Comparing China Input-Output Tables from Two Different Time Series: Major Differences and Potential Bias
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1 April 24, 2012 Comparing China Input-Output Tables from Two Different Time Series: Major Differences and Potential Bias Leona Li, Xiaoqin Li
2 What are the two different IO table series? One is the SUTs and IOTs from World Input-Output Database (WIOD), which adopted a SUT-RAS method to jointly estimate SUTs based on all publicly available statistics; the other is Use Table series and estimated IOTs from China KLEMS project, led by Professor Ren Ruoen, in corporation with Professor Dale Jorgenson and China s National Bureau of Statistics (NBS). China KLEMS project established close collaboration with China NBS, thus the compilation of their Use table time series involves a thorough inspection of all the historical Input-Output survey results, underlying materials and even the confidential policy files from the early-year planned economy system, which is also the initial reason why China compiled its first IOT under MPS framework. 2
3 What is the major difference in the compilation methodology? WIOD s compilation is mainly based on China s public benchmark tables and aggregate control totals from national accounts, assuming: 1. China s public benchmark tables are consistent; 2. China s IO account is consistent with GDP accounts; while the above assumptions are somehow problematic, because 1. China s statistical system is still improving over time, 2. 5-year intervals is a relatively long enough time to introduce some real structure change; 3. The Value-added from IO accounts are always slightly bigger than GDP accounts, because of the import tariffs and adjustments for consumption of financial services, etc. China KLEMS has included some internal information to make the necessary adjustments according to the above inconsistency issues. 3
4 What are the major additional information provided by NBS? For the industrial enterprises survey, there s scale-based system in China. For the enterprises with more than 5 million RMB s annual revenue, they supposed to follow a much detailed and systematic survey system. While for the enterprises under this designed scale, the cost and expense accounting is usually less detailed. Specific supply structure for SMEs in provided. China s IOTs total output by products is at producer price. The value-added tax expenditure by products is provided For import data in IOT, value creation for the import goods by the transportation enterprises, import tariff, consumption tax for imported goods, and adjustments for processing trade are all provided; For export data in IOT, the FOB price excluding turnover cost, and adjustments for processing trade are all provided; Most of underlying survey material comes from the use sectors, which accounting price is purchaser price. Turnover cost matrix (including business additional expenditure and transportation) is provided, which could be subtracted from the purchaser price table and construct the tables at producer price. 4
5 One example for adjustment details Table 2.1 The adjustments for processing trade in 2007 benchmark table Unit: billion RMB (1) (2) (3) (4) = (1) - (2)+(3) (5) (6) (7) = (5) - (6) 1 Agriculture Mining , , Construction Food and related Textiles, Leather, Apparel 1, , Wood and related Paper, printing and publishing Petroleum Chemicals & Rubber Other mineral Metals Machinery Electrical & Electronics 3, , , , Transport equipment Misc. manufacturing Utility Trade Transportation services Communications Finance, insurance and real estate Other private services Public services Total 10, , , , Notes: (1): All exports Exports of processing with (2): foreign supplied material Charges for processing with (3): foreign supplied material (4): Exports for IOT (5): All imports Imports for processing with (6): foreign supplied material (7): Imports for IOT 5
6 What are differences in the aggregate level? Table 2.2 Percentage difference of GDPs between China KLEM S/WIOT with National Accounts and 2002 Benchmark IO China KLEM S WIOD Deviation in % GDP, total Agriculture Industry Construction Service Deviation in % GDP, total Agriculture Industry Construction Service % 1.73% 3.33% 1.89% 2.57% % 0.00% 0.00% 0.00% 0.00% % 1.88% 3.19% 1.91% 2.59% % 0.00% 0.00% 0.00% 0.00% % 2.08% 2.91% 1.92% 2.60% % 0.00% 0.00% 0.00% 0.00% % 1.09% 2.14% 1.90% 2.60% % 0.00% 0.00% 0.00% 0.00% % 0.52% 1.43% 1.75% 2.44% % 0.00% 0.00% 0.00% 0.00% % 0.71% 0.02% 1.99% 2.60% % 0.00% 0.00% 0.00% 0.00% % 0.71% -0.06% 1.99% 2.69% % 0.00% 0.00% 0.00% 0.00% % 0.71% -0.12% 1.99% 2.68% % 0.00% 0.00% 0.00% 0.00% % 0.71% -0.38% 1.77% 2.58% % 0.00% 0.00% 0.00% 0.00% % 0.27% 0.30% 0.98% 1.66% % 0.00% 0.00% 0.00% 0.00% % 3.00% 0.92% 4.98% -0.78% % 0.00% 0.00% 0.00% 0.00% 2002 Benchmark IO 0.00% 0.00% -2.29% 0.00% 2.22% 2002 Benchmark IO -1.25% -0.71% -2.17% -1.95% -0.46% 6
