{"title":"评估关联性原则:估算、驱动因素和对政策的影响","authors":"Yang Li , Frank M.H. Neffke","doi":"10.1016/j.respol.2024.104952","DOIUrl":null,"url":null,"abstract":"<div><p>A growing body of research documents that the size and growth of an industry in a location depends on how much related activity is found there. This fact is commonly referred to as the “principle of relatedness”. However, there is no consensus on why we observe the principle of relatedness, how to best operationalize it empirically or how this empirical regularity can help inform local industrial policy. We try to make progress by performing a structured search over tens of thousands of specifications to identify robust procedures to determine how well industries fit the local economies of US cities that perform well in terms of out-of-sample predictions. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Portfolios of these different productive entities yield different relatedness matrices, each of which helps predict the size and growth of local industries. However, our specification search not only identifies ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that leverage information from inter-industry relatedness analysis.</p></div>","PeriodicalId":48466,"journal":{"name":"Research Policy","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating the principle of relatedness: Estimation, drivers and implications for policy\",\"authors\":\"Yang Li , Frank M.H. Neffke\",\"doi\":\"10.1016/j.respol.2024.104952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A growing body of research documents that the size and growth of an industry in a location depends on how much related activity is found there. This fact is commonly referred to as the “principle of relatedness”. However, there is no consensus on why we observe the principle of relatedness, how to best operationalize it empirically or how this empirical regularity can help inform local industrial policy. We try to make progress by performing a structured search over tens of thousands of specifications to identify robust procedures to determine how well industries fit the local economies of US cities that perform well in terms of out-of-sample predictions. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Portfolios of these different productive entities yield different relatedness matrices, each of which helps predict the size and growth of local industries. However, our specification search not only identifies ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that leverage information from inter-industry relatedness analysis.</p></div>\",\"PeriodicalId\":48466,\"journal\":{\"name\":\"Research Policy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research Policy\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0048733324000015\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Policy","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0048733324000015","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
Evaluating the principle of relatedness: Estimation, drivers and implications for policy
A growing body of research documents that the size and growth of an industry in a location depends on how much related activity is found there. This fact is commonly referred to as the “principle of relatedness”. However, there is no consensus on why we observe the principle of relatedness, how to best operationalize it empirically or how this empirical regularity can help inform local industrial policy. We try to make progress by performing a structured search over tens of thousands of specifications to identify robust procedures to determine how well industries fit the local economies of US cities that perform well in terms of out-of-sample predictions. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Portfolios of these different productive entities yield different relatedness matrices, each of which helps predict the size and growth of local industries. However, our specification search not only identifies ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that leverage information from inter-industry relatedness analysis.
期刊介绍:
Research Policy (RP) articles explore the interaction between innovation, technology, or research, and economic, social, political, and organizational processes, both empirically and theoretically. All RP papers are expected to provide insights with implications for policy or management.
Research Policy (RP) is a multidisciplinary journal focused on analyzing, understanding, and effectively addressing the challenges posed by innovation, technology, R&D, and science. This includes activities related to knowledge creation, diffusion, acquisition, and exploitation in the form of new or improved products, processes, or services, across economic, policy, management, organizational, and environmental dimensions.