Social capital, religion and small business activity

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Social capital, religion and small business activityR Steven C. Deller a,∗, Tessa Conroy b, Bjorn Markeson c a

Department of Agricultural and Applied Economics, Center for Community and Economic Development, 515 Taylor Hall - 427 Lorch St, University of Wisconsin - Madison, Madison, WI 53706, USA Center for Community and Economic Development, Department of Agricultural and Applied Economics, 521 Taylor Hall - 427 Lorch St, University of Wisconsin - Madison/Extension, Madison, WI 53706, USA c Department of Economics, Smith Collge, Northampton, MA, USA b

a r t i c l e

i n f o

Article history: Received 11 July 2017 Revised 30 July 2018 Accepted 6 September 2018 Available online xxx

a b s t r a c t Within the theoretical framework of social capital, we explore how different religious traditions influence small business activity in U.S. counties. We motivate the analysis by emphasizing the ways in which religious organizations may facilitate social capital, a key factor in business formation and performance. We find that communities with a large concentration of religious congregations have a correspondingly higher level of small business activity. We also find important differences across religious traditions, suggesting that religion should not be treated as a monolithic dimension of social capital. In addition, by exploring different traditions, beliefs, and norms, proxied by religion, finer insights into social capital and community economic development can be gained. Published by Elsevier B.V.

1. Introduction Social networks and network development are crucial for entrepreneurial success in the twenty-first century. Lans et al., p.458) The consumers, suppliers, market information, capital, and other resources that entrepreneurs and small business owners are able to access and leverage through their networks has been shown to help identify new opportunities, enhance productivity and profitability (Allen 20 0 0; Bauernschuster et al., 2010; Bhagavatula et al., 2010; Westlund et al., 2014), as well as gain legitimacy in the local community (Elfring and Hulsink, 20 03, 20 07; McKeever et al., 2014). Access to broader networks can increase a firm’s visibility and reputation within the community (Podolny, 2001). An individual may access networks through formal membership in business and professional organizations or more informally through social groups and family relationships. At the same time Gedajlovic et al., (2013) note that entrepreneurs and entrepreneurship is embedded within the social and cultural norms of their community. How entrepreneurs interact, or network, within that community is vital to the success of the entrepreneurial enterprise. For many communities, religious institutions can effectively provide these formal and informal networking opportunities for would-be entrepreneurs and existing small business owners. Religious institutions, such as churches, synagogues, mosques, and temples, can provide a forum for people with shared values or beliefs to gather together. Shared values, by

R ∗

An earlier version of this research was presented at the 2014 Annual Meetings of the Mid-Continent Regional Science Association, Madison, WI. June. Corresponding author. E-mail address: [email protected] (S.C. Deller).

https://doi.org/10.1016/j.jebo.2018.09.006 0167-2681/Published by Elsevier B.V.

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extension, lead to trust and behavioral norms within the group as well as relationships that are the basis of support networks. Trust and behavioral norms combined with the networks that arise out of regular meetings for worship, and other purposes such as volunteering for community service, provide the key elements of social capital (Halstead and Deller 2015). Increasingly the interplay between religiosity, cultural norms, social capital and the economy are becoming the focus of several research agendas (Smidt 1999, 2003). While the concept of social capital was discussed in the social progressive movements of the early 20th century, and imagined by Alexis de Tocqueville, David Hume, and Adam Smith, among other early philosophers (Farr 2004), it was not until the work of Coleman (1988) and Putnam (1995a, 1995b) that social capital surged in the academic and popular literature. Within the economic context, networks, norms, and trust—the foundational elements of social capital— can facilitate decisions about new business formation, closure and expansion. For example, in an analysis of entrepreneurship, defined in terms of small business activity, Markeson and Deller (2015) find that the concentration of associations that facilitate social capital, such as bowling alleys, public golf courses, labor, professional and business associations, among others, tends to have a positive impact on small business activity. Though the types of local institutions that can facilitate social capital are wide ranging, we focus on religious organizations as an almost ubiquitous community level factor that likely has consequences for entrepreneurship and small business activity. Coleman (2003:33) claims that “[i]t has become now almost cliché that religion in the United States generates more ‘social capital’ than any other American institution.” Specifically, there are arguments suggesting that religious beliefs can shape the entrepreneurial spirit and influence business practices (Williamson et al., 2007; Dana 2009, 2010; Altinay and Wang 2011; Riaz et al., 2016). Empirically, Rietveld and von Burg (2014) find that entrepreneurs in the Netherlands are more likely to be religious than other individuals. In a study of 30 OECD countries covering the period 1984–2010, Hoogendoorn et al., (2016) find a positive relationship between religion and business ownership based on those dimensions that reflect the internal aspects, belief and norms of behavior, of religiosity. Audretsch et al., (2007, 2013) find that some types of religious belief increase the likelihood of entrepreneurship, measured by self-employment, but others hinder it. For example, Islamic teaching promotes survival through hard work and independence (self-employment) over relying on others (salary and wage employment) to earn a living (Riaz et al., 2016). At the same time Islamic teaching also opposes debt, suggesting that business ventures be financed through savings, either personal savings or those of family (Ratten et al., 2017) thus making, starting or expanding a business more difficult for many Muslims. In Muslim culture, the tendency toward fatalism, or “If God wills it”, is generally associated with being less open to new products and business processes, particularly those with perceived higher risks, thus dampening the Schumpeterian notion of entrepreneurship where innovation is vital (Herbig and Dunphy 1998). Consistent with this literature, Gursoy et al. (2017) recently found that with Muslim entrepreneurs in Turkey, levels of religious adherence influence how entrepreneurs approach their business. This diverges from some past literature, such as a study of Turkish ethnic entrepreneurs in London, U.K., by Altinay and Wang (2011) who found that adherence to the Muslim faith played no role in business decisions. These latter results imply that the larger community context matters. While there is a growing literature exploring the effects of religious institutions and beliefs on various facets of economic development, there is disagreement if those effects foster or impede economic development at the regional level (Durlauf et al., 2012). We investigate the effects of local religious concentrations on entrepreneurship which we proxy with the rate and performance of proprietorships. We find that there is a relationship between religious traditions and small business activity as hypothesized. Most importantly, we find that the effects of religions organizations on small business activity differes across the traditions that we consider. In the end, higher concentrations of religious organizations do influence entrepreneurship and small business activity but, as noted by Fine (2018), care must be taken not to treat religion as a homogenous factor. Beyond these introductory comments, the study proceeds in five parts. First we will review the literature regarding the relationship between religion and various forms of social capital. Then we will look at the existing literature investigating the effects of religious beliefs on entrepreneurship and small business activity. In the next section, we provide a general stylized theory of how social capital, broadly defined, plays into entrepreneurship and small business activity. The methodology we use to investigate our hypothesis that higher concentrations of religious institutions influence the level of proprietorship at the community level is outlined. We use a heteroskedastic error specification of a spatial Durbin model to reflect how socioeconomic forces spill across community (or in our case U.S. county) boundaries. This is an improvement over much of the available literature looking at the interface of religion and entrepreneurship which tends to use simplistic statistical methods. Finally, we present our results, and a discussion of our findings.

