1

Chapter One

INTRODUCTION

1.1 Research Issue

Today it is commonly known that the human economic activities of

production and consumption affect the physical environment. In particular,

world economic and population growth have placed increasing pressure on

the environment, resulting in environmental destruction. Equally important is

consideration of the impact of this environmental destruction on human life.

This section briefly describes four types of environmental destruction -- air

pollution, contaminated water, infertile soil, and loss of biodiversity -- and

indicates their effect on human life. This section then presents the research

issue that this dissertation addresses.

The air pollution situation is alarming. In a recent study on ambient air

pollutants in 20 megacities around the world, the World Health Organization

(WHO) observes that at least one type of air pollutant exceeds the WHO

standard of allowable air pollutants in each of these megacities (UNEP and

WHO, 1992). Another study estimates that approximately 600 million people

live in urban areas where the sulfur dioxide (SO2) level exceeds the WHO

standard, and over 1.25 billion live in cities with an unacceptable level of

particulate matter (SPM) (GEMS-MARC, 1988). A high level of ambient air

pollutants is suspected to cause numerous health problems for humans. For

example, in Jakarta, with a total population of approximately nine million, it is

estimated that approximately 1558 cases of premature mortality, 39 million

cases of respiratory symptoms, 558 thousand cases of asthma attacks, 12

2

thousand cases of chronic bronchitis, 125 thousand cases of lower respiratory

illnesses for children, and 136 thousand cases of hypertension in 1990 were

associated with air pollutants (Ostro, 1994).

The quality of water in many developing countries is also alarming.

According to the World Bank (1992), at least 170 million people in urban areas

in developing countries lack access to clean water for drinking, cooking, and

washing; at least 855 million lack clean water in rural areas. Water supplies

are contaminated by disease-bearing human waste, toxic chemicals, and heavy

metals that are hard to remove from drinking water with standard purification

techniques. Use of polluted water spreads diseases that kill millions and

sicken more than one billion people each year (World Bank, 1992).

For the case of fertile soil, the United Nations Environment Programme

(UNEP) estimates that approximately 11 percent of the earth’s fertile soil has

been so eroded, chemically altered, or physically compacted as to reduce its

ability to process nutrients into a form usable for plants. Furthermore, the

UNEP also estimates that approximately three percent of the earth’s soil has

been degraded to the point where it can no longer perform its original biotic

function (WRI in collaboration with the UNEP and the UNDP, 1992). Clearly

infertile soil reduces agricultural productivity.

Loss of biodiversity is another important environmental problem

caused by human economic activities. For example, scientists estimate that

four to eight percent of tropical forest species may face extinction over the next

25 years (Reid, 1992). Damage to coral reefs also appears to be increasing. In

addition, more and more pressure is placed on wetlands. This loss of

biodiversity constitutes a serious threat to ecosystem balance (WRI in

collaboration with the UNEP and the UNDP, 1992).

3

Years ago, when pressure on the environment was relatively minor, a

tendency to ignore the role played by the environment in the productivity of

economic activities prevailed. Little justification for this lack of attention to

the relationship between the environment and the economy now exists. The

argument that environmental degradation, in the end, will reduce future

benefits from economic activities is well accepted (Lutz, 1993).

Currently many countries consider improvement in environmental

quality an integral part of their overall national objectives. Individual

countries also support and pursue international cooperation to improve

environmental conditions worldwide. For developing countries, however,

strong economic growth and a more equal income distribution, not

improvement in environmental quality, are still the immediate goals. These

countries do not favorably view policies that sacrifice economic objectives

simply to improve environmental quality.

This dissertation aims to determine the impact of environmental quality

improvement policies on economic growth and income distribution in

developing countries. This dissertation argues that developing countries can

achieve rapid economic growth and a more equal income distribution while

improving environmental quality.

1.2 Literature Review

Literature concerning environmental quality and economic activities

has been available since 1970. This section presents that literature. The

section first concentrates on the pioneering work of Leontief, Denison,

4

Bergman, Hazilla and Kopp, and Jorgenson and Wilcoxen. The section then

describes the contribution of this dissertation to economic literature.

Leontief (1970) was the first major economist to consider the

relationship between environmental quality and economic activities. In 1970,

he expanded an input-output table to include pollution generation and

abatement activities. In presenting his idea, Leontief used a small numerical

example. In this example, he aggregated production sectors into agriculture

and manufacture, both of which produced pollutants. Leontief then

introduced an additional activity -- the elimination of pollutants, i.e.

antipollution activity.

Table 1.1 Input-Output Table of an Economy with Antipollution Activity

Output Sectors Inputs and Pollutants' Sector 1 Sector 2

Output Agriculture Manufacture Antipollution Household Total Sector 1 Agriculture 26.12 23.37 55.00 104.49 (bushels) Sector 2 Manufacture 14.63 7.01 6.79 30.00 58.43 (yards) Pollutant (grams) 52.25 11.68 -33.93 30.00 Labor (man-years) 83.60 210.34 67.86 361.80

Table 1.1 presents Leontief’s example of the input-output table with

antipollution activity. From this table one can see that, in order to produce

104.50 bushels of agricultural products, the agricultural sector needs 26.12

bushels of agricultural products, 14.63 yards of manufactured products, and

5

83.60 man-years of labor; the agricultural sector emits 52.25 grams of

pollutants to the environment. Table 1.1 also shows that, in order to produce

58.43 yards of manufactured products, the manufacturing sector consumes

23.37 bushels of agricultural products, 7.01 yards of manufactured products,

and 210.34 man-years of labor; the sector dispenses 11.68 grams of pollutants.

