Factors Influencing Visitor's Choices of Urban Destinations in North ...

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Tourism Demand/Econometric Modelling LiteratureTourism Demand/Econometric Modelling Literature provides a range of econometrictechniques and models used to forecast tourism demand. Since most of the empiricalwork that was found was focused on determining tourist demand, Global Insight includedthe two together in this one category. Econometric techniques range from statistical timeseries that include simple ARIMA, autoregressive and “no change” models to morecomplicated error-correction models. Typical tourism demand models focus on theorigin-destination pair of countries.“A Review of International Tourism Demand Models”Lim (1997) provides a comprehensive summary of 100 articles on the topic of tourismdemand modelling published over the period 1961-94. Lim presents an overview of themost commonly used explanatory variables; classification by type of data used;dependent variables modelled; model specifications; and the qualitative factors thatinfluence tourism demand.Data Description: More than 50% of tourism studies used annual data. While thenumber of annual observations ranged from 5 to 28, the most common number ofobservations was 16. This small number of observations questions the reliability ofregression estimates for many of the studies. To address this concern, some studies usedmonthly or quarterly data, cross-section data, or pooled time-series data.Model Specification: Typical tourism demand model focuses on the origin-destinationpair of countries. The general demand model that is typically estimated is presentedbelow:DT ij = F(Y j , TC ij , RP ij , ER ij , QF i )Where:DT ij - demand for international travel services by origin j for destination i;Y j – income of origin j;TC ij – transportation cost between destination i and origin j;RP ij – relative prices (i.e. the ratio of prices in destination i to prices in origin j and inalternative destinations);ER ij – currency exchange rate, measured as units of destination i’s currency per unit oforigin j’s currency;QF i – qualitative factors in destination i.53
Model SpecificationTable 24: Model SpecificationsLog-linear single equations 56%Only linear-single equations 11%System of equations (i.e. complete demand model) 14%Other (combination of different models) 7%No model was used 2%Number of Studies Using this ModelSpecificationSource: Lim (1997)The main reason for using a log-linear model was related to the ease of interpretation ofthe coefficients as estimated elasticities. More than 80% of studies used OLS method ofestimation, either alone or in conjunction with other methods.Dependent Variable: The number of tourist arrivals and/or tourist departures was themost commonly used dependent variable. Other variables included the number of touristsper capita on independent travel; share of tourist arrivals; proportion of tourists to aparticular destination; visit rate; and the proportion of recreational and business tourists.One study used conference attendance as a dependent variable. Tourist expendituresand/or receipts expressed in nominal or real terms are also used to measure tourismdemand. Bakkal and Scaperlanda (1991) suggested that the number of nights spent attourist accommodation is superior to other proxies, because it accounts for time spent at ahotel excluding stay with friends and relatives.Number of explanatory variables: The number of independent variables used in thestudies ranges from one to nine. Typically, four explanatory variables were included inthe regression. Some studies used more than one qualitative factor. Qualitative factorsincluded tourists’ attributes (i.e. age, level of education, gender andemployment/profession); household size; population and population change in the origin;trip motive or frequency; destination attractiveness; political, social, and sporting eventsin a destination. Destination attractiveness refers to climate, culture, history, and naturalenvironment.54
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Table of ContentsI. EXECUTIVE SUMMA
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I. Executive SummaryHighlightsThe r
Page 6 and 7: Next StepsThis project has surfaced
Page 8 and 9: IV.coefficients from these models p
Page 10 and 11: series, simple autoregressive and
Page 12 and 13: Arts & Culture Museums Historymuseu
Page 14 and 15: The tourist visitations data was al
Page 16 and 17: Hotel Room Count and Hotel Property
Page 18 and 19: Table 9: Michelin’s RatingsRating
Page 20 and 21: value below one suggests that the s
Page 22 and 23: Table 13: Individual Attractions Th
Page 24 and 25: Table 16: Significant Results 14Equ
Page 26 and 27: The adjusted R 2 for this model is
Page 28 and 29: • On average across all cities, b
Page 30 and 31: Model 5C 17 : In Model 5C, the numb
Page 32 and 33: The model overpredicts the number o
Page 34 and 35: Attraction Elasticities for Ottawa
Page 36 and 37: Table 22: Marketing Budget Elastici
Page 38 and 39: VII.Technical AppendixA. Attraction
Page 40 and 41: • Metropolitan Opera House in New
Page 42 and 43: Model 3: Three-Star Quality-Rated A
Page 44 and 45: Model 5B: Three-Star Quality-Rated
Page 46 and 47: 3. The cognitive perspective involv
Page 48 and 49: also found that almost half of the
Page 50 and 51: Also, the model seems to completely
Page 52 and 53: strengths and its weaknesses, devel
Page 54 and 55: Items from QuestionnaireTable 23: M
Page 58 and 59: Table 25: Types of Explanatory Vari
Page 60 and 61: Results: Lim and McAleer found that
Page 62 and 63: Richards, Greg. (2002). “Tourism
Page 64 and 65: Figure 4: Overseas Visitors to U.S.
Page 66 and 67: Figure 6: Cities Person-Trips Volum
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