1. Introduction to CIP/China KLEMS Database Harry X. Wu Institute of Economic Research Hitotsubashi University (harry.wu@ier.hit-u.ac.jp)harry.wu@ier.hit-u.ac.jp Prepared for Asia KLEMS Database Meeting, Tokyo, Oct 17, 2014

2. This Introduction is based on the following forthcoming RIETI DPs on CIP/China KLEMS Reconstructing China’s National Accounts and Supply-Use and Input- Output Tables in Time Series 1.“Reconstructing China’s National Accounts and Supply-Use and Input- Output Tables in Time Series” by Harry X. Wu (Hitotsubashi University) and Keiko Ito (Senshu University), forthcoming RIETI DP #/2014 Constructing Employment and Compensation Matrices and Measuring Labor Input in China 2.“Constructing Employment and Compensation Matrices and Measuring Labor Input in China” by Harry X. Wu (Hitotsubashi University), Ximing Yue (Renmin University, Beijing) and George G. Zhang (University of Wisconsin, Madison), forthcoming RIETI DP #/2014 Constructing China’s Net Capital Stock and Measuring Capital Services in China 3.“Constructing China’s Net Capital Stock and Measuring Capital Services in China” by Harry X. Wu (Hitotsubashi University), forthcoming RIETI DP #/2014 Accounting for the Sources of Growth in the Chinese Economy 4.“Accounting for the Sources of Growth in the Chinese Economy” by by Harry X. Wu (Hitotsubashi University), forthcoming RIETI DP #/2014 H. Wu_Asia KLEMS Database Meeting_October 17, 2014 2

3. Contents 1.General background 1)Origin of CIP/China KLEMS 2)Coverage in time and space 3)Classification 4)Methodological framework 5)Sources of the basic data 2.Output and prices, and main results 3.Measuring labor input, and main results 4.Measuring capital input, and main results 5.Accounting for sources of growth 6.Concluding remarks with on-going work 7.Accessibility and acknowledgement H. Wu_Asia KLEMS Database Meeting_October 17, 2014 3

4. 1.1 Background to CIP/China-KLEMS The CIP (China Industry Productivity) Project began in 2010 jointly supported by RIETI and IER at Hitotsubashi University. According to the current arrangement, RIETI’s financial support will be continued up to March 2015. CIP began based on Wu’s China Growth and Productivity Database (Wu/CGPD) project, self initiated in 1995 and heavily involved in Angus Maddison’s work on China focusing on China’s aggregate economy since 1912 and manufacturing, mining and utility industries since 1949 (Maddison 1998 and 2007; Maddison and Wu 2008). From the very beginning CIP has been following the EU-KLEMS and WIOD methodological framework, and extended Wu/CGPD to an economy-wide coverage including agriculture, construction and services for the post-reform period 1980-2010. CIP is currently part of the World KLEMS. Thus, the CIP Project is also called CIP/China KLEMS Project, though our view is that “China KLEMS” can be a platform that any researcher or group may participate and make contribution to its database. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 4

5. 1.2 Coverage Currently, the CIP data work covers the Chinese economy with a breakdown of 37 sectors from 1980 to 2010. It aims to include all economic activities that are supposed to be covered by the national output accounts, as well as the corresponding income and use (in line with the SNA principles), regardless administrative level, size criteria, “formal” or “informal” activities. In other words, we do not allow any “residual” to be ignored or excluded, at least conceptually. Thus, in constructing the CIP data, we focus particularly on identifying and handling discrepancies in concept, coverage and classification in the official statistics, published in different times by different state agencies. The following CIP standard of industrial classification is EU-KLEMS classification compatible and convertible. It is used in the construction of all input and output data in CIP. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 5

6. 1.3 Classification (economy-wide 37 industries) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 6