The antipollution activity, using 6.79 yards of manufactured products and

67.86 man-years of labor, eliminates 33.93 grams of pollutants. Pollutants

emitted to the environment equal 30 grams.

Table 1.1 can be transformed into a system of equations. Let us define:

• X1 as the total output of agricultural products (= 104.49 bushels)

• X2 as the total output of manufactured products (= 58.43 yards)

• X3 as the total amount of eliminated pollutants (= 33.93 grams)

• L as the total employment (= 361.80 man-years)

• Y1 as the final demand for agricultural products (= 55.00 bushels)

• Y2 as the final demand for manufactured products (= 30.00 yards)

• Y3 as the total uneliminated amount of pollutants (= 30.00 grams)

• Y4 as the total amount of labor employed by households and other final

demand sectors (in Table 1.1, Y4 is assumed to equal zero).

Table 1.1 then becomes:

X X X Y

X X X X Y

X X X Y

X X X L Y

1 1 2 1

2 1 2 3 2

1 2 3 3

1 2 3 4

0 25 0 40014 012 0 20

0 50 0 200 80 3 60 2 00

− ⋅ − ⋅ =− ⋅ − ⋅ − ⋅ =⋅ + ⋅ − =

− ⋅ − ⋅ − ⋅ + =

. .. . .

. .. . .

(1.1)

or,

6

+ −− + −+ + −− − −

�

�

����

�

�

����

⋅

�

�

����

�

�

����

=

�

�

����

�

�

����

0 75 0 40 0 0014 0 88 0 20 00 50 0 20 100 00 80 3 60 2 00 1

1

2

3

1

2

3

4

. .

. . .

. . .

. . .

X

X

X

L

Y

Y

Y

Y

(1.2)

The typical simulation scenarios consider the variables in matrix Y as

exogenous, and the variables in matrix X as endogenous. To solve X as a

function of Y, the relationship in (1.2) is manipulated to:

X

X

X

L

Y

Y

Y

Y

1

2

3

11

2

3

4

0 75 0 40 0 0014 0 88 0 20 00 50 0 20 100 00 80 3 60 2 00 1

�

�

����

�

�

����

=

+ −− + −+ + −− − −

�

�

����

�

�

����

⋅

�

�

����

�

�

����

−. .. . .. . .. . .

(1.3)

or,

X

X

X

L

Y

Y

Y

Y

1

2

3

1

2

3

4

1573 0 749 0149 00 449 1404 0 280 00 876 0 655 1131 04 628 6 965 3 393 1

�

�

����

�

�

����

=

−−−−

�

�

����

�

�

����

⋅

�

�

����

�

�

����

. . .

. . .

. . .

. . .

(1.4)

In 1972, Leontief, working with Ford, applied this input-output (with

antipollution) method to the case of air pollutants in the United States. They

found that reducing pollutants emitted to the air could increase all prices of

goods and services (Leontief and Ford, 1972). Since 1972, there have been

several other studies which used the input-output method to analyze the

impact of environmental policies on the economy. Examples include the

studies developed by Carter in 1974, Pearson in 1989, and Duchin and Lange

in 1994.

Denison was also one of the pioneers in the subject of environmental

quality and economic activities. In 1979, he used a growth accounting model

7

to analyze the impact of environmental protection on economic growth in the

United States. In his growth accounting model, Denison allocated growth in

output to various factors which can be thought of as inputs to an aggregate

production function. For example, output Y is produced with capital K and

labor L, and the function changes systematically over time:

Y F K L t= ( , , ) (1.5)

Logarithmic differentiation of the relationship (1.5) yields:

d YKF

Fd K

LFF

d LFF

dtK L tln ln ln= + + (1.6)

Assuming that factors are paid according to their marginal products, this

expression shows that output growth is a weighted sum of the growth of

inputs, plus a technical change term. Denison carefully calculated the growth

rate of various inputs. By doing so, he was able to determine the contribution

of each factor to output growth. Denison’s results show that 0.04 percent of

the 1.3 percent reduction in US economic growth in the 1960s and 1970s was

due to the introduction of pollution abatement policies in the country

(Denison, 1979). Following Denison’s work, other studies used this growth

accounting methodology to determine the impact of environmental policies on

economic growth. Among these are Norsworthy et al. in 1979, Conrad and

Morrison in 1985, and Maddison in 1987.

The literature utilizing input-output and growth accounting models led

to the development of the Computable General Equilibrium (CGE) or General

8

Equilibrium (GE)1 models to analyze the impact of environmental policies on a

national economy. A CGE/GE is a system of equations that represent all agents'

behavior, i.e. consumers’ and producers’ behavior, and the market clearing

conditions of goods and services in the economy. In 1990, three CGE/GE

papers which analyzed environmental policy and the economy were

published by Bergman, by Hazilla and Kopp, and by Jorgenson and Wilcoxen.

Bergman observed the impact of controlling SOx and NOx on the

economic growth of Sweden. His CGE model includes one consumer

representative and ten production sectors. The production technology in each

production sector is represented by a nested production function. Starting

from the bottom of this function, the energy input in sector j is denoted by Qj,

where Qj is a CES function of electricity (E) and fuels (F). The energy input Qj

is then combined with capital (Kj) into the CES aggregate Uj. On the next level,

Uj is combined with natural resources (Nj) into a third CES aggregate Hj.

Again using a CES function, Hj is combined with labor (Lj) to produce the

composite input Yj. Finally, gross output in sector j, Xj, is determined by the

input in sector j of Yj and intermediate inputs produced by other sectors, Xij, in

accordance with a Leontief production function.

