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Master’s Thesis in Economics
An Analysis on the Non-Tariff Measure
Effect of the Korea-ASEAN FTA on
Bilateral Trade in Goods
䞲㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫㦮 ゚ὖ㎎㧻⼓ 䣾ὒ ㍳
August 2018
Department of Agricultural Economics and Rural Development
Seoul National University
YUNJI LEE
L
Abstract
An Analysis on the Non-Tariff Measure
Effect of the Korea-ASEAN FTA on
Bilateral Trade in Goods
The paper quantifies the trade volume effect of Korea-ASEAN Free Trade Agreement in light of liberalization through non-tariff measures. As the level of liberalization differ by sectors and product classes, the paper conducted analysis on more disaggregated level; processed food sector and nine product classes. To measure the trade volume effect of NTM liberalization took as part of the Korea-ASEAN FTA, the paper incorporates approaches from previous literature; 1) a fixed-effect model with a directional fixed effect that accounts for multilateral resistance 2) gravity model in a multiplicative form to account for zero trade observation and 3) use of a PPML estimator to provide a consistent, unbiased estimator. To quantify the impact of the liberalization through the NTMs, the paper uses the residual effect, which is quantified by Korea-ASEAN dummies, by controlling for all possible trade costs including tariff barriers. The estimation shows that the mitigation of NTMs from signing Korea- ASEAN FTA had increased the bilateral trade volume between economies by 85.06%. For food sector, the trade volume effect was 25.86%. The product classes that faced one of the biggest volume effects were tobacco and starch products.
Keywords : Non-Tariff Measures, Korea-ASEAN FTA, Student Number : 2016-21485
LLL
Table of Contents
List of Tablesv
1. Introduction1 1.1 Motivation and Background 1 1.2 Scope of the Study 4 1.3 Objective 6
2. Literature Review 8 2.1 The Quantification of Non-Tariff Measures8 2.2 The Gravity Model 11
3. Empirical analysis 15 3.1 The Empirical Model15 3.2 Empirical Specification 19 3.3 Data 21
4. Estimation Results27 5.1 Estimation Results27 5.2 The trade volume effect 30
LY ῃ
ῃⶎ㽞⪳37
References39
Appendix43 Table A1. Korea-ASEAN Trade Liberalization Schedule by the member
countries 43
Y
LIST OF TABLES
Table 1. Studies on quantifying the NTMs14 Table 2. Volume of the bilateral trade between the covered countries in the World trade 22 Table 3. Description of selected product classes23 Table 4. Summary statistics of bilateral export variable of food sector24 Table 5. Summary statistics of bilateral tariff by product level in percentage points 25 Table 6. Data and Sources26 Table 7. Gravity estimation results for total economy and food sector 27 Table 8. Gravity estimation results for nine product classes 28 Table 9. Trade volume effect calculated from the estimation 32
1.
Introduction
1.1
Motivation and Background
The Korea ASEAN Free Trade Agreement (Korea-ASEAN FTA), which took effect on June 1, 2007, has served as an important growth engine for trade and economic cooperation between Korea and ASEAN over the past decade1. Korean exports toward ASEAN have grown at a CAGR of 8.8%, and ASEAN has grown to become Korea’s second largest export destination since 2012 (K-Stat). The results are important because it played an important role in enhancing access to the emerging ASEAN market, the post-China production base and emerging consumer market. The Korea-ASEAN FTA involve not only tariff reductions between member countries but also extensive commitments on reduction of trade barriers induced by the non-tariff measures (NTMs). In fact, the recent survey points out that the negotiations or agreements on non-tariff measures are one of the most significant contributions and the subject of potential improvements for further integration at the same time.
According to the UNCTAD, NTMs are generally defined as “policy measures other than ordinary customs tariffs that can potentially have an economic effect
Korea-ASEAN FTA took different trade liberalization schedule for a different member of ASEAN. See Appendix Table 1. However, the two-track trade liberalization schedule was under schedule among the ASEAN members, too; for ASEAN6, tariff and NTM elimination deadline is 2010 (2012 for the Philippines on NTMs); for CMLV, 2015/2018 (Loreli C., 2007).
on international trade in goods, changing the quantities traded, or prices, or both” (UNCTAD, 2013). Ranging from border inspections to sanitary measures, the NTMs serve to correct market failures and promote consumer safety, yet some argue that NTMs act as trade barriers and serve as substitutes for tariffs.
On a survey conducted by KITA celebrating the 10th anniversary of the Korea-ASEAN FTA, Korean companies importing and exporting to Korea-ASEAN responded that the biggest contribution of Korea-ASEAN FTA came from mitigation of non-tariff measures (45.9%) and reduction of market prices due to non-tariff benefits (88.8%), respectively. On the other hand, of the five major difficulties that Korean companies face in the Korea-ASEAN FTA, four lied on the non-tariff measures (KITA 2017)2. Despite the importance of the NTMs and despite the importance of the ASEAN as Korea’s major trade partner, the existing papers fail to address such aspect of the Korea-ASEAN FTA.
Increasing attention to and significance of the NTMs in the regional trade agreements (RTAs) does not confine to the Korea-ASEAN FTA. For the last couple of decades, the bilateral and multilateral trade agreements have undergone two important shifts. The first is a relative shift in focus of trade negotiations from tariff reduction to the removal of NTM. While trade barriers from tariffs
The five major difficulties faced by the companies are: 1)Heterogeneity in regulatory standards on product classification, 2) Limited scope of coverage of tariff abolition due to reciprocal tariff rate, 3) disapproval of exceptions on the direct consignment 4) procedures on the certificate of origin and 5) exclusion of the ex-post application of FTA
have been reduced to the significant extent, the number and severity of the NTMs are reported to be increasing (Cadot et.al, 2015).
Secondly, relatively deep and comprehensive agreements compared to shallow tariff-only agreements are in effect and are under negotiation (Egger et al. 2015). The failure of the Doha Round triggered an emphasis on a negotiation of
Preferential Trade Agreements (PTAs), involving extensive commitments on reduction of trade barriers induced by the NTMs as well as the tariff. Fontagne et al (2013) even argues that gains from trade liberalization are much larger with regulatory cooperation or mitigation of NTMs than reductions on tariffs.
