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Economic Impact of Haze-Related Air Pollution on the Tourism Industry in Brunei Darussalam1
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Economic Analysis & Policy
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133
ECONOMIC IMPACT OF HAZE-RELATED AIR POLLUTION ON THE TOURISM INDUSTRY IN BRUNEI DARUSSALAM'
Kwabena A. Anaman and Chee N. Looi Department ofEconomics University ofBrunei Darussalam Bandar Seri Begawan BE1410 Brunei Darussalam
The impact of the 1997 and 1998 haze-related air pollution episodes on the tourism industry in Brunei Darussalamhas been analysed using multiple regression analysis and monthly arrival data of tourists from January 1995 to September 1999, asample of 57 monthly observations. The analysis is fIrst undertaken using ordinary least squares (OLS) regression. Count data models based on Poisson regression are then used to estimate the impact of the haze-related air pollution. Conservative estimates of impact of the haze-related air pollution using OLS regression are a reduction of 3.75 per cent in the number of tourists and direct economic loss to the tourism industry of about one million Brunei dollars (B$). Using the Poisson regression analysis, monthly arrivals are estimated to be reduced by about 28.7 per cent resulting in total direct loss of about B$8 million.
1. INTRODUCTION AND PROBLEM STATEMENT
Haze-related air pollution originating from forest fires in Indonesia engulfed much of Southeast Asia from July to September 1997, creating a major international environmental problem. By the end of 1997, weather conditions had returned to normal. However from mid-January 1998 to the end of April 1998, another series of major fires in Indonesia caused the return of haze-related air pollution. Brunei Damssalam (hereinafter referred to as Brunei) was mildly affected by the JulySeptember 1997 episode, but was seriously affected by the January-April 1998 event in terms of human health effects, impacts on economic sectors and closure of
schools and nursery centres for several weeks. The Economy and Environment Programme for SoutheastAsia (EEPSEA, 1998) undertook a study on the economic impact of the 1997 haze-related pollution episode on the economies ofIndonesia, Malaysia and Singapore, but not Brunei. While the health impacts of the 1997 haze-related episode were relatively minor in This study was sponsored by the University of Brunei Darussalam (UBD). We thank Kevin Ford and John Odihi for their comments of an earlier draft of this paper and the Academy of Brunei Studies. UBD, for granting pennission to publish this paper.
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Brunei, the effect on the tourism industry could have been considerable since at that time potential tourists stayed away from the entire Borneo Island (which includes Brnnei) and parts of Southeast Asia. To the authors' knowledge, no detailed economic study has been made ofthe impact ofeither the 1997 or 1998 haze-related air pollution on the tourism industry in Brunei. Given the high importance currently attached to the promotion of tourism in Brunei, it is appropriate to analyse the impact ofenvironmental disasters such as the 1997 and 1998 haze-related pollution episodes on this industry. Brunei's economy is heavily dependent on the oil and natural gas industry. Since 1929, when oil was fITst discovered, crude oil and natural gas have been the major contributors to the nation's gross domestic product (GDP). Brunei's GDP was estimated to be 8,111 million Brunei dollars (B$) in 1998, translating into a per capita GDP of B$25,065 (Government of Brnnei Darussalam, 2000). B$l.oo is equivalent to about 0.57 U.S.$ (in July 2000) and the B$ is at par with the Singapore dollar based on an interchangeability agreement between the two countries. An economic diversification strategy is being implemented by the Government of Brunei in view ofthe depletion ofthe oil and natural gas resources and the relatively low world prices of these commodities over the last decade. It is envisaged that Brunei will become a major tourist destination in Asia, especially in the niche market of ecotourism (Government of Brunei Darussalam, 1996). This policy on tourism promotion has been restated by the Brunei Economic Council established recently under the chairmanship of the Deputy Sultan (Head of State) to oversee implementation of economic reforms to make the economy sustainable over the long term. The year 200 I has been designated "Visit Brunei Year" with the Government aiming to boosttourisrn with a year-long focus on the country. The objectives ofthis study have been to determine the impact ofthe 1997 and 1998 haze-related air pollution on the total number of tourists to Brunei and to estimate direct economic losses to the tourism industry from reduced number of arrivals due to the pollution. The next section of the paper describes the theoretical framework used in this study. This is followed by a discussion of the empirical models and estimation procedures, including data and their sources. Results and conclusions follow.
