Pengaruh Batas Maksimum Residu Pestisida Pada Ekspor Teh Indonesia
THE TRADE-EFFECT OF MAXIMUM RESIDUE LIMITS FOR
PESTICIDES ON INDONESIA’S TEA EXPORT
NOVIANA KUS YUNIATI
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL INSTITUTE
BOGOR
2016
STATUTORY DECLARATION
I, Noviana Kus Yuniati, hereby declare that the master thesis entitled “The
Trade-Effect of Maximum Residue Limits for Pesticides on Indonesia’s Tea” 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. Source of
information originated from published and unpublished work are stated in the part of
references of this thesis. Herewith, I passed the thesis copyright to Bogor Agricultural
University.
Bogor, January 2016
Noviana Kus Yuniati
H151137084
RINGKASAN
NOVIANA KUS YUNIATI. Pengaruh Batas Maksimum Residu Pestisida pada Ekspor
Teh Indonesia. Dibimbing oleh RINA OKTAVIANI dan STEPHANIE MCWHINNIE.
Teh sebagai salah satu komoditas ekspor unggulan Indonesia dituntut untuk
memenuhi persyaratan keamanan pangan dalam perdagangan internasional. Salah satu
dari persyaratan tersebut adalah Batas Maksimum Residu (BMR) untuk pestisida.
Batas ini bertujuan untuk melindungi kesehatan konsumen dari kandungan berlebih
residu pestisida dalam pangan. Namun, BMR dapat menghambat perdagangan
internasional karena semakin ketatnya batas maksimum yang ditetapkan dan
pemberlakuan regulasi baru oleh negara-negara importir.
Tujuan dari penelitian ini adalah menganalisa pengaruh BMR terhadap ekspor
teh Indonesia. Penelitian ini menggunakan data panel dari 28 negara importir dari tahun
2001 sampai dengan 2014 dan dua model gravity dengan tiga metode estimasi yaitu
Ordinary Least Square (OLS), Fixed Effect (FE) dan Random Effect (RE).
Secara empiris, diperoleh hasil bahwa BMR yang semakin ketat berpengaruh
negatif dan signifikan terhadap ekspor teh Indonesia. Selain itu, hambatan tarif dan
harga internasional juga berpengaruh signifikan terhadap ekspor teh Indonesia.
Penelitian ini merekomendasikan Good Agricultural Practices (GAP) pada proses
penanaman teh serta pengujian laboratorium secara periodik untuk menjaga kualitas
teh Indonesia dan meningkatkan ekspor.
Kata kunci: batas maksimum residu, ekspor, teh
SUMMARY
NOVIANA KUS YUNIATI. The Trade-Effect of Maximum Residue Limits for
Pesticides on Indonesia’s Tea Export. Under supervision of RINA OKTAVIANI and
STEPHANIE MCWHINNIE.
Tea, as one of Indonesia’s important agricultural trade commodities, has
recently been confronted with the necessity to meet new requirements in food safety
for global trade. One of these requirements is maximum residue limits (MRLs) for
pesticides. These limits are set to protect consumers’ health from excessive pesticide
residues, which remain on food. However, MRLs are considered to hamper global trade
due to their increasing stringency and enforcement of stricter regulations.
Thus, the objective of this study is to estimate the impact of MRLs on
Indonesia’s tea exports. Based on a panel data set of 28 importing countries of
Indonesian tea from 2001 to 2014, this study employs two gravity models and applies
three estimation methods, Ordinary Least Square (OLS), Fixed Effect (FE) and
Random Effect (RE), to estimate the trade-effect of MRLs on Indonesian tea exports.
Empirical results show that increasing stringency of MRLs impedes export of
Indonesian tea. In addition, international prices and tariff barriers are statistically
significant, indicating a significant effect on exports. Based on these findings, this
study suggests that actions, such as implementing Good Agricultural Practices (GAP)
in tea farming and conducting laboratory testing on MRLs periodically, will improve
the quality of Indonesian tea and contribute to maximizing export opportunities.
Key words: maximum residue limits, export, tea
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THE TRADE-EFFECT OF MAXIMUM RESIDUE LIMITS FOR
PESTICIDES ON INDONESIA’S TEA EXPORT
NOVIANA KUS YUNIATI
Master Thesis
As a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL INSTITUTE
BOGOR
2016
External Advisory Committee Examiner: Prof Dr Ir Bambang Juanda, MS
ACKNOWLEDGEMENT
‘My Lord, increase me in knowledge’ (The Quran 20:114). All praises to Allah
SWT for the opportunities, strength and His blessing to complete this Masters
dissertation.
First, I would like to express my utmost gratitude to my supervisor from the
University of Adelaide, Dr. Stephanie McWhinnie, for her extensive assistance,
continuous support, kindness and encouragement throughout the process of this
dissertation. Her worthwhile ideas, suggestions and constructive comments have
contributed enormously to improve this work. Thereafter, my sincere gratitude to my
supervisor from Bogor Agricultural University, Prof. Dr. Ir. Rina Oktaviani, MS for
her assistance, knowledge, ideas, motivation and cooperation.
I would like also to appreciate Dr. Florian Ploeckl for his hard work to
coordinate the Masters of Applied Economics Dissertation program. Not forgotten, my
appreciation to Athena Kerley for her ideas, support and providing feedback for my
writing.
My appreciation to all lecturers in the double degree program of the School of
Economics of Bogor Agricultural University and University of Adelaide for the
valuable learning experiences and economics knowledge. I would like thank all staff
in both universities, especially Niranjala, Gus, Nicole, Regi and Dian for their support
and help during my study in Bogor and Adelaide.
I would like to thank the Ministry of Trade and the Australian Awards
Scholarship for providing a sponsorship to pursue a high education degree.
I would also like to acknowledge Dr. Dana Thomsen for providing editorial
assistance. Editorial intervention was restricted to Standards D and E of the Australian
Standards for Editing Practice.
I am also indebted to all of my college friends in Bogor and Adelaide for their
friendship, discussion and moral support during this study period.
I owe more honor to my parents, husband, sister and brother for their endless
love, encouragement and prayers throughout this challenging study period.
Bogor, January 2016
Noviana Kus Yuniati
i
TABLE OF CONTENTS
LIST OF FIGURES ...................................................................................................... ii
LIST OF TABLES ........................................................................................................ ii
1 INTRODUCTION .................................................................................................... 1
2 BACKGROUND ...................................................................................................... 1
The global market of tea ............................................................................................ 1
Indonesian tea ............................................................................................................ 1
Regulation of maximum residue limits for tea .......................................................... 1
3 LITERATURE REVIEW.......................................................................................... 1
Theoretical Perspective ............................................................................................. 1
Food Safety as Government Intervention .............................................................. 1
Food Safety in Global Trade .................................................................................. 1
Empirical Studies ...................................................................................................... 1
4 DATA AND METHODOLOGY ............................................................................. 1
The Gravity Model .................................................................................................... 1
Data ........................................................................................................................... 1
Methods of Estimation .............................................................................................. 1
5 THE EMPIRICAL RESULTS.................................................................................. 1
6 CONCLUSION AND POLICY IMPLICATIONS .................................................. 1
REFERENCES............................................................................................................ 19
APPENDIX ................................................................................................................... 1
ii
LIST OF FIGURES
Figure 2.1 The world volume and value export of tea between 2001 and 2014 ........... 1
Figure 2.2 Total world production of tea from 1990 to 2012 ....................................... 1
Figure 2.3 Volatility of tea price from august 2000 to february 2015 .......................... 1
Figure 2.4 Export of Indonesia’s tea from 2001 to 2014 .............................................. 1
Figure 2.5 Production of Indonesia’s tea from 2001 to 2012 ....................................... 1
Figure 3.1 Shifts in supply and demand curve due to MRLs ........................................ 1
Figure 3.2 Market equilibrium of MRLs in foreign trade results in zero import .......... 1
Figure 3.3 Market equilibrium of MRLs in foreign trade with import ......................... 1
LIST OF TABLES
Table 5.1. Regression results of the impact of MRLs on Indonesia’s tea exports ........ 1
Table A.1 Summary statistic ........................................................................................ 1
Table A.2 The estimation results of a regression version with only either deltamethrin
or the MRL enforcement periods for model 1 .............................................. 1
Table A.3 The estimation results of a regression version with only either deltamethrin
or the MRL enforcement periods for model 2 .............................................. 1
Table A.4 The result of Hausman test ........................................................................... 1
1
1 INTRODUCTION
The Uruguay Round Agreement on Agriculture (URAA) of the World Trade
Organization (WTO) has liberalized global agricultural trade by reducing tariff
barriers. On the other hand, concern has arisen regarding technical safety measures,
such as sanitary and phytosanitary (SPS) safety measures that increasingly impede
trade. Henson and Loader (2001) show that SPS measures restrict market access of
agricultural commodities from developing countries (DCs) to developed countries. The
reason is that SPS measures are prevalent in agro-food commodities as the main
objective of these measures is to protect the health of humans, animals and plants.
Furthermore, agro-food exports have a significant role in developing countries. The
total contribution of developing countries in global agricultural export was relatively
high at more than 36% in 2001, accounting for 162 billion USD (Aksoy, 2005).
Therefore, complying with SPS measures is substantial for developing countries to
penetrate the world market with regard to product competitiveness and consumer safety
(Henson & Loader, 2001).
This study empirically examines the impact of a specific set of SPS measures
on Indonesian tea exports. These measures are known as maximum residue limits
(MRLs). MRLs consist of the maximum permitted levels of pesticide residues in agrofood commodities. They have been established by many countries to assure safe
application of pesticides in farming and to protect countries from plant-disease risks of
imported food. In order to analyze the trade-effect of MRLs, trade data regarding export
value of Indonesian tea to 28 major importing countries from 2001 to 2014 and data of
MRLs for pesticide residues, namely deltamethrin, are used. Of 28 importing countries,
15 are from Asia and four are from the EU. Other countries include Egypt, the USA,
Canada, the Russian Federation, the United Kingdom, Fiji, Australia and New Zealand.
Three indicator variables for particular MRL regulation enforcement are created. This
regulation known as the default list system was imposed by three major importing
countries of Indonesian tea, namely Malaysia, Japan and the EU, in 2004, 2006 and
2008, respectively. The default list is the regulation of MRL which set the general
maximum limit for pesticide residue at a strict level of 0.01 ppm for any residues
unspecified in MRL regulation.