7 What are differences with public benchmark tables? Table 2.3 Weight Absolute Percentage Error between Benchmark IOT, WIOD and China KLEM S BU2002 vs. BU2007 BU2002 vs. WIOT2002 BU2007 vs. WIOT2007 WIOT2002 vs. WIOT2007 WIOT2002 vs. WIOT2005 Total Intermediate % Total Intermediate % by industry Direct consumption coefficient - all Final consumption coefficient - all Final household consumption Final government consumption Gross capital formation Export Import WIOT2005 vs. WIOT2007 BU2002 vs. CNUSE2002 CNUSE2005 vs. BU2002 CNUSE2005 vs. BU2007 CNUSE2002 vs. CNUSE2005 Total Intermediate % Total Intermediate % by industry Direct consumption coefficient - all Final consumption coefficient - all Final household consumption Final government consumption Gross capital formation Export Import
8 What are differences in the Input Coefficient Matrix? Table 2.4 Number of discrepancies between WIOD and China KLEMS in key input coefficient and zero input coefficient Key Input Coefficient Zero Input Coefficient Industry (#discrepancy) Total Total 1 Agriculture Mining Construction Food and related Textiles and related Leather Wood and related Paper related Petroleum Chemicals Rubber Other mineral Metals Machinery Electrical & Electronics Transport equipment Misc. manufacturing Utility Trade Transportation services Communications Finance related Other private services Public services
9 How do we measure the differences? (1) Table Weighted average percentage error between two Use table series by different level of aggregations (current price series) 30 X 24 - WAPE (%) X 19 - WAPE (%) Whole matrix Whole matrix Intermediate use matrix Intermediate use matrix Direct consumption coefficient matrix Direct consumption coefficient matrix Final use matrix Final use matrix Final consumption coefficient matrix Final consumption coefficient matrix Value-added Value-added Total output by industry Total output by industry X 9 - WAPE (%) X 19 - WAPE (%) Whole matrix Whole matrix Intermediate use matrix Intermediate use matrix Direct consumption coefficient matrix Direct consumption coefficient matrix Final use matrix Final use matrix Final consumption coefficient matrix Final consumption coefficient matrix Value-added Value-added Total output by industry Total output by industry X 9 - WAPE (%) Whole matrix Intermediate use matrix Direct consumption coefficient matrix Final use matrix Final consumption coefficient matrix Value-added Total output by industry
10 How do we measure the differences? (2) Table Weighted average percentage error between two Use table series by different level of aggregations (constant price series) 30 X 24 - WAPE (%) X 19 - WAPE (%) Whole matrix Whole matrix Intermediate use matrix Intermediate use matrix Direct consumption coefficient matrix Direct consumption coefficient matrix Final use matrix Final use matrix Final consumption coefficient matrix Final consumption coefficient matrix Value-added Value-added Total output by industry Total output by industry X 9 - WAPE (%) X 19 - WAPE (%) Whole matrix Whole matrix Intermediate use matrix Intermediate use matrix Direct consumption coefficient matrix Direct consumption coefficient matrix Final use matrix Final use matrix Final consumption coefficient matrix Final consumption coefficient matrix Value-added Value-added Total output by industry Total output by industry X 9 - WAPE (%) Whole matrix Intermediate use matrix Direct consumption coefficient matrix Final use matrix Final consumption coefficient matrix Value-added Total output by industry
11 Index of Sensitivity of Dispersion Index of Sensitivity of Dispersion Is there any bias in IPD/ISD analysis? (1) China KLEMS 1995 WIOD Index of Power of Dispersion Index of Power of Dispersion 11
12 Index of Sensitivity of Dispersion Index of Sensitivity of Dispersion Is there any bias in IPD/ISD analysis? (2) China KLEMS 2000 WIOD Index of Power of Dispersion Index of Power of Dispersion 12
13 Index of Sensitivity of Dispersion Index of Sensitivity of Dispersion Is there any bias in IPD/ISD analysis? (3) China KLEMS 2005 WIOD Index of Power of Dispersion Index of Power of Dispersion 13
