Do urbanization and public expenditure affect productivity growth? The case of Chinese Provinces

Abstract

In this paper, we examine the effect of infrastructure financing (public expenditure) and urbanization on productivity using a new concept. We call this concept Urban Kuznets curve (UKC) and define it as an inverted U-shaped relationship between per capita GDP and urbanization. We examine the UKC relationship from several angles. First, we explore its existence from a data driven approach (the scatter plot of log per capita GDP against urbanization). We then examine it after controlling for the input factors and a time trend. The first approach is ad hoc because no formal economic model is used. The second approach is grounded in the microeconomic theory with state-of-the-art econometric tools instead of using some popular ad hoc regressions where (log) per capita GDP is regressed on public expenditure and urbanization along with some other controls. In our pursuit of understanding the various channels through which urbanization may affect productivity, we use parametric and semiparametric partially linear econometric approaches. In the parametric approach we treat urbanization in the same way as inputs (capital, labor, etc.) and compute optimum levels of urbanization to examine whether the benefits of urbanization are exhausted for any of the Chinese provinces during our study period. In the semiparametric partially linear models, the UKC is detected from the estimated elasticity of urbanization without calculating the optimal levels of urbanization. For this study we use the Chinese province level An earlier version of this paper was presented at the ADBI workshop titled ''Urbanization in Asia: Economics and Social Consequences'' held in Sogang University, Seoul, on 15-16 December 2016. I would like to thank the participants of the workshop for their comments and the organizers for the invitation to present the paper.

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Regional Economic Efficiency and Its Influencing Factors of Beijing-Tianjin-Hebei Metropolitans in China Based on a Heterogeneity Stochastic Frontier Model

“…Researchers mainly investigated TFP and its contribution to economic growth on different spatial and temporal scales. From a macro-regional perspective, studies show that regional TFP has spatial differences (Liu et al, 2012;Otsuka and Goto, 2016;Akihiro, 2017) and that economic efficiency is significantly affected by a variety of factors, including knowledge capital (Cheng and Chen, 2017), technological progress (Diewert and Fox, 2018), economic agglomeration (Ehrl, 2013), urbanization (Kuo et al, 2010), and foreign investment (Kumbhakar, 2017). On the scale of a city cluster and city, studies show that the economic performance of a city increases significantly, the economic efficiency of a city is closely linked with its urban level (Zhao et al, 2016;Huang et al, 2017), technological progress is the main driving force of regional TFP (Bergeaud et al, 2018), and the growth rate of urban TFP follows the sequence: large city > small city > medium-sized city (Han et al, 2017).…”