1. Harmonising international trade data for inter- country input-output analysis: statistical issues Dong GUO, Norihiko YAMANO and Colin WEBB September 22 nd, 2008 WPGTS, Paris

2. Background Economic globalization: the significant trade in intermediate goods and services globally More fragmented production process across countries Inter-regional input-output model: tools for wide range of global analyses

3. Trade data and IO analysis Input-Output tables by OECD –Separated the domestic transaction from the imported transaction –Harmonised for 40 countries Multi-Regional IO –Trade structure integrated into IO analysis –Interregional feedback effects Challenges –Optimal data for trade structure between countries required (goods and services)

4. Sector 1 Country B Sector 2 Country A Sector 1 Sector 2 Country A IMD FD CP IP IMD FD CP IP Sector 1 Country C Sector 2 Country B Linking trade data with IO Country C Sector 1 Sector 2 + + = = IO

5. Trade in goods – Data issues Re-exports –Trade discrepancies: Classic case = China /HK/USA Un-allocated trade data –Possibly concentrated in certain industries and partners Trade in second-handed goods –Not linked to recent manufacturing production Scrap and waste products –Some identified by HS, others not (e.g. PCs) Trade by industry classifications –OECD IO: by industry; trade statistics: by products

6. China-US: trade discrepancies (official statistics, billion USD) Year US exports to China (US data) Chinese imports from the US (Chinese data) US imports from China (US data) Chinese exports to the US (Chinese Data) US- China trade balance (US data) US- China trade balanc e (Chine se data) 199511.716.145.624.7-33.8-8.6 200222.127.2125.270.0-103.1-42.8 Source: Fung and Lau (2003) Table1

7. Unallocated trade (% total trade) ExportsImports 199019952000199019952000 Australia14.415.919.13.62.32.1 Germany1.63.62.32.15.55.2 Ireland4.46.94.32.99.25.8 Norway5.67.34.40.80.91.0 USA3.93.33.5 3.44.4 Russia--12.3--13.2 South Africa--24.7--8.8

8. Trade in services / BOP Improvement in recent years with available data allows improved MRIO modelling Conceptual differences between EBOPS and Industry classification Particular issues –Goods sent abroad for processing –Merchanting

9. Differences in published trade statistics Merchandise trade statistics –ITCS v. COMTRADE: notably pre-2000 data; subtle differences after synchronization. Service trade statistics –Between OECD TIS and UNSITS Exports of manufactured goods –Between OECD BTD and I-O datasets

10. ITCS data as % of COMTRADE data, Germany, 2006 ExportsImports Total 100.0 Commodities not specified according to kind 66.286.8 63: Other textile articles; clothing etc 102.2100.0 73: Articles of iron or steel. 101.4100.0 82: Tools and implements of base metal 103.1100.9 84: Machinery and mechanical appliances and parts; boilers etc. 101.5100.1 85: Electrical machinery and equipment and parts thereof 100.8100.0 87: Vehicles other than railway or tramway rolling-stock; parts 104.7105.2 88: Aircraft, spacecraft, and parts thereof. 105.7106.0 Sum of all other 2-digit HS commodities 100.0

11. Possible solutions short-term –Re-exports: more information from countries, particularly for key I-O years (1995, 2000). – Un-allocated trade: more information on composition. longer-term –Systematic reporting of re-exports – New products codes to identify scrap, waste, recycled goods etc. – Reporting of BOP trade in goods by product groups

12. Summary More to international trade statistics than meets the eye Attempt at guide for researchers with limited experience of using trade statistics Highlights issues to be considered when using trade statistics – particularly for I-O related analyses

13. Thank you !