Faktor-Faktor Yang Mempengaruhi Pengenaan Non-Tariff Measures Dan Dampaknya Terhadap Ekspor Minyak Sawit Indonesia Pada Kasus Sps, Tbt, Dan Quantitative Restriction Measures
DETERMINANTS INFLUENCING THE IMPOSITION OF NON-TARIFF
MEASURE AND ITS IMPACT ON INDONESIAN PALM OIL EXPORT:
THE CASE OF SPS, TBT AND QUANTITATIVE RESTRICTION MEASURES
YUDHA AJISURYA PRATAMA
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
STATUTORY DECLARATION
I, Yudha Ajisurya Pratama, hereby declare that the master thesis entitled
“Determinants Influencing the Imposition of Non-Tariff Measure and Its Impact on
Indonesian Palm Oil Export: The Case of SPS, TBT and Quantitative Restriction
Measures” is my original work under the supervision of Advisory Committee and
has not been submitted in any form and to another higher education institution. This
thesis is submitted independently without having used any other source or means
stated therein. Any source of information originated from published and
unpublished work already stated in the part of references of this thesis. Herewith I
passed the thesis copyright to Bogor Agricultural University.
Bogor, January 2016
Yudha Ajisurya Pratama
H151137304
RINGKASAN
YUDHA AJISURYA PRATAMA. Faktor-Faktor yang Mempengaruhi Pengenaan
Non-Tariff Measures dan Dampaknya Terhadap Ekspor Minyak Sawit Indonesia
Pada Kasus SPS, TBT, dan Quantitative Restriction Measures. Dibimbing oleh
BAMBANG JUANDA dan RICHARD POMFRET.
Sejak krisis finansial dunia, penggunaan non-tariff measures (NTMs) dalam
perdagangan internasional menjadi lebih sering dan luas digunakan, sementara itu
pengaruhnya terhadap perdagangan masih ambigu. Penelitian-penelitian
sebelumnya menunjukkan bahwa NTMs dapat meningkatkan atau membatasi
perdagangan. Terlebih lagi, pengaruhnya dapat berubah seiring waktu dan berbeda
antar partner dagang satu dan lainnya. Kondisi ini menyebabkan studi tentang
pengaruh NTMs terhadap komoditas spesifik sangat dibutuhkan untuk mengukur
pengaruh aktualnya dalam perdagangan. Mengingat peran penting yang dimiliki
oleh industri minyak sawit terhadap sektor ekonomi dan sosial di Indonesia, maka
pengaruh dari pengenaan NTMs yang menyimpan ancaman terhadap potensi
perkembangan eksport minyak sawit perlu diperhitungkan. Tujuan dari penelitian
ini adalah untuk mengukur pengaruh dari faktor-faktor terhadap pengenaan NTMs
dalam bentuk sanitary and phytosanitary (SPS), technical barrier to trade (TBT)
dan quantitative restriction measures, serta bagaimana pengaruhnya terhadap
perdagangan minyak sawit Indonesia.
Model regresi logistik digunakan untuk menganalisis faktor-faktor yang
diduga mempengaruhi pengenaan NTMs pada ekspor minyak sawit Indonesia
dengan menggunakan data dari 40 negara importir terbesar minyak sawit Indonesia.
Sedangkan model gravity digunakan untuk mengukur pengaruh NTMs terhadap
ekspor minyak sawit Indonesia pada 23 negara importer terbesar. Hasil dari logistic
regression model menunjukan bahwa volume impor minyak sawit pada tahun 2012
dan 2013 serta with GDP dan GDP per kapita negara pengimpor memiliki pengaruh
terhadap pengenaan NTMs. Hasil dari gravity model mengindikasikan bahwa SPS
dan quantitative restriction measures memiliki pengaruh menghambat eksport
kelapa sawit Indonesia, sementara TBT memiliki pengaruh meningkatkan ekspor.
Empat rekomendasi kebijakan yang diajukan kepada pemerintah untuk
memformulasikan kebijakan yang komprehensif agar dapat secara efektif
menangkap peluang dan menghadapi tantangan dalam pasar minyak sawit dunia
adalah dengan cara mengembangkan industri hilir produk minyak sawit, membuat
peraturan yang mengharuskan penggunaan sertifkasi industry minyak sawit yang
berkelanjutan, melakukan penindakan hukum yang lebih ketat, meningkatkan
kesadaran lingkungan dari masyarakat.
Kata kunci: non-tariff measures, palm oil, sanitary and phytosanitary, technical
barrier to trade, quantitative restriction.
SUMMARY
YUDHA AJISURYA PRATAMA. Determinants Influencing the Imposition of
Non-Tariff Measure and Its Impact on Indonesian Palm Oil Export: The Case of
SPS, TBT and Quantitative Restriction Measures. Under Supervision of
BAMBANG JUANDA and RICHARD POMFRET.
Since the global financial crisis, the use of non-tariff measures (NTMs) in
international trade policy has become more frequent and widely used while their
effect on trade is still ambiguous. Previous studies have shown that NTMs can either
promote or restrict trade. Furthermore, the impact may potentially change over time
or affect various trade partners differently. These conditions make a study of the
impact of NTMs on specific trade commodities crucial to measuring their actual
impact on trade. Given the significant role that the palm oil industry plays in
Indonesian social and economic sectors, the imposition of NTMs may pose a threat
to the industry’s growth potential. The objectives of this study are to observe the
influence of factors which determine the imposition of NTMs in the forms of
sanitary and phytosanitary (SPS), technical barrier to trade (TBT) and quantitative
restriction measures, and how these affect palm oil trade.
The logistic regression model was developed to analyse factors influencing
the imposition of NTMs on palm oil exports using a dataset of the 40 largest
Indonesian palm oil importing countries in 2013. While, the gravity model was
developed to measure the impact of NTMs on palm oil exports in 23 largest
importing countries. Results of the logistic regression model show that import
volumes in 2012 and 2013 along with GDP and GDP per capita are very likely
influence the imposition of NTMs in importing countries. Meanwhile, results of the
gravity panel model indicate that SPS and quantitative restriction measures have a
trade impeding effect on Indonesian palm oil exports, while TBT has a trade
promoting effect. Four policy recommendations are proposed to the policy makers
to formulate a comprehensive policies to effectively seize opportunities and address
challenges in international palm oil market namely promote downstream industries
of palm oil products, develop regulations to enforce the use of sustainable palm oil
certification, promote more stringent law enforcement, and increase community
environmental awareness.
Keywords: non-tariff measures, palm oil, sanitary and phytosanitary, technical
barrier to trade, quantitative restriction.
© Copyright belongs to IPB, 2016
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1
DETERMINANTS INFLUENCING THE IMPOSITION OF NON-TARIFF
MEASURE AND ITS IMPACT ON INDONESIAN PALM OIL EXPORT:
THE CASE OF SPS, TBT AND QUANTITATIVE RESTRICTION MEASURES
YUDHA AJISURYA PRATAMA
Master Thesis
As a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
2
External Advisory Committee Examiner: Prof Dr Ir Rina Oktaviani, MS
4
5
ACKNOWLEDGEMENT
First and foremost, my greatest gratitude goes to Allah SWT, the God
Almighty, for His countless blessings and mercies. I would also like to express my
immense gratitude to my supervisors, Professor Richard Pomfret and Professor
Bambang Juanda, for their idea, worthwhile suggestions, time and patience for
assisting me throughout the process of writing this dissertation. I would also like to
express my gratitude to all staff at the University of Adelaide, most notably, Athena
Kerley, Dr. Florian Ploeckl, Niranjala Seimon, and Augustine Bhaskarraj for their
assistance and support throughout my study at The University of Adelaide.
I would also like to acknowledge with thanks to the Ministry of Trade and the
Australian Awards Scholarship for providing the sponsorship to undertake my
master in Bogor and Adelaide. I also wish to thank the University of Adelaide and
Bogor Agricultural University for providing me the chances to pursue my master
degree.
I would also like to acknowledge A plus Editing for providing me editorial
assistance to improve the quality of my thesis. The editorial intervention was
restricted to Standards D and E of the Australian Standards for Editing Practice.
I want to thank all my friends, especially my colleagues from batch 1 and 2
of IPB Kemendag for their support, laugh, and help during this challenging stage
of my study.
Finally I would like to thank my parents, younger sister and brother for their
support and encouragement throughout the entire process of my study. This
dissertation is dedicated to them.
All errors are solely my own.
Bogor, January 2016
Yudha Ajisurya Pratama
6
TABLE OF CONTENTS
LIST OF TABLES
vi
LIST OF FIGURES
vi
LIST OF APPENDICES
vi
1 INTRODUCTION
1
2 BACKGROUND
World Palm Oil Market
Indonesian Palm Oil Industries
Non-Tariff Measures
2
2
4
5
3 LITERATURE REVIEW
NTMs, Trade Distortion and Asymmetric Information
Previous Studies on NTMs
6
6
7
4 METHODOLOGIES AND DATA
Logistic Regression Model
Gravity Model
8
9
10
5 RESULTS AND DISCUSSION
Determinants Influencing the Imposition of NTMs on Palm Oil
The Impact of NTMs on Export
12
12
13
6 CONCLUSION AND POLICY RECOMMENDATIONS
Conclusion
Policy Recommendations
Promote Downstream Industries for Palm Oil Products
Increase Community Environmental Awareness
Develop Regulations to Enforce the Use of Sustainable Palm Oil
Certification
Promote More Stringent Law Enforcement
17
17
18
18
18
REFERENCES
20
APPENDICES
23
BIOGRAPHY
26
18
19
7
LIST OF TABLES
1
2
The odds ratio of logistic regression model for determinants influencing
the incidences of NTMs on palm oil
Regression Results for the impact of NTMs on indonesian palm oil
exports
12
14
LIST OF FIGURES
1 World vegetable oils consumption
2 Price comparison of major vegetable oils
3 Indonesian palm oil exports
4 Classification of non-tariff measures
5 NTMs enforced on Indonesian palm oil
3
3
4
5
6
LIST OF APPENDICES
1
2
NTMs enforced on Indonesian palm oil in 2014
40 largest importer of Indonesian palm oil in 2013
23
25
1
1 INTRODUCTION
For more than a decade, globalisation has changed the way the world’s
economy develops. Globalisation is a process which make peoples and nations
become more interconnected in every way, including economically (Lodge 1995).
One of the effects of globalisation in the economic sector is the emergence of trade
liberalisation. The fundamental philosophy of trade liberalisation is based on the
argument that its implementation will benefit the countries involved and improve
their welfare. The action needed to achieve this vision is by diminishing the factors
which limit trade flow between trade partners. Several concrete efforts have been
made in the form of the establishment of The General Agreement on Tariffs and
Trade (GATT) and The World Trade Organisation (WTO) and the world has since
seen a decline in tariff and non-tariff barriers imposed on international trade.