The empirical strategy used to examine the trade diversion effect of MRLs is
based on the gravity model. This model is selected to account for the standard
determinants of trade as MRLs will not be the only factor for altering tea exports. A
typical gravity dataset from the Institute for Research on the International Economy
(CEPII) is used. This data covers Gross Domestic Product (GDP), and distance
between Indonesia and its exporting partners from 2001 to 2014. Furthermore, this
study also considers tariff rate, international tea price, the production level of
Indonesia’s tea and population. Tariff rate is considered as another type of trade barrier
besides MRLs. The international price is included because it has impact on export
earnings. Production and population are considered to account for the supply and
demand side in the global market. In addition, this study considers a series of empirical
2
specifications namely the Ordinary Least Square (OLS), Fixed Effect (FE) and
Random Effect (RE) to estimate the impact of MRLs on trade, as well as the other
determinants.
Tea is a valuable commodity for Indonesia as it currently contributes economic
benefits and has increasing export opportunities. Indonesia is the fifth largest producer
of tea in the world of which around 65% is exported to foreign markets (Kustanti &
Widiyanti, 2007). In 2009, the total plantation area of tea was at approximately 125,000
hectares, of which almost 61% is cultivated by smallholders in rural areas (Syaipulloh,
2010). This indicates that the tea sector contributes substantially to the development of
rural areas. However, inconsistency of tea quality produced by smallholder growers is
one of the main challenges in regards to compliance with safety requirements of foreign
markets. In addition, the risk of pesticide residue remaining in tea is relatively high due
to simple manufacturing processes such as dehydrating and withering (Sood et al.,
2004). Consequently, non-compliance has led to export rejection. For example, some
of Indonesian tea exports to the EU experienced border rejection due to MRL
exceedances of β-cyflutryn, cypermethrin and fenvalerat (Suprihatini, 2006).
Empirical studies of MRLs exist, but the number is limited. Some studies
estimate the trade-effect of MRLs in terms of stringency and periods of enforcement
(Chen, Yang, & Findlay, 2008; Nugroho, 2013; Wei, Huang, & Yang, 2012a, 2012b;
Wilson & Otsuki, 2004). Other studies build different types of indices to compare
MRLs among countries and measure trade-effect (Drogué & DeMaria, 2012; Ferro,
Otsuki, & Wilson, 2015; Liu & Yue 2015; Xiong & Beghin, 2012). Generally, results
indicate that stricter MRLs and more heterogeneous MRLs among countries
empirically impede trade. The study of MRLs on Indonesian coffee export has been
conducted by Nugroho (2013). Thus, there is a gap for other valuable Indonesian export
commodities, such as tea. The findings of this study contribute to policy decision
making and trade facilitation to enhance the value of Indonesian tea exports.
This paper is organized into six sections. Following this introduction, the
background to the global tea market, profile of Indonesia’s tea sector and the regulation
of MRLs on tea are outlined. Section three reviews the literature in regard to the
theoretical framework and relevant empirical studies. Section four explains the data on
the Indonesian tea trade and MRLs and methodology. Section five discusses estimation
results of the trade-effect of MRLs on Indonesian tea export. Section six concludes that
MRLs have a detrimental impact on the export of Indonesian tea.
2 BACKGROUND
Tea is an agricultural commodity of DCs widely traded in international markets
due to the fact that tea is the second most popular beverage after water in the world and
is sold at a reasonably low price (Majumder, Bera, & Rajan 2010). The performance
of tea in terms of global trade and production is predicted to consistently grow until
3
2023 (Intergovernmental Group on Tea Food Agricultural Organization [IGG FAO],
2014b). In addition, this commodity makes a significant contribution to the
development of rural areas, reduction of poverty and food security in DCs (IGG FAO,
2014c).
The global market of tea
3.5
10
3
8
2.5
2
6
1.5
4
Billions USD
Millions tons
The trade performance of tea in the global market has varied over the last 13
years. Figure 2.1 illustrates global volume and value of tea exports between 2001 and
2014. Overall, the global export of tea shows an upward trend with the total volume of
tea exports increasing by 17.79% from 1.5 million tons in 2001 to 1.8 million tons in
2014, with 80% in the form of black tea. Similarly, the total value of tea exports rose
by around 42% from almost 3 billion USD to more than 7 billion USD between 2001
and 2014 and reached a peak in 2013 of almost 8 billion USD. This upward trend is
projected to continually increase and to reach approximately 2.4 million tons in 2023
with the proportion of black and green tea at around 1.7 million tons and 7.5 thousand
tons, respectively (IGG FAO, 2014b).
1
2
0.5
0
0
Year
Green tea
Black tea
value
Source: International Trade Centre (ITC) 2015
Figure 2.1 The world volume and value export of tea between 2001 and 2014
Worldwide production of tea has grown from 1990 to 2012. Figure 2.2 depicts
the total global production of tea over 22 years rising from 2.5 million tons in 1990 to
4.8 million tons in 2012. This growth was due to increase in yield harvested in major
production countries. In addition, there were major land recoveries in Kenya and Sri
Lanka in 2010 accounting for 18% and 13% of worldwide black tea production,
respectively (IGG FAO, 2012). Tea production in China, which is the largest tea
producer, increased from 33% of total world tea output in 2010 to 38% in 2013 (IGG
FAO, 2012; 2014b). This growth trend is predicted to continually rise at a slower rate
with black tea production estimated to reach 4.17 million tons in 2023 (IGG FAO,
2014b).
4
Production (millions tons)
6
5
4
3
2
1
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
Year
Source: Food Agriculture Organization Statistics (FAOSTAT) 2015
Figure 2.2 Total world production of tea from 1990 to 2012
Price volatility indicates the impact of macroeconomic factors of the world tea
economy on the income of producing countries. IGG FAO (2010) states that global tea
price impacts positively on export earnings. In addition, price volatility creates
uncertainty challenging producing countries in terms of plantation planning and
maximizing profit by providing balanced supply (United Nation Conference on Trade
and Development [UNCTAD], 2011). Tea prices experienced high volatility from July
to October 2010 increasing by 29% (UNCTAD, 2011). High prices tend to encourage
production due to the incentive offered by increasing export volume; however,
uncontrolled increase of output can lower tea prices dramatically due to unbalanced
supply-demand of tea (IGG FAO, 2010). IGG FAO (2014b) predicts that the current
situation of excess supply in the global tea market will continue until 2023. Thus, global
tea price affects the decision of producing countries to increase production and export;
however, instability of price in the international market complicates this decision.
4.5
Price
USD per kilogram
4
3.5
3
2.5
2
1.5
1
0.5
Nov-14
Feb-14
May-13
Aug-12
Nov-11
Feb-11
May-10
Aug-09
Feb-08
Nov-08
Aug-06
May-07
Feb-05
Nov-05
Aug-03
May-04
Feb-02
Nov-02
May-01
Aug-00
0
Month
Source: Index Mundi 2015
Figure 2.3 Volatility of tea price from August 2000 to February 2015
5
Generally, volatility of global tea price has fluctuated over the last 15 years.
Figure 2.3 shows the fluctuation in global tea prices from August 2000 to February
2015. Previously, international tea prices were relatively low; however, a slow steady
increase starting in February 2002 reached a 24 year record high at USD 3.21 per kg in
August 2008 (IGG FAO, 2014d). After several price drops between August 2012 and
January 2014, the global tea price reached a peak at slightly above USD 4 per kg in
July 2015. This average upward trend is due to restructuring of the global tea market
in balancing supply and demand of tea. Another reason is that there was an overall
increase in global food and beverage prices between 2006 and 2008 (IGG FAO,
2014d). Gilbert (as cited in IGG FAO, 2014d) concludes that the increase in food prices
was due to the nature of macroeconomic factors and the demand side of food. In
addition, reduction in the supply side due to long-term drought in major global tea
producing countries, such as Kenya, India and Sri Lanka, limits global tea output
increasing tea price (IGG FAO, 2010).
Indonesian tea
120
200
100
150
80
60
100
40
50
20
0
0
20012002200320042005200620072008200920102011201220132014
Year
export(tons)
export (value)
Source: (ITC, 2015)
Figure 2.4 Export of Indonesia’s tea from 2001 to 2014
export value (billions USD)
export (thousands tons)
Although Indonesia is not the world largest exporter of tea, this country
contributed to the global tea trade at about 3.5% in 2014, which places Indonesia fifth
in the world tea exporter rankings following China, Sri Lanka, Kenya and India. Figure
2.4 depicts the export of Indonesian tea from 2001 to 2014. The volume of tea exports
fluctuated between 2001 and 2008, declined consistently from 2008 to 2012, then
remained relatively steady at around 70 thousand tons from 2013 onward. In addition,
the value of Indonesia’s tea exports was highly affected by international prices. The
total value of Indonesia’s tea exports increased from 2001, peaking at 1.78 billion USD
in 2010. This was due to a growth trend in world tea prices from 2008. From 2010 to
2014, this value slowly decreased to 1.4 billion USD in 2014 which was due to global
price decline during this period. Thus, the increase Indonesia’s export value since 2008
is due to global price fluctuations in spite of increasing of export volume.
6
Production (thousands tons)
While production of Indonesia’s tea has declined over the years, this crop plays
an important role in the national economy. In 2000, the economic contribution of tea
was 1.2 trillion IDR which was 0.3% of total revenue from non-oil and gas sector
(Kustanti &Widiyanti, 2007). Figure 2.5 shows the fluctuation in production level of
Indonesia’s tea over a 12 year period starting from 2001. This figure shows a significant
decline from 167 thousand tons in 2005 to almost 149 thousand tons in 2006. This was
a result of the declining number of tea plantations due to land alteration to palm oil or
other more profitable crops (Kustanti &Widiyanti, 2007). However, from 2006 to 2009,
tea production increased as a result of the increase in international tea prices improving
the attractiveness of tea in the global market. After a slight fall in 2010, production
remained constant at about 150 thousand ton until 2012.
