Economic Impact of Haze Related Air Poll

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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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135

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 ｾ 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 ｘｩｾＧ

A; = ･ｸｰＨｘ［ｾＩ

(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 ｯｦｾＮ
The log likelihood junction, derived from Equations 3 and 4, is:
ｌＨｾＬｙｘＩ

n

L - ･ｸｰＨｘｩｾＩ

=
j

+ ＨｙｩｘｾＩ

- In(Y;!)

(5)

=1

The estimates of ｾ 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 ｾ are established. Because Equation 6 is a non-linear function ｯｦｾＬ
the
estimates of ｾ are derived by iterative algorithms such as the Newton-Raphson
method.
､ｌＨｾＬｙｘＩｬ

ｾ =

n

L

[Yi - exp( ｘ［ｾＩ｝ｻ

=0

(6)

i= 1

ｩｌＨｾＬｹｘＩｬ､ ｾ､

W= -

i: ｩｘｻＩｾＨｰｸ･

(7)

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 lossis therefore 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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