However, since the global financial crisis of 2007-08 there has been renewed
growing concern regarding the implementation of non-tariff measures (NTMs) on
foreign trade. During this period, many countries experienced slow domestic
growth and real exchange rate appreciation which led to market power loss and
subsequently resulted in reduced competitiveness. Consequently, this also led to the
reintroduction of protectionism, notably the use of NTMs, in international trade
policies (Bussière and others 2011; Georgiadis and Gräb 2013). The use of NTMs
has long been a contentious topic among economists. Many argue that the
implementation of NTMs has trade-impeding effects due to the impact on trade
costs while others claim that NTMs could also increase trade among countries due
to improved mutual understanding of product compliance between trade partners.
As one of the main export commodities in the agricultural sector, palm oil has
made an undisputed contribution to Indonesia, not only in enhancing economic
growth by maintaining trade surplus and foreign exchange earnings, but also in
improving social conditions in the country. According to World Growth (2011), the
palm oil industry provides developing countries (DCs) an opportunity to escape
from poverty by providing a livelihood for small plantation holders and their
workers to improve living standards. This argument is strengthened by Susila
(2004) who found that palm oil based-activity alleviated poverty by generating 63
per cent of household income in oil palm plantation communities. However, like
other agricultural products, this export commodity has been heavily challenged by
the implementation of NTMs.
As compared to other vegetable oils, palm oil is considered the most efficient
in terms of yield, land utilisation, supply reliability and is more economical. Palm
oil is also used in producing cleaner alternatives to fossil fuel (Mba and others 2015;
Monteiro de Carvalho and others 2015; World Growth 2011). However, NTM
implementation on palm oil may have a trade hindering effect. Thus, two questions
are raised:
1. What are the determinants influencing the imposition of NTMs on palm oil?
2. What are the impacts of NTMs, either SPS, TBT or quantitative restriction,
on Indonesian palm oil exports?
This study employs two models to answer these questions. First, the logistic
regression model is used to determine the potential driver that triggers the
occurrence of NTMs on 40 largest palm oil importing countries from Indonesia.
2
Second, the gravity model is used to estimate the influence of determinants
predicted to have an impact on Indonesian palm oil exports in 30 largest palm oil
importing countries from Indonesia.
This study is structured into six sections beginning with the introduction. The
second section provides an overview of the world palm oil market, Indonesian palm
oil industries and NTMs. Section three reviews the literature on trade distortion and
asymmetric information, and examines previous empirical studies on NTMs. The
fourth section discusses the data and methodology. The following section presents
and discusses the results, and the final section concludes the study.
2 BACKGROUND
The first part of this chapter highlights the prospect of palm oil in the world market,
the following part discusses the conditions for Indonesian palm oil industries, and
the last part examines NTMs on palm oil.
World Palm Oil Market
Vegetable oils is a term used to define an oil extracted from plant-based
products such as soybeans, oil palm, rapeseed and sunflowers. Besides used as
cooking oil, vegetable oil is also found in many food and non-food products. Along
with Indonesia, which currently is the largest producer of palm oil, there are several
countries which could be considered as major producers of vegetable oils, namely
the United States, India and China which all produce soybean, rapeseed and
sunflower oil, and the European Union (EU) which produces rapeseed and
sunflower oil, and Malaysia which produces palm oil.
In 2014, the world’s total consumption of vegetable oils multiplied by 8.4
times as compared to 1980, consuming 151,618 million metric tons (GAPKI 2014).
Most of the demand increase in the 34-year period was met by the rapid growth of
world palm oil production. In recent years, there has been a shift in vegetable oil
consumption patterns. Since 2008, palm oil has surpassed the hegemony of soybean
oil as the most consumed vegetable oil. As shown in figure 1, on the next page, the
market share of palm oil in the world vegetable oil market consistently increased
from 14.8 per cent in 1965 to 41.1 per cent in 2014 (GAPKI 2014). There are several
possible reasons for the driving forces behind this phenomena. Firstly, World
Growth (2011) explained that the rise in palm oil demand has been caused by the
shift in the consumer preferences of developed countries from trans-fats to highly
saturated fats, which are contained in palm oil, as they are better for human health.
Secondly, the increase in biodiesel production as a cleaner alternative to fossil fuel
has also positively shifted palm oil demand as it is one of the main raw materials
(Lim and Teong 2010; Mukherjee and Sovacool 2014; Rusmana and Listiyorini
2014). Thirdly, as pointed out by Carter and others (2007), using palm oil is also
relatively more economical due to its lower price as it is less costly to produce
compared to other vegetable oils. Lastly, palm oil is widely known for its function
as a raw material for the edible products industry (such as butter, cooking oil and
ice cream) and non-edible products industry (such as medicine, cosmetics,
oleochemical and biodiesel). Accordingly, its usefulness for consumption and
3
industry have made the demand for palm oil rise due to the increase in world
population and industries that utilise palm oil (FAO 2013; Mba and others 2015;
Rifin 2013).
160
BILLION(S) METRIC TON
140
120
100
80
60
40
20
0
1965
1980
1990
2000
Soybean Oil
Palm Oil
Sunflower Oil
Total
2010
2014
Rapeseed Oil
Source: GAPKI (2014)
Figure 1: World vegetable oils consumption
One factor presumed to be the determinant influencing palm oil demand is
price. It is well known that palm oil is the most competitive among other vegetable
oils in terms of price. As depicted in Figure 2, below, even though the price of the
four major vegetable oils are relatively volatile, the price of palm oil is consistently
been the cheapest, even when the world faced the global food crisis in 2008 and
2011. At that time palm oil price reached its zenith in February 2011 at 1,248.55
USD per metric ton.
2500
2000
1500
1000
500
Palm Oil
Soybean Oil
Rapeseed Oil
Sunflower Oil
Source: Index Mundi (2015)
Figure 2: Price comparison of major vegetable oils
Dec-14
Apr-12
Aug-13
Dec-10
Aug-09
Apr-08
Dec-06
Apr-04
Aug-05
Dec-02
Aug-01
Apr-00
Dec-98
Apr-96
Aug-97
Dec-94
Aug-93
Apr-92
Dec-90
Apr-88
Aug-89
Dec-86
Aug-85
0
4
Indonesian Palm Oil Industries
Since 1990 there has been a change in oil palm plantation cultivation patterns
in Indonesia. This transformation has been characterised by the growth of oil palm
small plantation holders. On average, the total growth of oil palm plantations, which
have spread across 22 of 33 provinces in Indonesia, is relatively high at 24 per cent
per year over a 13-year period. About 90 per cent of oil palm plantations are located
on the islands of Sumatra and Borneo, which account for 95 per cent of Indonesian
palm oil production. (GAPKI 2014). The interesting fact about palm oil plantations
is that state-owned enterprises and large enterprises coexist with smallholders
which is rarely observed in other agricultural commodity industries in Indonesia.
The palm oil industry produces a wide range of products for food, energy and
raw materials for other industries, which are demanded both domestically and
internationally. According to GAPKI (2014), in 2014 70 per cent of Indonesian
palm oil production was exported, while just 30 percent was consumed
domestically. This huge export volume has been consistently increasing over time.
As shown in Figure 3, below, despite a slight decrease in 2007 and 2010, the volume
of Indonesian palm oil exports increased each year from 2004-2014. In 2008 and
2011 the value of palm oil exports rose sharply compared to export volume increase
due to strong prices triggered by the global food crisis. Despite the increase in price,
which reached its zenith in 2011, palm oil exports are still rising due to strong
demand in the international market, especially from the development of
oleochemical and biodiesel industries.
25
20
18
16
14
15
12
10
10
8
6
5
Value of Export
Volume of Export
20
4
2
0
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Volume (Million(s) Metric ton)
Value (Billion(s) US$)
Source: WITS (2015)
Figure 3: Indonesian palm oil exports
Year
5
Non-Tariff Measures
NTMs are a form of policy that may create a distortion effect on foreign trade.
UNCTAD (2013) describes NTMs as “all policy-related trade costs incurred from
production to final consumer, with the exclusion of tariffs”. This added cost would
create a gap between the price in the domestic market and in importing countries.
Besides distorting price, NTMs could also limit trading quantity and in some cases
even have a prohibitive effect. Being one of the instruments which has an impact
on trade cost, appropriate NTM implementation might be vital not only to protect
domestic consumers and industries, but also to reduce disputes with trading
partners.
As shown in Figure 4, below, UNCTAD (2013) classifies NTMs into 16 types
which are divided into two main categories, import and export measures. The
import measures category is divided into two sub-categories, technical and nontechnical measures. Technical measures, such as technical barriers to trade (TBT)
and sanitary and phytosanitary (SPS) measures, are imposed to protect domestic
consumers by applying technical requirements related to standards and the quality
of products in terms of technical specifications, contents limitations or restrictions,
process of production and conformity assessment. Non-technical measures, such as
quantitative restrictions, countervailing measures and anti-dumping measures, are
NTMs usually imposed to protect domestic industries by either limiting imported
goods directly through quotas or indirectly through price control measures.
Source: UNCTAD (2013)
Figure 4: Classification of non-tariff measures
Based on the Integrated Trade Intelligence Portal (I-TIP) database, since 1990
Indonesian palm oil exports have been challenged by the enforcement of NTMs
from The Philippines in the form of quantitative restrictions. Since then, the number
of NTMs imposed increased and reached 71 in total from 20 countries in 2014.
Figure 5 shows that in 2009 and 2013 the number of NTMs and countries imposing
NTMs increased sharply, presumably due to protectionist policies introduced to
6
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
NUMBER OF COUNTRIES
NUMBER OF NTMS
correct market failure caused by financial crises in 2007-2008 and 2011. Primarily,
NTMs imposed on palm oil in 2014 are TBT and quantitative restrictions which
account for 38 and 35 per cent of NTMs imposed on palm oil, respectively, while
SPS measures account for only 13 per cent.
0
YEARS
NTMs
Countries
Source: I-TIP (2015)
Figure 5: NTMs enforced on Indonesian palm oil
3 LITERATURE REVIEW
NTMs is a determinant that might play a significant role in international palm
oil trade. Accordingly, understanding its impact on trade performance is
particularly important to study how NTMs might affect the Indonesian economy.
This chapter discusses some theoretical foundations and previous empirical studies
of the impact of NTMs on trade.
NTMs, Trade Distortion and Asymmetric Information
The enforcement of NTM policy, regardless of underlying intent, triggers
inefficiency in foreign trade because additional costs of compliance and conformity
assessment are incurred by producers in exporting countries in order to obtain
market access to the export destination enforcing the regulations. Maskus and
others (2013) examined the impact of enforced product standards on the cost of
production in developing country firms and found that on average the production
fixed cost rises by 4.7 per cent due to meeting export destination compliance. In
addition, these authors found that variable costs also increase, ranging from 0.06 to
0.13 per cent. Additional costs may distort trade directly in terms of price, and may
7
also either limit or restrict trade, and consequently might reduce welfare compared
to having a free market.
