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ECONOMICS OF REVENUE SYSTEMS
IN MALAYSIAN TIMBER CONCESSIONS

By

Awang Noor Abd. Ghani

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Forestry

1994

ABSTRACT

ECONOMICS OF REVENUE SYSTEMS
IN MALAYSIAN TIMBER CONCESSIONS

By

Awang Noor Abd. Ghani

Forest revenue systems in most tropical countries have failed to capture for
the government much of the available stumpage value or to provide incentives for
concessionaires to abide by logging regulations. Designing a forest revenue system
is very important to achieve the sustainable management of natural forests and to
raise government revenue.

This study addresses the economics of forest revenue systems in Peninsular
Malaysia through development of microeconomic static and dynamic models based
on uniform and ad valorem royalties. The models explicitly specify forest
management objectives and assume that concessionaires maximize profits. Empirical
analyses were performed using a simultaneous system of equations to estimate the
optimal royalties (volume-based charge), premium (area-based charge), harvest
volume, and government revenue. The analyses were based on data collected in
1990 from six concessions with different characteristics in the Permanent Forest
Estate in three states of Peninsular Malaysia.

The static model specifies that the govemment’s only objective is to
maximize revenue from current harvests. Three alternative timber fees were
analyzed: uniform royalty, ad valorem royalty, and premium. The analyses

predicted that the optimal uniform and ad valorem royalties would induce

concessionaires to leave timber above the cutting limits, and that the premium would
induce them to harvest below the cutting limits. The premium would provide the
highest government revenue.

The dynamic model specifies that the government’s objective function
includes not only current revenue but also the future impacts of over- or under-
harvesting. Two alternative fees were analyzed: uniform and ad valorem royalties.
The analyses predicted that the optimal uniform royalty would induce
concessionaires to leave timber above the cutting limits, whereas the optimal ad
valorem royalty would induce them to harvest below the cutting limits. Compared
to the static model, the dynamic uniform and ad valorem royalties had lower values,
which resulted in larger harvests. The static and dynamic optimal royalties and
premiums were found to be much higher than the actual rates.

The implications of the study are that the government must be clear about its
forest management objectives, and that it must know how concessionaires will
respond to alternative revenue systems. The government should carefully assess
forest revenue systems under different forest management objectives to ensure that
Peninsular Malaysia obtains maximum benefits from harvesting in its forests. The
present royalty and premium rates can be increased substantially to raise additional

revenue and to discourage concessionaires from harvesting too intensively.

Copyright by
AWANG NOOR ABD. GHANI
1994

ACKNOWLEDGMENTS

First and foremost, all my thanks are due to Allah, The Most Gracious, Most
Merciful; His Grace and Guidance have brought this work to fruition.

I would like to express my sincere thanks and appreciation to the following
people for their continuous support throughout my doctoral program and the
completion of this dissertation. I would like to thank Dr. Jeffrey R. Vincent, my
dissertation chairman, for his invaluable support, assistance, and encouragement
during the completion of this endeavor. His critical suggestions and superb technical
ability enabled me to develop complex economic models into understandable and
workable ones. Since the beginning of my doctoral program, he has provided me
with a model of critical thinking, which I hope to be able to follow.

Special appreciation is extended to Dr. Larry Leefers, who served as my
academic advisor during the last three years of my doctoral program at Michigan
State University. He was very helpful in providing me direction and was willing to
listen and talk throughout my program and during the preparation of this
dissertation. I also want to thank the other members of my graduate committee, Dr.
Karen Potter-Witter, Dr. James Stevens, and Dr. A. Allan Schmid, for their

contributions and guidance in the development and completion of this work.

I would like to thank the Malaysian government and the Universiti Pertanian
Malaysia for their financial support. My sincere appreciation is also due to the
World Wildlife Fund, the Conservation Foundation, and the Rockfeller Foundation
through the Osborn Center Forestry Policy Grants Program for their financial
support, which enabled me to carry out the inventory work in Peninsular Malaysia
that provided the data used in this study.

I also thank the Forestry Department Headquarters and State Forest Offices in
Pahang Darul Makmur, Terengganu, and Kelantan for helping me select research
sites and providing information from pre-felling inventories. I sincerely thank all
members of the post-felling inventory crews for their diligence and careful
measurements.

I also thank Uncle Johan Smith for generously agreeing to serve as a reader.

I owe a great debt to my late father, Abd. Ghani Mat Kik, and my mother,
Mek Wook Awang To’, who have supported me and helped me achieve my
educational goals. I am also indebted to my extended family members, who have
been supportive and proud of me throughout my doctoral studies.

Finally, my special and deepest thanks and love to my wife, Masitah Hashim,
for her love, sacrifice, patience, support, and encouragement. My children, Noor
Farihah, Ahmad Ilmi Farid, and Ahmad Ilmi Faiz, in their own ways, continuously
have provided me with love and inspiration. They patiently tolerated my

preoccupation with my studies and work. My love for theirs.

vi

TABLE OF CONTENTS

LIST OF TABLES .......................................... ix
LIST OF FIGURES .......................................... xi
CHAPTER ONE: INTRODUCTION ............................... 1
1.1 General Background .................................. 1
1.2 Structures of Forest Revenue Systems ...................... 5
1.3 Effects of Timber Fees ................................ 7
1.4 Objectives of the Study ................................ 11
1.5 Organization of the Dissertation .......................... 12

CHAPTER TWO: FORESTRY AND FOREST REVENUE SYSTEMS

2.1
2.2
2.3
2.4
2.5
2.6

IN PENINSULAR MALAYSIA ..................... 13
Introduction ........................................ 13
General Background of Malaysia ......................... 14
Forestry in Peninsular Malaysia .......................... 17
The Forest Concession Policy ............................ 21
The Forest Revenue System ............................. 26
The Forest Management System .......................... 33

CHAPTER THREE: MICROECONOMIC MODELS OF FOREST

3.1
3.2
3.3
3.4

3.5

3.6
3.7

REVENUE SYSTEMS ......................... 38
Introduction ........................................ 38
The General Approach ................................. 39
Assumptions of the Model .............................. 42
The Static Model of Forest Revenue Systems ................. 43

3.4.1 Model Structure ............................... 43
3.4.2 The Concessionaire’s Behavior .................... 43
3.4.3 The Government’s Behavior ...................... 52
The Dynamic Model of Forest Revenue Systems .............. 56
3.5.1 Conceptual Framework and Model Structure ........... 56
3.5.2 The Concessionaire’s Behavior .................... 58
3.5.3 The Government’s Behavior ...................... 58
Deriving Optimal Ad Valorem Royalty ..................... 63
Summary .......................................... 66

vii

CHAPTER FOUR: EMPIRICAL ANALYSIS: METHODS ............... 67

4.1 Introduction ........................................ 67
4.2 The Empirical Model .................................. 68
4.3 Research Sites ...................................... 75
4.3.1 General Remarks .............................. 75

4.3.2 Location and Characteristics ...................... 76

4.4 Data and Estimation .................................. 81
4.4.1 Timber Value Distribution (TVD) .................. 81
4.4.1.1 Model specification ...................... 81

4.4.1.2 Timber volumes ......................... 85

4.4.1.3 Log prices ............................. 86

4.4.1.4 Method of estimation ..................... 90

4.4.2 Calibrating the Production Function Parameters ......... 92

4.4.3 Logging Damage .............................. 96

4.4.4 Other Variables and Parameters .................... 96
CHAPTER FIVE: EMPIRICAL ANALYSIS: RESULTS ................ 100
5.1 Introduction ....................................... 100
5.2 Model Validation ................................... 100
5.3 Simulation Results .................................. 103
5.3.1 Static and Dynamic Uniform Royalty Systems ......... 103

5.3.2 Static and Dynamic Ad Valorem Royalty Systems ...... 110

5.3.3 Static Premium ............................... 112
CHAPTER SIX: DISCUSSION AND CONCLUSIONS ................. 116
6.1 Discussion ........................................ 116
6.2 Policy Implications .................................. 122
6.3 Conclusion and Recommendations for Further Studies .......... 124

APPENDIX A. Derivation of Ad Valorem Royalty:

Static and Dynamic Models ......................... 130
APPENDIX B. Comparative Static: Uniform Royalty Systems ............ 135
APPENDIX C. Data Used in the Empirical Analysis ................... 145
APPENDIX D. Simulation Results from SIML Command ............... 146
APPENDIX E. Royalty, Harvest Volume, and Government Revenue

Elasticities (Models 1, II, III, IV, V) ................... 158
LIST OF REFERENCES ...................................... 163

viii

LIST OF TABLES

1. Total Government Revenue Collection,

Peninsular Malaysia, (1971-89) ................................ 28
2. Premium Rates for Forest Concessions, Peninsular Malaysia, 1989

(Selected States) ......................................... 29
3. Royalty Rates for Logs, Peninsular Malaysia, (1978-89)

(Selected States and Species, RM/m3) ........................... 32
4. Definitions of Variables and Parameters ......................... 69
5. Structure of Model I ....................................... 70
6. Structure of Model 11 ...................................... 71
7. Structure of Model III ...................................... 72
8. Structure of Model IV ...................................... 73
9. Structure of Model V ...................................... 74
10. Characteristics of the Research Sites ............................ 79
11. List of Species Groups Used in the Analysis ...................... 87
12. Average Nominal Log Prices by Species and Sites (RM/m3) ........... 88
13. Price Reduction Factors .................................... 9O
14. OLS Results for the Timber Value Distribution .................... 91
15. NLS Results for the Timber Value Distribution .................... 91
16. Estimates of Cost Values and Production Function Parameters ......... 97
17. Logging Damage ......................................... 97

ix

18.

19.

20.

21.

22.

E1.

E2.

E3.

E4.

E5.

Simulation Results for the Uniform Royalty System ................ 104

Comparison of Optimal Uniform Royalty Rates With the
Present Royalty Rates in Pahang, Sabah, and Sarawak

(Selected Species, RM/m3) .................................. 107
Simulation Results for an Ad Valorem Royalty System ............... 111
Simulation Results for the Static Premium ....................... 114
Values of the Government Objective Function (RM/ha) .............. 119

Royalty, Harvest Volume, and Government Revenue Elasticities
of Model I ............................................. 158

Royalty, Harvest Volume, and Government Revenue Elasticities
of Model 11 ............................................ 159

Royalty, Harvest Volume, and Government Revenue Elasticities
of Model 111 ............................................ 160

Royalty, Harvest Volume, and Government Revenue Elasticities
of Model IV ............................................ 161

Royalty, Harvest Volume, and Government Revenue Elasticities
of Model V ............................................ 162

LIST OF FIGURES

1. Uniform Royalty system and Distribution of Stumpage Value

in a Concession Compartment ................................. 9
2. Map of Malaysia .......................................... 15
3. Taxonomy of the Forest Allocation System ........................ 23
4. The Determination of Optimal Labor Input ........................ 50
5. Location of the Research Sites ................................. 77
6. Timber Value Distribution .................................... 82

7. Royalty and Premium Shares of Government Revenue
(Uniform Royalty) ......................................... 109

8. Royalty and Premium Shares of Government Revenue
(Ad Valorem Royalty) ...................................... 113

9. Prescribed Harvest Volume Minus Actual Volume (VSY- V),
Static Models ............................................ 117

10. Prescribed Harvest Volume Minus Actual Volume (VH- V),
Dynamic Models .......................................... 121

xi

CHAPTER ONE

INTRODUCTION

1.1 General Background

Forests have played a significant role in the socioeconomic development of
many tropical countries. In their productive and protective roles, tropical forests
have contributed to industrial and agricultural development, regional development,
conservation, job creation, and poverty eradication. If these benefits are to be fully
realized and sustained for future development, forestry policies must be identified
and implemented in accordance with management of forest resources based on
biological and economic principles.

Unlike those in temperate countries, forests in most tropical countries are
owned and administered by government (Lanly, 1982). The government formally
transfers concession rights to a concessionairel to harvest timber or to manage a
specific area of a forest concession. A system of timber rules and procedures
through a concession contract confers rights to harvest timber to concessionaires.
Generally, property rights of forests in most tropical countries are not exclusive,

transferable, divisible, or enforceable (Vincent & Binkley, 1991). This may be due

 

‘Throughout this dissertation, the term "concessionaire" refers to a timber
operator or logger who is involved in timber harvesting and supplying logs from a
concession to his own mills, to other domestic mills, or to exporters.

l

2

to various provisions in the national constitution and the forest acts in each country.
Forests granted to concessionaires for timber harvest are therefore associated with
property rights. Concessionaires are likely to adopt a short-term behavior when they
harvest timber in the forests. This is because their concession contracts are typically
shorter than the average rotation period or growing period of the main timber crop.
In tropical countries where forests are managed under an uneven-aged system,
governments often decide on minimum-diameter cutting limits. The cutting limits,
which normally vary with respect to different species groups, are imposed to allow
immature, residual trees to grow naturally so that they can be harvested in the next
cutting cycle. Without logging regulations, short-term concessionaires would harvest
timber when the marginal log price equaled the marginal logging cost, which would
result in overharvesting, the felling of trees that are not yet financially mature.

In some countries, the government also grants long-term forest concessions in
an attempt to induce concessionaires to consider the long-term consequences of their
actions. A long-term forest concession refers to a lease period which is at least as
long as the cutting cycle (the frequency of entry to the stand for harvesting) (35
years) or the rotation (the age of trees at harvest) (60 to 100 years) (Paris and
Ruzicka, 1993). Such long-term concessionaires might also be unlikely to harvest at
"sustainable" intensities because their discount rates are often high and tropical forest
management is fraught with uncertainties (Vincent, 1993b). Therefore, the harvest
behaviors of short- and long-term concessionaires might not differ very much. A
direct role by the government in natural forest management might thus be

unavoidable.

3

In theory, governments can capture the full stumpage value,2 or resource
rent, of a concession without affecting coneessionaires’ harvest decisions, through
competitive auctioning. In practice, however, forest revenue systems in most
tropical countries are implemented by charging timber fees--based either on timber
volume (royalty) or the concession area (premium). Generally, timber fees are low
and do not reflect the true stumpage value. Low timber fees are associated with
inadequate mechanisms to adjust for timber scarcity and failure to offset
environmental costs of logging.

The most obvious effect of this fiscal undervaluation is low revenue
generation for the government and consequently large windfall profits for
concessionaires (Boado, 1988; Gillis, 1980, 1988a, 1988b, 1988c; Page, Pearson, &
Leland, 1976; Repetto & Gillis, 1988; Ruzicka, 1979; Vincent, 1990, 1992; Vincent,
Awang Noor, & Yusuf, 1993). Other problems include rapid depletion of forest
resources (Gillis, 1980), wasteful resource exploitation (Gillis, 1992; Repetto,
1988a), bias against conservation (Gillis, 1980), and illegal logging (Repetto, 1988b).

Undervaluation of the natural forests of Peninsular Malaysia has become
increasingly apparent. Results from previous studies reveal that the present forest
revenue system fails to capture much of the stumpage value or to provide incentives
for concessionaires to abide by logging regulations (Vincent, 1990; Vincent et al.,

1993). The estimated rent capture by the government does not exceed 50%, even

 

2 Stumpage value is the commercial value of timber in a standing tree. It is
calculated by taking the difference between the price a mill will pay for a log, and
the costs of logging and transporting the log from the forest to the mill. It is a type
of scarcity rent. It provides a return to a forest owner in a competitive economy.

4
with tendering (Awang Noor, Vincent, & Yusuf, 1992; Vincent et al., 1993).

Moreover, on average, the government rent capture was only 21.8% during the
period 1966 through 1985 (Vincent, 1990). Most timber fees have remained
virtually constant in most states in Peninsular Malaysia. Only five states have
increased their royalties since 1972 (Vincent, 1990).

Revenue that could be used by state governments to support public-sector
investments is instead captured by concessionaires at rates far exceeding true normal
profits. Timber harvesting is often connected with political patronage; thus,
enormous windfall profits captured by concessionaires may be distributed among
various parties. Who actually collects this rent is not known.

Previous researchers have attempted to estimate the amount and distribution
of stumpage value capture between the government and concessionaires, and others
have focused on policy options by the government to capture more stumpage value
from forests (Gillis, 1980, 1992; Gray, 1983; Sulaiman, 1977; Thompson, 1984;
Vincent, 1990; Vincent et al., 1993), or on the effects of timber fees on logging
behavior (Vincent et al., 1993). The studies have differed in terms of the methods
employed to estimate stumpage value and the types of data used. In general, these
studies have not been very explicit about government objectives in managing natural
forests. Also, conflicting discussions abound in the literature on the relationship
between revenue systems and the sustainability of tropical forest management (Hyde
& Sedjo, 1992; Paris & Ruzicka, 1991, 1993; Vincent & Binkley, 1991; Vincent,
1993a, 1993b). It is not clear that raising government revenue and encouraging

sustainable forest management are complementary objectives.

5

1.2 Structures of Forest Revenge Systems

Gillis (1980, 1992) and Gray (1983) described in detail the full range of

forest revenue systems in tropical countries. Heaps and Helliwell (1985) provided

related discussion on natural resource taxation. Some examples of the alternative

structure of forest charges are as follows (based on a classification from Gillis,

1980)

1. Taxes

A.

Ordinary profit taxes
-Profit tax and incentives, and withholding taxes
Windfall profit taxes or excess profit taxes
Export taxes
-Tax on logs and semiprocessed timber
Property taxes and custom duties
Production-sharing and profit-sharing arrangements
-Deduction of cost recovery, split of new production, in—kind

share of government, purchased equity, free equity

II. Forest fees

A. Royalties (stumpage fees)

B.

-Volume-based royalty (uniform, differentiated)
-Ad valorem royalty

License fees (area taxes)

C. Auction fees and bidding procedures

6

Forest charges can be based on income, profit, stumpage value, or timber
volume. Differences between countries reflect differences in forest resources, forest
management systems, socioeconomic development, socio-cultural background,
institutional structure, the historical background of forest management (Gray, 1983),
and, perhaps most important, differences in government objectives and the
constraints that governments face.

The methods of establishing the level of forest charges under each forest
revenue structure also differ from country to country. Gray (1983) noted that there
are six methods of setting the level of forest charges:

1. Administratively set, fixed-rate charges

2. Value-related (ad valorem) charges

3. Formula-based charges

4. Negotiation-based charges

5. Open-bid and sealed-bid auctions

6. Public log markets

When forests are under government control, and there is perfect information,
a perfect market, perfect tax administration, and perfect taxpayer compliance, the
best method is obtained either through a competitive auction or the application of
income taxes (Gillis, 1980). An auction would capture full payment of the stumpage
value of standing timber, and an income tax would capture the government’s normal
share of other property income from timber and associated operations. Auctions

provide many advantages: (a) ambiguity related to forest allocation is removed, (b)

7

risk (information from forest inventory) is transferred to bidders, (c) rent capture is
increased, and (d) incentives to lower the costs of timber harvesting are increased.

In tropical countries, however, a bidding auction (either open or sealed-bid)
may be weakened due to two unfulfilled requirements: (a) full information on
timber volumes, log prices, and logging costs, which is used in calculating stumpage
value; and (b) the absence of bidding collusion. These requirements are often not
fully satisfied due to the heterogeneity of species in tropical forests and political
intervention in the timber business. Collusion is prevalent not only in tropical
countries but in developed countries as well.3

Because of the above-mentioned problems, the most widely practiced forest
revenue systems in tropical countries are royalty-based forest charges. These royalty
structures are characterized by inflexible, undifferentiated, and low-level volume-
based charges. They are most commonly set administratively (uniform royalties) or

set proportional to log prices (ad valorem royalties).

1.3 Effects of Tijmber Fees
In general, resource-taxation analysis is particularly concerned with the
efficiency and equity or distributional aspects of resource development (Heaps &

Helliwell, 1985). The concept of efficiency in relation to forest revenue systems

 

3The problem of bidding collusion is related to the behavior of bidders and
market structure (Mead, 1967; Muraoka & Watson, 1983). Mead pointed out that
each bidder in an imperfect market develops a plan for accomplishing his objectives.
The highest bidder may not provide the highest return to the government, may not
be the most efficient harvesting and marketing concessionaire, may not plan to
harvest timber at the optimal time, and may practice collusion (Muraoka & Watson,
1983)

8

refers to the maximization of discounted stumpage value, whereas distribution refers
to the allocation of stumpage value between the government and the concessionaire.
The performance of alternative forest revenue systems can be measured using these
two aspects.

In general, a uniform royalty causes stumpage value to be split among at least
three components: government revenue, windfall profits, and timber left in the forest.
Figure 1 shows the relationship between log price (P) and cumulative timber volume
(V) within a given concession compartment,4 at a particular point in time. In a
typical tropical concession compartment, there are many timber species in varying
diameter classes. Prices for tropical logs vary according to species, grade, and
diameter class. The downward-sloping line P in Figure 1 shows the distribution of
timber volume according to log price. The value of logs decreases as one moves
from left to right along the horizontal axis. Potentially harvestable trees are those
with diameters above the commercial minimum. But due to sustained-yield
regulations, only trees above government-set minimum cutting limits are allowed to
be harvested.

The marginal cost of logging (MC) is assumed to be constant within the

compartment, and t is a uniform royalty. Without royalty, a profit-maximizing

 

4A homogeneous forest stand (based on physical characteristics such as terrain
and geographical area, not in terms of species composition) which is designated for
management purposes, analogous to analysis areas used by the US Forest Service
(Clutter, Fortson, Pienaar, Brister, & Bailey, 1983). Each compartment could be
treated as an independent decision unit in timber harvesting (such as determination
of cutting limits, issuance of license, etc.) and silvicultural treatment. Various names
(such as working areas, management units, and cutting units) also are used in forest
management textbooks.

 

 

 

 

 

 

RM/cu m
D
MC+t
E
MC
F \
P
0 CU m
v * v

Figure 1. Uniform Royalty System and Distribution
of Stumpage Value in a Concession
Compartment.

10

concessionaire would harvest V cubic meters of timber, at a point where price equals
marginal logging cost. The concessionaire would harvest V' in the presence of
royalty. The area ABDF is government revenue, the triangle BCD is the
concessionaire’s windfall profits, and the stumpage value of timber left in the forest
is DEF. Empirical evidence has shown that the government captures only a small
portion of the available stumpage value when forests are logged (Gillis, 1980; Page
et al., 1976; Repetto & Gillis, 1988; Ruzicka, 1979; Vincent, 1990; Vincent et al.,
1993). Most of the stumpage value is apparently captured by concessionaires. This
type of royalty also results in the most timber being left in the forest.

Gillis (1980, 1988a) showed that an ad valorem royalty or area-based charge,
on the other band, would yield higher government revenue (the government can even
capture 100% of the stumpage value) with less timber lefi in the forest. An income
tax and a differentiated royalty also would result in higher government revenue and
less timber left in the forest.

The differences in the effects of rent capture of alternative forest revenue
systems are a result of concessionaires’ decision on the marginal harvest. The level
of royalty affects the profitability of each unit of timber harvested. For instance, for
a given forest concession, an increase in the uniform royalty decreases the
profitability of timber that the concessionaire extracts. Lower-priced species might
offer negative profits. A rational concessionaire would prefer to leave such trees in
the forest rather than extract them.

An ad valorem royalty (percentage based on log price) would have less effect

on the concessionaire’s marginal harvest decision. Whether the government revenue

11

is greater, or windfall profits are less, depends on the magnitude of the ad valorem
rate.

Because a premium (area-based charge) is a fixed cost (not affected by
harvest level), it would be expected not to influence the harvest decision. However,
without government control, concessionaires facing large premiums might be
tempted to cover the fixed cost by harvesting timber below the cutting limits. An
empirical analysis by Vincent et al. (1993) revealed that this practice is apparent, at

least in the case of timber harvesting in Peninsular Malaysia.

1.4 Objectives of tl_1e Study

In this study, the researcher attempted to enhance the understanding of the
issues discussed above. Specific objectives were:

1. To develop a theoretical microeconomic model of timber production in a
typical concession and to analyze how optimal royalties and premiums are affected
by forest management objectives, concessionaires’ production technologies, and
characteristics of the forest.

2. To examine empirically how differences in forest revenue systems affect
concessionaires’ harvest behavior and the achievement of the government’s forest
management objectives.

