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De La Salle University De La Salle University

Animo Repository Animo Repository

Angelo King Institute for Economic and

Business Studies Units

11-2021

Non-Tariff Measures and Their Impacts on ASEAN Economic Non-Tariff Measures and Their Impacts on ASEAN Economic Integration

Integration

Myrna S. Austria

Follow this and additional works at: https://animorepository.dlsu.edu.ph/res_aki

Part of the Economic Policy Commons, International Economics Commons, and the Regional Economics Commons

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Non-Tariff Measures and Their Impacts on ASEAN Economic Integration

DLSU-AKI Working Paper Series 2021-11-074

By: Myrna S. Austria De La Salle University

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Non-Tariff Measures and Their Impacts on ASEAN Economic Integration Myrna S. Austria, Ph.D.

Abstract

Using a gravity model that accounts for the asymmetric effects of non-tariff measures (NTMs), the study examined the impact of the five most prevalent NTMs in the region on intra-ASEAN imports. The study found that all five NTMs are significant factors affecting intra-ASEAN imports.

However, their effects vary at the sectoral level, by pairs of trading partners, and whether the products are covered by mutual recognition and harmonization agreements (MRA) or not. For example, sanitary and phytosanitary (SPS) measures, in general, negatively affect imports and are trade-reducing. Exceptions are prepared foodstuff and medicinal products, both of which are covered by MRAs and harmonization agreements among the ASEAN. The positive effects of SPS measures on these two sectors provide evidence that NTMs that assure consumer safety and protection, while they could increase costs and price, increase consumer trust, and hence, promotes trade. Technical barriers to trade (TBT) measures are also deterrents to imports, in general.

However, they are found to promote imports and are trade-enhancing for products covered by MRAs and harmonization agreements such as electrical machinery and equipment, prepared foodstuff, telecommunications equipment, and medical devices. The study also found that regulatory distance between ASEAN Member States (AMS) contributes positively to the effects of SPS and TBT. This means that in instances when an SPS or TBT measure is a deterrent to imports, regulatory distance lessens the negative effects.

Key words: regional economic integration, gravity model, trade in goods, non-tariff measures, regulatory distance, PPML method

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Non-Tariff Measures and their Impacts on ASEAN Economic Integration

Myrna S. Austria, Ph.D.

School of Economics, De La Salle University

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Contents

Introduction ... 1

Non-Tariff Measures and Intra-ASEAN Imports ... 2

Intra-ASEAN Imports ... 2

Intra-ASEAN Imports and ASEAN MRAs and Harmonization Agreements ... 6

Intra-ASEAN Imports and Non-Tariff Measures ... 9

Assessing the Effects of NTMs on International Trade: A Review ... 16

Price-based Approach ... 17

Quantity-Based Approach ... 18

Price-based Approach vs. Quantity-Based Approach: Which is the Better Approach? ... 20

Theoretical Framework: Quantifying the Effects of NTMs on Trade ... 20

Case 1: Decrease in Demand ... 22

Case 2: Increase in Demand ... 23

Data and Methodology ... 23

Model Specification ... 23

Model Estimation ... 26

Data and Data Sources ... 26

Results and Discussion ... 28

Descriptive Statistics ... 28

Processing of Estimation Results ... 30

Test of Goodness-of-fit ... 30

Impact of Traditional Gravity Variables on ASEAN Imports... 31

Impact of NTMs on ASEAN Imports at the Sectoral Level ... 35

Impact of NTMs on ASEAN Importer-Exporter pairs ... 44

Effects of NTMs and MRAs and Harmonization Agreements ... 52

Effect of Regulatory Distance on the impact of NTMs on ASEAN Importer-Exporter Pairs ... 55

Conclusions and Recommendations ... 62

References ... 65

Appendix ... 68

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1

Non-Tariff Measures and their Impacts on ASEAN Economic Integration

1

Myrna S. Austria, Ph.D.

School of Economics, De La Salle University

Introduction

Tariff rates have gone down globally since the 1990s due to the liberalization of trade policies at all levels (unilateral, bilateral, regional, and multilateral). However, the tariff decline was accompanied by an increase in non-tariff measures (NTMs) over the years. NTMs have become a prominent feature in the regulation of international trade in goods. The nature of NTMs has also changed over time (Cadot & Gourdon, 2015). Prior to the 1990s, they were dominated by non-technical measures such as quota and price restrictions; however, technical measures, especially sanitary and phytosanitary (SPS) and technical barriers to trade (TBT), became prominent over time. Because of these shifts, NTMs have become the subject of discussions and debates, especially in deep regional integration efforts.

Broadly defined, NTMs refer to any measure other than tariffs that distort trade (United Nations Conference on Trade and Development [UNCTAD], 2015). Unlike tariffs, they take a variety of forms and have both trade and non-trade objectives. Technical measures, for example, are directed at strategic policy concerns such as consumer safety and the protection of plant and animal life and the environment. Some are designed to address market imperfections such as informational asymmetries and externalities (Berden & Francois, 2015). Because of the non-trade objectives, these measures are expected to continue to exist, and eliminating them may no longer be an option (Cadot et al., 2015). Nonetheless, although the pursuit of domestic policy objectives is legitimate, NTMs have the potential to become trade barriers.

There are emerging truths about NTMs, though. They increase production and trade costs and hence, products’ prices. The effects on demand for import, however, is ambiguous. Demand could decrease because of the price increase, but it could also be stimulated if the demand- enhancing effects outweigh the price-raising effects that may arise from compliance costs. The demand-enhancing effects could spring from NTMs that address market imperfections and, thus, provide signaling effects that could increase consumer trust (Xiong & Beghin 2014; Bratt, 2017;

Cadot et al., 2018). Hence, NTMs cannot be regarded solely as trade costs (Fugazza, 2013).

In the ASEAN, the member states have committed to removing non-tariff barriers (NTBs) and mutually recognize and harmonize each other’s NTMs. As a single production base, the free flow of goods in the region under the ASEAN Economic Community (AEC) is of utmost importance. This cannot be more emphasized than the greater role played by the ASEAN member

1The research project was funded by the Economic Research Institute for East Asia and the ASEAN (ERIA). The author would like to acknowledge Dr. Doan Thi Thanh Ha and other participants during the Technical Workshops organized by ERIA on April 13-14, 2019 and October 13, 2020 for their valuable comments & suggestions on the initial drafts of the paper. The author would also like to thank Eva Marie Aragones for the excellent research assistance provided.

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2 states (AMS) in the global production networks of multinational companies operating in the region. Yet, the incidence of NTMs has steadily increased in the region. According to Hirang (2017), the rising incidence of NTMs happened together with the increased participation of AMS in the production networks. At the same time, it was accompanied by an increase of NTMs in industries and sectors that are suffering a decline. The latter may indicate some protectionist motives. Thus, an interesting question has emerged—what has been the impact of NTMs on ASEAN trade?