Despite the additional cost incurred by the use of NTMs, several type of
NTMs, particularly SPS and TBT, are beneficial to reduce asymmetric information
on international trade. The term asymmetric information refers to the situation
where agents do not have the same level of information (WTO 2012). The
enforcement of labeling or conformity assessment on food safety standards is used
to reduce customer uncertainty regarding product quality and contents. Thus,
reducing asymmetric information on imported goods (Farida 2015).
From the discussion above, some conclusions can be drawn. First, in
importing countries prices of commodities on which NTMs are imposed tend to be
higher due to reduced competition from imported goods. This benefits producers
while harming consumers. Second, from a consumer protection view, consumers in
the importing country benefit because they are protected from low-quality or
contaminated goods. Third, for producers in exporting countries NTMs have
ambiguous effects. On one hand they face rising production costs leading to reduced
product competitiveness that might lead to less demand. On the other hand meeting
enforced compliance and conformity assessment of the export destination could
reduce asymmetric information by signaling about the quality of their products
which might lead to increased demand due to increased consumer confidence to
buy the products.
Previous Studies on NTMs
Many previous studies examining the impact of NTMs on trade performance
provide evidence that NTMs could have diverse results on cross border trade.
Mainly, those studies use a gravity model to determine the NTM impact. In this
study, several relevant studies are discussed to gain deeper understanding of how
NTMs could affect international trade.
The majority of studies on NTMs suggest that the imposition of NTMs results
in decreased export performance. Wall (1999) analysed the impact of import
protection on US trade and welfare using panel data analysis to measure the impact
of exporting country GDP, importing country GDP, and Trade Policy Index
variables on trade flows. Results showed that import protection imposition in the
US cause a 26.2 per cent decrease on US exports, while US import tariffs lower US
imports from non NAFTA countries by 15.4 per cent per year.
In reality, irrespective whether the intention of NTM imposition is
protectionist or not, evidence suggests that NTMs could trigger greater distortion
than tariffs. As observed by Chen and others (2008) export performance of Chinese
agricultural commodities and aquatic products are facing greater challenge from
food and safety standards than import tariffs. More stringent food standard applied
by the US, EU and New Zealand, compared to CODEX international standards,
would reduce China’s export to those countries by 21, 23, and 23 per cent,
respectively. While if equivalent import tariff was applied, it would only impact
export performance by 15 per cent to EU and 11 per cent to Japan.
In terms of impact magnitude, the imposition of NTMs might affect certain
countries or groups of countries differently compared to others. Disdier and others
(2008) examined the impact of SPS and TBT measures on agricultural commodities
8
export in G-90 countries. Using gravity model, they found that these policy reduce
export of developing countries and LDCs only, while export from OECD countries
are not significantly affected. This might happen because developed countries, like
OECD, have the technology and similar awareness towards environment and food
safety standards to comply with import country requirements.
Although most of previous literature found that imposition of NTMs have
unwanted consequences on trade performance, a considerable amount of study has
also shown that it may not always be the case. Farida (2015) explored the impact of
NTMs on Indonesian coffee trade. Using gravity panel with fixed effect and OLS
estimation, this author found that NTMs consistently have positive and statistically
significant effect on Indonesian coffee export, whether used as a single dummy
variable or when disaggregated into SPS and TBT variables. Moreover, when fixed
effect estimation is used, NTM variables still show positive signs. While, when
disaggregated into SPS and TBT, only TBT that consistently shows positive impact.
Fassarella and others (2011) examined the impact of SPS and TBT on Brazil’s
poultry meat export from 1996 to 2009 shows similar findings. Using gravity model
with fixed effect estimation, these authors show that TBT measure can stimulate
export, while SPS measure may have the opposite effect. These findings reveal that
besides impeding trade, NTMs can also act as trade catalyst, especially when
policies are intended to reduce asymmetric information between producer and
consumer regarding product quality, such as the use of labelling.
Another compelling finding on the impact of NTMs is that in several cases
the effect is not permanent, but changes after some period of time. This was
demonstrated by Peterson and others (2013) regarding the impacts of US
phytosanitary regulation for several fruit and vegetable commodities between 1996
and 2008. Using gravity equation on exports from 89 countries, they found that
initially SPS impacted negatively on US imports. However, the effect was not
permanent and altered after some time. The alteration was due to experience and
understanding gained by exporting countries about the standard required to enter
the US market. This understanding results in compliance with the requirement and
improved product quality. This empirical evidence is strengthened by Song and
Chen (2010) who obtained similar results when estimating the impact of food safety
standards on Chinese exports. They found that the short and long effects of the
regulations are different. While the short run had a statistically significant effect on
China’s Chinese export, the long run effect shows otherwise.
From the examples above, we can see that the impact of NTMs are diverse in
terms of direction and magnitude. Moreover, in some cases the effect can be time
bound and may also affect one country to another differently. Accordingly, we need
a deeper understanding when considering different characteristics and polices to
analyse the impact of NTMs in trade in order to legislate suitable policies to
overcome challenges that arise.
4 METHODOLOGIES AND DATA
This study aims to identify the incidences and effects of NTMs in the forms of SPS,
TBT and quantitative restriction on Indonesian palm oil exports. Several methods,
either generic or specific to NTMs, can be used for quantifying and estimating
9
NTM’s impact on trade. The logistic regression model is used to identify
determinants which cause the imposition of NTMs, while the gravity panel data
model is applied to estimate their impact on trade across borders by utilising dummy
variables to capture the incidence of NTMs on a country’s trade policies.
Logistic Regression Model
The logistic regression model, or the logit model, is a model developed to
measure the relationship between binary regressand and one or more regressors to
explain probabilities of the regressand (Hosmer and others 2013). This model
assumes that the response or dependent variables are categorical and coded as either
0 or 1.
As explained by Juanda (2009), logistic regression model is derived from
cumulative logistic probabilities function and has a basic form as follows:
1
1
=
=
=
+
=
−�
−
1+�
1 + � + ��
While the logit form of the model are shown below:
=�
=
1−
+
Where, e represents the base number of the natural logarithm (e=2.71828...),
represents odds ratio which is the probability ratio on the occurrence of
and
��
−��
one incident compared to the alternative incident.
Depending on the number of independent variables and observations, the
logistic regression model can be developed into a multiple logistic regression model
(Hosmer and others 2013) with k numbers for explanatory variables, and i numbers
for observations. Juanda (2009) shows that the multiple logistic regression model
takes the following form:
=
� =
1 + �−
+
While the logit form of the model are shown below:
� � =�
1−
�
�
=
+
1
� � +⋯+ � ���
� +⋯+
�
Where in this study � denotes the presence of NTM imposition on palm oil,
is the intercept,
are the estimated coefficients and � to � are variables
which are expected to have influence on the imposition of NTMs on palm oil.
Two multiple logistic regressions are developed in this study to measure the
influence of determinants that could trigger the incidences of NTMs affecting
Indonesian palm oil exports.
Model 1:
� �
�
−
� �
�
�
=
+
�
�
�
+
+
+
10
Model 2:
� �
�
−
� �
�
�
=
+
�
�
�
+
+
+
Where ‘IMPVOL’ indicate the total value of imported palm oil in the years
2013 measured in million metric ton, ‘GDP’ is the size of the economy which
represents the capacity to import palm oil products measured in billion US dollars.
‘GDPCAP’ is the income level of each country weighted by their population which
represents the purchasing power of the individual for consumption measured in
hundred US dollars. ‘POP’ indicates the country population which represents the
size of the country’s palm oil absorption capacity measured in million people. The
dependent variable is the incidences of NTMs in the year 2013, which takes the
value of 1 if at least one type of NTM is enforced by the country, otherwise it is 0
if no NTMs were imposed.
This model uses secondary data on 40 top palm oil importer countries
collected only for the years 2013 based on data availability. This study uses HS
15111 (palm oil and its fractions) as the analysed commodity.
Import price is calculated based on the data for palm oil imports from the
Ministry of Trade of Indonesia. Data for GDP, GDP per capita and population are
extracted from the World Development Indicators (WDI). The data for NTM
incidences are taken from the WTO.
Gravity Model
This study uses the gravity model to measure the impact of NTMs on
Indonesian palm oil exports. The gravity model was inspired by the Newtonian law
of gravity which states that gravitational force is proportionally related to the
objects masses and inversely related to the square distance between them. Gravity
models were first developed by Tinbergen (1963) to explain a positive correlation
between the volume of trade to GDP and a negative correlation to distance. Since
then, the gravity model has been widely used as an international trade model.
Gravity models represent trading volume as a function of GDP and distance
between two trading partners.
In its development, the gravity model has also been used to measure the
impact of trade policies on trade across country performances by introducing trade
policy determinants. To represent the incidences of NTMs, dummy variables are
introduced into the models. The dummy variable, or binary variable, is a method
frequently used to quantify implementation of NTMs on trade performance.
Dummy variables use a binary value, either 0 or 1, to represent the incidence of
NTMs on country’s trading policies. If there is at least one incidence of NTMs, the
dummy variables take the value of 1, otherwise the value is 0. The first step when
using a dummy variables approach in this study is by making an inventory of the
NTMs on Indonesian palm oil imposed by each countries across time. In this study,
dummy variables are divided into four categories, SPS, TBT, quantitative
restriction and NTMs themselves. After the inventory of NTMs have been
collected, this data will then be applied as dummy variables in the gravity panel
model.
11
�
�
Dummy NTM Model:
= + �
+
�
�
+
�
�
�
+
+
Disaggregated Dummy NTM Model:
= + �
+ �
�
+
�
�
+
�
+
�
�
+�
�
+
+
�
+
+
�
+
+
+
Where , , represent Indonesia as the exporting country of palm oil,
are the estimated
importing countries and the year of trade, respectively.
variables’ coefficients and � is the error term.
denotes the export volume of Indonesian palm oil to importing country
in year , measured in kilograms. The number of observed countries was 23, based
on the top 30 of the largest total export volume of palm exports from Indonesia in
2014 which have complete data (i.e., India, China, Pakistan, Italy, Netherlands,
Bangladesh, Egypt, Spain, Singapore, Malaysia, Russian Federation, USA,
Ukraine, South Africa, Benin, Philippines, Brazil, Turkey, Tanzania, Germany,
Ghana, Angola and Mauritania) in a 14 year period of observation from 2001 to
2014. This responding variable data was taken from the Ministry of Trade of the
Republic of Indonesia which stems from the Indonesian Central Bureau of Statistics
(BPS) database focusing on palm oil (HS code 1511) as the subject of study.
denotes importing countries’ GDP used as a proxy for palm oil
absorption capacity in importing countries, which measured in US dollars. This
variable captures economic size, demand for palm oil and purchasing power.
Statistics for GDP of each importing country were obtained from the World
Development Indicators (WDI) database.
