deforest as ion asia
TRANSCRIPT
The environmental effects of trade: timber export & deforestation
in the case of ASEAN
By
Alounxay Naphayvong
A thesis submitted for the partial fulfillmentof the requirements for the degree of
MASTERS OF ARTS ININTERNATIONAL DEVELOPMENT
at the
INTERNATIONAL UNIVERSITY OF JAPAN
2008
i
The thesis of Alounxay Naphayvong is approved by the thesis examining committee.
_______________________________
Nawalage S. Cooray (Examiner)
______________________________
Yuqing Xing (Supervisor)
INTERNATIONAL UNIVERSITY OF JAPAN
2008
ii
ACKNOWLEDGEMENTS
I gratefully acknowledge the contributions of my supervisor, Prof. Yuqing
Xing. He has made an enormous contribution with his useful suggestions and
invaluable support throughout this study. I am also highly thankful to Prof Nawalage
S. Cooray for valuable suggestions for this thesis.
I would like express my sincere appreciation to JICA and JICE from granting
the opportunity to study in Japan under the JDS scholarship programme.
This acknowledgment would not be complete without sincere thanks to all the
students that I have met in IDP who have discussed and shared ideas with me during
my study in IUJ. Moreover, my sincere thanks go to all the Lao students who shared
their happiness, sadness and experiences with me. In addition, I would like to thank
an anonymous reviewer for all the constructive comments provided on this study.
Finally, I would like to express my gratitude to my parents and brother for their
love and encouragement throughout my life.
iii
Table of Contents
ACKNOWLEDGEMENTS ..................................................................................... iiTable of Contents.................................................................................................... iiiList of tables ........................................................................................................... ivABSTRACT .............................................................................................................vCHAPTER ONE: INTRODUCTION....................................................................... 1
1.1.1.2.1.3.1.4.1.5.
Scope of the Study.................................................................................... 1Research Issues ........................................................................................ 3Purpose of this study ................................................................................ 3Methodology ............................................................................................ 4Organization............................................................................................. 5
CHAPTER TWO: OVERVIEW OF ASEAN’S DEFORESTATION AND TIMBERTRADE ................................................................................................................... 6
2.1.2.2.
Overview of deforestation in Southeast Asia............................................. 6Forest productions and trade in Southeast Asia ......................................... 9
2.2.1.2.2.2.
ASEAN’s roundwood production ..................................................... 9ASEAN’s roundwood export and import......................................... 10
2.3. Deforestation policies trend in ASEAN .................................................. 12CHAPTER THREE: LITERATURE REVIEW...................................................... 16CHAPTER FOUR: DATA AND MODEL SPECIFICATION ............................... 23
4.1. Specification of variables........................................................................ 234.1.1.4.1.2.
Annual change in forest area........................................................... 23Explanatory variables...................................................................... 24
4.2. Data sources ........................................................................................... 27HAPTER FIVE: THE ECONOMETRIC MODEL AND RESULTS ...................... 29
5.1.15.1.2.
The econometric model ...................................................................... 29Results and discussion ........................................................................ 31
CHAPTER SIX: CONCLUSION........................................................................... 36REFERENCES ...................................................................................................... 38APPENDICES ....................................................................................................... 42
iv
List of tables
Table 1: ASEAN’s deforestation rate 1990-2000...................................................... 8
Table 2: ASEAN’s roundwood exports and imports 1990-2000 ............................. 12
Table 3: Summarize literature survey on deforestation in national-country regression
model..................................................................................................................... 21
Table 4: Details of variables.................................................................................. 28
Table 5: Results .................................................................................................... 31
v
ABSTRACT
The environmental effects of trade: timber export & deforestation
in the case of ASEAN
by
Alounxay Naphayvong
Masters of Arts International Development
International University of Japan, 2008
Professor Yuqing Xing, Supervisor
This study examined the impact of roundwood export on deforestation during
the 1990-2005 period across ASEAN countries. Factors related to agricultural
production, population, economy, agricultural land, and protected area in each
country were hypothesed to influence deforestation. The study briefly reviews some
evidence on the links between timber trade and deforestation. Results of a panel
data’s regression show that population growth and agricultural production are
associated with forest degradation. Results also suggest that economic growth that
leads to poverty reduction can significantly decrease deforestation rate. Finally,
results show that the effect of roundwood export and protected area changes have an
insignificant influence on the depletion of forests.
JEL classification: L73, Q23, Q56
Key words: Deforestation, roundwood, ASEAN
1
CHAPTER ONE
INTRODUCTION
1.1. Scope of the Study
Environmental issues are commonly debated in international arenas, which have
drawn attention on the impact of economic development on environmental
degradation. The link between trade and environmental issues was spotlighted in
negotiations within the WTO at the Doha Ministerial Summit in 2001. This hasn't be
a surprise, as the relationship between trade and environment has been considered as
a major issue in discussions on the general agreement on tariffs and trade (GATT)
since the establishment of the post war global trading system in 1947. The wide
question of timber trade necessarily leads to a preoccupation for increased
deforestation which is an important topic for the trade and environment debate.
Theoretically, international trade encourages income to rise, which is associated with
change in the production and consumption structures. According to an ASEAN
report published in 2006, globalization of the economy in developing countries is
strongly associated with economic growth. International trade represents 14.7 percent
of the growth of the nominal value of total trade, or 6 percent of the growth rate of
GDP among the ASEAN countries.
International trade agreements have a great influence on the ratio of export
increase, through their impact on import tariff restrictions in developing countries.
More dynamic trade expected from tariff barriers reductions should allow an
economy to produce more output, hence an increase in demand on natural resources
2
which could lead to more environmental damage. In most developing countries,
agriculture, energy, mining and forestry create at least half of gross national product.
Expansion of timber trade could affect deforestation resulting in environmental
degradation because of its effect on biodiversity loss, climate change, flooding, soil
degradation and wildlife threat. In addition, forests play an important role in
maintaining water resources, wild life habitat and soil conservation. Moreover,
forests play an important role in carbon dioxide regulation by absorbing carbon
dioxide at the global level. A few thousand years ago, tropical rainforests constituted
about 6 millions square miles or 12 percent of the earth’s land. Today, tropical
rainforests cover less then 5 percent or 2.41 millions square miles of the earth’s land
(FAO, 2007). In the ASEAN context, deforestation emerged as a major phenomenon
with a rate of around 1.3 to 1.9 millions hectares per year. Indonesia deforestation
rate for example is about 0.6 million hectares a year in which represents about 0.5
percent of Indonesia’s forest area (FAO, 1995).
