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The tourism–economy causality in the United States: A sub-industry level examination
Tourism Management 30 (2009) 553–558
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Tourism Management journal homepage: www.elsevier.com/locate/tourman
The tourism–economy causality in the United States: A sub-industry level examination Chun-Hung (Hugo) Tang a, *, SooCheong (Shawn) Jang b,1 a b
School of Hotel and Restaurant Administration, Oklahoma State University, 210 HESW, Stillwater, OK 74078, USA Department of Hospitality and Tourism Management, Purdue University, 700 W. State Street, West Lafayette, IN 47907-2059, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 24 November 2007 Accepted 21 September 2008
This study analyzes the relationships between the performance of four tourism related industries (airlines, casinos, hotels, and restaurants) and GDP in the U.S., using cointegration and Granger causality tests. The results show no cointegration between economic growth and industry performance in the U.S. This suggests that mechanisms to increase the revenue of tourism related industries can potentially be successful in the long-run, even in the face of sustained economic stagnation. The results also indicate a temporal causal hierarchy among industry performance, which could be a good tool for timing and prioritizing the allocation of resources among industries to ensure better overall tourism and economic outcomes. Investors and managers could also use this temporal hierarchy to identify the best timing for investments and business strategies by observing performance trends of industries higher on the temporal hierarchy. Ó 2008 Elsevier Ltd. All rights reserved.
Keywords: Tourism performance Economic growth Cointegration Granger causality
1. Introduction Because of the potential economic benefits of tourism, such as increases in foreign exchange earnings, income, employment and taxes (Archer, 1995; Balaguer & Cantavella-Jorda, 2002; Dritsakis, 2004; Durbarry, 2002), many governments have engaged in tourism development for the purpose of promoting economic growth (Mill & Morrison, 2002; Sahli & Nowak, 2007). However, empirical studies have shown inconsistent or even contradictory results regarding the tourism-led economic growth hypothesis. For example, Dritsakis (2004) found a bi-directional causal relationship in Greece; Kim, Chen and Jang (2006) reported a bi-directional causality in Taiwan; and Oh (2005) discovered economy-driven tourism growth in Korea. Considering that these empirical studies are based upon different countries, the inconsistent results may be a reflection of the country effect. Because countries could be different in terms of the weight of tourism in the overall economy (Oh, 2005), the size and openness of the economy (Kim et al., 2006), and the production capacity constraints (Dwyer, Forsyth, Madden, & Spurr, 2000), the tourism–economy relationship could also differ from one country to another. Costello (1993) also concluded that a substantial
* Corresponding author. Tel.: þ1 405 744 7110; fax: þ1 405 744-6299. E-mail addresses: [email protected] (C.-H.(Hugo) Tang), jang12@purdue. edu (SooCheong(Shawn) Jang). 1 Tel.: þ1 765 496 3610; fax: þ1 765 494 0327. 0261-5177/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2008.09.009
fraction of changes in annual productivity could be attributed to nation-specific factors. The diverse relationships between tourism growth and economic development in different country settings (Balaguer & Cantavella-Jorda, 2002; Dritsakis, 2004; Kim et al., 2006; Oh, 2005) found in existing empirical studies further support the influence of country effect. The U.S. tourism industries are among the largest employers in the country and generate the largest tourism receipts in the world (World Tourism Organization, 2006). Tourism has created more than 7.5 million jobs and generates $104.9 billion in taxes (American Hotel & Lodging Association, 2006). Nevertheless, little effort has been made to investigate the relationship between tourism and economy in the U.S. Investigating the tourism–economy relationship in the U.S. would not only improve the understanding of the interaction between tourism industries and the economy in the U.S. but would also provide an opportunity for examining the tourism– economy relationship in the context of a developed economy. The inconsistent results among extant literature may also come from treating all tourism related businesses as a homogeneous industry. Unlike other industries that consist of firms offering similar goods and services, the tourism industry is more of a ‘system’ (Mill & Morrison, 2002) that incorporates a variety of different types of businesses and organizations, such as lodging establishments, airlines, restaurants, and casinos (American Hotel & Lodging Association, 2006). These tourism businesses may influence or respond to the same economic events differently in terms of timing and magnitude due to the difference in their offerings, albeit still related to tourism. Schmalensee (1985) also
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indicated that industry effect is the most important factor for profitability, which further suggests that business performance could also vary from industry to industry. Consequently, when the tourism related industries are pooled together, they might interact with the overall economy as a portfolio, whose overall performance is subject to the weights and performance of individual industries. This could cause the relationship between the portfolio of tourism industries and the overall economy to be unstable across countries since the weights and performance of individual industries could be different. Therefore, investigating the tourism–economy relationship on the sub-industry level could generate more precise outcomes on the dynamism between economic and tourism development. Additionally, since extant studies have focused on the relationship between overall tourism growth and economic development, this study also fills a research gap in the literature by investigating both the tourism–economy causality on the subindustry level. This study also aims to identify a temporal causal hierarchy among the performances of tourism industries. Until recently, industry cycle studies have primarily relied on macroeconomic variables (Choi, 2003; Guzhva & Pagiavlas, 2004; Wheaton & Rossoff, 1998). Extracting cues from related industries, the temporal hierarchy of industry performance could function as an easier and potentially more reliable alternative for predicting the turning points of industry performance. Therefore, the dual objectives of this study are 1) to examine the relationships between tourism industries’ growth and economic growth, and 2) to explore the interrelationships among the growth of tourism industries. 2. Literature review Analyzing the relationship between economic growth and tourism development has been a popular topic in recent tourism literature (Kim et al., 2006). However, researchers have reached mixed and sometimes conflicting results despite the common choice of time series techniques as the research methodology. Extending from the export-led economic growth hypothesis, Balaguer and Cantavella-Jorda (2002) tested and confirmed the tourism-led economic growth hypothesis through cointegration and causality testing. Through a bivariate Vector Autoregression model, Oh (2005) studied the relationship between gross domestic product (GDP) and aggregate tourism receipt in Korea. He found no long-run equilibrium but a one-way causal relationship for economy-driven tourism growth. When comparing his study to Balaguer and Cantavella-Jorda’s (2002), Oh (2005) pointed out that the tourism-led economic growth might not be applicable to countries other than Spain, where tourism plays an important role in the economy, accounting for 5.9% of the GDP. Using similar methods but a different proxy (tourist arrival) for tourism expansion, Kim et al.’s (2006) study based on Taiwanese data yielded opposite results from Oh’s (2005): long-run equilibrium and bi-directional causality between economic development and tourism expansion. Considering that Taiwan and Korea are similar in terms of economic development and tourism’s role in the economy,2 such conflicting results are unexpected. Kim et al. (2006) explained that the difference may arise from the size of the economy; Taiwan is more sensitive to tourism demand given its relatively smaller economy. Therefore, to confirm Kim et al.’s (2006) argument, it would be worthwhile to investigate the tourism–economy relationship in the U.S., where tourism also plays
2 According to World Tourism Organization, tourism receipts per capita are US$125 in Korea and US$178 in Taiwan. Based on CIA’s World Fact data, 2006 estimated GDP per capital is US$24,200 in Korea and US$29,000 in Taiwan. Therefore, the tourism receipts/GDP ratio is 0.52% for Korea and 0.61% for Taiwan.