�
denotes the GDP per capita of importing country in real terms
which indicates palm oil absorption capacity from household consumption as it is
used as the proxy for standard of living or income level. Statistics for GDP per
capita were extracted from WDI.
denotes geographical distance between Indonesia and importing
countries measured in kilometres. This independent variable was obtained from the
CEPII database and used as a resistance variable which acts as a proxy for
transportation cost.
denotes palm oil production in Indonesia measured in kilograms.
This variable was used to observe the potential export capacity. Data for palm oil
production was obtained from the Ministry of Agriculture of the Republic of
Indonesia.
�
denotes the international prices of palm oil measured in USD per
metric ton. Statistics for the palm oil price were taken from the Index Mundi.
denotes the real exchange rate between exporting and importing
countries. Statistics for this variable were taken from the UNCTAD database.
,
,
and
are the dummy variables which
denote the binary variables to capture the incidence of either NTMs as a whole or
when disaggregated into SPS, TBT and QR measures imposed by palm oil
importing countries. This study developed two models to evaluate the impact of
NTMs on Indonesian palm oil exports. The first model uses NTMs as a single
12
dummy variable, which takes on the value 1 if there is at least one NTM imposed
on palm oil, either SPS, TBT or quantitative restriction, and zero otherwise. The
second model uses NTM dummy variables which were disaggregated into three
dummy variables, i.e. SPS, TBT and QR, which take the value of 1 if the importing
country enforced at least one of the disaggregated NTMs and zero otherwise. These
measures were obtained from the WTO’s I-TIP database focusing on palm oil (HS
code 1511).
As a benchmark, alternative models were also developed through the
introduction of time dummies on both models. To avoid colinearity, the variables
such as palm oil price and production which are invariant across countries at a
particular time are omitted from these models. The introduction of time dummies
are intended to capture various effects that may have an influence in a given year.
These variables take the value of 1 for a particular year and zero otherwise.
This study used panel data consisting of 23 countries and 14 time periods,
with a total of 322 observations. In this study, estimation techniques were selected
using three tests in the forms of the F Test, Hausman Test and LM Test to decide
the preferred model with the most efficient and consistent estimation between the
pooled least squares, fixed effect model or random effect model.
5 RESULTS AND DISCUSSION
Determinants Influencing the Imposition of NTMs on Palm Oil
The results of the logistic regression model are shown in Table 1, below.
Three models are developed to measure the incidences of NTMs on Indonesian
palm oil exports in the year 2013. The time selection used for this model is due to
the significant increases in NTM incidences in the selected years, having almost
doubled from 34 incidences in the previous year to 66 incidences, while the
selection of determinants is based on Farida (2015) model which measures the
incidence of SPS and TBT measures on Indonesian coffee exports with some
modifications.
Table 1: The odds ratio of logistic regression model for determinants influencing
the incidences of NTMs on palm oil
Model 1
Import Volume
1.021447**
Importing Countries GDP
1.001263 ***
Importing Countries GDP per capita
Model 2
1.0012370**
1.005841***
Importing Countries population
0.987276 ***
0.999427***
Import Price
0.998407
0. 99825
*, **, and *** indicate significance at the ten, five and one per cent level, respectively.
Source: Author Calculations
From the results shown in Table 1, for all two models, the volume of imports
always has statistically significant coefficients at the five percent level. This
indicates that the imported volume of a country is related to the imposition of NTMs
policy to reduce imported goods. These 2 models predicts that if the import volume
13
of palm oil are increased by 1 million metric ton, the probability of NTMs
imposition will multiplied by 1.021447 and 1.001237 respectively. This happened
presumably due to two factors. First, as discussed in the previous chapter,
protectionist policies are used to protect domestic industries. This can be seen from
the I-TIP WTO database where the US and EU countries impose six and four types
of NTMs on palm oil imports, respectively. As is widely known, both the US and
EU produce competing products for palm oil in terms of soybean and rapeseed oil.
Secondly, concern raised by some environmental organisations has impacted on the
imposition of NTMs because a higher export value means higher production which
comes from increased rainforest deforestation for the expansion of palm oil
plantations.
GDP and GDP per capita show statistically significant coefficient estimations
at the five per cent level. Both variables are used to represent a country’s capacity
to absorb palm oil imports. While GDP represents absorption capacity of palm oil
as raw material for industrial activities, GDP per capita represents household
consumption of products derived from palm oil. The first model predicts that an
increase of a billion USD on importing countrie’s GDP will multiply the probability
of NTMs imposition by 1.001263 times, while the second model shows that an
increase of 100 USD on GDP per capita will multiply the probability of NTMs
imposition of palm oil by 1.005841.
The population determinant also shows a statistically significant coefficient.
As GDP per capita, population is also used to represent household absorption in
terms of consuming imported goods. However, the effect of population shows the
value of is less than 1 in the odds ratio for both models, this results indicate that the
increase in population might reduce the use of non-tariff measures due to the
increase in demand for palm oil in household consumption. These 2 models predicts
that if the population of the importing countrie’s are increases by 1 million people,
the probability of NTMs imposition will decrease by 0.987276 and 0.999427 times
respectively.
Meanwhile, the import price shows statistically insignificant estimation
results. This suggests that price is not associated with the imposition of NTMs on
palm oil. As highlighted earlier, even on reaching its highest price when the global
food crisis occurred, palm oil was still the cheapest vegetable oil compared to all of
its competitors. Thus, even when price rises competitiveness is still high, which
might explain why these two determinants are not related. Moreover, unlike other
commodities which have diverse qualities or grade (i.e. coffee and tea) palm oil is
rather homogenous which means its price is similar for all producers.
The Impact of NTMs on Export
This section presents results of the gravity model which estimates the impact
of NTMs on Indonesian palm oil exports. The results of the Hausman test and the
LM test suggest that the random effect model is preferred over the fixed effect
model and the pooled least squares. The results of the random effect model are
shown in Table 2, including the four models developed and estimated using the
STATA 13 application.
14
Table 2: Regression Results for the impact of NTMs on indonesian palm oil exports
Variables
Ln GDP
Ln GDP per
Capita
Ln Distance
Ln Production
Ln Price
Ln Real
Exchange
Rate
NTM
Dummy NTM Model
Without Time
With Time Dummies
Dummies
(1)
(2)
Disaggregated NTM Dummy Model
Without Time
With Time Dummies
Dummies
(3)
(4)
0.454387***
(0.1462118)
-0.1013467
(0.2555209)
-1.059489***
(0.3856029)
1.866794***
(0.3037308)
-0.5253073**
(0.2394016)
0.5006452***
(0.1355819)
-0.2756253
(0.2357179)
-1.173084***
(0.3372628)
1.970998***
(0.3028101)
-.5064464**
(0.2374695)
0.2028702
(0.1664388)
0.1405917
(0.1827732)
0.0430887
(0.1967608)
0.418084***
(0.1474821)
-0.13937
(0.2599049)
-1.05981
(0.3857702)
0.1763455
(0.1870353)
-0.6544767**
(.3139316)
0.3527208
(0.2318689)
-1.893878**
(9032158)
TBT
QUOTA
d2003
d2004
d2005
d2006
d2007
d2008
d2009
d2010
d2011
d2012
d2013
d2014
Constant
N
R2
-11.62824**
(5.618303)
332
0.4449
.2418322
(0.169754)
.0398457
(0.204631)
SPS
d2002
0.4689743***
(0.1367743)
-0.3200074
(0.2388214)
-1.17561***
(0.3379289)
0.3065448
(0.2590345)
0.1587109
(0.2686122)
0.6034176***
(0.279317)
0.9616572***
(0.2891091)
1.249985***
(0.3181759)
1.152765***
(0.3384132)
0.974287***
(0.3519337)
1.300777***
(0.3539882)
1.223884***
(0.3886389)
1.413193***
(0.4112395)
1.64367***
(0.410628)
1.919331***
(0.4094268)
2.148728***
(0.4034225)
16.0204
(4.541247)
332
0.4550
-12.50731***
(5.116731)
332
0.5127
-0.8120586**
(0.3316047)
0.4327762*
(0.2435475)
-1.874307**
(0.9048716)
0.3250217
(0.257535)
0.1976005
(0.2660161)
0.6350778***
(0.2749074)
1.006403***
(0.2837308)
1.323187***
(0.3095838)
1.244494***
(0.3280189)
1.077773***
(0.3404965)
1.309513***
(0.3418658)
1.237572***
(0.3731772)
1.583399***
(0.3095838)
1.813431***
(0.3998575)
2.089088***
(0.3989302)
2.313276***
(0.3936439)
16.83783***
(4.039343)
332
0.5179
*, **, and *** indicate significance at the ten, five and one per cent level, respectively. Standard
errors are in parentheses.
Source: Author Calculations
The estimation results of these 4 models have diverse results in the magnitude
of the coefficients. However, there are some consistent patterns on the sign of the
impact for all determinants between these four models. As shown in Table 2, GDP
estimated coefficients have robust results in terms of signs and statistical
significance. In all four models, the coefficients of GDP have positive signs and are
15
statistically significant at the one per cent level. These results imply that the increase
in importing countries GDP as a proxy for economic size would also raise
Indonesian palm oil exports.
The distance variable has consistent estimation signs, but opposite to GDP.
This result is consistent with gravity model theory that describes distance as a trade
impeding attribute in international trade. However, only three of four models where
distance is estimated are coefficients statistically significant at one per cent level.
Only in model 2 where NTM and time dummies are used does distance have
statistically insignificant coefficient. These results suggest that distance has a
restrictive effect on palm oil trade due to the need for higher transportation costs
when trading with more distant countries, thus resulting in lower export volume.
Due to its nature, which takes the same value for all of the countries in each
specific year, Indonesian palm oil production is only estimated in the model without
time dummies (columns 1 and 3). In both models, the production coefficients show
consistent positive signs and are statistically significant at the one per cent level,
suggesting that higher production would draw higher export quantity.
Similarly, price is only estimated in models 1 and 3 where time dummies were
not included. For both models, price has a negative sign and statistically significant
coefficients at the five per cent level. This is in line with the law of demand where
price and demand quantity have an inverse relationship.
Unexpected results are found with GDP per capita which has negative
coefficients sign but is statistically insignificant in all four models. These results
are in contrary to the initial hypothesis which predicts that it has positive correlation
with the quantity of palm oil imported to the country. This is possibly related to the
environmental Kuznets curve (EKC) which shows the relationship between
environmental degradation and income per capita (Tietenberg and Lewis 2011).
The EKC explains that beyond a certain level of income per capita people become
more aware and concerned about the environment. Thus, as palm oil industries are
often linked with deforestation, welfare growth may lead people to reduce the use
of palm oil. Moreover, most high income countries who imported palm oil also have
a viable domestic product substitute, such as rapeseed and sunflower oil in the EU,
and soybean, rapeseed and sunflower oil in US. These two conditions are presumed
to be the reasons for this phenomena.
