The multilateral nature of balterial trade in the age of ...

The multilateral nature of balterial trade in the age of ...

The multilateral nature of balterial trade in the age of global value chains (Work in progress) Fei Wang, Zhi Wang and Kunfu Zhu University of International Business and Economics Shang-Jin Wei Columbia Business School and NBER GLOBAL VALUE CHAIN TRAINING AND RESEARCH WORKSHOP UNIVERSITY OF INTERNATIONAL ECONOMICS AND BUSINESS BEIJING, JULY 29-AUGUST 4, 2018 Bilateral Trade Imbalance 2 Motivation Most economists agree that balance of trade is not a good measure of the effect of a countrys trade policy (Lawrence, 2018) and balances of bilateral trade should not be the focus of national policy due to the multilateral nature of international trade (Bergsten, 2006). However, as the bilateral trade balance is frequently headline news and a regular topic in the trade policy debate around the world, an analytical framework that is able to reveal the multilateral characters of bilateral trade in the age of global value chains, would help the public and policy makers to better understand the deeply rooted multilateral nature of many bilateral trade issues such as the role of exchange rate in current account adjustment. Understand the roles of third countries may play in balterial trade balance between two trading partners also help us to better understand the limited role of trade protection policies is able to achieve and the unintended results it may induce in the age of global value chains. 3 The Trade Balance of USA-China and the appreciation of RMB, 2004-2017 17 100.0 16 USD/100RMB 0.0 -100.0 15 14 -200.0 13 Agriculture -300.0 Mining 12 Manufacturing Service

-400.0 11 USD/100RMB Appreciation (Right) 10 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 -500.0 1995 Billion USD 2004 - 2014 How to measure third country effect in balterial trade?

The earlier study of such issues started with Hummels, Ishii and Yi (HIY, 2001). They propose using foreign value-added embodied in imported intermediate imports to export production as a measure of vertical specialization (VS) , the first measure of foreign production factors role in a countrys export production. A framework that extends HIY to multilateral setting, tracing value added and double counted items in aggregate gross trade flows is proposed by Koopman, Wang and Wei (KWW, 2014). Wang, Wei, and Zhu (2014) extend the KWW method, provides an accounting framework that resembles in spirit that of KWW (2014) at the bilateral, sector, and bilateral sector levels. For this reason, we might label the overall accounting framework as KWWZ method. The Major Contribution of Gross Trade Accounting method proposed by KWWZ Bridge two most important statistics (GDP in national account and Gross trade in custom statistics) and demonstrate the first time in the literature that the components of gross trade beyond DVA have specific types of relationships with GDP statistics. (1) Domestic value-added absorbed abroad (VAX): home countrys GDP used to satisfy foreign demand, factor content embodied in gross exports crosses national borders at least once; (2) Domestic value added returned and eventually consumed at home (RDV): not part of a country's exports of value-added, but account for part of the country's GDP, factor content crosses national borders at least twice; (1)+(2) = DVA: GDP in exports (3) Foreign value added in exports (FVA), part of other countries GDP, embodied factor content also crosses national borders at least twice; (4) Double counted terms due to intermediate goods being traded back and forth that cross border multiple times (PDC), counts no countrys GDP as it is the factor content that has already been counted by at least one of the three components 6 above and crosses national borders at least three times. The Major Contribution of Gross Trade Accounting proposed by KWWZ By identifying which parts of the gross trade transactions are double counted relative to GDP statistics and what the sources of double counting are, the KWW (and WWZ) method provides a way to correctly interpret gross trade transactions in value-added terms (relative to GDP) and offers a set of summary statistics that quantitatively measure what and how much is double counted for each bilateral/sector trade flow. All measures of trade in value-added and vertical specialization in the literature, such as the vertical specialization measure proposed by Hummels, Ishii and Yi (2001) and import content of exports proposed by the National Research Council (2006) can be expressed as linear combinations of various components of the KWW decomposition. 7 The roles of third countries in bilateral trade can be measures by 3 of the 8 detailed components # of border Relation to GDP crossing

