38

Chapter Two

THE IMPACT OF ENVIRONMENTAL POLICIES ON

HOUSEHOLD INCOMES FOR DIFFERENT

SOCIOECONOMIC CLASSES: THE CASE OF AIR

POLLUTANTS IN INDONESIA∗∗∗∗

Abstract With outdoor air pollutants in Indonesia as a case study, this essay expands a Social Accounting Matrix to include the link from the economy to the environment, as well as the link from the environment to the economy. This essay explores the relationship between production activities, pollution, and human health problems. It utilizes the Constrained Fixed Price Multipliers method to analyze the impact of policies designed to reduce the amount of pollutants in the air on household incomes for different socioeconomic classes in Indonesia. The results show that if policies designed to reduce the amount of pollutants in the air do not decrease the output of production sectors, then the policies also improve income distribution.

2.1 Introduction

Today the argument that environmental degradation will reduce future

benefits from economic activities is well accepted. Most countries consider

improvement of environmental quality an integral part of their overall

objectives (Lutz, 1993). For developing countries, however, strong economic

growth and better income distribution are still the immediate goals. These

countries view with disfavor policies that sacrifice economic objectives simply

to improve environmental quality.

Literature concerning the relationship between environmental quality

and economic activities has been available since 1970. In that year Leontief ∗ This essay, with Erik Thorbecke as the co-author, will be published in Ecological Economics (1996) (Amsterdam: Elsevier Science).

39

(1970) expanded an input-output table to include pollution generation and

abatement. Denison (1979) was also a pioneer in the subject of environmental

quality and economic activities. He used a growth accounting model to

analyze the impact of pollution abatement policies on US economic growth

between 1929-1978. Since then, many studies have focused on the relationship

between environmental quality and economic activities. Examples include the

studies developed by Bergman in 1990, Jorgenson and Wilcoxen in 1990, and

Duchin and Lange in 1994.

Most of this literature focuses on the relationship between economic

growth and the environment, but neglects the important relationship between

the environment and income distribution. This literature also shows the link

from economic activities to environmental quality, but not the link from

environmental quality feeding back to the economy. The first goal of this

essay is to present a methodology linking the economy to the environment as

well as feedback from the environment to the economy. The second goal is to

analyze the impact of environmental quality improvement policies on

household incomes for different socioeconomic classes.

This essay uses outdoor air pollution in Indonesia as a case study. This

case study was chosen for the following reasons:

1. The air pollution level in several large cities in Indonesia has become

alarming in the last few years. For example, in some parts of Jakarta, the

air pollution level is far above the allowable national standard for air

quality. The particle (SPM) concentration can reach 270 µg/m3, while the

Indonesian standard is 90 µg/m3. The lead concentration can reach 2

40

µg/m3, while the standard is 1 µg/m3. The NO2 concentration can reach

250 µg/m3, while the standard is 100 µg/m3 (Soedomo et al., 1991).

2. Since 1992 the Indonesian government has been preparing a national clean

air program to improve air quality by regulating the sources of air

pollutants. The government plans to start implementing this program in

the very near future (Lubis, 1994).

This essay is divided into six sections. The methodology section

presents a method to expand a social accounting matrix (SAM) to include the

link between production activities, ambient level of (outdoor) air pollutants,

and human health problems. This same section also presents a method to

analyze the impact of policies designed to improve air quality on household

incomes for different socioeconomic classes. The health cost section presents a

procedure to estimate the number of health problems and the health costs

associated with air pollutants. The Indonesian social and environmental

accounting matrix section describes the procedure to modify and to expand

the Indonesian SAM to incorporate the health effects associated with air

pollutants. The next section discusses several scenarios designed to simulate

the Indonesian clean air program. It is assumed that this clean air program

successfully reduces lead emissions by up to 62 percent, NO2 and SPM

emissions from the transportation sector by up to 50 percent, and NO2 and

SPM emissions from industrial sectors and open burning of municipal wastes

by up to 30 percent. The result section shows the impact of this air quality

program on household incomes. The essay ends with a discussion and

concluding section.

41

2.2 Methodology

Figure 2.1 shows the air pollution accounting matrix that records the

relationship between production activities, air pollutants, and human health

problems. Air pollutants are treated as the by-products of industrial and

transportation activities, i.e. “dirty” production sectors. The air pollutants

from these dirty production sectors increase the ambient level of air pollution.

As humans breathe this polluted air, they face a higher risk of contracting

health problems such as asthma, respiratory ailments, and high blood

pressure (Ostro, 1994). Individuals who actually contract those ailments are

likely to spend money for appropriate health treatment. The health costs

borne by these individuals and the government are defined, in this essay, as

the societal environmental economic costs of air pollutants.1

* Column headings and row headings are the same. For example, this "1" represents "Economy (prod. sectors)."