The relationship between trade and environment has been investigated by many
researchers addressing the question of whether there is significant relationship
between timber trade and tropical deforestation. According to Capistrano (1990),
timber trade is associated with deforestation. She examined the effect of timber trade
on deforestation in developing countries by analyzing data from forty five
developing countries. Capistrano concluded that export price of timber had a major
effect of deforestation. Similarly, the relationship between timer production and
forest clearance in topical area was investigated by Barbier et al. (1994). Their study
shows that timber production increases deforestation.
3
1.2. Research Issues
Barbier (1994) states that a few studies have examined the relationship between
trade and environment at a global level in the area of deforestation in developing
countries. Moreover, indicators use in these studies does not include potentially
important factors such as on timber export, agriculture land, population growth,
government policy. Therefore, further investigations are needed in order to provide
more satisfactory evidence about the relationship between trade of timber and
deforestation at a regional level as the ASEAN region. Quantitative can inform us
about the influence of factors on deforestation. However, most of quantitative
studies have some limitations due to data quality concerns. Therefore, large number
of countries and long length of time is employed for the global deforestation models.
Moreover, the limited degree of freedom, outliers, missing variables and
multicollinearity are main problems arising in the multi-country model.
1.3. Purpose of this study
The aim of this paper is to estimate the link between trade and environment, and
highlight the rate of cause and effect of deforestation in ASEAN region. The paper
investigates specific issues of timber trade in logging and deforestation in tropical
countries. In addition, the paper examines the following question: Does international
timber trade represent a risk for the degradation of the environment? This study will
provide information on the scale of deforestation process affected by timber export.
Furthermore, this investigation could assist policy makers in planning and managing
suitable policies in the ASEAN region and in other developing countries.
4
In addition, unlike previous studies, this work use data of the ten ASEAN
countries spread over fifteen five years, from 1990 to 2005, which is about the same
length of time used in the previous studies. However, this study employ every one
year annual change in change of forest area, rather ten year annual change in
deforestation rate is used by former studies. The study employs latest data available
to provide recent evidence about deforestation and policy recommendation regarding
to government restriction in Southeast Asia.
1.4. Methodology
This paper examines the link between international trade and environment in
relation to timber trade and deforestation by applying multi-country regression
model. The cross-sectional data from the nine ASEAN countries for the period from
1990 to 2005 were used. The regression model considered the rate of change in forest
area, index of agricultural production, population growth, GDP per capita growth,
roudwood export, agricultural land and protected area. Most of the data applied in
this study come from the FAO year book on forest products. Pool regression, fixed
effects and random effect are applied to estimate panel data for past fifteen years
across nine ASEAN countries. Due to lack of data for the roundwood export
variable, the multi-country regression’s simples are restricted to 9 country excluding
Singapore. Moreover, this study employs Hausman test technique to check efficient
of fixed and random effects, which method is preferred to use.
5
1.5. Organization
This paper is organized as follows: section two provided overview of
ASEAN’s deforestation and timber trade. The literature on country and multi-country
models related to deforestation is reviewed in section 3. Section 4 presents the data
specification and the source of data. The econometric model and results of study are
discussed in section 5, which followed by conclusions in section 6.
6
CHAPTER TWO
OVERVIEW OF ASEAN’S DEFORESTATION AND TIMBER TRADE
In this chapter, we will review a wide trend of deforestation and timber trade
from 1990 to 2005 in ten ASEAN countries. Then, key factors and problems about
deforestation will be discussed, as well as the linkage between timber trade and
deforestation, and policy factors implemented in ASEAN countries.
2.1. Overview of deforestation in Southeast Asia
The Association of Southeast Asian Nations (ASEAN) is known as one of the
biggest regions in the world. ASEAN is composed of 10 members, namely Brunei
Darussalam; Cambodia; Indonesia; Laos; Malaysia; Myanmar; Singapore; the
Philippines; Vietnam and Thailand. It had in 2005 a global population of about 555
millions individuals or 8.57 percent of world population, and a total country’s area of
448 millions hectares or 3.33 percent of world country’s surface (FAO, 2005).
The ASEAN region began to experience strong economic expansion in the
1990s with Indonesia; Malaysia; Singapore; Thailand and Vietnam taking the lead.
Growth acceleration increased and a diversified production developed to better meet
the consumption need of its population. Output gains on the average boosted the
welfare of millions of people. The rapid economic growth, however, was
accompanied by a degradation in environmental quality, i.e. a retreating bio-diversity
and an increasing forest, land and marine coastal degradation (United Nation
ESCAP, 2007).
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The deforestation in the ASEAN region is a serious concern. Growing
commercial harvesting results in a decline in forest area. High population growths
have also led to widespread tropical deforestation from fuel wood harvesting, timber
harvesting and shifting cultivation for domestic consumption. Others causes of forest
destruction are mining, irrigation and hydroelectric projects (FAO, 1997).
In the 1990s, Indonesia alone had a deforestation rate of 1.9 millions hectare
per year leading to 1.7 per cent per year of forest loss. About between 100,000 to
300,000 hectares per year were also lost in Cambodia, the Philippines and Thailand,
corresponding to 1.1, 2.8 and 0.7 per cent respectively of annual loss of forest area
during the same decade. About 466,000 hectares per year of forest cover were
dropped in Myanmar alone. Lao PDR and Malaysia each lost a little less than
100,000 hectares per year, corresponding nevertheless to 0.5 and 0.4 per cent
respectively of forest loss. FAO data shows that Cambodia, Laos and Vietnam lost
100.000 hectares per year in the 1981-1990period. Brunei and Singapore’s annual
lost of forest area were close to zero. The only country associated with positive forest
area change is Vietnam with 236,000 hectares per year increase (table 1).
Over the period 2000-2005, ASEAN’s deforestation rate accelerated to 1.27
per cent annually, resulting in a total decrease of over 2.8 millions hectares per year
of forest cover in a five years period. In fact, Cambodia, Indonesia, the Philippines
Malaysia and Myanmar have faced the largest serious deforestation in the ASEAN
region. Only Singapore and Vietnam had a positive annual forest area change (table
1), while the remaining Southeast Asia countries had a deforestation rate of 0.4 to 0.7
per cent, less than 100.000 hectares per year (table 1).
