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Economic Valuation of Health Impacts

of Smoke Haze Pollution in Malaysia

Jamal Othman

Mazrura Sahani

Mastura Mahmud

Md. Khadzir Sheikh Ahmad

February, 2015


Comments should be sent to:

Jamal Othman, Department of Agricultural and Resource Economics, Faculty of Economics,

Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia

Tel: +603 89213762

Fax: +603 89215789

Email: jortman@ukm.edu.my, j_othman@yahoo.com

The Economy and Environment Program for Southeast Asia (EEPSEA) was established in May 1993

to support training and research in environmental and resource economics. Its goal is to strengthen local

capacity in the economic analysis of environmental issues so that researchers can provide sound advice to

policymakers.

To do this, EEPSEA builds environmental economics (EE) research capacity, encourages regional

collaboration, and promotes EE relevance in its member countries (i.e., Cambodia, China, Indonesia, Lao PDR,

Malaysia, Myanmar, Papua New Guinea, the Philippines, Thailand, and Vietnam). It provides: a) research

grants; b) increased access to useful knowledge and information through regionally-known resource

persons and up-to-date literature; c) opportunities to attend relevant learning and knowledge events; and d)

opportunities for publication.

EEPSEA was founded by the International Development Research Centre (IDRC) with co-funding

from the Swedish International Development Cooperation Agency (Sida) and the Canadian International

Development Agency (CIDA). In November 2012, EEPSEA moved to WorldFish, a member of the Consultative

Group on International Agricultural Research (CGIAR) Consortium.

EEPSEA’s structure consists of a Sponsors Group comprising its donors (now consisting of IDRC and

Sida) and host organization (WorldFish), an Advisory Committee, and its secretariat.

EEPSEA publications are available online at http://www.eepsea.org.


ACKNOWLEDGEMENTS

Our utmost appreciation goes to the Economy and Environment Program for Southeast Asia

(EEPSEA) for providing us with the sponsorship needed to enable this study to be successfully undertaken.

Appreciation also goes to the Ministry of Health, Malaysia, for allowing us to have access to the relevant

database, without which the study could not have been completed. Finally, our thanks go to all the EEPSEA

resource personnel who gave their time to help ensure that this study was implemented to the highest

standards.


TABLE OF CONTENTS

EXECUTIVE SUMMARY 1

1.0 INTRODUCTION 2

1.1 Background and Review of Literature 2

1.2 Specific Objectives of the Study 4

1.3 Study Hypotheses 4

2.0 RESEARCH METHODOLOGY 5

2.1 Health Informatics Center, Malaysia 5

2.2 Analytical Techniques for Economic Valuation and Data Needs 5

3.0 RESULTS AND DISCUSSION 9

3.1 Profile Analysis 9

3.2 Regression Analysis 16

4.0 ECONOMIC VALUATION OF HAZE IMPACTS 22

5.0 SUMMARY OF RESULTS 23

6.0 POLICY IMPLICATIONSAND CONCLUSION 24

REFERENCES 26

APPENDIX 28


LIST OF TABLES

Table 1. Names of haze-related illnesses and their codes 8

Table 2. Selected CAQM stations in Selangor and the Federal Territory 9

Table 3. Inpatient cases per 10,000 population across API categories 10

Table 4. Mean daily inpatient cases and API/PM10 10

Table 5. Daily inpatient cases per 10,000 population across API and PM10 categories for each year 11

Table 6. Types of illness and API category (all years, absolute figures) 12

Table 7. API category and mean daily cases for major illnesses (all years, absolute figures) 13

Table 8. API and PM10 measurements across areas (all years) 14

Table 9. Distribution of total cases per 10,000 population for each age category and area (all years) 14

Table 10. Distribution of total cases for each age category and area (absolute figures, all years) 15

Table 11.

Distribution of total cases for major illness across area and age category (absolute figures,

all years)

Table12. Annual panel regression results (individual years, overall illness) 17

Table 13. Overall panel regression results (overall illness) 18

Table 14. Panel regression results for model with PM10 slope shifter (overall illness) 18

Table 15. Overall panel regression results for model with PM10 slope shifter (overall illness) 20

Table 16. Responsiveness of inpatient cases to PM10 changes across age groups 21

Table 17. Basic economic valuation parameters 22

15

LIST OF FIGURES

Figure 1. Cases of major illness across API categories 13


ECONOMIC VALUATION OF HEALTH IMPACTS

OF HAZE POLLUTION IN MALAYSIA

Jamal Othman, Mazrura Sahani, Mastura Mahmud, and Md. Khadzir Sheikh Ahmad

EXECUTIVE SUMMARY

This study was conducted to assess the economic value of inpatient health impacts due to haze

pollution caused by smoke in Kuala Lumpur and adjacent areas in Selangor state. Secondary data was

collected from seven hospitals for the years 2005, 2006, 2008 and 2009. Of the four years, the study found

that 99% of total haze-related inpatient cases were within the Good and Moderate Air Pollution Index (API)

categories. Only 1% of cases fell into the Unhealthy to Hazardous categories. Within the Moderate API days,

the Lower Moderate category constituted 4.15% of cases or 425 observations out of 10,239. There were only

7 and 13 cases of Very Unhealthy and Hazardous hazy days, respectively. All these cases occurred in 2005 and

2006. For the Unhealthy category, there were only 90 observations throughout the four years. Overall, there

were 535 days (5.2%) where the API was at least at the Lower Moderate level. This averages 19 days per year

or 1.6 days per month of hazy days. The highest daily mean of 0.35 per 10,000, or 35 cases per 1 million of

population, was noted in the Very Unhealthy API category. A striking observation is that the highest number

of cases was amongst young adults in all areas for both per 10,000 of population and in absolute terms for all

years. This is followed by adults, children and infants. This finding seems to indicate that young adults, who

largely represent the working population, tend to be more exposed to outdoor activities and so have a

greater probability of being hospitalized due to haze pollution.

The number of cases for all age categories on a per 10,000 population basis in both the urban

(0.293) and semi-urban (0.325) areas was substantially higher than in rural areas (0.202). The study also found

that of the 14 types of illness associated with air pollution, five were relatively dominant. The five illnesses

are: pneumonia, ischaemic heart diseases, acute upper respiratory tract infections, asthma, and hypertensive

diseases. These five illnesses represent about 63% of total inpatient cases for all illness under the Good and

Moderate API categories.

When the API was within the Good to Unhealthy range, on average, each hospital in the sample saw

about nine cases of haze-related admissions daily. When the API deteriorated to the Unhealthy to Very

Unhealthy range, mean cases went up to 12 (33%) but declined unexpectedly to 10 in the Hazardous

category. We believe the decline was simply a manifestation of precautionary behavior on the part of

affected residents to avoid greater risks due to poor visibility and increased air pollution exposure –

particularly as Malaysian Government statements via the media advise affected residents to remain indoors

during a haze hazard and to go outside only if there is an emergency.

The study found that the marginal increase in admission rates for all haze-related illnesses is highest

for infants, followed by children, young adults, and the elderly. Further regression results suggest that the

marginal impact of PM 10 (particulate matter) on total inpatient cases is highest for young adults, followed by

the elderly and infants. A zero dose-response coefficient for infants was observed while the coefficient for

children was not significant. The study estimated a dose-response coefficient of 0.001 (unadjusted) for the

PM 10 variable, which suggests a 10 unit increase in PM 10 daily, so the number of daily inpatient cases in

government hospitals for overall illness every 10,000 people will rise by 0.01 each day. The non-linear

specification denotes greater variation of the dose-response coefficient, ranging from 0.002-0.008. The

overall panel regression for the non-linear specification yields a mean dose-response coefficient of 0.003.

This reflects an increase of 1% in PM 10 daily, leading to total inpatient cases rising by 0.3%.

In all four years, the average daily PM 10 and API was 54.6 μg/m 3 and 50.1 (Upper Moderate category),

respectively. There were 535 hazy observations (19 days) in which the API level was at least 76 (Lower

Moderate category). The weighted mean PM 10 for the entire hazy days was 134 μg/m 3 and for the normal

days PM 10 was 50 μg/m 3 (with API being in the Upper Moderate category). Based on the adjusted doseresponse

coefficient of 0.00148, there was an increase of 1,707 inpatient cases for the entire population (7.2

million) or 2.4 cases per 10,000 population. This translates to an average increase in admissions of 142 cases

1 Economy and Environment Program for Southeast Asia


monthly or 4.7 daily each year. During the 19 hazy days on average each year, daily average inpatient rates

rose very substantially from 0.41 to 0.53 or 31%. Based on the unit economic value of MYR 160 (USD 53) for

an average hospital stay of two days, haze damage was valued at MYR 0.273 million (USD 91,000). This

averages MYR 23,000 (USD 7,700) per month or MYR 766 (USD 256) per day or an average of MYR 14,368

(USD 4,789) per hazy day.

Such estimates are rather small in both absolute and relative terms. Moreover, they would be much

lower if the actual unit economic value (hospital charges) of hospital admission of MYR 40 per day were to

be employed. Nonetheless, the estimated value pertains only to the designated study area, although the

affected population of 7.2 million is quite significant relative to the total population of Malaysia (28 million).

Furthermore, the estimated value only reflects the productivity loss due to hospital admissions, presuming

an average stay of two days. Technically, extreme haze may result in a host of other impacts, which can be

measured by preventive costs, mitigation costs for outpatient treatments, potential output loss (such as

tourism), productivity loss in various economic sectors, reduced leisure time, increased anxiety due to loss of

visibility, and the risk of traffic collisions.

While the estimated value may be minute in relative and absolute terms, it still has important

consequences, particularly for the allocation of scarce public healthcare resources. Should the haze trend of

2005-2009 continue, on average, the hospitals within the study area can expect to see an increase in the

number of beds required by 180 beds per day in any given haze episode each year. This may lead to

difficulty in resource allocation for some hospitals and potentially displaces other patients whose needs may

be more critical. In terms of physical resources and healthcare provision, the estimated daily increase of 180

beds during a haze episode is equivalent to the daily resource (physical and manpower) requirements of at

least one small to medium public healthcare facility in Malaysia.

The ASEAN has drawn up various anti-haze agreements and action plans since 1995. In 2003, the

ASEAN produced Guidelines for the Implementation of ASEAN Policy on Zero Open Burning, which provides for

the establishment of an ASEAN Coordinating Centre for Transboundary Haze Pollution Control to facilitate

cooperation related to haze pollution emanating from land and forest fires. There has been no lack of

regional plans and mechanisms as far as the Indonesian-source haze is concerned. Nevertheless, the haze

continues to recur almost annually with the worst episodes taking place in 2005 and 2006 since the lengthy

and most serious 1997 haze event. Hence, it is clear that the issue is not a lack of anti-haze plans to tackle

prevention/adaptation and mitigation – the main challenge remains in how to implement the plans

effectively.

It is proposed that international litigation demanding appropriate compensation via the

International Court of Justice (ICJ) may be effective enough to trigger responsible behavior from the various

economic agents in Indonesia. Through such bilateral action, Indonesia may be compelled to enact and

enforce laws and regulations that would impose substantial disincentives to local and foreign firms caught

acting in a manner detrimental to the environment and to human welfare.

1.1 Background and Review of Literature

1.0 INTRODUCTION

Haze episodes are an annual phenomenon in Malaysia. Severe smoke haze was recorded in April

1983, August 1990, June 1991, October 1991, and August 1994. However, the worst episode so far was in

1997 when almost the entire country was engulfed by thick smog for up to six months. Air Pollution Index

(API) readings for some areas went beyond the maximum range.

Severe haze episodes have continued to occur over the last seven years, most notably in 2005 and

2006, both in the month of August. However, relative to 1997, the low-level monsoon winds over Southeast

Asia (SEA) have been unable to transport the extremely high levels of smoke associated with the biomass

burned in Sumatra and Borneo (Mahmud 2009a, b). Biomass burning has contributed to increased aerosol

loads or smoke in the atmosphere (Reid et al. 2005).

