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
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.
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.
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
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.
xi
Autobiography
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
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
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
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
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
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.
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).
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.
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
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).
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).
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
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
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.
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.
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
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.
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
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.
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
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
28 pws : world sawnwood price ($/m3)
pwp : world plywood price ($/m3)
n , n : estimated parameters 0, 0 : constanta
, ν : residuals
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.
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
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
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
[image:48.595.117.511.83.264.2]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
34
“nwp” do not Granger-cause
“dp”
0.3058 0.7368
Plywood “dp” do not Granger-cause
“rwp”
8.1551 0.0004
“rwp” do not Granger-cause
“dp”
3.3617 0.0359
Sawn wood “ds” do not Granger-cause “rws” 6.9326 0.0011
“rws” do not Granger-cause “ds” 3.1179 0.0456
5.4 Stability Analysis
Stability analysis is conducted to evaluate the parameter instability in the VECM due to the presence of structural break. If we observe the sample split figures, it is found that only in the plywood model, all the bootstrap p-values are much higher than 0.1, which implies that all the parameters in the model are stable. Meanwhile, in the logwood model, there is one break point in which the bootstrap p-value is lower than 0.1. Furthermore, in the sawn wood model, generally it shows the evidence of instable parameter in the model, in which almost half of the bootstrap p-values are less than 0.1 as well as 0.005. Then, if we observe the break point figures, it is showed that all of the models are not stable in the parameter which is indicated by the presence of many break points.
35 Indonesian government amended the log export ban policy, which allows for export of several types of logwood.
Logwood
Sample Split Break Point
Plywood
Sample split Break Point
Sawn wood
[image:50.595.109.525.114.702.2]Sample Split Break Point
Figure 8 The Results of Chow Test
5.5 Model Selection
36 point the structural break takes place. Based on the results of stability analysis, there are several dummy variables which are developed in the analysis: 1)
Dlog0912, which implies the presence of structural break in the last quarter of 2009 for logwood model, and 2) Dsawn0609, which represents the presence of structural break in the third quarter of 2006 for sawn wood model.
Nevertheless, after introducing the dummy variables in the VECM for logwood and sawn wood, this study only finds the significant parameter for dummy in the logwood model. While in the sawn wood model, the dummy
variable is statistically insignificant and also produces „strange‟ results of price
transmission analysis, which might generate miss-understanding of interpretation. In spite of introducing dummy variable in the model, for plywood model, this study does not include any dummy variable since based on the sample split chow-test, it is found that all the parameters are stable and thus it does not show any structural break during the analysis, though this is contradictory with the results of the break point chow-test. According to this, therefore, this study employs the standard ECM for plywood and sawn wood, which assumes all parameters to be constants over time (linear error correction model). Furthermore, the number of lags used in the error correction model refers to several criterions: Akaike Information Criterion, Final prediction error, Hannan-Quin criterion, and schwarz criterion which are available in the software package.
5.6 The Results of Error Correction Model (ECM)
The results of error correction model for logwood, as shown in the table, do not find any statistically significant parameter from different market prices, which indicates there is no short run response between domestic price and world price of logwood. As we can see in the table below, the dummy variable i.e.
37 speed of adjustment of the world logwood price. Before the last quarter of 2009, the speed of adjustment of the domestic logwood price to correct market disequilibrium is 5.8 percent, whereas the speed of adjustment of the world logwood price is 10.3 percent. Meanwhile, after the last quarter of 2009, the speed of adjustment of the domestic logwood price is 1.2 percent higher than that before the break period, as well as the speed of adjustment of the world logwood price becomes 2.2 percent higher.
In addition, the estimated price adjustment of half-lives which implies the length of time required to adjust for half of the deviations from long-run market equilibrium before the last quarter of 2009 is calculated at about four months, and becomes faster after the last quarter of 2009 at about 3.19 months. Generally, the results from VECM do not confirm the Granger-causality test which tells there is no causality between the domestic logwood price and the world logwood price.
Table 7 Error Correction Model for Logwood
Variable Parameter Variable Parameter
Δdl(t Δrwl(t
ect(t-1) -0.058*** ect(t-1) 0.103***
Δdl(t-1) 0.019 Δdl(t-1) -0.094
Δrwl(t-1) 0.010 Δrwl(t-1) 0.445***
Long run
dl(t-1) Intercept
rwl(t-1) 0.0723***
Dlog0912(t-1)# -0.211***
Trend(t-1) 0.005***
38 percent. Furthermore, the speed of adjustment of the domestic plywood price is also slower than the speed of adjustment of the world price: 6.3 percent and 7.9 percent respectively. Based on this result, the estimated price adjustment of half-lives is a bit similar with that in the logwood market. Contradictory with the results in the logwood market, the VECM confirms the results of Granger-causality test which emphasizes the reciprocal Granger-causality from both domestic and world plywood price.
Table 8 Error Correction Model for Plywood
Variable Parameter Variable Parameter
Δdp(t Δrwp(t
ect(t-1) -0.063*** ect(t-1) 0.079***
Δdp(t-1) 0.405*** Δdp(t-1) 0.232***
Δrwp(t-1) 0.037*** Δrwp(t-1) 0.206***
Long run
dp(t-1)
Intercept 2.102**
rwp(t-1) 0.594***
Trend(t-1) 0.004***
39 Similar to the findings in the plywood model, the VECM confirms the results of Granger-causality test as well, which emphasizes both the domestic and the world price have an influence to each other.
Table 9 Error Correction Model for Sawn wood
Variable Parameter Variable Parameter
Δds(t Δrws(t
ect(t-1) -0.072*** ect(t-1) 0.037**
Δds(t-1) 0.277*** Δds(t-1) 0.294***
Δrws(t-1) -0.070 Δrws(t-1) 0.119*
Long run
ds(t-1)
Intercept 3.509***
rws(t-1) 0.350*
Trend(t-1) 0.004***
5.7 Residual Analysis
40 Table 10 The Results of Residual Analysis
Diagnostic Test p-value
Logwood Plywood Sawnwood Portmanteau test for
autocorrelation
0.1276 0.0173 0.8135
LM test for autocorrelation 0.0000 0.0010 0.5277 Test for non-normality 0.0000 0.0000 0.0000 ARCH-LM test U1 : 0.9667
U2 : 0.8496
U1 : 1.