2. Social capital and entrepreneurship There are two primary forms of social capital explored in the literature, bonding and bridging (Besser and Miller 2013; Rupasingha and Goetz 2007; Rupasingha et al., 2006). Inspired by Putnam (1995: 67) and following Halstead and Deller (2015), we define social capital in a community as the networks, norms, and trust that facilitate coordination and cooperation for mutual benefit. Further, we distinguish between bonding and bridging social capital and expect that religious adherence may impact both types. Bonding social capital is defined by networks, norms, and trust within a given institution (Rogers and Jarema 2015). Bridging social capital instead focuses on networks, norms, and trust across institutions Please cite this article as: S.C. Deller et al., Social capital, religion and small business activity, Journal of Economic Behavior and Organization (2018), https://doi.org/10.1016/j.jebo.2018.09.006

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(Emery and Flora 2006).1 As a secular example, a chamber of commerce that promotes broad networking of local businesses can be instrumental to building bonding social capital between business owners. Similarly, labor unions may bring workers together resulting in strong bonding social capital. The ability of the chamber and unions to work together for their mutual benefit is an example of bridging social capital. An adversarial relationship between the chamber and unions tends to be indicative of weak bridging social capital. Bonding and bridging social capital are also central to the framework of Porter-type clusters, which focus on mutually beneficial relationships between related businesses and/or public-sector partners (Porter 1990, 20 0 0, 20 03; Goetz et al., 20 09; Woodward and Guimarães 20 09). Religious adherence and features of a faith community, such as attending service together, likely facilitate each of the primary components of social capital—networks, norms, and trust. Potentially, religious adherence generates these components in a way that is conducive to building both bonding and bridging social capital. First, attending church gives one immediate access to a network—to a group of people who belong to the same faith organization—through ongoing and varying interactions. This church-based network may be a source of customers or input suppliers or key people in the business community, such as local lenders, which can specifically benefit an entrepreneur. Perhaps the most fundamental network is the group with which you attend service or know to be members of your organization. Even within a church, however, smaller and deeper networks may form—between volunteers, within a bible study, among parents, to name a few. While bonding social capital is arguably the more natural result of religious membership and adherence, there is also evidence that religious institutions are able to build bridging social capital through shared faith between people of different socio-economic or ethnic backgrounds, which facilitates relationships between them. The relationship between different people lends itself to connections between people of different socio-economic or ethnic background even outside the church (Smidt 1999, 2003; Wuthnow 2002). Further, one can imagine that a person identifies as belonging to a much larger national or international faith community which can build bridging social capital across communities. Catholics, for example, might attend Saint Michael’s parish but also identify with the global community of Catholics or even the Christian community at large. As another example of bridging social capital, some communities have a council of ministers where the local leaders of different denominational institutions gather regularly to talk about community issues. Another way in which religious organizations may increase the amount of bridging social capital is as a source of social services for those in need, as place for recruiting volunteers, as meeting places for self-help groups, and as administrative mechanisms for faith-based governmental initiatives (Ammerman 1997; Park and Smith 20 0 0; Becker and Dhingra 2001). Religious institutions can also be a social resource, one where the community builds relationships that help the community attain its civic goals (Loury 1987). This type of religious participation not only allows for bonding social capital within the congregation (place of worship) but also provides an environment for learning civic and communication skills that can then be brought to other civic or secular organizations (Lenski 1963). Religious participation itself tends to be associated with increased participation in civic organizations (Lenski 1963; McIntosh and Alston 1982; Smidt 1999, 2003). If a congregation has members that are active outside the church (synagogue, mosque, or temple)2 the strong networks in the church (bonding) can feed links (bridging) to outside engagement. We also expect members of faith community to exhibit some degree of shared values and beliefs which lead to behavioral norms. A core set of values shared among people likely encompasses a certain moral code if not specific choices that create some consistency in behavior thus strengthening a key component of social capital. One might observe behavioral norms that are specific to a particular church such as the dress code, way of greeting one another, or even financial contributions in addition to following the tenets of the faith. We expect that these norms build bonding social capital within the church membership. We might also expect that there are overarching norms across churches throughout a community to the extent that they share core beliefs and common behavior. Christians of different types, for example, might identify together across different specific churches. Even church-goers in general, may find common ground simply in being people of faith. Thus, we also expect that religious adherence may be conducive to bridging social capital across groups through behavioral norms. Last, these shared values and behaviors amongst the faith-based network can lead to trust. Within a church, we expect that people have relationships and expectations of one another that create accountability. The relationship, behavioral code, and accountability are conducive to generate trust between members of the organization. Further still, we expect that sharing the same faith such as Baptist, or even broadly defined such as “Christian” or “of faith”, could lead to some level of trust even among people who do not attend the same religious service. In effect, it is possible that religious adherence facilitates trust in a way builds both bonding and bridging social capital. One key aspect that determines the quality of social capital that stems from churches is the size of the congregation. With the exception of Catholic parishes, Jewish synagogues, and Protestant mega-churches (Thumma 1996), most congregations are relatively modest in size (see Chaves et al., 1999), making it comparatively easy for congregants to interact frequently and become familiar with one another. While some large churches do organize into smaller groups around special interest, for example, members of smaller congregations tend to have stronger support networks during periods of stress, suggesting that

1 Some, such as Teorell (2003) have suggested that bridging social capital be decomposed into two parts, traditional bridging along with linking social capital. Bridging social capital is connections between different groups of people while linking social capital is connections of individuals to institutions such as a business owner to a chamber of commerce. While this is an interesting alternative interpretation, for our purposes here we remain with the more traditional view of bridging social capital. 2 A central thesis of this study is to explore the heterogeneity of religious traditions on entrepreneurial and small business activity but for simplicity we will tend to use the word church as a generic term for all religious congregations including synagogues, mosques, or temples

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smaller congregations allow for stronger bonding social capital. Smaller congregations, however, limit the ability to network and learn from other business people. Large congregations, though they may offer extensive opportunities to network, may also have the downside of being relatively impersonal. Along these lines, Ellison et al., (2009) find that individuals who attend very large churches or those lacking a well-defined period for informal socializing before or after the worship service tend to report lower levels of anticipated support. While there is a growing empirical literature exploring the linkages between religion and social trust (Welch et al., 2004; Traunmuller, 2010; Olson and Li, 2015; Dingeman and Van Ingen, 2015) the bulk of the literature remains conceptual and theoretical. In contrast to the positive social capital mechanisms via religious affiliation already discussed, there are competing views of how religion-based social capital may affect economic outcomes (Young 2009). Developing strong bonding social capital may hinder or eventually preclude building the stock of bridging social capital. Built on symbolic boundaries used to define group membership, bonding social capital may increase members’ intolerance and prejudice toward out-groups. In some cases, this may result from a focus on expanding membership rather than building connections across different faith communities (Wuthnow 1999). The inward focus of bonding social capital is a type of group closure (Coleman 1988; Bourdieu 1986; Olson 1982) in which information and rewards are kept within a particular group to the exclusion of outsiders, thus weakening bridging social capital. Group closure can result in a judgmental environment that dampens entrepreneurial activity and disconnects the group from important external groups. As a consequence, potential entrepreneurs may miss out on opportunities to exchange knowledge and strategies across demographically diverse groups. These socially secluded religious groups represent a negative aspect of social capital (Portes and Landolt 1996; Fine 2018), one that promotes tight social ties marked by in-group trust and reciprocity but also often reinforces fanaticism and undemocratic ideologies (Fiorina 1999; Levi 1996). Despite the potential for negative outcomes, however, it is generally accepted that religious organizations contribute to the sustainability of civil society (Tocqueville, 1945; Coleman 1988; J. 2003; Leege 1988; Putnam 1995a, 1995b, 2001; Greeley 1997; Wuthnow, 2002; Smidt 2003). Particularly, they promote in-group bonding and a level of social networking and trust that can extend beyond group boundaries (Smidt, 1999; Wuthnow, 2002). How this interface between religion and social capital influences entrepreneurial activity and economic growth and development, has only recently been the focus of a modest but growing academic literature.

