, R. (2013), Visitors of two types of museums: a segmentation study, Expert Systems with Applications, Vol 40, No 6, pp Brida, J.G., Pulina, M., Riaño, E. and Zapata Aguirre, S. (2012a), Cruise passenger s experience embarking in a Caribbean Homeport, Ocean & Coastal Management, Vol 55, pp Brida, J.G., Bukstein, D., Garrido, N. and Tealde, E. (2012b), Cruise Passengers Expenditure in the Caribbean port of call of Cartagena de Indias: A Cross-Section data analysis, Tourism Economics, Vol 18, No 2, pp Brida, J.G, Bukstein, D., Garrido, N., Tealde, E y Zapata-Aguirre, S. (2010a), Impacto económico del turismo de cruceros. Un análisis del gasto de los cruceristas que visitan el Caribe colombiano, Estudios y Perspectivas en Turismo, Vol 19, pp Brida, J.G., Lanzilotta, B., Lionetti, S. and Risso, W.A. (2010b), The tourism-led-growth hypothesis for Uruguay, Tourism Economics, Vol 16, No 3, pp

13 Brida, J.G. and Risso, W.A. (2010), Cruise Passengers Expenditure Analysis and Probability of Repeat Visit to Costa Rica: a Cross-Section data analysis, Tourism Analysis, Vol 15, No 4, pp Brida, J.G. and Scuderi, R. (2013), Determinants of tourist expenditure: a review of microeconometric models, Tourism Management Perspectives, Vol 6, pp CLIA Cruise Lines International Association (2012), Cruise industry update, Retrieved 10 January 2013 from CLIA website. CLIA Cruise Lines International Association. (2011), The contribution of the North American Cruise Industry to the U.S. Economy in 2011, Retrieved 10 January 2013 from CLIA website. Douglas, N., & Douglas, N. (2004), Cruise ship passenger spending patterns in Pacific island ports, International Journal of Tourism Research, Vol 6, pp Dwyer L., Douglas N. & Livaic Z. (2004), Estimating the economic contribution of a cruise ship visit, Tourism in Marine Environments, Vol 1, No 1, pp Dwyer, L., & Forsyth, P. (1998), Economic significance of cruise tourism, Annals of Tourism Research, Vol 25, No 2, pp FCCA Florida-Caribbean Cruise Association. (2012), Cruise Industry Overview-2012, Retrieved 10 January 2013 from FCCA website. Heckman, J. J. (1979), Sample selection bias as a specification error, Econometrica, Vol 47, No 1, pp

14 Hong, G. S., Fan, J. X., Palmer, L., & Bhargava, V. (2005), Leisure travel expenditure patterns by family life cycle stages, Journal of Travel & Tourism Marketing, Vol 18, No 2, pp Hur, Y. K. & Adler, H. (2011), An Exploratory Study of the Propensity for South Koreans to take Cruises: Investigating Koreans Perceptions of Cruise Ship Travel, International Journal of Tourism Research, Vol 15, No 2, pp Jang, S. C. S. and Ham, S. (2009), A double-hurdle analysis of travel expenditure: Baby boomer seniors versus older seniors, Tourism Management, Vol 30, No 3, pp Jang, S. C. S., Ham, S., and Hong, G. S. (2007), Food-away-from-home expenditure of se- nior household in the United States: A double-hurdle approach, Journal of Hospitality & Tourism Research, Vol 31, No 2, pp Juan, P.-J. & Chen, H.-M. (2012), Taiwanese Cruise Tourist Behavior during Different Phases of Experience, International Journal of Tourism Research, Vol 14, pp Lekakou, M.B., Pallis, A. & Vaggelas, G. (2009), Which homeport in Europe: the cruise industry s selection criteria, Tourismos: An International Multidisciplinary, Vol 4, No 4, pp Lee, S. & Ramdeen, C. (2013), Cruise ship itineraries and occupancy rates, Tourism Management, Vol 34, pp MINTURD (2012a), Ministerio de Turismo y Deporte - Anuario Retrieved 5 January 2013 from Ministry of Tourism and Sports of Uruguay website.

