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The Power of Local Networks:
Returnee Entrepreneurs, School Ties, and Firm Performance
Elena Obukhova*
MIT Sloan School of Management
Yanbo Wang* Boston University School of Management
Jizhen Li
Tsinghua University School of Economics and Management
*The first two co-authors have contributed equally to the paper and their names appear in alphabetical order. Jizhen Li provided valuable contribution in field research, data collection and overall project development. For correspondence, please write to Elena Obukhova at <[email protected]> or Yanbo Wang at <[email protected]>. Acknowledgements: The research was partially funded by research support to Elena Obukhova from the Edward B. Roberts Entrepreneurship Center Fund, MIT Sloan and to Jizhen LI from National Natural Science Foundation of China (Project No. 71273152) and Tsinghua University. We received helpful suggestions on this paper from Olav Sorenson and participants in the MIT Economic Sociology Working Group.
The Power of Local Networks:
Returnee Entrepreneurs, School Ties, and Firm Performance
Returnee entrepreneurs—those who establish new ventures in their native developing countries
after studying or working in developed economies—appear to perform no better than
homegrown entrepreneurs. This is particularly surprising given the advantage they should gain
from the human and social capital they acquire abroad. While research has sought the solution to
this puzzle in institutional conditions in returnees’ home countries, little attention has been
devoted to the role of local social networks. In this study, we provide evidence that the lack of
social networks in the region where the venture is located contributes to this “returnee-liability.”
Using a unique, hand-coded dataset on Chinese high-technology firms in Beijing, we document
that ventures established by returnee entrepreneurs underperform homegrown ones and that
returnee entrepreneurs with more school ties in the region where their new ventures are located
are less disadvantaged, relative to homegrown entrepreneurs, than those who have fewer school
ties there. These findings expand our understanding of returnee entrepreneurs, as well as provide
important new evidence for the role of social networks in new venture creation.
3
The Power of Local Networks:
Returnee Entrepreneurs, School Ties, and Firm Performance
Returnee entrepreneurs— those who establish new ventures in their native developing
countries after studying or working in developed economies—appear to perform no better than
homegrown entrepreneurs (Obukhova 2009; Li et al. 2012; see also Amsden and Chu 2003 and
Breznitz 2005). This is particularly surprising given the advantage they should gain from the
human and social capital they acquire overseas (e.g., Saxenian and Hsu 2001; Saxenian 2006;
Dai and Liu 2009; see also Drori, Honig, and Wright 2009). For example, a returnee’s overseas
experience might help her identify business opportunities that homegrown entrepreneurs have
missed. In addition, she had opportunities while abroad to develop tacit knowledge of advanced
technologies and management practices that are unavailable in the developing economies. She
might also have been able to build internationally recognizable credentials and develop personal
relationships that might make it easier back home to mobilize the resources needed for a new
venture.
Research has sought the solution to this puzzle in institutional factors in the returnee
entrepreneur’s home country (e.g., Chen 2008; Obukhova 2011, 2012; Li et. al. 2012). Some
scholars have argued that returnee entrepreneurs might lack legitimacy in the developing
economy (Obukhova 2011; also see Wang 2012). It is plausible that because of the time
returnees have spent overseas or their potential commitments to overseas actors (such as their
foreign investors), resource-controlling actors in the returnee’s home country might view her as a
less desirable exchange partner, which would make it harder for a returnee entrepreneur to
mobilize resources. Other scholars have documented the lack of fit between returnee
4
entrepreneurs’ new ventures and the institutional environment in the home country (Chen 2008;
Obukhova 2012). It is plausible that because of their time overseas, returnees are more likely to
adopt strategies and organizational practices that are well suited to developed economies but
poorly suited to the institutional environments of their home countries, which might negatively
affect their new ventures’ performance.