175
170
165
160
155
150
145
140
135
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
Source: (FAOSTAT, 2015)
Figure 2.5 Production of Indonesia’s tea from 2001 to 2012
Tea plays a vital role as an income sources for people in remote areas. Tea is a
labor-intensive commodity contributing significantly to employment in remote areas
(IGG FAO, 2014c). Tea plantations are located only in certain rural areas due to
specific soil and climate requirements (Majumder et al., 2010). In Indonesia, most tea
plantations (78%) are situated in West Java and the remaining tea plantations (22%)
are located across 10 provinces (Syaipulloh, 2010). These plantations are mostly owned
by smallholders. In 2005, the numbers of tea plantations owned by smallholders
reached 180,000 with the labor ratio at about 0.75 per acreage, while tea processing
industries employed more than 51,000 workers (Kustanti &Widiyanti, 2007). The
productivity of tea cultivated by smallholders is typically poor compared to plantations
owned by state and private companies (The Ministry of Agriculture of Indonesia &
Indonesia Tea Board, 2014). However, the high number of tea smallholders reveals that
this commodity provides a livelihood for many people in rural areas.
7
Regulation of maximum residue limits for tea
To facilitate global trade, the sanitary and phytosanitary (SPS) agreement has
encouraged countries to adopt international standards. For agro-food commodities
including tea, this agreement recommends Codex as the international benchmark
because it is the international organization of food standard development. This
organization has developed 341 food standards since 1963 including the maximum
residue limits (MRLs) (Codex Alimentarius, 2015). Generally, MRLs set by Codex are
not overly restrictive for global trade as Codex accommodates different standard
compliance capacities and the infrastructure of different countries worldwide.
However, according to the SPS agreement, adhering to Codex standards is voluntary.
Therefore, countries with higher demand for safer food are allowed to establish and
apply more stringent safety requirements, such as MRLs.
In recent years, the relevant authorities of each country have set their own
national MRLs for tea. Thus, national regulation of MRLs across different countries is
diverse. For instance, the MRL of deltamethrin for tea which is regulated at different
limits between Russia and Malaysia at 10 ppm and 5 ppm, respectively (Food Safety
Information System of Malaysian [FoSIM], 2015; United States Department of
Agriculture [USDA], 2014). In addition, MRL stringency increases in terms of the
number of MRLs and strictness level for each pesticide residue. For instance, the EU
increased the total number of MRLs for tea from 67 in 2001 to 454 in 2014 (IGG FAO,
2001; IGG FAO, 2014a). Likewise, the quantity of MRLs regulated for tea in Japan
grew from seven in 2001 to 239 in 2015 (IGG FAO, 2001; The Japan Food Chemical
Research Foundation [FFCR], 2015). Furthermore, in 2005, Malaysia started to
incorporate MRLs into its national food laws with some substances such as dicofol
regulated at 5 ppm which was tighter than Codex’s MRL at 40 ppm (FoSIM, 2015;
Codex Alimentarius, 2015). In 2013, Russia amended its regulation on MRLs which
increased the number of MRLs for tea from four to 25 and established more stringent
MRLs than Codex (USDA, 2014).
The regulation of MRLs becomes more challenging for exporting countries due
to the default or positive list system. This system sets a single uniform limit for all
chemical substances unspecified in MRL regulations. For example, Japan established
a positive list system at 0.01 ppm in 2006 (FFCR, 2015). This means that agricultural
commodities entering the Japanese market cannot contain pesticide residues exceeding
their MRLs and limit at 0.01 ppm for any residue in the absence of MRL. The EU also
established a default system at 0.01 ppm in 2005 which came into force in September
2008 (European Food Safety Authority [EFSA], 2015). Malaysia also enforced a
default list in 2004; however, it applied in the absence of MRL in national regulation
and Codex’s MRLs (FoSIM, 2015).
8
3 LITERATURE REVIEW
This section discusses the relationship between MRLs and international trade
from both a theoretical perspective and according to relevant empirical studies.
Theoretical Perspective
Food Safety as Government Intervention
When markets fail to allocate resources efficiently, economics suggests that
government is required to take action to correct these failures. Market failures of MRLs
arises from imperfect information faced by consumers and producers (Antle, 2001).
This is because consumers cannot distinguish safe or unsafe products before consuming
them and it is often impossible to determine which product is resulting in illness
(Mitchell, 2003). Producers cannot directly determine the level of residues remaining
in their crops without laboratory testing (Antle, 2001). Government can intervene in
market failures by setting safety regulations such as MRLs (Henson, 2003). Figure 6
depicts the shift of supply and demand curve due to MRLs. Safety regulations shift the
supply and demand curve (Mitchell, 2003). The supply side shifts back due to higher
production costs and the demand side shifts forward due to a higher demand for safer
food. Therefore, prices will rise. The implications on quantity are different. One effect
on quantity is negative (refer to figure 3.1). This is because the increase in production
costs are higher relative to the willingness to pay of consumers. In the reverse scenario,
the quantity could rise but the price will certainly increase.
Source: (Mitchell, 2003)
Figure 3.1 Shifts in supply and demand curve due to MRLs
9
Food Safety in Global Trade
In theory, the trade-effect of MRLs can be explained in the demand and supply
diagram for global trade. However, it becomes complicated due to different safety
regulations for different countries (Mitchell, 2003). The trade-effect of safety
standards, such as MRLs, is different with tariff and unquantifiable barriers
(UNCTAD, 2013). There is no single conclusion as to whether safety standards hamper
or enhance trade (Swann, 2010). In terms of MRLs, the two dimensions of MRL
strictness, which are the number of MRLs and their limit value, imposed on each
commodity create difficulties in examining their trade-effect across countries and
commodities (Ferro et al., 2015).
Mitchell (2013) discusses two possible outcomes regarding the trade effect of
different MRL regulation across countries. The first consequence is zero imports. This
case occurs when MRL regulations of a domestic country are too strict. Thus, foreign
producers are incapable to produce safer food complying with these regulations at
reasonable prices. Figure 3.2 shows that the market equilibrium shifts from A-A’ to B
without imports. Secondly, foreign producers still can attain trade benefits through
exporting their product (Mitchell, 2003) because increasing world prices, due to MRL
regulations, is still below domestic prices. Thus, Figure 3.3 depicts a new equilibrium
occurring at point C-C’. At this point, consumers are better off because imports decline
the price of safer food. However, domestic production of safer food also decreases.
Source: (Mitchell, 2003)
Figure 3.2 Market equilibrium of MRLs in foreign trade results in zero import
10
Source: (Mitchell, 2003)
Figure 3.3 Market equilibrium of MRLs in foreign trade with import
Empirical Studies
Studies estimating the trade-effect of MRLs on agro-food commodities are
generally limited. However, among these some studies have focused on analysing the
trade-effect of MRL stringency on various agro-food products. As a part of these
studies, the trade-effect of MRL policy changes are estimated by introducing indicator
variables to measure the trade-effect of MRLs before and after the period of policy
enforcement. Other studies have focused on building indices to capture the level of
heterogeneity of MRLs among countries. In general, empirical results show that MRLs
have a negative impact on trade in terms of MRL stringency level, their increasing
number and heterogeneity among countries.
Typically, these studies use the panel data set. Some focus on a specific MRL
for one commodity of a particular exporting country to major importing countries. For
instance, the trade-effect of chloromycetin MRL on China’s honey (Wei et al., 2012a)
and the impact of carbaryl MRL on Indonesia’s coffee export (Nugroho, 2013). In
addition, Chen et al. (2008) investigate the trade-effect of two specific MRLs,
cholpyrifos and oxytetracycline on China’s exports of two category products covering
two aggregate products (vegetables, and fish and aquatic commodities) and three
individual products (garlic, onion and spinach). Other studies involve the interaction
of panel data from many exporting to many importing countries. Wilson and Otsuki
(2004) examine the trade-effect of stringency limits for chlorpyrifos on bananas from
21 exporting countries to 11 member countries of the Organization for Economic
Cooperation and Development (OECD).
Some studies employ indicator variables to examine the trade-effect of MRL
policy changes. These variables are used to indicate enforcement periods of stricter
MRL regulations. Wei et al. (2012b) introduced four indicators to capture stricter MRL
11
policy changes in the EU, Korea, Japan and Sri Lanka. Yue, Kuang, Sun, Wu, & Xu
(2010) estimated the impact of MRL policy change in the EU in 2001 on China’s tea
export by using the indicator variable. Similarly, Nugroho (2013) added two dummy
variables for the periods of MRL policy change in the EU and Japan. The results of
both studies show negative signs after stricter MRLs regulations are imposed. This
means that these regulations decrease trade.
The underlying theory applied for studies on MRLs is the gravity model. This
model has been frequently applied to estimate the impact of specific issues on bilateral
trade flow including MRLs. The simple gravity model involves main variables of Gross
Domestic Product (GDP) of importing countries and distances between exporting and
importing countries. Some add other variables, such as GDP of exporting countries,
population and cultural variables including common language and colony. In general,
previous studies apply the gravity model and include the additional variable of MRL
stringency (Wilson & Otsuki, 2004; Chen et al., 2008; Yue et al., 2010; Xiong &
Beghin, 2011; Wei et al., 2012a, 2012b; Nugroho, 2013).
In order to estimate trade effects of MRLs, different studies using a panel data
set have applied various techniques and estimators. For example, Nugroho (2013)
compared two applicable estimators for panel data, the fixed effect (FE) and the random
effect (RE), with ordinary least square (OLS). Similarly, Wei at al. (2012b) compared
estimation results of the OLS as a benchmark and the FE as this estimator is found to
be suitable for the gravity model. Yue et al. (2010) applied the OLS estimator as they
did not identify the problem of heteroscedasticity and autocorrelation. Some studies
encountering the problem of zero trade flow require specific estimation techniques.
This problem occurs when bilateral trade does not occur in some estimation periods.
Xiong and Beghin (2011) compared two estimation methods between the HelpmanMelitz-Rubinstein (HMR approach) and the zero-inflated negative binomial pseudomaximum-likelihood model (ZINBPML). Wei at al. (2012a) applied two approaches
and four estimators to address the zero trade flow problem. These approaches are
implemented by adding small positive numbers to all trade flow data and by applying
Heckman’s sample selection model. Furthermore, they compared estimation results of
the OLS, RE, FE and poisson pseudo maximum likelihood (PPML)
Other studies build indices to capture the trade-effect of heterogeneity of
regulated MRLs across countries. Instead of focusing on particular residue limits,
Drogué and DeMaria (2012) assessed the impact on trade of all MRLs on apples and
pears by generating indices of MRL similarities and introducing them to the gravity
model. The result suggests international harmonisation of MRLs. This
recommendation is supported by Liu and Yue (2015). Using similarities indices in the
variable elasticity of substitution (VES) model, these authors investigate the impact of
MRL heterogeneity on trade and social welfare. The result showed that heterogeneity
in MRL can lead to trade conflict between developing and developed countries. From
the perspective of restrictiveness level, Ferro et al. (2015) introduce indices of
restrictiveness representing the stringency level and number of MRLs regulated by
importing countries, concluding that MRLs mainly restrict exports from developing
countries.