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

2


Smoke haze is characterized by smog-like tiny suspended solid or liquid particles. Haze can emanate

from either domestic or transboundary sources. In Malaysia, the haze has been largely attributed to forest

fires from Indonesia. Haze from open burning or forest fires contains concentrated particulate matter

(organic matter, graphitic carbon) that is hazardous to health, especially to illnesses associated with the

lungs, heart, circulation and the eyes, particularly amongst susceptible population groups. The health effects

range from symptoms which lead to individuals seeking treatment via outpatient and emergency rooms, to

hospitalization (or hospital admissions) in moderate and severe cases, and to mortality in the worst-case

scenario. Prolonged and widespread haze may also provoke greenhouse gas effects that hasten negative

health effects.

Substantial literature has been established exploring the health effects of general air pollution

(Anderson 2009; Lee and Schwartz 1999; Kwon et al. 2002; Chen et al. 2004; Sunyer et al. 2000). Other studies

show that air pollution affects the respiratory and circulatory systems (Linaker et al. 2000; Lee et al. 2002). The

health effects range from mild eye irritation to mortality. Air pollution may induce alveolar inflammation,

which aggravates pre-existing lung disease and is associated with an increased risk of cardiovascular events.

In most cases, air pollutants aggravate pre-existing diseases or degrade a person’s health status, increasing

an individual’s susceptibility to respiratory infection or chronic disease.

A study conducted in nine US cities indicated that acute exposure to PM 10, CO, NO 2, and SO 2

significantly increased hospital admissions for ischemic stroke (Wellenius et al. 2005). Three studies in Europe

also showed that cerebrovascular admissions were significantly related to the short-term effects of NO 2

(Ballester et al. 2001) and PM 10 (Wordley et al. 1997). In Araraquara, Brazil, increased levels of Total

Suspended Particulate (TSP) were found to be linked to hypertension-related hospital admissions (Arbex et

al. 2010). In Shanghai, China, it was established that outdoor air pollution was associated with risk of

cardiovascular hospitalization during 2005-2007 (Chen et al. 2010).

Zanobetti and Schwartz (2009) found that fine and coarse particulate air pollution provoked

increases in total mortality and cardiovascular disease by 0.98% and 0.85%, respectively. Positive

associations were also found between day-to-day variation in PM2.5 concentration and hospital admissions

for all cardiovascular and respiratory disease (Dominici et al. 2006).

There are limited studies on the health impacts of extreme air pollution events such as haze and

Asian dust storms. In Indonesia, during the massive 1997 smoke episode, Seema (2009) reported that the

phenomenon caused over 15,600 inferred deaths or ‘missing children’ (1.2% of the affected birth cohort)

across the country throughout the five-month episode. The author especially attributed the ‘missing

children’ to prenatal exposure to extreme smoke pollution. Kwon et al. (2002) found strong associations

between Asian Dust Storm (ADS) events in Seoul and cardiovascular and respiratory mortality in the

population.

In Malaysia, empirical studies on the health effects of haze and general air pollution are progressing;

however, there have been relatively few comprehensive studies on the economic assessment of health

impacts due to haze. Such a valuation of health impacts is important in order to appreciate the scale of haze

hazards in relation to other health, social or environmental stressors. This comparison will also help in the

formulation of pertinent public policies to address the root causes of haze and appropriate mitigation

strategies.

The rather few local studies on the health effects of air pollution in Malaysia have focused on air

pollution sciences. Thus far, there has been only one study, by Othman and Shahwahid (1999), which

attempted to value the cost of haze impact on health along with other economy-wide impacts. The study

employed both the cost of illness (COI) approach and the dose-response function. The latter linked the API

and haze-related illness during the 1997 haze episode using a panel data regression. The haze-related

illnesses, as well as the associated ambient level, were treated in an aggregate fashion. The profile of hazerelated

illness and the socioeconomic characteristics of patients, such as age groups and spatial factors (e.g.,

rural vs. urban), were not investigated. A recent study by Pek and Othman (2011) used a choice experiment

model to estimate the economic value of air and water quality degradation due to poor solid waste disposal

technologies in the Semenyih area in the state of Selangor, Malaysia.

An empirical study by Sahani et al. (2001) examined daily hospital-based mortality data and air

pollution due to the 1997 haze using Poisson regression. They observed no significant association between

3 Economy and Environment Program for Southeast Asia


particulate matter (PM 10) air pollution on non-trauma, respiratory or cardiovascular mortality in Malaysia,

specifically in the three urban areas of Kuala Lumpur, Kota Baru and Kuching. However, the authors

cautioned that while there was no apparent impact on deaths they did not rule out other significant health

impacts. One possible explanation for the lack of haze-mortality association, besides the short time series,

was the fact that the affected population was urged to stay indoors, especially if they already had chronic

respiratory or cardiovascular diseases. Many residents in the worst affected regions also wore face masks to

mitigate haze pollution.

An investigation of specific haze-related illness during the 1997 haze period (August-September) in

a number of hospitals in Kuala Lumpur revealed that there were significant increases in asthma and acute

respiratory infections (Brauer and Hisham-Hashim 1998). It was reported that outpatient visits in Kuching,

Sarawak, increased between 100-200% during the peak haze period while daily respiratory illness outpatient

visits to Kuala Lumpur General Hospital increased by 200% (WHO 1998).

Daily data on inpatients seeking treatment in public hospitals throughout Malaysia has been

collected since 1998 and contains information on patients’ specific illnesses, ages, and areas of residence.

Outpatient data is also contained in this same data; however, no socioeconomic information is included. This

study empirically examines the linkage between air pollution stressors (measured by API and PM 10), hazerelated

illness, and socio-demographic variables (i.e., age and spatial factors) amongst inpatients in selected

areas of Peninsular Malaysia. Such links constitute the basis for estimating the economic values of the impact

of haze on health (inpatients) in Malaysia. Outpatients were excluded from the study as pertinent

information was not readily available from the national health data repository.

1.2 Specific Objectives of the Study

This study addresses the following specific questions, organized into three categories.

i. Profile of haze-related illness

What are the major and common types of haze-related illness?

The focus is on inpatients.

ii.

Economic valuation of the health impacts of haze

What are the economic costs of inpatient illness associated with haze?

Do differences in age and spatial (urban vs. rural) variables affect the sensitivity of individuals to

the health effects (inpatient cases) of haze pollution?

iii. Policy options

What are the economic costs and benefits associated with health protection measures to

deal with haze-related health impacts?

What are the domestic and cross-country (regional) policy instruments that will reduce hazerelated

health costs? Care will be observed in terms of attributing haze sources, as some of the

haze could also emanate from domestic sources.

1.3 Study Hypotheses

The following hypotheses were examined.

i. The dose-response coefficient across rural and urban areas is significantly different.

ii.

Urban areas demonstrate higher inpatient rates (e.g., per 10,000 population) relative to

rural/agricultural areas.

iii. Infants and elderly population groups are more susceptible to haze impacts relative to other

population groups.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

4


2.0 RESEARCH METHODOLOGY

2.1 Health Informatics Center, Malaysia

In Malaysia, three federal agencies are directly involved with haze or pollution matters: the

Department of Environment (DOE), the Malaysia Meteorological Department (MMD), and the Ministry of

Health (MOH). The DOE monitors the country’s ambient air quality through a network of 52 stations. These

monitoring stations are strategically located in residential, urban and industrial areas to detect any

significant change in the air quality that may be harmful to human health and the environment. The

National Air Quality Monitoring Network is supplemented by manual air quality monitoring stations (High

Volume Samplers) located at 19 different sites. At these sites, five criteria pollutants, which are carbon

monoxide (CO), nitrogen dioxide (NO 2), ozone (O3), sulphur dioxide (SO 2) and particulate matter (PM 10), are

monitored continuously. Meanwhile, lead (Pb) concentration is measured once every six days, at two

locations.

The MMD monitors atmospheric conditions related to microclimate and parameters on ambient air

quality, particularly humidity, wind conditions and TSP concentration in the air, which directly contributes to

haze intensity. On the other hand, the MOH, via the Health Informatics Center (HIC), has compiled and

managed the national database on visitation rates, health treatment and the illness types of patients

(inpatients and outpatients) in public hospitals and clinics throughout the country since 1998. This center

functions as the repository (known as the Health Information Management System or HIMS) for integrated

health information for the country. Full coverage of public facilities has been established and coverage of

private health care facilities was initiated in 2008, which have mainly focused on urbanized areas.

Information on socioeconomic profile, such as age and residential area, is provided only for inpatients.

Information on residential area can be used to ascertain if patients are from rural or urban areas or live close

to an industrial facility.

2.2 Analytical Techniques for Economic Valuation and Data Needs

The cost of illness approach is used to estimate the economic values of haze impacts on health.

2.2.1 Cost of illness approach

The cost of illness (COI) approach stems from the idea that the incremental cost of health treatment

can be attributed to haze pollution. The number of incremental health cases can be estimated via the doseresponse

function, which shows the quantitative relationship between doses of the stressor (haze pollutants,

particularly PM 10 or API) and corresponding reactions (i.e., morbidity statistics of haze-related illness,

specifically respiratory and cardiovascular). By multiplying the unit economic values with the change in

ambient concentration and dose-response coefficient, as well as population at risk, the total economic value

for each health variable can be estimated.

The unit economic value here refers to the average financial cost (direct and indirect costs) of

inpatient treatment for each specific haze-related illness for an average length of inpatient stay. This

information can be solicited from hospital management or industry sources. However, the financial cost of

hospital admission might grossly underestimate economic costs. Hence, this study uses the value of

productivity foregone to represent the unit economic value, which was estimated based on the average

monthly wages of patients.

Owing to data constraints, this study estimates empirically the cost of illness for inpatients at public

clinics/hospitals only. Extrapolation of the dose-response coefficient based on an estimated probability for

treatment seeking at private hospitals is made to provide estimates for inpatient costs affecting private

hospitals/clinics.

Not all individuals who suffered from the haze were able to pay a visit to a health center.

Disregarding this fact may grossly underestimate the economic values of inpatient costs. To provide a

remedy for this error, we will adjust the respective dose-response coefficient by a factor representing the

5 Economy and Environment Program for Southeast Asia


probability of individuals opting for self-treatment. The probability for treatment seeking at private or public

hospitals for those who opted for self-treatment is calculated based on a random survey of 200 individuals

within the designated study area. Admission rates for specific haze-related illnesses in the private

clinics/hospitals are also assumed to be the same as that of public hospitals/clinics.

Panel data regression (OLS estimator) is employed in the estimation of the dose-response function.

The general specification for the dose-response function is as follows:

Z i = f(H, D_HP, D_Age1, D_Age2, D_Age3, D_Age4, D_Urban, D_Rural, D_H*Age i) (Equation 1)

where:

Z i

: Number of inpatient admissions per day for total haze-related illnesses (or total haze-related

cases for each age category) per 10,000 population for every hospital service area i

H i : Intensity of haze variables (API or PM 10)

D_HP i : Dummy variable for hazy days (API > 100 or PM 10 > 142)

D_Age1 i : Dummy variable (or proxy) for infants

D_Age2 i : Dummy variable (or proxy) representing children

D_Age3 i : Dummy variable (or proxy) representing young adults

D_Age4 1 : Dummy variable (or proxy) for elderly patients (aged 60 and above)

D_Urban i : Dummy variable (or proxy) for inpatients residing in urban areas

D_Rural i : Dummy variable for inpatients residing in rural areas

D_H*Age t,l

: Interactive variable (slope dummy) for PM 10 and Age t

Note that the coefficient for haze (H) in the above specification constitutes the dose-response

coefficient. This is the most important parameter to be estimated in the empirical (econometric) analysis. It

reflects the impact of a marginal change in the intensity of the stressor (haze variable, either PM 10 or API) on

the number of inpatient admissions per 10,000 population per day, for all haze-related illness and for each

hospital on average.