Country/area
ForestArea Annual change rate
1990 2000 2005 1990-2000 2000-2005
1000 ha 1000 ha 1000 ha 1000 ha/yr
% 1000ha/yr
%
BruneiDarussalam 313 288 278 -2 -0.8 -2 -0.7
Cambodia 12,946 11,541 10,447 -140 -1.1 -219 -2.0Indonesia 116,567 97,852 88,495 -1,872 -1.7 -1,871 -2.0
Lao PDR 17,314 16,532 16,142 -78 -0.5 -78 -0.5Malaysia 22,376 21,591 20,890 -78 -0.4 -140 -0.7Myanmar 39,219 34,554 32,222 -466 -1.3 -466 -1.4
Philippines 10,574 7,949 7,162 -262 -2.8 -157 -2.1Singapore 2 2 2 0 0 0 0Thailand 15,965 14,814 14,520 -115 -0.7 -59 -0.4Viet Nam 9,363 11,725 12,931 236 2.3 241 2.0
Total South-east Asia 244639 216848 203089 -2779.10 -1.14 -2751.80 -1.27
8
Table 1: ASEAN’s deforestation rate 1990-2000
Source: FAO, FRA (2005)
Recently, an increase in forest’s plantation has lead to a slowdown loss of
forest cover. Brunei Darussalam, Myanmar, Thailand and Vietnam are the countries
that present the fastest forest’s plantation growth in the ASEAN region. However,
replacing timber production aims generally at developing plantation surface rather
than forest area which leads to an even decrease in bio-diversity. ASEAN’s forest
plantation reached over 9.0 per cent of the total forest cover of the region in 2005
(United Nation ESCAP, 2007).
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2.2. Forest productions and trade in Southeast Asia
With its rich biodiversity, forest in Southeast Asia has drawn a lot of attention
from the international community over the past couple of years. It is considered as a
main tropical timber source. Unsurprisingly, timber production and trade have been
carried out actively.
2.2.1. ASEAN’s roundwood production
Roundwood production in Southeast Asia continuously decreased from 325.2
million cubic meters (9.8 per cent of world’s Roundwood production) in 1990 to
297.5 million cubic meters (8.76 per cent of world’s Roundwood production) in
2000. After 2000, it gradually decreased to 265.5 million cubic meters, 7.74 per cent
of the world’s total production. Fuelwood and wood for charcoal account for large
percentage of roundwood consumption, about 73 per cent. Therefore, industrial
roundwood account about 26.9 per cent of total roundwood production in Southeast
Asia region. Around 4.79 per cent of the world’s industrial roundwood comes from
the ASEAN region. The percentage tended to gradually drop in the past couple of
years, with numbers going down from 97.2 million cubic meters in 1990 to 76.1
million cubic meters in 1994 (FAO, 2005).
During the 1990-2005period, ASEAN’s roundwood production gradually
declined year by year. The production comes mainly from Indonesia, which is a
production leader in the Southeast Asia Region, accounting for about 44 per cent of
ASEAN and 3.8 per cent of the world roundwood production. Indonesia is followed
by Vietnam, Thailand, Malaysia and the Philippines. In Indonesia, there was a
constant decrease in roundwood production from 164.4 million cubic meters in 1990
10
to 104.4 million cubic meters in 2005. In Malaysia, there was also decrease in
roundwood production from 45.2 million cubic meters in 1990 to 27.5 million cubic
meters in 2005. However, in Lao PDR there was an increase in production from 6.1
to 6.4 million cubic meters; there was also an increase in Myanmar from 21.2 to 42.5
million cubic meters in 2005. There was an increase in the Philippines up to 44
million cubic meters in 2000, followed by a decrease to 16 million cubic meters in
2005 (FAO 2005).
A large proportion of the population in many developing Asian countries still
rely on fuelwood and charcoal as the common sources of energy in rural and urban
areas. The largest amount of total roundwood production is dedicated to fuelwood.
According to FAO (2005), in 2000, more than 230 million cubic meters (77.6 %) of
the total roundwood production in Southeast Asia was for fuelwood; whereas 66
million cubic meters (22.3%) was for industrial purpose. This amount of ASEAN’s
fuelwood accounts for 12.8% of total roundwood in the world. In Indonesia, there
was a fuelwood production of 88.9 million cubic meters, 72.6% of total roundwood
production. Also, the fuelwood production in the Philippines was 40.9 million cubic
meters, or 93% of its total roundwood production. 26.6 million cubic meters of
fuelwood, or equivalent to 86.4% of its total roundwood production was produced in
Vietnam.
2.2.2. ASEAN’s roundwood export and import
Starting from the 1990s, there was an increasing trend to process the timbers
or turn them into high value products rather than export raw timber. This trend is
11
increasing as many countries in the region such as Indonesia, the Philippines, and
Malaysia have banned exports of unprocessed logs. In addition, many other countries
in the region are also considering or in the process of banning export of raw logs. In
the meantime, there was an increase in production and trade of processed products
constantly. This increase can be seen in the Indonesian and Malaysian cases, which
lead the world in production and export the wood products. Indonesia, the world
leader in plywood export, had a wood production of 10 million cubic meters in 1994
and exported 8 million cubic meters of tropical plywood. Also, Malaysia, the world
leader in sawnwood export, exported 4.6 million cubic meters of tropical sawnwood
in 1994 (FAO, 1997).
Table 2 shows a significant decrease in roundwood positive volume trade
balance from Southeast Asia. Roundwood imports plunged after 1980, while
roundwood exports dramatically declined from 32.9 million cubic meters in 1980 to
only 20.2 million cubic meters in 1990. The decline directly flows from the ban in
log exports in Indonesia after 1980. As a result, log exports from Indonesia decreased
from 16.3 million cubic meters in 1980 to around 1.6 million cubic meters during the
1991 to 1992period. In the meanwhile, there was an increase in roundwood exports
from Malaysia from 15.2 million cubic meters in 1980 to 19.8 million cubic meters
in 1985. After 1985, exports remained constant at around 17.8 to 19.5 million cubic
meters until 1992. Thereafter, exports decreased to 6.8 million cubic meters in 2000.
In the Philippines, there has been a slight net roundwood imports, whereas there was
a fluctuated trend in roundwood exports-imports in Singapore. For example, there
was a net roundwood import of 1.1 million cubic meters in 1980; but Singapore
Roundwood exports quantity (Cubicmeters)
Roundwood imports quantity(Cubic meters)
Country 1980 1990 2000 1980 1990 2000Brunei
Darussalam 1,035 1,503 189
Cambodia 6,300 35,839 100
Indonesia 16,313,800 45,682 1,608,700 61,226 186,700
Lao PDR 17,800 39,400 25,700 0Malaysia 15,194,500 18,084,860 6,845,300 41,500 9,533 758,000
Myanmar 209,300 1,416,680 1,115,500 54 0
Philippines 1,168,600 27,913 100 9,000 320,871 562,400Singapore 42,600 84,410 6,900 1,155,400 80,691 27,300
Thailand 700 46,601 200 104,900 2,035,949 493,000
Viet Nam 437,911 35,100 41,100 8,100
Total South-east Asia 32,953,600 20,219,296 9,638,635 1,351,900 2,509,827 2,035,689
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turned to become a net roundwood exporter around 0.05 to 0.17 million cubic meters
from 1990 to 1994. Thailand was the main importer with 1.37 million cubic meters
in 1995, while Myanmar was the main exporter with 1.1 million cubic meters in
2000, thereafter increasing to 1.14 million cubic meters in 2005.
