Factors Influencing Indonesian Cocoa Export to the European Union

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DECLARATION

I, Ratna Mega Sari, hereby declare that the thesis entitled:

FACTORS INFLUENCING INDONESIAN COCOA EXPORT TO THE EUROPEAN UNION

Submitted to fulfill a requirement for the award of Master of Science in Agribusiness from Bogor Agricultural University Indonesia and Georg August University of Goettingen Germany in the framework of international joint degree program between both universities is my own work 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 acknowledged and referenced herein.

Bogor, January 2013 Ratna Mega Sari NRP H451100071

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ABSTRACT

RATNA MEGA SARI, Factors Influencing Indonesian Cocoa Export to the European Union. Under direction ANDRIYONO KILAT ADHI, SUHARNO, andBERNHARD BRUEMMER

One of plantation commodity which is potentially developed in Indonesia is cocoa. Indonesia is the third largest exporter of cocoa bean after Ivory Coast and Ghana. Even though Indonesia is one of the biggest cocoa producer countries but only 1.07 percent of cocoa bean and 7.79 percent of cocoa butter and oil can enter European Union Market as the biggest cocoa consumer in the world. Based on that problem this paper will analyze what factors which can influence cocoa export of Indonesia to European Union and what policy implication can be conducted regarding to this condition.

Data processing was conducted by using stata which used panel data analysis with gravity model panel data. There are three models which can be estimated in panel data. This study deals with the flows of trade between Indonesia and Countries in European Union. Therefore the Fixed Effects will be a more appropriate model than random specification. The eleven importer countries are selected for the period 1996 - 2011.

Based on econometric results there are four significant variables that influence trade flows of cocoa code 1801 from Indonesia to European Union. Those are GDP of exporting country, population of exporting country, exchange rate and export tax. There is no significant variable which can explain trade flows of cocoa, code 1804 from Indonesia to European Union. It would be concluded that in this case gravity model is not the best model. The next research should consider the other variable which can explain the trade flows well.

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ABSTRAK

RATNA MEGA SARI, Faktor-Faktor yang Mempengaruhi Ekspor Kakao Indonesia - Uni Eropa. Dibawah Bimbingan ANDRIYONO KILAT ADHI,

SUHARNO, danBERNHARD BRUEMMER

Kakao adalah salah satu komoditi perkebunan yang potensial dikembangkan di Indonesia. Indonesia merupakan negara eksportir kakao terbesar ke tiga di dunia setelah Pantai Gading dan Ghana. Meskipun Indonesia merupakan salah satu produsen kakao terbesar di dunia namun hanya 1.07 persen biji kakao dan 7.79 persen minyak dan mentega kakao yang dapat memasuki pasar Uni Eropa sebagai konsumen kakao terbesar di dunia. Berdasarkan permasalahan tersebut penelitian ini akan menganalisis faktor-faktor apa saja yang mempengaruhi ekspor kakao Indonesia ke Uni Eropa dan bagaimana implikasi kebijakan yang dapat dilakukan terkait dengan kondisi tersebut.

Pengolahan data dilakukan dengan menggunakan stata. Analisis yang dilakukan adalah analisis panel data dengan menggunakan model gravitasi. Terdapat tiga model yang bisa diestimasi dalam panel data. Penelitian ini menganalisis aliran perdagangan antara Indonesia dan Uni Eropa. Oleh karena itu FE (Fixed Effects) lebih tepat dari pada menggunakan RE (Random Effects). Sebelas negara importir dipilih pada periode tahun 1996 - 2011. Adapun kesebelas negara tersebut adalah Jerman, Perancis, Belanda, Inggris, Belgia, Italia, Spanyol, Austria, Hongaria, Polandia dan Republik Ceko.

Berdasarkan analisis ekonometrik, terdapat empat variabel signifikan yang mempengaruhi perdagangan kakao kode 1801 dari Indonesia ke Uni Eropa. Variabel-variabel tersebut adalah GDP negara eksportir, populasi negara eksportir, nilai tukar dan pajak ekspor. Pada kode kakao 1804, tidak terdapat variabel yang signifikan yang dapat menjelaskan aliran perdagangan kakao dari Indonesia ke Uni Eropa. Oleh karena itu dapat disimpulkan bahwa pada kasus ini model gravitasi bukan merupakan model yang terbaik. Penelitian selanjutnya sebaiknya mempertimbangkan variabel lain yang dapat menerangkan aliran perdagangan dengan baik.

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SUMMARY

RATNA MEGA SARI, Factors Influencing Indonesian Cocoa Export to the European Union. Under direction ANDRIYONO KILAT ADHI, SUHARNO, andBERNHARD BRUEMMER.

Indonesia is the third largest exporter of cocoa bean after Ivory Coast and Ghana. Even though Indonesia is one of the biggest cocoa producer countries but only 1.07 percent of cocoa bean and 7.79 percent of cocoa butter and oil can enter European Union Market as the biggest cocoa consumer in the world. Based on that problem this paper will analyze what factors which can influence cocoa export of Indonesia to European Union and what policy implication can be conducted regarding to this condition.

Data used is panel data from 1998 - 2011 including cocoa export value of Indonesia to eleven countries, GDP of exporter and importer countries, population of exporter and importer countries, exchange rate and physical distance. Data processing was conducted by using stata which used panel data analysis with gravity model panel data. There are three models which can be estimated in panel data. This study deals with the flows of trade between Indonesia and Countries in European Union. Therefore the FE will be a more appropriate model than random specification. The eleven importer countries are selected for the period 1998 -2011. Those countries are Germany, France, Netherlands, United Kingdom, Belgium, Italy, Spain, Austria, Hungary, Poland and Czech Republic

In case of export tax, this variable also significantly influences trade flows of Indonesia cocoa, code 1801 to European Union positively. Exchange rate has the same condition with export tax which is not consistent with general previous studies. In this case cocoa bean exporter should not be worry about applied export tax in the years later and exchange rate. In specific condition (European Union Market), those variables do not influence cocoa demand. Indonesia cocoa exporter can focus on optimizing potential market, searching the new buyer etc. In order to achieve this goal, exporters have to pay attention to requirement of European Union people to cocoa they consume. Requirement could be related to the safety and quality of cocoa entering to their market.

Based on the output result, there is no significant variable which can explain trade flows of cocoa, code 1804 from Indonesia to European Union. It would be concluded that in this case gravity model is not the best model. The next research should consider the other variable which can explain the trade flows well. The model has to include variable.

Previous research related to Indonesia cocoa trade flows still treat cocoa in general. The different results of 1801 and 1804 show that we cannot make a generalization of factors influencing of cocoa export generally. It would be better

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if analysis is separated based on specific product. Therefore appropriate policy can be determined specifically based on condition and situation of cocoa product.

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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 Universitiy.

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FACTORS INFLUENCING INDONESIAN COCOA EXPORT

TO THE EUROPEAN UNION

RATNA MEGA SARI

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

BOGOR

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1. External Thesis Examiner : Dr. Amzul Rifin, SP.MA 2. Study Program Representative : Dr. Ir. Netti Tinaprilla, MM

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xiii Thesis Tittle : Factors Influencing Indonesian Cocoa Export to the

European Union

Name : Ratna Mega Sari

NRP : H451100071

Mayor : Agribusiness

Approved by

Advisory Committee

Agreed by

Examination Date : Submission Date :

Dr. Ir. Andriyono Kilat Adhi Chairman

Dr. Ir. Suharno, M.Adev. Member

Prof. Dr. Bernhard Bruemmer Member

Coordinator of Major Agribusiness

Prof. Dr. Rita Nurmalina, MS.

Dean of Graduate School

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ACKNOWLEDGEMENT

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 of the National Education Ministry of Indonesia for funding my study in Germany.

I would acknowledge my supervisors in Indonesia, Dr. Ir. Andriyono Kilat Adhi and Dr. Ir. Suharno, M. Adev from Bogor Agricultural University Indonesia, for their support and their insight to my research and my study. I also thank to Dr. Amzul Rifin S.P. M.A. and as examiners in my final examination for their constructive critics and comments. I am indebted to my supervisor in Germany Prof. Dr Bernhard Bruemmer from Goettingen University, who supports me academically and mentally in thesis writing from the beginning until the last step. I would like also to thank for their insight and his constructive criticism of my thesis.

My Special thank to my husband Efri Junaidi, Mas Rangga, Ratna SS, Krystal Lin, Pak Samsul, Mas Iqbal, Ria, Mas Adnan, Bang Firman, Mba Ira, Ulf Roemer, and Dek Am for discussion and proof reading this thesis. My sincere thank further to all my friends and family in SIA program and in Goettingen Indonesian Student Community, especially for the ‘Roko Jaya Family’ for providing me a friendly and warm environment during my study in Goettingen. Finally I would like to thank my family for their love and their support for me. I dedicate this work to my beloved mothers and fathers who always pray for me and give me their love and taught me the values of life.