7. 1.3 Classification … H. Wu_Asia KLEMS Database Meeting_October 17, 2014 7

8. 8 The CIP methodological framework exactly follows the growth accounting methodology as developed by Dale Jorgenson and associates as explained in Jorgenson, Gollop and Fraumeni (1987) and more recently in Jorgenson, Ho and Stiroh (2005), which is also used as the general framework in EU/KLEMS (O’Mahony and Timmer, 2009). It is based on PPF where the gross output of an industry j is a function of capital, labour, intermediate inputs and technology, indexed by time T, that is (Eq.1) Under the assumptions of competitive factor markets, full input utilization, and constant returns to scale, the growth of output can be expressed as the cost-share weighted growth of all inputs and technological change (Eq.2): 1.4 The CIP Data Framework

9. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 9 1.4 The KLEMS Framework … where and Note that the CIP data work is in fact to measure each input variable at its nominal cost to producers, which are controlled by the national accounts in an input-output framework and over the entire time span of the project. Thus, the growth of real output is expressed as:

10. 1.5 Sources of the Basic Data (skip…) Output-wise (DP 1): – Various types of Chinese “national accounts” under MPS and SNA, available in regular report in China Statistical Yearbook (NBS) and two historical data series published by Department of National Economic Accounts (DNEA, NBS). – National Input-Output Accounts (DNEA, NBS), available in full SNA- type tables by every five years, starting in 1987 and ending in 2007, and reduced tables extended between two full-table benchmarks – MPS input-output accounts for 1981 (DNEA, NBS). – Industry-level statistics on output as reported in the national and industrial censuses in 1985, 1995, 2004 and 2008, the tertiary sector census in 1992/93, and agriculture census in 2006. – Industry-level producer price indices (PPIs) for manufacturing, mining and utilities, and for agriculture – Relevant items available as CPI components that are used to construct PPIs for different services (DITS/DIS; various price survey teams, NBS). H. Wu_Asia KLEMS Database Meeting_October 17, 2014 10

11. 1.5 Sources of the Basic Data … Employment and compensation (DP 2): – At the national level, numbers employed are available in three broad sectors, namely primary, secondary and tertiary largely based on censuses and census-framework one-percent sample surveys (China Labor Statistical Yearbook, DPES, NBS). – Numbers employed by sectors, with various classifications over time (DPES and DITS/DIS, NBS). – Hours worked available in some population censuses, household income and expenditure surveys and other occasional surveys (Census Offices under State Council and NBS, and CHIP Database). – Data on demographic and human capital attributes from various censuses and household surveys as above listed. – Industry-level total labor compensation from the input-output accounts (DNEA, NBS). – Industry-level wage rate and total payroll from CLSY (DPES, NBS). H. Wu_Asia KLEMS Database Meeting_October 17, 2014 11

12. 1.5 Sources of the Basic Data … Investment and capital stock (DP 3): – At the national level, annual investment flows as GFCF (SNA-concept) are available in the national accounts published in CSY (DNEA, NBS). – Economy-wide and industry-level “total investment in fixed assets (TIFA)” and “newly increased investment in fixed assets (NIFA)” are available in China Fixed Assets Investment Statistics Yearbook (DFAIS, NBS). – Investment price index for the aggregate economy by equipment and structures is available in price statistics (price survey teams, NBS). – Detailed industry-specific asset prices are available for 1980-1998 (MoF) – PPIs of investment goods are available in industrial statistics (DITS/DIS, NBS) – Information on asset-specific service life is available for 2 or 3 benchmarks (MoF) – Data from 1950/51 national asset census (documents from national achieves) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 12

13. 2. Output and Prices Completed data work including: 1.The Chinese national accounts, reconstructed for the period 1980-2010, covering not only VA, but also GO and compensations for capital and labor based on the benchmark IO tables for 37 sectors. 2.Producer price index (PPI), constructed for each of the 37 sectors. 3.The time series of the input-output accounts, reconstructed after… 1)Converted the 1981 MPS IO accounts to SNA IO accounts based on the official 1987 MPS and SNA IO accounts 2)Added external transaction accounts to the existing IO accounts based on trade statistics and balance of payments accounts 3)Constructed benchmark “Supply-Use Tables” for 1981, 1987, 1992, 1997, 2002 and 2007 for which full “output” and “USE” accounts are available 4)Constructed “Supply-Use Tables” time series following the WIOD SUT-RAS approach 4.The real value added (VA), estimated using the standard (double) deflation approach assuming that the producers face sector-specific PPIs as their purchase prices. 13 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

