Background

The contemporary knowledge-based economies increasingly rely on businesses located in urban areas and on their capacity for innovations. Since the urban structure influences the operational preconditions and business activities of companies, land use and the way it is managed is a crucial factor for developing the vitality and attractiveness of urban regions. It has been shown that economic productivity increases along with a varied industrial structure in urban regions, the geographic proximity of businesses in the same industry and a high job density (Loikkanen & Susiluoto 2011; Loikkanen 2013). The purpose of our study is to look at organisation-level mechanisms underlying these advantages of agglomeration, and we will do so by analysing how the urban structure affects the formation of networks between companies and thus economic productivity. We approach the issue through methods of network analysis focussing on how the geographic proximity of knowledge-intensive businesses in the Helsinki Metropolitan Area influences workforce mobility and how this mobility is in turn linked to business activities.

There is a range of general research findings showing how networks between companies, on one hand, and between businesses and research organisations, on the other hand, have positive effects on innovation, knowledge capital, competitiveness and growth. Networking is considered to lead to the spread of knowledge and innovations between businesses, which increases their productivity (e.g. Audretsch & Feldman 2004). However, research literature often treats networking as a phenomenon on a rather general level, although the methods of network analysis in fact also allow a closer examination of local networks (e.g. Ter Wal & Boschma 2009; Maggioni & Uberti 2011).

Context

Economic activities are strongly concentrated in urban areas, and research and development, in particular, are locally agglomerated (Carlino et al. 2012). Agglomeration benefits brought about by clustered economic activities have been seen to bear a close relationship to the processes of the creation and spread of knowledge. At the same time, it has been predicted that evolution in information technologies may promote the geographical dispersion of innovation activities (Asheim & Gertler 2005). However, when we assess the role of geographic proximity we have to consider that companies can also be close in other ways apart from geography. It is essential to make an analytical distinction between the geographic and organisational dimensions of proximity (Boschma 2005). Geographic proximity is not, per se, a sufficient condition for the transfer of knowledge, and active participation in networks of knowledge exchange is necessary. Employing people from rival businesses, cooperation partners or other companies can be an important way of accessing such networks (Breschi & Lissoni 2003).

Thus, employee mobility between companies is seen as an important factor for the development of regional and urban economies and innovation. Research suggests that job switches between businesses promote structural change in the local job market and boost productivity (Maliranta et al. 2008; Böckerman & Maliranta 2012; Piekkola 2015). Transferring from one employer to another, employees not only move to a new workplace network, but they also create ties between the old network and the new one, thereby facilitating the spread of knowledge and ideas (Granovetter 1995). Although staff changing jobs can also have drawbacks for employers – such as losing a skilled employee to a rival company – studies maintain that employee mobility promotes learning processes within companies as well as their business success (e.g. Combes & Duranton 2006).

Modelling employee mobility should take into account not only organisational similarity but also the location of businesses in the urban area. In our statistical analysis, the purpose of a locational variable that controls area effects is to identify factors influencing the companies’ workforce demand or their locational decisions – factors that cannot be accounted for in the model – such as the companies’ finance situation (Haaparanta & Piekkola 2006) or their investments in the organisation (Piekkola 2015). When assessing the interrelation between geographic proximity and employee mobility, controlling for the location of companies is essential since the effects relating to the companies’ internal dynamics can be spatially distributed – as was the case with the reorganisation of the electronics industry and related business services in the Helsinki Metropolitan Area during the period of our study. Furthermore, location-related costs based on land prices influence the locational choices of companies so that different kinds of companies locate in different cost zones. This, in turn, influences the opportunities of companies to build networks (Arzaghi 2005).

Organisational proximity – which, like geographic proximity, has an important impact on the spread of knowledge – is strongly linked to the role of social capital in local networks. The concept of social capital is defined and used in literature in numerous ways. In the present study we distinguish between two interpretations of the concept: one focussing on social cohesion, the other focussing on “mediation” across “structural holes”. According to the concept of social capital understood as social cohesion, strong and dense relationships give network members the kind of opportunities to achieve their goals that they would otherwise lack. The interpretation that focusses on social capital as mediation across structural holes emphasises, in turn, the ties between groups or communities that allow for transfer of ideas and resources between them. According to the latter view, a dense network facilitates goal achievement only to a certain degree, since strong cohesion may lead to the sharing of common rather than new information.

Research data

In our study, we have used employees’ professional mobility between business establishments as an indicator of ties between organisations. We examined the job transfers on the basis of individual-level data from Statistics Finland’s employment statistics. We describe these links between organisations in terms of both the in- and the outflow of employees, because, according to research literature on employee mobility, companies learn from both new employees (e.g. Song et al. 2003) and from personnel who have moved to other organisations (t.ex. Corredoira & Rosenkopf 2010).

The assembled data covers job changes between business establishments in the knowledge-intensive industries in Helsinki Metropolitan Area in the years 2008–2012. During that period, a total of 52,500 job changes occurred in the selected industries. In our study, we classify as knowledge-intensive the following industries in the service and technology sectors:

• research and development

• data processing

• business services

• private and public education

• software publishing

• software industry

• information services

• electric and electronics industry

• other metal industry.

In 2012, these industries together accounted for around 18,400 workplaces in the Helsinki Metropolitan Area, with approximately 158,800 employees in total (HSY 2013). The labour flow networks identified in the study included 7,820 business establishments, which, during the period studied, had a total of 136,300 employees on average.

