Roots to Grow: Family Firms
and Local Embeddedness in
Rural and Urban Contexts
, Francesco Chirico
, Daniel Pittino
, and Johan Klaesson
The present study analyzes the nexus among business growth, ownership structure, and local
embeddedness—that is, the involvement of economic actors in a geographically bound social
structure—in rural and urban contexts. This work combines regional economics with studies on
family business and firm growth and uses a coarsened matched sample of privately held Swedish
firms. The findings indicate that family firms benefit more than nonfamily firms from local embed-
dedness and as such they achieve higher levels of growth and that this effect is more pronounced in
rural areas. Research implications are shared in the Conclusion section.
business growth, local embeddedness, urban–rural contexts, family firms
Previous research—particularly within the ﬁeld of regional studies—has addressed the inﬂu-
ence of local embeddedness, which is the involvement of economic actors in a geographically
bound social structure (Granovetter, 1973; Hess, 2004), on a number of ﬁrm-level outcomes
(e.g., Huggins, 2010; Johannisson, Ramirez-Pasillas, & Karlsson, 2002; Watts, Wood, &
Wardle, 2006; Welter, 2011), such as innovation (Huggins & Thompson, 2014), knowledge
sharing (Huggins & Johnston, 2009), new venture creation (Breitenecker, Harms, Weyh,
Maresch, & Kraus, 2017) and ﬁrm growth (Audretsch & Dohse, 2007; Dahl & Sorenson, 2012).
However, until recently (e.g., Backman & Palmberg, 2015; Bird & Wennberg, 2014), there
has been little cross-fertilization between regional studies and the family business literature.
Thus, our understanding of the link between family ﬁrms—characterized by some degree of
family involvement in ownership and management (Miller, Le Breton-Miller, Lester, &
Entrepreneurship Theory and Practice
!The Author(s) 2018
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Centre for Family Enterprise and Ownership (CeFEO), Jo
¨ping International Business School—Jo
´gico de Monterrey, EGADE Business School, Mexico
Centre for Entrepreneurship and Spatial Economics (CEnSE), Jo
¨ping International Business School—Jo
Research Institute of Industrial Economics, Stockholm, Sweden
Francesco Chirico, Centre for Family Enterprise and Ownership (CeFEO), Jo
¨ping International Business
¨ping University, P.O. Box 1026 SE-551 11 Jo
Cannella, 2007) —and the regional economic context remains quite limited (Stough, Welter,
Block, Wennberg, & Basco, 2015). This research gap is not a trivial issue, given the impor-
tance and potentially crucial role of local embeddedness in family ﬁrms’ behavior (e.g.,
Berrone, Cruz, & Go
´mez-Mejia, 2012; Bird & Wennberg, 2014; Zellweger, Nason,
Nordqvist, & Brush, 2013) and the key contribution of family ﬁrms to GDP and job creation
in diﬀerent regional contexts worldwide (e.g., Astrachan & Shanker, 2003; Basco, 2015;
Bjuggren, Johansson, & Sjogren, 2011; Memili, Fang, Chrisman, & De Massis, 2015).
This article aims to ﬁll this gap by investigating local embeddedness and family ﬁrm
growth in rural and urban contexts. We focus on business growth because it has been
proven to be among the key drivers of macroeconomic regional development (e.g.,
Carree & Thurik, 2003; Huggins & Thompson, 2014). Moreover, family ﬁrms’ attitude
toward growth is subject to a recent ongoing debate (e.g., Backman & Palmberg, 2015;
Bird & Wennberg, 2014; Bjuggren, Daunfeldt, & Johansson, 2013) that deserves
further attention from the family business research community (e.g., Miller, Steier, & Le
Breton-Miller, 2016). Using data covering privately held ﬁrms in Sweden in the 2004–2013
period, we assess family ﬁrms’ growth compared to that of nonfamily ﬁrms and how
this diﬀerence is contingent on the local embeddedness of the ﬁrm and the rural–urban
context in which the ﬁrm operates. Our ﬁndings indicate that family ﬁrms beneﬁt from
local embeddedness more than nonfamily ﬁrms and that this eﬀect is more pronounced in
Thus, our study makes several contributions. First, our work focuses on business growth,
which, although important, is a relatively new research topic in relation to family ﬁrms (see,
e.g., Casillas, Moreno, & Barbero, 2010). In particular, our results provide insights into the
theoretical mechanisms underlying the phenomenon of growth in family companies. Second,
we contribute to the ongoing debate about the family’s eﬀect on ﬁrm outcomes by considering
local embeddedness as a further intervening factor that helps explain the mixed ﬁndings in the
previous literature (e.g., Miller, Minichilli, & Corbetta, 2013; Wagner, Block, Miller,
Schwens, & Xi, 2015). Third, combining regional studies with the family business literature,
we answer the call for a deeper understanding of the contribution of family ﬁrms to regional
economic development and of the eﬀects of the regional context on ﬁrm behavior and out-
comes (Stough et al., 2015). Fourth, we contribute to the ﬁeld of regional economics by
detailing the importance of considering the impact of the spatial dimension (i.e., the urban
versus rural context) jointly with ﬁrm-level variables that account for family involvement and
territorial embeddedness. More speciﬁcally, we contribute to the literature on urban versus
rural ﬁrm growth (Tunberg, 2014) by investigating whether a rural context, properly leveraged
by ﬁrm-level behaviors, can maximize the beneﬁts of local embeddedness in speciﬁc organiza-
tional forms. Fifth, we contribute to the empirical studies on ﬁrm-level growth in family ﬁrms
using a comprehensive dataset that includes both micro and small ﬁrms, whereas most pre-
vious studies have tended to exclude smaller companies due to identiﬁcation constraints
(Backman & Palmberg, 2015).
The existing literature on embeddedness (Granovetter, 1973) suggests that economic activities
can be properly understood only if we consider the involvement of business organizations in
systems of interpersonal relationships and broader social structures. Various forms of
embeddedness can be identiﬁed according to the prevailing dimension in the formation of
relationships and social structures (Grabher, 1993; Granovetter, 1973). In particular,
2Entrepreneurship Theory and Practice 00(0)
economists and economic geographers have long been interested in the spatial distribution of
economic activity and in factors that can explain location patterns. The relationship between
location and economic activity can be interpreted using the speciﬁc concept of local embedd-
edness, which is deﬁned as the involvement of economic actors in a geographically delimited
network and/or institutional setting (Granovetter, 1973; Hess, 2004).
