Presentation on theme: "China KLEMS The First WORLD KLEMS Conference Harvard University 18-20 October 2010 Ren Ruoen Sun Linlin BeiHang University, China."— Presentation transcript:
China KLEMS The First WORLD KLEMS Conference Harvard University October 2010 Ren Ruoen Sun Linlin BeiHang University, China.
Motivation Whether Chinas rapid growth after 1978 is mainly driven by productivity or factor accumulation. Mostly existing studies of China TFP using aggregate data. This project estimated TFP based on industry data Pursue the guidelines about the policy making for improving industry international competitiveness Analyze the industry contribution to aggregate TFP Find what is the predominant source of China industry economic growth (input growth or productivity)
Goals Compute industry TFP based on the estimation of output, capital input, labor input and intermediate input index Quantify input and TFP contributions to output growth for each industry Highlight data and conceptual issues with industry data
Background In 1986 when I was working on my Ph.D. dissertation I knew Professor Jorgensons papers on rental cost and investment function. In 1988 I started to work on the expenditure-side purchasing power parity (PPP) estimates between China and US, using ICP approach. This study was published in Review of Income and Wealth as Ren Ruoen and Chen Kai An Expenditure - based Bilateral Comparison of Gross Domestic Product between China and the United States.
Background In 1993 I started to work on the production-side purchasing power parity (PPP) estimates between China and US, using ICOP approach and built the time series of comparative labor productivity between the two countries.
Background This study was published by China Economic Review as Adam Szirmai and Ren Ruoen Comparative Performance in Chinese Manufacturing, Since 1994 I started to write a book (Ren Ruoen China's Economic Performance in an International Perspective) for the OECD Development Centre. In this book, I reconciled the two studies on PPPs from expenditure and production sides to develop a estimate of Chinas GDP in US dollar and update it through 1994.
Background The first phase of the current project is ICPA -project funded by the Research Institute of Economy, Trade and Industry (RIETI), Tokyo, Japan, National Natural Science Foundation of China (grant no , , ) and the U.S. Environmental Protection Agency (EPA Work Order 3W NASX).
Background The ICPA – project has been focusing on an international comparison of productivity among Pan-Pacific countries to undertake a broad examination of the sources of economic growth and competitiveness through large-scale, internationally comparable databases of Pan-Pacific countries since 2001.
Background The estimation result is given in a chapter on the China in the book on Productivity in Asia: Economic Growth and Competitiveness edited by Dale Jorgenson, Masahiro Kuroda and Kazuyuki Motohashi, published by Edward Elgar Publishing in 2006 and in Cao, J., M. Ho, D. Jorgenson, R. Ren, L. Sun, X. Yue (2009) Industrial and Aggregate Measures of Productivity Growth in China, , Review of Income and Wealth, Volume 55, issue s1, 2009
Background The second phase of the current project is an international cooperation project on International Comparison of Productivity among China, EU Countries and US and IGEM Study for China funded by National Natural Science Foundation of China (grant no )
Industry Productivity The method used in this study is described in detail in many book and papers (see Jorgenson, Gollop and Fraumeni, 1987, Jorgenson and Stiroh 2000, and Gu and Ho 2000, Jorgenson et al., 2005). The economy is divided into 33 sectors producing 33 different commodities.
Industry Productivity Gross output of sector j is assumed to be produced with a Hicks-neutral production function: Productivity growth
Value-added Function The real value added of sector j is defined as output less an index of intermediate inputs : The following identity is implied:
Input Index Express the growth rate of each input as the weighted average of the growth rates of individual components: Weights are given by the average shares of each component
Input Output Series First phase: Construct the output and input indices for sectors during Second phase: Update the time series to 2005.
Input Output Series The NBS used the Material Product System (MPS) before Transformed to the System of National Accounts (SNA) after The IO time series were constructed with the National Bureau of Statistics of China.
Input-Output Time Series (First phase) Compiled annual nominal value of industry input and industry value-added, the final uses for total consumption, investment and net exports, for Constructed 4 current value benchmark I-O tables (1981,1987, 1992, 1997) including A tables and U tables Consistent with the coverage and definitions of the 1997 I-O table and scaled to the latest GDP series Constructed the current value U tables based on the 4 benchmark tables Constructed the real value U tables based on the current value U tables and price indices Estimated output and intermediate input index time series based on the I-O tables
Input-output series (Second Phase) Based on the suggestions made in our workshop on Oct. 12, 2009, we made some updates to our comparable Input-output table series. We compiled the IO table series from 2000 to 20 05, based on the new 2005 extended IO table and 2004 Census coverage and results. we updated the former 1981 to 1999 IO table series into the same standard of the second phase.
Input-output series (Second Phase) The 2007 benchmark IO table has been published and has been changed in coverage and other details from the 2005 extended IO table. If we decide to expand our comparable IO table series till 2007, we need cooperate with NBS team to look into all the differences between our IO series and their new 2007 benchmark table and build up a new comparable IO series from 1981 to 2007.