a relatively small role but the size of the economy is large. By investigating the tourism related industries in the U.S., this study also provides a chance to understand the interaction of tourism industries and economy in the context of developed economy. Treating tourism as a single industry with similar services and goods could be another reason for the inconsistent results among existing studies. Since individual tourism related industries could have different relationships with economic development, the overall relationship between tourism and economic development could be influenced by the weight and strength of the link between economic development and the performance of individual industries. Even though there is no empirical study that examines the tourism–economic relationship on the industry level, Chen’s (2007) study on the relationship between GDP and stock prices of tourism firms could provide an indication of the interaction between tourism industries’ performances (i.e. hotels, airlines, and travel agents) and economic development. In investigating the relationship between economic development and stock prices of tourism firms in Taiwan and China, Chen’s (2007) results indicate that the stock prices of travel agents show long-run equilibrium with GDP in China while those of hotels do not. Causality tests between hotels, airlines, and travel agents and GDP also show mixed results. Although the number of firms is not sufficient to represent the whole industry, Chen’s (2007) study did show that the interaction between economic development and industry performance could vary from industry to industry. Therefore, individual tourism industries may have different causal relationships with GDP. In addition to the relationship between tourism and economic development, this study intends to establish relationships among tourism related industries as an effort to provide an alternative industry cycle forecasting method. There are several studies that have focused on forecasting industry performance of the tourism related industries, such as the restaurant industry (Choi, 1999), the hotel industry (Choi, 2003; Wheaton & Rossoff, 1998), and the airline industry (Guzhva & Pagiavlas, 2004). In the shortrun, the development of tourism industries is expected to trail that of the economy because, unlike manufacturing industries, their supply cannot be quickly adjusted in response to changes in demand (Corgel, 2004). Wheaton and Rossoff’s (1998) empirical study supports this rationale by showing that hotel demand moves at as high a cyclic frequency as the economy, while supply follows the slower movements of investment. However, these studies generally focus on using macroeconomic factors, such as GDP, as indicators to forecast the business cycle of a single industry or the economic shock of a single event. Because these industries are all related to the tourism business and are affected by similar external factors such as tourist arrival, their performance should have higher correlations with each other than with the general economy. Therefore, the time series of related industries should hold more information in forecasting the cycle of a particular industry than unrelated industries. By establishing the timing of the interaction pattern among related industries, industry cycles can also be forecasted by looking at the cycle of related industries in addition to the economic indicators. 3. Data and econometric analyses 3.1. Data The American Hotel & Lodging Association (2006) indicates that there are 15 industries related to the tourism business. However, in order to obtain a meaningful sample size, only the industries with a large number of publicly traded companies were chosen for analysis in this study. Thus, four industries were included: the airline (AIR), casino (CASINO), hotel (HOTEL), and restaurant (REST) industries. These four tourism related industries are identified by
C.-H.(Hugo) Tang, SooCheong(Shawn) Jang / Tourism Management 30 (2009) 553–558
grouping NAICS defined industries with the same business nature. A detail composition of each industry is listed in Table 1. In measuring the performance of tourism industries, Chen (2007) constructed a value-weighted tourism price index based on the stock prices of tourism firms. However, in addition to the present performance, stock prices generally also incorporate investor’s views on the company’s future performance, which requires the estimation of future cash flows and proper discount rate. In this study, we adopted a more direct approach to measure industry performance by aggregating industry sales revenue to avoid the possible distortion caused by the prediction of future cash flows and discount rate. The industry aggregated revenue is calculated by summing the sales revenue of all individual companies in the same industry. The quarterly sales data from tourism related companies was collected from the COMPUSTAT database for the most recent 25-year period (Quarter 1, 1981 to Quarter 4, 2005) to measure the development of tourism related industries. Seasonally unadjusted quarterly gross domestic product (GDP) was used as a proxy for general economic development in the U.S. Both the GDP and aggregate industry sales revenue are not adjusted for seasonality because we want to keep as much information as possible and we are also interested in short-term causality. As shown in Fig. 1, the data from COMPUSTAT shows that the restaurant industry has the largest number of firms, followed by the casino, airline, and hotel industries. The number of firms may not accurately reflect the sizes of industries. Thus, we examined aggregate industry sales revenue. As shown in Fig. 2, the airline industry is the largest industry, followed by the restaurant, hotel, and casino industries. When the time series of sales figures are presented in first differences, the time trend disappears. This indicates that the first difference time series could be stationary. The correlation coefficients in the upper triangle of Table 2 show that the development of industries is highly correlated when measured by aggregate sales. However, when industry development is represented by growth rate, some of the correlations between industries become not significant. Such results imply that a deterministic trend might be present and shared by some of the aggregate industries sales time series. Additionally, the growth rate reflects the interaction between industries without the influence of the deterministic time trend.
140 120 100 80 60 40 20 0
82
84
86
88
90
92
94
AIR CASINO
96
98
00
02
04
HOTEL REST
Fig. 1. Time trend of the number of firms.