Knowing these theoretical and empirical relationships and their consequences
will provide a better understanding of the performance of different forest revenue

systems. This knowledge, in turn, may serve as a basis for developing policy

12

options for reforming forest revenue systems, which will eventually help to ensure

that forest management objectives are achieved.

1.5 Organization of the Dissertgtign

Forest concession policies, forest revenue systems, and forest management
systems in Peninsular Malaysia are highlighted in Chapter Two. This institutional
background provides the basis for developing a theoretical model of the forest
revenue system. This model is developed in Chapter Three. Methods of empirical
analysis used in this study are described in Chapter Four. Chapter Five contains the
results of the simulation analysis and a discussion of the significance of these
results. A general discussion of the results of the study is presented in Chapter Six.
Policy implications that emerged from the empirical analysis also are highlighted.

Finally, suggestions for further studies on forest revenue systems are offered.

CHAPTER TWO

FORESTRY AND FOREST REVENUE SYSTEMS IN

PENINSULAR MALAYSIA

2.1 Introduction

The forestry sector has played an important role, not only in Malaysia’s
economy but also in international tropical timber trade. The forest sector developed
rapidly in the 19603 and 19705 because of significant timber harvests as a result of
the large-scale conversion of lowland forests to planned agricultural development.
Since that time, the lowland forests have decreased in size, and timber production is
now concentrated largely in hill forests. Thus, the continuing role of the forestry
sector in Peninsular Malaysia’s socioeconomic development will depend on the
feasibility of hill forest management.

The Malaysian government is very much aware of the role of natural forests,
and its policies commit it to managing these forests for the benefit of the present and
future generations. Major public forest policies have been formulated and
implemented to meet this ultimate goal. Some of the public forest policies that are
relevant to the framework of this study are discussed in this chapter. These include
the concession allocation system, the forest revenue system, and the forest

management system.

13

14
2.2 General Background of Malaysia

Malaysia is located strategically at the maritime crossroads between China
and India. It is divided into two geographical regions: Peninsular Malaysia and East
Malaysia (Figure 2). Peninsular Malaysia comprises 11 states and the Federal
Territory of Kuala Lumpur. Two other states, Sabah and Sarawak, and the Federal
Territory of Labuan are situated in East Malaysia on the northern part of Borneo
Island. Peninsular Malaysia, then Malaya, became independent from British
Government rule on August 31, 1957. On September 13, 1963, Peninsular Malaysia,
Sabah, Sarawak, and Singapore formed a federal system of government, Malaysia.
However, in 1965, Singapore left Malaysia and became an independent country.

Politically, Malaysia is an elective monarchy with a constitutional
parliamentary system. With regard to administration, Malaysia has a federal system
of government, where the central government is responsible for certain matters such
as education, defense, international trade, public works, and so forth. Legislative
power is divided between the federal and state legislative assemblies, according to
the Federal List and the State List, as specified in the Ninth Schedule of the Federal
Malaysia Constitution. Each state has its own constitution, together with its own
legislative assembly. The ruler or head of the state appoints a state Executive
Council (EXCO)5 headed by the Menteri Besar or Chief Minister. The EXCO is
the top-level policy-making body at the state level and is empowered to carry out

duties concerning the rules and regulations pertaining to matters stated in the State

 

’EXCO members are the elected and appointed officials from the ruling party.
Their function is to set rules and regulations pertaining to the State List, such as
agriculture, forestry, mining, and so on.

15

 

 

ailand

    
   
 

. Pe 'nsular Malaysia

. Brune'
M A LA Y S l A !
Singapore ’ ’ I

   
 

 

Q l N o o N E s I A ,

Q Do
Jakarta 0
.00

Bali . <9

00:9 mwoo/Q

 

Figure 2. Map of Malaysia.

 

16

List. For instance, regulations pertaining to forestry matters, which include decisions
on timber allocation, revenue collection, land development, and other matters, are
made by the EXCO. The federal government provides technical assistance and
formulates national policies. In any event, decisions to implement policies at the
state level lie with the state itself.

From an economic perspective, Malaysia can be classified as an open
economy. The country has been fortunate to have been endowed with diverse and
vast natural resources such as tin, forests, oil, and natural gas. In the early stage of
economic development, the Malaysian economy depended mostly on primary
commodities and agricultural crops, particularly tin, timber, rubber, and oil palm.
Beginning in the early 19805, crude petroleum and natural gas became major
contributors to the gross national product (GNP). The economy has changed
structurally from primary- and agriculture-based exports to the export of petroleum
and manufactured goods. The effects of various economic and public policies under
the country’s Five-year Development Plans have strengthened the resilience of the
nation and contributed to its economic growth. Economic growth in Malaysia has
increased significantly during the last five years (the average 1988-1992 growth in
real gross domestic product (GDP) was 9.4%; it was 8.5% in 1992; Ministry of
Finance, 1992). Strong economic growth in the last few years has been a result of
the stimulus of growth from both domestic and external demands. A highly stable
political environment and conducive investment opportunities also have helped

Malaysia achieve impressive economic growth in the last few years.

17

2.3 Forestry ir_1 Peninsular Malaysia

The forestry sector has been one of the most important sectors in the
Malaysian economy. The government has been paying greater attention to this
sector in the last 20 years because of the significant role the forestry sector has
played in export earnings, job creation, regional development, environmental
protection, recreational opportunities, protection of wildlife, forest-based
industrialization, and so forth. As a result, policies and laws related to the
management and use of forest resources, forest-based industrialization, trade in
timber products, and environmental protection have been formulated and enacted by
the government. These include the Land Capability Classification (1965), the
Protection of Wildlife Act (1972), the National Forest Policy (1978), the National
Forestry Act (1984), the Wood-based Industries Act (1984), and the Industrial
Master Plan (1985). In one way or another, all of these acts and policies have had
significant influence on the development of the forestry sector in Peninsular
Malaysia.

Forests in Peninsular Malaysia can be divided into several types-Dipterocarp,
Peat Swamp, and Mangrove forests--based on their ecological and physical
conditions. Dipterocarp forests are the most extensive and the most important
commercially (constituting about 4.4 million hectares (ha) or 91% of the total
forested land). In this forest, the family Dipterocarpaceae predominates the area,
with many of the species from the genera Anisoptera, Dipterocarpaceae,
Dryobalanops, Hopea, Shorea, and Parashorea. The dipterocarp forests are divided

into lowland (0-300 m), hill (300-750 m), and upper hill (750-1200 m) forests.

18

Lowland dipterocarp forests were subject to rapid conversion during the 19608 and
19705 as a result of large-scale conversion of land for agricultural development
(Vincent & Yusuf, 1991). Most timber harvesting today is carried out in hill
dipterocarp forests.

To facilitate and coordinate development of the forestry sector in Peninsular
Malaysia, the National Land Council (NLC) established the National Forestry
Council (NFC)6 in 1971. The NFC serves as a forum for the federal and state
governments to discuss and resolve common problems and issues relating to forestry
policy, administration, and management. A significant policy effect of the NFC was
the formulation and acceptance of the National Forest Policy (NFP) in 1978. The
policy has been accepted by all states in Peninsular Malaysia. The NFP calls for the
establishment of Permanent Forest Estates (PFE),7 sufficient in area for timber
production, water supplies, environmental protection, recreation, education, research,
and conservation; these are strategically located throughout the country and in
accordance with the concept of rational land use. The PFEs are divided into three
major categories according to their functions and objectives: protective forests
(environmental amelioration such as water catchment), productive forests (timber
production), and amenity forests (conservation and other services such as recreation
and research). Forests intended to be converted to other uses are termed State Land

Forests (SLF). The total natural forest area in Peninsular Malaysia is estimated to

 

6The government dissolved the NFC in 1992. All forestry policies and related
issues are now under the jurisdiction of the NLC.

7Analogous to national forests in the United States.

19
be 6.1 million ha, 46.5% of the total land area (Forestry Department, 1991b). Out

of this area, 4.7 million ha are PFE. Production forests cover an area of 2.8 million
ha. Forest plantations are becoming increasingly important in meeting future log
requirements. To date, 45,368 ha of plantation forest have been established under
the Compensatory Forest Plantation Project.

In 1984, another major development in forest legislation occurred in
Peninsular Malaysia. The government enacted the uniform National Forestry Act of
1984 (NFA), which replaced the States Forest Enactrnent and Rules of 1935. The
NFA was promulgated to ensure effective forest administration, use, management,
harvesting, and reforestation in accordance with the principles of sustained yield
management. Under the NF A, every state is required to prepare and implement
forest management and working plans in the PFE. The plans describe the area to be
harvested, the species to be felled, the cutting limit and allowable cut to be
prescribed, penalties for poor timber harvesting practices, and so on. With this
development, the management, administration, and control of forest resources have
been systematically strengthened and upgraded.

F orest-based industrialization has grown rapidly during the past three
decades. The tremendous growth in the forestry sector has developed it into a major
contributor to foreign exchange and has established for the country a favorable
image as the top producer of high quality tropical hardwood. In the 1960s and
1970s, a large proportion of the logs that were harvested were exported. The
banning of logs of some popular species for export began in 1972. Since 1978,

virtually all logs have been processed locally. In the mid-19803, the government

20

encouraged the development of wood-based processing industries. The Industrial
Master Plan (IMP), launched in 1985, provides the general industrial-development
objectives and strategies for sectors with promising growth potential; wood-based
industries are one of such sectors. The growth strategy advocated by the IMP for
wood-based industries is to focus on the manufacture of high-value-added wood
products, such as furniture, joinery, and mouldings.

Forest-based industries have played a major role in regional development,
foreign exchange, government revenue, and local employment. The spatial pattern
of forest-based industries has been affected by such factors as proximity to timber
supply, labor, agglomeration economics, and government policy. In Peninsular
Malaysia, most of the forest-based industries were concentrated on the west coast
during the early stage of its development. The industrial shift to a timber-producing
region on the east coast began in the early 19705 due to an abundant supply of logs
and available labor (Jalaluddin, 1989).

Forest-based industries grew rapidly during the period from 1961 to 1985.
Domestic demand and export expansion were the main factors that contributed to the
growth (Vincent, 1986; Mohd. Shahwahid, 1986).

The current and future situation of forest-based industries depends primarily
on a sustained timber supply in the long run. Peninsular Malaysia is expected to
face a timber shortage in the mid-19905 (Thang, 1985). The scheduled reduction in
log production in Peninsular Malaysia is a major approach to maintaining a sustained
yield from the natural forests. The government has taken several measures to

address this situation. These include the designation of Permanent Forest Estates,

21

which are being managed under sustained yield; practicing sustainable forest
management through the Selective Management System (SMS); establishing
plantations under the Compensatory Forest Plantation Project; encouraging the
development of downstream processing industries; improving the timber concession
policy; ensuring sufficient funds for forest development projects; and enhancing

research and development.

2.4 The Forest Concession Policy

Under the federal constitution of Malaysia, forest lands are under the
jurisdiction of their respective state governments. This means that forests are owned
by the states, which can decide on the methods of forest allocation, development of
forest lands, and other related issues such as the levels of royalties and premiums,
the type of royalty payments, and so forth.

The forest allocation system can be regarded as a transfer of rights to log a
particular forest concession to individuals, independent private concessionaires,
private mill operators, or govemment-owned timber complexes. The transfer of
rights is made through a contract between the government and the concessionaire.
The contract specifies the rules and regulations as provided in the NFA, as well as
some regulations that may be added by the respective state government. With these
rights, a concessionaire is given a concession to harvest and extract timber, construct
roads, or carry out any activity related to timber harvesting and forest management

as specified in the contract. The states retain ownership of the concession area.

22

Any violation of the terms of the contract or any offense that contravenes the
NFA is subject to penalty or can be prosecuted in court. However, fines imposed by
the government and the terms of imprisonment under the existing NFA are
inadequate to deter concessionaires from encroaching and practicing what is known
as "illegal logging" (i.e., felling trees outside the demarcated boundary of the forest
concession approved for timber harvesting). To further strengthen the NFA, the
government has amended some provisions of the act. These include giving the army
the power of arrest those involved in illegal logging, increasing imprisonment from a
maximum of 3 years to 20 years, and increasing fines 50-fold (from a maximum of
RM10,000 to RM500,0008). Other possibilities also have been considered, such as
freezing concessionaires’ bank accounts in addition to confiscating their property,
and giving courts the power to revoke the licenses or permits of illegal
concessionaires.

In both Permanent Forest Estates (PFE) and State Land Forests (SLF),
Section 16 of the NFA clearly specifies that timber rights may be transferred by
state authority in three ways: (a) tendering, (b) negotiation, and (0) other processes
(depending on a particular case, such as grant or status). A taxonomy of forest
allocation systems in Peninsular Malaysia is depicted in Figure 3.

A short-term concession contract is allocated by two mechanisms--tender and
negotiation. The duration of the contract is normally from 1 to 3 years. Each
concession area averages 400 ha (Kumar, 1986). The terms of the contract include

definition of the felling area, description of forest products, period of harvesting,

 

8US$1.00=RM2.57 in 1993.

23

 

 

NATURAL FORESTS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

STATE LAND PERMANENT FOREST ESTATE
SHORT-TERM] LONG-TERM l SHORT-TERM LONG-TERM
[GRANT GRANT
TENDER NEGOTIATION TENDER NEGOTIATION

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3. Taxonomy of the Forest Allocation System.

24

payments due, location of checking stations for log scaling, and a list of species not
to be felled. Other harvesting regulations are prescribed in the guidelines of the
Selective Management System (SMS)9 (see section 2.6). The management of
natural forests logged under short-term contracts is the responsibility of the State
Forest Office (SF 0).

Tendering (auctioning) is a mechanism to allocate concessions in State Land
Forests that have been demarcated for agricultural development. However, in some
states this mechanism is extended to allocate concessions in Permanent Forest Estate.
There are two types of tenders: open tender (open bid) and closed tender (sealed

bid). Closed tenders are limited to bumiputera,10

whereas open tenders are
conducted for any parties. In both mechanisms of forest allocation, the concession is
normally awarded to the highest bidder.”

Under the negotiation mechanism, an applicant (individual, independent
concessionaire, or wood-based manufacturer) must meet certain requirements when
submitting an application to the SF O. Under regular and normal procedures, the
decision is made by the EXCO. A successful applicant will be notified by the SF 0

and sign an agreement with the state that specifies the rights of the concessionaire

and the state, and outlines the regulations governing the harvesting operation. The

 

9The guidelines and regulations of the SMS apply only to Permanent Forest
Estates.

loMalays and other indigenous groups; Chinese, Indians, and others are not
considered bumiputera.

l’Sometimes the successful concessionaire who obtains concession rights is
called the "concession holder" or "license holder." In this study, the terms
"concessionaire," "concession holder," and "license holder" are used interchangeably.

25

applicant is issued a logging permit (for a concession in a Permanent Forest
Reserve) or a license (for a concession in a State Land Forest) that is valid for a
specified short duration.

A long-term concession contract or "long-term agreement" is granted to
private wood-based manufacturers (contract period of 3 to 30 years) or to
government-owned timber complexes (known as integrated timber complexes--ITC;
contract period of 15 to 60 years). The objectives of granting a long-term
concession contract are to ensure a long-term timber supply for wood-based
industrial activity and to maximize the use of timber resources.

One important term of reference specified in the concession contract is that
forest management activities should be carried out by the concessionaire with direct
supervision from the SF 0. For instance, the ITCs have their own management
section, which is responsible for forest management activities such as forest
inventory, preparation of management and working plans, forest rehabilitation, and
research and development (Abd. Latif & Nik Mohd. Shah, 1984; Ahmad Zainal,
1984; Sheikh Ibrahim & Shaharuddin, 1984). The ITCs can be reimbursed for the
amount they have spent on forest management and development activities. In these
long-term concession areas, forest allocations are controlled by a separate permit,
which differs from the permit issued for a short-term contract. In some cases, the
ITCs also are permitted to tender out concession areas to independent private
concessionaires.

The loss of virgin forests as a result of agricultural expansion has reduced

forest areas available for logging. This has reduced log supplies and, consequently,

26

has tempted concessionaires to engage in illegal logging, i.e. to harvest timber
outside of or adjacent to the concession area or to harvest timber below the cutting
limits. This situation has prompted the government to review the forest concession
policy. The government is studying proposals to grant forest concessions only to
concessionaires who have processing mills or to appoint concessionaires to harvest
timber in a designated concession for a period of 5 years. In some states, other
proposals have been considered; for instance, existing concessionaires might be
required to log only a small area annually, thus extending the concession period12

and introducing new criteria governing the issuance of licenses.

2.5 The Forest Revenue System
A broad structure and level of fees on timber harvesting is employed in
Peninsular Malaysia. The methods of payment are related to the mechanisms of the
forest allocation system. In practice, the systems typically comprise a mix of

11 ll

voltune-based charges ("royalties, silvicultural cesses," and "tributes") and area-
based charges ("premiums"). In some states, government revenue collected from

these timber fees has been substantial and has been a significant source of public

funds. '3

 

12For instance, if a concessionaire had been promised a forest concession of
10,000 ha per year, the concessionaire would be allowed to harvest 1,000 ha per
year, and the concessionaire would continue to harvest 1,000 ha every year until the
10-year contract expired. The SFO may suggest to the state authority that the
contract period be extended (perhaps from 10 to 20 years, depending on the
situation), provided that the concessionaire reduces the yearly harvest.

l3Only royalties, premiums, charges on minor forest products, compensation, and
fines are channelled to the public fund, whereas the collection of silvicultural cesses
is used solely for forest management and development activities.

27

Table 1 shows total government revenue collection, according to method of
payment. The increase in government revenue was directly related to an increase in
log production. As can be seen in Table 1, royalty was the major contributor to
total forest revenue (on average, contributing 50%). The average contributions from
premium and silvicultural cess were 27% and 13%, respectively. Revenue from
minor forest products (such as bamboo, rattan, poles, resin, and so on) was not
substantial (about 6%).

Because area- and volume-based revenue systems were both analyzed in this
study, they require further explanation. The area-based forest charge is known as a
premium; it is a charge levied for the right to harvest a specific concession area
granted under a permit or a license. The premium is assessed per hectare, based on
the total area of the forest concession as shown in the permit or license. There are
two types of premiums, tendered and standard. The tendered premium is the
competitive bid price, appraised per hectare, for concessions that are allocated by a
tendering mechanism. Conceptually, this fee is independent of harvest volume, and
it may reflect the true total willingness of the concessionaire to pay in exchange for
the concession right. Thus, the tendered premium would be expected to vary with
respect to the productivity of the forest concession, location, accessibility,
characteristics of the concessionaire, price of logs, and other costs associated with
timber harvesting.

The standard premium applies to all concessions; the rate is set by the EXCO
or the SFO (the rates are shown in Table 2). Thus, the standard premium varies

according to forest type, administrative structure of the forest where the concession

28

Table 1. Total Government Revenue Collection, Peninsular Malaysia, (1971-89)

 

 

(RM‘ ’000 )
Year Premium Royalty Silvicultural Other2 Total
Cess
1971 972 47,810 - 1,301 50,083
1972 12,896 58,169 - 2,022 73,087
1973 20,883 60,960 4,855 4,428 91,126
1974 16,089 60,063 5,316 3,940 85,408
1975 25,998 54,090 4,871 3,513 88,472
1976 36,528 73,111 6,824 4,705 121,168
1977 NA NA 15,166 NA NA
1978 38,000 78,000 21,000 7,000 144,000
1979 42,572 75,892 22,705 42,991 184,160
1980 43,547 89,146 21,844 19,588 174,125
1981 62,678 80,531 21,658 9,135 174,002
1982 48,349 98,132 20,668 6,856 174,005
1983 97,996 97,470 22,651 11,588 229,705
1984 69,210 88,129 20,302 8,145 185,786
1985 65,630 76,630 16,696 9,802 168,758
1986 73,660 84,790 20,308 9,647 188,405
1987 67,676 102,174 24,484 31,132 225,466
1988 113,042 1 16,017 28,262 17,847 275,168
1989 115,451 120,490 31,237 18,025 285,203

 

 

 

 

 

 

NA = Not available.
lRM Ringgit Malaysia (Malaysian currency, US$1=RM2.57 in 1993).

2Including the collection of minor forest products, compensation, and fines.

Source: Forestry Department (1979, 1986, 1991a).

29

Table 2. Premium Rates for Forest Concessions, Peninsular Malaysia, 1989

(Selected States)

 

 

 

 

 

 

State Concession area Rate
Pahang Long-term agreement areas under RM250.00/ha/yr
Amanah Saham Pahang (ASPA)
Other long-term agreements
i. Virgin forests RMlOO0.00/ha/yr
ii. Logged over forests Assessed by SFO
Areas granted under special Assessed by SFO
allocation/other areas
Tendered areas Based on tendered
price
Areas granted for land clearance Charged at a premium
between 15 and 30
percent of royalty
collection
Terengganu Long term agreements RMlO0.00-300.00/ha/yr
Forest reserved and state land forests Based on tendered
price
Peat swamp and mangrove forests Based on tendered
price
Kelantan Forest reserved/state land forests, and
long term agreements
i. Virgin forests RM150.00/ha/yr
ii. Logged over forests RM75.00/ha/yr
Peat swamp forests
i. Virgin forests RM40.00/ha/yr
ii. Logged over forests RM20.00/ha/yr
Mangrove forests
i. Virgin forests RM50.00/ha/yr
ii. Logged over forests RM25.00/ha/yr

 

30

Table 2. (cont’d).

 

 

State Concession area Rate
Selangor Forest reserved (inland)
a. Virgin
i. Long term agreements RM600.00/ha/yr
ii. Areas given to timber RM600.00/ha/yr
complexes
iii. Bumiputera areas RM600.00/ha/yr

 

iv. Tendered areas

b. Logged over

State land forests
i. Virgin
ii. Logged over
iii. Land development
iv. Areas given to timber
complexes

Peatswamp forests
i. Virgin
ii. Logged over
Mangrove forests
i. Virgin
ii. Logged over

 

Based on tendered
price

RM3 OO/ha/yr

RM600.00/ha/yr

RM250.00-300.00/ha/yr
RM250.00-300.00/ha/yr
RM250.00-300.00/ha/yr

RM150.00/ha/yr
RM75.00/ha/yr

RM200.00/ha/yr
RMlO0.00/ha/yr

 

Source: Forestry Department (1991a).

31

is located, condition and status of forest, length of agreement, and type of
concession contract. The rate can be reviewed from time to time by the EXCO or
SFO in the respective state. The basis for establishing the rate varies. In the state
of Selangor, for example, the rate is sometimes calculated based on inventory data
(Sulaiman, 1977); it is fixed at 20% of the merchantable timber value. More
recently, one state has tripled its premiums, and another has proposed increasing
premiums 5- to 15-fold (depending on the site) (Awang Noor & Vincent, 1993).
Volume-based timber fees include royalty, silvicultural cess, and tribute.
Royalties are calculated and charged based on the actual volume of logs extracted
from the forest. The rates for selected species and states are shown in Table 3. The
rates vary by species, but they are uniform within each state. For a given species,
royalties are not differentiated according to stumpage value. The royalty is
independent of diameter class, quality of logs, terrain, location of forest, and type of
forest concession. Thus, royalties in Peninsular Malaysia are closer to uniform
royalties” than to differentiated royalties. The basis for setting royalty rates was
originally 10% of the market price of logs." As can be seen in Table 3, royalties
tend to be higher for heavy hardwood and lower for lesser-known species (grouped
under the category of other species). Even though log prices vary substantially, the

rates do not reflect these differences. In general, Kelantan charges the lowest

 

14This is equivalent to the specific uniform royalty used by Gillis (1980, 1988a).

1’In this sense, the rate can be considered as ad valorem. However, the rates are
fixed by the EXCO, and in practice they do not reflect the prices of logs.

