The clustering of knowledge-intensive business services in Greater Mexico City, 2010 through 2020

Abstract This article analyzes knowledge-intensive business services (KIBS) clustering and location patterns in Greater Mexico City. Although there is evidence of the importance of KIBS clustering as a factor that precedes innovation, no empirical work has applied point pattern analysis methods to identify intrametropolitan patterns. Little is known about KIBS firms clustering in Mexico and emerging economies in general. This study responds to both challenges, using the M and m functions, point pattern analysis methods that allow capturing concentration intensity and overdensity of same-type KIBS firms, respectively. Firm-level data are taken from Mexico’s National Statistics and Geography Institute’s (INEGI) open-source databases for 2010 and 2020. Results suggest different clustering patterns given our proposed KIBS classification. Overall, the clustering intensity of KIBS firms by class has increased during the analyzed period (2010–2020). Also, although central Greater Mexico City is the main clustering pole of attraction, urban subcenters display KIBS firms clustering depending on proposed KIBS classes. Clustering patterns are explained given existing intrametropolitan infrastructure and value-added differences, but also within- and between-class concentration variations. Despite the lack of firm-level economic data, results allow inferring possible agglomeration mechanisms behind clustering patterns.