15 MINTURD (2012b), Ministerio de Turismo y Deporte. Bases de datos. Encuestas de cruceros. Retrieved 15 December Morrison, A.M., Yang, C.H., O Leary, J.T., & Nadkarni, N. (2003), Comparative profiles of travellers on cruises and land-based resort vacations, Journal of Tourism Studies, Vol 14, pp Nicolau, J. L., & Más, F. J. (2005), Heckit modelling of tourist expenditure: Evidence from Spain, International Journal of Service Industry Management, Vol 16(3), pp Seidl, A., Guillano, F. and Pratt, L. (2007), Cruising for colones: cruise tourism economics in Costa Rica, Tourism Economics, Vol 13(1), pp Seidl, A., Guillano, F. and Pratt, L. (2006), Cruise tourism and community economic development in Central America and the Caribbean: The case of Costa Rica, Pasos, Vol 4(2), pp Sun, X, Gauri, D.K. & Webster, S. (2011), Forecasting for cruise line revenue management, Journal of Revenue and Pricing Management, Vol 10, pp Wanhill, S.R.C. (1982), Some aspects of cruise ships, Maritime Policy and Management, Vol 9, No 4, pp

16 TABLES Table 1. Cruise ship arrivals in Uruguay by port, Montevideo Punta del Este Total Season S T T/S S T T/S S T T/S 2004/ , , , / , , , / , , , / , , , / , , , / , , , / , , / , , Note: T: number of tourists; S: number of ships. Source: MINTUR (2012)

17 Table 2. Variables list and descriptive statistics. Numbers indicate percentages when not differently specified. Gender Month Port Male 36.8 December 16.3 Montevideo 44.7 Female 63.2 January 30.7 Punta del Este 55.3 Age February 24.8 Like < March 24.8 Buildings April 3.4 Landscape 7.1 > Residence Hygiene 9.9 Occupation Brazil 44.6 People 33.7 Housewife 3.8 Argentina 34.4 Tranquility 5.7 Manager. entrepreneur 14.8 Latin America 4.7 Food and beverage 5.1 Professional 35.1 North America 7.2 Other 1.2 Employee 11.6 Europe 7.3 Party size (mean) 2.44 Crew member 6.4 Other 1.8 Total expenditure (mean) Other 28.3 First time visitors 66.8 Total expenditure (median) 100

18 Table 3. Heckit estimation, marginal effects. Gender (ref. Woman) Heckit Unrestricted (UM) U Selection Outcome Heckit Restricted (RM) R Tobit T Selection Outcome Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Man ** ** Age (ref ) < * > Residence (ref. Argentina) Brazil 0.483*** *** *** *** *** Latin America 0.844*** *** *** North America *** *** Europe ** ** Other Previous experience First time in Uruguay ** ** Month (ref. December) January February 0.169* * ** March 0.156* * ** April 0.384** *** ** Port of call (ref. Montevideo) Punta del Este *** * *** *** Satisfaction Buildings Landscape Hygiene * People * Tranquility 0.430*** *** *** Food and drink 0.560*** *** *** Other Occupation Housewife Manager. entrepreneur 0.287*** *** * Professional 0.296*** *** Employee 0.219** *** * Crew member *** *** Party size *** *** *** Inverse Mills Ratio *** Note: N=3173; Number of censored observations: 585. Significance: *** p<0.01; ** p<0.05; * p<0.1 U Wald χ 2 (27)=284.79, p < T LR χ 2 (27)=383.17, p < 0.001, Log likelihood = , Pseudo R 2 = R Wald χ 2 (27)=259.00, p <

19 Table 4. Distribution of expenditure items, average amounts, USD, Brazilians and Latin Americans. Food beverage and All Brazilians Latin Americans Only Only spenders Diff., % All spenders Diff., % , ,32 Shopping , ,25 Tour , ,18 Transportation , ,22 Other , ,29

20 FIGURES Figure 1. Number of cruise passengers, million. Source: CLIA (2008) and FCCA (2012)