Abstract
This paper investigates the trade creation effects of Japan's free trade agreements (FTAs) using aggregate trade data for the years 1996–2015. We estimate various specifications of a gravity model. Our main finding is that the effects of Japan's FTAs are not clearly observed when the gravity model is specified with three types of fixed effects (i.e., exporter-year fixed effects, importer-year fixed effects, and country-pair fixed effects). In fact, the effects of FTAs vary substantially among trade partners and around half of the FTAs increase Japan's trade values. Our results also suggest that FTAs with small trade partners tend to have large effects on Japan as well as other countries. Recently enforced FTAs, however, increase Japan's import values more rapidly.
1. Introduction
Free trade agreements (FTAs) are currently the dominant form of commercial policy. The pattern of trade policies in the last two decades has been characterized mainly by the proliferation of FTAs. According to the Regional Trade Agreements Information System in the World Trade Organization (WTO), the cumulative number of physical regional trade agreements increased from 24 in 1992 to 286 in 2017.1
Japan started to seek trade liberalization through FTAs around 2000 and established the first FTA with Singapore in 2001. Since then, 15 FTAs with 17 countries have been established by August 2018.2 In addition, Japan recently signed two important multilateral trade agreements, the Comprehensive and Progressive Agreement for Trans-Pacific Partnership and the economic partnership agreement with the EU. Although East Asian countries including Japan began forming FTA networks only recently, they are catching up with Western countries and are expected to play a vital role against protectionism.
This paper investigates the trade creation effects of Japan's FTAs. We focus on Japan for three reasons. First, despite increasing interest among policymakers, the ex post evaluation of FTAs is extremely limited. No papers apply recently developed methods of analysis to Japan's FTAs. The gravity model, which is commonly used in ex post studies, is developing rapidly in the academic literature and is now estimated differently from the way it was ten years ago. Furthermore, some recent studies show that different FTAs have very different effects, and trade creation effects are not clearly observed in some cases. Even under FTAs, Japan has not eliminated trade barriers for agricultural goods, which are heavily protected by most favored nation (MFN) tariff rates. In addition, MFN tariff rates on manufacturing goods are already low and there is little scope to reduce them further through FTAs. It is therefore unclear whether Japan's FTAs have increased its own trade values. Trade creation is crucial for demonstrating the positive welfare effects of FTAs; if trade creation does not occur, a reconsideration of Japan's trade and commercial policies would be called for.
The second reason is that Japan provides a suitable case study for exploring the heterogeneous effects of FTAs in the context of the surge in regionalism and the proliferation of bilateral trade agreements since the 1990s. European countries trade mainly among themselves and they established trade agreements with each other soon after World War II. The United States also established trade agreements with its major trading partners, Canada and Mexico, around 1990 by signing the Canada–U.S. Free Trade Agreement and the North American Free Trade Agreement (NAFTA). In contrast, Japan, the world's fourth largest exporter and third largest economy, began negotiations of bilateral trade agreements with the countries of ASEAN after 2000.
Finally, Japan's FTA partners vary substantially in terms of economic development, ranging from Cambodia, Laos, and Myanmar to Australia, Singapore, and Switzerland. The differences in the trade creation effects across different FTAs can be attributed to the characteristics of the partner countries.
The objective of this paper is to evaluate Japan's FTAs. To this end, we first estimate the effects of Japan's and other countries’ FTAs using a state-of-the-art gravity model.3 We include three types of fixed effects and estimate using Poisson pseudo maximum likelihood (PPML).4 We estimate the gravity model using various specifications to compare the coefficients for the FTA dummies. In addition, the effects of each of the FTAs are separately identified to take heterogeneity into account. Then the estimated crude effects of individual FTAs are regressed on some variables to explore which FTAs have larger effects. This paper therefore contributes to the literature by examining the determinants of successful FTAs.
The structure of the paper is as follows. We briefly review the literature on the impacts of FTAs in the next section. In Section 3, we describe the source of the data and provide a descriptive analysis. Section 4 discusses the econometric methodology for estimating the gravity model. Results of the estimation are presented in Section 5, followed by conclusions in Section 6.
2. Related literature
FTAs are major instruments for promoting international trade in the 21st century. The ex post effects of FTAs are usually estimated using a gravity model; Baier and Bergstrand (2007) recommend the use of panel data to remove all time-invariant bilateral factors not controlled for in the traditional specification. A similar specification is applied in Magee (2008), although the effects of FTAs weaken when the gravity model is estimated with fixed effects. Cipollina and Salvatici (2010) conduct a meta-analysis and robustly reject the hypothesis that FTAs have no effects. Large effects are also confirmed in Eicher, Henn, and Papageorgiou (2012).
Although the effects of FTAs have been established in many papers, Kohl (2014) notes that the trade creation effects are heterogeneous and only about one-quarter of agreements are actually trade-promoting.5 Heterogeneity is also studied in Baier, Yotov, and Zylkin (2019) and Baier, Bergstrand, and Clance (2018). Zylkin (2016) examines the heterogeneous effects of FTAs, using the case of NAFTA. The differences between these papers and ours are three-fold. First, we focus on the heterogeneity of directional effects rather than agreement-specific effects or pair-specific effects. This is because Japan's FTAs are bilateral except for the agreements with ASEAN (ASEAN–Japan Comprehensive Economic Partnership, AJCEP), initially applied in December 2008 between Japan and four countries in ASEAN. Second, we compare the coefficients estimated in various specifications to consider what is important for the evaluation. This analysis provides a good benchmark for future studies because our state-of-the-art specification is computationally burdensome. Finally, our sample period, 1996–2015, includes recent agreements. Because Kohl (2014) finds that FTAs signed after 1990 have smaller effects, extending the sample period is not a trivial point.
Some papers examine FTAs in Japan or East Asia.6 Ando and Urata (2011) investigate the impact of the Japan–Mexico economic partnership agreement and find large trade-creation effects for some products. Ando and Urata (2015) conduct a similar analysis for three of Japan's FTAs with Malaysia, Thailand, and Indonesia. Yamanouchi (2017) examines the effects of Japan's FTAs by using the Trade Statistics of Japan published by the Ministry of Finance and Japan Customs. Although the effects of individual FTAs are estimated in these papers, only Japan's trade data are used. The trade values of Japan's FTA partners with third countries are not considered. Vietnam, for example, is undertaking rapid liberalization, including WTO accession in 2007. It is therefore useful to separately identify the effects of Japan's FTAs with Vietnam from the effects of Vietnam's unilateral trade liberalization. Okabe (2015) explores FTAs formed by ASEAN countries and their trade partners, so AJCEP is studied in that paper. She concludes that the impact of AJCEP is unclear. Furthermore, while the effects of each of Japan's FTAs are estimated, a country-pair dummy is not included in the specification.
In this paper, we use world trade data to estimate the effects of Japan's FTAs. We estimate various specifications of a gravity model and place emphasis on the importance of estimating the effects of FTAs using the correct specification. In addition, we discuss the characteristics of the partners with which FTAs are working well.
3. Data
The trade data used in this paper are obtained from UN Comtrade. The sample period extends from 1996 to 2015. We first construct a large data set of 156 countries to interpolate missing trade values. Then the sample to estimate the gravity model is limited to 69 countries.
We include a country in the data set if its import data are available for more than 11 years during the period 1996–2015. All of Japan's FTA partners are then added regardless of data availability. Many countries have some missing import data. We interpolate the missing import values using the export data reported by exporters. Because import values are reported as cost, insurance, and freight, and export values are reported as free on board, the gap must be estimated.7 We regress import values on a quartic of export values, a quartic of bilateral distance (population weighted), a quartic of log of importer GDP, a quartic of log of importer population, other gravity variables (a contingency dummy, a common colonizer dummy, and a common language dummy), variables related to trade policies (a FTA dummy, a customs union dummy, a partial scope agreement dummy, a common currency dummy, an importer EU dummy, and an importer WTO dummy), and an exporter-year fixed effect. We obtained these variables, other than some trade policy variables, from the CEPII Web site, constructed by Head, Mayer, and Ries (2010) and Head and Mayer (2014).8 The information on the FTA, customs union, and partial scope agreement dummies is obtained from the Mario Larch Regional Trade Agreements Database from Egger and Larch (2008).9
The actual and predicted values have a high correlation coefficient of 0.92, suggesting that the estimation is valid and the missing import values are well approximated by the corresponding export values.
The sample we use in the main analysis is smaller because of computational difficulty. We then select the countries by ranking trade values averaged over 20 years. The sample includes Japan's FTA partners and the countries with a ranking of export or import values higher than 60. Our sample includes 69 countries.10
Table 1 shows the evolution of Japan's exports to all countries and FTA partners. The trade flows under FTAs are shaded. Japan's exports to all countries were around US$ 410 billion in 1996. The total export value has increased rapidly over the period 2003–08. Although Japan's total exports collapsed during the 2008–09 global financial crisis, they soon recovered before decreasing slightly. From Table 1, it is difficult to identify the effects of Japan's FTAs because most of them were enforced during the expansion period, although the export values increased after the enforcement. In addition, although export values to developing countries such as Myanmar and Vietnam grew most rapidly, we must account for the impacts of the deepening integration of the world economy.