The emission of various pollutants is a crucial feature of the production

technology in the Bergman model. Two types of emission exist. First is the

emissions from combustion, emissions which are proportional to the quantity

of fuel used. Second is the emissions from industrial processes, emissions

1 Several authors prefer to call this type of modeling a Computable General Equilibrium (CGE) model, while others choose to call it a General Equilibrium (GE) model. This literature review section, depending on how the authors call their models in their papers, uses both the CGE and GE terms.

9

which are proportional to the output of each sector. Moreover, total emissions

can be reduced through cleaning activities that are available to all sectors.

Bergman developed air pollution policy scenarios that restrict the

amount of SOx and NOx emitted to the air. He compared the Gross Domestic

Product (GDP) resulting from the scenario in which air pollution abatement

policies are implemented with the GDP produced from a scenario in which no

air pollution abatement policies are implemented. Bergman’s results show

that, by the year 2000, reducing the ambient levels of SOx and NOx to 35

percent and 85 percent, respectively, of their initial “1980” levels produces a

four percent lower GDP than the GDP in a scenario with no air pollutant

control.

While Bergman constructed a CGE for Sweden, Hazilla and Kopp

developed an econometric GE model of the US economy to analyze the impact

of clean air and water regulations on national economic growth. The

regulations considered by the study were those implemented by the

Environmental Protection Agency (EPA) during the 1970s and 1980s. The

model is a intertemporal general equilibrium model that consists of one

household and 36 different production sectors.

A set of hierarchical indirect translog utility functions describes

household preferences. At the top of the hierarchy is the intertemporal

decision. The household must decide between present and future

consumption. Specifically, the household must allocate a lifetime wealth

endowment between present and future consumption of goods and leisure.

Following this choice, the household focuses on two sequential intratemporal

decisions. The first is to select the proportion of current period consumption

10

to allocate between goods and leisure. The second is to allocate current period

consumption among goods available in the economy.

On the production side, each sector, except for government services, is

algebraically formulated as a hierarchical system of translog cost functions

exhibiting constant returns to scale. This system gives rise to competitive

derived demand equations for capital and labor, four forms of energy, and 30

intermediate inputs.

The Hazilla and Kopp model shows that the implementation of air and

water regulations by the EPA during the 1970s and 1980s resulted in a 5.85

percent lower US GDP in 1990 than the GDP would have been if no air and

water regulations had been implemented.

Jorgenson’s and Wilcoxen’s GE model also analyzes the impact of clean

air and water regulations adopted by the EPA on the US economy during the

1970s and 1980s. Jorgenson’s and Wilcoxen’s GE model consists of one

household sector and 35 industrial sectors.

The household model is almost the same as that in Hazilla’s and Kopp’s

work. Jorgenson’s and Wilcoxen’s model, however, goes beyond Hazilla’s

and Kopp’s model, and develops a hierarchical system for consumption of

goods and services. On the top of this hierarchy, the household chooses

between present and future consumption. In the second level of the hierarchy,

the household selects the proportions of current period consumption to

allocate to leisure and aggregate consumption of goods. In the third level, the

household must select its aggregate consumption goods from among

composite energy, composite food, composite consumer goods, and composite

consumer services. Composite energy consists of five types of energy

resources. Four types of food form composite food. Composite consumer

11

goods consists of nine items, and composite consumer services consists of

sixteen different services.

Production in each industry is represented by a nested translog cost

function with constant returns to scale and zero in pure profits. The top level

of this nested function is a translog function of composite energy, composite

materials and services, capital, and labor inputs. Similar to the household

model, the composite energy input consists of five types of energy choices.

The composite materials and services input is formed using a translog

function of composite agricultural products, metal products, nonmetallic

products, and services inputs. Ten types of agricultural products compose the

composite agricultural products. The composite metal products consists of

eight different metal products. Five types of nonmetal products form the

composite nonmetal products. Seven different types of services constitute the

composite services.

Jorgenson and Wilcoxen argues that the US economic growth rate

would have been 0.191 percent higher if the EPA had not implemented any

pollution emission control regulations.

Since the 1990 models of Bergman, Hazilla and Kopp, and Jorgenson

and Wilcoxen, many CGE/GE models have focused on the relationship

between economic activities and environmental quality. This literature

includes the models developed by Robinson et al. in 1993, Lewis in 1993, and

Steininger and Farmer in 1994.

The majority of literature since 1970 shows the link from economic

activities to environmental quality, but not the link from environmental

quality to the economy. The literature also focuses on economic growth and

the environment, but neglects the important relationship between income

12

distribution and the environment. This dissertation addresses these two

issues. This dissertation presents a methodology linking the economy to the

environment, as well as the environment to the economy. It also analyzes the

impact of environmental quality improvement policies on both economic

growth and income distribution. Specifically, the contributions of this

dissertation to economic literature are:

• to present a procedure to broaden a Social Accounting Matrix (SAM) into a

Social and Environmental Accounting Matrix (SEAM) that includes the

impact of economic activities on the environment and vice versa.

• to demonstrate the use of the Constrained Fixed Price Multiplier (CFPM)

method to analyze the impact of environmental management on

household incomes for different socioeconomic classes.

• to develop a CGE model that incorporates the link from production

activities to environmental quality and the feedback link from

environmental quality to human health and to the effectiveness of

production activities.

• to show the difference between the impact on economic growth and

income distribution of environmental management policies in industrial

and transportation sectors, and in agricultural sectors.

1.3 Objectives of This Dissertation

13

The first goal of this dissertation is to develop appropriate

methodologies to model national economic activities and their links to the

environment, and to analyze the impact of national environmental

management policies designed to improve environment quality on national

economic growth and household incomes for different socioeconomic classes.