2. THEORETICAL FRAMEWORK Following Sinclair and Stabler (1997), a visit to a tourist destination (here Brunei) is considered as one of the many goods and services available to a potential tourist who is assumed to maximize histher utility of consumption of goods and services subject to a budgetary constraint. This specification allows for the analysis of demand for the Brunei tourist destination as a function of the direct price of a visit to Brunei, the income of people living in the originating countries of the tourists, the prices of visits to competing tourist destinations, the prices of visits to complementary destinations, and inflation rates in Brunei. High rates of inflation could be expected to make Brnnei less attractive to tourists. By adapting the general tourism demand theory outlined above to incorporate environmental disasters, it can be argued that events such as the haze-related air
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pollution that covered Borneo island (which includes Brunei, East Malaysia and the Kalimantan Region ofIndonesia) would increase the expected price of visits to the Borneo region for the tourist from outside Borneo island. Even if the haze-related air pollution was limited to one area, for example the Kalimantan Region (the major source of the fires responsible for the 1997 and 1998 haze episodes), potential tourists could still perceive the expected price for Borneo tourist destinations to have increased because of the closeness of the three areas and inadequate reporting of the extent ofthe haze-related pollution. This was especially applicable to theJuly to September 1997 haze-related pollution, which only mildly affected Brunei but led to reduced tourist arrivals because of the perceptions of a regional catastrophe that were carried by the international media. 2.1 Environmental disasters and the tourism industry
Environmental disasters such as haze-related air pollution, earthquakes, tornadoes, floods and volcanic eruptions have economic impacts on areas affected, including reduced tourist activities. Decision-making by tourists on travel plans is influenced by economic, technological, international/political, sociocultural and environmental factors including those directly relared to disasters and natural hazards (Mathieson and Wall, 1993). Sinclair and Stabler (1997) note that the range of goods and
services encompassing tourism includes natural resources and especially the oonpriced features and the positive benefits (positive externalities) derived from attractive environments and the negative effects ofpollution (negativeexternalities). The literature suggests that potential visitors are unlikely to go to a place affected by natural disasters until the situation returns to normal. This is because the perceived cost of travel increases by the realisation of natural disasters in affected areas, in addition to the reduced quality of events and scenery that normally attract
visitors. Several economic studies have analysed the impacts ofenvironmental disasters on tourism industry. A recent study in Australia found that there was a relationship between tropical cyclones (environmental disaster) and the income of hotels (tourism industry) along the Queensland Coast of Australia (Campos et aI., 1999). Chang (1983) evaluated the effect of a hurricane on the revenues of a municipality using input-output analysis. Chang found that hurricanes and other natural disasters
can decrease revenues to a municipality through reduced tourism revenues, although the increased flow of Federal Government assistance to the affected area could result in a net positive regional impact. The total economic losses to the tourism industry of Indonesia, Malaysia and Singapore from the July-September 1997 haze-related air pollution were estimated to be U.S. $256 million (EEPSEA, 1998). Several of the studies reported earlier used differences in average tourist arrivals to detenmne the impacts ofenvironmental disasters ontourism. Econometric analysis is used in this study to isolate the effect of haze-related air pollution on arrivals of tourists and other visitors, using a dummy variable specification of the
environmental disturbance.
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3. ESTIMATION OF EMPIRICAL MODELS AND DATA SOURCES Many empirical studies of international tourism demand have been undertaken since the 1970s. Forty four ofthese studies covering the period from 1970 to 1989 have been summarised by Crouch and Shaw (1992). Sinclair and Stabler (1997, pp. 4-12) give examples of studies undertaken on the demand for tourism from 1970 to 1997. The number of tourists is often used as a measure of tourism demand. Independent variables included income and relative prices. Environmental disasters were treated as disturbance factors and were often specified as dummy variables. In our study, the dependent variable is the total number of tourists entering Brunei each month. Independent variables are an average per capita income index of six countries which were major sources of tourists to Brunei, a haze-related air pollution disturbance factor, a seasonality or holiday season index, the monthly consumer price index of Brunei and a trend variable. These variables are further explained below. • Number of Tourists to Brunei (TOURIST). This variable refers to the narrow definition of tourists as those who entered Brunei for the purpose of touring or sightseeing. • Monthly Trend (TREND). The trend variable is included to capture any sustained upward or downward movement in tourism unrelated to the other independent variables. It carries a value of I in January 1995 through to 57 for September 1999. • Haze Period (HAZE). This is a dummy variable that takes the value of I for the months of the haze-related air pollution and zero otherwise. The haze episodes actually occurred from July-September 1997 and JanuaryApril 1998. However, a value of I is assigned for July to November 1997 and January to June 1998, on the premise that more than two months of absence of haze-related pollution are required for foreign visitors to perceive that pollution has completely ceased. (Lags of one to four months were tested, two months giving the best fit.) • Holiday Season (SEASON). This variable denotes the holiday season in Malaysia (which provides about 50 per cent of total tourists to Brunei) and other countries which are important sources of tourists to Brunei. The major school holiday season in Malaysia is in the months of November and December. December is also considered part of the summer holidays for Australia and New Zealand. The summer holiday season for countries in the Northern Hemisphere is July and August. The seasonality dummy variable takes a value of I for the months of July, August, November and December each year and zero otherwise. • Average Per Capita Income of Major Sources of Tourists to Brunei (WGDP). This is the weighted average per capita income measured in US dollars of six countries which together account for about 80 per cent of tourists coming to Brunei (Indonesia, Japan, Malaysia, Singapore, United Kingdom and United States). Monthly per capita incomes (purchasing power parity based on World Bank data) have been derived from Asiaweek
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•
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Magazine. The weighted average is based on per capita income of each country and proportion of total tourists to Brunei. INFLATION. The monthly inflation rate in Brunei measured by the changes in monthly consumer price index is included as an independent variable to capture the effect of increases in domestic prices on tourism demand. It was postulated that high rates of inflation decrease the number of tourists to Brunei especially those from Malaysia. Low levels of inflation in Brunei would attract more tourists especially from countries close to Brunei such as the East Malaysian states of Sabah and Sarawak and the Malaysian Federal Territory of Labuan which share land and sea borders with Brunei. JAN99DUMMY. This is a dummy variable for the month of January 1999 which had unusually low number of tourist arrivals to Brunei - only 321 compared to the average monthly number of tourists of 3213 over the period of study. This variable takes a value of I for January 1999 and zero for every other month.
3.1 Estimation Using OLS Regression Linear and log-linear visitor and tourist functions were specified as follows: TOURlST = Bo + B I TREND + Bz HAZE + B3 SEASON + B 4 WGDP + Bs INFLATION + B6JAN99DUMMY + U, LTOURlST = Co + C, LTREND + Cz HAZE + C3 SEASON + C. LWGDP + Cs INFLATION + C6JAN99DUMMY + Uz
(I)
(2)
where LWGDP and LTREND are the natural logarithms of WGDP and TREND respectively, and U I and U z are error terms assumed initially to have zero means and constant variances. Equation 2 is preferred to Equation I based on the MWD test proposed by MacKinnon et al. (1983) and summarised by Gujarati (1995, pp.265-266). In addition, the independent variables in the model which are non-dummy variables _LTREND,LWGDPandINFLATION-areallstationarybasedontheAugmented Dickey-Fuller test using a 10 per cent significance level. With the Phillips-Peron test of stationarity, LTOURlST, LWGDP and INFLATION are all stationary at the 10 per cent level. Equation 2 has therefore been estimated by OLS correcting for first-order autocorrelation. 3.2 Count Data Poisson Regression Model Acount process involves the generation ofevents or occurrences during aparticular fixed and constant interval of time, for example, the number of incoming tourists to a country for one month. An incoming tourist is therefore an event. It is generally accepted that the OLS regression model is inappropriate for cases where the dependent variable is a count variable because OLS assumes a normal random error. Estimating a model with an incorrectly specified random structure would lead to inconsistent and inefficient estimates. The random component of a count
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data model has to incorporate the non-negativity ofthe data and the fact that the data are expressedsolely asintegers (Winkehnann, 1994). A majorprobabilitydistribution fulfilling the above conditions is the Poisson distribution. Unlike the normal distribution, the Poisson distribution has only one parameter which determines both the mean and the variance, these parameters being equal. The variance assumptionis sometimes restrictive though the parameters are estimated consistently.