The primary objective of this paper is to evaluate the impact of NTMs on the imports of AMS from each other. Specifically, it aims to: (a) determine if the effects of NTMs are trade- reducing or trade-enhancing; (b) differentiate the effects of each NTM type on imports; (c) examine if there are variations on the effects of NTMs across products and sectors, and between trading partners; (d) compare the effects of NTMs on products covered by existing MRAs and harmonization agreements with products not subject to such agreements; (e) determine the effect of NTMs attributable to the regulatory distance between trading partners; and (f) recommend policies to ensure that NTMs enhance the region’s economic integration.

The paper is organized as follows. Section 0 briefly discusses the region’s intra-ASEAN import performance, the existing and forthcoming MRAs and harmonization agreements, and NTMs in the region. Section 0 reviews the literature on the methods employed to quantify the effects of NTMs on international trade. Section 0 discusses the theoretical framework to assess the effects of NTMs on ASEAN’s trade flows. Section 0 explains the model specification and data and data sources. Section 0 discusses the results and findings. Finally, Section 0 presents the conclusions and policy recommendations.

Non-Tariff Measures and Intra-ASEAN Imports Intra-ASEAN Imports

Total intra-ASEAN imports increased from US$244.4 billion in 2015 to US$306.7 billion in 2018 (Figure 1), or an average real growth rate of 5.6% per year during the period. Singapore accounted for the bulk of intra-ASEAN imports with an average annual share of 26.1%, whereas Brunei accounted for the least share, at less than 1% (Figure 2).

Total intra-ASEAN imports represent less than one-fourth of the region’s total imports, and the share has been gradually declining from 22.2% in 2015 to 21.5% in 2018 (Table 1).

Nonetheless, the region is an important source of imports for Brunei and the less developed AMS (Cambodia, Laos, and Myanmar). The region accounts for an average of 42%, 37%, 68%, and 41% of these countries’ total imports, respectively (Table 1). Cambodia also registered the highest growth rate of intra-ASEAN imports at 21.6% for the period 2015–2018, followed by the Philippines at 16.4%.

Intra-ASEAN imports are highly concentrated on a few products, as shown in Table 2. The top 15 accounted for 80% of intra-ASEAN imports (Table 2 and Figure 3). Mineral fuels and oil (HS 27) and electrical machinery and equipment (HS 85) represent the bulk, with each product group accounting for an average of 22% of the total. On the other hand, the fastest growing intra-

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3 ASEAN imports include mineral fuels and oils (HS 27), vehicles (HS 87), iron and steel (HS 72), and copper (HS 74); these products registered an average annual growth rate of at least 10%.

Figure 1

Intra-ASEAN Imports, 2015-2018 (US$ Billions)

Source: UN Comtrade Database, https://comtrade.un.org; Author’s calculations

Figure 2

Average Annual Share in Intra-ASEAN Imports (%)

Source: UN Comtrade Database, https://comtrade.un.org; Author’s Calculations

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4 Table 1

Average Annual Growth and Share of Intra-ASEAN to Country’s Total Imports, 2015- 2018 (%)

Country Ave. Annual Growth (%)

Share of Intra-ASEAN imports to country’s total imports

2015 2016 2017 2018

Brunei Darussalam -2.25 43.01 48.35 43.17 32.35

Indonesia 2.87 27.19 25.58 25.03 24.36

Cambodia 21.66 33.25 37.23 38.46 40.14

Lao PDR 9.18 73.55 74.32 59.35 65.33

Myanmar 2.54 41.54 37.65 39.58 44.80

Malaysia 4.50 26.57 24.58 25.66 25.51

Philippines 16.40 24.29 26.18 26.11 24.92

Singapore 4.90 21.05 21.47 21.65 21.16

Thailand 4.05 18.97 18.81 18.57 18.26

Vietnam 7.29 14.33 13.77 13.30 13.43

ASEAN 5.55 22.22 21.80 21.77 21.53

Source: UN Comtrade Database, https://comtrade.un.org; Author’s Calculations

Table 2

Intra-ASEAN Imports by Product, 2015-2018, Top 15 Products

HS Code Description

Value of Intra-ASEAN Imports (US$ Billions)

Share in

Intra-ASEAN Imports (%)

Ave.

Annual Growth 2015 2016 2017 2018 2015 2016 2017 2018 (%)

27

Mineral fuels, mineral oils and products of their distillation;

bituminous substances;

mineral waxes

56.14 46.34 63.07 75.77 22.97 19.57 23.03 24.71 10.00

85

Electrical machinery and equipment and parts thereof; sound recorders and

reproducers; television image and sound recorders and reproducers, parts and accessories of such articles

52.95 53.20 61.52 67.02 21.66 22.47 22.47 21.86 6.07

84

Nuclear reactors, boilers, machinery and mechanical appliances;

parts thereof

26.85 26.55 27.42 29.25 10.98 11.21 10.02 9.54 0.56

87

Vehicles; other than railway or tramway rolling stock, and parts and accessories thereof

11.24 13.52 14.44 16.18 4.60 5.71 5.27 5.27 10.17

39 Plastics and articles

thereof 10.19 10.17 11.19 12.65 4.17 4.30 4.09 4.13 4.76

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5 Table 2

Intra-ASEAN Imports by Product, 2015-2018, Top 15 Products (continued)

HS Code Description

Value of Intra-ASEAN Imports (US$ Billions)

Share in

Intra-ASEAN Imports (%)

Ave.

Annual Growth (%) 2015 2016 2017 2018 2015 2016 2017 2018

71

Natural, cultured pearls;

precious, semi-precious stones; precious metals, metals clad with precious metal, and articles thereof;

imitation jewelry; coin

6.36 7.51 5.97 6.97 2.60 3.17 2.18 2.27 2.73

29 Organic chemicals 5.40 4.72 6.21 6.43 2.21 1.99 2.27 2.10 4.58

90

Optical, photographic, cinematographic, measuring, checking, medical or surgical instruments and apparatus; parts and accessories

4.43 4.69 5.27 5.55 1.81 1.98 1.93 1.81 5.52

15

Animal or vegetable fats and oils and their cleavage products;

prepared animal fats;

animal or vegetable waxes

3.78 3.64 4.34 4.10 1.55 1.54 1.58 1.34 0.43

40 Rubber and articles

thereof 3.53 3.31 4.18 4.02 1.44 1.40 1.53 1.31 2.76

73 Iron or steel articles 3.69 3.44 3.39 3.63 1.51 1.45 1.24 1.18 -2.82 48 Paper and paperboard;

articles of paper pulp, of paper or paperboard

3.35 3.29 3.63 3.92 1.37 1.39 1.32 1.28 2.81 38 Chemical products n.e.c. 2.92 3.14 3.65 4.10 1.19 1.33 1.33 1.34 9.35 72 Iron and steel 2.62 2.46 3.23 4.54 1.07 1.04 1.18 1.48 18.74 74 Copper and articles

thereof 2.53 2.76 3.72 3.67 1.04 1.17 1.36 1.20 11.42

Intra-ASEAN Imports

(Top 15 Products) 195.97 188.76 221.24 247.81 80.17 79.73 80.80 80.81 5.91 OTHERS 48.47 47.98 52.57 58.85 19.83 20.27 19.20 19.19 4.12

Total Intra-ASEAN Imports 244.44 236.74 273.81 306.66 100.00 100.00 100.00 100.00 5.55

Notes: Product ranking is based on an average annual share in intra-ASEAN imports.