Moreover, the study can lead to meaningful implications for future negotiations with emerging economies. In April 2015, Korean government announced a new FTA strategy, which targets mostly developing countries in Latin America, the Middle East, Africa and Asia. On February 21, 2018, Korea signed the FTA deal with five Central American countries; Panama, Costa Rica, Honduras, El Salvador, Nicaragua. Although the size of bilateral trade between these countries andGKorea is relatively small, the effects on Korea's food sector cannot be overlooked considering the experience of implementing FTAs. In this sense, the implication from Korea-ASEAN FTA from this study can bring significant implications on promoting future FTA centered on developing countries. Thus, assessment of the trade agreement in light of liberalization through non-tariff measures is necessary at this point to set guideline on further negotiations and utilize the Korea-ASEAN FTA effectively.
Korea-ASEAN FTA has been revised several times over the past decade, pointing out that the liberalization level is relatively low. By quantifying the effect of regulatory cooperation and reducing trade barriers of NTMs, The paper
aims to provide a basis for the future negotiations between Korea and ASEAN.
1.2
Scope of the Study
The paper focuses on the trade effect of Korea-ASEAN FTA in light of liberalization through non-tariff measures. The analysis covers fifty countries from 2001 to 2015. Note that the ASEAN countries covered in the study are Indonesia, Malaysia, Philippines, Thailand, Singapore, Brunei, and Vietnam. The paper will address not only the total economy but also food sector and class of products of food sector. The paper addresses two industries: agriculture and food sector with 18 and 6 product classes, respectively.
The paper chooses Korea-ASEAN FTA as the subject of analysis not only because of the reported significance of NTMs to the agreement but also because of the significance of the ASEAN as one of the most important political and economic partner of Korea now and forward. The Association of Southeast Asian Nations (ASEAN) is a political and economic organization for regional stability and economic growth among its members3. The Korean export toward ASEAN has been robust for last decade with an average growth rate of 8.8%, and the expansion is incomparable to any trade partners of Korea (K-stat). ASEAN members have rapidly growing economies and robust population growth, with the average GDP growth rate of 6.9% for CLMV and 4.3% for ASEAN6 (ASEAN
ASEAN member countries are Indonesia, Malaysia, Philippines, Thailand, Singapore, Brunei, Vietnam, Myanmar, Cambodia, and Lao PDR.
Secretariat). With such background, ASEAN market is considered as the post-China production base and emerging consumer market.
The paper will address not only the total economy but also food sector and class of products of processed food sector, for two reasons: First, The multilateral resistance terms and trade costs, including both tariff and NTMs, significantly vary by sector and product classes. Secondly, the elasticity of substitution also varies across sectors. This is important because the parameters of the trade cost function are joint estimates of the elasticity of substitution and the elasticity of trade costs with respect to particular factors. Thus, it is important to take account of this variation by sectors to achieve accurate estimates.
Secondly, among various industries, the processed and agricultural food sector is one of the fields facing highest NTMs while expanding rapidly in Korea-ASEAN bilateral trade. Since the implementation of the FTA between Korea and ASEAN, both imports and exports on the processed food have increased
significantly. According to KREI, food imports from ASEAN increased from $1.43 billion in 2006 to $ 4.6 billion in 2014, which is an increase of 220.9%, making ASEAN the third biggest food exporting economy of Korea after US and China. In the same period, food exports to ASEAN also increased 265.9%(KREI 2016). Among agro-food imports from ASEAN, processed food accounted for 56.8% (as of 2014), far higher than crops or fruits. Major export items are also concentrated on processed food, accounting for 77.2% of the total (KREI 2016). Thus, analysis on processed food products, or food sector, in more disaggregated level can bring significant implications.
1.3
Objective
A recent survey reveals that the agreements in the non-tariff measures are one of the most significant contributions and a subject of potential improvements for further integration of Korea-ASEAN FTA. As government launches new FTA strategy, which targets mostly developing countries, the assessment of the trade agreement in light of liberalization through non-tariff measures is necessary at this point to set guideline on further FTA negotiations and to utilize the ASEAN FTA effectively. Thus, the paper focuses on the trade effect of Korea-ASEAN FTA in light of liberalization through non-tariff measures. As the level of liberalization and trade costs differ by sectors and product classes, analysis on a more disaggregated level is necessary. The paper addresses the processed food sector, which is traditionally known as the sector with protectionist NTM intervention.
To quantify the impact of the liberalization through the NTMs, the paper uses the residual effect, which is quantified by Korea-ASEAN dummies, by
controlling for all possible trade costs including tariff barriers. The analysis covers fifty countries from 2001 to 2015. The paper will address not only total economy but also a sector and subsequent classes of products. The paper addresses two industries: processed food sector with nine product classes.
The organization of this paper is as follows. Section 2 provides a literature review on quantification of NTMs and recent developments of the gravity model, the model the paper uses to analyze such effect. The Section 3 will discuss the foundation of structural gravity model and introduces model specification and the
data used in the analysis. Section 4 respectively discusses the empirical result of the estimation and the final section of the study concludes.
2.
Literature Review
2.1
The Quantification of Non-Tariff Measures
The NTMs are multi-dimensional and thus extremely difficult to incorporate such measures into a single index. For this matter, even with its significant impact on the trade flow, there is no universal consensus on how NTMs should be accounted for in empirical research. Over the years, several analytical approaches were developed in order to tackle the challenging task of quantification of NTMs. The approaches can be classified into two basic
categories; measures that require the use of the economic technique with those do not.
The most common and simple methodology is the inventory approach that quantifies the incidence of NTMs, such as the frequency index and the coverage ratio. These measures do not require the use of econometric techniques. The frequency index is the percentage of products that are subject to one or more NTMs. The coverage ratio measures the percentage of imports that are subject to one or more NTMs. These methods can easily capture the number and
importance of the NTMs in a country’s trade, but it does not take into account the measure’s stiffness. Regardless of the severity, each NTM is treated equally and thus can bias the severity of the effect on a trade flow.
The most common method using of econometric techniques to assess the effect of NTMs is to yield an ad-valorem equivalent (AVEs) of the NTMs. The basic concept of AVEs is to calculate the trade cost that would induce the same level of impact on the trade flow as the NTM in question. AVEs are by far the best way to
quantify the severity of NTMs into a manageable variable. Two basic avenues exist to estimate AVEs of the NTM; price-based and quantity-based approach.
