USING TOURISM INDICES TO DETERMINE TRENDS AND FORECASTS: THE CASE OF INTERNATIONAL OVERNIGHT PLEASURE TRIPS TO CANADA

Dongkoo Yun, Ph.D. School of Hospitality and Tourism Management

University of Guelph Guelph, Ontario N1G 2W1

Canada Phone: 519.362.2110

Email: dyun@uoguelph.ca

Marion Joppe, Ph.D. Director, Professor

School of Hospitality and Tourism Management University of Guelph

Guelph, Ontario N1G 2W1 Canada

Phone: 519.824.4120 ext. 56118 Email: mjoppe@uoguelph.ca

HS Chris Choi, Ph.D. Assistant Professor

School of Hospitality & Tourism Management University of Guelph

Guelph, Ontario N1G 2W1 Canada

Phone: 519.824.4120 ext. 53370 Email: hwchoi@uoguelph.ca

USING TOURISM INDICES TO DETERMINE TRENDS AND FORECASTS: THE CASE OF INTERNATIONAL OVERNIGHT PLEASURE TRIPS TO CANADA

ABSTRACT Using the international travel survey data sets for overseas and US resident trips to Canada, a number of tourism index time series were produced in this study. These indices reflected both quantity and quality of the variables used. By using these indices to determine trends and forecasts, the study provides some insightful marketing implications. Consequently, the results indicate that the index scores were identified as useful tools to examine and monitor market changes and better understand the dynamics of international tourism by overseas and US overnight pleasure travellers. INTRODUCTION Travel from overseas nations into Canada rose for the second straight year, following three consecutive years of declines. The number of overnight trips to Canada from countries other than the United States increased 7.1% to more than 4.2 million. Overseas tourists spent $5.7 billion in Canada in 2005, up 6.5% from 2004. However, Americans not only made fewer trips to Canada, but spent less as well. The number of overnight trips from the U.S. fell 4.6% to only 14.4 million, and US tourists spent about $7.5 billion in Canada, down 8.6% compared to 2004 (Statistics Canada, 2006). In addition, the recent increase in Canada’s exchange rate has been even stronger than during 1986-1991. But the only observable impact has been to discourage Americans from travelling to Canada, although some of this decline is also part of the negative fallout from the September 11 attacks (Roy, 2005). Reliable figures and facts on trends in tourism are always important in light of the high value placed on such data by planners and investors (Lapage, 1987). Available indicators such as destination visits, lodging and spending information, activities participated in at destinations, along with other consumer consumption information, provide gross estimates of tourism trends. Even questions of travel market shifts, interactivity substitutions, changing travel interests and frequencies, and travel intentions reflect a more ideal level of detail for tourism planning at particular destinations (Lapage, 1987). Such trends data can best be generated by longitudinal data or specific studies of populations over time, and work of this kind can set the scene for undertaking accurate forecasting (Bakalis, Morris, & Wilson, 1994) or making projections which extrapolate historical experience or past trends based on the assumption that the future will be a logical extension of the past (Moeller & Shafer, 1987). Therefore, forecasting based on past trends can provide at least some indication of the expected level of demand which consequently enables tourism businesses to plan and control their resources accordingly. The purposes of this study are to explore and monitor tourism trends and performance by overseas and US overnight pleasure trips to Canada, utilizing tourism indices related to information of the international travel survey, to forecast future trends and expectations of the indices by performing time-series forecasting methods, and to provide insightful marketing implications. TOURISM INDICES AS PERFORMANCE INDICATORS In general, tourism professionals/marketers/policy-makers need to assess and monitor market changes to better understand the dynamics of the travel and tourism industry or destinations, and to improve the direction of marketing programs. A tourism index is one of the useful tools to track and explore market changes and evolution (Backman & Uysal, 1987). Tourism index scores have been broadly used to measure the industry’s performance at every level, from international to the state to a destination (Hogan & Rex, 1984; Judd & Rulison, 1983; Uysal & Crompton, 1985; Witt, 1980). The advantage of tourism indices is that they provide opportunities to examine and compare by both markets of origin and destinations, and to help in setting standards as performance indicators. In addition, the index scores as a time series can be used to compose graphical information that portrays images and patterns of changes in the travel and tourism markets,