Figure 2.1. Air Pollutant Accounting Matrix.

1 This essay certainly underestimates the total societal costs of air pollutants. Since other costs associated with air pollutants, such as loss in human productivity, premature mortality cases, and damage to crops, buildings, and vehicles, are very difficult to estimate, limiting the analysis to human health costs appears a reasonable choice.

1. Economy (prod. sectors)

2. Ambient air pollutants

3. Health problems

1* 2 3

air poll. from dirty prod. sec.

health cases

health cost

42

* Column headings and row headings are the same. For example, this "1" represents

"Factors."

Figure 2.2. Principles of the Social and Environmental (Air Pollutant) Accounting Matrix.

Combining the air pollution matrix with a SAM results in a

consolidated social and environmental (air pollutant) accounting matrix

(SEAM) as in Figure 2.2. A SAM is a traditional double-entry accounting model

that records all economic transactions among agents in the economy and

provides information about the social structure of the economy. The upper left

portion of the SEAM (i, j = 1 to 4) in Figure 2.2 is the SAM; the rest captures

the air pollutant flow and associated health effects and costs.

The method used to analyze the impact of policies designed to improve

air quality on household incomes is the constrained fixed price multiplier

1. Factors

2. Institutions

3.a. Dirty prod. sectors

4. Other accounts

5. Amb. air pollutants

6. Health problems

TOTAL

TOTAL 1* 2 3.a. 4 5 6

T13a

T3a2 T3a4 T3a3a

T42

T21 T24

T41 T43a

T22

y1 T14

y2

y3a

y3b

y1' y2' y3a' y4'

M3a5

M56

m63c

3.b. Clean prod. sectors

3.c. Air Pollutant-Health

3.c. 3.b.

T13b t13c

T43b t43c

T3b4

y3c

y4

y3b' y3c'

T3b2

t3c2

T3a3b t3a3c

T3b3a T3b3b t3b3c

43

(CFPM) method. This method primarily utilizes the upper left side (SAM

part) of the SEAM. The health sector and the dirty production sectors are the

constrained outputs, i.e. any change in these sectors’ outputs is determined

exogenously. The final equation from the derivation of the CFPM method is

(see Chapter One for the derivation of the CFPM method2):

dyx

I CR I

I QI C

dxy

NC

C

NC

C

NC

C

�

��

�

�� =

−− −

�

��

�

�� ⋅

− −�

��

�

�� ⋅

�

��

�

��

−( ) |

||| ( )

00

1

(2.1)

where:

yNC represents vector outputs of the non-constrained sectors

yC represents vector outputs of the constrained sectors

xNC represents exogenous vectors of the non-constrained sectors

xC represents exogenous vectors of the constrained sectors

( ) ||

|| ( )

I CR I

I QI C

NC

C

−− −

�

��

�

�� ⋅

− −�

��

�

��

−0

0

1

is the constrained fixed price

multiplier.

The procedure to apply the CFPM method is as follows:

1. Determine the impact of air pollutant reduction on the output of the sector

that produces the air pollutants. For example, air pollutant reduction can

decrease the sector’s output.3

2. Formulate a new matrix of constrained fixed price multipliers. The

reduction of ambient air pollutants will alleviate health problems, and

therefore reduce health costs associated with air pollutants. Urban

2 See also Lewis and Thorbecke (1992), and Parikh and Thorbecke (forthcoming). 3 For the justification of this example, see the explanation of the Pessimistic Setting. Another possibility is the Optimistic Setting. Both Settings are explained in the Simulation Scenario section.

44

households and the government will spend this “extra income” for other

goods and services.4 This behavior will produce a new matrix of

constrained fixed price multipliers.

3. Calculate the relationship (2.1) using the new constrained fixed price

multiplier matrix, the new health costs, and the new output of dirty

production sectors. The impact of ambient air pollutant reduction on

household incomes of different socioeconomic classes can then be

observed.

2.3 Estimation of the Health Costs Associated with Air Pollutants

This essay uses the 1990 SAM from Indonesia, which is the latest

Indonesian SAM available. The health costs that are estimated are also from

1990.

In his recent work5, Ostro (1994) summarized the impact of air

pollutants on health. From epidemiological literature, he collected the dose-

response functions that relate health impacts to ambient levels of air pollution.

