An integrative empirical analysis of several regional economic outcome variables in China for the period of 1995–2013 reveal the major sources of regional growth differences in China. Patterns of growth in population, per capita income, gross regional product, housing prices and changes in unemployment rates are identified using principal components analysis. Regression analysis of principal component scores is applied to identify geographic and administrative status patterns in the sources of the growth. The analysis suggests that shifts in labor supply largely were responsible for the regional growth differences over the period, though shifts in labor demand were nearly equally as important. The results have implications for evaluating the success of regional development policies such as the Western Development Strategy.
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Chen (2016) constructs a multiple factor panel model to examine differences across 28 major regions in China for 1953–2013. However, the study focuses solely on per capita GRP and identifies supply shocks as those having permanent effects, while demand shocks are assumed to have temporary effects.
The second tier consists of autonomous prefectures, autonomous counties and cities that makeup the provinces and autonomous regions in the first tier. The third tier consists of townships, ethnic minority townships, and towns that makeup the counties, autonomous counties and cities in the second tier.
Because this paper focuses only on mainland China, we exclude the two special administrative regions of Hong Kong and Macao.
In March, 1999, the Chinese Constitution was amended to include private ownership and the rule of law (Tung 2005).
Morrison (2015) traces many of the policies implemented by China since 2006 to the document, The National Medium- and Long-Term Program for Science and Technology Development (2006–2020).
Chongqing was included in the Sichuan province until year 1997. Thus, we use 1997 as the beginning year to calculate Chongqing’s growth rate. For Tibet, we use 1996 as the beginning year to calculate the growth rate because 1995 data were missing.
The online database for rural and urban population is only available since 2005 and afterward. Therefore, we calculated the 2013 urbanization ratio using rural and urban population. For 1999, we adopted the ratio from http://www.doczj.com/doc/886469aad0d233d4b04e6916.html.
The principal components and regression analysis were conducted using the EViews 7 program.
Because of the descriptive nature of the regression analysis, and broad geographic scope of the regions, we do not estimate the equation using spatial econometric techniques.
Chongqing is ranked 23rd for its first principal component score. In order, Heilongjiang and Guangdong are the other two regions with scores above one for the first principal component.
Dropping Tibet from the regression causes the positive coefficient for the Northwest to become statistically significant (\(t=2.58\)), while the negative coefficient for the Northeast remains as the only other statistically significant coefficient.
The third principal component did not exhibit a readily identifiable pattern of correlations with the outcome variables: positive correlations for population, unemployment, per capita income and gross regional product; and approximately no correlation with housing prices. The fifth principal component has an eigenvalue of less than 0.5 and also does not exhibit a readily identifiable pattern of correlations: positive correlations with population, housing prices and per capita income; and negative correlations with gross regional product and unemployment.
The results of estimating sub-periods 1995–2004 and 2005–2013 showed some differences across the two periods. The first sub-period though was the most positively correlated with the overall period. Changing economic patterns may be related to government programs in the West (Wu 2007) or by regional forces (Wei 2002).
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Wang, H., Rickman, D. Regional growth differences in China for 1995–2013: an empirical integrative analysis of their sources. Ann Reg Sci 60, 99–117 (2018). https://doi.org/10.1007/s00168-017-0847-0