On May 17, Lucien Chaffa presented his paper, co-authored with Thierry Warin, titled “Deciphering Economic Clusters in Real-time: Applying Machine Learning to Registre des entreprises du Québec Data” at the 63rd annual conference of the Société canadienne de science économique (SCSE). This event marks a major milestone for us as it showcased preliminary findings from our very first research paper to a group of eminent academics.
Economic clusters are an integral dimension of our economic analysis in the bi-national St. Lawrence – Great Lakes region. Firms from an industry form linkages with firms from other industries, resulting in them colocating with each other. The grouping of industries into such clusters is useful to better understand the structure of regional economies and the industrial dynamics within them. Identifying the presence of clusters and subsequently analyzing their industrial composition, highlights the hidden interdependencies between firms and their economic impact at a very granular scale.
Clusters have become a household tool for policymakers to enhance regional competitiveness. Yet, cluster theory and its definitions have often relied on a qualitative, case-study driven approach where empirical data is used to validate hypotheses in a region-specific context. With this paper, we operationalize our inductive approach by using data science methods to revisit economic phenomena. By using firm level data from the Registre des entreprises du Québec, we create a new quantitative measure to define clusters and analyze their changing dynamics in near real-time.
The methodology and findings of this research paper will soon be published on our website. We invite you to refer to the following presentation for more detailed insights.