including seasonal variations, trends, cycles, and fluctuation. According to Backman, Uysal and Backman (1992), major considerations in the development of tourism indices are seasonal variation, the strength of the index, and the data problem. A review of the literature reveals that a variety of index measures pertinent to travel and tourism exist, including national tourism indicators (Statistics Canada, 1996), trip index (Clough & Pearce, 1987; Uysal & McDonald, 1989) developed by Pearce & Elliott (1983), travel price index (Backman & Uysal, 1987; Morley, 1994), travel activity index (Backman & Uysal, 1987; Backman, et al., 1992; Huan & O’Leary, 1999; Tierney, 1990; Vina, Hollas, Merrifield, & Ford, 1994), destination diversification index developed by Fesenmaier and Lieber (1988), destination perception index (Rafferty, 1990), tourism barometers (Bond & McDonald, 1978), travel frequency (Schmidhauser, 1975), travel propensity index (Bond & McDonald, 1978; O’Leary, Hsieh, Uysal, Bailie, 1993; Hudman, 1979, 1980; Uysal, Fesenmaier, & O Leary, 1994), tourism function (Hinch, 1990; Keogh, 1984; Potts & Uysal, 1992) developed by Defert (1967), gross travel propensity (Schmidhauser, 1975, 1976; Uysal, Oh, & O’Leary, 1995), travel intensity (O’Leary et al., 1993), tourist intensity (Keogh, 1984; Potts & Uysal, 1992), travel dispersal index (Oppermann, 1992), main destination ratio (Leiper, 1989), lodging index (Wassenaar & Stafford, 1991), spatial association index (Smith, 1995) developed by Lee (1979), and many more. In the following section, seven specific tourism indices, produced in this study, are described and discussed in terms of concepts and measurement (formulas) with a brief review of literature. METHODOLOGY Data Source Canada’s International Travel Survey (ITS) data was used for this study. The ITS contains four main parts: Canadian resident trips abroad, Canadian resident trips to United States, overseas resident trips to Canada, and United States resident trips to Canada. Of the four main categories, data sets of both overseas and U.S. resident trips to Canada during the period from January 1990 to September 2005 were used (Statistics Canada, 2005a, 2005b). Samples Table 1 presents the ITS data characteristics and samples used. A total of 195,504 samples of overseas resident trips to Canada and 567,765 samples of U.S. resident trips to Canada were collected from 1990 till September 2005 by Statistics Canada. Of these, 78,892 samples of overseas resident trips to Canada and 151,374 samples of U.S. resident trips to Canada were used for calculating a series of tourism indices and forecasting the index scores in this study. The number of samples used indicates overseas and U.S. overnight pleasure trips to Canada rather than same day trips and other purposes of trip. Analytical Method of Tourism Indices Trip Index (TI) The TI developed by Pearce & Elliott (1983) was to measure travel time. The TI relates the length of the stay in a country or at a destination to the total length of the trip (Clough & Pearce, 1987; (Oppermann, 1992, 1993; Uysal & McDonald, 1989). The formula is:

100TI ×=Tripthe on SpentNights Total

nDestinatio Particulara at SpentNights

Demographic Index (DI) The DI is used to measure and monitor the demographic backgrounds of travellers (Huan & O’Leary, 1999). Specifically, the demographics in the ITS refer to the number of adults and/or children who are part of the same travel party (party composition) (Statistics Canada, 2005a,

2005b). The formulas are:

100Index Youth Female) (or Male ×=Party Travelthe of Number

19-0 ofAge s Female)' (orMale of Number

100Index Adult AgeFemale) (or Male ×=Party Travelthe of Number

34-20 ofAge s Female)' (orMale of Number

100Index AgeMiddle Female) (or Male ×=Party Travelthe of Number

54-35 ofAge s Female)' (orMale of Number

100Index Senior Female) (or Male ×=Party Travelthe of Number

55 over ofAge s Female)' (orMale of Number

Destinations Visited Index (DVI) The DVI indicates the number of destinations visited in Canada (Statistics Canada, 2005a, 2005b). Thus, the DVI as one of the tourism performance measures in Canada can also be calculated using the number of provinces visited by their travelling party. The following formula was applied:

100DVI ×=(15)Canada inPlaces Reportable of Number Total

Canada in VisitedPlaces Specific of Number

Table 1. The ITS Data Characteristics and Samples

Overseas Resident Trips to Canada U.S. Resident Trips to Canada

Year of Survey

Year Total

Overnight Trips

Overnight Pleasure Trips a

Year Total

Overnight Trips

Overnight Pleasure Trips b

1990 11,110 10,709 4,038 40,443 30,165 12,689 1991 12,292 11,875 4,343 36,426 25,346 10,253 1992 13,742 13,290 5,030 35,533 24,384 8,630 1993 14,048 13,557 5,474 35,902 23,749 9,458 1994 13,932 10,709 5,587 31,739 20,562 8,239 1995 12,556 12,004 5,256 29,766 18,420 8,480 1996 12,658 12,019 5,389 30,601 18,315 8,924 1997 10,214 9,650 4,052 30,910 19,652 9,240 1998 11,567 11,005 4,797 28,450 17,265 7,846 1999 8,108 7,663 3,473 35,105 18,968 9,246 2000 14,566 13,926 6,848 38,487 19,526 9,800 2001 15,682 15,029 7,165 41,782 20,244 10,758 2002 12,782 12,195 5,329 40,137 19,403 10,336 2003 11,595 10,977 4,343 39,680 18,366 9,652 2004 11,225 10,677 4,295 41,639 18,894 10,097 2005 (Q3) 9,427 8,976 3,473 31,165 14,605 7,726 Total 195,504 186,988 78,892 567,765 327,864 151,374

Note: a and b indicate sampled overnight pleasure travellers used in this study.

Travel Activity Index (TAI) The TAI represents an aggregate index that measures relative changes in activity at a destination (Backman & Uysal, 1987; Backman et al., 1992; Tierney, 1990). Travel activity participation (or attendance) data at given destinations over a period of time is one significant index of tourism performance. In the ITS data, single TAI can be calculated by rescaling measures: “0 equals 0” and “1 equals 100”, which is the same as the calculation of percentages. In case of the ITS, the following formula can be applied as an aggregate TAI:

100TAI ×=(19) Surveyed Activities Travel Total of Number

edParticipat Activities Travel of Number

Accommodation Index (AI) The AI is used to measure and monitor the type of accommodation used by travellers at a particular destination. The ITS tracks accommodation used in each location visited: hotel, motel, home of friends or relatives, camping, cottage or cabin and other (Statistics Canada, 2005a, 2005b). The formula is:

100AI ×=(6)ions Accommodat Selectable Total of Number

Visited nDestinatio Particular at Stayed ionAccommodat Specific of Number

Travel Expenditure Index (TEI) The TEI represents a measure of relative spending rates in each expenditure category. The ITS contains five categories of expenditure, including accommodation, transportation, food and beverage, recreation and entertainment, and other spending including souvenirs, shopping, photos, etc., and total spending from travel parties (Statistics Canada, 2005a, 2005b). Therefore, the TEI can be calculated by using the following formula:

100TEI ×=Party Travel by Spending Total

Party Travel byItem Specifica on Spending

Travel Service Index (TSI) The TSI represents a measure of relative perception of five different aspects of a trip in Canada, including transportation, accommodation, hospitality of local people, value for money, and variety of things to see and do (Statistics Canada, 2005a, 2005b). In the ITS data, each TSI can be produced by rescaling: “Good (= 100)”, “Average (= 50)” and “Poor (= 0)”. In addition, an aggregate TSI can be calculated by using the following formula:

(5)Items Service Travel of Number TotalItemService Travel Specifica in RescaledScores Rating

=TSI

Forecasting using Tourism Indices As discussed under the analytical method of tourism indices, each tourism index in this study can be identified through a time-series, which is a collection of observations made sequentially through time (Frechtling, 1996; Witt & Witt, 1992). They are univariate time series. Thus, univariate forecasting methods were applied in this study, where forecasts depend only on present and past values of the single series being forecasted (Chaffiled, 2001; WTO, 1980). Basically, a univariate model describes the distribution of a single variable at time t, namely Xt, in terms of its relationship with past values of Xt and its relationship with a series of white-noise random shocks defined as a sequence of uncorrelated, identically distributed random variables with zero mean and constant variance (purely random process) (Brocklebank & Dickey, 2003).