Table 3. As Enders (1995) indicated, the Dickey–Fuller tests assume the errors to be independent and have constant variance, while the Phillips–Perron test allows for fairly mild assumptions about the distribution of errors. Test results show that the sales revenue time series are non-stationary but the first-differenced time series are stationary. When considering multiple series together, it is not always clear whether causal relationships exist between the series and, if so, what directions of these relationships are. One of the statistical approaches that allows for the examination of these relationships is the vector autoregression (VAR) model, which treats all variables symmetrically without assuming any restrictions on the causal relationship between the time paths of series. The possibility of a long-run relationship among variables was examined by a VAR-based cointegration test using the methodology developed by Johansen (1991). In this procedure, a VAR of order P with k non-stationary I(1) time series (Eq. (1)) is rewritten as a VAR of the first-differenced time series (Eq. (2)) as shown below:
yt ¼ A1 yt1 þ . þ Ap ytp þ 3t
Dyt ¼ Pyt1 þ
p1 X
(1)
Gi Dyti þ 3t
(2)
i¼1
3.2. Unit root and cointegration tests
where P ¼
Before estimation, it is important to examine the stationarity of the individual series. Stationarity refers to the fact that mean, variance, and autocovariance are all time invariant (Enders, 1995). If a time series is found to be non-stationary, a filtering mechanism such as the first difference of the variable can be employed to induce stationarity for univariate model estimation. Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used to test the null hypothesis of unit root non-stationarity in the level and the first difference of sales revenue series. Results are presented in Table 1 Industry definition.
Pp
i¼1
Ai I, Gi ¼
Pp
j ¼ iþ1
Aj .
The lag length of differenced equation in Eq. (2) is the lag length of the level time series minus 1. The optimal lag length of Eq. (1) was identified using the Schwartz Bayesian criterion. The maximum likelihood procedure by Johansen has good large- and finite-sample properties and is more efficient than the two-step
40000 30000
Industry
Variable name NAICS description
NAICS code
Airline
AIR
Scheduled passenger air transportation
481111
Casino
CASINO
Casino hotels Casinos (except casino hotels) Other gambling industries
721120 713210 713290
Hotel
HOTEL
Hotels (except casino hotels) and motels 721110
Restaurant REST
555
Full-servi ce restaurants Limited-service restaurants Cafeterias Snack and nonalcoholic beverage bars
722110 722211 722212 722213
20000 10000 0
82
84
86
88
90
92
AIR CASINO
94
96
98
00
02
04
HOTEL REST
Fig. 2. Time trend of aggregated industry sales revenue by quarter (millions).
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Table 2 Correlation coefficients.
GDP AIR CASINO HOTEL REST
Table 4 Pairwise cointegration test results.
GDP
AIR
CASINO
HOTEL
REST
1962Q1 0.110 0.047 0.428*** 0.524***
0.883*** 1965Q4 0.561*** 0.112 0.098
0.934*** 0.793*** 1965Q3 0.170* 0.301***
0.942*** 0.795*** 0.859*** 1962Q1 0.455***
0.978*** 0.812*** 0.947*** 0.921*** 1965Q1
Time series pair
Upper triangle represents the correlation of aggregate industry sales. Lower triangle represents the correlation of the first differences of industry sales. Diagonal cells are the starting quarter of sales data. All time series end in 2005Q4. ***: Significant at 1%; ** significant at 5%; *: significant at 10%.
approach of Engle and Granger (Chen, 2007). Two likelihood ratio test statistics, the trace statistic, ltrace, and the maximum eigenvalue statistic, lmax, are computed as
ltrace ¼ T
k X
li ln 1 b
and
lmax ¼ T ln 1 bl rþ1 ;
Trace statistics
GDP and AIR GDP and CASINO GDP and HOTEL GDP and REST AIR and CASINO AIR and HOTEL AIR and REST CASINO and HOTEL CASINO and REST HOTEL and REST
[5] [5] [5] [5] [3] [4] [4] [5] [1] [2]
Max-eigenvalue statistics
Rank ¼ 0
Rank 1
Rank ¼ 0
Rank 1
23.460* 15.333 12.845 15.139 9.152 16.251 17.954 10.448 23.175 14.478
9.493 4.795 4.610 5.600 3.334 4.498 4.407 3.375 7.537 6.044
13.967 10.538 8.234 9.538 5.818 11.753 13.547 7.075 15.638 8.433
9.493 4.795 4.610 5.600 3.334 4.498 4.407 3.374 7.537 6.044
MacKinnon–Haug–Michelis (1999) p-values were employed. ***: significant at 1%; ** significant at 5%; *: significant at 10%. Figures in the brackets indicate the optimal lag length. Linear deterministic trend is assumed in testing.
i ¼ rþ1
li where r is the hypothesized number of cointegrating equation, b the ith largest eigenvalue of the coefficient matrix, and T is the sample size used for estimation. If the cointegration test shows that, for example, GDP and AIR are cointegrated, we can state that economic development and airline industry performance tend to move together in the long-run, while experiencing short-run transitory deviations from this long-run relationship. Because this study hypothesizes that the relationship between individual industries and GDP could be different, the cointegration test was conducted pairwise between GDP and each industry. Pairwise cointegrations between industries were also tested. The level sales time series was used for the cointegration tests. The optimal lag length of the pairwise VAR models was determined by the Schwartz Bayesian criterion (SBC). Although both Akaike information criterion (AIC) and SBC are commonly used models for selection criteria, SBC was perceived to have some advantages for this study. SBC’s construction allows it to select a more parsimonious model than the AIC. The SBC also has superior large sample properties (Enders, 1995). When the sample size approaches infinity, the SBC is asymptotically consistent while the AIC is biased toward selecting an over-parameterized model. The results presented in Table 4 indicate that only the airline industry had a weak cointegration with GDP while there was no long-run equilibrium between GDP and other tourism related industries. There was also no long-run equilibrium among tourism related industries. The results suggest that in the long-run the developments in tourism industries and economy do not influence each other. 3.3. Granger causality test and results Since the tests show no cointegration between any two time series, the non-stationary first-differenced time series were
modeled in the VAR models pairwise. The interrelationship among the four industries and GDP were then examined by the Granger causality test. This method is best suited to determine whether the lags of one variable enter into the equation for another variable (Enders, 1995). The Granger causality test utilizes a conventional F test in order to test the restriction that foreign lag variables are not significantly in VAR model specification. For example, if we want to test whether AIR Granger causes GDP in the model, the joint significance of the coefficients of all AIR lag variables in the GDP equation should be tested with an F-statistics. The proper VAR model specification (lag length) is determined using SBC criterion. The results in Table 5 indicated uni-directional causality from GDP to the four tourism industries. Three uni-directional causality relationships were observed between industries: from AIR to CASINO, from AIR to HOTEL, and from HOTEL to REST. One bi-directional causality exists between HOTEL and CASINO. These results are robust to different lag selections. (The results using different lags are not reported.) The results suggest that tourism industries in the U.S. generally benefit from economic development in the short term while lacking long-term equilibrium with the economy due to the lack of cointegration between series. Considering that tourism receipts accounted for only 0.58% of the GDP in the U.S., the results support Oh’s (2005) argument that when tourism is only a small portion of the GDP, the relationship is more likely to be economy-driven tourism growth. This economy-driven tourism industry growth is also consistent with Corgel’s (2004) argument that hotel cycles lag behind business cycles because the supply cannot be easily adjusted to accommodate the change in demand. This is especially true for the hotel and airline industries whose major products, such as room-night and seat-mile, are tied to fixed assets. For policy makers, the results suggest that allocating limited resources for economic development could stimulate tourism growth in the short term. For practitioners, the uni-directional
Table 3 Unit root test on individual series. Variable
GDP AIR CASINO HOTEL REST
Sales revenue ADF
PP
1.393 2.732 0.405 2.608 1.244
0.686 3.479** 1.010 3.551** 1.755
Table 5 Granger causality tests.