12
4 DATA AND METHODOLOGY
This section describes the gravity model frequently used to analyze trade effects
of MRLs and also explains data sources and estimation methods.
The aim of this study was to examine the trade-effect of MRLs on Indonesia’s
tea exports. Previous literature shows that the export of tea is also determined by many
other characteristics. Therefore, this study uses the standard gravity model of trade flow
to form the basis of analysis.
The Gravity Model
The gravity model is an essential empirical tool to analyze trade flow. This
model is successful in estimating bilateral trade flow (Gomez-Herrera, 2013) as it can
provide the most robust economic findings Chaney (2013). By inserting additional key
variables the augmented gravity model is able to estimate the impact of certain issues
on trade flow (Sargento, 2007). Thus, this model is more flexible for addressing
specific issues related to trade including policy changes related to MRLs. Generally,
studies on MRLs apply the gravity model by inserting the variable of MRL stringency
and dummy variables for the periods of MRL enforcement.
The traditional gravity model explains that bilateral trade flow is determined by
country size indicated by Gross Domestic Product (GDP) and trade restrictions
indicated by the distance between countries. This model follows Newton’s gravity
equation so that the traditional gravity model equation becomes:
��
=
��� � ���
�� 2
In another gravity specification, country size can also be associated with
population to capture the market size in trade flow (Reinert, Rajan, & Glass, 2009).
Anderson and van Wincoop (2004) highlight that non-tariff barriers are one type of
trade restriction. Thus, non-tariff barriers, such as SPS measures and MRLs, can be
inserted as variables into the gravity equation. To estimate this equation, empirical
studies convert it into a natural logarithm linear form (Bacchetta et al., 2012). In
addition, empirical studies use the expected signs of GDP and distance to determine
the appropriate method of estimation (Salvitci, 2013).
This study uses two models of the gravity equation and includes four measures
(Deltamethrin, MYS2004, JPY2006 and EU2008) to examine the trade-effect of MRLs
on Indonesia’s tea exports. In addition, price, production and tariff barriers as other
determinants of Indonesia’s tea trade are also taken into account. The specifications of
the two models are:
13
Model 1:
Ln(EXPIjt) = β0 + β1ln(DELTAMETHRINjt) + β2MYS2004 + β3JPN2006 + β4EU2008
+ β5ln(100+TARIFFjt) + β6ln(PRODit-1) + β7ln(PRICEt) +β8ln(GDPjt) +
β9ln(POPjt) + β10ln(DISTij) +εijt
Model 2:
Ln(EXPIjt) = β0 + β1ln(DELTAMETHRINjt) + β2MYS2004 + β3JPN2006 + β4EU2008
+ β5ln(100+TARIFFjt) + β6ln(PRODit-1) + β7ln(PRICEt) +
β8ln(GDPjt*GDPIt)) + β9ln(POPjt*POPIt) +β10ln(DISTij) + εijt
Where the variables and subscripts are:
I = Indonesia
j = Importing countries of Indonesia’s tea
t = Trade year
EXPIjt
: Export value of tea from Indonesia to countries j in year t
DELTAMETHRINjt : The variable of MRL stringency of countries j in year t
MYS2004
: Indicator for default list enforcement periods by Malaysia
JPY2006
: Indicator for default list enforcement periods by Japan
EU2008
: Indicator for default list enforcement periods the EU
TARIFFjt
: Simple average tariff rates of countries j set for Indonesia’s tea
PRODit-1
: Production of Indonesia in year t-1
PRICEt
: International prices in year t
GDPjt
: GDP of country j in year t
GDPIt
: GDP of Indonesia in year t
POPjt
: Population of countries j in year t
POPIt
: Population of Indonesia
DISTij
: Distances the between capital cities of Indonesia and importing
countries
β0 to β10
: Coefficients to be estimated
εijt
: Error term
Data
This study uses panel data covering the time period from 2001 to 2014 and 28
importing countries. These data were collected from various sources. Export data are
from the United Nation Commodity Trade Statistics Database (UNCOMTRADE)
focusing on tea (HS: 0902). GDP data, in nominal terms, is from the World Bank
Indicator (WDI). Price data, in nominal term, is from the Index Mundi. Export, GDP
and Price data are measured in US$ and estimated in real terms. Thus, to convert the
nominal into real terms the Consumer Price Index (CPI) and GDP deflator were used.
Both the CPI and GDP deflators are in the base year 2010 and from the WDI. Likewise,
population data is from the WDI. Distance data, measured in km, is from the Institute
for Research on the International Economy (CEPII). Tariff data is from the World
Trade Organization (WTO). Production data, measured in tons, is from Food
Agriculture Organization Statistics (FAOSTAT). One year lag of production was used
to avoid potential endogeneity (Wei et al., 2012b).
14
The data representing MRL stringency, deltamethrin, were obtained from
sources related to the national regulations of importing countries. Deltamethrin is one
type of pesticide residue measured in ppm. Deltamethrin was used because this limit is
applied in the majority of countries importing Indonesia’s tea. The expected sign for
deltamethrin is positive because the lower the value of the MRLs the more stringent.
Thus, they hamper trade. For some countries which do not set a specific MRL on
deltamethrin, I followed the approach used in previous studies and assume that the
MRL of deltamethrin is the least stringent MRL regulated by these importers of
Indonesia’s tea. This is because Indonesia, as the producer of tea, is required to adjust
its production process to meet the least stringent MRL to export to its trading partners
(Chen et al., 2008; Wei et al., 2012a, 2012b; Wilson & Otsuki, 2004).
In addition, three indicator variables, MYS2004, JPY2006 and EU2008, were
created. These indicators, in the form of dummy variables, were used to capture the
trade effect of policy changes regarding pesticide regulation in importing countries.
The dummy is a binary variable which is valued 0 prior to the period of enforcement
and 1 for the year in which the regulation is initiated and afterwards. MYS2004 is
valued 1 for the period 2004 to 2014, JPN2006 is valued 1 for the period 2006 to 2014,
and EU2008 is valued 1 from 2008 to 2014.
Methods of Estimation
Two models of the gravity equation were estimated using the OLS, FE and RE.
The OLS is useful as a basic comparison model. However, the OLS is only consistent
under specific assumptions that rarely are present and do not account for the two
dimensions of time and cross-section of the panel data set. Thus, the FE and RE were
used as estimation methods for the panel data. Using the FE to estimate the gravity
model is useful to avoid heterogeneity bias (Cheng & Wall, 2005). This is because the
FE assumes that countries have fixed-pair factors, such as distance and cultural
variables, correlated with their trade value (Cheng & Wall, 2005). Consequently, the
FE omits the estimation of time invariant variable coefficients, such as distance. Thus,
this study applied the RE which is able to estimate time invariant variables and to
control the heterogeneity effect of the panel data (Bacchetta et al., 2012). To select
suitable estimators between the FE and RE, this study used the Hausman test (Bacchetta
et al., 2012).
5 THE EMPIRICAL RESULTS
In this section, estimation results of the two gravity models using the OLS, FE
and RE are explained. As presented in Table 5.1, results show consistent signs and very
similar coefficients for each variable in both models. Six independent variables, namely
GDP, population, distance, price, tariff and deltamethrin as the indicator of MRL
15
stringency, have a statistically significant impact on Indonesia’s tea trade. However,
production and indicators for MRL enforcement periods are insignificant. In addition,
a regression version with only either deltamethrin or MRL enforcement periods was
run and found to generate similar results (refer to Tables A.2 & A.3 in the Appendix).
The Hausman test shows that RE is more suitable estimator than FE (refer to Table A.4
in the Appendix).
Table 5.1. Regression results of the impact of MRLs on Indonesia’s tea exports
Ln Deltamethrin
MYS2004
JPY2006
EU2008
Ln Tariff
Ln Production
Ln Price
Ln GDP
OLS
0.293***
(4.43)
0.118
(0.62)
-0.197
(-0.81)
-0.131
(-0.73)
-3.292***
(-17.25)
-1.721
(-0.94)
1.193***
(4.73)
0.413***
(9.85)
Model 1
FE
0.604**
(2.91)
0.272*
(2.31)
-0.108
(-0.82)
-0.0773
(-0.78)
-0.214
(-0.26)
-1.078
(-1.03)
0.896***
(5.54)
0.0164
(0.08)
RE
0.451**
(2.88)
0.157
(1.45)
-0.124
(-0.94)
-0.0788
(-0.80)
-2.237***
(-4.30)
-0.883
(-0.87)
0.835***
(5.49)
0.307**
(2.63)
Ln (GDPIt*GDPjt)
Ln Population
0.344***
(6.19)
0.718
(1.94)
_cons
-0.305***
(-3.44)
29.37
(1.36)
392
0.6265
(.)
9.181
(0.57)
392
RE
0.435**
(2.77)
0.120
(1.08)
-0.151
(-1.14)
-0.174
(-1.68)
-2.233***
(-4.28)
-0.701
(-0.68)
0.875***
(5.72)
0.408***
(9.76)
-0.0737
(-0.48)
0.215*
(2.04)
0.348***
(6.28)
-0.302***
(-3.40)
6.037
(0.28)
392
0.625
0.711
(1.96)
(.)
0.466
(0.02)
392
0.461**
(2.96)
-0.0389
(-0.13)
-0.162
(-0.01)
392
0.400*
(2.52)
Ln (POPIt*POPjt)
Ln Distance
OLS
0.292***
(4.41)
0.0381
(0.20)
-0.243
(-1.00)
-0.281
(-1.55)
-3.294***
(-17.22)
-1.233
(-0.67)
1.194***
(4.73)
Model 2
FE
0.599**
(2.89)
0.276*
(2.26)
-0.107
(-0.81)
-0.0882
(-0.79)
-0.187
(-0.23)
-1.151
(-1.09)
0.946***
(6.00)
-0.102
(-0.34)
15.95
(1.25)
392
Observations
R-squared
R-squared overall
0.2142
0.5844
0.1668
t statistics in parentheses * p
PESTICIDES ON INDONESIA’S TEA EXPORT
NOVIANA KUS YUNIATI
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL INSTITUTE
BOGOR
2016
STATUTORY DECLARATION
I, Noviana Kus Yuniati, hereby declare that the master thesis entitled “The
Trade-Effect of Maximum Residue Limits for Pesticides on Indonesia’s Tea” 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. Source of
information originated from published and unpublished work are stated in the part of
references of this thesis. Herewith, I passed the thesis copyright to Bogor Agricultural
University.