Univariate forecasting methods include linear regression, decomposition methods such as ARIMA (autoregressive integrated moving average), X-11-ARIMA and X-12-ARIMA widely used in economics and government, especially in Canada, England, and the USA, and exponential smoothing techniques. In general, the method by which a forecasting model is selected depends upon components and characteristics or a function of time in a time-series data such as linear trend, cycle, seasonal variation, and irregular fluctuation. In this study, seasonal ARIMA (abbreviated SARIMA) models, sometimes called Box-Jenkins models (Box & Jenkins, 1970), are applied. Although not the only models for this type of estimation, it is the most appropriate since the index data sets were identified as having significant seasonal variations and fluctuations by plotting past trends and analyzing ANOVA tests. A series of 77 indices in seven index categories (TI, DI, DVI, TAI, AI, TEI and TSI) covering the period from January 1990 to September 2005 are used as explanatory variables. The index scores are produced and forecasted monthly and then aggregated yearly as shown in Figures 1 to 12. The forecasting procedures processed in this study by performing “Proc ARIMA” in the SAS/ETS program are as follows (Brocklebank & Dickey, 2003; SAS Institute, 1988):

Step 1: Model identification (ACF: autocorrelation functions, IACF: inverse autocorrelation functions, PACF: partial autocorrelation functions, and autocorrelation check for white noise with Chi-square statistics)

Step 2: Model estimation (CLS: conditional least squares, ULS: unconditional least squares, and ML: maximum likelihood)

Step 3: Model fit checks (AIC: Akaike’s information criterion, SBC: Schwartz’s Bayesian criterion, Chi-square check of residuals, differencing AR and MA)

Step 4: Forecasts for variable X A SARIMA model with non-seasonal terms of order (p, d, q) and seasonal terms of order (P, D, Q) is abbreviated as “SARIMA (p, d, q) × (P, D, Q)s” model where ‘p’ and ‘P’ are AR (autoregressive) terms, ‘q’ and ‘Q’ are MA (moving average) terms, and ‘s’ denotes the number of seasonal periods (e.g., quarterly or seasonally = 4 and monthly = 12). If the original data series is differenced d times before fitting the ARMA (p, q) process, then the model for the original undifferenced series is said to be an ARIMA (p, d, q) process, where the letter ‘I’ stands for integrated and ‘d’ denotes the number or degree of differences taken. With ARIMA models, there is an additional problem in choosing the required order of differencing (i.e., what is d?) (Chaffiled, 2001). Therefore, applications for a time-series after finding out the appropriate (p, d, q) or (P, D, Q) in SARIMA procedures are the most important steps. In this study, a number of different SARIMA models for each index time-series are estimated to find better models by repeating steps 1 to 3 with identifying fit statistics and then step 4 is processed for the forecasts. Each forecasting is estimated by using a monthly index data set but plotting the result is provided using yearly bases in this study. RESULTS Seasonality Checks Butler (1994) describes seasonality in tourism as “a temporal imbalance in the phenomenon of tourism, [which] may be expressed in terms of dimensions of such elements as number of visitors, expenditure of visitors, traffic on highways and other forms of transportation, employment and admission to attractions (p. 332).” The basic method for measuring tourism seasonality is through determining the number of visitors in relation to such periods as quarters, seasons, months, weeks, and days. Simple tests for seasonality include the Poisson distribution, Chi-square tests, Gini-coefficient, and F-tests (Lundtorp, 2001). In this study, a series of F-test were analyzed to examine seasonality (seasonal variation) and a number of plots for the original data were produced to observe trend, cycle, and fluctuation in the index data during the period. F-test results indicate that all tourism index scores were significantly different for months, quarters, seasons and even years at the 0.05 level. The results of trend plots also indicate that there exist seasonal trends, cycles, and regular or irregular fluctuation.

Trends and Forecasts of Tourism Indices Table 2 presents selected SARIMA models in each tourism index time-series by specifying correct differencing and appropriate transformations for the raw index data. Provincial’ Trip Index (TI) The results of TI’s trends and forecasts by overseas pleasure markets are plotted in Figure 1. Yearly TI scores for overseas markets range from 4.67 to 74.73 during the period and forecasted from 13.91 to 70.26 for the years 2005 to 2007. Of 12 provinces and territories, TI scores for British Columbia, Quebec, and Ontario are relatively higher than other provinces, whereas those of Saskatchewan, Northwest Territories, Prince Edward Island, and New Brunswick are relatively lower during the period. Figure 1. Trends and Forecasts of Provinces’ Trip Index (TI) by Overseas Pleasure Markets