3. Religious institutions and entrepreneurship Even though social capital is a predictor of entrepreneurial and small business activity (Anderson and Jack 2002; Bauernschuster et al., 2010; Korunka et al., 2010, 2011; Kwon and Arenius 2010; Besser and Miller 2013; McKeever et al., 2014; Stam et al., 2014; Markeson and Deller 2015; Lans et al., 2015) and there is a clear, although complex, relationship between religious belief and social capital, how religion influences entrepreneurship and small business activity is generally not well understood (Dodd and Seaman 1998; Dodd and Gotis 2007; Abereijo and Afolabi 2017). The evidence ranges from conceptual or theoretical arguments, such as the Calvinist teaching that labor and earthly success were indicators of salvation (Weber, 1904), to statistical analysis that indicates a direct positive relationship between religiosity and entrepreneurial activity (Audretsch et al., 2007, 2013; Kingma and Yeung 2014; Gursoy et al., 2017) or success in entrepreneurial behavior (Bellu and Flume 2004). In a study of 23 countries with a Christian tradition, Galbraith and Galbraith (2007) find that higher levels of what they refer to as “intrinsic” religiosity, is positively tied to early stage entrepreneurial activity (i.e., start-ups) and subsequent economic growth. They conclude that to fully understand how social capital and the culture of communities influence economic activity, including entrepreneurship, we must explore the role of religion. In another study of 27 predominately Christian countries, Parboteeah et al., (2015) find, through survey work, that a religious faith matters in entrepreneurship, but not nearly as much as a country’s investment in knowledge and technology. They reaffirm the conclusions of Galbraith and Galbraith (2007) that exploring religion as a major factor of norms and attitudes must be expanded if we are to fully understand entrepreneurship. Building on the sociology of religion, and in particular new institutional theory, Henley (2016) finds in a study of 74 countries, a significant association between entrepreneurial activity and evangelical/Pentecostal Christian religious affiliation along with evidence that the impact of religion on entrepreneurship is mediated through pluralism and regulation. These latter elements could be interpreted as the negative elements of social capital that dampen entrepreneurial activity. Kingma and Yeung (2014) found that Catholics are less likely than Protestants to be entrepreneurs and both are less likely than Jews, which is consistent with the findings of Zelekha et al., (2014). Minns and Rizov’s (2005) in their study of entrepreneurship in Canada at the beginning of the 20th Century showing that Catholics were less likely to become entrepreneurs than Protestants. In a study of risk preferences, an important element of entrepreneurship, Noussair et al., (2013) found that more religious people (proxied by church membership or attendance) tend to be more risk adverse and Protestants, despite evidence that they are more entrepreneurial, are slightly more risk averse than Catholics suggesting that risk preferences alone do not explain entrepreneurial propensity. The relationship is broader than merely the Christian tradition. Audretsch et al., (2007, 2013) find that in India, Islam and Christianity favor entrepreneurship, while Hinduism inhibits it. Riaz et al. (2016) note that the teachings of Islam encourage hard work, perseverance, and independence (i.e., self-employment) over reliance on others (i.e., wage/salary employment) Please cite this article as: S.C. Deller et al., Social capital, religion and small business activity, Journal of Economic Behavior and Organization (2018), https://doi.org/10.1016/j.jebo.2018.09.006

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all of which points to entrepreneurship as part of Islamic culture (Vargas-Hernández et al., 2010). Yet, Guisa, et al. (2007) conclude that “Buddhism and Christianity seem most conducive to capitalism, and Islam the least.” Entrepreneurs appear to be religious across several indicators. First, nine out of ten entrepreneurs are affiliated with a religious tradition (Dougherty et al., 2013). A detailed survey offered by Dana (2009, 2010), where, among other things, examples of financial, employment and information networks that emerge between people of the same religion are presented. Ethnographic evidence suggests that entrepreneurs might cluster in certain types of “pro-business” congregations where they find encouragement to both start a business and make a profit (Marti 2005). Benjamin et al., (2016) find that Protestants tend to be more trusting than Catholics and suggest that the centralized vertical hierarchy tradition of Catholics, as opposed to Protestant and mainstream American synagogues which are more autonomous and horizontally organized, may foster a lack of trust of those outside the church, thus limiting bridging social capital. At the same time, in the business literature there are several qualitative and ethnographic contributions. Carswell and Rolland (2004) question whether the positive effect of Protestant work ethic on entrepreneurship could be negatively affected by the increasing ethnic and religious diversity associated with increased migration, particularly immigration. While not using the terminology of bridging social capital, Carswell and Rolland (2004) question if increased religious diversity across a community hinders entrepreneurship. In their analysis of New Zealand, Carswell and Rolland (2004), find no evidence that growing religious diversity hinders entrepreneurial activity, suggesting that the negative effects of intense bonding social capital leading to weak bridging capital are weak if present at all. In the end, the small but growing literature seeking to better understand the role of religion on entrepreneurship, whether through the lens of social capital or some other theoretical framework, consistently finds that religion matters but how it matters varies across different religious traditions. Each author concludes that the available evidence is encouraging but the complexity of studying religion and entrepreneurship requires more study using both case studies and increased rigorous analysis. 4. Theoretical framework This research is informed by a theoretical framework for how firms use social capital. Assume there is an infinite number of regions, including region g, where individual i lives. Individual i is the owner of a firm that enters the market when the profit function

πig = P∗Qi − F Cig − V Cig ≥ 0

(1)

is satisfied such that profits are positive, or at least non-negative. The profit of a firm owned by an individual i living in region g is represented by π ig . P is the price of the good being sold at a quantity Qi . The quantity sold can be represented by

Q i = Q ( k i , s k i , s k g ).

(2)

The quantity sold is a function of business skill (ki ), as well as the bonding (ski ) and bridging (skg ) social capital of the owner (i) with other members in her faith community as well as other people from other faith communities, where and ski ≥ and skg ≥ 0.3 Since small firms are a focus of this research, the model assumes that the social capital of the firm and the social capital of the owner of the firm, the entrepreneur, are the same. It is also assumed that the value (Qi ) is increasing in both owner skill and both types of social capital. An individual with a high level of bonding social capital based on her faith community, in the form of mutual trust and influence on shared norms, will sell a larger quantity goods through personal relationships and network effects. In a sense, a higher level of bonding social capital creates a thicker market for the good or services that the individual/firm is supplying resulting in higher sales. Whereas an entrepreneur’s bonding social capital (ski ) reflects how well she is connected to other business owners and  community members ( N n=1 ), an entrepreneur’s bridging social capital (skg ) reflects how entrepreneurs and community N members ( n=1 ) across faith communities in region g are connected to each other. Higher levels of bridging social capital are associated with richer networks of information, mutual trust, and reciprocity as well as thicker markets for goods or services similar to the effect of bonding social capital. Thus we expect the function Qi is increasing in both social capitals. The costs faced by a firm are composed of both fixed costs and variable costs. It is assumed in this research that fixed costs are a function of the owner’s skill, both types of social capital, and the local entrepreneurial ecosystem (eg ), broadly defined.

F Cig = F C (ki , ski , skg , eg )

(3)

3 While we can conceive of negative social capital we restrict social capitals to positive values to better represent our conception of social capitals with some mathematical ease. From a profitability perspective, we assume there is no meaningful difference in profit between the cases of zero and negative social capital. In either scenario, we assume a business owner will have no market-based interaction (i.e. buying or selling) of consequence with members of the group in question. Therefore, the effect on profitability is zero in either case and we can reasonably make the simplification. In our view, profit-damaging relationships with network members may be better represented by a profit penalty or penalties for diverging from a social group such as a faith-community. Given the complexity of the issue which includes potentially competing penalties from different groups, we leave this augmentation of the model to subsequent work.

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An important distinction must be made between local social capital and the regional entrepreneurial context (eg ). High levels of social capital of either type do not necessarily support entrepreneurship. There is potential for a strongly cohesive group or network to exist in a region that is less conducive to entrepreneurship—perhaps due to weak business associations, the industrial composition, demographics, or cultural norms that stymie entrepreneurialism. For example, in some communities there is legacy or presence of large monolithic industries like mining or heavy manufacturing: in these “company towns,” factor markets and employee-mindset can be antithetical to entrepreneurship in a way that affects regional attitudes and institutions for decades (Chinitz 1961; Glaeser et al., 2015). This type of barrier—an averse local entrepreneurial context—leads to added time, money, and psychic costs. Conversely, in industrially diverse regions with favorable demographics and strong business support, as well as norms that are supportive of risk taking, it is less costly to start a new business. Therefore, it is assumed that if the local entrepreneurial context (eg ) is supportive of risk taking and entrepreneurial activity, then high levels of social capital will make it easier to start a business by decreasing fixed costs. On the other hand, if the local entrepreneurial context (eg ) discourages risk-taking and entrepreneurship, then high levels of social capital will enforce those norms and make it more difficult to operate by increasing fixed costs. Formally, eg ∈[− 1,1] is a measure of the local entrepreneur context eg . For purposes of this model, eg is restricted to this range for ease of computation. Additionally, the regional entrepreneurial context is defined such that as eg →1 the entrepreneurial ecosystem in region g is more supportive of entrepreneurship and risk-taking. On the other hand, as eg → − 1 the entrepreneurial ecosystem in region g is weak, discouraging entrepreneurship and risk-taking. Let fixed costs take the form

F Cig = X −α F C (ki ).

(5)

Let X > 1 be an arbitrary scalar affecting a firm’s fixed costs, and α is a function of (ski , skg , eg ). That is

α = eg ∗ ( skg + ski ).