32

Table 3. Royalty Rates for Logs, Peninsular Malaysia, 1978-1989
(Selected States and Species, RM/m’)

 

 

 

 

 

State Species 1978 1985 1989
Pahang Cengal 14.00 19.00 19.00
Balau 10.00 14.00 14.00
Kapur 8.00 13.00 13.00
Keruing 5.50 13.00 13.00
DRMl 5.50 16.00 16.00
Jelutong 4.00 14.00 14.00
Sepetir 4.00 11.00 11.00
Other LHW2 4.00 6.00 6.00
Terengganu Cengal 14.00 18.00 18.00
Balau 7.00 9.00 12.00
Kapur 7.00 9.00 12.00
Keruing 7.00 9.00 12.00
DRM' 11.00 15.00 15.00
Jelutong 8.00 l 1.00 12.00
Sepetir 5.50 8.00 10.00
Other LHW2 5.50 8.00 8.00
Kelantan Cengal 14.00 1 8.00 18.00
Balau 8.00 11.00 11.00
Kapur 8.00 1 1.00 1 1.00
Keruing 5.50 8.00 8.00
DRM' 5.50 8.00 8.00
Jelutong 4.00 8.00 8.00
Sepetir 4.00 6.00 6.00
Other LHW2 4.00 6.00 6.00
Selangor Cengal 14.00 20.00 20.00
Balau 10.00 25.00 25.00
Kapur 8.00 12.00 13.00
Keruing 7.00 1 1.00 13.00
DRMl 7.00 18.00 18.00
Jelutong 4.00 16.00 16.00
Sepetir 4.00 12.00 12.00
Other LHW2 4.00 8.00 8.00

 

 

lDRM = Dark Red Meranti (see Table 11 for species listed in this group).

2LHW = Light Hardwood (see Table 11 for species listed in this group).

Source: Forestry Department (1979, 1986, 1991a).

33

royalty, whereas Perak has the highest royalty. '6 For a particular single species, the
highest royalty rate is RM30 per m3, whereas the lowest rate is RM5.3O per m3. The
royalty rates have not shown any significant increase during the past several years
and have not actually corresponded to the price trend for logs.

Another volume-based charge, known as silvicultural cess, has been collected
in all states since 1973; the revenues are to be used solely for forest rehabilitation
and development ("Forest Resource," 1979). Specific flat rates are charged on each
cubic meter of timber extracted, but these rates vary among states, from RMO.6O to
RM2.8O per m3. The tribute is a special payment made by integrated timber

complexes (ITC) in certain states. The tribute rates vary among states.17

2.6 The Forest Management System

The stated objectives of forest management in Peninsular Malaysia are to
ensure sustainability of forest resources, obtain maximum social and economic
benefits, and safeguard environmental stability ("Forest Resource," 1979; Salleh,
1983). The implementation of forest management with regard to Permanent Forest
Estates is specifically stated in the National Forest Policy as follows (Forestry
Department, 1978):

"The Permanent Forest Estate should be managed to provide optimum

production of all forms of forest produce and other benefits for the welfare of
the community.

 

16Based on statistics published by the Forestry Department (1991a). The royalty
rates for Perak are not shown in the table.

l7Cess and tribute are not explicitly specified in the theoretical and empirical
models. "Royalty" in these empirical analyses implicitly includes these fees.

34

The Director-General of Forestry is responsible to the Federal Government
for the proper and efficient management of the Nation’s Permanent Forest
Estate, which is achieved by providing State Governments, through their
respective State Directors of Forestry, with technical advice, assistance and
training facilities. In the interest of the community and the Nation, State
Governments may accept technical and professional advice given by the
Director General of Forestry for implementation.

If there should be a need in the future for harvesting Amenity and Protective

Forests, the State Government may make such decision upon the advice of

the Director of Forestry.

State Governments shall ensure that the Permanent Forest Estate is adequately

protected from encroachment or damage." (p. 20)

Forest management and harvesting practices vary with regard to the status of
forest lands. In State Land Forests (Conversion Forests), clear felling is practiced
and there is no minimum-diameter cutting limit. Timber with a diameter as low as
27 cm dbh (diameter at breast height) is commonly sold in the market. Selective
felling is implemented in production forests of the Permanent Forest Estates. Strict
rules and regulations are imposed on timber harvesting. The cutting cycle is

determined first. Then the minimtun-diameter cutting limits are decided.18 Only

trees above the minimum-diameter cutting limits may be harvested.

 

l8This differs from forest management in even-aged forests with single species,
in which a forest owner only decides when to harvest. Most problems associated
with even-aged forest management involve determining optimal rotation and
subsequently to estimate optimal harvest volume. Optimal rotation varies with
respect to factor ownership, and economic and other policy variables. The optimal
rotation length has its roots in the Faustrnan formula. There is extensive literature
on this subject (for examples, Heaps & Helliwell, 1985; Hyde, 1980; Johansson &
Lofgren, 1985; Neher, 1990; Newman, Gilbert, & Hyde, 1985; Synder &
Bhattacharyya, 1990). The effect of forest taxation on optimal rotation has also been
studied extensively (Chang, 1982; Klemperer, 1976a, 1976b; Ollikainen, 1990).

35

Increasing the cutting limits affects the amount of stumpage value, and hence
the distribution of resource rent and efficient harvest level. The economics of
cutting regimes have been analyzed by Mohd. Shahwahid (1985) and Vincent et a1.
(1993). However, the methods used in their studies were different. The findings
suggest that the cutting limits are not economically justified under the current SMS
because of low rates of return relative to the Opportunity cost of capital (if nontimber
benefits are not taken into account). Higher cutting limits are economically justified
if nontimber benefits are considered.

It should be noted that forest management in the natural lowland and natural
hill dipterocarp forests is entirely different because of variations in forest structures
and the ability of seedlings to regenerate after harvest. The management system
developed for natural lowland forests rich in meranti (a group of Shorea spp.) is
known as the Malayan Uniform System (MUS). This management system has been
found to be very effective in regenerating such forests (Tang, 1987). The cutting
cycle under the MUS is 70 years.

The current management practice in the natural hill forests of Permanent
Forest Estates is based on the Selective Management System (SMS). This system
attempts to prescribe cutting regimes that yield an economically viable harvest
volume while leaving sufficient residual trees of advanced regeneration to ensure
future harvests at intervals of 30 years (Tang, 1987; Thang, 1986). The SMS is
designed to optimize the management objectives of economic and better harvesting
practices, sustainability of the forest and timber supply, and minimum forest

development costs under prevailing socioeconomic conditions.

36

In practice, the SMS is implemented by setting minimum-diameter cutting
limits for dipterocarp and nondipterocarp by analyzing data from pre-felling (pre-F)
inventories. Cutting limits typically are no lower than 50 cm dbh for dipterocarp
and 45 cm dbh for nondipterocarp timber species. Minimum residual stocking is
also required, which should be not less than 32 marketable trees of good quality in
diameter class 30 to 45 cm or its equivalent per ha.

To endow the next harvest with a greater proportion of dipterocarp than
nondipterocarp species groups, the difference in cutting limits prescribed between the
two groups should be at least 5 cm for any one compartment. The SFO attempts to
choose the cutting regime that best balances current timber production and protection
of advanced regeneration. There is no standard set of cutting limits because the
choice depends on particular forest conditions.

The Forest Department carries out the following activities in implementing
the SMS: (a) boundary clearing before harvesting," (b) pre-felling (pre-F)
inventory, (0) tree marking (all commercial trees that are larger than the prescribed
cutting limits, ((1) enforcement during and after harvesting, (e) post-felling (post-F)
inventory, and (i) prescribed silvicultural treatments. The types of silvicultural
treatments performed are climber cutting, poisoning of defective and unwanted trees,
and enrichment planting.

All logging operations in a concession (managed under the SMS or the MUS)

are under the general supervision of the SFO. Logging must be carried out during

 

19This activity is not very destructive as it involves the cutting of undergrowth
and small trees, not larger timber species. The purpose of boundary clearing is to
distinguish the felling area from other adjacent compartments.

37

the period when the permit or license is valid. Normally, a concessionaire submits
an application for permission to terminate the logging operations when almost all of
the sound marked trees have been harvested. However, in some cases,
concessionaires will leave marked trees because they are difficult to extract and are
likely to be defective (for instance, hollow trunks). The government allows
concessionaires to leave as many as 20% marked trees. Concessionaires are subject
to fines if they do not continue logging. They can also be fined for felling
unmarked trees (trees that are below the cutting limits) or felling trees in other
compartments (timber trespass or illegal felling).

A recent management practice in the implementation of the SMS is to
incorporate volume control into the area-control approach ("Perlaksanaan," 1993;
Alias, 1993). Under this approach, a periodic harvest schedule will be determined,
based on joint considerations of area, volume, and silvicultural conditions of the
forest stands. This will ensure that concessionaires harvest an allowable timber
volume from forest concessions, as prescribed in the pre-F inventory. A 100% tree
marking for trees above the cutting limits will be undertaken to further confirm the
harvest volume extracted. Strict enforcement of log flow from the forest will be
conducted at checking stations, where concessionaires’ harvest volume will be
compared with the estimated timber volume from pre-F and tree-marking data.
Through this practice the Forest Department hopes that illegal logging will be

eliminated and that a substantial increase in government revenue can be expected.

CHAPTER THREE

MICROECONOMIC MODELS OF FOREST REVENUE SYSTEMS

3.1 Introductiuu

In this chapter, static and dynamic microeconomic models of optimal forest
revenue systems for Malaysian forests are described and developed. The models
differ in terms of the nature of the government’s objective function, whether it is to
maximize short-run revenue only (the static model) or to maximize revenue and
future forestry benefits jointly (the dynamic model). The revenue system consists
solely of a royalty (uniform and ad valorem), although some implications for the
premium are examined. The models assume that concessionaires act according to
neoclassical production theory, as firms that maximize profits, taking forest revenue
policies as given. Other assumptions underlying the two models are the same. In
each model, the optimal royalty is derived for three types of logging technologies:
Cobb-Douglas, Leontief, and Linear. Comparative static analysis is carried out to
examine the direction of changes in production function parameters and
characteristics of forest concessions and their effect on the optimal royalty.

The main hypothesis is that alternative forest revenue systems have

significant effects on concessionaires’ harvest behavior and government revenue, and

38

39

that in designing an optimal forest revenue system to achieve specified government

objectives, these effects must be taken into account.

3.2 The General Approach

Generally, two approaches can be taken to study log production: the
household production model and the timber production model. The application of
each approach depends on the behavior of the household and the firm. The
household production model is appropriate when forests are privately owned,
particularly when nontimber outputs are valued by the owner (Binkley, 1987). The
household production model treats the landowner as a utility-maximizing consumer
of nontimber forest outputs and consumptive goods. The timber production model is
best used when logging firms operate under a defined market structure whereby
production behavior is described as a solution to profit maximization. In this
approach, three methods can be used: primal (direct), dual (indirect), and statistical
analysis (Cardellichio & Kirjasniemi, 1987).

The primal and dual approaches are based on the neoclassical production
theory. Based on this theory, production behavior is described which incorporates
information on both economic behavior and available technology. A system of
output supply and input demand functions are then derived. The statistical analysis
approach, however, uses arbitrary output supply and input demand functions without
explicit consideration of the underlying technology. A calibrated model falls in the

first two categories.

40

Functional relationship of output supply or input demand functions can be
estimated econometrically or using the "calibration" method. The application of
each method depends on data availability. Cross sectional or time series data, which
can be gathered from published reports or through sample survey, will permit the
use of econometric method to estimate parameter values. If data are lacking or
unavailable, the calibration method can be used.

This study employed a direct approach based on timber production of profit-
maxirnizing behavior of a firm in a competitive economy using a Constant Elasticity
of Substitution (CES) production function. This approach is appropriate because
forest concessions in Peninsular Malaysia are owned by the government. Timber
harvesting is carried out by concessionaires, subject to government regulation. The
concessionaire is thus considered as a profit-maximizing firm in the short run. The
government, not the concessionaires, is responsible for forest management activities
following logging.

The timber production system involves both human and natural components.
The human component comprises the government and concessionaires. Through
decisions made by the government and concessionaires about timber production, the
natural systems are affected. The focus of this study is not the influence of human
decisions on natural systems, but rather the interaction between the revenue system
and timber production. These two systems (forest revenue and timber production
systems) are interrelated because of the roles played by the government and the
concessionaire. The two systems are governed and controlled by the National

Forestry Act, which facilitates the administration and development of the forestry

41

sector in Peninsular Malaysia. The concessionaire employs labor and fixed capital
(for examples, roads and equipment) in timber production. The concessionaire
obtains timber right-~a designated forest concession--through a concession contract.
The logging industry is assumed to be perfectly competitive.20 It is therefore
assumed that the concessionaire takes log prices, the wage rate, the price of capital,
and royalties as given. The concessionaire attempts to maximize profits subject to
these prices and the production technology employed. If capital is fixed and labor is
the only other factor, the concessionaire’s decision is to determine the level of labor
that will maximize profits.

The government, on the other hand, either attempts to maximize forest
revenue in the short-run, or adopts a long-run objective of current forest revenue
together with other forest management objectives, subject to the concessionaire
maximizing profits. These objectives are consistent with the stated objectives of
forest management in Peninsular Malaysia (section 2.6). The economic benefits
could be viewed as the short-run objective, and the sustainability of forest resources
is the long-run objective. The government views the forest revenue system as a
policy instrument to influence the concessionaire’s harvest behavior to achieve forest
management objectives. Determining the optimal level of royalty can be viewed as

a process that simultaneously determines the optimal labor input and the optimal

royalty.

 

20The degree of "competitiveness" of the logging industry is difficult to
determine because there has been no single study carried out on this subject.
However, the number of concessionaires could be used as an indicator. In 1993,
there are about 1,200 concessionaires in Peninsular Malaysia (Dan, 1993). This
indicates that the logging industry is competitive.

42

3.3 Assumptions of tl_1e Model

In formulating the static and dynamic models, the following conditions were
assumed:

1. The logging industry exhibits perfect market competition. The
concessionaire behaves as a price taker--to take log prices, royalties, and factor
inputs (price of labor and capital) as given-~when he determines an optimal policy.
Timber fees levied by the government are assumed to have no effect on log prices.

2. The concessionaire is small relative to the industry. The harvest decision
made by one concessionaire does not affect the decisions of other concessionaires.

3. Log prices vary among diameter classes and by species group. Variations
in log prices are a result of differences in the quality of logs and how mills perceive
the value of each species in the marketplace.

4. The concessionaire behaves rationally with respect to output and input
prices and timber harvesting. This behavior is facilitated by accessible, relevant, and
free information, as well as minimal transaction costs.

5. Forest concessions are managed by the Forestry Department, not the
concessionaire, under the Selective Management System (SMS) with 25- to 30-year
cutting cycles. The length of the cutting cycle and the cutting limits are taken as
given and are assumed to be optimal.

6. The government is responsible for forest management activities. A
nominal forest development fee is charged to the concessionaire, but this fee is

assumed to be negligible and contained within the royalty.

43

3.4 The Stflc Model of Forest Reveuue Systems

3.4.1 Model Structure

The static model developed here is based on the competing interests of two
principal agents in the short run--the government and the concessionaire. The
government owns, manages, and controls the forests, whereas the concessionaire is
directly involved in harvesting timber from a particular forest concession. The
concessionaire is treated as a single firm producing an intermediate good--logs. A
contract to harvest timber in a particular compartment is made through a short-term
agreement. This agreement establishes the royalty to be paid for timber harvested
and extracted, and the premium is due based on the total area logged. In practice,
the logging operation in a concession compartment normally is carried out within a
year. Thus, the concessionaire is characterized by short-term harvest behavior, and
this behavior is reflected by the short-run profit function.

Basically, the choice variable for the government is the timber royalty, with
the forest revenue systems being obtained as functions of the choice variable (labor
input) from the concessionaire’s profit-maxirnization problem. Therefore, the basic
tenet of the static model is that the government is attempting to maximize forest
revenue subject to the production technology of the concessionaire, who is

attempting to maximize profits.

3.4.2 fie Coucessionuire’s Beluwior
The concessionaire harvests timber from a given forest concession within a

given contract period and sells the logs at competitive prices in the market. The

44

concessionaire employs labor L and fixed capital K. The area of forest to be
harvested is fixed by the contract; thus, there is no allocation of land over a period
of harvest.

The concessionaire’s log production function is defined on a per-hectare basis
and is taken as Constant Elasticity of Substitution (CBS). The CBS production
function was chosen for this study because it allows the analysis of a number of
other more specialized production functions--Cobb-Douglas, Leontief, and Linear
(Silberberg, 1978; Varian, 1984). The CBS production function was first introduced
by Arrow, Chenery, Minhas, and Solow (1961). Moreover, Sato (1975b) pointed
out that this and the Cobb-Douglas nonlinear types of functions are frequently used
in the field of economic analysis. Detailed explanations about characteristics,
economic justifications, and types of CES functions have been provided by Arrow et
a1. (1961), McFadden (1963), Sato (1975a, 1975b), and Yacui (1965).

Formally, the general CES production function for the concessionaire is

written as

-_1. (1)
V = A[YL“’+(1-Y)K"’] "

where V is timber output per hectare, L is labor input, and K is fixed capital input.
The parameter A is the "efficiency parameter," the parameter 7 is the "distribution
parameter" (0 < 7 < 1), and the parameter p is the "substitution parameter." A
change in A changes output by the same proportion for any given set of inputs. The

value of parameter A, at least, would vary from site to site. The parameter 7

45

indicates that for any given value of p, the distribution of revenue between factors is
determined by the extent to which the technology is labor intensive (7 is large) or
capital intensive (7 is small). A change in p changes the elasticity of substitution.
It is assumed that p 2 -1. If p = -1, the function is Linear; if p = 0, it is Cobb-
Douglas; and if p :> 00, it is Leontief. The production function has the following
properties:

The marginal product of labor is positive:

VL > 0,”
but it exhibits diminishing returns:

22

 

2'This is obtained by differentiating the CES function with respect to L,

at! 1 4'1
E’ = V, = A(--p-)[YL"’+(1-Y)K"’1 P [-pTL'p'11

-1-
= ATIYL'p+(l-Y)K“’] " l[L‘P'l]

-_l(1+p

)
= AYIYL"p+(l-7)K'P] P [L‘(‘*")]

= lV(1+p)L-(1+P) =. l(Z)l+p > 0,
A" A" L

22The partial derivative of VL with respect to L is
V
a Y _ 1+9
{A p( L) 1

6L

 

 

LVL—V
) < 0,
L2

=L . Z"
Apa pr><

46

This production fimction also exhibits homogeneity of degree one (Arrow et al.,
1961)

Harvest behavior of the concessionaire is derived from a short—run profit
function that treats labor L as the input to be chosen. Thus, the objective fimction of

the concessionaire can be written as:

Max 1: = (l-o)f0V.P(V)dv—wL(1+6)—rK(1+6)—t(l-o)V—m (2)
L

where PM is timber value distribution, w wage rate, r price of capital, t uniform
royalty rate, m premium rate, 6 normal profit margin, and a logging damage. Note
that V is the actual harvest volume of timber felled by the concessionaire, but due to
logging damage, only a portion of this timber, (1-0), is actually extracted and sold.
It should be pointed out that uncertainties and risks are not treated explicitly, but
they are implicitly embodied in total factor costs through the inclusion of normal
profit margins, 1 + 6.23

Equation 2 comprises three components:

1. Total revenue = (1-0) 1 0V. PMdv
2. Total factor costs = wL(1+6) +rK(1+6)
3. Total timber fees = 1(1-0) V+m

 

because LVL - V < O by the Euler theorem (under constant returns to scale).

23Neher (1990) pointed out that it is not easy to know how to incorporate
uncertainty. Normally, the treatment of uncertainty in modeling frameworks is
employed in the expected-value and expected-utility approaches.

47

The first component, total revenue, is the integral of the timber value
distribution. The timber value distribution is a descriptive relationship between log
prices and timber volume. It indicates the price of each incremental cubic meter of
timber that is harvested within the forest concession. The timber value distribution
would be a step function if it were plotted out for an actual compartment. For

simplicity, PM is modeled by a linear function, as in Figure 1:

1’00 = a—BV (3)

where a is a constant (measures the price of highest-valued timber species) and B is
the slope of the timber value distribution (measures how steeply prices fall off as
less valuable timber is extracted). The timber value distribution slopes downward
due to differences in species, diameter, and grade. Although PM is downward
sloping, it is not a demand function. This functional relationship is consistent with
figures in Page et a1. (1976), Ruzicka (1979), Awang Noor et a1. (1992), and
Vincent et a1. (1993).

The second component, total factor costs, is the payment for labor and fixed
capital inputs. These include a normal profit margin, 5, which is what the
concessionaire receives for his management effort. The profit margin is net of
payments of corporate income taxes. Only normal profits are earned by all

concessionaires if the government captures all the stumpage value.

48

The last component, total timber fees paid by the concessionaire, is part of
the concessionaire’s total costs. Royalty is charged at a uniform“ rate I for each
species on each cubic meter of timber extracted from the concession area. V is
multiplied by l-a to adjust for logging damage during timber harvesting. A
premium rate m is charged, based on per hectare area logged. 2’

To maximize profit, Equation 2 is differentiated with respect to L and

equated to zero: 2"

 

24There are basically three royalty structures for logs: specific royalties, per tree
royalties, and ad valorem royalties (Gillis, 1992). In the case of specific royalties,
there are two forms--uniform specific royalties and differentiated specific royalties.
In this study, the uniform royalty refers to uniform specific royalties used in Gillis
(1988a, 1992) or to uniform fixed royalties used by Hyde & Sedjo (1992).

25As explained in Chapter Two, the model considers a forest revenue system that
comprises a mix of volume-based and area-based charges. Even though silvicultural
cess and tribute are not explicitly specified, these charges are implicitly included in
t.

2(’The second-order condition for profit-maxirnizing behavior requires that
(321/sz < O; that is, the profit function must be strictly concave in the neighborhood
of the optimal level of labor use. Thus, by taking the partial derivative of VL we
have

a2
52’:- = (1‘011VL(‘13V1)+(“‘PnVuJ'ta‘COI/LL < 0,

which can simply be written as
(a-BV-th-BWJ’ < 0.

which holds because VL > 0, Vu. < 0. The concessionaire would not harvest at a
point where a—B V-t < O, as this implies that the royalty, t, exceeds the log price, a-
6V.

49

2:

6L (l-o)(a-BV-I)VL-w(l+6) = 0 (4)
This equation can be rearranged to yield the standard result that the net

marginal value product of labor (NMVPL) equals the marginal factor cost of labor

(MFCL)=

(l-o)(a-BV-t)VL = w(1+6) (5)

The lefi-hand side of the equation represents the net revenue from hiring one more
unit of labor: incremental harvest (VL), times price (oz-3V) minus royalty (t), adjusted
for logging damage (l-a). On the right-hand side is the marginal cost of labor
(including overhead costs, i.e. normal profit) (MFCL). Therefore, the concessionaire
will employ labor input until the net marginal value product of the L'th unit of labor
equals its factor cost. This condition is presented in Figure 4.

The horizontal axis represents the labor used in harvesting logs within a
given concession compartment. In this case, capital is fixed at level K. VL is, as
discussed earlier, a concave function (VL > 0, VLL < O). MFCL does not vary with
labor input L and is drawn horizontally.

In the absence of royalty, the concessionaire will harvest logs when the gross
marginal value product of labor equals the alternative wage rate of labor, including

overhead costs, ceteris paribus. The optimal labor input is at L‘.

50

 

/ (1- OX a- B V)VL=MVPL

 

 

 

 

 

5 IVL
.3 (1- o)( a— B V-I)VL=NMVPL
8.
3
I:
a w(l+ 5)=MFCL
‘6"
U
La L1 Db“

Figure 4. The determination of Optimal Labor Input.

51

The net marginal value product curve is drawn below the marginal value
product of labor because the concessionaire has to pay t ringgit27 per unit of timber
harvested. The vertical distance between the MVPL and the NMVPL shows the
amount of royalty paid for an incremental unit of timber harvested. This will induce
the concessionaire to reduce output below the level where the gross marginal value
product of labor equals its alternative wage rate. As a result, the concessionaire has
to reduce the amount of labor used in timber production, and thus the level of
harvest. The optimal labor input is now at L'.

The optimal labor input L. is found by solving the first order condition of

Equation 4:

 

L, : A"w(1+6) 131,, (6)
T(l-0)(a-DV-t)

 

where (l/1+p) is the elasticity of substitution. Equation 6 can be simplified as

L’ = lgrkIa—pV—m" (7)
AP

where

k

7(l-a)/w(1+6), and

0

l/ (1+p).
Equation 7 indicates that the optimal labor input in timber production, and thus the

optimal harvest, will depend on output and input prices and the technology used.

 

27Malaysian currency (abbreviated as RM for Malaysian Ringgit).