0

10

20

30

40

50

60

70

80

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

NF PE NS NB QC ON MA SA AB BC YU NW

Figure 2 presents the results of TI’s trends and forecasts by US overnight pleasure markets. Yearly TI scores range from 13.87 to 92.21 and forecasted from 20.82 to 92.34 during the same period. TI scores of US markets for Ontario, British Columbia, Quebec are relatively higher than other provinces, whereas those of Northwest Territories, New Brunswick, Newfoundland and Labrador, Prince Edward Island, and Saskatchewan are relatively lower over time. Figure 2. Trends and Forecasts of Provinces’ Trip Index (TI) by U.S. Pleasure Markets

0

20

40

60

80

100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

NF PE NS NB QC ON MA SA AB BC YU NW

Table 2. Selected SARIMA Models Tourism

Index Specific

Time Series Selected SARIMA Model Tourism Index

Specific Time Series Selected SARIMA Model

NF SARIMA (0,1,1) × (0,1,2) s=12 NF SARIMA (0,1,1) × (0,1,1) s=12PE SARIMA (0,1,1) × (0,1,1) s=12 PE SARIMA (0,1,1) × (0,1,2) s=12NS SARIMA (0,1,1) × (0,1,2) s=12 NS SARIMA (0,1,1) × (2,1,0) s=12NB SARIMA (0,1,1) × (0,1,2) s=12 NB SARIMA (0,1,1) × (2,1,0) s=12QC SARIMA (0,1,1) × (0,1,1) s=12 QC SARIMA (0,1,1) × (0,1,2) s=12ON SARIMA (0,1,1) × (0,1,2) s=12 ON SARIMA (0,1,1) × (0,1,1) s=12MA SARIMA (0,1,1) × (0,1,1) s=12 MA SARIMA (0,1,1) × (0,1,1) s=12SA SARIMA (0,1,1) × (0,1,2) s=12 SA SARIMA (0,1,1) × (0,1,2) s=12AB SARIMA (0,1,2) × (0,1,1) s=12 AB SARIMA (0,1,1) × (0,1,2) s=12BC SARIMA (0,1,2) × (0,1,1) s=12 BC SARIMA (0,1,1) × (0,1,1) s=12YU SARIMA (1,1,1) × (0,1,1) s=12 YU SARIMA (0,1,1) × (0,1,1) s=12

Provinces’ TI by overseas in Figure 1

NW SARIMA (0,1,1) × (0,1,2) s=12

Provinces’ TI by U.S.

in Figure 2

NW SARIMA (1,1,1) × (0,1,1) s=12Male Youth SARIMA (0,1,1) × (0,1,1) s=12 Male Youth SARIMA (0,1,1) × (0,1,1) s=12Male Adult SARIMA (0,1,1) × (0,1,1) s=12 Male Adult SARIMA (0,1,1) × (0,1,1) s=12Male Middle SARIMA (0,1,2) × (0,1,2) s=12 Male Middle SARIMA (0,1,1) × (0,1,1) s=12Male Senior SARIMA (0,1,1) × (0,1,1) s=12 Male Senior SARIMA (0,1,1) × (0,1,1) s=12Female Youth SARIMA (0,1,1) × (0,1,2) s=12 Female Youth SARIMA (0,1,1) × (0,1,1) s=12Female Adult SARIMA (0,1,1) × (0,1,2) s=12 Female Adult SARIMA (0,1,1) × (0,1,1) s=12Female Middle SARIMA (0,1,1) × (0,1,2) s=12 Female Middle SARIMA (1,1,1) × (0,1,1) s=12

DI by overseas in Figure 3

Female Senior SARIMA (0,1,1) × (0,1,1) s=12

DI by U.S.

in Figure 4

Female Senior SARIMA (0,1,1) × (0,1,1) s=12OV Overnight Pleasure Trips SARIMA (0,1,1) × (0,1,1) s=12 OV Overnight

Pleasure Trips SARIMA (0,1,2) × (1,1,0) s=12DVI in Figure 5 US Overnight

Pleasure Trips SARIMA (0,1,1) × (0,1,1) s=12

TAI in Figure 6 US Overnight

Pleasure Trips SARIMA (2,1,0) × (0,1,1) s=12

Hotel SARIMA (0,1,1) × (0,1,1) s=12 Hotel SARIMA (0,1,1) × (0,1,1) s=12Motel SARIMA (1,1,1) × (0,1,1) s=12 Motel SARIMA (0,1,1) × (1,1,0) s=12