Source: UN Comtrade Database, https:comtrade.un.og; Author’s Calculations

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6 Figure 3

Value and Share in Intra-ASEAN Imports of the Top 15 Products, 2015-2018

Source: UN Comtrade Database, https://comtrade.un,org; Author’s Calculations

Intra-ASEAN Imports and ASEAN MRAs and Harmonization Agreements

To help facilitate the seamless movement of goods within the region and to minimize trade protection and compliance costs associated with non-tariff measures (NTMs), the ASEAN has been implementing mutual recognition agreements (MRAs) and harmonization agreements as early as 2002 (Table 3). Agreements that are currently implemented cover cosmetics, electrical machinery and electronic equipment, prepared foodstuff, medical equipment, medicinal products, and telecommunications. On the other hand, ongoing work and negotiation on MRAs covering automotive products and building and construction materials are nearing completion.

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7 Table 3

List of MRAs and Harmonization Agreements, ASEAN

Title Code Date Signed HS Codes

Covered Existing MRAs/Harmonization Agreements

ASEAN Sectoral MRA on Electrical and Electronic Equipment

Electrical Machinery April 5, 2002 HS 85 (except 8517, 8544) ASEAN MRA on Inspection and

Certification System on Food

Hygiene for Prepared Foodstuff Prepared Foodstuff April 27, 2018 HS 16-22 ASEAN Sectoral MRA for GMP

Inspection of Manufacturers for

Medicinal Products Medicinal Products April 10, 2009 HS 30

ASEAN Sectoral MRA on Conformity Assessment of

Telecommunications Equipment Telecommunications - HS 8517, 8544

Agreement on the ASEAN Harmonised Cosmetic Regulatory Scheme with ASEAN Cosmetic Directive

Cosmetics September 2, 2003 HS 33-34

ASEAN Medical Device

Directive Medical Devices November 21, 2014 HS 9018, 9019,

9022 Forthcoming MRAs/Harmonization Agreements

ASEAN MRA on Type Approval

for Automotive Product Automotive (for finalization) HS 87

ASEAN MRA on Building and

Construction Materials Construction (for finalization)

HS 32, 38, 39, 44, 68, 69, 70, 72, 73,

76

Note: This list includes the MRAs and Harmonizati on Agreements covered by the study; the HS codes covered were determined by the researchers based on the provisions and scope of each Agreement

Products covered by existing MRAs and harmonization agreements account for about 28%

of total intra-ASEAN imports, whereas forthcoming agreements account for 16% (Table 4).

However, it is worth noting that a large portion of the region’s total imports is intra-ASEAN (Table 4). For example, the share of prepared foodstuff in intra-ASEAN imports is only 3%–4%, but the share of intra-ASEAN to the total imports of the products has increased from 39% in 2015 to 43.4% in 2018. Similarly, although the share of cosmetics products in total intra-ASEAN imports is less than 2%, the region supplies around 28.5% of the region’s total imports of the products. A similar pattern can be observed for automotive products. These products represent only 5% of total intra-ASEAN imports, but the share of the total imports of the region went up from 28.45% in 2015 to 34.25% in 2018. Intra-ASEAN imports of electrical machinery account for 22% of the region’s total imports of the products.

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8 Table 4

Intra-ASEAN Imports Covered by MRAs or Harmonization Agreements, 2015-2018

MRA

Value of Intra-ASEAN Imports (US$ Billions)

Share in Intra-ASEAN Imports (%)

Ave.

Annual Growth (%)

2015 2016 2017 2018 2015 2016 2017 2018 2015- 2018 Existing MRAs and Harmonization Agreements

Electrical Machinery 45.66 46.49 53.56 58.61 18.68 19.64 19.56 19.11 6.56 Prepared Foodstuff 8.24 9.57 10.02 11.63 3.37 4.04 3.66 3.79 9.27 Medicinal Products 1.00 1.01 1.07 1.05 0.41 0.43 0.39 0.34 -0.99 Telecommunications 7.30 6.71 7.96 8.41 2.98 2.84 2.91 2.74 2.95

Cosmetics

3.16 3.39 3.58 4.10 1.29 1.43 1.31 1.34 6.27

Medical Devices 0.73 0.86 0.83 0.93 0.30 0.36 0.30 0.30 6.39

Forthcoming MRAs and Harmonization Agreements

Automotive 11.24 13.52 14.44 16.18 4.60 5.71 5.27 5.27 10.17

Construction 26.55 25.79 28.37 32.75 10.86 10.89 10.36 10.68 4.79 Intra-ASEAN Imports

Covered by

MRAs/Harmonization Agreements

103.88 107.34 119.82 133.67 42.50 45.34 43.76 43.59 6.29

Non-MRA Imports 140.56 129.40 153.99 172.99 57.50 54.66 56.24 56.41 5.12 ASEAN 244.44 236.74 273.81 306.66 100.00 100.00 100.00 100.00 5.55 Source: UN Comtrade Database, https://comtrade.un.org; Author’s Calculations

The relative importance of products covered by MRAs and harmonization agreements (both existing and forthcoming) can also be seen from the imports of individual AMS (Table 5). For most of the AMS, the bulk of their imports of these products are sourced from the region. Take the case of prepared foodstuff; the share of intra-ASEAN to the AMS total imports of the products range from a low of 26.7% for Singapore to a high of 94.5% for Laos. For cosmetics, at least 50%

of the total imports of Brunei, Cambodia, Laos, Myanmar, and the Philippines are sourced from the region. At least 20% of imports of electrical and electronics products, telecommunications products, and automotive products are also from the region for the majority of the AMS. This could be due to the important role the ASEAN plays as host to the global production networks of these industries.