(6)

As the level of either social capital increases, paired with a conducive regional entrepreneurial context, so does the propensity for new ventures due to input advantages and lower fixed costs. On the other hand, in communities with an averse entrepreneurial context, higher levels of social capital make it more difficult to start a business due to higher fixed costs. When eg >0, as total social capital increases, fixed costs decrease. When eg <0, as total social capital increases, fixed costs increase. In addition to fixed costs, a firm faces variable costs that are dependent on the quantity of goods a firm produces. Social capital affects these costs as well. Variable costs are represented as





V Cig = wig ∗Li + [rig ∗Ki ].

(7)

There is sufficient evidence that both the effective wage rate (Sabatini, 2008) and the rental rate of capital (Allen, 20 0 0; Berg, 2014) are affected by the level of social capital. As such

wi = w(ski , skg ).

(8)

With respect to social capital’s impact on wages, assume that as a firm owner’s social capitals increase, the ties of trust and reciprocity increase such that employees are more productive and shirk less. Since workers with stronger bonds of trust and reciprocity are more productive and more productive workers can command a higher wage, it is reasonable to assume that higher marginal productivity of labor ought to be associated with higher wages. Additionally, this research assumes that increases in a region’s bonding social capital leads to information sharing and network learning effects that increase worker productivity. Since workers with higher levels of human capital have higher rates of marginal productivity, and regions with more social capital, such as clusters, have more opportunities for learning, it is reasonable to assume that higher social capital ought to be associated with increased marginal productivity. Again, higher marginal productivity of labor ought to be associated with higher wages. Thus, the wage function wi is increasing in both social capitals. The cost of capital is also a function of a firm owner’s social capitals such that

ri = r ( ski , skg )

(9)

As a firm owner’s social capital increases, investors and lenders have additional information regarding the owner’s ability to repay. For example, a person who regularly sacrifices time resources to attend service and perhaps financial resources through tithing sends a signal about their trustworthiness and reliability. Further a personal relationship with a lender that develops out of the faith-based network can result in higher quality information flows related to a business venture. That is, the level of risk decreases through more accurate information, which is reflected in a lower rental rate of capital. Additionally, the entrepreneurial supportiveness of the region’s entrepreneurial context increases the willingness to lend and invest in a community, thus cost of capital is decreasing in social capitals. In its entirety:

πig = P∗Q (ki , ski , skg ) − X −(eg (ski +skg )) F C (ki ) − Li ∗w(ski , skg ) − Ki ∗r (ski , skg ).

(10)

Assume that individual skill (k) and the regional entrepreneurial context (eg ) are both fixed, and the region is conducive to ventures (eg = 1 ), then changes in a firm’s profits are dependent upon changes in the social capitals of individual (i). The Please cite this article as: S.C. Deller et al., Social capital, religion and small business activity, Journal of Economic Behavior and Organization (2018), https://doi.org/10.1016/j.jebo.2018.09.006

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incremental effect on profits associated with changes in individual bonding social capital in region g can be represented as

 πig = P

       ∂Q ∂w ∂r − ( ski +skg ) ∗ s k i + ( s k i ) ∗ X ∗ lnX ∗ F C (ki ) − Li ∗ ski − Ki ∗ s k i . ∂ ski ∂ ski ∂ ski

(11)

The way bonding social capital affects firm i’s change in profits depends on the quantity sold and the amount of the firm’s costs. If the gains in quantity sold combined with fixed and capital cost savings outweigh the increases in labor costs, the firm will be more profitable from an increase in bonding social capital. The increased profitability would thus make entrepreneurship more probable for agent i in region g. The symmetric relationship is true for bridging social capital. In the case of averse regional entrepreneurial conditions (eg = −1 ), fixed costs would be increasing and reduce profitability. The model also captures two additional intuitive features. For a given sum of bonding and bridging social capital, an increase in bonding social capital will reduce fixed costs. If we consider a comparatively larger sum of social capitals with an increase in bonding social capital, the reduction in fixed costs will be smaller. Thus, the model intuitively captures diminishing returns to the sum of social capital. Perhaps most interesting is the case with strong bonding social capital where faith community members are strongly in-group focused to the exclusion of other faiths resulting in weak or zero bridging social capital. In such a scenario, the benefits of social capital are relatively limited resulting in relatively modest profits compared to the case of strong combined social capitals. 5. Methodology One of the limitations of this small but growing literature that seeks to better understand the role of religion and entrepreneurship is the lack of quantitative methods. Some studies, such as Vallieri (2008), take an ethnographic approach where personal interviews are the foundation of the study while others (e.g., Dana 2009, 2010; Gursoy et al., 2017) are more descriptive in nature focusing on survey results. Some studies, for example, Galbraith and Galbraith (2007) and Parboteeah et al., (2015), use country level data that may not adequately reflect local networking opportunities that are vital to entrepreneurs. The more rigorous studies, such as Hoogendoorn et al., (2016), use simple statistical analysis such as correlation coefficients and classical regression analysis. In this study, we move the literature forward by using a spatial Durbin model with heteroskedastic errors to explicitly model how concentrations of different religious congregations influence small business activity. The central research question faces three empirical challenges: (1) how do we measure levels of religious activity, (2) how do we measure concentrations of entrepreneurial or small business activity, and (3) what are the appropriate control variables that must be accounted for in the analysis. The answer to the first question hinges on the availability of data at the community level or, in our case, U.S. counties. Here we used the decennial Religious Congregations and Membership Study carried out by the Association of Statisticians of American Religious Bodies (ASARB). This is an extensive survey that is intended to mimic a census type inventory of congregations by type, membership levels and levels of adherence.4 The data collected by ASARB allow us three basic measures: concentrations of the number of congregations within a community, adherence rates, and congregational sizes. While the count of congregations provided by the ASARB is reliable the estimates of adherence rates and membership levels are less reliable as they are self-reported and thus incorporate personal variations on what defines adherence and who is a member varies across religious traditions. For example, are children counted as members and is adherence simply attending services periodically? Thus, we limit our analysis to just the number of congregations. We aggregated the 236 different religious groups in the ASARB study into seven larger classifications based on the absolute size within the U.S. We based these classifications on the work of the Pew Research Center on Religion and Public Life’s Religious Land Scape Study and these include Evangelical, Black Protestant, Catholic, Latter-day Saints (Mormon), Muslim, Jewish and Eastern traditions.5 Our measure of religion concentration is not without its limitations. Kine (2018) notes that religion, particularly within the context of social capital, is multidimensional and reducing that complexity to a single element, such as the number of different congregations per capita aggregated into broad religious traditions, is a serious oversimplification. For example, unlike the finding from Altinay and Wang (2011) for Muslims in London (UK), Gursoy et al. (2017) find that, for Muslims in Turkey, levels of religious adherence is a strong predictor of how entrepreneurs approach their business. Given the limitations to our measure of religion, we must limit our hypothesis testing to more narrowly defined elements of the religion, social capital, and entrepreneurship interactions. Specifically, (1) does a higher concentration of congregations influence small business activity, (2) does that relationship vary across different religious traditions, broadly defined, and (3) do higher levels of religious diversity aid or hinder small business activity? One of the biggest challenges in studying community factors that affect entrepreneurial activity is one of empirical measurement. Entrepreneurship is multifaceted, involving aspects such as risk-taking and innovation, which are difficult if not impossible to quantify. Ideal data that capture such aspects do not exist. Most studies of entrepreneurship focus on one 4 These data were downloaded from the Association of Religion Data Archives at: http://www.thearda.com/Archive/ChCounty.asp. The 2010 survey allowed for 236 religious groups ranging from 217 Christian denominations to four Jewish, Muslims and Zoroastrian among others. The 236 groups reported a total of 344,894 congregations with 150,686,156 adherents, comprising 48.8 percent of the total U.S. population of 308,745,538 in 2010. 5 This work can be found at: http://www.pewforum.org/religious- landscape- study/

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S.C. Deller et al. / Journal of Economic Behavior and Organization 000 (2018) 1–17 Table 1 Proprietorship index: Correlations and eigenvector weights.

