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«Paper No. 365 Determinants of self-employment among commuters and non-commuters Mikaela Backman Charlie Karlsson May, 2014 The Royal Institute of ...»

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In this section, we have illustrated that social networks play a critical role for the diffusion of information and, in particular, knowledge. The information and knowledge that a person possess is function of how well developed his/her social networks is and, in particular, how rich his/her instrumental links are, since the instrumental links can be expected to provide more varied information and knowledge than the expressive links in the private part of the social network. In the following section, we discuss the set of links in a person’s network that we Certainly, people can develop links via social media but here we assume that it is links formatted through faceto-face meetings that are the critical links for entrepreneurship.

expect provide information and knowledge of special importance for a person’s decision to become entrepreneur, namely direct and indirect links to entrepreneurs and managers. We use the term “entrepreneurship” networks for this set of links in a person’s social network.

2.3 “Entrepreneurship” networks We are now in a position to start a discussion about what links that are most critical for a person’s decision to become an entrepreneur. Thus, we are interested in testing the importance of one particular type of (potential) network for the decision to become an entrepreneur, namely the accessibility to existing entrepreneurs and managers, i.e. to people that are self-employed or managers of a firm in a locality. We call such networks “entrepreneurship” networks. Individuals that work and live in the same community are exposed to one such network, while people that commutes are exposed to two such networks, one in the “work” locality and one in the “home” locality. Some individuals have both direct and indirect links to entrepreneurs and/or managers, while other people might only have indirect links. Of course, some of the links might represent participation in formal networks (Lawton Smith and Romeo 2013).

Since in modern economies each individual by necessity only has incomplete and scattered information and knowledge on any subject, we assume that “entrepreneurship” networks and in particular their density measured in terms of accessibility play a critical role i) in helping individuals in discovering, evaluating and exploit entrepreneurial opportunities (Dow and Zolnik 2012), and ii) in influencing the decisions of individuals actually to become entrepreneurs. Thus, most problems including entrepreneurial problems must be solved by processes of mutual interactive learning and for such learning to take place a potential entrepreneur is dependent upon the quality and the density of his/her “entrepreneurship” network. Actually the characteristics of “entrepreneurship” networks are determined by five factors (Cappelin 2003): i) the information, knowledge, experience and competence accumulated in each node, i.e. individual, ii) the travel time distance between the different nodes, i.e., individuals, in the network, iii) the connectivity to other interacting networks, iv) the speed of change of the links and the creation and destruction of links, and v) the overall trajectory of the structure of the network.

“Entrepreneurship” networks are critical because they provide role models for potential entrepreneurs and offer general and professional encouragement and acceptance of entrepreneurial endeavours as well as an environment conducive to entrepreneurial activities and the denser the network the larger the stimuli for entrepreneurial activities. The density of “entrepreneurship” networks is critical also from other aspects, since denser networks i) imply a richer set of information and knowledge corridors and processes that can channel information and knowledge to potential entrepreneurs (Shane 2000; Baker et al. 2005), ii) facilitate the integration of multi-disciplinary knowledge that is tacit and therefore embodied in people, and iii) allow for the rapid decision-making needed to cope with uncertainty (Patel and Pavitt 1991). This effect might be very important since it has been suggested that differences in entrepreneurial behaviour may be caused by information and knowledge asymmetries across potential entrepreneurs (Hayek 1945; Kirzner 1973).

Denser “entrepreneurship” networks, i.e., “entrepreneurship” networks in localities where entrepreneurs and managers have clustered spatially, in principle offer better conditions for personal contacts and social and professional interactions between on the one side potential entrepreneurs and on the other side active and retired entrepreneurs and managers. These contacts and interactions may over time develop mutual trust among the actors and reduce the costs of interaction, the transfer of information and knowledge, cooperation, and even doing business (Goldstein and Gronberg 1984). We may assume that the potential for such proximity effects are larger, the denser the “entrepreneurship” network (Love and Roper 2001).

Actually, the available empirical evidences on cross-locality differences in various outcomes seem to be difficult to explain without human capital in account as well as the tendency for information and knowledge to spillover between individuals through face-to-face interaction (Gennaioli et al. 2013). In the current case, we can say that the spillovers of information and knowledge relevant for entrepreneurial actions through “entrepreneurship” networks generate entrepreneurship externalities, which are greater, the denser the “entrepreneurship” network.

Our assumption about the critical role of the density of “entrepreneurship” networks is based upon the results of several empirical studies, which have found that information and knowledge spillovers tend to be greater in localities with a higher population and industrial density (Brunello and De Paola 2004; Audretsch and Lehmann 2005; Audretsch and Keilbach 2007). There is no reason what so ever to assume that other conditions should apply for entrepreneurship-relevant information and knowledge.

We assume that “entrepreneurship” externalities are a product of spontaneous meetings between people with complementary sets of skills, know-how and experiences relevant for entrepreneurship (Glaeser 1999). Due to the tyranny of distance most social, business and professional interactions take place rather close either to the place of residence or to the place of work, since the probability of them taking place depreciates with distance. This implies that entrepreneurial learning to a high extent is localised to the place of residence or the place of work (Rosenthal and Strange 2008). This implies that localities with dense “entrepreneurial” networks have a comparative advantage in the sense that they offer more opportunities for entrepreneurial interactions and learning within the time budgets of individuals.