The objective of this study is to measure the impact of NTMs in the form of
SPS, TBT and quantitative restriction. Four models have been developed to achieve
this objective by utilising dummy variables to capture the non-tariff measures. The
first and second models used only one NTM dummy which represent
MEASURE AND ITS IMPACT ON INDONESIAN PALM OIL EXPORT:
THE CASE OF SPS, TBT AND QUANTITATIVE RESTRICTION MEASURES
YUDHA AJISURYA PRATAMA
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
STATUTORY DECLARATION
I, Yudha Ajisurya Pratama, hereby declare that the master thesis entitled
“Determinants Influencing the Imposition of Non-Tariff Measure and Its Impact on
Indonesian Palm Oil Export: The Case of SPS, TBT and Quantitative Restriction
Measures” is my original work under the supervision of Advisory Committee and
has not been submitted in any form and to another higher education institution. This
thesis is submitted independently without having used any other source or means
stated therein. Any source of information originated from published and
unpublished work already stated in the part of references of this thesis. Herewith I
passed the thesis copyright to Bogor Agricultural University.
Bogor, January 2016
Yudha Ajisurya Pratama
H151137304
RINGKASAN
YUDHA AJISURYA PRATAMA. Faktor-Faktor yang Mempengaruhi Pengenaan
Non-Tariff Measures dan Dampaknya Terhadap Ekspor Minyak Sawit Indonesia
Pada Kasus SPS, TBT, dan Quantitative Restriction Measures. Dibimbing oleh
BAMBANG JUANDA dan RICHARD POMFRET.
Sejak krisis finansial dunia, penggunaan non-tariff measures (NTMs) dalam
perdagangan internasional menjadi lebih sering dan luas digunakan, sementara itu
pengaruhnya terhadap perdagangan masih ambigu. Penelitian-penelitian
sebelumnya menunjukkan bahwa NTMs dapat meningkatkan atau membatasi
perdagangan. Terlebih lagi, pengaruhnya dapat berubah seiring waktu dan berbeda
antar partner dagang satu dan lainnya. Kondisi ini menyebabkan studi tentang
pengaruh NTMs terhadap komoditas spesifik sangat dibutuhkan untuk mengukur
pengaruh aktualnya dalam perdagangan. Mengingat peran penting yang dimiliki
oleh industri minyak sawit terhadap sektor ekonomi dan sosial di Indonesia, maka
pengaruh dari pengenaan NTMs yang menyimpan ancaman terhadap potensi
perkembangan eksport minyak sawit perlu diperhitungkan. Tujuan dari penelitian
ini adalah untuk mengukur pengaruh dari faktor-faktor terhadap pengenaan NTMs
dalam bentuk sanitary and phytosanitary (SPS), technical barrier to trade (TBT)
dan quantitative restriction measures, serta bagaimana pengaruhnya terhadap
perdagangan minyak sawit Indonesia.
Model regresi logistik digunakan untuk menganalisis faktor-faktor yang
diduga mempengaruhi pengenaan NTMs pada ekspor minyak sawit Indonesia
dengan menggunakan data dari 40 negara importir terbesar minyak sawit Indonesia.
Sedangkan model gravity digunakan untuk mengukur pengaruh NTMs terhadap
ekspor minyak sawit Indonesia pada 23 negara importer terbesar. Hasil dari logistic
regression model menunjukan bahwa volume impor minyak sawit pada tahun 2012
dan 2013 serta with GDP dan GDP per kapita negara pengimpor memiliki pengaruh
terhadap pengenaan NTMs. Hasil dari gravity model mengindikasikan bahwa SPS
dan quantitative restriction measures memiliki pengaruh menghambat eksport
kelapa sawit Indonesia, sementara TBT memiliki pengaruh meningkatkan ekspor.
Empat rekomendasi kebijakan yang diajukan kepada pemerintah untuk
memformulasikan kebijakan yang komprehensif agar dapat secara efektif
menangkap peluang dan menghadapi tantangan dalam pasar minyak sawit dunia
adalah dengan cara mengembangkan industri hilir produk minyak sawit, membuat
peraturan yang mengharuskan penggunaan sertifkasi industry minyak sawit yang
berkelanjutan, melakukan penindakan hukum yang lebih ketat, meningkatkan
kesadaran lingkungan dari masyarakat.
Kata kunci: non-tariff measures, palm oil, sanitary and phytosanitary, technical
barrier to trade, quantitative restriction.
SUMMARY
YUDHA AJISURYA PRATAMA. Determinants Influencing the Imposition of
Non-Tariff Measure and Its Impact on Indonesian Palm Oil Export: The Case of
SPS, TBT and Quantitative Restriction Measures. Under Supervision of
BAMBANG JUANDA and RICHARD POMFRET.
Since the global financial crisis, the use of non-tariff measures (NTMs) in
international trade policy has become more frequent and widely used while their
effect on trade is still ambiguous. Previous studies have shown that NTMs can either
promote or restrict trade. Furthermore, the impact may potentially change over time
or affect various trade partners differently. These conditions make a study of the
impact of NTMs on specific trade commodities crucial to measuring their actual
impact on trade. Given the significant role that the palm oil industry plays in
Indonesian social and economic sectors, the imposition of NTMs may pose a threat
to the industry’s growth potential. The objectives of this study are to observe the
influence of factors which determine the imposition of NTMs in the forms of
sanitary and phytosanitary (SPS), technical barrier to trade (TBT) and quantitative
restriction measures, and how these affect palm oil trade.
The logistic regression model was developed to analyse factors influencing
the imposition of NTMs on palm oil exports using a dataset of the 40 largest
Indonesian palm oil importing countries in 2013. While, the gravity model was
developed to measure the impact of NTMs on palm oil exports in 23 largest
importing countries. Results of the logistic regression model show that import
volumes in 2012 and 2013 along with GDP and GDP per capita are very likely
influence the imposition of NTMs in importing countries. Meanwhile, results of the
gravity panel model indicate that SPS and quantitative restriction measures have a
trade impeding effect on Indonesian palm oil exports, while TBT has a trade
promoting effect. Four policy recommendations are proposed to the policy makers
to formulate a comprehensive policies to effectively seize opportunities and address
challenges in international palm oil market namely promote downstream industries
of palm oil products, develop regulations to enforce the use of sustainable palm oil
certification, promote more stringent law enforcement, and increase community
environmental awareness.
Keywords: non-tariff measures, palm oil, sanitary and phytosanitary, technical
barrier to trade, quantitative restriction.
© Copyright belongs to IPB, 2016
All rights Reserved Law
Prohibited quoting part or all of this paper without including or mentioning the
source. The quotation is only for educational purposes, research, scientific
writing, preparation of reports, writing criticism, or review an issue; and
citations are not detrimental to the interest of IPB.
Prohibited announcing and reproducing part or all papers in any form without
permission of IPB
1
DETERMINANTS INFLUENCING THE IMPOSITION OF NON-TARIFF
MEASURE AND ITS IMPACT ON INDONESIAN PALM OIL EXPORT:
THE CASE OF SPS, TBT AND QUANTITATIVE RESTRICTION MEASURES
YUDHA AJISURYA PRATAMA
Master Thesis
As a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
2
External Advisory Committee Examiner: Prof Dr Ir Rina Oktaviani, MS
4
5
ACKNOWLEDGEMENT
First and foremost, my greatest gratitude goes to Allah SWT, the God
Almighty, for His countless blessings and mercies. I would also like to express my
immense gratitude to my supervisors, Professor Richard Pomfret and Professor
Bambang Juanda, for their idea, worthwhile suggestions, time and patience for
assisting me throughout the process of writing this dissertation. I would also like to
express my gratitude to all staff at the University of Adelaide, most notably, Athena
Kerley, Dr. Florian Ploeckl, Niranjala Seimon, and Augustine Bhaskarraj for their
assistance and support throughout my study at The University of Adelaide.
I would also like to acknowledge with thanks to the Ministry of Trade and the
Australian Awards Scholarship for providing the sponsorship to undertake my
master in Bogor and Adelaide. I also wish to thank the University of Adelaide and
Bogor Agricultural University for providing me the chances to pursue my master
degree.
I would also like to acknowledge A plus Editing for providing me editorial
assistance to improve the quality of my thesis. The editorial intervention was
restricted to Standards D and E of the Australian Standards for Editing Practice.
I want to thank all my friends, especially my colleagues from batch 1 and 2
of IPB Kemendag for their support, laugh, and help during this challenging stage
of my study.
Finally I would like to thank my parents, younger sister and brother for their
support and encouragement throughout the entire process of my study. This
dissertation is dedicated to them.
All errors are solely my own.
Bogor, January 2016
Yudha Ajisurya Pratama
6
TABLE OF CONTENTS
LIST OF TABLES
vi
LIST OF FIGURES
vi
LIST OF APPENDICES
vi
1 INTRODUCTION
1
2 BACKGROUND
World Palm Oil Market
Indonesian Palm Oil Industries
Non-Tariff Measures
2
2
4
5
3 LITERATURE REVIEW
NTMs, Trade Distortion and Asymmetric Information
Previous Studies on NTMs
6
6
7
4 METHODOLOGIES AND DATA
Logistic Regression Model
Gravity Model
8
9
10
5 RESULTS AND DISCUSSION
Determinants Influencing the Imposition of NTMs on Palm Oil
The Impact of NTMs on Export
12
12
13
6 CONCLUSION AND POLICY RECOMMENDATIONS
Conclusion
Policy Recommendations
Promote Downstream Industries for Palm Oil Products
Increase Community Environmental Awareness
Develop Regulations to Enforce the Use of Sustainable Palm Oil
Certification
Promote More Stringent Law Enforcement
17
17
18
18
18
REFERENCES
20
APPENDICES
23
BIOGRAPHY
26
18
19
7
LIST OF TABLES
1
2
The odds ratio of logistic regression model for determinants influencing
the incidences of NTMs on palm oil
Regression Results for the impact of NTMs on indonesian palm oil
exports
12
14
LIST OF FIGURES
1 World vegetable oils consumption
2 Price comparison of major vegetable oils
3 Indonesian palm oil exports
4 Classification of non-tariff measures
5 NTMs enforced on Indonesian palm oil
3
3
4
5
6
LIST OF APPENDICES
1
2
NTMs enforced on Indonesian palm oil in 2014
40 largest importer of Indonesian palm oil in 2013
23
25
1
1 INTRODUCTION
For more than a decade, globalisation has changed the way the world’s
economy develops. Globalisation is a process which make peoples and nations
become more interconnected in every way, including economically (Lodge 1995).
One of the effects of globalisation in the economic sector is the emergence of trade
liberalisation. The fundamental philosophy of trade liberalisation is based on the
argument that its implementation will benefit the countries involved and improve
their welfare. The action needed to achieve this vision is by diminishing the factors
which limit trade flow between trade partners. Several concrete efforts have been
made in the form of the establishment of The General Agreement on Tariffs and
Trade (GATT) and The World Trade Organisation (WTO) and the world has since
seen a decline in tariff and non-tariff barriers imposed on international trade.