. | . | Export value in 1996 = 100 . | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FTA partner . | Export 1996 (million US dollars) . | 1997 . | 1998 . | 1999 . | 2000 . | 2001 . | 2002 . | 2003 . | 2004 . | 2005 . | 2006 . | 2007 . | 2008 . | 2009 . | 2010 . | 2011 . | 2012 . | 2013 . | 2014 . | 2015 . |
Singapore | 23,842 | 97 | 71 | 77 | 97 | 67 | 61 | 64 | 80 | 80 | 83 | 90 | 108 | 78 | 102 | 110 | 99 | 85 | 84 | 77 |
Mexico | 3,837 | 116 | 118 | 132 | 168 | 210 | 243 | 197 | 275 | 340 | 398 | 425 | 424 | 297 | 391 | 429 | 460 | 445 | 457 | 452 |
Malaysia | 19,225 | 90 | 59 | 70 | 89 | 73 | 74 | 74 | 88 | 86 | 90 | 98 | 101 | 80 | 107 | 111 | 104 | 93 | 87 | 71 |
Chile | 950 | 111 | 104 | 66 | 73 | 57 | 56 | 74 | 104 | 133 | 154 | 208 | 338 | 167 | 357 | 308 | 273 | 260 | 248 | 221 |
Thailand | 20,445 | 78 | 49 | 59 | 74 | 67 | 72 | 89 | 109 | 127 | 126 | 146 | 163 | 122 | 185 | 206 | 242 | 200 | 174 | 152 |
Indonesia | 8,504 | 97 | 50 | 34 | 63 | 55 | 51 | 49 | 71 | 81 | 64 | 76 | 177 | 115 | 199 | 228 | 267 | 226 | 199 | 155 |
Brunei | 139 | 169 | 72 | 61 | 68 | 51 | 147 | 89 | 100 | 124 | 154 | 127 | 242 | 192 | 176 | 201 | 192 | 150 | 104 | 171 |
Laos | 49 | 80 | 54 | 72 | 70 | 45 | 62 | 54 | 58 | 78 | 90 | 149 | 263 | 297 | 67 | 89 | 147 | 226 | 185 | 148 |
Myanmar | 106 | 90 | 84 | 93 | 273 | 367 | 171 | 213 | 191 | 186 | 221 | 359 | 514 | 549 | 206 | 377 | 835 | 1605 | 1544 | 1447 |
Vietnam | 1,208 | 113 | 120 | 152 | 190 | 180 | 207 | 246 | 294 | 337 | 389 | 512 | 682 | 618 | 746 | 861 | 960 | 957 | 1064 | 1174 |
Philippines | 7,578 | 104 | 84 | 86 | 91 | 92 | 104 | 109 | 106 | 111 | 101 | 95 | 93 | 76 | 96 | 92 | 91 | 73 | 73 | 89 |
Switzerland | 2,112 | 100 | 100 | 108 | 110 | 95 | 83 | 100 | 116 | 111 | 117 | 138 | 183 | 156 | 167 | 221 | 238 | 190 | 188 | 166 |
Cambodia | 81 | 104 | 86 | 109 | 72 | 75 | 78 | 96 | 102 | 123 | 159 | 173 | 140 | 146 | 192 | 305 | 294 | 214 | 324 | 520 |
India | 2,186 | 97 | 112 | 116 | 101 | 81 | 97 | 106 | 135 | 168 | 216 | 266 | 356 | 305 | 378 | 513 | 565 | 479 | 455 | 440 |
Peru | 440 | 109 | 121 | 108 | 110 | 97 | 93 | 83 | 81 | 101 | 128 | 178 | 290 | 210 | 311 | 298 | 341 | 326 | 251 | 244 |
Australia | 7,859 | 106 | 106 | 110 | 119 | 106 | 115 | 141 | 163 | 175 | 174 | 202 | 229 | 171 | 223 | 236 | 250 | 228 | 196 | 188 |
World (156 countries) | 410,174 | 102 | 94 | 101 | 117 | 103 | 106 | 119 | 142 | 151 | 163 | 180 | 198 | 149 | 188 | 206 | 206 | 189 | 182 | 164 |
. | . | Export value in 1996 = 100 . | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FTA partner . | Export 1996 (million US dollars) . | 1997 . | 1998 . | 1999 . | 2000 . | 2001 . | 2002 . | 2003 . | 2004 . | 2005 . | 2006 . | 2007 . | 2008 . | 2009 . | 2010 . | 2011 . | 2012 . | 2013 . | 2014 . | 2015 . |
Singapore | 23,842 | 97 | 71 | 77 | 97 | 67 | 61 | 64 | 80 | 80 | 83 | 90 | 108 | 78 | 102 | 110 | 99 | 85 | 84 | 77 |
Mexico | 3,837 | 116 | 118 | 132 | 168 | 210 | 243 | 197 | 275 | 340 | 398 | 425 | 424 | 297 | 391 | 429 | 460 | 445 | 457 | 452 |
Malaysia | 19,225 | 90 | 59 | 70 | 89 | 73 | 74 | 74 | 88 | 86 | 90 | 98 | 101 | 80 | 107 | 111 | 104 | 93 | 87 | 71 |
Chile | 950 | 111 | 104 | 66 | 73 | 57 | 56 | 74 | 104 | 133 | 154 | 208 | 338 | 167 | 357 | 308 | 273 | 260 | 248 | 221 |
Thailand | 20,445 | 78 | 49 | 59 | 74 | 67 | 72 | 89 | 109 | 127 | 126 | 146 | 163 | 122 | 185 | 206 | 242 | 200 | 174 | 152 |
Indonesia | 8,504 | 97 | 50 | 34 | 63 | 55 | 51 | 49 | 71 | 81 | 64 | 76 | 177 | 115 | 199 | 228 | 267 | 226 | 199 | 155 |
Brunei | 139 | 169 | 72 | 61 | 68 | 51 | 147 | 89 | 100 | 124 | 154 | 127 | 242 | 192 | 176 | 201 | 192 | 150 | 104 | 171 |
Laos | 49 | 80 | 54 | 72 | 70 | 45 | 62 | 54 | 58 | 78 | 90 | 149 | 263 | 297 | 67 | 89 | 147 | 226 | 185 | 148 |
Myanmar | 106 | 90 | 84 | 93 | 273 | 367 | 171 | 213 | 191 | 186 | 221 | 359 | 514 | 549 | 206 | 377 | 835 | 1605 | 1544 | 1447 |
Vietnam | 1,208 | 113 | 120 | 152 | 190 | 180 | 207 | 246 | 294 | 337 | 389 | 512 | 682 | 618 | 746 | 861 | 960 | 957 | 1064 | 1174 |
Philippines | 7,578 | 104 | 84 | 86 | 91 | 92 | 104 | 109 | 106 | 111 | 101 | 95 | 93 | 76 | 96 | 92 | 91 | 73 | 73 | 89 |
Switzerland | 2,112 | 100 | 100 | 108 | 110 | 95 | 83 | 100 | 116 | 111 | 117 | 138 | 183 | 156 | 167 | 221 | 238 | 190 | 188 | 166 |
Cambodia | 81 | 104 | 86 | 109 | 72 | 75 | 78 | 96 | 102 | 123 | 159 | 173 | 140 | 146 | 192 | 305 | 294 | 214 | 324 | 520 |
India | 2,186 | 97 | 112 | 116 | 101 | 81 | 97 | 106 | 135 | 168 | 216 | 266 | 356 | 305 | 378 | 513 | 565 | 479 | 455 | 440 |
Peru | 440 | 109 | 121 | 108 | 110 | 97 | 93 | 83 | 81 | 101 | 128 | 178 | 290 | 210 | 311 | 298 | 341 | 326 | 251 | 244 |
Australia | 7,859 | 106 | 106 | 110 | 119 | 106 | 115 | 141 | 163 | 175 | 174 | 202 | 229 | 171 | 223 | 236 | 250 | 228 | 196 | 188 |
World (156 countries) | 410,174 | 102 | 94 | 101 | 117 | 103 | 106 | 119 | 142 | 151 | 163 | 180 | 198 | 149 | 188 | 206 | 206 | 189 | 182 | 164 |
Source: UN Comtrade. Trade values are basically reported by importers, and missing values are interpolated from corresponding export values.
The evolution of Japan's import values is like that of its export values. As shown in Table 2, total imports almost doubled over the period 2003–08. After the collapse in 2009, import values recovered quickly but then decreased. As for each of the FTA partners, import values have grown more rapidly than export values. For example, imports from Cambodia have increased by 147 times in the last two decades.
