PARTICIPATION IN GVCS: ONGOING AND FUTURE WORK Javier Lopez - PowerPoint PPT Presentation

DEVELOPING COUNTRIES PARTICIPATION IN GVCS: ONGOING AND FUTURE WORK Javier Lopez Gonzalez, Development Division, OECD Trade and Agriculture Directorate Bangkok 13 June 2014

Background • The international fragmentation of production is re-shaping the world economy. 3 key systems: ‘Factory Europe’, ‘Factory Asia’ and ‘Factory • North America’(Baldwin and Lopez -Gonzalez, 2013. • Heightened ‘interconnectedness’; implies that trade is increasingly complementary rather than competing . • From a policy stand-point this means that impediments may not just affect foreign firms but also the competitiveness of domestic ones (Barriers to imports are barriers to exports) This presents new opportunities for policy coordination • geared to meet common goals (FTAs, BITS, MFN reduction). • Regulatory frameworks appear to be increasingly important in view of promoting further specialisation and international competitiveness. 2

Aim • Unravelling GVC activity: i. Mapping participation; ii. Identifying drivers – policy and non-policy related; iii. Understanding consequences (jobs, distribution of gains etc); OECD TAD work falls along these lines • • Before, a brief note on how we measure it. 3

Measuring: Trade flows • We have traditionally relied on tariff headings labelled ‘parts and components’, but: – products are not exclusive to one end-use (i.e. think milk or tyres) – trade statistics give us no indication of; i) how products are combined (linkages between buying and selling sectors); or ii) about the final destination of the resulting output. – Are measured gross and not net which can mislead analysts into wrongfully attributing location of value added (iPhone – Kraemer et al. (2011)) • This does not mean that trade statistics are useless! We still need to track product movement. That is where trade policy happens (tariffs). 4

Measurement: What the factories are doing...  To produce a $10k car a factory uses  Direct domestic value added (capital and labour)  Intermediates (domestic steel + imported gear boxes) 5

What the workers are doing (value added)... 6

Mapping • Inter-Country Input-Output table measures: – Backward linkage (sourcing): Foreign value added content of gross exports. – Forward linkages (selling): Domestic value added sold to other countries for these to produce gross exports. – Value added in final demand (Los et al. 2014) – Other: length or distance to consumer. • Trade data: – By end use  Intermediate good imports and exports (primary and processed) using BTDxE • Firm level: – Using targeted surveys or case studies (ultimately it is firms and not countries which engage in GVCs). 7

Global Matrix of Value Added Trade 8

Backward Participation 9

Forward Participation 10

... A bigger pie? Value Added Content of one unit of Value of Chinese Exports of Electrical Chinese Electrical and Optical and Optical Equipment by origin Equipment Exports 700,000 100% 6% 78,747 12% 600,000 3% 90% 2% 30,425 6% 5% 18,366 2% 3% 80% 31,359 5% 500,000 20,852 3% 70% 400,000 60% 50% 300,000 78% 439,944 40% 67% 200,000 30% 20% 100,000 10% 1,951 1,993 1,045 603 6% 692 41,640 4% 26,510 0% - 1,238 1995 2009 1995 2009 EU CHN TWN JPN KOR USA RoW EU CHN TWN JPN KOR USA RoW 11

South East Asia increasingly looking inwards for sources of intermediates… 12

… Mix of services, primary and electrical equipment domestic value added in exports… 1 1 1 1 1 1 1 1 . 8 . 8 .8 .8 .8 .8 .8 .8 . 6 . 6 .6 .6 .6 .6 .6 .6 . 4 . 4 .4 .4 .4 .4 .4 .4 . 2 . 2 .2 .2 .2 .2 .2 .2 0 0 0 0 0 0 0 0 1 995 200 5 20 09 1 995 200 5 20 09 1 995 200 5 200 9 19 95 200 5 200 9 19 95 2 005 200 9 199 5 2 005 200 9 199 5 2 005 2 009 199 5 2 005 2 009 TH A KHM SG P I DN M Y S VNM PHL BRN Chemicals&Fuel Electrical_Equipment Transport_Equipment light_manufacturing manufacturing_machinery primary services 13

… with foreign value added mainly in services… 1 1 1 1 1 1 1 1 . 8 . 8 .8 .8 .8 .8 .8 .8 . 6 . 6 .6 .6 .6 .6 .6 .6 . 4 . 4 .4 .4 .4 .4 .4 .4 . 2 . 2 .2 .2 .2 .2 .2 .2 0 0 0 0 0 0 0 0 1 995 200 5 20 09 1 995 200 5 20 09 1 995 200 5 200 9 19 95 200 5 200 9 19 95 2 005 200 9 199 5 2 005 200 9 199 5 2 005 2 009 199 5 2 005 2 009 TH A KHM SG P I DN M Y S VNM PHL BRN Chemicals&Fuel Electrical_Equipment Transport_Equipment ligth_manufacturing manufacturing_machinery primary services 14

