to economic linkages with Bali were exacerbated by migrants who had been ...... Time series evidence from some MENA countries', Ohio State University,.
‘I’VE BEEN TO BALI TOO’ (AND I WILL BE GOING BACK): ARE TERRORIST SHOCKS TO BALI’S TOURIST ARRIVALS PERMANENT OR TRANSITORY? Russell Smyth, 1 Ingrid Nielsen 2 and Vinod Mishra 3 4 Discussion Paper 24, 2006
ABSTRACT International visitor arrivals to Bali are examined using univariate and panel Lagrange Multiplier (LM) unit root tests with one and two structural breaks to ascertain if shocks to the time path of tourist arrivals are permanent or transitory. The univariate LM unit root tests with one and two structural breaks fail to reject the null hypothesis of a unit root in international visitor arrivals to Bali. However, the panel LM unit root tests with one and two structural breaks applied to a panel of Bali’s 11 major source markets rejects the null and supports the alternative hypothesis of a joint trend-stationary series with transitory shocks. This result suggests that the effects of the recent terrorist acts on Bali on the growth path of tourist arrivals from major markets are only transitory and that as a consequence Bali’s tourism sector is sustainable in the long run.
1
Department of Economics, Monash University, 900 Dandenong Road, Caulfield East, VIC. 3145, Australia. Department of Management, Monash University, VIC. 3800, Australia. 3 Department of Economics, Monash University. 900 Dandenong Road, Caulfield East, VIC. 3145, Australia. 4 The authors thank Fara Azmat, Jade Bilardi and Fang-Fah Lam for research assistance with this project. 2
1
‘I’VE BEEN TO BALI TOO’ (AND I WILL BE GOING BACK): ARE TERRORIST SHOCKS TO BALI’S TOURIST ARRIVALS PERMANENT OR TRANSITORY?
1 INTRODUCTION The pop song ‘I’ve Been to Bali Too’ by the group Redgum caricatures Bali as the tourist destination for Australian holiday makers. That such a song has been written underscores the appeal of Bali to many Australians who have long flocked to the island for its famed surf. However, towards the end of 2002 Bali’s reputation as a peaceful haven, largely separated from the political and religious difficulties engulfing the rest of Indonesia, was shattered. The bombings in Kuta in October 2002 killed 202 people, including 88 Australians. Tourist arrivals from Australia and other source countries fell dramatically. Direct arrivals to Bali dropped from 150,747 in September 2002 to 31,497 in November following the bombing (World Bank/UNDP, 2006). In the week prior to the bombing occupancy rates at starred hotels ranged from 65 to 100 per cent, but by the end of October 2002 occupancy rates were 10 per cent (World Bank/UNDP, 2006). Tourism began to return to normal throughout 2004 and the first eight months of 2005. In 2004 visitor arrivals to Bali peaked at 1.5 million, 44 per cent higher than in 2003. The positive trend continued into 2005 with arrivals exceeding 1 million until August, 5.6 per cent higher than the comparable period in 2004 (EIU, 2005). But then a second suicide bombing in Kuta and Jimbaran in October 2005 killed 23 people including the three suicide bombers. Following the second terrorist attack tourist arrivals fell from 10,000 to 5,000 people per day and the average hotel occupancy rate was estimated to be between 30 and 40 per cent (Jakarta Post, 16 February, 2006). In the one-and two-star hotel businesses in Kuta, which prior to the first bombing received 3000 Australian guests each month, numbers declined to around 200 per month (Bisnis Indonesia, 8 April 2006). The fall in tourist arrivals has had adverse economic implications for the Balinese economy. A joint UNDP and World Bank survey in early 2003 found that household income in Bali fell by 25 per cent following the first bombing (World Bank/UNDP, 2006). Official statistics on formal sector job losses showed that following the first bombing, out of the 58,000 people employed in the hotel and restaurant sectors in Bali, by May 2003 1,400 hotel workers had been made redundant. However the World Bank/UNDP (2006) estimated that as many as three-quarters of those employed in the hotel sector were either working reduced shifts or had temporarily been made redundant. There were also flow-on effects to other industries dependent on tourists for survival. In the joint UNDP and World Bank survey, market traders, beach vendors and taxi drivers reported a drop in sales revenue between 32 per cent in Pasar Badung and 71 per cent in Pasar Ubud (World Bank/UNDP, 2006). Small and medium enterprises in service sectors such as retail trade that are reliant on tourists reported shedding as much as 50 to 60 per cent of jobs between October 2002 and May 2003 (World Bank/UNDP, 2006). Bali is a hub for tourists to other parts of Indonesia. More than 60 per cent of visitors to Indonesia go to Bali before going to Lombok, Yogyakarta or elsewhere (Asiamoney, 10 April, 2006). In Lombok, average income fell by 50 per cent in most districts following the first bombing reflecting a fall in tourists traveling to Lombok and a reduction in demand in Bali for handicrafts made in Lombok (World Bank/UNDP, 2006). The economy of East Java has strong economic linkages with that of Bali with an estimated 35 per cent of small and medium enterprise production in East Java bound for the Bali market. The World Bank/UNDP (2006) report found that silver and wood industries in Pasuruan, granite and metal producers in Tulungagung and bamboo and wood producers in Banyuwangi all experienced more than 50 per cent reductions in turnover in the months following the first bombing. The direct effects of the economic down turn in East Java due to economic linkages with Bali were exacerbated by migrants who had been working in Bali returning to their villages in East Java in search of employment. The 9/11 terrorist attack reduced airline travel in the United States creating a financial crises for airlines in the United States (Blunk et al., 2006). Net reductions in the profits of U.S. airlines totalled $US3.2 billion in the third quarter of 2001 and $US4.4 billion for all of 2001 (FAA, 2001).
