Abstract

Introduction

What makes knowledge creation a more or less effective means of stimulating economic growth? The knowledge spillover theory of entrepreneurship (KSTE) asserts that incumbent firms create knowledge and exploit that which they deem commercially valuable. The residual knowledge is then transferred intentionally or unintentionally to entrepreneurial individuals through what is conceptualized as a knowledge filter. Knowledge that is not commercialized by incumbent organizations is thus considered a source of entrepreneurial opportunities for individuals within or outside of firm boundaries (CITATION).  In fact, research has provided empirical support for the idea that knowledge creation stimulates entrepreneurial activity (Acs, Audretsch, Braunerhjelm, & Carlsson, 2012; Colombelli, 2016), which in turn can catalyze economic growth (Braunerhjelm, Acs, Audretsch, & Carlsson, 2010).

The factors that enable this initial transfer of knowledge, however, are less thoroughly addressed in the literature on knowledge spillovers. It is suggested that knowledge is transferred through information channels (CITATION), but there are few indications of the characteristics of these channels that restrict or facilitate an effective flow of information for the purposes of realizing entrepreneurial opportunities. One possible explanation exists in the established literature on social relationships as a means of transferring information. Even still, attempts to measure particular aspects of social relationships have proven difficult. However, a focus on opportunities for social interaction at an aggregate level, rather the aspects of particular social relationships, may provide a new perspective from which to examine this social phenomenon. By examining the structure of the regional social environment, it may be possible to discover conditions that facilitate the conversion of knowledge to increased entrepreneurial and economic activity.   To this end, we seek to explore the following question: What aspects of the social environment may impact the ability of new knowledge to stimulate entrepreneurial activity, and economic growth?

In response to this question, we suggest an integration of an established literature on social relationships from the field of psychology into the KSTE research stream to help inform how the conditions for building social relationships are beneficial to entrepreneurial growth. Social relationships emerge through (1) social integration and (2) social support and are hindered by (3) potentially negative interactions (Cohen, 2004). While the psychology literature suggests that these aspects of social relationships contribute to overall health at the individual level (Cohen, 2004), we theoretically develop and empirically test a model which proposes that similar aspects of the regional social environment may impact the degree to which new knowledge can stimulate both entrepreneurial activity and economic growth (see Figure 1). In order to test the proposed framework, we compile a unique dataset from a number of publicly-available sources including the FBI’s Uniform Crime Reporting Program, the US Census Bureau of Economic Analysis, and the US Patent and Trademark Office.

The outcomes of this research are likely to be of practical and theoretical significance.  First, by incorporating the existing streams of research on social relationships and social environments with the KSTE research, we hope to add granularity to the KSTE, further answering the question of ‘when’ new knowledge may stimulate economic growth and entrepreneurship.  Additionally, the moderating variables of the social environment are levers that are potentially impacted by policy.  If these factors are indeed found to promote economic growth, then policy makers may be able to affect changes utilizing the guidelines provided by this research.   This paper will proceed in the following manner…

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Theory and Hypotheses

Endogenous growth theory posited that the creation of new knowledge could lead to economic growth in an economy (Lucas, Robert, 1988; Romer, 1986).  While this was a breakthrough insight, theories of endogenous growth assumed that this knowledge automatically spilled over, somehow morphing into economic activity, without specifying the mechanism that actually facilitated that process.  The knowledge spillover theory of entrepreneurship (KSTE) picked up where endogenous growth theory left off, and asserted that entrepreneurship is one potential mechanism that facilitates the spillover of knowledge, leading to economic growth (Acs, Audretsch, Braunerhjelm, & Carlsson, 2004; Acs, Stam, Audretsch, & O’Connor, 2017; Audretsch, Keilbach, & Lehmann, 2006; Braunerhjelm et al., 2010).

As our understanding of this phenomenon has grown, research has begun to take important steps to understand the nuances of the relationships between the variables included in KSTE.  For example, research has shown that the type of new knowledge matters in terms of the type of entrepreneurial activity that is spurred.  Specifically, new knowledge that is related to products, instead of processes, is more likely to lead to new entrepreneurial activity outside of the existing firm, because the product knowledge is less likely to be firm-specific (Wong et al., 2008).  Perhaps one of the most important conditions that has been identified for knowledge spillovers is related to geography; generally speaking, the spillover of new knowledge is geographically bounded, so that the closer one moves to the source of the knowledge, the more likely the effects hypothesized by KSTE will be observed (Lee et al., 2013).  The extensive and continuing work done in this area demonstrates that there is indeed great interest in understanding this phenomenon, because of its potentially positive implications for economic entities and regions.