Table 2: ASEAN’s roundwood exports and imports 1990-2000
Source: FAO, FRA (2005)
2.3. Deforestation policies trend in ASEAN
Historical availability and timber price have influenced the usage of timber
for construction and other usages in different regions and cultures. Increasing
population, economic development, and increasing consumption levels have led to a
rapid growth in global aggregate demand for forest products. According to the 2004
report by Mihoko et al, producing countries such as Indonesia still enjoy export
13
competitiveness in forest products as they have large remaining areas of tropical
forest. However, they are certain to be facing the need for reforestation as have
renowned log-importing countries such as Thailand and the Philippines.
Over the past decade the management of forest has been focusing on
sustainable forest management concept that aims at balancing environmental, socio-
cultural and economic objectives, in compliance with the 1992 agreement of
UNCED (United Nations Conference on Environment and Development).
Nonetheless, forest production management, as the ITTO 2000 (International
Tropical Timber Organization) initiative that encourages sustainable forest
management in tropical timber consumer countries, is focused in broader objectives.
Additionally, regional and national forest harvesting codes such as the Certification
of forest products, a market-based mechanism devised to encourage sustainable
management of forests, have been developed and received positive attention (FAO,
2000).
The economic regulation and direction imposed by governments has an
impact on forestry sector in terms of international trades. In particular, countries in
the Southeast Asia region regulate their economic in form of tariffs and non tariff
barriers such as duties and surcharges, and rules and regulation respectively. Import
restrictions policies aim at discouraging and restricting import of foreign products in
order to promote domestic activities and preserve lower economic efficiency and
welfare. On the other hand, export restrictions aim at reallocating resource in order to
boost domestic consumption. Thus countries have enough reasons to impose such
barriers. In both cases, the forest sector is directly affected by these trade policies
(FAO, 2000).
14
Forest rich countries and regions have utilized Log export ban (LEB) policy
for a long time in an attempt to encourage domestic economic development. Many
countries have been actively discussing the impacts of log export bans policies. The
bans have been adopted in an attempt to reduce aggregate demand that result in
easing pressure on forest resources. Its ultimate goal is to minimize deforestation and
deprivation of forest. Nevertheless, many experts believe that declining demand due
to tightening export market would lead to lower timer price. As a result, the soaring
profitability of forest operations would lead to lower fund for forest management.
Ultimately, the large forest area will be replaced by more profitable agricultural
alternatives such as rice and coffee (Resosudarmo and Yusuf, 2006).
To maintain and increase timber harvest and supplement forest products, a
high rate of forest growth must be achieved; thus, commercial timer interests have
driven the development of reforestation policies. Nonetheless, many national and
international objectives such as soil conservation, habitats protection, watershed
maintenance, and other environmental preoccupations have continuously considered
forest preservation as promotional instrument in recent years. Re-vegetating barren
forestland, improving stock or land condition are the primary objectives of various
national forest sector strategies within the ASEAN region, with a preoccupation for
the extent of forest cover and the declining of potential productivity and economic
gain due to forest exploitation (FAO, 1997).
Many countries have moved to protect their forest under national parks
schemes or other types of conservation projects in the past two decades. The level of
protection and the effectiveness differ between countries. In some countries,
protected area covers all of their forest under the forestry legislation while many
15
other countries only report legally protected areas that meet international standards.
The World Conservation Union (IUCN) and World Conservation Monitoring Centre
(WCMC) (1994) report that the status of ASEAN’s protected area equaled 55.2, 83
and 86.5 million hectares in 1990, 2000 and 2005 respectively. These protected
areas in 1990, 2000 and 2005 accounted for only 12.3, 18.5 and 19.3 percent
respectively of the total Southeast Asia’s land area. In 2005, the 46.5 million hectares
of Indonesia land areas were the largest national land protected followed by 11.1
million hectares in Thailand while Cambodia, Malaysia and the Philippines had 4.3,
8.3 and 6.3 million hectares respectively. Lao and Myanmar protected areas were
similar at 3.7 and 3.8 million hectares respectively. In addition, Brunei Darussalam
had the highest rate when using proportionate area protected method with 59.2
percent, Malaysia came second with 25.2 percent , and Cambodia and Indonesia third
with about 24 percent of land protected . Unfortunately, two countries’ protected
areas were under 10 percent, Singapore and Vietnam, which are associated with
positive change in forest area.
16
CHAPTER THREE
LITERATURE REVIEW
This section aims to review the linkage between tropical deforestation and
timber trade. The measures of the relationship between trade in timber and
deforestation have generated a large literature in recent years. In the global literature,
reviews highlight similarity rather than differences between countries and regions. In
order to review the deforestation effect of timber export, regression models is mainly
discussed in this literature review. The purpose of this chapter is to provide a review
of the literature on the link between deforestation and timber trade at two levels,
country (regional) and multi-country levels models.
Most of single country studies used regressions to measure the correlation
between tropical deforestation and timber trade. Kummer and Sham (1994) examined
the relationship between timber trade in logging and tropical deforestation in forest
cover in the Philippines over the 1957-1980 period. In their study, Kummer and
Sham used explanatory variables related to population growth and lower
transportation costs to estimate their effect on deforestation. They found that both
explanatory variables have a positive relationship with deforestation. Furthermore,
they concluded that there are differences in the percentage of land covered by forest
and by region. Similarly, Panayotou and Sugsuwan (1994) provide a useful survey at
a national level in Thailand by sampling sixteen provinces. They developed a model
to directly investigate the effect of logging as well as other explanatory variables on
forest clearing. They empirically demonstrated that higher agricultural price,
population growth and lower transportation costs are positively associated with forest
17
clearing. Furthermore, they established that the wood prices and deforestation have a
negative relationship.