52

This equation is treated as a constraint in formulating the objective function of the

government.

3.4.3 _'I;he Goverument’s Behavior

The government employs a single criterion or objective--to choose the royalty
that will maximize forest revenue R in any specific time period. This objective
function is subject to solution of the first-order condition of the concessionaire

(Equation 7). Formally, the static objective function can be written as

Max R = t(l-o)V+m-c(l-0)V (8)

subject to 61r/ISL = O. The equation indicates that the government attempts to
maximize forest revenue collection from a given hectare of a forest concession (the
first two terms), and c ringgits must be spent for royalty collection. These costs are
the payment to forestry staff at checking stations, where royalties are assessed. Note
that the per-hectare premium, m, is not treated as a choice variable; more on this is
provided below.

The first-order condition for maximizing forest revenue is obtained by
differentiating Equation 8 with respect to t, and setting the resulting derivative

equals to zero:

an at at
.57 = (l-o)V+t(l-o)VL at -c(l-o)VL at = o (9)

 

 

53

For an interior solution, I > 0. To interpret the first-order condition more easily,

Equation 9 is rearranged as

. 10
V“ = «page; ( )

 

The left-hand side of Equation 10 represents increased revenue on timber currently
harvested if the government raises the royalty. The right-hand side is the
opportunity cost of net marginal forest revenue from timber that the higher royalty
causes the concessionaire to leave in the forest. Referring to Figure 4, an increase in
royalty causes labor input to fall (dL‘lat < O), which also causes harvest to fall (VL >
0). A decrease in harvest level will lead to a lower net revenue received from each
cubic meter of timber left in the forest (t-c). Thus, at the optimal level of royalty,
the marginal increase in revenue from current timber harvest and the marginal loss
in net forest revenue are equal. In other words, maximization of government
revenue requires simultaneous determination of the labor input and the royalty rate.
So for every L', one can choose the optimal t. This result is similar to the one
worked out by Hyde and Sedjo (1992, p. 349), which was corrected by Vincent
(1993b).

The optimal royalty can be obtained by solving the first-order condition of
Equation 10. But Equation 10 cannot explicitly be solved for t because VL and

(if/at also are functions of t. VL is obtained from Equation 5,

54

L = “41““) (11)
(1 —o)(a ‘11 V")

and (if/(3t is obtained by differentiating Equation 7 partially with respect to t, using

the chain rule,

a 6

 

 

 

where g is given by k(a-B V-t).
Substituting VL and dLVc’it in Equation 10, yields a general formula for the

optimal royalty, 1':

1-9
1* = _g__-l(a_py_t)+ipy+c (13)
ApeekVL 6 A99

When combined with Equations 1 and 7, it forms a system of three simultaneous
equations that can be used to predict optimal values of the three unknown variables,
t’, L', and V‘.

Equation 13 can be converted to optimal-level-of-royalty formulas for three
types of production technologies, Cobb-Douglas, Leontief, and Linear, by inserting

the appropriate values of 0:

55
Cobb-Douglas (0=1):

 

(step = [a-v(a—c)1+(2v-1)BV* (14)

Leontief (9:0):
tsfu: = [a—‘fi’g’l-BV‘ (15)

Linear (9:00):
tstm = c+pV* (16)

where the subscript S refers to the static objective, and the subscripts CD, LF, and
LN correspond to the Cobb-Douglas, Leontief, and Linear production technologies,
respectively.

In general, therefore, the optimal level of royalty is influenced by output and
input prices, logging damage, and the characteristics of a forest concession. The
extent to which these factors affect the optimal level of royalty depends on the
nature of the production function of the logging industry.

Note that for the Cobb-Douglas technology, the relationship between the
optimal royalty, t', and the optimal harvest volume, V, is seen to depend on the
distribution parameter in the production function, 7. If 7 > 1/2, t’ is positively related
to V (that is, a higher royalty rate is associated with a larger harvest); if 7 < 1/2, t’ is

negatively related to V (that is, a lower royalty rate is associated with a lower

56

harvest); and if 7 = 1/2, the royalty rate is independent of the volume of harvest. In
the extreme, the optimal royalty in the Cobb-Douglas technology is exactly equal to
the optimal royalty in the Linear technology when 7 = 1 (labor is the only input).
In addition to royalty, the government can collect revenue by levying a
premium. This can range from zero to a maximum value that limits concessionaire
profits to normal levels: 1r = 0 in Equation 2. If m is so high that 1r < 0, the
concessionaire would not operate. Once the optimal royalty is determined, Equation
2 can be used to determine m as a residual.28 As the premium is not chosen
directly, it is not necessarily an optimal value. The premium is thus the difference
between total revenue and the sum of fixed costs, variable costs, and royalty
payments. The premium can be regarded as the maximum amount that the
concessionaire would be willing to pay in order to obtain concession rights, given
the optimal royalty. If no royalty is levied, this is the total stumpage value of the

compartment.

3.5 The Dynamic Model of Forest Reverule Systehnys

3.5.1 Conceptual Frumeworh and Model Structure
The conventional timber harvest objective in a long term context is to harvest

forests to maximize the sum of discounted stumpage value from present and future

 

28It should be pointed out that the premium may not provide the best solution
due to input assumptions used in the model. It is expected that if royalties are raised
and volume of harvest reduces then residual damage would increase at a rate higher
than the proportional decline in timber harvest. The effects of logging damage and
revenue collection costs on premium are not taken into account since they are
assumed fixed with respect to different harvest levels.

57
timber harvests (Hyde, 1980; Heaps and Helliwell, 1985; Johansson & Lofgren,

1985; Sedjo & Lyon, 1990; Neher, 1990). The classical economic problem is to
determine the optimal rotation length (i.e. when a forest stand should be harvested).
This concept is mainly applied in even-aged forests, in which all trees are harvested
at the same time. Dipterocarp forests in Peninsular Malaysia are, however, managed
as uneven-aged stands. The long term aspects of forest management are the timing
of harvests (the cutting cycle) and the selection of minimum-diameter cutting limits,
which determine the size of current harvests and affect future ones through impacts
on stand composition.

The implementation of minimum-diameter cutting limits for particular species
groups, given a fixed cutting cycle, results in some portion of timber with positive
stumpage in the current period being left in the forest as advanced regeneration to
restore the forest. It follows that a long-run formulation of the government’s
objective function should not be restricted to revenue maximization alone but should
also include the opportunity cost of deviations from the minimum cutting limits.
The static model assumes that the government’s only objective is to maximize forest
revenue without considering the long-term impacts of the present harvest level. This
revenue-driven objective fails to reflect sustainable management of natural forests
for long-term, socially optimal behavior.

To model this, an additional term that reflects the opportunity cost of harvest
deviations from the prescribed level must be included in the government’s objective
function. The inclusion of this term might even signify a long-term interest in

managing natural forests for multiple benefits. Over- or under-harvesting might

58

affect the sustainability of multiple benefits following harvesting. Therefore, the
basic purpose of the dynamic model is to analyze optimal royalties that are optimal

for long-term forest management objectives.

3.5.2 The Concessionaire’s Behuvior

In this model, concessionaires are assumed to continue to behave in a short-
run manner due to the structure of concession contracts, which are shorter than the
cutting cycle. Because of this and the fact that forests are owned by the
government, concessionaires would not expect to capture stumpage value from future
harvests. Therefore, the objective function of the concessionaire in the dynamic
model remains the same as in the static model.29 Equation 7 is used as a constraint

in formulating the objective function of the government.

3.5.3 flie Government’s Behavior
The objective function of the government must accommodate a long-term

forest management objective and may be expressed as follows:

Mfzx R = t(1-o)V+m-c(l-o)V-|.I(VSY-V)2, (17)

subject to the optimal level of labor input (Equation 7). This objective function is

similar to the expression in Equation 8, but with one extra term; u( V”- V)’, which is

 

29Vincent (1993b) pointed out that if the owner (government) makes the optimal
decisions and the concessionaire abides by them, the stumpage value of timber
harvested today is the financially optimal amount, regardless of whether the
concession is short term or long term.

59

a loss function giving the Opportunity cost to the government of harvest deviations
(either under- or over-harvesting) from the SMS prescriptions. Since the term is an
opportunity cost, it must be subtracted in Equation 17, which implies that the
government maximizes forest revenue net of forgone future benefits.

V” is the prescribed harvest level to implement a sustained-yield management
regime that maximizes the sum of discounted stumpage value (and perhaps other
values, as noted in section 3.5.1) from present and future harvests. It should be
recognized that V" is the policy instrument for long-term, multiple benefits even
though these benefits are not explicitly specified. The difference, V”- V, exists due
to logging behavior by the concessionaire in response to timber fees levied by the
government, market conditions, and other factors. The larger the difference, V"- V,
the larger the deviation of the harvest from the prescribed level. In practice, there
are two possibilities that relate to the concessionaire’s harvest behavior in a
concession compartment, i.e., whether V > V" or V < V”.

If V > V”, i.e. if the actual harvest is greater than the prescribed harvest
level, the concessionaire harvests too much timber by harvesting below the cutting
limits. Vincent et a1. (1993) confirmed that this actually happens under the present
forest management system in Peninsular Malaysia. The discrepancy creates a
dynamic cost because fewer residual trees are left to grow until the next harvest. If
V < V”, i.e. if the actual harvest is less than the prescribed harvest level, this implies
too little harvest--timber is left in the forest above the cutting limits. The
discrepancy also creates a dynamic cost because the timber left to grow until the

next harvest is overmature. Therefore, any deviation (negative or positive) of the

60

actual timber harvest level V from the prescribed harvest level V" generates losses,
which reduce the government’s objective function. To show this in Equation 17, the
expression VSY- V, can be made arbitrarily squared and subtracted, so that all
deviations, whether positive or negative, yield a loss. This specification also
indicates that the government is more concerned with great deviations than with
small ones. Because VSY-V is squared, the cost rises exponentially as the deviation
increases.

The deviations must be valued by a shadow price, 11, in order to be compared
to revenue effects. Because a multiplies by (VSY- V)2, it is the per squared-unit cost
of deviations. If only timber values are important, it can be regarded as the
reduction in discounted forest values due to overrnature timber left in the forest (if V
< V") or immature timber harvested in the current cutting cycle (if V > V”). a is
analogous to shadow price of a resource in optimal control theory (Chiang, 1992;
Dorfman, 1969; Neher, 1990).

The first-order condition for maximization is:

Q = (1-o)V+t(1-O)VLa§t -c(l-o)VLa§t +2p(VS"-V)VL% = 0 (18)

 

 

At the Optimum, the first-order condition for maximization indicates that

2M
t— +
-l( e) (H, )

 

VSY- __V)“, at (19)
V‘ at

61

Compared to Equation 10, the right-hand side of Equation 19 has one extra term, the
opportunity cost for deviations from the SMS prescriptions. Thus, it is expected that
the harvest level in the dynamic model is greater than the harvest in the static model
if the harvest in the static model is less than V" (not enough timber is harvested
from the standpoint of the dynamic objective), and less otherwise.

To obtain the optimal level of royalty, the same procedure is followed as in
the static case. Substituting VL (Equation 11) and (TU/(3t (Equation 12) in Equation

19 and obtaining the general expression for t,

1—6 SY_
t : ._g_—l(a—pV—t)+_1_pV+c—_2L(V__fl 20
1 W, Ape (l-o) ( )
A99 L

Equation 20 can be used to derive the optimal royalty, t’, for three types of

production technologies:

Cobb-Douglas (9:1):

 

. = _ _ +2uVSY + _ + 2‘“; .. 21

taco {(1 Y1“ C (l-o)“ [(2Y 1113 —(1-o)]V ( )
Leontief (0:0):

43).; = [a-Ml-DV‘ (22)

(1-0)

62

Linear (0:00):

_ 2I1(V"'V’) (23)
(1 ’0)

II; M = c+ B V‘
where the subscript D refers to the dynamic royalty, and the subscripts CD, LF, and
LN correspond to the Cobb-Douglas, Leontief, and Linear production technologies,
respectively. As before, the (not necessarily optimal) premium m can be obtained as
a residual by using the zero-profit condition. Compared to the static royalties, the
dynamic royalties of the Cobb-Douglas and Linear production technologies are either
less or greater than the static royalties, depending on the size of the deviation from
the SMS prescriptions. In the case of the Leontief technology, the static and
dynamic royalties are the same. These results are summarized in the following
equations:

a) flaw < t’SCD,30 if V” > V,-

O O 0 Y .
t D,CD > taco, 1f VS < V,

 

3oRewriting Equation 21, one has

tic» = [a-v(a-c)1+(2v-1)IW‘-—27—"(V"—V')
0—0)

The first two terms on the right-hand side represent the static royalty in Equation 14.
Hence,

2711 sr_ .
(l-o)(V V)

It is obvious that the optimal dynamic royalty (t’D'CD) relative to the static royalty
(t’SCD) depends on the size of VSY-V'.

 

# _ ‘
‘D,CD ‘ ts,CD‘

63

e _ t 3]
b) t D,I.F _ t S.I.F9

c) t'mN < 1’3“,” if V" > V‘;

I‘D” > {SW if V” > V‘.

Based on these results, the levels of premiums in the dynamic model of the
Cobb-Douglas and Linear production technologies are likely to be higher or lower
than the level of premium in the static model. When the dynamic royalty is lower
than the static royalty, the dynamic premium is higher than the static premium. This
is because the lower royalty induces a larger harvest, with a higher aggregate
stumpage value. The reverse is true when the dynamic royalty is higher than the
static royalty. The optimal static premium equals value of m when (=0, but this does
not apply for the dynamic premium because the concessionaire has no incentive to
recognize V” in the absence of t. The static and dynamic premiums for the Leontief

technology would be the same.

3.6 Deriving Optimal Ad Valorem Royalty

The government also can levy timber fees based on a flat rate or ad valorem

royalty. In this study, the royalty base is the market value of timber harvested, and

 

3'This is obvious from Equations 15 and 22.

32Similarly, from Equations 16 and 23, one can write

a _ a 211(VSY-V‘)
1“” _ ts”- (l-o)

 

Thus, it is obvious that the optimal dynamic royalty relative to the static royalty
depends on the size of V”- V.

64

royalty, t, is levied on an ad valorem basis (that is, percentage of log price).33 To
derive an optimal ad valorem royalty, the conditions and assumptions as specified in
the case of uniform royalty remain unchanged. The only difference is the
specification of the concessionaire’s and government’s objective functions.
Therefore, the same approach as was employed in the static and dynamic uniform
royalties can be used to derive optimal ad valorem royalties. Because the
concessionaire pays a royalty based on a percentage of log price, (l-t)P(V), the

concessionaire’s revenue net of royalty payments is therefore:

(1 -o)(l -t)foV’P(V)dv

As in the case of uniform royalty, the concessionaire’s static and dynamic objective
functions are assumed to be the same (that is, maximize profits). Therefore, the

concessionaire’s static and dynamic objective functions are expressed as follows:

Max 1: = (1-a)(1-t)f0"'P(V)dv-wL(1+5)-rK(1+5)-m (24)
L

The total government revenue from ad valorem royalty is then equal to the integral

of timber value distribution times the royalty rate:

 

33Ad valorem royalty can also be defined as percentage charges on net revenues
(Hyde & Sedjo, 1992). However, Gillis (1980, 1988a) and most tropical country
governments have defined ad valorem royalty as the charge on log price.

65
(1-a)tfo"’P(V)dv

The integral is multiplied by 1-0, to adjust for logging damage.

As in the case of uniform royalty, the structure of the problem for the
government is to maximize revenue (static objective) or to maximize revenue and
future benefits with sustained logging practices (dynamic objective). The static

objective of the government is written as:

Max R = (l-o)tf0V'P(V)dv+m-c(1-o)V. (25)

and the dynamic objective function is given by:

Max R = (1-<r)tf0’"P(V)dwnz-c(1-a)V—II(VSY_V)2 (26)

A more detailed derivation is provided in Appendix A. The optimal static
and dynamic ad valorem royalties for the Cobb-Douglas production technology are
given below.

a. Static ad valorem royalty

 

tsica = “If” , (27)
z+v(a-BV)

where

66

z = [aV—EW'fIn-w—W' ]
2 (a-BV‘)

b. Dynamic ad valorem royalty

+ _ 211 sr_ . .
_ z [c (l-o)(V V)]yV (28)

_ z+TV‘(a-BV‘)

 

‘
taco

3-7 Sin—myrx

The static and dynamic microeconomic models of optimal forest revenue
systems for the uniform royalty were described and developed in this chapter. The
same derivations were also employed to determine the static and dynamic
microeconomic models for the ad valorem royalty. From these models, the residual
static premium, which is not necessarily optimal, can be developed.

Given these optimal royalties, comparative statics can also be investigated.
The purpose is to study the effect of changes in certain parameters on the
equilibrium (optimal) level of decision variables when certain assumptions change,
holding other variables or parameters constant. The analysis considers the direction
of the changes on the equilibrium value rather than the magnitude of the changes.
Two parameters were considered because of their importance in the forest revenue
system--one representing technology, 7, and the other representing forest

characteristics, 5. Appendix B provides detailed results for the uniform royalty.

CHAPTER FOUR

EMPIRICAL ANALYSIS: METHODS

4.1 Introduction

The theoretical models formulated in Chapter Three are applied to data on
Peninsular Malaysia in this chapter. The main purpose is to determine optimal
royalties and to examine how differences in forest revenue systems affect
concessionaire’s behavior given a specific forest managment objective.

For this purpose, production behavior is modeled using the Cobb-Douglas
function. This function provides a more suitable representation of the production
function of the logging industry than does either the linear fimction, which assumes
that labor and capital are perfect substitutes, or the Leontief function, which assumes
they cannot substitute at all for each other. The analysis provides quantitative
estimates of the optimal royalty and the effects of changes in exogenous variables
and parameters on the optimal royalty, harvest volume, and government revenue.

The chapter begins with a brief description of the full model. The research
sites and the methods for estimating key functions and parameters are described in
the remainder of the chapter. Results of sensitivity analysis are presented in Chapter

Five.

67

68

4.2 The Empirical Model

The empirical model is based on equations derived in Chapter Three. Five

models are developed, depending on government objectives and the structure of the

royalty systems. The models are:

Model 1: Static Model: Uniform Royalty
Model 11: Dynamic Model: Uniform Royalty
Model 111: Static Model: Ad Valorem Royalty
Model IV: Dynamic Model: Ad Valorem Royalty
Model V: Static Model: Premium

Models I through IV comprise six simultaneous equations.
1.

2.

5.

6.

Production function

Labor demand function
Profit fimction

Government revenue function
Optimal royalty equation

Premium equation

The equations are:

Model V is a special case when the royalty is zero (t=0). The Optimal royalty

equation is therefore excluded from this model. Table 4 summarizes the definitions

of all variables and parameters, which are the same as in Chapter Three. Tables 5

through 9 list the model equations.

With reference to Tables 5 through 9, the models differ in terms of the

structure of Equations 2, 3, 4, and 6. Equation 1 is the same in all models.

Equations 2, 3, 4, and 6 are the same in Models I and II, but they are different

69

Table 4. Definitions of Variables and Parameters

 

S‘kal‘s

Symbol
Endogenous

Definition

Volume of timber felled
Labor input

Profit

Government revenue
Royalty

Premium

 

‘3 gain}: Q1309

~1ng

Exogenous

Parameter

 

Intercept of the timber value distribution
Slope of the timber value distribution
Logging damage

Cost of harvest deviation (shadow price)
Efficiency parameter

Capital

Prescribed harvest volume

Royalty collection costs

Labor share
Profit margin
Wage rate
Interest rate

 

70
Table 5. Structure of Model I

 

. Production function

V = .41anH

. Labor demand function

L V[W(l+6)(a IW ()1

. Profit function

x = (1-a)(aV-12’—V2)-wL(1+a)—rK(1+5)—t(1-a)V—m

. Government revenue function

R = t(1 —o)V+m—c(1-O)V

. Optimal royalty equation
t = a-Y(a-c)+(27-l)13V

. Premium equation

m = (l-o)(aV—%Vz)—WL(1+6)-rK(1+6)-t(1-O)V

 

71
Table 6. Structure of Model 11

 

. Production function

V = ALYKI'Y

. Labor demand function

z Y_(1_‘22 - _
L Vlw(l+6)(a 13V 0]

. Profit function

x = (1-o)(aV—%Vz)-WL(1+O)-rK(l+6)-t(l-o)V-m

. Government revenue function

R = t(1-o)V+m—c(1—o)V

. Optimal royalty equation

= _ -.2uV". _ .2741.
t a via 0 (1_o)1 [(27 1M3 (1_O)]V

 

. Premium equation

m =(1-o)(aV-%V2)-WL(1+6)—rK(1+6)-t(1—o)V

 

72
Table 7. Structure of Model IH

 

O\

. Production function

V = AL’IKI"I

Labor demand function

= 191'). - _
L Vlw(1+5)(1 t)(a 13V)l
. Profit function

it = (1 -o)(l -t)((x V-%V2)-WL(1+O)-rK(l+6)-m

Government revenue function

R = (l-o)t(aV——g-V2)+m-c(l-O)V

. Optimal royalty equation

_2 2 _ + YBV +
(«V 2mm x ——(a_m1 W

_E 2 _ .4211... _
(«V 2mm 7 (“_MI you (IV)

t :

 

. Premium equation

m =(l-o)(1-t)(aV-—g-V2)—WL(1+6)—rK(1+6)

 

73
Table 8. Structure of Model IV

 

. Production function

V = ALIK"I

. Labor demand function

: Y(l-0) _ _
L VI—w(1+5)(l 001 13V)]

. Profit function

x =(1—o)(1-t)(aV—%V2)-WL(1+6)-rK(l+6)—m

. Government revenue function

R =(1-o)t(aV—%Vz)+m-c(l-O)V

. Optimal royalty equation

(«V-gVZm—y+—1’-‘3’—I+rc-—2L(V"-V>IYV

 

, = (a-BV) (1-0)
_fi 2 _ + YBV + __
(«V 2V)Il v ——(a_M1 TV(a (W)

. Premium equation

m =(1-o)(1—t)(or V-gV2)-WL(1 +6) -rK(l +6)

 

74
Table 9. Structure of Model V

 

. Production function

V = ALYKI'Y

. Labor demand function

: 7(1-0) _
L V[——W(1+b)(a BV)]

. Profit function

it =(1—o)(ct V-%V2)-WL(1+6)-rK(l+6)-m

. Government revenue function

R = m—c(1-O)V

. Premium equation

m =(1—a)(a V-%V2)-WL(1 +6)-rK(1+6)

 

75

compared to Models 111 and IV. The optimal royalty equation (Equation 5) differs
in every model.

The equations specified for each model show how endogenous variables are
determined in the model. Parameters and exogenous variables are assumed to be
independent of one another. Therefore, any new value can be assigned to any one
of them without affecting the values of the others. This is an important criterion for
sensitivity analysis, whereby the value of an exogenous variable or parameter is
increased individually in order to obtain new values for the endogenous variables.
The values of endogenous variables after the change can be used to calculate
elasticities for the royalty, harvest volume, and government revenue.

The endogenous variables were arranged such that they appeared on the lefi
side of the equations. Except in Model V, all six equations were solved
simultaneously. In Model V, the five equations (excluding the optimal royalty
equation) were solved under the constraint that the royalty is zero. Data source,
estimation, and computational procedures for all variables and parameters are

discussed in the following sections.

4.3 Research Sites

4.3.1 General Remarks

Data for this study were drawn primarily from a concession-level study on
forest revenue systems in Peninsular Malaysia by Awang Noor et a1. (1992). That
study was carried out in eight concession compartments in three east-coast states of

Peninsular Malaysia (Pahang, Terengganu, and Kelantan). Since eight concession

76

compartments were used in that study, there are eight data sets that could have been
used in this study. However, only six data sets were actually used. The reason for
excluding the other two is given in the next section.

For each compartment, the database consists of timber volumes before and
after logging, which were aggregated by species group, diameter class, and average
log prices. Other available information includes the average logging costs within
each state, logging damage, logging fines, and profit margin. In addition to this
database, other information was also collected from published reports and other
sources. This information includes interest rate, wage rate of logging workers, and
the government cost of collecting royalty.

Further details on data and data sources are contained in the remainder of this

section and also in section 4.4.