Local embeddedness is particularly important for business growth (see e.g., Davidsson &
Wiklund, 2006) because it shapes the basic conditions that support productivity and competi-
tiveness (Jacobs, 1969; Thompson, 1965). The crucial role of local embeddedness has been
highlighted with respect to ﬁrm outcomes at the local and regional/domestic levels (e.g.,
Cooke, 2007; Cooke, Clifton, & Oleaga, 2005) and competitiveness at the international
scale (e.g., Al-Laham & Souitaris, 2008). In particular, local embeddedness favors contacts
with customers and suppliers in a geographic area (e.g., Cooke, 2007) and facilitates access to
tangible assets, such as technology and a skilled labor force, and intangible assets, such as
localized knowledge. Localized knowledge is characterized largely by being tacit; thus, it is
diﬃcult to codify, transfer and replicate. Therefore, localized knowledge can play a central
role in building a ﬁrm’s competitive advantage (Grant, 1991; Nonaka, 2008; Spender, 1993).
The local context may also favor the pursuit of expansion strategies in international markets,
as local institutions and linkages often help to develop legitimacy and capabilities that can
sustain ﬁrms’ international competitiveness (e.g., Al-Laham & Souitaris, 2008; Peng, Lee, &
The translation of localized knowledge and resources into competitive advantage
depends crucially on the capacity of the ﬁrm to assimilate, utilize, and exchange such
resources, and this, in turn is related to the ﬁrm’s preexisting knowledge, organizational
processes and strategies (Cohen & Levinthal, 1990). The implications of local embedded-
ness vary also according to the type of region in which a ﬁrm is located. Regions are
heterogeneous, and the levels of economic activity, development and resources diﬀer sub-
stantially within a given region. Economic activities and resources are indeed unevenly
distributed across space within a country. This uneven distribution leads some locations to
be characterized by an abundance of resources and others by a scarcity of resources, as
reﬂected in the urban–rural spatial context. Urban regions are larger and denser than rural
regions, and they present advantages in terms of (a) a diversiﬁed supply of various pro-
ducer services; (b) a regional network for information ﬂows regarding new production
techniques, products, customers, and suppliers; and (c) a large and diﬀerentiated labor
supply (Norton, 1992).
Local embeddedness is a particularly important feature of family ﬁrms. As shown, for
example, by Bird and Wennberg (2014: 424), ‘‘family businesses are more embedded within
the regional community than their non-family counterparts,’’ which in turn aﬀects their
strategic choices. Family ﬁrms are organizations in which ownership is concentrated
within a family, with multiple family members involved in business operations while striving
to maintain intraorganizational family-based relatedness (Arregle, Hitt, Sirmon, & Very,
2007; Miller, et al., 2007). Given these features, family business owners are particularly
sensitive to organizational longevity, and therefore, they often emphasize the preservation
of durable relationships with local stakeholders both through the establishment of cohesive
internal communities involving employees (e.g., Miller, Lee, Chang, & Le Breton-Miller,
2009; Pittino, Visintin, Sternad, & Lenger, 2016) and through the creation of connections
with external actors (Arregle et al., 2007). Family ﬁrms’ higher local embeddedness is linked
to family owners’ noneconomic goals (Chrisman, Chua, Pearson, & Barnett, 2012). These
goals are often related to strengthening a ﬁrm’s community citizenship, to the preservation
`et al. 3
of binding and long-term ties with local stakeholders and to the contribution to the eco-
nomic and social development of the area in which the company is located (e.g., Berrone
et al., 2012; Berrone, Cruz, Go
´mez-Mejia, & Larraza-Kintana, 2010; Deephouse &
Next, we argue that whereas certain features of family ﬁrms inhibit ﬁrm growth in com-
parison to nonfamily ﬁrms, local embeddedness and the rural context positively moderate and
thus counterbalance this negative eﬀect.
Family Firms and Business Growth
Business growth is considered a key factor of the creation of wealth and employment
(Davidsson & Wiklund, 2006). As several reviews highlight (e.g., Davidsson, Achtenhagen,
& Naldi, 2010; Delmar, 1997; Gilbert, McDougall, & Audretsch, 2006; Shepherd & Wiklund,
2009; Wiklund, 1998), research on this topic has focused on both the antecedents and out-
comes of growth, yet empirical evidence is somehow conﬂicting (Achtenhagen, Naldi, &
Melin, 2013; Davidsson & Wiklund, 2006; Shepherd & Wiklund, 2009). However, one
rather established fact in this area of research is that growth outcomes are aﬀected by ﬁrm
owners’ preferences (e.g., Cliﬀ, 1998; Wiklund, Davidsson, & Delmar, 2003; Wiklund, Patzelt,
& Shepherd, 2009).
In particular, previous studies have suggested that in the pursuit of nonﬁnancial goals
(Chrisman et al., 2012), family owners tend to be more risk averse than nonfamily ﬁrms
with regard to growth opportunities (e.g., Bjuggren et al., 2013; Daily & Dollinger, 1992;
Hamelin, 2013). For instance, Go
´a, Haynes, Nu´ n
˜ez-Nickel, Jacobson, and Moyano-
Fuentes (2007) ﬁnd that family owners are loss averse in terms of threats to their socioemo-
tional wealth, even if this aversion means accepting a greater probability of below-target
performance. Put diﬀerently, avoiding nonﬁnancial losses has a greater weight than pursuing
ﬁnancial gains. Additionally, growth often implies the participation of external investors or a
reliance on debt ﬁnancing or equity funding, which are likely to diminish the capacity of the
family to exert control over the business (Chrisman & Patel, 2012; Go
´mez-Mejia et al., 2014).
For instance, although obtaining debt ﬁnancing does not dilute the family’s ownership of a
ﬁrm, lenders often impose restrictive covenants and reporting requirements (Smith & Warner,
1979), which again reduce the family’s decision-making discretion and, overall, the family’s
nonﬁnancial wealth endowment, such as family identity and the potential perpetuation of the
family dynasty (Go
´mez-Mejia, Cruz, Berrone, & De Castro, 2011; Hoskisson, Chirico, Zyung,
& Gambeta, 2017).
In contrast, nonﬁnancial considerations are less pronounced among nonfamily ﬁrms—for
example, in ﬁrms dominated by an entrepreneurial team with no family ties in the business—
which are less likely to face the nonﬁnancial-ﬁnancial trade-oﬀs related to growth (Chrisman
& Patel, 2012). These ﬁrms may ﬁnd nonﬁnancial considerations to be less salient and frame
their strategic choices around ﬁnancial criteria, which in turn may aﬀect growth. Indeed,
existing research suggests that compared to family ﬁrms, nonfamily ﬁrms are less emotionally
attached to the business and more ﬁnancially motivated (Miller, Le Breton-Miller, &
Scholnick, 2008). Thus, they favor strategies of rapid growth in sales through quick expansion
by using leverage, building up cash reserves, and attracting external equity investors (Miller
et al., 2011). Thus, we propose the following:
Hypothesis 1: Family ﬁrms exhibit lower business growth than non-family ﬁrms.