The general form he used to estimate health impacts is:6

dHi = bi · POPi · dA (2.2)

where:

dHi is the change in number of people that contract health effect i

bi is the slope of the dose-response curve

4 Households spend this “extra income” in accordance with their marginal expenditure propensities. 5 Ostro estimated the health effects of air pollutants in Jakarta. His estimates have been accepted by several researchers in the Indonesian Ministry of Health and the Environmental Impact Management Agency (Lubis, 1994). 6 See also Appendix A.

45

POPi is the population within the polluted area under consideration,

i.e. the population at risk of health effect i

dA is the change in air pollution under consideration.

The health costs of pollutants under consideration are:

dTC = Σi Vi · dHi (2.3)

where:

Vi is the treatment cost of health effect i.

As Ostro suggested, in applying the dose-response functions this essay

will assume that there exists a threshold level of air pollution, below which

unfavorable health effects do not occur. The threshold level, or the allowable

air pollutants level, used in this essay is the Indonesian Air Pollutant Standard

(Ostro, 1994). The dA in the relationship (2.2) is the ambient level of pollutants

in the air over and above the Indonesian Standard.

From the dose-response functions collected by Ostro, this essay limits

itself to the dose-response functions for particulate matter (SPM), NO2, and

lead. The reason for choosing these three types of air pollutants is that the

relevant data are relatively available in contrast with the scarcity of

information relating to other air pollutants. Although the data for SO2 is also

available, the SO2 ambient level in Indonesia is still below the standard.

Therefore, this essay assumes that no significant health problem associated

with SO2 exists.

To estimate the health effects of air pollution, maps of air pollutant

levels (isopleths of annual average air pollutants) are used. The Indonesian

Agency for the Assessment and Application of Technology (BPPT)

46

collaborated with the German Ministry of Technology (BPPT and KFA, 1993)

to publish maps of air pollutant levels on the island of Java in 1990. These

maps indicate that three cities on Java have air pollutant levels above the

Indonesian Standard. The three cities are Jakarta, Bandung, and Surabaya,

with respective populations of 8.0 million, 1.9 million, and 2.3 million in 1990.

In addition to these air pollution maps of Java, detailed city maps of air

pollutants for Jakarta, Bandung, and Surabaya in 1989 developed by the

Bandung Institute of Technology (Soedomo et al., 1991) were used.7 These

maps show ambient levels of air pollutants in the different neighborhoods in

each city. It is assumed that the ambient level of air pollutants in the three

cities in 1990 was at the same level as in 1989. Based on these maps,

population distribution data, and the dose-response functions, the number of

health cases associated with air pollutants in Jakarta, Bandung, and Surabaya

can be estimated in a detailed way.

For regions outside Java, an approximation must be made. The most

heavily populated city outside Java is Medan. It boasts a 1990 population of

1.8 million. Since the population of Medan is close to that of Bandung,

Medan is assumed to have as many cases of health problems associated with

air pollutants as Bandung. The next most populated city outside Java is

Palembang. In 1990 its population was 1.1 million. According to BPPT’s air

pollutant maps, Javanese cities with populations around 1.1 million, such as

Semarang and Bogor, do not have serious health problems associated with air

pollutants. All cities outside Java besides Medan, hence, are assumed to have

no serious health problems associated with air pollutants. The total number of

7 See Appendix C.

47

health problems associated with air pollutants in Indonesia for 1990 can be

seen in Table 2.1.

Table 2.1 Health Effects Associated with Air Pollutants Above the Ambient Air Pollutant Standard in Indonesia in 1990

Air Pollutant Health Effect Number Health Costs* of cases (in millions of rupiahs)

SPM Hospital Admissions 2,518 3,776.69

Emergency room visits 49,391 740.86 Lower respiratory illnesses (children) 126,588 189.88 Asthma attacks 564,300 2,821.50 Respiratory symptoms 38,396,298 32,636.85 Chronic bronchitis 12,841 224.71

NO2 Respiratory Symptoms 1,979,842 1,682.87

Lead Hypertension 211,323 2,641.54 Non-fatal heart attacks 283 735.71

Total health costs 45,450.61

* The health costs include government subsidies. Urban Low households account for 58.7 percent of the total health costs associated with air pollutants. Urban Non-labor and Urban High households account for an additional 5.2 and 3.3 percent, respectively, with government subsidies forming the remaining 32.5 percent.

The information on the cost of medical/health treatments (including

the information on government subsidies) is based on interviews with medical

doctors working in public hospitals and public health centers in Jakarta. Table

2.1 also shows the total health costs paid by patients and government. Table

2.2 provides information on the size of these health costs associated with air

pollutants relative to household spending on other goods and services.

