DEVELOPING COUNTRIES PARTICIPATION IN GVCS: ONGOING AND FUTURE WORK

Transcription

1 DEVELOPING COUNTRIES PARTICIPATION IN GVCS: ONGOING AND FUTURE WORK Javier Lopez Gonzalez, Development Division, OECD Trade and Agriculture Directorate Bangkok 3 June 24

2 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, 23. 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

3 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

4 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. (2)) This does not mean that trade statistics are useless! We still need to track product movement. That is where trade policy happens (tariffs). 4

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

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

7 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. 24) 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

8 Global Matrix of Value Added Trade 8

9 Backward Participation 9

10 Forward Participation

11 ... A bigger pie? % 9% 8% 7% 6% 5% 4% 3% 2% % % Value Added Content of one unit of Chinese Electrical and Optical Equipment Exports 6% 2% 3% 6% 2% 78% 2% 5% 3% 5% 3% 67% 4% 6% EU CHN TWN JPN KOR USA RoW 7, 6, 5, 4, 3, 2,, - Value of Chinese Exports of Electrical and Optical Equipment by origin 78,747 3,425 8,366 3,359 2, ,944,45,993, ,5,238 4, EU CHN TWN JPN KOR USA RoW

12 South East Asia increasingly looking inwards for sources of intermediates 2

13 Mix of services, primary and electrical equipment domestic value added in exports TH A KHM SG P I DN M YS VNM PHL BRN Chemicals&Fuel Transport_Equipment manufacturing_machinery services Electrical_Equipment light_manufacturing primary 3

14 with foreign value added mainly in services TH A KHM SG P I DN M YS VNM PHL BRN Chemicals&Fuel Transport_Equipment manufacturing_machinery services Electrical_Equipment ligth_manufacturing primary 4

15 and interesting complementarities between domestic and foreign value added Change in domestic value added share Change in domestic value added share Thailand Philippines Electrical_Equipment ligth_manufacturing primary Chemicals&Fuel Transport_Equipment manufacturing_machinery services Electrical_Equipment Change in imported value added share Fitted values Change Dom ligth_manufacturing servicestransport_equipment manufacturing_machinery Chemicals&Fuel primary Change in imported value added share Fitted values Change Dom Chemicals&Fuel Indonesia Electrical_Equipment Transport_Equipment manufacturing_machinery primary services Cambodia ligth_manufacturing Chemicals&Fuel Electrical_Equipment Transport_Equipment manufacturing_machinery services ligth_manufacturing primary Change in imported value added share Fitted values Change Dom Change in imported value added share Fitted values Change Dom 5

16 Moving forward but at different speeds? 6

17 but what determines participation? A simple econometric approach (Policy versus Non-policy or structural): BACKWARD it FORWARD it = f(npol it,, NPOL N it, POL it,, POL M it, ε it ) = f(npol it,, NPOL N it, POL it,, POL M it, ε it ) where: (NPOL i,, NPOL N i and NPOL j,, NPOL N j ) are country-specific indicators of non-policy characteristics of country i in year t and (POL i,, POL M i and POL j,, POL M j ); are the country-specific indicators of policy determinants of GVC trade; and (ε k ij ) is the error term. Clustering standard errors to correct for country and year-specific omitted factors Reiterating the exercise for four broad types of activities Quintile regressions 7

18 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 Mostly structural but policy can play a role.7 Backward participation (ratio) Non-policy & constant Trade policy Investment opennness Residual Actual ratio 8

19 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

20 What about developing and leastdeveloped 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. 2

21 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, 24). But 75% of household income is derived from wages (OECD, 23). 2

22 3.5 Ratio of top 5 on bottom Gini calculated with 'Value Added' Gini calculated with 'wages' Ratio of top on bottom Ratio of top on botton Global inequality falling but adjustment at the top end of distribution World inequality measured with r9t World inequality measured with WIOD_GINI World inequality measured with r9t5 World inequality measured with WIOD_GINI year year World inequality measured with r5t year year year 22

