The Implication of Wood Price Changes on Deforestation in Indonesia: A Market Integration Approach

THE IMPLICATION OF WOOD PRICE CHANGES ON

DEFORESTATION IN INDONESIA:

A MARKET INTEGRATION APPROACH

ANISA DWI UTAMI

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

ii

DECLARATION

I, Anisa Dwi Utami, hereby declare that the thesis entitled:

THE IMPLICATION OF WOOD PRICE CHANGES ON

DEFORESTATION

IN

INDONESIA:

A

MARKET

INTEGRATION APPROACH

Submitted to fulfill a requirement for the award of Master of Science

in Agribusiness from Bogor Agricultural University is my own piece

work produced through the guidance of my academic advisors and to

the best of my knowledge. It has not been submitted for the award of

any degree in any other academic institutions. This thesis does not

contain any pieces of work of other person, except those are duly

acknowledge and referenced in the text.

Bogor, November 2012

ANISA DWI UTAMI

NRP H451100091

iii

Abstract

ANISA DWI UTAMI, the Implication of Wood Price Changes on Deforestation in Indonesia: A Market Integration Approach (ANDRIYONO KILAT ADHI as a Chairman and LUKMAN M BAGA, and BERNHARD B BRUEMMER as Member of Advisory Committee)

Being one of the largest tropical countries in the world, Indonesia faces increasing problems with deforestation. Timber industry is one of the main sources of income from forestry sector in Indonesia. It is commonly assumed that deforestation is driven by the dynamics of supply and demand of forest products, particularly with regard to wood products. Based on the economic theory, price is an indicator of how market works and thus drives supply and demand reactions. This research is aimed to investigate the market integration between domestic and world market of wood products and the implications of wood price changes on deforestation in Indonesia. We conducted price transmission analysis which uses Error Correction Model and regression model to see the relations between wood prices with deforestation. According to the results of price transmission analysis, it is found that domestic logwood market of Indonesia is integrated with the world market as well as in the plywood and sawn wood market, though the domestic logwood market is protected by the enactment of export ban since 1987. The results of regression model show positive correlation between wood prices and deforestation which concludes that the higher prices of wood products will lead to the more potential deforestation in Indonesia. In the presence of market integration of some processed wood products between domestic and world market, the deforestation may not be hindered only by imposing export ban on logwood.

iv

Summary

ANISA DWI UTAMI, the Implication of Wood Price Changes on Deforestation in Indonesia: A Market Integration Approach (ANDRIYONO KILAT ADHI as a Chairman and LUKMAN M BAGA, and BERNHARD B BRUEMMER as Member of Advisory Committee)

Nowadays, Indonesian forestry sector has to deal with deforestation problem which is related to the logging activities as well as the international trade of timber products. Indonesia has been entering into a period of decreasing forest resource availability due to a highly degraded growing stock and declining in forest area. It is commonly assumed that deforestation is driven by the dynamics of supply and demand of forest products, particularly with regard to wood products. Based on the economic theory, price is an indicator of how market works and thus drives supply and demand reactions. This study are aimed to: 1) To investigate the market integration between domestic and world market of wood products, and 2) To investigate the implications of wood price changes on deforestation in Indonesia

To analyze the wood industry, this study uses secondary time series data taken from FAO, Ministry of Trade of Indonesia, Ministry of Forestry of Indonesia, Statistical Bureau of Indonesia, International Tropical Timber Organization (ITTO), and World Bank. Due to the data availability, this study only focus on the three main products in the Indonesian wood industry i.e. logwood, plywood, and sawn wood. The data which are used for the transmission analysis are monthly price data. Meanwhile, the data which are used for the econometric modelling of deforestation are annual data from 1978 until 2007. Error Correction Model (ECM) is applied by using software package of J-Multi. There are some statistical testings are applied with regard to ECM : 1) Unit root test for testing data stationarity, 2) Testing for cointegration, 3) Granger-causality test, 4) Stability analysis, and 5) Residual analysis for checking the occurence of heteroscedasticity, autocorrelation, and testing for normality. regression model is applied to answer the question of how is the impliation of wood price changes on deforestation in Indonesia. The term deforestation in this study refers to the potential deforestation, not to the real deforestation condition due to the data availability on deforestation. There are two proxies to represent the potential deforestation. First, it refers to the distortion rate which is calculated from the deviation between annual allowable cut and the real production. The negative distortion implies the presence of over-harvesting activities in wood industry. Second, it refers to the illegally logwood export. Since the logwood export has been banned since 1987, it is assumed that the present of logwood export is considered as illegal activities.

According to the results of price transmission analysis, it is found that domestic logwood market of Indonesia is integrated with the world market as well as in the plywood and sawn wood market. From this result, it reveals that, though the domestic logwood market is protected by the enactment of export ban since 1987, but the market is still integrated. The integration implies that every change in the world market will be transmitted to the domestic market reflected by the

v price response. Implicitly, loggers will not only react to the changes on demand and prices in the domestic market, but also to the changes in the world market.

The results of regression model show positive correlation between wood prices and deforestation. Based on these findings, generally it can be concluded that the higher prices of wood products will lead to the more potential deforestation in Indonesia. Indeed, these findings confirm the previous literatures as well as the economic theory. In the presence of market integration of some processed wood products e.g. plywood and sawn wood, between domestic and world market, the deforestation may not be hindered only by imposing export ban on logwood. Therefore, partial trade restriction may not be effective to reduce the rate of deforestation from logging activities.

vi

Copyright© 2012. Bogor Agricultural University All Right

Reserved

1.

No part or all of this thesis maybe excerpted without inclusion

and mentioning the sources.

a.

Excerption only for research and education use, writing for

scientific papers, reporting, critical writing or reviewing of a

problem.

b.

Excerption does not inflict a financial loss in the proper

interest of Bogor Agricultural University

2.

No part of or entire of this thesis maybe translated and

reproduced in any form or by any means without written

permission from Bogor Agricultural University

vii

THE IMPLICATION OF WOOD PRICE CHANGES ON

DEFORESTATION IN INDONESIA:

A MARKET INTEGRATION APPROACH

ANISA DWI UTAMI

A thesis

Submitted to the Graduate School in Partial Fulfillment of the Requirement for Master of Science

Degree in Agribusiness

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

viii

1. External Thesis Examiner : Dr. Ir. Nunung Kusnadi, MS

ix

Thesis Tittle : The Implication of Wood Price Changes on Deforestation in Indonesia: A Market Integration Approach

Name : Anisa Dwi Utami Registration Number : H451100091

Approved

Advisory Committee

Examination Date :

Submission Date :

Dr. Ir. Andriyono Kilat Adhi Chairman

Ir. Lukman M Baga, M.AEc Member

Prof. Dr. Bernhard B Bruemmer Member

Agreed

Coordinator of Major Agribusiness

Prof. Dr. Rita Nurmalina, MS

Dean of Graduate School

x

ACKNOWLEDGMENT

This research would have been impossible without the support from many people. I would like to appreciate everything they have given to me.

First of all, all praise to God, who the most precious and the most merciful for His blessing from the first until the last step of the research process.

I would like to acknowledge the support from the Ministry of Education of Indonesia for funding my study in Germany.

I would like to thank to my supervisors from Bogor Agricultural University: Dr. Andriyono Kilat Adhi and Ir. Lukman M Baga, M.A.Ec, who supports me academically in thesis writing, for their evaluation and valuable comments on this research from the beginning until the last step. I would like also to thank my supervisor from Goettingen University, Prof. Dr. Bernhard B Bruemmer, for his insight and his constructive criticism of my thesis. I would also acknowledge to Prof. Dr. Rita Nurmalina, MS as the head of Magister Science of Agribusiness with regard to the joint degree program between Magister Science of Agribusiness, Bogor Agricultural University with Master of International Agribusiness and Rural Development, Goettingen University.

My sincere thank further to all my friends in Magister Science of Agribusiness IPB (MSA 1) as well as in SIA program- Goettingen University, for a friendly and warm environment during my study.

My deepest appreciation goes to my lovely husband, Reza Fathurrahman, for his great supports. I dedicate this work to my beloved parents who always give me their love and teach me the values of life.