Bogor, January 2013 Ratna Mega Sari

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AUTOBIOGRAPHY

Ratna Mega Sari, the author of this thesis, was born in Dumai, on 16th of August 1987. She completed her primary education in 1999 at SDN 002 Pangkalan Sesai Dumai Barat. She did her Junior high school at SLTPN 4 Dumai in 2002 and completed her senior high school at SMAN 2 Dumai in 2005. She spent her bachelor degree in Bogor Agricultural University with major Agribusiness. She got her B.A in 2009 supported by Dumai Government.

She was active in some organizations during her study period in Bachelor degree such as DKM Al-hurriyah, HIPMA IPB, FORCES IPB and IKPMR. She participated in some paper competition and being a winner in some events. In 2009, She got scholarship to be a participant of IELSP (Indonesian English Languange Study Program) in Ohio University, United States of America.

She has been working as a teacher in Study Club, Nurul Fikri Bogor. She continued her study to master’s 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 National Education Ministry of Indonesia.

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Page

LIST OF TABLES... xix

LIST OF FIGURES... xx

LIST OF APPENDICES ... xxi

LIST OF ABBREVIATION... xxii

I. INTRODUCTION ... 1

1.1. Background ... 1

1.2. Problem Statements... 5

1.3. Objectives... 7

1.4. Significance of the Research ... 7

II. LITERATURE REVIEW ... 9

2.1. International Trade of Indonesia Cococa ... 9

2.2. Gravity Models of Trade... 11

2.3. Gross Domestic (GDP), Population, Physical Distance, Exchange Rates, and Export Tax ... 15

2.3.1. Gross Domestic Product ... 15

2.3.2. Population... 15

2.3.3. Distance ... 16

2.3.4. Exchange Rates... 17

2.3.5. Export Tax ... 17

III. THEORITICAL FRAMEWORK ... 19

3.1. Theory of International Trade on Indonesian Cocoa ... 19

3.2. International Demand and Supply of Cocoa... 23

3.3. Export Import Theory... 25

3.4. Operational Framework ... 26

IV. RESEARCH METHOD... 29

4.1. Scope of Study ... 29

4.2. Types of Data and Sources ... 29

4.3. Data Analysis Methods ... 30

4.3.1. Pooled Least Square ... 31

4.3.2. Fixed Effect Model ... 33

4.3.3. Random Effect Model ... 33

4.4. Model Formulation ... 35

4.4.1. Model Formulation of HS 1801 (Cocoa Beans, Whole Or Broken, Raw or Roasted... 35

4.4.2. Model Formulation of HS 1804 (Cocoa Butter, Fat and Oil ... 36

V. DESCRIPTION OF INDONESIAN COCOA... 39

5.1. Development of Indonesian Cocoa... 39

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5.3. Cocoa Development Policy in Indonesia ... 40

VI. RESULT AND DISCUSSION... 45

6.1. HS 1801 (Cocoa Beans, Whole or Broken, Raw or Roasted).. 45

6.2. HS 1804 (Cocoa Butter, fat, and Oil)... 51

VII. CONCLUSION, POLICY AND RESEARCH OUTLOOK... 53

REFERENCES ... 55

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LIST OF TABLES

Number Page

1. Export and Import of Cocoa and Cocoa’s

Processing In Indonesia ... 4

2. The Progression of Cocoa Consumption in 2001/2002 -2006/2007 ... 5

3. Absolute Advantages ... 19

4. Comparative Advantages ... 20

5. Sources of Data ... 29

6. Budget Recapitulation of Cocoa Program for Three Years 2009 - 2011 ... 41

7. Fixed Effect Regression of HS 1801 ... 46

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LIST OF FIGURES

Number Page

1. Gross Domestic Product on The Basis of Constant Price

in 2000 According to Sector in 2004 - 2008 ... 1 2. Export Value of Indonesian Plantation Commodity

in 2004 -2008... 2 3. World Cocoa Producing Countries... 3 4. The Progression of Export Volumes of Cocoa Bean

According to the Destination Country in 2004 - 2008 ... 6 5. Operational Framework Diagram ... 27

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xxi

LIST OF APPENDICES

Number Page

1. HS 1801 ( Export tax as dummy variable)... 61 2. HS 1801 (Export tax as percentage value)... 62 3. HS 1804 (Export tax as percentage value)... 63

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LIST OF ABBREVIATION

FE Fixed Effects

GDP Gross Domestic Product

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0 20000 40000 60000 80000 100000 120000 140000 160000

2004 2005 2006 2007 2008

C o n tr ib u ti o n t o G D P ( B il io n R u p ia h ) Year Crops Plantation Animal husbandry Forestry Fishery

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0,00 2.000,00 4.000,00 6.000,00 8.000,00 10.000,00

2004 2005 2006 2007 2008

(

Year

Oil Palm Rubber Cocoa Coffee Tea Tobacco

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percent). Hence, smallhol production (Wahyudi et a

Cocoa can be pr Nevertheless, today’s coc masker, soap, and many ot made by leaf and bark of

Most of cocoa be Coast and Ghana in Afric largest grower of cocoa 2003. Contribution of e following chart.

Figure 3. World Cocoa Pr (Source: ICCO, 2012

There are six code Those are HS 1801 (coc (cocoa shells, husks, skins

Nigeria 5,6 Others 10,6 % Brasil 4,6 % Ecuador 3,4 % Indonesia 10,4 %

llholder plantation contributes 90 percent of t al, 2008).

processed to be many kind of food and ocoa is not only for food or beverage industries but

y other beauty products. It can be turned to be w k of cocoa.

beans in the world are produced by three count frica and also Indonesia. In 2002, Indonesia was oa bean after Ivory Coast. But this condition of each country to World’s cocoa trading is

Producing Countries. O, 2012)

odes of cocoa in the four digit codes of harmon cocoa beans, whole or broken, raw or roasted) skins and other cocoa waste), HS 1803 (cocoa past

Cameroon 5,3 % Ivory Coast 35,1 % Ghana 23,8 % Indonesia 10,4 % Papua New Guinea 1,1 % 3

nt of national

nd beverage. es but also for

be woof that is

ountries, Ivory as the second on changed in is shown by

mony system. ed), HS 1802 paste, whether Ivory Coast

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or not defatted), HS 1804 (cocoa butter, fat, and oil), HS 1805 (cocoa powder, not containing added sugar or other sweetening matter), and HS 1806 (chocolate and other food preparations containing cocoa). Most of Indonesian cocoa is exported in the form of cocoa beans, whole or broken, raw or roasted (HS 1801) and cocoa butter, fat and oil (HS 1804). Trade balance of cocoa each code can be shown by Table 1.

The high contribution of cocoa toward national economic is indicated by improving of devise, providing of job occupation, and increasing of farmer’s income. This condition makes cocoa commodity has potential opportunity to be continuously developed for optimal result. So that it is needed some of researches related to cocoa as one of potential commodities in Indonesia.

Table 1. Export and Import of Cocoa and Cocoa’s Processing in Indonesia

Year Description Volume (Ton)

HS 1801 HS 1802 HS 1803 HS 1804 HS 1805 HS 1806

2007 Export 379,829 1,860 22,173 51,149 32,232 16,280

Import 19,655 n/a 529 354 6,955 16,120

Balance 360,174 n/a 21,644 50,795 25,277 160

2008 Export 380,513 2,164 30,056 55,584 34,408 12,814

Import 22,968 125 4,723 21 7,797 17,697

Balance 357,545 2,039 25,333 55,563 26,611 -4,883

2009 Export 439,305 1,102 13,393 41,606 27,540 12,244

Import 27,230 659 1,054 4 10,709 7,210

Balance 412,075 443 12,339 41,602 16,831 5,034

2010 Export 432,427 1,201 20,014 46,687 36,354 16,159

Import 24,831 2,382 2,291 5 11,556 6,351

Balance 407,596 -1,181 17,723 46,682 24,798 9,808

2011 Export 210,067 4,672 54,922 82,535 41,494 16,520

Import 19,100 274 5,778 42 9,580 8,888

Balance 190,967 4,398 49,144 82,493 31,914 7,632

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5 1.2 Problem Statements

World’s cocoa consumption tends to increase over year and even world’s

cocoa consumption is more than production. Europe is the biggest cocoa consumer in the world. European Union has the highest number of consumption per capita in the world. FAO (2003) explained that world cocoa market is concentrated in EU around 40 percent of world production are consumed. This condition is caused some countries in Europe such as Germany and Holland have big downstream industries so that cocoa bean is needed for that purpose. The other big cocoa consumer country is America especially United States of America.