14 Is there any bias in DPIC analysis? (1) Table DPIC by Final household consumption China KLEM S WIOD Agriculture M ining Construction Food and related Textiles, Leather, Apparel Wood and related Paper, printing and publishing Petroleum Chemicals & Rubber Other mineral M etals M achinery Electrical & Electronics Transport equipment Misc. manufacturing Utility Trade Transportation services Communications Finance, insurance and real estate Other private services Public services Total
15 Is there any bias in DPIC analysis? (2) Table DPIC by Gross Capital Formation (GCF) China KLEM S WIOD Agriculture M ining Construction Food and related Textiles, Leather, Apparel Wood and related Paper, printing and publishing Petroleum Chemicals & Rubber Other mineral M etals M achinery Electrical & Electronics Transport equipment Misc. manufacturing Utility Trade Transportation services Communications Finance, insurance and real estate Other private services Public services Total
16 Is there any bias in DPIC analysis? (3) Table DPIC by Exports (EX) China KLEM S WIOD Agriculture M ining Construction Food and related Textiles, Leather, Apparel Wood and related Paper, printing and publishing Petroleum Chemicals & Rubber Other mineral M etals M achinery Electrical & Electronics Transport equipment Misc. manufacturing Utility Trade Transportation services Communications Finance, insurance and real estate Other private services Public services Total
17 What are the sources of Japan-China IO tables? National Bureau of Statistics of China and Japan International Cooperation Agency compiled a Japan-China IOT 2007 (NBS-JICA) WIOT 40-country IOT 2007 transformed into two-country IOT 2007 Examples of adjustment: Enterprises consumption expenditure Office facilities R&D activity 17
18 How large is the difference between the two Japan-China tables? (1) Aggregate level China-China Japan-Japan Intercountry linkage Table WAPE of Total Output, Value-added Share, Direct Consumption Coefficient and Final Consumption Coefficient Intermediate Use Final Use Total Output Japan China Japan China Japan/China Japan China ROW VA share Output
19 How large is the difference between the two Japan-China tables? (2) Sectoral breakdown of export China Export to Japan (in mil. RMB) NBS-JICA WIOT Japan Export to China (in mil. RMB) NBS-JICA WIOT Other private services Communications Trade Misc. manufacturing Electrical & Instrument Metals Chemicals & Rubber Paper, printing and publishing Textiles, Leather, Apparel Construction Agriculture 0 50, , , , , ,000 Other private services Communications Trade Misc. manufacturing Electrical & Instrument Metals Chemicals & Rubber Paper, printing and publishing Textiles, Leather, Apparel Construction Agriculture 0 50, , , , , , , ,000 19
20 How large is the difference between the two Japan-China tables? (3) Final Use% of export China Export to Japan ( Final Use %) NBS-JICA WIOT Japan Export to China ( Final Use %) NBS-JICA WIOT Other private services Communications Trade Misc. manufacturing Electrical & Instrument Metals Chemicals & Rubber Paper, printing and publishing Textiles, Leather, Apparel Construction Agriculture 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Other private services Communications Trade Misc. manufacturing Electrical & Instrument Metals Chemicals & Rubber Paper, printing and publishing Textiles, Leather, Apparel Construction Agriculture 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 20
21 Is there discrepancy in IPD/ISD analysis? Rank correlation of IPD and ISD between the two series are both at 0.94 with less than 1% significance; Country Industry* IPD IPD ISD ISD NBS-JICA WIOT NBS-JICA WIOT Japan Mining Japan Textiles, Leather, Apparel Japan Petroleum Japan Misc. manufacturing Japan Utility Japan Trade Japan Communications Japan Public services China Mining China Wood and related China Petroleum China Metals China Machinery China Misc. manufacturing China Utility China Trade China Other private services * selected industries with high discrepancies 21
22 Is there discrepancy in inducement analysis? (1) Japan s domestic production is mainly driven by final household consumption (43%) and gross capital formation (23%); while that of China is driven by gross capital formation (34%) and export to RoW (29%); Domestic inducement structure very similar between two tables; Large variation observed at China s inducement of Japanese household and government consumption, and Japan s inducement of Chinese government consumption. (mil. RMB) Domestic Production Inducement Coefficient Japan China Japan China Country Final Demand Items NBS-JICA WIOT NBS-JICA WIOT NBS-JICA WIOT NBS-JICA WIOT Japan Household consumption 26,905,299 28,506,064 1,291, , Japan Government consumption 8,368,342 9,182,963 81, , Japan Gross capital formation 14,336,791 14,443, , , Japan Export to ROW 10,482,602 10,449, , , China Household consumption 327, ,739 20,835,671 19,840, China Government consumption 86, ,082 7,695,792 7,359, China Gross capital formation 913, ,632 28,120,993 27,994, China Export to ROW 698, ,426 23,582,552 24,277,