If Y is a Poisson random variable with parameter A, the probability density function is as in Equation 3 (where exp denotes an exponential function). P(Y=k) = exp·). Ak lIe!
(3)
Let Y be the dependent variable, X the independent variables and ~ the parameters. The Poisson regression model assumes that A is a function of the independent variables, as in Equation 4. This also implies thattheconditional mean of Y is an exponential function of Xi~'
A; = exp(X;~)
(4)
Given an independent sample, the joint probability distribution of a sample is the product of the individual probabilities. These individual probabilities are a function of~. The log likelihood junction, derived from Equations 3 and 4, is: n
L - exp(Xi~) + (YiXi~) -
L(~,Y,X) = j
In(Y;!)
(5)
=1
The estimates of ~ are those which maximize the log-likelihood function. These estimates are derived by applying the first ordercondition shown in Equation 6. The Hessian matrix is indicated in Equation 7, adapted from Winkehnann (1994). Since this matrix is negative definite; the second order conditions for a maximum
estimate of ~ are established. Because Equation 6 is a non-linear function of~, the estimates of ~ are derived by iterative algorithms such as the Newton-Raphson method. dL(~,Y,X)ld ~ =
n
L
[Yi - exp( X;~)]X{ = 0
(6)
i: exp(Xi~)X{Xi
(7)
i= 1
iL(~,y,X)ld ~d W= -
i= I
The assumption of independent sampling is more realistic for cross-sectional data than time-series of count data. With a Poisson process involving time-series data, dependency patterns may exist violating the assumptions of independence and stationarity thus leading to autocorrelation (Winkelmann, 1994). The problem of dependencies across time intervals using count data is sometimes tackled by
incorporating lagged dependent variables and trend terms into the model or adjusting the model to accommodate autocorrelation (Zeger, 1988).
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3.3 Estimation of the Poisson Regression Model for This Study The Poisson regression model has been estimated using maximum likelihood techniques available from the Time Series Processor software (Hall and Cummins, 1999). The estimation is based on Equation 8 for I..; and Equation 9 for the loglikelihood function L.
1..;: exp(Do + Dt LTREND + D2 HAZE + D3 SEASON + D4 LWGDP + DslNFLATION + D6JAN99DUMMY) (8) L: -I..; +(TOURJST*LOG(I..;» - LOG(TOURJSTl)
(9)
3.4 Data Sources This study encompassed the period from January 1995 to September 1999. No monthly data on tourist arrivals are available for before January 1995. Arrivals data for October 1999 and beyond have not been processed by Brunei Government for public release. Data on tourist arrivals were collected from the Department of Economic Planning and Development, Ministry ofFinance. Data on tourist arrivals for the three months from October to December 1998 were estimates; the rest were reported figures. Consumer price index data were obtained from the Brunei Darussalam Statistical Yearbook 1999 editionpublishedby the Ministry ofFinance (Government ofBrunei Darussalam, 2000). The data series used for the estimation of the tourist functions are available from the authors on request. 4. RESULTS
4.1 Results for OLS Regression Estimation Results ofthe log-linear touristfunction estimated by OLS are summarised in Table I. These indicate absence of heteroscedasticity as measured by the White (1980) general heteroscedasticity test. Similarly, the variance inflation factor (VIF) of all three independent variables is low indicating that multicollinearity was not a problem. The sigus of the estimated coefficients for both models were as expected from economic theory. The seasonality index is not significant. On the other hand, the inflation rate has a significantly negative impact on numbers of tourists. The HAZE dummy variable is statistically significant indicating that the haze-related air pollution reduces the number of tourists visiting Brunei. Given the estimate of -0.294, the initial assessment ofthe proportional reduction in the number oftourists for the haze period (compared to the non-haze period) is e-O·294 - I or 25.5 per cent. Dividing the estimates of parameters derived from OLS regression analysis by the mean ofthe dependent variable would yield a more plausible indication of the size of the parameters when dealing with count data models (Cameron and Trivedi, 1998). The mean ofthe dependent variable in this study (LTOURIST) is 7.994. The adjusted estimates in Table I are the parameter estimates divided by 7.994.
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4.2 Results for the Poisson Regression Analysis The results ofthe estimation of the tourist function by Poisson regression analysis are presented in Table 2. All the estimates of the parameters are statistically significant and also have the correct sign based on a priori expectations. The parameter estimate of the haze factor is -0.338, sligbtly larger than the initial estimate derived from the OLS regression.
