1. Estimate value added in Chinese exports: micro data issues Shunli Yao (UIBE) and Hong Ma (Tsinghua) EU-China Global Value Chains Workshop 20 March 2012, Beijing

2. Presentation Outline I. WTO MiWi and methodology II. “Proportionality assumption” & its drawbacks III. Import use in China’s 2007 IO tables IV.China micro data: available, but usable? V.NBS firm import use survey VI.Steps for better exp VA estimation

3. I. WTO MiWi and methodology (1/3) the idea is simple

4. I. WTO MiWi and methodology (2/3) OECD and WTO, 2011, "Trade in Value-added: Concept, Methodologies and Challenges," 6 June, mimeo “……this work requires a full set of inter-country I-O tables, where all bilateral exchanges of intermediate goods and services are accounted for: in other words a global input-output table.” (para 8) Methodology is intuitive: suppliers’ suppliers……

5. I. WTO MiWi and methodology (3/3) Constructing an IIO is not easy, but “……the key challenges in the immediate future concern the quality of trade statistics and the assumptions made to allocate imports to users……” (para 61) How to meet the key immediate challenges? - OECD/Eurostat joint TEC exercise (para 46) - Linking traders to the manufacturers will form an important part of the work. (para 48)

6. II. “Proportionality assumption” & its drawbacks (1/4) IO tables are data intensive Aggregate data are easier to obtain, while disaggregate data are often off-limits to researchers Allocate imports to users: aggregate → disaggregate, requires micro info When not available, assumptions have to be made: “Proportionality or homogeneity assumption” But it’s a bad assumption!

7. II. “Proportionality assumption” & its drawbacks (2/4) Conceptually import use of a product varies across industries: e.g.: iron ore in steel industry; cotton in textile industry higher proportion than national average firms are heterogeneous: e.g.: large and small firms behave differently

8. II. “Proportionality assumption” & its drawbacks (3/4) Empirically US import matrix: Feenstra & Jensen (2009) v.s. BEA

9. II. “Proportionality assumption” & its drawbacks (4/4) Improve IO with micro data Feenstra & Jensen (2009): USA LFTTD database: link prod & import data, prod inputs by sector  develop firm-level input-output tables  aggregate to get industry-level import use by industry Winkler and Milberg (2009): Germany Ahmad and Araujo (2011a,b): OECD and Turkey

10. III. Import use in China 2007 IO (1/4) Competitive IO tables 42/135 sectors, public release Competitive & Non-competitive IO tables 42/135 sectors, internal use Non-competitive IO table w/ processing & normal exp, domestic prod 42 sectors, internal use

12. III. Import use in China 2007 IO (3/4) How to determine the shares of import use by sector? Semi-survey, first time in Chinese IO tables 10,000 large firms, sampled from enterprise dataset Yangtze Delta 3500, Guangdong 1300 (Shanghai 1100, Zhejiang 1100, Jiangsu 1300) Focus given to FFEs

13. III. Import use in China 2007 IO (4/4) No use of firm level trade data 170,000 firms Biased in regional and size distribution small firms behave differently from large ones roughly 1/3 in Yangtze, 1/3 in Guangdong

14. IV. China micro data: available, but usable? (1/2) Calculate domestic content Custom Trade Data firm ID HS-8 Product (6000-9000) export value import value (inputs, K, C) customs regime (normal/proc trade) ownership Production Data firm ID CIC industry (500) gross output value added total input use ownership (equity share)

15. More problems: Imports resold to other firms Imported inputs not used in current year (inventory) Firm produces in multiple sectors The data hardly usable in its current form !! IV. China micro data: available, but usable? (2/2)

16. V. NBS firm import use survey Crucial info to be supplemented But data (even questionnaire) not available

17. VI. Steps for better exp VA estimates (1/) Step 1: maximum use of existing data firm trade data (170,000 firms) enterprise data (160,000 ~ 400,000 firms) NBS import use survey (10,000 firms) Step 2: questionnaire design & firm sampling Step 3: target survey

18. Thanks! Shunli Yao shunliyao@yahoo.com