While these institutional mechanisms have attracted the most attention, there is also a
strong theoretical rationale for expecting returnee entrepreneurs to be hindered by having smaller
or less effective social networks in the region where they establish their new ventures than their
homegrown counterparts. We know that entrepreneurs’ social networks are an important factor
in the performance of new ventures (Burton, Sorensen, and Beckman 2002; Shane and Stuart
2002; Stuart and Sorenson 2005, 2008). For nascent entrepreneurs, prior working experiences in
the region provide important opportunities to build social networks there (Sorenson and Audia
2000; Dahl and Sorenson 2012). However, those who are not in the region prior to establishing
their new ventures might miss out on opportunities to build local social networks. Thus, we
might expect that institutional differences aside, a venture established by an entrepreneur who
has been away from the region where she is starting that venture is likely to perform worse than
ventures established by those who have been in the region all along (Dahl and Sorenson 2012).
In this study, we provide empirical evidence that the lack of local social networks helps
explain why returnee entrepreneurs perform no better than homegrown ones. To do so, we
examine the variation in access to local social networks among returnee entrepreneurs. We
argue that those who before their time abroad had experiences in the region that were likely to
lead to formation of local social network, when they come back are more likely to have social
networks or will find it easier to create new social networks. In particular, we focus on school
5
ties—relationships to students, faculty and administrators affiliated with the university a returnee
attended before going overseas—that can be an important resource for entrepreneurs (Roberts
1991; Lerner and Malmednier 2007; Kacperszyk 2011). Thus we expect that those returnee
entrepreneurs who had experiences in the region that were likely to lead to formation of school
ties in the region where their new ventures are located are less disadvantaged, relative to
homegrown entrepreneurs, than those returnees who did not have these experiences .
Our analysis proceeds in three steps. We begin by confirming that the returnee
entrepreneurs in our sample perform no better (and in fact, on average, worse) than homegrown
entrepreneurs. Then, we show that, consistent with our argument about the power of local social
networks, those returnee entrepreneurs who attended a university in the region—and whom we
might expect to have more school ties there—are less at a disadvantage compared to homegrown
entrepreneurs than those who attended a university elsewhere. To rule out the possibility that
institutional factors at the university level account for our results, we show that among returnee
entrepreneurs who attended an educational institution in the region where they start their new
ventures, those who attended educational institutions with larger alumni networks—and whom
we might expect to have more school ties in the region—are less disadvantaged, relative to
homegrown entrepreneurs, then those who attended educational institutions with smaller alumni
networks.
Our study uses a unique hand-coded dataset on Chinese high-technology firms in Beijing,
about a quarter of which were founded by returnee entrepreneurs. Our site has a number of
important advantages for this study. Beijing has the best institutional environment for high-tech
entrepreneurship in China, including China’s first high-tech park, generous government support,
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and plentiful human capital (Segal 2003; Fan, Wang, and Zhu 2010). This makes Beijing a
strategic site for our study, because the city attracts returnees from all over China, including
those who have attended universities elsewhere. This also means that Beijing is a conservative
site in which to evaluate our argument. We might expect that because Beijing has well-
developed formal institutions supporting entrepreneurship, entrepreneurs there would have less
need to rely on local social networks than in less developed cities. If we find that returnee
entrepreneurs need regional embeddedness to perform well even in Beijing, the same pattern is
likely to hold in the rest of the country where such institutions are less developed.
THEORY AND HYPOTHESES
Although early theoretical work has predicted that returnees should have important
advantages over homegrown entrepreneurs because of the unique resources acquired overseas
(e.g., Saxenian and Hsu 2001; Saxenian 2006; Dai and Liu 2009; see also Drori, Honig, and
Wright 2009), the weight of the empirical evidence to date suggests that returnees perform no
better, and frequently worse, than homegrown entrepreneurs. While some returnee-
entrepreneurs have established ventures with stellar performance attracting widespread media
attention, no systematic study has found that returnee entrepreneurs outperform domestic ones.1
Instead, research among Chinese returnee entrepreneurs suggests that returnee-firms might
actually underperform homegrown entrepreneurs’ firms (Obukhova 2009; Li et al. 2012).