48

Table 2.2 Urban Household Groups: Population, Income, and Expenditure Pattern

Socioeconomic Urban Household Groups Urban Low Urban Non-

labor Urban High

Proportion: 1. From Total Populationa 0.16 0.01 0.05 2. From Total Household Incomeb 0.13 0.04 0.25

Expenditure: 1. Transfer Among Households 4.04% 1.07% 3.27% 2. Food and Other Agricultural Products 17.87% 24.00% 27.45% 3. Textile and Leather 2.31% 1.43% 2.38% 4. Wood and Construction 1.68% 0.53% 0.25% 5. Paper and Metal Products 2.02% 3.36% 5.75% 6. Chemical and Basic Metal 5.09% 4.28% 3.19% 7. Electricity and Gas 1.37% 1.22% 0.99% 8. Water Supply 0.33% 0.29% 0.23% 9. Transportation 9.31% 11.80% 6.30% 10. Financial Institutional Products 7.88% 2.56% 4.29% 11. Air Pollution-Health Service 0.13% 0.04% ∗.∗∗%c 12. Other Health Service 0.76% 1.10% 0.18% 13. Public Services 5.19% 6.75% 1.75% 14. Other Services 24.15% 24.20% 23.54% 15. Income Tax 0.10% 1.66% 1.22% 16. Household Saving 17.77% 15.71% 19.21%

Total 100.00% 100.00% 100.00% a Total population in Indonesia in 1990 was approximately 180 million people. b Total household income was approximately 158,000 billion rupiahs. c *.**% = less than 0.005%

2.4 Indonesian Social and Environmental Accounting Matrix

The modification of the original SAM published by the Indonesian

Central Bureau of Statistics separates the Air Pollutant-Health Service sector

(health service activities associated with air pollutants) from the Public Service

sector. The previous section estimated the total health costs associated with

air pollutants, i.e. the total income of the Air Pollutant-Health Service sector.

The spending pattern of that sector is estimated using the spending pattern of

public health; the latter pattern is available in the 161x161 Indonesian Input-

49

Output Table for 1990. The estimate of how much different urban

socioeconomic classes of households have to pay in health costs associated

with air pollutants is based on research conducted by Achmadi (1989). Basing

his study on people’s occupations in Jakarta, he estimated the health risks

associated with air pollutants.

The information needed to determine which production sectors are

responsible for polluting the air and how much each sector contributes to this

pollution is available from the World Bank (1993). The World Bank estimated

the air pollution contribution of industrial, land transportation, electric utility,

and open burning of municipal waste sectors in Jakarta for 1990.

2.5 Simulation Scenarios

Since 1992 the Indonesian government has been preparing a national

clean air program called the Blue Sky Program (BSP) to improve air quality by

regulating the sources of air pollutants.8 The BSP is divided into two

programs. The goal of the first program is to control air pollutants from

mobile sources such as motor vehicles. The Indonesian government expects to

launch this program in the near future. The second program, expected to

follow the first program, will attempt to control air pollutants from stationary

sources such as factories and open burning of municipal wastes.

8 Several government agencies participate in the preparation of BSP. The Environmental Impact Management Agency, an agency under the Ministry of Environment, is in charge of coordinating the efforts of government agencies. The Ministry of Transportation, Jakarta Regional Government, Ministry of Health, and Agency for the Assessment and Application of Technology are currently involved in designing the first program of BSP. The second BSP program is in its very early stages. The Ministry of Industry, Ministry of Energy, and regional governments are expected to be actively involved in the preparation of this second BSP program.

50

Policies considered by the Indonesia government to control air

pollutants from vehicles (the first program of BSP) include:

1. Reducing the Lead Content of Gasoline: The lead content of gasoline in

Indonesia is presently about 0.40 g/l. The government plans to first reduce

the lead content to 0.15 g/l (low lead gasoline), and then to 0.04 g/l

(“unleaded” gasoline).

2. Promoting the Recovery of Vapor Emissions: The goal is to reduce the

amount of gasoline vapors that are emitted into the atmosphere, when

gasoline tanks are filled.

3. Introducing Emissions Standards for New Vehicles: Besides gradually

reducing pollutants in the air, this policy aims to limit the growth of air

pollutants as vehicle numbers rise.

4. Phasing Out Two-Stroke Engines: The reason for this policy is that two-

stroke engines generate approximately 40 percent more pollution than

four-stroke engines of the same size (World Bank, 1993).

5. Establishing a Roadside Inspection Program:9 This policy is designed to

control air pollutants from vehicles in use. It is suspected that the 10

percent worst polluting vehicles generate about half of the pollution

(World Bank, 1993).