14. CIP Approach to SUT and IOT time series 14 Required Annual data: - Gross output by industry - Exports and imports by product, inventory changes by product (linearly interpolated for non-benchmark years) - Gross output deflators by industry (PPIs) - Value-added to gross output ratios by industry - Control totals and cross industry structures Benchmark SUTs (59 products x 38 industries): -1981, 1987, 1992, 1997, 2002, 2007  Supplementary using benchmark IOTs at detailed industry level SUTRAS program Annual IOTs Converted from the SUTs Annual SUTs Estimated H. Wu_Asia KLEMS Database Meeting_October 17, 2014

15. Converting the 1981 MPS to SNA IOT Differences between MPS IOT and SNA IOT Estimate (2) and (3) : Derived from the differences between MPS IOT and SNA IOT for 1987  1987 industry distribution is applied. Estimate (4) – (16): Derived from the estimated values in (3) and the IO coefficients for 1987  1987 composition of final demand is applied. 15 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

16. Transformation of SUTs to Symmetric industry-by-industry IOTs 16 We employ the transformation methodology assuming that product sales structure is fixed (Model D in the Eurostat Manual of Supply, Use, and Input-Output Tables, 2008 edition, Chapter 11) H. Wu_Asia KLEMS Database Meeting_October 17, 2014

17. MAIN RESULTS ON OUTPUT & PRICES 17 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

18. e.g. 1: Implicit value added deflators - Industry (CIP* vs NBS) 18 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

19. e.g. 2: Implicit value added deflators - “Non-material” services 3 19 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

20. Implicit value added deflators - Total GDP 20 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

21. Thus, size of GDP has been lowered (1981=100) 21 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

22. GDP structure, nominal (NAs, IO based) 22 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

23. GDP structure, in 2000 prices (CIP/China KLEMS, “double deflation”) 23 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

24. 3. Labor Input Completed data work include: 1.The “control totals” for three broad-sectors, reconstructed in numbers by filling all gaps (e.g. “informal” and military personnel, etc.) and handling serious structural breaks (e.g. 1990 break). 2.The “above-size” industry-level employment series, reconstructed as the “hard core”. 3.The “residual” distributed to the most labor-intensive sectors based on censuses and surveys on small enterprises, e.g. village enterprises. 4.The full-dimension “number matrix” (= 2 gen x 7 age x 5 edu x 37 sec), constructed by several “marginal matrices” using available census data for benchmarks with gaps filled by the IPF (iterative proportion filling) approach. 5.The time series of the full-dimension “number matrix”, constructed by interpolations. 6.The full-dimension “hour matrix”, estimated based on all available data from censuses, household surveys (CHIP) and other surveys. 7.Finally, the matching full-dimension “compensation matrix”, constructed using the labor income accounts in the IO tables as “control totals”, cross-classified wage rates (for formal and above size), as well as parameters obtained by estimating Mincer equations. 24 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

25. The CIP Approach to Employment and Compensation Matrices Construction - IPF We adopt iterative proportional filling (IPF) approach to build up full- dimensioned matrix from partial or marginal matrices Assume we have a set of partial matrices, J for a specific benchmark. For simplicity of notation, we also assume the number of elements in set J is J. A matrix j ∈ J is constructed from the intersection of some of the 4 human capital dimensions and the relative number of employment in a specific cell i of j is denoted as e ij. Also denote as an operation of summation of employment number of all cells in a specific partial matrix. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 25

26. MAIN RESULTS ON LABOR QUANTITY & SERVICES 26 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

27. Estimated annual hours worked/per employed in China, compared to the East Asia at the same stage of development (at 1990 PPP$ 2000-8000) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 27

28. Hours worked/head, the previous problem! 28 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

29. 29 Changes of gender ratio of Chinese workforce, 1980-2010

30. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 30 Changes of age structure of Chinese workforce, 1980-2010

31. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 31 Changes of education attainment structure in Chinese workforce, 1980-2010

32. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 32 25-39 aged versus senior-high/above education attainment in Chinese workforce

33. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 33 Changes of the industrial structure in Chinese workforce, 1980-2010

34. Growth of numbers, hours, “quality” and labor input, (1987=100) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 34

35. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 35 Growth of labor input by dimension (1987=100)

36. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 36 Main effects on “quality” change, (1987=100)

37. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 37 Marginal effects by order on “quality” change, The case of “sector effect” (1987=100)

38. 4. Capital Input Completed data work include: 1.Industry-level investment flows for the industrial sectors (manufacturing, mining and utilities), using official end-year stocks at historical costs for formal or “above size” enterprises. 2.Industry-level investment flows for the non-industrial sectors, using official fixed asset investment statistics. 1)The final investment flows are available in two asset types, i.e. “non-residential structures” and “equipment”. 3.The 1980 “initial stock”, based on the 1950-51 asset census and GFCF as “control totals” for 1952-2010. 4.Industry-level “net capital stock”, estimated by PIM using industry-specific depreciation rate (using official asset lives and BEA declining balance rate) and investment deflator (based on the Ministry of Finance assets surveys for 1980- 1998, and extended by PPIs of producer goods industries for 1999-2010). 5.Net capital stock for “below-size” enterprises, estimated by industry-level K/L ratios. 6.Finally, the full capital stock accounts by 37 sectors, constructed by distributing the “residual” between the sum of industry-level capital stocks and GFCF-based national capital stock proportionally 38 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

39. The CIP Approach to Reconstructing Investment Flows (The Industrial Sector) If we denote the SNA concept of GFCF as I, finished fixed assets and transferred from producers to users (investors) in a given period and denote the Chinese TIFA as O=the “ work load ” of investment projects monitored and recorded by the planning authorities, and NIFA as N= “ newly increased FA ” in a given period (i = 0, 1, 2, …  ) A large part of O cannot be put into production in the year of investment, and some of O may never meet production standards or completely wasted, thus, N is better than O  But both O and N contain residential structures and exclude all investment projects less than half million yuan; and both could be over- /under-reported for political or technical 39 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

40. Also, to use N one needs to adjust it for residential structures (  ) and missing/underreporting ( ) (assuming no over-reporting): Problems? Little information on  and ; no detailed industry breakdown; N in a much shorter series compared with O. Summary: none of the official investment indicators, GFCF, TIFA or NIFA cannot be accepted as the “investment” variable as defined in the PIM equation … Further adjustment 40 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

41. Recall that gross capital stock can be defined as:  where S denotes scrapings; therefore, rearranging the equation, the current period investment should be:  In the Chinese case, the investment flow by asset i can be derived as:  PIM is then applied to the estimated I, and for other variables see below… Reconstruction of investment flows 41 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

42. MAIN RESULTS ON CAPITAL STOCK AND SERVICES 42 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

43. Total capital stock Initial stock based on the 1950-51 asset census, adjusted and checked with the steady state estimate GFCF is used, but residential housing is not yet removed Deflator: weighted prices for structures and equipment, higher than NBS implicit investment deflator Depreciation rate: 5, 6, 7% alternatively; 7% is very close to the industrial depreciation rate that is based on the Hulten- Wykoff approach adopted by BEA 43 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

44. GFCF (investment flows) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 44

45. Price deflators for fixed asset investment H. Wu_Asia KLEMS Database Meeting_October 17, 2014 45

46. Net capital stock, national total H. Wu_Asia KLEMS Database Meeting_October 17, 2014 46

47. Industry-specific deflators H. Wu_Asia KLEMS Database Meeting_October 17, 2014 47

48. Annual Change of Net Capital Stock by Industry: Wu (K1/Blue) vs Official (K2/Red) 48 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

49. Estimated Net Capital Stock by Industry: Wu (K1/Blue) vs Official (K2/Red) (1985=100) 49 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

50. Examine quality change of capital input, e.g. “SF&F” industries H. Wu_Asia KLEMS Database Meeting_October 17, 2014 50

51. Examine quality change of capital input, e.g. “Services II” industries H. Wu_Asia KLEMS Database Meeting_October 17, 2014 51