. | . | Import value in 1996 = 100 . | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FTA partner . | Import 1996 (million US dollars) . | 1997 . | 1998 . | 1999 . | 2000 . | 2001 . | 2002 . | 2003 . | 2004 . | 2005 . | 2006 . | 2007 . | 2008 . | 2009 . | 2010 . | 2011 . | 2012 . | 2013 . | 2014 . | 2015 . |
Singapore | 7,323 | 80 | 64 | 74 | 87 | 73 | 68 | 74 | 85 | 91 | 102 | 96 | 107 | 83 | 111 | 118 | 119 | 101 | 107 | 107 |
Mexico | 1,890 | 85 | 65 | 87 | 126 | 106 | 95 | 94 | 114 | 134 | 149 | 167 | 201 | 148 | 184 | 210 | 233 | 223 | 226 | 251 |
Malaysia | 11,750 | 96 | 73 | 92 | 123 | 109 | 95 | 107 | 120 | 124 | 131 | 148 | 197 | 142 | 193 | 259 | 279 | 253 | 248 | 182 |
Chile | 2,763 | 107 | 86 | 91 | 102 | 88 | 77 | 95 | 151 | 185 | 262 | 295 | 286 | 192 | 280 | 355 | 337 | 290 | 294 | 217 |
Thailand | 10,212 | 93 | 80 | 86 | 103 | 101 | 102 | 116 | 138 | 152 | 165 | 179 | 203 | 156 | 205 | 240 | 231 | 215 | 212 | 200 |
Indonesia | 15,194 | 96 | 71 | 82 | 107 | 97 | 93 | 108 | 123 | 137 | 158 | 174 | 214 | 143 | 186 | 224 | 212 | 190 | 168 | 130 |
Brunei | 1,392 | 100 | 74 | 75 | 118 | 121 | 109 | 131 | 135 | 164 | 167 | 179 | 326 | 239 | 295 | 409 | 430 | 340 | 288 | 168 |
Laos | 23 | 90 | 85 | 58 | 51 | 29 | 28 | 31 | 34 | 34 | 52 | 51 | 77 | 115 | 161 | 415 | 527 | 458 | 491 | 416 |
Myanmar | 103 | 96 | 87 | 98 | 116 | 99 | 106 | 135 | 174 | 198 | 239 | 287 | 306 | 331 | 375 | 573 | 653 | 738 | 837 | 840 |
Vietnam | 2,018 | 108 | 86 | 97 | 130 | 129 | 125 | 153 | 191 | 225 | 262 | 303 | 450 | 344 | 405 | 572 | 747 | 705 | 764 | 750 |
Philippines | 4,522 | 110 | 98 | 117 | 159 | 141 | 144 | 155 | 182 | 170 | 176 | 193 | 186 | 141 | 175 | 197 | 206 | 204 | 225 | 196 |
Switzerland | 3,563 | 96 | 84 | 94 | 92 | 92 | 92 | 108 | 135 | 141 | 143 | 146 | 180 | 176 | 190 | 220 | 230 | 204 | 202 | 207 |
Cambodia | 7 | 200 | 245 | 526 | 795 | 1005 | 1141 | 1362 | 1519 | 1606 | 1831 | 2117 | 1843 | 2172 | 3165 | 4691 | 6158 | 8882 | 11761 | 14755 |
India | 2,843 | 93 | 76 | 79 | 92 | 78 | 73 | 76 | 91 | 112 | 142 | 146 | 184 | 131 | 200 | 240 | 246 | 248 | 245 | 171 |
Peru | 427 | 127 | 67 | 68 | 82 | 99 | 100 | 101 | 159 | 164 | 309 | 523 | 495 | 389 | 510 | 548 | 656 | 619 | 411 | 290 |
Australia | 14,229 | 102 | 91 | 90 | 104 | 101 | 98 | 105 | 136 | 172 | 196 | 219 | 334 | 244 | 316 | 398 | 396 | 358 | 338 | 244 |
World (156 countries) | 327,328 | 98 | 81 | 90 | 110 | 102 | 98 | 112 | 133 | 151 | 169 | 182 | 223 | 162 | 203 | 251 | 261 | 245 | 239 | 182 |
. | . | Import value in 1996 = 100 . | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FTA partner . | Import 1996 (million US dollars) . | 1997 . | 1998 . | 1999 . | 2000 . | 2001 . | 2002 . | 2003 . | 2004 . | 2005 . | 2006 . | 2007 . | 2008 . | 2009 . | 2010 . | 2011 . | 2012 . | 2013 . | 2014 . | 2015 . |
Singapore | 7,323 | 80 | 64 | 74 | 87 | 73 | 68 | 74 | 85 | 91 | 102 | 96 | 107 | 83 | 111 | 118 | 119 | 101 | 107 | 107 |
Mexico | 1,890 | 85 | 65 | 87 | 126 | 106 | 95 | 94 | 114 | 134 | 149 | 167 | 201 | 148 | 184 | 210 | 233 | 223 | 226 | 251 |
Malaysia | 11,750 | 96 | 73 | 92 | 123 | 109 | 95 | 107 | 120 | 124 | 131 | 148 | 197 | 142 | 193 | 259 | 279 | 253 | 248 | 182 |
Chile | 2,763 | 107 | 86 | 91 | 102 | 88 | 77 | 95 | 151 | 185 | 262 | 295 | 286 | 192 | 280 | 355 | 337 | 290 | 294 | 217 |
Thailand | 10,212 | 93 | 80 | 86 | 103 | 101 | 102 | 116 | 138 | 152 | 165 | 179 | 203 | 156 | 205 | 240 | 231 | 215 | 212 | 200 |
Indonesia | 15,194 | 96 | 71 | 82 | 107 | 97 | 93 | 108 | 123 | 137 | 158 | 174 | 214 | 143 | 186 | 224 | 212 | 190 | 168 | 130 |
Brunei | 1,392 | 100 | 74 | 75 | 118 | 121 | 109 | 131 | 135 | 164 | 167 | 179 | 326 | 239 | 295 | 409 | 430 | 340 | 288 | 168 |
Laos | 23 | 90 | 85 | 58 | 51 | 29 | 28 | 31 | 34 | 34 | 52 | 51 | 77 | 115 | 161 | 415 | 527 | 458 | 491 | 416 |
Myanmar | 103 | 96 | 87 | 98 | 116 | 99 | 106 | 135 | 174 | 198 | 239 | 287 | 306 | 331 | 375 | 573 | 653 | 738 | 837 | 840 |
Vietnam | 2,018 | 108 | 86 | 97 | 130 | 129 | 125 | 153 | 191 | 225 | 262 | 303 | 450 | 344 | 405 | 572 | 747 | 705 | 764 | 750 |
Philippines | 4,522 | 110 | 98 | 117 | 159 | 141 | 144 | 155 | 182 | 170 | 176 | 193 | 186 | 141 | 175 | 197 | 206 | 204 | 225 | 196 |
Switzerland | 3,563 | 96 | 84 | 94 | 92 | 92 | 92 | 108 | 135 | 141 | 143 | 146 | 180 | 176 | 190 | 220 | 230 | 204 | 202 | 207 |
Cambodia | 7 | 200 | 245 | 526 | 795 | 1005 | 1141 | 1362 | 1519 | 1606 | 1831 | 2117 | 1843 | 2172 | 3165 | 4691 | 6158 | 8882 | 11761 | 14755 |
India | 2,843 | 93 | 76 | 79 | 92 | 78 | 73 | 76 | 91 | 112 | 142 | 146 | 184 | 131 | 200 | 240 | 246 | 248 | 245 | 171 |
Peru | 427 | 127 | 67 | 68 | 82 | 99 | 100 | 101 | 159 | 164 | 309 | 523 | 495 | 389 | 510 | 548 | 656 | 619 | 411 | 290 |
Australia | 14,229 | 102 | 91 | 90 | 104 | 101 | 98 | 105 | 136 | 172 | 196 | 219 | 334 | 244 | 316 | 398 | 396 | 358 | 338 | 244 |
World (156 countries) | 327,328 | 98 | 81 | 90 | 110 | 102 | 98 | 112 | 133 | 151 | 169 | 182 | 223 | 162 | 203 | 251 | 261 | 245 | 239 | 182 |
Source: UN Comtrade. Trade values are basically reported by importers, and missing values are interpolated from corresponding export values.
Overall, we cannot conclude from this simple analysis that FTAs have had significant effects on Japan's international trade, although trade with some countries increased rapidly after an FTA was enforced. Instead, we turn to explore the contribution of FTAs using a correctly specified gravity model with three types of fixed effects.