To control air pollutants from factories (the second program of BSP) the

Indonesian government plans, among others, on:

1. Promoting Energy Efficiency Technologies: A mid-1980s survey of 67

industrial establishments identified an energy efficiency improvement

potential of about 23 percent in Indonesia (World Bank, 1993).

9 As a complement of this program, an emission standard for in-use vehicles will also be introduced.

51

2. Phasing Out Coal Use in Urban Areas: As a substitute for coal, the

government plans to promote the use of natural gas.

3. Facilitating the Development of Combine Heat and Power Generation

(CHP): It is estimated that up to 18 percent of factories in Indonesia could

potentially adopt this technology. CHP could reduce fuel requirements by

10 to 30 percent (World Bank, 1993).

4. Introducing Industrial Emission Standard. Factory owners would be

asked to gradually reduce their air pollutants down to a certain standard.

For the open burning of municipal wastes, the government plans to build

incinerators in big cities and to encourage the participation of private

entrepreneurs in building these incinerators.

This essay attempts to analyze the short-run impacts of the BSP on the

Indonesian economy. Note that in the SEAM all vehicles are pooled in the

Land Transportation sector, factories in the industrial sectors, and open

burning of municipal wastes in the Public Service sector. The scenarios in this

essay are:

1. First Stage of BSP:10 The Land Transportation sector reduces its lead

emissions by 62 percent (using low lead gasoline) and reduces its NO2 and

SPM emissions by 50 percent; air pollutants from other sectors are

assumed constant.11

10 The information on approximately how much the BSP is expected to reduce the ambient level of air pollutants in the short run is based on several interviews with government officers and researchers at the Environmental Impact Management Agency, Agency for the Assessment and Application of Technology, and Jakarta Regional Government. 11 This emission reduction decreases the ambient level of air pollutants in the four biggest cities in Indonesia. The estimates of health problems, hence, have to be recalculated using the dose-response functions.

52

2. Second Stage of BSP: First Stage of BSP is implemented; also, the

industrial sectors reduce their NO2 and SPM emissions by 30 percent; air

pollutants from the Public Service sector are assumed constant.

3. Third Stage of BSP: Second Stage of BSP is implemented; in addition, the

Public Service sector reduces its NO2 and SPM emissions by 30 percent.

In conducting the three scenarios (the numbers below refer to the three

scenarios, i.e. the stages of BSP), this essay will simulate two extreme settings:

a) Optimistic Setting (Scenarios 1a, 2a, and 3a): This first extreme setting

assumes that the reduction in ambient air pollutants can be achieved with

no reduction in the sectors’ output (1a assumes that land transportation

output is unaffected; 2a assumes that both land transportation and

industrial sectors’ output remain constant; and 3a assumes that, in addition

to 2a, public sector output remains constant). This situation can be

interpreted as if there were technological improvements available to

reduce air pollutants at relatively inexpensive cost. All vehicles and

factories would then be able to reduce their air pollutant emissions as

foreseen by the government.

b) Pessimistic Setting (Scenarios 1b, 2b, and 3b): In scenario 1b, it is

assumed that the reduction in air pollutants can only be achieved through

a 10 percent reduction in the Land Transportation sector’s output. The

justification for this assumption is that approximately 10 to 15 percent of

vehicles in Indonesia are at least 15 years old and generate considerable

pollution. The pessimistic setting presumes that these vehicles could not

be modified to achieve the emission standard and owners would not be

able to replace them with new vehicles. In scenarios 2b and 3b, it is further

assumed that, in addition to the above ten percent drop in the Land

53

Transportation sector’s output, the reduction in air pollutants can only be

achieved through a 5 percent drop in the industrial sectors’ output. Since

the government has not implemented strict regulations concerning types of

technology that can be applied in the industrial sectors, many factory

owners have adopted technologies that generate significant amounts of

pollution per unit of production. Underlying these scenarios is the

assumption that some of the existing pollution-generating technologies

could not be modified to achieve the emission standard.

2.6 Results of Simulations

The results of all simulations can be seen in Table 2.3 which shows the

absolute and relative changes in total incomes due to the implementation of

air pollutant regulations. These total income changes are listed for each

socioeconomic class of household.

