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.

Keywords:

• KIBS
• knowledge-based services
• agglomeration
• Mexico City
• point pattern analysis

Notes

1 Knowledge spillovers are monetary and productive benefits stemming from proximity and collaboration between firms, expressed by the use of “partially non-rival” (e.g., workforce education) and exclusive human capital, such as patents (Henderson 2007, 497).

2 For a definition of the medium-high and high-technology industrial sector, see OECD (2011), and Shearmur et al. (2015).

3 The concept of territorial servitization refers to the geographical proximity between KIBS and manufacturing given their productive integration in the search for new monetization flows (see Lafuente, Vaillant, and Vendrell-Herrero 2017, 2019).

4 Urbanization economies of agglomeration refers to pecuniary and nonpecuniary benefits accrued through labor market sharing or pooling, propensity to reach out to more clients (city size), and diversity of economic activity (for discussions, see Brunow, Hammer, and McCann 2020; Peng et al. 2022).

5 It should be noted that most studies concerning KIBS in Mexico do not follow a spatial approach (see Arroyo-López and Cárcamo-Solís 2009; López and Ramos 2013; Pérez-Campuzano, Sánchez-Zárate, and Cuadrado-Roura 2018; Romero-Amado, López-Toache, and Sánchez-Daza 2018; Garrido-Rodríguez and Pérez-Campuzano 2019). There are, however, case studies that present city- and industry-specific findings (see Carrillo and Matus 2020; Graizbord and Santiago 2021).

6 In Mexico, the municipio is the smallest political administrative division of the country’s territory.

7 Acronym for North American Industrial Classification System (see INEGI 2018b). A five-digit NAICS level is used in this study (see Table 1).

8 This is an important fact of the M and m functions as there are no edge corrections to be made to estimations (e.g., in Ripley’s K; see Ripley 1977). The total area of analysis is determined by firms’ locations and not administrative area sizes (Marcon and Puech 2017).

9 But also, of similar point pattern methods in general; see Gómez-Antonio and Alañón-Pardo (2020) for a thorough review of these methods.

10 AGEB is the Spanish acronym for Area Geoestadística básica (Basic Geostatistical Area).