… and interesting ‘complementarities’ between domestic and foreign value added… Philippines Thailand .3 .04 Electrical_Equipment .2 .02 primary ligth_manufacturing Chemicals&Fuel Transport_Equipment manufacturing_machinery .1 0 services Transport_Equipment 0 -.02 manufacturing_machinery Chemicals&Fuel Electrical_Equipment -.1 primary -.04 ligth_manufacturing services -.1 0 .1 .2 -.15 -.1 -.05 0 .05 .1 Change in imported value added share Change in imported value added share Fitted values Change Dom Fitted values Change Dom Indonesia Cambodia .05 .2 primary services Chemicals&Fuel Electrical_Equipment .1 Transport_Equipment manufacturing_machinery ligth_manufacturing 0 Chemicals&Fuel Electrical_Equipment Transport_Equipment manufacturing_machinery 0 -.1 -.05 services -.2 primary ligth_manufacturing -.1 -.3 -.1 -.05 0 .05 .1 -.2 -.1 0 .1 Change in imported value added share Change in imported value added share Fitted values Change Dom Fitted values Change Dom 15

Moving forward but at different speeds? 16

… but what determines participation? • A simple econometric approach (Policy versus Non-policy or structural): 1 , … , 𝑂𝑄𝑃𝑀 𝑗𝑢 𝑂 , 𝑄𝑃𝑀 𝑗𝑢 1 , … , 𝑄𝑃𝑀 𝑗𝑢 𝑁 , 𝜁 𝑗𝑢 ) 𝐶𝐵𝐷𝐿𝑋𝐵𝑆𝐸 𝑗𝑢 = 𝑔 ( 𝑂𝑄𝑃𝑀 𝑗𝑢 1 , … , 𝑂𝑄𝑃𝑀 𝑗𝑢 𝑂 , 𝑄𝑃𝑀 𝑗𝑢 1 , … , 𝑄𝑃𝑀 𝑗𝑢 𝑁 , 𝜁 𝑗𝑢 ) 𝐺𝑃𝑆𝑋𝐵𝑆𝐸 𝑗𝑢 = 𝑔 ( 𝑂𝑄𝑃𝑀 𝑗𝑢 1 , … , 𝑂𝑄𝑃𝑀 𝑗 𝑂 and 𝑂𝑄𝑃𝑀 𝑘 1 , … , 𝑂𝑄𝑃𝑀 𝑘 𝑂 ) are country-specific indicators of non-policy where: ( 𝑂𝑄𝑃𝑀 𝑗 1 , … , 𝑄𝑃𝑀 𝑗 𝑁 and 𝑄𝑃𝑀 𝑘 1 , … , 𝑄𝑃𝑀 𝑘 𝑁 ); are the country-specific characteristics of country i in year t and ( 𝑄𝑃𝑀 𝑗 𝑙 ) is the error term. indicators of policy determinants of GVC trade; and ( 𝜁 𝑗𝑘 • Clustering standard errors to correct for country and year-specific omitted factors • Reiterating the exercise for four broad types of activities Quintile regressions • 17

Mostly structural but policy can play a role Backward participation (ratio) 0.7 0.6 0.6 0.5 0.4 0.4 0.3 0.2 0.2 0.1 0 0 -0.1 -0.2 -0.2 -0.3 -0.4 -0.4 SAU BRN RUS USA ARG AUS BRA JPN NOR ZAF CHL IDN IND NZL GBR TUR GRC FRA CAN DEU PRT ITA ESP HKG LVA CHE POL MEX BGR DNK AUT SWE FIN NLD VNM CHN KOR KHM ISR THA LTU ISL CZE MLT SVN MYS BEL TWN PHL IRL EST SVK HUN SGP LUX Non-policy & constant Trade policy Investment opennness Residual Actual ratio 18

Drivers vary significantly by sector • Market size plays less of a role in backward and forward integration in agriculture and mining • Level of development is a differentiating factor of integration across sectors: • E.g. the higher the GDP per capita the lower the backward engagement in agriculture and the higher the forward engagement in manufacturing • FDI openness has a more pronounced impact in mining and services as compared to manufacturing or agriculture • Tariffs and RTAs seem to impede GVC integration more in manufacturing than in agriculture or mining and extractive industries

What about developing and least- developed country participation? • Hard to assess due to data availability. – But a lot can be done using trade data intelligently (intensive, extensive margins, duration, netowork analysis, Haussman-Hidalgo) – Need to evaluate other source of IO tables such as EORA. – Look into combining IO data with trade data to add granularity. • Think about what upgrading means, how we can capture it and what its determinants are. • But also how GVC participation and inequality are linked. 20

GVCs and wage-income inequality? • Aim: To shed light on how the proliferation of GVC activity has affected the distribution of wage-income within the working population. • Data: WIOD for calculation of both GVC indicators and wages • Caveats: Wage-income does not capture the Bill Gates or the unemployed… No capital returns (Piketty, 2014). But 75% of household income is derived from wages (OECD, 2013). 21

Global inequality falling but adjustment at the top end of distribution… World inequality measured World inequality measured with r90t10 with r90t50 180 World inequality measured World inequality measured 40 with WIOD_GINI1 with WIOD_GINI0 .8 .8 Ratio of top 10 on botton 50 160 35 140 30 .7 .7 120 25 Gini calculated with 'wages' 100 20 1995 2000 2005 2010 1995 2000 2005 2010 year year .6 .6 World inequality measured with r50t10 5.5 .5 .5 5 4.5 4 .4 .4 1995 2000 2005 2010 1995 2000 2005 2010 year year 3.5 1995 2000 2005 2010 year 22