4.3.2 Locution and Characteristics

The research sites were located in four concessions in virgin hill forests in
Permanent Forest Reserves in three east-coast states (Pahang, Terengganu, and
Kelantan) of Peninsular Malaysia (Figure 5). These states accounted for 63% of the
timber harvest in Peninsular Malaysia in 1989. Their forests are broadly similar in
terms of forest types and productivity. However, the forest revenue systems vary
among the three states due to state autonomy over forestry matters. Timber fees are
characterized as follows (refer to section 2.5 for details of the Forest Revenue
System):

Pahang: high royalty, high premium.

77

 

 

Lv'.‘. .fl....".'. mu
0 t.» ., MAID In. so 9 an an a...
D: ';
V .v. .'
KEDAH .' *." ‘fl .‘ /
S” ,. .I "
~--. 5 ' H l ‘é
M ‘ a 6
mm ’ k
' t .J /'J I 3
SE ~ ‘ I. / KELAW l g
PEN '00 . ‘1 1 X z
3} M. ‘gsmu “
was»: 4 5".) ‘Vmw M. g
3"“ \
a I
9. ‘x .1
3 ‘ \ l
=3 '1 x “x."
or . \)
"u, 1.
Q 77" '\‘/\ PAHANG
3
E SELANGG!‘ X
e 3"“.
a. I. \-
r' is s
‘1 J ‘ w ' In
0 \
. O.Ja\. .
’e0’0'7' \
.Iouoa

 

 

 

Figure 5. Location of the Research Sites.

78

Terengganu: moderate royalty, moderate premium.

Kelantan: low royalty, low premium.

The characteristics of the research sites are shown in Table 10. Each
research site is denoted by a three-symbol abbreviation. The first symbol (P, T) is
the first letter of the state where the site was located. The second symbol (1 or 2),
applies only to Pahang, and distinguishes the concessions within a state. The third
symbol distinguishes the compartments within a concession (a or b). Sites Kla and
Klb were excluded from the empirical analysis due to inconsistencies between pre-F
and post-F data (thus, they are not shown in the table). Compartment size ranged
from 239 ha to 401 ha. These differences meant that harvesting activities took
longer to complete in some compartments than others. The duration of logging
ranged from 6 months (P2b) to 17 months (Tla). The average volume of timber
extracted per hectare over the life of the concession, based on volumes recorded at
checking stations, ranged from approximately 35 m3 to nearly 70 m3. Regarding the
forest revenue system, premiums were higher in Pahang than those in Terengganu.
It should be noted that the premiums for P2a and P2b, which were sites in tendered
compartments, exclude the tender price, which is reported lower in the table.
Average royalties are expressed both per hectare and per m3. They varied among
sites because of variations in stocking and species composition. Silvicultural cess
and tributes are set at fixed rate per m’. The rate of silvicultural cess is the same in
all sites. In Terengganu, concessionares are not required to pay the tribute.

The minimum-diameter cutting limits varied little across sites--limits were 60-

65 cm for dipterocarps and 50—55 cm for nondipterocarps. Standing timber volumes

79

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u:u:am

 

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33.88225

 

33m :233M 2: he nuumtouoahaao .3 93:.

80

before and after logging varied substantially among sites. Timber volumes before
and after logging were based on pre-F and post-F inventories, respectively. (See
section 4.4.1.2 for explanation of pre-F inventory). The post-F inventories were
carried out in a SO-ha block within each compartment. The selection of a 50-ha
block was based on the judgement that it was the minimum size that would be
representative of the terrain and initial forest composition of an average
compartment. A local timber inventory consulting firm was hired to carry out the
inventory. The sampling framework and the information collected are essentially
identical to those in the pre-F inventory. Thus, in each block there were 50
sampling plots. The post-F inventories covered one-fourth to one-eight of the area
covered by the pre-F inventories. Therefore, the pre-F and post-F data are not
exactly comparable.

Timber volume in the table is defined as the volume of trees having a
diameter at breast height (dbh) of at least 30 cm. The highest pre-F timber volume
was 122.88 tha in Plb. Most of the sites are very well stocked, having pre-F
volumes between approximately 95 m3/ha and nearly 123 m3/ha. There were also
substantial differences in species composition. In three of the sites (Plb, Tla, le),

dipterocarps accounted for two-thirds or more of the pre-F timber volume.

81

4.4 Data and Estimation

4.4.1 Timber Value Distribution (TVD)

4.4.1.1 Model specification. The relationship between log prices and
cumulative timber volume within a forest concession can be graphed as a step
function (Page et al., 1976; Ruzicka, 1979; Vincent et al., 1993). This is because
log prices vary due to differences in species, grade, and log diameter. The timber
value distribution patterns for the compartments are plotted and shown in Figure 6.

The horizontal axis represents the cumulative timber volume across all
species groups. To calculate cumulative timber volume, each species group
disaggregated by diameter class was ordered from highest to lowest, according to the
magnitude of the average log price. Then the cumulative timber volume was
obtained by adding the volume of timber in a given group to the timber volume for
all the more valuable groups. The vertical axis indicates log price for a given,
incremental species/diameter group. Each species/diameter group defines a step,
with the width being the volume in the group and the height the log price.

The model requires that these step functions be represented by single
equations. Two functional forms of timber value distribution (TVD) were
considered: linear and semilog equations. The functional forms of the equations are

written as follows:

Price (RM/cu m)

Price (RM/cu m)

82

(a) Site Pla

 

450 L
.l

 

400

350

 

300

 

250

 

200

 

150

 

 

100

 

 

50 I I I T I

 

 

o 10 20 30 40 5'0 60
Cumulative timber volume (cu m/ha)

(b) Site Plb

 

450

 

400

 

350

 

 

300

 

250

 

 

 

150

 

100

 

 

 

so I I I I I
0 20 40 60 80 100
Cumulative timber volume (cu m)

Figure 6. Timber Value Distribution.

1&0

140

Price (RM/cu m)

Price (RM/cu m)

400

350

83

(0) Site P2a

 

“'IT

 

300

 

250

 

200

 

150

 

100

 

50

 

 

 

300

250

200

150

100

50

I

0 1O

20

I I I

30 40 50 6'0 7'0 8'0

Cumulative timber volume (cu m/ha)

(d) Site P2b

I

90

 

100

 

 

 

 

 

 

 

 

 

I

20

4‘0 6'0 8'0
Cumulative timber volume (cu m/ha)

Figure 6. (cont’d).

100

120

Price (RM/cu m)

Price (RM/cu m)

500

84

(e) Site Tla

 

450

 

400

 

350

 

300

 

250

 

 

150

 

 

 

 

100

50

 

 

 

 

2'0 4'0 6'0 8'0
Cumulative timber volume (cu m/ha)

(f) Site le

160

 

120

 

350

 

300

250‘

 

 

 

200

150

 

 

100

 

 

 

50

 

 

O

I I I I

20 40 60 80
Cumulative timber volume (cu m/ha)

Figure 6. (cont’d).

100

120

Linear:

Semilog:

where

V

E

85

_ a+BVij
Pi]. - e +617

the price of logs,
cumulative timber volume,

error term,

or and B are parameters to be estimated, 1' is species group, and j is diameter

class.

Data required for estimating timber value distribution are timber volumes and log

prices. They are discussed below.

4.4.1.2 Timber volumes. This study used pre-F inventory data on timber

volumes before logging for each site. These data were provided by the SF05. As

discussed in Chapter Two, pre-F inventories are normally carried out by the SFO for

an individual compartment 1 to 2 years before it is logged. This inventory is

required under the SMS in the Permanent Forest Reserves. The inventory is based

on a 10-percent systematic linear sampling method. The inventory records stocking

(number of trees) per diameter class (as classes, in 5 to 15 cm increments) by

86

individual species. The inventory also records information on log quality, crown
status, and presence of climbers. The Forestry Department uses formulas based on
volume tables to estimate timber volumes from these data. These data then were
aggregated to correspond to species groups in Table 11 and timber volumes by
species group and diameter class were calculated. These groups correspond to the

ones used to report monthly log prices for Peninsular Malaysia in AMSKAYU a

 

monthly timber bulletin published by the Malaysian Timber Industry Board (MTIB).

4.4.1.3 Log Prices. Data on log prices were gathered from MASKA YU.
Average domestic log prices paid by mills (in Malaysian ringgit) per cubic meter are
reported for 21 individual species and species groups and three regions--East Coast,
Central and West Coast. The states of Pahang and Terengganu are in the East Coast
region. These prices are mean reported log prices provided by mills and the
logger’s association in each region. The reported log prices are normally gathered

1.34

through direct information provided by mills or via telephone cal Only selected
mills are included in providing this information.

The data pertain to logs with diameters of 55 cm and above. The average
East Coast log prices for the months when logging actually occurred were used.
These prices are provided in Table 12. Reduction factors were used to adjust log

prices by diameter class (Table 13). For instance, a factor of 0.3 for dbh 30 to 45

cm indicates that price per cubic meter is only 70% of the full value.

 

3"These data are collected through MTIB regional offices, which compile the
information into a monthly report called ’Area Timber Price’ and send it to MTIB
Headquarters. These log prices are then published in MASKA YU.

87

Table 11. List of Species Groups Used in the Analysis

 

Species Group

 

Dipterocarps/

Nondipterocarps'

Symbol

 

 

Species (Common Name)

 

HEAVY HARDWOOD (HHW)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Balau DIP BL Balau and Sengkawang Air
Cengal DIP CGL Cengal
Merbau NONDIP MRB Merbau
Red Balau DIP RB Damar Laut Merah, Membantu
Other Heavy Hardwoods DIP/NONDIP OHHW Giam, Resak, etc.
MEDIUM HARDWOOD (MHW)
Kapur DIP KPR Kapur and Keladan
Kempas DIP KPS Kempas
Keruing DIP KRG All Keruings
Mengkulang NONDIP MK Mengkulang
Other Medium Hardwoods NONDIP OMHW Keledang, Punah, Simpoh, etc.
LIGHT HARDWOOD (LHW)
Dark Red Meranti DIP DRM Seraya, Meranti Bukit, etc.
Jelutong NONDIP JLG Jelutong
Light Red Meranti DIP LRM Meranti Rarnbai Daun, Meranti Batu,
Meranti Tembaga, Meranti Kepong, etc.
Mersawa DIP MA All Mersawas
Nyatoh NONDIP NY Nyatoh
Red Meranti DIP RM Gerutu, Gerutu Pasir, etc.2
Sepetir NONDIP SP Sepetir
White Meranti DIP WM Meranti Bakau, Meranti Belang, Meranti
Bumbung, Meranti Jerit, etc.
Yellow Meranti DIP YM Damar Hitam, Damar Katup, etc.
Other Light Hardwoods NONDIP OLHW Penarahan, Machang, Kembang Semangkuk,
Bintanggor, Kedondong, etc.
CONIFER
Damar Minyak/Kuari NA DM Damar Minyak/Kuari

 

 

 

 

NA = Not applicable. Damar Minyak and Kuari are conifers.

' These two groups are important for determining the cutting limits. Cutting limits for dipterocarps
are slightly higher than those for nondipterocarps.

2 Some of the species are listed under LRM.

88

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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90

Table 13. Price Reduction Factors

 

 

DBH Class (cm) Reduction Factor
30 - 45 0.300
45 - 50 0.150
50 - 55 0.025
55 and above 0.000

 

 

The information in Table 13 was obtained from the MTIB (Ahmad, 1990).
These prices were expressed in nominal terms for two reasons: the logging within
each site typically occurred in periods of one year or less, with 29 months as the
longest period in one site, and inflation was low during this period.35

Although log prices in Pahang were generally close to the East Coast
averages, log prices in Terengganu were often 10% to 13% lower. This is because
the state of Pahang is relatively close to many timber manufacturing plants located
in Selangor. Thus, logs in Pahang would be offered at higher prices because of

lower transportation costs.

4.4.1.4 Method of estimation. The linear equation was estimated by the
Ordinary Least Squares (OLS) method and the semilog equation by the Nonlinear
Least Squares (NLS) method. The results are shown in Tables 14 and 15. The
results indicate that both models were consistent with a negative slope of the timber

value distribution curve. All of the coefficients were highly significant at the .001

 

35The percentage annual increases of consumer price index (CPI) for 1989-1991
were between 2.9% to 4.7%.

Table 14. OLS Results for the Timber Value Distribution

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Estimate
Site N R2 Adjusted R2
a B

Pla 380.449' -3.6048’ 1320 .8948 .8948
(2.1200) (0.0340)

Plb 474.536' -3.2935' 1111 .9175 .9174
(2.7473) (0.0297)

P2a 327.5 88' -3 .3437 ° 671 .8244 .8241
(3.1609) (0.0596)

P2b 236.946‘ -1.6621' 704 .8755 .8753
(1.3377) (0.0237)

Tla 336.458' -2.2214' 594 .9516 .9516
(1.7308) (0.0206)

le 298.053' - 1 .9295 ' 737 .9516 .9516
(1.3327) (0.0160)

Figures in parentheses indicate standard errors.
' Significant at the .001 level.
Table 15. NLS Results for the Timber Value Distribution
Estimate
Site N R2 Adjusted R2
a B

Pla 6.0904' -0.0184' 1320 .9445 .9445
(0.00485) (0.00012)

Plb 6.3496' -0.0138’ 1111 .8911 .8910
(0.0093) (0.00014)

P2a 5.9867' -0.0203 ' 671 .9538 .9538
(0.0059) (0.00016)

P2b 5.5370‘ -0.0107 ' 704 .9264 .9263
(0.0049) (0.00011)

Tla 5.9384' -0.0114' 594 .9199 .9198
(0.0089) (0.00013)

le 5.7679' -0.0104' 737 .9162 .9160
(0.0072) (0.00010)

 

 

 

 

 

 

Figures in parentheses indicate standard errors.

‘ Significant at the .001 level.

92

level, and the coefficients of determination (R2) were very high. The relative values
of R2 between the two models in each site did not show any large differences.
Semilog and linear models each give better fits in three cases. Shortcomings
of the semilog function are that it implies an infinite timber volume and an infinitely
high maximum log price, and that deriving the theoretical model using the semilog

function is difficult. Hence, the linear function was used in the analysis.

4.4.2 @ibrating the Production Function Parameters

A Cobb-Douglas production function can be econometrically estimated either
from time-series or cross-sectional data by the Ordinary Least Squares (OLS)
technique (Greene, 1990; Kmenta, 1986). The data required for estimation are
outputs (might be measured by value added) and labor and capital inputs for each
firm across region or yearly data of all industries across a region. In Peninsular
Malaysia, time-series or cross-sectional data on timber production in relation to input
requirements were not available for the logging industry. Thus, the functional
relationship of the Cobb-Douglas production function cannot be estimated
econometrically.

Kmenta (1986) suggests that faced with this problem, one can choose
parameter values using the "calibration" method instead of estimating them
econometrically. In this study, the approach is to use an exogenous estimate of the
labor share to calibrate the production function to data for the endogenous variables

(labor and capital). The calibration procedure used in this study is described below.

93

The short-run total costs (TC) per hectare for the concessionaire in timber
production must equal total variable costs (TVC) plus total fixed costs (TFC).
Variable costs include such items as labor and fuel; fixed costs include such items as
road construction and payments for machinery. The relationship between total costs
and total variable and total fixed costs is given as:

TC = TVC + TFC.

The short-run variable and fixed costs can be represented as follows:

TVC = wL, and TFC = rK.

If the short-run average total costs (ATC) are defined as total costs per unit of
output, then

TC = ATC * V.

Given estimates of ATC and V, total costs can be calculated from the preceding
equation. If the share between TVC and TF C is known, the amount of TVC and
TF C can be estimated from the total costs (TC). Moreover, if the values of w and r
are known, the amount of labor input L and fixed level of capital K can be estimated
using the following relationships:

L = TVC/w,

K = TFC/r.

Given this information, the parameters of the Cobb-Douglas production
function can easily be determined. The Cobb-Douglas production function has a
labor share parameter (7) and an efficiency parameter (A). The labor factor share is

estimated as follows:

 

Once the factor share is determined, the efficiency parameter can be determined by

inserting V, L, K, and 7 into the Cobb-Douglas production function:

V
LrKl-v

 

A:

Data required for calibrating those production function parameters include
timber volumes, average logging cost, wage rate, interest rate, and labor share. Data
on timber volumes are the same as described in section 4.4.1.2. Actual harvest
volumes were adjusted for logging damage, based on checking station records.

The average logging costs for each state were RM63.83/m3 in Pahang and
RM60.83/m3 in Terengganu. The estimate in Pahang was based on a previous study
(Apong, 1989), and in Terengganu it was estimated from information provided by a
concessionaire. It was assumed that the average logging costs were constant within
the state. The estimates of average logging costs were in nominal terms and
included a normal, post-tax profit margin (5). The pre-tax profit margin was
estimated to be 22%, based on information provided by logging contractors. It was
assumed that the corporate income tax was 40%. Thus, the estimated post-tax profit
margin was 13.2%. The average post-tax logging costs, exclusive of profit margins,

were MR56.39/m3 in Pahang and MR53.74/m3 in Terengganu.

95

Wage differentials in the logging industry vary according to type of
occupation, skill level, nature of work, and region. The wage paid is based on a
monthly payment or a piece-rate, depending on the nature of work done by the
workers. In this study, the wage rate for felling activities was used to reflect the
average wage rate of all categories of workers in the logging industry. The wage
rate was set equal to RM30.00 per man-day in all states (Ku Azmi, 1993;
Shaharuddin, 1993).

The interest rates should necessarily reflect the cost of capital to the logging
industry. In this study, prime or base lending rates (BLR) were readily available and
could be used as a general measure of the cost of capital. However, in practice, the
actual interest rates charged by commercial banks and finance companies are
normally 2% higher than the reported prime lending rates. Information obtained
from commercial banks and finance companies indicates that the market interest
rates range from 10% to 12%. In this study, the interest rate was set at 12%.

The labor share was calculated using logging cost data for 1979 to 1981
reported by Chung (1984). The calculated value of 7 was 0.53, and it was treated as
constant for all states. Using all the information described above, the estimates of
TC, TVC, TFC, L, K, and A were obtained and are shown in Table 16.

The estimates presented in Table 16 represent only two major costs (labor, L
and capital, K), because the production function assumes that harvest is a function of
L and K only. K can be thought of as the sum of all fixed factors (e.g., road

construction and overhead, as well as equipment), while L can be thought of as all

96

variable factors. Timber management costs are not included because the government
is responsible for forest management activities.

If one had used an econometric approach to estimate the relationship among
labor (L), capital (K), and harvest (V) at the individual concessionaire level, the vales
of the factor shares (7, 1-7) and the efficiency parameter (A) might have differed.

Uncertainty of these values is evaluated through sensitivity analysis.

4.4.3 Logging Damage

Logging damage can be in the form of physical damage to the trees
(seedlings and saplings and advanced growth) or to the environment (soil, water,
nutrient, habitat for wildlife, so on). In this study, logging damage referred to all
dead or damaged, standing or down, trees above the cutting limits.

The parameter of logging damage was calculated using the following
formula:

0 = VD/ V * 100, where o is the percentage of logging damage, V is actual
harvest volumes, and VD is volume of timber damaged. The actual harvest volume
(V) was based on checking station records. Checking stations record information on
the actual volume of timber extracted from the forest. The values of a for each site

are provided in Table 17.

4.4.4 Other Variables and Parameters
Information on logging fines and volume of timber above the cutting limits
left in the forest was used to estimate u. The fine is based on RM400 per tree. This

fine is also applied to trees harvested below the cutting limits. If one assumes that

97

Table 16. Estimates of Cost Values and Production Function Parameters

 

 

 

 

 

 

 

 

 

 

 

Site Total Costs Total Variable Total Fixed Labor Capital Efficiency
Costs Costs Parameter
(TC) (1‘ VC) (T F C) (L) (K) (A)
(RM/ha) (RM/ha) (RM/ha) (RM/ha)
Pla 3,648.19 1,933.54 1,714.65 64 14,289 0.0832
Plb 2,976.48 1,577.53 1,398.94 53 1 1,658 0.0832
P28 2,010.29 1,065.45 944.84 36 7,874 0.0832
P2b 3,326.84 1,763.23 1,563.62 59 13,030 0.0832
Tl a 3,389.98 1,796.69 1,593.29 60 13,277 0.0793
le 3,979.23 2,108.99 1,870.24 70 15,585 0.0793
Table 17. Logging Damage
Site Volume (m3/ha) Percent
Pla 1.93 2.84
Plb 2.46 4.44
P2a 2.01 3.48
P2b 2.01 3.13
Tla 3.26 5.42
le 0.68 0.96

 

 

 

98

a loss function giving the opportunity cost of deviations from the SMS prescriptions
(u(V5’- V)2) equals the total fine per hectare for timber above the cutting limits lefi in

the forest, then:

400m = p(VS’-V)2,

where n is the total number of unfelled trees above the cutting limits, then u can

simply be estimated using the following formula:

400*n
(VSY-Vf'

The numerator is the total fine per hectare for timber left in the forest: logging fine
(RM400.00 per tree), times the number of trees above the cutting limit (n), left in
the forest per hectare. The denominator is the square of the total volume of timber
above the cutting limit left in the forest. This residual volume was calculated from
the post-F inventory, as was n. The values of u were estimated separately for each
site.

The cost of royalty collection (c) was estimated at MR3.20/m3 in Pahang and
MR3.10/m3 in Terengganu. The estimated values were based on 5% of tree-marking
and post-F inventory costs of the total logging costs plus the cost at the checking
station. Information on the proportion of tree-marking and post-F inventory costs
was based on concessionaires’ estimates, and the cost at the checking station was

calculated from data provided by the Forestry Department.

99

The values for each variable are provided in Appendix C. These values were
used to solve all empirical models shown in Tables 5 through 9. The models were
solved using the SIML command of the econometric computer program, PC-TSP
Version 4.2A (Hall, 1991). The simulation results for all five models are included

in Appendix D. The results are discussed in the next chapter.

CHAPTER FIVE

EMPIRICAL ANALYSIS: RESULTS

5.1 Introduction
The results of the five models outlined in Chapter Four are presented in this
chapter. First, the results of model validation are presented. Then the simulation
results for four key endogenous variables (royalties, premiums, government

revenues, and harvest levels) are presented.

5.2 Model Validation

Model validation refers to a procedure in which the performance and the
structure of the model are compared empirically and theoretically to the real world
situation (Isaac & Michael, 1982; Robinson & Frey, 1983). Validity tests indicate
whether a model provides accurate and acceptable predictions of the behavior of the
system being simulated (Hiller & Lieberman, 1980). Model validation is also
required because of uncertainty in assumptions and data used, and omissions of
some variables in model structure (Hufschmidt, James, Meister, Bower, & Dixon,
1983; Meerschaert, 1993; Robinson & Frey, 1983). The main objective of model

validation is therefore to determine whether the model provides a sufficiently

100

101

accurate representation of reality to be used in examining the potential effects of
optimal royalties.

The calibration approach employed in this study implies that, on an equation-
by-equation basis, the model replicates real-world behavior perfectly. Comparing
predicted values to actual values is therefore not a meaningful way of validating a
calibrated model. An appropriate approach is to evaluate whether the individual
equations provide reasonable representations of behavior, and to conduct sensivity
analysis. As noted in Chapter Four, the use of the Cobb-Douglas production
function provides a reasonable representation of concessionaires’ harvest behavior.
The fact that the models did not predict actual harvest well is not surprising and of
little consequence to the results. In the real world, concessionaires face minimum
diameter cutting limits, which the model ignores, as the focus was on revenue
system alone as a means of regulating harvest behavior. If the cutting limit had
been included in the model, then the predicted harvest would have been
approximately near to actual harvest.