Bogor, November 2012 Anisa Dwi Utami

xi

Autobiography

Anisa Dwi Utami, the author of this thesis, was born in Pekalongan, on 21st of October 1987. She completed her primary education in 1999 at SD N I Legokkalong, Pekalongan. She did her Junior high school at SMP N I Wonopringgo, Pekalongan in 2002 and completed her senior high school at SMAN I Pekalongan in 2005. She spent her bachelor degree in Bogor Agricultural University with major Agribusiness. She got her B.A in 2009. In 2010, she continued her study to the master degree with joint-degree program between Department of Agribusiness of Bogor Agricultural University, Indonesia and Department of International Agribusiness and Rural Development of Goettingen University, Germany. She spent her first year in Bogor and her second year in Goettingen. She got scholarship from Ministry of National Education of Indonesia.

xii

Table of Contents

Abstract ... iii

Summary ... iv

Acknowledgment ... x

Table of Contents ... xii

List of Tables... xiv

List of Figures ... xv

1 INTRODUCTION ... 1

1.1 Background ... 1

1.2 Research Problem ... 3

1.3 Research Objective ... 5

2 LITERATURE REVIEW ... 6

2.1 The Concept of Deforestation : Definition and Causes ... 6

2.2 The Role of International Timber Trade on Deforestation ... 9

2.3 Review of Economic Modeling of Deforestation ... 12

3 THEORETICAL FRAMEWORK ... 15

3.1 Market Integration ... 15

3.2 Spatial Market Efficiency and Equilibrium ... 17

3.3 Deforestation : Supply and Demand Theory for Forest Product ... 18

4 METHODOLOGY ... 20

4.1 Data Description ... 20

4.2 Price Transmission Analysis ... 20

xiii 5 PRICE TRANSMISSION ANALYSIS BETWEEN DOMESTIC AND

WORLD PRICE OF INDONESIAN WOOD PRODUCTS ... 29

5.1 Unit Root Test ... 30

5.2 Cointegration Analysis ... 31

5.3 Granger Causality Test ... 33

5.4 Stability Analysis ... 34

5.5 Model Selection ... 35

5.6 The Results of Error Corection Model (ECM) ... 36

5.7 Residual Analysis ... 39

5.8 Discussions ... 40

6 THE IMPLICATION OF WOOD PRICE CHANGES ON DEFORESTATION IN INDONESIA ... 43

6.1 Potential Deforestation in Indonesia ... 43

6.2 Model Selection ... 45

6.3 Discussions : The Impacts of Wood Prices on Potential Deforestation in Indonesia ... 47

6.4 Policy Implications ... 49

7 CONCLUSION ... 51

8 REFERENCES ... 52

xiv

List of Tables

Table 1 Deforestation rate in Seven Main Islands in Indonesia during 2000-2005 1

Table 2 Diagnostic Tests for Residual Analysis ... 26

Table 3 ADF test Result ... 31

Table 4 The Result of Engel-Granger-Two-Step Procedure ... 32

Table 5 Results of Johansen Trace test ... 32

Table 6 The Result of Granger-Causality Test ... 33

Table 7 Error Correction Model for Logwood ... 37

Table 8 Error Correction Model for Plywood ... 38

Table 9 Error Correction Model for Sawnwood ... 39

Table 10 The Results of Residual Analysis ... 40

Table 11 The Result of Regression analysis before omitting ... 46

xv

List of Figures

Figure 1 Tropical Logwood Production of Five Top Producer Countries During

2008-2010 ... 2

Figure 2 Annual Export of Wood Primary Wood Products of Indonesia During 1995-2011 ... 3

Figure 3 Production and Consumption of Logwood of Indonesia 1996-2010... 4

Figure 4 Supply and Demand of Forest Products ... 19

Figure 5 Plot Series of Logwood ... 19

Figure 6 Plot Series of Plywood... 30

Figure 7 Plot Series of Sawnwood ... 30

Figure 8 The Results of Chow Test... 35

Figure 9 Annual allowable cut 2004-2007 in Five Main Islands ... 44

1

1

INTRODUCTION

1.1 Background

Indonesian forestry sector faces some problems seen both from economic and environmental perspective. Being one of the largest tropical countries in the world, Indonesia faces increasing problems with deforestation and related environmental degradation issues. For some periods, natural forests have been increasingly and unsustainably exploited for gaining some economic benefits. Although Indonesia has comparative advantage with a given condition of forest resource abundance, but nowadays, Indonesia has been entering into a period of decreasing forest resource availability due to a highly degraded growing stock and declining in forest area. If it is compared to the other countries which have experienced substantial declines in forested land during 1990 until 2005, Indonesia ranks second in terms of the absolute loss of area and fourth in relative terms with about 15 % of the total land area (World Trade Organization, 2010). Even though there are various estimations of the rate of deforestation, but it is generally believed that deforestation in Indonesia reaches the level of at almost one million hectares annually, with a significant portion resulted from conversion to large-scale estate crops and timber plantation (Ministry of Forestry of Indonesia, 2005).

Table 1 Deforestation rate in Seven Main Islands in Indonesia during 2000-2005

Island 2000- 2001

2001- 2002

2002- 2003

2003-2004

2004-2005

Average

Sumatera 259.500 202.600 339.000 208.700 335.700 269.100 Kalimantan 212.000 129.700 480.400 173.300 234.700 246.020 Sulawesi 154.000 150.400 385.800 41.500 134.600 173.260 Maluku 20.000 41.400 132.400 10.600 10.500 42.980 Papua 147.200 160.500 140.800 100.800 169.100 143.680 Jawa 118.300 142.100 343.400 71.700 37.300 142.560 Bali & Nusa

Tenggara 107.200 99.600 84.300 28.100 40.600 71.960 Indonesia 1.018.200 926.300 1.906.100 634.700 962.500 1.089.560 Source: Ministry of Forestry of Indonesia (2006)

2 Timber industry is one of the main sources of income from forestry sector in Indonesia. Due to the increasing growth of GDP and domestic demand from construction industry, Indonesia has grown to become the largest tropical timber producer in the world with 32.4 million m3 annual production since 2007 (ITTO, 2008), and thus has contributed to cover almost a half of world supply of tropical logwood as shown in the figure 1. The share of tropical timber in total world timber is relatively high. In 2009, reported by ITTO (2010), the share of tropical wood products to total world timber production is 13 percent for logwood, 14 percent for sawn wood, and 24 percent for plywood. As it is in the production, its share in total world timber export is high as well, and even a bit higher, i.e. 22 percent for logwood, 11 percent for sawn wood, and 30 percent for plywood.

Figure 1 . Tropical Logwood Production of Five Top Producer Countries During 2008-2010

(Source: International Tropical Timber Organization, 2010)

A major shift has been occurred in Indonesian timber industry from its earlier role as major tropical logwood exporter in the world market, which contributed for over 40 percent of world exported log in 1979, to processed products exporter. This was due to the change in the government‟s orientation to

promote wood processing export since the early 1980‟s and the introduction of log

0 5000 10000 15000 20000 25000 30000 35000 40000

Others Nigeria Malaysia India Brazil Indonesia

2010 2009 2008

3 export ban in 1987. As reported by the Ministry of Forestry (2005), for most tropical wood products, Indonesia has been known as a country with a relatively high share of export to production for sawn wood (22.2 percent), plywood (62.8 percent), wood pulp (37.4 percent), new print (64.77 percent), and paper (36.8 percent). However, in the last decade, Indonesian timber industry has experienced a declining trend in production as well as its share in the world market as shown in the figure 2. During 1995-2011, the export of plywood statistics has shown a substantial decline. In 2003 the country experienced an extreme reverse production slope with a decline of more than fifty percent in compare to the previous year. It is quite contrast to the fact that, before 2002, Indonesia was the largest exporter of plywood, before it was replaced by China afterwards. While for the sawn wood, the trend is quite contradictory. During 1995-2004 the export of sawn wood was increased before it started to decline afterwards.

Figure 2 Annual Export of Wood Primary Wood Products of Indonesia During 1995-2011

(Source: FAO, 2012)

1.2 Research Problem

Generally, the problems faced by the Indonesian timber industry come from supply and demand disparity as we can see in figure 3. From the supply side, Indonesia has to deal with the resource constraints in supplying sufficient timber to fulfill the need from wood processing industry. On the other hand, the government promotion to stimulate more wood processing industry has brought

0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 9000000 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 plywood sawnwood

4 high capacity of forest industry. Therefore, given this situation, all of Indonesia‟s major processing sectors are operating under capacity nowadays. As mentioned before, throughout the late 1990‟s, Indonesia had supplied more than 50 percent

of the world‟s tropical plywood exports, but then the production dropped

substantially over the past decade as large diameter logs became increasingly scarce. It was reported that, with 110 operating plywood mills and annual production capacity of 11.3 million m3, in 2003 the production could only reach 6.5 million m3 (Ministry of Forestry, 2007). While from the demand side, the increase of world population and economic growth have led to an increase in global demand of wood-based products. Consequently, this situation has contributed to over harvesting of timber through illegal logging and resulted in natural forest damage. The government argued that in 2006 the supply shortage of about 40 million m3 was met with illegally harvested logs.