First difference
[6] [4] [4] [4] [4]
ADF
PP
3.702** 3.763** 3.205* 5.807*** 8.394***
31.820*** 12.289*** 9.953 18.106*** 56.144***
‘‘Y’’ [5] [3] [3] [4] [2]
***: Significant at 1%; **: significant at 5%; *: significant at 10%. The intercept and linear trend are included in the test. The optimal lags in brackets are selected with the lowest values of Schwartz Bayesian criterion. The Phillips–Perron test is based on OLS AR Spectral estimation method.
DGDP ‘‘X’’
DGDP DAIR DCASINO DHOTEL DREST
1.203 [4] 1.442 [4] 0.622 [4] 2.422 [4]
DAIR
DCASINO
DHOTEL
DREST
7.387***
6.502*** 9.733***
2.710** 3.891*** 3.276**
3.514*** 1.653 2.037 6.644**
0.087 [2] 1.263 [3] 1.625 [2]
2.569** [4] 0.995 [1]
2.051 [1]
Null hypothesis: ‘‘X’’ does not Granger Cause ‘‘Y.’’ Figures in the cell are F-statistics, ***: significant at 1%; ** significant at 5%. The optimal lags in brackets are selected by the Schwartz Bayesian criterion.
C.-H.(Hugo) Tang, SooCheong(Shawn) Jang / Tourism Management 30 (2009) 553–558
AIRLINE
HOTEL
GDP
CASINO
REST
Fig. 3. Graphical presentation of Granger causality. Note: REST refers to restaurant.
causality from GDP to industries suggests that consideration should be placed on the overall economy while planning industrial level investments or strategies for the near term. More positively, since the performance of the industries trail that of the economy; practitioners can take hints from the economy in order to better time their strategy implementation or responses to the economy. When the results of the Granger Causality test were demonstrated graphically in Fig. 3, a temporal causal hierarchy (Rao, 2007) among the performances of tourism industries emerged. The hierarchy starts from the airline industry, moves to the hotel and casino industries, and finally reaches the restaurant industry. The airline industry appears to be a leading industry because its performance precedes other industries (hotel and casino). Subsequently, airline industry’s performance growth could ignite the development of other industries. However, its influence on the restaurant industry is only indirect, via the hotel industry. The hotel industry appears to be the most ‘‘connected’’ industry with causal links to the rest of the industries. It completes the performance causal network of the tourism industries. Without the hotel industry, the causal links would be broken and result in a weakened multiplying effect (Dwyer et al., 2000), which is assumed in most of the economic analyses. This illustrates the hotel industry’s central role in tourism development. Like the hotel industry, the casino industry also follows the airline industry but does not have direct links to the restaurant industry. Its bi-directional causal relationship with the hotel industry indicates that the performance of the casino industry is in sync with that of the hotel industry. The restaurant industry appears to be a pure beneficiary of this ‘‘tourism system’’ given it occupies the lowest position in the causal hierarchy.
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strengthen the pull factors of the country as a destination and eventually benefit the overall economy. The temporal causal hierarchy developed from the results of Granger causality tests offers practical value for both public and private sectors. From policy maker’s perspective, it provides a good reference for timing and prioritizing the allocation of resources among industries for better overall tourism and economic outcomes. For the purpose of stimulating tourism growth, the performance of the airline and hotel industries appears to be essential. The most efficient strategy would be allocating more resources to these two industries rather than distributing resources equally to all industries because the performance of other industries follows that of the airline industry and are connected by the hotel industry. For investors and managers, this temporal hierarchy also could function as a tool for forecasting turning points of industry performance. Instead of relying on the leading economic indicators, investors and industry practitioners can simply project where their industry is heading by taking cues from other tourism related industries. In other words, they could identify the best timing for investments of business strategies based on the observation of the performance trend of the industries higher on the temporal hierarchy. Finally, the bi-directional causal relationship between hotels and casinos suggests that there exists some potential value associated with bundling the services of these two industries. Although this study focuses on the relation between tourism growth and economic development; the aggregated industry sales revenues used for analysis are more likely to include non-tourism revenues. For example, some hotels and restaurants could derive a large portion of revenue from local patrons instead of tourists. Airlines might also benefit from passengers traveling to visit friends and relatives. Due to the limitation of data, it is not possible to isolate the non-tourism revenues. This limitation could be addressed if tourism receipts were segregated at the industry level. By incorporating industry-segregated tourism receipts into the causality consideration, the whole process of the interactions between overall tourism growth, the performance of tourism industries, and the overall economy could be better understood. Extending from this study, the performance of tourism industries could also be further segregated spatially into standard statistical metropolitan areas. Such analysis could provide more specific, perhaps more useful, information for tourism managers. Future exploration on the relative importance of individual industries in relation to economic development is also expected to benefit policy makers and industry practitioners in their decision making processes. In order to further address the inconsistent findings in extant literature regarding the cointegration between tourism and economic development, an investigation on the determinants of cointegration between tourism and economic development is also suggested for future studies.