However, since the global financial crisis of 2007-08 there has been renewed
growing concern regarding the implementation of non-tariff measures (NTMs) on
foreign trade. During this period, many countries experienced slow domestic
growth and real exchange rate appreciation which led to market power loss and
subsequently resulted in reduced competitiveness. Consequently, this also led to the
reintroduction of protectionism, notably the use of NTMs, in international trade
policies (Bussière and others 2011; Georgiadis and Gräb 2013). The use of NTMs
has long been a contentious topic among economists. Many argue that the
implementation of NTMs has trade-impeding effects due to the impact on trade
costs while others claim that NTMs could also increase trade among countries due
to improved mutual understanding of product compliance between trade partners.
As one of the main export commodities in the agricultural sector, palm oil has
made an undisputed contribution to Indonesia, not only in enhancing economic
growth by maintaining trade surplus and foreign exchange earnings, but also in
improving social conditions in the country. According to World Growth (2011), the
palm oil industry provides developing countries (DCs) an opportunity to escape
from poverty by providing a livelihood for small plantation holders and their
workers to improve living standards. This argument is strengthened by Susila
(2004) who found that palm oil based-activity alleviated poverty by generating 63
per cent of household income in oil palm plantation communities. However, like
other agricultural products, this export commodity has been heavily challenged by
the implementation of NTMs.
As compared to other vegetable oils, palm oil is considered the most efficient
in terms of yield, land utilisation, supply reliability and is more economical. Palm
oil is also used in producing cleaner alternatives to fossil fuel (Mba and others 2015;
Monteiro de Carvalho and others 2015; World Growth 2011). However, NTM
implementation on palm oil may have a trade hindering effect. Thus, two questions
are raised:
1. What are the determinants influencing the imposition of NTMs on palm oil?
2. What are the impacts of NTMs, either SPS, TBT or quantitative restriction,
on Indonesian palm oil exports?
This study employs two models to answer these questions. First, the logistic
regression model is used to determine the potential driver that triggers the
occurrence of NTMs on 40 largest palm oil importing countries from Indonesia.
2
Second, the gravity model is used to estimate the influence of determinants
predicted to have an impact on Indonesian palm oil exports in 30 largest palm oil
importing countries from Indonesia.
This study is structured into six sections beginning with the introduction. The
second section provides an overview of the world palm oil market, Indonesian palm
oil industries and NTMs. Section three reviews the literature on trade distortion and
asymmetric information, and examines previous empirical studies on NTMs. The
fourth section discusses the data and methodology. The following section presents
and discusses the results, and the final section concludes the study.
2 BACKGROUND
The first part of this chapter highlights the prospect of palm oil in the world market,
the following part discusses the conditions for Indonesian palm oil industries, and
the last part examines NTMs on palm oil.
World Palm Oil Market
Vegetable oils is a term used to define an oil extracted from plant-based
products such as soybeans, oil palm, rapeseed and sunflowers. Besides used as
cooking oil, vegetable oil is also found in many food and non-food products. Along
with Indonesia, which currently is the largest producer of palm oil, there are several
countries which could be considered as major producers of vegetable oils, namely
the United States, India and China which all produce soybean, rapeseed and
sunflower oil, and the European Union (EU) which produces rapeseed and
sunflower oil, and Malaysia which produces palm oil.
In 2014, the world’s total consumption of vegetable oils multiplied by 8.4
times as compared to 1980, consuming 151,618 million metric tons (GAPKI 2014).
Most of the demand increase in the 34-year period was met by the rapid growth of
world palm oil production. In recent years, there has been a shift in vegetable oil
consumption patterns. Since 2008, palm oil has surpassed the hegemony of soybean
oil as the most consumed vegetable oil. As shown in figure 1, on the next page, the
market share of palm oil in the world vegetable oil market consistently increased
from 14.8 per cent in 1965 to 41.1 per cent in 2014 (GAPKI 2014). There are several
possible reasons for the driving forces behind this phenomena. Firstly, World
Growth (2011) explained that the rise in palm oil demand has been caused by the
shift in the consumer preferences of developed countries from trans-fats to highly
saturated fats, which are contained in palm oil, as they are better for human health.
Secondly, the increase in biodiesel production as a cleaner alternative to fossil fuel
has also positively shifted palm oil demand as it is one of the main raw materials
(Lim and Teong 2010; Mukherjee and Sovacool 2014; Rusmana and Listiyorini
2014). Thirdly, as pointed out by Carter and others (2007), using palm oil is also
relatively more economical due to its lower price as it is less costly to produce
compared to other vegetable oils. Lastly, palm oil is widely known for its function
as a raw material for the edible products industry (such as butter, cooking oil and
ice cream) and non-edible products industry (such as medicine, cosmetics,
oleochemical and biodiesel). Accordingly, its usefulness for consumption and
3
industry have made the demand for palm oil rise due to the increase in world
population and industries that utilise palm oil (FAO 2013; Mba and others 2015;
Rifin 2013).
160
BILLION(S) METRIC TON
140
120
100
80
60
40
20
0
1965
1980
1990
2000
Soybean Oil
Palm Oil
Sunflower Oil
Total
2010
2014
Rapeseed Oil
Source: GAPKI (2014)
Figure 1: World vegetable oils consumption
One factor presumed to be the determinant influencing palm oil demand is
price. It is well known that palm oil is the most competitive among other vegetable
oils in terms of price. As depicted in Figure 2, below, even though the price of the
four major vegetable oils are relatively volatile, the price of palm oil is consistently
been the cheapest, even when the world faced the global food crisis in 2008 and
2011. At that time palm oil price reached its zenith in February 2011 at 1,248.55
USD per metric ton.
2500
2000
1500
1000
500
Palm Oil
Soybean Oil
Rapeseed Oil
Sunflower Oil
Source: Index Mundi (2015)
Figure 2: Price comparison of major vegetable oils
Dec-14
Apr-12
Aug-13
Dec-10
Aug-09
Apr-08
Dec-06
Apr-04
Aug-05
Dec-02
Aug-01
Apr-00
Dec-98
Apr-96
Aug-97
Dec-94
Aug-93
Apr-92
Dec-90
Apr-88
Aug-89
Dec-86
Aug-85
0
4
Indonesian Palm Oil Industries
Since 1990 there has been a change in oil palm plantation cultivation patterns
in Indonesia. This transformation has been characterised by the growth of oil palm
small plantation holders. On average, the total growth of oil palm plantations, which
have spread across 22 of 33 provinces in Indonesia, is relatively high at 24 per cent
per year over a 13-year period. About 90 per cent of oil palm plantations are located
on the islands of Sumatra and Borneo, which account for 95 per cent of Indonesian
palm oil production. (GAPKI 2014). The interesting fact about palm oil plantations
is that state-owned enterprises and large enterprises coexist with smallholders
which is rarely observed in other agricultural commodity industries in Indonesia.
The palm oil industry produces a wide range of products for food, energy and
raw materials for other industries, which are demanded both domestically and
internationally. According to GAPKI (2014), in 2014 70 per cent of Indonesian
palm oil production was exported, while just 30 percent was consumed
domestically. This huge export volume has been consistently increasing over time.
As shown in Figure 3, below, despite a slight decrease in 2007 and 2010, the volume
of Indonesian palm oil exports increased each year from 2004-2014. In 2008 and
2011 the value of palm oil exports rose sharply compared to export volume increase
due to strong prices triggered by the global food crisis. Despite the increase in price,
which reached its zenith in 2011, palm oil exports are still rising due to strong
demand in the international market, especially from the development of
oleochemical and biodiesel industries.
25
20
18
16
14
15
12
10
10
8
6
5
Value of Export
Volume of Export
20
4
2
0
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Volume (Million(s) Metric ton)
Value (Billion(s) US$)
Source: WITS (2015)
Figure 3: Indonesian palm oil exports
Year
5
Non-Tariff Measures
NTMs are a form of policy that may create a distortion effect on foreign trade.
UNCTAD (2013) describes NTMs as “all policy-related trade costs incurred from
production to final consumer, with the exclusion of tariffs”. This added cost would
create a gap between the price in the domestic market and in importing countries.
Besides distorting price, NTMs could also limit trading quantity and in some cases
even have a prohibitive effect. Being one of the instruments which has an impact
on trade cost, appropriate NTM implementation might be vital not only to protect
domestic consumers and industries, but also to reduce disputes with trading
partners.
As shown in Figure 4, below, UNCTAD (2013) classifies NTMs into 16 types
which are divided into two main categories, import and export measures. The
import measures category is divided into two sub-categories, technical and nontechnical measures. Technical measures, such as technical barriers to trade (TBT)
and sanitary and phytosanitary (SPS) measures, are imposed to protect domestic
consumers by applying technical requirements related to standards and the quality
of products in terms of technical specifications, contents limitations or restrictions,
process of production and conformity assessment. Non-technical measures, such as
quantitative restrictions, countervailing measures and anti-dumping measures, are
NTMs usually imposed to protect domestic industries by either limiting imported
goods directly through quotas or indirectly through price control measures.
Source: UNCTAD (2013)
Figure 4: Classification of non-tariff measures
Based on the Integrated Trade Intelligence Portal (I-TIP) database, since 1990
Indonesian palm oil exports have been challenged by the enforcement of NTMs
from The Philippines in the form of quantitative restrictions. Since then, the number
of NTMs imposed increased and reached 71 in total from 20 countries in 2014.
Figure 5 shows that in 2009 and 2013 the number of NTMs and countries imposing
NTMs increased sharply, presumably due to protectionist policies introduced to
6
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
NUMBER OF COUNTRIES
NUMBER OF NTMS
correct market failure caused by financial crises in 2007-2008 and 2011. Primarily,
NTMs imposed on palm oil in 2014 are TBT and quantitative restrictions which
account for 38 and 35 per cent of NTMs imposed on palm oil, respectively, while
SPS measures account for only 13 per cent.
0
YEARS
NTMs
Countries
Source: I-TIP (2015)
Figure 5: NTMs enforced on Indonesian palm oil
3 LITERATURE REVIEW
NTMs is a determinant that might play a significant role in international palm
oil trade. Accordingly, understanding its impact on trade performance is
particularly important to study how NTMs might affect the Indonesian economy.
This chapter discusses some theoretical foundations and previous empirical studies
of the impact of NTMs on trade.
NTMs, Trade Distortion and Asymmetric Information
The enforcement of NTM policy, regardless of underlying intent, triggers
inefficiency in foreign trade because additional costs of compliance and conformity
assessment are incurred by producers in exporting countries in order to obtain
market access to the export destination enforcing the regulations. Maskus and
others (2013) examined the impact of enforced product standards on the cost of
production in developing country firms and found that on average the production
fixed cost rises by 4.7 per cent due to meeting export destination compliance. In
addition, these authors found that variable costs also increase, ranging from 0.06 to
0.13 per cent. Additional costs may distort trade directly in terms of price, and may
7
also either limit or restrict trade, and consequently might reduce welfare compared
to having a free market.