First, the price-based approach quantifies the price gap between the import prices in the importing country (“internal price”) with the reference prices of comparable products in markets without such distortions (“external price”). The approach allows the direct estimation of NTMs, based on the observed internal and external prices. For example, Dean et al (2009) estimates the TCEs of 65 countries on four sectors, using city-level retail price data to estimate the impact to core NTMs. Studies such as Bradford(2003, 2005) and Ferrantino (2006) also employ price-based approach. However, not only finding a fully comparable and non-distorted market is very difficult but also internal prices may be
unobservable or uninformative, given the list prices on the domestic wholesale market may not reflect the actual prices.
On the other hand, the quantity-based approach uses the gravity model to quantify the effect of NTM on the bilateral trade flow. The quantity-based approach captures the difference between the observed (with NTMs) and the estimated (non-distorted) trade volume or value. Compared to price-based approach, the approach provides a more indirect estimation of NTMs, influenced by the estimated value, which is "subject to various uncertainties surrounding econometric specifications" (Fontagne et al., 2013). Although the debate between two approaches is ongoing, a quantity-based approach is more common in the literature due to a relatively light requirement of the data and it’s convenience on large-scale analyses with large country coverage. The paper employs the
approach since the quantity-based approach is most commonly used approach to assess the impact of regional trade agreements.
Previous literatures employing quantity-based approach to estimate the AVEs of NTM are as follows: Kee et al. (2009), Berden et al (2009) and Winchester (2009) estimated the economic effect of existing NTMs in bilateral trade pattern, while Egger and Larch (2011), and Egger (2015) estimated the non-tariff effect of the trade agreements between countries.
Kee et al (2009) estimates AVEs of NTMs at tariff line level and generated trade restrictive index, the World Bank OTRI index, to quantify the severity of existing NTMs for 60 countries. To quantify the existing NTMs, Kee et al (2009) uses NTM variable, a binary dummy variable indicating 1 if NTM exists in such tariff line and zero otherwise. Berden et al (2009) estimates the existing NTMs between EU and USA using an NTB (Non-Tariff Barrier) index, generated from a business survey with 5,500 data points from both large firms and small and medium enterprises of EU and US. Winchester (2009) uses the residual border effect from controlling for all possible trade costs to assess the non-tariff barriers between New Zealand and its trading partners.
Egger and Larch (2011) and Egger (2015) discern the tariff-effect from the non-tariff effect of entering trade agreements, such as Europe Agreement(EA) and Interim Agreement (IA) and Transatlantic Trade and Investment Partnership (TTIP), respectively. Egger and Larch (2011) uses EA, IA and RTA dummy variable with a variable capturing the tariffs between countries explicitly to “discern pure tariff effects from other effects”. Egger and Larch (2011) not only measures the trade effect but also intensive, extensive margins and trade creation, a diversion from EA and IA, by taking both one-part two-part estimation. The Egger (2015) used EU dummy with PTA depth variable, an indicator variable ranging from 1 to 6 depending on the depth of the preferential trade agreements.
The paper aims to assess the trade effect of Korea-ASEAN FTA induced by NTM arrangements by controlling for all possible trade costs, including tariff barriers. To my knowledge, the paper is one of the first studies to assess the NTM induced trade effect of Korea-ASEAN FTA.
2.2
The Structural Gravity Model
The gravity model first adopted by Timbergen (1962) to analyze international trade patterns by applying the gravity model of physics. The intuition behind the model is simple;the trade volume between the two countries is proportional to the size of the economy and is inversely proportional to distance. Despite its remarkable empirical success, the model stayed outside the mainstream of trade research until recently, due to lack of theoretical foundations. The Anderson and van Wincoop (2003) model revolutionized the field by introducing the
multilateral resistance term and providing structural gravity model with a solid theoretical framework. The structural gravity model is a workhorse of
international trade analyzing, delivering a tractable framework for trade policy analysis in a multi-country environment.
Since Anderson van Wincoop (2003), numerous scholars developed a range of different theoretical microfoundations and econometric version of the model. The main developments that are widely applied to current gravity model literature are a treatment of multilateral resistance terms, zero trade flows, and
heteroscedasticity of trade data. To address such issues, various econometric techniques have been developed accordingly.
(2003), is based on the intuition that not only the bilateral but also multilateral trade friction4 for both importing and exporting country should be considered. The problem was that the multilateral resistance terms are not directly observable. The studies attempted to control multilateral resistance by using so-called
“remoteness indexes” approximated by the reduced-form approaches. However, it was criticized to bear little resemblance to the theoretical counterpart of
multilateral terms. Thus studies such as Hummels (2001) and Feenstra (2004) suggested fixed effect estimation which enabled fully accounting the multilateral resistance terms while overcoming the computational difficulties. The directional fixed effect5not only account for the unobservable multilateral resistance terms but also absorb other observable and unobservable country-specific
characteristics.
Helpman et al. (2008) suggested that zero trade flows are not random zeros but observations that contain information about the trade pattern between counties. Helpman et al. (2008) introduced a Tobit model with theoretically-founded two-stage estimation process which can take the zero trade observations into account. The model suggests that heterogeneity in firm productivity exists, and only the exporters who can absorb some market-entry fixed cost will export to the market.
For example, New Zealand-Australia pair (NZ-A), 4,155km apart fromeach other, face similar or even more bilateral trade friction than United Kingdom-Spain pair (UK-S), 1,263km apart. However, NZ-A will be more dependent to each other on trade because when taken multilateral trade friction (e.g. distance from the rest of the world) to account, the trade friction between NZ-A will be significantly less than UK-S.
Thus, the model performs a first-stage Probit estimation, which determines the probability to export, then proceed to OLS estimation.
However, Santos Silva and Tenreyro (2006) points out the OLS estimator is not only biased but also inconsistent with the gravity model, due to the presence of heteroscedasticity. Thus, Santos Silva and Tenreyro(2006) suggest Poisson Pseudo Maximum Likelihood (PPML) estimator and use of a gravity model in multiplicative form rather than the OLS logarithmic form. PPML estimator, applied to the gravity model expressed in a multiplicative form, accounts for the heteroscedasticity of trade data and the zero trade flows. Moreover, PPML estimator provides consistent estimates of a nonlinear model and is consistent in the presence of fixed effects.