4. Discussion and conclusion
References
The results of this study have several implications for the industry and academia. From the cointegration test, the lack of a long-run relationship between the economy and tourism industry development indicates that the industry is not completely at the whim of general economic growth. That is, mechanisms to increase the revenue of tourism industries could potentially be successful in the long-run, even in the face of sustained economic stagnation. From the Granger causality test, the uni-directional causality from GDP to industry performance may reflect the small contribution of the overall economy to these industries in the short-run. By improving the general economic/business environment, tourism related industries could benefit from the favorable economic situation and offer better service and goods, which may in turn
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Tourism Management journal homepage: www.elsevier.com/locate/tourman
The tourism–economy causality in the United States: A sub-industry level examination Chun-Hung (Hugo) Tang a, *, SooCheong (Shawn) Jang b,1 a b
School of Hotel and Restaurant Administration, Oklahoma State University, 210 HESW, Stillwater, OK 74078, USA Department of Hospitality and Tourism Management, Purdue University, 700 W. State Street, West Lafayette, IN 47907-2059, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 24 November 2007 Accepted 21 September 2008
This study analyzes the relationships between the performance of four tourism related industries (airlines, casinos, hotels, and restaurants) and GDP in the U.S., using cointegration and Granger causality tests. The results show no cointegration between economic growth and industry performance in the U.S. This suggests that mechanisms to increase the revenue of tourism related industries can potentially be successful in the long-run, even in the face of sustained economic stagnation. The results also indicate a temporal causal hierarchy among industry performance, which could be a good tool for timing and prioritizing the allocation of resources among industries to ensure better overall tourism and economic outcomes. Investors and managers could also use this temporal hierarchy to identify the best timing for investments and business strategies by observing performance trends of industries higher on the temporal hierarchy. Ó 2008 Elsevier Ltd. All rights reserved.
Keywords: Tourism performance Economic growth Cointegration Granger causality
1. Introduction Because of the potential economic benefits of tourism, such as increases in foreign exchange earnings, income, employment and taxes (Archer, 1995; Balaguer & Cantavella-Jorda, 2002; Dritsakis, 2004; Durbarry, 2002), many governments have engaged in tourism development for the purpose of promoting economic growth (Mill & Morrison, 2002; Sahli & Nowak, 2007). However, empirical studies have shown inconsistent or even contradictory results regarding the tourism-led economic growth hypothesis. For example, Dritsakis (2004) found a bi-directional causal relationship in Greece; Kim, Chen and Jang (2006) reported a bi-directional causality in Taiwan; and Oh (2005) discovered economy-driven tourism growth in Korea. Considering that these empirical studies are based upon different countries, the inconsistent results may be a reflection of the country effect. Because countries could be different in terms of the weight of tourism in the overall economy (Oh, 2005), the size and openness of the economy (Kim et al., 2006), and the production capacity constraints (Dwyer, Forsyth, Madden, & Spurr, 2000), the tourism–economy relationship could also differ from one country to another. Costello (1993) also concluded that a substantial
* Corresponding author. Tel.: þ1 405 744 7110; fax: þ1 405 744-6299. E-mail addresses: [email protected] (C.-H.(Hugo) Tang), jang12@purdue. edu (SooCheong(Shawn) Jang). 1 Tel.: þ1 765 496 3610; fax: þ1 765 494 0327. 0261-5177/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2008.09.009
fraction of changes in annual productivity could be attributed to nation-specific factors. The diverse relationships between tourism growth and economic development in different country settings (Balaguer & Cantavella-Jorda, 2002; Dritsakis, 2004; Kim et al., 2006; Oh, 2005) found in existing empirical studies further support the influence of country effect. The U.S. tourism industries are among the largest employers in the country and generate the largest tourism receipts in the world (World Tourism Organization, 2006). Tourism has created more than 7.5 million jobs and generates $104.9 billion in taxes (American Hotel & Lodging Association, 2006). Nevertheless, little effort has been made to investigate the relationship between tourism and economy in the U.S. Investigating the tourism–economy relationship in the U.S. would not only improve the understanding of the interaction between tourism industries and the economy in the U.S. but would also provide an opportunity for examining the tourism– economy relationship in the context of a developed economy. The inconsistent results among extant literature may also come from treating all tourism related businesses as a homogeneous industry. Unlike other industries that consist of firms offering similar goods and services, the tourism industry is more of a ‘system’ (Mill & Morrison, 2002) that incorporates a variety of different types of businesses and organizations, such as lodging establishments, airlines, restaurants, and casinos (American Hotel & Lodging Association, 2006). These tourism businesses may influence or respond to the same economic events differently in terms of timing and magnitude due to the difference in their offerings, albeit still related to tourism. Schmalensee (1985) also
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indicated that industry effect is the most important factor for profitability, which further suggests that business performance could also vary from industry to industry. Consequently, when the tourism related industries are pooled together, they might interact with the overall economy as a portfolio, whose overall performance is subject to the weights and performance of individual industries. This could cause the relationship between the portfolio of tourism industries and the overall economy to be unstable across countries since the weights and performance of individual industries could be different. Therefore, investigating the tourism–economy relationship on the sub-industry level could generate more precise outcomes on the dynamism between economic and tourism development. Additionally, since extant studies have focused on the relationship between overall tourism growth and economic development, this study also fills a research gap in the literature by investigating both the tourism–economy causality on the subindustry level. This study also aims to identify a temporal causal hierarchy among the performances of tourism industries. Until recently, industry cycle studies have primarily relied on macroeconomic variables (Choi, 2003; Guzhva & Pagiavlas, 2004; Wheaton & Rossoff, 1998). Extracting cues from related industries, the temporal hierarchy of industry performance could function as an easier and potentially more reliable alternative for predicting the turning points of industry performance. Therefore, the dual objectives of this study are 1) to examine the relationships between tourism industries’ growth and economic growth, and 2) to explore the interrelationships among the growth of tourism industries. 2. Literature review Analyzing the relationship between economic growth and tourism development has been a popular topic in recent tourism literature (Kim et al., 2006). However, researchers have reached mixed and sometimes conflicting results despite the common choice of time series techniques as the research methodology. Extending from the export-led economic growth hypothesis, Balaguer and Cantavella-Jorda (2002) tested and confirmed the tourism-led economic growth hypothesis through cointegration and causality testing. Through a bivariate Vector Autoregression model, Oh (2005) studied the relationship between gross domestic product (GDP) and aggregate tourism receipt in Korea. He found no long-run equilibrium but a one-way causal relationship for economy-driven tourism growth. When comparing his study to Balaguer and Cantavella-Jorda’s (2002), Oh (2005) pointed out that the tourism-led economic growth might not be applicable to countries other than Spain, where tourism plays an important role in the economy, accounting for 5.9% of the GDP. Using similar methods but a different proxy (tourist arrival) for tourism expansion, Kim et al.’s (2006) study based on Taiwanese data yielded opposite results from Oh’s (2005): long-run equilibrium and bi-directional causality between economic development and tourism expansion. Considering that Taiwan and Korea are similar in terms of economic development and tourism’s role in the economy,2 such conflicting results are unexpected. Kim et al. (2006) explained that the difference may arise from the size of the economy; Taiwan is more sensitive to tourism demand given its relatively smaller economy. Therefore, to confirm Kim et al.’s (2006) argument, it would be worthwhile to investigate the tourism–economy relationship in the U.S., where tourism also plays
2 According to World Tourism Organization, tourism receipts per capita are US$125 in Korea and US$178 in Taiwan. Based on CIA’s World Fact data, 2006 estimated GDP per capital is US$24,200 in Korea and US$29,000 in Taiwan. Therefore, the tourism receipts/GDP ratio is 0.52% for Korea and 0.61% for Taiwan.
