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WIOD Why WIOD? Erik Dietzenbacher This project is funded by the European Commission, Research Directorate General as part of the 7th Framework Programme,

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Presentation on theme: "WIOD Why WIOD? Erik Dietzenbacher This project is funded by the European Commission, Research Directorate General as part of the 7th Framework Programme,"— Presentation transcript:

1 WIOD Why WIOD? Erik Dietzenbacher This project is funded by the European Commission, Research Directorate General as part of the 7th Framework Programme, Theme 8: Socio-Economic Sciences and Humanities. Grant Agreement no: 225 281

2 1.What is (in) WIOD 2.Factor intensity of trade 3.Vertical specialization 4.CO2 emissions: Consumer responsibilities and their sensitivity 5.Does the Rest of the World matter? 6.Is there a life after WIOD?

3 WIOD WORLD INPUT-OUTPUT DATABASE: Construction and Applications

4 Background Policies are designed at a detailed level of industries and products Production is characterized by interdependent structures Globalization increases the importance of cross- border interdependencies, which makes inclusion of trade in analyses more essential than ever Analyzing policy issues requires an all-encompassing database with three dimensions: time, industries/products and countries

5 Objectives of WIOD To build a time series of global inter-country input- output tables; To build socio-economic and environmental satellite accounts; To measure and analyze trends in trade, economic growth, technological change and environmental pressures; To provide policy support to the European Commission on socio-economic and environmental issues. Full database will become publicly available in May 2012

6 WIOD: Data and Coverage Time series in current and constant prices of: Harmonized national supply and use tables Harmonized IO tables Bilateral trade flows of goods and services Inter-country IO tables Socio-economic accounts and environmental accounts The tables in the WIOD-database will cover: The period from 1995 to 2006 (and for some major countries back to 1980) 27 EU countries and 13 other major countries More than 30 industries and at least 60 products

7 WIOD: Work Packages WP1-3: Construction of harmonized supply and use tables, national input-output tables, price deflators, trade flows and intercountry input-output tables WP4: Construction of environmental satellite accounts (energy use, greenhouse gas emissions, etc.) WP5: Construction of socio-economic satellite accounts (skill levels, investment, accumulation of intangibles) WP6: Methodological research WP7-9: Development of new models and extension/adaptation of models with track record within EC

8 Who is in WIOD? University of Groningen (The Netherlands) Institute for Prospective Technological Studies (Sevilla, Spain) Wiener Institut für Internationale Wirtschaftsvergleiche (Vienna, Austria) Zentrum für Europäische Wirtschaftsforschung (Mannheim, Germany) Österreichisches Institut für Wirtschaftsforschung (Vienna, Austria) Konstanz University of Applied Sciences (Germany) The Conference Board Europe (Brussels, Belgium) CPB Netherlands Bureau for Economic Policy Analysis (The Hague, The Netherlands) Institute of Communication and Computer Systems (Athens, Greece) Central Recherche SA (Paris, France) * Organization for Economic Co-operation and Development (Paris France)

9 My personal interest: What (types of) questions can be answered? What difference does it make? Examples of preliminary studies

10 Factor intensity of trade Leontief paradox: labor content of 1 million $ of exports versus labor content of 1 million $ of imports Crucial: same technology assumption use the matrix of technical input coefficients ($ of steel per $ of US cars, no matter whether US steel or German steel is used)

11 Factor intensity of trade Problem 1: labor content of US exports includes German workers solution: use domestic input coefficients Problem 2: domestic input coefficients of the US cannot be used for labor content of US imports Countries that are similar in terms of technical input coefficients, may have very different domestic input coefficients because their dependence on imported inputs differs

12 Factor intensity of trade Exercise: Intercountry IO tables for 6 European countries GE, FR, IT, NL, BE, DK 1985, 1975

13 Factor intensity of trade 19751985 GE1215 FR1113 IT1011 NL28 BE2736 DK1617 Exports as % of total output

14 Factor intensity of trade Exercise: LAB(GEFR) = GE labor embodied in GE exports to FR LAB(FRGE) = FR labor embodied in FR exports to GE all exports amount to: 1 million ECU K/L ratios for GE and FR

15 Factor intensity of trade LAB(GEFR) = GE labor embodied in GE exports to FR LAB(FRGE) = FR labor embodied in FR exports to GE K/L ratios for GE and FR K/L GE > K/L FR : according to HO, FR exports labor to GE : LAB(FRGE) > LAB(GEFR) Bilateral comparisons: If yes: GREEN If no: RED