The second goal of this dissertation is to find an elaboration of a

national environmental management policy that controls the amount of

pollutants emitted, and generates a rapid rate of national economic growth

and a more equal income distribution in a nation.

1.4 Basic Methodology

This section describes the methodology utilized in this dissertation. First,

this section shows how to broaden a Social Accounting Matrix to become a Social

and Environmental Accounting Matrix (SEAM) which includes the impact of

environmental destruction on a national economy and vice versa. Second, this

section describes how to use the Constrained Fixed Price Multiplier (CFPM)

method, which is derived from the SEAM, to observe the likely impacts of

environmental policies on household income for different socioeconomic classes.

Third, this section describes how to develop a Computable General Equilibrium

(CGE) model that contains links from economic activities to the environment as

well as feedback links from the environment to the economy. Finally, this section

demonstrates how to utilize the CGE to analyze the impact of environmental

policies on national economic growth and income distribution.

1.4.1 Social and Environment Accounting Matrix

14

The Social Accounting Matrix (SAM) for a national economy is a

traditional double-entry accounting model that records all economic transactions

among agents, particularly transactions among production activities, institutions

(including households), and factors, in the national economy (see Figure 1.1). A

SAM also provides information about the social structure of a nation, specifically

information on the structure of production, the resulting factorial and

household (by socioeconomic groups) income distributions, and the

expenditure pattern of various institutions (including the different household

groups). In general, a SAM is the closest approximation to a general

equilibrium accounting framework available to economists and social

scientists (Thorbecke, 1985).

A Social and Environmental Accounting Matrix (SEAM) is an extension of

a SAM. The difference between the two is that a SEAM includes environmental

accounts which record the impact of production activities on the environment,

and the impact of environmental degradation on the economy. Figure 1.2 depicts

a simplified SEAM. The upper-left of the SEAM (∀ i, j = 1 to 4) is the traditional

SAM; the rest captures the environmental accounts.

This dissertation treats pollutants as the by-products of industrial

activities in the “dirty” production sectors of the SEAM. Figure 1.2 shows the

ambient level of pollutants from these sectors in column 5, row 3a. A high level

of pollutants (from dirty production sectors) in the environment inflicts damage

on society. In the case of air pollutants, a high pollution level increases the

number of people who contract health problems such as asthma, respiratory

ailments, and high blood pressure (Ostro, 1994). A high level of air pollutants

15

ProductionActivities

T33

Institutions(incl. household

income dist.)T22

T32 T13

T21

Factors(factorial income

distribution)

Note that Ts represent matrices of economic transactions as follows: • T13 is the matrix of factorial income distribution • T21 is the matrix of income distribution to households and other institutions • T22 is the matrix of transfers such as taxes and subsidies among institutions • T32 is the matrix of institutional demand for good and services • T33 is the matrix of interindustrial demand for good and services

Figure 1.1 Economic Transactions Among Agents in the Economy

also might damage crops, buildings, and vehicles (BPPT and KFA, 1993).

Column 6, row 5 records this societal damage caused by air and other pollutants.

The damage created by a high level of pollutants in the environment

causes society to conduct activities for necessary recovery “treatments.” In the

case of air pollutants, individuals who actually contract air pollutant-related

illnesses are likely to seek appropriate health treatment. Vehicle and building

16

owners must fix vehicle and building damage caused by air pollutants. The

SEAM defines the activities associated with necessary treatments to recover from

pollutant-related damage as the Pollutant Recovery activities. The cost of these

activities (borne by society) is defined as the societal cost associated with

pollutants, as shown in column 3c, row 6.

The next step is to change the damage caused by pollutants and the

ambient level of pollutants from physical to monetary units. This dissertation

defines that the monetary value of damage created by a particular pollutant as

the amount spent by society on treatments to recover from the damage. The

monetary value of the ambient level of a particular pollutant also equals the

amount spent by society on treatments to recover from the damage associated

with that pollutant.2

Note that the row Subtotal only sums the numbers in columns 5 and 6

(the same definition also holds for the column Subtotal). Another important

point is that the number defining Demand for pollutant recovery in the row Total

should be the same as that in the row Subtotal. These numbers represent the

societal cost of pollutants in the environment.

1.4.2 Constrained Fixed Price Multiplier Method

Utilizing a SAM and relying on the assumption of fixed prices, the

CFPM method finds a relationship that defines the change in the output of

certain sectors (non-constrained endogenous sectors) as a function of

exogenous sectors, where the output of other sectors (constrained endogenous

2 One can keep columns 5 and 6 in physical units. Transforming all the physical units into monetary units facilitates determination of the societal cost of emitting a certain pollutant into the environment.

17

sectors) are held constant. This relationship is then utilized to analyze the

impact of environmental quality improvement on household incomes.

In the SAM part of the SEAM in Figure 1.2, let us define Other

Accounts and Government as the exogenous sectors, Dirty Production sectors

and Pollutant Recovery sector as the constrained endogenous sectors, and

other sectors as the non-constrained endogenous sectors (see Figure 1.3).