4Entrepreneurship Theory and Practice 00(0)
Local Embeddedness and Business Growth
We also predict that as local embeddedness increases, family ﬁrms are more likely than their
nonfamily counterparts to take advantage of localized knowledge and resources and further
enhance them through training and socialization processes supported by their rich tacit
knowledge and (family) ﬁrm-speciﬁc assets (Block & Spiegel, 2013). As argued by Bird and
Wennberg (2014) and Chirico, Ireland, and Sirmon (2011), family ﬁrms indeed possess a
superior capacity to leverage local resources and networks. In fact, they often build their
competitive advantage by relying not only on tacit knowledge and the network-embedded
resources available within the business (Carnes & Ireland, 2013; Habbershon & Williams,
1999; Sirmon & Hitt, 2003) but also and most importantly on those outside of the business
(Bird & Wennberg, 2014; Chirico et al., 2011; Miller & Le-Breton Miller, 2005).
First, we argue that the superior capacity of family versus nonfamily ﬁrms to exploit the
beneﬁts of higher local embeddedness derives from the fact that the utilization of social capital
is often a distinctive component of a family ﬁrm’s strategy (e.g. Zahra, 2010). In family ﬁrms,
social relationships are indeed the building blocks of the family organizational structure
(Arregle et al., 2007) and are characterized by long-term generalized reciprocity and trust
among ﬁrm members (Long & Mathews, 2011; Pearson & Marler, 2010). In this context, the
family is a source of competitive advantage because of the uniqueness that it oﬀers to the ﬁrm
in terms of interactions between individual family members and the business. These features
tend to be replicated outside of the ﬁrm, thus creating an environment that facilitates the use
and exchange of knowledge and resources with the external network of local stakeholders on
the basis of informal and trust-based interactions (Miller, Lee, Chang, & Le Breton-Miller,
2009; Lester & Cannella, 2006; Peake, Cooper, Fitzgerald, & Muske, 2017) sustained by
family members’ personal commitment and personalized business relationships (Carney,
2005; Gedajlovic & Carney, 2010) and through the dissemination of the family business’
values and norms in the local community (Arregle et al. 2007; Benavides-Velasco,
´a, & Guzma
´n-Parra, 2013; Danes et al., 2009).
Second, family ﬁrms are more likely than nonfamily ﬁrms to exploit the beneﬁts of higher
local embeddedness because they obtain both ﬁnancial and nonﬁnancial utilities from such
behavior. More speciﬁcally, increasing levels of local embeddedness may help family ﬁrms to
reconcile the trade-oﬀ between the pursuit of ﬁnancial and noneconomic goals, thus sustain-
ing business growth at a higher rate than the growth of nonfamily ﬁrms—whose focus is on
ﬁnancial utilities only. For instance, in the context of increasing local embeddedness the
preservation of nonﬁnancial goals of family ﬁrms may motivate future-oriented ﬁnancial
strategies that prioritize the business’ long-term continuity in the community by maximizing
current growth opportunities (e.g., Zahra, Hayton, & Salvato, 2004; Miller et al., 2009). That
is, when local embeddedness is high, the pursuit of business growth helps family ﬁrms to
achieve not only higher ﬁnancial returns but also help them to fulﬁll their noneconomic
preferences, for instance, in terms of family reputational concerns, a feeling of responsibility
towards local stakeholders and the economic development of the territorial community, and
the desire for a long-term presence in the local community (Berrone et al., 2012). As such,
although we expect that family ﬁrms exhibit lower business growth than nonfamily ﬁrms, we
contend that as local embeddedness increases, family ﬁrms achieve higher levels of growth.
Hypothesis 2: Local embeddedness positively moderates the relationship between family involvement
and business growth in such a way that as local embeddedness increases, the growth of family ﬁrms
increases at a higher rate than the growth of non-family ﬁrms.
`et al. 5
Local Embeddedness in Urban Versus Rural Contexts
In rural areas, knowledge externalities are less frequent, and ﬁrms face less demand for their
products in both their immediate surrounding environment and in adjacent non-metropolitan
regions (e.g., Duranton & Puga, 2004). Moreover, rural areas often lack important resources
needed for ﬁrm growth, such as skilled labor and/or ﬁnancial capital, or at least the supply of
these production factors is less diversiﬁed in rural regions than in larger and denser urban
regions, which often oﬀer the most favorable conditions (Backman, 2013). These factors may
tend to lower the average ﬁrm growth in rural areas relative to that of ﬁrms located in more
urban settings (Tunberg, 2014).
The ﬁrm-region nexus is further inﬂuenced by the level of ﬁrm linkage—that is, the extent
to which a ﬁrm depends on and interacts with its rural–urban surroundings—and what the
local community can oﬀer in terms of economic opportunities (Dicken & Malmberg, 2001).
Davidsson and Honig (2003) ﬁnd that small ﬁrms located in a rural context are heavily reliant
on the resources and knowledge that can be accessed through friends and family. Consistent
with that result, Meccheri and Pelloni (2006) establish that in rural areas, ﬁrm outcomes
depend strongly on the ﬁrm’s human capital and social local anchoring. Thus, especially in
rural areas where resources are scarcer (Uzzi, 1999), local embeddedness may become more
relevant for the identiﬁcation and exploitation of growth opportunities.
Extending this argument to the comparison between a family and nonfamily ﬁrm, we
postulate that in rural areas the advantage of family ﬁrms in leveraging locally embedded
knowledge and resources for growth is particularly pronounced. Arregle et al. (2007) explain
that family ﬁrms are characterized by ‘‘network closure’’; that is, a family ﬁrm’s network is
characterized by densely rather than sparsely connected ties. As discussed above, compared to
nonfamily ﬁrms, family ﬁrms tend to build stronger and more durable relationships with the
local community, which can provide them with critical knowledge and resources for ﬁrm
growth. This phenomenon is particularly valid for rural areas, ‘‘where families have the
possibility to form alliances and build close connections with the community and are exposed
less to the anonymity of urban areas’’ (Bird & Wennberg, 2014: p. 425).
Additionally, given that rural ﬁrms often rely on informal relationships (Felzensztein,
Gimmon, & Carter, 2010) and on ﬁnancing through friends and family who are in close
proximity (Davidsson & Honig, 2003; Meccheri & Pelloni, 2006) while emphasizing commu-
nity-level shared values and norms as a platform for knowledge exchange (Falk & Kilpatrick,
2000; Summers, 1986), family ﬁrms have an important advantage over nonfamily ﬁrms in this
regard because of their informal and trust-based relationships and their strong local roots in
the region (Astrachan, 1988; Block, 2010; Fitzgerald, Haynes, Schrank, & Danes, 2010).
Hence, we predict that the positive moderating eﬀect of local embeddedness on the relation-
ship between family involvement and business growth is further enhanced in rural areas
compared to urban areas. In formal terms:
Hypothesis 3: The positive moderating eﬀect of local embeddedness on the relationship between family
involvement and business growth will be more pronounced in rural contexts than in urban contexts.