Thus, the paper incorporates approaches from previous literature; 1) a fixed-effect model with a directional fixed fixed-effect that accounts for multilateral resistance 2) gravity model in a multiplicative form to account for zero trade observation and 3) use of a PPML estimator to provide a consistent, unbiased estimator. Egger and Larch (2011) also incorporate approaches mentioned above. Thus, The paper follows the model Egger and Larch (2011) used to "discern pure tariff effects from other effects" of EA, IA. However, since the paper intends to cover only trade effect from Korea-ASEAN FTA, the paper will take one-part estimation with PPML estimator.
Ta b le 1 . Em pirical St udies on qua n ti fy ing th e N T M s i n l ig ht o f t he three a p pr oa ch es Ke e et a l ( 2009 ) E gge r & L ar ch ( 2011 ) E gge r e t al . ( 20 15) Wi n ch e ster ( 2009 ) F oundation Le am er (1988 ) Av W (2003) Av W ( 2003) A v W (2003) C ountr y C ov erag e 6 0 c oun trie s 157 e co nom ie s 12 re g io n s 37 r eg ion s a nd R OW Esti m ati on P art ia l Eq. g ra v ity a naly si s F ix ed E ffect Grav it y Analy si s F ix ed E ffect Grav it y Analy si s P art ia l Eq. g ra v ity a naly si s M u lt il ate ra l Re si st an ce YE S Y ES YE S Y ES Z ero Obser v at ion NO YE S YES Y es Hete roscedas ti c Disturban ce NO ( O LS ) YE S ( P P ML) YE S ( P PML) YES ( P P ML) NTM v ar iab le NTM v ar iab le (0,1) R T A dumm y (0,1) EU dum m y , P T A de pth v ar iab le R T A dumm ie s (0, 1) S ecto ra l Es t. YE S (H S6, a p p. 4000 ) NO Y ES ( 11 secto rs ) Y ES ( 23 se cto rs )
3.
The Empirical Model
3.1
The Empirical Model
The paper incorporates approaches from previous literature; 1) a fixed-effect model with a directional fixed effect that accounts for multilateral resistance 2) gravity model in a multiplicative form to account for zero trade observation and 3) use of a PPML estimator to provide a consistent, unbiased estimator. The model must account for both zero and positive trade observations to avoid selection bias. However, estimating using PPML enables estimations with both zero and positive observations without modifying the model. Thus, the paper uses theoretical foundations and structural gravity model Anderson van Wincoop provided. A model of positive and zero bilateral trade with multilateral resistance can be characterized as follows.
The model assumes monopolistic competition, symmetric trade costs for both marginal and fixed, and constant elasticity of substitution (CES) utility. Assume the CES utility function of consumers in country j is as below:
ሺͳሻܷ ൌ ߚ ଵିఙ ఙ ή ܿ ሺఙିଵሻ ఙ ே ୀଵ ൩ ఙ ఙିଵ ǡ ߪ ͳ
where ߚ is a CES preference parameter (an inverse measure of quality), ߪ is
the elasticity of substitution among different varieties, ܿis consumption of the
Consumers maximize utility subject to the following budget constraint:
ሺʹሻܧൌ ܿ ே ୀଵ
whereܧ is the total expenditure in country j, is the price of goods from
country i cosumed in country j and differ depending on the importer i due to trade costs(ݐሻ: ൌ ݐ where is the factory-gate prices in the country of
origin. The trade costs follow “iceberg” structure which only fraction of shipped goods from i arrives in j, defined as ݐൌ
ଵ
ఛೕǡ ߬ ͳǤ
Solving the consumer’s optimization problem with equation (1) and (2) yields the following demand function as below:
ሺ͵ሻܺൌ ቆߚ߬ ܲ
ቇ
ଵିఙ
ܧ
where ܺis the exports from i to j, ܲ is the CES price index of country j: ܲ ൌ
ቒσ ൫ߚ߬൯ ଵିఙ ே ୀଵ ቓ ଵȀሺଵିఙሻ .
Applying yielded demand function to the market clearing condition ൌ
ሺͶሻሺߚሻଵିఙൌ ୧ σ ൬߬ܲ ൰ ଵିఙ ܧ
The terms can be arranged as follows by defining world nominal GDP as ௪ൌ σ ܻ and dividing equation (4) by ௪:
ሺͷሻሺߚሻଵିఙൌ ୧ ܻ௪ σ ൬߬ܲ ൰ ଵିఙܧ ܻ௪
Now, expand the RHS of the equation (5) to insert back to equation (3) to get:
ሺሻܺ ൌ ቆ ߬ ܲቇ ଵିఙ ܧܻ ܻ௪ අቆߚ߬ ܲ ቇ ଵିఙ ܧ ܻ௪ ඉ ିଵ
Anderson and van Winncoop (2003) define the denominator of equation (5) as ȫଵିఙൌ σ ൬ ఛೕ ೕ൰ ଵିఙா ೕ ೢ
ǤRearranging equation (6) with the definition of ȫଵିఙ
yields the structural gravity model of Anderson van Wincoop (2003):
ሺሻܺൌ ܧܻ ܻ௪ ቆ ߬ ܲȫቇ ଵିఙ
where ܲ and ȫ are multilateral resistance terms of country j and i,
ሺͺሻȫଵିఙ ൌ ቆ߬ ܲቇ ଵିఙ ܧ ܻ௪ ሺͻሻܲଵିఙൌ ൬߬ ȫ ൰ ଵିఙ ܻ ܻ௪
As mentioned previously, the paper also addresses more disaggregated level of goods on its analysis. This is important because the elasticity of substitution, multilateral resistance terms and trade costs, including both tariff and NTMs, significantly vary by sector and product classes. Fortunately, the structural gravity model is separable; the bilateral expenditure across countries are
separable from output and expenditure at the country level at both aggregate and disaggregated level (Larch and Yotov, 2016b). The structural gravity model of the sector/good k at time t will be as follows:
ሺͳͲሻܺ௧ ൌܧ௧ ܻ ௧ ܻ௪௧ ቆ ߬௧ ܲ௧ȫ௧ ቇ ଵିఙೖ
Alternatively, the gravity model can be estimated with pooled data. However, fixed effect models become easily unmanageable when including sector fixed effects into the model, in this case importer-sector-time fixed and exporter-sector-time fixed effect. Thus, various studies recommend estimating the model
separately for each sector in the dataset. Through such estimation, each sector represents a separate estimation sample allowing the elasticity of substitution, multilateral resistance terms and trade costs vary accordingly. The paper thus estimates the model separately for each sector in the dataset.