4. Estimation method
Equation (2) is estimated initially by ordinary least squares (OLS). Recently, however, the bias of the OLS estimator has been pointed out. Santos Silva and Tenreyro (2006) show that when a log-linearized model such as the gravity model is estimated by OLS, heteroskedasticity affects both consistency and efficiency. They recommend specifying the conditional variance as proportional to the conditional mean and estimating the log-linearized model by PPML. Our baseline specification is therefore the estimation with three types of fixed effects by PPML.12 We can deal with the zero trade flows problem by PPML. In addition, the use of PPML is supported by the need to satisfy the adding up constraint (Arvis and Shepherd 2013; Fally 2015).
5. Estimation results
In this section, we provide the estimation results of equations (2)–(4). We first estimate the average treatment effects of all FTAs. Japan's FTAs are then separated from the other FTAs. We further decompose the effects of Japan's FTAs by partner countries. Individual FTAs all over the world are also investigated to explore the determinants of successful FTAs.
5.1 Trade creation effects of all FTAs
Before estimating the effects of Japan's FTAs, we first estimate the effects of all FTAs. We start with the results of the traditional gravity specification (equation [4]) by OLS. As reported in column (1) of Table 3, the coefficients for the FTA dummy and the customs union dummy are both positive and statistically significant. The coefficients for the other gravity variables are also consistent with the standard gravity model estimates in previous studies.
Variables . | (1) OLS . | (2) OLS . | (3) OLS . | (4) PPML . | (5) PPML . | (6) PPML . | (7) PPML w/o zero . |
---|---|---|---|---|---|---|---|
FTA | 0.367*** | 0.202*** | 0.118*** | 0.374*** | 0.104** | −0.00703 | −0.00786 |
(5.856) | (6.216) | (3.266) | (5.655) | (2.051) | (−0.127) | (−0.142) | |
CU | 0.238** | 0.561*** | 0.292*** | 0.656*** | 0.448*** | 0.0606 | 0.0535 |
(2.363) | (9.950) | (4.390) | (6.536) | (6.739) | (0.786) | (0.688) | |
PSA | 0.0181 | 0.0768 | 0.0663 | −0.154 | 0.0993 | −0.000895 | −0.00176 |
(0.231) | (0.989) | (0.837) | (−1.431) | (1.631) | (−0.0207) | (−0.0407) | |
Common currency | −0.584*** | −0.139*** | −0.00833 | −0.122 | 0.0302 | −0.0386 | −0.0396 |
(−6.059) | (−4.521) | (−0.209) | (−1.590) | (0.907) | (−1.251) | (−1.284) | |
ln(Distance) | −1.108*** | −0.669*** | |||||
(−27.46) | (−17.48) | ||||||
Contiguity | 0.612*** | 0.353** | |||||
(4.019) | (2.257) | ||||||
Common colonizer | 0.635*** | 0.407*** | |||||
(5.358) | (6.170) | ||||||
Common language | 0.509*** | 0.122* | |||||
(6.936) | (1.938) | ||||||
Exporter ln(GDP) | 0.0710*** | 0.268*** | |||||
(7.134) | (3.145) | ||||||
Importer ln(GDP) | 0.218*** | 0.740*** | |||||
(16.61) | (23.22) | ||||||
Exporter ln(Population) | 1.448*** | 0.698*** | |||||
(12.86) | (5.788) | ||||||
Importer ln(Population) | 0.555*** | 0.0680 | |||||
(7.393) | (0.611) | ||||||
Exporter ln(Remoteness) | 0.233** | 0.746*** | |||||
(2.035) | (5.415) | ||||||
Importer ln(Remoteness) | −0.0708 | −0.119 | |||||
(−0.601) | (−0.898) | ||||||
Exporter WTO | 0.594*** | 0.366*** | |||||
(12.65) | (5.632) | ||||||
Importer WTO | 0.465*** | 0.226*** | |||||
(10.33) | (4.945) | ||||||
Observations | 91,267 | 91,262 | 91,262 | 93,840 | 93,760 | 93,760 | 91,262 |
Exporter-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Importer-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Exporter-importer fixed effects | No | Yes | Yes | No | Yes | Yes | Yes |
Variables . | (1) OLS . | (2) OLS . | (3) OLS . | (4) PPML . | (5) PPML . | (6) PPML . | (7) PPML w/o zero . |
---|---|---|---|---|---|---|---|
FTA | 0.367*** | 0.202*** | 0.118*** | 0.374*** | 0.104** | −0.00703 | −0.00786 |
(5.856) | (6.216) | (3.266) | (5.655) | (2.051) | (−0.127) | (−0.142) | |
CU | 0.238** | 0.561*** | 0.292*** | 0.656*** | 0.448*** | 0.0606 | 0.0535 |
(2.363) | (9.950) | (4.390) | (6.536) | (6.739) | (0.786) | (0.688) | |
PSA | 0.0181 | 0.0768 | 0.0663 | −0.154 | 0.0993 | −0.000895 | −0.00176 |
(0.231) | (0.989) | (0.837) | (−1.431) | (1.631) | (−0.0207) | (−0.0407) | |
Common currency | −0.584*** | −0.139*** | −0.00833 | −0.122 | 0.0302 | −0.0386 | −0.0396 |
(−6.059) | (−4.521) | (−0.209) | (−1.590) | (0.907) | (−1.251) | (−1.284) | |
ln(Distance) | −1.108*** | −0.669*** | |||||
(−27.46) | (−17.48) | ||||||
Contiguity | 0.612*** | 0.353** | |||||
(4.019) | (2.257) | ||||||
Common colonizer | 0.635*** | 0.407*** | |||||
(5.358) | (6.170) | ||||||
Common language | 0.509*** | 0.122* | |||||
(6.936) | (1.938) | ||||||
Exporter ln(GDP) | 0.0710*** | 0.268*** | |||||
(7.134) | (3.145) | ||||||
Importer ln(GDP) | 0.218*** | 0.740*** | |||||
(16.61) | (23.22) | ||||||
Exporter ln(Population) | 1.448*** | 0.698*** | |||||
(12.86) | (5.788) | ||||||
Importer ln(Population) | 0.555*** | 0.0680 | |||||
(7.393) | (0.611) | ||||||
Exporter ln(Remoteness) | 0.233** | 0.746*** | |||||
(2.035) | (5.415) | ||||||
Importer ln(Remoteness) | −0.0708 | −0.119 | |||||
(−0.601) | (−0.898) | ||||||
Exporter WTO | 0.594*** | 0.366*** | |||||
(12.65) | (5.632) | ||||||
Importer WTO | 0.465*** | 0.226*** | |||||
(10.33) | (4.945) | ||||||
Observations | 91,267 | 91,262 | 91,262 | 93,840 | 93,760 | 93,760 | 91,262 |
Exporter-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Importer-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Exporter-importer fixed effects | No | Yes | Yes | No | Yes | Yes | Yes |
Source: Author's calculations.
Note: Robust t-statistics in parentheses. CU = customs union dummy; FTA = free trade agreement dummy; PSA = partial scope agreement dummy. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
We also estimate equation (3) and report the results in column (2). Although the coefficient for the FTA dummy is significantly positive, it has halved in value. This implies that FTAs are more likely to be signed between country pairs with high ex ante trade values, conditional on gravity variables. This is the opposite result to that of Baier and Bergstrand (2007), but it is qualitatively consistent with Magee (2008). The coefficient for the customs union dummy increases. Overall, the coefficients for the other country-year variables are positive and statistically significant as expected.
Column (3) of Table 3 shows the estimation results with all three types of fixed effects. Again, although the coefficient for the FTA dummy is positive and statistically significant, it has decreased compared with columns (1) and (2). This result implies that the role of trade policies is overestimated when the gravity model is specified with all three types of fixed effects.
The results of the estimations by PPML are almost the same as those by OLS. As reported in columns (4)–(6), the coefficients for the FTA dummies and the customs union dummies are positive and statistically significant. One notable difference between the results of OLS and PPML is that when three types of fixed effects are included, the coefficients for the FTA dummies and the customs union dummies decrease to almost zero, which means that these trade policies have no trade creation effects on average.
A potential reason for the lack of significant effects is our choice of the sample period. Baier and Bergstrand (2007) and Magee (2008), for example, use the sample periods: 1960–2000 and 1980–98, respectively. The coefficient for the FTA dummy in the present paper reflects the impacts of only recently signed FTAs because the trade creation effects of FTAs enforced before 1996 are absorbed into the country-pair fixed effect. Kohl (2014) points out that FTAs enforced after the 1990s performed poorly.
5.2 Trade creation effects of Japan's FTAs
We next turn to the trade creation effects of Japan's FTAs. Table 4 shows the results of estimation, in which the effects of Japan's FTAs are separated from those of others. Other covariates are included in the estimation, but not reported in the table to save space. As reported in column (1) of Table 4, the coefficients for Japan's FTA dummies are positive and statistically significant for exports and positive and slightly insignificant for imports when the country-year fixed effects are included but the country-pair fixed effects are not. Although the results change when the country-pair fixed effects are included in the regression, the point estimates are still large in the estimation with three types of fixed effects. In addition, this result holds when the gravity model is estimated by PPML. As reported in column (6), the point estimates of the coefficients for Japan's FTA dummies are 0.067 for exports and 0.086 for imports, but they are not statistically significant.