End. Sec. Exog. Sec. TOTAL Nonc. Cons. Sum Sum

E 1 Factors n Nonc. 2a Institutions (w/o Gov.) MNC MQ nNC XNC xNC yNC

d 3b Clean Production S 3a Dirty Production e Cons. 3c Pollutant Recovery MR MC nC XC xC yC

c Exog. 2b Government Sec. 4 Other Accounts LNC LC l T t yE

TOTAL yNC' yC' yE'

Figure 1.3 A Simplified SAM

Let us now derive the relationship between exogenous sectors and the

output of non-constrained sectors. Algebraically the table in Figure 1.3 can be

written as:

yy

nn

xx

NC

C

NC

C

NC

C

�

��

�

�� =

�

��

�

��+

�

��

�

�� (1.7)

Under the assumption that prices are fixed, it follows from the relationship

(1.7) above:

18

E X P E N D I T U R E1 2 3 4 5 6

Institutions Production Activities Other Accounts Ambient Pollutant-Factors Including a b c a b TOTAL Level of related SUBTOTAL

Households Dirty Prod. Clean Prod. Pollutant Combined Rest of Pollutants DamagesActivities Activities Recovery Capital the World

Income Factors Factorial income distribution of

(T13) factorsIncome

Institutions distribution Transfers, Including to households taxes, and Income of Households and other subsidies institutions

institutions (T22)(T21)

a Dirty Prod. Institutional Gross output Pollutants TotalActivities demand Gross of dirty prod. pollutants

Production b Clean Prod. for goods Interindustry demand capital Exports Gross outputActivities Activities and services (T33) formation of clean prod.

c Pollutant (T32) Output ofRecovery poll. recovery

Balance ofa Combined Domestic payments Aggregate

Capital savings current acc. savingsOther deficit

Accounts Rest Imports of Foreignb of complementary Imports of competitive goods exchange

the World goods outflowExpenditure Expenditure Gross Gross Demand for Aggregate Foreign

TOTAL of of demand for demand for pollutant investment exchangefactors institutions dirty goods clean goods recovery inflow

Ambient Damage Total Level caused by damage of Pollutants pollutants Pollutant- Costs of related treatment Damages to recover

Demand for Ambient SUBTOTAL pollutant level of

recovery pollutants

Figure 1.2 A Simplified SEAM

dyy

dnn

dxx

NC

C

NC

c

NC

C

�

��

�

�� =

�

��

�

��+

�

��

�

�� (1.8)

or

dyy

CR

QC

dyy

dxx

NC

C

NC

C

NC

C

NC

C

�

��

�

�� =

�

��

�

�� ⋅

�

��

�

��+

�

��

�

��

||

(1.9)

where:

C

RQC

NC

C

||

�

��

�

�� is the matrix of marginal expenditure propensities

CNC is the matrix of non-constrained marginal expenditure

propensities

CC is the matrix of constrained marginal expenditure

propensities.

19

A matrix of marginal expenditure propensities shows, for each sector,

how much expenditure on goods and services changes when income

marginally changes from the initial condition. Note that information on

expenditure elasticities for each sector is needed to calculate the matrix of

marginal expenditure propensities. Supposing that this information is

available, then the procedures to calculate the marginal expenditure

propensities utilizing information from a SAM are as follows:

• Calculate the average expenditure propensity for each good in each sector

in the SAM. Specifically, the average expenditure propensity aij is the

average change in sector j expenditure on good i when the income of sector

j changes. Let mij be a number in matrix MM

M

MNC

R

Q

C

||

�

��

�

�� , and yj be the

output/income of sector j. The average expenditure propensity aij is:

am

yijij

j

= (1.10)

• Calculate the marginal expenditure propensities using the information on

expenditure elasticities and the average expenditure propensities. Let eij be

the expenditure elasticity of sector j for good i, and cij be the marginal

elasticity of sector j for good i. The marginal elasticity cij is thus:

c a eij ij ij= ⋅ (1.11)

Let us return to the relationship (1.9). This relationship can be written

as two relationships:

dyNC = CNC · dyNC + Q · dyC + dxNC (1.12)

20

and

dyC = R · dyNC + CC · dyC + dxC (1.13)

Furthermore, the relationship (1.13) can be manipulated to become:

dxC = -R · dyNC + (I - CC ) · dyC (1.14)

Hence, the relationships (1.12) and (1.14) can be written as:

( ) |

||| ( )

I CR I

dyx

I QI C

dxy

NC NC

C C

NC

C

−− −

�

��

�

�� ⋅

�

��

�

�� =

− −�

��

�

�� ⋅

�

��

�

��

00

(1.15)

or

dyx

I CR I

I QI C

dxy

NC

C

NC

C

NC

C

�

��

�

�� =

−− −

�

��

�

�� ⋅

− −�

��

�

�� ⋅

�

��

�

��

−( ) |

||| ( )

00

1

(1.16)

where:

( ) |

||| ( )

I CR I

I QI C

NC

C

−− −

�

��

�

�� ⋅

− −�

��

�

��

−0

0

1

is the matrix of constrained fixed price

multipliers.

The relationship (1.16) shows the relationship between exogenous

sectors and the output of non-constrained sectors, while the output of

constrained sectors remains constant. Note that relationship (1.16) can also

show the changes in the output of the non-constrained sectors (yNC) as a

function of the changes in the output of the constrained sectors (yC), while

holding exogenous sectors constant. Total income of each household group is

one of the outputs of non-constrained sectors. The output of the Pollutant

Recovery sector is one of the outputs of constrained sectors. Let us suppose

that the implementation of a pollutant abatement regulation is able to reduce

21

the ambient level of pollutants. This reduction decreases the quantity of

pollutant recovery activities, i.e. reduces the output of Pollutant Recovery

sector. With the relationship (1.16), hence, one can find the impact of this

reduction in the output of Pollutant Recovery sector on household incomes,

i.e. the impact of improvement in the ambient level of pollutants on household

incomes.3

1.4.3 Computable General Equilibrium Method

As mentioned in the Literature Review section, a Computable General

Equilibrium model of a national economy is a system of equations that represent

all agents' behavior, i.e. consumers’ and producers’ behavior, and market

clearing conditions of goods and services in the national economy. This system

of equations usually is divided into six blocks of equations. The blocks are:

• Production Block: Equations in this block represent the structure of

production activities and producers’ behavior.