Bogor, January 2016
Noviana Kus Yuniati
H151137084
RINGKASAN
NOVIANA KUS YUNIATI. Pengaruh Batas Maksimum Residu Pestisida pada Ekspor
Teh Indonesia. Dibimbing oleh RINA OKTAVIANI dan STEPHANIE MCWHINNIE.
Teh sebagai salah satu komoditas ekspor unggulan Indonesia dituntut untuk
memenuhi persyaratan keamanan pangan dalam perdagangan internasional. Salah satu
dari persyaratan tersebut adalah Batas Maksimum Residu (BMR) untuk pestisida.
Batas ini bertujuan untuk melindungi kesehatan konsumen dari kandungan berlebih
residu pestisida dalam pangan. Namun, BMR dapat menghambat perdagangan
internasional karena semakin ketatnya batas maksimum yang ditetapkan dan
pemberlakuan regulasi baru oleh negara-negara importir.
Tujuan dari penelitian ini adalah menganalisa pengaruh BMR terhadap ekspor
teh Indonesia. Penelitian ini menggunakan data panel dari 28 negara importir dari tahun
2001 sampai dengan 2014 dan dua model gravity dengan tiga metode estimasi yaitu
Ordinary Least Square (OLS), Fixed Effect (FE) dan Random Effect (RE).
Secara empiris, diperoleh hasil bahwa BMR yang semakin ketat berpengaruh
negatif dan signifikan terhadap ekspor teh Indonesia. Selain itu, hambatan tarif dan
harga internasional juga berpengaruh signifikan terhadap ekspor teh Indonesia.
Penelitian ini merekomendasikan Good Agricultural Practices (GAP) pada proses
penanaman teh serta pengujian laboratorium secara periodik untuk menjaga kualitas
teh Indonesia dan meningkatkan ekspor.
Kata kunci: batas maksimum residu, ekspor, teh
SUMMARY
NOVIANA KUS YUNIATI. The Trade-Effect of Maximum Residue Limits for
Pesticides on Indonesia’s Tea Export. Under supervision of RINA OKTAVIANI and
STEPHANIE MCWHINNIE.
Tea, as one of Indonesia’s important agricultural trade commodities, has
recently been confronted with the necessity to meet new requirements in food safety
for global trade. One of these requirements is maximum residue limits (MRLs) for
pesticides. These limits are set to protect consumers’ health from excessive pesticide
residues, which remain on food. However, MRLs are considered to hamper global trade
due to their increasing stringency and enforcement of stricter regulations.
Thus, the objective of this study is to estimate the impact of MRLs on
Indonesia’s tea exports. Based on a panel data set of 28 importing countries of
Indonesian tea from 2001 to 2014, this study employs two gravity models and applies
three estimation methods, Ordinary Least Square (OLS), Fixed Effect (FE) and
Random Effect (RE), to estimate the trade-effect of MRLs on Indonesian tea exports.
Empirical results show that increasing stringency of MRLs impedes export of
Indonesian tea. In addition, international prices and tariff barriers are statistically
significant, indicating a significant effect on exports. Based on these findings, this
study suggests that actions, such as implementing Good Agricultural Practices (GAP)
in tea farming and conducting laboratory testing on MRLs periodically, will improve
the quality of Indonesian tea and contribute to maximizing export opportunities.
Key words: maximum residue limits, export, tea
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THE TRADE-EFFECT OF MAXIMUM RESIDUE LIMITS FOR
PESTICIDES ON INDONESIA’S TEA EXPORT
NOVIANA KUS YUNIATI
Master Thesis
As a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL INSTITUTE
BOGOR
2016
External Advisory Committee Examiner: Prof Dr Ir Bambang Juanda, MS
ACKNOWLEDGEMENT
‘My Lord, increase me in knowledge’ (The Quran 20:114). All praises to Allah
SWT for the opportunities, strength and His blessing to complete this Masters
dissertation.
First, I would like to express my utmost gratitude to my supervisor from the
University of Adelaide, Dr. Stephanie McWhinnie, for her extensive assistance,
continuous support, kindness and encouragement throughout the process of this
dissertation. Her worthwhile ideas, suggestions and constructive comments have
contributed enormously to improve this work. Thereafter, my sincere gratitude to my
supervisor from Bogor Agricultural University, Prof. Dr. Ir. Rina Oktaviani, MS for
her assistance, knowledge, ideas, motivation and cooperation.
I would like also to appreciate Dr. Florian Ploeckl for his hard work to
coordinate the Masters of Applied Economics Dissertation program. Not forgotten, my
appreciation to Athena Kerley for her ideas, support and providing feedback for my
writing.
My appreciation to all lecturers in the double degree program of the School of
Economics of Bogor Agricultural University and University of Adelaide for the
valuable learning experiences and economics knowledge. I would like thank all staff
in both universities, especially Niranjala, Gus, Nicole, Regi and Dian for their support
and help during my study in Bogor and Adelaide.
I would like to thank the Ministry of Trade and the Australian Awards
Scholarship for providing a sponsorship to pursue a high education degree.
I would also like to acknowledge Dr. Dana Thomsen for providing editorial
assistance. Editorial intervention was restricted to Standards D and E of the Australian
Standards for Editing Practice.
I am also indebted to all of my college friends in Bogor and Adelaide for their
friendship, discussion and moral support during this study period.
I owe more honor to my parents, husband, sister and brother for their endless
love, encouragement and prayers throughout this challenging study period.
Bogor, January 2016
Noviana Kus Yuniati
i
TABLE OF CONTENTS
LIST OF FIGURES ...................................................................................................... ii
LIST OF TABLES ........................................................................................................ ii
1 INTRODUCTION .................................................................................................... 1
2 BACKGROUND ...................................................................................................... 1
The global market of tea ............................................................................................ 1
Indonesian tea ............................................................................................................ 1
Regulation of maximum residue limits for tea .......................................................... 1
3 LITERATURE REVIEW.......................................................................................... 1
Theoretical Perspective ............................................................................................. 1
Food Safety as Government Intervention .............................................................. 1
Food Safety in Global Trade .................................................................................. 1
Empirical Studies ...................................................................................................... 1
4 DATA AND METHODOLOGY ............................................................................. 1
The Gravity Model .................................................................................................... 1
Data ........................................................................................................................... 1
Methods of Estimation .............................................................................................. 1
5 THE EMPIRICAL RESULTS.................................................................................. 1
6 CONCLUSION AND POLICY IMPLICATIONS .................................................. 1
REFERENCES............................................................................................................ 19
APPENDIX ................................................................................................................... 1
ii
LIST OF FIGURES
Figure 2.1 The world volume and value export of tea between 2001 and 2014 ........... 1
Figure 2.2 Total world production of tea from 1990 to 2012 ....................................... 1
Figure 2.3 Volatility of tea price from august 2000 to february 2015 .......................... 1
Figure 2.4 Export of Indonesia’s tea from 2001 to 2014 .............................................. 1
Figure 2.5 Production of Indonesia’s tea from 2001 to 2012 ....................................... 1
Figure 3.1 Shifts in supply and demand curve due to MRLs ........................................ 1
Figure 3.2 Market equilibrium of MRLs in foreign trade results in zero import .......... 1
Figure 3.3 Market equilibrium of MRLs in foreign trade with import ......................... 1
LIST OF TABLES
Table 5.1. Regression results of the impact of MRLs on Indonesia’s tea exports ........ 1
Table A.1 Summary statistic ........................................................................................ 1
Table A.2 The estimation results of a regression version with only either deltamethrin
or the MRL enforcement periods for model 1 .............................................. 1
Table A.3 The estimation results of a regression version with only either deltamethrin
or the MRL enforcement periods for model 2 .............................................. 1
Table A.4 The result of Hausman test ........................................................................... 1
1
1 INTRODUCTION
The Uruguay Round Agreement on Agriculture (URAA) of the World Trade
Organization (WTO) has liberalized global agricultural trade by reducing tariff
barriers. On the other hand, concern has arisen regarding technical safety measures,
such as sanitary and phytosanitary (SPS) safety measures that increasingly impede
trade. Henson and Loader (2001) show that SPS measures restrict market access of
agricultural commodities from developing countries (DCs) to developed countries. The
reason is that SPS measures are prevalent in agro-food commodities as the main
objective of these measures is to protect the health of humans, animals and plants.
Furthermore, agro-food exports have a significant role in developing countries. The
total contribution of developing countries in global agricultural export was relatively
high at more than 36% in 2001, accounting for 162 billion USD (Aksoy, 2005).
Therefore, complying with SPS measures is substantial for developing countries to
penetrate the world market with regard to product competitiveness and consumer safety
(Henson & Loader, 2001).
This study empirically examines the impact of a specific set of SPS measures
on Indonesian tea exports. These measures are known as maximum residue limits
(MRLs). MRLs consist of the maximum permitted levels of pesticide residues in agrofood commodities. They have been established by many countries to assure safe
application of pesticides in farming and to protect countries from plant-disease risks of
imported food. In order to analyze the trade-effect of MRLs, trade data regarding export
value of Indonesian tea to 28 major importing countries from 2001 to 2014 and data of
MRLs for pesticide residues, namely deltamethrin, are used. Of 28 importing countries,
15 are from Asia and four are from the EU. Other countries include Egypt, the USA,
Canada, the Russian Federation, the United Kingdom, Fiji, Australia and New Zealand.
Three indicator variables for particular MRL regulation enforcement are created. This
regulation known as the default list system was imposed by three major importing
countries of Indonesian tea, namely Malaysia, Japan and the EU, in 2004, 2006 and
2008, respectively. The default list is the regulation of MRL which set the general
maximum limit for pesticide residue at a strict level of 0.01 ppm for any residues
unspecified in MRL regulation.