52. More works completed… Estimate the initial stock using the 1950-51 asset census, check by the steady state model results; establish sectoral distribution for 1952 (industry-specific K(52)) Use GFCF to establish national “control totals” in investment and net stock Investment flows for the non-industrial sectors are established using adjusted fixed asset investment statistics (no official stock data available) Industry-specific depreciation rate is estimated using official accounting rules on asset lives and declining balance used in BEA (market economy empirics) Industry-specific deflator is constructed using MOF asset survey information and PPIs of capital goods industries H. Wu_Asia KLEMS Database Meeting_October 17, 2014 52

53. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 53 Our methodological framework exactly follows the growth accounting methodology as developed by Dale Jorgenson (1966) and its application and further development in Jorgenson, Gollop and Fraumeni (1987) and more recently in Jorgenson, Ho and Stiroh (2005) (also see EU/KLEMS, O’Mahony and Timmer, 2009). It is based on PPF where the gross output (not value added) of an industry j is a function of capital, labour, intermediate inputs and technology, indexed by time T, that is Under the assumptions of competitive factor markets, full input utilization, and constant returns to scale, the growth of output can be expressed as the cost-share weighted growth of all inputs and technological change: – Note that this is also our framework for the data construction 5. Productivity

54. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 54 5… …where and The right-hand side of each equation indicates the proportion of output growth accounted for by growth in capital services, labour services, intermediate inputs, and technical change as measured by TFP, respectively. Next, we have to consider the aggregation problem Following JHS (2005) we introduce the Domar weights that take into account the productivity effect of the upper-stream on the down-stream industries (an accumulative effect)

55. 5…The aggregation problem Domar aggregation considers the link between aggregate and industry-level measures, explored by Domar (1961), further elaborated by Hulten (1978). For an industry-wide equivalent, we postulate the existence of an industry-wide PPF that relates available primary factor inputs to deliveries to the final demand. Aggregate productivity change is defined as a shift of the aggregate PPF over time, or the rate of change of A (i.e. TFP), which can be measured as the difference between the rate of change in total final demand (FD) and the rate of change in primary factor inputs (Z=L*K) and imported intermediate inputs (M): H. Wu_Asia KLEMS Database Meeting_October 17, 2014 55

56. 5. … Following the aggregate productivity change as discussed above, the industry-level productivity change can be aggregated as: Finally, Domar’s aggregation formula: A direct consequence of this integration is that weights do not sum to unity, implying that productivity growth amounts to more/less than a weighted average of industry-level productivity growth. This reflects the fact that productivity gains in M do not only have an “own” effect but in addition they lead to reduced or increased prices in the downstream industries, and the effects cumulated. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 56

57. Three important “weights” in the Domar aggregation under APPF 1.The share of industry j in total value added (v V,j ) 2.The value added share in gross output of industry j (w j ) 3.The share of factor income (K and L) in the gross output of industry j (v K,j, v L,j ) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 57

58. Domar-weighted TFP growth and reallocation effect of K and L H. Wu_Asia KLEMS Database Meeting_October 17, 2014 58

59. MAIN RESULTS ON SOURCES OF GROWTH 59 H. Wu_Asia KLEMS Database Meeting_October 17, 2014

60. Some descriptive observations based on the constructed data (value added) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 60

61. 5… H. Wu_Asia KLEMS Database Meeting_October 17, 2014 61

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63. Results by individual group (GO-based growth accounting) H. Wu_Asia KLEMS Database Meeting_October 17, 2014 63

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65. Results: Aggregated in the APPF growth accounting H. Wu_Asia KLEMS Database Meeting_October 17, 2014 65

66. … if from a labor productivity perspective H. Wu_Asia KLEMS Database Meeting_October 17, 2014 66

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68. Results: Sector contributions and reallocation effects H. Wu_Asia KLEMS Database Meeting_October 17, 2014 68