2
At the beginning of 2006 the Indonesian national airline, Garuda Indonesia, announced that it had failed to meet repayments on an unspecified amount of debt. The reason was the fall in tourist numbers coming to Bali coupled with other factors such as high fuel prices and competition from low cost carriers such as the Singapore-based Tiger Airways (Courier Mail, 7 January, 2006). Garuda’s response to the fall in tourism to Bali was to give away for free 10,000 return tickets as part of a promotional campaign to boost tourism to the island (Jakarta Post, 1 December 2005); of which 1600 were offered to Australians (Courier Mail, 7 January, 2006). In February 2006 the Indonesian government responded to the growing crisis in Bali through announcing a 67-billion rupiah (US$7 million) package to assist in the recovery of Bali’s tourist industry (Jakarta Post, 16 February 2006). The Bali Tourism Office has also run promotional campaigns in Beijing and Guangzhou in China and Berlin, Hamburg and Frankfurt in Germany encouraging people to return to Bali (Jakarta Post, 18 March 2006). The objective of this paper is to examine whether the Bali bombings have had a permanent or transitory effect on tourist arrivals in Bali through analyzing the time series properties of data on tourist arrivals. Specifically the paper applies univariate and panel Lagrange Multiplier (LM) unit root tests with one and two structural breaks to test the null hypothesis that tourist arrivals to Bali contain a unit root. If tourist arrivals contain a unit root this suggests that following a shock, such as a terrorist attack, tourist arrivals will not return to their stable growth path and the effects of the shock will be permanent. However, if the null hypothesis of a unit root in tourist arrivals is rejected, this is indicative that following the shock, tourist arrivals will return to their long-run growth path and the impact of the shock on varied tourist numbers will only be transitory. The findings from such an analysis have important policy implications in the context of recent debate about the sustainability of Bali’s tourism industry. The World Bank/UNDP (2006) report concluded that Bali was too reliant on tourism, making it vulnerable to shocks and recommended that the Indonesian government initiate reforms to build a more diverse and sustainable economy. If tourist arrivals contain a unit root this provides empirical support for this conclusion because the effects of shocks will be permanent, but if the null of a unit root in tourist arrivals is rejected, the shocks will be short-lived indicating that the long-run returns from investment in the tourist industry in Bali are sustainable. The paper is set out as follows. The next section provides an overview of the economic importance of tourism to Bali. Section III considers the relevant literature. Section IV discusses the data. The methodology and results are contained in Sections V and VI.
2
ECONOMIC IMPORTANCE OF TOURISM IN BALI
Tourism is an increasingly important industry in Indonesia and one that is of particular significance to Bali. In 2001, prior to the bombings, an estimated 5.1 million tourists visited Indonesia, contributing US$5.2 billion to Indonesia’s Balance of Payments and representing 9.2 per cent of total exports (World Bank/UNDP 2006). Many of these tourists come to Bali at some point in their visit. In 2001 direct international tourist arrivals to Bali were 1.36 million representing 26 per cent of Indonesia’s direct arrivals (Bali Tourist Authority, 2001). The fifteen major markets for direct tourist arrivals to Bali over the period 2001 to 2005 are shown in Table 1. In 2005 the top five source markets were Japan, Australia, Taiwan, South Korea and the United Kingdom. After the bombings when international tourist arrivals fell, tourists from other parts of Indonesia became more important. There are considerable differences in expenditure between domestic and international tourists. Average daily visitor expenditures by foreign visitors in Bali (including accommodation) are estimated to be $US74, with substantial variation across nationalities, including $US110 for Japanese, $US62 for Americans and $US55 for Australians. Domestic tourists are estimated to spend on average $US18 per day with tourists from Yogyakarta ($US38) and Jakarta ($US23) spending the most and those from East and West Java the least ($US10) (World Bank/UNDP, 2006).