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9 Table 5

Average Annual Share of Intra-ASEAN Imports to Total Imports by Country, Products Covered by MRAs/Harmonization Agreements, 2015-2018 (%)

Country COS- ME- TICS

ELEC- TRICAL MACHI-

NERY

PRE- PARED FOOD- STUFF

MEDI- CAL DEVI- CES

MEDI- CINAL

PRO- DUCTS

TELE- COMM- UNICA- TIONS

AUTO- MOTIVE

CONS- TRUC- TION Brunei

Darussalam 80.84 26.83 82.00 37.61 85.65 31.07 24.17 28.10

Indonesia 30.53 24.56 44.52 14.92 8.25 24.32 32.78 24.42

Cambodia 79.77 38.19 82.50 26.86 27.88 44.98 37.76 34.61

Lao PDR 80.89 66.19 94.52 35.81 63.87 46.14 59.38 58.54

Myanmar 77.14 21.97 60.30 18.32 31.58 19.64 21.80 24.77

Malaysia 36.03 23.74 32.20 19.00 9.46 21.59 33.26 19.90

Philippines 55.83 18.53 51.57 21.60 15.35 21.62 56.22 19.69

Singapore 8.22 26.36 26.68 13.72 4.08 18.27 18.95 20.80

Thailand 27.23 22.21 36.42 8.25 4.54 21.58 19.07 11.96

Vietnam 44.92 10.26 46.58 3.87 5.58 3.12 31.20 11.42

ASEAN 28.52 21.84 40.33 13.37 8.67 15.61 31.86 17.42 Source: UN Comtrade Database, https://comtrade.un.org; Author’s Calculations

Intra-ASEAN Imports and Non-Tariff Measures

The average tariff rate in the region has gone down for most products and in most AMS, reaching 0% for most products in 2018 (Table 6). The exceptions are Cambodia and Thailand, where the average tariff rate is higher than 10% for certain products, like prepared foodstuff, cosmetics, and automotive products. The decline in tariff rate, however, is accompanied by the rising number of NTMs. According to Ing et al. (2016), the major non-tariff measures prevailing in the ASEAN include the following: sanitary and phytosanitary measures (A); technical barriers to trade (B); pre-shipment inspection and other formalities (C); non-automatic licensing, quotas, prohibitions, and quantity control measures other than SPS or TB measures (E); and price control measures including additional taxes and charges (F).

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10 Table 6

Average Tariff Rate per Country by Sector, 2018 (%)

SECTOR BRN IDN KHM LAO MMR MYS PHL SGP THA VNM Existing MRAs and Harmonization Agreements

Electrical Machinery

0.03 0.00 14.66 0.00 0.08 2.93 0.00 0.00 5.51 0.00 Prepared Foodstuff

0.00 9.84 26.30 0.82 1.93 3.08 0.10 0.00 16.38 0.40 Medicinal Products

0.00 0.33 0.00 0.02 0.00 0.00 0.00 0.00 5.68 0.31 Telecommunications

0.00 0.00 4.20 0.00 0.00 4.43 0.00 0.00 2.35 0.00 Cosmetics

0.57 1.82 17.86 0.34 0.12 2.24 0.00 0.00 11.37 0.00 Medical Devices

0.00 0.00 1.83 0.00 0.07 0.00 0.00 0.00 0.65 0.00 Forthcoming MRAs and Harmonization Agreements

Automotive

0.00 0.00 14.32 0.23 0.14 16.35 0.00 0.00 31.06 0.00 Construction

0.37 0.05 6.92 0.20 0.42 8.90 0.00 0.00 5.28 0.21 No MRA or Harmonization Agreement

Mineral fuels, oils, and waxes;

bituminous substances (HS 27)

0.00 0.00 7.02 0.41 0.39 0.65 0.00 0.00 1.28 1.32 Rubber (HS 40) 0.00 0.00 10.40 0.00 0.10 16.93 0.00 0.00 6.38 1.26 Paper and paperboard (HS 48) 0.00 0.00 6.37 0.65 0.54 11.58 0.00 0.00 4.63 0.05 Copper (HS 74) 0.00 0.00 5.76 0.18 0.59 1.96 0.00 0.00 1.55 0.00 Nuclear reactors, boilers,

machinery and mechanical

appliances (HS 84) 0.00 0.00 12.59 0.05 0.28 2.87 0.00 0.00 2.24 0.00 Pearls; Precious, Semi-

precious stones; Precious

metals; Jewelry; Coin (HS 71) 0.00 0.00 1.74 0.00 0.03 0.39 0.00 0.00 0.00 0.00 Organic chemicals (HS 29) 0.00 0.01 5.83 0.03 0.05 0.12 0.00 0.00 0.16 0.00 Optical, photographic,

cinematographic instruments

and apparatus (HS 90) 0.00 0.00 14.87 0.00 0.02 0.49 0.00 0.00 1.81 0.00 Animal or vegetable fats, oils,

and waxes (HS 15) 0.00 0.00 6.57 0.04 0.27 2.63 0.00 0.00 0.82 0.00

OTHERS 0.30 0.08 10.11 0.88 0.37 3.87 0.17 0.00 9.10 0.31

Note: The sectors include those covered by MRA and harmonization agreements, top 15 intra-ASEAN imports whi ch are not covered by the agreements; and Others, which include all ot her products not among the top 15 and not covered by the agreements.

Source: World Trade Organization Tariff Download Facility, http://tariffdata.wto.org/Default.aspx

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11 Figure 4 to Figure 8 show the frequency index by type of NTM for each of the AMS during the period 2015–2018. The index measures the percentage of tariff lines affected by at least one NTM measure. TBT measures are the most frequent forms of NTMs in the region, whereas pre- shipment inspection measures are the least. At least 60% of tariff lines are affected by TBT in Cambodia, Vietnam, and the Philippines, but the frequency index for the rest of the AMS is less than 40% (Figure 5). Furthermore, the index has increased from 2015 to 2018 for Indonesia, Cambodia, Laos, Myanmar, and the Philippines. On the other hand, pre-shipment inspection is hardly used in Singapore, Brunei, and Cambodia (Figure 6).

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12 Figure 4

Frequency Index (SPS), ASEAN, 2015–2018

Figure 5

Frequency Index (TBT), ASEAN, 2015–2018

Figure 6

Frequency Index (Pre-Shipment Inspections), ASEAN, 2015–2018

Figure 7

Frequency Index (Quantity Control), ASEAN, 2015–2018

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13 Figure 8

Frequency Index (Price Control), ASEAN, 2015–2018

Source: UNCTAD TRAINS NTMs: The Global Database on Non-tariff Measures, http:// asean.i-tip.org/Forms/Analysis.aspx; Author’s Calculations Note: Frequency Index of each NTM type provides the share of tariff lines affected by at least one measure of that NTM type

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12 SPS measures affect about 20% of the tariff lines of AMS, except Malaysia and Singapore with only 17% (Figure 4). The index has not changed from 2015 to 2018, except Vietnam, where there was a substantial decline in 2017. Quantity control measures also affect a greater portion of the tariff lines (Figure 7). The index is more than 35% for Brunei, Indonesia, Cambodia, Myanmar, and the Philippines; 30% for Vietnam, 26% for Singapore, and less than 20% for Laos and Thailand. For price control measures, Laos has the highest percentage of tariff lines affected by the NTM (88%), followed by Singapore (36%) (Figure 8).