• Consumption Block: This block consists of equations that represent the

behavior of households and other institutions.

• Export-Import Block: This block models the country’s decision to export

or import goods and services.

• Investment Block: Equations in this block simulate the decision to invest

in the economy, and the demand for goods and services used in the

construction of the new capital.

3 Detailed procedures for implementation of the CFPM will be explained in Chapters Two and Three.

22

• Market Clearing Block: Equations in this block determine the market

clearing conditions for labor, goods, and services in the economy. National

balance of payment is also in this block.

• Intertemporal Block: This block consists of dynamic equations that link

economic activities in the current year to future economic conditions.

This section focuses only on the production and consumption blocks.

The section develops the links between economic activities and the

environment in these two blocks. Regarding the other four blocks, one can

develop them on one’s own, or take them from existing CGE models as long

as the equations in those blocks are appropriate for the country that one

studies.

Figure 1.4 presents the relationships, implemented in this dissertation,

between the economy and the ambient level of pollutants. This dissertation

treats pollutants as by-products of production activities which use “toxic”

materials. Toxic materials are defined as material inputs used in production

activities that emit pollutants into the environment. Examples of these

materials include gasoline, diesel, and pesticides. A high level of ambient

pollutants causes increasing cases of pollutant-related damage such as human

health problems, reduction in soil fertility, and damage to buildings and

vehicles.

ECONOMY

Production activities

Ambient level of

pollutants

23

Figure 1.4 Links Between the Economy and Pollutants in the Environment

Two immediate impacts of pollutant-related damage exist. The

pollutant-related damage reduces the productivity of factor inputs in

production activities. For example, people who contract health problems miss

work, and thus reduce the number of workdays that they are available for

production activities. The pollutant-related damage also induces a societal

cost, borne by households and the government. This societal cost represents

the cost of conducting pollutant recovery activities. For example, individuals

who contract health problems associated with pollutants may have to visit a

doctor for medical care. The resultant societal cost reduces the ability of

households and the government to consume other goods and services.

Pollutant recovery activities

Pollutant- related damage

24

OutputX

IntermediateInput

ValueAdded

X1 X2 Xn Labor Capi-tal Land

Figure 1.5 Structure of the Sectoral Production Function

Detailed modeling of the relationships between economic activities and

the environment follows. Let us suppose Figure 1.54 represents the structure

of sectoral production activities. The links between these sectoral production

activities and environmental quality are manifested in the value-added

function and the amount of “toxic” materials consumed in production

activities.

Value added is a function of pollutant-related damages and factor

inputs, i.e. land, labor, and capital. The general form of the value-added

function is:

VA HE f LB K LNi i i i i= ⋅ ( , , ) (1.17)

25

where:

i is the index for production sectors

VAi is the value-added input for sector i

HEi is the pollutant-related damage variable. This variable

represents the impact of pollutant-related damage on the value-

added production activities.

LBi is the amount of labor input used in sector i

Ki is the amount of capital used in sector i

LNi is the quantity of land used in sector i.

The pollutant-related damage HEi is a function of damage to factor inputs

caused by pollutants. Examples include restricted activity days (for humans)

due to pollutant-related illnesses, reduction in soil fertility, and damage to

roads and buildings. The equation below represents this pollutant-related

damage:

HE f POLDAM d Di i d= ∀ ∈( ; ), (1.18)

where:

d is the index for different types of pollutant-related

damage

D is the set of different types of pollutant-related damage

POLDAMi,d is the quantity of pollutant-related damage d in sector i.

Important to note is that many different types of pollutant-related damage are

highly correlated with each other. One may wish to select only one type of

4 One can certainly use other structures to represent the production function. The procedure to develop the link between production activities and the environment would remain the

26

pollutant-related damage among the ones that are highly correlated in the

relationship (1.18).

The amount of toxic material used in production activities represents a

second link between production activities and the environment. The amount

of toxic material determines the quantity of pollutants emitted, which affects

the ambient level of pollutants in the environment. In other words, the

ambient level of pollutant p is a function of the quantity of all toxic materials

(used in all production sectors in the economy) that release pollutant p. The

equation below represents this ambient level of pollutant p:5

AMB f X tx TX i Ip tx i= ∀ ∈ ∀ ∈( ; ; ), (1.19)

where:

p is the index for different types of pollutants. Examples include

particulate matter and sulfur dioxide in the air, and nitrate in

groundwater.

AMBp is the ambient level of pollutant p in the environment

Xtx,i is the quantity of toxic material input tx in production sector i

I is the set of production activities in the economy

TX is the set of toxic material inputs that produce pollutant p.

Let us now consider the consumption block. Each household group in

the CGE model is assumed to maximize its utility subject to the group budget

constraint. The utility of each household group is a function of the group’s

same. 5 If time-series data on emission and ambient level of pollutants are available, one might want to develop the relationship (1.19) so that it becomes: AMB f AMB X tx TX i Ip

tpt

tx i= ∀ ∈ ∀ ∈−( , ; ; ),1

where t is the index for the time period.

27

consumption of all goods and services, except for pollutant recovery activities.

The household consumption decision problem can be modeled as follows:6

MAX U U HCD i I i aphh i h= ∀ ∈ ≠( : & ), (1.20)

subject to:

PQ HCD YH HTAX HSAV CDHE HHTRi i hi aph

h h h h h⋅ ≤ − − − −≠� , (1.21)

where:

h is the index for household groups

aph is the index for pollutant recovery activities

YHh is the income of household h

HCDi,h is household consumption of commodity i

PQi is the price of commodity i

HTAXh is income taxes

HSAVh is household savings

HHTRh is net household transfers

CDHEh is costs for pollutant recovery activities.