3
Table 1: The fifteen major markets for direct tourist arrivals to Bali: 2001-2005 Nationality
Rank
2001
Rank
2002
Rank
2003
Rank
2004
Rank
2005
Japan +/-% Australia +/-% Taiwan +/-% South Korea +/-% United Kingdom +/-% Germany +/-% Malaysia +/-% United States +/-% France +/-% Netherlands +/-% Singapore +/-% Italy +/-% New Zealand +/-% Switzerland +/-% P.R. China +/-%
I
296,282 -18.22 238,857 3.07 154,575 -1.92 35,634 159.36 116,323 8.53 84,028 0.18 17,496 7.65 68,359 -13.97 42,944 -1.40 40,633 22.94 18,925 8.95 32,939 -16.13 26,018 0.18 16,614 -16.77 1,898 152.06
I
301,380 1.72 183,561 -23.15 168,756 9.17 41,036 15.16 96,806 -16.78 72,599 -13.60 19,960 14.08 50,007 -26.85 43,623 1.58 39,638 -2.45 27,919 47.52 32,531 -1.24 22,388 -13.95 13,543 -18.48 4,232 122.97
I
185,751 -38.37 139,018 -24.27 170,533 1.05 46,365 12.99 50,043 -48.31 53,374 -26.48 34,820 74.45 35,937 -28.14 29,628 -32.08 32,567 -17.84 42,931 53.77 12,130 -62.71 15,624 -30.21 9,727 -28.18 7,524 77.79
I
326,397 75.72 267,520 92.44 183,624 7.68 80,273 73.13 55,546 11.00 70,050 31.24 62,974 80.86 50,516 40.57 40,441 36.50 32,805 0.73 43,113 0.42 19,964 64.58 20,231 29.49 16,035 64.85 21,651 187.76
I
310,129 -4.98 249,001 -6.92 128,194 -30.19 78,146 -2.65 75,845 36.54 73,998 5.64 66,568 5.71 51,739 2.42 44,869 10.95 41,998 28.02 35,164 -18.44 19,388 -2.89 17,182 -15.07 17,155 6.98 17,137 -20.85
II III IX IV V XIV VI VII VIII VIII X XI -
II III VIII IV V XIII VI VII IX XI X XII -
III II VI V IV IX VIII XI X VII XIII XII -
II III IV VII V VI VIII X XI IX XIV XIII XV XII
Source: Bali Tourism Authority http://www.balitourismauthority.net/news/statistics.asp (accessed June, 2006)
4
II III IV V VI VII VIII IX X XI XII XIII XIV XV
The size of the tourist industry in Bali in terms of numbers employed belies its importance to the local economy. The 58,000 people working in hotels and restaurants represent just 3.3 per cent of Bali’s workforce, but they contribute 21 per cent of Bali’s provincial GDP. When retail trade, manufacturing and construction are included, it is estimated that tourism contributes more than half of Bali’s income (World Bank/UNDP, 2006). Booth (1990) utilized data on foreign tourist expenditures and an input-output table to calculate the national value added to Indonesia of tourism. She calculated that the foreign visitor to employment ratio for 1989 in Indonesia was 0.42. Hence, every one million international visitor arrivals generated 420,000 jobs. Based on evidence from Egypt complied by Tohamy and Swinscoe (2000) the World Bank/UNDP (2006) suggested that the effect of international tourism on employment is two to three times the contribution of hotels and restaurants to GDP, implying 42-63 per cent in Bali’s case. On this basis, as a crude approximation, the World Bank/UNDP (2006) suggested that one foreign visitor to Bali per year is responsible for just under half a local job and that a 20 per cent drop in international tourists coming to Bali - or roughly 860,000 visitors - could cost 361,000 jobs, equivalent to just over 20 per cent of current employment in Bali.