Figure 9a to Figure 13b show the NTM frequency index by sectors. TBT, quantity control, and price control measures are the most common forms of NTM in the ASEAN region. The percentage of tariff lines affected by TBT measures range from 83% to 100% across sectors, except telecommunications with 60% (Figure 10a & Figure 10b). On the other hand, the indices range from 60% to 80% for both quantity control measures (Figure 12a & Figure 12b) and price control measures (Figure 13a & Figure 13b). The figures also show that the indices have increased from 2015 to 2017.

As shown in Figure 9a and Figure 9b, the index is also high for SPS but only for prepared foodstuff, medicinal products, cosmetics, medical devices, and animal or vegetable fats (HS15).

However, both figures also show that all sectors, except automotive, were initially affected by SPS measures but these were either removed or substantially reduced in some sectors in 2017 and 2018, for example, electrical machinery, telecommunications, paper & cupboards (HS48), copper (HS74), nuclear reactors, boilers, machinery and mechanical appliance (HS84), organic chemicals (HS29), and optical, photographic, cinematographic instruments and apparatus (HS90).

All sectors are affected by pre-shipment inspections, with the following sectors as the most affected: prepared foodstuff, medicinal products, medical devices, automotive, construction, mineral fuels, oils, and waxes (HS27), pearls, precious, semi-precious stones, precious metals, a nd jewelry (HS71), organic chemicals (HS29), and animal or vegetable fats, oils, and waxes (HS15) (Figure 11a & Figure 11b). At least 50% of the tariff lines of these sectors are affected by the NTM.

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13 Figure 9a

Frequency Index (SPS), Sectors with Existing/Forthcoming MRAs, 2015–2018

Figure 10a

Frequency Index (TBT), Sectors with Existing/Forthcoming MRAs, 2015–2018

Figure 9b

Frequency Index (SPS), Products not Covered by an MRA, 2015–2018

Figure 10b

Frequency Index (TBT), Products not Covered by an MRA, 2015–2018

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14 Figure 11a

Frequency Index (Pre-Shipment Inspections), Sectors with Existing/Forthcoming MRAs, 2015–2018

Figure 12a

Frequency Index (Quantity Control), Sectors with Existing/Forthcoming MRAs, 2015–2018

Figure 11b

Frequency Index (Pre-Shipment Inspections), Products not Covered by Existing/Forthcoming MRAs, 2015–2018

Figure 12b

Frequency Index (Quantity Control), Products not Covered by an MRA, 2015–2018

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15 Figure 13a

Frequency Index (Price Control), Sectors with Existing/Forthcoming MRAs, 2015–2018

Figure 13b

Frequency Index (Price Control), Products not Covered by an MRA, 2015–2018

Source: UNCTAD TRAINS NTMs: The Global Database on Non-tariff Measures, http://asean.i-tip.org/Forms/Analysis.aspx; Author’s Calculations

Note: Frequency Index of each NTM type provides the share of tariff lines affected by at least one measure of that NTM type

The import coverage ratio, which is the percentage of the total value of imports affected by an NTM, confirms the findings above. More than 50% of the value of intra-ASEAN imports were affected by TBT measures during the period 2015–2017 (Table 7). The ratio increased from 39%

to 46% for quantity control measures and from 26% to 34% for price control measures. On the other hand, less than 15% of imports are covered by SPS and pre-shipment inspections.

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16 Table 7

Import Coverage Ratio, by NTM Type, Intra-ASEAN Imports, 2015–2018

NTM Type

Value of Intra-ASEAN Imports

(US$ Billions) Import Coverage Ratio (%) 2015 2016 2017 2018 2015 2016 2017 2018

(A) SPS 35.83 37.72 33.04 37.89 14.66 15.93 12.07 12.35

(B) TBT 128.78 124.81 142.84 174.96 52.68 52.72 52.17 57.05

(C) Pre-Shipment

Inspections 22.22 19.35 23.76 37.61 9.09 8.17 8.68 12.27

(E) Quantity Control

Measures 95.11 95.39 116.57 140.73 38.91 40.29 42.57 45.89

(F) Price Control

Measures 63.86 66.04 84.07 104.58 26.12 27.89 30.70 34.10

Sub-Total 345.80 343.30 400.28 495.78 141.47 145.01 146.19 161.67 Adjustment to

Prevent Double-

Counting -194.03 -194.36 -234.77 -292.53 -79.38 -82.10 -85.74 -95.39 Value of Imports

Covered by NTMs 151.77 148.94 165.51 203.25 62.09 62.91 60.45 66.28 Value of Imports Not

Covered by NTMs 92.67 87.81 108.31 103.41 37.91 37.09 39.55 33.72 Total Intra-ASEAN

Imports 244.44 236.74 273.81 306.66 100.00 100.00 100.00 100.00 Source: UN Comtrade Database, https://comtrade.un.org; UNCTAD TRAINS NTMs: The Global Database on Non- tariff Measures, http://asean.i-tip.org/Forms/Analysis.aspx; Author’s Calculations

The impact of NTMs on ASEAN’s imports will be discussed in Section 6 of this paper.

Assessing the Effects of NTMs on International Trade: A Review

Because of the increasing incidence of NTMs, there has been a growing interest in quantifying their effects on international trade. Yet, quantifying their effects has proven to be difficult (Ferrantino, 2006, 2010). Because of their varying objectives, the effects of NTMs are not directly quantifiable, unlike tariffs (Fugazza, 2013).

Nonetheless, the literature has progressed over the years. There is a growing body of empirical work looking at the impacts of NTMs. This was made possible by the development of alternative frameworks, improvement in the availability and accessibility of data, and advances in econometric modeling, all of which allowed new methodologies to be developed (Arita et al., 2015; Ferrantino, 2010; Beghin & Bureau, 2001). Most studies covered technical regulations, especially TBT and SPS, because they have both trade-cost effects and demand-enhancing effects;

and also because other NTMs like quantitative restrictions are being phased out under the WTO.

Ferrantino (2006, 2010) and Beghin and Bureau (2001) provided an exhaustive review of earlier works on the impacts of NTMs on trade and welfare, focusing on methodological and estimation issues, data constraints, and implications of research findings. The review done in this

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17 paper focuses only on works that are relevant to the study, the highlights of which are shown in Appendix Table 1.

NTMs affect the price of a good and hence, the demand. Hence, there are two general approaches in quantifying the impacts of NTMs on trade: the price-based approach and the quantity-based approach. Although the two approaches differ in data requirements and estimation techniques, their goal is the same, that is, to estimate the price effects associated with an NTM in terms of tariff equivalent or, more specifically, the ad valorem equivalent (AVE). As the measuring unit, the AVE translates the impact of NTMs into a single metric and thus allows easy comparison with tariff rates.