Social Capital in KSTE

Although the social capital dynamics that are so important to the phenomenon of entrepreneurship are largely unexplored in regards to the KSTE, there are rare exceptions. Though not entrepreneurship per se, it has been suggested that large firms commercialize new knowledge through partnerships with research centers (Autio, Hameri, & Vuola, 2004). In doing so, they rely on social capital to develop collective processes that align the vision and goals of the research-industry partnership, provide expanded network benefits to the industrial companies, and facilitate informal access to complementary resources. Whereas Autio et al. focused on international big science centers, research universities have also been presented as sources of knowledge creation (Wang & Shapira, 2012). Wang & Shapira examined collaborations between technology firms and university scientists and concluded that social capital resource spillovers, in terms of increased network access, was not impactful in regards to a firm’s anticipated technology potential, especially when compared to resource spillovers of intellectual capital. The authors suggested that this might occur because mobilization, not merely access to or existence of an expanded network, is what makes social capital impactful.

Another interesting finding related to social capital influences on KSTE suggests that although localized competition constrains the conversion of knowledge to entrepreneurial activity, increased population density erases this negative effect (Plummer & Acs, 2014). When individuals are less likely to exploit opportunities due to substantial competition, this hesitancy is negated by bountiful access to resources necessary to launch and develop a venture. Those resources are made available by increased population density in the same manner that social networks provide entrepreneurs with access to those same resource resources (Hoang & Antoncic, 2003). Highlighting once again the importance of mobilizing social capital rather than simply obtaining access, a recent effort in grounded theory suggests ways that social enterprises leverage social capital to collect and commercialize knowledge spillovers (Ko & Liu, 2015). Specifically, the social enterprises in this study were said to have used social capital to identify potentially mobile workers with valuable knowledge, interact with other individuals who may possess knowledge, and acquire additional, specific knowledge domains that are necessary for continued venturing efforts.

Our interest in the present research is in examining factors that enhance the conversion of new knowledge into entrepreneurial activity, and thereby economic growth.  Particularly, we examine constructs that have the potential to be acted upon or manipulated by policies and policy-makers, as these types of variables stand to be the most meaningful in terms of generating practical outcomes.  Although the literature on KSTE and social capital are both, separately, extensive, their combined implications have yet to be examined.  In the following paragraphs, we will draw on these literatures to develop theoretical arguments as to why social capital could interact with the generation of new knowledge, in spurring entrepreneurial activity.  The various connections hypothesized in Figure 1 will be explained in the following pages.

The Direct Relationship Between New Knowledge and Entrepreneurship

H1:

The Indirect Relationship Between New Knowledge and Economic Growth

As has been illustrated, it is highly probable that knowledge creation will lead to economic growth in a region.  However, studies have found varying impact from knowledge creation to economic growth <<citation>>.  Therefore, we theorize that there are likely mediating circumstances which intervene.  As the knowledge spillover theory of entrepreneurship (KSTE) has found support for the mediating effect of entrepreneurial activity on the relationship between knowledge creation and economic growth  (Acs et al., 2017; Audretsch et al., 2006; Braunerhjelm et al., 2010), in this section we further discuss the basis for this argument.

Empirical analysis has largely supported the general relationship proposed in the KSTE.  For example, research has demonstrated that new knowledge is generally related to increased entrepreneurial activity (Colombelli, 2016; Guerini & Rossi-Lamastra, 2014; Lee et al., 2013; Wong et al., 2008).  Furthermore, entrepreneurial activity is positively associated with both economic growth (Braunerhjelm et al., 2010) and with employment growth (M Stuetzer et al., 2018).  Therefore, much of the existing literature accepts that entrepreneurship is one conduit that allows the conversion or spillover of new knowledge into new commercial knowledge or economic growth (Acs et al., 2012; Mueller, 2006).