Home of F&R SARIMA (0,1,1) × (0,1,1) s=12 Home of F&R SARIMA (0,1,1) × (0,1,1) s=12Camping SARIMA (0,1,1) × (0,1,2) s=12 Camping SARIMA (0,1,2) × (1,1,0) s=12

Cottage/Cabin SARIMA (0,1,1) × (0,1,2) s=12 Cottage/Cabin SARIMA (0,1,1) × (0,1,1) s=12

AI by overseas in Figure 7

Other SARIMA (0,1,1) × (0,1,1) s=12

AI by U.S.

in Figure 8

Other SARIMA (0,1,1) × (0,1,2) s=12Accommodation SARIMA (1,1,1) × (0,1,1) s=12 Accommodation SARIMA (0,1,1) × (0,1,1) s=12Transportation SARIMA (0,1,1) × (0,1,1) s=12 Transportation SARIMA (0,1,1) × (0,1,1) s=12

Food & Beverage SARIMA (0,1,1) × (1,1,0) s=12 Food & Beverage SARIMA (0,1,1) × (0,1,2) s=12Recreation & Entertainment SARIMA (0,1,1) × (0,1,2) s=12 Recreation &

Entertainment SARIMA (0,1,1) × (0,1,1) s=12

TEI by overseas in Figure 9

Other spending SARIMA (0,1,1) × (0,1,1) s=12

TEI by U.S.

in Figure 10

Other spending SARIMA (0,1,1) × (0,1,1) s=12Transportation

services SARIMA (1,1,1) × (0,1,1) s=12 Transportation services SARIMA (0,1,1) × (0,1,1) s=12

Accommodation services SARIMA (0,1,1) × (0,1,2) s=12 Accommodation

services SARIMA (0,1,1) × (0,1,1) s=12

Hospitality of local people SARIMA (0,1,1) × (0,1,2) s=12 Hospitality of

local people SARIMA (0,1,1) × (0,1,1) s=12

Value for money SARIMA (0,1,1) × (0,1,1) s=12 Value for money SARIMA (1,1,0) × (0,1,2) s=12Variety things to

see and do SARIMA (0,1,1) × (0,1,2) s=12 Variety things to see and do SARIMA (0,1,1) × (0,1,1) s=12

TSI by overseas in Figure 11

TSI by U.S.

in Figure 12

Value of US $ in Canadian $ SARIMA (0,1,1) × (0,1,1) s=12

Note: The data of exchange rates was obtained from the Bank of Canada (2006).

Demographic Index (DI) The results of DI’s trends and forecasts for overseas pleasure markets are shown in Figure 3. Yearly DI scores by overseas markets range from 2.94 to 22.07 during the period and forecasted from 3.32 to 18.63 for the years 2005 to 2007. Middle aged (35-54) groups of overseas markets have ranked on top since 1996 (male) and 1998 (female). In addition, middle aged (35-54) travellers and seniors (over 55) have seen a steady increase, whereas male and female adults (20-34) have seen significant decline over time. Figure 3. Trends and Forecasts of Demographic Index (DI) by Overseas Pleasure Markets

0

5

10

15

20

25

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Male Youth Age (0-19) Male Adult Age (20-34) Male Middle Age (35-54) Male Senior Age (over 55)

Female Youth Age (0-19) Female Adult Age (20-34) Female Middle Age (35-54) Female Senior Age (over 55)

Figure 4 plots the results of DI’s trends and forecasts for US overnight pleasure markets. Yearly DI scores for US markets range from 3.33 to 21.57 and forecasted from 4.03 to 22.31 during the same period. Senior groups (over 55) of US markets have ranked on top since 2000 (male) and 2001 (female). Only male and female seniors (over 55) have seen a significant increase, whereas male and female adults (20-34) and middle aged (35-54) men and women have seen significant declines during the period. Figure 4. Trends and Forecasts of Demographic Index (DI) by U.S. Pleasure Markets

0

5

10

15

20

25

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Male Youth Age (0-19) Male Adult Age (20-34) Male Middle Age (35-54) Male Senior Age (over 55)

Female Youth Age (0-19) Female Adult Age (20-34) Female Middle Age (35-54) Female Senior Age (over 55)

Destinations Visited Index (DVI) The results of DVI’s trends and forecasts are plotted in Figure 5. Yearly DVI scores range from