In this study, the individual patient is the unit of analysis. However, the variables representing the

individuals such as Z i and Age i do not enter the model directly. Rather the Z i variable is expressed as the

ratio of number of inpatients per 10,000 population unit. Population unit here is defined as the population

(individuals) from the relevant area/zone served by the sampled hospital.

To obtain an estimate of the size of population unit attributed to each hospital, we first identified

the continuous air quality monitoring (CAQM) stations located closest to the selected study hospitals within

the study area. Some meaningful service zones (such as circular zones or the shortest route from main

residential areas) for each sampled hospital were identified with the cooperation of hospital management.

This implicitly assumes that most patients prefer visiting the hospital closest to their homes to minimize

travel costs. A priori knowledge also lent support to this assumption. The number of population was

obtained with assistance from the Malaysian Department of Statistics.

This study on the effects of haze air pollution on hospital admissions is an ecological study design.

Such studies are those in which the unit of analysis is represented by grouped observations, as in the abovementioned

Z i variable. Results from these designs have to be interpreted with caution due to the inherent

risk of biases attributed to the absence of information about individual characteristics such as pre-existing

illness, exposure to cigarette smoke, self-treatment, or localized pollution. All these variables are assumed to

be constant across time and space.

One important hypothesis of this study is to examine the influence of age variables on the

sensitivity of individuals to the health effects of haze pollution. Since the dependent variables, Z i, are

essentially grouped observations, age cannot be captured by the econometric model specification directly.

Instead, age is proxied or represented by the ratio of inpatients for each age category (i.e., infants, children,

young adults, and the elderly). Nominal binary data was assigned for age categories, which was

proportionately higher for each day’s inpatient cases. A value of 1 was assigned to each age category, which

obtained a value of at least 0.52. Hence, each observation may have either one age category being assigned

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

6


a value of 1 or none at all. Thus, such nominal values are akin to the usual intercept dummy variables

representing the age category that is relatively more dominant in terms of inpatient admissions.

Data for the estimation of dose-response function came from the national health data repository,

especially HIMS (see Section 2.1). Appropriate meteorological data was sourced from the MMD. Specific data

needs are discussed in Sections 2.2.4 to 2.2.6.

This study encompasses both the with-haze episodes period and the without-haze episodes for the

same selected study areas. The without-haze period acts as a control to discern the true effects of haze on

health cases. This approach avoids the use of a separate non-haze area as a control group. For the latter,

recent studies have used the more sophisticated case cross-over design using conditional logit models in

the econometric regression (Sunyer et al. 2000). The use of the traditional panel data regressions are

appropriate given the issues and objectives this study seeks to address.

2.2.2 Estimation of aggregate values

The aggregate economic values (AEV) of inpatient health costs due to the haze in the study area

were calculated using the following formula:

AEV = β*(1+F)*∆H*∆HD*POPN*UEV (Equation 2)

where:

AEV : Aggregate economic values of inpatient health costs in both public and private healthcare

entities in the study area

β : Dose-response coefficient for all haze-related illness, denoting the number of admission cases

per 10,000 population per hospital in the study area

F : Adjustment factor to take into consideration private hospital as well as self-treatment patients

∆H : Average incremental change in air pollutant level (relative to normal level), e.g., PM 10

∆HD : Number of hazy days (beyond moderate API or PM 10)

POPN : Population size in the study area (haze-impacted population)

UEV : Average unit economic value for both public and private hospitals

2.2.3 Study sample

The study sample was all respiratory and cardiovascular cases who were treated and admitted to

the sampled hospitals in Kuala Lumpur (Federal Territory) and selected areas within the adjacent state of

Selangor (districts of Klang and Kuala Selangor) between 1 January and 31 December in 2005, 2006, 2008

and 2009.

There are 11 public hospitals within the designated study area in both Selangor state and the

Federal Territory and we examined seven of them. Private hospitals were excluded as information was not

available for the selected years. The hospitals sampled were:

Kuala Lumpur (Federal Territory)

1. Pediatric Institute

2. Institute of Neuroscience

3. Main Hospital 1

4. Main Hospital 2

Selangor

5. Hospital Tengku Ampuan Rahmah (District of Klang)

6. Tanjung Karang Hospital (District of Kuala Selangor)

7. Selayang Hospital (District of Gombak)

Both Klang and Kuala Selangor districts are situated along the coast of Selangor facing the Straits of

Malacca and are thus relatively closer to the hot spot areas in Sumatera, Indonesia. Kuala Lumpur and

Selayang are about 40 km to the west of Klang while Tanjung Karang is 20 km to the north of Klang.

Given the choice of government and semi-government hospitals, we may define the study

population as patients that came from the lower to middle socioeconomic classes as such hospitals normally

charge subsidized medical rates. For instance, the admission cost for a regular treatment in a first-class

7 Economy and Environment Program for Southeast Asia


public hospital ward is a mere MYR 40 per day. Patients from the upper-income class who normally

patronize private hospitals were excluded from the empirical study. This portion of the population will be

somewhat captured via extrapolation of the dose-response coefficient as described in Sections 2.2.1 and

2.2.2.

2.2.4 Study period and population

We originally compiled data for seven years (i.e., 2002, 2003, 2004, 2005, 2006, 2008, 2009) and parts

of 2010 (up to March). However, for some hospitals, the basic data were not available for some years (2002-

2004). Hence, to enable a balanced panel data analysis, we proceeded to examine the data for only four

years (i.e., 2005, 2006, 2008 and 2009). Fortunately, the selected years still allowed us to capture the main

haze episodes of 2005 and 2006 (both in August) and to enable comparisons with that of the normal years.

The study population, as earlier noted, was all respiratory and cardiovascular disease inpatients

treated in selected government and semi-government hospitals located within the designated Selangor and

Federal Territory area. As of 2010, the population of the area was 7.2 million, representing some 25% of the

total population of Malaysia.

2.2.5 Hospital inpatient data

Daily inpatient data from selected hospitals during the study period were based on daily treatments

at the hospitals. The disease diagnosis was based on a patient’s discharge diagnosis, which was classified

according to the International Classification of Disease, Tenth Revision (ICD 10). The 14 illnesses selected for

this study are all natural causes of haze/air pollution-related respiratory and cardiovascular diseases. The

name of the illnesses and their ICD codes are listed in Table 1.

Table 1. Names of haze-related illnesses and their codes

Illness

ICD code

1. Hypertensive diseases I10-I15

2. Ischaemic heart diseases I20-I25

3. Cerebrovascular diseases I60-I69

4. Diseases of arteries, veins and lymphatic vessels I70-I99

5. Diseases of pulmonary circulation and other forms of heart disease I00-I09, I26-I52

6. Acute upper respiratory tract infections J00-J06

7. Influenza J10-J11

8. Pneumonia J12-J18

9. Rhinitis and sinusitis J30-J32

10. Bronchitis J40-J42

11. Emphysema J43

12. Other chronic obstructive pulmonary diseases J44

13. Asthma J45-J46

14. Other diseases of the respiratory system J20-J22, J33-J39, J47-J99

2.2.6 Air quality data

Data on air quality were obtained from Alam Sekitar Malaysia Sdn. Bhd.’s (ASMA) continuous air

quality monitoring (CAQM) stations located closest to the selected study hospitals through the assistance of

the DOE. The distribution of the CAQM stations for selected locations in Selangor state and the Federal

Territory area is shown in Table 2. The data is composed of mean daily ambient concentrations of particulate

matter less than 10 microns (PM 10) in ug/m 3 , sulfur dioxide (SO 2) in ppm, nitrogen dioxide (NO 2) in ppm,

ozone (O 3) in ppm, and carbon monoxide (CO) in ppm.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

8


Table 2. Selected CAQM stations in Selangor and the Federal Territory

Station location Category Parameters

1. Gombak Water Service Department Residential

2. Raja Zarina Secondary School Residential

3. Kuala Lumpur Traffic

4. Seri Petaling Primary School,

Petaling Jaya

5. Cheras Residential

6. Sek. Keb. Raja Muda, Shah Alam Residential

CO, O 3, SO 2,

NO 2, PM 10, THC,

UV

CO, O 3, SO 2,

NO 2, PM 10, THC,

UV

CO, O 3, SO 2,

NO 2, PM 10, THC

Industrial SO 2, NO 2, PM 10

CO, O 3, SO 2,

NO 2, PM 10, THC,

UV

CO, O 3, SO 2,

NO 2, PM 10, THC,

UV

Nearest hospital

to the air station

Selayang Hospital

Klang Hospital

Kuala Lumpur Hospital

University Malaya Medical

Center (UMMC)

National University of Malaysia

Medical Centre (PPUKM)

No public hospital

Each CAQM station houses an integrated ambient air quality monitoring system designed to

monitor ambient air for specific pollutants. This is accomplished through the continuous operation of a

number of ambient air analyzers and sensors. The data from these analyzers and sensors are recorded on a

microcomputer-based data acquisition system (DAS), which also provides output control to the various

analyzers and sensors. The data are then transferred to a central computer every hour for evaluation and

reporting. The monitoring instruments and operation protocols of the CAQM stations were those approved

by the United States Environmental Protection Agency (USEPA).

It is important to note that air quality data surrounding patients’ houses was not available.

Therefore, we presumed that the air quality data collected from the closest monitoring station to the

sampled hospital pertains to the patients’ vicinities.

3.1 Profile Analysis

3.0 RESULTS AND DISCUSSION

This section presents the findings of the study. We first highlighted the profile analysis followed by

the economic valuation of haze impacts on associated inpatient health cases.

3.1.1 Inpatient cases

Tables 3 to 7 give the descriptive statistics for total daily inpatient cases per 10,000 population for all

hospitals for each ordinal category of API within the vicinity of the hospital area. Recall that the daily data

was collected for seven hospitals over a period of four years (i.e., 2005, 2006, 2008, 2009). The API categories

in this study are defined as follows:

1 = API between 0-50 (Good)

2 = API between 51-100 (Moderate)

2_1 = API between 51-75 (Upper Moderate)

2_2 = API between 76-100 (Lower Moderate)

3 = API between 101-200 (Unhealthy)

4 = API between 201-300 (Very Unhealthy)

5 = API above 301 (Hazardous)

9 Economy and Environment Program for Southeast Asia


Table 3 shows that almost all inpatient cases (99%) were in the Good and Moderate API categories,

with only 1% in the Unhealthy to Hazardous categories. Within the Moderate API category, the Lower

Moderate constitutes 4.15% or 425 observations. There were only 7 and 13 cases of Very Unhealthy (Category

4) and Hazardous (Category 5) haze cases, respectively. All these cases occurred in 2005 and 2006. For the

Unhealthy category, there were only 90 observations throughout all four years observed. Overall, there were

535 observations (5.2%) where the API was at least at the Lower Moderate category. This averages 19 days

per year or 1.6 days per month for each hospital.

The highest daily mean of 0.35 per 10,000, or 35 cases per 1 million of population, was observed in

the Very Unhealthy API category (4), followed by the Unhealthy category (3).

Table 3. Inpatient cases per 10,000 population across API categories

API category Obs. Mean API

Mean PM 10

Inpatient cases

(μg/m 3 ) Min. Max. Mean

1 (Good) 5282 40.23 39.21 0 1.203 0.276

2_1 (Upper Moderate) 4422 58.49 63.43 0 1.547 0.303

2_2 (Lower Moderate) 425 83.60 111.63 0 1.184 0.309

3 (Unhealthy) 90 118.5 177.07 0 1.037 0.340

4 (Very Unhealthy) 7 241.9 390 0 0.790 0.349

5 (Hazardous) 13 352.6 426 0.074 0.617 0.318

Overall 10,239 54.6 51.1 0 1.547 0.289

Source: Calculated by the authors from raw data.