The amount of entrepreneurship in a region will likely positively impact economic growth to a point, however over a certain level, the positive impact of that relationship might begin to diminish.  The Global Entrepreneurship Monitor (GEM) brought with it the unique ability to compare entrepreneurship in countries on a macro level and to more accurately determine potential antecedents, and consequences, of nascent entrepreneurship in a country.  Wennekers et al. (2005) found that, while developed nations experienced positive growth, developing nations seemed to experience a u-shaped relationship between entrepreneurship and economic development.  However, further exploration of this relationship using a sample of 23 OECD countries, included an attempt to determine an ideal rate of equilibrium in a country for entrepreneurship.  The results of this study indicated that an L-shaped relationship was more likely in that low levels of entrepreneurship were less likely to have an impact on economic development but high levels of entrepreneurship continued having a positive effect (Carree, Van Stel, Thurik, & Wennekers, 2007).

Studies in different contexts which attempt to better explain this relationship seem to agree on the likelihood of some type of u-shaped relationship.  A study in West Germany exploring the impact of research and development spending on small business employment (a leading indicator which would likely lead to economic growth) found a u-shaped relationship between start-up activity and changes in employment (Fritsch & Schroeter, 2011).  Exploring the impact of family firms on economic growth using state-level data, Memili et al. (2015) found support for an inverted u-shaped relationship between the proportion of small- and mid-sized family firms and economic development.

Therefore, we propose that:

H2: The relationship between new knowledge and economic growth is partially mediated by entrepreneurial activity, such that new knowledge is positively related to entrepreneurial activity, and entrepreneurial activity has a u-shaped relationship with economic growth, where entrepreneurship over a given level leads to diminishing returns in terms of economic growth.

Integration

H3:

Social Support

Unemployment, underemployment, lack of economic opportunities, lack of adequate training and lack of social safety nets are intrinsic factors to poverty (Kesten, 2009).  And in a place where poverty exists almost never thoughts on economic growth or wealth generation. One of the solutions for eradicating these social constraints is social programs. Several studies show how effective these are in reducing these indicators that so much concern the society. For those living in poverty, hard work is often the only means available to improve well-being and the existence of social projects in order to bring social support (SS), often catalyzing the search for improvement of society (Zahra, Gedajlovic, Neubam & Shulman, 2009). The creation of productive employment opportunities is fundamental to reducing poverty and promoting sustainable economic and social development, as well as providing monetary security and emancipation. In communities with a strong presence of social projects, they have acquired an understanding of opportunities to increase growth and attract more investment and wealth (Porter & Kramer, 2006).

However, the effects of how that local social support affects the generation of wealth and growth is still little explored. Social contextual forces encroach on the various factors and remain important to all forms of entrepreneurship (Austin, Stevenson, & Wei-Skillern, 2006; Greve & Salaff, 2003). Living in a locality that exists in the presence of social projects can stimulate the economy in general (Johns, 2006), inciting regional development in education and the generation of new knowledge (Allen, 1984), but little is known how the presence of social actions interferes with the relation of income generation and economic growth. Therefore, we hypothesize that social support acts as a moderator between new knowledge and economic growth as well as new knowledge and entrepreneurial activity:

H4a: SS moderates the relationship between new knowledge and economic growth. SS enhances the positive relationship that new knowledge has with economic growth.

H4b: SS moderates the relationship between new knowledge and economic growth. SS enhances the positive relationship that new knowledge has with entrepreneurial activity.

Negative Interactions

Cohen (2004) argues that another important determinant of the social environment is the presence (or absence) of negative interactions.  As it relates to physical health, negative interactions may elicit stress, which has both negative psychological and behavioral implications, and thereby increases one’s risk for physical disease (Cohen, 2004).  While the physical implications of negative interactions are important, here we consider that negative interactions may also hamper the ability of new knowledge to trigger both economic growth and entrepreneurial activity.

Crime is one salient type of negative interaction that may have a particular impact on economic outcomes at the regional level of analysis.  Crime can both create stress in that it inspires fear for one’s personal safety and it also threatens individuals’ ability to protect their own property.  If an incumbent creates new knowledge, two ways that it can convert that new knowledge to economic growth include using the new technology to attract skilled individuals to its workforce, or simply selling the product or service, and thereby stimulating the local economy.  However, it is unlikely that a firm will be able to convert new knowledge to economic growth if crime rates deter individuals from moving to the region to take a new job, or to conduct business because they fear for their personal safety (Gartner & Subodh, 2000).  Therefore:

H5a: Negative interactions will negatively moderate the direct relationship between new knowledge and economic growth.