2.6.1 First Stage of the BSP

In scenario 1a (Optimistic Setting), household incomes for all

agricultural and rural classes increase. Household incomes for all urban

classes fall. The links below illustrate how improvement in air quality affects

household incomes:

54

• The improvement in air quality reduces the number of health problems

associated with air pollutants. This reduction in the number of health

problems, in turn, leads to a fall, in the output as well as the income of the

Air Pollutant-Health Service sector. Clerical Paid Urban laborers,

Professional Paid Urban laborers, and Chemical and Basic Metal sectors

suffer the most from the reduction in Air Pollutant-Health Service

activities. Clerical Paid Urban laborers belong mostly to the Urban High

and Low households, respectively. Professional Paid Urban workers are

found mostly among the Urban High households. The drop in income

affecting the Chemical and Basic Metal sector decreases significantly the

rent received by the Unincorporated Capital Urban sector. Since the Urban

Table 2.3 Estimated Impact of Various Air Pollution Scenarios on Incomes of Socioeconomic Groups(based on fixed price multipliers formSEAM)

(in billions of rupiahs and percentages)

ScenariosBase Trans.1 Trans. + Indus.2 Trans. + Indus. + PS3

Total Incomes Opt.a (1a) Pes.b (1b) Opt. (2a) Pes. (2b) Opt. (3a) Pes. (3b)Ag Employee 5860.00 0.056 -28.710 0.103 -242.130 0.111 -242.123

0.001% -0.490% 0.002% -4.132% 0.002% -4.132%Small Farmer 29268.00 0.115 -151.689 0.212 -1217.609 0.230 -1217.598

*.* -0.518% 0.001% -4.160% 0.001% -4.160%Medium Farmer 6714.65 0.051 -34.249 0.094 -287.412 0.103 -287.405

0.001% -0.510% 0.001% -4.280% 0.002% -4.280%Large Farmer 8929.80 0.065 -44.975 0.119 -381.611 0.130 -381.603

0.001% -0.504% 0.001% -4.273% 0.001% -4.273%Rural Low 8840.23 0.061 -65.233 0.112 -342.762 0.122 -342.754

0.001% -0.738% 0.001% -3.877% 0.001% -3.877%Rural Non-labor 2981.47 0.028 -18.511 0.051 -102.562 0.056 -102.558

0.001% -0.621% 0.002% -3.440% 0.002% -3.440%Rural High 25413.35 0.177 -190.898 0.325 -924.457 0.353 -924.434

0.001% -0.751% 0.001% -3.638% 0.001% -3.638%Urban Low 21134.08 -0.586 -190.425 -1.078 -789.785 -1.171 -789.883

-0.003% -0.901% -0.005% -3.737% -0.006% -3.737%Urban Non-labor 6690.93 -0.142 -66.017 -0.262 -274.494 -0.284 -274.518

-0.002% -0.987% -0.004% -4.102% -0.004% -4.103%Urban High 42180.83 -3.678 -361.295 -6.769 -1467.929 -7.351 -1468.520

-0.009% -0.857% -0.016% -3.480% -0.017% -3.481%1 Trans. = Land Transportation sector a Opt. = Optimistic Setting2 Indus. = Industrial sectors b Pes. = Pessimistic Setting3 PS = Public Service sector *.* = less than 0.0005%

55

Non-labor households (i.e., the retired, students and rentiers) receive

incomes mostly from that sector, they are negatively affected.12

• The reduction in the number of health problems associated with air

pollutants allows urban households to spend more of their incomes on

goods and services other than Air Pollutant-Health Services. This

increasing demand for goods and services boosts domestic production

activities (except land transportation and “dirty” industrial sectors13). and

raises household incomes. For Urban High, Non-labor, and Low

households, however, the income benefits from the increase in domestic

production activities resulting from a shift away from health expenditures

to expenditures on other goods and services (e.g., food) cannot compensate

for their income reduction caused by the decrease in Air Pollutant-Health

activities.

In scenario 1b (Pessimistic Setting), all household incomes fall. Urban

households bear the major burden of this environmental regulation. In

particular, the total income of the Urban Non-labor class is reduced by almost

1 percent. The link between Land Transportation and Unincorporated Capital

Urban sectors explains why the total income of Urban Non-labor households

decreases the most. The reduction in the output of land transportation

activities lowers the rent received by the Unincorporated Capital Urban sector

and, in turn, the incomes of Urban Non-labor households.

2.6.2 Second Stage of the BSP 12 These various links and relationships can be read off from the matrix of marginal expenditure propensities of SEAM. 13 The Land Transportation and “dirty” industrial sectors are the constrained sectors.

56

The impact of scenario 2a on income distribution is similar to that of

scenario 1a, except that it is twice as great. While all other types of households

experience an increase in total income, the Urban High, Non-labor, and Low

households experience a decrease.

Scenario 2b affects agricultural households the most; their total income

falls by approximately 4 percent. In particular, the Medium Farmer

households undergo the greatest reduction in incomes. The links between

Food-Drink-Cigarette, Food Crop, and Agricultural Unpaid Rural sectors

explains how scenario 2b affects Medium Farmer households.