Table 4 describes the mean daily inpatient cases for each year and the corresponding API and PM 10

averages. Mean inpatient cases have been increasing marginally over the years while mean API and PM 10

show a slight downtrend. It would be misleading to gauge if there has been a significant, positive correlation

between mean inpatient cases and API/PM 10 levels by simply considering overall long-run averages.

Table 4. Mean daily inpatient cases and API/PM 10

Years Parameters Obs. Min. Max. Mean

API 2555 18.00 519.00 54.57

2005 PM 10 (μg/m 3 ) 2555 13.00 614.00 59.46

TOTAL CASES 2555 0.00 1.14 .27

API 2567 0.00 165.00 51.56

2006 PM 10 (μg/m 3 ) 2567 0.00 250.00 55.71

TOTAL CASES 2567 0.00 1.11 .28

API 2562 0.00 106.00 48.01

2008 PM 10 (μg/m 3 ) 2562 0.00 170.00 49.16

TOTAL CASES 2562 0.00 1.19 .29

API 2555 15.00 131.00 50.44

2009 PM 10 (μg/m 3 ) 2555 19.00 194.00 54.15

TOTAL CASES 2555 0.00 1.55 .32

Source: Calculated by the authors from raw data.

Table 5 depicts the mean inpatient statistics across API categories and years. The table shows 90

Unhealthy cases (see Table 3) spread over 2005 (47 observations), 2006 (25 observations), 2008 (4

observations) and 2009 (14 observations). Clearly, mean inpatient cases for each year, especially the 2005

data, show positive correlation with API levels. The mean cases for Unhealthy to Hazardous days were

substantially higher relative to the Good and Moderate categories.

A simple regression of the mean inpatient cases with API and PM 10 annual averages, as depicted in

Table 5, suggests a unit increase in both parameters provokes a significant change of 0.1% in inpatient cases

per 10,000 population (Adj R2 = 0.11 and 0.18, respectively).

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

10


Table 5. Daily inpatient cases per 10,000 population across API and PM 10 categories for each year

Year API Parameters Obs. Min. Max. Mean

Good TOTAL CASES 1248 0.00 1.14 0.26

PM 10 1248 13.00 77.00 39.12

API 1248 18.00 50.00 40.57

Upper Moderate TOTAL CASES 1116 0.00 1.11 0.28

PM 10 1116 35.00 166.00 65.14

API 1116 51.00 74.00 59.60

Lower Moderate TOTAL CASES 124 0.00 0.96 0.31

PM 10 124 41.00 212.00 107.77

2005

API 124 75.00 100.00 83.23

Unhealthy TOTAL CASES 47 0.00 1.04 0.32

PM 10 47 87.00 396.00 186.85

API 47 101.00 186.00 120.85

Very Unhealthy TOTAL CASES 7 0.00 0.79 0.35

PM 10 7 226.00 464.00 390.00

API 7 217.00 288.00 241.86

Hazardous TOTAL CASES 13 0.07 0.62 0.32

PM 10 13 261.00 614.00 426.00

API 13 305.00 519.00 352.62

Good TOTAL CASES 1266 0.00 1.03 0.26

PM 10 1266 0.00 71.00 38.58

API 1266 0.00 50.00 40.01

Upper Moderate TOTAL CASES 1118 0.00 1.11 0.29

PM 10 1118 24.00 187.00 64.10

2006

API 1118 51.00 74.00 58.45

Lower Moderate TOTAL CASES 158 0.00 0.98 0.27

PM 10 158 62.00 241.00 114.78

API 158 75.00 100.00 84.39

Unhealthy TOTAL CASES 25 0.00 0.92 0.34

PM 10 25 121.00 250.00 174.68

API 25 101.00 165.00 121.16

Good TOTAL CASES 1416 0.00 1.19 0.27

PM 10 1416 0.00 71.00 39.27

API 1416 0.00 50.00 40.03

Upper Moderate TOTAL CASES 1112 0.00 1.05 0.30

PM 10 1112 28.00 113.00 59.79

2008

API 1112 51.00 74.00 57.03

Lower Moderate TOTAL CASES 30 0.10 0.58 0.40

PM 10 30 57.00 165.00 109.90

API 30 75.00 100.00 82.30

Unhealthy TOTAL CASES 4 0.20 0.40 0.33

PM 10 4 93.00 170.00 138.75

API 4 103.00 106.00 104.25

Good TOTAL CASES 1352 0.00 1.14 0.31

PM 10 1352 19.00 96.00 39.84

API 1352 15.00 50.00 40.35

Upper Moderate TOTAL CASES 1076 0.00 1.55 0.34

PM 10 1076 32.00 139.00 64.71

2009

API 1076 51.00 74.00 58.90

Lower Moderate TOTAL CASES 113 0.00 1.18 0.34

PM 10 113 80.00 184.00 111.90

API 113 75.00 99.00 83.25

Unhealthy TOTAL CASES 14 0.07 0.84 0.42

PM 10 14 141.00 194.00 159.43

API 14 103.00 131.00 109.93

Source: Calculated from raw data by the authors.

11 Economy and Environment Program for Southeast Asia


3.1.2 Distribution of illness

Table 6 depicts the mean daily cases in absolute figures for each of the 14 haze-associated illnesses

across the five API categories. Also shown is the daily average for each illness for overall API for the purposes

of comparison. There was an average of 14 daily inpatient cases for each hospital in the study area for the

Good to Unhealthy API category. This admission rate rose to 19 (35%) and 18 (29%) daily when the API was

Very Unhealthy and Hazardous, respectively.

Table 6. Types of illness and API category (all years, absolute figures)

Types of illness

Good Moderate

API category

Unhealthy

Very

Overall

Hazardous

Unhealthy

API

Obs. Mean Obs. Mean Obs. Mean Obs. Mean Obs. Mean

1. Hypertensive diseases 5282 1.190 4847 1.692 90 2.111 7 1.143 13 1.692 1.436

2. Ischaemic heart

diseases

5282 1.850 4847 2.297 90 2.500 7 3.000 13 2.538 2.069

3. Cerebrovascular

diseases

5282 0.955 4847 1.190 90 1.322 7 1.143 13 1.385 1.070

4. Diseases of arteries,

veins and lymphatic 5282 0.683 4847 0.719 90 0.711 7 0.571 13 0.308 0.700

vessels

5. Diseases of

pulmonary circulation

and other forms of

5282 1.149 4847 1.432 90 1.622 7 2.000 13 1.077 1.288

heart diseases

6. Acute upper

respiratory tract 5282 1.585 4847 2.030 90 2.911 7 2.000 13 1.538 1.808

infections

7. Influenza 5282 0.170 4847 0.307 90 0.244 7 0.286 13 0.077 0.236

8. Pneumonia 5282 2.305 4847 2.539 90 3.111 7 2.857 13 2.769 2.424

9. Rhinitis and sinusitis 5282 0.044 4847 0.044 90 0.033 7 0.000 13 0.077 0.044

10. Bronchitis 5282 0.013 4847 0.009 90 0.011 7 0.000 13 0.000 0.011

11. Emphysema 5282 0.003 4847 0.004 90 0.022 7 0.000 13 0.000 0.004

12. Other chronic

obstructive

5282 0.504 4847 0.630 90 0.744 7 0.714 13 0.692 0.566

pulmonary diseases

13. Asthma 5282 1.302 4847 1.569 90 1.656 7 2.714 13 1.077 1.432

14. Other diseases of the

respiratory system

5282 1.080 4847 1.312 90 1.811 7 1.571 13 1.000 1.196

TOTAL 13 16 19 18 14 14

Source: Calculated by the authors from raw data.

The five major illnesses, based on their mean values, are summarized in Table 7. The table shows

that the five major illnesses are pneumonia, ischaemic heart diseases, acute upper respiratory tract

infections, asthma, and hypertensive diseases. These five illnesses represent about 63% of total cases for all

illness under the Good and Moderate API categories. The proportion of these illnesses increased marginally

to 65% and 69%, respectively, under the Very Unhealthy and Hazardous API categories. From the table, it can

be seen that under the Good to Unhealthy API each hospital sees, on average, about nine cases of daily

admission due to the five illnesses. When the API deteriorated to Unhealthy to Very Unhealthy, mean cases

went up to 12 (33%) but declined, rather unexpectedly, to 10 in the Hazardous category.

Pneumonia and ischaemic diseases rank consistently as the first and second highest, respectively, in

terms of inpatient illnesses for all API categories. Mean cases of hospital admissions due to asthma especially

saw a pronounced jump (by 62%) when API moved from the Unhealthy to the Very Unhealthy range. This

provides strong empirical evidence denoting a positive association of asthma episodes with deterioration of

air quality due to haze. Figure 1 summarizes the distribution of cases of major illness across the API

categories.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

12


Table 7. API category and mean daily cases for major illnesses (all years, absolute figures)

API category

Major illnesses

Very

Good Moderate Unhealthy

Unhealthy

Hazardous

Hypertensive diseases 1.19 (5) 1.692 (4) 2.111 (4) 1.143 (5) 1.692 (3)

Ischaemic heart diseases 1.85 (2) 2.297 (2) 2.5 (3) 3 (2) 2.538 (2)

Acute upper respiratory tract infections 1.585 (3) 2.03 (3) 2.911 (2) 2 (4) 1.538 (4)

Pneumonia 2.305 (1) 1 2.539 (1) 3.111 (1) 2.857 (1) 2.769 (1)

Asthma 1.302 (4) 1.569 (5) 1.656 (5) 2.714 (3) 1.077 (5)

TOTAL

8

10

12

12

10

( 63%) 2 (63%) (65%) (65%) (69%)

Source: Calculated by the authors from raw data.

1, 2

Values are ranking of illness in order of magnitude and percentage to total cases for all illnesses.

Number of cases

per 10,000 population

15

10

5

0

1 2 3 4 5

API category

Asthma

Pneumonia

Acute upper respiratory tract infections

Ischaemic heart diseases

Hypertensive diseases

Figure 1. Cases of major illness across API categories

3.1.3 Inpatient cases, API and PM 10 levels across areas

In this section all the hospitals in the study area are grouped into three categories based on the

intensity of urbanization of hospital coverage areas. The major factors thought to define urbanization extent

include the density of private and industrial vehicles as well as the extent of vegetation within each area. The

three categories are:

1 = Urban – all the sampled hospitals in the Kuala Lumpur area

2 = Semi-urban – Klang Hospital and Selayang Hospital

3 = Rural – Tanjung Karang Hospital

The urban area is the densest relative to other areas in terms of commercial and private vehicle

activity and has the least vegetation. Selected statistics are compared and appraised for each area. It is

generally thought that the urban area will demonstrate higher inpatient rates relative to other areas for a

given air pollution level.

Table 8 compares the API and PM 10 levels across the three areas for the selected years. All three

areas show comparable mean API levels, at around 50-52. However, mean levels for PM 10 in the semi-urban

and rural areas (about 58) were unexpectedly higher relative to the urban area (52). During the hazy days

(defined by API > 100), the semi-urban areas demonstrated the lowest mean level of API (147) and PM 10

(214). This shows that there is no clear association between PM 10 and API levels in any given area across the

sampled hospitals.

According to Table 8, the mean total daily inpatient cases per 10,000 population for all the selected

years was highest in semi-urban areas (0.325), followed by urban areas (0.293) and rural areas (0.202). The

mean for urban and semi-urban areas was higher by 60% and 45%, respectively, relative to rural areas. This

may suggest that the urban population is more susceptible to haze impacts, resulting in a higher level of

hospitalization.