43 Tourism Revenue Losses Due to Haze-Related Pollutiou The mean number of monthly tourist arrivals to Brunei during the II haze months was 2257.64 (standard deviation of 194.07). During the 46 non-haze months, the mean number was 3442.46 (standard deviation of 1243.58). As measured by the tstatistic value of -6.156, there is a statistically significant difference between these two monthly means, hence haze-related pollution appears to reduce the number of tourists coming to Brunei. As indicated earlier, dividing parameter estimates derived from OLS by the mean of the dependent variable would yield a more plausible indication of the true parameter values. The adjusted parameter estimate of HAZE from OLS regression was derived as the raw estimate (-0.294) divided by the mean of the dependent variable, LTOURIST (7.994), yielding a value of -0.037. This adjusted estimate corresponds to a proportional reduction factor of -3.75 per cent which, when multiplied by the average number of monthly tourist arrivals during the non-haze TABLE! RESULTS OF THE ESTIMATED LOG-LINEAR TOURIST ARRIVALS FUNCTION (LTOURlST) FOR BRUNEI BASED ON THE OLS METHOD CORRECTING FOR AUTOCORRELATION Explanatory Variable
Parameter Estimate
T-statistic
INTERCEPT LTREND
15.867 0.285 ·0.294 0.039 ·2.421 -8.046 -2.275
1.555 1.245 -3.392 0.572 -1.142 -1.942 -8.050
HAZE SEASON
LWGDP INFLATION
JAN99DUMMY R2
Adjusted R2 F-value Durbin-Watson Statistic First-order autocorrelation coefficient Level of significance of White (1980) general heteroscedasticity test
P-value
Standardiud
VlF
Estimate 0.127 0.219 0.001' • 0.570 0.259 0.058" 0.000'-
0.000 0.245 0.286 0.047 0.231 0.155 0.740
Adjusted
Estimate-
0.000 6.177 1.139 1.067 6.553 1.024 1.351
1.985 0.036 -0.037 0.005 -0.303 -1.007 -0.285
0.700 0.662 18.618 2.039 -0.022 0.360
* The adjusted estimates are based on dividing the actual estimates by the mean ofLTOURIST (7.994). ** denotes statistical significance at 10% level.
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period (3,442.46) indicates the reduction in number of tourists as a result of the haze-related air pollution episode (129 per month). There were 11 haze-related months, which translates into a total reduction of 1419 tourists. The average spending per day of tourists is taken as B$200 with B$139 being average nightly hotel accommodation charge, based on the work of Ali (1998, p. 83). The average length of stay is taken as 3.7 days, also derived from Ali (1998: pp. 52 and 81). Direct losses due to reduced tourist arrivals are therefore B$I,050,060, Le. (1419)(200)(3.7). This could be regarded as a conservative estimate based on use of adjusted OLS estimates. TABLE 2 RESULTS OF THE ESTlMATED TOURIST ARRIVALS FUNCTION FOR BRUNEI BASED ON POISSON REGRESSION FUNCTION Explanatory Variable
Poisson Parameter Estimate
Poisson t-value
24.067
396.296'
0.156
249.827'
-0.338
-343.222'
SEASON
0.183
461.988'
LWGDP
-1.742
-264.148'
INFLATION
-5.017
-122.077'
-3.088
-604.257'
CONSTANT LTREND HAZE
JAN99DUMMY Log likelihood ratio
-7028.44
* Statistically significant at 10% level. Using the HAZE coefficient estimate derived from the Poisson regression analysis of -0.338 implies a 28.7 per cent reduction of tourists during the haze months compared to the non-haze months. The equivalent reduced tourist arrivals based on the Poisson estimate are 10, 860 during the 11 months of the haze-related pollution. Thedirect economic lossistherefore B$8,036,400, Le.(I0,860)(200)(3.7); this could be considered as the maximum direct loss. 5. CONCLUSIONS
Based on conservaclve estimates from OLS regression, the 1997 and 1998 episodes caused about 3.75 per centreduction in the number oftourists to Brunei. Total direct economic loss suffered by the tourism industry in Brunei are estimated to be about B$l million. Using Poisson regression analysis, there has been a reduction of about
28.7 per cent in tourist arrivals and direct loss of about B$8 million. Haze-related pollution episode appears to affect the tourism industry for about two months after the event ends. A policy implication related to this study is that Brunei may need to mount intensive publicity campaigns to inform the international public after the
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endofhaze-relatedairpollution episodes. While such events areoften overdramatised by the international media when they occur, the international public is not fully informed when they have ended. This information strategy to attract tourists to Brunei is also applicable to other events which occur in neighbouring countries which may drive tourists away from Brunei because of the erroneous impression that the disturbances are widespread in the Borneo region or in Southeast Asia. These events include, for example, the recent Sipadan hostage crisis in Malaysia (2000) and major riots in Kalimantan, Indonesia in 1999.
REFERENCES Ali, N.D. (1998), A Study of the Hotel Industry in Negara Brunei Darussalam, Bachelor of Arts (Economics) Thesis, Department of Economics, Universiti Brunei Darussalam, Bandar Seri Begawan (unpublished). Cameron, A.C. and P.K. Trivedi (1998), Regression Analysis of Count Data, Cambridge University Press, London. Chang, S. (1983), "Disasters and Fiscal Policy: Hurricane Impact on Municipal Revenue", Urban Affairs Quarterly, 18(4),511-523. Crouch, G.I. and R.N. Shaw (1992), "International Tourism Demand: A MetaAnalytical Integration of Research Findings", In Johnson, P. and B. Thomas (eds.), Chaice and Demand in Tourism. Mansell, London, 175-207. Campos, M., L. Drake and K. A. Anaman (1999), "Impact of Tropical Cyclones Warning Information on Incomes of Commercial Tourist Accommodation Operators Along the Queensland Coast", Economic Analysis and Policy, 29(2), 185-193. Economy and Environment Programme ForSouth East Asia (1998), The Indonesian Fires and Haze of1997: The Economic Toll (4th June). Hall, B.H. and C. Cummins (1999), Time Series Processor Version 4.5: User's Guide, TSP International, Pal Alto. Government of Brunei Darussalam (1996), Seventh National Development Plan 1996 - 2000, Ministry of Finance, Bandar Seri Begawan. Government ofBrunei Darussalam (2000), Brunei Darussalam Statistical Yearbook 1999, Department of Economic Planning and Development, Ministry of Finance, Bandar Seri Begawan. Gujarati D.N. (1995), Basic Econometrics: Third Edition, McGraw-Hili, New York. Mckinnon, J., H. White and R. Davidson (1983), "Tests for Model Specifications in the Presence of Alternative Hypothesis: Some Further Results", Journal of Econometrics, 21, 53-70. Mathieson, A. andG. Wall (1993), Tourism: Economic, Physical & Social Impacts, Longman Scientific & Technical, New York.