Price-based Approach

The price-based approach measures the extent to which NTMs increase domestic prices by comparing the price of products affected by NTMs with similar products without NTMs. The measurement is done either econometrically or by direct price-gap comparison. The latter adjusts the price gap for other factors which may influence the price (e.g., taxes, tariffs, transport and distribution costs, wholesale and retail margins, and subsidies). Ferrantino (2006) and Deardorff and Stern (1998) provided a number of price-gap formulae which vary depending on the adjustments made. The estimation is a simple arithmetic exercise, and the estimated price gap is considered as the tariff equivalent.

On the other hand, the econometric method involves a price estimation model where NTM is included as one of the explanatory variables. It looks for proof that the domestic price is higher than it otherwise would be. The AVE is then calculated directly by taking the exponential of the coefficient of NTM.

Among the empirical works that used the econometric approach, the following have made significant contributions to the literature: Dean et al. (2006), Cadot and Gourdon (2015), Ing and Cadot (2017), Cadot et al. (2018), and Vanzetti et al. (2018). Using a differentiated product model of retail prices covering 47 consumer products from 60 countries, Dean et al. (2006) found that NTMs are a significant source of trade restrictiveness for many countries and products. For example, the prices of fruits & vegetables and meats are higher by 44% and 54%, respectively, because of NTMs. Although NTMs are restrictive in many countries, they appear to be less restrictive in Sub-Saharan African, Eastern European, and some Middle Eastern countries; and more restrictive in the E.U., U.S., and some Southeast Asian countries.

Apart from the price effects, Cadot and Gourdon (2015) took account of the role of deeper integration efforts on the impact of NTMs on prices in their estimation framework. This was their main contribution to the literature. Regional trade agreements (RTAs) include clauses on harmonization and mutual recognition arrangements (MRA) of technical regulations (SPS and TBT) and conformity assessment procedures. They are expected to reduce compliance costs and hence, product prices. The findings of the study confirmed that NTMs increase prices; however, harmonization and MRA reduce their price-raising effects by about a quarter. This means that harmonization or MRAs lowers the compliance-cost component of product prices. Unlike Dean et al. (2009), the study disentangled the effects of NTM by types, such as TBT, SPS, and other measures (quantitative restrictions and price measures). The findings showed that different NTMs

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18 affect goods differently. In particular, the AVEs of SPS are high for food and agricultural products, but the AVEs of TBT are high for automobiles.

The paper by Ing and Cadot (2017) differs from Dean et al. (2009) and Cadot and Gourdon (2015) on three areas. First, the paper focused on the ASEAN member economies (AMS). Second, instead of a dummy variable to capture the effects of NTMs, the number of NTMs was employed to capture the cumulative burden of NTMs to exporters. Third, the estimation model allowed country-specific estimates of AVEs, where AVEs are interpreted as tariff equivalents of compliance costs. The results showed that for manufactured products, AVEs for TBT are low for both the AMS (4.5%) and the entire sample economies (5%). In contrast, for agricultural products, AVEs for SPS are slightly higher for both the AMS (6.5%) and the entire sample (6.7%).

The novel contribution of Cadot et al. (2018) in the literature was their attempt to estimate both the price and volume effects of NTMs. That is, they disentangled the trade-cost effects of NTMs from their demand-enhancing effects due to information asymmetries. This was accomplished by employing both the price-based approach for the price effects and the quantity- based approach for the volume effects. Note, though, that the AVE was not calculated for the latter.

The findings of Cadot et al. (2018) showed that AVEs for SPS in agriculture are higher compared to AVEs for TBT in manufacturing. For SPS and TBTs, AVEs are associated with compliance costs. But higher AVEs do not necessarily mean more distortions. It could mean that exporters need to upgrade product quality or product design. NTMs also lower the volume of trade.

However, for SPS, although it increases trade costs, it also increases trade volume.

The paper by Vanzetti et al. (2018), apart from the price effects of NTMs, also examined the effect of regulatory distance on trade prices in the ASEAN. Their findings show that similarity in regulations between the importer and exporter can substantially reduce the costs effects of NTMs.

In particular, regulatory reform towards convergence, without increasing or decreasing the number of NTMs, could reduce the cost effects of NTMs by 15%–20%.

Quantity-Based Approach

A quantity-based approach is an indirect approach of measuring the impact of NTMs on prices; indirect because the approach involves a two-step process. An import demand function of bilateral flows is first estimated, where NTM is one of the explanatory variables. The estimation looks for evidence that trade with NTMs is lower than what it otherwise would be (Ferrantino, 2006). The quantity impact of NTM is then converted into AVE using import demand elasticities.

Empirical works commonly use the gravity framework to estimate the quantity impact of NTMs. Similar to Newton’s law of gravity, the model assumes that bilateral trade flows increase as the economic size of the trading partners increase and decrease as trade costs increase (Arita et al., 2015). Although the framework is similar, the model specification varies depending on the availability of data. The pseudo-Poisson maximum likelihood (PPML) is commonly used to estimate the possibility of zero trade flows, especially for products at very disaggregated levels (Ferrantino, 2006, 2010; Beghin & Bureau, 2001).

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19 Among the pioneering works on the quantity-based approach, the paper by Kee et al. (2009) is the most often cited in the literature because it was the first paper to estimate at the multi-country level and at a very disaggregated product level (HS 6-digit). Using Leamer’s comparative advantage framework, the impact of core NTMs and domestic support were estimated. Their findings showed that the importance of NTMs as a protectionist tool is higher than the tariff and that poor countries have more restrictive regimes. Their approach allows the estimation of product- specific AVEs but not for importer-specific AVEs. This limitation was addressed by interacting dummy variables for NTM presence with country characteristics; thus, it allowed them to use the coefficients to estimate the predicted country-specific AVEs.

Ghodsi et al. (2016a) extended the approach of Kee et al. (2009) to a panel analysis, allowing them to improve on the model specification. The estimation allowed for importer-specific AVE for each product, which was done by interacting the NTM variables with importer dummies.

The effects of various NTM types were differentiated, but the approach was different. The effect of an NTM type in focus was distinguished while controlling for the effects of all other types of NTMs considered in the model. Instead of dummy, the intensity use of NTM (number of NTMs imposed) was considered similar to Ing and Cadot (2017) and Cadot et al. (2018). Unlike Kee et al. (2009), which restricted NTMs to be trade-reducing, Ghodsi et al. (2016a) considered the ambiguous effects of NTMs due to market imperfections. Thus, NTMs have the potential to increase trade. The use of panel data made it possible for the study to employ lagged policy variables because import demand does not react immediately to policy changes.

With their improved model specification and using more recent estimates of import demand elasticities by Ghodsi et al. (2016b) for the calculation of AVEs, the major findings of Ghodsi et al. (2016a) showed that, in general, SPS and TBT measures have both trade-impending and trade- enhancing effects, depending on the imposing country and product under consideration.

Furthermore, the AVEs are smaller for developed countries than less developed countries, despite the former imposing more NTMs than the latter. At the product level, AVEs are highest for NTMs, affecting products related to gross fixed capital formation.