13 Economy and Environment Program for Southeast Asia


Table 8. API and PM 10 measurements across areas (all years)

Area category Parameters Obs. Min. Max. Mean Std. deviation

API 5852 15 325 50.07 17.24

PM 10 (μg/m 3 ) 5852 13 465 51.82 26.64

Urban

PM 10 for API > 100 56 158 465 224.21 100.24

API > 100 48 103 325 159.92 78.20

Inpatient cases per

10,000 population

5852 0 1.546 0.293 0.263

API 2924 0 495 52.76 20.52

PM 10 (μg/m 3 ) 2924 0 590 58.54 32.82

Semi-urban

PM 10 for API > 100 47 150 590 214.83 97.75

API > 100 41 101 495 147.41 82.14

Inpatient cases per

10,000 population

2924 0 1.203 0.325 0.153

API 1463 8 519 52.18 22.17

PM 10 (μg/m 3 ) 1463 21 614 57.98 34.01

Rural

PM 10 for API > 100 21 150 614 228.05 124.70

API > 100 21 101 519 153.43 102.99

Inpatient cases per

10,000 population

1463 0 0.803 0.202 0.118

Source: Calculated by the authors from raw data (Ministry of Health database).

3.1.4 Distribution of inpatient cases by area and age

Tables 9 and 10 present the distribution of inpatient cases (total and selected illnesses) based on

area as well as age categories. Age categories refer to the age group of respondents where Age 1 = infants,

Age 2 = children and teenagers (aged 1-17), Age 3 = young adults (aged 18-59), Age 4 = elderly patients

(aged 60 and above).

Table 9 (all years’ observation) shows that the number of cases for all age categories on a per 10,000

population basis in both the urban area (0.293) and semi-urban area (0.325) is substantially higher than the

rural area (0.202). The differences are statistically significant at 0.01% significance level. This finding is

expected as hospitalization rates in both areas may also be influenced by other urban-related pollution

factors such as the heat island effect and emissions from vehicles and industrial activities.

Table 9. Distribution of total cases per 10,000 population for each age category and area (all years)

Area Age category Obs. Min. Max. Mean Std. deviation

Age 1 5852 0 0.568 0.041 0.081

Urban

Age 2 5852 0 0.749 0.054 0.098

Age 3 5852 0 1.233 0.121 0.167

Age 4 5852 0 0.628 0.078 0.116

Age 1 2924 0 0.240 0.035 0.031

Semiurban

Age 2 2924 0 0.333 0.053 0.037

Age 3 2924 0 0.656 0.141 0.088

Age 4 2924 0 0.342 0.097 0.049

Age 1 1463 0 0.158 0.016 0.030

Rural

Age 2 1463 0 0.402 0.025 0.039

Age 3 1463 0 0.653 0.085 0.074

Age 4 1463 0 0.374 0.076 0.064

Age 1 10239 0 0.568 0.036 0.065

Overall

Age 2 10239 0 0.749 0.049 0.079

Age 3 10239 0 1.233 0.122 0.139

Age 4 10239 0 0.628 0.083 0.095

Source: Calculated by the authors from raw data.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

14


A striking observation is the highest number of cases from the Age 3 category (young adults) for all

areas for both per 10,000 population and in absolute terms for all years (Tables 9 and 10). This is followed by

Age 4, Age 2 and Age 1. This finding seems to indicate that young adults, who largely represent the working

population group and so tend to be more exposed to outdoor activities, have a greater probability of being

hospitalized due to the haze stressor. More robust analysis of the influence of age factors on hospitalization

rates is presented in the regression analysis.

Table 10. Distribution of total cases for each age category and area (absolute figures, all years)

3.1.5 Distribution of major illnesses across age groups and areas

Table 11 depicts the distribution of cases for the top three haze-related illnesses. Table 7 earlier

indicated that the top three illnesses according to mean inpatient cases are pneumonia, followed by

ischaemic heart diseases, and acute respiratory tract infections.

For pneumonia, which is the most prevalent haze-related illness, the semi-urban area demonstrates

a relatively higher number of cases for all age categories. Age category 3 seems to be rather striking for the

semi-urban area, while Age category 1 dominates urban areas. Another striking finding is the substantially

lower mean cases for all age categories in rural areas. However, overall, no age group seems to be

particularly notable. For ischaemic heart diseases and acute respiratory tract infections, Age category 2 and

Age category 3 appear to be most dominant, respectively, for all areas as well as for the overall sample.

Table 11. Distribution of total cases for major illness across area and age category (absolute figures, all years)

Area Age Obs. Min. Max. Mean Std. deviation

Age 1 5852 0 23 2 3.336

Urban

Age 2 5852 0 31 2 4.016

Age 3 5852 0 51 5 6.863

Age 4 5852 0 26 3 4.741

Age 1 2924 0 18 3 2.355

Semiurban

Age 2 2924 0 25 4 2.907

Age 3 2924 0 54 11 7.049

Age 4 2924 0 25 7 3.920

Age 1 1463 0 3 0 0.578

Rural

Age 2 1463 0 8 0 0.761

Age 3 1463 0 13 2 1.436

Age4 1463 0 7 1 1.251

Source: Calculated by the authors from raw data.

Area Age category Obs. Pneumonia

Ischaemic heart Acute respiratory

diseases

tract infection

Age 1 5852 0.610 0.515 0.001

Urban

Age 2 5852 0.593 0.797 0.002

Age 3 5852 0.549 0.300 0.866

Age 4 5852 0.397 0.034 0.633

Age 1 2924 0.880 0.751 0.003

Semiurban

Age 2 2924 0.954 1.370 0.015

Age 3 2924 1.145 0.652 2.249

Age 4 2924 0.907 0.089 1.621

Age 1 1463 0.142 0.076 0.000

Rural

Age 2 1463 0.126 0.208 0.002

Age 3 1463 0.172 0.077 0.386

Age 4 1463 0.197 0.011 0.319

Age 1 10239 0.620 0.520 0.001

Overall

Age 2 10239 0.629 0.876 0.006

Age 3 10239 0.665 0.369 1.192

Age 4 10239 0.514 0.046 0.870

Source: Calculated by the authors from raw data.

15 Economy and Environment Program for Southeast Asia


3.2 Regression Analysis

The data was organized as daily panel data with seven groups (hospitals) across the years 2005,

2006, 2008 and 2009. In the regression analysis, we first examined the factors that influence the variation in

the number of total inpatient cases for all the haze-associated illnesses. The main aim was to derive an

estimate of the coefficient for the environmental stressor (PM 10 or API) that influences the level of inpatient

cases. This coefficient constitutes the core parameter in the economic valuation of haze impacts on

inpatients, which is discussed in the next section.

Since the data was on a daily basis (360 observations per year per hospital), where degrees of

freedom would not be a constraint, we ran separate panel regressions for each year alongside the entire

four-year panel data set for all groups (hospitals). A number of specifications with various estimators,

including Generalized Linear as well as Poisson regression, were attempted. Based on econometric

considerations as well as the plausibility of the coefficient signs and significance level, the Least Squares

Dummy Variable (LSDV) estimator was found to explain the variation in the level of inpatient cases best. The

LSDV model in this study is essentially a fixed effects model with the assumption that there are differences in

the intercept for some of the groups and inpatient demographic characteristics, particularly age. Table 12

presents the regression results for the model specification with intercept shifter for age variables for the

individual years, while Table 13 shows the results for the overall years. We also examined models that

incorporate slope shifters for the haze stressor (PM 10). The latter was achieved by multiplying PM 10 with the

age variables. Tables 14 and 15 present the regression results for the individual years and overall data,

respectively. For all the regression models we present both the results for non-linear as well as the linear

specifications.

The most outstanding result from all the models is the finding that API has not been significant in

influencing the variation of inpatient cases per 10,000 population, regardless of whether or not there was a

haze episode. On the other hand, PM 10 has been able to explain the variation in the said variable very

significantly. This finding is not unexpected. Studies have indicated that airborne particles (aerosols) emitted

from burned biomass have shown a relatively high correlation coefficient of 0.85 with PM 10 concentrations at

several air quality stations in Peninsular Malaysia (Mahmud 2009b).

A simple regression of API against PM 10 indicates that PM 10 is associated very significantly with API,

where a unit increase in API provokes an increase in PM 10 by 1.43 unit (Adj R2 = 0.83) or 1.72% (Adj R2 =

0.67). On the other hand a unit increase in PM 10 induces a change in API by 0.582 (Adj R2 = 0.81) or 0.8% (Adj

R2 = 0.61). This shows that the marginal effect of API on PM 10 is substantially higher than that of PM 10 on API.

Indirectly, it suggests that changes in PM 10 are still strongly attributed to variation in API.

Based on the linear model in Table 12, the annual dose-response coefficient as well as the overall

average (Table 13) for the PM 10 variable is 0.001. This suggests that given a daily 10-unit increase in PM 10 the

number of daily inpatient cases in government hospitals for overall illness every 10,000 people will rise by

0.01. The non-linear specification denotes greater variation of the dose-response coefficient, ranging from

0.002-0.008. The overall panel regression for the non-linear specification yields a mean dose-response

coefficient of 0.003 (see Table 13). A daily increase of 1% in PM 10 would lead to an increase in total inpatient

cases of 0.3%. Our valuation of the haze impacts in the next section will be based on the dose-response

coefficient of the PM 10 variable.

An interesting research question is: Which age group of patients has higher hospitalization rates

relative to others on any given day? This is ascertained by examining the relative magnitude of the coefficients

for the age intercept dummies (D_age). The intercept dummies are nominal binary data representing the

proportion of daily inpatient cases for each age category. A value of 1 is assigned to the age category whose

proportion is greater than 0.50. As noted earlier, such nominal values may be akin to the usual intercept

dummy variables. However, it is not an intercept dummy in the strict sense as each observation can take

either a value of 1 or none at all. While the order of magnitude for the age dummy coefficient across the

individual years may vary slightly, in general the regression findings for both the individual year’s

observation (Table 12) and overall years (Table 13) indicate the coefficient for Age category 1 (infants) is

highest, followed by Age 2 (children), Age 3 (young adults) and Age 4 (elderly adults). Since the nominal

binary values are based on the relative ratio of inpatient cases for each group, the coefficients may not be

indicative of the relative magnitude of mean cases for each age category. They may, however, be interpreted

as the relative order of the change in inpatient rates (relative marginal change) when the respective age

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

16


group constitutes more than 50% of inpatient cases on any given day. Hence, such information will not be

readily comparable with that of the mean cases calculated from raw data.

It is also interesting to investigate whether the dose-response coefficient is substantially different

across age categories. This is done by incorporating an interactive variable for age category and PM 10. The

results are shown in Table 14 for the individual years and Table 15 for all years. The results generally suggest

that the marginal impact of PM 10 on total inpatient cases is highest for Age category 3 (young adults),

followed by Age 4 (elderly adults). Based on the regression results for all years (Table 15, linear model), the

estimated dose-response coefficient for Age 3 and Age 4 is 0.0006 and 0.0003, respectively. Note that a zero

dose-response coefficient for Age category 1 (infants) was observed while Age 2 was not significant. This

result is consistent with the fact that the Age category 3 (young adults) shows the highest mean cases, both

per 10,000 population and in absolute terms, for all the chosen years (Table 10). This seems to reinforce the

view that young adults, who largely represent the working population group, tend to exhibit greater

marginal impacts during haze events due to their greater participation in outdoor activities.

This study also examines whether there are any differences in the mean inpatient cases across rural,

semi-urban and urban areas. Earlier, our profile analysis of the means indicated that inpatient cases have

been highest in semi-urban areas, followed by urban and rural areas (Table 8), and the statistical analysis

shows that the differences were significant. However, our regression models (Tables 12 to 15) for all

specifications indicate that while semi-urban is highest, there has been no consistency in terms of which

area is ranked second or third. The linear specification consistently shows the mean case has been highest

for the semi-urban area, followed by urban and rural areas, similar to the outcome of the profile analysis. On

the other hand, the non-linear specification denotes rural areas as having a higher number of mean cases

relative to the urban area. Based on the profile analysis of raw data and the results of the linear model, we

may conclude that urban areas on average have had higher numbers of inpatient cases for all illnesses,

relative to rural areas.