10.65 to 24.59 during the period and forecasted from 12.10 to 25.30 for the years 2005 to 2007. Overall, overseas’ DVI scores are much higher than US’s. Overseas’ DVI scores have been significantly increasing since 1997, whereas US’s DVI scores have been slightly decreasing since 1998. Figure 5. Trends and Forecasts of Destinations Visited Index (DVI)

5

10

15

20

25

30

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Overseas Overnight Pleasure Trips US Overnight Pleasure Trips

Travel Activity Index (TAI) The results of TAI’s trends and forecasts are presented in Figure 6. Yearly TAI scores range from 19.18 to 29.13 during the period and forecasted from 19.47 to 29.02 for the years 2005 to 2007. Overall, overseas’ TAI scores are much higher than US’s. Overseas’ TAI scores have been slightly increasing since 1996, although they have seen several irregular fluctuations, whereas US’s TAI scores have been slightly decreasing since 1999. Figure 6. Trends and Forecasts of Travel Activity Index (TAI)

15

20

25

30

35

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Overseas Overnight Pleasure Trips US Overnight Pleasure Trips

Accommodation Index (AI) The results of AI’s trends and forecasts for overseas pleasure markets are plotted in Figure 7. Yearly AI scores for overseas markets range from 0.44 to 10.97 during the period and forecasted from 0.46 to 11.58 for the years 2005 to 2007. Hotel index scores for overseas markets have ranked highest over time. Motel indices have registered a dramatic decrease, whereas hotel index scores have significantly increased. Other accommodation and camping index scores have also slightly increased since 1998.

Figure 7. Trends and Forecasts of Accommodation Index (AI) by Overseas Pleasure Markets

0

2

4

6

8

10

12

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Hotel Motel Home of Friends or Relatives Camping Cottage or Cabin Other

Figure 8 presents the results of AI’s trends and forecasts for US overnight pleasure markets. Yearly AI scores by US markets range from 0.61 to 4.54 during the period and forecasted from 0.55 to 4.48 for the years 2005 to 2007. Hotel index scores for US markets have ranked top over time. Motel indices have registered a dramatic decrease, whereas hotel and other accommodation index scores have slightly increased during the period. Figure 8. Trends and Forecasts of Accommodation Index (AI) by U.S. Pleasure Markets

0

1

2

3

4

5

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Hotel Motel Home of Friends or Relatives Camping Cottage or Cabin Other

Travel Expenditure Index (TEI) The results of TEI’s trends and forecasts for overseas pleasure markets are presented in Figure 9. Yearly TEI scores by overseas markets range from 7.65 to 21.67 during the period and forecasted from 7.06 to 17.25 for the years 2005 to 2007. Of categories of travel expenditure, spending on accommodation by overseas markets are highest over time. Other spending (souvenirs, shopping, photos, etc.), spending on food and beverage, transportation, and recreation and entertainment, follow in that order.

Figure 9. Trends and Forecasts of Travel Expenditure Index (TEI) by Overseas Pleasure Markets

5

10

15

20

25

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Accommodation Transportation Food & Beverage Recreation & Entertainment Other

Figure 10 presents the results of TEI’s trends and forecasts for US overnight pleasure markets. Yearly TEI scores by US markets range from 7.32 to 30.50 during the period and forecasted from 7.72 to 27.43 for the years 2005 to 2007. Spending on accommodation by US markets are highest over time. Spending on food and beverage, recreation and entertainment, other spending (souvenirs, shopping, photos, etc.), and transportation follow in that order. Figure 10. Trends and Forecasts of Travel Expenditure Index (TEI) by U.S. Pleasure Markets

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35

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Accommodation Transportation Food & Beverage Recreation & Entertainment Other

Travel Service Index (TSI) The results of TSI’s trends and forecasts for overseas pleasure markets are shown in Figure 9. Yearly TSI scores for overseas markets range from 52.75 to 94.49 during the period and forecasted from 73.48 to 96.40 for the years 2005 to 2007. Of five different aspects of travel services, the index scores of perception of hospitality by local people for overseas markets have been highest over time, whereas index scores of value for money have been lowest since 2000. Accommodation services, variety of things to see and do, and transportation services follow in that order.