3
RELATED LITERATURE
The existing literature on tourism in Bali or Indonesia more generally is limited in scope and largely descriptive in nature. Some studies have examined the role of tourism in stimulating economic growth and/or alleviating poverty (Jayasuriya and Nehen, 1989; Booth, 1990; Shah and Gupta, 2000). Other studies have considered how to manage conflict between tourism and indigenous culture (McTaggart, 1980; Lietaer and De Meulenaere, 2003) or the linkages between tourism and small-scale entrepreneurship (Hitchcock, 2000). Hitchcock (2001) provided an overview of the effect of the Asian financial crisis on tourist arrivals in Bali. One of the few econometric studies examined the determinants of tourist flows to Malaysia and Indonesia from six major markets; namely, Australia, Germany, Japan, US, UK, Singapore over the period 1980 to 1997 (Tan et al. 2002). Not surprisingly, the study found that real income in the source markets and price competitiveness of the destinations are important determinants of demand. The literature on the effect of the bombings on tourism in Bali is predominantly limited to media reports or reports written by lending agencies in the aftermath of the bombings that provide initial assessments such as the World Bank/UNDP (2006) report. The paper builds on a limited number of studies that use a similar methodology to examine the effects of shocks on tourism in other countries. Aly and Strazicich (2000) used a univariate LM unit root test with two structural breaks to examine whether terrorist attacks had a permanent or transitory effect on tourist arrivals in Egypt and Israel. Their results rejected the null and support the alternative hypothesis of a trend-stationary series with transitory shocks. Bhattacharya and Narayan (2005) applied panel unit root tests to examine whether shocks to visitor arrivals to India were permanent or transitory and found that shocks were only transitory. In a series of articles Narayan (2005, 2005a, 2005b) examined the effect of the 1987 Rabuka coups on tourist arrivals and tourist expenditures in Fiji using unit root tests with structural breaks proposed by Zivot and Andrews (1992), Lumsdaine and Papell (1997), Vogelsang (1997) and Sen (2003). In each case he found that the coups had a transitory effect on tourist arrivals and tourist expenditure in Fiji. Finally, Narayan (2006) applied univariate and panel LM unit root tests with one and two structural breaks to examine whether shocks to tourist arrivals to Australia are permanent or transitory and found them to be transitory.
5
4
ECONOMETRIC METHODOLOGY
Unit root tests without structural breaks In order to provide a benchmark for the LM unit root tests we begin through applying the Augmented Dickey Fuller (ADF) and Phillips-Perron unit root tests as well as the Dickey Fuller Generalized Least (DF-GLS) and Point Optimal unit root tests proposed by Elliot et al. (1996). The ADF unit root test is based on the auxiliary regression: k
Δy t = κ + αy t −1 + ωt + ∑ d j Δyt − j + ε t
(1)
j =1
The ADF auxiliary regression tests for a unit root in
yt ,
where y refers to international tourist
arrivals in Bali, t = 1,...T is an index of time and ∆yt-j is the lagged first differences to accommodate serial correlation in the errors. Equation (1) tests the null hypothesis of a unit root against a trend stationary alternative. In Equation (1) the null and the alternate hypotheses for a unit root in y t are: H 0 α = 0 and H1 α < 0 . The Phillips-Perron unit root test is also based on Equation (1), but without the lagged differences. While the ADF test corrects for higher-order serial correlation by adding lagged difference terms to the right-hand side, the Phillips-Perron test makes a non-parametric correction to account for residual serial correlation. Monte Carlo studies suggest that the Phillips-Perron test generally has greater power than the ADF test (see Banerjee et al., 1993). The unit root test statistics proposed by Elliot et al. (1996) have substantially improved power over the ADF test when an unknown trend is present. Univariate LM unit root test with one and two structural breaks A limitation of the ADF, Phillips-Perron, DF-GLS and point optimal unit root tests is that these tests do not take into account potential structural breaks in visitor arrivals. Perron (1989) was the first to point out that power to reject the unit root null declines if the data contains a structural break that is ignored. In addition to the bombings, potential structural breaks in international tourist arrivals to Bali could be due to events such as the Asian financial crisis (October 1997), political riots in Jakarta (May 1998), the East Timor crisis (August 1999); 9/11 attacks in the US (September, 2001); SARS (March, 2003) and