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Sinclair, M.T. and M. Stabler (1997), The Economics of Tourism, Routledge, London. White, H. (1980), "A Heteroscedasticity Consistent Covariance Matrix Estimator and a Direct Test of Heteroscedasticity", Econometrica, 48, 817-818. Winkelmann, R. (1994), Count Data Models: Econometric Theory and an Application to Labor Mobility, Springer-Verlag, Berlin. Zeger, S.L. (1988), "A Regression Model for Time Series of Counts", Biometrika, 75(4),621-629.
200105088
Limited on (02) 93947600 (ph) or (02) 93947601 (fax)
Economic Analysis & Policy
Vol.30 No.2. September 2000
133
ECONOMIC IMPACT OF HAZE-RELATED AIR POLLUTION ON THE TOURISM INDUSTRY IN BRUNEI DARUSSALAM'
Kwabena A. Anaman and Chee N. Looi Department ofEconomics University ofBrunei Darussalam Bandar Seri Begawan BE1410 Brunei Darussalam
The impact of the 1997 and 1998 haze-related air pollution episodes on the tourism industry in Brunei Darussalamhas been analysed using multiple regression analysis and monthly arrival data of tourists from January 1995 to September 1999, asample of 57 monthly observations. The analysis is fIrst undertaken using ordinary least squares (OLS) regression. Count data models based on Poisson regression are then used to estimate the impact of the haze-related air pollution. Conservative estimates of impact of the haze-related air pollution using OLS regression are a reduction of 3.75 per cent in the number of tourists and direct economic loss to the tourism industry of about one million Brunei dollars (B$). Using the Poisson regression analysis, monthly arrivals are estimated to be reduced by about 28.7 per cent resulting in total direct loss of about B$8 million.
1. INTRODUCTION AND PROBLEM STATEMENT
Haze-related air pollution originating from forest fires in Indonesia engulfed much of Southeast Asia from July to September 1997, creating a major international environmental problem. By the end of 1997, weather conditions had returned to normal. However from mid-January 1998 to the end of April 1998, another series of major fires in Indonesia caused the return of haze-related air pollution. Brunei Damssalam (hereinafter referred to as Brunei) was mildly affected by the JulySeptember 1997 episode, but was seriously affected by the January-April 1998 event in terms of human health effects, impacts on economic sectors and closure of
schools and nursery centres for several weeks. The Economy and Environment Programme for SoutheastAsia (EEPSEA, 1998) undertook a study on the economic impact of the 1997 haze-related pollution episode on the economies ofIndonesia, Malaysia and Singapore, but not Brunei. While the health impacts of the 1997 haze-related episode were relatively minor in This study was sponsored by the University of Brunei Darussalam (UBD). We thank Kevin Ford and John Odihi for their comments of an earlier draft of this paper and the Academy of Brunei Studies. UBD, for granting pennission to publish this paper.
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Brunei, the effect on the tourism industry could have been considerable since at that time potential tourists stayed away from the entire Borneo Island (which includes Brnnei) and parts of Southeast Asia. To the authors' knowledge, no detailed economic study has been made ofthe impact ofeither the 1997 or 1998 haze-related air pollution on the tourism industry in Brunei. Given the high importance currently attached to the promotion of tourism in Brunei, it is appropriate to analyse the impact ofenvironmental disasters such as the 1997 and 1998 haze-related pollution episodes on this industry. Brunei's economy is heavily dependent on the oil and natural gas industry. Since 1929, when oil was fITst discovered, crude oil and natural gas have been the major contributors to the nation's gross domestic product (GDP). Brunei's GDP was estimated to be 8,111 million Brunei dollars (B$) in 1998, translating into a per capita GDP of B$25,065 (Government of Brnnei Darussalam, 2000). B$l.oo is equivalent to about 0.57 U.S.$ (in July 2000) and the B$ is at par with the Singapore dollar based on an interchangeability agreement between the two countries. An economic diversification strategy is being implemented by the Government of Brunei in view ofthe depletion ofthe oil and natural gas resources and the relatively low world prices of these commodities over the last decade. It is envisaged that Brunei will become a major tourist destination in Asia, especially in the niche market of ecotourism (Government of Brunei Darussalam, 1996). This policy on tourism promotion has been restated by the Brunei Economic Council established recently under the chairmanship of the Deputy Sultan (Head of State) to oversee implementation of economic reforms to make the economy sustainable over the long term. The year 200 I has been designated "Visit Brunei Year" with the Government aiming to boosttourisrn with a year-long focus on the country. The objectives ofthis study have been to determine the impact ofthe 1997 and 1998 haze-related air pollution on the total number of tourists to Brunei and to estimate direct economic losses to the tourism industry from reduced number of arrivals due to the pollution. The next section of the paper describes the theoretical framework used in this study. This is followed by a discussion of the empirical models and estimation procedures, including data and their sources. Results and conclusions follow.