A sensitivity analysis was carried out to investigate the impacts of uncertainty
about data. As explained in chapter four, some of the parameters used are known
with certainty, while others are not. Sensitivity analysis enables one to examine the
extent to which uncertainty about the data affects confidence in model output.
Meerschaert (1993) suggests that a useful way is to calculate relative change or
percent change, rather than absolute values. This approach was adopted in this

study. Each model was solved to determine the initial values of the endogenous

102

variables; then each model was rerun by increasing by 1% a given exogenous

variable or parameter. Elasticities were then calculated using the following formula:

Y, is the initial value of an endogenous variable, and Yl is the value after the change.
Thus, elasticities measure sensitivity of model output to a parameter change. If the
elasticity is high, then the model is very sensitive to values of that parameter.
Detailed results for three key endogenous variables (royalty, harvest volume,
and government revenue) are presented in Tables E1 through E5 in Appendix B.
These variables, and three parameters (7, oz, and 6) were chosen to explain the
results because of their importance as decision variables for the government and
concessionaires. As seen in Tables E1 through E5, it is surprising that government
revenue and royalty are sensitive to parameters in the timber value distribution. As
expected, the labor share (in a model in which labor is the only private choice
variable) has a great impact on harvest volume. Further results indicate that
royalties were elastic with respect to labor share (7) in four of the models.
Government revenues were elastic with respect to maximum log price (a) in all
models. Royalty was sensitive to the slope of timber value distribution (3) in the
dynamic models. The results also showed that harvest volumes were elastic with
respect to labor share (7) in static models, and to maximum log price (a) in Model

III only.

103

The results of sensitivity analysis therefore indicate that simulation output is
very sensitive to these three parameters. Nevertheless the simulation results might
be acceptable with respect to a and 5. Since data on log prices were obtained from
an official publication, it is less likely that these data were unreliable. This was also
true with a and B. In the regression analysis done in the study, the estimated
coefficients of determination in estimating a and B were high. However, the
parameter 7 was based on much less reliable data. If more data are collected, labor

share parameter should be given due consideration.

5.3 Simulation Results
The magnitudes of the optimal levels of royalties, premiums, harvest
volumes, and government revenues from the base-case analysis for all models are
presented in Tables 18, 20, and 21. Major findings of the analyses are presented in

the following sections.

5.3.1 Static and Dynthic Uniforn_i Royalty System

 

As seen in Table 18, the optimal levels of royalty rates differed substantially
in the two models. Within a given model, the rates also differed among sites.
Depending on a particular site, Model 11 royalty rates were 23% to 108% less,
compared to those in Model I. The royalty rates obtained in Model 11 conformed to
theoretical expectations: that is, the royalty rates in Model 11 were less than those in
Model I when the volume of the prescribed harvest level (W) was greater than the
simulated static harvest level (V). Model I harvests were below the prescribed

harvest level. Hence, Model 11, in which the government values this deviation,

104

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105

predicts that royalty should be decreased to induce the concessionaire to harvest
more. The royalty rates in Model I ranged from RM117.44 to RM235.36 per m3;
they ranged from RM76.42 to RM119.83 per m3 in Model II. The differences
reflect the variation in maximum log price and the slope of timber value distribution.
In comparison to the figures shown in Table 14, the results indicate that higher
royalties can be imposed on more productive sites (i.e., sites with large values of a
and B).

In Model 11, the cost of deviation, ,1, imposed on unharvested timber above
the cutting limit or timber harvested below the cutting limit, discourages the
concessionaire from deviating too far from V”. Because the concessionaire’s
objective function is the same in both the dynamic and static models, the
government reduces the royalty to induce larger harvests. The lower royalty
provides concessionaires an incentive to hire additional labor.

The results also show that the ranking of royalties can differ in Model 11 as
compared to Model I. For example, the two highest royalty rates in Model I are for
sites Pla and Plb, but the rates are moderate in Model II. On the contrary,
moderate royalty rates are found in sites Tla and le in Model I, but these sites
show the highest royalty rates in Model 11. However, the lowest royalty rates in
sites P2a and P2b are also the lowest in Model 11. These variations result from
differences in the cost of deviation, u. Sites P2a and P2b have the highest values of
p. All else being equal, the royalty rate in Model 11 is low when the cost of
deviation is high. The results indicate that the government is more willing to lower

the royalty rate when it highly values compliance with V".

106

The royalty rates in both models are higher than the actual royalty rates
(compare to Table 19). This suggests that the present royalty rates are well below
the optimal level, from either a static or a dynamic perspective. For instance, the
lowest optimal static and dynamic rates which are for site P2b, are each at least four
times larger than the actual royalty rates in Pahang (Table 19). They are also higher
than those in Sarawak, but they are mixed when compared to the rates in Sabah for
export logs. The optimal royalty rates found in this study also tend to be larger than
the actual rates reported for other tropical countries (Gillis, 1980; Gillis & Repetto,
1988; Gray, 1983; Page et al., 1976; Ruzicka, 1979).

The premium indicates the maximum lump-sum amount that the government
could charge the concessionaire in addition to royalty. In Table 18, the premium
rates are much higher than the present rates: 3 to 8 times higher in Model I, and 10
to 25 times higher in Model II. Model II premiums are higher than those premiums
in Model I by a factor of two in four of the six sites. Thus, the decrease in the
royalty in Model II is offset by a corresponding increase in the premium. In
general, the lower the royalty, the higher the premium.

Premiums vary among sites because of variations in timber value distribution
parameters. These variations affect the concessionaire’s total revenue. The reason
the premium is so high in site Plb is obvious from the maximum log price. Site
Plb has the highest value of 0: compared to the values of a in the other sites. If the
government collects revenue solely through a uniform royalty, then the premiums
indicated in Table 18 represent the magnitude of windfall profits to the

concessionaire.

107

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108

It is important to consider the results of harvest volume when evaluating
concessionaires’ harvest behavior under different government objectives. This is
because the simulated harvest volume will determine the degree to which
concessionaires deviate from the prescribed harvest level. In Table 18, Model I
harvests are smaller than Model 11 harvests. In absolute terms, the deviations range
from 14.30 m3 per ha to 53.17 m3 per ha in Model I, and from 21.48 m3 per ha to
49.71 m3 per ha in Model II. The results indicate that the uniform royalty system
induces concessionaires not to harvest all timber above the cutting limits. This is
expected because higher royalty will reduce labor input, which causes
concessionaires to reduce harvests. This finding is consistent with the conclusions
from Gillis (1980, 1988a).

Government revenues in both models are substantial. Surprisingly, Model 11
revenues are greater than those of Model I. One would expect that the higher
royalty in Model I should provide higher government revenue. However, further
examination reveals that the significant share of premium plays a role in relation to
government revenue (Figure 7). Thus, premium payment becomes more significant
than royalty payment in the dynamic model. The reasons for this were explained
earlier. The average shares of premium are 22% and 46% for Models I and II,
respectively. The significance of premium share to government revenue in Model II
suggests that the government could lower the royalty rate and obtain a higher
premium to induce a larger harvest, in order to achieve the dynamic forest

management objective. A government that chooses to use only the royalty to

109
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40-’
30-’

«awoken.

 

T1b

T1a

Site

 

 

“:3 Royalty Premium |

Figure 7. Royalty and Premium Shares of Government Revenue

(Uniform Royalty).

110

maximize revenue, and determines the premium residually, will actually raise less

revenue than one whose objective is not simply to raise revenue.

5.3.2 _S_tgtic and Dvna_r_nic AMlorem Rovgm Systems

As can be seen in Table 20, the optimal levels of ad valorem royalty rates
vary with respect to different government objectives and among sites. The estimated
royalty rates in Model IV are lower than those in Model 111. The lower royalty rate
estimates obtained in the dynamic model are parallel to the results obtained in the
case of uniform royalty. The rates obtained in this study are higher than the
stipulated 10% of the log price. The rates in Model IV range from 11% to 16%;
they range from 26% to 33% in Model 111. The variations in the two models with
respect to different sites are not very large. The largest variations are 12% and 5%
in Models 111 and IV, respectively. Variations among sites in ad valorem royalties
are not very large because of the structure of the optimal royalty formula: it is the
ratio between the two values. Even though there are differences in timber value
distribution, cost of collecting revenue, and harvest level, these parameters do not
significantly affect the ratio.

As for premium, the rates obtained in the static model are lower than the
rates found in the dynamic model, and they also vary by site. As can be seen in
Table 20, the premium rates in Model III range from about RM2,400 to RM13,000;
they range from RM6,000 to RM22,500 in Model IV. Thus, the premiums in the
dynamic model are about double those in the static model. The higher premiums in

Model IV are associated with lower optimal royalty.

111

3.80. 525.55% 2.. .2. .55.. .538 o... 08......

 

 

 

 

 

 

 

 

 

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.13.... 5...” 8.3. m... 3.... .mdn .96 mm... 2.2. 2...... 2...
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u... .o 5. M2 .9 .5. .55
0....0>0% 05......» 5.93% 0.503% 05......» 3.93% 3.5....
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.05...e> .05...e>
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2:08.... .0.0..055eu

 

 

 

 

 

50.9mm 5.53% 50.3.5; 3... .5 5.. 8.53% 555.555 ...N 03:...

112

Model 111 harvests are below the prescribed harvest level in all sites. In
contrast, Model IV harvests are above the prescribed harvest level in four of the
sites. The results indicate that the static ad valorem royalty encourages leaving
timber above the cutting limits, but the dynamic royalty could either result in leaving
timber above the cutting limits or harvesting timber below the cutting limits. In
reality, these effects cannot be observed because the royalty system in Peninsular
Malaysia is only loosely an ad valorem system (refer to section 2.5).

The results also show that government revenues are substantial. Model IV
revenues are greater than those in Model 111. A substantial share of government
revenue comes from royalties, about 62% and 82% in Models 111 and IV,
respectively (Figure 8). Thus, a lower royalty as found in Model IV is offset by
higher premiums, which contributes greatly to higher government revenue. This
suggests that a government that chooses to use only the royalty to maximize
revenue, and determines the premium residually, will actually raise less revenue than

one whose objective is not simply to raise revenue.

5.3.3 Static Premium

Premium, harvest volume, and government revenue from the simulation
analysis of Model V are presented in Table 21. The premiums indicate the
maximum amount that could be charged per ha when a concession is to be harvested
(without levying a volume-based royalty). The premium rates differ substantially
among sites, and they vary by a factor of more than two. In four of the sites, the

premium is more than RM10,000 per ha. The highest premium rate is in Site Plb,

113

(a) Model III

Percent

 

P1 a P1 b P2a P2b T1 a T1 b
Site

 

ll: Royalty - Premium J

(b) Model Iv

Percent

 

P1 a P1 b P2a P2b T1a T1 b
Site

 

|[::j Royalty - Premium I

Figure 8. Royalty and Premium Shares of Government Revenue
(Ad Valorem Royalty).

5.350.. 8.5.55.5 0... 8.. .55.. .5505 055 050......

 

114

 

59.3. ovdo. Manx“. $.55 $5.: 5....

25.2 55.8. :3: 2.5.. 3.2. 5....

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mm...» :55 35.x 59mm $65 5a..

oov.mm $5.: «gram 55.2: mem. a...

m.m.m. 55.3. Sim. 35> 2.3 5...

.5555 .255 .5525 .555 .255

05>.
55...... 555.6

0.505% 053.5 0... 0.52.3 .50 an N.

5055.025 50.55% 55.50.... .05...0> .05....5 0.5
50.55% 5052050.... .35.... 5.205550

 

 

 

 

 

 

83.5.... 535 2.. a... 5...... 55.355 ..N 2......

115

whereas the lowest is in Site P2b. It is not surprising that the static premium
induces a harvest below the cutting limits, which approaches the commercial timber
volume. The harvest volume is greater than the prescribed harvest level in five of
the sites, a range from 8 m3 per ha (10%) to 40 m3 per ha (60%). The harvest
volume for site Plb is very close to the prescribed harvest level (harvest deviation is
very small, 1.8%). Government revenue ranges from RM8,000 per ha to about

RM22,4OO per ha, indicating 4 to 14 times more actual government revenue.

CHAPTER SIX

DISCUSSION AND CONCLUSIONS

6.1 Discussion

The economic and empirical models developed in this study differ in view of
the government’s forest management objectives: maximizing revenue (static models--
Models 1, III, and V) and maximizing revenue and future benefits (dynamic models—-
Models II and IV). Comparing the simulation results to draw lessons for policy
requires comparing the results for models having the same objective.

When the forest management objective is only maximizing revenue, the
preferred revenue system is one based on the premium (Model V). This suggests
that the government could collect almost 100% of the stumpage value by auctioning
without distorting logging decisions. The results for the premium could be used as
reservation price in auctioning.

Model V predicts that concessionaires are likely to harvest almost all timber,
in some cases close to commercial-sized timber volumes (compare Tables 18 and
21). Overharvesting occurs because concessionaires have no incentive to recognize
V57 without sufficient enforcement of logging regulations. On the other hand, if the
government relies on uniform or ad valorem systems, much mature timber goes

unharvested (Figure 9). Hence, Models I and III show royalty-induced selective

116

Percent

117

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-20-

\\.\.\\\._ \ \ \\
\

 

 

 

 

-4o-

 

 

 

 

 

 

-60 r r r r r 1
P1 b P1a P2a T1a P2b T1 b

Site

 

[:| Model I Model Ill Model V

 

 

 

Figure 9. Prescribed Harvest Volume Minus Actual Volume (VSY-V),
Static Models.

118

logging. The results indicate that the deviations are greater under uniform royalty
than they are under ad valorem royalty. It appears that the results obtained in this
study are consistent with Gillis’s (1980, 1988a) and Hyde and Sedjo’s (1992)
findings that uniform and ad valorem royalties result in incomplete harvests above
the cutting limits. The larger timber harvest below the cutting limits obtained in
Model V is expected because the concessionaires’ harvest decision is not affected by
premituns. With the tendering system, the first-order condition for the choice of
labor input (L) is not affected by the premium (m). Thus, the profit-maximizing
behavior of concessionaires is not distorted; they would otherwise be tempted to
harvest more timber that provides positive stumpage value. Under the royalty
systems, the optimal labor input will be determined once the optimal royalty has
been decided by the government. Because the royalty rate is fixed by the
government, the concessionaires will reduce their optimal labor input, causing them
to deviate from V”.

Without considering future benefits and at prevailing public and private sector
discount rates in Malaysia, there is indeed an economic loss when mature timber
goes unharvested, as shown in Models I and III. Given that royalties are chosen to
maximize short-run revenue and ignore future benefits, the losses may be very large.
The government suffers a loss in revenue, which reduces the funds that potentially
could be used for forest management, among other uses. The loss, on average, is
estimated at 28% and 21% from Models I and 111, respectively, relative to the

amount of revenue that would have been estimated under the tendering system.

119

In the dynamic objective, the government seeks to maximize not only current
revenue but also future forest benefits. The preferred revenue system can be
determined by comparing the values of the objective functions for Models II and IV.
The values of the objective function were computed by subtracting the opportunity
cost of over- or under-harvesting from total government revenue. The cost of
deviation indicates the reduction in the discounted value of future harvests, due to
either overmature timber left in the forest or immature timber harvested in the
current cutting cycle. The results are presented in Table 22. The results shown in
the table indicate that the values of the objective function in Model IV are much
higher than those in Model 11. This suggests that ad valorem royalty is superior to
the uniform royalty under the dynamic forest management objective. This finding
seems to support Gillis (1980), Gillis & Repetto (1988), and Vincent (1990), who
called for replacing the uniform fixed royalty, which is the most common type of

royalty levied by tropical countries, with alternative fees.

Table 22. Values of the Government Objective Function (RM/ha)

 

 

Site Model II Model IV
P1a 11,038 13,452
Plb 18,432 26,361
P2a 7,777 9,999
P2b 6,924 7,361
T1a 11,537 14,586
T1b 10,386 11,504

 

 

 

120

The ad valorem royalty induces small violations of the cutting limits, ranging
from 1% to 10% of the prescribed harvest volume in four of the sites (Figure 10).
Limiting harvests to the prescribed volumes would require sufficient enforcement of
cutting-limit regulations, which involves substantial cost. On the other hand, the
uniform royalty system may result in leaving timber volume above the cutting limits.
The results show that unharvested mature timber ranges from 4% to 21% of the
prescribed harvest volume, which is within the allowable timber volume that
concessionaires can leave in the forest. Thus, the uniform royalty may not entail
greater long-term costs than the Forestry Department is willing to tolerate. Both
mature timber going unharvested and immature timber being harvested cause
dynamic losses (that is, the fourth term on the right-hand side of Equation 17) . The
dynamic losses are higher under a uniform royalty system, ranging from 1% to 10%
of the government revenue, compared to 0.2% to 3% percent under an ad valorem
royalty system.

It should be pointed out that the management of forests for static and
dynamic objectives does not depend solely on the forest revenue system. As seen
above, different levels and structures of forest revenue systems affect socially
optimal behavior and government revenue. Other public options also play an
important role in achieving forest management objectives. These include the
structure of the concession contract, provision for financial incentives, and
privatization of forest management activities. These options can be achieved

through legislation and other related forest policies and laws.

Percent

121

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-10-

 

 

 

 

 

-15 1' 1’ .' 1' .' fl
P1 b P1 a T1 a P2a T1 b P2b
Site

 

1:] Model ll

si\‘\‘\§t Model IV

 

 

 

 

Figure 10. Prescribed Harvest Volume Minus Actual Volume (V SY-V),
Dynamic Models.

122
6.2 Policy Implications

The government is now concerned about forest revenue system because of its
impact not only on revenue but also on concessionaires’ harvest behavior. One
important assumption in making policy prescriptions is that the government will
continue to maintain forest ownership. Hence, the amount of stumpage value
captured by the government must be sufficient for financing forest management.
There are two options available for increasing revenue--raising the present royalty
rate or changing the forest revenue system. The results of this study provide some
insights into these options.

If the capture of stumpage value from current harvest is obviously low, then
the main policy suggestion is to raise the present royalty rates consistent with the
government’s real forest management objective. The government’s real forest
management objective is to maximize long-term socio-economic benefits of forest
resources (section 2.6). Therefore, the estimated dynamic uniform royalty rates
could be used as a basis for setting the optimal rates.

The present royalty rates have remained virtually constant over a long period
of time. Only recently have some of the states increased their timber fees. The
results from this study suggest that royalty and premium rates are in fact far below
their optimal levels. These rates could be increased without eliminating normal
profit margins for concessionaires. The new rates should take into account many
factors, such as timber harvesting costs, log prices, profit margins, transaction costs,
and risks and uncertainty. Higher timber fees not only provide more funds for

respective state governments, but they also provide more financial allocations for

123

forest management activities. Thus, increasing timber fees can be fully justified for
long-term sustainable harvest level. The optimal rates can be analyzed if further
additional information (such as logging cost, normal profit margins, cost of
deviation) is available. The availability of computer hardware and software for
economic modeling now makes such efforts possible.

The second policy option--changing the forest revenue system--will also
depend on the government objective. If the government objective is purely
maximizing revenue, the preferred forest revenue system is tendering. A tendering
system is already being used in State Land Forests, where logging is carried out by
clear felling (that is, without a minimum-diameter cutting limit). Tendering also
shifts the risks from the government to the concessionaire. This is because the
government can formulate a reservation price based on information from the pre-F
inventory. The tender price can be estimated in advance based on stumpage value
for a given compartment. The types of tendering, however, can be decided on
depending upon the type of concessionaire (bumiputera, or concessionaires who have
wood-based plants) or the scale of operation (small, medium, or large). Tendering
would require a certain degree of competition. Given that a competitive market is
apparently functioning well in the logging industry, the tendering system is likely to
be preferred over uniform or ad valorem royalties.

If the government objective is maximizing both revenue and future benefits,
the government could probably replace the uniform royalty with the ad valorem
royalty. This would mean setting the royalty rate based on the percentage of log

price for extracted logs. The premium can be determined by tendering, which can

124

be estimated in advance as a residual using zero-profit condition of the
concessionaire. The minimum reservation premium rate can be decided if the
government is responsible for forest management. On the other hand, the
government can provide sufficient financial incentive to the concessionaire if the
concessionaire is allowed to be involved in and responsible for forest management.
Giving a financial incentive to the concessionaire would make the benefit of the ad
valorem royalty more apparent.

This royalty system can best be applied to forests in the Permanent Forest
Estates (PFE), which restrict logging to trees whose dbh is above a specified
minimum-diameter cutting limit according to the SMS. This is because the
government is concerned with small harvest deviations (VSY- V), which is necessary
for second-growth and protecting nontimber benefits. The model predicts that the
deviation is small, and this requirement can be fully justified. In addition, strict
enforcement of logging regulations might not be needed. New guidelines for timber
harvesting (incorporating the volume-area approach in timber harvesting) and well
defined legislation would make ad valorem royalty more favorable than uniform
royalty. However, information on log prices needs to be maintained and updated

regularly.

6.3 Conclusion a_nd Recommendations for Further Studies
In conclusion, the empirical analyses revealed that the present royalty and
premium rates are far below the optimal level, regardless of whether the

government’s objective is short-run or long-run. Optimality in this sense refers to

125

the optimal level of royalty that is associated with the highest revenue or revenue
and future benefits to the government without affecting the normal profit margin for
the concessionaire. Tendering is the preferred system when the only objective for
forest management is to maximize revenue. Tendering captures the full stumpage
value, even though harvesting below the cutting limits is greater. The static uniform
and ad valorem royalties induced leaving timber above the cutting limits, which
implies an economic loss. The driving mechanism is the royalty rates, which signal
concessionaires to reduce their harvest levels below the prescribed level. However,
when the objective of forest management is to maximize revenue and future benefits,
the preferred forest revenue system would rely upon ad valorem royalty. This is
because the highest value of the dynamic objective function (which is accounted for
by the reduction in the discounted value of overharvesting timber below the cutting
limits) could be obtained, compared to those provided by a uniform royalty.

Because overharvesting is too small under an ad valorem royalty, the appropriate
royalty rate could be determined to provide a greater incentive to concessionaires to
abide by the cutting-limit regulations.

Although the static objective is expected to generate higher revenue than the
dynamic objective for uniform and ad valorem royalties, this was not the case when
the premium was included and determined residually. The government revenue for
uniform and ad valorem royalties is always higher in the dynamic models than in the
static model when revenue from the residually determined premium is included as
well. The lower royalty rates obtained in the dynamic model induce larger harvests,

which significantly affect the magnitude of government revenue in the dynamic

126

model. Choosing the optimal royalty to maximize revenue and future benefits, and
determining premiums residually, will actually raise more revenue than choosing the
optimal royalty only to maximize revenue. The values of both static and dynamic
optimal royalties and premiums were found to be much higher than actual rates.
Government efforts aimed at revising and increasing the present royalty and
premium rates will increase government revenue and enhance future forestry
benefits.

The results of this study demonstrate the importance of revising the forest
revenue system in Peninsular Malaysia and suggest the magnitude of optimal
royalties. However, there are some caveats which affect the reliability of the results
and to what degree they can be used in making generalization. The assumptions
used and data available for this study were subject to some constraints. These
include simplifying assumptions about the role of natural systems, optimal cutting
cycle and diameter cutting limits, and the use of uniform data such as log price,
logging cost, damage rate, and labor share. The interrelationship between the human
system and the natural system is complex. Excluding the natural system from the
modeling framework makes it impossible to study other types of environmental
impacts of decisions made by the government and concessionaires. The length of
cutting cycle and cutting limits were taken as given and were assumed optimal.
This restricted the analysis to calculate optimal royalties. Ideally, the choice of
optimal cutting cycle, cutting limits, and forest revenue system should be considered
simultaneously to study combined effects on harvest behavior. Some errors related

to volume estimation might have occurred due to sampling procedures and formulas

127

used. Some of the data used in the study were based on average condition or
uniform values--uniform price for all species within each of the three miscellaneous
categories (i.e, OLHW, OMHW, and OHHW), uniform logging cost for all timber
within a given compartment, uniform damage rate for all timber, and uniform labor
share for all sites. Values might not be uniform in reality. The use of uniform
values could result in upward or downward biases in the solutions. Due to these
limitations, efforts to improve the models through future studies should be pursued.

Several additional studies are outlined below. Firstly, the fact that the high
cost of deviation affects concessionaires’ harvest decision suggests that logging fines
should be reviewed. Logging fines need to be determined for timber above and
below the cutting limits. A study should also be done to analyze the effects of
financial incentives on concessionaires’ harvest behavior.

Secondly, a study is needed to analyze the effects of proposed forest
concession policies in relation to transaction costs (cost of contracting, information,
and policing). This relationship has been given little attention in the past. Even
though it was included in this study, the analysis is not comprehensive enough to
take into account all associated costs involved in the contractual arrangements. This
is important if the government’s policy is to restructure the concession contracts.
The evaluation of risks and uncertainty in concession contracts should also be
included in such a study, as should concessionaires’ willingness to pay for the off-
site environmental Costs of logging.