the South and Southeast Asia Tsunami and Earthquake (December 2004). Perron (1989) incorporated an exogenous structural break into an ADF test. The subsequent literature has extended the ADF-type unit root test to incorporate one and two endogenous structural breaks (Zivot and Andrews, 1992; Lumsdaine and Papell, 1997). As an alternative to ADF-type tests, Lee and Strazicich (2003, 2004) developed LM unit root tests with one and two structural breaks. The Zivot and Andrews (1992) and Lumsdaine and Papell (1997) ADF-type endogenous break unit root tests both have the limitation that the critical values are derived while assuming no break(s) under the null hypothesis. Nunes et al. (1997) showed that this assumption leads to size distortions in the presence of a unit root with structural breaks. As a result, when utilizing ADF-type endogenous break unit root tests, one might conclude that a time series is trend stationary, when in fact it is non-stationary with break(s), meaning that spurious rejections might occur. In contrast to the ADF-type endogenous break tests, the LM unit root test has the advantage that it is unaffected by breaks under the null (Lee and Strazicich, 2001). The LM unit root test can be explained using the following data generating process (DGP): yt = δ ′Z t + et , et = β et −1 + ε t . Here, Z t consists of exogenous variables and ε t is an error term with classical properties. Lee and Strazicich (2004) developed two versions of the LM unit root test with one structural break. Using the nomenclature of Perron (1989), Model A is known as
6
the “crash” model, and allows for a one-time change in the intercept under the alternative
hypothesis. Model A can be described by Z t = [1, t , Dt ] , where Dt = 1 for t ≥ TB + 1, and zero '
otherwise, TB is the date of the structural break, and δ' = ( δ1 , δ2 , δ3 ). Model C, the “crash-cumgrowth” model, allows for a shift in the intercept and a change in the trend slope under the B
alternative hypothesis and can be described by Z t = [1, t , Dt , DTt ] , where DTt = t − TB for t ≥ TB + 1, '
and zero otherwise. Lee and Strazicich (2003) developed a version of the LM unit root test to accommodate two structural breaks. The endogenous two-break LM unit root test can be considered as follows. Model AA, as an extension of Model A, allows for two shifts in the intercept and is described by
Z t = [1, t , D1t , D2t ] where D jt = 1 for t ≥ TBj + 1, j = 1, 2, and 0 otherwise. TBj denotes the date when '
the breaks occur. Note that the DGP includes breaks under the null (β= 1) and alternative (β< 1) hypothesis in a consistent manner. In Model AA, depending on the value of β, we have the following null and alternative hypotheses:
H 0 : yt = μ0 + d1 B1t + d 2 B2t + yt −1 + v1t , H A : yt = μ1 + γ t + d1 D1t + d 2 D2t + v2t , where v1t and v2t are stationary error terms; B jt = 1 for t = TBj + 1, j = 1, 2, and 0 otherwise. Model CC, as an extension of Model C, includes two changes in the intercept and the slope and is described by Z t = [1, t , D1t , D2t , DT1t , DT2t ] , where '
DT jt = t − TBj for t ≥ TBj + 1, j = 1, 2, and 0
otherwise. For Model CC we have the following hypotheses:
H 0 : yt = μ0 + d1 B1t + d 2 B2t + d3 D1t + d 4 D2t + yt −1 + v1t , H A : yt = μ1 + γ t + d1 D1t + d 2 D2t + d3 DT1t + d 4 DT2t + v2t , where v1t and v2t are stationary error terms; B jt = 1 for t = TBj + 1, j = 1, 2, and 0 otherwise. The LM unit root test statistic is obtained from the following regression:
Δy t = δ ′ΔZ t + φS t −1 + μ t where S t = yt − ψˆ x − Z t δˆ t , t = 2 ,...,T ; δˆ are coefficients in the regression of Δy t on ΔZ t ; ψˆ x is given by y t − Z t δ ; and y1 and Z 1 represent the first observations of yt and Z t respectively. The LM test statistic is given by: τ = t-statistic for testing the unit root null hypothesis that φ = 0 . The
location of the structural break (TB ) is determined by selecting all possible break points for the minimum t-statistic as follows:
Infτ~ (λi ) = ln f τ~ (λ ) , where λ = TB T . λ
The search is carried out over the trimming region (0.15T, 0.85T), where T is the sample size. We determined the breaks where the endogenous two-break LM t-test statistic is at a minimum. Critical
7
values for the one break case are tabulated in Lee and Strazicich (2004), while critical values for the two break case are from Lee and Strazicich (2003). Panel LM unit root test with one and two structural breaks Consider a model of the form: yit = δ i' X it + eit , eit = β i ei ,t −1 + ε it where yit is international visitor arrivals, i represents the cross-section of source markets (i = 1, . . . ,N), t represents the time period (t = 1, . . . , T), eit is the error term and X it is a vector of exogenous variables. The test for the null hypothesis of a unit root in international visitor arrivals is based on the parameter βi while