Furthermore, even in the Taiwanese IT industry, which provided fertile ground for early
theorizing on the advantages of returnee entrepreneurs (e.g., Hsu 1997; Saxenian and Hsu 2001),
1 Dai and Liu (2009) claim to find that Chinese returnee entrepreneurs outperform homegrown ones, but the empirical results they present do not actually address this issue.
7
both Amsden and Chu (2003) and Breznitz (2005) find little evidence suggesting that returnees
have been more successful than homegrown entrepreneurs.
Research has identified two related but distinct institutional mechanisms that seem to
contribute to this “returnee liability.” The first mechanism is the returnee entrepreneur’s lack of
legitimacy in the developing economy (Obukhova 2011).2 It is plausible that, because of the
time a returnee has spent overseas or her potential commitments to overseas actors (such as her
foreign investors), resource-controlling actors in the returnee’s home country might view her as a
less desirable exchange partner. For example, Obukhova (2011) argues that, in China, state
actors view returnee firms as less legitimate partners in achieving state goals such as
development, employment, technological self-sufficiency, and military security. As a result,
state actors might withhold resources from ventures established by returnee entrepreneurs unless
these returnee entrepreneurs demonstrate their commitment to the goals of the state. Consistent
with the institutional legitimacy argument, research finds that returnee entrepreneurs in China
who have connections to legitimacy-granting public educational institutions (Chen 2008) or to
state firms (Li et al. 2012) outperform those returnee entrepreneurs who do not.
The second mechanism is the lack of fit between returnee entrepreneurs’ new ventures
and the institutional environment in the home country (e.g., Chen 2008; Obukhova 2012).3 The
institutional environment in the countries where the returnees acquired knowledge and social
networks is considerably different from the institutional environment in the developing economy,
making it costly for returnees-entrepreneurs to adapt to the new environment. For example,
2 In a related stream of research, Wang (2012) shows that the effect of returnee-employees on organizational practices is larger in countries that have lower xenophobia (and vise versa). 3 Interestingly, the early work on returnees actually considered institutional differences as a potential asset for returnee entrepreneurs, because it predicted that returnees will engage in institutional arbitrage (Saxenian and Hsu 2001, Khanna 2007; see also Nanda and Khanna 2010).
8
Chen (2008) argues that many aspects of the industrial-district ecosystems that returnees might
have experienced in the West are largely absent in Beijing. Consistent with Chen’s argument,
Obukhova (2012) finds that, in Shanghai’s semiconductor design industry, returnee
entrepreneurs’ firms suffer from the lack of fit with the local institutional environment in two
ways. First, they often cannot locate skilled engineering labor—which was much easier to find
in the West—and are therefore forced to provide costly on-the-job training. Second, once they
have invested heavily in their labor force, they find it hard to retain workers because of the lack
of institutional support for stock options. Both problems take their toll on the new venture’s
performance.
Toward a network explanation
While these institutional explanations have received the most attention in the literature,
little attention has been devoted to the role that social networks might play in this process. Such
an omission is puzzling because we know that entrepreneurs’ social relationships are an
important factor in the performance of new ventures (Burton, Sørensen, and Beckman 2002;
Shane and Stuart 2002; Stuart and Sorenson 2005, 2008). Having social networks in their
environment helps entrepreneurs to identify new business opportunities. Knowing exchange
partners and having relationships with them can help nascent entrepreneurs to overcome the
problems of information asymmetry and opportunistic behaviors in accessing external resources.
Furthermore, because nascent entrepreneurs often lack legitimacy, social relationships can be an
important signal of quality that helps a nascent entrepreneur mobilize critical resources.