3.2
Empirical Specification
The paper will conduct a fixed effect estimation with exporter-time and importer-time fixed effects. This is to control for unobservable multilateral resistance terms and potential unobservable characteristics of exporter and importer that vary over time. The gravity model used in the paper to perform fixed effects estimation is as below.
(11) ܺ௧ ൌ ሺߟ௧ ߤ௧ ߚூௌ் ܦܫݏݐ ߚ்ேீܥܶܰܩ
ߚேீܮܣܰܩ ߚி்ܭܣܨܶܣ ߚோ்ܴܶܣ௧ ߚ௧൫ͳ ݐܽݎ݂݂݅௧൯ሻ ߝ௧
The ܺ௧ is export from country i to country j in a given time t. ߟ௧ is the
exporter-time fixed effect, which reflects ܻ௧ߎ௧ଵିఙ and all other possibly
unobservable determinants of ܺ௧ which are specific to country i and time t.
ߤ௧is an importer-time fixed effect, which reflects ܻ௧ܲ௧ଵିఙ and all other
possibly unobservable determinants of ܺ௧ which are specific to country j and
time t.
The three variables, ܦܫݏݐǡ ܥܶܰܩǡand ܮܣܰܩ are the three most
widely used and robust gravity proxies for trade costs. The variable ܦܫݏݐ
denotes the logarithm of bilateral distance between trading partners i and j. CNTG and ܮܣܰܩ denote indicator variables that captures the presence of
shared border or language between country i and j, respectively. The ܴܶܣ௧ and
ܭܣܨܶܣrepresent indicator variables that take a value of one when country i
respectively. The regional trade agreement is a comprehensive term that includes free trade agreement, preferential agreement, and currency union. In the paper, the ܴܶܣ௧ takes a value of one for all enacted regional trade agreement except
for the Korea-ASEAN FTA, which will be denoted by the ܭܣܨܶܣ variable.
As mentioned in the previous section, the structural gravity model is separable, which implies that equation (10) can be estimated for each sector as if the data were aggregated. For any sector or product class k, the gravity model used in the paper to perform fixed effects estimation is as below.
(12)ܺ௧ ൌ ሺߟ௧ ߤ௧ ߚூௌ் ܦܫݏݐ ߚ்ேீ ܥܶܰܩ ߚேீ ܮܣܰܩ
ߚி் ܭܣܨܶܣ ߚோ் ܴܶܣ௧ ߚ௧ ݈݊൫ͳ ݐܽݎ݂݂݅௧൯ ߝ௧
The structural gravity model enables to translate the effects of concluding any trade policy variable into a trade volume effect. The volume effect triggered by the presence of Korea-ASEAN RTA can be calculated in percentage terms as follows (Yotov et al 2016):
3.3
Data
The paper use data on nominal bilateral export flows among 50 countries6 over the years 2001-2015. The period covers seven years before and after the June 1st, 2007 when the Korea-ASEAN FTA was signed. The countries were selected based on its size of bilateral exports and data availability. The countries are the top exporting countries from 2012 to 2016, while countries without tariff data for two consecutive years are excluded from the list. Note that Cambodia, Myanmar and Lao PDR were excluded due to tariff data availability. This will not cause a significant bias since the bilateral trade volume between Korea and three countries were trivial; below 2% for 2016. This tendency persists in the food sector; for food sector, the seven ASEAN members account for an average of 98.6% of imports from and 93.43% of exports to the ASEAN since enactment of the Korea-ASEAN FTA.
The nominal bilateral exports (imports) by countries covered in the paper accounts for approximately 88% (85%) of the total world trade from 2001 to 2015. Thus, the sample well represents the bilateral trade patterns of the world.
The countries covered in the estimation is as follows: Argentina, Australia, Austria, Belgium-Luxembourg, Brazil, Brunei Darussalam, Canada, Chile, China, Croatia, Czech Rep., Denmark, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Italy, Japan, Kuwait, Malaysia, Mexico,
Netherlands, New Zealand, Nigeria, Norway, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Rep. of Korea, Russian Federation, Saudi Arabia, Singapore, Spain, Sweden, Switzerland, Thailand, Turkey, United Arab Emirates, United Kingdom, Uruguay, USA, Viet Nam
The breakdown of the bilateral trade by years is as follows:
Table 2. The volume of the bilateral trade between the covered countries in the World Trade
Unit: 1000,000 US Dollars
Year
Bilateral Trade of 50 Country Coverage
Total Amount of Trade (Worldwide) Total Amount of Exports (Portion) Total Amount of Imports (Portion) 2012 15,416 (87%) 14,727 (83%) 17,716 2013 15,818 (87%) 15,067 (83%) 18,169 2014 15,934 (88%) 15,259 (83%) 18,189 2015 13,996 (89%) 13,230 (84%) 15,779 2001-2015 184,857 (88%) 177,620 (85%) 210,470
Source: BACI Database, calculations made by author
The paper further disaggregates its analysis into food sector according to the UN ISIC Rev.3. The paper will first analyze the food sector as a whole and proceed to the selected subsequent product classes. The selection is based on the size of the bilateral trade volume between Korea and ASEAN members. Among eighteen, nine product classes are selected, accounting for 89% of the total bilateral trade. The coverage for product classes is presented in Table 3.