16 1975 GEFRITNLBEDK GE--- FR--- IT--- NL--- BE--- DK---

17 1975 GEFRITNLBEDK GE--- FR--- IT--- NL--- BE--- DK--- Large countries behave according to HO Small countries behave according to HO

18 1985 GEFRITNLBEDK GE--- FR--- IT--- NL--- BE--- DK---

19 1985 GEFRITNLBEDK GE--- FR--- IT--- NL--- BE--- DK--- Large countries behave almost according to HO Small countries behave according to HO

20 Factor intensity of trade 6 out of 6 in 1975 and 5 out of 6 in 1985 is a wonderful score!

21 Factor intensity of trade 6 out of 6 in 1975 and 5 out of 6 in 1985 is a wonderful score! But (admittedly) the sample size is rather small Use WIOD: to include more countries to include refinements (types of labor, capital)

22 Vertical Specialization Production processes more and more split up in subsequent phases, carried out in different countries Trade in intermediate goods and services becomes more and more important increase in interconnectedness of industries across countries intercountry IO tables reflect exactly that measure vertical specialization in intercountry IO tables

23 Vertical Specialization Measuring vertical specialization Hummels, Ishii & Yi (JIE, 2001): import content of the exports

24 Vertical Specialization Zcex M v´v´ x´x´ Z = intermediate deliveries matrix c = domestic consumption vector e = gross exports vector x = gross output vector M = imports matrix v ´ = value added vector

25 Vertical Specialization Zcex M v´v´ x´x´ Z = intermediate deliveries A = input coefficients (I – A) -1 = Leontief inverse

26 Vertical Specialization Zcex M v´v´ x´x´ Z = intermediate deliveries A = input coefficients (I – A) -1 = Leontief inverse M(I – A) -1 e = imports necessary for exports s ´ M(I – A )-1 e = total imports necessary for exports, s ´ = summation row vector = (1,…,1)

27 Z 11 Z 12 c1c1 e1e1 x1x1 Z 21 Z 22 c2c2 e2e2 x2x2 ZR1ZR1 ZR2ZR2 (v1)´(v1)´ (v2)´(v2)´ (x1)´(x1)´ (x2)´(x2)´ Z 12 = intermediate deliveries from 1 to 2 c 1 = domestic consumption in country 1 e 1 = exports to consumers in country 2 plus all exports to the Rest of the World (= R) Z R1 = imports from R to country 1

28 Z 11 Z 12 c1c1 e1e1 x1x1 Z 21 Z 22 c2c2 e2e2 x2x2 ZR1ZR1 ZR2ZR2 (v1)´(v1)´ (v2)´(v2)´ (x1)´(x1)´ (x2)´(x2)´ Three types of final demand that drive the model

29 ExportsImports from 2Imports from R e1e1

30 ExportsImports from 2Imports from R e 1 (production in 1)

31 ExportsImports from 2Imports from R e 1 (production in 1)inputs

32 ExportsImports from 2Imports from R e 1 (production in 1)inputs (production in 2) inputs

33 ExportsImports from 2Imports from R e 1 (production in 1)inputs (production in 2) inputs (production in 1)

34 ExportsImports from 2Imports from R e 1 (production in 1)inputs (production in 2) inputs (production in 1)inputs (production in 2) inputs

35 ExportsImports from 2Imports from R e 1 (production in 1)inputs (production in 2) inputs (production in 1)inputs (production in 2) inputs (production in 1)inputs (production in 2) inputs

36 ConsumptionExportsImports from 2Imports from R c 1 (production in 1)inputs (production in 2) inputs (production in 1)inputs (production in 2) inputs (production in 1)inputs (production in 2) inputs

37 Final demand 2ExportsImports from 2Imports from R c 2 + e 2 (production in 2) inputs (production in 1)inputs (production in 2) inputs (production in 1)inputs (production in 2) inputs

38 Vertical Specialization Collect all exports and all imports (from 2 and from R) Use some matrix algebra, then: it is exactly the same as for the single country case Conclusion: to measure vertical specialization of a country it is not necessary to use an intercountry IO table VS = all imports from 2 + all imports from R all exports by 1

39 Two exercises on CO2 Central question: does it matter whether we use an intercountry IO table (and how much)? Data from Nori Yamano 37 countries, 16 sectors, 80% of world-GDP

40 Two exercises on CO2 Abuse the data: exports to RoW become part of domestic consumption imports from RoW become part of value added Why? we want to work with a perfect world-IO table if you cannot construct the table, adapt your world Hence: our world consists of 37 countries that is, USA USA, USA = USA

