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on China |
| By: | Yinhua Mai; Philip Adams |
| Abstract: | This study analyses the effects of removing Tariff Rate Quota (TRQ) and other barriers on wool imports into China using the Monash Multi-Country (MMC) model, a dynamic Computable General Equilibrium Model of Australia, China and the Rest of the World. The study suggests that TRQ on greasy wool represents the most restrictive barriers to wool imports into China, if the current level of quota holds. The elimination of TRQ on greasy wool is found to boost Chinese imports of wool from Australia and Chinese exports of textiles and clothing products to the Rest of the World significantly. The Australian wool and Chinese textiles and clothing industries stand to gain from the elimination of TRQ on greasy wool. Both countries also gain in terms of a slightly higher growth in real GDP and real GNP due to the elimination of TRQ on wool imports into China. |
| Keywords: | Wool, China, FTA |
| JEL: | D58 F15 Q17 |
| Date: | 2005–10 |
| URL: | http://d.repec.org/n?u=RePEc:cop:wpaper:g-156&r=cna |
| By: | Xiaoyu Shi; Karen R. Polenske |
| Abstract: | Since the start of its economic reforms in 1978, China's energy prices relative to other prices have increased. At the same time, its energy intensity, i.e., energy consumption per unit of Gross Domestic Product (GDP), has declined dramatically, by about 70%, in spite of increases in energy consumption. Is this just a coincidence? Or does a systematic relationship exist between energy prices and energy intensity? In this study, we examine whether and how China’s energy price changes affect its energy intensity trend during 1980-2002 at a macro level. We conduct the research by using two complementary economic models: the input-output-based structural decomposition analysis (SDA) and econometric regression models and by using a decomposition method of own-price elasticity of energy intensity. Findings include a negative own-price elasticity of energy intensity, a price-inducement effect on energyefficiency improvement, and a greater sensitivity (in terms of the reaction of energy intensity towards changes in energy prices) of the industry sector, compared to the overall economy. Analysts can use these results as a starting point for China's energy and carbon emission forecasts, which they traditionally conduct in China without accounting for energy-intensity changes. In addition, policy implications may initiate new thinking about energy policies that are needed to conserve China's energy resources and reduce carbon emissions. |
| Date: | 2006–05 |
| URL: | http://d.repec.org/n?u=RePEc:mee:wpaper:0606&r=cna |
| By: | R.S. Eckaus |
| Abstract: | As oil prices rose in 2004, a large part of the blame was laid at the feet of the emerging colossus of the East. Newspaper stories wrote of the, “surging,” and, “insatiable demand,” coming from China, describing it as the, “engine of oil demand growth,”1 and explaining the change, "More than a billion Chinese are joining the oil market…. How can prices go down?”2 There were moderately dissenting voices, e.g., from a professional at the International Energy Agency, "It is neither fair nor accurate to blame China for most of the rise in oil prices.3 The measured increases in China’s international oil imports are based on international data and are quite real and not related to the probable overestimates of China’s overall rate of economic expansion. The very high growth rates of Chinese oil imports in 2004 and previous years are shown in Table 1. The implied growth rates are so high as to be almost unbelievable. From the fourth quarter of 2003 to the third quarter of 2004, there was a 30 per cent increase in crude oil imports. Such a high growth rate is not the way economies, in general, actually behave or, in particular, the manner in which the Chinese economy has functioned in the past, even in the course of its remarkable expansion. Yet the growth is real, so how can it be explained? That is the puzzle! |
| Date: | 2004–12 |
| URL: | http://d.repec.org/n?u=RePEc:mee:wpaper:0422&r=cna |
| By: | Yinhua Mai; Philip Adams; Mingtai Fan; Ronglin Li; Zhaoyang Zheng |
| Abstract: | In this study, we simulated three potential scenarios of an Australia-China Free Trade Agreement (FTA): removal of border protection on merchandise trade, investment facilitation, and removal of barriers to services trade. The analytical framework is a multi-country, multi-sector computable general equilibrium model, the Monash-Multi-Country (MMC) model. The FTA is found to deepen the two-country's economic partnership developed in the past fifteen or so years. On one hand, it sharpens the competitiveness of the Chinese manufacturing sector by reducing its costs of intermediate inputs. On the other hand, it raises the welfare of Australian consumers through improved terms of trade. In achieving a better utilisation of resources, adjustment of labour between sectors does occur. However, such adjustment is small in scale compared with what is occurring in the two countries amid globalisation without an FTA. |
| Keywords: | China, Australia, FTA, investment liberalisation |
| JEL: | D58 F15 F21 O53 |
| Date: | 2005–10 |
| URL: | http://d.repec.org/n?u=RePEc:cop:wpaper:g-153&r=cna |
| By: | Yinhua Mai |
| Abstract: | Computable general equilibrium models have been widely applied in analysing the effects of removing tariffs. However, not nearly as much effort has been devoted to their application on investment liberalisation that is increasingly an integral part of trade liberalisation agreements. The Monash-Multi-Country (MMC) model is developed to meet such policy needs. The MMC model is an advanced dynamic CGE model with bilateral investment flows between countries/regions modelled explicitly at an industry level. This paper describes the model structure and data of the MMC model. Its application is illustrated by a simulation of a potential investment liberalisation in China's oil industry. |
| Keywords: | China, oil industry, investment liberalisation, CGE modelling |
| JEL: | D58 F15 F21 |
| Date: | 2005–10 |
| URL: | http://d.repec.org/n?u=RePEc:cop:wpaper:g-150&r=cna |
| By: | Dennis Ahlburg (Leeds School of Business, University of Colorado-Boulder); Eric R. Jensen (Department of Economics, College of William and Mary); Ruyan Liao (Carlson School of Management, University of Minnesota) |
| Abstract: | This study uses the China Healthy Longevity Survey of Oldest-Old to investigate the health status of the oldest-old in China. We found that the different measures of health collected in the survey were only moderately related. That is, there is not a single construct called "health". We found that work history was modestly related to some measures of health. We also found that childhood health and socioeconomic status were correlated with health even at advanced ages. To the best of our knowledge, this is the first study to examine this connection in developing countries and at such advanced ages. |
| Keywords: | Aging, oldest-old, longevity |
| JEL: | J14 I10 |
| Date: | 2006–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:cwm:wpaper:34&r=cna |
| By: | Harry X. Wu |
| Abstract: | The Chinese statistical authorities have recently adjusted the Chinese GDP level and growth rate for the period 1993-2004 following China's first national economic census. However, their methodology used in the adjustment is opaque. Using a trend-deviation interpolation approach, this study has managed to replicate the basic procedures of the adjustment and reproduced the official estimates. Through this exercise, it has found that the estimates that could be obtained by the normal interpolation procedures were significantly and arbitrarily modified to satisfy certain needs. Based on some political economy argument, we attempt to explain why the adjustment had to leave the growth rate of 1998 intact and why it had to bypass the price issue and directly work on the real growth rate adjustment. Based on previous studies and other observations, we also challenge the census results on non-service industries. |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:hst:hstdps:d06-176&r=cna |