statistics Home GDP satisfies At least final demand in partner once country Homes GDP satisfies final demand in third countries Homes GDP satisfies own domestic final demand through At least twice international trade Trading partners VA used in Partners GDP satisfies production of exports that return to final demand in partner and absorbed by partner country Third countries VA used in Third countries GDP production of exports that finally satisfies final demand in absorbed by partner partner country Pure double counting in gross Non countrys GDP exports sourced from third countries At least Pure double counting in gross Non countrys GDP three times exports sourced from home Pure double counting in gross Non countrys GDP exports sourced from partner Core KWW Detailed Economic interpretation decomposition items VAX_G DVA_DIR Domestic VA in production of Value added exports that finally absorbed by exports trading partner DVA_IND Domestic VA in production of exports that finally absorbed by third countries RDV_G RDV_G Domestic VA first exported but Returned DVA finally returned home and consumed there FVA Foreign value added MVA

OVA PDC Pure double counting ODC DDC MDC An example of trade decomposition Japan R&D China US Re-exports Consumer 5 6 3 1 7 Chinese Steel 4 Japanese Auto industry Chinese Consumer 2 AUS Iron Ore Japanese Consumer DVA_DIR: 1-2, DVA_IND: 1-3, MVA: 6-1-2, MDC: 6-1-3/4/5; OVA: 7-1-2, ODC: 7-1-3/4/5. RDV_G: 1-4, DDC: 1-5; 9 Preliminary: Inter-Country Input-Output Table Outputs Inputs Intermediate Uses

Country Country Country Final Uses Total Outputs 1 2 G 1 2 G 1 Z11 Z12 Z1G Y11 Y12 Y1G X1 2 Z21 Z22 Z2G Y21 Y22 Y2G X2 G ZG1 ZG2 ZGG YG1 YG2

YGG XG Value Added VA1 VA2 VAG Total Inputs X1 X2 XG Intermediate Inputs 10 Matrix partition: diagonal and off-diagonal 1g 0 Y F and Y = g 1 are GN by GN domestic and imported input coefficient matrix respectively Y 0 [ ] = l11ss l12ss 0 0 1 a11ss a12ss 0 0 ss ss ss ss l l 0 0 a 1 a

0 0 22 L 21 22 rr rr 21 rr rr 0 0 l11 l12 0 0 1 a11 a12 rr rr rr rr 0 0 l l 0 0 a 1 a 21 22 21 22 ss ss 1 a a ss 11 12 L ss ss a 1 a 22 21 Ass A D 0 0 0 Arr

0 1 0 0 Att ss b B ss 11ss b21 0 A F Ars Ats A sr 0 Atr A st Art 0 1 b11ss b12ss ss ss b b 21 22 B rs b11 b12rs rs rs b21 b22 b11sr b21sr b11rr rr b21 b12sr b22sr b12rr rr b22 b12ss ss b22 Ass

A A D A F Ars Ats A sr Arr Atr Ast Art Att Additional Notations :GN by GN global Leontief inverse matrix; local Leontief inverse matrix; , Bs are GN by GN diagonal and off diagonal matrix; is a GN by G diagonal block matrix of total outputs. E is the exports directly consumed by trade partner without further boarder crossing. GN by GN diagonal matrix of direct value-added coefficients; Y, and : GN by G diagonal matrix of final consumption; Local Leontief Inverse Matrices: an analytical tool to isolate pure domestic production Production and use balance, or the row balance condition: X=f(Y) (1) Where superscript D represents diagonals, F represents off diagonals (2) Value-added multiplier (3) Revised KWWZ gross trade flow decomposition to study third country effect (4) (5) Where DVA_DIRa is home DVA directly absorbed by partner. DVA_DIRb is home DVA indirectly absorbed by partner. RDV_G, DVA_DIRb, DDC, MVA and MDC are all loop effects in the two trading partners 14