To test our hypotheses, we use detailed micro-data from Statistics Sweden. The data cover
individuals and privately held ﬁrms for the years 2004 to 2013 and are structured as matched
6Entrepreneurship Theory and Practice 00(0)
employee–employer longitudinal data. We excluded ﬁrms with only one employee (i.e., indi-
viduals who are self-employed) during the period considered. We further limited our sample
to independent companies (by excluding ﬁrms controlled by other organizations) of which the
information of who are the owners of the companies was available. We also excluded ﬁrms
established in 2004 (i.e., the matching year) given that we could not calculate business growth
in the ﬁrst year of existence of a ﬁrm. Finally, we considered companies with complete
information in the studied period. Thus, by utilizing this dataset, we cover a population of
over 40,000 privately held companies.
Variables and Measures
Business growth. Following previous studies (e.g., Delmar, Davidsson, & Gartner, 2003), we
measured business growth as relative (percentage) sales growth (i.e., [sales
. There is a 1-year lag between the measure of the dependent variable and all inde-
pendent and control variables.
Family firm measure. We adopt a binary measure of family ﬁrms. We deﬁne family ﬁrms as
those in which two or more family members own and manage the company
(Miller et al.,
2007), either in a household (spousal couple) or in a biologically linked family (i.e., father,
mother, and children who live in the same or another household) (see also Wennberg,
Wiklund, Hellerstedt, & Nordqvist, 2011; Wiklund, Nordqvist, Hellerstedt, & Bird, 2013).
Thus, the variable is coded as ‘‘1’’ when the company is a family ﬁrm and ‘‘0’’ otherwise.
Local embeddedness. We focus on capturing owners’ embeddedness within a location.
Following Dahl and Sorenson (2012), we measure local embeddedness as the log of the
average number of years that the owners have lived in the municipality in which the ﬁrm is
Firm context—rural versus urban. Following the work of Westlund, Larsson, and Olsson (2014)
and Nilsson (2015), Swedish municipalities are separated into two categories. The ﬁrst cate-
gory is the urban context, which includes both metropolitan and urban municipalities that
supply a large variety of services to the individuals and ﬁrms that are based in the location.
The second category is the rural context, which includes rural and sparsely populated rural
municipalities that host a limited range of services. The variable takes the value of 1 for the
rural context and 0 for the urban context.
First, because owners’ age may aﬀect ﬁrm growth (Hambrick & Mason, 1984), we controlled
for the average age of all owners.Second, we controlled for the human capital within the ﬁrm,
calculated as the ratio of employees with a graduate education, given that the intensity of
high-human-capital workers leads to stronger ﬁrm outcomes (Colombo & Grilli, 2005;
Ganotakis, 2012; Moretti, 2004; Pennings, Lee, & Witteloostuijn, 1998). Third, we controlled
for the log of total assets as the amount of prior investments might eﬀect ﬁrm growth (Anand
& Singh, 1997). Fourth, we controlled for high- and low-discretion slack, given their potential
eﬀects on ﬁrm growth (George, 2005). High-discretion slack was measured as the level of cash
reserves, whereas low-discretion slack was measured as the debt-to-equity ratio (George,
2005). Following Chen (2008), we standardized these two measures and summed them to
obtain a general slack index. Fifth, we controlled for ﬁrm performance in terms of Return on
`et al. 7
(Wales, Parida, & Patel, 2013). Sixth, we controlled for environmental dyna-
mism and muniﬁcence (Keats & Hitt, 1988), which may inﬂuence ﬁrm growth (Bradley,
Shepherd, & Wiklund, 2011). Muniﬁcence is measured by averaging the regression coeﬃcients
of a given industry’s (four-digit NACE code) sales over a 5-year period. Dynamism is calcu-
lated as the average of the standard errors of the regression slopes for the sales regression
equations used in calculating industry muniﬁcence over a 5-year period (see Keats & Hitt,
1988). Seventh, we controlled for the log of the population living in each Swedish municipality,
as it captures the potential agglomeration beneﬁts that may increase ﬁrm growth (Audretsch
& Dohse, 2007; Baptista & Preto, 2011; Raspe & Oort, 2008). Eight, we calculated the
location quotients (LQ) to control for the match between the ﬁrm’s specialization at the
two-digit SIC code level and the specialization of the municipality in which the ﬁrm is located
(Boix & Trulle
´n, 2007). LQ measures the relative specialization for a municipality in relation
to the reference unit, in this case, the rest of Sweden. The LQ compares the employment share
of a speciﬁc sector in the municipality to the rest of the country, as described in Equation 1.
Thus, a value of one indicates that the municipality has the same share of industry employ-
ment as the rest of the country. A value above one indicates a higher industry share compared
to the rest of the country (overrepresentation), and a value below one indicates a lower share
compared to the rest of the country (underrepresentation). A higher value indicates a higher
level of specialization and has been conﬁrmed in previous studies to correlate with higher ﬁrm
outcomes (Gabe & Kraybill, 2002; Wennberg & Lindqvist, 2010). Finally, to control for time
dependency, the log of time was also incorporated into the analyses (Box-Steﬀensmeier &
Employment in sector j in municipality kEmployment in municipality k
Total employment in sector j Total employment
We relied on coarsened exact matching (CEM) to improve the balance between the treated
(family) and control (nonfamily) groups (Blackwell, Iacus, King, & Porro, 2009; Iacus, King,
& Porro, 2011). CEM improves the estimation of causal eﬀects using a monotonic, imbalance-
reducing matching method that mitigates selection bias, causal estimation error, ineﬃciency
and model dependence (Blackwell et al., 2009). We used the ‘‘k2k’’ matching technique, which
prunes observations from a CEM solution within each stratum until the solution contains the
same number of treated and control units within all strata, enabling compensation for the
diﬀerential strata sizes.
Speciﬁcally, we matched on four variables capturing the age of a ﬁrm
, ﬁrm size in terms of
number of full time employees, the municipality in which the ﬁrm is located and the industry in
which the ﬁrm operates (at the two-digit SIC level) in the year 2004 and followed the com-
panies in the subsequent years. Young and small ﬁrms can be aﬀected by the liability of
newness and smallness, which can aﬀect ﬁrm growth (Hannan & Freeman, 1977). In addition,
diﬀerent municipality locations and industries may encourage companies to be more or less
positioned for growth (Eisenhardt & Schoonhoven, 1990; Hoover & Vernon, 1959). Given
that coarser matching may reduce the value of matching when adjusting for diﬀerences across
the case and control groups, we opted for a ﬁner-grained exact matching for number of
employees and industry in which Stata forces CEM to avoid coarsening the matching
8Entrepreneurship Theory and Practice 00(0)
variables (Blackwell et al., 2009; Iacus et al., 2011). The treatment variable is a binary variable
that distinguishes family ﬁrms from nonfamily ﬁrms. The matching procedure yielded a ﬁnal
matched sample of 15,658 companies (7,829 family ﬁrms and 7,829 nonfamily ﬁrms). The
means (M), standard deviations (SD), minimum (Min) and maximum (Max) of the matching
variables and dependent and independent variables of the study before the matching and in
the year of the matching are presented in Table 1 for family (FF) and nonfamily ﬁrms (NFF).