The empirical strategy used to examine the trade diversion effect of MRLs is
based on the gravity model. This model is selected to account for the standard
determinants of trade as MRLs will not be the only factor for altering tea exports. A
typical gravity dataset from the Institute for Research on the International Economy
(CEPII) is used. This data covers Gross Domestic Product (GDP), and distance
between Indonesia and its exporting partners from 2001 to 2014. Furthermore, this
study also considers tariff rate, international tea price, the production level of
Indonesia’s tea and population. Tariff rate is considered as another type of trade barrier
besides MRLs. The international price is included because it has impact on export
earnings. Production and population are considered to account for the supply and
demand side in the global market. In addition, this study considers a series of empirical
2
specifications namely the Ordinary Least Square (OLS), Fixed Effect (FE) and
Random Effect (RE) to estimate the impact of MRLs on trade, as well as the other
determinants.
Tea is a valuable commodity for Indonesia as it currently contributes economic
benefits and has increasing export opportunities. Indonesia is the fifth largest producer
of tea in the world of which around 65% is exported to foreign markets (Kustanti &
Widiyanti, 2007). In 2009, the total plantation area of tea was at approximately 125,000
hectares, of which almost 61% is cultivated by smallholders in rural areas (Syaipulloh,
2010). This indicates that the tea sector contributes substantially to the development of
rural areas. However, inconsistency of tea quality produced by smallholder growers is
one of the main challenges in regards to compliance with safety requirements of foreign
markets. In addition, the risk of pesticide residue remaining in tea is relatively high due
to simple manufacturing processes such as dehydrating and withering (Sood et al.,
2004). Consequently, non-compliance has led to export rejection. For example, some
of Indonesian tea exports to the EU experienced border rejection due to MRL
exceedances of β-cyflutryn, cypermethrin and fenvalerat (Suprihatini, 2006).
Empirical studies of MRLs exist, but the number is limited. Some studies
estimate the trade-effect of MRLs in terms of stringency and periods of enforcement
(Chen, Yang, & Findlay, 2008; Nugroho, 2013; Wei, Huang, & Yang, 2012a, 2012b;
Wilson & Otsuki, 2004). Other studies build different types of indices to compare
MRLs among countries and measure trade-effect (Drogué & DeMaria, 2012; Ferro,
Otsuki, & Wilson, 2015; Liu & Yue 2015; Xiong & Beghin, 2012). Generally, results
indicate that stricter MRLs and more heterogeneous MRLs among countries
empirically impede trade. The study of MRLs on Indonesian coffee export has been
conducted by Nugroho (2013). Thus, there is a gap for other valuable Indonesian export
commodities, such as tea. The findings of this study contribute to policy decision
making and trade facilitation to enhance the value of Indonesian tea exports.
This paper is organized into six sections. Following this introduction, the
background to the global tea market, profile of Indonesia’s tea sector and the regulation
of MRLs on tea are outlined. Section three reviews the literature in regard to the
theoretical framework and relevant empirical studies. Section four explains the data on
the Indonesian tea trade and MRLs and methodology. Section five discusses estimation
results of the trade-effect of MRLs on Indonesian tea export. Section six concludes that
MRLs have a detrimental impact on the export of Indonesian tea.
2 BACKGROUND
Tea is an agricultural commodity of DCs widely traded in international markets
due to the fact that tea is the second most popular beverage after water in the world and
is sold at a reasonably low price (Majumder, Bera, & Rajan 2010). The performance
of tea in terms of global trade and production is predicted to consistently grow until
3
2023 (Intergovernmental Group on Tea Food Agricultural Organization [IGG FAO],
2014b). In addition, this commodity makes a significant contribution to the
development of rural areas, reduction of poverty and food security in DCs (IGG FAO,
2014c).
The global market of tea
3.5
10
3
8
2.5
2
6
1.5
4
Billions USD
Millions tons
The trade performance of tea in the global market has varied over the last 13
years. Figure 2.1 illustrates global volume and value of tea exports between 2001 and
2014. Overall, the global export of tea shows an upward trend with the total volume of
tea exports increasing by 17.79% from 1.5 million tons in 2001 to 1.8 million tons in
2014, with 80% in the form of black tea. Similarly, the total value of tea exports rose
by around 42% from almost 3 billion USD to more than 7 billion USD between 2001
and 2014 and reached a peak in 2013 of almost 8 billion USD. This upward trend is
projected to continually increase and to reach approximately 2.4 million tons in 2023
with the proportion of black and green tea at around 1.7 million tons and 7.5 thousand
tons, respectively (IGG FAO, 2014b).
1
2
0.5
0
0
Year
Green tea
Black tea
value
Source: International Trade Centre (ITC) 2015
Figure 2.1 The world volume and value export of tea between 2001 and 2014
Worldwide production of tea has grown from 1990 to 2012. Figure 2.2 depicts
the total global production of tea over 22 years rising from 2.5 million tons in 1990 to
4.8 million tons in 2012. This growth was due to increase in yield harvested in major
production countries. In addition, there were major land recoveries in Kenya and Sri
Lanka in 2010 accounting for 18% and 13% of worldwide black tea production,
respectively (IGG FAO, 2012). Tea production in China, which is the largest tea
producer, increased from 33% of total world tea output in 2010 to 38% in 2013 (IGG
FAO, 2012; 2014b). This growth trend is predicted to continually rise at a slower rate
with black tea production estimated to reach 4.17 million tons in 2023 (IGG FAO,
2014b).
4
Production (millions tons)
6
5
4
3
2
1
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
Year
Source: Food Agriculture Organization Statistics (FAOSTAT) 2015
Figure 2.2 Total world production of tea from 1990 to 2012
Price volatility indicates the impact of macroeconomic factors of the world tea
economy on the income of producing countries. IGG FAO (2010) states that global tea
price impacts positively on export earnings. In addition, price volatility creates
uncertainty challenging producing countries in terms of plantation planning and
maximizing profit by providing balanced supply (United Nation Conference on Trade
and Development [UNCTAD], 2011). Tea prices experienced high volatility from July
to October 2010 increasing by 29% (UNCTAD, 2011). High prices tend to encourage
production due to the incentive offered by increasing export volume; however,
uncontrolled increase of output can lower tea prices dramatically due to unbalanced
supply-demand of tea (IGG FAO, 2010). IGG FAO (2014b) predicts that the current
situation of excess supply in the global tea market will continue until 2023. Thus, global
tea price affects the decision of producing countries to increase production and export;
however, instability of price in the international market complicates this decision.
4.5
Price
USD per kilogram
4
3.5
3
2.5
2
1.5
1
0.5
Nov-14
Feb-14
May-13
Aug-12
Nov-11
Feb-11
May-10
Aug-09
Feb-08
Nov-08
Aug-06
May-07
Feb-05
Nov-05
Aug-03
May-04
Feb-02
Nov-02
May-01
Aug-00
0
Month
Source: Index Mundi 2015
Figure 2.3 Volatility of tea price from August 2000 to February 2015
5
Generally, volatility of global tea price has fluctuated over the last 15 years.
Figure 2.3 shows the fluctuation in global tea prices from August 2000 to February
2015. Previously, international tea prices were relatively low; however, a slow steady
increase starting in February 2002 reached a 24 year record high at USD 3.21 per kg in
August 2008 (IGG FAO, 2014d). After several price drops between August 2012 and
January 2014, the global tea price reached a peak at slightly above USD 4 per kg in
July 2015. This average upward trend is due to restructuring of the global tea market
in balancing supply and demand of tea. Another reason is that there was an overall
increase in global food and beverage prices between 2006 and 2008 (IGG FAO,
2014d). Gilbert (as cited in IGG FAO, 2014d) concludes that the increase in food prices
was due to the nature of macroeconomic factors and the demand side of food. In
addition, reduction in the supply side due to long-term drought in major global tea
producing countries, such as Kenya, India and Sri Lanka, limits global tea output
increasing tea price (IGG FAO, 2010).
Indonesian tea
120
200
100
150
80
60
100
40
50
20
0
0
20012002200320042005200620072008200920102011201220132014
Year
export(tons)
export (value)
Source: (ITC, 2015)
Figure 2.4 Export of Indonesia’s tea from 2001 to 2014
export value (billions USD)
export (thousands tons)
Although Indonesia is not the world largest exporter of tea, this country
contributed to the global tea trade at about 3.5% in 2014, which places Indonesia fifth
in the world tea exporter rankings following China, Sri Lanka, Kenya and India. Figure
2.4 depicts the export of Indonesian tea from 2001 to 2014. The volume of tea exports
fluctuated between 2001 and 2008, declined consistently from 2008 to 2012, then
remained relatively steady at around 70 thousand tons from 2013 onward. In addition,
the value of Indonesia’s tea exports was highly affected by international prices. The
total value of Indonesia’s tea exports increased from 2001, peaking at 1.78 billion USD
in 2010. This was due to a growth trend in world tea prices from 2008. From 2010 to
2014, this value slowly decreased to 1.4 billion USD in 2014 which was due to global
price decline during this period. Thus, the increase Indonesia’s export value since 2008
is due to global price fluctuations in spite of increasing of export volume.
6
Production (thousands tons)
While production of Indonesia’s tea has declined over the years, this crop plays
an important role in the national economy. In 2000, the economic contribution of tea
was 1.2 trillion IDR which was 0.3% of total revenue from non-oil and gas sector
(Kustanti &Widiyanti, 2007). Figure 2.5 shows the fluctuation in production level of
Indonesia’s tea over a 12 year period starting from 2001. This figure shows a significant
decline from 167 thousand tons in 2005 to almost 149 thousand tons in 2006. This was
a result of the declining number of tea plantations due to land alteration to palm oil or
other more profitable crops (Kustanti &Widiyanti, 2007). However, from 2006 to 2009,
tea production increased as a result of the increase in international tea prices improving
the attractiveness of tea in the global market. After a slight fall in 2010, production
remained constant at about 150 thousand ton until 2012.