Table 2. The Progression of Cocoa Consumption in 2001/2002 - 2006/2007

Region Cocoa’s Consumption (Thousand Ton)

2001/02 2002/03 2003/04 2004/05 2005/06 2006/07

Europe 1,282 1,320 1,347 1,379 1,456 1,541

Germany Holland Others 195 418 669 193 450 677 224 445 678 235 460 684 306 455 695 357 465 719

Africa 421 446 464 501 485 515

Ivory Coast Others 290 131 315 131 335 129 364 137 336 149 336 179

America 768 813 852 853 881 854

Brazil USA Others 173 403 192 195 410 208 207 410 235 209 419 225 223 432 226 224 418 212

Asia & Oceania 416 499 575 622 698 699

Indonesia Malaysia Others 105 105 206 115 150 234 120 203 252 115 249 258 140 267 291 140 270 289

Total 2,887 3,078 3,238 3,355 3,520 3,609

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0 50000 100000 150000 200000

2004 2005 2006 2007 2008

Singapore Brazil Thailand China Canada Germany Holland Others

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Cocoa which is imported by European Union is processed to be some products. Although European Union is the biggest cocoa consumer in the world, Indonesia cannot export cocoa to this region in big volume. Competitors of Indonesian cocoa in European Union are Ivory Coast which is supplied 41.54 percent of Cocoa and followed by Ghana, Nigeria, Cameroon, Brazil, Ecuador and Switzerland.

Based on that description, the problem that will be analyzed in this research are what factors that can influence cocoa export of Indonesia to European Union and what policy implication can be conducted regarding to this condition.

1.3 Objective

According to the background and problem statement which has been explained, so the purposes of this research is to identify factors that influence Indonesia cocoa export to European Union and briefly determine policy implication regarding factors influencing Indonesia Cocoa Export to European Union.

1.4 Significance of the Research

The Study is expected to:

1. Provide information in the formulation of international trade policy of cocoa. 2. Increase knowledge in applying science which has been obtained in analytical

problem solving skills.

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II. LITERATURE REVIEW

2.1 International Trade of Indonesia Cocoa

Trade relations among countries occur because of differences in potential resources, cost of production and tastes, differences in demand and supply, as well as desire to expand market and to raise foreign exchange. In international trade there are many factors affecting exports. It can be analyzed from demand and supply that occurred in those commodities both domestically and internationally. Theoretically, export volume of a particular commodity from one country to another is difference between higher domestic supply and domestic demand which is referred to excess supply. At that time, domestic excess supply will be used by other country that is having excess demand. In addition, Solvatore (1997) showed that exports are also affected by commodity price and other factors that may also affect either directly or indirectly. There are some main export products including plantation commodity in Indonesia.

Plantation products are widely traded commodity. Cocoa is one of the plantation commodities which have an important contribution to the national economy. This was demonstrated by research of Hadi and Mardianto (2004) which showed that cocoa products are one group commodities in 1999 - 2001 (when Indonesia had decreasing of export competitiveness) has a positive composition effect so need to get greater attention to be exported.

Lolowang (1999) conducted an analysis of supply and demand for Indonesian cocoa in domestic and international markets that are formulated in the form of simultaneous equations. Model is suspected by Three Stage Least Squares (3SLS). This research aims to analyze supply and demand for Indonesian cocoa in

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domestic and international markets in the form of simultaneous equations. Result of this study indicates that behavior of plant areas in west and east Indonesia in the short time are not responsive to the domestic cocoa price, domestic coffee price, labor rates and bank interest rates. Productivity of cocoa in western and eastern of Indonesia in the short time is not responsive to the domestic price of cocoa, fertilizer price and plant areas.

Exports of cocoa beans are the difference between productions or consumption reduced by domestic demand coupled with the stock of the previous year. According to research Nurasa and Muslim (2008) Indonesian cocoa exports have a tendency to increase from year to year. But still be exported cocoa beans that have not undergone processing.

According to research of Widianingsih (2009), Indonesian cocoa export to Malaysia, Singapore and China is determined by some factors. Those are export price, population of Malaysia, Singapore and China, exchange rate and gross domestic product per capita. Her research found that export price has not significant and negative correlation to export demand of Indonesia cocoa. Population has positive significant influence because increasing population will increase cocoa consumption. Exchange rate and GDP per capita also has positive influence to export demand.

Sitorus (2009) also conducted the cocoa trade analysis of Indonesia, Malaysia, Singapore, Hongkong and Thailand to China. She concluded that cocoa export is significantly influenced by exporter GDP, exporter population, exchange rate and physical distance.

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2.2 Gravity Models of Trade

The gravity model is one of the great success stories of economics. The success of the model is its great explanatory power. The equations fit well statistically and give quite similar answers across many different datasets-inferred bilateral trade cost are big, varying with distance and border crossings. Gravity model became very popular because of its quite simple usage combined with a substantial power of explaining the flows in general. The gravity equation has been exploited as an instrument to model not only international trade flows but also tourism or migration. Subsequently it has been recognized that gravity equation can be derived from different models including Ricardian, Hecksher-Ohlin, and the monopolistic competition model. Gravity has long been one of the most successful empirical models in economics. In corporating deeper theoretical foundations of gravity in to recent practice has led to a richer and more acurate estimation and interpretation of the spatial relations described by gravity.

Despite this success, the inferred trade costs have had little impact on the broader concerns of economics until very recently. There are two difficulties. First, national buyer and seller responses to bilateral trade cost depend on their incidence instead of the full cost. Second, the high dimensionality of bilateral trade cost requires aggregation, both for elementary comprehension of magnitude

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and for use in the wide class of trade models that focus on resource and expenditure allocation as sectoral aggregates (Anderson, 2003).

The classical conception of gravity model originally reported by Tinbergen (1963) was inspired by the Newton’s law of universal gravitation. This law states

that every point mass attracts every other point mass with a gravity force Fg that is directly proportional to the product of their masses M1 and M2 and inversely proportional to the square of the distance between them:

= 1 2

Before Tinbergen, Ravenstein (1885) and Zipf (1946) used gravity concepts to model migration flows. Independently from Tinbergen, Poeyhoenen (1963), inspired by Leo Tornqvist, published a paper using a similar approach. Tinbergen’s student and team-member of the Netherlands Economic Institute, Hans Linneman published a follow up study (Linneman 1966) which extended the analysis and discussed the theoritical basis of the gravity equation using the Walrasian model as a benchmark. By the 1970s the gravity equation was already a must. The famous international trade book by Edward Leamer and Robert Stern included almost an entire chapter on it (Leamer and Stern 1970, pp.157-170), based on the contribution of Savage and Deutsch (1960). Leamer and Stern’s book

introduced trade economist to the term resistance, that entered their glosary as a synonim for distance and other trade impediments. To make a long story short, from the first conceptualisation of Tinbergen (1962) the gravity equation has been used time and again to empirically analyse trade between countries. It has been defined the workhorse of international trade and has been considered as a “fact of

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life” in the field of research (Deardorff, 1998). The gravity equation’s ability to

correctly approximate bilateral trade flows makes it one of the most stable empirical relationship in economics (Leamer and Levinson 1995)

Gravity model for international trade considers the bilateral trade as the “gravity force” between two countries and suggests the same relationship between this force, masses of the countries proxied by GDP and the distance between them.

The basic gravity model is developed by Tinbergen in the 1960s explaining bilateral trade between two countries depending positively on their economic sizes and negatively distances between them. Tinbergen (1963) explained that an economic model describing international trade flows can be formulated in varying degrees of detail. The model consists of only one equation in which the value of total exports from one country to another is explained by a small number of variables. The explanatory variables that play a preponderant role are:

a. The Gross National Product (GNP) of the exporting country; b. The Gross National Product of the importing country; and c. The distance between the two countries.

In several calculations other explanatory variables were introduced; however, their contribution to an explanation of the value of exports was very limited as compared to that the three main variables. Other important characteristics of the present analysis are that:

a. No separate demand and supply functions for exports are introduced-meaning that the equation is a turnover relation in which prices are not specified; and

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b. Only a statistic analysis is made – no attention is paid to the development of exports over time.

For estimating purposes, the traditional gravity model of international trade could be written in the form:

Xji= β0 GDP_jβ1GDP_iβ2D_ijβ3εij

Where Xji stands for the bilateral trade between countries i and j; Dij is a

distance between these two countries; εij stands for the error term and β0, β1, β2

and β3are parameters to be estimated.

