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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

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DEVELOPING COUNTRIES PARTICIPATION IN GVCS: ONGOING AND FUTURE WORK

Javier Lopez Gonzalez, Development Division, OECD Trade and Agriculture Directorate Bangkok 13 June 2014

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  • 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

Background

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  • 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

Aim

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  • We have traditionally relied on tariff headings labelled

‘parts and components’, but:

– products are not exclusive to one end-use (i.e. think milk

  • r 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

  • utput.

– 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

Measuring: Trade flows

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 To produce a $10k car a factory uses  Direct domestic value added (capital and labour)  Intermediates (domestic steel + imported gear

boxes)

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Measurement: What the factories are doing...

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

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  • 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

Mapping

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Global Matrix of Value Added Trade

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Backward Participation

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Forward Participation

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... A bigger pie?

4% 6% 78% 67%

2% 3% 6% 5% 2% 3% 3% 5% 6% 12% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1995 2009

Value Added Content of one unit of Chinese Electrical and Optical Equipment Exports

EU CHN TWN JPN KOR USA RoW 1,238 41,640 26,510 439,944 692 20,852 1,993 31,359 603 18,366 1,045 30,425 1,951 78,747

  • 100,000

200,000 300,000 400,000 500,000 600,000 700,000 1995 2009

Value of Chinese Exports of Electrical and Optical Equipment by origin

EU CHN TWN JPN KOR USA RoW

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South East Asia increasingly looking inwards for sources of intermediates…

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… Mix of services, primary and electrical equipment domestic value added in exports…

.2 .4 .6 .8 1 TH A 1 995 200 5 20 09 .2 .4 .6 .8 1 KHM 1 995 200 5 20 09 . 2 . 4 . 6 . 8 1 SG P 1 995 200 5 200 9 . 2 . 4 . 6 . 8 1 I DN 19 95 200 5 200 9 .2 .4 .6 .8 1 M Y S 19 95 2 005 200 9 .2 .4 .6 .8 1 VNM 199 5 2 005 200 9 .2 .4 .6 .8 1 PHL 199 5 2 005 2 009 .2 .4 .6 .8 1 BRN 199 5 2 005 2 009

Chemicals&Fuel Electrical_Equipment Transport_Equipment light_manufacturing manufacturing_machinery primary services

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… with foreign value added mainly in services…

.2 .4 .6 .8 1 TH A 1 995 200 5 20 09 .2 .4 .6 .8 1 KHM 1 995 200 5 20 09 . 2 . 4 . 6 . 8 1 SG P 1 995 200 5 200 9 . 2 . 4 . 6 . 8 1 I DN 19 95 200 5 200 9 .2 .4 .6 .8 1 M Y S 19 95 2 005 200 9 .2 .4 .6 .8 1 VNM 199 5 2 005 200 9 .2 .4 .6 .8 1 PHL 199 5 2 005 2 009 .2 .4 .6 .8 1 BRN 199 5 2 005 2 009

Chemicals&Fuel Electrical_Equipment Transport_Equipment ligth_manufacturing manufacturing_machinery primary services

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… and interesting ‘complementarities’ between domestic and foreign value added…

Chemicals&Fuel Electrical_Equipment Transport_Equipment ligth_manufacturing manufacturing_machinery primary services

  • .1

.1 .2 .3

  • .1

.1 .2 Change in imported value added share Fitted values Change Dom

Philippines

Chemicals&Fuel Electrical_Equipment Transport_Equipment ligth_manufacturing manufacturing_machinery primary services

  • .04
  • .02

.02 .04

  • .15
  • .1
  • .05

.05 .1 Change in imported value added share Fitted values Change Dom

Thailand

Chemicals&Fuel Electrical_Equipment Transport_Equipment ligth_manufacturing manufacturing_machinery primary services

  • .1
  • .05

.05

  • .1
  • .05

.05 .1 Change in imported value added share Fitted values Change Dom

Indonesia

Chemicals&Fuel Electrical_Equipment Transport_Equipment ligth_manufacturing manufacturing_machinery primary services

  • .3
  • .2
  • .1

.1 .2

  • .2
  • .1

.1 Change in imported value added share Fitted values Change Dom

Cambodia

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Moving forward but at different speeds?

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… but what determines participation?