Eigenvector Weights Number of Proprietorships per 1,0 0 0 Population Share of Wage and Salary Employment from Proprietorships Proprietorship Income per Number of Proprietorships (0 0 0$) Proprietorship Income per Capita (0 0 0$) Variance Explained

0.3844

Share of Wage and Salary Employment from Proprietorships

Proprietorship Income per Number of Proprietorships (0 0 0$)

Proprietorship Income per Capita (0 0 0$)

0.48201∗∗∗ (0.001)

−0.1708∗∗∗ (0.001) 0.19294∗∗∗ (0.001)

0.42373∗∗∗ (0.001) 0.55779∗∗∗ (0.001)

0.5263

0.4130

0.70817∗∗∗ (0.001)

0.6361 0.5444

Marginal significance in parentheses. ∗ Significant at 90.0% level. ∗∗ Significant at 95.0% level. ∗∗∗ Significant at 99.9% level.

fundamental aspect, namely self-employment or business ownership, which undoubtedly spans a spectrum of innovators, risk-takers, stakeholders, and owners. As outlined in detail by Goetz and colleagues (2010) and Low and Isserman (2015), the data are further limited for small areas such as counties. For this study, we narrow our focus to the rate and success of proprietors and use data that is widely available at the community (county) level from the U.S. Department of Commerce’s Bureau of Economic Analysis, Regional Economic Information System (BEA-REIS). While using widely available data may place some limits on the analysis, it allows for other researchers to replicate and expand on our analysis. Our measures include: • • • •

Number of Proprietorships per 1,0 0 0 Population Share of Wage and Salary Employment from Proprietorships Proprietorship Income per Number of Proprietorships (0 0 0$) Proprietorship Income per Capita (0 0 0$)

No single one of these measures is appropriate, rather all four taken together in aggregate better reflect the entrepreneurial characteristics of the community. As such we construct a Proprietorship Index using principal component analysis. Because principal components can be sensitive to differences in the scaling of individual variables we standardize the four variables to have a zero mean and standard deviation of one. The index weighting scheme, based on the correlation structure of the four variables comprising the index, for the final measure along with a simple correlation matrix are provided in Table 1.6 The variables comprising our Proprietorship Index all move in unison except for the concentration of proprietorships and proprietorship income per proprietorship which move in the opposite direction. The size of the correlation coefficients, however, is relatively small (−0.1708). All four measures enter the final index with positive weights with the largest single component being proprietorship income per capita followed by share of wage and salary employment from proprietorships. The concentration of proprietorships has the smallest weight. The final index accounts for 54.4 percent of the total variation in the four individual variables. Thus, higher values of the Proprietorship Index are associated with higher levels of small business activity, which is our proxy for entrepreneurship. The third question centers on the selection of a set of control variables. Here we draw on prior studies that use similar proxies of entrepreneurship via small business activity including Markeson and Deller (2012, 2015), Goetz and Rupasingha (2009, 2014), Low and Weiler (2012) among others such as Contreras and Rupashingha (2014). We include the share of income from wage and salary employment and the unemployment rate as proxies for non-self-employment opportunities, both of which, however, may have competing effects. We might expect that communities that have more opportunities for wage and salary employment will place downward pressure on entrepreneurship. Also, lower unemployment rates should also place downward pressure on necessity entrepreneurship. Here necessity entrepreneurship occurs when people prefer wage and salary employment but the lack of such opportunities forces people into starting their own businesses to meet basic economic necessities as reaction to poor economic conditions. In both cases of low wage-and-salary employment and high unemployment, economic pressures spurring necessity entrepreneurship are expected to be lower resulting in lower Proprietorship Index. It may also be the case, however, that an expanding economy featuring a low unemployment rate and competitive wage and salary positions, would increase economic confidence and encourage opportunistic entrepreneurs.

6 There are numerous alternative methods to our use of the correlation matrix to build the final measure of entrepreneurial activity from different approaches to scaling the data to the use of the covariance matrix to a wide range of methods within factor analysis correspondence analysis and even cluster analysis. Unfortunately, there is no prior social science rooted theoretical justification of one approach over another. Using the simplest approach possibly limits the introduction of researcher bias.

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We also include two age profile variables to reflect the size of the community least likely to be in the labor market: percent under the age 18 and percent over the age 65. Generally, the very young (under 18) and older (over 65) are less interested in starting businesses. Singh and DeNoble (2003) maintain, however, that as our life expectancy has grown over time more retirees are reentering the workforce but in the form of starting their own businesses. We also control for the education profile of the community by constructing an education index based on the distribution of educational attainment levels for those age 25 and over. Using six educational attainment categories, ranging from less than a 9th grade education to having a graduate/professional degree, we use the 3rd moment of the distribution as our index. If the index is negative the distribution is skewed toward a more highly educated population and a positive value of the index indicates that the distribution is skewed toward a less educated population. A value of zero means that the distribution is symmetric favoring neither higher or lower education levels. To capture the economic base of the community (county) we include the share of earnings from construction, manufacturing, and retailing as well as government employment. Because so many construction companies are smaller scale, (general and subcontractors) we expect a larger construction industry to be associated with more small business activity. At the same time, most manufacturing is larger in scale thus we expect communities with more manufacturing activity to have a lower level of small business activity as we have defined it. We also expect more earnings from government to be linked to lower levels of small business activity. Dependency on retail could be either positively or negatively related to business activity. For example, many small, non-store, internet-based retail businesses are sole-proprietors but at the same time bigbox stores can present formidable competition to smaller businesses. We include median household income as an overall metric of demand and the population density to capture the rural-urban spectrum of U.S. counties. We expect more small business activity in urban places compared to rural places. Finally, as a very simple measure of social capital we include the concentration of civic and social organizations (number of organizations per 1,0 0 0 population). Our final set of control variables is aimed at capturing the growth patterns of the community. We expect that communities (counties) that are experiencing more growth will have heightened opportunities for entrepreneurship. Whereas declining or stagnate communities may see fewer opportunities for entrepreneurship primarily due to decreased demand: if a local market is struggling or declining, the likelihood of starting a successful new business is weak. On the other hand, these declining communities often times see an increase in necessity entrepreneurship as people find it necessary to create a job for themselves. This points to fundamental limitation to the available secondary data in that we cannot separate what Goetz et al., (2010) describe as Schumpeterian entrepreneurs, normal or mundane entrepreneurs or necessity entrepreneurs.7 Our growth measures include the rate of growth from 20 0 0 to 2010 for population, employment and per capita income. Our general model can be expressed as

P I = α + β1 DM + β2 EC + β3 LG +

θ RL + ε

(12)

where PI is our principal component derived Proprietorship Index, DM are the demographic metrics, EC are the economic structure metrics, LG is the three lagged growth metrics, and RL are our religious concentration metrics. In addition to concentration of congregations (number of congregations per 1,0 0 0 population) we also include a religious diversity measure. The measure of religious diversity is a simple Herfindahl-type index looking at the distribution across the number of congregations and adherence rates. Though the literature is somewhat mixed, we expect that higher levels of diversity across different religions lead to higher levels of bridging social capital thus having a positive relationship to small business activity.8 Drawing on the work of Pijnenburg and Kholodilin (2014) we use a heteroskedastic error spatial Durbin specification and estimator to explicitly capture the spatial dependency in the data but also allow for us to explicitly model the presence and level of spatial spillover effects between geographically proximate observations (Anselin 1988; LeSage and Pace 2009). Pijnenburg and Kholodilin (2014) found evidence of such structural spatial spillovers in their study of entrepreneurial activity across German regions. In essence, the political boundaries at which our data are collected and reported do not reflect the relevant economic region. The potential for this type of spatial spillover effects are not reflected in any of the religion and entrepreneurship literature available to date. The formal model can be rewritten as:

P I = α + ρW · P I + β11 DM + β12W · DM + β21 EC + β22W · EC + β31 LG + β32W · LG +

θ1 RL + θ2W · RL + ε

(13)

The term W is a spatial connectivity matrix that defines the geographical extent of economic spillovers. It can be defined in several ways and may simply reflect counties that are contiguous to county i, flows between counties (e.g., commuting), 7 Schumpeterian entrepreneurs are those that bring new products to market and are growth oriented. Normal, or what Julien (2007) refers to as mundane, entrepreneurs are those that see local market opportunities and elect to start a business. These businesses are generally not considered growth oriented and examples range from a barber shop to a hardware store to an interior design business. Reactionary entrepreneurs, as already described, are reacting to limited economic opportunities and open a business to satisfy their own personal economic necessities. 8 As documented by Silverman (2002), many religions have strong ties to race, for example, Latino/as are predominately Catholic and Asians are more likely to follow Eastern religions, thus to control for ethnic influences we include an ethnic diversity index based on the share of the population across  2 si which represents the probability of two six races. Specifically, if si is the share of the population identified as belonging to the ith race, the index is randomly selected people within the community (county) are of the same race. Thus higher values of the index is interpreted as a more homogenous community. While the role of race and diversity on community level entrepreneurship has been widely studied (e.g., Low, 2009; Low and Isserman, 2015; McGranahan, Wojan and Lamber 2011; Walzer and Blanke, 2013), the results are largely inconsistent and contradictory. Thus, we include racial diversity as a robustness check and find no substantial differences in our core results. These alternative specifications are available from the authors as requested.