41 Consumer responsibility CO2 Consumer responsibility (CR) of country 1= all CO2 emissions (all over the world) that are necessary for producing the consumption in country 1 Crucial element of the carbon footprint of country 1 Calculate the CR of country 1: using the full world-table yields true answer various cases with limited information measure the percentage error for country 1 Do this for country 1, …, country 37 (unweighted) average % error

42 Z input coefficients A emission coefficients, row vectors (w 1 )´, (w 2 ) ´, (w 3 ) ´ true consumer responsibility for country 1: [(w 1 )´, (w 2 )´, (w 3 )´](I – A) -1 f 1 A 11 A 12 A 13 f 11 x1x1 A 21 A 22 A 23 f 21 x2x2 A 31 A 32 A 33 f 31 x3x3 Af1f1 x

43 Case 1: only technical coefficients available for country 1 consumer responsibility (w 1 )´(I – A 1 ) -1 f 1 Average error: -37.5% i.e. reported CR is (on average) only 62.5% of true CR A 11 f 11 x1x1 +A 21 +f 21 x2x2 +A 31 +f 31 x3x3 A1A1 f1f1 x

44 Case 2: information for imports from RoW use true emission coefficients: (w 1 )´, (w 2 ) ´, (w 3 ) ´ [(w 1 )´, (w 2 )´, (w 3 )´](I – A) -1 f 1 average error: -27.6% use emission coefficients of country 1 only [(w 1 )´, (w 1 )´, (w 1 )´](I – A) -1 f 1 average error: -31.0% A 11 00f 11 x1x1 A 21 00f 21 x2x2 A 31 00f 31 x3x3 Af1f1 x

45 Case 3: technical coefficients for all other countries use true emission coefficients: (w 1 )´, (w 2 ) ´, (w 3 ) ´ [(w 1 )´, (w 2 )´, (w 3 )´](I – A) -1 f 1 average error: +0.3% use emission coefficients of country 1 only [(w 1 )´, (w 1 )´, (w 1 )´](I – A) -1 f 1 average error: -7.9% A 11 00f 11 x1x1 A 21 A2A2 0f 21 x2x2 A 31 0A3A3 f 31 x3x3 Af1f1 x

46 Case 4: aggregated RoW use true emission coefficients: (w 1 )´, (w RoW ) ´ [(w 1 )´, (w RoW )´](I – A) -1 f 1 average error: -29.0% use emission coefficients of country 1 only [(w 1 )´, (w 1 )´](I – A) -1 f 1 average error: -31.0% A 11 0f 11 x1x1 A 21 +A 31 0f 21 +f 31 x2+x3x2+x3 Af1f1 x

47 Case 5: aggregated RoW, estimate RoW using country 1 use true emission coefficients: (w 1 )´, (w RoW ) ´ [(w 1 )´, (w RoW )´](I – A) -1 f 1 average error: -16.1% use emission coefficients of country 1 only [(w 1 )´, (w 1 )´](I – A) -1 f 1 average error: -20.9% A 11 0f 11 x1x1 A 21 +A 31 A1A1 f 21 +f 31 x2+x3x2+x3 Af1f1 x

48 Consumer responsibility CO2 Conclusion: underestimation is (on average) substantial unless a lot of information is available (Case 3)

49 Estimating RoW effects We will never be able to cover all countries there will always remain a RoW How does this affect our findings, and what can we do? Our world covers only 37 countries delete one of them (which plays RoW) and consider the effects on consumer responsibility CO2 large effects: neglecting RoW matters small effects: who cares about RoW?

50 Delete country 3 Case 1: CR country 1: [(w 1 )´, (w 2 )´, (w av )´](I – A) -1 f 1 % error country 1 CR country 2: [(w 1 )´, (w 2 )´, (w av )´](I – A) -1 f 2 % error country 2 w av = average emission coefficients (of countries 1 and 2) A 11 A 12 0f 11 f 12 x1x1 A 21 A 22 0f 21 f 22 x2x2 000f 31 f 32 x3x3 Af1f1 f2f2 x

51 Delete country 3 % error country 1, % error country 2 finally: weighted average % error Example: delete China (i.e. treat China as RoW) % error in any other country is on average -5% A 11 A 12 0f 11 f 12 x1x1 A 21 A 22 0f 21 f 22 x2x2 000f 31 f 32 x3x3 Af1f1 f2f2 x

52 Delete country 3 Case 2: extra information on imports from RoW CR country 1: [(w 1 )´, (w 2 )´, (w av )´](I – A) -1 f 1 % error country 1 CR country 2: [(w 1 )´, (w 2 )´, (w av )´](I – A) -1 f 2 % error country 2 A 11 A 12 0f 11 f 12 x1x1 A 21 A 22 0f 21 f 22 x2x2 A 31 A 32 0f 31 f 32 x3x3 Af1f1 f2f2 x