Thirdly, an extensive empirical analysis is needed to study the effects of

timber fees on harvest volume and government revenue in other states of Malaysia.

128

This is because optimal timber fees may be highly sensitive to log prices and
logging costs with respect to locations. In particular, results from sensitivity
analyses have shown that data on log prices, forest characteristics, and labor share
should be collected intensively. Emphasis also should be given to analyzing the
trend of log prices. Thus, an adjustment can be made to the level of royalty rates
with respect to changes in log prices. In addition, such a study should focus on the
structure and distribution of forest growth under varying forest revenue systems.

Fourthly, a study of harvesting costs under different spatial locations of forest
concessions and harvesting methods is necessary. Such a study would be timely
because no intensive research has been carried out to investigate the economics of
timber harvesting. Information on logging costs, normal profit margins, and
elasticity of substitution is limited or not readily available. Because any optimal
forest revenue system will depend heavily on the economics of timber harvesting,
such information is critical.

Fifthly, a study of cutting cycle and diameter cutting limit in relation to
production functions and how they are affected by changes in timber fees, log price,
management costs, and interest rate is required. Efforts might be directed at
developing a comprehensive microeconomic model of concessionaires’ and
government behaviors, which incorporates all these variables. Detailed empirical
analysis can be performed to estimate parameter values using an econometric model.
Since databases are still lacking for testing models specification and functional

forms, data collection on variables identified above should also be given high

priority.

129

All of the proposed studies could be done at the local, regional, and national
levels. An integrated approach is useful in gaining a better understanding of the
optimal forest revenue system and its effect on concessionaires’ harvest behavior and

government revenue.

APPENDIX A
Derivation of Ad Valorem Rwalty: Static and Dynamic Models

1. Static Model

The concessionaire’s production function is taken as CBS

.1 (Al)
V = AWL-”"0 ‘Y)K-p] "

The objective function of the concessionaire is written as

Max 1: = (1-o)(l-t)f0V.P(V)dv-WL(1+6)—rK(l+6)-m (A2)
L

The timber value distribution P(V) is linear, i.e.,

P(V) = a-BV

Integrating this function yields

fOV'Pmdv = (a V-sz) (A3)

130

131

The first-order condition for a maximum profit is

(1-o)(l-t)(a-BV)VL = w(1+6) (A4)

Note that the net marginal value product of labor will shift downward at every level
of labor input. Royalty payment is thus equal to percentage of log price for each
cubic meter of logs extracted. From the first-order condition, the marginal product
of labor is given by

V = ”0““) (A5)
L (1-0)(1-t)(a-BV)

The optimal level of labor input, L' is obtained by equating Equation A5 and

the partial derivative of Equation A1 with respect to L,
(A6)
L “ = V‘A "’°[k(l -t)(a -[5V"')]6
where

__y(1-o) and 6 - .
w(1+5)’ l+p

 

132

The objective function of the government is written as

 

. (A7)
Max R = (l-o)tf0V P(V)dv+m—c(1—o)V‘
t
The first-order condition of government maximization yields
(«Vt—EV”) = -[t(a-cV*)-c]VLaL (A8)
2 a:
where aL’/6t is obtained by differentiating Equation A6 with respect to t,
t _ t -p0 _ 1:
6L = VA 6k(a BV) (A9)

 

 

at g 1'6 'A ‘°°[Y -k0(l -t)l3 ViVL]

and g is given by k(l-t)(a-BV). Substitute (if/at in Equation A9, and obtain the

general expression for t

(a V‘-—'23—V‘2)[11-°—A ‘99” -ke(1—t)p V‘VL}]

= [t(a -l3 V‘)-c]A "peVL[V*6k(a -[3 V*)] (A10)

To solve Equation A10 for optimal royalty, t', substitute the values of 9
corresponding to the Cobb-Douglas and Leontief production functions, and after

doing much calculus, the following results were obtained:

133
Cobb-Douglas (9 = 1):

(a V*—-g—V'2>[1—v+-—Y-9-Z—1+ch‘

 

tstcn = “1:?” (A11)
(a V‘-—"—V‘2)[1—y+—Y—9—1+v(a—BV‘)
2 (a- V')
Leontief (9 = O):
p = 1-1 _1 A12
5'” k[(a-BV‘) ( )

2. Dynamic Model
The objective function of the concessionaire and results obtained in the static
model still hold and are used in the dynamic model. The objective function of the

government is written as

Max R = (1—a):f0"'1>(V)dv+m-c(1—a)V“-p(V"—V*)2 (A13)

The first-order condition of government maximization yields

(a V‘—%V‘z) = —[t(a —BV‘)—c+2u(VSY-V’)]VL% (A14)

where 61.761, as shown by Equation A9. After substituting 6Li/6t into Equation

A14, the general expression for t can be written as

134

(«V‘-%V'2>[1'P°-A"Pew-keu-W‘Vgn

= Ma -9 V')-c+62£_)(VSY—V‘)]VL[V‘Bk(a —p V*)] (A15)
’0

Using the same procedure as in the static model, the optimal level of royalty
is obtained as follows:

Cobb-Douglas (G = 1):

(«V’gV‘ZHI-v+—Y[fll;]+[c-—2i‘—(Vs’-V*)IYV‘]

 

t£,ca= (“"1"”) * (1”) (A16)
(a V'-£V‘2)[1-v+—YEV—-1+YV*(a~91")
2 (a—pvu
Leontief (9 = O):
t‘ = 1-1 —1—— (A17)
D” k[(a-BV‘)]

which is the same as Equation A12 derived in the static model.

APPENDIX B

Comparative Static: Uniform Royalty System

The purpose of comparative static analysis is to study the effect of changes in
certain parameters on the equilibrium (optimal) level of decision variables when
certain assumptions change, holding other variables or parameters constant (Hadar,
1971; Silberberg, 1978). The analysis considers the effects of the direction of the
changes in the equilibrium value rather than the magnitude of the changes.

In this study, comparative static analysis examines how the optimal level of
royalty changes in response to changes in two parameters, one representing
technology change (measured by labor share, 7), and the other representing forest
characteristics (measured by the slope of timber value distribution, 5). Perhaps
uncertainty about these parameters is of utmost importance from the forest revenue
systems point of view. Changing the values of 'y and 5 shifts the shape of the
production function and the slope of timber value distribution. This will affect the
optimal level of royalty. Only the results of uniform royalty are presented.
Comparative static analysis for ad valorem royalty requires extensive work; thus, it

is not presented here.

135

136
Model I (Static Uniform Royalty)

l. A shift ir_1 the Prodaction Technology Parameter (1)

1.1 Cobb-Douglas

Totally differentiating Equation 14 (in the text) with respect to 7 yields:

‘1‘ = -(a-c)+(2y-I)BVL6L +2BV‘ (Bl)
dy 67

 

 

To determine the sign of the above total differentiation, it is necessary, among other
things, to know the sign of (£767. It also depends on the value of 7. By
differentiating Equation 7 (in the text) with respect to 7, one obtains the general
expression for aL'/6'y,

i6g"V“
6L ‘ y (B2)

6v 1-g9VL+ekg°-lpV*VL

 

 

where

 

= w(1+6) >
L (l-o)(a-BV-t) ’
g = k(a-BV-t),
k = 70"”

w(1+5)’

 

In the case of the Cobb-Douglas technology, substituting for 0 = l in

Equation B2, BLVB'y becomes

1 .

. —8V

i : Y > 0
av l-gVL-kfl V‘VL

(B3)

 

because the denominator is always > 0. To show that the denominator is > O, the

denominator in Equation B3 can be written as
1-k(a -|3 V-t) VL +kl3 VVL,
l-kVL(a - B V—t- [3 V),

1 -kVL(a -2p V-t).

If it is found that a-2fiV-t < 0, the above expression is clearly > 0.

If a—2fiV-t > O, the above expression is found to be

1_y(a-2[3V-t) > O
(a-BV-t)

 

138

because 7 cannot be > 1 or < O, and a-ZBV-t < a—BV-t. Because the optimal royalty
is related to the specific value of 7, dt/d7 can be evaluated for two extreme cases: 7
= O and 7 = 1.
Case 1. 1L: 0

Substituting 7 = O in Equation B1,

dt‘ aL"
=-a-c- V +2 V’<,>0 (B4)

 

 

Clearly, the definite sign of dt'/d7 cannot be determined because one cannot be sure
of the combined effects of all the terms on the RHS.
Case 2. 7 = 1

Substituting 7 = l in Equation Bl,

0" = “(a-C)+BVL6L +213V“ <. > o (35)
611! 6?

 

 

Similarly, the definite sign of dt'/d7 cannot be determined because one cannot be

sure of the combined effects of all the terms on the RHS.

1.2 Leontief

Totally differentiating Equation 15 (in the text) with respect to 7, one obtains

dt ___ _BVL 61. = 0, (B6)
dv 6)!

 

 

139

because the sign of (if/67 = 0. In the case of the Leontief technology, substituting

for 0 = O in Equation (B2), it is found that (if/67 = 0.

1.3 Linear
Because the optimal level of royalty is equivalent to the revenue collection
costs (assumed fixed for each cubic meter of logs extracted), the effect of 7 on t is

zero .

2. A Shift in thi Steegness of the Tinaber Value Distribution (Q)

2.1 Cobb-Douglas

Following the same steps as described in section 1, one can obtain the total

derivative of t with respect to B of Equation 14 (in the text):

(It "

 

 

= (2y—l)V‘+BVL:lE; (B7)

Similarly, to determine the sign of dt'/d6, we need to know the sign of (if/BB.
Differentiating Equation 7 (in the text) with respect to 5, the general expression for
61.736 can be written as:

av z -6g°’1kV‘2

(BB)
613 l-geVL+6kg°‘1[3V‘VL

 

 

Substituting the above equation for 0 = 1, one has

140

aL * = -kV“2 < 0 (B9)

613 l—gVL+kB V’VL

 

 

because the denominator is always > 0 (from section 1.1). Following the same
procedure for evaluating dt'/67, the sign of the dt'/dB can be determined for two
extreme values of 7.

Case 1. y = 0

Substituting 7 = O in Equation B7, one has

 

 

dt : "V7-BVL6L <, > 0, (B10)
0'13 <913

because one cannot be sure whether V > 3V1. * Elf/63 or V < 5V1, * BLVaB, even
though the sign of 8L'/6B < 0.
Case 2. y = 1

Substituting 7 = 1 in Equation B7, one has

 

 

dt ___ V7+BVL6L <, > 0, (Bll)
an an

This is for the same reason as explained in the first case.

141

2.2 Leontief

Totally differentiating Equation 15 (in the text) with respect to B
.511. = 4-915% < o, (1312)

because the sign of (if/66 = O. From the general expression in Equation B8 it is

clear that, when 0 = 0, (if/63 = O.

2.3 Linear
Because the optimal level of royalty is equivalent to the revenue collection
costs (assumed fixed for each cubic meter of logs extracted), the effect of B on t is

zero.
Model II (Dynamic Uniform Royalty)
3. A shift in the ProdILtion Technology Parameter (7)

3.1 Cobb-Doglas

Totally differentiating Equation 21 with respect to 7 yields:

 

 

dt‘ = _ _ +2uVSY + _ _ 2ij 21::
dv [a c (1_0)] [(27 DB —(1_0)]VL By
2}; .
+[2p-—]V (BIB)

(1-0)

142

The sign of the (Mi/67 is the same as the one found in the static model (Equation
B3). Therefore, one can evaluate the sign of dt'/d7 for two extreme cases: 7 = O
and 7 = 1.

Case 1. 7 = 0

Substituting 7 = 0 in Equation B13, one obtains:

 

 

 

 

 

 

 

 

t SY It
dt = [a c+2uV I‘BVLaL
d7 (1-0) BY
+[213- 2" ]V‘ <, > o (1314)
(1-0)
Case 2. 1: 1
Substituting 7 = 1 in Equation B13, one has:
dt’ 2uVSY 6L"
= -[a-c+ 1+[(B- —]V
dv (l-o ) (12— a) L a
+[2p-(121‘O,)]V* <, > 0 (B15)

3.2 Leontief
The results of the comparative static were found to be same as the ones
derived in the static model because the optimal royalty in the dynamic model is the

same as it is in the static case.

143

3.3 Linear

Totally differentiating of Equation 23 (in the text) with respect to 7 yields:

(1! = 2p VLaL >0 (B16)
(17 (1-0) 67

 

This is because 61,767 > O, which was obtained from Equation B2. Substituting 0 =

00 in Equation B2 yields

 

iv.
22:- v .0 (B17)
av kBV‘VL

4. A Shift in tl_1e Steepness of the Timber Value Distribution (Q)

4.1 Cobb-Douglas

Following the same steps as described above, one obtains the total derivative

of t with respect to 6 in Equation 21 (in the text) :

d": _ 5.4.2" E 18)
dB (2Y1)V us (l-o)]V"67 (B

 

 

Similarly, to determine the sign of dt'/d[3, one needs to know the sign of 6L'/6B.

From (B9), 61.766 < O, and evaluating dti/dfi for two extreme values of 7:

144
Case 1.7 = 0

Substituting 7 = 0 in Equation B18, one has

 

 

dt = -V*-BVLaL <’ > 0 (B19)
dB 59

Case 2. 7 =1

Substituting 7 = 1 in Equation B18, one has

dt _ 614'.I B20
dB +[l3 Jaw Lap <. > 0 ( )

       

 

4.2 Leontief
The results of the comparative static were found to be same as the ones
derived in the static model because the optimal royalty in the dynamic model is the

same as it is in the static model.

4.3 Linear

Totally differentiate Equation 23 (in the text) with respect to 6 yields:

dt = 2].! VLaL <0 (B21)
dB (1-0) 613

 

This is because 6L'/6B < O, which was obtained from Equation B2. Substituting 0 =

00 in Equation B2 yields 6L" : _ V‘ < 0
67 BVL

 

 

APPENDIX C

Data Used in the Empirical Analysis

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Site V r R t m L K
P1a 67.89 19,599 1,887 l l 500 64 14,289
Plb 55.39 20,441 1,738 13 500 53 11,658
P2a 37.41 8,388 3,749 10 2,526 36 7,874
P2b 61.91 9,258 2,747 10 930 59 13,030
T1a 60.12 14,089 1,677 14 300 60 13,277
T1b 70.57 15,042 1,791 13 300 70 15,585
Variable and Parameter

Site (1 B a c A n V"
P1a 380.45 3.6048 .028 3.20 .0832 6.56 76.65
Plb 474.54 3.2935 .044 3.20 .0832 5.35 106.96
P2a 327.59 3.3437 .054 3.20 .0832 13.54 55.68
P2b 236.95 1.6621 .032 3 .20 .0832 16.94 62.16
T1a 336.46 2.2214 .054 3.10 .0793 5.02 74.15
T1b 298.05 1.9295 .010 3.10 .0793 6.53 66.58

 

 

 

 

 

 

 

 

145

Simulation of Empirical Models in TSP Using SIML Command

? FREQ NONE

?? READ IN DATA FOR THE SIMULATION ANALYSIS FROM THE TSP DATA BANK FILE

AWANG.TLB

? IN AWANG

? OUT AWANG
? SMPL 1 6

?? PRINT DATA BANK FOR THE RUN

? DBPRINT AWANG

APPENDIX D

Values for all series in Databank AWANG.TLB

QMAwNF-d @MbWNF‘

@LII-h-UJN—d

V
67.89000
55.39000
37.41000
61.91000
60.12000
70.57000

GR
1 887.00000
l 73 8.00000
3749.00000
2747.00000
1 677.00000
l 791 .00000

A
0.083189
0.083189
0.083189
0.083 189
0.079279
0.079279

L
64.4513
52.58445
35.51516
58.77421
59.88968
70.29964

K
14288.73633
l 1657.87402

7873.64307
13030.13184
13277.42969
15585.29883

COST

3 .20000
3.20000
3 .20000
3 .20000
3. l 0000
3 . l 0000

T

1 1.00000 19598.99023
13.00000 20440.58398
10.00000 8387.68164
10.00000 9258.20020
14.00000 14088.70801
13.00000 15041 .60449

ALPHA
380.44901
474.53601
327.58801
236.94600
336.4580]
298.05301

U
6.56000
5.35000

13.54000
16.94000
5.02000
6.53000

146

B
3.60480
3.29350
3.34370
1.66210
2.22140
1.92950

M
500.00000
500.00000
2526.00000
930.00000
300.00000
300.00000

S
0.028428
0.044412
0.053729
0.032466
0.054225

0.0096358

QMAMNfl GLAJ>~LHN—‘ QMAMNH ONklt-bUJN—

?? DISPLAY THE CONTENTS OF DATA BANK

VSY

76.65000
106.96000
55.68000
62.16000
74.1 5000
66.58000

SL
64.45132
52.5 8445
35.51516
58.77421
59.88968
70.29964

DV
67.89000
55.39000
37.41000
61.91000
60.12000
70.57000

SPA

19598.99023
20440.58398
8387.68164
9258.20020
14088.70801
15041.60449

DLA

64.45132
52.5 8445
35.51516
58.77421
59.88968
70.29964

‘7 DBLIST AWANG
Contents of Databank AWANG.TLB

SERIES

Name

g-u—ir‘<

0

OST

DT
1 1.00000
1 3.00000
1 0.00000
1 0.00000
14.00000
1 3.00000

SP
19598.99023
20440.58398

83 87.68 1 64
9258.20020
14088.70801
15041 .60449

DL
64.45132
52.58445
35.51516
58.77421
59.88968
70.29964

SGRA
1887.00000
173 8.00000

3749.00000
2747.00000
1677.00000
1791 .00000

DPA
19598.99023
20440.58398

83 87.68 164
9258.20020
14088.70801
15041 .60449

147

DGR
l 887.00000
1 73 8.00000
3749.00000
2747.00000
1677.00000
1 79 1 .00000

SGR

1 887.00000

l 73 8.00000
3749.00000
2747.00000

1677.00000

1 79 1 .00000

DP
19598.99023
20440.58398

83 87.68 164
9258.20020
14088.70801
15041.60449

STA
0.50000
0.50000

0.50000
0.50000
0.50000
0.50000

DGRA
1887.00000
173 8.00000

3749.00000
2747.00000
1677.00000
1791 .00000

Description

ST
0.50000
0.50000
0.50000
0.50000
0.50000
0.50000

SM
500.00000
500.00000
500.00000
500.00000
300.00000
300.00000

SVA
67.89000
55.39000
37.41000
61 .91000
60.12000
70.57000

SMA
500.00000
500.00000

2526.00000
930.00000
300.00000
300.00000

DTA
0.50000
0.50000
0.50000
0.50000
0.50000
0.50000

1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency

DM
500.00000
500.00000

2526.00000
930.00000
300.00000
300.00000

SLA
64.45132
52.58445
35.51516
58.77421
59.88968
70.29964

DVA

67.89000
55.39000
37.41000
61.91000
60.12000
70.57000

DMA

500.00000
500.00000
2526.00000
930.00000
300.00000
300.00000

EQUATION

SGR

DV
SGRA
STA
SMA
DL
DP
SPA
DVA
DLA
DPA
DGRA
DTA
DMA
SVA
SLA

PROFIT
PREMIUM
VOLUME
REVENUE
ROYALTY
LABOR
DVOLUME
DLABOR
DPROFIT

DREVENUE
DROYALTY
DPREMIUM

AVOLUME
APROFIT

AREVENUE
AROYALTY

ALABOR

APREMIUM

VOLUMEA
LABORA
PROFITA

6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from
6 obs. from

formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula
formula

148

1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency
1-6, no frequency

149

REVENUEA formula
PREMIUMA formula
ROYALTYA formula

SCALAR GAMMA parameter 0.53000
W parameter 30.00000
DELTA parameter 0.13200
R parameter 0.12000

?? PRINT ALL EQUATIONS

? PRINT VOLUME LABOR PROFIT REVENUE ROYALTY PREMIUM DVOLUME DLABOR
DPROF IT DREVENUE DROYALTY DPREMIUM AVOLUME ALABOR APROFIT AREVENUE
AROYALTY APREMIUM VOLUMEA LABORA PROFITA REVENUEA ROYALTYA
PREMIUMA

EQUATION: VOLUME
FRML VOLUME V = (A* L" GAMMA)* K" (1- GAMMA)

EQUATION: LABOR
FRML LABOR L = v* (((GAMMA‘ (1- S))/ (w* (1+ DELTA)))* ((ALPHA- B* V)-

1))
EQUATION: PROFIT

FRML PROFIT P = ((((1- S)‘ (ALPHA‘ v- (B/ 2)* v" 2)- (w* L)* (1+
DELTA))- (R* K)* (1+ DELTA))— (T* V)‘ (l- S))- M

EQUATION: REVENUE
FRML REVENUE GR = (((1- S)* V)’ ”H M)- ((1- S)* V)* COST

EQUATION: ROYALTY
FRML ROYALTY T = (ALPHA- GAMMA"I (ALPHA- COST))+ ((2“ GAMMA- l)* B)* V

EQUATION: PREMIUM
FRML PREMIUM M = (((1- S)* (ALPHA* v- (B/ 2)* vM 2)- (w* L)* (1+
DELTA))- (R* K)* (1+ DELTA))- (T* V)* (1- S)

EQUATION: DVOLUME
FRML DVOLUME DV = (A* DL“ GAMMA)* K" (1- GAMMA)

EQUATION: DLABOR
FRML DLABOR DL = DV“ (((GAMMA* (1- S))/ (w* (1+ DELTA)))* ((ALPHA- 3*
DV)- DD)

EQUATION: DPROFIT
FRML DPROFIT DP = ((((1- S)‘ (ALPHA* DV— (B/ 2)* DV" 2)- (w* DL)* (1+
DELTA))- (R* K)’ (1+ DELTA))- ((1- 8)“ my DV)- DM

EQUATION: DREVENUE
FRML DREVENUE DGR = (((1- S)* DV)’ DT+ DM)- ((1- S)* DV)* COST

150

EQUATION: DROYALTY
FRML DROYALTY DT = (ALPHA- GAMMA* ((ALPHA- COST)+ «2* U)* vsv)/ (1-
S)))+ «2* GAMMA- I)* 3+ «2* GAMMA)* U)/ (1- S))* Dv

EQUATION: DPREMIUM
FRML DPREMIUM DM = «(1- S)* (ALPHA* DV- (B/ 2)* Dv** 2)- (w* DL)* (1+
DELTA))- (R* K)‘ (1+ DELTA))- ((1- S)* DT)* DV

EQUATION: AVOLUME
FRML AVOLUME SVA = (A* SLA“l GAMMA)* K" (1- GAMMA)

EQUATION: ALABOR
FRML ALABOR SLA = SVA* («(GAMMA* (1- S))/ (w* (1+ DELTA)))* (ALPHA- B*
SVA))* (1- STA))

EQUATION: APROFIT
FRML APROFIT SPA = ((((1- S)* (1- STA))* (ALPHA* SVA- (B/ 2)* SVA** 2)-
(w* SLA)* (1+ DELTA))- (R* K)* (1+ DELTA))— SMA

EQUATION: AREVENUE
FRML AREVENUE SGRA = (((l- S)* STA)* (ALPHA* SVA- (B/ 2)* SVA** 2)+
SMA)- «1- S)* SVA)‘ COST

EQUATION: AROYALTY

FRML AROYALTY STA = «ALPHA* SVA- (B/ 2)* SVA** 2)* «1- GAMMA)+
«GAMMA* B)* SVA)/ (ALPHA- B* SVA))+ (GAMMA* COST)* SVA)/ «ALPHA* SVA- (B/
2)* SVA** 2)* ((1- GAMMA)+ «GAMMA* B)* SVA)/ (ALPHA- B* SVA))+ (GAMMA* SVA)*
(ALPHA- B* SVA))

EQUATION: APREMIUM
FRML APREMIUM SMA = «(1- S)* (1- STA))* (ALPHA* SVA- (B/ 2)* SVA** 2)-
(w* SLA)* (1+ DELTA))- (R* K)* (1+ DELTA)

EQUATION: VOLUMEA
FRML VOLUMEA DVA = (A* (DLA* GAMMA))* K** (1- GAMMA)

EQUATION: LABORA
FRML LABORA DLA = «DVA* «GAMMA* (1- S))/ (w* (1+ DELTA))))* (ALPHA-
B* DVA))* (1- DTA)

EQUATION: PROFITA
FRML PROFITA DPA = ((((1- S)* (1- DTA))* (ALPHA* DVA- (B/ 2)* DVA** 2)-
(w* DLA)* (1+ DELTA))— (R* K)* (1+ DELTA))— DMA

EQUATION: REVENUEA
FRML REVENUEA DGRA = (((1- S)* DTA)* (ALPHA* DVA- (B/ 2)* DVA** 2)+
DMA)- «1- S)* COST)* DVA

EQUATION: ROYALTYA
FRML ROYALTYA DTA = ((ALPHA* DVA- (B/ 2)* DVA" 2)* ((1- GAMMA)+

«GAMMA* B)* DVA)/ (ALPHA- B* DVA))+ ((COST- «2* U)/ (1- S))* (VSY- DVA))*
GAMMA)* DVA)/ «ALPHA* DVA- (B/ 2)* DVA** 2)* «1- GAMMA)+ «GAMMA* B)* DVA)/

151
(ALPHA- B* DVA))+ (GAMMA* DVA)* (ALPHA- B* DVA))

EQUATION: PREMIUMA
FRML PREMIUMA DMA = «(1- S)* (1- DTA))* (ALPIIA* DVA- (B/ 2)* DVA** 2)-
(w* DLA)* (1+ DELTA))- (R* K)* (1+ DELTA)

?? SET PARAMETERS FOR THE BASE RUN
? PARAM W 30, GAMMA 0.53, R 0.12, DELTA 0.132

?? SIMULATION OF MODEL I
? SIML (ENDO=(V,L,P,GR,T,M)) VOLUME LABOR PROFIT REVENUE ROYALTY PREMIUM

MODEL SIMULATION
#*************t*
NUMBER OF EQUATIONS: 6
METHOD: NEWTON STEPSIZE METHOD: BARD
PRINT OPTIONS: PRINT = F PRNRES = F PRNDAT = F PRNSIM = T
SILENT = F DEBUG = F
STATIC SIMULATION

Working space used by SIML = 1182

F= 0.99921E+08 FNEW= 0.30037E+08 ISQZ= 0 STEP= 1.0000 CRIT= 0.99921E+08
F= 0.30037E+08 FNEW= 90903. ISQZ= 0 STEP= 1.0000 CRIT= 0.30037E+08
F= 90903. FNEW= 2858.8 ISQZ= 0 STEP= 1.0000 CRIT= 90903.

