{"title":"Relatedness-Based Industrial Exit Paths and Economic Complexity: Evidence From Chinese Regions","authors":"Wei Li, Yiming Fu, Zhen Liu, Ying Wu","doi":"10.1111/grow.70044","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Industrial exit plays a critical role in shaping regional industrial dynamics. Evolutionary economic geography studies often use co-occurrence and density methods to assess the likelihood of related and unrelated industries exiting a region. However, these traditional methods lack the ability to quantitatively distinguish related from unrelated exiting industries. This paper addresses this gap by introducing a novel quantitative method for differentiating related and unrelated exiting industries. We then explore the correlation between (un)related exits and economic complexity using data from the China Customs Database (2000–2012). The findings suggest that related exits dominate China's industrial exit paths, with relatively few unrelated exits, and the disparity between them is widening. Second, a significant and positive correlation between related exits and economic complexity is observed, while unrelated exits display a significant and negative correlation. Third, differences emerge between related and unrelated exits across various sectors and regions in China. The novel method for distinguishing related and unrelated exit industries holds the potential for application in other countries and regions, contributing to a more precise understanding of the patterns of regional industrial exit.</p>\n </div>","PeriodicalId":47545,"journal":{"name":"Growth and Change","volume":"56 3","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Growth and Change","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/grow.70044","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DEVELOPMENT STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
Industrial exit plays a critical role in shaping regional industrial dynamics. Evolutionary economic geography studies often use co-occurrence and density methods to assess the likelihood of related and unrelated industries exiting a region. However, these traditional methods lack the ability to quantitatively distinguish related from unrelated exiting industries. This paper addresses this gap by introducing a novel quantitative method for differentiating related and unrelated exiting industries. We then explore the correlation between (un)related exits and economic complexity using data from the China Customs Database (2000–2012). The findings suggest that related exits dominate China's industrial exit paths, with relatively few unrelated exits, and the disparity between them is widening. Second, a significant and positive correlation between related exits and economic complexity is observed, while unrelated exits display a significant and negative correlation. Third, differences emerge between related and unrelated exits across various sectors and regions in China. The novel method for distinguishing related and unrelated exit industries holds the potential for application in other countries and regions, contributing to a more precise understanding of the patterns of regional industrial exit.
期刊介绍:
Growth and Change is a broadly based forum for scholarly research on all aspects of urban and regional development and policy-making. Interdisciplinary in scope, the journal publishes both empirical and theoretical contributions from economics, geography, public finance, urban and regional planning, agricultural economics, public policy, and related fields. These include full-length research articles, Perspectives (contemporary assessments and views on significant issues in urban and regional development) as well as critical book reviews.