Given the important role of social networks in entrepreneurship, we might expect that
lack of local social networks is one factor that contributes to the poor performance of returnee
9
entrepreneurs compared to homegrown ones. We know that working experiences in the region
are an important source of local social networks for nascent entrepreneurs (Sorenson and Audia
2000; Dahl and Sorenson 2012). It is likely that because of their time away from the region,
returnee entrepreneurs have fewer local social networks than homegrown entrepreneurs
Specifically, by being abroad, returnees miss out on opportunities to form new local relationships
which might be useful to their local ventures (Qin 2008). It is also likely that some of their social
networks in the region became weakened or outdated. As a result, when they come back to their
home countries, they might find it difficult to identity business opportunities and mobilize
resources to exploit them.
The social networks arguments presented above provide a baseline prediction for our
research:
Hypothesis 1: Returnee entrepreneurs perform worse than homegrown entrepreneurs.
Because the baseline prediction of our social network mechanism does not differ from
that of two institutional mechanisms discussed above, to provide support for the network
explanation, we focus on the variation in the amount of local social networks among returnee
entrepreneurs. While we might expect that, on average, returnees have fewer social relationships
than homegrown entrepreneurs, we also have plausible reasons to expect that some returnees
have more local social networks than others. Specifically, we expect that a returnee
entrepreneur’s experiences in the region before she went abroad will influence the size of the
local social network she can reactivate once she comes back. Those who had experiences in the
region that were likely to lead to formation of local social network are likely to be less
disadvantaged compared to homegrown entrepreneurs than those who did not have these
experiences. As we discuss below, school ties— relationships to students, faculty and
10
administrators affiliated with the university in the home country a returnee attended before going
overseas —provide an important opportunity to test our arguments.
Returnee entrepreneurs, school ties, and firm performance
For nascent entrepreneurs, university experiences are important sources of social
networks. To start, universities foster interaction among individuals, which encourages tie
formation. Specifically, through coursework, living arrangements, and extracurricular activities,
university students have repeated interaction with other students, professors, and staff. Social
relationships (including marriages) are likely to result (Feld 1981, 1982). Furthermore,
affiliation with a university tends to give people a sense of social similarity and common destiny
that can lead to the formation of ties even amongst people who never met while they were there
(Nann et al. 2010; Rider 2011). This means that by attending a university, individuals also
potentially gain access not only to those whom they directly interacted with, but also to a larger
network of those who have worked or studied at the university in the past or will work or study
there in the future.
We also have compelling reasons to expect that school ties can have important
consequences for subsequent entrepreneurship (Roberts 1991; Lerner and Malmednier 2007;
Kacperczyk 2011). University ties are an important source of advice, referrals, and resources—
such as investment and technical information that can shape both an entrerepenur’s strategy and
the subsequent performance of her ventures (Roberts 1991). Research also suggests that one’s
university peers exert social influence on one’s decision to enter entrepreneurship by promoting
transfer of information about new ventures and reducing the uncertainly associated with staring a
11
new venture (Kacperczyk 2011). While we might expect that some of the similarities in
outcomes among people who attended the same university are due to selection (Manski 1993),
Lerner and Malmednier (2007) and Shue (2011) demonstrate substantively significant peer
network effects in the context of randomly assigned student groups.
Those returnee entrepreneurs who have attended a university in the region can potentially
re-activate their existing school ties, which should improve their new ventures performance.
Even though ties tend to decay with time (Burt 2000), some ties can remain dormant for a long
period of time and be reactivated when needed (Levin, Walter, Murninghan 2011). Leveraging
their “previously attained common understandings and feelings” (Granovetter 1992:34, cited in
Levin, Walter, Murninghan 2011), individuals can reconnect a past relationship and get access to
valuable resources associated with it. While we have reasons to expect that during their time
away returnees were not able to build new ties, those who have attended a university in the
region should have access to more actual and potential ties that they can re-activate. Because
those who had educational experiences in the region where they start their new ventures are more
likely to have school ties in that region than those who went to a university elsewhere, we
hypothesize that:
Hypothesis 2. Among returnee entrepreneurs, those who attended educational
institutions in the region where they start their new ventures are less disadvantaged, relative to
homegrown entrepreneurs, then those who did not attend educational institutions in that region.