How to measure the multilateral factor or third country effect in balterial trade Need analyze more detailed DVA items to find how final demand in third countries impact balterial trade between two trading partners (6) DAV_IND to gross trade ratio can be used to measure how important the partner country as a transfer planform for the home countrys DVA absorbed in third countries. Not only determined by the production sharing arrangement between the home and partner country; but also driven by final demand in third countries. 15 How to measure the multilateral factor or third country effect in balterial trade Need analyze detailed components in VS to find how the supply capacities in third countries impact balterial trade between two trading partners (7) OVA to gross trade ratio can be used to measure how important third countries play in the home countrys export production. Not only drive by final demand in partner country, but also determined by the production sharing arrangement between the home and third countries. ODC to gross trade ratio can be used to measure how complex the third country effect. It is independent to any countries final demand, and determined by production technology and the production arrangement among home, partner and third countries. 16 What cause the deviation between balance of bilateral trade in gross and in value-added terms? (8) Proposition In bilateral trade, only and only if VS embodied in the two way trade equal each other, Balance of Trade (BOT) in gross term and value-added term are the same. The larger the difference of VS between trading partners, the bigger the deviation VA BOT from gross BOT. 17 Multilateral Nature of US-China balterial Trade: Gross Trade US exports to China 100 200 90 180 80

160 70 140 60 120 50 100 40 80 30 60 20 40 10 20 0 0 1995 2000 2007 DVA_IND DVA_DIR Net Imports 80 40 500 90 450 80 400 70 350

60 300 50 250 40 200 30 150 20 100 10 50 0 0 2007 RDV+DDC+MVA+MDC 2014 100 2000 OVA+ODC 60 China exports to the US 1995 CHN-US trade balance 2014 20 0 50 0 Third country transfer played important role, but decline in recent years Multilateral Nature of US-China balterial Trade:

Manufacture China exports to the US CHN-US trade balance 100 500 90 450 OVA+ODC RDV+DDC+MVA+MDC 80 400 DVA_IND DVA_DIR 70 350 Net Imports 60 300 50 250 40 200 30 150 20 100 10 50 0 80 60 0 1995

2000 2007 2014 40 US exports to China 100 120 20 90 100 80 70 0 50 80 60 0 50 60 40 40 30 20 20 10 0 0 1995 2000 2007 2014 Third country transfer played important role, but decline in recent years Growth of US-China trade in manufacturing products and contribution of third country effects

Period Trade Value Contributio Contributio Contribution n of n of of DVA_DIR DVA_IND RDV+DDC+MC US exports to China Contributio n of OC 19952000 20002007 20072014 10.0 44.2 21.2 17.6 17.1 35.0 40.8 28.9 14.1 16.2 52.7 59.4 15.2 China Exports to US 9.7 15.7 19952000 20002007 20072014 38.5 43.6 3.8 7.0

45.5 183.7 51.2 5.8 5.1 37.9 57.2 8.9 5.5 US net imports from China 28.3 19952000 20002007 2007- 159.7 28.5 43.5 -2.3 3.3 55.5 148.7 53.7 0.3 3.0 43.0 107.0 56.2 5.8 3.5 34.6 Multilateral Nature of US-China balterial Trade: ICT Products China exports to the US 100 180

90 160 80 140 70 CHN-US trade balance 120 60 OVA+ODC RDV+DDC+MVA+MDC DVA_IND DVA_DIR Net Imports 100 50 80 40 60 30 20 40 10 20 0 0 1995 2000 2007 80 60 2014 80 40 US exports to China

100 60 30 20 90 40 25 80 70 20 0 20 60 50 0 15 40 10 30 20 5 10 0 0 1995 2000 2007 2014 Third country transfer played important role, but decline in recent years Growth of US-China trade in ICT products and contribution from each component Period Trade Value Contributio

n of DVA_DIR Contributio n of DVA_IND Contribution of RDV+DDC+MC Contributio n of OC US Exports to China 19952000 20002007 20072014 3.8 8.6 11.1 45.3 27.9 55.8 17.0 39.2 28.4 15.3 22.4 15.6 22.4 10.5 0.2 11.3 7.3 7.1 67.0 54.2 33.8 China Exports to US 19952000 20002007 20072014 12.2 76.6 72.1 18.4 34.2 52.1 3.2 4.4 7.1