Table 12. Annual panel regression results (individual years, overall illness)

Year

Semi Log Dependent Variable

Linear

B Std. error VIF B Std. error VIF

2005

(Constant) -1.4810 0.0530*** 0.2420 0.0120***

Dummy Urban -0.5300 0.0460*** 1.63 -0.0670 0.0100*** 1.54

Dummy Rural -0.4890 0.0580*** 1.32 -0.1320 0.0140*** 1.33

D_Age 1 0.7820 0.0870*** 1.24 0.1760 0.0200*** 1.18

D_Age 2 0.6750 0.0720*** 1.41 0.1300 0.0160*** 1.30

D_Age 3 0.2580 0.0450*** 1.55 0.1400 0.0100*** 1.39

D_Age 4 -0.2540 0.0590*** 1.36 0.0120 0.0130 1.26

API -0.0010 0.0020 6.77 0.0000 0.0000 6.67

PM 10 0.0020 0.0010** 6.82 0.0010 0.0000*** 6.71

Adj R2 0.126 0.129

CI 14 14

2006

(Constant) -1.3830 0.0770*** 0.2700 0.0180***

Dummy Urban -0.6940 0.0460*** 1.85 -0.1040 0.0110*** 1.77

Dummy Rural -0.5630 0.0570*** 1.39 -0.1460 0.0130*** 1.39

D_Age 1 0.5290 0.0870*** 1.21 0.0890 0.0200*** 1.18

D_Age 2 0.6080 0.0680*** 1.38 0.1130 0.0160*** 1.33

D_Age 3 0.1410 0.0440*** 1.59 0.0840 0.0100*** 1.50

D_Age 4 -0.0570 0.0630 1.29 0.0430 0.0140*** 1.25

API 0.0010 0.0030 5.65 0.0000 0.0010 5.62

PM 10 0.0010 0.0010 5.68 0.0000 0.0000 5.66

AdjR2 0.115 0.07

CI 21 21

17 Economy and Environment Program for Southeast Asia


Table 12 continued

Year

Semi Log Dependent Variable

Linear

B Std. error VIF B Std. error VIF

2008

(Constant) -1.6190 0.0780*** 0.1900 0.0190***

Dummy Urban -0.5120 0.0440*** 1.83 -0.0520 0.0110*** 1.79

Dummy Rural -0.2510 0.0540*** 1.38 -0.0670 0.0130*** 1.38

D_Age 1 0.7770 0.0900*** 1.19 0.1360 0.0220*** 1.17

D_Age 2 0.6630 0.0650*** 1.39 0.1160 0.0160*** 1.36

D_Age 3 0.1670 0.0420*** 1.61 0.0810 0.0100*** 1.55

D_Age 4 -0.2420 0.0550*** 1.30 -0.0120 0.0130 1.27

API -0.0030 0.0030 4.56 0.0000 0.0010 4.57

PM 10 0.0080 0.0020*** 4.59 0.0020 0.0010*** 4.59

AdjR2 0.125 0.08

CI 24 24

2009

(Constant) -1.1930 0.0800*** 2.01 0.3220 0.0220***

Dummy Urban -0.6100 0.0480*** 1.42 -0.0820 0.0130*** 1.96

Dummy Rural -0.6390 0.0580*** 1.23 -0.1730 0.0160*** 1.41

D_Age 1 0.7190 0.0860*** 1.41 0.1340 0.0240*** 1.21

D_Age 2 0.6490 0.0690*** 1.72 0.1540 0.0190*** 1.37

D_Age 3 0.1490 0.0460*** 1.34 0.0880 0.0130*** 1.65

D_Age 4 -0.2580 0.0620*** 5.38 -0.0290 0.0170* 1.30

API -0.0030 0.0030 5.41 -0.0010 0.0010 5.40

PM 10 0.0040 0.0020** 2.01 0.0010 0.0000*** 5.43

AdjR2 0.124 0.095

CI 23 23

Table 13. Overall panel regression results (overall illness)

Year

Semi Log Dependent Variable

Linear

B Std. error VIF B Std. error VIF

(Constant) -1.357 0.031*** 0.278 0.0078***

Dummy Urban -0.582 0.023*** 1.8 -0.078 0.0056*** 1.7

Dummy Rural -0.488 0.028*** 1.3 -0.132 0.0070*** 1.4

D_Age 1 0.679 0.044*** 1.2 0.131 0.0109*** 1.2

D_Age 2 0.632 0.034*** 1.4 0.127 0.0084*** 1.3

D_Age 3 0.161 0.022*** 1.6 0.096 0.0054*** 1.5

D_Age 4 -0.228 0.030*** 1.3 -0.001 0.0073 1.3

API -0.001 0.001 6.0 0.000 0.0003 6.0

PM 10 0.003 0.001*** 6.0 0.001 0.0002*** 6.0

Adj R2 0.118 0.081

CI 18 18

Obs 9965 10,239

Source: Regression results.

Table 14. Panel regression results for model with PM 10 slope shifter (overall illness)

Year

Semi Log Dependent Variable

Linear

B Std. error VIF B Std. error VIF

2005

(Constant) -1.530 0.060*** 0.229 0.014***

Dummy Urban -0.529 0.046*** 1.6 -0.067 0.010*** 1.5

Dummy Rural -0.491 0.059*** 1.3 -0.133 0.014*** 1.3

D_Age 1 0.927 0.129*** 2.7 0.211 0.030*** 2.6

D_Age 2 0.720 0.122*** 4.0 0.140 0.028*** 3.8

D_Age 3 0.320 0.073*** 4.0 0.159 0.016*** 3.7

D_Age 4 -0.143 0.098 3.6 0.041 0.022 3.5

D_Age1*PM10 -0.003 0.002 2.6 -0.001 0.000 2.6

D_Age2*PM10 -0.001 0.002 3.8 0.000 0.000 3.7

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

18


Table 14 continued

Year

Semi Log Dependent Variable

Linear

B Std. error VIF B Std. error VIF

D_Age3*PM10 -0.001 0.001 4.2 0.000 0.000 4.1

D_Age4*PM10 -0.002 0.001 3.5 0.000 0.000 3.4

API -0.001 0.002 7.0 -0.001 0.000 6.9

PM10 0.003 0.001*** 9.0 0.001 0.000*** 8.5

AdjR2 0.127 0.129

CI 16 16

N 2427 2554

2006

(Constant) -1.463 0.088 0.235 0.020***

Dummy Urban -0.694 0.047*** 1.8 -0.106 0.011*** 1.7

Dummy Rural -0.562 0.057*** 1.4 -0.146 0.013*** 1.4

D_Age 1 0.763 0.186*** 5.5 0.166 0.043*** 5.5

D_Age 2 1.008 0.156*** 7.3 0.223 0.036*** 7.2

D_Age 3 0.205 0.093** 7.1 0.130 0.021*** 6.8

D_Age 4 0.084 0.139 6.3 0.117 0.032*** 6.2

D_Age1*PM10 -0.004 0.003 5.3 -0.001 0.001** 5.3

D_Age2*PM10 -0.008 0.003*** 6.8 -0.002 0.001*** 6.8

D_Age3*PM10 -0.001 0.001 6.8 -0.001 0.000*** 6.7

D_Age4*PM10 -0.002 0.002 6.1 -0.001 0.001*** 6.1

API 0.002 0.003 5.6 0.000 0.001 5.6

PM10 0.002 0.002 6.6 0.001 0.000** 6.6

AdjR2 0.117 0.081

CI 22 23

N 2501 2562

2008

(Constant) -1.696 0.093*** 0.165 0.0227***

Dummy Urban -0.514 0.044*** 1.8 -0.053 0.0108*** 1.8

Dummy Rural -0.252 0.054*** 1.3 -0.068 0.0133*** 1.3

D_Age 1 0.583 0.367 19 0.071 0.0914 19

D_Age 2 0.760 0.221*** 16 0.135 0.0549*** 16

D_Age 3 0.375 0.119*** 12 0.153 0.0294*** 12

D_Age 4 -0.164 0.167 12 0.020 0.0413 11

D_Age1*PM10 0.004 0.007 19 0.001 0.0018 19

D_Age2*PM10 -0.002 0.004 15 0.000 0.0011 15

D_Age3*PM10 -0.004 0.002* 12 -0.001 0.0006*** 12

D_Age4*PM10 -0.002 0.003 11 -0.001 0.0008 11

API -0.002 0.003 4 0.000 0.0007 4.5

PM 10 0.010 0.002*** 5 0.003 0.0006*** 5.6

AdjR2 0.125 0.081

CI 26 26

N 2524 2562

2009

(Constant) -1.226 0.094*** 0.317 0.0261***

Dummy Urban -0.606 0.049*** 2.0 -0.081 0.0133*** 1.9

Dummy Rural -0.636 0.058*** 1.4 -0.173 0.0160*** 1.4

D_Age 1 0.914 0.214*** 7.5 0.213 0.0597*** 7.5

D_Age 2 0.583 0.179*** 9.4 0.099 0.0499** 9.4

D_Age 3 0.173 0.103* 8.5 0.084 0.0286*** 8.4

D_Age 4 -0.112 0.154 8.3 0.018 0.0428 8.2

D_Age1*PM10 -0.004 0.004 7.3 -0.002 0.0011 7.3

D_Age2*PM10 0.001 0.003 9.3 0.001 0.0009 9.2

D_Age3*PM10 0.000 0.002 8.7 0.000 0.0005 8.6

D_Age4*PM10 -0.003 0.003 8.2 -0.001 0.0008 8.1

API -0.003 0.003 5.4 -0.001 0.0008 5.4

PM 10 0.004 0.002** 6.7 0.001 0.0005*** 6.6

AdjR2 0.133 0.092

CI 25 25

N 2513 2555

Source: Regression results.

19 Economy and Environment Program for Southeast Asia


Table 15. Overall panel regression results for model with PM 10 slope shifter (overall illness)

Overall Years

Semi Log Dependent Variable

Linear

B Std. error VIF B Std. error VIF

(Constant) -1.407 0.0362*** 0.2628 0.0089***

Dummy Urban -0.581 0.0231*** 1.8 -0.0780 0.0056*** 1.7

Dummy Rural -0.487 0.0283*** 1.4 -0.1315 0.0070*** 1.4

D_Age 1 0.853 0.0820*** 4.3 0.1808 0.0205*** 4.2

D_Age 2 0.722 0.0724*** 6.2 0.1420 0.0180*** 6.1

D_Age 3 0.221 0.0429*** 6.0 0.1168 0.0105*** 5.8

D_Age 4 -0.117 0.0601** 5.4 0.0349 0.0149** 5.3

D_Age1*PM10 -0.003 0.0013*** 4.1 -0.0010 0.0003*** 4.1

D_Age2*PM10 -0.002 0.0012 5.9 -0.0003 0.0003 5.9

D_Age3*PM10 -0.001 0.0007* 6.1 -0.0004 0.0002*** 6.0

D_Age4*PM10 -0.002 0.0010** 5.2 -0.0007 0.0002*** 5.2

API -0.001 0.0011 6.0 -0.0004 0.0003 6.0

PM 10 0.003 0.0008*** 7.6 0.0010 0.0002*** 7.4

AdjR2 0.118 0.082

CI 20 19

Obs. 9965 10,239

Source: Regression results

3.2.1 Factors influencing the variation of total cases across age groups

The preceding sections examine the relative marginal contribution of each age group to total illness

as PM 10 changes. This section further evaluates the relative magnitude of the dose-response coefficient

attributed to age. This provides information on the relative vulnerability of each age group in the face of a

haze episode. Two regression models were appraised. The first model employs the natural logarithms of

total illness per 10,000 population for each age group for overall years, while the second considers the ratio

of total illness accrued to each age group to total illness for all age groups. The results are shown in Table 16.