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71. 6. On-going Work… Output – – To adjust income accounts based on estimating “returns on land”. – To decompose income of self-employed into labor and capital income. – To further improve Supply Table by considering secondary products in the “output table”. – To fixe some inconsistencies identified in the derived IO coefficients. – To construct import matrix at product level for 1987 and 1992 IO accounts. Labor – – To improve the estimation for hours worked. – To improve the estimation for agricultural employment based on land use. – To improve the compensation matrix. Capital – – To better reconcile GFCF with reconstructed industry-level investment flows – To estimate land stock and land services – To estimate R&D capital stock and services – To estimate ICT capital stock and services H. Wu_Asia KLEMS Database Meeting_October 17, 2014 71

72. 7.1 Accessibility The CIP/China KLEMS data are eventually “public goods”. Transparency is our primary principle, which ensures that our data work can be fully accessed. CIP will never use any “secret” or “internal” data that cannot be publicized. Our data work and sources of basic data used are given in the “background papers” (the latest version of these papers will be available on line as RIETI Discussion Papers scheduled in October 2014). The first version of the CIP data, CIP Round 1.0, was made on-line available since March 2012 without capital input. It has since been substantially revised. CIP Round 2.1 data are internal, which have been used for Special CIP Meeting on March 20 and May 22, 2014 for evaluation purpose by international experts. CIP Round 2.2, though still not yet finalized, are currently used for several analytical papers at the 3 rd World KLEMS Conference in Tokyo, APPC 2014 in Brisbane, and IIOA 2014 in Lisbon. The finalized CIP 2.2 will become available in Quarter 4, 2014. The scope of the CIP 2.2 is yet to be decided. H. Wu_Asia KLEMS Database Meeting_October 17, 2014 72

73. 7.2 The CIP Team (now and past) The CIP project is led by Harry X. Wu from IER, Hitotsubashi University. The current CIP Team includes Kyoji Fukao, also acting as the project’s co- leader (Hitotsubashi University), Keiko Ito (Senshu University), Tomohiko Iuni (Gakushuin University), Gaaitzen J. de Vries (GGDC, Groningen University), Tangjun Yuan (Fudan University), Ximing Yue (Renmin University) and George G. Zhang (Wisconsin, Madison) The work is currently supported by research assistants, Zhan Li, Tao Liang, Shuai Yuan (all at Hitotsubashi University) and Reiko Ashizawa (Secretary of Wu Office). Researchers and research assistants who previously contributed to CIP and Wu/CGPD include Esther Shea (HK Polytechnic University), Lei Fu (former HK Polytechnic University), Xinxin Ma (Kyoto University), Jingming Wang (Hitotsubashi University), Lena Maruyama (free lance), Xiaoqin Li (former TCB) and Mingxia Zhang (TCB). H. Wu_Asia KLEMS Database Meeting_October 17, 2014 73

74. 7.3 Acknowledgement (Project Reviewing and Continuous Moral Support) First, I would like to thank CIP/China KLEMS reviewers for critical comments and constructive suggestions, including Mun Ho (Harvard), Jiemin Guo (BEA), Bo Meng (IDE, JETRO), Ximing Yue (Renmin), Hiroshi Sato (Hitotsubashi), Tomohiko Inui (Gakushuin), Toshiyuki Matsuura (Keio) and Abdul Erumban (TCB and Graningen). The following institutions and their research leaders have provided persistent support to the CIP/China KLEMS Project, including: – The Groningen Growth and Development Centre (GGDC) and its EU-KLEMS Project, directed by Marcel Timmer, at Groningen University, The Netherlands – The World KLEMS Project, directed by Dale Jorgenson, Harvard University – The Japanese Industrial Productivity (JIP) Project directed by Kyoji Fukao – The Asian KLEMS, organized by Hak Kil Pyo, Seoul National University – The Conference Board (TCB) Headquarter (New York) under Chief Economist Bart van Ark – The Conference Board (TCB) China Center (Beijing) directed by David Hoffman – National Bureau of Statistics (NBS), especially Department of National Accounts, Department of Industrial Statistics and Department of Population and Employment Statistics, under the leadership of Vice Commissioner Xianchun Xu 74 H. Wu_Asia KLEMS Database Meeting_October 17, 2014