Additionally, Table 2 shows a breakdown of the family and nonfamily ﬁrms by industry (SNI
To test the proposed hypotheses, we used panel data analysis with a random eﬀect speciﬁca-
tion. The descriptive statistics and the Pearson correlation coeﬃcients of the study’s variables
are presented in Table 3. Inspection of the variance inﬂation factors (VIFs) and tolerance
showed that multicollinearity was not a concern. Indeed, all VIF coeﬃcients were below the
cutoﬀ of 5 (O’Brien, 2007).
Table 1. Matching Variables, Dependent and Independent Variables Before the Match and in the Year of
the Match (2004).
Pre-match (N¼45,282) Post-match (N¼15,658)
Nonfamily firms Family firms Nonfamily firms Family firms
Firm age M: 6.41 M: 7.76 M: 6.31 M: 6.64
SD: 3.54 SD: 3.79 SD: 3.54 SD: 3.55
Min: 2 Min: 2 Min: 2 Min: 2
Max: 14 Max: 14 Max: 14 Max: 14
Firm size (number of employees) M: 7.72 M: 9.23 M: 5.86 M: 5.86
SD: 12.33 SD: 13.26 SD: 5.55 SD: 5.55
Min: 2 Min: 2 Min: 2 Min: 2
Max: 335 Max: 547 Max: 119 Max: 119
Local embeddedness M: 9.39 M: 10.45 M: 9.49 M: 10.25
SD: 5.92 SD: 5.66 SD: 5.90 SD: 5.68
Min: 0 Min: 0 Min: 0 Min: 0
Max: 15 Max: 15 Max: 15 Max: 15
Firm context (rural ¼1) M: 0.32 M: 0.41 M: 0.33 M: 0.37
SD: 0.47 SD: 0.49 SD: 0.47 SD: 0.48
Min: 0 Min: 0 Min: 0 Min: 0
Max: 1 Max: 1 Max: 1 Max: 1
(relative sales growth)
M: 0.13 M: 0.09 M: 0.14 M: 0.12
SD: 0.39 SD: 0.31 SD: 0.50 SD: 0.45
Min: 0.92 Min: 0.94 Min: 0.92 Min: 0.94
Max: 3.75 Max: 3.77 Max: 3.75 Max: 3.77
`et al. 9
Table 2. Distribution of the Firms Per Each Industry (SNI 2002) in the Year of the Match (2004).
Nonfamily firms Family firms
A Agriculture, hunting and forestry 350 350 4.47%
B Fishing 0 0
C Mining and quarrying 2 2 0.03%
DA Manufacture of food products; beverages and
80 80 1.02%
DC Manufacture of leather and leather products 0 0
DD Manufacture of wood and wood products 81 81 1.03%
DE Manufacture of pulp, paper; publishing and
113 113 1.44%
DF Manufacture of coke, refined petroleum products
and nuclear fuel
DG Manufacture of chemicals, chemical products and
5 5 0.06%
DH Manufacture of rubber and plastic products 12 12 0.15%
DI Manufacture of other nonmetallic mineral
12 12 0.15%
DJ Manufacture of basic metals and fabricated metal
217 217 2.77%
DK Manufacture of machinery and equipment n.e.c. 93 93 1.19%
DL Manufacture of electrical and optical equipment 50 50 0.64%
DM Manufacture of transport equipment 18 18 0.23%
DN Manufacturing n.e.c. 46 46 0.59%
E Electricity, gas and water supply 0 0
F Construction 1,050 1,050 13.41%
G Wholesale and retail trade; repair of motor vehi-
cles, and personal goods
2,198 2,198 28.08%
H Hotels 588 588 7.51%
I Transport, storage and communication 503 503 6.42%
J Financial intermediation 0 0
K Real estate, renting and business activities 1,734 1,734 22.15%
L Public administration and defence; compulsory
M Education 65 65 0.83%
N Health and social work 275 275 3.51%
O Other community, social and personal service
337 337 4.30%
P Activities of households 0 0
Q Extra-territorial organizations and bodies 0 0
10 Entrepreneurship Theory and Practice 00(0)
Table 3. Correlation Matrix and Summary Statistics (2004–2013).
Mean SD 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.
1. Business growth
2. Family firm
0.60 0.49 0.00
12.75 7.51 0.04 0.08
4. Firm context
0.36 0.48 0.02 0.04 0.20
5. Owners’ age 48.85 9.19 0.12 0.05 0.18 0.05
6. Firm human
0.11 0.22 0.04 0.04 0.10 0.20 0.04
7. Firm total assets 4,384.87 5,568.54 0.06 0.07 0.02 0.01 0.04 0.06
8. Firm slack 0.01 0.11 0.06 0.03 0.01 0.03 0.02 0.01 0.43
9. Firm performance 2.61 18.95 0.16 0.15 0.06 0.06 0.04 0.13 0.16 0.12
1.00 0.00 0.00 0.02 0.05 0.01 0.04 0.01 0.12 0.10 0.03
0.98 0.03 0.04 0.00 0.02 0.01 0.03 0.07 0.05 0.04 0.01 0.02
12. Municipality size 177,124.88 262,129.80 0.02 0.07 0.20 0.44 0.05 0.28 0.01 0.03 0.06 0.01 0.04
13. Location quotients 1.44 2.35 0.00 0.01 0.04 0.10 0.01 0.02 0.07 0.03 0.02 0.04 0.02 0.05
14. Firm age 10.42 4.63 0.16 0.10 0.22 0.05 0.38 0.04 0.14 0.05 0.06 0.01 0.12 0.03 0.04
15. Firm size (#
6.69 7.50 0.08 0.06 0.07 0.07 0.07 0.07 0.45 0.35 0.15 0.00 0.06 0.09 0.01 0.05
16. Time (log) 1.35 0.74 0.12 0.12 0.27 0.02 0.21 0.01 0.11 0.04 0.04 0.03 0.06 0.00 0.02 0.60 0.06
17. Business growth
0.06 0.29 0.27 0.00 0.03 0.01 0.09 0.01 0.01 0.01 0.01 0.00 0.02 0.00 0.01 0.03 0.11 0.01
18. Sales 8,798.27 13,053.17 0.10 0.05 0.08 0.05 0.04 0.02 0.67 0.32 0.19 0.10 0.04 0.06 0.00 0.04 0.60 0.00 0.01
19. Family involve-
0.00 1.89 0.00 0.82 0.02 0.01 0.00 0.02 0.12 0.07 0.17 0.02 0.00 0.02 0.01 0.13 0.14 0.18 0.00 0.11
20. Top managers’
8.27 8.95 0.03 0.04 0.54 0.10 0.10 0.06 0.11 0.04 0.02 0.06 0.02 0.10 0.02 0.08 0.09 0.05 0.01 0.13 0.00
Table 3. Continued
Mean SD 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.