US net imports from China 19952000 20002007 2007- 8.5 68.0 61.0 6.5 35.0 51.4 -2.9 0.0 3.2 9.6 5.3 5.5 86.7 59.7 39.9 The increasing of US net imports from China and contribution of multilateral effects Period 19952000 20002007 20072014 19952000 20002007 20072014 Net Imports Contributio Contributio Contribution n of n of of DVA_DIR DVA_IND RDV+DDC+MC All Sectors Contributio n of OC 35.1 53.8 -1.6 1.6 46.3 172.6

62.2 -1.3 1.6 37.5 106.9 60.1 4.4 2.2 33.3 Manufacture 43.5 -2.3 3.3 55.5 148.7 53.7 0.3 3.0 43.0 107.0 56.2 5.8 3.5 34.6 9.6 5.3 86.7 59.7 28.5 ICT 19952000 20002007 2007- 8.5 68.0

6.5 35.0 -2.9 0.0 23 Value-added structure of US net imports from China, billion US dollars, 2014 Role of third countries in bilateral trade TEXP DVA_dir DVA_ind RDV MC+DD C OC (1) =2+3+4+5 (2a) (2b) (3) (4) (5) Value 335.2 202.8 0.5 -9.7 15.7 125.8 Share 100 60.5 0.2 -2.9 4.6

37.5 Value 301.6 160.5 5.4 -6.1 15.9 126 Share 100 53.2 1.8 -2.0 5.2 41.8 Value 141.2 56.3 1.7 -2.7 10.7 75.1 Share 100 39.9 1.2 -1.9 7.6 53.1 Sectors All sectors Manufacture

ICT sector 24 Data source: Author computed from OECD ICIO Table with processing trade in China and Mexico Value-added structure of US-China bilateral trade in ICT products, 2014 120.0 US-China bilateral trade in ICT products, 2014 100.0 80.0 8.4 46.4 60.0 40.0 20.0 0.0 OVA+ODC 53.2 RDV+DDC +MVA+M DC 29.7 5.5 1.2 DVA_IND DVA_DIR China exports to US US exports to China US-China trade balance OVA+ODC is the largest portion of Chinas exports. China need use upstream inputs from third countries; DVA_IND is the largest portion of US exports , which are US ICT products imported by China used as inputs to produce Chinas ITC exports for third country markets (US exports through China). Change value-added structure of US-China Net imports in ICT products 1995-2014 80 160 70 140 60 120 50

100 40 80 30 60 20 40 10 20 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 -10 0

-20 Net Imports DVA_IND OVA ODC DVA_DIR RDV MC+DDC The difference of value-added structure in China exports to US and US exports to 26 China are the fundamental factor of US-China trade imbalance in ICT products What we can learn from the decomposition The results not only reveal the misleading nature of balance of trade computed from gross trade statistics but also the sources of the error. Based on the value added method, in 2014, US-China bilateral balance of manufacturing trade is only about 55% of what is indicated by the gross trade data. Domestic value-added absorbed by importing countries (US here) only took 53% (column 2a) of the total gross imbalance; US actually ran surplus in terms (3): there are more US value-added in US exports to China re-export by China back to US than Chinese value added in China export to US re-export by US back to China, indicating that US intermediate exports producers are located more upstream in various GVCs than Chinese firms; The rest came from third country effects: 42% due to Chinese exports to the US using more value-added from third countries than that in US exports to China (column 5+6); 4.1% due to Chinese exports to the US including more US value added than Chinese value-added embodied in US exports to China (column 4); 1.1% due to more double counting in Chinas exports to the US than that in US exports to China (column 7), indicating Chinese value-added has more border crossing than US value-added before they reach their final consumers. Multilateral Nature of Germany-US balterial Trade: Total

Germany exports to the US 100 500 90 450 80 400 70 350 60 300 50 250 45 40 200 40 30 150 20 100 10 50 0 0 1995 2000 2007 US exports to Germany 2014 DEU-US trade balance OVA+ODC RDV+DDC+MVA+MDC

DVA_DIR Net Imports DVA_IND 50 35 30 50 25 20 100 200 90 180 80 160 10 70 140 5 60 120 50 100 40 80 30 60 20 40 10 20 0 0 1995