Among studies which used national-level regression models in insular South
East Asia’s deforestation models, most examined deforestation in Indonesia,
Philippines and Thailand. They conclude that roundwood production, which is
measured as a logging variable, has been major cause of deforestation in South East
Asia between 1970s and 1980s (Katila 1995; Kummer and Sham 1994; Osgood
1994; Panayotou and Sungsuwan 1994). In addition, population growth is found to
have a positive relationship with deforestation (Cropper et al 1997; Katila 1995;
Kummer and Sham 1994; Panayotou and Sungsuwan 1994). However, Panayotou
and Sungsuwan (1994) found higher income was associated with less deforestation.
Nevertheless, Katila (1995) argued that there were no correlation between
deforestation and income variable.
In the context of a single country Kaimowitz and Angelson (1998) conclude
that logging is the major determinant of deforestation in South East Asia rather than
agricultural cultivation as in Latin America. However, they conclude that single-
country regression’s models may have some causality difficulties. There might be
large correlation or high muticollinearity among the independent or explanatory
variables such as population and agriculture land area and agriculture prices.
Various cross-sectional regression (muti-country regression) models to study
the link between deforestation and timber trade, have been developed at multi-
national level to estimate deforestation process of larger size and draw empirically
conclusions at a more global level. Capistrano (1990) analyzed the effect of forest
area, population, national income, external debt and exchange rate on deforestation
18
in forty five developing countries during the 1967 to 1971 period. She showed that
population, agricultural export prices and exchange rate had a positive relation with
deforestation. Moreover, she concluded that high income growth leads to higher
natural resources consumption associated with greater deforestation. However, high
level of income per capita caused environment improvement in the case of high
income countries.
Rudel and Roper (1997) hypothesized that there were differences in
deforestation process in countries with small and large size of forest area. They
pointed out that external debt and rural population influence deforestation. In their
study, they investigated the effect of agricultural exports as percentage of GNP on
deforestation in the 1980-1990 in fifty one countries. Their simulation results
indicated that deforestation has a positive correlation with rural density. However,
they didn't find that income per capita, external debt and road construction might
have an effect on tropical deforestation.
Among multi-country studies that introduced total forest area, national
income and population scale as independent variables (Allen and Barnes 1985;
Capistrano 1990; Kant and Redants 1997; Rudel 1989). According to Capistrano
(1990), higher income was found to be associated with more utilization of forest and
agriculture products, resulting in greater deforestation. Moreover, pressure on forest
conservation is occurring in higher income countries. However, some authors claim
that Latin America’s higher income countries are significantly associated with a
lower lost of forest area (Palo and Lehto 1996). Furthermore, they found that
fuelwood consumption, which is a great part of forest production, and high income
are negatively correlated.
19
Most of the authors hypothesized that population is positively correlated with
forest area decline. Population growth was found to be a determinant of
deforestation. Burgess (1991), Palo et al (1987) and Rudel and Roper (1996) found
that higher population growth was highly associated with greater decreasing in forest
cover area. Nevertheless, Burgess (1991) and Inman (1990) argue that higher
deforestation was significant associated with lower population growth.
Other factors found in multi-country regression studies are the direct sources
of forest clearing which is defined as yield expansion. Increasing crop land area is a
major source of deforestation (Kaimowitz and Angelsen 1998). However, Burgress
(1993) argues that fuelwood and logging removing do not correlate with complete
deforestation. Most national regression models have been concerned about
agriculture and logging variables that effect deforestation. Therefore, increasing in
agriculture area, per capita food production, roundwood production and timber
exports are significantly associated with greater deforestation, as shown in table 3.
Moreover, Capistrano (1990), Gullison and Losos (1993) and Kant and Redantz
(1997) state that agricultural and roundwood export price variables, which are
associated with enhanced trade of agricultural and forest product export, benefit
from forest converting. According to Kant and Redantz (1997), timber exports are
significant in the case of Asia, but the impact on deforestation is greater in Latin
America.
Few studies have included forest protected area as defined governments in
economic deforestation model. Implementing forest’s protected area diminishes the
probability of deforestation occurring (Deininger and Minten 1996; Krutilla et al
1995). However, Kaimowitz and Angelsen (1998) underlie limitations of such
20
studies due to missing variable, heteroskedasticity, muticollinearity and small
number’s degree of freedom. They suggest that these problems will possibly be
solved by adding explanatory variables interactions to verify the effect of each
others. Most of authors employ data from forest resources assessment and production
yearbooks of FAO. Reliability of data from FAO is acceptable for global
deforestation’s model (Rudel and Roper 1997).
In sum, the link between tropical deforestation and timber trade has been
studied in single country and at a cross-countries level. The advantage of single
country’s study is that it allows to directly determine the impact of trade policy on
tropical deforestation. At the same time, the advantage of cross-country studies is
that it allows to classify alternative traders and to measure the impact of deforestation
on region (Barbier, Burgess and Strand, 1995). The linkage between agriculture,
logging, income and population variables; and deforestation in national-country
regression models is showed in table 3 as below.
StudyDependent
variableExplanatory
variables Effect Main featureSource offorest data
Allen and
Barnes (1985)
Decreasing
inforest area
Population No effect
The panel analysis between1968 and 1978 of 39
countries presented thatdeforestation is related toincreases in agriculture inthe short term, and woodproduction for export is
related to deforestation inthe long term.
FAO
yearbooksIncome No effect
Agriculture Increase
Logging Increase
Bawa and
Dayanandan(1997)
Decreasing
inforest area
Population IncreaseDeforestation is positiverelated to increasing in
agricultural production inAsia, Africa and Latin
America. However, loggingwas associated with
deforestation only in Asiaand Latin America.
FAO, WRI
(1994)
Income No effect
Agriculture Increase
Logging Increase
Burgess
(1993)
Decreasing
inforest area
Population Increase Logging removal is morecontroversial. Many authorsargue these activities onlyharmful forests. However,
Logging usually is notassociated with complete
deforestation.
Lany(1998)Income Increase
Agriculture NA
Logging Increase
Kant and
Redantz(1997)
Decreasing
inforest area
Population Increase They find that one hectareof agricultural expansion
lead to 2.8 hectares ofdeforestation in Africa, andto 0.5 hectares in Asia and
Latin America.
FRA, FAO
(1990)
Income Increase
Agriculture Increase
Logging Increase
Mainardi
(1996)
Decreasing
inforest area
Population NA Higher national incomesper capita in developingcountries are associated
with higher forest clearingduring the period of 1980sfor 48 countries in sample.
WRI
(1994),FAO
Income Increase
Agriculture Increase
Logging No effect
21
Table 3: Summarize literature survey on deforestation in national-country regressionmodel
StudyDependent
variableExplanatory
variables Effect Main featureSource offorest data
Palo et al.
