THE EFFECT OF INNOVATION SUBSIDIES ON REGIONAL TRANSFORMATION: TAKING THE ECONOMIC TEXTURE INTO ACCOUNT

Published: 10-02-2020| Version 1 | DOI: 10.17632/9fznjcx6p9.1
Contributor:
Li Fengshu

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These data are original data for threshold regression. The data includes independent variables, dependent variables, threshold variables, and control variables. The names of each variable can correspond to the original manuscript.

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his study investigates the relationship between government subsidies, average firm size, and transformation rates in a region by focusing on China’s high-tech industry. It is defined as an industry that produces high-tech products with modern cutting-edge technology and it is characterized by intensive knowledge and technology and low energy consumption (Hong et al, 2016). According to the China Statistics Yearbook on High Technology Industry, China's high-tech industrial sector consists of five sub-sectors, namely (1) pharmaceutical industry (2) aircraft and spacecraft, (3) electronic and communication equipment, (4) computer and office equipment, and (5) medical equipment and instrument manufacturing industry. In general, the high-tech industry is one of the main industries in a knowledge-based economy and is the most important force for the transformation of technology patents into economic value (Chen et al., 2018). China's national and local governments are keen to develop its high-tech industry by providing substantial R&D grants and facilitating/improving transformation in this sector. As such, we believe it is a relevant context to investigate our research question. This study uses data on the high-tech industry of 27 provinces and cities in mainland China during 2009-2016. Although there are 31 provinces and autonomous regions in mainland China, China’s regional economic development is unbalanced. Some regions, namely Tibet, Xinjiang, Inner Mongolia and Qinghai have very little high-tech activities and, as a result, the data on these four regions is largely missing. Removing these four regions, leads to 27 remaining provinces and cities, distributed in three major regions of China. The panel data used in this study are obtained from three official sources: China statistics yearbook on high technology industry (2010-2017), China statistical yearbook on science and technology (2010—2017) and China statistical yearbook (2010—2017), which cover the annual data from 27 provinces and autonomous regions. We use the interpolation method and the average value to supplement missing values (including for the calculation of the dependent variable as described below). We use Stata software for empirical analysis, mainly using a threshold regression model.