175
170
165
160
155
150
145
140
135
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
Source: (FAOSTAT, 2015)
Figure 2.5 Production of Indonesia’s tea from 2001 to 2012
Tea plays a vital role as an income sources for people in remote areas. Tea is a
labor-intensive commodity contributing significantly to employment in remote areas
(IGG FAO, 2014c). Tea plantations are located only in certain rural areas due to
specific soil and climate requirements (Majumder et al., 2010). In Indonesia, most tea
plantations (78%) are situated in West Java and the remaining tea plantations (22%)
are located across 10 provinces (Syaipulloh, 2010). These plantations are mostly owned
by smallholders. In 2005, the numbers of tea plantations owned by smallholders
reached 180,000 with the labor ratio at about 0.75 per acreage, while tea processing
industries employed more than 51,000 workers (Kustanti &Widiyanti, 2007). The
productivity of tea cultivated by smallholders is typically poor compared to plantations
owned by state and private companies (The Ministry of Agriculture of Indonesia &
Indonesia Tea Board, 2014). However, the high number of tea smallholders reveals that
this commodity provides a livelihood for many people in rural areas.
7
Regulation of maximum residue limits for tea
To facilitate global trade, the sanitary and phytosanitary (SPS) agreement has
encouraged countries to adopt international standards. For agro-food commodities
including tea, this agreement recommends Codex as the international benchmark
because it is the international organization of food standard development. This
organization has developed 341 food standards since 1963 including the maximum
residue limits (MRLs) (Codex Alimentarius, 2015). Generally, MRLs set by Codex are
not overly restrictive for global trade as Codex accommodates different standard
compliance capacities and the infrastructure of different countries worldwide.
However, according to the SPS agreement, adhering to Codex standards is voluntary.
Therefore, countries with higher demand for safer food are allowed to establish and
apply more stringent safety requirements, such as MRLs.
In recent years, the relevant authorities of each country have set their own
national MRLs for tea. Thus, national regulation of MRLs across different countries is
diverse. For instance, the MRL of deltamethrin for tea which is regulated at different
limits between Russia and Malaysia at 10 ppm and 5 ppm, respectively (Food Safety
Information System of Malaysian [FoSIM], 2015; United States Department of
Agriculture [USDA], 2014). In addition, MRL stringency increases in terms of the
number of MRLs and strictness level for each pesticide residue. For instance, the EU
increased the total number of MRLs for tea from 67 in 2001 to 454 in 2014 (IGG FAO,
2001; IGG FAO, 2014a). Likewise, the quantity of MRLs regulated for tea in Japan
grew from seven in 2001 to 239 in 2015 (IGG FAO, 2001; The Japan Food Chemical
Research Foundation [FFCR], 2015). Furthermore, in 2005, Malaysia started to
incorporate MRLs into its national food laws with some substances such as dicofol
regulated at 5 ppm which was tighter than Codex’s MRL at 40 ppm (FoSIM, 2015;
Codex Alimentarius, 2015). In 2013, Russia amended its regulation on MRLs which
increased the number of MRLs for tea from four to 25 and established more stringent
MRLs than Codex (USDA, 2014).
The regulation of MRLs becomes more challenging for exporting countries due
to the default or positive list system. This system sets a single uniform limit for all
chemical substances unspecified in MRL regulations. For example, Japan established
a positive list system at 0.01 ppm in 2006 (FFCR, 2015). This means that agricultural
commodities entering the Japanese market cannot contain pesticide residues exceeding
their MRLs and limit at 0.01 ppm for any residue in the absence of MRL. The EU also
established a default system at 0.01 ppm in 2005 which came into force in September
2008 (European Food Safety Authority [EFSA], 2015). Malaysia also enforced a
default list in 2004; however, it applied in the absence of MRL in national regulation
and Codex’s MRLs (FoSIM, 2015).
8
3 LITERATURE REVIEW
This section discusses the relationship between MRLs and international trade
from both a theoretical perspective and according to relevant empirical studies.
Theoretical Perspective
Food Safety as Government Intervention
When markets fail to allocate resources efficiently, economics suggests that
government is required to take action to correct these failures. Market failures of MRLs
arises from imperfect information faced by consumers and producers (Antle, 2001).
This is because consumers cannot distinguish safe or unsafe products before consuming
them and it is often impossible to determine which product is resulting in illness
(Mitchell, 2003). Producers cannot directly determine the level of residues remaining
in their crops without laboratory testing (Antle, 2001). Government can intervene in
market failures by setting safety regulations such as MRLs (Henson, 2003). Figure 6
depicts the shift of supply and demand curve due to MRLs. Safety regulations shift the
supply and demand curve (Mitchell, 2003). The supply side shifts back due to higher
production costs and the demand side shifts forward due to a higher demand for safer
food. Therefore, prices will rise. The implications on quantity are different. One effect
on quantity is negative (refer to figure 3.1). This is because the increase in production
costs are higher relative to the willingness to pay of consumers. In the reverse scenario,
the quantity could rise but the price will certainly increase.
Source: (Mitchell, 2003)
Figure 3.1 Shifts in supply and demand curve due to MRLs
9
Food Safety in Global Trade
In theory, the trade-effect of MRLs can be explained in the demand and supply
diagram for global trade. However, it becomes complicated due to different safety
regulations for different countries (Mitchell, 2003). The trade-effect of safety
standards, such as MRLs, is different with tariff and unquantifiable barriers
(UNCTAD, 2013). There is no single conclusion as to whether safety standards hamper
or enhance trade (Swann, 2010). In terms of MRLs, the two dimensions of MRL
strictness, which are the number of MRLs and their limit value, imposed on each
commodity create difficulties in examining their trade-effect across countries and
commodities (Ferro et al., 2015).
Mitchell (2013) discusses two possible outcomes regarding the trade effect of
different MRL regulation across countries. The first consequence is zero imports. This
case occurs when MRL regulations of a domestic country are too strict. Thus, foreign
producers are incapable to produce safer food complying with these regulations at
reasonable prices. Figure 3.2 shows that the market equilibrium shifts from A-A’ to B
without imports. Secondly, foreign producers still can attain trade benefits through
exporting their product (Mitchell, 2003) because increasing world prices, due to MRL
regulations, is still below domestic prices. Thus, Figure 3.3 depicts a new equilibrium
occurring at point C-C’. At this point, consumers are better off because imports decline
the price of safer food. However, domestic production of safer food also decreases.
Source: (Mitchell, 2003)
Figure 3.2 Market equilibrium of MRLs in foreign trade results in zero import
10
Source: (Mitchell, 2003)
Figure 3.3 Market equilibrium of MRLs in foreign trade with import
Empirical Studies
Studies estimating the trade-effect of MRLs on agro-food commodities are
generally limited. However, among these some studies have focused on analysing the
trade-effect of MRL stringency on various agro-food products. As a part of these
studies, the trade-effect of MRL policy changes are estimated by introducing indicator
variables to measure the trade-effect of MRLs before and after the period of policy
enforcement. Other studies have focused on building indices to capture the level of
heterogeneity of MRLs among countries. In general, empirical results show that MRLs
have a negative impact on trade in terms of MRL stringency level, their increasing
number and heterogeneity among countries.
Typically, these studies use the panel data set. Some focus on a specific MRL
for one commodity of a particular exporting country to major importing countries. For
instance, the trade-effect of chloromycetin MRL on China’s honey (Wei et al., 2012a)
and the impact of carbaryl MRL on Indonesia’s coffee export (Nugroho, 2013). In
addition, Chen et al. (2008) investigate the trade-effect of two specific MRLs,
cholpyrifos and oxytetracycline on China’s exports of two category products covering
two aggregate products (vegetables, and fish and aquatic commodities) and three
individual products (garlic, onion and spinach). Other studies involve the interaction
of panel data from many exporting to many importing countries. Wilson and Otsuki
(2004) examine the trade-effect of stringency limits for chlorpyrifos on bananas from
21 exporting countries to 11 member countries of the Organization for Economic
Cooperation and Development (OECD).
Some studies employ indicator variables to examine the trade-effect of MRL
policy changes. These variables are used to indicate enforcement periods of stricter
MRL regulations. Wei et al. (2012b) introduced four indicators to capture stricter MRL
11
policy changes in the EU, Korea, Japan and Sri Lanka. Yue, Kuang, Sun, Wu, & Xu
(2010) estimated the impact of MRL policy change in the EU in 2001 on China’s tea
export by using the indicator variable. Similarly, Nugroho (2013) added two dummy
variables for the periods of MRL policy change in the EU and Japan. The results of
both studies show negative signs after stricter MRLs regulations are imposed. This
means that these regulations decrease trade.
The underlying theory applied for studies on MRLs is the gravity model. This
model has been frequently applied to estimate the impact of specific issues on bilateral
trade flow including MRLs. The simple gravity model involves main variables of Gross
Domestic Product (GDP) of importing countries and distances between exporting and
importing countries. Some add other variables, such as GDP of exporting countries,
population and cultural variables including common language and colony. In general,
previous studies apply the gravity model and include the additional variable of MRL
stringency (Wilson & Otsuki, 2004; Chen et al., 2008; Yue et al., 2010; Xiong &
Beghin, 2011; Wei et al., 2012a, 2012b; Nugroho, 2013).
In order to estimate trade effects of MRLs, different studies using a panel data
set have applied various techniques and estimators. For example, Nugroho (2013)
compared two applicable estimators for panel data, the fixed effect (FE) and the random
effect (RE), with ordinary least square (OLS). Similarly, Wei at al. (2012b) compared
estimation results of the OLS as a benchmark and the FE as this estimator is found to
be suitable for the gravity model. Yue et al. (2010) applied the OLS estimator as they
did not identify the problem of heteroscedasticity and autocorrelation. Some studies
encountering the problem of zero trade flow require specific estimation techniques.
This problem occurs when bilateral trade does not occur in some estimation periods.
Xiong and Beghin (2011) compared two estimation methods between the HelpmanMelitz-Rubinstein (HMR approach) and the zero-inflated negative binomial pseudomaximum-likelihood model (ZINBPML). Wei at al. (2012a) applied two approaches
and four estimators to address the zero trade flow problem. These approaches are
implemented by adding small positive numbers to all trade flow data and by applying
Heckman’s sample selection model. Furthermore, they compared estimation results of
the OLS, RE, FE and poisson pseudo maximum likelihood (PPML)
Other studies build indices to capture the trade-effect of heterogeneity of
regulated MRLs across countries. Instead of focusing on particular residue limits,
Drogué and DeMaria (2012) assessed the impact on trade of all MRLs on apples and
pears by generating indices of MRL similarities and introducing them to the gravity
model. The result suggests international harmonisation of MRLs. This
recommendation is supported by Liu and Yue (2015). Using similarities indices in the
variable elasticity of substitution (VES) model, these authors investigate the impact of
MRL heterogeneity on trade and social welfare. The result showed that heterogeneity
in MRL can lead to trade conflict between developing and developed countries. From
the perspective of restrictiveness level, Ferro et al. (2015) introduce indices of
restrictiveness representing the stringency level and number of MRLs regulated by
importing countries, concluding that MRLs mainly restrict exports from developing
countries.