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  • A simple econometric approach (Policy versus Non-policy or

structural):

  • Clustering standard errors to correct for country and year-specific
  • mitted factors
  • Reiterating the exercise for four broad types of activities
  • Quintile regressions

𝐶𝐵𝐷𝐿𝑋𝐵𝑆𝐸𝑗𝑢 = 𝑔(𝑂𝑄𝑃𝑀𝑗𝑢

1 , … , 𝑂𝑄𝑃𝑀𝑗𝑢 𝑂 , 𝑄𝑃𝑀𝑗𝑢 1 , … , 𝑄𝑃𝑀𝑗𝑢 𝑁, 𝜁𝑗𝑢 )

𝐺𝑃𝑆𝑋𝐵𝑆𝐸𝑗𝑢 = 𝑔(𝑂𝑄𝑃𝑀𝑗𝑢

1 , … , 𝑂𝑄𝑃𝑀𝑗𝑢 𝑂 , 𝑄𝑃𝑀𝑗𝑢 1 , … , 𝑄𝑃𝑀𝑗𝑢 𝑁, 𝜁𝑗𝑢 )

where: (𝑂𝑄𝑃𝑀𝑗

1, … , 𝑂𝑄𝑃𝑀𝑗 𝑂 and 𝑂𝑄𝑃𝑀𝑘 1, … , 𝑂𝑄𝑃𝑀𝑘 𝑂) are country-specific indicators of non-policy

characteristics of country i in year t and (𝑄𝑃𝑀𝑗

1, … , 𝑄𝑃𝑀𝑗 𝑁 and 𝑄𝑃𝑀𝑘 1, … , 𝑄𝑃𝑀𝑘 𝑁 ); are the country-specific

indicators of policy determinants of GVC trade; and (𝜁𝑗𝑘

𝑙 ) is the error term.

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Mostly structural but policy can play a role

  • 0.4
  • 0.2

0.2 0.4 0.6

  • 0.4
  • 0.3
  • 0.2
  • 0.1

0.1 0.2 0.3 0.4 0.5 0.6 0.7

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

Backward participation (ratio)

Non-policy & constant Trade policy Investment opennness Residual Actual ratio

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  • 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

Drivers vary significantly by sector

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  • 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.

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What about developing and least- developed country participation?

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  • 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).

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GVCs and wage-income inequality?

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Global inequality falling but adjustment at the top end of distribution…

.4 .5 .6 .7 .8 1995 2000 2005 2010 year with WIOD_GINI1

World inequality measured

.4 .5 .6 .7 .8 Gini calculated with 'wages' 1995 2000 2005 2010 year with WIOD_GINI0

World inequality measured

100 120 140 160 180 1995 2000 2005 2010 year

with r90t10

World inequality measured

20 25 30 35 40 Ratio of top 10 on botton 50 1995 2000 2005 2010 year

with r90t50

World inequality measured

3.5 4 4.5 5 5.5 1995 2000 2005 2010 year

with r50t10

World inequality measured

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Mainly driven by within changes across skill levels between countries…

.5 1 1.5 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

for countries

Theil decomposition of World inequality

Between variation Within variation .5 1 1.5 Theil index 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

for skills

Theil decomposition of World inequality

Between variation Within variation .5 1 1.5 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

for sectors

Theil decomposition of World inequality

Between variation Within variation .5 1 1.5 Theil index 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

for development

Theil decomposition of World inequality

Between variation Within variation

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Strong Development dimension

IND IDN ROM BGR BRA RUS LTU TUR POL HUN CZE KOR SVN PRT ESP FIN SWE GBR AUS BEL DNK AUT CAN NLD USA LUX

.1 .2 .3 .4 .5 .6 7 8 9 10 11 Per Capita GDP (natural logarithm) 95% Confidence Interval Linear Prediction

  • bs
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Preliminary evidence of negative correlation wrt backward participation

JPN RUS USA BRA AUS IND CHN GBR IDN TUR ITA DEU FRA LVA POL ROM CAN ESP FIN AUT KOR SWE DNK PRT MEX GRC CYP LTU NLD TWN BGR SVN CZE BEL SVK EST IRL HUN MLT LUX

.1 .2 .3 .4 .5 .6 .2 .4 .6 Backward Participation 95% Confidence Interval Linear Prediction

  • bs
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The long-run: Countries with higher backward participation have lower wage-income inequality…

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  • Fall along 5 broad categories
  • Predictions of literature on impact are not

unambiguous (HOS, trade in tasks predict different effects) therefore it is an empirical issue.

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Determinants

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But in the short-run, +ve changes in participation lead to higher inequality

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  • To tease out mechanisms and in particular

justify long and short term differences

  • To look at how the composition of the

backward linkage (whether low, medium

  • r high-skill) matters.
  • To further differentiate the origin of these

backward linkages

  • To think about how the forward linkage

and inequality could be linked.

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More research is needed

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Thanks! Javier.lopezgonzalez@oecd.org This is joint work with: Przemislaw Kowalski, Alexandros Ragoussis and Cristian Ugarte and Pascal Archard

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Thanks

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  • Going back to the basics… What intermediates are countries

trading and with whom?

  • Caveats:

– what is an intermediate good? Hard to define products by end-use and added complication of exclusivity of current methods (milk example) – What is it being used for? Hard to establish how production is connected. Who is the selling and the using sector and therefore interlinkages – What value is being added to this good? Trade stats are gross and therefore hard to establish nature of activity – Very little data on services flows and no decomposition by end use

  • But still very useful: remember that trade policy mainly

based on products not value added.