21. # years of a firm in
11.74 5.70 0.14 0.06 0.34 0.09 0.40 0.07 0.11 0.06 0.01 0.03 0.11 0.03 0.04 0.08 0.00 0.06 0.12 0.03 0.04 0.18
8.07 0.54 0.00 0.15 0.06 0.11 0.12 0.23 0.25 0.10 0.23 0.06 0.04 0.10 0.01 0.11 0.14 0.40 0.03 0.19 0.16 0.06 0.02
23. Household size 2.12 0.63 0.01 0.23 0.09 0.02 0.10 0.00 0.02 0.00 0.01 0.02 0.01 0.06 0.02 0.90 0.02 1.00 0.01 0.00 0.18 0.05 0.06 0.40
Note. Values greater than j0.02jare significant at p<.05.
First Stage: Controlling for Endogeneity
Firm growth may also be endogenous to the unique features of family ﬁrms. In other words,
factors that might inﬂuence ﬁrm growth could also inﬂuence the desirability of keeping the
ﬁrm as a family business. Although we lagged the independent and control variables by 1 year
and the matching approach addresses the most pressing endogeneity concerns (De Figueiredo,
Meyer-Doyle, & Rawley, 2013; Wooldridge, 2012), we further identiﬁed two instrumental
variables correlated with the family ﬁrm variable but not with the dependent variable: (a)
household size (average number of family members living with the owner) and (b) household
income (log of the average of the total incomes of family members living with the owner). In
fact, a family may be more likely to maintain control of their ﬁrm when many family members
are part of their household (e.g., Miller, Fitzgerald, Winter, & Paul, 1999) and when their
income is high (Haynes, Walker, Rowe, & Hong, 1999). However, these two factors may not
directly aﬀect ﬁrm growth (Heck, Owen, & Rowe, 1995).
We employed a two-stage residual inclusion (2SRI) model (see Terza, Basu, & Rathouz,
2008) to control for endogeneity. In linear models, 2SRI is similar to the popular two-stage
least squares (2SLS) method and is, therefore, consistent in terms of results (Terza et al., 2008:
534). Empirically a 2SRI estimator is like the linear 2SLS, except that in the second-stage
regression, the endogenous variables are not replaced by the ﬁrst-stage predictors. Instead,
ﬁrst-stage residuals are included as additional regressors (endogeneity score in Table 4). Thus,
we controlled for the endogeneity score in the ﬁnal analyses to mitigate any potential endo-
geneity issues (Terza et al., 2008). Table 4 (Model 1) presents the results of our ﬁrst stage
Second Stage: Hypotheses Test
We report the ﬁndings of our analyses in Table 4. Hypothesis 1 (Table 4, Model 3) is not
supported. In fact, the family ﬁrm coeﬃcient is positive and signiﬁcant, suggesting that family
ﬁrms achieve higher levels of business growth than nonfamily ﬁrms do. Hypotheses 2 and 3
consider the moderation eﬀects of local embeddedness and ﬁrm location, respectively. To test
Hypothesis 2, we employed a two-way interaction (Aiken & West, 1991) between the family
ﬁrm and local embeddedness. The interaction term is positive and signiﬁcant (Table 4, Model
4). To interpret this result, we plotted the two-way interaction in Figure 1. In support of
Hypothesis 2, the ﬁgure shows that as local embeddedness increases, the growth of family
ﬁrms increases at a higher rate than the growth of nonfamily ﬁrms. As expected, this diﬀer-
ence is largest when local embeddedness is high. Interestingly, Figure 1 also shows that when
embeddedness is low nonfamily ﬁrms achieve higher levels of growth than family ﬁrms.
Finally, to test Hypothesis 3, we employed a three-way interaction (Dawson & Richter,
2006) among the family ﬁrm, local embeddedness and the rural/urban context. The interac-
tion eﬀect is signiﬁcant (see Table 4, Model 5), and the plot of the interaction (Figure 2)
conﬁrms that the highest eﬀect of local embeddedness on business growth occurs among
family ﬁrms that operate in a rural context.
First, to test the sensitivity of our results to our matching procedure, we included the obser-
vations dropped by CEM into the sample. Second, following previous studies (Backman &
Palmberg, 2015; McKelvie & Wiklund, 2010; Shepherd & Wiklund, 2009), we used an alter-
native measure of business growth in terms of relative employment growth. Additionally,
`et al. 13
Table 4. 2SRI. Family Firm, Local Embeddedness, Firm Context and Business Growth (2004–2013).
First stage Second stage
1. 2. 3. 4. 5.
Firm owner’s age 0.863*** 0.170*** 0.173*** 0.174*** 0.174***
(0.184) (0.009) (0.009) (0.009) (0.009)
Firm human capital 1.193*** 0.017* 0.018* 0.018* 0.018*
(0.174) (0.008) (0.008) (0.008) (0.008)
Firm total assets 0.233*** 0.018*** 0.018*** 0.018*** 0.018***
(0.030) (0.001) (0.001) (0.001) (0.001)
Firm slack 0.148 0.098*** 0.097*** 0.097*** 0.097***
(0.218) (0.013) (0.013) (0.013) (0.013)
Firm performance 0.011*** 0.004*** 0.004*** 0.004*** 0.004***
(0.001) (0.000) (0.000) (0.000) (0.000)
Industry dynamism 19.878þ0.392 0.287 0.289 0.291
(11.224) (0.548) (0.548) (0.548) (0.548)
Industry munificence 1.887** 0.185*** 0.185*** 0.186*** 0.187***
(0.646) (0.041) (0.041) (0.041) (0.041)
Municipality size 0.373*** 0.000 0.001 0.001 0.001
(0.034) (0.001) (0.002) (0.002) (0.002)
Location quotients 0.001 0.001 0.001 0.001 0.001
(0.014) (0.001) (0.001) (0.001) (0.001)
Log of time 0.944*** 0.048*** 0.049*** 0.049*** 0.049***
(0.024) (0.002) (0.002) (0.002) (0.002)
Family firm 0.007* 0.003 0.002
(0.003) (0.006) (0.007)
Local embeddedness 0.009*** 0.004 0.005*
(0.001) (0.002) (0.002)
Firm context (rural) 0.006 0.006 0.010
(0.005) (0.010) (0.013)
Family firm Local
Family firm Firm context 0.015* 0.043**
Local embeddedness Firm
Family firm Local
Instrument Household size
14 Entrepreneurship Theory and Practice 00(0)
we ran our analyses using absolute measures of sales and employees with a ﬁxed eﬀect
speciﬁcation (Backman & Palmberg, 2015). Third, we adopted a continuous measure of
family involvement (Chrisman & Patel, 2012) in terms of (a) the total number of family
owners, that is, family members who declared partial ownership to tax authorities, and (b)
the total number of family managers (e.g., Le Breton-Miller, Miller, & Lester, 2011). Given
that these measures converged by loading together as a single factor, we built a composite
measure of family involvement by summing the standardized values of the two measures (see
Finkelstein, 1992; Martin, Go
´mez-Mejia, & Wiseman, 2013 for a similar procedure). Fourth,
we used an alternative measure of local embeddedness that captures top managers’ embedded-
ness within a location (that is, the number of years that managers have lived in the munici-
pality in which the ﬁrm is located). In all the above cases, the results were substantially similar
to those reported in the main analyses. Yet, the three interaction eﬀect with the top managers’
Figure 1. Local embeddedness and business growth among family and non-family firms.
