Agglomeration and coagglomeration of co-working spaces and creative industries in the city

ABSTRACT

The present paper investigates the location patterns and the effects co-working spaces (CWS) generate on the urban context. The focus is on Barcelona, one of the most important creative hubs in Europe in terms of knowledge-based, creative, digital, and sharing economy, and the city hosting the largest number of co-working spaces in Spain. The paper addresses three main questions: (1) Which are the location patterns of co-working spaces in Barcelona? (2) Do CWS agglomerate in the same areas? And, (3) Do CWS coagglomerate with the creative industries (CIs)? To do that, this paper uses open data on Barcelona neighbourhoods’ socioeconomic composition provided by the Statistics Department of the Council of Barcelona and micro-geographic data of private CWS and creative labs in Barcelona. By using Geographical Information Systems (GIS) and Kd functions of agglomeration and coagglomeration, results show that CWS are highly concentrated in central areas of Barcelona where there are greater chances to meet customers and suppliers, the proximity to urban amenities and the fact of being associated to a specific place-image. Moreover, they coagglomerate with CIs firms, especially with symbolic and synthetic knowledge-based CIs. These results are relevant when assessing the actual goal of urban policies in Barcelona.

KEYWORDS:

• Co-working spaces
• creative industries
• distance-based methods
• agglomeration
• coagglomeration
• Barcelona

JEL:

• R00
• R39
• Z00

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Co-working spaces are innovative workplaces where independent knowledge-based, creative, and digital workers – mainly freelancers or self-employed professionals – share their work spaces. They rent a desk (for months, days, or even just hours) in return for different kinds of services: both traditional (such as, for instance, administrative offices, meeting rooms, or spaces of aggregation) and digital (such as, for instance, wifi connections, or printers) (Mariotti, Pacchi, and Di Vita 2017).

2 It is worth noting that geographical proximity per se does not guarantee more innovation and entrepreneurial options and other dimensions of proximity such as cognitive, organizational, social or institutional (Boschma 2005) may have an impact on the innovation and entrepreneurial performance of CWS. However, since the aim of this paper is to provide evidence on the location, agglomeration and coagglomeration of CWS and the CIs, we leave for future works the analysis of the impact of the different kinds of proximities on CWS location and effective knowledge diffusion.

3 There are three definitions of knowledge base for innovative and creative activities – analytical, synthetic and symbolic – which are defined according to a mixture of tacit and codified knowledge, the possibilities and limitations of knowledge codification, and the competences and skills required for the development of their activity (Asheim, Coenen, and Vang 2007; Asheim and Hansen 2009).

4 The creative economy is based on the concentration of creative people and industries with traded and untraded agglomeration externalities. Its main core is the maximization of opportunities for face-to-face meetings, which make it possible the exchange of tacit knowledge contributing to sustainable growth, jobs, and social cohesion (DCMS 2001, 2013; Florida 2002, 2005; Scott 2006; Pratt 2008; European Commission 2017).

5 The sharing economy is an economic system that enables a shift away from a culture where consumer's own assets, toward a culture where consumers share access to assets. This shift is driven by internet peer-to-peer platforms which will disrupt the unsustainable practices of hyper-consumption that drive capitalist economies (Botsman and Rogers 2011; Martin 2016).

6 See, for instance, the following papers for alternative definitions of CWS: Spinuzzi (2012); Capdevila (2014), Moriset (2014), Merkel (2014) or Mariotti, Pacchi, and Di Vita (2017).

7 The Silicon Sentier District is home in the heart of Paris to numerous creative spaces: tech incubators, co-working spaces, and digital learning spaces (please, visit https://en.convention.parisinfo.com/latest-news/fresh-news/2018/silicon-sentier-district-innovation-paris, for more details). The 22@ was created in 1998 as a public initiative to encourage the concentration of activities closely related to innovation and creativity by providing networking and workspace facilities and granting support (Pareja-Eastaway and Piqué 2011; Viladecans-Marsal and Arauzo-Carod 2012).

8 Cowocat is a non-profit association created in 2013 that gathers different CS located throughout Catalonia (https://cowocat.cat/). The list of CS is regularly updated, adding any new CS that has applied to join the association.

9 SABI database includes several firms’ characteristics including year of entry, balance sheets, income, expenditure accounts, number of employees, industry, sales, assets, and georeferenced location (i.e., X-Y coordinates). SABI collects data from the Mercantile Register, where all limited liability companies and corporations are obliged by law to deposit their balance sheets. This is the most widely used dataset in Spain and Portugal when firm georeferentiation is required and it is provided by Bureau van Dijk. Previous studies have used this database (See for instance, Jofre-Monseny, Marín-López, and Viladecans-Marsal 2015 or Coll-Martínez, Moreno-Monroy, and Arauzo-Carod 2019) and some of them have explored its representativeness by computing the correlation between SABI and the Social Security Register and finding a high correlation of around 0.90 (Jofre-Monseny, Marín-López, and Viladecans-Marsal 2014).

10 The reason to use 2015 variables is the lack of data available for recent years. Table A2 provides the main descriptive statistics for all these variables.

11 The Central Business District (CBD) of Barcelona corresponds to Diagonal Avenue, Passeig de Gràcia and Plaça Catalunya areas.

12 All calculations use a 0.05 significance level, using 1000 simulations. The dashed line corresponds to the benchmark scenario, that is the density of all the economic activity (All Creative firms in our case) and the shaded area is the confidence interval.

13 The classification of firm size is small (less than 50 employees), medium (less than 250) and large (250 or more).

Additional information

Funding

This work was supported by Departament d’Innovació, Universitats i Empresa, Generalitat de Catalunya [grant number 2014 SGR 299]; Ministerio de Economía, Industria y Competitividad, Gobierno de España [grant number ECO2014-55553-P and ECO2017-88888-P]. And the paper is supported by COST Action CA18214 ‘The geography of New Working Spaces and the impact on the periphery', which is funded by the Horizon 2020 Framework programme of the European Union (project website: http://www.new-working-spaces.eu/; European Union Website: https://www.cost.eu/actions/CA18214).