Figure 3 Production and Consumption of Logwood of Indonesia 1996-2010 (Source: ITTO, 2010)

With the assumption that prices can reflect the market condition, given this situation, it is needed to understand how much is the potential deforestation when the price of wood products changes. High prices may lead to uncompetitiveness of Indonesian wood products in the world market, but on the other hand, as an incentive for people to engage in this wood business, it can lead illegal logging as well, particularly with the scarce of raw material. Moreover, the low prices might

0 5000 10000 15000 20000 25000 30000 35000 40000

production consumption

5 threaten the profitability of wood industry and therefore, it also can impact on illegal logging and deforestation. Given this situation, basically, it is needed to investigate the implication of wood price changes on deforestation in Indonesia In addition to that, with regard to the position of Indonesia as one of the largest producer as well as exporter of several tropical wood products in the world, it is also interesting to see whether the Indonesian wood market is integrated with the world market in order to give better understanding on how wood market works and its implication on deforestation.

Based on the explanation above, the research questions of this study are: 1) Is the Indonesian wood market integrated with the world market ?

2) How is the wood prices in the world market are transmitted to the domestic market of Indonesia?

3) How is the implication of wood price changes on deforestation in Indonesia ? 4) If the Indonesian wood market is integrated with the world market, then what is

its implication to deforesation in Indonesia?

1.3 Research Objectives

According to those research questions, the objectives of this study are: 1) To investigate the market integration between domestic and world market of

wood products

2) To investigae the implications of wood price changes on deforestation in Indonesia

6

2

LITERATURE REVIEW

2.1 The Concept of Deforestation: Definition and Causes

Tropical deforestation is a global issue concerning environmental problem regarding the value of tropical forests in conservation of biodiversity and its role in limiting green house-effect (Angelsen et al, 1999). One of the main problems in quantifying the extent of deforestation is coming from the definition itself of deforestation. The term deforestation refers to the complete destruction of the forest cover (Amnelung and Diehl, 1992); the removal of trees from forested site and the conversion of land for another use like agriculture (Van Kooten, 2000). However, according to the scope of definition, deforestation can also be defined with narrower or broader concept. The broad definition takes into account both forestland conversion and reduction on forest quality (i.e. density and structure, ecological services, biomass stocks, species, diversity, and so forth) also known as forest degradation. Meanwhile narrower version only refers to changes on forest land use (Mahapatra and Kant, 2005).

The relevant concept of deforestation is important to precisely identify what factors cause deforestation and which sector gives the greater contribution. Pearce and Brown (1994) identify two main factors affecting deforestation: 1) competition for the remaining land as indicated by the conversion of forest land to uses of agriculture, infrastructure, urban development, industry, and so forth. 2) Failures of economic systems in reflecting the value of environment as the result of no market for many tropical forests functions and thus being ignored by the decision makers. In addition to that, fiscal and the other financial incentives have played role in the decisions to convert tropical forests. Rudel and Roper (1997) identified entrepreneurs, companies, and small farmers as the important agents of deforestation.

Within the existing literatures, the distinctions have been drawn between direct and indirect causes of deforestation, as well as specific deforestation among developing regions [Mainardi (1998), Thorkil et al (2004)]. Shafik (1994) and Thorkil et al (2004) observed that direct causes of deforestation are fairly obvious such as by timber harvesting activities, and the land conversion for agriculture and

7 livestock. Regarding the link between timber harvesting and agriculture activities, Hartwick (2001) found three stages which have occurred i.e. first, starting with clearing with net costly forest removal; second, clearing with profitable timbering; and third, profitable timbering but with a net loss on the land switched from forest to agricultural use. Similar to this, in some cases, deforestation process is also observed as the replacement of logging activities by widespread forest clearing for subsistence agriculture (Pearson, 1995). This conversion from timber harvesting to agricultural is mainly driven by high initial price of agricultural output and land (Hartwick, 2001). He confirms the same result with Angelsen (1995) in the case of shifting cultivation in Indonesia. This occurs when they have few other economic opportunities and thus decide to clear additional land (Rudel and Roper, 1997).

The direct causes of deforestation, which is usually known as sources of deforestation (Caviglia, 1999) or first level of proximate causes [Panayotou (1992). Barbier and Rauscher (1994)] are sometime difficult to be distinguished notably in the practical matter due to the interaction between different types of agents. Therefore, it is difficult to separate their impacts and determine their relative importance and to blame one specific sector for deforestation, when the forest resources have been used jointly by several sectors. For example, ranchers and loggers often facilitate small farmers to enter into forested areas, whereas farmers engage in logging to finance agricultural expansion, and ranchers follow small farmers into agricultural frontier areas (Anmelung and Diehl, 1992).

Thorkil et al (2004) identified some indirect causes such as migration, export prices, property rights, and government policies. Mahaprata and Kant (2005) also found that growth in population, road construction, and debt service growth has significant effect on deforestation. In most of less-developing countries with forest-resource abundant, a relatively larger timber processing activities are likely to be less associated with the level of development and thus generally have serious implications on the forest exploitation rate. High pressures brought by population in urban and rural areas have encouraged governments to establish large-scale infrastructural projects commonly such as hydroelectric power, irrigation, and transport system.

8 The indirect causes of deforestation tend to be more complex and debatable covering both factors which bring immediate effect on the decisions of agents to deforest (e.g. output and input prices) and those with delayed impact on

agents‟ decision-making (e.g. underlying terms of trade and technological progress) (Scrieciu, 2003). The identification of the sectors involved is always dealing with the complex system of incentives and disincentives that indirectly cause the forest disruption (Amnelung and Diehl, 1992). Furthermore, in the case of forest degradation, the estimation is even more difficult since degradation is sometime gradual. Moreover, forest destructions are varied from one particular location to another as well, e.g. from places with large forest and those with small forest (Rudel and Roper, 1997). The major drawback of the direct-indirect classification is that it incorporates both the immediate and the underlying causes with the same label as indirect or second level factors. Because the underlying causes determine the decision parameters, these mixing may have an implication with regard to the cause-effect relationship and thus produces serious problems in the empirical workings and regression models, in particular, such as high level of multicollinearity (Angelsen et al 1999). Therefore, Scrieciu (2003) suggested to classify the factors into three distinct groups : sources of deforestation, local-level of deforestation, and macro-level causes of forest depletion in order to avoid this.

Most authors seem to agree that market and policy failures, coupled with demographic pressures and poverty, have been the most relevant structural determinants of deforestation in less-developing countries. Without denying the relevance of market and policy failures, some empirical analysis focus exclusively on population pressures and poverty as the underlying causes of all sources of deforestation (Cropper-Griffiths, 1994). The situation where total social costs are not accounted entirely in the economy is called „economic failures‟, which can be classified into three types of failures i.e. local market failures, policy failures, and global appropriation failures (Panayotou, 1990). Market failures are present when the prices are fail to reflect the real social costs and benefits from resource use and thus deliver bias information about resource scarcity. Meanwhile, policy failures are exist when misguided intervention to mitigate the failures produces worse outcomes. Global appropriate failures are present because, in the case of tropical

9 forests, market allocation poorly depict the benefits of preservation, biodiversity, and the value of the genetic pool in developing new medicines, crops, and pest control agents. The absence of first best policies that could effectively internalize the externalities arising from the economic failures strengthen the factors which drive the people to deforest. (Scrieciu, 2003)

With the regard to the government policies on sustainability, Grainger and Malayang (2006) identified three phases of forest policy evolution: 1) exploitative, when both actual and stated policy promote exploitation, 2) ambiguous, when the stated policy promotes sustainability but at the same time, the actual effect is quite the contrary, and 3) sustainable, when both the actual and the stated policy promote sustainability. The progress of this evolution is dependent on political situation which relates to democratization and pluralization. The shifting from exploitative policy into sustainable policy is influenced by the effectiveness of pressures strength on policy maker, mainly from internal protectionist group in the system rather than external pressure. The external pressures might change stated policy but cannot guarantee the changes on actual policy. Deacon (1995) examined some policies such as transportation improvements, taxes and royalties on timber harvests, control on log export, a variety of agricultural policies, tax incentives to promote domestic processing industries, and employment opportunity enhancement, to assess the relationship between policy and deforestation. The results showed the importance to emphasize patterns of substitution among inputs and outputs in cases where forests are free to be exploited.