Findings from the first model indicate very strongly that Age category 3 is the age group most

vulnerable to PM 10 increases as the API rises beyond the Moderate level (marginal change = 0.3%). This is

followed by Age category 4 (elderly adults) with a marginal change of 0.25%. Age categories 1 and 2 share

the same ranking, with a marginal change estimate of 0.18%.

The second model, where the dependent variable is expressed in terms of the natural logarithms of

ratio of total cases incurred by each category to total illness for all ages, shows more intuitive findings. The

model incorporates a slope shifter for PM 10 levels during hazy days. Results signify that the dose-response

coefficient for Age category 4 (elderly adults) is largest (0.2%), followed by Age category 1 (infants) at 0.1%,

while all the rest are negative.

Note that the influence of age on total inpatient cases in the preceding analysis was captured

indirectly via the construct of dummies that capture the relative proportion of each age group to total cases.

In this section, the dependent variable is total inpatient cases for each age group. Hence the dose-response

coefficients in this section are thought to better reflect the responsiveness of inpatient cases to changes in

air pollution levels. Given this perspective, we may conclude that elderly adults (Age category 4) are more

susceptible to hospitalization during haze episodes. The next most affected age group is infants.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

20


Table 16. Responsiveness of inpatient cases to PM 10 changes across age groups

Total cases per 10,000 population

Ratio of total cases by age to overall

Age

for each age group (semi log dependent cases (semi log dependent variable)

variable) [Model 1]

[Model 2]

B Std. error VIF B Std. error VIF

AGE 1

(Constant) -3.3878 0.0310*** -1.993 0.033*** 6.6

PM 10 0.0018 0.0008*** 6.3 -0.002 0.001*** 6.6

API -0.0016 0.0012 6.3 -0.002 0.001* 1.1

D_Urban 1.3182 0.0214*** 1.1 1.267 0.019*** 1.1

D_Rural 0.5414 0.0359*** 1.1 0.829 0.031*** 4.9

D_Haze -0.194 0.168 6.7

D_H*PM 10 0.003 0.001*** 6.6

CI 15 16.3

R2 0.470 0.54

N 4301 4300

AGE 2

(Constant) -3.1957 0.0359*** 5.9 -1.803 0.040***

PM 10 0.0018 0.0008** 5.8 -0.002 0.001*** 6.2

API 0.0003 0.0013 1.1 0.001 0.001 6.1

D_Urban 0.7564 0.0224*** 1.1 0.727 0.021*** 1.1

D_Rural 0.3179 0.0365*** 5.9 0.596 0.035*** 1.1

D_Haze 0.151 0.213 5.2

D_H*PM 10 0.000 0.001 6.6

CI 16 16

R2 0.17 0.192

N 5457 5456

AGE 3

(Constant) -2.3498 0.0347*** -1.042 0.023***

PM 10 0.0030 0.0008*** 5.9 0.001 0.000*** 6.3

API 0.0001 0.0013 5.9 0.001 0.001 6.1

D_Urban -0.1193 0.0220*** 1.2 0.346 0.012*** 1.2

D_Rural -0.2190 0.0312*** 1.2 0.163 0.018*** 1.2

D_Haze 0.128 0.119 5.1

D_H*PM 10 -0.002 0.001*** 6.6

CI 18 17

R2 0.018 0.089

N 8133 8132

AGE 4

(Constant) -2.5200 0.0316*** -1.155 0.021***

PM 10 0.0025 0.0008*** 6.2 0.000 0.000 6.7

API -0.0016 0.0012 6.2 0.000 0.001 6.5

D_Urban 0.0098 0.0207 1.19 0.333 0.011*** 1.2

D_Rural 0.0347 0.0285 1.2 0.408 0.015*** 1.2

D_Haze -0.319 0.102*** 4.9

D_H*PM 10 0.002 0.000*** 6.6

CI 16 17

R2 0.003 0.144

N 7290 7289

Source: Regression results.

21 Economy and Environment Program for Southeast Asia


4.0 ECONOMIC VALUATION OF HAZE IMPACTS

This section reports the results of the estimation of economic values of smoke haze on health

following Equation 1. We focus on estimating the health impact of the haze through all four of the selected

years based on the total population of Kuala Lumpur and the adjacent area in Selangor state for 2008. The

average yearly, monthly, and, consequently, daily cost of the haze on health is estimated. The basic

parameters are outlined in Table 17. Some of the basic information was obtained from our small

socioeconomic survey of 200 respondents. In this survey we examined the respondents’ treatment behavior,

particularly their choice of public, private, or self-treatment in the face of a haze event. Details of the profiles

of respondents and their treatment-seeking behavior are presented in the Appendix.

Table 17. Basic economic valuation parameters

Items Parameters Data sources

PM 10 Coefficient (dose-response) 0.001 Regression estimates, Table 13

Mean PM 10 (Upper Moderate API category) 112

Mean PM 10 (Unhealthy API category) 177 Raw data (DOE)

Mean PM 10 (Very Unhealthy API category) 390 Raw data (DOE)

Mean PM 10 (Hazardous API category) 426 Raw data (DOE)

Number of hazy observations (2005) 535 Raw data (DOE)

Upper Moderate API category 425

Unhealthy API (category 3) 90 Raw data (DOE)

Very Unhealthy API (category 4) 7 Raw data (DOE)

Hazardous API (category 5) 13 Raw data (DOE)

Weighted average PM 10 for hazy days 134 μg/m 3 Raw data (DOE)

Weighted average normal (non-hazy days)

Raw data (DOE)

50.24 μg/m

PM 10 (all years)

Total population for Klang Valley (2010) 7.2 million Secondary sources

Klang 823,200

Kuala Lumpur 1,703,000

Selayang 673,200

Petaling 1,782,000

Hulu Langat 915,600

K Selangor 201,400

Gombak 682,000

Sepang 212,000

Kuala Langat 222,000

Probability of getting treatment in

government clinics and hospitals (PG)

0.455 Calculated from survey data,

Table A2 in Appendix

Probability of getting treatment in private

Calculated from survey data,

0.2

clinics and hospitals (PC)

Table A2 in Appendix

Probability of self-treatment (PS) 0.02 Survey data, Table A2

Factor for adjustment of dose-response

(PC+PS)/PG

0.483

coefficient (F)

Unit Economic Value (UEV) MYR160 (2 days) Table A1 in Appendix

An important piece of information for the economic valuation of the health impacts of the haze is

the adjustment factor for the dose-response coefficient. Recall that the data was based on inpatient cases in

government hospitals only. This can be used to extrapolate the factor to capture inpatient cases in private

hospitals as well as self-treatment. The estimated dose-response coefficient (0.001) is seen as a reflection of

the visitation rates of the general public to government hospitals. Note that this is the coefficient for the

PM 10 variable estimated using the linear model shown in Table 13. Using our survey to identify visitation

rates to private hospitals and for self-treatment, shown in Table 17, the adjustment factor is calculated at

0.483. Therefore, the corrected dose-response coefficient is 0.001484.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

22


It is also important to determine the economic value of each inpatient case. In this study, as

indicated in the methodology section, we employed a productivity perspective and utilized the average

wage rate via our survey data. Presuming an average inpatient stay of two days per case, the unit value is

estimated at MYR 160 (Table 17). Of course, the average wage rate for the entire population of the

designated area, obtainable from secondary data, may also be used.

For all of the four chosen years, average daily PM 10 and API was 54.6 μg/m 3 and 50.1 (Upper

Moderate category), respectively. There were 535 hazy observations or 19 days per year, on average, in which

the API level was at least 76 (Lower Moderate category). The weighted mean PM 10 for all the hazy days was

134 (μg/m 3 ), and for the normal days PM 10 = 50.2 μg/m 3 (API is Upper Moderate). Given the estimated doseresponse

coefficient, annually the haze episodes induced an increase of 1,707 inpatient cases for the entire

population (7.2 million) within the study area, or 2.4 cases per 10,000 population. This translates to an

average increase of 142 cases monthly or 4.7 daily each year for the entire study area. During the 19 hazy

days each year, average daily inpatient rates rose very substantially from 0.41 (normal days) to 0.53,

representing an increase of 90 cases, or 31%.

Based on the unit economic value of MYR 160 (USD 53) for two days, the smoke haze health

damages were valued at MYR 273,000 (USD 91,000) annually. This averages out to MYR 23,000 per month or

MYR 766 (USD 256) per day, or an average of MYR 14,368 (USD 4,789) per hazy day.

The above estimation was for a yearly or daily average based on four years of observations. The

number of hazy days varied across the years. Therefore the estimates for each year would be multiples of the

average daily costs with the exact number of hazy days for the year, presuming the same daily incremental

increases in PM 10 or API. For instance, in 2005 and 2006 there were some 27 hazy days each year where the

API was above the moderate level. Multiplying the estimated costs per hazy day of MYR 14,368 by 27 yields a

cost estimate of MYR 387,936 (USD 129,312) each year.

5.0 SUMMARY OF RESULTS

The study has shown that 99% of all haze-related inpatient admissions in the study area in the years

2005, 2006, 2008 and 2009 took place while API levels were Good to Moderate. Only 1% of inpatients were

admitted when the API was Unhealthy to Hazardous. The Lower Moderate days (425 observations)

constituted 4.15% of all observations (10,239). There were only between 7 and 13 cases of Very Unhealthy

(category 4) and Hazardous (category 5) hazy days, respectively; all these cases occurred in 2005 and 2006.

For the Unhealthy category, there were only 90 observations throughout all four chosen years. Overall, there

were 535 observations (5.2%) where the API was at least at the Lower Moderate category. This averages to 19

days per year or 1.6 days per month in which the API level was at least 76.

The highest daily mean of 0.35 per 10,000 or 35 cases per 1 million population was observed in the

Very Unhealthy API (category 4), followed by Unhealthy (category 3). The number of cases for all age groups

on a per 10,000 population basis in both the urban (0.293) and semi-urban (0.325) areas was substantially

higher than in rural areas (0.202). The differences are statistically significant at the 0.01% significance level.

This finding is expected as hospitalization in both areas may also be induced by other urban-related

pollution factors such as the heat island effect and emissions from vehicles and industrial activity.

The study found that the five major haze-related illnesses were pneumonia, ischaemic heart

diseases, acute upper respiratory tract infections, asthma and hypertensive diseases. These five illnesses

represent about 63% of the total cases for all illnesses under the Good and Moderate API category. The

proportion of these illnesses increased marginally to 65% and 69% respectively under the Very Unhealthy to

Hazardous API categories.

Regression findings for both individual years and the overall years indicate that the probability of

any one age group constituting more than 50% of inpatient cases is highest for Age 1 (infants), followed by

Age 2 (children), Age 3 (young adults) and Age 4 (adults). However, since the nominal binary values are

based on the relative ratio of inpatient cases for each group, the coefficients may not be indicative of the

relative magnitude of mean cases for each age category.

23 Economy and Environment Program for Southeast Asia


Results generally suggest that the marginal impact of PM 10 on total inpatient cases is highest for

Age 3 (young adults) followed by Age 4 (elderly adults). This result is consistent with the fact that the Age 3

category (young adults) shows the highest mean cases, both on per 10,000 population and in absolute terms

for all the years.

Based on the linear model, the annual dose-response coefficient as well as the overall average for

the PM 10 variable is 0.001. This suggests that a daily 10-unit increase in PM 10 will lead to an increase in daily

inpatient cases of 0.01 every 10,000 of population. The non-linear specification exhibits greater variation of

the dose-response coefficient, ranging from 0.002-0.008. The overall panel regression for the non-linear

specification yields a mean dose-response coefficient of 0.003. This reflects an increase of 1% in PM 10 daily,

meaning total inpatient cases would rise by 0.3%. Our valuation of the haze impacts was thus based on the

dose-response coefficient of the PM 10 variable.