2000 2007 2014 15 0 0 Germany also important base for US MNE affiliates export to rest of the world, especially other EU countries Multilateral Nature of Germany-US balterial Trade: Motor Vehicles Germany exports to the US 100 DEU-US trade balance 35 90 30 80 70 25 60 20 50 40 15 30 10 20 120 OVA+ODC RDV+DDC+MVA+MDC DVA_IND DVA_DIR 30 Net Imports

100 25 80 20 60 15 40 10 20 5 5 10 0 0 1995 2000 2007 2014 US exports to Germany 100 6 90 5 80 70 4 60 50 3 40 2 30 20 1 10 0

0 1995 2000 2007 2014 0 0 1995 2000 2007 2014 Third country effects play increasingly important role in US net auto imports from Germany Value-added Structure of US net imports from Germany, 2014 TEXP DVA_dir DVA_ind RDV MC+DD C OC (1) =2+3+4+5+6 +7 (2a) (2b) (3) (4) (5) Value 40.6 30.0 -8.1 -2.2

2.6 18.1 Share 100 74.0 -19.9 -5.4 6.6 44.7 Value 54.8 34.7 0.5 -0.8 2.4 18 Share 100 63.2 0.9 -1.5 4.5 32.9 Value 26.4 16.9 1.3 -0.1 0.8 7.5 Share

100 64.1 5.0 -0.4 3.1 28.2 Sectors All sectors Manufacture Motor vehicles sector 30 Data source: Author computed from OECD ICIO Table without considering processing trade Change value-added structure of US net imports from Germany 1995-2014 100 50 Germany-USA Motor vehicles BOT, 1995-2014 80 80 30 40 71 70 25 60 30 40 20 60 20 50 16 40 20 15

10 30 0 0 10 20 20 5 10 -20 -10 Net Imports RDV_G ODC DVA_Dir DDC+MC DVA_Ind OVA Total BOT OVA+ODC DVA_DIR DVA_IND 13 20 11 20 09 20 07 20 05 20 03 20 01 20 99 19 19 -20

19 -40 97 0 95 0 MVA+MDC+DDC Change role of third countries on US-Germany trade balance in ICT products 1995-2014 USA-Germany Transport and storage BOT, 1995-2014 35 9 30 7 25 20 5 15 3 10 5 1 0 5 9 996 997 998 999 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 9 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 -5 -10

-1 -3 Net Imports DVA_Ind OVA ODC Change role of third countries on US-Japan bilateral trade in Auto and Auto part 1995-2014 15 50 12 40 9 30 6 20 3 10 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 0 -3 -10 -6 -20 Net Imports DVA_Ind OVA ODC The difference of value-added structure in China exports to US and US exports to 33 China are the fundamental factor of US-China trade imbalance in ICT products What we can learn from the decomposition

US-China balterial trade is not special. Similar pattern also can be observed in US-Germany and many other bilateral trade routes; A special feature of US net imports from Germany is that there is much larger portion of US intermediate goods export to Germany were re-exported back to US or to third countries than that in US imports from Germany (Column 2b and 3), in these two portions US actually ran a large surplus, especially in services sectors, further demonstrating the complex composition and offsetting factors inside gross net trade flows. Both supply capacity and final demand in third countries increased their impacts on the balance of trade of auto between US and Japan in recent years. This implies that net bilateral imports no longer be a uniform measure for import penetration as the time when final good trade dominate, since its value-added structure can vary significantly across country/sector and bilateral routes in the age of global value chains. 34 This also implies that any bilateral trade policy change will have a third country effect that policy maker can not overlook. Value-added structure of US-Mexico bilateral trade 2014 120.0 100.0 21.0 18.5 33.7 80.0 60.0 7.9 OVA+ODC RDV+DDC+ MVA+MDC 6.2 16.9 DVA_IND 40.0 DVA_DIR 20.0

0.0 Mexico exports to US US exports to Mexico US-Mexico trade balance Value-added structure of US-Canada bilateral trade 2014 120.0 100.0 13.2 15.3 4.3 OVA+ODC 80.0 35.6 11.4 5.9 RDV+DDC+ MVA+MDC 60.0 40.0 62.1 20.0 0.0 63.4 DVA_IND 56.4 DVA_DIR Value-added structure of UK-Ireland bilateral trade 2014 200.0 150.0 OVA+ODC 100.0 39.3 19.7 RDV+DDC+ MVA+MDC 50.0 0.0 -50.0 -100.0