F= 2858.8 FNEW= 0.41877 ISQZ= OSTEP= 1.0000 CRIT= 2858.8

F= 0.41877 FNEW= 0.10410E—07 ISQZ= 0 STEP= 1.0000 CRIT= 0.41877

CONVERGENCE ACHIEVED AFTER 5 ITERATIONS.

F= 0.13513E+09 FNEW= 0.80106E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.13513E+09
F 0.80106E+07 FNEW= 95588. ISQZ= 0 STEP= 1.0000 CRIT= 0.80106E+07
F= 95588. FNEW= 269.36 ISQZ= 0 STEP= 1.0000 CRIT= 95588.

F= 269.36 FNEW= 0.11942E-0218QZ= 0 STEP= 1.0000 CRIT= 269.36

F 0.11942E-02 FNEW= 0.43471E—13 ISQZ= 0 STEP= 1.0000 CRIT= 0.11942E-02

CONVERGENCE ACHIEVED AFTER 5 ITERATIONS.

F= 0.18086E+08 FNEW= 0.55528E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.18086E+08
F= 0.55528E+07 FNEW= 0.14264E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.55528E+07
F= 0.14264E+07 FNEW= 77642. ISQZ= 0 STEP= 1.0000 CRIT= 0.14264E+07
F= 77642. FNEW= 212.35 ISQZ= 0 STEP= 1.0000 CRIT= 77642.

F= 212.35 FNEW= 0.10725E—01 ISQZ= OSTEP= 1.0000 CRIT= 212.35

F= 0.10725E-01 FNEW= 0.12458E-10 ISQZ= 0 STEP= 1.0000 CRIT= 0.10725E-01

CONVERGENCE ACHIEVED AFTER 6 ITERATIONS.

F= 0.23839E+08 FNEW= 0.96165E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.23839E+08
F= 0.96165E+07 FNEW= 0.62992E+06 ISQZ= 0 STEP= 1.0000 CRIT= 0.96165E+07
F= 0.62992E+06 FNEW= 47546. ISQZ= 0 STEP= 1.0000 CRIT= 0.62992E+06
F= 47546. FNEW= 77.508 ISQZ= 0 STEP= 1.0000 CRIT= 47546.

F 77.508 FNEW= 0.17434E-02 ISQZ= 0 STEP= 1.0000 CRIT= 77.508

F= 0.17434E—02 FNEW= 0.31706E-12 ISQZ= 0 STEP= 1.0000 CRIT= 0.17434E-02

CONVERGENCE ACHIEVED AFTER 6 ITERATIONS.

152

F= 0.61192E+08 FNEW= 0.14080E+08 ISQZ= 0 STEP= 1.0000
F= 0.14080E+08 FNEW= 0.15776E+07 ISQZ= 0 STEP= 1.0000

CRIT= 0.61192E+08
CRIT= 0.14080E+08

F= 0.15776E+07 FNEW= 69763. ISQZ= 0 STEP= 1.0000 CRIT= 0.15776E+07
F= 69763. FNEW= 87.611 ISQZ= 0 STEP= 1.0000 CRIT= 69763.

= 87.611 FNEW= 0.73322E-03 ISQZ= 0 STEP= 1.0000 CRIT= 87.611

F= 0.73322E-03 FNEW= 0.23006E—13 ISQZ= 0 STEP= 1.0000 CRIT= 0.73322E-03

CONVERGENCE ACHIEVED AFTER 6 ITERATIONS.
F= 0.64954E+08 FNEW= 0.19063E+08 ISQZ= 0 STEP= 1.0000
F= 0.19063E+08 FNEW= 0.10192E+07 ISQZ= 0 STEP= 1.0000

CRIT= 0.64954E+08
CRIT= 0.19063E+08

F= 0.10192E+07 FNEW= 52111. ISQZ= 0 STEP= 1.0000 CRIT= 0.10192E+07
F= 52111. FNEW= 54.045 ISQZ= 0 STEP= 1.0000 CRIT= 52111.

F= 54.045 FNEW= 0.31010E—03 ISQZ= 0 STEP= 1.0000 CRIT= 54.045

F= 0.31010E-03 FNEW= 0.44014E—14 ISQZ= 0 STEP= 1.0000 CRIT= 0.31010E-03

CONVERGENCE ACHIEVED AFTER 6 ITERATIONS.

SIMULATION RESULTS

V L P GR T
1 41.69238 25.68651 -7.16116D-17 9424.01654 189.52459
2 53.78943 49.75426 0.00000 16400.71316 235.35726
3 32.49839 27.23214 0.00000 6310.46623 162.18226
4 43.97059 30.81855 0.00000 5573.05224 117.44563
5 50.48446 43.07424 0.00000 9973.10187 166.50704
6 52.28072 39.91577 0.00000 9187.60391 147.78045
M
l 1876.54188
2 4467.70969
3 1421.39913
4 712.69856
5 2170.91424
6 1696.49156

?? SIMULATION OF MODEL II
? SIML (ENDO=(DV,DL,DP,DGR,DT,DM)) DVOLUME DLABOR DPROFIT DREVENUE
DROYALTY PREMIUM

MODEL SIMULATION
*$#**¥lttttttttt
NUMBER OF EQUATIONS: 6
METHOD: NEWTON STEPSIZE METHOD: BARD
PRINT OPTIONS: PRINT = F PRNRES = F PRNDAT = F PRNSIM = T
SILENT = F DEBUG = F
STATIC SIMULATION

Working space used by SIML = 1268

F= 0.99911E+08 FNEW= 0.23623E+06 ISQZ= 0 STEP= 1.0000
F= 0.23623E+06 FNEW= 3.5258 ISQZ= 0 STEP= 1.0000
F= 3.5258 FNEW= 0.10185E-07 ISQZ= OSTEP= 1.0000

CRIT= 0.99911E+08
CRIT= 0.23623E+06
CRIT= 3.5258

153

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.

F= 0.13511E+09 FNEW= 0.37762E+08 ISQZ= 1 STEP= 0.71818 CRIT= 0.13511E+09
F= 0.37762E+08 FNEW= 51527. ISQZ= 0 STEP= 1.0000 CRIT= 0.37762E+08
F= 51527. FNEW= 0.33160 ISQZ= 0 STEP= 1.0000 CRIT= 51527.

F= 0.33160 FNEW= 0.28229E-10 ISQZ= 0 STEP= 1.0000 CRIT= 0.33160

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.18082E+08 FNEW= 0.46408E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.18082E+08
F= 0.46408E+07 FNEW= 12473. ISQZ= 0 STEP= 1.0000 CRIT= 0.46408E+07
F= 12473. FNEW= 0.50305E—01 ISQZ= 0 STEP= 1.0000 CRIT= 12473.

F 0.50305E-01 FNEW= 0.10894E-11 ISQZ= 0 STEP= 1.0000 CRIT= 0.50305E-01

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.23838E+08 FNEW= 25971. ISQZ= 0 STEP= 1.0000 CRIT= 0.23838E+08
F= 25971. FNEW= 0.52401E—01 ISQZ= 0 STEP= 1.0000 CRIT= 25971.

F= 0.52401E—01 FNEW= 0.46324E-12 ISQZ= 0 STEP= 1.0000 CRIT= 0.52401E-01

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.

F= 0.61183E+08 FNEW= 0.84287E+06 ISQZ= 0 STEP= 1.0000 CRIT= 0.61183E+08
F= 0.84287E+06 FNEW= 267.53 ISQZ= 0 STEP= 1.0000 CRIT= 0.84287E+06
F 267.53 FNEW= 0.57287E-04 ISQZ= 0 STEP= 1.0000 CRIT= 267.53

F= 0.57287E-04 FNEW= 0.24760E—17 ISQZ= 0 STEP= 1.0000 CRIT= 0.57287E-04

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F== 0.64958E+08 FNEW= 0.10960E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.64958E-108
F= 0.10960E+07 FNEW= 159.11 ISQZ= 0 STEP= 1.0000 CRIT= 0.10960E+07
F 159.11 FNEW= 0.27740E-0418QZ= 0 STEP= 1.0000 CRIT= 159.11

F 0.27740E-04 FNEW= 0.40313E—18 ISQZ= 0 STEP= 1.0000 CRIT= 0.27740E-04

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.
SIMULATION RESULTS

DV DL DP DGR DT
1 62.85710 55.73344 2.22045D-16 12286.11169 95.38561
2 84.06930 115.54618 0.00000 21235.75124 105.49442
3 49.88693 61.13094 0.00000 8230.48911 77.80545
4 59.86407 55.16327 0.00000 7013.78018 76.42068
5 65.30999 70.01680 8.67362D—19 11928.98938 118.74663
6 62.41279 55.75700 0.00000 10499.00884 119.82822
DM
1 6656.31986
2 13017.86927
3 4708.62229
4 2772.80176
5 4785.66286
6 3283.87487

154

?? SIMULATION OF MODEL 111
? SIML (ENDO=(SVA,SLA,SPA,SGRA,STA,SMA)) AVOLUME ALABOR APROFIT
AREVENUE AROYALTY APREMIUM

MODEL SIMULATION
*tifitittttfififiitt
NUMBER OF EQUATIONS: 6
METHOD: NEWTON STEPSIZE METHOD: BARD
PRINT OPTIONS: PRINT = F PRNRES = F PRNDAT = F PRNSIM = T
SILENT = F DEBUG = F
STATIC SIMULATION

Working space used by SIML = 1746

F= 0.15495E+09 FNEW= 0.31284E+06 ISQZ= 0 STEP= 1.0000 CRIT= 0.15495E+09
F 0.31284E+06 FNEW= 353.46 ISQZ= O STEP= 1.0000 CRIT= 0.31284E+06
F= 353.46 FNEW= 0.16303E-03 ISQZ= 0 STEP= 1.0000 CRIT= 353.46

F 0.16303E-03 FNEW= 0.72070E-16 ISQZ= 0 STEP= 1.0000 CRIT= 0.16303E—03

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.15819E+09 FNEW= 0.11136E407 ISQZ= 0 STEP= 1.0000 CRIT= 0.15819E+09
F= 0.11136E+07 FNEW= 240.83 ISQZ= 0 STEP= 1.0000 CRIT= 0.11136E+07
F= 240.83 FNEW= 0.87608E-04 ISQZ= 0 STEP= 1.0000 CRIT= 240.83

F= 0.87608E-04 FNEW= 0.20586E—16 ISQZ= 0 STEP= 1.0000 CRIT= 0.87608E-04

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.41743E+08 FNEW= 1111.2 ISQZ= 0 STEP= 1.0000 CRIT= 0.41743E+08
F 1111.2 FNEW= 0.15614E-03 ISQZ= OSTEP= 1.0000 CRIT= 1111.2

F= 0.15614E-03 FNEW= 0.10274E-14 ISQZ= 0 STEP= 1.0000 CRIT= 0.15614E-03

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.

F= 0.41928E+08 FNEW= 67253. ISQZ= 0 STEP= 1.0000 CRIT= 0.41928E+08
F= 67253. FNEW= 0.45187 ISQZ= 0 STEP= 1.0000 CRIT= 67253.

F= 0.45187 FNEW= 0.15036E-08 ISQZ= 0 STEP= 1.0000 CRIT= 0.45187

F 0.15036E-08 FNEW= 0.17062E-23 ISQZ= 0 STEP= 1.0000 CRIT= 0.15036E-08

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.80666E+08 FNEW= 1254.4 ISQZ= 0 STEP= 1.0000 CRIT= 0.80666E+08
F 1254.4 FNEW= 0.17635E-04 ISQZ= 0 STEP= 1.0000 CRIT= 1254.4

F= 0.17635E-04 FNEW= 0.88010E-17 ISQZ= 0 STEP= 1.0000 CRIT= 0.17635E-O4

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.

F= 0.94984E+08 FNEW= 90052. ISQZ= 0 STEP= 1.0000 CRIT= 0.94984E+08
F= 90052. FNEW= 0.32351E-01 ISQZ= 0 STEP= 1.0000 CRIT= 90052.

F= 0.32351E-01 FNEW= 0.31081E-11 ISQZ= 0 STEP= 1.0000 CRIT= 0.32351E-01

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.
SIMULATION RESULTS

SVA SLA SPA SGRA STA
52.35097 39.46832 0.00000 1 1 107.31064 0.74068
65.66228 72.48737 0.00000 18744.51206 0.71340
38.14135 36.83652 0.00000 7085.83864 0.67310
50.72184 40.35108 0.00000 6262.13651 0.65485

kWh)—

155

M

59.31419 58.38410 0.00000 11218.60441 0.67422
6 61.75683 54.65648 -1.96918D-17 10422.59146 0.67992

SMA
492.18783
2543.90207
792.13492
159.1771 1
1 158.66655
695.27685

@LAQUJN—

?? SIMULATION OF MODEL IV
? SIML (ENDO=(DVA,DLA,DPA,DGRA,DTA,DMA)) VOLUMEA LABORA PROFITA
REVENUEA ROYALTYA PREMIUMA

MODEL SIMULATION
*ttttttttttttttt
NUMBER OF EQUATIONS: 6
METHOD: NEWTON STEPSIZE METHOD: BARD
PRINT OPTIONS: PRINT = F PRNRES = F PRNDAT = F PRNSIM = T
SILENT = F DEBUG = F
STATIC SIMULATION

Working space used by SIML = 1850

F= 0.15496E+09 FNEW= 84479. ISQZ= O STEP= 1.0000 CRIT= 0.15496E+09
F= 84479. FNEW= 5.0629 ISQZ= 0 STEP= 1.0000 CRIT= 84479.

F= 5.0629 FNEW= 0.24470E-06 ISQZ= 0 STEP= 1.0000 CRIT= 5.0629

F 0.24470E-06 FNEW= 0.90883E-21 ISQZ= 0 STEP= 1.0000 CRIT= 0.24470E-06

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.15820E+09 FNEW= 0.86418E+08 ISQZ= 0 STEP= 1.0000 CRIT= 0.15820E+09
F= 0.86418E+08 FNEW= 0.27311E+06 ISQZ= 0 STEP= 1.0000 CRIT= 0.86418E+08
F= 0.27311E+06 FNEW= 3590.2 ISQZ= 0 STEP= 1.0000 CRIT= 0.27311E+06
F= 3590.2 FNEW= 0.30059E-01 ISQZ= 0 STEP= 1.0000 CRIT= 3590.2

F= 0.30059E-01 FNEW= 0.11472E-10 ISQZ= 0 STEP= 1.0000 CRIT= 0.30059E-01

CONVERGENCE ACHIEVED AFTER 5 ITERATIONS.

F= 0.41745E+08 FNEW= 0.35619E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.41745E+08
F= 0.35619E+07 FNEW= 305.01 ISQZ= 0 STEP= 1.0000 CRIT= 0.35619E+07
F= 305.01 FNEW= 0.67117E—0518QZ= 0 STEP= 1.0000 CRIT= 305.01

F 0.67117E-05 FNEW= 0.19315E—20 ISQZ= 0 STEP= 1.0000 CRIT= 0.67117E-05

CONVERGENCE ACHIEVED AFTER 41TERATIONS.

F= 0.41941E+08 FNEW= 16569. ISQZ= 0 STEP= 1.0000 CRIT= 0.41941E+08
F= 16569. FNEW= 0.16247E-03 ISQZ= 0 STEP= 1.0000 CRIT= 16569.

F= 0.16247E-03 FNEW= 0.24572E-20 ISQZ= 0 STEP= 1.0000 CRIT= 0.16247E—03

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.
F= 0.80679E+08 FNEW= 0.49857E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.80679E+08

156

F= 0.49857E+07 FNEW= 464.68 ISQZ= 0 STEP= 1.0000 CRIT= 0.49857E+07
F= 464.68 FNEW= 0.99084E-06 ISQZ= 0 STEP= 1.0000 CRIT= 464.68
F= 0.99084E-06 FNEW= 0.34652E-21 ISQZ= 0 STEP= 1.0000 CRIT= 0.99084E-06

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.95005E+08 FNEW= 59584. ISQZ= 0 STEP= 1.0000 CRIT= 0.95005E+08
F= 59584. FNEW= 0.18804E—0218QZ= 0 STEP= 1.0000 CRIT= 59584.

F 0.18804E-02 FNEW= 0.85225E-19 ISQZ= 0 STEP= 1.0000 CRIT= 0.18804E-02

CONVERGENCE ACHIEVED AFTER 3 ITERATIONS.
SIMULATION RESULTS

DVA DLA DPA DGRA DTA
74.51969 18.84006 -4.79368D-20 15007.97952 0.85089
103.72185 28.85476 0.00000 27223.81776 0.85967
56.60369 18.93653 0.00000 10593 .60397 0.83623
64.09544 16.92230 0.00000 8847.64784 0.86593
81.17078 22.28956 0.00000 16109.86040 0.88085
73.65058 18.75708 0.00000 13577.26721 0.89434

O\Lh-h UJhJ-—

DMA
1 76.41926
2 1661.06256
3 330.84958
4 -817.51132
5 -307.71187
6 -1004.60925

?? SIMULATION OF MODEL V

7? SET ROYALTY EQUALS ZERO

? GENR T=T*0

?? EXCLUDES ROYALTY EQUATION

? SIML (ENDO=(V,L,P,GR,M)) VOLUME LABOR PROFIT REVENUE PREMIUM

MODEL SIMULATION
*tttttfittttttttt
NUMBER OF EQUATIONS: 5
METHOD: NEWTON STEPSIZE METHOD: BARD
PRINT OPTIONS: PRINT = F PRNRES = F PRNDAT = F PRNSIM = T
SILENT = F DEBUG = F
STATIC SIMULATION
Working space used by SIML = 980
F= 0.10404E+09 FNEW= 0.69949E+06 ISQZ= 0 STEP= 1.0000 CRIT= O.10404E+09
F= 0.69949E+06 FNEW= 1955.1 ISQZ= 0 STEP= 1.0000 CRIT= 0.69949E+06
F= 1955.1 FNEW= 0.13326E—01 ISQZ= OSTEP= 1.0000 CRIT= 1955.1
F 0.13326E-01 FNEW= 0.91211E—1218QZ= OSTEP= 1.0000 CRIT= 0.13326E—01

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.
F= 0.14390E+09 FNEW= 0.10320E+09 ISQZ= 2 STEP= 0.62500E-01 CRIT= 0.14390E+09
F= 0.10320E+09 FNEW= 0.36985E+06 ISQZ= 0 STEP= 1.0000 CRIT= 0.10320EL109

157

F= 0.36985E+06 FNEW= 120.71 ISQZ= 0 STEP= 1.0000 CRIT= 0.36985E406
F= 120.71 FNEW= 0.38840E-04 ISQZ= 0 STEP= 1.0000 CRIT= 120.71

CONVERGENCE ACHIEVED AFTER 41TERATIONS.

F= 0.18634E+08 FNEW= 0.11568E+08 ISQZ= 1 STEP= 0.25000 CRIT= 0.18634E+08
F 0.11568E+08 FNEW= 0.38000 ISQZ= 0 STEP= 1.0000 CRIT= 0.11568E+08
F= 0.38000 FNEW= 0.96775E-03 ISQZ= 0 STEP= 1.0000 CRIT= 0.38000

F 0.96775E-03 FNEW= 0.72495E-14 ISQZ= 0 STEP= 1.0000 CRIT= 0.96775E-03

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.26638E+08 FNEW= 0.41639E+07 ISQZ= 0 STEP= 1.0000 CRIT= 0.26638E+08
F 0.41639E+07 FNEW= 92502. ISQZ= 0 STEP= 1.0000 CRIT= 0.41639E+07

F 92502. FNEW= 42.736 ISQZ= 0 STEP= 1.0000 CRIT= 92502.

F= 42.736 FNEW= 0.29019E-04 ISQZ= 0 STEP= 1.0000 CRIT= 42.736

F= 0.29019E-04 FNEW= 0.74781E-17 ISQZ= 0 STEP= 1.0000 CRIT= 0.29019E-04

CONVERGENCE ACHIEVED AFTER 5 ITERATIONS.

F= 0.68078E+08 FNEW= 0.31905E+08 ISQZ= 1 STEP= 0.25000 CRIT= 0.68078E+08
F= 0.31905E+08 FNEW= 5727.2 ISQZ= 0 STEP= 1.0000 CRIT= 0.31905E+08
F= 5727.2 FNEW= 1.3993 ISQZ= 0 STEP= 1.0000 CRIT= 5727.2

F 1.3993 FNEW= 0.40130E—08 ISQZ= 0 STEP= 1.0000 CRIT= 1.3993

CONVERGENCE ACHIEVED AFTER 4 ITERATIONS.

F= 0.72069E+08 FNEW= 0.24158E+08 ISQZ= 0 STEP= 1.0000 CRIT= 0.72069E+08
F= 0.24158E+08 FNEW= 0.72712E+06 ISQZ= 0 STEP= 1.0000 CRIT= 0.24158E+08
F= 0.72712E+06 FNEW= 984.19 ISQZ= 0 STEP= 1.0000 CRIT= 0.72712E+06
F= 984.19 FNEW= 0.61220E-0218QZ= 0 STEP= 1.0000 CRIT= 984.19

F= 0.61220E-02 FNEW= 0.14186E-l2 ISQZ= 0 STEP= 1.0000 CRIT= 0.61220E—02

CONVERGENCE ACHIEVED AFTER 5 ITERATIONS.
SIMULATION RESULTS

V L P GR M
84.45967 97.31548 0.00000 13218.92461 13481.51219
108.8861 1 188.23854 0.00000 22409.37871 22742.33943
66.1 1558 104.00349 0.00000 8777.80685 8978.00929
89.90096 1 18.81024 0.00000 8028.30050 8306.643 53
102.64054 164.30614 -1.31297D—l6 13910.43112 14211.36322
106.39759 152.54230 0.00000 12966.28753 13292.94186

ONMAWN—

APPENDIX E

158

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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