2. THEORETICAL FRAMEWORK Following Sinclair and Stabler (1997), a visit to a tourist destination (here Brunei) is considered as one of the many goods and services available to a potential tourist who is assumed to maximize histher utility of consumption of goods and services subject to a budgetary constraint. This specification allows for the analysis of demand for the Brunei tourist destination as a function of the direct price of a visit to Brunei, the income of people living in the originating countries of the tourists, the prices of visits to competing tourist destinations, the prices of visits to complementary destinations, and inflation rates in Brunei. High rates of inflation could be expected to make Brnnei less attractive to tourists. By adapting the general tourism demand theory outlined above to incorporate environmental disasters, it can be argued that events such as the haze-related air
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pollution that covered Borneo island (which includes Brunei, East Malaysia and the Kalimantan Region ofIndonesia) would increase the expected price of visits to the Borneo region for the tourist from outside Borneo island. Even if the haze-related air pollution was limited to one area, for example the Kalimantan Region (the major source of the fires responsible for the 1997 and 1998 haze episodes), potential tourists could still perceive the expected price for Borneo tourist destinations to have increased because of the closeness of the three areas and inadequate reporting of the extent ofthe haze-related pollution. This was especially applicable to theJuly to September 1997 haze-related pollution, which only mildly affected Brunei but led to reduced tourist arrivals because of the perceptions of a regional catastrophe that were carried by the international media. 2.1 Environmental disasters and the tourism industry
Environmental disasters such as haze-related air pollution, earthquakes, tornadoes, floods and volcanic eruptions have economic impacts on areas affected, including reduced tourist activities. Decision-making by tourists on travel plans is influenced by economic, technological, international/political, sociocultural and environmental factors including those directly relared to disasters and natural hazards (Mathieson and Wall, 1993). Sinclair and Stabler (1997) note that the range of goods and
services encompassing tourism includes natural resources and especially the oonpriced features and the positive benefits (positive externalities) derived from attractive environments and the negative effects ofpollution (negativeexternalities). The literature suggests that potential visitors are unlikely to go to a place affected by natural disasters until the situation returns to normal. This is because the perceived cost of travel increases by the realisation of natural disasters in affected areas, in addition to the reduced quality of events and scenery that normally attract
visitors. Several economic studies have analysed the impacts ofenvironmental disasters on tourism industry. A recent study in Australia found that there was a relationship between tropical cyclones (environmental disaster) and the income of hotels (tourism industry) along the Queensland Coast of Australia (Campos et aI., 1999). Chang (1983) evaluated the effect of a hurricane on the revenues of a municipality using input-output analysis. Chang found that hurricanes and other natural disasters
can decrease revenues to a municipality through reduced tourism revenues, although the increased flow of Federal Government assistance to the affected area could result in a net positive regional impact. The total economic losses to the tourism industry of Indonesia, Malaysia and Singapore from the July-September 1997 haze-related air pollution were estimated to be U.S. $256 million (EEPSEA, 1998). Several of the studies reported earlier used differences in average tourist arrivals to detenmne the impacts ofenvironmental disasters ontourism. Econometric analysis is used in this study to isolate the effect of haze-related air pollution on arrivals of tourists and other visitors, using a dummy variable specification of the
environmental disturbance.
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3. ESTIMATION OF EMPIRICAL MODELS AND DATA SOURCES Many empirical studies of international tourism demand have been undertaken since the 1970s. Forty four ofthese studies covering the period from 1970 to 1989 have been summarised by Crouch and Shaw (1992). Sinclair and Stabler (1997, pp. 4-12) give examples of studies undertaken on the demand for tourism from 1970 to 1997. The number of tourists is often used as a measure of tourism demand. Independent variables included income and relative prices. Environmental disasters were treated as disturbance factors and were often specified as dummy variables. In our study, the dependent variable is the total number of tourists entering Brunei each month. Independent variables are an average per capita income index of six countries which were major sources of tourists to Brunei, a haze-related air pollution disturbance factor, a seasonality or holiday season index, the monthly consumer price index of Brunei and a trend variable. These variables are further explained below. • Number of Tourists to Brunei (TOURIST). This variable refers to the narrow definition of tourists as those who entered Brunei for the purpose of touring or sightseeing. • Monthly Trend (TREND). The trend variable is included to capture any sustained upward or downward movement in tourism unrelated to the other independent variables. It carries a value of I in January 1995 through to 57 for September 1999. • Haze Period (HAZE). This is a dummy variable that takes the value of I for the months of the haze-related air pollution and zero otherwise. The haze episodes actually occurred from July-September 1997 and JanuaryApril 1998. However, a value of I is assigned for July to November 1997 and January to June 1998, on the premise that more than two months of absence of haze-related pollution are required for foreign visitors to perceive that pollution has completely ceased. (Lags of one to four months were tested, two months giving the best fit.) • Holiday Season (SEASON). This variable denotes the holiday season in Malaysia (which provides about 50 per cent of total tourists to Brunei) and other countries which are important sources of tourists to Brunei. The major school holiday season in Malaysia is in the months of November and December. December is also considered part of the summer holidays for Australia and New Zealand. The summer holiday season for countries in the Northern Hemisphere is July and August. The seasonality dummy variable takes a value of I for the months of July, August, November and December each year and zero otherwise. • Average Per Capita Income of Major Sources of Tourists to Brunei (WGDP). This is the weighted average per capita income measured in US dollars of six countries which together account for about 80 per cent of tourists coming to Brunei (Indonesia, Japan, Malaysia, Singapore, United Kingdom and United States). Monthly per capita incomes (purchasing power parity based on World Bank data) have been derived from Asiaweek
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Magazine. The weighted average is based on per capita income of each country and proportion of total tourists to Brunei. INFLATION. The monthly inflation rate in Brunei measured by the changes in monthly consumer price index is included as an independent variable to capture the effect of increases in domestic prices on tourism demand. It was postulated that high rates of inflation decrease the number of tourists to Brunei especially those from Malaysia. Low levels of inflation in Brunei would attract more tourists especially from countries close to Brunei such as the East Malaysian states of Sabah and Sarawak and the Malaysian Federal Territory of Labuan which share land and sea borders with Brunei. JAN99DUMMY. This is a dummy variable for the month of January 1999 which had unusually low number of tourist arrivals to Brunei - only 321 compared to the average monthly number of tourists of 3213 over the period of study. This variable takes a value of I for January 1999 and zero for every other month.