DVA_IND Ireland exports to UK UK exports to Ireland UK-Ireland trade balance -76.7 DVA_DIR VA structure of UK-France bilateral trade 2014 12 10 24.2 22.0 32.0 8 OVA+ODC RDV+DDC+M VA+MDC 6 4 DVA_IND 2 DVA_DIR France exports t... -2 UK exports to Fr... UK-France trade bal... The country source of third country effect (supply side) in US net imports from China, Germany and Japan; 2014 Net Imports Exports Sector (Billions USD) OVA (%) Share in (1) (1) Value Source Countries of the Multi Effect Upper middle Lower middle High income (%) income (%) income (%) Share in (2) Share in (2) Share in (2) (2) (3) USA Net Imports from Germany

Manufacture LTI MTI HTI 54.81 -1.25 -1.30 57.35 Manufacture LTI MTI HTI 30.59 -16.56 -0.94 48.09 Manufacture LTI MTI HTI 301.59 53.60 34.87 213.12 32.88 79.80 -17.01 45.48 -34.37 135.22 30.28 76.67 USA Net Imports from Japan 19.66 29.86 15.08 55.50 23.03 100.11 18.16 38.85 USA Net Imports from China 41.76 70.92 26.01 56.51 25.98 59.38 48.30 72.90 (4) (5) 11.19 30.43

-39.61 13.42 9.02 24.09 4.39 9.91 38.30 27.86 51.58 35.18 31.84 16.64 -51.68 25.97 11.09 15.90 13.92 10.28 17.99 27.59 26.70 16.82 The country sources of OVA (third country supply): For Germany, mainly from high income countries, about 80%, while 10% from high and low middle countries each; For China, about 70% from high income countries and the percentage increase as technology intensity increase, least from countries in the same income group at about 10%, Japan is different, about 40% from middle high income countries, each 30% from high and middle low income countries. The Geographic Sources of Third Country Effects in Global Trade Country source of OVA+ODC value, 1995-2014 12% 10% 8% 90% 11% 80% 71% 8% 70%

8% 60% 6% 50% 4% 40% 28% 2% 30% 2% 20% 0% 5 96 97 9 8 99 00 01 02 03 04 05 06 07 08 0 9 10 11 12 13 1 4 10% 9 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 -2% 0% USA JPN CHN Developed(Right) DEU Emerging(Right) 41 Emerging economies play increasing role in third country effect, especially China, increased 5 times. While the share contributed US, Japan and Gemerny declined from 40% in 1995 to less than 30% in 2014 Summary and Conclusion National income accounts record domestic output in value added terms but standard trade statistics record trade in gross terms. Our method is the first one in the literature to reveal the significant role that third countries have played in balterial trade relations in the age of global value chains that has been masked by official trade statistics. We apply KWWZ gross trade accounting framework and revise the decomposition by regroup the detailed items in their original decomposition formula to make it suitable to measure the role of multiterminal factor in bilateral trade. Apply our method to most recent version of OECD ICIO table, we show that net bilateral imports no longer be a uniform measure for import penetration and trade shocks as the time when final good trade dominate, since its value-added structure can vary significantly across country/sector and bilateral routes in the age of global value chains. Because the multileral nature of modern trade, the goal of trade policies should not be focus on bilateral trade balances but instead on eliminating trade barriers along GVCs, including what behind national borders. 42 The roles of third countries in bilateral trade can