Table 4. Continued
First stage Second stage
1. 2. 3. 4. 5.
Endogeneity score 0.002* 0.002** 0.002** 0.002**
(0.001) (0.001) (0.001) (0.001)
4,557.00 7,005.13 7,049.88 7,056.16 7,065.42
0.000 0.000 0.000 0.000 0.000
N15,658 15,658 15,658 15,658 15,658
Note. þp<.1. *p<.05. **p<.01. ***p<.001.
`et al. 15
embeddedness measure was signiﬁcant at p¼.081. Additionally, as an alternative proxy for
embeddedness, we counted the number of years a ﬁrm has spent in the same municipality
since 1990 (ﬁrst data available on this variable). As expected, the plot showed that within
highly embedded companies, family ﬁrms operating in rural areas are those that achieve the
highest level of growth. Finally, we tested for nonlinear relationships and found no empirical
Our results highlight the importance of the local context for the growth of family versus
nonfamily ﬁrms. Regarding family ﬁrms’ general attitude towards business growth, the lack
of support for the ﬁrst hypothesis suggests that family ﬁrms may instead opt to preserve future
nonﬁnancial wealth through increasing current revenues (e.g., Go
´mez-Mejia et al., 2014;
Hoskisson et al., 2017). Additionally, as the level of embeddedness increases, family ﬁrms
may be further motivated to grow because of their commitment to local stakeholders and
communities. Family ﬁrms appear to be more capable than nonfamily ﬁrms to translate
spatially embedded resource endowments into growth performance, suggesting their superior
ability to beneﬁt from ‘‘favorable community attitudes’’ (Bird & Wennberg, 2014; p. 433).
Family ﬁrms better use locally embedded knowledge and resources to build a competitive
advantage based on tacit knowledge and ﬁrm-speciﬁc capital, which in turn translates into
superior growth. In particular, Figure 1 shows that the positive eﬀect of increasing local
embeddedness on business growth is higher among family ﬁrms in comparison to nonfamily
ﬁrms. Nonfamily ﬁrms seem thus to derive lower beneﬁts than family ﬁrms from increased
local embeddedness (see Figure 1). One possible explanation for this result could be related
with the quality of relationships established at the local level. For example, it might be that
nonfamily ﬁrms are less able to develop valuable social capital from the relationships with
Figure 2. Local embeddedness, rural and urban contexts and business growth among family and nonfamily
16 Entrepreneurship Theory and Practice 00(0)
their stakeholders. For example, if exchanges are restricted, as opposed to generalized (e.g.,
Daspit, Holt, Chrisman, & Long, 2016) —that is, they privilege the transactional/market
dimension over the relational one, the embeddedness might be less beneﬁcial to achieve
positive ﬁrm outcomes. However, our measure of embeddedness does not allow to capture
the quality of the relationships, and this represents a limitation of the present study, which is
further discussed in the concluding section.
Finally, we ﬁnd support for Hypothesis 3, which predicts that locally embedded connec-
tions and bonds are particularly important for family ﬁrms versus nonfamily ﬁrms in rural
contexts, where weak infrastructure and the low density of the population and of economic
activities make it more diﬃcult for all types of ﬁrms to attract resources, ﬁnd inputs, and
access information and knowledge. In such conditions, it becomes even more important to
eﬀectively leverage the available resources. Figure 2 shows that the positive eﬀect of local
embeddedness on business growth is strongest among family ﬁrms operating in a rural con-
text. This evidence suggests that the ‘roots to grow’ are important and family ﬁrms gain
‘‘localized’’ competitive advantages in rural contexts, using their local social connections
and networks to attract human capital, knowledge-based assets and ﬁnancial capital
(Backman & Palmberg, 2015).
Our study has relevant theoretical implications for family business research. First, our work
represents one of the ﬁrst recent attempts to study business growth and the contextual con-
ditions aﬀecting business growth in family ﬁrms (e.g., Casillas & Moreno, 2010; Casillas et al.,
2010; Eddleston, Kellermanns, Floyd, Crittenden, & Crittenden, 2013). In contrast to our
predictions and the existing literature (e.g., Arregle, Batjargal, Hitt, Webb, & Tsui, 2013;
Colombo, De Massis, Piva, Rossi-Lamastra, & Wright, 2014), our results show that family
ﬁrms grow more than nonfamily ﬁrms. Additionally, in terms of growth, family ﬁrms beneﬁt
more from the embeddedness within the local community than nonfamily ﬁrms (Backman &
Palmberg, 2015; Bird & Wennberg, 2014). As such, our work furthers the idea that family
ﬁrms strongly leverage tacit knowledge and ﬁrm-speciﬁc resources in their strategic conduct
(e.g., Chirico et al., 2011) and rely on external social relationships to address resource scarcity
and resource deployment issues (Kim, Im, & Slater, 2013; Sirmon & Hitt, 2003). Thus, we
oﬀer interesting insights for future studies exploring the family, business and environmental
mechanisms that lead to positive outcomes for family ﬁrms.