In a more recent paper, Ghodsi et al. (2017), using the same model as Ghodsi et al. (2016a) but with more updated dataset, estimated the effects of NTMs on the quantity of imports only. The AVEs, however, were not estimated. The results show the trade-impending effects of NTMs for about 60% of their estimates. The effects of NTMs differ by the imposing country. Trade-reducing effects are highest for SPS measures and QRs in Sub-Saharan Africa. On the other hand, the most trade-supportive effects are in South Asia for SPS measures and TBTs.

Bratt (2017) further enriched the model specification of earlier empirical works by considering the asymmetric impact of an NTM imposed by a country on trade with its trading partners. That is, the same NTM can affect exporters differently depending on how well they are prepared to respond to the NTMs of their trading partners. The specification, therefore, allows for the estimation of AVEs for each importer-exporter pair by product. The main results showed that the AVEs of high-income importers are lower than those of low-income importers and that high- income exporters are less affected by NTMs than low-income exporters. The paper, however, analyzed the impacts of NTMs in general without differentiating the impacts by NTM type.

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20 There are also empirical studies that deal with the impact of NTMs on specific products or countries for specific NTM types. Arita et al. (2015), for example, examined the impact of SPS and TBT for select agricultural commodities (beef, poultry, pork, corn, soy, fruits, vegetables, nuts, wheat) for the U.S.–E.U. trade. The results showed that SPS and TBT are significant impediments to U.S.–EU agricultural trade. Estimated AVEs of NTMs are larger than existing tariffs and tariff- rate quotas on the same products.

Other studies focused only on the demand effect of NTMs for specific products and specific countries without estimating the AVEs. Examples of these include Disdier et al. (2008) on the impact of NTMs under the SPS and TBT agreements of WTO; Song and Chen (2010) on the impact of food safety regulations on China’s agricultural exports; Wei et al. (2012) on food standards on China’s exports of tea; and Nguyen (2018) on the impact of SPS on Vietnam’s exports of rice. The common finding of these studies attests to the negative effects of NTMs, particularly SPS and TBT, on exports of agricultural products.

Price-based Approach vs. Quantity-Based Approach: Which is the Better Approach?

The debate on which of the two approaches is the most appropriate in examining the effects of NTM on international trade continues. Each approach has its own strengths and limitations.

Although the price-based approach allows for the direct calculation of the AVEs from the coefficients in the estimation without the need for the price elasticity of import demand, the key issue is the availability and comparability of price data for a large set of products at a very disaggregated level across countries.

On the other hand, the quantity-based approach is more suitable for large-scale analyses involving multi-country and highly disaggregated product levels (HS 6-digit), primarily because of the availability, accessibility, and comparability of trade data at the global level. However, the calculation of AVEs is highly dependent on the price elasticities of import demand. Recent empirical works used the import demand elasticities taken from the work of Kee et al. (2009).

Also, as the effect of NTMs on trade flows may be more of direct interest to policymakers than the effects on prices, the quantity-based approach would be a better approach for policy analysis (Ferrantino, 2006).

Both approaches have one thing in common though; that is, neither takes account of the differences in quality between domestic and imported products. Notwithstanding their strengths and limitations, the results of both approaches largely depend on the validity of the econometric specification (Ferrantino, 2010; Beghin & Bureau, 2001; Bratt, 2017).

Theoretical Framework: Quantifying the Effects of NTMs on Trade

The theoretical framework in assessing the effects of NTMs on trade in the ASEAN and its dialogue partners draws on Bratt (2017) on the asymmetric effects of NTMs on trading partners.

The relative condition of a country will have a bearing on how well it can meet the NTMs imposed by its trading partners. For example, similarities in domestic laws and regulations, particularly in technical regulations, between an exporter and importer will give the exporter an edge over another exporter whose domestic laws and regulations differ from the importer’s. The regulatory distance

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21 indicator developed by Cadot et al. (2015) measures the similarities in NTM measures between countries and across sectors or products.

Similarities in NTM measures are best exemplified in deep regional integration commitments that include harmonization and mutual recognition agreements (MRAs) on technical measures. These deep integration efforts narrow the “standards divide” between trading partners, most especially between developed and developing countries. Hence, they lower, if not eliminate, compliance costs. An exporter that belongs to the same RTA as the importer will therefore have an advantage over another exporter that is outside of the RTA. As pointed out in the preceding section, the study by Cadot and Gourdon (2015) showed that such deep integration efforts lower the price-raising effects of NTMs.

NTMs that address market imperfections such as externalities and information asymmetries can alter the effects of NTMs on trade. Marette and Beghin (2007) showed, for instance, that NTMs that correct an externality associated with the consumption of a good are pro- trade when foreign producers are more efficient than domestic producers in addressing the externality. Also, an NTM that sets the standard or quality for a particular good lessens information asymmetries and, therefore, reduces the producers’ transaction costs. It could also result in an increase in consumer trust. Both outcomes could result in an increase in exports from countries that meet the importing country’s standards. Based on the study by Cadot et al. (2018), SPS measures, while they increase trade costs, they also increase trade volume.

To illustrate the theoretical framework, suppose there are two countries exporting the same good, q. As in Bratt (2017), both countries are small and hence, are price-takers, but they differ in their cost functions. Due to its lack of or less rigid regulations, exporter L has a lower cost function than exporter H, which has more rigid regulations. Their profit functions take the following forms:

Π = 𝑝 ∗ 𝑞 − 𝜆 𝑐 ∗ 𝑞 +1 2𝑓 𝑞

(1) Π = 𝑝 ∗ 𝑞 − 𝜆 𝑐 ∗ 𝑞 +1

2𝑓 𝑞

where c and f represent variable costs, where 𝑐 < 𝑐 and 𝑓 < 𝑓 . On the other hand, 𝜆 represents compliance cost on any NTM, where 𝜆 = 𝜆 = 1 implies the absence of NTM.

Exporter H is more efficient than exporter L in dealing with NTMs. Hence, 1 ≤ 𝜆 ≤ 𝜆 . Profit maximization implies the following supply curves:

𝑞 = 𝑝 − 𝜆 𝑐

𝜆 𝑓 (2)

𝑞 =𝑝 − 𝜆 𝑐 𝜆 𝑓

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22 The total domestic supply of good q comes solely from both exporters as there are no domestic producers in the importing country. Thus, 𝑞 = 𝑞 + 𝑞 . Demand is linear in prices, 𝑞 = 𝑎 − 𝑝, where a denotes market size.

Figure 14 illustrates the effect on both exporters when the importing country imposes an NTM on good q. Prior to the imposition, the domestic market clears at point A, where the demand curve and total supply curve (𝑆 , ) intersects; and 𝑞 and 𝑝 are the equilibrium quantity and price, respectively. As the low-cost country, exporter L, with a supply curve 𝑆 , , exports more than exporter H, with the supply curve 𝑆 , , at the equilibrium price 𝑝, that is, 𝑞 > 𝑞 .

