{"title":"Regional Resilience in China: The Response of the Provinces to the Growth Slowdown","authors":"Anping Chen, N. Groenewold","doi":"10.52324/001c.35253","DOIUrl":null,"url":null,"abstract":"Since 2007 China’s real GDP growth rate has slowed from a level of over 10% per annum to below 7%. Given China’s regional diversity, an important aspect of the slowdown is the possible spatial variation in its experience. This is the issue we consider in this paper and we analyse this question in the context of the regional economic resilience framework. We proceed in two stages. In the first we analyse a measure of provincial slowdown (a sensitivity index) based just on growth rates and use cross-section regressions to investigate the determinants of this index, using a range of provincial characteristics common in the resilience literature. We find that economic structure, demographic factors and education all play a role, although with signs that are often at odds with the existing literature. In the second stage we decompose regional growth rates into national and province-specific components using a VAR model and argue that since resilience concerns the response of provinces to a national shock, it is properly analysed using just the national component of the growth rate rather than the growth rate as such. We therefore analyse a sensitivity index based just on the national component of growth and find many differences between the two sets of results. Using the second index matters for the determinants which are significant as well as for the magnitude of their coefficients. It appears that some of the influences found to be significant in the first stage are there only because of their influence on growth via the province-specific component of the growth rate and in this sense are spurious.","PeriodicalId":44865,"journal":{"name":"Review of Regional Studies","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Review of Regional Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52324/001c.35253","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 1
Abstract
Since 2007 China’s real GDP growth rate has slowed from a level of over 10% per annum to below 7%. Given China’s regional diversity, an important aspect of the slowdown is the possible spatial variation in its experience. This is the issue we consider in this paper and we analyse this question in the context of the regional economic resilience framework. We proceed in two stages. In the first we analyse a measure of provincial slowdown (a sensitivity index) based just on growth rates and use cross-section regressions to investigate the determinants of this index, using a range of provincial characteristics common in the resilience literature. We find that economic structure, demographic factors and education all play a role, although with signs that are often at odds with the existing literature. In the second stage we decompose regional growth rates into national and province-specific components using a VAR model and argue that since resilience concerns the response of provinces to a national shock, it is properly analysed using just the national component of the growth rate rather than the growth rate as such. We therefore analyse a sensitivity index based just on the national component of growth and find many differences between the two sets of results. Using the second index matters for the determinants which are significant as well as for the magnitude of their coefficients. It appears that some of the influences found to be significant in the first stage are there only because of their influence on growth via the province-specific component of the growth rate and in this sense are spurious.