Trade flows

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  • UN-BEC nomenclature identifies i) intermediate; ii)

capital; and iii) consumption goods.

  • But complex GVC participation requires further

‘digging’.

  • OECD-BTDIxE provides this granularity and can

easily be extended to decompose trade along 11 different end-use categories.

Decomposing trade by end use

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Global trade and contribution to export growth

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  • Changes mainly in Int-Prim, fuel, medicaments and phones but

contribution to export growth still mainly intermediates

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Focus SEA: Evidence of moving away from simple assembly?

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OTH ESA MEN WCA SAS SEA INT-PRIM 3.9 16.1 2.7 22.3 5.8 2.1 INT 47.6 43.8 18.0 12.1 37.4 40.5 FUEL 4.6 14.3 58.8 54.8 0.5 3.5 CONS 15.9 18.9 15.5 7.4 51.6 23.4 CAP 16.5 4.9 4.0 3.0 3.1 17.2 XMEDIC 1.7 0.2 0.3 0.0 1.0 0.2 XPC 3.0 0.3 0.3 0.0 0.2 7.1 XCARS 6.0 1.4 0.3 0.2 0.3 5.6 XPHONE 0.8 0.1 0.2 0.0 0.0 0.5 XPRCS 0.0 0.0 0.0 0.0 0.0 0.0 XMISC 0.1 0.0 0.0 0.1 0.0 0.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Export shares in 1998/99 OTH ESA MEN WCA SAS SEA INT-PRIM 6.3 13.9 1.9 18.0 8.7 2.5 INT 42.2 34.0 18.3 18.9 34.1 43.1 FUEL 12.7 31.8 68.1 51.8 14.8 6.3 CONS 13.1 8.7 7.1 6.4 30.5 18.2 CAP 15.1 4.6 2.6 4.5 4.5 18.2 XMEDIC 3.1 0.1 0.6 0.1 3.0 0.3 XPC 1.1 0.1 0.1 0.0 0.2 6.2 XCARS 5.1 2.2 0.7 0.2 1.6 3.0 XPHONE 0.9 0.1 0.1 0.0 1.2 2.1 XPRCS 0.4 4.3 0.4 0.2 1.4 0.1 XMISC 0.1 0.1 0.1 0.0 0.0 0.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Export shares in 2010/11

  • Focus on SEA
  • In 98/99 - relative to

world, mainly Consumption and Capital goods.

  • by 2010/11

Intermediates rise but Cons declines

  • Evidence of moving

away from assembly?

  • But perhaps not for

Xphone?

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Factory Asia?

ESA MEN WCA SAS SEA INT-PRIM 7.7 3.6 4.0 14.6 3.8 INT 43.4 44.4 27.1 44.8 50.2 FUEL 17.4 23.5 23.7 12.2 11.1 CONS 17.7 19.6 21.6 19.1 12.2 CAP 11.3 5.9 21.5 6.5 15.3 XMEDIC 0.6 0.8 0.3 0.6 0.2 XPC 0.5 0.6 0.1 0.0 4.4 XCARS 1.2 1.3 1.6 2.0 1.1 XPHONE 0.1 0.2 0.0 0.2 1.7 XPRCS 0.1 0.2 0.1 0.0 0.0 XMISC 0.0 0.0 0.0 0.0 0.0 Total 100.0 100.0 100.0 100.0 100.0 ESA MEN WCA SAS SEA INT-PRIM 14.6 1.7 19.8 8.3 1.8 INT 33.0 14.9 17.8 33.4 39.0 FUEL 33.4 73.9 55.5 15.0 3.6 CONS 7.7 5.5 4.4 31.3 21.6 CAP 3.8 2.2 2.2 4.4 19.9 XMEDIC 0.1 0.6 0.0 3.1 0.3 XPC 0.1 0.1 0.0 0.1 7.3 XCARS 2.4 0.6 0.0 1.5 4.1 XPHONE 0.1 0.1 0.0 1.3 2.3 XPRCS 4.8 0.4 0.2 1.5 0.1 XMISC 0.1 0.1 0.0 0.0 0.0 Total 100.0 100.0 100.0 100.0 100.0 Exports within the region Exports out of the region

  • SEA intra regional exports

represent 36% of total exports

  • Composition of these
  • verwhelmingly

intermediates

  • Extra-regional exports

(64%) also important intermediates but more geared towards final products (consumption and Capital

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  • Further dig into the data:

– Combining trade and IO data to obtain measures of vertical specialisation – Exploit firm level datasets – Explore Eora

  • Add dimensionality:

– Network analysis – Further refine analysis of products traded (Hausman-Hidalgo)

36

Way forward