Table 3. Description of selected product classes
Division Class Description
Manufacturing of food products and
beverages
1512 Processing and preserving of fish and fish products 1513 Processing and preserving of fruit and vegetables 1514 Manufacture of vegetable and animal oils and fats 1532 Manufacture of starches and starch products 1541 Manufacture of bakery products
1542 Manufacture of sugar
1543 Manufacture of cocoa, chocolate and sugar confectionery
1549 Manufacture of other food products n.e.c. 1600 Manufacture of tobacco products Source: UN Department of Economic and Social Affairs - Statistics Division
The paper uses bilateral trade flows data from BACI database. The CEPII’s BACI database is based on UNCOMTRADE bilateral trade flow data, while providing more reliable data than the raw data. The figures from BACI database addresses shortfalls on the measurement, especially correcting prevailing
discrepancies between reported import and export figure to treat the inaccuracies in disaggregated trade statistics (Gaulier et al. 2010). The bilateral trade flows and applied bilateral tariffs disaggregated at the HS6 level were collected and were classified into ISIC Rev.3 to conduct analysis in the level of sector and product classes. The summary statistics of the bilateral export variable is as
follows. Note that zero trade observations are increasing to a significant amount in more disaggregated level. On product class level, the zero trade flow accounts more than one third of the bilateral trade flows. Thus, considering the increasing portion of the zero trade flows in disaggregated level, it is essential to include zero observations, as the paper intend to do so by using PPML estimator, to treat for possible bias from omitting zero observations.
Table 4. Summary statistics of a bilateral export variable of the food sector
Total Sector Class
Country Coverage 50 50 50
Time Coverage 15 15 (’01-’15) 15 (’01-’15)
Sector/Product Coverage 1 1 9
Number of Observation 36,750 36,750 330,750
Zero trade flow (%) 406 (1.1%) 2,604 (7.1%) 117,348 (35.5%)
For measuring tariff barriers, the paper use log of the most-favored nation (MFN) applied rate of the coverage nations provided by World Bank's World Integrated Trade Solution(WITS) database. Following the approach of Egger and Larch (2011) towards the tariff, the tariff variable is the direct measure of the one plus “applied (outside the RTA and zero else) tariff rate” between countries i and j. Also, note that the heterogeneity in the tariff rates are significant in the product class level as below table presents.
Table 5. Summary statistics of bilateral tariff by product level in percentage points
Variable Mean SD Min Max
Total Economy 23.29 12.51 0.04 87.92
Processed Food Sector 14.45 6.55 0 44.07 Processed Fish Product 9.87 7.65 0 52.43 Processed Fruit Product 17.68 13.69 0 97.09 Animal, Vegetable Oil Product 11.72 11.95 0 81.58 Starch Products 30.80 32.25 0 187.06 Bakery Product 13.18 11.47 0 100.00 Sugar Products 28.57 22.21 0 135.00 Confectionery 11.54 7.13 0 78.33 Other N.E.C 15.17 13.41 0 84.86 Tobacco Products 44.07 57.79 0 1,018.60
Ta b le 6 . Da ta and S ou rce s Va r iab le Un it S ou r ces Dependen t No m in al Export In m illi on U S do llar s BACI Tr ade D atab ase of C EPII Inde pende nt ln( 1 +tari ff ) Log of the tari ff (1+ MF N app lied tari ff rat e) World B ank 's Wor ld I nte g ra te d T ra de S olutio n(WI T S) da tabase ln( d is tanc e) Log (K ilom ete rs ) G eog ra p hi ca l d at abas e of C EP II C om m on bor der s Sha re d bor d ers = 1, O th erwi se = 0 Geog ra phical d at aba se of CEPII C om m on la ng uag e S ha re d l ang uag e= 1, Other w ise = 0 Geog raphical d at abas e o f C EPII R T A RT A = 1, O th erwis e = 0 Ma rio Larch' s RT A Da taba se K or ea-AS EAN F T A K or ea-AS EAN F T A = 1, Other w is e = 0 F T A P o rtal o f Kor ea G ov er nm ent
4.
Estimation Results
4.1
The Estimation Results
The paper employs a fixed-effect model with directional fixed effect described above to estimate the trade volume effect of Korea-ASEAN FTA in the light of the NTMs. The total economy and food sector results are presented in Table 7. The results from product classes are presented in Table 8.
Table 7. Gravity estimation results for total economy and food sector
Definition Total Economy Processed Food Sector
ln(distance) - 0.6485 (0.010) *** - 0.5949 (0.014)*** Common borders 0.4462 (0.271) *** 0.6243 (0.033)*** Common language 0.0035 (0.025) 0.3430 (0.030)*** ln(1+tariff) - 0.7247 (0.293) ** - 0.6664 (0.207)*** RTA 0.1645 (0.024) *** 0.4400 (0.053) *** K-ASEAN FTA 0.6155 (0.070) *** 0.2300 (0.118) ** Observation 36,750 36,750 R2 0.8662 0.8325
Note: Asterisks indicate statistical significance at the * 10%, ** 5%, and *** 1% levels.
Table 8. Gravity estimation results for nine product classes
Variable Korea-ASEAN
FTA RTA Ln(1+Tariff)
Processed Fish products 0.234 (0.19) 0.078 (0.07) -1.361 (0.67) ** Processed Fruit products 0.866 (0.14) *** 0.741 (0.056) *** 1.031 (3.45) Animal, Vegetable Oil products -0.411 (0.18)** 0.356 (0.185) *** -3.067 (0.42)*** Starch products 1.847 (0.180) *** 1.282 (0.068) *** 0.169 (0.18) Bakery product 0.912 (0.057) *** 0.857 (0.560) *** -0.898 (0.35)*** Sugar 0.280 (0.438) -0.612 (0.187) ** -6.794 (0.66) *** Confectionery 0.898 (0.190) *** 1.007 (0.070) *** 0.479 (0.56) Other food products n.e.c. 0.345 (0.137)** 0.839 (0.070)*** 2.010 (0.420) *** Tobacco products 1.588 (0.333) *** 1.147 (0.137) *** -2.685 (0.404)***
Note: Asterisks indicate statistical significance at the * 10%, ** 5%, and *** 1% lev.
The estimation results for total economy and processed food sector mostly reflects the consensus of previous literature. The estimation results all show
negative signs to the ܦܫܵܶ, which means the larger distances between two
countries, the less countries trade with each other. The tariffs have negative impact on the trade flow while sharing borders or signing an RTA have positive effect on the trade flow. The RTA, which denotes to all other RTAs between country pairs except Korea-ASEAN FTA, have larger coefficient in food sector. This can mean that Korea-ASEAN FTA benefited less from non-tariff measure arrangements than average RTAs on food sector. However, for the total economy and agriculture sector, Korea-ASEAN FTA has higher coefficients than RTAs. Thus, on a more aggregated level, the Korea-ASEAN FTAs contributed to bilateral trade flows more than average RTAs.