3.1 Estimation Using OLS Regression Linear and log-linear visitor and tourist functions were specified as follows: TOURlST = Bo + B I TREND + Bz HAZE + B3 SEASON + B 4 WGDP + Bs INFLATION + B6JAN99DUMMY + U, LTOURlST = Co + C, LTREND + Cz HAZE + C3 SEASON + C. LWGDP + Cs INFLATION + C6JAN99DUMMY + Uz
(I)
(2)
where LWGDP and LTREND are the natural logarithms of WGDP and TREND respectively, and U I and U z are error terms assumed initially to have zero means and constant variances. Equation 2 is preferred to Equation I based on the MWD test proposed by MacKinnon et al. (1983) and summarised by Gujarati (1995, pp.265-266). In addition, the independent variables in the model which are non-dummy variables _LTREND,LWGDPandINFLATION-areallstationarybasedontheAugmented Dickey-Fuller test using a 10 per cent significance level. With the Phillips-Peron test of stationarity, LTOURlST, LWGDP and INFLATION are all stationary at the 10 per cent level. Equation 2 has therefore been estimated by OLS correcting for first-order autocorrelation. 3.2 Count Data Poisson Regression Model Acount process involves the generation ofevents or occurrences during aparticular fixed and constant interval of time, for example, the number of incoming tourists to a country for one month. An incoming tourist is therefore an event. It is generally accepted that the OLS regression model is inappropriate for cases where the dependent variable is a count variable because OLS assumes a normal random error. Estimating a model with an incorrectly specified random structure would lead to inconsistent and inefficient estimates. The random component of a count
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data model has to incorporate the non-negativity ofthe data and the fact that the data are expressedsolely asintegers (Winkehnann, 1994). A majorprobabilitydistribution fulfilling the above conditions is the Poisson distribution. Unlike the normal distribution, the Poisson distribution has only one parameter which determines both the mean and the variance, these parameters being equal. The variance assumptionis sometimes restrictive though the parameters are estimated consistently.
If Y is a Poisson random variable with parameter A, the probability density function is as in Equation 3 (where exp denotes an exponential function). P(Y=k) = exp·). Ak lIe!
(3)
Let Y be the dependent variable, X the independent variables and ~ the parameters. The Poisson regression model assumes that A is a function of the independent variables, as in Equation 4. This also implies thattheconditional mean of Y is an exponential function of Xi~'
A; = exp(X;~)
(4)
Given an independent sample, the joint probability distribution of a sample is the product of the individual probabilities. These individual probabilities are a function of~. The log likelihood junction, derived from Equations 3 and 4, is: n
L - exp(Xi~) + (YiXi~) -
L(~,Y,X) = j
In(Y;!)
(5)
=1
The estimates of ~ are those which maximize the log-likelihood function. These estimates are derived by applying the first ordercondition shown in Equation 6. The Hessian matrix is indicated in Equation 7, adapted from Winkehnann (1994). Since this matrix is negative definite; the second order conditions for a maximum
estimate of ~ are established. Because Equation 6 is a non-linear function of~, the estimates of ~ are derived by iterative algorithms such as the Newton-Raphson method. dL(~,Y,X)ld ~ =
n
L
[Yi - exp( X;~)]X{ = 0
(6)
i: exp(Xi~)X{Xi
(7)
i= 1
iL(~,y,X)ld ~d W= -
i= I
The assumption of independent sampling is more realistic for cross-sectional data than time-series of count data. With a Poisson process involving time-series data, dependency patterns may exist violating the assumptions of independence and stationarity thus leading to autocorrelation (Winkelmann, 1994). The problem of dependencies across time intervals using count data is sometimes tackled by
incorporating lagged dependent variables and trend terms into the model or adjusting the model to accommodate autocorrelation (Zeger, 1988).
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3.3 Estimation of the Poisson Regression Model for This Study The Poisson regression model has been estimated using maximum likelihood techniques available from the Time Series Processor software (Hall and Cummins, 1999). The estimation is based on Equation 8 for I..; and Equation 9 for the loglikelihood function L.
1..;: exp(Do + Dt LTREND + D2 HAZE + D3 SEASON + D4 LWGDP + DslNFLATION + D6JAN99DUMMY) (8) L: -I..; +(TOURJST*LOG(I..;» - LOG(TOURJSTl)
(9)
3.4 Data Sources This study encompassed the period from January 1995 to September 1999. No monthly data on tourist arrivals are available for before January 1995. Arrivals data for October 1999 and beyond have not been processed by Brunei Government for public release. Data on tourist arrivals were collected from the Department of Economic Planning and Development, Ministry ofFinance. Data on tourist arrivals for the three months from October to December 1998 were estimates; the rest were reported figures. Consumer price index data were obtained from the Brunei Darussalam Statistical Yearbook 1999 editionpublishedby the Ministry ofFinance (Government ofBrunei Darussalam, 2000). The data series used for the estimation of the tourist functions are available from the authors on request. 4. RESULTS
4.1 Results for OLS Regression Estimation Results ofthe log-linear touristfunction estimated by OLS are summarised in Table I. These indicate absence of heteroscedasticity as measured by the White (1980) general heteroscedasticity test. Similarly, the variance inflation factor (VIF) of all three independent variables is low indicating that multicollinearity was not a problem. The sigus of the estimated coefficients for both models were as expected from economic theory. The seasonality index is not significant. On the other hand, the inflation rate has a significantly negative impact on numbers of tourists. The HAZE dummy variable is statistically significant indicating that the haze-related air pollution reduces the number of tourists visiting Brunei. Given the estimate of -0.294, the initial assessment ofthe proportional reduction in the number oftourists for the haze period (compared to the non-haze period) is e-O·294 - I or 25.5 per cent. Dividing the estimates of parameters derived from OLS regression analysis by the mean ofthe dependent variable would yield a more plausible indication of the size of the parameters when dealing with count data models (Cameron and Trivedi, 1998). The mean ofthe dependent variable in this study (LTOURIST) is 7.994. The adjusted estimates in Table I are the parameter estimates divided by 7.994.