We assume that the error term εij is statistically independent on the other

regressors; moreover, we further assume that E (εij GDPi, GDPj, Dij) = 1. This

assumption leads to:

E (Xji GDPi, GDPj, Dij) = β0 GDP_jβ1GDP_iβ2D_ijβ3

However, the gravity model is identified in multiplicative form, which does not permit for employing standard estimation techniques. The traditional way in the literature how to deal with estimation of multiplicative form of the model is to estimate the logarithmic transformed model:

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15 2.3 Gross Domestic Product (GDP), Population, Physical Distance, Exchange

Rate, and Export Tax 2.3.1 Gross Domestic Product

Gross domestic product is used to measure the country’s total output. It is

one of the primary indicators used to gauge the health of a country’s economy.

Economic production and growth, what GDP represents, has a large impact on nearly everyone within that economy. According to the gravity model, a large economy spends more on imports and exports. GDP influence country’s ability

towards trade flows. The higher GDP of one country means more trade for a country. Bergstrand (1989) reports a positive GDP per capita coefficient. He interprets a negative GDP per capita coefficient in a way that the product group which is subject to the estimation is not capital intensive but labor intensive. However, in the long run higher population has a tendency to decrease income per capita, making every individual poorer, and therefore it may cause production and exports to decrease. In addition to that, lower income per capita tends to decrease the demand for imports as well.

2.3.2 Population

Big population can possibly increase trade flows between countries. It is possible to extend the basic gravity model by including the populations of exporting and importing countries to see what the effect of population on bilateral trade flows between two countries is. It is possible to modify the basic gravity model by including populations of exporting and importing counties to know the effect of population on bilateral trade flows between two countries. Matyas (1997) conclude that population has a tendency to increase trade and the level of specialization by producing gains from specialization. On contrary Dell’Ariccia

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(1999) finds a negative population coefficient which means a negative relationship between population and trade flows, suggesting that imports and exports are capital intensive in production.

Moreover, according to Bertrand (1989) the impact of population on trade may also differ depending on the length of the estimation period (short term vs. long term). Population may have a positive impact on trade flows in the short run, since it may increase the amount of labor force, the level of specialization and more products to export as a result.

2.3.3 Distance

The concept of bilateral distance is the main determining characteristic of the gravity model and thus measurement issues related to distance are key to the validity of any empirical application, but also to the interpretation of the result of the econometric findings. Economics is no physics. In the natural sciences distance is well defined and its measurement can be exact and unambiguous. Economic distance however is a multifaceted concept, and measurement and interpretation accordingly are subject to continuous debate. Originally distance entered the model because it could be used as an approximation of transportation cost and transport time. Also distance was used as a measure for the “mental”

distance of exporters and importers that increases with distance. New and challenging measures of intangible distances related to different legal and economic institutions, different cultures and different technologies have recently been added to the gravity model (Martinez-Zarzoso and Marquez-Ramos 2005; Dekker et al.2006). Furthermore, larger distances between countries are expected to decrease bilateral trade (Rose et al, 2000).

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17 2.3.4 Exchange Rates

Another variable supposed to affect the level of international trade is the exchange rates. Including exchange rates is also a common practice in the gravity literature, as the depreciation of a currency makes the exports of a given economy more competitive in the rest of the world as they get cheaper (Anderson and Van Wincoop, 2003). We expect a positive sign for exchange rates because depreciation of home country relative to the foreign country currency will lead to more export and less export for the home country.

Zarzoso and Lehman (2003) apply the gravity trade model to asses Mercosur-European Union trade and trade potential following the agreement reached recently between both trade blocs. The model is tested for sample 20 countries, the four formal members of Mercosur, Chile, and fifteen members of the European Union. The research finds that exchange rate is one of the important variables of bilateral trade flows.

Exchange rates, in some cases, have no influence to explain some country’s trade. Rahman (2006) who analyzes the Bangladesh’s trade with its

major trading partners using the panel data estimation finds that exchange rate has no effect on the Bangladesh’s import. This also happen in export behavior of

Ethiopia which is investigated by Taye (2009). He finds that real exchange rate is statistically insignificant to determine Ethiopia’s export performance.

2.3.5 Export Tax

The compound effect of export taxes on trade is ambiguous, depending on market structure and market power of the applying country. Export tax can decrease domestic production, but increase domestic consumption and production

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of downstream industries. Overall the impact of export tax on trade is expected to be negative (Solleder, 2012) that export taxes negatively affect export values and quantities of tax imposing nations. Burger (2007) conducted research on cocoa export tax by a case study on Cocoa commodity contained in the Ivory Coast. His research concluded that the Ivory Coast who has contributed 40 percent of world cocoa demand almost did not get the benefit of the export tax changes.

Moreover with the above mentioned variables, international or bilateral trade is affected by many other factors such as common language, common border, and colonial ties, being in the same trade union or free trade area, sharing a common culture and religion and so on.

Mehanna (2003) analyzes the effects of politics, as represented by political freedom and corruption, and cultures as represented by religion and language affiliation, on Intra-Middle East trade for the period 1996-1999 for sample of 33 countries. It employs an extended version of the gravity model by controlling for oil exporting countries. The results showed that religion and culture has a statistically significant effect on the Middle-East trade. However, corruption is shown to have a highly statistically negative effect on both exports and imports in the Middle East. In addition she finds that the level of political freedom in these countries does not statistically affect Middle-East trade.

Gassebneret.al (2006) even includes disaster variable to gravity model in

their paper which examine the impact of major disasters on international trade flows using a gravity model. Their research consists of more than 170 countries for the years 1962 - 2004 yielding approximately 300.000 observations.

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IV. THEORITICAL FRAMEWORK

3.1 Theory of International Trade on Indonesian Cocoa.

No country in the world that can live without interaction with other countries. As rich as any natural resources, a country would require other states in ensuring the survival of its people. International trade is a form of interaction between countries is one important issue in world economic activity. It is then not only related to economic issues, but then also extends to political and social issues.

International trade in principle arises as a result of the interaction between demand and supply is competitive. This is not apart from the concept of absolute advantage and comparative advantage. Adam Smith stated that the concept of inter-state trade is affected by absolute advantage. If a country produces a commodity with a more cost efficient than other commodities and also compared with other countries so the concept of specialization of production would be more beneficial for the country. This means that a country does not need to produce all the required items.

Tabel 3. Absolute Advantages

Commodity Indonesia Malaysia

Rubber (1 unit) 2 4

Tobacco (1 unit) 4 2

Total 6 6

With the advantage absolute different in each country, each country does not need to produce all the goods. Malaysia and Indonesia can share the role. For one unit of rubber Indonesia requires less labor than Malaysia. Meanwhile, for a unit of Malaysian tobacco requires less labor than Indonesia. Therefore, Indonesia

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and Malaysia can share the role. To focus on Indonesian rubber production activities while Malaysia produces tobacco.

In contrast to absolute advantage, comparative advantage put forward by David Ricardo explained that there is a condition in which a country has a better production efficiency compared with other countries. This can be exemplified as follows:

Table 4. Comparative Advantages

Commodity Indonesia Malaysia

Rubber (1 unit) 2 4

Tobacco (1 unit) 4 6

Total 6 10

Table 4 shows that Indonesia has an absolute advantage for the two commodities are rubber and tobacco. This does not mean that Indonesia should produce both commodity and export it to Malaysia. Trade will not happen if Malaysia does not produce anything and sell anything to Indonesia.

Indeed international trade has the principle of mutual benefit between the countries involved. However, it is not always possible. Often there is a case in which one party better off than others. This can be caused by the unfair agreement on when starting an agreement or violation of agreements that have been made.

Trade relations between countries occurs because of differences in the potential and resources, production costs, tastes, differences in demand and supply, as well as the desire to expand the market to raise foreign exchange. In international trade are many factors that affect the exports that could be analyzed from the demand and supply that occurred in those commodities both domestically and internationally.

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Theoretically, the volume of exports of a particular commodity from one country to another is the difference between domestic supply is referred to as excess supply. At the time of the excess supply of domestic used by other countries that are experiencing excess demand. In addition, exports are also affected by commodity price and other factors that may also affect either directly or indirectly (Salvatore, 1997).

Offering country exports of cocoa beans is the difference between productions or consumptions is reduced by domestic demand coupled with the stock of the previous year. According to research Nurasa and Muslim (2008) Indonesian cocoa exports have a tendency to increase from year to year. But still be exported cocoa beans that have not undergone processing.

Although exports have increased from year to year, Indonesia experienced obstacles in marketing their cocoa to the EU. Indonesia must compete with the cocoa from Africa who enters the EU without tariffs. This rate gives a negative effect for cocoa exports. Burger (2007) conducted research on cocoa export tax by a case study on Cocoa commodity contained in the Ivory Coast. His research concluded that the Ivory Coast who has contributed 40% of world cocoa demand almost did not get the benefit of the export tax changes. In the long run this will have negative consequences for farmers. Welfare of farmers will be affected by high taxes.