Despite the additional cost incurred by the use of NTMs, several type of
NTMs, particularly SPS and TBT, are beneficial to reduce asymmetric information
on international trade. The term asymmetric information refers to the situation
where agents do not have the same level of information (WTO 2012). The
enforcement of labeling or conformity assessment on food safety standards is used
to reduce customer uncertainty regarding product quality and contents. Thus,
reducing asymmetric information on imported goods (Farida 2015).
From the discussion above, some conclusions can be drawn. First, in
importing countries prices of commodities on which NTMs are imposed tend to be
higher due to reduced competition from imported goods. This benefits producers
while harming consumers. Second, from a consumer protection view, consumers in
the importing country benefit because they are protected from low-quality or
contaminated goods. Third, for producers in exporting countries NTMs have
ambiguous effects. On one hand they face rising production costs leading to reduced
product competitiveness that might lead to less demand. On the other hand meeting
enforced compliance and conformity assessment of the export destination could
reduce asymmetric information by signaling about the quality of their products
which might lead to increased demand due to increased consumer confidence to
buy the products.
Previous Studies on NTMs
Many previous studies examining the impact of NTMs on trade performance
provide evidence that NTMs could have diverse results on cross border trade.
Mainly, those studies use a gravity model to determine the NTM impact. In this
study, several relevant studies are discussed to gain deeper understanding of how
NTMs could affect international trade.
The majority of studies on NTMs suggest that the imposition of NTMs results
in decreased export performance. Wall (1999) analysed the impact of import
protection on US trade and welfare using panel data analysis to measure the impact
of exporting country GDP, importing country GDP, and Trade Policy Index
variables on trade flows. Results showed that import protection imposition in the
US cause a 26.2 per cent decrease on US exports, while US import tariffs lower US
imports from non NAFTA countries by 15.4 per cent per year.
In reality, irrespective whether the intention of NTM imposition is
protectionist or not, evidence suggests that NTMs could trigger greater distortion
than tariffs. As observed by Chen and others (2008) export performance of Chinese
agricultural commodities and aquatic products are facing greater challenge from
food and safety standards than import tariffs. More stringent food standard applied
by the US, EU and New Zealand, compared to CODEX international standards,
would reduce China’s export to those countries by 21, 23, and 23 per cent,
respectively. While if equivalent import tariff was applied, it would only impact
export performance by 15 per cent to EU and 11 per cent to Japan.
In terms of impact magnitude, the imposition of NTMs might affect certain
countries or groups of countries differently compared to others. Disdier and others
(2008) examined the impact of SPS and TBT measures on agricultural commodities
8
export in G-90 countries. Using gravity model, they found that these policy reduce
export of developing countries and LDCs only, while export from OECD countries
are not significantly affected. This might happen because developed countries, like
OECD, have the technology and similar awareness towards environment and food
safety standards to comply with import country requirements.
Although most of previous literature found that imposition of NTMs have
unwanted consequences on trade performance, a considerable amount of study has
also shown that it may not always be the case. Farida (2015) explored the impact of
NTMs on Indonesian coffee trade. Using gravity panel with fixed effect and OLS
estimation, this author found that NTMs consistently have positive and statistically
significant effect on Indonesian coffee export, whether used as a single dummy
variable or when disaggregated into SPS and TBT variables. Moreover, when fixed
effect estimation is used, NTM variables still show positive signs. While, when
disaggregated into SPS and TBT, only TBT that consistently shows positive impact.
Fassarella and others (2011) examined the impact of SPS and TBT on Brazil’s
poultry meat export from 1996 to 2009 shows similar findings. Using gravity model
with fixed effect estimation, these authors show that TBT measure can stimulate
export, while SPS measure may have the opposite effect. These findings reveal that
besides impeding trade, NTMs can also act as trade catalyst, especially when
policies are intended to reduce asymmetric information between producer and
consumer regarding product quality, such as the use of labelling.
Another compelling finding on the impact of NTMs is that in several cases
the effect is not permanent, but changes after some period of time. This was
demonstrated by Peterson and others (2013) regarding the impacts of US
phytosanitary regulation for several fruit and vegetable commodities between 1996
and 2008. Using gravity equation on exports from 89 countries, they found that
initially SPS impacted negatively on US imports. However, the effect was not
permanent and altered after some time. The alteration was due to experience and
understanding gained by exporting countries about the standard required to enter
the US market. This understanding results in compliance with the requirement and
improved product quality. This empirical evidence is strengthened by Song and
Chen (2010) who obtained similar results when estimating the impact of food safety
standards on Chinese exports. They found that the short and long effects of the
regulations are different. While the short run had a statistically significant effect on
China’s Chinese export, the long run effect shows otherwise.
From the examples above, we can see that the impact of NTMs are diverse in
terms of direction and magnitude. Moreover, in some cases the effect can be time
bound and may also affect one country to another differently. Accordingly, we need
a deeper understanding when considering different characteristics and polices to
analyse the impact of NTMs in trade in order to legislate suitable policies to
overcome challenges that arise.
4 METHODOLOGIES AND DATA
This study aims to identify the incidences and effects of NTMs in the forms of SPS,
TBT and quantitative restriction on Indonesian palm oil exports. Several methods,
either generic or specific to NTMs, can be used for quantifying and estimating
9
NTM’s impact on trade. The logistic regression model is used to identify
determinants which cause the imposition of NTMs, while the gravity panel data
model is applied to estimate their impact on trade across borders by utilising dummy
variables to capture the incidence of NTMs on a country’s trade policies.
Logistic Regression Model
The logistic regression model, or the logit model, is a model developed to
measure the relationship between binary regressand and one or more regressors to
explain probabilities of the regressand (Hosmer and others 2013). This model
assumes that the response or dependent variables are categorical and coded as either
0 or 1.
As explained by Juanda (2009), logistic regression model is derived from
cumulative logistic probabilities function and has a basic form as follows:
1
1
=
=
=
+
=
−�
−
1+�
1 + � + ��
While the logit form of the model are shown below:
=�
=
1−
+
Where, e represents the base number of the natural logarithm (e=2.71828...),
represents odds ratio which is the probability ratio on the occurrence of
and
��
−��
one incident compared to the alternative incident.
Depending on the number of independent variables and observations, the
logistic regression model can be developed into a multiple logistic regression model
(Hosmer and others 2013) with k numbers for explanatory variables, and i numbers
for observations. Juanda (2009) shows that the multiple logistic regression model
takes the following form:
=
� =
1 + �−
+
While the logit form of the model are shown below:
� � =�
1−
�
�
=
+
1
� � +⋯+ � ���
� +⋯+
�
Where in this study � denotes the presence of NTM imposition on palm oil,
is the intercept,
are the estimated coefficients and � to � are variables
which are expected to have influence on the imposition of NTMs on palm oil.
Two multiple logistic regressions are developed in this study to measure the
influence of determinants that could trigger the incidences of NTMs affecting
Indonesian palm oil exports.
Model 1:
� �
�
−
� �
�
�
=
+
�
�
�
+
+
+
10
Model 2:
� �
�
−
� �
�
�
=
+
�
�
�
+
+
+
Where ‘IMPVOL’ indicate the total value of imported palm oil in the years
2013 measured in million metric ton, ‘GDP’ is the size of the economy which
represents the capacity to import palm oil products measured in billion US dollars.
‘GDPCAP’ is the income level of each country weighted by their population which
represents the purchasing power of the individual for consumption measured in
hundred US dollars. ‘POP’ indicates the country population which represents the
size of the country’s palm oil absorption capacity measured in million people. The
dependent variable is the incidences of NTMs in the year 2013, which takes the
value of 1 if at least one type of NTM is enforced by the country, otherwise it is 0
if no NTMs were imposed.
This model uses secondary data on 40 top palm oil importer countries
collected only for the years 2013 based on data availability. This study uses HS
15111 (palm oil and its fractions) as the analysed commodity.
Import price is calculated based on the data for palm oil imports from the
Ministry of Trade of Indonesia. Data for GDP, GDP per capita and population are
extracted from the World Development Indicators (WDI). The data for NTM
incidences are taken from the WTO.
Gravity Model
This study uses the gravity model to measure the impact of NTMs on
Indonesian palm oil exports. The gravity model was inspired by the Newtonian law
of gravity which states that gravitational force is proportionally related to the
objects masses and inversely related to the square distance between them. Gravity
models were first developed by Tinbergen (1963) to explain a positive correlation
between the volume of trade to GDP and a negative correlation to distance. Since
then, the gravity model has been widely used as an international trade model.
Gravity models represent trading volume as a function of GDP and distance
between two trading partners.
In its development, the gravity model has also been used to measure the
impact of trade policies on trade across country performances by introducing trade
policy determinants. To represent the incidences of NTMs, dummy variables are
introduced into the models. The dummy variable, or binary variable, is a method
frequently used to quantify implementation of NTMs on trade performance.
Dummy variables use a binary value, either 0 or 1, to represent the incidence of
NTMs on country’s trading policies. If there is at least one incidence of NTMs, the
dummy variables take the value of 1, otherwise the value is 0. The first step when
using a dummy variables approach in this study is by making an inventory of the
NTMs on Indonesian palm oil imposed by each countries across time. In this study,
dummy variables are divided into four categories, SPS, TBT, quantitative
restriction and NTMs themselves. After the inventory of NTMs have been
collected, this data will then be applied as dummy variables in the gravity panel
model.
11
�
�
Dummy NTM Model:
= + �
+
�
�
+
�
�
�
+
+
Disaggregated Dummy NTM Model:
= + �
+ �
�
+
�
�
+
�
+
�
�
+�
�
+
+
�
+
+
�
+
+
+
Where , , represent Indonesia as the exporting country of palm oil,
are the estimated
importing countries and the year of trade, respectively.
variables’ coefficients and � is the error term.
denotes the export volume of Indonesian palm oil to importing country
in year , measured in kilograms. The number of observed countries was 23, based
on the top 30 of the largest total export volume of palm exports from Indonesia in
2014 which have complete data (i.e., India, China, Pakistan, Italy, Netherlands,
Bangladesh, Egypt, Spain, Singapore, Malaysia, Russian Federation, USA,
Ukraine, South Africa, Benin, Philippines, Brazil, Turkey, Tanzania, Germany,
Ghana, Angola and Mauritania) in a 14 year period of observation from 2001 to
2014. This responding variable data was taken from the Ministry of Trade of the
Republic of Indonesia which stems from the Indonesian Central Bureau of Statistics
(BPS) database focusing on palm oil (HS code 1511) as the subject of study.
denotes importing countries’ GDP used as a proxy for palm oil
absorption capacity in importing countries, which measured in US dollars. This
variable captures economic size, demand for palm oil and purchasing power.
Statistics for GDP of each importing country were obtained from the World
Development Indicators (WDI) database.