(1996)Forest cover
Population Increase They find that increasing in
incomes for Latin Americalead to deceasing in
deforestation. However,they show the finding forother regions are opposite
effect.
FRA, FAO
(1990)Income Increase
Agriculture Increase
Logging No effect
Rudel (1989)Decreasing
inforest area
Population IncreaseBy using 36 countries insample for 15 years since1976, the study find that
affect of deforestation rateshave high proportion with
large forest areas especiallyin Brazil, Indonesia and
Papua New Guinea.
FRA, FAO
(1980)Income Increase
Agriculture No effect
Logging No effect
Rudel and
Roper (1997)
Decreasing
inforest area
Population Increase Higher deforestation isassociated with greater
income per capita level for1976-90 for countries thathave small forest areas.
Estimates
by authors
Income IncreaseAgriculture NA
Logging Increase
22
Source: Compiled by the authorNA= Not applicable
23
CHAPTER FOUR
DATA AND MODEL SPECIFICATION
In the previous sections some background issues on Southeast Asian’s
deforestation and timber trade were discussed. Then a brief review of literature and
other studies were presented. The data description the source of data and econometric
model are now explained.
4.1. Specification of variables
The sample consisted of the nine ASEAN developing countries listed in
Appendix 1. Due to data availability and missing data, Singapore is the only AEAN
country excluded from our sample. This country has a zero rate of deforestation. The
study design is a panel analysis of Southeast Asian countries across the 1990-2005
period with a total of 134 observations.
4.1.1. Annual change in forest area (rate of deforestation)
The total of forest area is used to measure the deforestation rate defined as the
annual percentage change in forest cover during a period. According to FAO
organization (FRA, 2005), the definition of forest cover includes the area of bamboo
and palm surface, open and closed forests as national protected parks, windbreaks
and firebreak, protected areas as history and culture areas. However, forest area doest
not include agriculture and urban extended land use, or garden and urban parks. The
rate of change in forest is measured by the change in units of forest cover of 1000
hectares. This study defined rate of forest degradation as the annually change in
are
24
forest area. The annually percent change rate of forest area was calculated by
dividing the forest area of the current year (t) by the forest area of the previous year
(t-1) levels, subtracting 1 and dividing by the number of years considered:
areat areat1 1
areat1
number of years
Deforestation rate, which is defined as the annual change in forest area, is
employed to estimate the correlation between timber trade and tropical deforestation.
Due to up-to-date data availability, annual 1990 to 2005 data were used. Data on
deforestation rate was mainly taken from the FAOSTAT statistical database of the
Food and Agriculture Organization of The United Nations (FAO, 2005). Because of
data availability in the past, most studies have employed FAO data for performing
multi-country regression to estimate over ten year the change of deforestation rate.
However, this study used recent FAO data available year by year, which made an
analysis of the deforestation on a yearly basis possible.
4.1.2. Explanatory variables
Growth of GDP per capita
Growth of gross domestic product (GDP) per capita, in constant 2000 US
dollar, reflects national income level and socioeconomic development. In this
context, there is a positive relationship between national income level and
environmental degradation. High growth of GDP may lead to an increase in demand
25
for forest and agriculture production and can be associated with deforestation.
However, a high GDP growth may slow down and decrease the deforestation. In high
GDP countries, there is a demand for environmental protection, which protects from
deforestation (Culas, 2006). The data are directly sourced from the world
development online indicators of the World Bank database.
Population growth rate
Population growth rate, which is an indicator of socioeconomic
developments, might be related to deforestation rate and economic growth. Growth
rate of total population is the annual rate change of of a country population size
calculated at mid-year. Population is a determinant of the demand for forest product
and land use, which might result in deforestation. Therefore, this study hypothesizes
that the coefficient of relationship between change in forest area and population
growth should be positive. In this study, all population indicators were obtained from
the database of United Nations, Department of Economic and Social Affairs,
Population Division, 2006.
Agricultural production index
The agricultural production index consists of all crop and livestock products
produced in each country over the period 1999 to 2001. The agricultural production
index refers to the agricultural component of economic growth in developing
countries. The increase in agricultural production index could boost GDP. Export of
crops and labor might cause deforestation. Therefore, the variable of Agricultural
production index is assumed to be positive in its relation with deforestation. The
26
original data set was taken from the Food and Agriculture Organization of the United
Nations (FAO) 2005, FAOSTAT Online Statistical Service.
Trade in forest product (roundwood export)
Data on trade in forest product were drawn from the 2007 FAOSTAT on-line
statistical service of Food and Agriculture Organization of the United Nations (2005
Edition). There is commercial timber harvesting for roundwood export between
countries. Increasing roundwood products might be associated with greater forest
production which could also be leading source of decline in forest cover. However,
the roundwood export has declined over time from 1980, and has represented a small
amount of forest products (roundwood production) in each ASEAN country.
Furthermore, commercial timber export may not be a key factor leading to
deforestation. Its rather the roundwood export that might be a key factor to use in a
ASEAN’s deforestation model. Roundwood export is hypothesized to be positively
correlated with the decline in forest clearing.
Harvested area (agriculture land)
The total harvested area is the total area used for agriculture. Agriculture land
is defined as arable land under permanent harvest, as wheat, barley, maize, rye, oats,
millet, sorghum, rice, quinoa, and rice lands (FAO, 2005). The expansion of
agricultural area is commonly associated with degradation of forest area. In the
ASEAN case, rice which is the main agricultural production is assumed to be
associated with deforestation. Data were taken from the FAO database (2005).
27
Government restrictions (protected area)
Governments might impose restrictions on protected areas through its
environment policy and regulation on public goods. The protected area variable used
in this study is the total area to be protected for its biodiversity as defined by the
World Conservation Union (IUCN). The dataset is taken from the 2006 World
Database on Protected Areas (WDPA). This study hypothesizes that a greater
protected area is associated with a lower deforestation. In addition, according to data
trends, protected areas imply that country with severe deforestation have a greater
protected areas implementation.
4.2. Data sources
The dataset used in the study are original data coming from the FAO (2005),
the World Bank (World development Indicators, WDI) and the United Nations
Statistics Division. Data on forest areas (annual change in forest area or decline in
deforestation) came from the FAO (FRA, 2005) which provides more reliable and
up-to-date data on yearly forest area. The regression was performed using a panel
dataset from 1990 to 2005. Details on the variables, the variables units and the
expected coefficient sign are described and summarized in table 4 as below.