Second, we contribute to the literature on noneconomic goals in family ﬁrms (e.g.,
Chrisman et al., 2012; Go
´mez-Mejia, et al., 2007) by theorizing that the level of local embedd-
edness may be a dimension that aﬀects the trade-oﬀ between economic and noneconomic
goals, with this trade-oﬀ waning when the pursuit of community-related noneconomic goals is
also a way to build resources that support the family business’s competitive advantage. Third,
we add to the ﬁeld of regional economics by investigating whether local embeddedness in a
rural context can function as a substitute for external economies, such as the agglomeration
beneﬁts and positive externalities that are present in an urban context. In an urban region of
suﬃcient size, spatially bounded markets function more eﬀectively. One reason for this eﬀect
is the sheer size of the market, and another is the diversity of resources in a large market as a
result of diﬀerent ﬁxed costs and indivisibilities. Thus, markets for labor, ideas and ﬁxed
resources give rise to matching, learning, and sharing eﬀects, all of which beneﬁt ﬁrms that
can obtain these positive externalities in a larger urban market. In a smaller, more rural
region, the above eﬀects are much more limited. One way to achieve similar eﬀects in a smaller
rural location is by relying not on the market process to the same extent but instead on locally
`et al. 17
embedded network exchanges. The value added of greater local embeddedness is that actions
can be coordinated and result in eﬃciency gains that oﬀset the lack of agglomeration gains
that can be obtained in a larger market. The observed eﬀects provide interesting extensions to
both theoretical arguments and empirical evidence in favor of the prevalence of family ﬁrms in
low-resource-intensive environments (Carney, 2005) and in regions that are economically
depressed or that feature lower levels of development (Chang, Chrisman, Chua, &
Finally, by combining regional studies and the family business literature, we answered the
call for further research to combine the two ﬁelds (e.g., Stough et al., 2015) and provided a
way to (a) better understand the contribution of family businesses to growth in various types
of local contexts (e.g., Welter, 2011) and (b) investigate the issue of ﬁrm-level heterogeneity
and behavior in the context of regional studies (e.g., Block & Spiegel, 2013; Maskell, 2001).
Limitations and Directions for Future Research
The present study has also several limitations that indicate important directions for future
research. First, an aspect that is not fully explored in the present study is how local embedd-
edness diﬀers across industries. In this study, we match for industry; however, to understand
the diﬀerences in a more profound way, ﬁrms of diﬀerent industries should be analyzed
separately. This separate analysis can certainly be combined with the regional context,
where, for example, knowledge-intensive ﬁrms may be more reliant on being located in a
urban setting. Second, due to data limitations, we did not measure local embeddedness
through actual owners’ network contacts in a region. However, we agree with Dahl and
Sorenson (2012: 1063), who note that although embeddedness may be measured by directly
observing whether owners have close personal contacts in a region, owners’ tenure in the
region represents a better measure of embeddedness. Speciﬁcally,
one must worry about the meaning of relationships. At issue is the fact that people select into
them. Diﬀerences in the quality or number of connections available to individuals might therefore
reﬂect individual-level heterogeneity rather than random variation in their relationships.
Measuring these connections directly raises a reﬂection problem that could bias the estimates.
Yet, future research may assess local embeddedness utilizing also measures accounting for the
quality of the relationships (e.g. restricted versus generalized social exchanges between a ﬁrm
and its community; Daspit et al., 2016). A further limitation of our measure of local embedd-
edness is that it may not account for the network that previous family generations may pass
down to current owners. This topic may represent a promising area of future research in
connection to family business succession, given the need to transfer social networks across
generations and since embeddedness in a community is considered an important factor of
successor selection (see Cabrera-Sua
´rez, & Garcı
´a-Almeida, 2001; Steier, 2001).
Third, our measure of family ﬁrms does not take into account the amount (percentage) of
family ownership of the ﬁrm. As such, our family ﬁrm sample might include ﬁrms where the
majority of owners are nonfamily investors or exclude ﬁrms where family mangers are not
present due to the fact that the ﬁrm is in later generations of family control. Fourth, another
underexplored issue is related to the potential shifts in localizations of family and nonfamily
ﬁrms over time and whether such shifts may be aﬀected by the level of local embeddedness. By
analyzing localization patterns and their determinants, future studies may contribute to the
existing literature on ﬁrm localization. Fifth, this study is conducted in a sample of Swedish
ﬁrms; it would be interesting to observe whether similarities or diﬀerences exist across speciﬁc
18 Entrepreneurship Theory and Practice 00(0)
countries. Finally, an interesting ﬁnding that may deserve future investigation is the positive
relationship between local embeddedness and owners’ age and their eﬀects on
business growth. In particular, the negative eﬀect of owners’ age on growth is in line with
previous evidence that family ﬁrms with long-tenured family owners exhibit lower growth
propensity (e.g., Zahra, 2005). Yet, future research may explore whether local embeddedness
has a diminishing or increasing eﬀect on growth over time, thus suggesting a nonlinear
In summary, we believe that our study provides important insights and has the potential to
stimulate further work on the interesting but underexplored topic of local embeddedness and
rural–urban contexts for business growth in family versus nonfamily ﬁrms.
We are indebted to the editor, James Chrisman, and the two anonymous reviewers as well as the
organizers and participants of the TOFE Conference at the University of St. Gallen for their insightful
and developmental feedback. We would also like to thank Jo
¨ping International Business School, the
Centre for Family Enterprise and Ownership (CeFEO) and the related CeFEO workshop and partici-
pants for their support and valuable suggestions received during the development of the present
Declaration of Conflicting Interests
The author(s) declared no potential conﬂicts of interest with respect to the research, authorship, and/or
publication of this article.
The author(s) received no ﬁnancial support for the research, authorship, and/or publication of this
1. To avoid outliers, the sale values are trimmed at the 1st and 99th percentiles.
2. Family members working part-time were also considered.
3. To avoid outliers, the ROA values are trimmed at the 1st and 99th percentiles (winsor2; trim replace).
4. Due to the high correlation between generational involvement and the family firm dummy, we did not
control for the number of family generations involved in the business (Sciascia, Mazzola, & Chirico,
5. Information on firm age is not available for all firms. CEM offers an approach to address missing
values through ‘‘matching on missingness,’’ that is, CEM treats the missing values as an additional
category on which to match (Blackwell et al., 2009).
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`is Assistant Professor at Jonkoping International Business School, Centre for
Family Enterprise and Ownership–CeFEO, Sweden.
Francesco Chirico is Professor at Jonkoping International Business School and Co-Director of
the Centre for Family Enterprise and Ownership–CeFEO, Sweden. He is also a Star Professor
at Tecnologico de Monterrey, EGADE Business School, Mexico.
Daniel Pittino is Associate Professor at Jonkoping International Business School, Centre for
Family Enterprise and Ownership–CeFEO, Sweden.
Mikaela Backman is an assistant professor in Economics at Jo
¨ping International Business
School and affiliated to Centre for Entrepreneurship and Spatial Economics.
Johan Klaesson is Professor of Economics and director of the Center for Entrepreneurship
and Spatial Economics, Jo
¨ping International Business School, Jo
¨ping University and
affiliated researcher at The Research Institute of Industrial Economics, Stockholm, Sweden.
26 Entrepreneurship Theory and Practice 00(0)