2.2 The Role of International Timber Trade on Deforestation

Most of the existing literatures have emphasized the important role of timber production as one of the major drivers of deforestation, with greater relevance to the international trade. However, the current debates have been emphasized on the discussion whether the reduction of international timber trade can bring a significant benefit to reduce deforestation as well as its impacts on the economy. Consequently, those impacts may in turn threaten the environment rather than positive effects. Anmelung and Diehl (1992) pointed out that a large

10 reduction of international trade in tropical hardwood might not directly lower logging significantly, since the reduction of export revenues implies the losses of economic growths potential and at the same time, the hardwood is consumed in tropical countries as well. Furthermore, deforestation may even increase because the economic value of forest will decrease when there is insufficient attention to cultivate forest areas, and also due to the uncounted ecological function. In addition to that, if the forestry sector becomes less profitable, it may lead to the other conversion of forestland to agriculture or industrial uses, which may consequently accelerate the rate of deforestation (Maestad, 2001).

Export restriction of unprocessed timber products is one of the favoring policies of large producer countries in order to mitigate degradation of forest stock as well as facilitating domestic wood processing industry. This restriction could implicate an increase in price and severe regional disparities during the adjustment process in the industrialized countries, since some countries heavily rely on tropical resources, as, for instance, Japan on tropical round wood (Amnelung and Diehl, 1992). On the contrary, this is not always the case. Even though Log Export Ban (LEB) policy might increase volume share of value-added products, but it cannot guarantee the increase in wood products prices (Amoah et al, 2009). By applying this restriction, the exporting countries might face the reduction of log price which may lead to inefficient logging and processing techniques. High-cost local wood-processing industries will occur, commonly characterized by lower capacity utilization to produce (Repelto-Gillis, 1998). Subsidized and inefficient wood-processing industries may cause a higher waste of logs relative to wood production (Pearce-Brown, 1994). This situation consequently would lead to a greater pressure on the forest resource (Tumaneng-Diele et al, 2005). Though there is an increased processing capacity in the concerned country as expected during the restriction, but in fact, exporting countries have to pay an economic price in the form of subsidy and inefficiency (Boscolo and Vincent, 2000). In addition, LEB policy could reduce the potential the country‟s potential export revenue from forestry sector in total (Manurung and Buongiorno, 1997).

Although log export restriction may not be the first best policy (Goodland and Daly, 1996), but it could support forest conservation in a short run. In spite of

11 inefficient instrument in terms of economic welfare, log export ban is still necessary mainly under situation of inability to control corruption and illegal logging, relative to less restrictive forest and trade policies. It may allow required incentives for the revival of the wood-based industry (Tumaneng-Diele et al, 2005).

Along with the increasing trade liberalization in the world market, reducing tariff of wood based products is discussed within the existing literatures, particularly with regard to its impact on forest sustainability. Turner et al (2005) emphasized that the impact of removing tariff on forest products solely would be relatively small, but stronger impact would be the case if complete multi-sector liberalization effect on country‟s income is taken into account. Reductions in tariffs on forest products are likely to generate merely very modest increases in worldwide trade, and production as well as the price [Zhu et al (2001), Sedjo and Simpson (1999)]. The timber harvest would change in a number of countries, but the net effect at the world level would be small as well, whereas the composition of commodity would shift from raw materials to more processed products as expected (Zhu et al, 2001). As the consequence, the increased harvest pressure on forests due to tariff reduction should be quite modest (Serdjo and Simpson, 1999).

Incorrect pricing of forests which is revealed by low level of rent out of forest exploitation relative to the real costs of reforestation, compared to public programs for other sectors, reduces the incentives to sustainable development. This is implied by the situation in which the existing institutional and legal framework do not involve forests in the domain of markets which is un-owned, unpriced, and unaccounted for (Von Amsberg, 1994), pushing up the excessive use and destruction of forests, in spite of their growing true economic value (Scrieciu, 2003). Tropical timber prices which is associated with a permanent loss of forest areas, should take into account the cost of the foregone economic value entailed by deforestation (Barbier et al, 1995). If the producer country considers its tropical forests solely as an income source of timber export, then it will cause a smaller forest stock in the long run which is significantly smaller than if the other values provided by the forest, such as watershed protection, genetic diversity, tourism, microclimatic functions, and so forth are taken into account. Meanwhile,

12 if importing countries expect the exporting countries to conserve more forests, trade interventions may appear to be the second-best way to achieve this. However, under some certain conditions, those interventions may be counter-productive. Barbier and Rauscher (1994) proved that international transfers, which in contrast can reduce the dependency of the producer country on the exploitation of the forest in gaining export earnings, are more effective in promoting conservation of the forest stock.

Some notions are proposed with regard to market power associated with its implication on the possibility to conserve more forest stock. Theoretically, market power has positive correlation with the possibility to the act of conserving since the general rule is : the greater the market power, the higher the returns per unit of output and the less the need to exploit tropical forests more heavily, even in the monopoly case. According to this rule, some producer countries with a higher market power and a relatively more diversified production base tend to set restrictions to unprocessed log exports, while at the same time aim to promote domestic processing activities. Moreover, the aim to increase market power might encourage a conservationist approached by these countries, particularly supported by international financial assistance (Barbier and Rauscher, 1994). When the government favors logging as an export earning source, as well as land conversion due to agricultural export expansion, the policies have often attempted to redress pressing macroeconomic constraints, such as the decrease of foreign exchange earnings from other alternative sources and the need to service foreign debt obligations. Devaluation of the exchange rate appears to stimulate logging activities to export, and thus enhance deforestation(Mainardi, 1998).

2.3 Review of Economic Modeling of Deforestation

Along with the increasing concern on deforestation issue as one of major environmental problems, there have been some economic models which have been developed with various kinds of approaches. Those various models are mainly developed to answer the questions on what factors cause deforestation, and thus have emerged with some conclusions on the policy formulation. However, these economic models still have to deal with the problem of data limitation, both

13 in quality and availability. With this kind of situation, those models have come up with the use of proxy variable as emphasized by an econometric analysis. It is important to distinguish the use of different proxies for the phenomenon and to compose relevant explanatory variables (Scrieu, 2003).

Based on the level of analysis, Kaimawotz and Angelsen (1998) identified at least three variations of deforestation models i.e. 1) households and firm-level models, 2) regional level models, and 3) national and macro-level models. Within the existing literature, magnitude and location of deforestation is the main dependent variable for most models. While explanatory variables were developed commonly starting from the question of which part of society have driven deforestation, the so-called agents of deforestation in some literature, and what factors influence their decisions. Agent of deforestation refers to the individuals or companies involved in land-use change. The characteristics of these agents are mostly considered as exogenous in the models, whereas the variables related to the activities of agents are on the other way around. For any particular agent or group of agents, the decisions with respect to the choice variables could determine the amount of forest cleared. Most models classify the factors influencing the

agents‟ decision as exogenous. However, sometime this is not the case, for example, the case of general equilibrium model, which considers prices as endogenous macro-level variables and explicitly model the markets Furthermore, some macro-level variables are also developed with regard to policy instruments,

which do not affect the agents‟ decision directly. These variables are usually

14 Magnitude and Location of Deforestation

Characteristics of deforestation agents : Initial population

Objectives and preferences

Initial resource endowments and knowledge

Cultural attributes Choice Variables : Land allocation

Labor allocation and migration Capital allocation

Consumption

Other technological and management decision

Agents‟ decision parameters : Output prices

Labor costs

Other factor (input) prices Accessibility

Available technology and information

Risk

Property regimes

Government restrictions Other constraints on factor use Environmental factors

Macro-level variables and policy instruments :

Demographics

Government policies World market prices Asset distribution Macroeconomic trends Technology

15

3 THEORETICAL FRAMEWORK

This chapter provides the theoretical framework of this study, which underlines the analysis on how market is working to answer the research objective. From the perspective of economic science, price is an important instrument with many implications, not only on the economy, but also external effect such as on environment as discussed in this study. A good understanding on how the wood market works, especially on how the wood price is transmitted from world market to domestic market, is undoubtedly required prior to the analysis of the impact of wood price changes on deforestation. Moreover, such understanding could also give useful information how shock in the wood market is reflected either in the short or long run equilibrium. Therefore, several theories on market integration, particularly with regard to spatial market integration is presented, along with the underlying assumptions used in this study.