Figure 14

Asymmetric Impact of an NTM on Two Exporters

p1**

p1* p0*

q1L q0H q1H q0L q1* q0* Quantity q1L** q1H** q1**

Source: Expanded version of Bratt (2017).

Case 1: Decrease in Demand

Suppose the importing country imposes an NTM with rigid regulations. The NTM increases the costs of both exporters but more so for exporter L (𝑆 , is higher than 𝑆 , ). The market clears at point D, with a lower equilibrium quantity 𝑞 but a higher equilibrium price 𝑝. With its own rigid standards prior to the imposition of the NTM on its exports, exporter H is now

Demand1

Price

Demand B

F E

C

D

A H G

I

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23 in a better position than exporter L in meeting the rigid regulations of the importing country. This results not only in an increase in exporter H’s share in the domestic market (𝑞 > 𝑞 ) but also to a higher market share than exporter L, that is, 𝑞 > 𝑞 .

Case 2: Increase in Demand

Suppose the NTM imposed by the importing country improves the quality or safety of a product, resulting in an increase in consumers’ trust. Instead of a decrease, it increases the demand for the product, as shown by the shift of the demand curve to the right (Demand 1). Both the equilibrium price ( 𝑝∗∗) and quantity (𝑞∗∗) are now higher at the equilibrium point G. However, the efficient exporter H experiences a much higher increase in its exports (𝑞 ∗∗) and hence, a much higher share in the market while exporter L suffers a decline (𝑞 ∗∗).

Both cases 1 and 2 illustrate the asymmetric impact of NTMs. That is, the effects of NTMs vary across trading partners. Also, although NTMs raise an exporter’s costs, it matters how much the increase is relative to the increase in the costs of other exporters.

The above theoretical framework best illustrates the case of the ASEAN member economies, given the differences in their domestic laws and regulations and in their capacities and institutions to meet each other’s NTMs. The deep integration efforts in the region through the AEC is a test of how narrow or how wide the “standards divide” is in the region.

Data and Methodology Model Specification

To account for the asymmetric effects of NTMs, the model specification should allow for the estimation of the quantity effect for each importer-exporter pair and for each product and NTM type. Drawing on Bratt’s (2017) framework and methodology, the following gravity model specification is used:

𝑚 , = exp 𝛼 + 𝛼 , + 𝛼 , + 𝛽 ln 𝐺𝐷𝑃, + 𝛽 ln 𝐺𝐷𝑃, + 𝛾 𝐶𝐴 , + 𝛾 𝐶𝐴, + 𝜌 𝑅𝐷 , + 𝛿 𝑡 , + 𝜙 , 𝑁𝑇𝑀 , 𝜀 ,

(3)

The dependent variable in Equation (3) is imports—denoted as 𝑚—by the importer 𝑖 from exporter 𝑗, of product 𝑛 at year 𝑡. Products refer to tariff lines at the 6-digit level of the Harmonized System (HS). The 𝛼 , 𝛼 , , and 𝛼 , account for different fixed effects; 𝛼 captures product- specific fixed effects, or the unobserved heterogeneity arising from differences in each product;

𝛼 , accounts for country-pair specific fixed effects or time-invariant heterogeneity unique to each country-pair, such as whether the country-pair shares a border, shares a common language; 𝛼 , accounts for time-specific fixed effects or the unobserved heterogeneity arising from economic shocks occurring at specific time periods.

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24 𝛽 and 𝛽′ measure the elasticity of import quantities to the importer’s and exporter’s GDP in the same year, denoted by 𝐺𝐷𝑃, and 𝐺𝐷𝑃, respectively. 𝐶𝐴 denotes comparative advantage in each factor 𝑘, of each country at year 𝑡 and their impacts on imports of product 𝑛 are captured by 𝛾 and 𝛾 . Following Bratt (2017), the comparative advantage in each factor 𝑘 is measured as the ratio of each factor—agricultural land, capital, and labor—to the country’s GDP, demeaned across the sample of countries.

Meanwhile, 𝜌 measures the effect of regulatory distance (𝑅𝐷) between country pairs on product 𝑛 at time 𝑡. On the other hand, 𝑡 , is the bilateral tariff on product 𝑛 at time 𝑡 and its impact is measured by 𝛿 .

The impact of importer NTMs are given by 𝜙 , . The NTM variable is a dummy variable2 such that:

𝑁𝑇𝑀 , = 1 0

if an NTM has been imposed by the importer on product 𝑛 as of that year

otherwise (4)

Similar to Bratt’s (2017) specification, the coefficient 𝜙 , varies per product, for each country-pair in a given year. This is done by interacting the importer NTM with the comparative advantage variables, the GDPs of each country, and the regulatory distance between each country pair. As explained in the previous section, the varying effects of NTMs between country-pairs could be explained by how well an exporter is able to meet an importer’s NTM, which in turn could be captured by country differences in these factors. Thus, 𝜙 , is decomposed into:

𝜙 , = 𝜙 + 𝜙 , 𝑙𝑛𝐺𝐷𝑃, + 𝜙 , 𝑙𝑛𝐺𝐷𝑃, + 𝜙 , 𝐶𝐴, + 𝜙 , 𝐶𝐴 , +𝜙𝑛𝑡,𝑖𝑗𝑅𝐷 𝑅𝐷𝑡,𝑖𝑗 (5)

where 𝜙 captures the product-specific average effect of an NTM imposition by the importer. The coefficients 𝜙 , and 𝜙 , measure the differential impact on imports of each country’s GDP, given an NTM imposition by the importer, while 𝜙 , and 𝜙 , capture the differential impact on imports of the comparative advantage variables, given an NTM imposition by the importing country. Similarly, 𝜙 , measures the differential impact on imports of the regulatory distance between the trading partners, given an NTM imposition by the importer. The sum of all these coefficients result in 𝜙 , , which is unique to each product and each country-pair trading that product for a given year.

2 The use of dummy variables to represent NTM presence, instead of NTM count per product, is necessary to enable the interaction with the country-specific variables such as GDP, comparative advantage variables, and regulatory distance. This interaction, in turn, is necessary to obtain country-pair specific estimates for the effect of NTM presence on imports.

Pigura

Table 8   Variable List
Table 9   Descriptive Statistics
Figure 27 further confirms the earlier discussion on the impact of TBT for products to be  covered by  forthcoming  MRAs  and harmonization agreements
Figure 31   Average 𝝓
+5

Mga Sanggunian

NAUUGNAY NA DOKUMENTO

DEPARTMENT OF REGISTRAR AND ADMISSION SERVICES APPLICATION FOR ADMISSION Name of student: __________________________ Date: _______________ Address: _________________________________

2 Specifically, the explanatory variables were expressed as: brgy = barangay dummy 1 if barangay is San Lorenzo, 0 if barangay is Bani; age = age of respondent years; gender dummy 1 if