12
4 DATA AND METHODOLOGY
This section describes the gravity model frequently used to analyze trade effects
of MRLs and also explains data sources and estimation methods.
The aim of this study was to examine the trade-effect of MRLs on Indonesia’s
tea exports. Previous literature shows that the export of tea is also determined by many
other characteristics. Therefore, this study uses the standard gravity model of trade flow
to form the basis of analysis.
The Gravity Model
The gravity model is an essential empirical tool to analyze trade flow. This
model is successful in estimating bilateral trade flow (Gomez-Herrera, 2013) as it can
provide the most robust economic findings Chaney (2013). By inserting additional key
variables the augmented gravity model is able to estimate the impact of certain issues
on trade flow (Sargento, 2007). Thus, this model is more flexible for addressing
specific issues related to trade including policy changes related to MRLs. Generally,
studies on MRLs apply the gravity model by inserting the variable of MRL stringency
and dummy variables for the periods of MRL enforcement.
The traditional gravity model explains that bilateral trade flow is determined by
country size indicated by Gross Domestic Product (GDP) and trade restrictions
indicated by the distance between countries. This model follows Newton’s gravity
equation so that the traditional gravity model equation becomes:
��
=
��� � ���
�� 2
In another gravity specification, country size can also be associated with
population to capture the market size in trade flow (Reinert, Rajan, & Glass, 2009).
Anderson and van Wincoop (2004) highlight that non-tariff barriers are one type of
trade restriction. Thus, non-tariff barriers, such as SPS measures and MRLs, can be
inserted as variables into the gravity equation. To estimate this equation, empirical
studies convert it into a natural logarithm linear form (Bacchetta et al., 2012). In
addition, empirical studies use the expected signs of GDP and distance to determine
the appropriate method of estimation (Salvitci, 2013).
This study uses two models of the gravity equation and includes four measures
(Deltamethrin, MYS2004, JPY2006 and EU2008) to examine the trade-effect of MRLs
on Indonesia’s tea exports. In addition, price, production and tariff barriers as other
determinants of Indonesia’s tea trade are also taken into account. The specifications of
the two models are:
13
Model 1:
Ln(EXPIjt) = β0 + β1ln(DELTAMETHRINjt) + β2MYS2004 + β3JPN2006 + β4EU2008
+ β5ln(100+TARIFFjt) + β6ln(PRODit-1) + β7ln(PRICEt) +β8ln(GDPjt) +
β9ln(POPjt) + β10ln(DISTij) +εijt
Model 2:
Ln(EXPIjt) = β0 + β1ln(DELTAMETHRINjt) + β2MYS2004 + β3JPN2006 + β4EU2008
+ β5ln(100+TARIFFjt) + β6ln(PRODit-1) + β7ln(PRICEt) +
β8ln(GDPjt*GDPIt)) + β9ln(POPjt*POPIt) +β10ln(DISTij) + εijt
Where the variables and subscripts are:
I = Indonesia
j = Importing countries of Indonesia’s tea
t = Trade year
EXPIjt
: Export value of tea from Indonesia to countries j in year t
DELTAMETHRINjt : The variable of MRL stringency of countries j in year t
MYS2004
: Indicator for default list enforcement periods by Malaysia
JPY2006
: Indicator for default list enforcement periods by Japan
EU2008
: Indicator for default list enforcement periods the EU
TARIFFjt
: Simple average tariff rates of countries j set for Indonesia’s tea
PRODit-1
: Production of Indonesia in year t-1
PRICEt
: International prices in year t
GDPjt
: GDP of country j in year t
GDPIt
: GDP of Indonesia in year t
POPjt
: Population of countries j in year t
POPIt
: Population of Indonesia
DISTij
: Distances the between capital cities of Indonesia and importing
countries
β0 to β10
: Coefficients to be estimated
εijt
: Error term
Data
This study uses panel data covering the time period from 2001 to 2014 and 28
importing countries. These data were collected from various sources. Export data are
from the United Nation Commodity Trade Statistics Database (UNCOMTRADE)
focusing on tea (HS: 0902). GDP data, in nominal terms, is from the World Bank
Indicator (WDI). Price data, in nominal term, is from the Index Mundi. Export, GDP
and Price data are measured in US$ and estimated in real terms. Thus, to convert the
nominal into real terms the Consumer Price Index (CPI) and GDP deflator were used.
Both the CPI and GDP deflators are in the base year 2010 and from the WDI. Likewise,
population data is from the WDI. Distance data, measured in km, is from the Institute
for Research on the International Economy (CEPII). Tariff data is from the World
Trade Organization (WTO). Production data, measured in tons, is from Food
Agriculture Organization Statistics (FAOSTAT). One year lag of production was used
to avoid potential endogeneity (Wei et al., 2012b).
14
The data representing MRL stringency, deltamethrin, were obtained from
sources related to the national regulations of importing countries. Deltamethrin is one
type of pesticide residue measured in ppm. Deltamethrin was used because this limit is
applied in the majority of countries importing Indonesia’s tea. The expected sign for
deltamethrin is positive because the lower the value of the MRLs the more stringent.
Thus, they hamper trade. For some countries which do not set a specific MRL on
deltamethrin, I followed the approach used in previous studies and assume that the
MRL of deltamethrin is the least stringent MRL regulated by these importers of
Indonesia’s tea. This is because Indonesia, as the producer of tea, is required to adjust
its production process to meet the least stringent MRL to export to its trading partners
(Chen et al., 2008; Wei et al., 2012a, 2012b; Wilson & Otsuki, 2004).
In addition, three indicator variables, MYS2004, JPY2006 and EU2008, were
created. These indicators, in the form of dummy variables, were used to capture the
trade effect of policy changes regarding pesticide regulation in importing countries.
The dummy is a binary variable which is valued 0 prior to the period of enforcement
and 1 for the year in which the regulation is initiated and afterwards. MYS2004 is
valued 1 for the period 2004 to 2014, JPN2006 is valued 1 for the period 2006 to 2014,
and EU2008 is valued 1 from 2008 to 2014.
Methods of Estimation
Two models of the gravity equation were estimated using the OLS, FE and RE.
The OLS is useful as a basic comparison model. However, the OLS is only consistent
under specific assumptions that rarely are present and do not account for the two
dimensions of time and cross-section of the panel data set. Thus, the FE and RE were
used as estimation methods for the panel data. Using the FE to estimate the gravity
model is useful to avoid heterogeneity bias (Cheng & Wall, 2005). This is because the
FE assumes that countries have fixed-pair factors, such as distance and cultural
variables, correlated with their trade value (Cheng & Wall, 2005). Consequently, the
FE omits the estimation of time invariant variable coefficients, such as distance. Thus,
this study applied the RE which is able to estimate time invariant variables and to
control the heterogeneity effect of the panel data (Bacchetta et al., 2012). To select
suitable estimators between the FE and RE, this study used the Hausman test (Bacchetta
et al., 2012).
5 THE EMPIRICAL RESULTS
In this section, estimation results of the two gravity models using the OLS, FE
and RE are explained. As presented in Table 5.1, results show consistent signs and very
similar coefficients for each variable in both models. Six independent variables, namely
GDP, population, distance, price, tariff and deltamethrin as the indicator of MRL
15
stringency, have a statistically significant impact on Indonesia’s tea trade. However,
production and indicators for MRL enforcement periods are insignificant. In addition,
a regression version with only either deltamethrin or MRL enforcement periods was
run and found to generate similar results (refer to Tables A.2 & A.3 in the Appendix).
The Hausman test shows that RE is more suitable estimator than FE (refer to Table A.4
in the Appendix).
Table 5.1. Regression results of the impact of MRLs on Indonesia’s tea exports
Ln Deltamethrin
MYS2004
JPY2006
EU2008
Ln Tariff
Ln Production
Ln Price
Ln GDP
OLS
0.293***
(4.43)
0.118
(0.62)
-0.197
(-0.81)
-0.131
(-0.73)
-3.292***
(-17.25)
-1.721
(-0.94)
1.193***
(4.73)
0.413***
(9.85)
Model 1
FE
0.604**
(2.91)
0.272*
(2.31)
-0.108
(-0.82)
-0.0773
(-0.78)
-0.214
(-0.26)
-1.078
(-1.03)
0.896***
(5.54)
0.0164
(0.08)
RE
0.451**
(2.88)
0.157
(1.45)
-0.124
(-0.94)
-0.0788
(-0.80)
-2.237***
(-4.30)
-0.883
(-0.87)
0.835***
(5.49)
0.307**
(2.63)
Ln (GDPIt*GDPjt)
Ln Population
0.344***
(6.19)
0.718
(1.94)
_cons
-0.305***
(-3.44)
29.37
(1.36)
392
0.6265
(.)
9.181
(0.57)
392
RE
0.435**
(2.77)
0.120
(1.08)
-0.151
(-1.14)
-0.174
(-1.68)
-2.233***
(-4.28)
-0.701
(-0.68)
0.875***
(5.72)
0.408***
(9.76)
-0.0737
(-0.48)
0.215*
(2.04)
0.348***
(6.28)
-0.302***
(-3.40)
6.037
(0.28)
392
0.625
0.711
(1.96)
(.)
0.466
(0.02)
392
0.461**
(2.96)
-0.0389
(-0.13)
-0.162
(-0.01)
392
0.400*
(2.52)
Ln (POPIt*POPjt)
Ln Distance
OLS
0.292***
(4.41)
0.0381
(0.20)
-0.243
(-1.00)
-0.281
(-1.55)
-3.294***
(-17.22)
-1.233
(-0.67)
1.194***
(4.73)
Model 2
FE
0.599**
(2.89)
0.276*
(2.26)
-0.107
(-0.81)
-0.0882
(-0.79)
-0.187
(-0.23)
-1.151
(-1.09)
0.946***
(6.00)
-0.102
(-0.34)
15.95
(1.25)
392
Observations
R-squared
R-squared overall
0.2142
0.5844
0.1668
t statistics in parentheses * p