2.4 Market Integration

Market Integration is one of the most important measures to understand how market works. The existing literature reveals that market integration is commonly emphasized as a degree of the flow of goods and information across time, space, and form (Barret, 1996; Lutz et al 1994). Other defines it as the extent to which demand and supply, the so-called contestability, in one location of market is transmitted to the other [Fackler (1996), McNew (1996), McNew and Fackler (1997), Fackler and Goodwin (2001)]. Similar to this, Barrett and Li (2002) also define market integration as tradability or contestability between markets with a focus on physical trade as tradability in the application. Under the terms of market integration, tradability refers to the condition where a good is traded between markets at the time when arbitrageurs face zero marginal returns (contestability). Tradability transfers the information when there is an excess demand of a good in one market to other market as captured in potential physical flows of the good. The level of tradability of a particular good is determined by the level of transportability driven by arbitrage forces on transfer costs constraints. Besides related to tradability and contestability, the concept of market integration also refers to the existence of price co-movement between markets

16 implied by the law of one price (LOP) as defined by Chen and Knez (1995) who describe it as existence of law of one price (LOP) or no-arbitrage opportunities between markets. An integrated market exists when connected markets exhibit high price correlation [Harris (19790, Ravalion (1986)]. If trade occurs between a couple of markets for a homogenous product, the price in the source market (Pi) is

equal to that in the destination market (Pj) plus transfer costs (Ci). In its strong

form, The LOP is expressed as Pi – Pj = Cij

In the competitive equilibrium condition when efficient arbitrage occurs, the changes of price in the source market will be transferred to the destination market on one-for-one basis immediately. While in the weaker form of LOP, it allows a temporary deviation after a price shock occurs, but then it will return the equilibrium situation in the long run. The concept of LOP assumes that all related agents in the markets should have all information needed to perform optimal arbitrage and no-barriers to trade or when transportation costs between market is insignificant [McNew (1996), Jensen (2007)]. Nevertheless, some literatures view this assumption too idealistic which rarely happens in practice.

Though two markets do not trade each other, but as long as they are part of a marketing system, it is still possible for both markets to be integrated. Thus, market integration does not automatically mean market competitiveness [Baulch (1997), Fackler and Goodwin (2001)]. In a similar sense, the physical connectivity of arbitrage process does not guarantee the existence of market integration. This is the case when market agents are indifferent in participating arbitrage. This absence of market integration is called market segmentation which underlines uncorrelated inter-market prices. This situation occurs when supply and demand in one market cannot affect trade, as well as the price of a homogenous good in the other market. Since both phenomena implies the LOP, empirical results of spatial price transmission could be similar under markedly different market regimes [Baulch (1997), Barrett and Li (2002)].

17

2.5 Spatial Market Efficiency and Equilibrium

An efficiency of a market refers to the condition of how all relevant information are fully reflected by a market, particularly in determining prices of commodities marketed in the concerned market [Fackler and Goodwin (2001), Lence and Falk (2005)]. In the analysis of market integration, the information includes demand, supply, and transaction costs. The definition of efficiency also underscores whether the price at a particular period (t) can be the best forecast value for the price at the next period (t+i) , i=1,2,3,...n, since it is assumed to have all available information of the market (Barret, 1996). According to the spatial market integration term, the efficiency is determined by the response of arbitrageurs to demand and supply shock in the market. Arbitrageurs will transport the commodity from the lower price market to the higher one, until the inter-market price differences is equal to the transaction costs and thus make profit from that. These processes ensure equality of transfer costs and inter-market price differences which imply a long run relationships or co-integration between the markets involved.

In competitive markets, ensured by the arbitrageurs‟ actions, the inter -market price difference should not be higher than the transfer cost of trading the good between markets. If this condition is not fulfilled, i.e. the transfer costs for a commodity between markets are higher than the inter-market price difference, and then the efficiency of the markets is not achieved. However, it is important to consider that market efficiency, which is necessary for market equilibrium, is different from market integration, the mainly emphasizes on the flow of commodity and price information, without requiring the condition in which arbitrage opportunities are fully exhausted. This implies that the presence of trade is not required for the accomplishment of market integration as well as market efficiency. Furthermore, the minimization of inter-market transfer costs and quasi rents from binding quota is necessary for market efficiency in spatial distinct markets (Barrett, 2001). If transaction costs of trade are excessively high, due to trade barriers, poor transport infrastructure, and so forth, markets can be uncompetitive spatial equilibrium and yet not be socially efficient. Therefore, it implies that the presence of market integration does not guarantee the

18 maximization of welfare unless the trade costs and the quasi-rents due to binding trade quotas are minimized (Barret, 2005).

The spatial equilibrium condition (SEC) will occur when PA = PB + TBA,

where PA is importing price from B to A, PB is exporting price to A, and TBA is

transaction (trade) cost from B to A. Except this equilibrium conditions, there two possible situations, i.e.

1) If PA > PB + TBA, there will be more trade from B to A until restoring the SEC.

This is when arbitrage occurs.

2) If PA < PB + TBA, there will be no trade until PA gets too small or PB so big, that

PB > PA + TBA, than trade will occur from A to B.

It is necessary to notice that TBA ≠ TAB due to geography aspect namely “backhauls” phenomenon.

2.6 Deforestation: Supply and Demand Theory for Forest Product

The existing literatures assumed that deforestation is driven by the dynamics of supply and demand of forest products, particularly with regard to the wood products. Based on this assumption, the theory of supply and demand is applied to understand the implication of wood price changes on deforestation. Adapted from Hyde and Seve (1993), it considers D(t) as aggregate demands for wood at a given time (t), while the supply (MCt) consists of two components : MCi as the supply of indigenous tree which is subsided over time, and MCe as the

supply of plantings. The private marginal cost of indigenous harvests refers to the cost of collecting wood plus the other related transaction costs such as fee paid to government i.e. forestry department.

When the growing stock of indigenous trees is decreasing, the remaining stock becomes more remote, which will generate higher harvesting costs, and thus the supply of indigenous tree will shift to the left over time (MCi (t + n)). While

MCe is considered as constant over time assuming that there is no cost-reducing

technical change in tree growth. From this point, sustainable supply occurs when the growing costs is equal to the harvesting costs. Assuming that population and economy are growing as time passes, then the aggregate demand of forest products will shift outward as a consequence, and the supply will shift in the

19 MCi (t + n)

MCi (t)

MCt (t)

D (t + n)

D (t)

Volume (m3) MCe

Price

P (t+n)

P (t)

opposite way as mentioned before. As we can see in the figure 6, MCi (t) will shift

to the left MCi (t+n), and D(t) will shift outwards to D(t+n). An equilibrium

condition will be achieved as the price increases from P(t) to P(t+n).

When price increases e.g. due to the increasing aggregate demand of forest products, then the indigenous forest cover which is accessible will be gone. While some forest areas which are less accessible and have higher harvesting costs will still exist but are not attractive to be harvested. Similarly, if harvesting costs is increasing (MCi shifts to the left) then open access harvest opportunities will

diminish as well as the indigenous supply. Given this situation, when wood price is increasing, sustainable supply (supply of wood which comes from managed land i.e. plantation) will dominate. Indeed, the open access availability will accelerate the reducing indigenous forest and cause more rapid conversion to sustainable supply as well as price responds to increase. If there are a plentiful supply of wood and MCi increase slowly, deforestation may occur rapidly. The

equilibrium price increases slowly as well without inducing a significant forest sector adjustment. On the other hand, if MCi is steeper and shifts rapidly to the

left, the equilibrium wood price will increase rapidly and the forestry sector is responsive.

Figure 4 Supply and Demand of Forest Products (Source: Hyde and Seve, 1993)

20

4

METHODOLOGY

This chapter provides some information with the regard to methodological approach used in this study. First, it begins with the information about data description: the type of data, sources of data, and so forth. Second, it continues with the analysis: how to process the data, some statistical tests, hypothesis, and so on. Referring to the research objectives, there are two main analyses in this study: 1) price transmission analysis and 2) econometric modeling of deforestation, which emphasizes on the implication of wood price changes on deforestation in Indonesia.

4.1 Data Description

To analyze the wood industry, this study uses secondary time series data taken from some various organizations including FAO, Ministry of Trade of Indonesia, Ministry of Forestry of Indonesia, Statistical Bureau of Indonesia, International Tropical Timber Organization (ITTO), and World Bank. Due to the data availability, this study only focus on the three main products in the Indonesian wood industry i.e. logwood, plywood, and sawn wood. The data which are used for the transmission analysis are monthly price data: 1) nominal price series of wood products which are converted to real prices deflated by US GDP deflator for the world wood products prices, and 2) wholesale price indexes for the domestic prices of Indonesian wood products.