a relatively small role but the size of the economy is large. By investigating the tourism related industries in the U.S., this study also provides a chance to understand the interaction of tourism industries and economy in the context of developed economy. Treating tourism as a single industry with similar services and goods could be another reason for the inconsistent results among existing studies. Since individual tourism related industries could have different relationships with economic development, the overall relationship between tourism and economic development could be influenced by the weight and strength of the link between economic development and the performance of individual industries. Even though there is no empirical study that examines the tourism–economic relationship on the industry level, Chen’s (2007) study on the relationship between GDP and stock prices of tourism firms could provide an indication of the interaction between tourism industries’ performances (i.e. hotels, airlines, and travel agents) and economic development. In investigating the relationship between economic development and stock prices of tourism firms in Taiwan and China, Chen’s (2007) results indicate that the stock prices of travel agents show long-run equilibrium with GDP in China while those of hotels do not. Causality tests between hotels, airlines, and travel agents and GDP also show mixed results. Although the number of firms is not sufficient to represent the whole industry, Chen’s (2007) study did show that the interaction between economic development and industry performance could vary from industry to industry. Therefore, individual tourism industries may have different causal relationships with GDP. In addition to the relationship between tourism and economic development, this study intends to establish relationships among tourism related industries as an effort to provide an alternative industry cycle forecasting method. There are several studies that have focused on forecasting industry performance of the tourism related industries, such as the restaurant industry (Choi, 1999), the hotel industry (Choi, 2003; Wheaton & Rossoff, 1998), and the airline industry (Guzhva & Pagiavlas, 2004). In the shortrun, the development of tourism industries is expected to trail that of the economy because, unlike manufacturing industries, their supply cannot be quickly adjusted in response to changes in demand (Corgel, 2004). Wheaton and Rossoff’s (1998) empirical study supports this rationale by showing that hotel demand moves at as high a cyclic frequency as the economy, while supply follows the slower movements of investment. However, these studies generally focus on using macroeconomic factors, such as GDP, as indicators to forecast the business cycle of a single industry or the economic shock of a single event. Because these industries are all related to the tourism business and are affected by similar external factors such as tourist arrival, their performance should have higher correlations with each other than with the general economy. Therefore, the time series of related industries should hold more information in forecasting the cycle of a particular industry than unrelated industries. By establishing the timing of the interaction pattern among related industries, industry cycles can also be forecasted by looking at the cycle of related industries in addition to the economic indicators. 3. Data and econometric analyses 3.1. Data The American Hotel & Lodging Association (2006) indicates that there are 15 industries related to the tourism business. However, in order to obtain a meaningful sample size, only the industries with a large number of publicly traded companies were chosen for analysis in this study. Thus, four industries were included: the airline (AIR), casino (CASINO), hotel (HOTEL), and restaurant (REST) industries. These four tourism related industries are identified by
C.-H.(Hugo) Tang, SooCheong(Shawn) Jang / Tourism Management 30 (2009) 553–558
grouping NAICS defined industries with the same business nature. A detail composition of each industry is listed in Table 1. In measuring the performance of tourism industries, Chen (2007) constructed a value-weighted tourism price index based on the stock prices of tourism firms. However, in addition to the present performance, stock prices generally also incorporate investor’s views on the company’s future performance, which requires the estimation of future cash flows and proper discount rate. In this study, we adopted a more direct approach to measure industry performance by aggregating industry sales revenue to avoid the possible distortion caused by the prediction of future cash flows and discount rate. The industry aggregated revenue is calculated by summing the sales revenue of all individual companies in the same industry. The quarterly sales data from tourism related companies was collected from the COMPUSTAT database for the most recent 25-year period (Quarter 1, 1981 to Quarter 4, 2005) to measure the development of tourism related industries. Seasonally unadjusted quarterly gross domestic product (GDP) was used as a proxy for general economic development in the U.S. Both the GDP and aggregate industry sales revenue are not adjusted for seasonality because we want to keep as much information as possible and we are also interested in short-term causality. As shown in Fig. 1, the data from COMPUSTAT shows that the restaurant industry has the largest number of firms, followed by the casino, airline, and hotel industries. The number of firms may not accurately reflect the sizes of industries. Thus, we examined aggregate industry sales revenue. As shown in Fig. 2, the airline industry is the largest industry, followed by the restaurant, hotel, and casino industries. When the time series of sales figures are presented in first differences, the time trend disappears. This indicates that the first difference time series could be stationary. The correlation coefficients in the upper triangle of Table 2 show that the development of industries is highly correlated when measured by aggregate sales. However, when industry development is represented by growth rate, some of the correlations between industries become not significant. Such results imply that a deterministic trend might be present and shared by some of the aggregate industries sales time series. Additionally, the growth rate reflects the interaction between industries without the influence of the deterministic time trend.
140 120 100 80 60 40 20 0
82
84
86
88
90
92
94
AIR CASINO
96
98
00
02
04
HOTEL REST
Fig. 1. Time trend of the number of firms.