23 Is there discrepancy in inducement analysis? (2) For both countries, around 97% of gross value-added is induced by domestic final demand; Pattern of discrepancy is identical to that of production inducement. (mil. RMB) VA Inducement Coefficient Japan China Japan China Country Final Demand Items NBS-JICA WIOT NBS-JICA WIOT NBS-JICA WIOT NBS-JICA WIOT Japan Household consumption 15,562,551 16,078, , , Japan Government consumption 4,753,078 5,482,960 24,713 34, Japan Gross capital formation 6,522,918 6,368, , , Japan Export to ROW 4,103,538 4,104,814 82,124 79, China Household consumption 134, ,003 8,020,191 7,919, China Government consumption 35,207 43,071 3,116,921 3,066, China Gross capital formation 361, ,322 8,094,469 8,180, China Export to ROW 275, ,897 6,536,891 6,794,
24 Is there discrepancy in decomposition of energy consumption? Following Miller and Blair (2009) and Zhang and Lahr (2011), we decompose change in gross energy consumption between 1995 and 2005 into 1) change in energy efficiency, defined as energy input per unit of gross output; 2) change in production structure, described by Leontief inverse matrix; 3) change in final demand structure; 4) change in final demand level. Caveat on interpretation: China KLEMS uses double deflation to construct constant price IOT series 24
25 Is there discrepancy in decomposition of energy consumption? Energy efficiency; Final demand structure; China KLEMS WIOD Changes in energy consumption and its determinants China KLEMS IOT, Changes in energy consumption and its determinants WIOT, % 80% 60% 60% 40% 40% 20% 0% -20% -40% -60% total Production structure FD level energy efficiency FD structure 20% 0% -20% -40% -60% total Production structure FD level energy efficiency FD structure 25
26 Is there discrepancy in decomposition of CO2 emission? China KLEMS WIOD Changes in CO2 emission and its determinants China KLEMS IOT, Changes in CO2 emission and its determinants WIOT, % 80% 60% 60% 40% 40% 20% 20% 0% -20% % -20% % -40% -60% total Production structure FD level energy efficiency FD structure -60% total Production structure FD level energy efficiency FD structure 26
27 Is there discrepancy in labor IPD/ISD analysis? Relative position and ranking of IPD and ISD resembles each other closely; Larger variation in ISD than in IPD; China KLEMS WIOT China KLEMS WIOT Industry* Mining Textiles, Leather, Apparel Wood and related Misc. manufacturing Utility Transportation services Communications Finance, insurance and real estate Other private services * selected industries with high discrepancies IPD ISD 27
28 Is there discrepancy in aggregate labor inducement analysis? Increase in household consumption in Agriculture has a predominant yet declining effect on aggregate labor employment, followed by Other private services and Trade; Portrait by two IOT series similar: except for household consumption in 2001, GCF in 1995, and export in 1997, all rank correlations are significant at less than 1%. Spearman's Rho HH con. GCF Export * * * * NOT significant at 10% 28
29 Is there discrepancy in labor compensation inducement analysis? Spearman's High-skilled Medium-skilled Low-skilled Rho HH con. GFC Export HH con. GFC Export HH con. GFC Export * * 0.40* -0.09* * * 0.50* * * * 0.16* * 0.06* * * * * 0.40* * * 0.17* 0.44* 0.21* * * 0.15* 0.17* * * 0.40* -0.48* 0.22* * -0.46* * NOT significant at 10% 29
30 Conclusion Overall comparable and generate similar patterns in inducement analysis; From sectoral perspective, service industries and heavily state-owned sectors are more problematic; Sectoral level discrepancy High (6) Medium (5) Low (11) Mining Construction Agriculture Wood and related Other mineral Food and related Other private services* Machinery Textiles, Leather, Apparel Misc. manufacturing* Utility* Paper, printing and publishing* Trade Transportation services* Petroleum Communications Chemicals & Rubber Metals Electrical & Electronics* Transport equipment Finance, insurance and real estate Public service * with concordance issue betn. WIOT and China KLEMS From year perspective, and seem to be with higher variation; Implication is research-question-driven: e.g. energy and utility analysis. 30
31 Q & A Thank you! 31
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