ε it is a zero mean error term that allows for heterogeneous variance structure across crosssectional units but assumes no cross-correlations. The parameter βi allows for heterogeneous measures of persistence. A structural break in the model is incorporated by specifying X it as [1, t , Dit , Tit ] , where Dit is a '
dummy variable that denotes a mean shift while Tit denotes a trend shift. If a structural break for country i occurs at TBi , then the dummy variable Dit = 1 if t > TBi , zero otherwise, and Tit = t − TB if
t > TBi , zero otherwise. Two structural breaks are incorporated by specifying X it
as [1, t , D1it , D 2it T 1it , T 2it ] , where D1it and D 2it are dummy variables that capture the first and '
second structural break respectively. D1it = 1 if t > TB1 , zero otherwise; D 2it = 1 if t > TB 2 , zero otherwise and T 1it = t − TB1 if t > TB1 , zero otherwise; T 2it = t − TB 2 if t > TB 2 , zero otherwise. The panel LM test statistic is obtained by averaging the optimal univariate LM unit root t-test statistic estimated for each country. This is denoted as LM iτ :
LM barNT =
1 N
N
∑ LM τ . i =1
i
Im et al. (2005) constructed a standardized panel LM unit root test statistic by letting E(LT) and V(LT) denote the expected value and variance of LM iτ respectively under the null hypothesis. Im et al. (2005) then compute the following expression:
ψ LM =
N ⎡⎣ LM barNT − E ( LT ) ⎤⎦ . V ( LT )
The numerical values for E(LT) and V(LT) are in Im et al. (2005). The asymptotic distribution is unaffected by the presence of structural breaks and is standard normal.
5
Data
The univariate unit root tests are applied to monthly international visitor arrivals in Bali from January 1983 to December 2005. The panel unit root tests are applied to annual international visitor arrivals in Bali from 1986 to 2005 from each of the island’s 11 major markets as at the end of 2005, treating each source market as a panel. Bali’s 11 major tourist markets in 2005 were Japan, Australia, Taiwan, South Korea, United Kingdom, Germany, Malaysia, United States, France,
8
Netherlands and Singapore (see Table 1). Treating each source market as a panel follows the approach adopted in previous studies by Bhattacharya and Narayan (2005) for India and Narayan (2006) for Australia. All data are expressed in logarithms and are extracted from BPS (various).
6
EMPIRICAL RESULTS
The results for the ADF, Phillips-Perron, DF-GLS and point optimal unit root tests are reported in Table 2. None of the tests are able to reject the null hypothesis of a unit root in international visitor arrivals to Bali at the 5 per cent level or better. Table 3 presents the results for the LM unit root test with one break in the intercept (Model A) and the LM unit root test with a break in the intercept and slope (Model C). The null hypothesis of a unit root cannot be rejected in either Model A or Model C at the 5 per cent level or better. In Model A the break in the intercept is statistically significant at the 1 per cent level and occurs in October 2002 in the same month as the Bali bombing. Following the bombing, several governments including those of Australian, Canada, United States and United Kingdom issued travel warnings for Bali advising their citizens not to travel to the island. In Model C the breaks in the intercept and slope are statistically significant at the 1 per cent level and occur in May 2001, in the months following the East Timor crisis (August 1999) and in the lead-up to the 9/11 attacks in New York and Washington (September, 2001). This was during a period that saw increasing Islamic militancy in Indonesia, attributed to the terrorist network Jemaah Islamiyah, reflected in events such as the bombing of churches in nine Indonesian cities on Christmas Eve of 2000. In addition to increased fears of terrorism there was rising concern about “sweeping activity” (ie. anti-western harassment) in bars and restaurants throughout Indonesia (DFAT, 2003). However, it is most likely that the break in May 2001 in Model C is in fact associated with 9/11 and the ensuing war in Afghanistan. In addition to the general fear of travel generated by 9/11 (Blunk et al., 2006), in October 2001, Australia and the United Kingdom participated in the United Statesled “Coalition of the Willing” in Afghanistan. In addition to several broadcasts threatening the safety of United States and United Kingdom citizens abroad, on November 3, 2001, al Jazeera television broadcast a statement by Osama bin Laden specifically threatening Australian tourists in Indonesia in retaliation for Australia’s involvement in Afghanistan as part of the “Coalition of the Willing” and Australia’s support for East Timor’s independence from Indonesia.