be measures by 3 of the 8 detailed components DAV_IND to gross trade ratio is used to measure how important the partner country as a transfer planform for the home countrys DVA absorbed in third countries. Not only determined by the production sharing arrangement between the home and partner country; but also driven by final demand in third countries. OVA to gross trade ratio is used to measure how important third countries play in the home countrys export production. Not only drive by final demand in partner country, but also determined by the production sharing arrangement between the home and third countries. ODC to gross trade ratio is used to measure how complex the third country effect. It is independent to any countries final demand, and determined by production technology and the production arrangement among home, partner and third countries. Thank you for your attention! Zhi Wang, Professor and Dean, Research Institute of Global Value Chain, University of International Business Economics Research Professor and Senior Policy Research Fellow, Schar School of Policy and Government , George Mason University Email: [email protected] Website: https://schar.gmu.edu/about/faculty-directory/zhi-wang UIBE GVC indictor database: http://139.129.209.66:8000/d/daedafb854/ When use this database, please make a Reference to: UIBE GVC Index Team, Data files structure of the UIBE GVC index system http://139.129.209.66:8000/d/daedafb854/ Any questions and suggestions about UIBE GVC Index, please contact GVC index team at UIBE. Contact: team leader, Professor Fei Wang; E-mail: [email protected] The 2017 GVC development report: https://www.wto.org/english/res_e/publications_e/gvcd_report_17_e.htm http://www.worldbank.org/en/topic/trade/publication/global-value-chain-development-report-measuri ng-and-analyzing-the-impact-of-gvcs-on-economic-development The discussion of the 2017 GVC development report at Brookings https://www.brookings.edu/events/the-impact-of-global-value-chains-on-rich-and-poor-countries/ Major Reference Koopman, R., Wang, Z., Wei, S.J. (2014). Tracing Value-Added and Double Counting in Gross Exports. American Economic Review, 104(2), 459-94. Lawrence, Z. Robert (2018) Five Reasons Why the Focus on Trade Deficits Is Misleading Policy Brief, 18-6, Peterson Institute of International Economics. Los, B., M. P. Timmer, and G. J. de Vries (2016): Tracing Value-Added and Double Counting in Gross Exports: Comment, The American Economic

Review, 106, 19581966. Wang, Z., Wei, S.J., Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels. NBER Working Paper No. 19677. http://www.nber.org/papers/w19677 Leontief, W. (1936). Quantitative input and output relations in the economic system of the United States. The Review of Economic and Statistics, 18, 10525. Trade in Value-Added Developing New Measures of Cross Border Trade, coedited with Aaditya Mattoo and Shangjin Wei, CEPR/World Bank, April 2013. http://documents.worldbank.org/curated/en/2013/01/18821638/trade-value-adde d-developing-new-measures-cross-border-trade Change role of third countries in US net imports from Germany 1995-2014 Germany-USA BOT, 1995-2014 60 Germany-USA Motor vehicles BOT, 1995-2014 50 50 35 9 30 8 25 7 20 6 15 5 10 4 5 3 0 2 -5

1 -10 0 40 40 30 30 20 20 10 10 0 -10 Net Imports OVA DVA_Ind ODC Net Imports DVA_Ind OVA 20 13 20 11 20 09 20 07 20 05 -20 20 03 -40 20 01 -30 19 99

-10 19 97 -20 19 95 0 ODC Value-added structure of UK-Gemerny bilateral trade 2014 12 10 25.4 21.4 34.9 OVA+ODC 8 RDV+DDC+MV A+MDC 6 4 DVA_IND 2 DVA_DIR Germany exports t... -2 UK exports to Ger... UK-Germany trade bal... Apply KWWZ method to analyze third country effects in balterial trade (0) Gross exports (goods and services) (E) Regroup detailed items based on issue at hand (1)+(2)+(3) Domestic valueadded absorbed abroad (VAX_G) (4) Domestic value-added first exported then

returned home (RDV_B) (6)+(8) Foreign valueadded (FVA) (5)+(7)+(9) Pure double counted terms (PDC) (1)+(2) Domestic VA absorbed by direct importer (3) Domestic VA absorbed by other countries (5) Pure double counting from domestic sources (6)+(7) Importer content in gross exports (DVA_DIR) (DVA_IND) (DDC) (MVA+MDC) Domestic Value-added (DVA) (8)+(9) Other countries content in gross exports (OVA+ODC) Vertical Specialization (VS) 48 E can be at country-sector, country aggregate, bilateral -sector or bilateral aggregate; both DVA and RDV are based on backward industrial linkages

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