23 Theil index Theil index Theil index Theil index.5.5 Mainly driven by within changes across skill levels between countries Theil decomposition of World inequality for countries Theil decomposition of World inequality for skills Between variation Within variation Between variation Within variation Theil decomposition of World inequality for sectors Theil decomposition of World inequality for development Between variation Within variation Between variation Within variation 23

24 WIODGINI Strong Development dimension IND BRA RUS IDN ROM BGR TUR LTU POL HUN KOR PRT USA SVN ESPAUS CAN NLD BEL CZE GBR AUT FINDNK SWE LUX Per Capita GDP (natural logarithm) 95% Confidence Interval Linear Prediction obs 24

25 WIODGINI Preliminary evidence of negative correlation wrt backward participation BRA IND RUS CHN MEX IDN JPNUSA AUS TUR TWN LVA KOR HUN PRT ROM GRC CYPLTU BGR EST ITA DEU FRA POL CAN ESP SVN NLD GBR AUT CZE BEL SVK IRL FINDNK SWE MLT LUX Backward Participation 95% Confidence Interval Linear Prediction obs 25

26 The long-run: Countries with higher backward participation have lower wage-income inequality 26

27 Determinants 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. 27

28 But in the short-run, +ve changes in participation lead to higher inequality 28

29 More research is needed 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 or high-skill) matters. To further differentiate the origin of these backward linkages To think about how the forward linkage and inequality could be linked. 29

30 Thanks Thanks! This is joint work with: Przemislaw Kowalski, Alexandros Ragoussis and Cristian Ugarte and Pascal Archard 3

31 Trade flows 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. 3

32 Decomposing trade by end use 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 different end-use categories. 32

33 Global trade and contribution to export growth Changes mainly in Int-Prim, fuel, medicaments and phones but contribution to export growth still mainly intermediates 33

34 FUEL CONS CAP XMEDIC XPC Focus SEA: Evidence of moving away from simple assembly? XCARS XPHONE XPRCS XMISC Total Export shares in 24/5 OTH ESA Export MEN shares in 998/99 WCA SAS SEA INT-PRIM OTH 4.3 ESA 9.8 MEN.7 WCA 2. SAS 8.5 SEA.9 INT-PRIM FUEL INT CONS FUEL CAP CONS XMEDIC CAP XPC XMEDIC XCARS XPC XPHONE XCARS XPRCS XPHONE XMISC XPRCS Total XMISC Total Export shares in 2/ OTH ESA Export MEN shares in 24/5 WCA SAS SEA INT-PRIM OTH ESA MEN.9 WCA 8. SAS 8.7 SEA 2.5 INT INT-PRIM FUEL INT CONS FUEL CAP CONS XMEDIC CAP XPC XMEDIC XCARS XPC XPHONE XCARS XPRCS XPHONE XMISC XPRCS Total XMISC Total Export shares in 2/ OTH ESA MEN WCA SAS SEA Focus on SEA In 98/99 - relative to world, mainly Consumption and Capital goods. by 2/ Intermediates rise but Cons declines Evidence of moving away from assembly? But perhaps not for Xphone? 34

35 Factory Asia? Exports within the region Exports within the region ESA MEN WCA SAS SEA ESA MEN WCA SAS SEA INT-PRIM SEA intra.8.4 regional.5 exports.9.4 INT-PRIM INT INT FUEL represent.8 36% of total.8 4. FUEL CONS CONS CAP CAP XMEDICexports..... XMEDIC XPC XPC XCARS Composition..2 of.2these..4 XCARS XPHONE XPHONE XPRCS..... XPRCS overwhelmingly XMISC..... XMISC..... Total Total..... intermediates Extra-regional exports Exports out of the region Exports out of the region ESA MEN WCA SAS SEA ESA MEN WCA SAS SEA INT-PRIM INT-PRIM INT INT (64%) also important FUEL FUEL CONS CONS CAP intermediates but 2. more CAP XMEDIC XMEDIC XPC geared. towards..final. 4.6 XPC XCARS XCARS XPHONE XPHONE products (consumption XPRCS XPRCS XMISC..... XMISC..... Total Total..... and Capital 35

36 Way forward 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