Table 2: Results for unit root tests without structural breaks Unit root test
Test statistics
Lag Length Bandwidth
P-value
ADF
-0.7116
12
0.9706
DF-GLS
-0.6254
12
0.5323
Phillips-Perron
-3.3973
11
0.0538
Point Optimal
60.8411
12
-
Note: The lag lengths for the ADF and DF-GLS tests are based on the SIC. The bandwidth selection for the Phillips-Perron test is based on Newey-West method using Bartlett Kernel. For the point optimal P statistic, the spectral estimation method is AR spectral (OLS). The critical value for the point optimal test at 5% level is 5.6448. (5.) All the unit root tests were performed with the assumption of constant term and linear trend in the time series. (6.) The maximum lag length selected in all cases was 15 based on the formula lag lengthmax=int(12(T/100)0.25) suggested by Hayashi (2000, p.594).
9
Table 3: Results of the LM unit root test with one structural break Model A LM Test Statistic -0.082 (-2.082)
Model C LM Test Statistic
TB
B(t)
Oct. 2002
-0.887*** (-6.983)
-0.276* (-4.399)
TB
B(t)
D(t)
May 2001
-0.367*** (-2.787)
0.125*** (3.735)
Note: The optimal lag selected in both the models was 12. The maximum lag length was 15 based on the formula lag lengthmax=int(12(T/100)0.25) suggested by Hayashi (2000, p.594). Lag length was selected using the general to specific criteria recommended by Hall (1994). TB is the date of the structural break; Bt is the dummy variable for the structural break in the intercept; D(t) is the dummy variable for the structural break in the slope. Figures in parentheses are t-values. For Model A critical values for the LM test statistic from Lee and Strazicich (2004) at the 10%, 5% and 1% significance levels are -3.211, -3.566, -4.239. For model C the critical values for the LM test statistic depend on the location of the break and are as follows: Location of break, λ
0.1
0.2
0.3
0.4
0.5
1% significance level
-5.11
-5.07
-5.15
-5.05
-5.11
5% significance level
-4.50
-4.47
-4.45
-4.50
-4.51
10% significance level
-4.21
-4.20
-4.18
-4.18
-4.17
Critical values for the dummy variables follow the standard normal distribution. * (**) *** denote statistical significance at the 10%, 5% and 1% levels respectively. The numbers in parenthesis represent t- statistics.
Table 4 presents the results for the LM unit root test with two breaks in the intercept (Model AA) and the LM unit root test with two breaks in the intercept and slope (Model CC). The null hypothesis of a unit root in international visitor arrivals to Bali cannot be rejected in either Model AA or Model CC at the 5 per cent level or better. In Model AA the breaks in the intercept are statistically significant. The first break occurs in April 1998 at the height of the Asian financial crisis, which reduced tourist flows from major Asian source markets for Bali such as Japan, South Korea and Taiwan and was one month prior to political riots in Jakarta, which may have increased uneasiness in the minds of some potential tourists about the safety of Indonesia. The second break in Model AA coincides with the Bali bombing in October 2002. In Model CC the breaks in intercept and slope are statistically significant and occur in February 1992 and October 2002. A possible reason for the failure of the univariate LM unit root test to reject the unit root null for international tourist arrivals to Bali based on Models A, C, AA and CC is the time span of the data. As is well-known, univariate unit root tests generally have low power when the sample size is small (Shiller and Perron, 1985). While there are a large number of observations in this study, the actual time span of the data is relatively short. To address this issue we applied the panel LM unit root test. We first applied the panel LM unit root test without a break and with one and two breaks to the full panel of Bali’s 11 major tourist source markets. The results are reported in Table 5. In the panel LM unit root test without a structural break, the null hypothesis of a unit root in international tourist arrivals cannot be rejected, but in the one and two break cases, the unit root null is rejected at the 1 per cent level suggesting that international tourist arrivals to Bali from Bali’s 11 major source markets are jointly trend stationary with transitory shocks.