Table 3. As Enders (1995) indicated, the Dickey–Fuller tests assume the errors to be independent and have constant variance, while the Phillips–Perron test allows for fairly mild assumptions about the distribution of errors. Test results show that the sales revenue time series are non-stationary but the first-differenced time series are stationary. When considering multiple series together, it is not always clear whether causal relationships exist between the series and, if so, what directions of these relationships are. One of the statistical approaches that allows for the examination of these relationships is the vector autoregression (VAR) model, which treats all variables symmetrically without assuming any restrictions on the causal relationship between the time paths of series. The possibility of a long-run relationship among variables was examined by a VAR-based cointegration test using the methodology developed by Johansen (1991). In this procedure, a VAR of order P with k non-stationary I(1) time series (Eq. (1)) is rewritten as a VAR of the first-differenced time series (Eq. (2)) as shown below:
yt ¼ A1 yt1 þ . þ Ap ytp þ 3t
Dyt ¼ Pyt1 þ
p1 X
(1)
Gi Dyti þ 3t
(2)
i¼1
3.2. Unit root and cointegration tests
where P ¼
Before estimation, it is important to examine the stationarity of the individual series. Stationarity refers to the fact that mean, variance, and autocovariance are all time invariant (Enders, 1995). If a time series is found to be non-stationary, a filtering mechanism such as the first difference of the variable can be employed to induce stationarity for univariate model estimation. Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used to test the null hypothesis of unit root non-stationarity in the level and the first difference of sales revenue series. Results are presented in Table 1 Industry definition.
Pp
i¼1
Ai I, Gi ¼
Pp
j ¼ iþ1
Aj .
The lag length of differenced equation in Eq. (2) is the lag length of the level time series minus 1. The optimal lag length of Eq. (1) was identified using the Schwartz Bayesian criterion. The maximum likelihood procedure by Johansen has good large- and finite-sample properties and is more efficient than the two-step
40000 30000
Industry
Variable name NAICS description
NAICS code
Airline
AIR
Scheduled passenger air transportation
481111
Casino
CASINO
Casino hotels Casinos (except casino hotels) Other gambling industries
721120 713210 713290
Hotel
HOTEL
Hotels (except casino hotels) and motels 721110
Restaurant REST
555
Full-servi ce restaurants Limited-service restaurants Cafeterias Snack and nonalcoholic beverage bars
722110 722211 722212 722213
20000 10000 0
82
84
86
88
90
92
AIR CASINO
94
96
98
00
02
04
HOTEL REST
Fig. 2. Time trend of aggregated industry sales revenue by quarter (millions).
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Table 2 Correlation coefficients.
GDP AIR CASINO HOTEL REST
Table 4 Pairwise cointegration test results.
GDP
AIR
CASINO
HOTEL
REST
1962Q1 0.110 0.047 0.428*** 0.524***
0.883*** 1965Q4 0.561*** 0.112 0.098
0.934*** 0.793*** 1965Q3 0.170* 0.301***
0.942*** 0.795*** 0.859*** 1962Q1 0.455***
0.978*** 0.812*** 0.947*** 0.921*** 1965Q1
Time series pair
Upper triangle represents the correlation of aggregate industry sales. Lower triangle represents the correlation of the first differences of industry sales. Diagonal cells are the starting quarter of sales data. All time series end in 2005Q4. ***: Significant at 1%; ** significant at 5%; *: significant at 10%.
approach of Engle and Granger (Chen, 2007). Two likelihood ratio test statistics, the trace statistic, ltrace, and the maximum eigenvalue statistic, lmax, are computed as
ltrace ¼ T
k X
li ln 1 b
and
lmax ¼ T ln 1 bl rþ1 ;
Trace statistics
GDP and AIR GDP and CASINO GDP and HOTEL GDP and REST AIR and CASINO AIR and HOTEL AIR and REST CASINO and HOTEL CASINO and REST HOTEL and REST
[5] [5] [5] [5] [3] [4] [4] [5] [1] [2]
Max-eigenvalue statistics
Rank ¼ 0
Rank 1
Rank ¼ 0
Rank 1
23.460* 15.333 12.845 15.139 9.152 16.251 17.954 10.448 23.175 14.478
9.493 4.795 4.610 5.600 3.334 4.498 4.407 3.375 7.537 6.044
13.967 10.538 8.234 9.538 5.818 11.753 13.547 7.075 15.638 8.433
9.493 4.795 4.610 5.600 3.334 4.498 4.407 3.374 7.537 6.044
MacKinnon–Haug–Michelis (1999) p-values were employed. ***: significant at 1%; ** significant at 5%; *: significant at 10%. Figures in the brackets indicate the optimal lag length. Linear deterministic trend is assumed in testing.
i ¼ rþ1
li where r is the hypothesized number of cointegrating equation, b the ith largest eigenvalue of the coefficient matrix, and T is the sample size used for estimation. If the cointegration test shows that, for example, GDP and AIR are cointegrated, we can state that economic development and airline industry performance tend to move together in the long-run, while experiencing short-run transitory deviations from this long-run relationship. Because this study hypothesizes that the relationship between individual industries and GDP could be different, the cointegration test was conducted pairwise between GDP and each industry. Pairwise cointegrations between industries were also tested. The level sales time series was used for the cointegration tests. The optimal lag length of the pairwise VAR models was determined by the Schwartz Bayesian criterion (SBC). Although both Akaike information criterion (AIC) and SBC are commonly used models for selection criteria, SBC was perceived to have some advantages for this study. SBC’s construction allows it to select a more parsimonious model than the AIC. The SBC also has superior large sample properties (Enders, 1995). When the sample size approaches infinity, the SBC is asymptotically consistent while the AIC is biased toward selecting an over-parameterized model. The results presented in Table 4 indicate that only the airline industry had a weak cointegration with GDP while there was no long-run equilibrium between GDP and other tourism related industries. There was also no long-run equilibrium among tourism related industries. The results suggest that in the long-run the developments in tourism industries and economy do not influence each other. 3.3. Granger causality test and results Since the tests show no cointegration between any two time series, the non-stationary first-differenced time series were
modeled in the VAR models pairwise. The interrelationship among the four industries and GDP were then examined by the Granger causality test. This method is best suited to determine whether the lags of one variable enter into the equation for another variable (Enders, 1995). The Granger causality test utilizes a conventional F test in order to test the restriction that foreign lag variables are not significantly in VAR model specification. For example, if we want to test whether AIR Granger causes GDP in the model, the joint significance of the coefficients of all AIR lag variables in the GDP equation should be tested with an F-statistics. The proper VAR model specification (lag length) is determined using SBC criterion. The results in Table 5 indicated uni-directional causality from GDP to the four tourism industries. Three uni-directional causality relationships were observed between industries: from AIR to CASINO, from AIR to HOTEL, and from HOTEL to REST. One bi-directional causality exists between HOTEL and CASINO. These results are robust to different lag selections. (The results using different lags are not reported.) The results suggest that tourism industries in the U.S. generally benefit from economic development in the short term while lacking long-term equilibrium with the economy due to the lack of cointegration between series. Considering that tourism receipts accounted for only 0.58% of the GDP in the U.S., the results support Oh’s (2005) argument that when tourism is only a small portion of the GDP, the relationship is more likely to be economy-driven tourism growth. This economy-driven tourism industry growth is also consistent with Corgel’s (2004) argument that hotel cycles lag behind business cycles because the supply cannot be easily adjusted to accommodate the change in demand. This is especially true for the hotel and airline industries whose major products, such as room-night and seat-mile, are tied to fixed assets. For policy makers, the results suggest that allocating limited resources for economic development could stimulate tourism growth in the short term. For practitioners, the uni-directional
Table 3 Unit root test on individual series. Variable
GDP AIR CASINO HOTEL REST
Sales revenue ADF
PP
1.393 2.732 0.405 2.608 1.244
0.686 3.479** 1.010 3.551** 1.755
Table 5 Granger causality tests.