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or it may capture counties that are within a given distance to county i. In the simplest sense, economic conditions in nearby counties may influence a county’s entrepreneurial intensity. While the specification of W has been a source of significant debate within the spatial econometrics literature, LeSage and Pace (2014) suggest that if the model is correctly specified, the exact construction of the matrix is secondary to simply accounting for the spatial interdependency. Here the spatial parameters ρ , β 12 , β 22 , β 32 and θ 2 capture different elements of the spatial dependency within the data. The spatial autoregressive parameter, ρ , gives us the degree to which small business activity is influenced by the degree of small business activity in a neighboring county. In our case, the spatial connectivity we are most interested in detecting arises because our spatial unit of observation is located near other counties that have a high intensity of proprietor activity (all else constant). This spatial pattern captured by using the spatial Durbin specification may produce complementary spillover effects that facilitate even more entrepreneurial activities in the county of interest – specifically, something akin to an “entrepreneurial region”, as was indirectly implied in the work of Pijnenburg and Kholodilin (2014), or it may substitute for local entrepreneurial activities, essentially crowding it out. Given the interaction terms embedded in the spatial Durbin specification, direct interpretation of the coefficients is not possible. To see this, consider the general form of the spatial Durbin model which can be expressed as y = ρW y + β X + δW X + e and in reduced form as y = (I − ρW )−1 β X + (I − ρW )−1 δW X + (I − ρW )−1 e. Let V (W ) = (I − ρW )−1 then write the reduced form as y = V (W )β X + V (W )δW X + V (W )e. Because V(W) is a matrix and not a scalar, the common approach of using point estimates to test the hypothesis as to whether or not spatial spillovers exist can lead to erroneous conclusions (LeSage and Pace 2009, p.74). Instead we need to use the partial derivatives to properly interpret the impact of changes to the variables. Specifically, ∂∂ yx = V (W )β + V (W )δW or ∂∂ yx = direct + indirect = total. Here the direct effect is the impact within the county and the indirect is the impact across counties, or the neighborhood effect. For reporting purposes, LeSage and Pace (2009) suggest the using the averages of the diagonal element of V(W)β for the direct effects, or within geographical unit effects, as well as the averages of the sum of the columns or rows (the symmetric nature of the matrix makes it irrelevant if one used the columns or rows) of the V(W)δ W for the indirect effects, or across geographical unit effects. The one limitation to interpreting the spatial Durbin indirect effects, of the spillover effect across community boundaries is the inability to say which direction the spillover is flowing. We cannot say, for example, that the spillover is flowing from the community in the center out to its neighbors or it is flowing from the neighbors into the community. Here appeals must be made to theory to draw implications. Because of the significant spatial heterogeneity across the U.S. and the large differences in how the effects of the recent Great Recession played out over the U.S., we would expect some spatial heterogeneity to exist in small business concentrations and entrepreneurial activity. Following LeSage and Pace (2009) we allow for this possibility by incorporating a heteroscedastic error structure: ε ∼ N (0, σ 2V ) V = diag(v1 , . . . , vn ), vi = v j . The set of variance scalars (v1 , v2 , . . ., vn ) are unknown parameters that need to be estimated. If spatial dependence is present in the data, it seems more reasonable to allow for the potential that the variance (σ 2 ) varies over space (v1 , . . . , vn ). From a broader perspective, the heteroscedastic model is a more general form and less restrictive than the homoscedastic error model. This specification requires us to move beyond traditional maximum likelihood because the number of integrals makes the estimation problem intractable. Using diffuse prior information, our econometric analysis relies on Bayesian estimation methods. We assume a normal-gamma conjugate prior for β , θ and σ , and a uniform prior for ρ . With the prior distributions denoted by π , the Bayesian priors are: π (β , θ ) ∼ N(c, N), π (1/σ 2 ) ∼ (d, v) and π (ρ ) ∼ U[0, 1]. The prior distribution for the vi terms takes the form of an independent χ 2 (r)/r distribution where χ 2 is a single parameter distribution with r as the parameter. By adding the single parameter r this allows the estimation of the n parameters vi . The prior distributions are indicated using (π ), a normal-gamma conjugate prior for σ and a uniform prior for ρ .9

6. Results To simplify the discussion, we report the results for the base model (i.e., θ1 = θ2 = 0) in Table 2 and then report the results for the models that include metrics of religion in Table 3 but suppress the results for the control variables. We find that the results for the control variables are stable across all four specifications of the models (base model plus three religion-augmented models) lending some confidence in the robustness of the base model. Because of this stability we elected to suppress the results of the control variables and focus attention on the different religion measures. Based on the simple R2 the models explain slightly more than one-third of the variation in our Proprietorship Index and we find significant evidence of spatial dependency in the data as evident from the statistically significant spatial ρ parameter and strong indirect spillover effects. This is consistent with the findings of Pijnenburg and Kholodilin (2014) and suggests that prior studies that ignore these spatial patterns in the data are biased and inconsistent. We also test for multicollinearity

9 The Gibbs sampling procedure must be repeated until the values of the estimates converge. For this study we use 10 0,0 0 0 draws with the first 2,0 0 0 draws removed in effect acting as a ”burn-in” to minimize the likelihood of poor starting values.

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Table 2 Base model results, proprietorship index. Direct Growth in Population (20 0 0 to 2010) Growth in Employment (20 0 0 to 2010) Growth in Per Capita Income (20 0 0 to 2010) Share of Income From Wages and Salary Percent of the Population Under Age 18 Percent of the Population Over Age 65 Education Index Concentration of Civic and Social Organizations Unemployment Rate Share of Earning from Construction Share of Earnings from Government Employment Share of Earnings from Manufacturing Share of Earnings from Retail Mean Household Income Population Density Ethnic Diversity

R2 Spatial ρ

Indirect ∗∗∗

Total ∗∗∗

1.3974 (0.0 0 01) −2.3519∗∗∗ (0.0 0 01) −0.0622 (0.5933) −0.3877∗∗∗ (0.0 0 01) −4.8774∗∗∗ (0.0 0 01) 1.5052∗∗ (0.0070) −0.0935∗∗ (0.0145) −0.1183 (0.2881) −6.5535∗∗∗ (0.0 0 01) 1.1538∗∗ (0.0170) −2.0287∗∗∗ (0.0 0 01) −1.5390∗∗∗ (0.0 0 01) 2.9620∗∗∗ (0.0 0 01) 2.5556∗∗∗ (0.0 0 01) 0.0 0 01 (0.1466) −0.0371 (0.7803)

−2.2993 (0.0 0 01) 2.4441∗∗∗ (0.0 0 01) 1.0749∗∗∗ (0.0 0 01) −0.8133∗∗ (0.0066) 2.6068∗∗ (0.0328) −2.4087∗∗ (0.0418) −0.1099 (0.1567) −0.1425 (0.6248) −0.0033 (0.9983) −0.0992 (0.9376) −1.0259∗∗ (0.0177) 0.6013 (0.2280) 7.5518∗∗ (0.0 0 04) −0.7582∗∗ (0.0170) 0.0 0 0 0 (0.3120) 0.0577 (0.7899)

−0.9019 (0.1496) 0.0922 (0.8663) 1.0127∗∗ (0.0 0 03) −1.2010∗∗ (0.0 0 03) −2.2706∗ (0.0525) −0.9035 (0.4401) −0.2034∗∗ (0.0065) −0.2608 (0.3967) −6.5568∗∗∗ (0.0 0 01) 1.0546 (0.4623) −3.0547∗∗∗ (0.0 0 01) −0.9376∗ (0.0803) 10.5139∗∗∗ (0.0 0 01) 1.7974∗∗∗ (0.0 0 01) 0.0 0 01 (0.4969) 0.0206 (0.9134)