4.2 Price Transmission Analysis

This analysis is used to answer the research questions of spatial market integration as well as the price transmission of wood products, which links the domestic market of Indonesia with the international market. Data were runned by using software package of J-Multi. There are some statistical testings are applied in this section : 1) Unit root test for testing data stationarity, 2) Testing for cointegration, and 3) Granger-causality test. Coupled with those tests, to model the price transmission between the world prices and the domestic prices, this study applies standard linear error correction model (ECM). After applying ECM, then it continues with stability analysis to detect the presence of structural break and

21 the residual analysis as well for checking the occurence of heteroscedasticity, autocorrelation, testing for normality, and so forth which are available in the software package.

a) Unit Root Test

Unit root test is the test for time series data to see whether the data is stationary or not. Stationary is needed to prevent the presence of spurious regression. Spurious regression implies that the result of the regression may not be as good or significant as they seem. This study employed Augmented Dickey Fuller test (ADF) which is a modification of Dickey Fuller test (DF) to check the existence of unit root for each variable in the model. Mathematically the equation is written as follows:

∆ Yt= α0+ α1T + tYt-i+ Σ i∆Yt-i+ t, ~ΠD (0, 2) ( 1)

Where Δ represents a first difference of ΔYt = Yt – Yt-1, and n is number of lag

lengths. If the null hypothesis of | t| = 0 is rejected then the conclusion is that the

data series is stationary, and vice-versa.

b)Co-integration Test

Before examining any further analysis of price transmission, it is important to test whether a set of price data have co-integrated relationship. Since many price data are found to be non-stationary and commonly integrated in the first difference i.e. I (1), a valid regression will be achieved only when the variables are co-integrated. Theoretically, co-integration of two markets implies that in the short run, the dynamics of prices in the two markets may not fulfill the LOP, but in the long run will move towards the LOP. If both prices in the two markets are integrated in the same order i.e. I(d) and there is a linear relationship between price series, then it can be said that those two markets are co-integrated. Practically, it can be seen if based on the test of stationarity, Pit ~ I(1) as well as Pit

~ I(1), then co-integration between Pit and Pjt is occured when is such that Pit + Pj

t ~ I(0). Co-integration implies a long run equilibrium relationship.

Commonly there are two approaches use to examine the existence of co-integration i.e. two-step approach of Engel-Granger (1987) and Johansen variance

22 autoregressive (VAR) approach (1990). The former is used for bivariate models whereas the latter can be used for multivariate analysis.

 Two-step approach of Engel-Granger

The first step of Engel granger approach is starting from the estimation of equation :

Pit = α + Pjt+ t (2)

using OLS, then is followed by ADF test for the residuals. This unit root test on residuals is conducted to obtain the order of integration by performing regression below :

Δἕt= + α1ἕt-1 + t + ∑ Δ t-j (3)

As analogue as mentioned before, the null hypothesis in this test is |α1| = 0. If the

null hypothesis is rejected, then it concludes that the residual series do not contain any unit root, and thus the two price series are co-integrated, and vice versa. When this is the case, it is said that the two price series are co-integrated in the order (1,1).

 Johansen and Juselius technique

As mentioned before, to deal with the multivariate price series is using the approach of Johansen and Juselius (1990) technique. With this technique, all the variables are considered as endogenous so that it can work simultaneously in handling the response of variables. Furthermore, Johansen method can overcome the problem of normalization emerged in the granger method, and is possible to use when the variables have different orders of integration. Mathematically, the estimation of this approach is explained as the following vector autoregressive equations:

ΔPt= 0t + ∑ 0iΔPt-1+ 0t (4)

23 Where Pt is a vector of ordered prices, t is the time dimensional vector random errors, t is the intercept, and is matrices of coefficients estimated. The likelihood ratio test statistic is employed by using the vectors of random errors ( t)

which determine the number of co-integration vectors.

There are two test statistics for estimating the null hypothesis of no co-integration i.e. 1) trace statistics and 2) maximum eigen value test. The first test statistics is used to see the number of the most r-cointegrating vectors in Pt as

typically characterized by :

λtrace (r) = -T∑ ln (1-λ (6)

where λ implies p –r smallest correlations of 0t with respect to 1t, and T is the

number of observations. While the second test is used to determine the exactly cointegrating vectors in Pt as expressed by:

λmax = (r, r+1) = -Tln (1- λ r+1) (7)

λtrace λmax

Null hypothesis Alternative hypothesis

Null hypothesis Alternative hypothesis r = 0 r > 0 r = 0 r = 1

r ≤ 1 r > 1 r = 1 r = 2

r ≤ β .... r > 2 .... r = 2 .... r = 3 ....

.... r ≤ k .... r > k .... r = k .... r = k +1

The number of co-integrating vectors is determined according to how many null hypothesis (r = k) in the table is rejected. If all of k null hypothesis are rejected it concludes that there are k integrating vectors and thus data series are co-integrated. If none of the k null hypothesis is rejected under the trace and maximum eigen value test, no co-integrating vectors are observed.

24

c) Granger Causality Test

Granger (1960) causality test evaluates the presence of price transmission between two markets and in which direction. Consider Pit and Pjt are the two

prices, if both current and lagged values of Pit can forecast the value of Pjt then Pit

granger-causes Pjt (Judge et al, 1988). With error correction and vector

autoregressive (VAR), Granger-causality test employs to the extent to which both current and past price changes in one market can explain current price changes in the other market (Baulch, 1997). This Granger-causality models are typically characterised as follows:

Pit = ∑ 1Pjt-1 + ∑ kPjt-k + 1t (8)

Pjt = ∑ 1Pjt-1 + ∑ kPjt-k + 2t (9)

Those two equations above tell that price in each market (market i and j) is dependent on the lagged prices in both markets i and j respectively. While 1t and

2t are error term series for each equation.

According to the Granger-causality test, there are three categories of causality which probably occur:

i. No causality between two prices : this occurs when the coefficients of both lagged exogenous variables (Pjt-1 and Pit-1) are not statistically different from

zero (∑ i = 0 and ∑ k = 0)

ii. Unidirectional causality : this occurs when there is only one direction of causality between prices e.g. Pit can be predictive of Pjt but not the other way

around. Statistically, it can be shown if the coefficients of Pjt-1 in the former

equation, as a group, are statistically different from zero i.e. ∑ i ≠ 0, whereas

the coefficients of Pit-k in the latter equation, as a group, are statitically not

different from zero i.e. ∑ k = 0.

iii.Bilateral causality (Reciprocal causality) : this means that both prices can be predictive of each other. Both coefficients of lagged exogenous variables, as a group, are statistically different from zero in both equations i.e. ∑ i ≠ 0 and

25

d)Error Correction Model (ECM) Estimation

Based on the granger representation theorem, if the two variables are co-integrated then there is a valid error correction model (ECM) that depicts the relationship between those variables and vice versa. Derived from a standard vector autoregression (VAR) :

P1t = 01+ 11P1t-1+ 21P2t+ 31P2t-1 (10)

P2t = 02+ 12P1t-1+ 22P2t+ 32P2t-1 (11)

the estimation of error correction model is as the following equations :

ΔP1,t= 01+ 11ΔP1t-1+ β1ΔP2t-1+ α1ectt-1 + u1 (12) ΔP2,t= 02+ 1βΔP1t-1+ ββΔP2t-1+ α2ectt-1 + u2 (13)

where ectt-1 = P1,t-1 – Φ0 – Φ1P2,t-1 which implies the deviation from long run

equilibrium condition.

e) Stability Analysis

Stability analysis is conducted to evaluate the parameter instability in the model due to the presence of structural break. Structural changes is one of the main consideration when someone works with time series data in order to hinder bias estimation (Candelon and Cubadda, 2006). This study applies the Chow test to conduct stability analysis which is available in the software package. Basically, the Chow test (1960) is applied by splitting the sampel into some separated sub-periods, introducing the presence of break, and testing whether the parameters are equal among the sub-period. The null hyphotesis of this test is that the parameters

are stable ( 0 = 1= 2=...= n).

This study employs the Chow test for VEC models, which refers to sample-split (SS) and break point (BP) statistics, provided by the J-Multi. BP statistics checks whether any of the parameters of model under the analysis vary (except the cointegration parameters), which is calculated as :

ΛBP = (T1 + T2) log det ∑ - T1log det ∑ - T1log det ∑ ~ X2 (k)

(14)

We suppose that T is the number of observations, T1 is the first observation, T2 is

26 < TB and T2 ≤ T - TB. Meanwhile k denotes the difference between the sum of

number of parameter , which also includes the residuals, estimated in the first and last sub-periods and the number of parameters in the full sample model. The null hypothesis will be rejected when ΛBP is too large.