Using Statistics Finland’s codes for establishments and enterprises we combined our individual-level employment data with information on the location and business activities of establishments. The latter were obtained from Statistics Finland’s Business Register and Statistics Finland’s data on companies’ R&D and export activities and the educational level of the employees within the organisations. By combining the results of the network analysis based on employment statistics with data on companies’ locations and business activities, we were able to analyse the interrelations of the different forms of proximity between workplaces and their position in the network, on one hand, and their network position and their business activities, on the other hand.

The network perspective that we applied differs in terms of the methods of statistical analysis from the kind of economic research on agglomeration advantages that relies on econometric modelling. Econometric models are usually based on the assumption of companies functioning independently of each other, only reacting to consumer demand (Granovetter 1985). While autocorrelation models in spatial econometrics identify interdependence between regions, network analysis methods enable an analysis of dependences between companies. In the network-analytical approach, individuals or organisations are placed in the network of social and economic relations, and the main focus of the analysis is, expressly, on the interdependences between actors (c.f. Abbott 1997).

Findings

In the light of our study, employee mobility and a common labour market – which both are important for the spread of knowledge – seem to have a strong spatial dimension: our statistical analysis shows that geographic proximity is linked to employee mobility between establishments also when other forms of proximity and organisational factors have been taken into account. Our findings suggest that workplaces located closer  to each other in the urban area form both stronger and denser labour flow networks . In the knowledge-intensive industries in the Helsinki Metropolitan Area that were the focus of our study, one-quarter of job changes occurred between workplaces located under one kilometre from each other and half within four kilometres. The city centre and the fringe areas of Inner Helsinki, as well as the Otaniemi–Tapiola–Leppävaara axis in Espoo, form the strongest agglomeration of labour flows in knowledge-intensive industries in the capital region, complemented by Pitäjänmäki and the more peripheral employment zones in the region.

The relationship observed between proximity and workforce mobility can be seen as supporting the view that matching between employers and employees take place not only through the price mechanisms of the labour market, but that the processes of job seeking are also affected by social factors that require close personal contact (Granovetter 1995). Employee mobility between companies links networks together, which creates social cohesion between these companies. This creates networks that can enhance mobility even further. The social processes that influence job seeking may be local, because short distances facilitate contacts between people and enable the kind of knowledge exchange that happens more easily through face-to-face interaction (cf. Storper & Venables 2004). This can be the case especially in well-networked knowledge-intensive service industries where informal interaction plays a key role for economic activities (Arzaghi & Henderson 2008). With the rapid development of information technologies, networks are no longer necessarily geographically limited, but spatial proximity still seems to be of importance for economic activity.

Networking through switches of jobs has – as our findings suggest – a positive correlation with higher productivity at workplaces measured in our study in terms of turnover per employee. This supports the view that workforce mobility raises productivity and competitiveness in businesses, industries and regions (e.g. Maliranta et al. 2008; Böckerman & Maliranta 2012; Piekkola 2015). The link between productivity and the network contacts made when people change jobs is interesting also from the perspective of social capital theories, since the different approaches have opposing views on how networks generate social capital.

According to our study, productivity of establishments appears to have a positive correlation with strong and dense network ties within the labour flow networks – more so than brokering relationships, considered to be essential for the access to new ideas, information sources and resources and thus for competitiveness. This may be explained by the fact that employee mobility is limited and that even the densest labour flow networks are consequently rather sparse. Thus also the denser networks include actors who are not connected with each other in such a way as to provide grounds for brokerage and ensure them a favourable position in terms of information and resource flows. However, the selection of businesses with different levels of productivity into different network positions makes it difficult to assess the impact of network structures on productivity.

Conclusions

Although geographic proximity plays its role in parallel and in interaction with organisational forms of proximity, and the labour flow networks and the productivity of businesses are, in turn, connected through complex interrelations, our primary conclusion from viewpoint of land use planning is that these phenomena correlate positively with one another. If proximity between companies correlates with increasing employee mobility, the local labour market may be made more functional by planning land use in a way that guarantees better conditions for businesses to be located near each other.

In 2015, there were about 610,000 jobs in Helsinki Metropolitan Area, of which 160,000, or one quarter, were in the knowledge-intensive industries that were the scope of our study (HSY 2016). These industries account for almost all of the exports of services in the metropolitan area and the majority in Finland as a whole. Through the technology industry, they also account for the majority of the exports of goods in the Helsinki Metropolitan Area. In a longer perspective, the growth of production and jobs has been significantly faster in the knowledge-intensive industries than in all industries on average. These industries also represent, in terms of both ownership and personnel, the most international part of the Finnish economy. Creating favourable conditions for knowledge-intensive industries to locate and function well is of great importance for economic development in both the metropolitan area and Finland in general. 

Companies locate into urban areas and create contacts with other businesses and stakeholders on the basis of their own business criteria. However, cities can develop the conditions for successful business clusters. The supply of offices is crucial, as are transit connections and local services for companies and their employees. Our findings suggest that it would be important for the knowledge-intensive industries in the Helsinki Metropolitan Area that the cities focus their support on already existing clusters in order to help these zones grow larger, denser, more varied and more functional. To achieve this, local authorities must enable infill construction and the renewal of old structures so that the supply of offices can adapt to the changing needs of enterprises in the attractive zones. To ensure the conditions for successful entrepreneurship, it is also vital to exploit the potential of new rail connections and easily accessible station areas in developing the availability of offices.

Tamás Lahdelma is Researcher at City of Helsinki Executive Office.

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