The amount of household spending on pollutant recovery activities

depends on the quantity of pollutant-related damage that occurs. The

quantity of pollutant-related damage is a function of the ambient level of

pollutants in the environment:

POLDAM f AMP p Ph d p, ( ; )= ∀ ∈ (1.22)

where:

6 In this CGE, since government consumption of goods and services is set exogenously, there is no need to explain of the government consumption pattern.

28

P is the set of pollutants in the environment. Certainly only

pollutants that cause damage d are in equation (1.22).

Using relationships (1.17) to (1.22), one can see that the increase in the

ambient level of pollutants might reduce the productivity of factor inputs and

increase costs to households. Conversely, pollutant regulations that are able

to reduce the ambient level of pollutants in the environment might increase

the productivity of factor inputs in production activities and reduce household

costs associated with pollutant-related damage. The increase in productivity

of factor inputs tends to increase the supply of goods and services. The

reduction in household costs associated with pollutant-related damage

enables households to spend more on other goods and services, i.e. this

reduction in costs may induce an increase in demand for other goods and

services. In the end, the increase in supply of and demand for goods and

services affects national economic growth and household incomes.

1.5 Scope of This Dissertation

This section first explains the reasons for choosing Indonesia as the focus

of the analysis in this dissertation. The section then describes the case studies

examined in this dissertation.

The reasons for choosing Indonesia are:

• In the 1970s and 1980s, Indonesia experienced rapid development in its

industrial and agricultural sectors. On average, the annual growth rates were

approximately 12 and 4 percent, respectively (Thorbecke, 1992, and Woo et

al., 1994). Indonesia is expected to continue experiencing high growth rates

in its industrial and agricultural sectors in the next decade.

29

• In addition to rapid development in its industrial and agricultural sectors,

significant poverty alleviation has occurred during the last two decades in

Indonesia. The proportion of the population under the poverty line fell

from 40 percent in 1976 to 18 percent in 1987 (Thorbecke, 1992). A

continuing process of poverty alleviation is also expected to occur in the

next decade.

• On the other hand, the amount of environmental destruction in Indonesia

has increased during the last two decades. Examples include the alarming

level of ambient air pollutants in large cities (Soedomo et al., 1991), highly

polluted rivers and contaminated groundwater in urban areas (World

Bank, 1994), the overuse of pesticides in agricultural sectors (Oka, 1995),

and the high rate of the ongoing deforestation (FAO, 1990). Increasing

numbers of Indonesians are concerned with this environmental

destruction.

• In the beginning of 1990s, the Indonesian government started various

programs to control environmental destruction. Examples include the Blue

Sky Program to control air pollutants in urban areas, the Clean River

Program to reduce pollutants in urban rivers, and the Integrated Crop Pest

Management Program to decrease the use of pesticides in agricultural

sectors. The effect of these pollution abatement policies on the process of

rapid economic growth and on the creation of a more equal income

distribution concerns many Indonesians.

• Finally, data on the national economy and the environment are relatively

available in Indonesia, as compared to other developing countries.

30

Figure 1.6 shows the case studies examined and the methodologies

used in this dissertation. The two case studies are outdoor air pollutants

originating in industrial and transportation sectors, and pesticides used in the

agricultural sector. The two case studies highlight the difference between the

impact of environmental policies implemented in industrial and

transportation sectors, and environmental policies implemented in the

agricultural sector on economic growth and income distribution. This

dissertation uses two methodologies, the CFPM (derived from a SEAM) and

the CGE.

This dissertation aims to find an appropriate national environmental

policy for Indonesia. Hopefully the Indonesian government will consider this

policy as a recommendation concerning the proper course to follow. This

dissertation is also valuable as a comparative study for other developing

countries.

Note: CFPM = Constrained Fixed Price Multiplier CGE = Computable General Equilibrium

PESTICIDES

AIR POLLUTANTS

CFPM CGE

Income Distribution

Income Distribution

Growth &

Income Distribution

Growth &

Income Distribution

31

Figure 1.6 Methodologies and Case Studies

Note, however, that results from this dissertation should be properly

qualified. Since data are limited, the SEAM and the CGE cannot capture

perfectly all relationships within the economy, within the environment, and

between the economy and the environment. Furthermore, one must pay

careful attention to the assumptions underlying the SEAM, the CGE, and the

simulation scenarios.

1.6 Organization of This Dissertation

The main body of this dissertation consists of four essays. Chapter Two

presents the first essay. This essay develops a Social and Environmental

Accounting Matrix (SEAM) for the case of air pollution in Indonesia. It

utilizes the Constrained Fixed Price Multiplier method to show the impact of

the Indonesian national clean air program on household income for different

socioeconomic classes.

The second essay follows in Chapter Three. This essay presents the

SEAM for the case of pesticides and describes two Indonesian government

programs to reduce the number of pesticide poisoning cases. The essay then

analyzes the impact of reducing the number of pesticide-related illnesses on

income distribution in Indonesia.

Chapter Four presents the third essay. This essay builds a Computable

General Equilibrium model that includes the link from the use of pesticides in

agricultural sectors to farmer health, and the link from farmer health to

agricultural activities. This essay describes the impact of the Integrated Crop

32

Pesticide Management program on household incomes and economic growth

in Indonesia.

Chapter Five presents the final essay. This essay develops a

Computable General Equilibrium model that includes the link from the

economy to air quality and the feedback link from air quality to the economy.