First difference
[6] [4] [4] [4] [4]
ADF
PP
3.702** 3.763** 3.205* 5.807*** 8.394***
31.820*** 12.289*** 9.953 18.106*** 56.144***
‘‘Y’’ [5] [3] [3] [4] [2]
***: Significant at 1%; **: significant at 5%; *: significant at 10%. The intercept and linear trend are included in the test. The optimal lags in brackets are selected with the lowest values of Schwartz Bayesian criterion. The Phillips–Perron test is based on OLS AR Spectral estimation method.
DGDP ‘‘X’’
DGDP DAIR DCASINO DHOTEL DREST
1.203 [4] 1.442 [4] 0.622 [4] 2.422 [4]
DAIR
DCASINO
DHOTEL
DREST
7.387***
6.502*** 9.733***
2.710** 3.891*** 3.276**
3.514*** 1.653 2.037 6.644**
0.087 [2] 1.263 [3] 1.625 [2]
2.569** [4] 0.995 [1]
2.051 [1]
Null hypothesis: ‘‘X’’ does not Granger Cause ‘‘Y.’’ Figures in the cell are F-statistics, ***: significant at 1%; ** significant at 5%. The optimal lags in brackets are selected by the Schwartz Bayesian criterion.
C.-H.(Hugo) Tang, SooCheong(Shawn) Jang / Tourism Management 30 (2009) 553–558
AIRLINE
HOTEL
GDP
CASINO
REST
Fig. 3. Graphical presentation of Granger causality. Note: REST refers to restaurant.
causality from GDP to industries suggests that consideration should be placed on the overall economy while planning industrial level investments or strategies for the near term. More positively, since the performance of the industries trail that of the economy; practitioners can take hints from the economy in order to better time their strategy implementation or responses to the economy. When the results of the Granger Causality test were demonstrated graphically in Fig. 3, a temporal causal hierarchy (Rao, 2007) among the performances of tourism industries emerged. The hierarchy starts from the airline industry, moves to the hotel and casino industries, and finally reaches the restaurant industry. The airline industry appears to be a leading industry because its performance precedes other industries (hotel and casino). Subsequently, airline industry’s performance growth could ignite the development of other industries. However, its influence on the restaurant industry is only indirect, via the hotel industry. The hotel industry appears to be the most ‘‘connected’’ industry with causal links to the rest of the industries. It completes the performance causal network of the tourism industries. Without the hotel industry, the causal links would be broken and result in a weakened multiplying effect (Dwyer et al., 2000), which is assumed in most of the economic analyses. This illustrates the hotel industry’s central role in tourism development. Like the hotel industry, the casino industry also follows the airline industry but does not have direct links to the restaurant industry. Its bi-directional causal relationship with the hotel industry indicates that the performance of the casino industry is in sync with that of the hotel industry. The restaurant industry appears to be a pure beneficiary of this ‘‘tourism system’’ given it occupies the lowest position in the causal hierarchy.
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strengthen the pull factors of the country as a destination and eventually benefit the overall economy. The temporal causal hierarchy developed from the results of Granger causality tests offers practical value for both public and private sectors. From policy maker’s perspective, it provides a good reference for timing and prioritizing the allocation of resources among industries for better overall tourism and economic outcomes. For the purpose of stimulating tourism growth, the performance of the airline and hotel industries appears to be essential. The most efficient strategy would be allocating more resources to these two industries rather than distributing resources equally to all industries because the performance of other industries follows that of the airline industry and are connected by the hotel industry. For investors and managers, this temporal hierarchy also could function as a tool for forecasting turning points of industry performance. Instead of relying on the leading economic indicators, investors and industry practitioners can simply project where their industry is heading by taking cues from other tourism related industries. In other words, they could identify the best timing for investments of business strategies based on the observation of the performance trend of the industries higher on the temporal hierarchy. Finally, the bi-directional causal relationship between hotels and casinos suggests that there exists some potential value associated with bundling the services of these two industries. Although this study focuses on the relation between tourism growth and economic development; the aggregated industry sales revenues used for analysis are more likely to include non-tourism revenues. For example, some hotels and restaurants could derive a large portion of revenue from local patrons instead of tourists. Airlines might also benefit from passengers traveling to visit friends and relatives. Due to the limitation of data, it is not possible to isolate the non-tourism revenues. This limitation could be addressed if tourism receipts were segregated at the industry level. By incorporating industry-segregated tourism receipts into the causality consideration, the whole process of the interactions between overall tourism growth, the performance of tourism industries, and the overall economy could be better understood. Extending from this study, the performance of tourism industries could also be further segregated spatially into standard statistical metropolitan areas. Such analysis could provide more specific, perhaps more useful, information for tourism managers. Future exploration on the relative importance of individual industries in relation to economic development is also expected to benefit policy makers and industry practitioners in their decision making processes. In order to further address the inconsistent findings in extant literature regarding the cointegration between tourism and economic development, an investigation on the determinants of cointegration between tourism and economic development is also suggested for future studies.
4. Discussion and conclusion
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The results of this study have several implications for the industry and academia. From the cointegration test, the lack of a long-run relationship between the economy and tourism industry development indicates that the industry is not completely at the whim of general economic growth. That is, mechanisms to increase the revenue of tourism industries could potentially be successful in the long-run, even in the face of sustained economic stagnation. From the Granger causality test, the uni-directional causality from GDP to industry performance may reflect the small contribution of the overall economy to these industries in the short-run. By improving the general economic/business environment, tourism related industries could benefit from the favorable economic situation and offer better service and goods, which may in turn
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