Jiang China

Trade, Foreign Direct Investment, and Development Strategies
1. Auflage 2014
ISBN: 978-1-78063-443-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band 63, 232 Seiten

Reihe: Chandos Asian Studies Series

Trade and foreign direct investment (FDI) play a crucial role in shaping China's development strategies. The development of China's economy creates strong pressures for continuous restructuring. China therefore explores how trade and FDI affect China's development strategies regarding the process and pattern of restructuring. The book aims to provide the reader with findings that shed new light on the related issues and problems. The first chapter introduces the topics, and subsequent chapters enter into theoretical discussion and empirical analysis of trade, FDI, and development strategies, followed by a concluding final chapter. - Based on a rigorous theoretical framework - Applies various statistical and econometric methods - Uses rich and comprehensive data

Yanqing Jiang is associate professor at Shanghai International Studies University, in Shanghai, China. His recent research is mainly on China's opening up, growth and development. He started his research in this area in 2004 when he was affiliated to the Hanken School of Economics and the Helsinki Centre of Economic Research as a doctoral researcher in Helsinki, Finland. He has published many articles on China in various refereed journals.

Autoren/Hrsg.

Jiang, Yanqing

Leseproben

2 Regional growth and its decomposition
Abstract
Along with China’s rapid economic growth in the past few decades, substantial disparities have emerged in productivity and per-capita income across the different regions of China. In this chapter, in preparation for subsequent examinations of regional development and spatial inequality in China, we construct a theoretical framework within which regional growth in total output can be broken down into the growth of its various contributors. As unbalanced growth and development in China can be seen to be a result of the uneven regional growth of productivity and production factors, we propose a coherent framework, not only to investigate the potential forces shaping the pattern of China’s spatial disparities, but also to evaluate their relative importance by quantitatively breaking the inequality down into its constituent parts. In sum, our discussion in this chapter constitutes an analytic foundation on which our analyses in subsequent chapters can be built. Key words economic growth productivity inequality growth decomposition intensive growth extensive growth JEL classification codes O47 O571. Introduction
Before 1978, China had a centrally planned economy, characterized by low productivity, widespread poverty, and very low inequality in income. Thanks to the post-1978 reforms, China has achieved spectacular economic growth in the ensuing 35 years. However, great disparities have emerged in productivity and per-capita income across the different regions of China. The Gini coefficient, for example, which measures economic inequality in society, rose by about 40 percent in total from 0.33 in 1980 to 0.46 in the early 2000s (Sisci, 2005; WB, 2005; Fan and Sun, 2008; Knight, 2008). Such a rate of increase, according to the World Bank, was the fastest in the world. Spatial income disparities, especially those between urban and rural areas and between coastal and inland regions, have been on the rise and became a prominent issue in China during the country’s transition and development (Yin, 2011). By the end of the 1990s, interregional income inequality had exceeded that in any other country, and by 2005 the average per-capita income of the richer coastal regions was at least 2.5 times higher than that of inland regions (Yang, 1999; Zhu et al., 2008). Some researchers claim that the growing inequality may ‘threaten the social compact and thus the political basis for economic growth and social development’ (Fan et al., 2009). Why have some regions in China become so much richer than others? In spite of regional preferential policies, there are a number of other factors that may also play a role in shaping interregional income inequality. These factors, often interrelated, may include geographical differences (Demurger et al., 2002), regional infrastructure development (Demurger, 2001), regional openness and the process of globalization (Zhang and Zhang, 2003; Kanbur and Zhang, 2005; Wan et al., 2007), development of the regional industrial mix (Huang et al., 2003), openness and development of regional township and village enterprises (Yao, 1997; DaCosta and Carroll, 2001), the process of marketization (Jian et al., 1996), effects of regional structural shocks and structural transformation (Jiang, 2010), and investment in and accumulation of regional human capital (Fleisher et al., 2010), to name a few. The influencing factors just listed may contribute to interregional income inequality through their impacts either on regional growth of productivity or on regional accumulation of physical and human capital. Differential rates of regional productivity growth and regional physical and human capital accumulation will lead to different rates of regional output growth and ultimately shape the pattern of the evolution of interregional income inequality across China’s different regions. Therefore, in order to empirically examine regional development and interregional inequality in China, we first need to construct a theoretical framework within which regional growth in total output can be broken down into growth of its constituent parts. A theoretical framework for output decomposition
In this section we apply the Solow growth model and break output growth down theoretically. We can use this framework to empirically examine interregional inequality in China. Moreover, we augment the traditional Solow model by incorporating human capital into the aggregate production function. Specifically, we assume that, at any given point in time, output is produced according to the following function =FKAH   (2.1) where Y denotes the level of output, K denotes the level of physical capital stock, H denotes the level of human capital–augmented labor used in production, and A denotes the level of productivity (technology), which is, for convenience, assumed to be labor augmenting (Harrod neutral). As A and H enter the production function 2.1 multiplicatively, we refer to AH as effective labor. We further assume that each unit of labor (each worker) is identical within the economy and is trained with E years of education. That is, human capital intensity is determined by =exp?E   (2.2) where human capital intensity h is defined as per-worker human capital (i.e., h = H/L). By assuming ?(0) = 0, the function ?(E) reflects the relative efficiency of a worker with E years of education compared with one who receives no education (see, for example, Hall and Jones, 1999). In order to make the model workable, we have to assume that the production function 2.1 exhibits constant returns to scale in its two arguments: physical capital and effective labor.2 This assumption allows us to work conveniently with the production function in intensive form. We therefore define ^=K/AH and ^=Y/AH, and under the assumption of constant returns to scale we have KAH1=1AHFKAH   (2.3) which can be rewritten in the intensive form as ^=fk^   (2.4) where we define k^=fk^1. Thus we can write output per unit effective labor as a function of physical capital per unit effective labor. We assume that k^ satisfies 0=0,f'k^>0, and ?k^<0, which implies that the marginal product of physical capital is positive, but that it declines as capital (per unit effective labor) rises. The model distinguishes three sources of variation in per-worker output Y/L: differences in per-worker physical capital K/L, differences in technology A, and differences in per-worker human capital h. It follows directly from Eq. 2.1 that =FkAh   (2.5) where y = Y/L and k = K/L, which are defined as per-worker output and per-worker physical capital, respectively. An accounting approach can thus be applied to account for variation in per-worker output y in terms of per-worker physical capital k, technology A, and per-worker human capital h, provided that the functional form of Eq. 2.1 is specified. If we adopt the well-known Cobb–Douglas functional form Y = F(K, AH) = Ka(AH)1-a, then =FkAh=kaAh1-a   (2.6) Taking logs then yields y=alnk+1-alnh+1-alnA   (2.7) In terms of growth rates, we have ?y=ak?k+1-ah?h+1-aA?A   (2.8) where a dot over a variable indicates the first-order derivative with respect to time. Thus the growth rate (or level) of per-worker output can be accounted for by the growth rates (or levels) of technology, per-worker physical capital, and per-worker human capital. However, the growth accounting framework has a serious shortcoming. It ignores the causal linkage between the growth (or level) of technology (or per-worker human capital) and the growth (or level) of per-worker physical capital. To understand this point, we need to consider the dynamics of the Solow model. We further assume that technology A and human capital–augmented labor H grow exogenously at constant rates ?/A=g   (2.9) ?/H=?   (2.10) Output can be...