Variables . | (1) OLS . | (2) OLS . | (3) OLS . | (4) PPML . | (5) PPML . | (6) PPML . | (7) PPML w/o zero . |
---|---|---|---|---|---|---|---|
Japan's FTA (export) | 0.672*** | −0.191* | 0.178 | 0.504*** | −0.00362 | 0.0672 | 0.0667 |
(3.636) | (−1.758) | (1.583) | (3.245) | (−0.0249) | (0.781) | (0.775) | |
Japan's FTA (import) | 0.760 | −0.0525 | 0.106 | 0.423* | 0.173** | 0.0864 | 0.0855 |
(1.615) | (−0.513) | (0.808) | (1.821) | (2.516) | (1.312) | (1.296) | |
Other FTA | 0.397*** | 0.154*** | 0.0517 | 0.296*** | 0.0738 | −0.0439 | −0.0460 |
(6.164) | (4.789) | (1.437) | (4.267) | (1.430) | (−0.684) | (−0.716) | |
Observations | 91,267 | 91,262 | 91,262 | 93,840 | 93,760 | 93,760 | 91,262 |
Exporter-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Importer-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Exporter-importer fixed effects | No | Yes | Yes | No | Yes | Yes | Yes |
Variables . | (1) OLS . | (2) OLS . | (3) OLS . | (4) PPML . | (5) PPML . | (6) PPML . | (7) PPML w/o zero . |
---|---|---|---|---|---|---|---|
Japan's FTA (export) | 0.672*** | −0.191* | 0.178 | 0.504*** | −0.00362 | 0.0672 | 0.0667 |
(3.636) | (−1.758) | (1.583) | (3.245) | (−0.0249) | (0.781) | (0.775) | |
Japan's FTA (import) | 0.760 | −0.0525 | 0.106 | 0.423* | 0.173** | 0.0864 | 0.0855 |
(1.615) | (−0.513) | (0.808) | (1.821) | (2.516) | (1.312) | (1.296) | |
Other FTA | 0.397*** | 0.154*** | 0.0517 | 0.296*** | 0.0738 | −0.0439 | −0.0460 |
(6.164) | (4.789) | (1.437) | (4.267) | (1.430) | (−0.684) | (−0.716) | |
Observations | 91,267 | 91,262 | 91,262 | 93,840 | 93,760 | 93,760 | 91,262 |
Exporter-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Importer-year fixed effects | Yes | No | Yes | Yes | No | Yes | Yes |
Exporter-importer fixed effects | No | Yes | Yes | No | Yes | Yes | Yes |
Source: Author's calculations.
Note: Robust t-statistics in parentheses. OLS = ordinary least squares; PPML = Poisson pseudo maximum likelihood. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
Overall, Japan's FTAs do not appear to have positive effects on trade values when the model is correctly specified. Although the coefficients are statistically insignificant, the point estimates are not small. These results weakly suggest that the effects of Japan's FTAs are heterogeneous.
5.3 Trade creation effects of Japan's individual FTAs
The results of the previous subsection suggest that some of Japan's FTAs are working well, but others are not. To examine this point further, we decompose the effects of Japan's FTAs by partner countries.
Table 5 presents the coefficients for Japan's FTA dummies. We regard column (4), which includes three types of fixed effects and estimates by PPML, as the most reliable result.13 We also add the results from other studies, Ando and Urata (2015) and Yamanouchi (2017), in columns (5) and (6). Based on column (4), the export values from Japan to Australia, Chile, India, Indonesia, Mexico, Myanmar, Thailand, and Vietnam are positively affected by the FTAs with these countries. The FTA with Myanmar (AJCEP) has the largest effect and it increased Japan's exports to Myanmar by . This result is surprising because Myanmar's tariff rates were not lowered in the sample period under AJCEP. Therefore, this implies that removing nontariff barriers is crucial for trade creation. In contrast, FTAs with Brunei, Cambodia, Laos, Peru, the Philippines, and Switzerland have no significant effects on Japan's exports. The coefficients for FTAs with Malaysia and Singapore are negative and statistically significant.
. | (1) OLS . | (2) PPML . | (3) PPML . | (4) PPML . | (5) Ando and Urata (2015) OLS . | (6) Yamanouchi (2017) PPML . | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables . | Export . | Import . | Export . | Import . | Export . | Import . | Export . | Import . | Export . | Import . | Export . | Import . |
FTA (Australia) | 0.285** | 0.350*** | 0.312** | 1.469*** | 0.0401 | 0.139*** | 0.153** | 0.190** | ||||
(2.202) | (3.352) | (2.415) | (3.658) | (0.896) | (3.442) | (2.135) | (2.008) | |||||
FTA (Brunei) | 0.132 | 0.803*** | 0.188 | 2.473*** | 0.0685 | 0.557*** | 0.108 | 0.627*** | −0.0429 | 0.370*** | ||
(1.495) | (3.597) | (0.951) | (6.187) | (1.600) | (16.12) | (1.119) | (3.025) | |||||
FTA (Cambodia) | 0.125 | −0.245 | −0.491** | −0.0673 | 0.134*** | 1.245*** | 0.0570 | 0.610*** | 0.114*** | 0.413*** | ||
(0.988) | (−1.595) | (−2.226) | (−0.192) | (2.809) | (31.42) | (0.470) | (3.252) | |||||
FTA (Chile) | 0.744*** | 0.132 | 0.147 | 1.350*** | 0.462*** | 0.369*** | 0.397*** | 0.155 | 0.593*** | 0.0533 | 0.595*** | 0.109** |
(5.023) | (1.228) | (1.094) | (4.393) | (10.85) | (9.481) | (2.728) | (1.603) | (4.556) | (0.578) | |||
FTA (India) | −0.175 | −0.252*** | −0.631*** | −0.639*** | 0.469*** | 0.330*** | 0.170** | −0.0246 | ||||
(−1.156) | (−3.389) | (−3.347) | (−2.680) | (11.24) | (9.168) | (2.511) | (−0.323) | |||||
FTA (Indonesia) | 0.271** | −0.0343 | 0.585*** | 0.993*** | 0.302*** | −0.000329 | 0.265** | −0.0129 | −0.106 | −0.303*** | 0.0122 | −0.201** |
(2.508) | (−0.458) | (4.385) | (5.322) | (6.463) | (−0.00699) | (2.561) | (−0.213) | (−0.710) | (−2.856) | |||
FTA (Laos) | 0.853*** | 1.041*** | −0.634 | −0.730** | −0.180*** | 1.161*** | −0.151 | 0.594*** | 0.560*** | 1.108*** | ||
(5.839) | (5.470) | (−1.192) | (−1.994) | (−3.274) | (22.66) | (−1.097) | (2.650) | |||||
FTA (Malaysia) | −0.520*** | 0.0652 | 0.796*** | 0.711*** | −0.452*** | 0.231*** | −0.178** | 0.304*** | −0.148 | −0.0262 | −0.220*** | −0.0515 |
(−3.965) | (0.853) | (6.387) | (3.270) | (−8.978) | (6.386) | (−2.490) | (3.599) | (−1.201) | (−0.301) | |||
FTA (Mexico) | 0.192* | −0.223** | 0.361*** | −0.799*** | 0.534*** | 0.214*** | 0.428*** | 0.00810 | 0.628*** | 0.264*** | 0.498*** | 0.141** |
(1.795) | (−2.340) | (3.012) | (−3.572) | (12.84) | (3.680) | (4.567) | (0.128) | (4.800) | (2.844) | |||
FTA (Myanmar) | 0.864*** | 0.874*** | 0.515** | 0.211 | 0.0588 | 0.530*** | 0.517*** | 0.679*** | ||||
(6.553) | (4.820) | (2.334) | (0.547) | (1.074) | (5.468) | (3.776) | (2.734) | |||||
FTA (Peru) | 0.148 | 0.238* | 0.0522 | 0.632** | 0.185*** | 0.341*** | 0.0251 | −0.0108 | ||||
(1.263) | (1.929) | (0.356) | (2.182) | (4.430) | (9.035) | (0.225) | (−0.0902) | |||||
FTA (Philippines) | −0.0955 | −0.199** | 0.267 | 0.433* | −0.716*** | −0.158*** | −0.144 | 0.199* | −0.216 | −0.400*** | −0.181*** | −0.369*** |
(−0.871) | (−2.169) | (1.544) | (1.958) | (−15.71) | (−4.556) | (−1.583) | (1.841) | (−1.464) | (−3.822) | |||