In the SS test, it is assumed that the residual covariance is constant. It checks against the alternative that the other parameters may vary, as calculated:

ΛSS = (T1 + T2) [log det ∑ - log det {(T1+ T2)-1(T1∑ +T2 ∑ )}]

(15)

The SS test also follows X2 distribution. The degrees of freedom is noted as the number of restrictions which assumes the constant coefficient rather than a break in period TB. J-Multi offers bootstrap p-values to conduct those tests.

f) Residual Analysis

Residual analysis is conducted for checking the adequacy of estimated VECM with regard to residual autocorrelation, non-normality, and conditional heteroscedasticity. The tests provided by J-Multi are as shown in the table below.

Table 2 Diagnostic Tests for Residual Analysis

Diagnostic Test The Hypothesis Portmanteau test for

autocorrelation

H0 : E (ut u1t-i) = 0, i = 1,...,h

LM test for autocorrelation H0 : B1* = ....= Bh* = 0

H1 : B1* ≠ ....or Bh* ≠ 0

Ut = B1* Ut-1 +....+ Bh* Ut-h + errort

Test for non-normality H0 : non-normality

ARCH-LM test H0 : B1 = ....= Bq= 0

H1 : B1≠ ....or Bq≠ 0

Vech ( t 1t) = B0 + B1vech( t-1 1t-1) +...+

Bqvech( t-q 1t-q) +

27

4.3 Econometric Modeling of Deforestation

The term deforestation in this study refers to the potential deforestation, not to the real deforestation condition due to the data availability on deforestation. There are two proxies to represent the potential deforestation. First, it refers to the distortion rate which is calculated from the deviation between annual allowable cut and the real production. The negative distortion implies the presence of over-harvesting activities in wood industry. This study assumes that with the present of negative distortion, the potential deforestation will likely be higher. Second, it refers to the illegally logwood export. Since the logwood export has been banned since 1987, it is assumed that the present of logwood export is considered as illegal activities. Both proxies are converted to the amount of hectares which is calculated under the following formula:

dist = (AACi– Qi)

where dist denotes the rate of distortion, AAC stands for annual allowable cut, Q is the logwood production quantity, and i denotes one particular period.

To address the implication of wood price changes on deforestation, it simply regress the proxy variable of deforestation (the dependent variable) with the variable of domestic prices of wood products, particularly as described in the following equation. This study employs two regression models according to the proxy of deforestation: 1) semi logarithmic regression, and 2) logarithmic regression.

dist = 0 + 1lnpdl + 2lnpdl + 3 lnpdl + 4 lnpdl + 5 lnpdl + 6lnpdl + (1θ

where :

dist : distortion rate

pdl : domestic logwood price (IDR/m3) pds : domestic sawnwood price (IDR/m3) pdp : domestic plywood price (IDR/m3) pwl : world logwood price ($/m3)

28 pws : world sawnwood price ($/m3)

pwp : world plywood price ($/m3)

n , n : estimated parameters 0, 0 : constanta

, ν : residuals

To check the significance of each independent variable refers to the values of t-statistic. Meanwhile, the significance of the overall model is evaluated by observing the value of F-statistics. If the p-value of the statistics is less than the desired level of significance (1 percent, 5 percent, and 10 ten percent), it can be concluded that the variable is statistically significant. Furthermore, whether the variance of the model can represent the variance of the dependent variable is checked by referring to the value of square and adjusted square. The R-square denotes how many percent the model (the independent variables which are involved in the model) can explain the variance of the dependent variable.

29

5

PRICE TRANSMISSION ANALYSIS BETWEEN

DOMESTIC AND WORLD PRICE OF INDONESIAN

WOOD PRODUCTS

This chapter explains the result of price transmission analysis as mentioned in the first objective of this study. First, it begins with the analysis of stationarity of data using Augmented Dickey Fuller test which is necessary for the next step of analysis. Second, it continues with co-integration analysis between world and domestic level of wood prices using two-step engel-granger and Johansen techniques. Then it also provides results from granger causality test. The price transmission between world and domestic wood products prices is estimated by using the standard linear error correction model. Stability analysis is also conducted in the ECM by using Chow test. The last part is discussion which gives the summary of the whole analysis in this chapter emphasizing whether the prices in world and domestic market are co-integrated and its implications. Some recommendations for further research are mentioned as well mainly with regard to price transmission model.

Initially, monthly price data, which are available for this study, are from 1997 until 2011, but only data from 1998 are included in the analysis due to the presence of economic crisis during 1997. As shown in the three figures: figures 6, 7, and 8, it is indicated the extreme contradictory trend between the domestic price and the world price which could generate the strange result from statistical perspective. Meanwhile, the data which are used for the econometric modeling of deforestation are annual data from 1978 until 2007.

30 Figure 5 Plot Series of Logwood

Figure 6 Plot Series of Plywood

Figure 7 Plot Series of Sawn wood

5.1 Unit Root Test

Stationarity is a necessary condition in the time series analysis to produce unbiased estimation. Data is stationary when there is no unit root included, thus

31 testing of the stationarity is usually known as a unit root test. As most recent studies use, this study employed Augmented Dickey Fuller (ADF) test. All price variables are transformed into logarithmic form. As shown in the table 3, we can see that the values of test statistics of all variables are higher than -1.94 at the level implying that the null hypothesis of non-stationarity cannot be rejected for five percent level of significance. While for the first difference, the results are contradictory i.e. all value of test statistics are even less than -2.56 which means that the null hypothesis of non-stationairity is rejected at the one percent level of significance. According to these results, it can be concluded that all price variables in the model are not stationary at level but stationary in the first difference i.e. ~I(1).

Table 3 ADF test Result

Variable Value of test statistics Number of Lags (Akaike) Level First Difference Level First

Difference dl_log 2.9823 -12.7408 0 0 dp_log 2.5193 -6.8294 1 0 ds_log 3.6914 -8.4119 0 0 rwl_log 0.3503 -8.3923 1 0 rwp_log 0.3515 -10.7933 0 0 rws_log 1.1805 -14.4396 0 0

5.2 Co-integration Analysis

With the presence of un-stationary price series, than we proceed co-integration testing. As mentioned in the chapter 4, there are two techniques to check the co-integration relationships of wood price series between world and domestic market : two-step Engel- Granger and Johansen techniques. Based on the former technique, to check the co-integration relationship is by employing ADF test for the residuals from the regression between a set prices series i.e. world price and domestic price for any particular wood product as we can see in equation 2 in the chapter 4. The results of ADF test showed that all of the value of

32 test statistics for the residuals is statistically significant for rejecting the null hypothesis of non-stationarity. According to this, it can be concluded that based on the two-step Engel Granger approach, there are co-integration relationships between world price and domestic price for all wood products studied in this study i.e. logwood, plywood, and sawn wood.

Table 4 The Result of Engel-Granger-Two-Step Procedure

Wood Product Variable Parameter ADF Test for u(t)#

Logwood dl(t) -2.6431

Intercept 4.506*** rwl(t) 0.157*** Trend(t) 0.007***

Plywood dp_log(t) -6.8727

Intercept 0.477*** rwp_log(t) 2.668*** Trend(t) 0.005***

Sawn wood ds(t) -4.7252

Intercept 0.220*** rws(t) 4.132*** Trend(t) 0.005*** the asterisk (#) denotes the value of test statistics in the ADF test

Generally, the results from Johansen technique showed the similar conclusion with the Engel-Granger approach, implying the existence of co-integration for all wood product prices between world and domestic market. As we can see in the table 5, for plywood and sawn wood, the null hypothesis of r = 0, which implies that there is no co-integration relationship, is rejected at both five percent and one percent level of significance for all wood products. Meanwhile, for logwood, the null hypothesis of r = 0 is rejected for ten percent level of significance. The null hypothesis of one co-integrating relation i.e. r = 1, is not rejected for all wood products.

33 Wood Product r0 LR p-value Number of

Lags Logwood 0 24.80 0.0659 2

1 5.92 0.4813

Plywood 0 34.99 0.0021 2 1 7.65 0.2905

Sawn wood 0 42.43 0.0001 1 1 4.47 0.6771

5.3 Granger Causality Test

Granger causality test is conducted to indicate the presence of price transmission between two markets and to which direction. As shown in the table 6, the result of Granger-causality test does not find any Granger-causality for logwood as both the null hypothesis are not rejected. Meanwhile, for plywood and sawn wood, the Granger-causality test produces the same result i.e. reciprocal causality in which both the null hypothesis to the extent that domestic prices do not affect world prices and that world prices do not affect domestic prices are rejected at five percent level of significance. According to granger causality test, thus it can be concluded that the domestic price and the world price of plywood and sawn wood have an influence to each other, and on the contrary, the domestic and the world price of logwood have no influence each other.