Similarly, past work has shown that crime has a negative impact on small business owners’ growth aspirations for their business (Gartner & Subodh, 2000).  This may be the case because an individual who fears that their property will be appropriated from them will be less willing to invest the time and energy into a venture that is necessary to ensure growth (McMullen, Bagby, & Palich, 2008; Miller & Kim, 2017).  Likewise, in an area plagued by crime, residents should be less willing even to start a business to capitalize on new scientific knowledge, because their property may be stolen from them, which would prevent them from reaping the returns from their efforts.  Hence:

H5b: Negative interactions will negatively moderate the relationship between new knowledge and entrepreneurial activity.

Method

Sample and Procedure

Measures

New Knowledge.  Following precedence in the literature, we operationalize new knowledge as a count of utility patents in a given MSA (Colombelli, 2016; Lee et al., 2013; Plummer & Acs, 2014; Rothaermel & Ku, 2008).  In their study connecting regional knowledge production and entrepreneurship, Lee, Hong and Sun (2013) outlined several reasons patent data is appropriate in this context.  First, patents provide an objective way to track knowledge production.  Second, patents tend to be a leading indicator of commercial opportunities that may be created due to the new knowledge that has been created.  Third, there is a high correlation between patents and investment in research & development, providing a justification for using patents as a proxy for R&D investment.  Data regarding utility patents was retrieved from the U.S. Patent and Trademark Office and includes all patent applications in a given MSA.

Entrepreneurial Activity.  Researchers testing the Knowledge Spillover Theory of Entrepreneurship are consistent in operationalizing entrepreneurial activity as the creation of new firms (Audretsch & Keilbach, 2007; Stenholm, Acs, & Wuebker, 2013).  Therefore, we operationalize entrepreneurial activity as new firms created within a given MSA.  This data was available through the U.S. Census Business Dynamics Statistics (BDS) database.  The BDS is compiled from the Longitudinal Business Database which tracks births and deaths for all establishments in the United States.  While this dataset includes all firms which are formally established as entities within a given MSA, it only counts an entity as being established once it hires employees.  It also excludes most government agencies, as well as those who are self-employed, domestic service workers, railroad employees, agricultural production workers, employees on ocean–borne vessels, and employees in foreign countries.  An additional point of interest of the BDS is that it tracks establishments, which may or may not be a subsidiary of another company (https://www.census.gov/ces/dataproducts/bds/methodology.html).  As our study is most interested in the establishment of firms which have a high probability of growing, this will likely not hinder our study.

Economic Growth. Using GDP as a measure of economic growth has a solid basis in economics literature (Bruns, Bosma, Sanders, & Schramm, 2017; Kreft & Sobel, 2005; Pinillos & Reyes, 2011).  GDP by MSA was available from the U.S. Census Bureau of Economic Analysis and includes all industries in the given MSA.

Measures of Opportunity for Social Interaction in a Region

Opportunities to engage in social interaction and build social relationships in a geographical region have been observed by capturing the level of integration (Cohen, 2004; Samila & Sorenson, 2017), extent of social support (Cohen, 2004), and potential for negative interactions (Cohen, 2004; Palmer, Ziersch, Arthurson, & Baum, 2005). The following section addresses each of these moderating variables individually.

Integration. Measures of integration in a region were obtained from the United States Census Bureau American Fact Finder. We extracted population estimates of race, educational attainment, and annual income in each region at the census tract level. This required us to compile the data from each census tract located within every MSA in the United States. With the census tract-level data, we were able to use the Theil Index to calculate the extent of segregation at the MSA level (Iceland, 2004; Samila & Sorenson, 2017). These measures provided separate scores for racial, educational, and income integration in a region. An overall summated score of integration was used in our analysis.

Social Support. Measures of social support in a region were obtained from the Council for Community and Economic Research. Using this database, we were able to gather information on government programs that included their category, type, business need met, and geographic focus. We used this information to develop a count of programs specific to each MSA region that indicate support for entrepreneurial risk-taking.

Potential Negative Interactions. Social interaction in a region will be constrained by the potential for negative interactions, often in the form of violent and property crimes (Cohen, 2004; Palmer et al., 2005). We obtained counts of violent and property crimes each year, in each MSA from the FBI’s Uniform Crime Reporting Program.

Analytic Strategy

Results

Discussion

Limitations and Directions for Future Research

Practical Implications

Conclusion

References

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Figure 1

Conceptual Model