FTA (Singapore) | −0.316*** | −0.113 | 0.397** | −0.348* | −0.486*** | −0.202*** | −0.307*** | −0.138 | −0.0863 | −0.118 | −0.485*** | −0.407*** |
(−3.012) | (−1.357) | (2.563) | (−1.705) | (−9.341) | (−4.993) | (−4.272) | (−0.884) | (−0.445) | (−0.857) | |||
FTA (Switzerland) | 0.0576 | 0.184** | −0.0444 | 0.471* | 0.251*** | 0.137*** | 0.0990 | −0.00131 | 0.914*** | 0.0844 | 0.391*** | 0.128** |
(0.544) | (2.246) | (−0.325) | (1.945) | (6.393) | (2.615) | (0.919) | (−0.0139) | (4.704) | (0.612) | |||
FTA (Thailand) | 0.343*** | −0.281*** | 1.203*** | 0.506*** | 0.208*** | 0.295*** | 0.211*** | 0.0366 | 0.0366 | −0.0761 | 0.0664 | −0.0248 |
(3.687) | (−3.308) | (9.873) | (2.762) | (4.124) | (7.980) | (3.393) | (0.444) | (0.281) | (−0.824) | |||
FTA (Vietnam) | −0.0664 | −0.448*** | 0.575*** | 0.598** | 0.321*** | 0.551*** | 0.209* | −0.153 | 0.239 | 0.0845 | 0.182*** | 0.175*** |
(−0.520) | (−3.978) | (3.814) | (2.436) | (5.802) | (10.54) | (1.856) | (−1.177) | (1.599) | (0.799) | |||
Observations | 91,262 | 93,840 | 93,760 | 93,760 | 360 | 360 | 1,908 | 1,908 | ||||
Exporter-year fixed effects | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | ||||
Importer-year fixed effects | Yes | Yes | No | Yes | No | No | No | No | ||||
Exporter-importer fixed effects | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes |
. | (1) OLS . | (2) PPML . | (3) PPML . | (4) PPML . | (5) Ando and Urata (2015) OLS . | (6) Yamanouchi (2017) PPML . | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables . | Export . | Import . | Export . | Import . | Export . | Import . | Export . | Import . | Export . | Import . | Export . | Import . |
FTA (Australia) | 0.285** | 0.350*** | 0.312** | 1.469*** | 0.0401 | 0.139*** | 0.153** | 0.190** | ||||
(2.202) | (3.352) | (2.415) | (3.658) | (0.896) | (3.442) | (2.135) | (2.008) | |||||
FTA (Brunei) | 0.132 | 0.803*** | 0.188 | 2.473*** | 0.0685 | 0.557*** | 0.108 | 0.627*** | −0.0429 | 0.370*** | ||
(1.495) | (3.597) | (0.951) | (6.187) | (1.600) | (16.12) | (1.119) | (3.025) | |||||
FTA (Cambodia) | 0.125 | −0.245 | −0.491** | −0.0673 | 0.134*** | 1.245*** | 0.0570 | 0.610*** | 0.114*** | 0.413*** | ||
(0.988) | (−1.595) | (−2.226) | (−0.192) | (2.809) | (31.42) | (0.470) | (3.252) | |||||
FTA (Chile) | 0.744*** | 0.132 | 0.147 | 1.350*** | 0.462*** | 0.369*** | 0.397*** | 0.155 | 0.593*** | 0.0533 | 0.595*** | 0.109** |
(5.023) | (1.228) | (1.094) | (4.393) | (10.85) | (9.481) | (2.728) | (1.603) | (4.556) | (0.578) | |||
FTA (India) | −0.175 | −0.252*** | −0.631*** | −0.639*** | 0.469*** | 0.330*** | 0.170** | −0.0246 | ||||
(−1.156) | (−3.389) | (−3.347) | (−2.680) | (11.24) | (9.168) | (2.511) | (−0.323) | |||||
FTA (Indonesia) | 0.271** | −0.0343 | 0.585*** | 0.993*** | 0.302*** | −0.000329 | 0.265** | −0.0129 | −0.106 | −0.303*** | 0.0122 | −0.201** |
(2.508) | (−0.458) | (4.385) | (5.322) | (6.463) | (−0.00699) | (2.561) | (−0.213) | (−0.710) | (−2.856) | |||
FTA (Laos) | 0.853*** | 1.041*** | −0.634 | −0.730** | −0.180*** | 1.161*** | −0.151 | 0.594*** | 0.560*** | 1.108*** | ||
(5.839) | (5.470) | (−1.192) | (−1.994) | (−3.274) | (22.66) | (−1.097) | (2.650) | |||||
FTA (Malaysia) | −0.520*** | 0.0652 | 0.796*** | 0.711*** | −0.452*** | 0.231*** | −0.178** | 0.304*** | −0.148 | −0.0262 | −0.220*** | −0.0515 |
(−3.965) | (0.853) | (6.387) | (3.270) | (−8.978) | (6.386) | (−2.490) | (3.599) | (−1.201) | (−0.301) | |||
FTA (Mexico) | 0.192* | −0.223** | 0.361*** | −0.799*** | 0.534*** | 0.214*** | 0.428*** | 0.00810 | 0.628*** | 0.264*** | 0.498*** | 0.141** |
(1.795) | (−2.340) | (3.012) | (−3.572) | (12.84) | (3.680) | (4.567) | (0.128) | (4.800) | (2.844) | |||
FTA (Myanmar) | 0.864*** | 0.874*** | 0.515** | 0.211 | 0.0588 | 0.530*** | 0.517*** | 0.679*** | ||||
(6.553) | (4.820) | (2.334) | (0.547) | (1.074) | (5.468) | (3.776) | (2.734) | |||||
FTA (Peru) | 0.148 | 0.238* | 0.0522 | 0.632** | 0.185*** | 0.341*** | 0.0251 | −0.0108 | ||||
(1.263) | (1.929) | (0.356) | (2.182) | (4.430) | (9.035) | (0.225) | (−0.0902) | |||||
FTA (Philippines) | −0.0955 | −0.199** | 0.267 | 0.433* | −0.716*** | −0.158*** | −0.144 | 0.199* | −0.216 | −0.400*** | −0.181*** | −0.369*** |
(−0.871) | (−2.169) | (1.544) | (1.958) | (−15.71) | (−4.556) | (−1.583) | (1.841) | (−1.464) | (−3.822) | |||
FTA (Singapore) | −0.316*** | −0.113 | 0.397** | −0.348* | −0.486*** | −0.202*** | −0.307*** | −0.138 | −0.0863 | −0.118 | −0.485*** | −0.407*** |
(−3.012) | (−1.357) | (2.563) | (−1.705) | (−9.341) | (−4.993) | (−4.272) | (−0.884) | (−0.445) | (−0.857) | |||
FTA (Switzerland) | 0.0576 | 0.184** | −0.0444 | 0.471* | 0.251*** | 0.137*** | 0.0990 | −0.00131 | 0.914*** | 0.0844 | 0.391*** | 0.128** |
(0.544) | (2.246) | (−0.325) | (1.945) | (6.393) | (2.615) | (0.919) | (−0.0139) | (4.704) | (0.612) | |||
FTA (Thailand) | 0.343*** | −0.281*** | 1.203*** | 0.506*** | 0.208*** | 0.295*** | 0.211*** | 0.0366 | 0.0366 | −0.0761 | 0.0664 | −0.0248 |
(3.687) | (−3.308) | (9.873) | (2.762) | (4.124) | (7.980) | (3.393) | (0.444) | (0.281) | (−0.824) | |||
FTA (Vietnam) | −0.0664 | −0.448*** | 0.575*** | 0.598** | 0.321*** | 0.551*** | 0.209* | −0.153 | 0.239 | 0.0845 | 0.182*** | 0.175*** |
(−0.520) | (−3.978) | (3.814) | (2.436) | (5.802) | (10.54) | (1.856) | (−1.177) | (1.599) | (0.799) | |||
Observations | 91,262 | 93,840 | 93,760 | 93,760 | 360 | 360 | 1,908 | 1,908 | ||||
Exporter-year fixed effects | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | ||||
Importer-year fixed effects | Yes | Yes | No | Yes | No | No | No | No | ||||
Exporter-importer fixed effects | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes |
Source: Columns (1)–(4) are calculated by the author. Column (5) is taken from Ando and Urata (2015, Table 10). Column (6) is taken from Yamanouchi (2017, Tables 43 and 47).
Note: Robust t-statistics in parentheses. OLS = ordinary least squares; PPML = Poisson pseudo maximum likelihood. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
Import values, however, increased significantly because of FTAs with Australia, Brunei, Cambodia, Laos, Malaysia, Myanmar, and the Philippines. The coefficients are statistically insignificant for the other FTAs. The largest effect on imports is also observed for the FTA with Myanmar.
We also check the symmetry of the trade creation effects using a joint test of the hypothesis that all pairs of coefficients are equal for exports and imports. The chi-square statistic is 103.1 and the hypothesis is clearly rejected.