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Table 4: Results of the LM unit root test with two structural breaks Model AA
Model CC
LM Test Statistic
-0.123 (-2.605)
TB1
TB2
B1(t)
B2(t)
Apr. 1998
Oct. 2002
-0.395*** (-3.195)
-0.890*** (-6.893)
LM Test Statistic
-0.373 (-5.009)
TB1
TB2
B1(t)
B2(t)
D1(t)
D2(t)
Feb. 1992
Oct. 2002
-0.388*** (-3.119)
-0.990*** (-7.681)
0.154*** (4.398)
0.124*** (3.908)
Note: The optimal lag selected in both models was 12. The maximum lag length was 15 based on the formula lag lengthmax=int(12(T/100)0.25) suggested by Hayashi (2000, p.594). Lag length was selected using the general to specific criteria recommended by Hall (1994). TB1 and TB2 are the dates of the structural breaks; B1(t) and B2(t) are the dummy variables for the structural breaks in the intercept; D1(t) and D2(t)are the dummy variables for the structural breaks in the slope. Figures in parentheses are t-values. For model AA critical values for the LM test at 10%, 5% and 1% significance levels are -3.504, -3.842, -4.545. For model CC, critical values depend on the location of the breaks and are as follows: Critical values for St-1 λ2
0.4
0.6
0.8
λ1
1%
5%
10%
1%
5%
10%
1%
5%
10%
0. 2
6.16
5.59
5.27
6.41
5.74
5.32
6.33
5.71
5.33
0. 4
-
-
-
6.45
5.67
5.31
6.42
5.65
5.32
0. 6
-
-
-
-
-
-
6.32
5.73
5.32
Figures in parentheses are t-values. λj denotes the location of breaks. * (**) *** denote statistical significance at the 10%, 5% and 1% levels respectively.
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Table 5: Results of the panel LM unit root test for Bali’s 11 major markets Panel Size
Full Panel
Panel LM test statistic
No Breaks
One Break
Two Breaks
-1.332
-12.309***
-11.515***
5.793
-
-
-
-3.238***
-
-
-
-7.657***
‘Unit Root 6’ ( excluding Netherlands,, Malaysia, Singapore, Taiwan, USA)
‘Unit Root 7’ ( excluding Australia, Malaysia, Singapore, USA)
‘Unit Root 9’ ( excluding Malaysia, USA)
Notes: The full panel includes Australia, France, Germany, Japan, Korea, Malaysia, Netherlands, Singapore, Taiwan, UK, and the US. The 1%, 5% and 10% critical values for the panel LM unit root tests are -2.326, 1.645 and -1.282 respectively. *** denotes statistically significant at the 1% level.
However, Taylor and Sarno (1998) suggested that rejection of the null hypothesis of joint nonstationarity using panel data tests might be due to as few as one of the series being stationary. Thus we also applied the LM unit root test without structural breaks and with one and two structural breaks to smaller panels excluding those source markets for which univariate LM unit root tests without a break and with one and two breaks rejected the unit root null. i For the univariate LM unit root test with no breaks, the unit root null could be rejected for five countries (Malaysia, Netherlands, Singapore, Taiwan and the United States). Thus for the panel LM unit root test without a structural break the smaller panel is a ‘unit root six’. For the univariate LM unit root test with one break the unit root null could be rejected for four countries (Australia, Malaysia, Singapore and the United States). Thus for the panel LM unit root test with one structural break the smaller panel is a ‘unit root seven’. For the univariate LM unit root test with two breaks the unit root null could be rejected for two countries (Malaysia and the United States). Thus for the panel LM unit root test with two structural breaks the smaller panel is a ‘unit root nine’. The results for the smaller panels, which are also reported in Table 5, made no difference to the conclusions. In the case of the panel LM unit root test without a structural break the null hypothesis of a unit root in international arrivals cannot be rejected, but for the LM unit root test with one and two breaks, the unit root null is rejected at the 1 per cent level.
7
CONCLUSION
Terrorism and tourism are inextricably linked. There is a lot of evidence that tourists alter their behavior in response to terrorist attacks (Pizam and Smith, 2000). Bali is an interesting destination to examine for effects of shocks on tourism, since it has experienced well-known acts of terrorism and relies on tourism as an important source of income. The World Bank/UNDP (2006) report has recently suggested that Bali is too reliant on tourism, making it vulnerable to shocks, such as terrorist attacks. The World Bank/UNDP (2006, p.66) concluded: “Bali needs to balance its future
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development strategy to …… support other sectors to mitigate against the inherent risks of the tourism sector”. This paper has considered this claim through examining the time series properties of international tourist arrivals in Bali. The univariate unit root tests with and without structural breaks were unable to reject the null hypothesis of a unit root in international tourist arrivals in Bali; however, the panel LM unit root tests with one and two structural breaks were able to reject the joint null for Bali’s 11 major tourist source markets. This result implies that shocks to international tourist arrivals from Bali’s 11 major source markets are jointly trend stationary with transitory shocks. The policy implication is that tourism is a sustainable industry and that following shocks, international tourist arrivals from Bali’s major source markets will revert to their long-run growth path. Thus, while some diversification may be desirable in the interests of a more balanced economy, Bali should not neglect or downplay its tourist sector on the premise that shocks will have permanent effects on international tourist arrivals to the island.
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NOTES i
The results of the univariate LM unit root tests applied to individual source markets are not reported, but are available on request.
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