53 Delete country 3 Case 3: extra information on imports from RoW and estimate technical coefficients of RoW CR country 1: [(w 1 )´, (w 2 )´, (w av )´](I – A) -1 f 1 % error country 1 CR country 2: [(w 1 )´, (w 2 )´, (w av )´](I – A) -1 f 2 % error country 2 A av = average technical coeffients of countries 1 and 2 A 11 A 12 0f 11 f 12 x1x1 A 21 A 22 0f 21 f 22 x2x2 A 31 A 32 A av f 31 f 32 x3x3 Af1f1 f2f2 x

54 Case 1Case 2Case 3 Germany-2.3-1.5+1.4 Japan-1.3-0.8+2.0 USA-4.8-3.4+2.2 China-5.3-4.9-3.0 Russia-4.8-4.5-4.0 Average-0.9-0.7+0.2 Estimating RoW effects Average % errors

55 Case 1Case 2Case 3 Germany-2.3-1.5+1.4 Japan-1.3-0.8+2.0 USA-4.8-3.4+2.2 China-5.3-4.9-3.0 Russia-4.8-4.5-4.0 Average-0.9-0.7+0.2 Move from left to right: add more information average underestimation decreases Delete GE/USA/Japan (clean and efficient countries) estimate them by the average (which is less clean and efficient) NL depends on USA, but US emissions are overestimated hence consumer responsibility of NL is overestimated

56 Case 1Case 2Case 3 Germany-2.3-1.5+1.4 Japan-1.3-0.8+2.0 USA-4.8-3.4+2.2 China-5.3-4.9-3.0 Russia-4.8-4.5-4.0 Average-0.9-0.7+0.2 Tentative conclusion for WIOD: GDP of RoW in WIOD is approximately 15% omission of USA from 37 world has limited effects (less than 5%) suggests that WIOD will generate reliable results but: these are average results for specific countries the errors may be substantial oil-producing countries are not included in WIOD

57 Is there a life after WIOD? Consider the effects involved in off-shoring or processing trade in general Lot of imports and exports, little value added little activity, little CO2 emissions Recent debate about Chinas CO2 emissions claim: a substantial part is in Chinas exports Use an IO analysis

58 Is there a life after WIOD? In 2002: of all CO2 emitted in production activities 20.3% due to exports (and thus 79.7% due to domestic consumption) 20.6% for SO2, 20.7% for Nox Strongly increasing over time: Weber et al (2008), for CO2 21% in 2002 33% in 2005

59 Is there a life after WIOD? Claim: these numbers are seriously overestimated Why? Production activities related to processing trade requires: essentially imported inputs almost no domestically produced inputs involves little emissions at home Ordinary production: large dependence on domestically produced inputs little dependence on imported inputs

60 Is there a life after WIOD? Input-output tables reflect the average production structure processing trade 50% of exports, but a minor share of production (>1 billion Chinese consumers) production related to processing trade receives a small weight in the average production Conclusion: ordinary IO tables cannot do the job we need special IO tables better reflecting the input structure of processing trade

61 Tripartite IO table (Chen et al., 2006) Z DD Z DP Z DN fDfD 0xDxD 0000ePeP xPxP Z ND Z NP Z NN fNfN eNeN xNxN MDMD MPMP MNMN (vD)(vD)(vP)(vP)(vN)(vN) (xD)(xD)(xP)(xP)(xN)(xN) Distinguish between three types of production D = for domestic use only P = for processing exports N = for non-processing exports and other production of foreign-invested enterprises (high dependence on imports and many indirect exports)

62 Is there a life after WIOD? In 2002: of all CO2 emitted in production activities 20.3% due to exports Using the tripartite IO table: of all CO2 emitted in production activities 12.6% due to exports CO2 emissions due to exports are overestimated by 61%!!

63 Is there a life after WIOD? In 2002: of all CO2 emitted in production activities 12.6% due to exports CO2 emissions due to exports are overestimated by 61%!! Is this to be viewed as a measurement error? The measurement error has the size of the total CO2 emissions of the Netherlands, or Poland or 1.5 times Turkey

64 Is there a life after WIOD? Conclusion: using ordinary IO tables grossly overestimates the CO2 emissions involved in exports when processing trade is present this applies to countries for which: processing trade is a major part of exports large domestic market characteristic production structure is not reflected in the average production structure (which is dominated by the structure for domestic production)

65 Is there a life after WIOD? Yes, there is a life after WIOD!

66 Is there a life after WIOD? Yes, there is a life after WIOD! Thank you for your attention.


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