For all four chosen years, average daily PM 10 and API was 54.6 μg/m 3 and 50.1 (Upper Moderate

category), respectively. There were 535 hazy observations (19 days) in which the API level was at least 76

(Lower Moderate category). The weighted mean PM 10 for the entire hazy days was 134 μg/m 3 and for the

normal days PM 10 was 50 μg/m 3 (with API being in the Upper Moderate category). This resulted in an increase

of 1,707 inpatient cases for the entire population of some 7.2 million population or 2.4 cases per 10,000

population each year. This suggests an average increase of 142 cases monthly or 4.7 daily. During the 19

hazy days each year, daily inpatient rates rose from 0.41 (normal days) to 0.53, representing an increase of

31%.

Based on the unit economic value of MYR 160 (USD 53) for a two-day hospital stay, the health

damages due to haze pollution each year were valued at MYR 273,000 (USD 91,000). This averages to MYR

766 (USD 256) per day or MYR 14,368 (USD 4,789) per hazy day.

6.0 POLICY IMPLICATIONS AND CONCLUSION

The cost of MYR 0.273 million per year, measured by the value of productivity foregone for inpatient

treatment cases due to smoke haze pollution over four years (2005, 2006, 2008, 2009) may be minute in both

absolute and relative terms. Moreover, it is much lower if the unit economic value (financial cost) of hospital

admission of MYR 40 per day is employed. Nonetheless, the estimated value pertains only to the designated

study area, although the population at risk of 7.2 million is quite significant relative to the total population of

Malaysia (28 million). Furthermore, the cost only reflects the productivity loss due to hospital admissions

presuming an average stay of two days. Extreme haze may result in a host of other impacts, which can be

measured by preventive costs, mitigation costs for outpatient treatments, potential output loss (such as

tourism), productivity loss in various economic sectors, reduced leisure time, and increased anxiety due to

loss of visibility and the risk of traffic collisions. Thus the total damages due to haze could be very substantial.

The study of the 1997 smoke haze episode by Othman and Shahwahid (1999) estimated a value of some

MYR 30 million for health impacts alone. The impacts encompassed outpatient and inpatient treatments,

productivity loss, as well as increased anxiety. According to Othaman and Shahwahid’s study, the overall

cost to the Malaysian economy was estimated at MYR 800 million.

It is important to note that hospitalization rates dropped quite substantially when API readings

went beyond the Very Unhealthy category. We believe that such an observation did not truly reflect a decline

in health cases, rather it was a manifestation of precautionary behavior to avoid greater risks due to poor

visibility and increased air pollution exposure. Moreover, Malaysian Government statements through the

media caution affected residents and urge them to remain indoors during a haze hazard unless there is an

emergency, so only very critical cases opt to travel for immediate treatment. Such highly protective behavior

may underestimate the true impacts of haze on health.

While the estimated value may be minute in both relative and absolute terms, it still poses

important questions, particularly about the allocation of scarce public healthcare resources. As noted, the

estimated increase in the number of inpatient cases was 90 per day on hazy days, as measured by an API at

the Upper Moderate level. This inevitably implies the need for an additional 180 beds per day (90 × 2 = 180).

This may lead to difficulty in resource allocation for some hospitals and can potentially displace other

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

24


patients whose needs may be more critical. The economic cost and social consequences of such possibilities

have not been considered in this study.

A large hospital such as the Tengku Ampuan Rahimah Hospital (in Klang district) has a capacity of

some 800 beds. Hence, in terms of physical resources and healthcare provision, the estimated additional

daily need of 180 beds during a haze episode is equivalent to the daily resource requirements (physical and

manpower) of a quarter of such a large healthcare facility. With such a quantification and comparison, the

magnitude of haze impacts, albeit on inpatient health treatment alone, can now be appreciated.

The ASEAN has drawn up various agreements and action plans since 1995, when the first ASEAN

Cooperation Plan on Transboundary Pollution was incepted. As a consequence of the worst haze episode, in

1997, the ASEAN Haze Action Plan was formulated and ratified. The plan was to prevent land and forest fires

through improved policies and enforcement as well as to establish mechanisms to monitor land and forest

fires while strengthening regional-based mitigating measures. In 2003, ASEAN produced the Guidelines for

the Implementation of ASEAN Policy on Zero Open Burning. This provides for the establishment of an ASEAN

Coordinating Centre for Transboundary Haze Pollution Control to facilitate cooperation related to haze

pollution emanating from land and forest fires. Thus, there has been no lack of regional plans and

mechanisms as far as Indonesian-source haze is concerned. Nevertheless, the haze continues to recur almost

annually with the worst episodes since 1997 being in 2005 and 2006. It is clear that the issue at hand is not a

dearth of anti-haze plans that tackle both prevention/adaptation and mitigation. The main challenge

remains to be on how to implement these plans effectively.

An area which has been largely neglected but could potentially be effective in resolving

transboundary pollution is the possibility of the countries affected by haze initiating bilateral decentralized

measures based on the polluter-pays principle. The ASEAN environment ministerial level talks on haze thus

far have, understandably, avoided mentioning, let alone raising the possibility of such an approach for fear

of potential repercussions. But perhaps international litigation demanding appropriate compensation via

the International Court of Justice (ICJ) may effectively provoke responsible behavior by the various economic

agents in Indonesia in relation to land and forest fires. Indonesia could also be compelled to enact and

enforce laws and regulations that would impose substantial disincentives on local and foreign firms caught

acting in a manner detrimental to the environment and human welfare within and across their national

borders.

25 Economy and Environment Program for Southeast Asia


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27 Economy and Environment Program for Southeast Asia


Appendix. Profile analysis of survey data

A survey to identify the attitude of the public towards health treatment in the face of the haze was

conducted in the designated study area (Kuala Lumpur and the adjacent area in Selangor state) from

October-November 2011. Two hundred (200) respondents were chosen randomly in various locations across

the area. Table A1 depicts the basic socioeconomic and demographic information of the sample.

Majority (70%) of the respondents were in the 18-30 age group, while some 27% were in the 31-50

age group. About half of the respondents were single. Malays formed the majority (80%) of the respondents.

There were slightly more female respondents, at 55%. Almost half of the respondents (45%) had received

secondary education or lower; almost one-fourth (22%) achieved a college diploma. Some 25% of

respondents had access to tertiary education. The largest percentage of respondents worked in the public

(government) sector (41.5%), followed by private sector employees (30%). Some 27% of respondents were

college students and entrepreneurs. Mean income level was found to be about MYR 2352 per month (Table

A1).

Almost all of the respondents (95%) claimed to be concerned about haze (Table A2). About 45.5% of

respondents had gone for treatment to government clinics and hospitals, while 20% of respondents used

private clinics and hospitals. The analysis observed that 32.5% of respondents did not seek treatment at any

treatment center during a haze episode. Table A3 depicts the average distance to the various treatment

centers and their average treatment costs. The majority of respondents who did not seek treatment reported

that they had no need of treatment as the haze was not serious enough to warrant concern (Table A4).

Some 6% of respondents claimed that family members were admitted to hospital due to haze. All

were reportedly admitted to government hospitals (Table A5).

The cost of mitigation actions against haze was estimated at MYR 152 per respondent (Table A6).

This cost includes the purchase of face masks, food supplements and the cost of reduced outdoor activities,

valued by multiplying the value of an hourly-wage by the number of hours of outdoor labor foregone,

presuming an average respondent spends six hours engaged in outdoor activities on a normal day.

A Contingent Valuation study was attempted to estimate respondents’ willingness to pay (WTP) to

reduce anxiety from potentially increased risk of vehicle collision or accidents due to poor visibility from the

haze. The payment vehicle used was in the form of payment (insurance premium) for a special insurance

scheme that provides comprehensive medical and hospitalization coverage in the event of vehicle collision

or accidents attributable to the haze. Via an open-ended WTP elicitation format, the study found that 53% of

the respondents were willing to purchase the insurance. Average WTP was calculated at MYR 369 per year

(Table A7).

Table A1. Socioeconomic profile of respondents

Particulars Frequency Percentage

Age

18–30 140 70

31–40 33 16.5

41–50 21 10.5

> 51 6 3.0

Mean = 27

Gender

Male 90 45

Female 110 55

Race

Malay 160 80

Indian 17 8.5

Chinese 23 11.5

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

28


Table A1 continued

Particulars Frequency Percentage

Household size

1–4 78 39.0

5–8 98 49.0

9–12 22 11.0

> 13 2 1.0

Highest education level

Secondary school and lower 90 45.0

High school certificate 15 7.5

Diploma 44 22.0

First degree 31 15.5

Graduate degree 20 10.0

Marital status

Single 113 56.5

Married 85 43.0

Single parents 1 0.5

Occupation

Government staff 83 41.5

Private staff 60 30.0

Business 22 11.0

Retirees 2 1.0

Students 33 16.5

Household income

MYR 20,000 1 0.5

Mean = MYR 2,352

Residential areas

Urban 65 32.5

Semi-urban 64 32.0

Rural 71 35.5

Source: Analysis of survey data.

Table A2. Concerns about haze and health treatment

Particulars Frequency Percentage

Levels of concern regarding haze

Very concerned 93 45.5

Somewhat concerned 100 50.0

Not concerned 5 2.5

Not at all concerned 2 1.0

Where treatment for haze obtained

Government clinics 63 31.5

Private clinics 37 18.5

Government hospitals 28 14.0

Private hospitals 3 1.5

Traditional treatment 1 0.5

Self-treatment 3 1.5

Did not go for any treatment 65 32.5

Obtained medical certificate (those who went for treatment)

Yes 64 49

No 67 51

Source: Analysis of survey data.

29 Economy and Environment Program for Southeast Asia


Table A3. Distance to clinics/hospitals and treatment costs

Entities Average distance (km) Average cost (MYR)

Government clinic 3.85 11.39

Private clinic 4.21 44.15

Government hospital 6.64 8.55

Private hospital 7.00 38.33

Source: Analysis of survey data.

Table A4. Reasons for not getting treatment

Reasons Frequency Percentage

Haze impact not serious enough 39 61

I am using a mask 13 20

I always stay indoors 8 12

Do not have time 4 6

High cost of treatment 1 1

Total 64 100

Source: Analysis of survey data.

Table A5. Hospital admissions and treatment

Particulars Frequency Percentage

Family members admitted due to haze?

Yes 12 6

No 188 94

Type of hospital admitted in

Government 12 100

Private 0

History of respiratory problems among family members?

Yes 38 19

No 162 81

What specific respiratory problems?

Asthma 35 17.5

Sinus 1 0.5

Breathing difficulty 2 1.0

Does the haze aggravate respiratory problems?

Yes 33 16.5

No 3 1.5

Source: Analysis of survey data.

Table A6. Main action taken to mitigate against haze and estimated costs

Action taken to mitigate against haze Frequency Percentage

Average cost per

episode (MYR)

Purchase mask 94 47.0 9

Purchase of additional food supplements 12 6.0 113

Reduction in outdoor activities 81 40.5 30 +

Others 13 6.5

Total 200 100 152

Note: + A normal outdoor activity time of six hours is presumed for each respondent, multiplied by reported reduction in outdoor time

(48%), and hourly wage.

Source: Analysis of survey data.

Economic Valuation of Health Impacts of Haze Pollution in Malaysia

30


Table A7. Results of contingent valuation survey

Particulars Frequency Percentage

Willingness to purchase insurance coverage

Yes 107 53.5

No 93 46.5

Total 200 100

Willingness to pay an insurance premium annually (MYR)

10–250 73 68

251–450 9 8

451–650 8 7

651–850 1 1

851–1050 7 7

1051–1250 2 2

1251–1450 1 1

1451–1850 2 2

> 1851 4 4

Mean WTP = MYR 369

Total 107 100

Source: Analysis of survey data.

31 Economy and Environment Program for Southeast Asia

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