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4.2 Results for the Poisson Regression Analysis The results ofthe estimation of the tourist function by Poisson regression analysis are presented in Table 2. All the estimates of the parameters are statistically significant and also have the correct sign based on a priori expectations. The parameter estimate of the haze factor is -0.338, sligbtly larger than the initial estimate derived from the OLS regression.
43 Tourism Revenue Losses Due to Haze-Related Pollutiou The mean number of monthly tourist arrivals to Brunei during the II haze months was 2257.64 (standard deviation of 194.07). During the 46 non-haze months, the mean number was 3442.46 (standard deviation of 1243.58). As measured by the tstatistic value of -6.156, there is a statistically significant difference between these two monthly means, hence haze-related pollution appears to reduce the number of tourists coming to Brunei. As indicated earlier, dividing parameter estimates derived from OLS by the mean of the dependent variable would yield a more plausible indication of the true parameter values. The adjusted parameter estimate of HAZE from OLS regression was derived as the raw estimate (-0.294) divided by the mean of the dependent variable, LTOURIST (7.994), yielding a value of -0.037. This adjusted estimate corresponds to a proportional reduction factor of -3.75 per cent which, when multiplied by the average number of monthly tourist arrivals during the non-haze TABLE! RESULTS OF THE ESTIMATED LOG-LINEAR TOURIST ARRIVALS FUNCTION (LTOURlST) FOR BRUNEI BASED ON THE OLS METHOD CORRECTING FOR AUTOCORRELATION Explanatory Variable
Parameter Estimate
T-statistic
INTERCEPT LTREND
15.867 0.285 ·0.294 0.039 ·2.421 -8.046 -2.275
1.555 1.245 -3.392 0.572 -1.142 -1.942 -8.050
HAZE SEASON
LWGDP INFLATION
JAN99DUMMY R2
Adjusted R2 F-value Durbin-Watson Statistic First-order autocorrelation coefficient Level of significance of White (1980) general heteroscedasticity test
P-value
Standardiud
VlF
Estimate 0.127 0.219 0.001' • 0.570 0.259 0.058" 0.000'-
0.000 0.245 0.286 0.047 0.231 0.155 0.740
Adjusted
Estimate-
0.000 6.177 1.139 1.067 6.553 1.024 1.351
1.985 0.036 -0.037 0.005 -0.303 -1.007 -0.285
0.700 0.662 18.618 2.039 -0.022 0.360
* The adjusted estimates are based on dividing the actual estimates by the mean ofLTOURIST (7.994). ** denotes statistical significance at 10% level.
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period (3,442.46) indicates the reduction in number of tourists as a result of the haze-related air pollution episode (129 per month). There were 11 haze-related months, which translates into a total reduction of 1419 tourists. The average spending per day of tourists is taken as B$200 with B$139 being average nightly hotel accommodation charge, based on the work of Ali (1998, p. 83). The average length of stay is taken as 3.7 days, also derived from Ali (1998: pp. 52 and 81). Direct losses due to reduced tourist arrivals are therefore B$I,050,060, Le. (1419)(200)(3.7). This could be regarded as a conservative estimate based on use of adjusted OLS estimates. TABLE 2 RESULTS OF THE ESTlMATED TOURIST ARRIVALS FUNCTION FOR BRUNEI BASED ON POISSON REGRESSION FUNCTION Explanatory Variable
Poisson Parameter Estimate
Poisson t-value
24.067
396.296'
0.156
249.827'
-0.338
-343.222'
SEASON
0.183
461.988'
LWGDP
-1.742
-264.148'
INFLATION
-5.017
-122.077'
-3.088
-604.257'
CONSTANT LTREND HAZE
JAN99DUMMY Log likelihood ratio
-7028.44
* Statistically significant at 10% level. Using the HAZE coefficient estimate derived from the Poisson regression analysis of -0.338 implies a 28.7 per cent reduction of tourists during the haze months compared to the non-haze months. The equivalent reduced tourist arrivals based on the Poisson estimate are 10, 860 during the 11 months of the haze-related pollution. Thedirect economic lossistherefore B$8,036,400, Le.(I0,860)(200)(3.7); this could be considered as the maximum direct loss. 5. CONCLUSIONS
Based on conservaclve estimates from OLS regression, the 1997 and 1998 episodes caused about 3.75 per centreduction in the number oftourists to Brunei. Total direct economic loss suffered by the tourism industry in Brunei are estimated to be about B$l million. Using Poisson regression analysis, there has been a reduction of about
28.7 per cent in tourist arrivals and direct loss of about B$8 million. Haze-related pollution episode appears to affect the tourism industry for about two months after the event ends. A policy implication related to this study is that Brunei may need to mount intensive publicity campaigns to inform the international public after the
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endofhaze-relatedairpollution episodes. While such events areoften overdramatised by the international media when they occur, the international public is not fully informed when they have ended. This information strategy to attract tourists to Brunei is also applicable to other events which occur in neighbouring countries which may drive tourists away from Brunei because of the erroneous impression that the disturbances are widespread in the Borneo region or in Southeast Asia. These events include, for example, the recent Sipadan hostage crisis in Malaysia (2000) and major riots in Kalimantan, Indonesia in 1999.
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Sinclair, M.T. and M. Stabler (1997), The Economics of Tourism, Routledge, London. White, H. (1980), "A Heteroscedasticity Consistent Covariance Matrix Estimator and a Direct Test of Heteroscedasticity", Econometrica, 48, 817-818. Winkelmann, R. (1994), Count Data Models: Econometric Theory and an Application to Labor Mobility, Springer-Verlag, Berlin. Zeger, S.L. (1988), "A Regression Model for Time Series of Counts", Biometrika, 75(4),621-629.