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|Title:||The productivity dynamics of Chinese manufacturing firms||Authors:||Wang, Zhifeng||Keywords:||DRNTU::Social sciences||Issue Date:||2017||Source:||Wang, Z. (2017). The productivity dynamics of Chinese manufacturing firms. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||The Chinese economy has been growing at a very high speed rate since 1978. Productivity growth is considered as the engine of the long-run sustainable economy growth. Unlike the previous researches that use aggregate level data to investigate the contribution of productivity growth, this thesis uses a firm-level data set of Chinese manufacturing sector. The advantage of using the micro-level data is that we can study questions with macro-level implications by estimating firm-level production function instead of imposing a growth accounting framework. Chapter 1 provides the motivation of this thesis. Chapter 2 aims to quantify the contribution of productivity growth in the Chinese manufacturing sector. Using a growth accounting framework, Zhu (2012) argues that China's total factor productivity (TFP) growth is mainly driven by resource reallocation due to market liberalization and institutional reforms. How much has the growth of Chinese manufacturing sector been driven by TFP growth? What's the contribution of resource reallocation? What is the distribution of productivity across different ownership types and regions? This chapter answers these three questions using Chinese manufacturing firms spanning 1998-2007. In particular, we empirically employ three production function estimation methods, i.e., ordinary least squares (OLS hereafter) , Ackerberg, Caves and Frazer (2006, 2015) (ACF hereafter) and Blundell and Bond (2000) (BB hereafter). Chapter 3 focuses on the productivity of public infrastructure investment. The role of public infrastructure in promoting economic growth is still under investigation. This issue has become more important after the burst of global financial crisis since 2008. Existing studies mainly use macro-level data, and thus to tackle the inherited reverse causality problem becomes a challenging task. In this chapter, we employ a model of endogenous productivity to calculate the return rate of public infrastructure investment. It matches the firm-level data with the province-level public infrastructure investment data to address those identification challenges. With the constant elasticity of substitution demand system, the short-run Keynesian demand effect can be separated from the long-run productivity effect. The estimated return rates of infrastructure are 9.2% and 2.5% for revenue-based and quantity-based total factor productivity. If spillover effects are considered, the return rates almost triple. Chapter 4 investigates the dynamic learning by exporting effect in Chinese manufacturing firms. International trade plays a key role in promoting China's economic growth during 1998-2007. The productivity of exporters in China, however, is found to be unexceptional in literature. Previous researches usually focus on the comparison of the productivity of exporters with that of non-exporters. This research examines the dynamic learning by exporting effect. We find that the dynamic learning by exporting effect is significantly heterogeneous across industries. Only several industries significantly gain productivity growth advantage through exporting. Processing-trade firms have lower productivity growth in most of industries. The learning by exporting effect is positively related with firm's capital intensity. The protection policy in the international trade also contributes to the lower productivity growth rate of exporters. Chapter 5 summarizes our results and contributions.||URI:||http://hdl.handle.net/10356/72856||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
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