Under scenario 2b, the quantity of food processing activities falls

significantly. This reduction in food processing affects the output of the Food

Crop sector negatively, which, in turn, leads to a fall in the income received by

the Agricultural Unpaid Rural class, largely represented by medium-size

farmers.

2.6.3 Third Stage of the BSP

The impact of scenario 3a on income distribution is similar to that of

scenario 2a. Again, while other households benefit from this scenario, the

total incomes of the Urban High, Non-labor and Low households decline.

The impact of scenario 3b on income distribution is also similar to that

of scenario 3a. With this scenario, the owners of medium farms experience the

greatest decrease in incomes.

2.7 Discussion and Conclusion

57

This essay develops a procedure to expand a SAM into a social

environmental accounting matrix (SEAM), capturing the link between

production activities, ambient level of air pollutants, and associated human

health problems. Thus in the SEAM and the policy simulations based on it,

the health costs borne by individuals and the government resulting from air

pollution (above the standards) are defined as the societal environmental costs

of air pollutants. As such the methodology used in this essay underestimates

the total societal costs of air pollutants.14 If more information on the societal

costs of air pollution were available, a more sophisticated SEAM could be

developed using the same procedure as that presented in this essay.

This essay relies on the use of the constrained fixed price multiplier

(CFPM) method to analyze the impact of alternative policies designed to

improve air quality on household incomes for different socioeconomic classes.

Although this method restricts the number of possible scenarios that can be

developed (in particular those relating to taxing pollution activities), this

simple mathematical method yields valuable information.

Three scenarios were designed to simulate the Indonesian clean air

program, i.e. the Blue Sky Program (BSP). The first scenario (representing the

First Stage of BSP) assumes that this clean air program successfully reduces

lead emissions by up to 62 percent, NO2, and SPM emissions from the

transportation sector by up to 50 percent. The second scenario (representing

the Second Stage of BSP) maintains the same assumption as the first scenario

but, in addition, assumes that NO2 and SPM emissions from industrial sectors

drop by 30 percent. The third scenario (representing the Third Stage of BSP)

retains the same assumption as the second scenario but, in addition, assumes 14 For more detail see footnote 1.

58

that NO2 and SPM emissions from open burning of municipal wastes decline

by 30 percent. The three scenarios are simulated under Optimistic and

Pessimistic Settings, respectively. The Optimistic Setting postulates that

improvement in air quality can be achieved with no reduction in the sectors’

output through the adoption of available alternative technologies. The

Pessimistic Setting, in contrast, presumes that implementing air pollution

regulation on Land Transportation sector reduces land transportation output

by 10 percent, and implementing air pollution regulation on industrial sectors

reduces industrial output by 5 percent.

Table 2.3 summarizes the estimated impact of the various simulated

anti-pollution scenarios -- under the optimistic and pessimistic settings -- on

the incomes of the ten socioeconomic household groups. It can be seen that

under the optimistic scenarios (1a, 2a, 3a) the relative impact of the anti-

pollution policies on household groups incomes would be negligible. On the

other hand, under the pessimistic scenarios, the relative negative impact is

significant -- ranging from -0.5 percent to almost -1 percent for case 1b and

from -3.4 percent to -4.3 percent for cases 3b and 3c, respectively. It is

noteworthy that the urban high income and rural high income households are

somewhat less negatively affected than the other socioeconomic groups. This

suggests that if the Pessimistic Settings were to prevail, the income

distribution would become more unequal.

Under the Optimistic Setting, simultaneously implementing the BSP in

all dirty sectors benefits all socioeconomic classes of households, except the

urban households. Since the average income of urban households on a per

capita basis is higher than that of agricultural and rural households, a drop in

59

average income in the former category combined with a rise in average

income in the latter should contribute to a more equal income distribution.

To determine which type of households will be affected the most by the

implementation of air pollutant regulations, it is important to know whether

the Optimistic Setting or the Pessimistic Setting is more likely to prevail.

There are no studies yet within the context of Indonesia shedding any light on

this question. However, in 1994, BPPT conducted a survey on the availability

of technologies to reduce air pollutants in Indonesia (Nurrohim et al., 1994).

BPPT concluded that such technologies are relatively available. This would

suggest that the actual outcomes of the simulated policies are likely to fall

within the range bounded by the optimistic and pessimistic settings but closer

to the former.