Industry Output Index Results Wide range of industry output growth rates varies from %(oil and gas extraction) to 22.10%(electrical machinery) We compare two sub-period, , Some industry output growth decelerate Food and kindred products(11% to 7%); Apparel(19% to 7%); Paper and allied(19% to 11%); Leather(19% to 9% Communication(17% to 0%); Finance(15% to 2%). Some industry output growth accelerate Petroleum and coal products(4% to 11%); Primary metal(9% to 14%); Transportation(9% to 14%); Electric utilities( 9% to 14%)
Industry output Growth(%) Note: Industries sorted by growth, in percentage points. Annex Table A1 in text.
Industry Intermediate Input Index Results Wide range of industry intermediate input and energy input growth rates varies from 7.93%(textile) to 18.08% (electrical machinery) Varies from -0.52%(public service) to 14.16%(electrical machinery) Intermediate input experienced stronger growth than capital input and labor input.
Industry Intermediate Input Index (%) Note: Industries sorted by growth, in percentage points.
Industry Energy Input Index (%) Note: Industries sorted by growth, in percentage points.
Labor Input Index Labor input is a divisia aggregate over workers distinguished by sex, age and education attainment using wages as weights. Sex: male, female Educational attainment: college, high school, junior high school, elementary school, no schooling. Age: 16-34, 35-54, 55+
Labor input index Number of workers for benchmark is based on the Population Cencuses(1982,1990,2000), and Sample Population Surveys(1987,1995,2005). Number of workers of other years is estimated from the Labor Force of Society prior to 1990), the annual Population Change Surveys (since 1990). Hours data is from the 1995 Sample Population Survey, and incorporate the changes in institutional arrangement. Relative costs of different workers using the Chinese Household Income Project (CHIP) Surveys. Sum of different categories of workers is equal to the labor compensation of IO table for each industry.
Industry labor Input Index results Wide range of industry labor input growth rates varies from 1.18%(metal and nonmetallic mining ) to 13.16%(gas utilities) Larger labor growth for labor intensive manufacturing (such as apparel, leather, lumber and wood), as well as energy sectors (gas, electrical), some service( communication, finance). We compare two sub-period, , Labor input increase for all service industry. Labor input fell for agriculture, mining sectors, and some manufacturing (chemical, machinery).
Industry Labor Input Index (%) Note: Industries sorted by growth, in percentage points.
Capital Input Index Estimated the capital stock under the Perpetual Inventory Method with the geometrically declining pattern, classified by asset type – structure, equipment and auto Estimated the capital rental price with the help of the property compensation from the input- output series Aggregation of capital services over different asset types with the weight of capital rental price
Capital input index We adjust the fixed asset investment to gross capital formation of each industry. Asset price index comes from the NBS, the IO table series. Depreciation rate is estimated base on the asset life assumption. We estimate the land capital stock for agriculture. We consider the self-employed compensation problem during the internal rate of return estimation.
Industry Capital Input Index Results Wide range of industry capital input growth rates, s ervice had a high capital accumulation speed. varies from 1.02%(machinery) to 15.91% (communication) We compare two sub-period, , Most manufacturing experience higher capital input. (apparel, lumber, furniture, paper, instrument, etc). Only few industries experience lower capital input. (metal and nonmetallic mining, oil and gas extraction, finance insurance and real estate, other private services).
Industry Capital Input Index (%) Note: Industries sorted by growth, in percentage points.
Industry TFP Results Wide range of industry TFP growth rates varies from -9.71%(oil and gas extraction) to 6.46%(electrical machinery) Many energy industries (oil and gas extraction, gas, petroleum and coal products), and some service (finance, public service, communication) show negative TFP growth rate. Some manufacturing especially ICT manufacturing see high TFP rates.( electrical manufacturing, machinery, motor vehicle, instrument, paper).
Industry TFP Results Compare 2 sub-period, , Some industries TFP slowdown: apparel, paper, leather, transportation equipment, transportation, communication. Few industries TFP accelerate: coal mining, Primary metal, metal and nonmetallic mining.
Industry TFP Growth(%) Note: Industries sorted by growth, in percentage points
Domar-Weighted TFP China has a moderate productivity growth, Domar-weighted TFP growth is estimated at 3.89% , and experienced negative domar-weighted TFP TFP acceleration is obvious during the beginning of reform, Agriculture and electrical machinery – biggest contribution to aggregate TFP among 33 industries agriculture, for the largest average share of gross output to total value-added over Electrical machinery, for the fastest TFP growth rate over
Domar-Wtd Productivity Contributions Note: Industries sorted by productivity contribution, in percentage points.
Industry Source of Output Growth Input growth: predominant source of output growth in most industries Intermediate input growth: primary source of output growth in most industries over , and , TFP growth: primary source of output in most industries over Capital input growth: became a more important source of output growth over the four periods Labor input growth: few industries relied on labor input as primary source of expansion
Predominant Source of Output Growth
Conclusions Domar-weighted TFP growth is estimated at 3.89%. TFP acceleration is obvious during the beginning of reform, Agriculture and electrical machinery – biggest contribution to aggregate TFP among 33 industries. Intermediate input growth is the most important source of growth