4.2
The Trade Volume Effects
The trade volume effects calculated from the estimation results are presented in Table 9. The table also includes rough estimate of actual bilateral trade growth in its third column. The actual growth rates are percentage growth of trade from 2006, year before the enactment of Korea-ASEAN FTA, and 2015, the last year of our estimation. The estimation shows the trade volume effect was 85.06% for the bilateral trade of the total economy. This means that mitigation of NTMs from signing the Korea-ASEAN FTA had triggered 85.06% increase in trade volume. For processed food sector, the volume effect triggered from the mitigation of NTMs of the Korea-ASEAN FTA were 25.86%. The processed food sector faces less boost from Korea-ASEAN FTA than the total economy, in the light of non-tariff measure.
Table 9. Trade volume effect calculated from the estimation
Variable Trade Volume
Effect
Actual Bilateral Trade Growth
Total Economy 85.06% 58%
Processed Food Sector 25.86% 143 %
Processed Fish products 26.36% 222%
Processed Fruit
products 137.74% 106%
Animal, Vegetables Oil
products -33.70% 217%
Starch products 534.08% 112%
Bakery product 148.93% 99%
Sugar 32.31% 170%
Confectionery 145.47% 244%
Other food products 41.20% 270%
Tobacco products 389.40% 256%
When broken down to the product classes, the trade volume effect was biggest for starch products with 534.08% followed by tobacco products with 389.40% and bakery product with 148.93%. For starch products, trade volume effect from mitigation of NTMs are estimated to be far greater than actual bilateral trade
growth. This can be due to the fact that Tapioca, a major product in the starch product class, is included in the Highly Sensitive List and was exempted from tariff reduction. However, Tapioca export to Korea almost tripled after Korea-ASEAN FTA. The other food products were the product class that had showed largest growth in actual bilateral trade, while showing relatively low trade volume effect on non-tariff measures. This could be due to the fact that other food product classes did not see benefits from the mitigation of the NTMs, but also due to that complexity of the product class; the other food products consists of various different products that does not fall under the previous product specification. The least liberalized product class was other food products with negative impact; the Korea-ASEAN FTA has actually decreased trade volume effect by 33.70% for animal, vegetable oils products. The trade volume effects of Korea ASEAN FTA in product classes are positive except for the oil products.
However, estimates of the trade effects of the FTA derived by quantitative analysis are rarely precisely identical to the actual trade effects are usually accompanied by estimation errors (standard errors), large or small. Therefore, it is safe to interpret the actual result of the Korea-ASEAN FTA trade effect as the probability of belonging to a specific interval calculated using the estimates and standard errors. For example, the method of calculating the confidence interval of the FTA estimation coefficient belongs to this. However, when the effects of the ASEAN-Korea FTA are expressed as confidence intervals, it is difficult to make
meaningful interpretations by defining too wide a range according to the standard error.
5.
Concluding Remarks
A recent survey reveals that the agreements in the non-tariff measures are one of the most significant contributions and a subject of potential improvements for further integration of Korea-ASEAN FTA. As government launches new FTA strategy, which targets mostly developing countries, the assessment of the trade agreement in light of liberalization through non-tariff measures is necessary at this point to set guideline on further FTA negotiations and to utilize the Korea-ASEAN FTA effectively. Thus, the paper focuses on the trade effect of Korea-ASEAN FTA in light of liberalization through non-tariff measures. As the level of liberalization and trade costs differ by sectors and product classes, analysis on a more disaggregated level is necessary. The paper addresses the processed food sector, which is traditionally known as the sector with largest protectionist NTM intervention.
To quantify the impact of the tariff-equivalent effect of the Korea-ASEAN liberalization through the NTMs, the paper use trade volume effect and tariff-equivalent measures, which indicate the ad-valorem tariff whose removal would have generated the same impact as the trade policy in question. The paper uses the residual effect by controlling for all possible trade costs including tariff barriers. The estimation shows that the mitigation of NTMs from signing the Korea-ASEAN FTA had triggered 85.06% increase in trade volume. For processed food sector, the
volume effect triggered from the mitigation of NTMs of the Korea-ASEAN FTA were 25.86%. The processed food sector faces less boost from Korea-ASEAN FTA than the total economy, in the light of non-tariff measure. The product classes that faced one of the biggest volume effects were starch products, tobacco, confectionary, and bakery products. These are the product classes that also faced the significant increase in trade volume in the actual bilateral trade flows.
There are several limitations to the analysis. There exist possible endogeneity in the tariff and RTA data with the amount of the bilateral exports. Recent literature proves that endogeneity between RTA and the bilateral trade flow biases the impact of RTAs. Likewise, the past or present volume of the bilateral export can affect both the RTA and tariff levels. This can lead to overestimation of the results, and overall, the estimation results predict larger impacts than the actual growth. The analysis could be further extended by addressing endogeneity problem with panel techniques with instrument variables, which are used by Bergstrand et al. (2013). However, the techniques proposed by Bergstrand et al. (2013) addresses the impact of the comprehensive RTAs and fails to discern the impact of NTM arrangements. To my knowledge, there are no studies that discerned NTM effects of the RTA that controlled for the endogeneity. Moreover, since the level of NTMs is highly heterogeneous by the tariff line, the analysis can be extended to a more disaggregated level such as HS6 level to reveal more meaningful implications. The paper faced statistically insignificant or biased values partially due to the
complexity of the product classes, especially for other food products. Thus, conducting the analysis in a more disaggregated level will provide more accurate estimates.