Research on cocoa has also been performed by Armanda (2009) and Lolowang (1999). The results Armanda (2009) showed that the response of cocoa area significantly affected by the variable price of coffee a year earlier, the price

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of CPO a year earlier, the area under the previous cocoa, cocoa prices the previous year and the rainfall the previous year.

Generally, the analysis of international trade is done by using simultaneous equations model. According to Nachrowi (2006), simultaneous equations consist of endogenous and exogenous variables. Endogenous variables are variables whose values are determined in the model. Although not identical, the endogenous variables is similar to the dependent variable in the regression equation, where its value can be determined if the value of independent variables has been determined in advance. While the exogenous variables are variables which are determined from outside of the model. Endogenous variables in an equation affect the endogenous variables in other equations.

In a simultaneous equations model there are two types of structural models and model reduction. Structural model is also called behavioral models, has a form based on the underlying theory to fit the behavior or structure of existing markets. While model reduction is a simple structural model.

Studies using simultaneous equation analysis have been done by Lolowang (1999) Sanjaya (2009), Setiawan (2005) and Sihotang (1996). In his research, Lolowang (1999) and Sihotang (1996) using the estimation methods Three Stage Least Squares (3SLS) while Sanjaya (2009) analyzed the response deals with the estimation method of Ordinary Least Square (OLS).

Cocoa bean is one of the export commodities that are able to contribute in the efforts to increase foreign exchange Indonesia. As specified in the objectives of this study emphasize the study of the factors that influence development of Indonesian cocoa beans with the response approaches the area and productivity of

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cocoa, domestic consumption of cocoa beans, cocoa beans Indonesia export supply to the EU and the impact of economic policies and external to the grain market balance Indonesian cocoa. As a commodity traded on world markets, cocoa Indonesia is more oriented to export. With these considerations the model formulated must be related to the order of the market in cocoa beans producer and consumer countries.

Indonesia exports of cocoa beans were analyzed based on the country's main export destination of Indonesia to find out whether there are differences in the behavior of Indonesian cocoa exports offerings based on the segmentation or differentiation of export destinations. Analysis of the domestic price of cocoa beans is expected to inform the extent to which the prospect of the domestic price of cocoa beans is affected by the change from the side of consumers and producers, as well as domestic policy.

3.2 International Demand and Supply of Cocoa

Cocoa export is difference between production and consumption which is reduced by domestic demand and coupled with stock of the previous year. Therefore cocoa exports are as follows:

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Where:

QXt= Total exports of cocoa in the year t

QPt= Total production of cocoa in the year t

QCt= Total consumption of cocoa in the year t

St-1= Stock of previous year

Assumptions used in this equation are import of cocoa-exporting countries is relatively small when compared with production, so it can be ignored. Then, considering the amount of cocoa production when compared with demand stock demand in producer countries suspected rather than functioning as a buffer to adjust market conditions, but the rest of the production at the end of the year that are not sold and entered into this year offerings, because these factors are relatively constant value then the variables can be excluded from the model. Then the formula becomes as follows:

QXt= QPt- QCt

QPt= Area (At) x Productivity (Yt)

Commodity price is positively related to production. It means that higher price commodity will increase production. It is similar to area. Higher land area will increase production. For wages and interest rates, the hypothesis is negatively related. It means that increasing of wage and interest rate will reduce production. Cocoa production produced in part will be consumed and the rest is used for export. Amount of cocoa beans that are required or consumes at a certain period is affected by important variables, namely domestic prices (PD), average household

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income (Y), related-price commodity (HL), Number of population (Pop). Mathematically, function of cocoa consumption is as follows:

QCt=f(PDt, Yt, HLt, Popt)

3.3 Export Import Theory

Exports are a product that a country produces more than it can consume in order to ship to other countries for a profit. An Import is product that a country gets from another country because it has a lower price than if they were to produce it themselves.

Exports consist of transaction in good and services from residents to non residents. Meanwhile imports consist of transaction in good and services from non residents to residents. An export of a good occurs when there is a change of ownership from a resident to a non resident. Export of commercial quantities of goods normally requires involvement of the customs in both the country of export and the country of import.

International trade can give devise contribution. This condition will increase economic development of country. Amir (1995) stated that general characteristic of a commodity which is potential to be exported are:

1. Having production surplus.

2. Having certain advantages such as: scarce, good quality compared to the same products from the other countries.

3. Export oriented.

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(Export activities contribute big benefit to country. Benefit of export for government are increasing country’s devise, enlarging benefit of national resources etc).

3.4 Operational Framework

Cocoa is one of the most important commodity in Indonesia. Export value of cocoa bean has third rank after oil palm and rubber. Indonesia is also the big three of cocoa producing countries in the world. But apparently Indonesia cannot export cocoa to the European Union in a big volume although FAO (2003) explained that world cocoa market is concentrated in EU around 40 percent of world production are consumed. By using gravity model, this research will determine what factors which can influence indonesian cocoa trade flows to European Union. There are two codes of cocoa which will be analyzed in this paper. Those are HS 1801 and HS 1804. Determination of these codes based on the two highest cocoa export of Indonesia. Based on this condition operational framework of this research can be explained by figure 5.

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IV. RESEARCH METHOD

4.1 Scope of Study

This research was conducted in Indonesia focusing on factors that influence Indonesian cocoa export to the European Union.

4.2 Types of Data and Sources

The type of data used in this research is secondary data time series (time series) and cross section as much as 14 years, start from 1998 until 2011. The data obtained from several agencies such as: Statistics of Indonesia, Agricultural Ministry, ICCO, Global Trade Atlas Navigator and other institutions. Data used are export volume of cocoa, the distance between countries, Gross Domestic Product and population.

Table 5. Sources of Data

Data Unit Sources of Data

Export Value of Indonesian Cocoa US$ Global Trade Atlas Navigator Exchange Rate of Rupiah towards Dollar Rp/EUR OANDA

GDP PPP of European Union US$ World Bank

GDP PPP of Indonesia US$ World Bank

Population of European Union (Million People)

Million People

FAOSTAT Population of European Union (Million

People)

Million People

FAOSTAT

Export Tax Financial Ministry of

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4.3 Data Analysis Methods

Data processing was conducted by using stata which use panel data Analysis with gravity model panel data. We often find problem regarding to data availability. Sometimes time series data provided are short and sometimes cross section data provided are limited. In Econometrics this problem can be solved by using pooled data in order to get efficient estimation.

A panel data set, while having both a cross sectional and a time series dimension, differs in some important respects from independently pooled cross section. To collect panel data, sometimes called longitudinal data, we follow (or attempt to follow) the same individuals, families, firms, cities, states, or across time. For example a panel data set on individual wages, hours, education and other factors is collected by randomly selecting people from a population at a given point in time. Then, these same people are interviewed at several subsequent points in time. This gives us data on wages, hours, education, and so on, for the same group of people in different years.

Panel data sets are fairly easy to collect for school districts, cities, counties, states, and countries, and policy analysis is greatly enhanced by using panel data sets. Hsiao (2003) lists several benefits from using panel data.

1. Controlling for individual heterogeneity.

2. Panel data give more informative data, more variability, less collinearity among the variables, more degrees of freedom and more efficiency.

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4. Panel data are also well suited to study the duration of economic states like unemployment and poverty, and if these panels are long enough, they can shed light on the speed of adjustments to economic policy changes.

5. Panel data are better able to identify and measure effects that are simply not detectable in pure cross section or pure time-series data.

6. Panel data models allow us to construct and test more complicated behavioral models than purely cross-section or time series data.

7. Micro panel data gathered on individuals, firms and household may be more accurately measured than similar variables measured at the macro level. Biases resulting from aggregation over firms or individuals may be reduced or eliminated.

8. Macro panel data on the other hand have a longer times and unlike the problem of nonstandard distribution typical of unit roots tests in time series analysis.

There are three model which can be estimated in panel data. These are Pooled Least Square, Fixed Effects and Random Effects.

4.3.1 Pooled Least Square

Pooled least square use panel data by using cross section, time series and pooling. Every observation (each period) has regression. We can know N (Quantity of unit cross section) and T (period of time). From all of the observations (N.T), we can write function

Yit= α + Xitβj + εit

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where:

Yit = endogenous variable

Xit = exogenous variable

α = intercept β = slope i = individual i t = period year t ε = error

N = Quantity of unit cross section T = Quantity of time period

The simplest approach to estimate this function is ignoring cross section and time series dimension from panel data and estimating by ordinary least square which is determined by pool data.