Tables 6 and 7 present the matrices of the root mean square differences between the coefficients for the FTA dummies across specifications. Table 6 shows the root mean square differences of the coefficients for Japan's exports to its FTA partners. The coefficients in our baseline specification (column [6]) differ little from those estimated without country-year dummies. The choice of estimator does not matter much. However, the specifications without the country-pair dummies (columns [1] and [4]) show very different coefficients. Table 7 shows the same matrix for Japan's imports. The results are like the case of exports, and misspecification is problematic if the country-pair fixed effects are excluded from the estimation. If the country-pair fixed effects are not included, the trade creation effects of the FTAs are overestimated because of endogeneity.
Export . | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
---|---|---|---|---|---|---|
(1) OLS, no pair dummies | 0.00 | |||||
(2) OLS, no country-year dummies | 1.20 | 0.00 | ||||
(3) OLS, three types | 0.68 | 0.60 | 0.00 | |||
(4) PPML, no pair dummies | 0.89 | 0.79 | 0.66 | 0.00 | ||
(5) PPML, no country-year dummies | 0.97 | 0.29 | 0.43 | 0.65 | 0.00 | |
(6) PPML, three types | 0.85 | 0.43 | 0.32 | 0.51 | 0.23 | 0.00 |
Export . | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
---|---|---|---|---|---|---|
(1) OLS, no pair dummies | 0.00 | |||||
(2) OLS, no country-year dummies | 1.20 | 0.00 | ||||
(3) OLS, three types | 0.68 | 0.60 | 0.00 | |||
(4) PPML, no pair dummies | 0.89 | 0.79 | 0.66 | 0.00 | ||
(5) PPML, no country-year dummies | 0.97 | 0.29 | 0.43 | 0.65 | 0.00 | |
(6) PPML, three types | 0.85 | 0.43 | 0.32 | 0.51 | 0.23 | 0.00 |
Source: Author's calculations.
Note: OLS = ordinary least squares; PPML = Poisson pseudo maximum likelihood.
Import . | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
---|---|---|---|---|---|---|
(1) OLS, no pair dummies | 0.00 | |||||
(2) OLS, no country-year dummies | 1.89 | 0.00 | ||||
(3) OLS, three types | 1.62 | 0.46 | 0.00 | |||
(4) PPML, no pair dummies | 1.29 | 1.08 | 0.92 | 0.00 | ||
(5) PPML, no country-year dummies | 1.74 | 0.44 | 0.53 | 0.99 | 0.00 | |
(6) PPML, three types | 1.69 | 0.38 | 0.32 | 0.89 | 0.34 | 0.00 |
Import . | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
---|---|---|---|---|---|---|
(1) OLS, no pair dummies | 0.00 | |||||
(2) OLS, no country-year dummies | 1.89 | 0.00 | ||||
(3) OLS, three types | 1.62 | 0.46 | 0.00 | |||
(4) PPML, no pair dummies | 1.29 | 1.08 | 0.92 | 0.00 | ||
(5) PPML, no country-year dummies | 1.74 | 0.44 | 0.53 | 0.99 | 0.00 | |
(6) PPML, three types | 1.69 | 0.38 | 0.32 | 0.89 | 0.34 | 0.00 |
Source: Author's calculations.
Note: OLS = ordinary least squares; PPML = Poisson pseudo maximum likelihood.
In this subsection, the effects of the individual FTAs are explored. We found that each of Japan's FTAs affects trade values in a different way. Therefore, we conclude that one of the reasons for the absence of the trade creation effects of Japan's FTAs is that aggregation across all trade partners obscures the positive effects. We should note that about half of Japan's FTAs have positive and statistically significant impacts.
5.4 Trade creation effects of each individual FTA
In this subsection, we consider the determinants of the trade creation effects. To this end, we first obtain the coefficients for each individual FTA. We then regress the estimated coefficients on some variables. This two-step estimation is suggested in Baier, Yotov, and Zylkin (2019).
The first stage is estimated by OLS as in Kohl (2014) because of the computational difficulty. As explained in the previous subsection, the differences between coefficients in the two estimates are small. Among the 725 directional flows within active FTAs, 256 (35 percent) of these flows have positive and statistically significant values at the 5 percent level. Mean and median values are 0.113 and 0.105, respectively.14 Compared with Baier, Yotov, and Zylkin (2019), these values are small and suggest that recent FTAs have weaker effects.
The estimation results of equation (5) are shown in Table 8. In the first column, the signs of the coefficients are as expected. The FTAs generally increase trade values if the trading countries are smaller and the relationship between the two countries is weak. In addition, the results imply that relatively old FTAs are more effective.
. | (1) . | (2) . | (3) . | (4) . | (5) . |
---|---|---|---|---|---|
Importer-year fixed effect | −0.0529*** | −0.0550*** | −0.0369 | ||
(−3.828) | (−3.729) | (−1.519) | |||
Importer-year fixed effect | −0.0759 | −0.0869*** | |||
× Export from Japan | (−1.423) | (−3.583) | |||
Exporter-year fixed effect | −0.0447* | −0.0437* | −0.0631* | ||
(−1.822) | (−1.650) | (−1.868) | |||
Exporter-year fixed effect | −0.0860 | −0.0716** | |||
× Import to Japan | (−1.331) | (−2.121) | |||
Country pair fixed effect | −0.128*** | −0.139*** | −0.160*** | −0.163*** | −0.178*** |
(−4.329) | (−4.531) | (−3.241) | (−4.786) | (−3.366) | |
Country pair fixed effect | −0.0191 | −0.0182 | 0.137 | 0.146** | |
× Export from Japan | (−0.128) | (−0.368) | (1.534) | (2.123) | |
Country pair fixed effect | 0.171** | 0.0406 | 0.161*** | 0.0637 | |
× Import to Japan | (2.070) | (0.526) | (4.753) | (1.000) | |
Number of years under FTA | 0.0147*** | 0.0158*** | 0.0253*** | 0.0231** | 0.0289** |
(2.795) | (2.981) | (3.088) | (2.540) | (2.351) | |
Number of years under FTA | 0.0249 | −0.0328*** | 0.0147 | 0.00231 | |
× Export from Japan | (1.319) | (−4.009) | (1.095) | (0.153) | |
Number of years under FTA | 0.000187 | −0.00323 | −0.0536*** | −0.0391*** | |
× Import to Japan | (0.0142) | (−0.286) | (−5.884) | (−3.888) | |
Observations | 725 | 725 | 723 | 723 | 721 |
Adjusted R-squared | 0.070 | 0.075 | 0.184 | 0.155 | 0.250 |
Exporter fixed effects | No | No | Yes | No | Yes |
Importer fixed effects | No | No | No | Yes | Yes |
. | (1) . | (2) . | (3) . | (4) . | (5) . |
---|---|---|---|---|---|
Importer-year fixed effect | −0.0529*** | −0.0550*** | −0.0369 | ||
(−3.828) | (−3.729) | (−1.519) | |||
Importer-year fixed effect | −0.0759 | −0.0869*** | |||
× Export from Japan | (−1.423) | (−3.583) | |||
Exporter-year fixed effect | −0.0447* | −0.0437* | −0.0631* | ||
(−1.822) | (−1.650) | (−1.868) | |||
Exporter-year fixed effect | −0.0860 | −0.0716** | |||
× Import to Japan | (−1.331) | (−2.121) | |||
Country pair fixed effect | −0.128*** | −0.139*** | −0.160*** | −0.163*** | −0.178*** |
(−4.329) | (−4.531) | (−3.241) | (−4.786) | (−3.366) | |
Country pair fixed effect | −0.0191 | −0.0182 | 0.137 | 0.146** | |
× Export from Japan | (−0.128) | (−0.368) | (1.534) | (2.123) | |
Country pair fixed effect | 0.171** | 0.0406 | 0.161*** | 0.0637 | |
× Import to Japan | (2.070) | (0.526) | (4.753) | (1.000) | |
Number of years under FTA | 0.0147*** | 0.0158*** | 0.0253*** | 0.0231** | 0.0289** |
(2.795) | (2.981) | (3.088) | (2.540) | (2.351) | |
Number of years under FTA | 0.0249 | −0.0328*** | 0.0147 | 0.00231 | |
× Export from Japan | (1.319) | (−4.009) | (1.095) | (0.153) | |
Number of years under FTA | 0.000187 | −0.00323 | −0.0536*** | −0.0391*** | |
× Import to Japan | (0.0142) | (−0.286) | (−5.884) | (−3.888) | |
Observations | 725 | 725 | 723 | 723 | 721 |
Adjusted R-squared | 0.070 | 0.075 | 0.184 | 0.155 | 0.250 |
Exporter fixed effects | No | No | Yes | No | Yes |
Importer fixed effects | No | No | No | Yes | Yes |
Source: Author's calculations.