Variable Explanation Unit SourceExpected
sign*
FORCH Rate of annually change in forest
area (decreasing in rate change offorest area is equal todeforestation)
%
www.fao.org
POPG Population growth % www.un.org Negative
GDPGGDP per capita growth
(in 2000 market price)
% World Bank
(WDI, 2007)Negative
RWEX Roundwood exportmillion
ha www.fao.org Negative
AGRL Agriculture landmillion
ha www.fao.org Negative
AGRPI Agriculture production index
Base
year(1999-2001)
www.fao.org Negative
PROTA Protected areamillion
ha
www.unep-
wcmc.orgPositive
28
Table 4: Details of variables
*expected sign for Rate of annually change in forest area is defined as change
in forest growth. Therefore, if rate change of forest area is associated with negative
sign, there is deforestation occurring.
29
HAPTER FIVE
THE ECONOMETRIC MODEL AND RESULTS
In the previous parts of this study report, the definitions of the deforestation
explanatory variables were presented. This section will explain the econometric
model, and provide the regression results.
5.1.1 The econometric model
The model aims at measuring that the influence on f forest area changes, of
the following variables: export of forest products, agriculture production, economic
development and land use. This study suggests that the explanatory variables have
influenced most of the key causes of deforestation degradation found in the literature.
In this study’s model, the dependent variable is the rate of decline in forest area
which is defined as the annual change in forest cover. The explanatory variables are
provided in table 1 above. Since, panel data which include time series and cross-
sectional data is used, i is defined as countries and t as year. The coefficients of the
variables are provided by , the error term by it and the constant by it .
The ASEAN deforestation’s empirical model is represented as:
FORCH it it1POPG it 2GDPPCG it 3RWEX it
4AGRL it 5AGRPI it 6PROTA it it
30
Where; FORCH it
POPG it
GDPPCG it
RWEX it
AGRL it
AGRPI it
PROTA it
= Rate of annually change in forest area
= Population growth
= GDP per capita growth (in 2000 market price)
= Roundwood export
= Agriculture land
= Agriculture production index
= Protected area
Three major methods, simple pooled regression, fix effects and random
effects, were tested to estimate the coefficient values for ASEAN’s deforestation.
Due to data limitation for RWEX (roundwood export), this study had to exclude one
country, Singapore. The study econometric model determines the relationship
between deforestation and the net impact of income, socioeconomics development,
wood use and restriction by the governments. Since, panel data methods are
employed in this analysis, the limited number of observations, the small degree of
freedom and high high collinearity between the independent variables are reduced
and resolved by using random and fix effects methods. Further information on the
limitations and advantages of applying the method of panel data can be found in
study of Batagi (1995) and Hsiao (1986). For testing the fixed effects method against
the random effects regression, this study applied the Hausman test statistics to select
the most appropriate method.
Data description and correlation matrix checking are presented in Appendix
2. Agricultural production index and agricultural land are assumed to be greatly
correlated with population growth and protected area respectively. Multicollinearity
31
between independent variables is not thought to be that main factor that might cause
a problem of this study. Because of missing observations for some cross-section
units, the unbalanced panel is applied to resolve general attrition.
5.1.2. Results and discussion
Table 1 presents the final results for the ASEAN’s deforestation regressions
on the effects of the chosen explanatory variables on the growth rate of forest area.
Table 5: Results
Dependent variable: annually change in forest area (decline in forest cover)Pool
Independent regressi Fixed Randomvariable
POPG
on
-0.754
Prob
0.006***
effects
-0.728
Prob
0.031**
effects
-0.754
Prob
0.005***(0.271) (0.334) (0.271)
GDPPCG 0.059 0.034** 0.079 0.030** 0.059 0.033**(0.028) (0.036) (0.028)
RWEX 0.037 0.314 0.0305 0.467 0.037 0.313(0.036) (0.0418) (0.0367)
AGRPI -0.015 0.019** -0.015 0.253 -0.015 0.018**(0.0065) (0.013) (0.0065)
AGRL -0.0585 0.080* -0.0622 0.184 -0.0585 0.078*(0.0332) (0.0465) (0.0332)
PROTA 0.0171 0.589 0.0215 0.604 0.0171 0.588
(0.0316) (0.0413) (0.0316)
Constant 2.35 0.033** 2.228324 0.092* 2.350074 0.031**
(1.0891) (1.3108) (1.0891)
dfR squaredF statistics(df)
1270.2725.53
1130.20475.14
-0.27-
***Significant at 0.01 level; **Significant at 0.05 level; *Significant at 0.10 level.Standard errors are in (parenthesis)
32
All coefficient independent variables in three regression methods are presented, as
well as their standard deviation and the probability estimation.
Population factor and deforestation
The results confirm that the increase in population growth is associated with
spoil of forest area. The coefficient relating population growth to forest area change
(all about1 = -0.7) shows a negative relationship and is significant at 0.01 and 0.05
level for pool regression and random effects, and fixed effects, respectively. A
negative coefficient indicates that ASEAN’s countries with high population growth
rate also have high rate of deforestation. Therefore, the study’s finding strengthens
previous studies showing that population growth is positively correlated with forest
degradation (Cropper et al 1997; Katila 1995; Kummer and Sham 1994; Panayotou
and Sungsuwan 19994). In ASEAN’s countries, population factor plays an important
role in deforestation.
Income and deforestation
GDP growth in connection with increasing natural resources consumption has
been regarded as one factor of deforestation. The results of our study indicate that the
coefficients for GDP per capita growth ( 2 = 0.059, 0.079 for pool regression and
random effects, and fixed effects, respectively) are positive and significant at the
0.05 level. A positive correlation implies that an increasing GDP growth is
associated with decreasing deforestation. According to Palo and Lehto (1996), higher
income in Latin America countries is significantly associated with lower
deforestation. Our result confirms this study. Our finding, however, is different from
33
Capistrano's results (1990) that state that higher income is associated with more
forest and agriculture products, resulting in greater loss of forest area.
Roundwood export and deforestation
The regression results find an inverse relationship between Roundwood
exports and annual change in forest area suggesting that increasing forest products
exports increase annual change of forest area, by declining deforestation rate. The
most important finding is that roundwood exports in ASEAN’s countries were not
significantly correlated to deforestation. As mentioned earlier, this result may be due
to the fact that roudwood exports contributed to a small among of forest production
in the case of ASEAN countries during the 1990 to 2005 period. Therefore, this
multi-country regression suggests that in Southeast Asia roundwood export may not
be an important reason for the decline in forest cover. This finding is consistent with
the finding by Rudel (1989) that there is no effect of timber exports on deforestation
in national country regression models. Rudel suggested that agricultural and timber
export were not associated with increasing deforestation. The demand for timber did
not account also for a large amount of deforestation in Africa and Latin American.