In this method, model assume that variable’s intercept is the same, then

this model also assume that coefficient slope from two variables is identical for all unit cross section. This is strict assumption. Although PLS method (pooled least square) is relatively easy, but model possibly distort the real relationship between Y and X in unit of cross section.

Pooled Least Square models are consistent if the dependent variables are not correlated to the error. Pooled models also produce an unbiased estimator if the unit effects (αi ) are uncorrelated with the independent variable (x). But,

commonly αi is correlated to the x, therefore pooled and pa tend to produce a bias

estimator of β (Clark and Linzer, 2012). Fixed effects and random effects model could solve this problem. Clark and Linzer (2012) stated that the fixed effects

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model will produce unbiased estimates of β, but those estimates need a high

variability on the sample.

4.3.2 Fixed Effect Model

Fixed effect model is model which considers eliminated variables can change intercept of cross section and time series. Dummy variables can be added to the model to make intercept changes possible. Afterward model is estimated by using Ordinary Least Square (OLS)

Yit=αiDi+ β Xit+ εit

Where:

Yit = endogenous variable

Xit = exogenous variable

α = intercept

D = dummy variable

β = slope

i = individual i

t = period year t

ε = error

4.3.3 Random Effect Model

Additional of dummy in fixed effect can reduce quantity of degree of freedom. This condition will also reduce efficiency of estimated parameter. Random effect model can be used to solve this problem. In this model, different parameter between individual and time is included to error. Random effect model can be explained by this equation:

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Yit= Xitβj+ εit

εit = uit+vit+ wit

Where:

uit~ N(0,δu2) = component of cross section error

vit~ N(0,δv2) = component of time series error

wit~ N(0,δv2) = component of combination error

It can also be assumed that individual error and combination error is not correlated each other. Using of random effect model can reduce using of degree of freedom. It has implication that estimated parameter will be more efficient.

Nachrowi and Usman (2006) suggested that it is better to use fixed effects model if we have T (time) bigger than amount of individual. On contrary, if we have amount of individual is bigger than amount of time, so it would be better if we use random effects model. Egger (2000) explained that since individual effects are include in the regressions a decision should be made whether they are treated as random or fixed. A random effects model can be more appropriate when estimating the flows of trade between a randomly drawn sample of trading partners from a large population. A fixed effects model would be a better model when estimating flows of trade between an ex ante predetermined selection of countries).

This study deals with the flows of trade between Indonesia and Countries in European Union which is main importer of Indonesia cocoa. Those are Germany, France, Netherlands, United Kingdom, Belgium, Italy, Spain, Austria, Hungary, Poland and Czech Republic. Therefore the fixed effect will be a more

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appropriate model than random specification. The eleven importer countries are selected for the period 1998 - 2011.

4.4 Model Formulation

There are two codes of cocoa which will be analyzed in this paper. Those are HS 1801 and HS 1804. Determination of these codes based on the two highest cocoa export of Indonesia.

4.4.1 Model Formulation of HS 1801 (Cocoa Beans, Whole or Broken, Raw or Roasted)

Analysis used in this research is Gravity Model approach which consists of Dependent variables and some Independent variables. Independent variables used are GDP of exporter and importer countries, population of exporter and importer countries, physical distance, exchange rate and export tax.

We will divide Analysis of code HS 1801 (Cocoa beans, whole or broken, raw or roasted) into two analyses. Firstly, export tax is treated as dummy variable and secondly, export tax is analyzed as percentage value. It is intended to know the effect of the export tax to European Union as whole, before and after export tax policy and also the effect of export tax (in percentage value) to trade flows (export value). The model formulation could be written as follows:

ln Yijt = β0+ β1lnGit+β2lnGjt+β3lnSit+β4lnSjt+ β5ln Eijt + β6lnLij+ β7Tt+ ε

where:

β0 = Intercept

β1, β2,β5 = Parameter of each variable which will be tested statistically and

econometrically

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i,j = (1,…,N) Bilateraltrades between country i and j

Yijt = Trade flows (Export Values) of Cocoa from country i to j in the

year t

Git = GDP of country i in the year of t

Gjt = GDP of country j in the year of t

Sit = Population of country i in the year t (people)

Sjt = Population of country j in the year t (people)

Eijt = Exchange rate of country i and j in the year t

Lij = The distance between exporter countries and importer countries

(Kilometres)

T = Export Tax (dummy and percentage)

ε = Error

4.4.2 Model formulation of HS 1804 (Cocoa Butter, Fat, and Oil)

In case of cocoa HS 1804, we still use the same variables with cocoa HS1801. Since there is no export tax imposed on this cocoa code, here we eliminate export tax variable. Independent variables used are GDP of exporter and importer countries, population of exporter and importer countries, physical distance, and exchange rate. The model formulation could be written as follows

Yijt = β0+ β1lnGit+ β2lnGjt+β3lnSit+β4lnSjt+ β5ln Eijt + β6lnLij+ ε

where:

β0 = Intercept

β1, β2,β7 = Parameter of each variable which will be tested statistically and

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t = (1,…,T) between 1998–2011

i,j = (1,…,N) Bilateral trades between country i and j

Yijt = Trade flows (Export Values) of Cocoa from country i to j in the

year t

Git = GDP of country i in the year of t

Gjt = GDP of country j in the year of t

Sit = Population of country i in the year t (people)

Sjt = Population of country j in the year t (people)

Eijt = Exchange rate of country i and j in the year t (Rp/Euro)

Lij = The distance between exporter countries and importer countries

(Kilometres)

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V. DESCRIPTION OF INDONESIA COCOA

5.1 Development of Indonesia Cocoa

Cocoa is a commodity from Amazon River and originated from tropical forests in Central land South America (Wahyudi et.al, 2008). Cocoa was brought to Indonesia through North Sulawesi by Spanish in 1560s and it became an important commodity in Indonesia. Its growth is strongly influenced by climatic and soil factors which also has implication on its production. Rainfall, temperature and sunlight are parts of factors that determine climate whereas chemical and physical properties of the soil will affect root absorption of soil nutrients.

Generally, there are three types of cocoa that can be grown in tropical areas. They are Criolo, which is consisted of Criolo from Central and South

America,Forasterowhich is known as bulk or ordinary cocoa andtrinitariowhich

is derived from crossing species between Criolo and Forastero. According to

Supriatna (2004), the most developed cocoa is fine or flavor and bulk cocoa in Indonesia. Noble cocoa comes from Criolowith red fruit and bulk cocoa from

ForasteroandTrinitariowith green fruit.

5.2 Production of Indonesia Cocoa

Indonesia is the third biggest cocoa producer in the world. The total cocoa acreage in Indonesia reached 1,563,423 by 2008. This was dominated by smallholders (93.11 percent),and the number of farmers who are directly involved are as many as 1,526,271 households. Cocoa evenly spread in almost all the major islands in Indonesia. Cocoa spreads in Sulawesi, as much as 62.3 percent,

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Sumatera; 17.3 percent, Java; 5.6 percent, Nusa Tenggara and Bali; 4.1 percent, Borneo; 3.7 percent, Maluku and Papua for 7.0 percent (Ministry of Agriculture, 2009).

Centrals of Cocoa plantations largely are concentrated in four provinces namely South Sulawesi, Southeast Sulawesi, West Sulawesi, and Central Sulawesi. There are three types of ownership of these plantations; government, private and smallholders.

According to the Ministry of Agriculture (2009), South Sulawesi is a province with the largest cocoa grower areas in Indonesia, which covers 262,807 ha, followed by Central Sulawesi with of 221,667 ha, and then Southeast Sulawesi and West Sulawesi with 197,449 ha and 153,043 ha. Although South Sulawesi is the largest grower of cocoa in Indonesia, but in contrast, it has the lowest production.

5.3 Cocoa Development Policy in Indonesia

Cocoa in Indonesia began to be developed in 1980. This indicates that cocoa plans in Indonesia are old now; therefore they need rejuvenation, rehabilitation and intensification. Due to this condition, government began running a program related to cocoa production and quality to increase national income. The total budget spent to perform this program reached 13.7 trillion rupiah. The following is a recapitulation of the budget to run the program.