0.3345 0.3357∗∗∗ (0.001)

— —

— —

Sample Size: 3,236 Marginal significance in parentheses. ∗ Significant at 90.0% level. ∗∗ Significant at 95.0% level. ∗∗∗ Significant at 99.9% level.

in the base model and find the condition index10 is 61.386 which is slightly high, but the highest variance inflation factor11 for any given variable was only 2.899. 6.1. Base model results The lagged growth metrics, designed to capture the dynamics of the community, have a complex impact on entrepreneurship activity. If we look at the impact of growth within the county (direct effect) growth in population has a positive impact on entrepreneurship while growth in employment has a negative impact. Both of these are as expected: growing population means market opportunities are expanding while growth in employment may be creating wage and salary employment opportunities lowering the likelihood of reactionary entrepreneurship. We find that growth in income has no direct effect. Looking at spillover effects across county boundaries (indirect) the relationships move in opposite directions. For example, though population has a positive home-county effect, the indirect effect is negative, suggesting that population growth at home can expand the economy and perhaps enhance amenities and incentivize entrepreneurship, perhaps even pulling would-be entrepreneurs from neighboring counties. While employment growth in the home county constrains home-county entrepreneurship, the rise in employment may be increasing demand for complementary services in surrounding areas leading to an increase in entrepreneurship in neighboring counties. For population and employment, the cross-flowing effects appear to cancel each other out and the total effect is statistically insignificant. For income growth, the positive indirect

10

A condition index is an eigenvalue-based measure of multi-collinearity in the independent variables of a regression. The variance inflation factor is also a measure of multi-collinearity in a regression. It estimates how much the variance is inflated because of multicollinearity for each coefficient. 11

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S.C. Deller et al. / Journal of Economic Behavior and Organization 000 (2018) 1–17 Table 3 Religion influence on proprietorship index (Control Variables Suppressed for Space). Direct Concentration of Total Number of Congregations R2 Spatial ρ Concentration of Evangelical Congregations Concentration of Black Protestant Congregations Concentration of Catholic Congregations Concentration of Mormon Congregations Concentration of Muslim Congregations Concentration of Jewish Congregations Concentration of Eastern Congregations R2 Spatial ρ Religious Diversity on Number of Congregations R2 Spatial ρ

∗∗∗

0.0940 (0.001) 0.3436 0.3326∗∗∗ (0.001) 0.1672∗∗∗ (0.0 0 01) 0.0671 (0.5847) −0.0215 (0.7692) 0.1074 (0.4251) −3.1136 (0.1312) 1.3334 (0.2479) 0.8399 (0.2509) 0.3542 0.3114∗∗∗ (0.001) −0.0045∗∗ (0.0017) 0.3424 0.3255∗∗∗ (0.001)

Indirect

Total

0.0146 (0.7394) — —

0.1087∗∗ (0.0110) — —

0.0908 (0.1404) −0.5988∗∗ (0.0057) 0.1829 (0.2807) 0.1666 (0.3984) −18.7998∗∗ (0.0163) 2.0355 (0.6711) 3.1900∗∗ (0.0475) — —

0.2581∗∗∗ (0.0 0 01) −0.5317∗∗ (0.0077) 0.1614 (0.3609) 0.2741∗∗ (0.0409) −21.9134∗∗ (0.0110) 3.3689 (0.5101) 4.0300∗∗ (0.0328) — —

−0.0028 (0.2745) — —

−0.0072∗∗ (0.0015) — —

Marginal significance in parentheses. ∗ Significant at 90.0% level. ∗∗ Significant at 95.0% level. ∗∗∗ Significant at 99.9% level.

effect outweighs the statistically insignificant direct effect suggesting that growth in income will have a positive overall (total) effect on proprietorship activity. The conclusion is that historical growth is important in understanding proprietorship activity, but a regional perspective must be taken. As expected, a larger share of income that comes from wage and salary employment corresponds to less proprietorship activity. We also find that the share of the population that is under age 18 tends to place downward pressure on proprietorship activity. But again, the direct and indirect effects move in opposite directions but the total effect is consistent with our expectations. We find that within the county a higher share of the population that is over age 65 is actually associated with higher levels of entrepreneurial activity but the indirect effect is negative resulting in the total effect being negative but statistically insignificant. One interpretation of the positive direct effect is that having a larger share of older persons opens up business opportunities, but a more likely interpretation is older people reentering the labor market but in the form of small business owners. This latter result is somewhat consistent with the findings of Lambert et al. (2007) where retirement-destination rural U.S. counties had higher than expected small business activity. The education index performs as expected where the data supports the notion that a community with a higher level of education tends to have more entrepreneurial activity although the indirect or spillover effects are weak. Our very simple measure of social capital, based on civic and social organizations, is not statistically significant for either the within (direct) or spillover (indirect) effects. It is likely this simple measure is not sufficient to capture non-religious elements of community social capital. High unemployment is associated with lower levels of entrepreneurship as proxied by our Proprietorship Index. Given the concept of necessity entrepreneurship, we might expect the opposite result to hold. Three possible explanations for this unexpected result. First, it may be that the lingering effects of the Great Recession, which was coupled with financial crisis that has limited access to capital for entrepreneurs, are introducing sufficient noise in the data to distort the results. Second, and more likely, a better measure could be sustained periods of chronic unemployment. Third, the result is driven by opportunistic entrepreneurs who are pulled into business activity by an expanding economy with low unemployment. Our four measures of economic structure provide results that are consistent with expectations. Greater share of earnings from construction tends to be linked to higher levels of entrepreneurship, but this is only within the county (direct) as the indirect effect is insignificant and weak enough to drive the total effect to being insignificant. Higher levels of earnings from both the public sector (government) and manufacturing places downward pressure on entrepreneurship, again, as expected. We see a strong positive impact on entrepreneurial activity associated with higher dependency on retail for earnings. If we consider the nature of the businesses that tend to make up these industries (save for the public sector), these results make Please cite this article as: S.C. Deller et al., Social capital, religion and small business activity, Journal of Economic Behavior and Organization (2018), https://doi.org/10.1016/j.jebo.2018.09.006

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intuitive sense. Median household income has a strong positive direct effect and slightly weaker negative indirect effect resulting in the overall total effect to be positive and significant. This suggests that communities with higher income levels and are surrounded by lower income communities will have higher levels of proprietorship activity. Population density, which is our measure of the urban-rural spectrum, is not statistically significant, though perhaps unsurprisingly given the range of controls which capture several aspects of rural-urban variation. Perhaps the most important is the finding of strong spatial spillovers across communities. While the directions of the spillover effects can appear to be contradictory on the surface, the insights gained can be powerful in helping understand entrepreneurial patterns across communities. In particular, if local policy makers are interested in fostering a higher level of entrepreneurship they must look at the characteristics of not only their own community but that of the surrounding communities. 6.2. Effects of religion The expanded models where we include the concentration of religious congregations is provided in Table 3. Again, because the results of the control variables are stable across the expanded models, we elected to suppress those results for space considerations. The simplest religion-augmented model includes the concentration of all congregations regardless of the denomination. We find that the direct effect is positive and statistically significant but the indirect effect, although positive, is statistically insignificant. The total effect, which reflects both the direct and indirect is positive and statistically significant. This result provides evidence that religious organizations, in the form of congregations, can have a positive impact on entrepreneurial activity and this effect is strongest within the home community. The more interesting question that we address in this study is if the positive relationship varies across different types of religious faiths. First, we do find evidence of strong differences across the seven religions we examine. Second, the results tend to be consistent with the findings of the handful of other studies examining the interface of religion and entrepreneurship. Consistent with Henley (2016), we find that a higher concentration of Evangelical congregations is linked to higher levels of our Proprietorship Index, particularly within the home community. This may be driven by institutional mechanisms within the faith community. For example, most evangelical churches are financially independent, as opposed to the Catholic churches which are financed at least partly hierarchically. For evangelical churches, the funding mechanism may cultivate higher levels of social capital as people rely on each other to collectively sustain the church and pastors have a direct incentive to foster and grow a strong community. A higher concentration of Black Protestant churches, however, is tied to lower levels of entrepreneurship as indicated by the total effect. This effect of black protestant churches seems to be primarily driven by indirect spillover effects. While the role of Black Protestant churches has not been previously explored in the literature, the lower level of entrepreneurship in African-American dominated communities is consistent with other findings in the literature (e.g., Bogan and Darity 2008). The unique result of our study which highlights the spillover rather than home-community effect may be driven partly by the spatial segregation of minority groups. To the extent to which these communities are isolated, they may also be surrounded by an economically underperforming region resulting in weaker entrepreneurial outcomes.12 Consistent with Kingma and Yeung (2014) and Minns and Rizov (2005) we find that Catholic congregations have no influence on entrepreneurial activity. We have the same result with the concentration of Jewish congregations. This result is somewhat surprising because the strength of networks within certain Jewish dominated industries, such as the diamond wholesale market in New York City, has been widely used to describe notions of social capital in a business context (Coleman 1988). We do find that a higher concentration of Mormon congregations has a positive impact on proprietorship activity but in a spatially unique manner. Neither the direct or indirect effects are statistically significant in isolation, but taken together there is a statistically significant impact. This may be due to characteristically strong lay participation and leadership as well as extensive volunteerism that can be conducive to bonding social capital (McBride 2007). Further, the Mormon population is also organized geographically such that there all small wards (i.e. parishes) that are connected to other wards in their same stake (i.e. diocese). This structure may be the basis for forming bridging social capital across units and small but widely dispersed entrepreneurial gains. The results on the concentration of Muslim mosques has a very strong negative impact on our measure of entrepreneurship, which is consistent with the findings of Guisa and colleagues (2007). As with Black Protestant the direct effect, while negative, is not statistically significant but the indirect effect is significant to such an extent that the total effect is negative and significant. We suspect that the spillover effect is partly driven by the spatial segregation of minority communities and the extent to which they may be isolated from the business center and/or within an underperforming region. As such, the home community may function sufficiently well due to support from local members but surrounding areas may fall further