These results should be appropriately qualified. First, the use of a

national SAM, rather than a regional SAM, might underestimate the impact of

improvements in air quality on household incomes in Jakarta, Bandung,

Surabaya, and Medan. On the other hand, the use of a national SAM might

overestimate the impact of improvements in air quality on household incomes

outside the large cities just mentioned. Secondly, although households have

been classified into ten different socioeconomic classes, each household

category covers a range of incomes and socioeconomic characteristics.

Consequently, some households in a particular class might be affected more

than others in the same class.

60

2.8 References

Achmadi, U.F. “Analisis Resiko Efek Pencemaran Udara CO dan Pb terhadap

Penduduk Jakarta.” Department of Public Health Working Papers,

University of Indonesia, Jakarta, 1989 (in Indonesian).

Bergman, L. “Energy and Environmental Constraints on Growth: A CGE

Modeling Approach.” Journal of Policy Modeling, 12 (4 1990): 671-91.

Badan Pengkajian dan Penerapan Teknologi (BPPT) and Forschungszentrum

Jülich GmbH (KFA). Environmental Impact of Energy Strategies for

Indonesia: Final Summary Report. Jakarta: Badan Pengkajian dan

Penerapan Teknologi, 1993.

Denison, E.F. Accounting for Slower Economic Growth: The United States in the

1970s. Washington, D.C.: the Brookings Institution, 1979.

Duchin, F. and G.M. Lange. “Strategies for Environmentally Sound Economic

Development.” Investing in Natural Capital: The Ecological Economics

Approach to Sustainability. A.M Jansson, J. Hammer, C. Folke, and R.

Costanza, eds., pp. 250-65. Washington, D.C.: Island Press, 1994.

Jorgenson, D.W. and P.J. Wilcoxen. “Environmental Regulation and the U.S.

Economy.” Rand Journal of Economics, 21 (2 1990): 314-90.

Keuning, S. and E. Thorbecke. “The Social Accounting Matrix and

Adjustment Policies: the Impact of Budget Retrenchment on Income

Distribution.” Adjustment and Equity in Indonesia. E. Thorbecke et al., pp.

63-84. Paris: OECD Publications, 1992.

Leontief, W. “Environmental Repercussions and the Economic Structure: An

Input-Output Approach.” The Review of Economics and Statistics, 52 (3

1970): 262-71.

61

Lewis, B. and E. Thorbecke. “District-Level Economic Linkages in Kenya:

Evidence Based on a Small Regional Social Accounting Matrix.” World

Development, 20 (6 1992): 881-97.

Lubis, S.M. “Permasalahan Pencemaran Udara di Indonesia dan Program

Langit Biru.” Paper presented in Kursus Pengendalian Pencemaran

Udara BAPEDAL dan KP2L-DKI, 12-23 January 1994, Serpong, Indonesia

(in Indonesian).

Lutz, E. “Toward Improved Accounting for the Environment: An Overview.”

Toward Improved Accounting for the Environment. E. Lutz, ed., pp. 1-4.

Washington, D.C.: World Bank, 1993.

Nurrohim, A., M.S. Boedoyo, and C. Malik. “Technological Options of Air

Pollution Abatement, Cost, and Benefits.” Agency for the Assessment

and Application of Technology (BPPT) Internal Report, Jakarta, 1994.

Ostro, B. “Estimating the Health Effects of Air Pollutants: A Method with an

Application to Jakarta.” Policy Research Working Paper No. 1301, World

Bank, 1994.

Parikh, A. and E. Thorbecke. “Impact of Rural Industrialization on Village

Life and Economy: A SAM Approach.” Economic Development and

Cultural Change, forthcoming.

Pyatt, G. and J. Round. “Accounting and Fixed Price Multipliers in a Social

Accounting Matrix Framework.” Economic Journal, 89 (1979): 850-73.

Soedomo, M., K. Usman, and M. Irsyad. Analisis dan Prediksi Pengaruh Strategi

Pengendalian Emisi Transportasi Terhadap Konsentrasi Pencemaran Udara di

Indonesia: Studi Kasus di Jakarta, Bandung dan Surabaya. Bandung: Institut

Teknologi Bandung, Bandung, 1991 (in Indonesian).

62

Sutamihardja, R.T.M. “Air Quality Management.” Paper presented in the

Workshop on Urban Air in Jakarta, May 26-27, 1994, Jakarta.

United Nations. Integrated Environmental and Economic Accounting (Interim

Version). Studies in Methods, Series F: 61, the United Nations, 1993.

World Bank. Indonesia. Energy and the Environment: A Plan of Action for

Pollution Control. Report No. 11871-IND, World Bank, 1993.

World Bank. Indonesia. Environment and Development: Challenges for the Future.

Report No. 12083-IND, World Bank, 1994.