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We estimate productivity growth for 33 industries covering the entire Chinese economy using a time series of input-output tables covering 1982-2000. Capital input is measured using detailed investment data by asset and labor input uses demographic information from household surveys. We find a wide range of productivity performance at the industry level. We then show how these industry growth accounts may be consistently aggregated to deliver a decomposition of aggregate GDP growth. For the 1982-2000 period aggregate TFP growth was 2.5 percent per year; decelerating from a rapid rate in the early 1980s to negative growth during 1994-2000. The main source of growth during the 1982-2000 period was capital accumulation, with a small negative contribution from the reallocation of factors across industries. IntroductionWhile it is widely agreed that the Chinese economy has grown rapidly since the reforms started in 1978, there is disagreement about both the magnitude and sources of that growth. Was the dominant factor the accumulation of capital, total factor productivity growth, or the restructuring of the economy from agriculture to manufacturing and services? A question related to the structural transformation of the economy is how estimates of aggregate GDP growth may be reconciled with the estimates at the industry level. These questions are difficult to answer given the quality and quantity of data available. The answers to them, however, are important in understanding the effects of past economic policies and hence to devise future policies.Note: We thank two anonymous referees for helpful comments. We also thank members of the National Accounts Department, NBS, who helped us with the data: Xu Xianchun, Qi Shuchang, Liu Liping, Dong Lihua and Zhao Tonglu. We are also grateful for the assistance of Li Xiaoqin and Ma Xiangqian from Beihang University. *Correspondence to: Jing Cao, School of Economics and Management, Tsinghua University, 100084, Beijing, P.R. China (caojing@sem.tsinghua.edu.cn). 485This paper estimates the sources of growth of industry output-the growth of capital, labor and intermediate inputs, and total factor productivity (TFP). To do this we introduce newly developed data, including a time series of input-output tables and estimates from a survey of the labor force. Our measures account for the changing composition of the labor force and investment. The second aim of the paper is to discuss how these industry measures may be aggregated to GDP. We describe three aggregation approaches to highlight the methodological issues of separating out the roles of factor accumulation, factor reallocation and sectoral total factor productivity growth: (i) aggregate production function; (ii) aggregate production possibility frontier (PPF); and (iii) direct Domar-weighted aggregation. The first approach may be familiar to many readers; the aggregate PPF method relaxes the strict assumptions of that approach and allows us to identify the effects of reallocating value-added across industries. The third method...

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The crowd panic and its contagion play non-negligible roles at the time of the stock crash, especially for China where inexperienced investors dominate the market. However, existing models rarely consider investors in networking stocks and accordingly miss the exact knowledge of how panic contagion leads to abrupt crash. In this paper, by networking stocks of sharing common mutual funds, a new methodology of investigating the market crash is presented. It is surprisingly revealed that the herding, which origins in the mimic of seeking for high diversity across investment strategies to lower individual risk, will produce too-connected-to-fail stocks and reluctantly boosts the systemic risk of the entire market. Though too-connected stocks might be relatively stable during the crisis, they are so influential that a small downward fluctuation will cascade to trigger severe drops of massive successor stocks, implying that their falls might be unexpectedly amplified by the collective panic and result in the market crash. Our findings suggest that the whole picture of portfolio strategy has to be carefully supervised to reshape the stock network.

This paper presents a detailed bilateral comparison of GDP between China and the U.S. with 1986 as a reference date, using the purchasing power parity (PPP) approach formulated by the United Nations International Comparison Program (ICP). An estimate of PPP over GDP made for Chinese currency in this study was used t o estimate China's dollar per capita GDP in 1986 and 1991. The specific issues in the comparisons of the housing and the comparison‐resistant services categories were discussed and an approach similar to the estimation of shadow rent was exercised. The possible errors in the bilateral comparison were analyzed.

Abstract:In response to the call of the Chinese government to support low-carbon development, the issue has come to the view gradually as to whether the behaviors of banks' green credit will contribute to easing their own credit risk. To reflect the behaviors of green credit of banks in detail, an indicator, named the carbon intensity of loans (CIL), is first proposed in this paper to measure the carbon emissions with association of the loans for commercial banks, on basis of the series of the input-output table. Then, a panel data model is used to explore the relationship between CIL and non-performing loan ratio, which measures the credit risk of banks. Based on the data of China's commercial banks from 2007 to 2014, an empirical study has been conducted to investigate the impacts of CIL upon the non-performing loan ratio from a microscopic-level perspective. The result indicates that CIL has a positive effect on the non-performing loan ratio of banks. Since CIL is considered a significant indicator for the banks' green credit, this paper comes to a conclusion that the green credit policy not only contributes to achieving of the emission-reduction targets for the society, but also promotes the development of banks' credit risk.

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