Table 6 The Result of Granger-Causality Test Wood

Products

Null Hypothesis (Ho) Test-Statistics

P-value

69 Appendix 3. Output Sawnwood

 Unit Root Test ds_log

*** Tue, 11 Sep 2012 10:58:21 *** ADF Test for series: ds_log

sample range: [1998 M2, 2011 M12], T = 167 lagged differences: 0

no intercept, no time trend asymptotic critical values

reference: Davidson, R. and MacKinnon, J. (1993),

"Estimation and Inference in Econometrics" p 708, table 20.1, Oxford University Press, London

1% 5% 10% -2.56 -1.94 -1.62 value of test statistic: 3.6914 regression results:

--- variable coefficient t-statistic --- x(-1) 0.0012 3.6914 RSS 0.0982

OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA sample range: [1998 M12, 2011 M12], T = 157

optimal number of lags (searched up to 10 lags of 1. differences): Akaike Info Criterion: 8

Final Prediction Error: 8 Hannan-Quinn Criterion: 1 Schwarz Criterion: 0  Unit Root Test ds_log_d1 *** Tue, 11 Sep 2012 10:59:12 *** ADF Test for series: ds_log_d1

sample range: [1998 M2, 2011 M12], T = 167 lagged differences: 0

no intercept, no time trend asymptotic critical values

reference: Davidson, R. and MacKinnon, J. (1993),

"Estimation and Inference in Econometrics" p 708, table 20.1, Oxford University Press, London

1% 5% 10% -2.56 -1.94 -1.62 value of test statistic: -8.4119 regression results:

70 ---

variable coefficient t-statistic --- x(-1) -0.5695 -8.4119 RSS 0.0855

OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA sample range: [1998 M12, 2011 M12], T = 157

optimal number of lags (searched up to 10 lags of 1. differences): Akaike Info Criterion: 7

Final Prediction Error: 7 Hannan-Quinn Criterion: 0 Schwarz Criterion: 0  Unit Root Test rws_log

*** Tue, 11 Sep 2012 10:59:28 *** ADF Test for series: rws_log

sample range: [1998 M2, 2011 M12], T = 167 lagged differences: 0

no intercept, no time trend asymptotic critical values

reference: Davidson, R. and MacKinnon, J. (1993),

"Estimation and Inference in Econometrics" p 708, table 20.1, Oxford University Press, London

1% 5% 10% -2.56 -1.94 -1.62 value of test statistic: 1.1805 regression results:

--- variable coefficient t-statistic --- x(-1) 0.0004 1.1805 RSS 0.1571

OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA sample range: [1998 M12, 2011 M12], T = 157

optimal number of lags (searched up to 10 lags of 1. differences): Akaike Info Criterion: 2

Final Prediction Error: 2 Hannan-Quinn Criterion: 0 Schwarz Criterion: 0

71  Unit root Test rws_log_d1

*** Tue, 11 Sep 2012 11:00:39 *** ADF Test for series: rws_log_d1

sample range: [1998 M2, 2011 M12], T = 167 lagged differences: 0

no intercept, no time trend asymptotic critical values

reference: Davidson, R. and MacKinnon, J. (1993),

"Estimation and Inference in Econometrics" p 708, table 20.1, Oxford University Press, London

1% 5% 10% -2.56 -1.94 -1.62 value of test statistic: -14.4396 regression results:

--- variable coefficient t-statistic --- x(-1) -1.0417 -14.4396 RSS 0.1581

OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA sample range: [1998 M12, 2011 M12], T = 157

optimal number of lags (searched up to 10 lags of 1. differences): Akaike Info Criterion: 1

Final Prediction Error: 1 Hannan-Quinn Criterion: 1 Schwarz Criterion: 0

 Johansen Trace test ds_log rws_log *** Tue, 11 Sep 2012 10:58:49 *** Johansen Trace Test for: ds_log rws_log

sample range: [1998 M2, 2011 M12], T = 167 included lags (levels): 1

dimension of the process: 2 trend and intercept included response surface computed:

--- r0 LR pval 90% 95% 99% --- 0 42.43 0.0001 23.32 25.73 30.67 1 4.47 0.6771 10.68 12.45 16.22

72 sample range: [1998 M11, 2011 M12], T = 158

optimal number of lags (searched up to 10 lags of levels): Akaike Info Criterion: 2

Final Prediction Error: 2 Hannan-Quinn Criterion: 1 Schwarz Criterion: 1  Unit Root Test for residual (u1) *** Tue, 11 Sep 2012 11:06:07 *** ADF Test for series: u1

sample range: [1998 M2, 2011 M12], T = 167 lagged differences: 0

no intercept, no time trend asymptotic critical values

reference: Davidson, R. and MacKinnon, J. (1993),

"Estimation and Inference in Econometrics" p 708, table 20.1, Oxford University Press, London

1% 5% 10% -2.56 -1.94 -1.62 value of test statistic: -4.7252 regression results:

--- variable coefficient t-statistic --- x(-1) -0.0859 -4.7252 RSS 0.0824

OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA sample range: [1998 M12, 2011 M12], T = 157

optimal number of lags (searched up to 10 lags of 1. differences): Akaike Info Criterion: 8

Final Prediction Error: 8 Hannan-Quinn Criterion: 1 Schwarz Criterion: 0  Granger causality test

*** Tue, 11 Sep 2012 14:17:08 *** TEST FOR GRANGER-CAUSALITY: H0: "ds_log" do not Granger-cause "rws_log" Test statistic l = 6.9326

73 TEST FOR INSTANTANEOUS CAUSALITY:

H0: No instantaneous causality between "ds_log" and "rws_log" Test statistic: c = 1.1471

pval-Chi( c; 1) = 0.2842

*** Tue, 11 Sep 2012 14:17:13 *** TEST FOR GRANGER-CAUSALITY: H0: "rws_log" do not Granger-cause "ds_log" Test statistic l = 3.1179

pval-F( l; 2, 314) = 0.0456

TEST FOR INSTANTANEOUS CAUSALITY:

H0: No instantaneous causality between "rws_log" and "ds_log" Test statistic: c = 1.1471

pval-Chi( c; 1) = 0.2842  Residual Analysis

*** Tue, 11 Sep 2012 14:17:37 ***

PORTMANTEAU TEST (H0:Rh=(r1,...,rh)=0) tested order: 16

test statistic: 48.3267 p-value: 0.8135 adjusted test statistic: 50.9623 p-value: 0.7321 degrees of freedom: 58.0000 *** Tue, 11 Sep 2012 14:17:37 ***

LM-TYPE TEST FOR AUTOCORRELATION with 5 lags LM statistic: 18.9098

p-value: 0.5277 df: 20.0000

*** Tue, 11 Sep 2012 14:17:37 *** TESTS FOR NONNORMALITY Reference: Doornik & Hansen (1994) joint test statistic: 2861.9290 p-value: 0.0000 degrees of freedom: 4.0000 skewness only: 15.8346 p-value: 0.0004 kurtosis only: 2846.0945

74 p-value: 0.0000

Reference: Lütkepohl (1993), Introduction to Multiple Time Series Analysis, 2ed, p. 153

joint test statistic: 2828.2270 p-value: 0.0000 degrees of freedom: 4.0000 skewness only: 14.9082 p-value: 0.0006 kurtosis only: 2813.3188 p-value: 0.0000

*** Tue, 11 Sep 2012 14:17:37 *** JARQUE-BERA TEST

variable teststat p-Value(Chi^2) skewness kurtosis u1 2793.4340 0.0000 -0.2735 23.0891 u2 36.0209 0.0000 -0.6967 4.8073 *** Tue, 11 Sep 2012 14:17:37 ***

ARCH-LM TEST with 16 lags

variable teststat p-Value(Chi^2) F stat p-Value(F) u1 27.0360 0.0411 2.0613 0.0136 u2 6.8185 0.9766 0.4464 0.9666 *** Tue, 11 Sep 2012 14:17:37 ***

MULTIVARIATE ARCH-LM TEST with 5 lags VARCHLM test statistic: 50.8836

p-value(chi^2): 0.2531 degrees of freedom: 45.0000  Plot ect