The extent to which entrepreneurial learning takes place is, among other things, a function of the absorptive capacity of individuals, i.e. of their complementary skills and knowledge sets and of their ability to internalize entrepreneurial information and knowledge and to use it for commercial ends (Cohen and Levinthal 1990). It is also a function of the cognitive distance between people and the larger distance the higher the dynamic transaction costs (Langlois 1992), and thus the higher the costs of learning from and persuading, negotiating and coordinating with others. This, in particular, affects potential entrepreneurs, who have limited financial and time resources to bear such costs. We assume here that differences in the density of “entrepreneurial” networks between localities generate significant spatial differences in the accessibility to critical entrepreneurial information and knowledge, which might explain differences between localities in the rate of entrepreneurship.

Interactions in “entrepreneurial” networks play a role when the peer group (Scheinkman

2008) affects the actions of potential entrepreneurs. It seems to be well established in the literature that influences from “entrepreneurial” networks often are critical in inducing individuals to consider an entrepreneurial career by providing entrepreneurial role models and in smoothing the start-up process for potential entrepreneurs as well as for running a new firm with a profit (Andersson and Larsson 2013). It is well known that a person that have entrepreneurs in the family or who knows entrepreneurs personally have a higher propensity to consider a career as an entrepreneur. In addition, active entrepreneurs and managers might have identified possible entrepreneurial ventures that they themselves for different reasons are not interested in pursuing and might be willing to transfer their business idea to other people within the “entrepreneurship” network that they think has the potential to become good entrepreneurs. Furthermore, people living and/or working in localities with dense “entrepreneurial” networks might be induced to become entrepreneurs seeing the examples set by others or they might feel forced to become entrepreneurs to become socially accepted. In both cases, this can be interpreted as that they demand a lower risk premium or lower discount factor than people living and/or working in localities with less dense “entrepreneurial” networks.

“Entrepreneurial” networks may provide information and advice that reduce the start-up costs of new firms. This is true for all the processes that a potential entrepreneur has to go through before starting a firm, such as product development, market intelligence and marketing, creating the firm as a legal entity, finding and renting facilities and equipment, hiring employees, and setting up an accounting system. The “entrepreneurial” network may also help in finding the necessary financial resources. A potential entrepreneur who via links in an “entrepreneurial” network is known by potential financiers might easier get access to financing, since being part of such a network reduce the problem with asymmetric information. Potential entrepreneurs with embedded relationships and links in an “entrepreneurial” network are more likely to get seed money and long-term financing (Uzzi 1999; Porter 2000; Gompers and Lerner 2001). Being linked into an “entrepreneurial” network may also help potential and new entrepreneurs in identifying customers and markets with the necessary purchasing power, organizing production and distribution at reasonable costs, and thus contribute to a newly started firm reaching break-even within a shorter time horizon.

Potential entrepreneurs that live and work in the same locality can take advantage of the “entrepreneurial” network in that locality. Potential entrepreneurs who commute can take advantage of the “entrepreneurial” network in both their “home” locality and their “work” locality. The aim of this paper is to test which of these two “entrepreneurial” networks, which is most important for those potential entrepreneurs who decide to become entrepreneurs, i.e. to start an new firm. Our hypothesis is that “entrepreneurial” externalities develop in close proximity to the place of work (Jacobs 1969). The motivation for this hypothesis is that that it is in the work situation that people mainly are able to observe and get in contact with active entrepreneurs and managers, and thus to develop those diversified instrumental links that might provide role models as well as the information and knowledge necessary to take the decision to become self-employed.

An alternative hypothesis could be that “entrepreneurial” externalities develop in close proximity to the place of living, not least since there normally exist a strong segregation in housing areas and that entrepreneurs and managers tend to live in localities and neighbourhoods where many entrepreneurs and managers live. However, we assume that the links and contacts coupled to the place of living are less business oriented and more having to do with having children in the same school, attending the same church, being members of the same golf and boat clubs, etc. and thus more related to spare time activities than to business-related activities, i.e. we can expect that a high share of the links people have in the locality where they live to be expressive links.

Ideally, we would like to be able to follow the life story of different individuals in detail but that is seldom possible for large groups of people. By controlling for a number of background factors of individuals we might come some way to control for some important differences in life stories. Here we do something much simpler, we identify a number of groups of individuals that behave differently in the labour market. Actually, we identify three different groups: i) those who work and live in the same locality, ii) those who work in one locality but live in another locality in the same commuting region, and iii) those who work in a locality in one labour market region and live in a locality in another labour market region. The basic hypothesis is that individuals develop one type of links in the locality where they live and another type of links in the locality where they live. By this approach we are able to test this hypothesis, since we for the two last groups in the econometric tests can include the characteristics of both the work locality and the “home” locality and how these characteristics influence potential network formation.

Unfortunately, we are not able to identify directly the relevant peer groups (Scheinkman

2008) or to directly observe the links of different potential entrepreneurs, which implies that we are forced to use proxy variables. We assume that that the intra-locality accessibility to self-employed people can be used as an indicator of the density of the “entrepreneurial” networks that potential entrepreneurs can access. For people that commute we get one measure for their “work” locality and one measure for their “home” locality.

In this section we have highlighted the strategic role that “entrepreneurship” networks have for individuals’ decisions to become self-employed. We have also presented our main hypothesis that it is the “entrepreneurship” networks related to place of work and not the “entrepreneurship” networks related to place of living that are the critical networks for the decision to become self-employed.

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