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Table 6. Budget recapitulation of cocoa program for three years (2009 - 2011)

No Source Cost (Milion)

1 Central Government (National Budget) 2.521.634,7

2 Province Government (Local Budget I) 257.594,5

3 Regency Government (Local Budget II) 786.482,2

4 Banking (plantation revitalization) 6.716.289,3

5 Private (quality standard socialization) 2.500

6 Farmer (labour) 3.464.989,8

Total 13.749.490,5

Source: Portal Nasional Republik Indonesia, 2009

According to the General Directorate of Plantations (2009), locations of te program cover nine provinces in 40 districts, they are:

a. West Sulawesi in five districts: Mamasa, Polewali Mandar, Majene, Mamuju and North Mamuju.

b. South Sulawesi in 10 districts: Bantaeng, Bone, Soppeng, Wajo, SindenrengRappang, Pinrang, Enrekang, Luwu, North Luwu.

c. Southeast Sulawesi in five districts:Konawe, Kolaka North, South and MunaKonawe.

d. Central Sulawesi in eight districts: Donggala, Moutong, Parigi, Poso, Morowali, Banggai, ToliToli, Buol and Tojo Una-Una.

e. East Nusa Tenggara in Sikka and Ende, Tabanan and Jembrana. f. Maluku in the District of West Seram and Buru.

g. West Papua in Manokwari and Sorong.

h. Yapen Islands of Papua in Sarmi, Keerom and Jayapura districts.

There are some activities which are conducted in this Production Improvement and Quality of National Cocoa Program. The main activities are

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rejuvenating 70,000 ha plantations, rehabilitating 235.000 ha plantations, intensification which covered 145,000 ha areas, farmer training for 450,000 people to realize quality improvement.

Supporting activities are training 360 people, constructing sub-station research, building four units of experimental garden and strengthening seven units of field laboratories, manufacturing cocoa cultivation technology database systems, rehabilitating 90 units of UPP, soil and leaf analysis for fertilizer recommendation, monitoring and evaluating are done by universities.

Cocoa production and quality improvement program involves various parties to exploit potential available resources. They include central government, provinces, foreign countries, private companies, banks and farmers with the duties and responsibilities as follows:

1. Central government: providing financing for planting materials, fertilizers, rejuvenation, rehabilitation and intensification, labor assistance for farmers, pest control tools and materials, professional assistants, farmer

empowerment, development of sub-station study, strengthening and developing labs and field application of quality or socialize the implementation of quality standards.

2. Provincial government: allocating budget to support program

implementation and cocoa certification and providing land for sub-station construction.

3. District Government: providing budget to support the program and selecting farmer candidate and land candidate.

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4. Banking: providing credit to finance revitalization of farm fertilizers, pesticides, agricultural tools and land certificate.

5. Private: providing financing for SNI Implementation. 6. Farmers: providing shade trees and labor.

The implementation of cocoa production and quality improvement program will provide these benefits:

1. Increasing cocoa productivity in program location. 2. Increasing cocoa production in program location. 3. Increasing farmer income in program location. 4. Increasing money supply in rural location.

5. Increasing foreign exchange earning in program location. 6. Increasing cocoa quality in accordance with SNI.

7. Fulfilling raw material needs of domestic industry.

In April 2010 Indonesian government started to impose tax policy for cocoa bean under decree No. 67/2010. The Finance Minister imposed a five percent tax on exported cocoa beans, and priced ranging from US$ 2000 - 2750 per ton. This tax rate is increased to 10 percent for beans sold for more than $ 2750.

This tax policy was aimed to push domestic cocoa downstream industry. Government considers that the cocoa tax policy will revive the cocoa industry. It was made to encourage more production of cocoa beans in Indonesia, to improve the benefit from marketing value-added product for the country. It would benefit not only the cocoa industry but also cocoa farmers, who currently have more options in selling their beans. Instead of being depended on exports; farmers have

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the option to sell their beans to domestic processors. Government can also use the funds gathered from tax to help cocoa farmers, particularly to improve their productivity as well as the quality of their products.

On the other hand, there are some organizations or people who do not agree with this policy. They believe the tax would lead to decreased competitiveness of the nation’s cocoa export, compared to the competitors such as

Ivory Coast and Ghana. They argue that domestic processors were able to get more than enough cocoa beans already. The domestic industry will not suffer from the lack of raw materials if an export is not imposed.

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VI. RESULT AND DISCUSSION

6.1 HS 1801 (Cocoa Beans, Whole or Broken, Raw or Roasted)

Overall there are seven variables used in the analysis of HS 1801 with the gravity model. Those are GDP of exporting country, GDP of importing country, population of exporting country, population of importing country, physical distance, exchange rate and export tax. The Analysis starts with pooled least square as the basic model and then is extended with fixed effect.

Analysis of code HS 1801 (cocoa beans, whole or broken, raw or roasted) with export tax is divided into two analyses. Firstly, export tax is treated as dummy variable and secondly, export tax is analyzed as percentage value. From these two analyses, it is intended to know the effect of the export tax to European Union as whole, before and after export tax policy and also the effect of export tax (in percentage value) to trade flows. The result of those econometrics models are shown in Table 7.

Table 5 shows that the coefficients and standard error for each model vary. All of models are jointly significant, as known from an F test of all of models which are less than 0.10.

For export tax as a dummy, we can see that R-Squared is 0.1444 or 14.44 percent. It means that 14.44 percent of export value is explained by the input variables’ variance (GDP of exporting country, GDP of importing country,

population of exporting country, population of importing country, exchange rate, physical distance and export tax).

P-value of each coefficient provides the likelihood that they are real results and did not occur by chance. The lower the P-value, the higher the

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likelihood that coefficient is valid. In this paper, we can justify one variable as a significant variable if p-value of each variable is under 0.10 (10 percent).

Table 7. Fixed Effect Regression of HS 1801

Description Export Tax as a Dummy Export Tax as percentage value

Ln gdpi 4.24738

(0.085)*

4.439333 (0.070)*

Ln gdpj 4.29403

(0.352)

4.340007 (0.349)

Ln population -43.4491

(0.023)*

-43.82033 (0.022)*

Ln population -0.5542183

(0.964)

-0.5566403 (0.963)

Ln Exchangerate -0.4414836

(0.055)*

-0.4486994 (0.051)*

Ln Physicaldistance Omitted Omitted

Exporttax 2.048882 (0.038)* 0.0955555 (0.041)* Constant 782.834 (0.033) 788.0725 (0.033)

Observations 109 109

R-Squared 0.1444 0.1291

F-test 0.0022 0.0023

*significant at 10 percent level

Physical distance is omitted by stata. The omission of variables can happen if there is collinearity between variables in the model. Under real condition, physical distance will not influence the cocoa trade flows so much. There is only one exporting country (Indonesia) and eleven importing countries. These eleven importing countries are closely located to each other.

We would conclude that one percentage change in GDP of exporting country results in around a 4.24738 percent change in export value. One percentage change in GDP of importing country results in around a 4.29403 percent change in export value. One percentage change in population of exporter country results in around 43.4491 percent change in export value (decreasing). One percentage change in population of importer country results in around

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0.5542183 percent change in export value (decreasing). One percentage change in exchange rate results in around 0.4414836 percent change in export value. One percentage change in export tax result in around 2.048882 percent change in export value.

Significance of F-value indicates the probability of the regression output being obtained by chance. A small significance of F confirms the validity of the regression output. Based on the output the significance of F is 0.00, there is only a 0 percent chance that the regression output was merely chance occurrence.

The model that treats export tax as a percentage value shows almost the same result with the previous model (model that treats export tax as a dummy variable). The difference between those models is in the coefficient and P-value of export tax.

Table 7 shows that the coefficients and standard error for each model vary. But all of models are jointly significant, as known from F test of all of models which are less than 0.10.

In the model that treats export tax as percentage value, R-squared is 0.1291 or 12.91 percent meaning 12.91 percent of export value is explained by the input variables’ variance (GDP of exporting country, GDP of importing country,

population of exporting country and population of importing country, exchange rate physical distance and export tax).

P-value of each coefficient provides the likelihood that they are real results and did not occur by chance. The lower the P-value, the higher the likelihood that coefficient is valid. In this paper, we can justify one variable as a significant variable if p-value of each variable is under 0.10 (10 percent). Like the

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previous model, there is one variable which is omitted by Stata (physical distance).

Based on the output with export tax as percentage value we would conclude that one percentage change in GDP of exporting country results in around a 4.439333 percent change in export value. One percentage change in GDP of importing country results in around a 4.340007 percent change in export value. One percentage change in population of exporter country results in around 43.82033 percent change in export value (decreasing). One percentage change in population of importer country results in around 0.5566403 percent change in export value (decreasing). One percentage change in exchange rate results in around 0.4486994 percent change in export value. One percentage change in export tax result in around 0.1979622 percent change in export value.

Significance of the F-value indicates the probability that the regression output could have been obtained by chance. A small significance of F confirms the validity of the Regression output. Based on the output the significance of F is 0.0023 there is only a 0 percent chance that the regression output was merely chance occurrence.