Note: Robust t-statistics in parentheses. OLS = ordinary least squares; PPML = Poisson pseudo maximum likelihood. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
In columns (2)–(5), the interaction terms with Japan's export and import dummies are added to explore the characteristics of Japan's FTAs.16 Although the results are not stable, the effects of Japan's FTAs are larger when the partner countries are smaller. However, the trade linkages before FTAs are signed are not important. More interestingly, recently enforced FTAs increase Japan's import values. In the case of Japan, agricultural goods are excluded from the negotiations in initial agreements. Protection is likely to be weakened and tariff rates for some products such as beef are lowered in the recent agreements with Australia.17,18
In Table 9, we present the estimation results of equation (6). While the main results are unchanged from the previous analyses, we find some notable differences. First, exporter GDP does not explain the effectiveness of an FTA.19 This implies that some factors related to export values but not to GDP are key determinants of trade creation effects. One potential explanation is the endowment of natural resources. The export values of natural resources are not closely related to tariff rates, so the countries specializing in those resource sectors cannot increase exports via FTAs. Similarly, the coefficients for distance are statistically insignificant in some specifications, and therefore the role of distance is less clear than the country-pair fixed effects. However, distance is important for Japan's import under FTAs.
. | (1) . | (2) . | (3) . | (4) . | (5) . |
---|---|---|---|---|---|
Importer ln(GDP) | −0.0345** | −0.0350* | −0.0238 | ||
(−2.047) | (−1.893) | (−0.878) | |||
Importer ln(GDP) | −0.101** | −0.118*** | |||
× Export from Japan | (−2.263) | (−4.345) | |||
Exporter ln(GDP) | −0.0251 | −0.0261 | −0.0307 | ||
(−0.957) | (−0.905) | (−0.942) | |||
Exporter ln(GDP) | −0.124** | −0.148*** | |||
× Import to Japan | (−2.427) | (−4.550) | |||
ln(Distance) | 0.0751* | 0.0728* | 0.105 | 0.0904 | 0.174* |
(1.756) | (1.671) | (1.474) | (1.309) | (1.689) | |
ln(Distance) | 0.347** | 0.271*** | 0.0148 | 0.161 | |
× Export from Japan | (2.439) | (3.799) | (0.743) | (1.573) | |
ln(Distance) | 0.409*** | 0.0326** | 0.144** | −0.151 | |
× Import to Japan | (2.695) | (2.393) | (2.089) | (−1.370) | |
Number of years under FTA | 0.0143*** | 0.0150*** | 0.0210** | 0.0210** | 0.0268** |
(2.621) | (2.694) | (2.420) | (2.544) | (2.107) | |
Number of years under FTA | −0.0361 | −0.0456*** | 0.000480 | 0.0109 | |
× Export from Japan | (−1.444) | (−5.261) | (0.0209) | (0.383) | |
Number of years under FTA | −0.0357 | −0.0416*** | −0.0617*** | −0.0383*** | |
× Import to Japan | (−1.604) | (−3.720) | (−7.478) | (−3.884) | |
Observations | 725 | 725 | 723 | 723 | 721 |
Adjusted R-squared | 0.010 | 0.008 | 0.119 | 0.067 | 0.181 |
Exporter fixed effects | No | No | Yes | No | Yes |
Importer fixed effects | No | No | No | Yes | Yes |
. | (1) . | (2) . | (3) . | (4) . | (5) . |
---|---|---|---|---|---|
Importer ln(GDP) | −0.0345** | −0.0350* | −0.0238 | ||
(−2.047) | (−1.893) | (−0.878) | |||
Importer ln(GDP) | −0.101** | −0.118*** | |||
× Export from Japan | (−2.263) | (−4.345) | |||
Exporter ln(GDP) | −0.0251 | −0.0261 | −0.0307 | ||
(−0.957) | (−0.905) | (−0.942) | |||
Exporter ln(GDP) | −0.124** | −0.148*** | |||
× Import to Japan | (−2.427) | (−4.550) | |||
ln(Distance) | 0.0751* | 0.0728* | 0.105 | 0.0904 | 0.174* |
(1.756) | (1.671) | (1.474) | (1.309) | (1.689) | |
ln(Distance) | 0.347** | 0.271*** | 0.0148 | 0.161 | |
× Export from Japan | (2.439) | (3.799) | (0.743) | (1.573) | |
ln(Distance) | 0.409*** | 0.0326** | 0.144** | −0.151 | |
× Import to Japan | (2.695) | (2.393) | (2.089) | (−1.370) | |
Number of years under FTA | 0.0143*** | 0.0150*** | 0.0210** | 0.0210** | 0.0268** |
(2.621) | (2.694) | (2.420) | (2.544) | (2.107) | |
Number of years under FTA | −0.0361 | −0.0456*** | 0.000480 | 0.0109 | |
× Export from Japan | (−1.444) | (−5.261) | (0.0209) | (0.383) | |
Number of years under FTA | −0.0357 | −0.0416*** | −0.0617*** | −0.0383*** | |
× Import to Japan | (−1.604) | (−3.720) | (−7.478) | (−3.884) | |
Observations | 725 | 725 | 723 | 723 | 721 |
Adjusted R-squared | 0.010 | 0.008 | 0.119 | 0.067 | 0.181 |
Exporter fixed effects | No | No | Yes | No | Yes |
Importer fixed effects | No | No | No | Yes | Yes |
Source: Author's calculations.
Note: Robust t-statistics in parentheses. OLS = ordinary least squares; PPML = Poisson pseudo maximum likelihood. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level.
In this subsection, we discussed which types of countries have effective FTAs. In a nutshell, a small ex ante trade value means substantial scope to increase trade via FTAs. Japan's FTA partners are so far mainly located in the Asia–Pacific region and actively transacting with Japan before signing the agreements. Japan can be integrated with the global market through FTAs along with many developing countries.
6. Conclusion
FTAs are one of the major commercial policies of the 21st century, yet Japan's FTAs have not been evaluated adequately. This paper therefore investigates the effects of Japan's FTAs using a recently developed gravity framework and explores the determinants of the effects of FTAs.
Our estimation results do not indicate the presence of trade creation effects for Japan's FTAs on average. Nonetheless, the effects of Japan's FTAs vary substantially across partners and around half of the FTAs increase Japan's trade values. Positive impacts on Japan's exports are more likely to be observed for small partners. Japan's imports from FTA partners tend to increase when the partner countries are small and distant. More importantly, Japan's recent FTAs have larger effects.
Our results suggest that there is a little scope to increase trade values with some countries. This implies that political resources should be directed toward negotiations with developing countries if the government is aiming to integrate the economy into the global market through FTAs. Large-scaled multilateral trade agreements may not be effective in terms of trade creation, however, even though large amounts of effort are spent on the negotiation. Of course, the impacts on investment and other forms of international cooperation are different and those multilateral agreements may play important roles in regulating world trade systems and supporting new types of globalization. These are issues open for future research.
Notes
I am grateful to Sungbae An, Mateus Silva Chang, Vu Quoc Huy, Banri Ito, Hayato Kato, Fukunari Kimura, Kozo Kiyota, and participants at the 7th Spring Meeting of the Japan Society of International Economics at Matsuyama University and the Asian Economic Panel at Keio University for useful comments and suggestions.
See http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx (last accessed 30 August 2018).
In the first stage, we estimate the crude effects of FTAs. Various kinds of related factors such as interactions among other FTAs and the formation of production networks are included.
The roles of these fixed effects are discussed in Section 4.
Among the 166 agreements studied in Kohl (2014), only 44 agreements have a trade-promoting effect.
In our data set, the correlation of trade values reported by importers and by exporters is 0.88, but the mean of the logged trade gap (the difference between log of import values and log of export values) is 0.26 and the median is 0.14.
The data set is available at Centre d'Etudes Prospectives et d'Informations Internationales (CEPII): www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=8 (last accessed 2 September 2018).
The data set is available at www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html (last accessed 2 September 2018).
Mongolia is included in the sample as an FTA partner of Japan; however, the FTA was not enforced in the sample period.
The estimated coefficients for the FTA dummies are considered to be average effects over time. Although we do not explicitly consider the phase-in effects in the first stage, it is partially addressed in the second stage.
PPML with high-dimensional fixed effects is computationally demanding. In this paper, we use the Stata command ppml_panel_sg written by Larch et al. (2017). See their paper for the detailed procedure.
301 (42 percent) are insignificant and 168 (23 percent) are negatively significant.
Yamanouchi (2017) does not find phase-in effects for Japan's FTAs.
We cannot see the effects of lowered tariff rates using simple statistics because Japan's import values of agricultural goods from Australia decreased after the FTA entered into force. As explained in Section 3, we cannot make conclusions about the effects of FTAs from the descriptive analysis. In fact, Australia's total exports also decreased in 2015.
It is difficult to explain the large effects of Japan's recent FTAs by the differences of contents across Japan's FTAs. Almost all FTAs have chapters on investment and trade in services. Provisions on intellectual property, movement of natural persons, and government procurement are not limited to recent FTAs.
Baier, Yotov, and Zylkin (2019) report positive coefficients for both exporter and importer GDP.