�
denotes the GDP per capita of importing country in real terms
which indicates palm oil absorption capacity from household consumption as it is
used as the proxy for standard of living or income level. Statistics for GDP per
capita were extracted from WDI.
denotes geographical distance between Indonesia and importing
countries measured in kilometres. This independent variable was obtained from the
CEPII database and used as a resistance variable which acts as a proxy for
transportation cost.
denotes palm oil production in Indonesia measured in kilograms.
This variable was used to observe the potential export capacity. Data for palm oil
production was obtained from the Ministry of Agriculture of the Republic of
Indonesia.
�
denotes the international prices of palm oil measured in USD per
metric ton. Statistics for the palm oil price were taken from the Index Mundi.
denotes the real exchange rate between exporting and importing
countries. Statistics for this variable were taken from the UNCTAD database.
,
,
and
are the dummy variables which
denote the binary variables to capture the incidence of either NTMs as a whole or
when disaggregated into SPS, TBT and QR measures imposed by palm oil
importing countries. This study developed two models to evaluate the impact of
NTMs on Indonesian palm oil exports. The first model uses NTMs as a single
12
dummy variable, which takes on the value 1 if there is at least one NTM imposed
on palm oil, either SPS, TBT or quantitative restriction, and zero otherwise. The
second model uses NTM dummy variables which were disaggregated into three
dummy variables, i.e. SPS, TBT and QR, which take the value of 1 if the importing
country enforced at least one of the disaggregated NTMs and zero otherwise. These
measures were obtained from the WTO’s I-TIP database focusing on palm oil (HS
code 1511).
As a benchmark, alternative models were also developed through the
introduction of time dummies on both models. To avoid colinearity, the variables
such as palm oil price and production which are invariant across countries at a
particular time are omitted from these models. The introduction of time dummies
are intended to capture various effects that may have an influence in a given year.
These variables take the value of 1 for a particular year and zero otherwise.
This study used panel data consisting of 23 countries and 14 time periods,
with a total of 322 observations. In this study, estimation techniques were selected
using three tests in the forms of the F Test, Hausman Test and LM Test to decide
the preferred model with the most efficient and consistent estimation between the
pooled least squares, fixed effect model or random effect model.
5 RESULTS AND DISCUSSION
Determinants Influencing the Imposition of NTMs on Palm Oil
The results of the logistic regression model are shown in Table 1, below.
Three models are developed to measure the incidences of NTMs on Indonesian
palm oil exports in the year 2013. The time selection used for this model is due to
the significant increases in NTM incidences in the selected years, having almost
doubled from 34 incidences in the previous year to 66 incidences, while the
selection of determinants is based on Farida (2015) model which measures the
incidence of SPS and TBT measures on Indonesian coffee exports with some
modifications.
Table 1: The odds ratio of logistic regression model for determinants influencing
the incidences of NTMs on palm oil
Model 1
Import Volume
1.021447**
Importing Countries GDP
1.001263 ***
Importing Countries GDP per capita
Model 2
1.0012370**
1.005841***
Importing Countries population
0.987276 ***
0.999427***
Import Price
0.998407
0. 99825
*, **, and *** indicate significance at the ten, five and one per cent level, respectively.
Source: Author Calculations
From the results shown in Table 1, for all two models, the volume of imports
always has statistically significant coefficients at the five percent level. This
indicates that the imported volume of a country is related to the imposition of NTMs
policy to reduce imported goods. These 2 models predicts that if the import volume
13
of palm oil are increased by 1 million metric ton, the probability of NTMs
imposition will multiplied by 1.021447 and 1.001237 respectively. This happened
presumably due to two factors. First, as discussed in the previous chapter,
protectionist policies are used to protect domestic industries. This can be seen from
the I-TIP WTO database where the US and EU countries impose six and four types
of NTMs on palm oil imports, respectively. As is widely known, both the US and
EU produce competing products for palm oil in terms of soybean and rapeseed oil.
Secondly, concern raised by some environmental organisations has impacted on the
imposition of NTMs because a higher export value means higher production which
comes from increased rainforest deforestation for the expansion of palm oil
plantations.
GDP and GDP per capita show statistically significant coefficient estimations
at the five per cent level. Both variables are used to represent a country’s capacity
to absorb palm oil imports. While GDP represents absorption capacity of palm oil
as raw material for industrial activities, GDP per capita represents household
consumption of products derived from palm oil. The first model predicts that an
increase of a billion USD on importing countrie’s GDP will multiply the probability
of NTMs imposition by 1.001263 times, while the second model shows that an
increase of 100 USD on GDP per capita will multiply the probability of NTMs
imposition of palm oil by 1.005841.
The population determinant also shows a statistically significant coefficient.
As GDP per capita, population is also used to represent household absorption in
terms of consuming imported goods. However, the effect of population shows the
value of is less than 1 in the odds ratio for both models, this results indicate that the
increase in population might reduce the use of non-tariff measures due to the
increase in demand for palm oil in household consumption. These 2 models predicts
that if the population of the importing countrie’s are increases by 1 million people,
the probability of NTMs imposition will decrease by 0.987276 and 0.999427 times
respectively.
Meanwhile, the import price shows statistically insignificant estimation
results. This suggests that price is not associated with the imposition of NTMs on
palm oil. As highlighted earlier, even on reaching its highest price when the global
food crisis occurred, palm oil was still the cheapest vegetable oil compared to all of
its competitors. Thus, even when price rises competitiveness is still high, which
might explain why these two determinants are not related. Moreover, unlike other
commodities which have diverse qualities or grade (i.e. coffee and tea) palm oil is
rather homogenous which means its price is similar for all producers.
The Impact of NTMs on Export
This section presents results of the gravity model which estimates the impact
of NTMs on Indonesian palm oil exports. The results of the Hausman test and the
LM test suggest that the random effect model is preferred over the fixed effect
model and the pooled least squares. The results of the random effect model are
shown in Table 2, including the four models developed and estimated using the
STATA 13 application.
14
Table 2: Regression Results for the impact of NTMs on indonesian palm oil exports
Variables
Ln GDP
Ln GDP per
Capita
Ln Distance
Ln Production
Ln Price
Ln Real
Exchange
Rate
NTM
Dummy NTM Model
Without Time
With Time Dummies
Dummies
(1)
(2)
Disaggregated NTM Dummy Model
Without Time
With Time Dummies
Dummies
(3)
(4)
0.454387***
(0.1462118)
-0.1013467
(0.2555209)
-1.059489***
(0.3856029)
1.866794***
(0.3037308)
-0.5253073**
(0.2394016)
0.5006452***
(0.1355819)
-0.2756253
(0.2357179)
-1.173084***
(0.3372628)
1.970998***
(0.3028101)
-.5064464**
(0.2374695)
0.2028702
(0.1664388)
0.1405917
(0.1827732)
0.0430887
(0.1967608)
0.418084***
(0.1474821)
-0.13937
(0.2599049)
-1.05981
(0.3857702)
0.1763455
(0.1870353)
-0.6544767**
(.3139316)
0.3527208
(0.2318689)
-1.893878**
(9032158)
TBT
QUOTA
d2003
d2004
d2005
d2006
d2007
d2008
d2009
d2010
d2011
d2012
d2013
d2014
Constant
N
R2
-11.62824**
(5.618303)
332
0.4449
.2418322
(0.169754)
.0398457
(0.204631)
SPS
d2002
0.4689743***
(0.1367743)
-0.3200074
(0.2388214)
-1.17561***
(0.3379289)
0.3065448
(0.2590345)
0.1587109
(0.2686122)
0.6034176***
(0.279317)
0.9616572***
(0.2891091)
1.249985***
(0.3181759)
1.152765***
(0.3384132)
0.974287***
(0.3519337)
1.300777***
(0.3539882)
1.223884***
(0.3886389)
1.413193***
(0.4112395)
1.64367***
(0.410628)
1.919331***
(0.4094268)
2.148728***
(0.4034225)
16.0204
(4.541247)
332
0.4550
-12.50731***
(5.116731)
332
0.5127
-0.8120586**
(0.3316047)
0.4327762*
(0.2435475)
-1.874307**
(0.9048716)
0.3250217
(0.257535)
0.1976005
(0.2660161)
0.6350778***
(0.2749074)
1.006403***
(0.2837308)
1.323187***
(0.3095838)
1.244494***
(0.3280189)
1.077773***
(0.3404965)
1.309513***
(0.3418658)
1.237572***
(0.3731772)
1.583399***
(0.3095838)
1.813431***
(0.3998575)
2.089088***
(0.3989302)
2.313276***
(0.3936439)
16.83783***
(4.039343)
332
0.5179
*, **, and *** indicate significance at the ten, five and one per cent level, respectively. Standard
errors are in parentheses.
Source: Author Calculations
The estimation results of these 4 models have diverse results in the magnitude
of the coefficients. However, there are some consistent patterns on the sign of the
impact for all determinants between these four models. As shown in Table 2, GDP
estimated coefficients have robust results in terms of signs and statistical
significance. In all four models, the coefficients of GDP have positive signs and are
15
statistically significant at the one per cent level. These results imply that the increase
in importing countries GDP as a proxy for economic size would also raise
Indonesian palm oil exports.
The distance variable has consistent estimation signs, but opposite to GDP.
This result is consistent with gravity model theory that describes distance as a trade
impeding attribute in international trade. However, only three of four models where
distance is estimated are coefficients statistically significant at one per cent level.
Only in model 2 where NTM and time dummies are used does distance have
statistically insignificant coefficient. These results suggest that distance has a
restrictive effect on palm oil trade due to the need for higher transportation costs
when trading with more distant countries, thus resulting in lower export volume.
Due to its nature, which takes the same value for all of the countries in each
specific year, Indonesian palm oil production is only estimated in the model without
time dummies (columns 1 and 3). In both models, the production coefficients show
consistent positive signs and are statistically significant at the one per cent level,
suggesting that higher production would draw higher export quantity.
Similarly, price is only estimated in models 1 and 3 where time dummies were
not included. For both models, price has a negative sign and statistically significant
coefficients at the five per cent level. This is in line with the law of demand where
price and demand quantity have an inverse relationship.
Unexpected results are found with GDP per capita which has negative
coefficients sign but is statistically insignificant in all four models. These results
are in contrary to the initial hypothesis which predicts that it has positive correlation
with the quantity of palm oil imported to the country. This is possibly related to the
environmental Kuznets curve (EKC) which shows the relationship between
environmental degradation and income per capita (Tietenberg and Lewis 2011).
The EKC explains that beyond a certain level of income per capita people become
more aware and concerned about the environment. Thus, as palm oil industries are
often linked with deforestation, welfare growth may lead people to reduce the use
of palm oil. Moreover, most high income countries who imported palm oil also have
a viable domestic product substitute, such as rapeseed and sunflower oil in the EU,
and soybean, rapeseed and sunflower oil in US. These two conditions are presumed
to be the reasons for this phenomena.
The objective of this study is to measure the impact of NTMs in the form of
SPS, TBT and quantitative restriction. Four models have been developed to achieve
this objective by utilising dummy variables to capture the non-tariff measures. The
first and second models used only one NTM dummy which represent