Like the previous model that treats export tax as dummy variable, based on the output result there are four significant variables. Those are GDP of exporting country and Population of exporting country, exchange rate and export tax. GDP of exporting country has positive correlation influencing export value. It means that if GDP of Indonesia is increasing, Export value of Indonesia cocoa (code 1804) will be increasing as well. Population of Indonesia has negative

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correlation influencing export value. It means that if population of Indonesia is increasing, Export value of Indonesia cocoa will be decreasing.

According to the gravity theory, the income of a country affects its trade in positive way. We can know the health of a country’s economy by using its Gross Domestic Product. GDP influence country’s ability towards trade flows.

From the output results, we can see that the coefficient sign is consistent with the theory. Indonesia income will influence cocoa trade flows to European Union positively.

Population of Indonesia has negative effect to cocoa trade flows to European Union. It can be interpreted that if Indonesian population higher, people have a less tendency to export because they need more products to be consumed. Cocoa bean consumption per capita tends to increase year to year in Indonesia. Previous data which was mentioned that it was 0.14 kg per capita per year is not relevant anymore. Now Indonesia cocoa bean consumption is 1 kg per capita per year1.

Based on the model exchange rate negatively influence trade flows between Indonesia and European Union countries. This is not consistent with theoretical expectation. Exchange rate should have positive effect to export value. We expect a positive sign for exchange rates because depreciation of home country relative to the foreign country currency will lead to more export and less export for the home country.

Actually this condition is consistent with previous research. Hariadi (2008) tried to find factors influencing non oil/gas export performance. He finds

Azwar AB, Director of Spice Plants and Plantation for Drinks, Ministry of Agriculture. http://www.bumn.go.id/ptpn12/publikasi/target-ekspor-kakao-gusur-ghana/

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that exchange rate of Rupiah (IDR) to Euro (€) has a negative coefficient. This result shows that exchange rate depreciation of rupiah the towards dollar will not influence export enhancement.

This is appropriate with the previous model. In the case of export tax, there is a different empirical result compared to the previous literature review in this analysis. Based on the analysis, export tax has positive correlation to trade flows of Indonesia cocoa to European Union and also has significant P-value. Normally, it is expected to be negative sign. When export tax increases, the trade flows of Indonesia cocoa to European Union will be decreasing.

The possible reason for this condition is that the share Indonesia cocoa to European Union is very small compared to European Union needs. As we know most of cocoa consumed by European Union is supplied by Ivory Coast and Ghana. Indonesia cocoa production tends to increase over time. In another side, European Union cocoa consumption is increasing as well. It means that Indonesia has big market potential in European Union. Therefore, Export tax will not really influence the trade flows between Indonesia and European Union.

This condition is also explained by Balasa (1976) in his paper, Export Incentives and Export Performance in Developing Countries: A comparative Analysis. He explained that exports from the developing countries are indicated by the fact that these countries account only for seven percent of the imports of goods in the developed nations were to increase at an average annual rate of 5 percent during a decade and the developing countries were to supply one-twentieth of this increment, they could increase their exports of manufactured goods to the developed nations.

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The other strong reason, why variable of export tax has significant positive coefficient to the trade flows is the decreasing production of Ghana and Ivory Coast cocoa. Cocoa production of Ghana and Ivory Coast (as Indonesia cocoa market competitors) was decreasing because of bad weather in 2010 and 2011. Therefore Indonesia cocoa export to European Union was increasing significantly in the year when export tax imposed (2010 - 2011). Because of this reason, the impact of export tax which was supposed to be negative is covered by the impact of decreasing of Ivory Coast and Ghana cocoa production. This condition proves that cocoa export (case HS 1804) is shock sensitive.

6.2 HS 1804 (Cocoa Butter, Fat, and Oil)

Overall, there are six variables used in analysis of HS 1804 with gravity model. Those are GDP of exporter country, GDP of importer country, population of exporter country and population of importer country, physical distance and exchange rate. We do not include export tax variable in the model since there is no export tax for code 1804 (cocoa butter, fat and oil). Analysis is started with pooled least square as the basic model and then extended with fixed effect. The result of those econometrics models are shown in Table 8.

Table 8 shows that the coefficients and standard error for each model are vary. But all of models are jointly significant. It is known from F test of all of models which are less than 0.10. R-Squared is 0.0013 or 0.13 percent. It means that only 0.13 percent of export value is explained by the input variables’ variance (GDP of exporter country, GDP of importer country, population of exporter country and population of importer country, physical distance and exchange rate).

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viii

World Bank. 2012. GDP per capita, PPP (current international $)

http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD/countries?displ

ay=default. Html [24 July 2012].

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F test that all u_i=0: F(10, 92) = 9.21 Prob > F = 0.0000 rho .63813417 (fraction of variance due to u_i)

sigma_e 1.9809429 sigma_u 2.6305961

_cons 782.8342 362.5901 2.16 0.033 62.69886 1502.969 exporttaxd~y 2.048882 .9707714 2.11 0.038 .1208462 3.976918 lphysicald~e (omitted)

lexchanger~e -.4414836 .2267774 -1.95 0.055 -.891883 .0089159 lpopulationj -.5542183 12.11934 -0.05 0.964 -24.62428 23.51584 lpopulationi -43.4491 18.7347 -2.32 0.023 -80.65783 -6.240382 lgdpj 4.294034 4.593279 0.93 0.352 -4.828614 13.41668 lgdpi 4.24738 2.440261 1.74 0.085 -.5991892 9.09395 lexportvalue Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.1984 Prob > F = 0.0022 F(6,92) = 3.75 overall = 0.1444 max = 16 between = 0.0135 avg = 9.9 R-sq: within = 0.1966 Obs per group: min = 2 Group variable: country Number of groups = 11 Fixed-effects (within) regression Number of obs = 109

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F test that all u_i=0: F(10, 92) = 9.26 Prob > F = 0.0000 rho .65002168 (fraction of variance due to u_i)

sigma_e 1.9826144 sigma_u 2.7019768

_cons 788.0725 363.6103 2.17 0.033 65.91102 1510.234 exporttax .1979622 .0955555 2.07 0.041 .0081808 .3877437 lphysicald~e (omitted)

lexchanger~e -.4486994 .2272991 -1.97 0.051 -.900135 .0027363 lpopulationj -.5566403 12.12969 -0.05 0.963 -24.64724 23.53396 lpopulationi -43.82033 18.81078 -2.33 0.022 -81.18016 -6.460505 lgdpj 4.340007 4.614615 0.94 0.349 -4.825016 13.50503 lgdpi 4.439333 2.417572 1.84 0.070 -.3621742 9.24084 lexportvalue Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.2234 Prob > F = 0.0023 F(6,92) = 3.72 overall = 0.1291 max = 16 between = 0.0041 avg = 9.9 R-sq: within = 0.1952 Obs per group: min = 2 Group variable: country Number of groups = 11 Fixed-effects (within) regression Number of obs = 109

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F test that all u_i=0: F(9, 93) = 16.79 Prob > F = 0.0000 rho .98983125 (fraction of variance due to u_i)

sigma_e 1.3433583 sigma_u 13.25375

_cons 297.5496 280.4809 1.06 0.292 -259.4299 854.5291 lphysicald~e (omitted)

lexchanger~e -.068118 .1590604 -0.43 0.669 -.3839805 .2477445 lpopulationj -13.88411 9.114166 -1.52 0.131 -31.98304 4.214819 lpopulationi -4.395567 13.92192 -0.32 0.753 -32.04173 23.2506 lgdpj 2.099067 3.48831 0.60 0.549 -4.828027 9.02616 lgdpi 2.624555 1.657752 1.58 0.117 -.6674114 5.916522 lexportvalue Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9828 Prob > F = 0.0603 F(5,93) = 2.20 overall = 0.0013 max = 16 between = 0.1846 avg = 10.8 R-sq: within = 0.1060 Obs per group: min = 2 Group variable: country Number of groups = 10 Fixed-effects (within) regression Number of obs = 108

Factors Influencing Indonesian Cocoa Export to the European Union

DECLARATION

ABSTRACT

ABSTRAK

SUMMARY

FACTORS INFLUENCING INDONESIAN COCOA EXPORT

TO THE EUROPEAN UNION

RATNA MEGA SARI

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

ACKNOWLEDGEMENT

AUTOBIOGRAPHY

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF APPENDICES

LIST OF ABBREVIATION

II.

LITERATURE REVIEW

IV. THEORITICAL FRAMEWORK

IV. RESEARCH METHOD

V. DESCRIPTION OF INDONESIA COCOA

VI. RESULT AND DISCUSSION

World Bank. 2012. GDP per capita, PPP (current international $)

http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD/countries?displ

ay=default. Html [24 July 2012].

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