Agriculture land and production, and deforestation
The coefficients in the agriculture land and production are statistically
significant at 0.05 and 0.1 levels, respectively. But they were not found significant
when the fixed effects method was used. In line with this study model, higher levels
of agriculture land and production are related with an increased the loss of forest
area, thereby with higher deforestation. This finding is consistent with most previous
34
results that showed that agriculture land and production imply an expansion of land
for crop use. Increasing agriculture land leads to an increase in deforestation (Kant
and Redantz 1997; Mainardi 1996). Higher food production is associated with lower
loss in forest area (Bergess 1991; Palo et al 1987). According to our results, a 1%
increase in agriculture production decreased forest area by 0.015%. And a 1 million
hectares increase in agriculture land led to an increase deforestation by 0.0585%.
Protected area and deforestation
Government policy and restriction leading to an expansion of protected area
has been considered as one major contributor to the growth of forest area. Higher
protected area is associated with lower deforestation. However, the coefficient of
protected area ( 2 = 0.059, 0.079 for pool regression and random effects, and fixed
effects, respectively), although positive as expected, is not statistically significant.
This implies that there is no correlation between deforestation and protected area in
the case of Southeast Asia. This supports the argument that protection area might be
ineffective regarding protected area management. In fact, illegal logging is known to
take place in protected area because of poor law enforcement (Najam et al 2007).
Croper et al (1997) have shown that protected forest area did not decrease the chance
of forest decline in Thailand.
Hausman test
This study has used panel data regressions to build a multi-country
deforestation model for developing countries. Fixed effects and random effects were
employed to estimate cross-section data from 1990 to 2005. In the case of
35
correlation, a fixed effect estimation is necessary when there is a risk of bias due to
omitted variables. In contrast, in case there is no correlation, the random effects
method is appropriate (Greene, 1997). When there is a need to choose between the
random effects and the fixed effects approaches under panel data estimation, the
Hausman technique is employed.
the Hausman test provided Prob>chi2 = 0.9904. According to Stock and
Watson (2003), when there is an insignificant P-value, which is defined as Prob>chi2
greater than 0.05, using the random effects method is appropriate. But, when the P-
value is significant, the random effects method should be preferred to the fixed
effects method. Therefore, the random effect is the preferred approach to produce
estimates in the panel data regressions.
36
CHAPTER SIX
CONCLUSION
This work used a quantitative model to study the effect of GDP per capita
growth, population growth, agricultural land change and production in forest
products, on deforestation, across ASEAN countries over the 1990 –2005 period of
time. Results from regression suggest that population growth, agricultural land use
and agricultural production directly aggravate deforestation. However, timber export
is not likely to influence deforestation. Also, protected area change tends not to
hamper loss of forest area.
A panel-data analysis for Southeast Asia countries, excluding Singapore,
showed that the random effects method is appropriate to study deforestation in
ASEAN. The conclusions from this study are that population growth, agricultural
land use and production can influence deforestation. These results are consistent with
previous findings that there is a positive relationship between deforestation and
population growth, agricultural land use and production. An interesting finding of
this study was that roundwood export had no statistically influence on deforestation
for the Southeast Asia case. As discussed above, this result may be due to the size of
roundwood export in the ASEAN.
In the past 16 years, forestry policies have increased rapidly in Southeast
Asia. In fact, the increase in protected area may be regarded as a measure to counter
severe deforestation. However, protected area does not necessarily lead to reduced
deforestation. This can be explained by the fact that governments are not always able
37
to enforce regulation on protected areas such as punishment for illegal logging. Thus
forestry policy needs to be strengthened in ASEAN countries.
Considering the significant effect and negative sign of the growth of GDP per
capita on deforestation as appeared in all pool regressions, fixed effects and random
effects methods, the results from this study suggest that strengthening poverty
reduction would reduce deforestation. One percent increase in GDP will reduce
deforestation by 0.06 to 0.08 percent. Thus, it might be more effective to focus
efforts on poverty reduction for controlling deforestation, rather than to limit
population growth, agricultural land use and agricultural production. Therefore, the
paper brings out a number of policy implications. The most significant is the
importance of poverty reduction in Southeast Asia countries for effective forest
conservation in long term.
It is recommended that further studies are conducted to better understand the
effect of political, institution and government policies in the field of forestry policies.
It is suggested that modeling techniques should improve in the future, as will data
quality for use in regressions on deforestation. Further models will certainly be more
appropriate to overcome the limited degrees of freedom of the regressions.
38
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Rudel, T.K. and Roper, J. (1996). Regional patterns and historical trends in tropicaldeforestation, 1976-1990: a qualitative comparative analysis. Ambio 25: 160-6.
Rudel, T.K. and Roper, J. (1997). Forest fragmentation in the humid tropics: a crossnational analysis. Singapore Journal of Tropical Geography, 18.
Stock, James H., and Mark W. Watson (2003): Introduction to Econometrics,Addison Wesley (First Edition)
United Nations. (2008). United Nations Statistics Division online database. Fromhttp://unstats.un.org/
WCMC. (1994). Biodiversity and Protected Areas: the World Database on ProtectedAreas. From http://www.unep-wcmc.org/wdpa/ .
Variable Mean SD Min Max Obs
Rate of annually change in forest area -0.807 1.266 -3.196 2.522 135
Population growth 1.893 0.645 0.7 3.526 135
GDP per capita growth 3.539 3.888 -14.238 12.467 134Roundwood export 1.194 3.184 0 19.511 144
Agriculture production index 90.298 21.040 33 142.8 144
Agriculture land 12.148 12.712 0.013 47.8 144Protected area 8.211 12.141 0.011 46.536 144
42
APPENDICES
Appendix 1: List of countries
Brunei DarussalamCambodiaIndonesiaLao PDRMalaysiaMyanmarPhilippinesThailandViet Nam
Appendix 2: Descriptive statistics
Appendix 3: Correlation matrix
FORCH POPG GDPPCG RWEX AGRPI AGRL PROTAFORCH 1.0000
POPG -0.0306 1.0000GDPPCG 0.2249 -0.2685 1.0000
RWEX 0.0681 0.2232 0.1237 1.0000AGRPIAGRL
-0.1186-0.2975
-0.5496-0.5241
0.18830.0302
0.0053-0.0736
1.00000.2851 1.0000
PROTA -0.3282 -0.3397 -0.0530 0.0215 0.2726 0.9411 1.0000