This essay focuses on the Indonesian clean air program and shows the impact

of this program on national economic growth and income distribution.

Finally, Chapter Six presents several conclusions based on the results

from the four essays and suggests areas for future research.

33

1.7 References

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

Penduduk Jakarta.” Department of Public Health Working Paper,

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

____. “Agricultural Injuries in Indonesia: Estimates and Surveys.”

Department of Public Health Working Paper, University of Indonesia,

Jakarta, 1991.

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.

Carter, A.P. “Energy, Environment, and Economic Growth.” Readings in

Input-Output Analysis: Theory and Applications. I. Sohn, ed., pp. 578-92.

Oxford: Oxford University Press, 1974.

Central Bureau of Statistics (CBS). Statistical Yearbook of Indonesia 1990.

Jakarta: Central Bureau of Statistics, 1991.

Conrad, K. and C.J. Morrison. “The Impact of Pollution Abatement

Investment on Production Change: An Empirical Comparison of the U.S.,

Germany, and Canada.” National Bureau of Economic Research

Working Paper No. 1763, 1985.

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

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

34

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.

Food and Agricultural Organization (FAO). Situation and Outlook for the

Forestry Sector in Indonesia. Report for the Indonesian Ministry of

Forestry, Jakarta, 1990.

Global Environment Monitoring System (GEMS)/AIR Monitoring Project,

Monitoring and Assessment Research Center (MARC). Assessment of

Urban Air Quality. London: Monitoring and Assessment Research

Center, 1988.

Hazilla, M. and R.J. Kopp. “Social Cost of Environmental Quality Regulations:

A General Equilibrium Analysis.” Journal of Political Economy, 98 (4

1990): 853-73.

IPM National Program Monitoring and Evaluation Team. The Impact of IPM

Training on Farmers’ Behavior: A Summary of Results from the Second Field

School Cycle. Jakarta: BAPPENAS, 1993.

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

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

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

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

1970): 262-71.

Lewis, J.D. “A Computable General Equilibrium (CGE) Model of Indonesia.”

Development Discussion Papers, Harvard Institute for International

Development, Cambridge, 1991.

35

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, 1994 (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.

Maddison, A. “Growth and Slowdown in Advanced Capitalist Economies:

Techniques of Quantitative Assessment.” Journal of Economic Literature,

25 (2 1987): 649-98.

Norsworthy, J.R., M.J. Harper, and K. Kunze. “The Slowdown in Productivity

Growth: Analysis of Some Contributing Factors.” Brooking Papers on

Economic Activities, 2 (1979): 387-421.

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, 1994.

Oka, I.N. “Success and Challenges of the Indonesian National Integrated Pest

Management Program in the Rice-based Cropping System.” Crop

Protection 10 (3 1991): 163-65.

____. “Integrated Crop Pest Management With Farmer Participation in

Indonesia.” Working Papers Food Crop Research Center, Bogor, 1995.

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.

36

Pearson, P.J.G. “Proactive Energy-Environment Policy Strategies: A Role for

Input-Output Analysis.” Environment and Planning Analysis, 21 (10 1989):

1329-48.

Pimentel, D., H. Acquay, M. Biltonen, P. Rice, M. Silva, J. Nelson, V. Lipner, S.

Giordano, A. Horowitz, and M. D’AMore. “Environmental and

Economic Costs of Pesticide Use.” BioScience, 42 (November 1992): 750-

60.

Pimentel, D. “Green Revolution Agriculture and Chemical Hazards.” Paper

presented to the Working Group on Chemical Hazards in Developing

Countries, Pontifical Academy of Sciences, October 21-23, 1993.

Reid, W.V. “How Many Species Will There Be?” Tropical Deforestation and

Species Extinction. T. Whitmore and J. Sayer, eds., pp. 55-74. London:

Chapman and Hall, 1992.

Robinson, S., S. Subramanian, and J. Geoghegan. “A Regional, Environmental,

Computable General Equilibrium Model of the Los Angeles Basin.”

Working Paper No. 646, Department of Agricultural and Resource

Economics, University of California, Berkeley, 1993.

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, 1991 (in Indonesian).

Steininger, K. and K. Farmer. “Sustainable Air Quality and Sectorial

Production in an Intertemporal CGE-Model of Austria.” Proceedings of

the International Symposium: Models of Sustainable Development,

Universite Pantheon-Sorbonne, Paris, March 16-18, 1994.

37

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

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

Thorbecke, E. “The Social Accounting Matrix and Consistency Type Planning

Models.” The Social Accounting Matrices: A Basis for Planning. G. Pyatt and

J.I. Round, eds., pp. 207-56. Washington, D.C.: World Bank, 1985.

____. Adjustment and Equity in Indonesia. Paris: OECD Publications, 1992.

United Nations. Integrated Environmental and Economic Accounting (Interim

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

United Nations Environment Programme (UNEP) and the World Health

Organization (WHO). Urban Air Pollution in Megacities of the World.

Oxford: Blackwell, 1992.

Woo, W.T., B. Glassburner, and A. Nasution. Macroeconomics Policies, Crises

and Long-run Growth: The Case of Indonesia 1965-1990. Washington, D.C.:

World Bank, 1994.

World Bank. World Development Report, 1992. New York: Oxford University

Press, 1992.

____. Indonesia. Energy and the Environment: A Plan of Action for Pollution

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

____. Indonesia. Environment and Development: Challenges for the Future. Report

No. 12083-IND, World Bank, 1994.

World Resources Institute (WRI) in collaboration with the United Nations

Environment Programme (UNEP) and the United Nations Development

Program (UNDP). World Resources 1992-93. New York: Oxford

University Press, 1992.