WIOD Why WIOD? Erik Dietzenbacher

1. 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 WIODWhy WIOD?Erik Dietzenbacher

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

3. WIODWORLD 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 Exports as % of total output Exports as % of total output

14. Factor intensity of trade • Exercise: • LAB(GE→FR) = GE labor embodied in GE exports to FR • LAB(FR→GE) = 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(GE→FR) = GE labor embodied in GE exports to FR • LAB(FR→GE) = FR labor embodied in FR exports to GE • K/L ratios for GE and FR • K/LGE > K/LFR : according to HO, FR “exports” labor to GE • : LAB(FR→GE) > LAB(GE→FR) • Bilateral comparisons: • If yes: GREEN • If no: RED

17. Large countries behave according to HO Small countries behave according to HO

19. 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 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 Z = intermediate deliveries → A = input coefficients → (I – A)-1 = Leontief inverse

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

27. Z12 = intermediate deliveries from 1 to 2 c1 = domestic consumption in country 1 e1 = exports to consumers in country 2 plus all exports to the Rest of the World (= R) ZR1 = imports from R to country 1

28. Three types of final demand that drive the model

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

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 (w1)´, (w2)´, (w3)´ “true” consumer responsibility for country 1: [(w1)´, (w2)´, (w3)´](I – A)-1f•1

43. Case 1: only technical coefficients available for country 1 consumer responsibility (w1)´(I – A1)-1f1 Average error: -37.5% i.e. reported CR is (on average) only 62.5% of “true” CR

44. Case 2: information for imports from RoW • use true emission coefficients: (w1)´, (w2)´, (w3)´ • [(w1)´, (w2)´, (w3)´](I – A)-1f•1 • average error: -27.6% • use emission coefficients of country 1 only • [(w1)´, (w1)´, (w1)´](I – A)-1f•1 • average error: -31.0%

45. Case 3: technical coefficients for all other countries • use true emission coefficients: (w1)´, (w2)´, (w3)´ • [(w1)´, (w2)´, (w3)´](I – A)-1f•1 • average error: +0.3% • use emission coefficients of country 1 only • [(w1)´, (w1)´, (w1)´](I – A)-1f•1 • average error: -7.9%

46. Case 4: aggregated RoW • use true emission coefficients: (w1)´, (wRoW)´ • [(w1)´, (wRoW)´](I – A)-1f•1 • average error: -29.0% • use emission coefficients of country 1 only • [(w1)´, (w1)´](I – A)-1f•1 • average error: -31.0%

47. Case 5: aggregated RoW, estimate RoW using country 1 • use true emission coefficients: (w1)´, (wRoW)´ • [(w1)´, (wRoW)´](I – A)-1f•1 • average error: -16.1% • use emission coefficients of country 1 only • [(w1)´, (w1)´](I – A)-1f•1 • average error: -20.9%

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: [(w1)´, (w2)´, (wav)´](I – A)-1f•1 → % error country 1 CR country 2: [(w1)´, (w2)´, (wav)´](I – A)-1f•2 → % error country 2 wav = average emission coefficients (of countries 1 and 2)