ῃ ῃⶎ㽞⪳
䞲-㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫㦮 ゚ὖ㎎㧻⼓
䣾ὒ ㍳
㍲㤎╖䞯ᾦ ╖䞯㤦 ⏣ἓ㩲㌂䣢䞯 ⏣㠛Ř㧦㤦ἓ㩲䞯㩚Ὃ 㧊㥺㰖 2007⎚ 6㤪 1㧒 䣾 ♲ 䞲-㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫㦖 㰖⋲ 10 ⎚ ☯㞞 䞲ῃὒ 㞚㎎㞞 Ṛ㦮 ⶊ㡃 ἓ㩲 䡧⩻㦚 㥚䞲 㭧㣪䞲 㡃䞶㦚 㑮䟟 䟊㢪┺. 㞚㎎㞞㠦 ╖䞲 䞲ῃ㦮 㑮㿲㦖 㡆䘟‶ 8.8 % ㎇㧻䟞ἶ, 2012 ⎚ 㧊䤚 㞚㎎㞞㦖 䞲ῃ㦮 ⚦ ⻞㱎⪲ 䋆 㑮㿲╖㌗ῃ㦒⪲ Ệ❃⌂┺. KITAṖ 10㭒⎚㦚 ⰴ㞚 㔺㔲䞲 ㍺ⶎ 㫆㌂㠦㍲ 㑮㿲㠛㧦✺㦖 䞲-㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫㦮 Ṗ㧻 䋆 ₆㡂⯒ ゚ὖ㎎㧻⼓ 㢚䢪(45.9 %)⪲, ┺㎅Ṗ㰖 㭒㣪 Ṳ㍶㩦 㭧 ⍺ Ṗ㰖⯒ ゚ὖ㎎㧻⼓ ὖ⩾ 䟃⳿㦒⪲ ↓㞮┺. ⽎ 㡆ῂ⓪ ゚ὖ㎎㧻⼓ 㢚䢪⯒ 䐋䞲 㧦㥶䢪⧒⓪ ὖ㩦㠦㍲ 䞲-㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫㦮 ⶊ㡃 ′⳾ 䣾ὒ⯒ 㩫⨟䢪䞲┺. 㧦㥶䢪㦮 㑮㭖㧊 ㌆㠛 㩲䛞 ⮮㠦➆⧒ 䘎㹾Ṗ 䋂┺⓪ 㩦㠦 㹿㞞䞮㡂 ⽎ 㡆ῂ⓪ ⽊┺ ㎎䢪 ♲ ṖὋ 㔳䛞 㟒 9 Ṳ㦮 㩲䛞 ⮮㠦 ╖䟊 ㍳㦚 㑮䟟䞲┺. 䞲-㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫㦮 㧒䢮㦒⪲ 䀾䟊㰚 ゚ὖ㎎㧻⼓㦮 㢚䢪Ṗ ⶊ㡃 ′⳾㠦 ⹎䂲 㡗䟻㦚 䁷㩫䞮₆ 㥚䟊 ⽎ 㡆ῂ⓪ 1) ┺㧦Ṛ 㩖䟃(Multilateral Resistance)㦚 ㍺ⳛ䞮⓪ ἶ㩫 䣾ὒ ⳾◎㦚 㧊㣿䞮Ⳇ 2) 㟧㧦Ṛ ⶊ㡃 㔺㩗㧊 㠜⓪ 㧦⬢ (zero trade observation)㦚 䙂䞾䞮㡂 㿪㩫䞮Ⳇ 3) PPML (Poisson Pseudo-Maximum Likelihood)⯒ 㩗㣿䞲┺. ⽎ 㡆ῂ㦖 ὖ㎎㧻⼓㦚 䙂䞾䞲 ⳾✶ ⶊ㡃 ゚㣿㦚 䐋㩲䞾㦒⪲㖾 䞲-㞚㎎㞞 ▪⹎⯒ 䐋䟊 㧪㡂 䣾ὒ⯒ ㌂㣿䞮㡂 ゚ὖ㎎㧻⼓㦮 㢚䢪⯒ Ἒ⨟䞲┺. 䞲-㞚㎎㞞 㧦㥶ⶊ㡃 䡧㩫 㼊ἆ㠦㍲ ゚ὖ㎎㧻⼓ 㢚䢪⯒ 䐋䞲 㟧ῃ Ṛ ᾦ㡃 㯳Ṗ⓪ 85.06 %⪲ ⋮䌖⋮Ⳇ ṖὋ㔳䛞 ⶎ㦮 ἓ㤆 25.86 % 㯳Ṗ䞲 ộ㦒⪲ ⋮䌖⌂┺. Ṗ㧻 䋆 㯳Ṗ⯒ ⽎ 㩲䛞ῆ㦖 ╊⺆ 㩚 㩲䛞⮮ 㡖┺. 㭒㣪㠊 : 䞲-㞚㎎㞞 㧦㥶ⶊ㡃䡧㩫, ゚ὖ㎎㧻⼓, Poisson Pseudo-Maximum Likelihood, 㭧⩻⳾䡫 䞯 ⻞ : 2016-21485
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[23] ⺆㺂ῢ½ₖ㡗‖½ὓ㎇㧒½ṫ㭖ῂ½ₖ䡗䢿½⁞䡲㥺½㧊㭖㤦½㔶⹒⁞½ₖ☚䧂OYWX\PSG ˈ䞲½hzlhuGm{hG 㧊䟟㌗䢿G 䘟ṖG 㾲㫛⽊ἶ㍲ˉG ╖㣎ἓ㩲㩫㺛㡆ῂ㤦ૹG G [24] 㰖㎇䌲½㧊㑮䢮½㥶㩫䢎½㥶㭒㡗OYWX^PSG ˈ䞲½㞚㎎㞞G m{hG 䣾G XW ⎚SG ⏣㿫㌆ⶒG ᾦ㡃G ⼖䢪㢖G ὒ㩲ˉSG 䞲ῃ⏣㽢ἓ㩲㡆ῂ㤦ૹG rylpG ⏣㩫䙂䄺㓺G 㩲G X[^ 䢎G
[25]G㰖㎇䌲½㧊䡚⁒½㧊㑮䢮½㥶㩫䢎OYWX\PSG ˈ⏣㠛G 䞲½㞚㎎㞞G m{hG 㧊䟟G 㔺䌲
㢖G 㔲㌂㩦ˉSG 䞲ῃ⏣㽢ἓ㩲㡆ῂ㤦ૹG G
Appendix
Table A1. Korea-ASEAN Trade Liberalization Schedule by the member countries
Country Name
The Korea- ASEAN Trade in Goods Agreement
The Korea- ASEAN Trade in Service Agreement Korea June 1st, 2007 May 1st, 2009 Malaysia Singapore Indonesia - Myanmar May 1st, 2009 Vietnam Philippines January 1st, 2008 Brunei July 1st, 2008 Lao PDR October 1st, 2008 - Cambodia November 1st, 2008 -
Thailand January 1st, 2010 January 1st, 2010