Figure 11. Trends and Forecasts of Travel Service Index (TSI) by Overseas Pleasure Markets

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100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Transportation services Accommodation services Hospitality of local peopleValue for money Variety things to see and do

Figure 11 shows the results of TSI’s trends and forecasts for US overnight pleasure markets. Yearly TSI scores for US markets range from 31.60 to 86.11 during the period and forecasted from 66.70 to 87.91 for the years 2005 to 2007. The index scores of perception of hospitality of local people by US markets have been highest except between 1998 to 2000. The index scores for accommodation services have been lowest over time. However, the index scores of value for money have ranked lowest since 2004. Variety of things to see and do and transportation services follow in that order. It is noteworthy to point out that the US overnight pleasure trips to Canada have fluctuated with the value of the US dollar in Canadian $. Figure 12. Trends and Forecasts of Travel Service Index (TSI) by U.S. Pleasure Markets

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100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Transportation services Accommodation services Hospitality of local peopleValue for money Variety things to see and do Value of US dollar in Canadian $

IMPLICATIONS AND CONCLUSIONS Some implications for international travel markets and tourism marketing are discussed based upon the results of trends and forecasts of tourism indices. Although the three provinces of ON, QC, and BC have ranked top in terms of trip index scores over time, BC has been more likely to receive benefits from overseas travel, whereas Ontario has been more likely to be the primary destination for Americans. The Prairies (especially Alberta and Saskatchewan) have seen an increase in visitors from overseas and US, whereas the Atlantic region has seen the least

improvement. Historically, overseas overnight pleasure travellers have stayed longer on average than Americans in Canada. However, average overseas’ TI scores have been lower than US’s in this study, meaning that overseas travellers have been more likely to visit more provinces or tourism regions while travelling in Canada, whereas Americans have been more likely to stay at specific destinations. The result implies that tourism marketers or tour operators who want to attract more international travellers need to consider the travel propensity of international travellers when they promote or produce travel products or packages. Including more destinations may be the better option for overseas pleasure markets, whereas fewer destinations may be more appropriate for US markets. The demographic index trends highlighted some weaknesses in Canada. Adults aged 20-34 from overseas pleasure markets and adults aged 20-34 as well as middle aged (35-54) travellers from the US markets have drastically declined over the years. Therefore, market research is needed to find the reasons and promote these demographic groups. The scores of destinations visited index for overseas pleasure markets have been significantly increasing since 1997, whereas the index scores for US markets have been slightly decreasing since 1998. In addition, the results of overall travel activity index scores show similar trends and forecasts as DVI scores for overseas and US. The results indicate that overseas pleasure travellers have been more likely to be involved in diverse activities at different destinations (provinces) while travelling in Canada, whereas US pleasure travellers have been more likely to enjoy specific activities within a particular destination. These results and implications are very similar to the findings of the trip index. Thus, market and travel behaviour research is needed to develop and promote specific activity-based products at particular destinations. Looking at the results of the accommodation index, hotel indices have seen significant increases by overseas and US markets over the years, whereas motel index scores have dramatically decreased. The results imply that investment in hotels rather than motels should be considered when targeting international pleasure markets. Another finding is that camping and other accommodation scores for overseas markets and cottage/cabin and other accommodation indices for US markets have slightly increased. Therefore, tourism marketers or tour operators may need to consider camping, cottage or cabin as a preferred accommodation product. The results of travel expenditure indices indicate that overall, trends and forecasts have seen slightly decreases over the years. These imply that the average amount of expenditure per travelling party has been more likely to be less than that of past years despite increases of the number of international pleasure travellers over time. In addition, overseas travellers have been more likely to spend on shopping, whereas US travellers have been more likely to spend on recreation and entertainment. Therefore, tourism marketers need to notice the changes in spending patterns over time in order to receive more tourism dollars and promote specific markets. Excellent hospitality service by local people is identified as a significant tourism resource in Canada. Overall, travel service index scores for overseas and US pleasure markets have seen a dramatic increase since 2000. However, the index scores of value for money by overseas markets have irregularly fluctuated with other service indices over the years. Noticeably, US resident trips to Canada have fluctuated with the value of the US dollar. Therefore, tourism marketers need to consider the current situation in Canada, that is to say the relatively poor exchange rate against the Canadian dollar, the high price of gas, and the new passport requirements faced by all U.S. travellers. In conclusion, some trends of indices produced may be viewed differently as some trends of quantitative statistics or indicators (arrivals, revenue generated, overnight stays, and so on). Tourism indices utilized in this study, on the whole, were identified as useful tools to examine and monitor market changes and better understand the dynamics of international tourism in Canada by overseas and US overnight pleasure travellers.

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