12 While we have offered some insights into to why we find these specific spatial spillover patterns (for three religions statistically insignificant direct or within county effects, but significant indirect or neighboring effects) care must be taken in drawing too strong of an interpretation. While the spatial Durbin specification we adopted (eq.13) specifically allows for spatial spillover, the direction of that spillover is unknown. For example, if we have two neighboring counties (A and B) with a large concentration of Eastern tradition congregations in one county (A), but not in the other (B), we do not know the direction of the indirect effect. We do not know if the effect is flowing from A to B or B to A, all we can state is that is a positive spillover and the total regional effect is positive and significant. Thus, while the direct and indirect effects can provide additional insights, the core hypotheses around the influence of religion, via our measures, on small business activity and entrepreneurship should focus on the total effects.

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behind. Hamtramck, Michigan, for example, has a large Muslim population in part of the larger Detroit area, much of which is still struggling (Lu and Mellnik, 2015). Finally, as found by Vallieri (2008) a higher concentration of congregations that follow Eastern religious traditions does not hinder entrepreneurial behavior and actually has a strong total effect. Indeed, in their study of Buddhist tradition and entrepreneurship, Liu et al., (2018) found that Buddhist life philosophies, including mediation, helped entrepreneurs better deal with the stresses of starting a business. Our last religion focused variable of interest is the Herfindahl-type index of diversity. Lower values of the index are associated with a more diversified mix of religions while higher values are associated with a less diversified or homogenous religious community. We hypothesized that greater diversity in religious organizations, once other community characteristics are controlled for, would likely enhance entrepreneurial activity because of the potential for building bridging social capital. We find indeed, increased diversity is associated with higher levels of entrepreneurial activity. This is consistent with the findings of Carswell and Rolland (2004). Rather than creating tensions within the community, religious diversity fosters entrepreneurial activity. 7. Discussion Our analysis lends support to our central hypothesis; religious institutions and traditions can influence levels of entrepreneurial activity through the mechanism of social capital. More importantly our results suggest that care must be taken with making broad generalizations. Specifically, if we return to Coleman’s claim that “[i]t has become now almost cliché that religion in the United States generates more ‘social capital’ than any other American institution” our results suggest that with respect to entrepreneurship and small business activity this is an over-generalization. We find that the influence of religion on entrepreneurship varies significantly across different religious traditions and religious diversity. In general, a greater concentration of religious congregations and a greater diversity in congregations being associated with higher levels of our Proprietor Index is consistent with congregations as a venue to connect with potential suppliers and consumers, network, gather information and learn through bonding and bridging social capital. The greater diversity of different congregations might be a proxy for a more open community, which in turn, is more conducive to entrepreneurial activity. The implications of these findings are fairly straightforward. Congregations are likely a mechanism for building social capital that then enables and supports entrepreneurial behavior. For example, a large number of congregations in a county, irrespective of religious diversity, generate a higher level of association and trust among individuals within and between those groups. This corresponds to a higher rate of and more successful proprietorships within these communities. These results support the central social capital hypothesis in a broad sense. From a purely local or regional economic development perspective local religious institutions should not be overlooked as an important part of the local economic milieu. Consistent with much of the available literature, different religions have different influences on entrepreneurship. The Evangelical tradition is generally associated with higher levels of proprietorship activity. Both Mormonism and Eastern religious traditions are also associated with higher levels of proprietorship. Muslim along with Black Protestant traditions correspond to lower values of our measure of proprietorship. Higher concentrations of Catholic churches tend to have no association with entrepreneurship. Thus, religions and the social capital building opportunities they present do help us understand entrepreneurship. Treating religion as a monolithic element of social capital and entrepreneurial and small business activity would be in error. The bottom line to our analysis is that the religious characteristics of a community (county) can help us better understand the entrepreneurial and small business activity within the community. The theoretical lens of social capital, both bonding and bridging, can help us think through how this relationship plays out in the community. More importantly, we found that the impact of religious congregations on entrepreneurial and small business activity varies significantly across different religious traditions. For local economic development practitioners, these insights cannot only help them better understand the entrepreneurial spirit of the local economy but also potentially identify unique, and perhaps new, roles for local religious institutions in community and economic growth and development efforts. Something as simple as helping form and facilitate a local “council of ministers”, where local religious leaders from different faiths come together to discuss community issues, can help build an important characteristic of bridging social capital. 8. Conclusions People coming together through religious activities has long been held as a means of building social capital within a community. At the same time, within the economic growth and development literature, the role of social capital in fostering entrepreneurial and small business activity is becoming more widely accepted. The question is if a better understanding of the religious nature of a community will help us better understand how social capital develops and how it impacts entrepreneurial and small business activity. Theoretically, participating in religious activities provides an opportunity for entrepreneurs and small business owners to build and utilize networks. These networks can be within a particular congregation, in the form of bonding social capital, and across congregations, in the form of bridging social capital. Across a broad set of specifications, we can draw several general conclusions. First, the religious characteristics of the community (county) do influence the rate and performance of proprietorships within the community. This is consistent with the hypothesis that religious institutions, or congregations, build bonding social capital and may create opportunities for Please cite this article as: S.C. Deller et al., Social capital, religion and small business activity, Journal of Economic Behavior and Organization (2018), https://doi.org/10.1016/j.jebo.2018.09.006

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bridging social capital. Further, our results indicate that diversity is also associated with more entrepreneurial activity providing additional evidence of bridging social capital. While diversity of religion generally corresponds to more entrepreneurship, the results also suggest that the impact of any particular religion is unique. Thus, religion should not be treated as a monolithic element of social capital with homogenous effects. There are significant differences within and across different religious traditions. For example, we find that a higher concentration of Muslims corresponds to less proprietorship activity at the county-level but a higher concentration of Evangelicals has a positive impact on small business activity. It is beyond the scope of this study to explore why there are these differences between religious traditions, rather we sought to simply understand if specific aspects of religiosity in a county affect small business activity via social capital and if that relationship varies across religious traditions. Our analysis indicates that this is the case; consideration for religious traditions within a community can help us move forward in understanding small business activity. Future work in this area must refine our understanding of why different religious traditions have different impacts on entrepreneurial and small business activity. Our work is limited to reaffirming that understanding the religious characteristics of the community can help us better understand entrepreneurial and small business activity and it would be an error to treat religion as a monolithic element of social capital. 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Please cite this article as: S.C. Deller et al., Social capital, religion and small business activity, Journal of Economic Behavior and Organization (2018), https://doi.org/10.1016/j.jebo.2018.09.006