Despite the theoretical reasons to expect that educational experiences help to create social
networks that can be useful in entrepreneurship, we need to consider an alternative institutional
explanation for Hypothesis 2. Many universities provide support for entrepreneurship by former
12
alumni; for example, by sponsoring incubators or providing access to university technology.
Research suggests that these institutional supports might influence performance of new ventures
established by faculty and researchers (Murray 2002; Shane 2004; Colyvas and Powell 2006). It
is plausible that these factors would also affect performance of new ventures established by
former students. It is therefore possible that returnee entrepreneurs who have attended an
university in the region where they start their new ventures are better at overcoming returnee-
liability because of such institutional support.
To provide additional support for role of entrepreneur’s local social networks as a factor
that affect new venture’s performance, we examine how size of the alumni network affects
returnees’ new venture performance. that Alumni networks is one type of school tie that is
linked to performance of new ventures(Roberts 1991; Nann et al. 2010). We would expect that
entrepreneurs who attended universities with larger alumni networks have access to more social
capital than those who attended universities with smaller alumni network. However, there is no
reason to expect an association between the size of a university’s alumni network and the
university’s institutional support for entrepreneurship.4 Thus, to the extent that the size of an
alumni network in the region has a positive effect on the performance of the returnee
entrepreneur’s new venture, we might infer that the causal mechanism responsible for the effect
of educational experiences in the region on returnee entrepreneur firm’s performance is
entrepreneur’s local social networks and not the university’s institutional support for
entrepreneurship.
4 In some context this association might be more likely, if for example successful entrepreneurial alumni donate to their alma maters. However, alumni philanthropy is a relatively new phenomenon in China, so this is not a significant factor in our context.
13
Hypothesis 3. Among returnee entrepreneurs who attended an educational institution in
the region where they start their new ventures, those who attended educational institutions with
larger alumni networks are less disadvantaged, relative to homegrown entrepreneurs, then those
who attended educational institutions with smaller alumni networks.
DATA
To test our hypotheses, we use a hand-coded dataset of small and medium-sized high-
technology firms in Beijing that had applied in 2005 for prestigious funding awards granted by
the Ministry of Science and Technology (MOST). The Innofund is modeled after the U.S. Small
Business Innovation Research (SBIR) program. It was established in 1999 and, by 2010, had
financed 25,855 projects with a total investment of RMB 16.66 billion.5 To qualify as an
applicant, a firm must have business activities in an area of “intensive technology content,” a top
management team composed of scientists or engineers, a minimum of 20 percent of employees
with college degrees, and at least three percent of sales dedicated to R&D activities (Innofund
2010). Over the past decade, most of the grant has gone to companies active in such areas as
electronics and information, biopharmaceuticals, new energy, advanced materials and technology
services. Among the most noteworthy companies to have received financing from fund are some
of China’s industry giants, including Lenovo, Huawei and Suntech Power.
5 The exchange rate in November of 2012 was 1 US$ = 6.24 RMB.
14
While our data does not represent a random sample of all high-tech firms in Beijing, it
provides a fairly accurate representation of most active firms engaged in high-technology
activities. Our interviews with grant administrators and firms that have applied for the grant
suggest that firms have both financial and non-financial reasons for applying. The grant amount
for each project generally ranges between RMB 500K and RMB 1 million, with a maximum of
RMB 2 million for key projects. The Innofund is set up in such a way that its recipients
automatically receive matching funds and other contributions from the provinces, the
municipalities and even the science parks in which the firms are located. Once matching funds
and contribution from local government are taken into consideration, grant recipients receive a
considerable financial wherewithal. Furthermore, the award conferees on the firm national
recognition that makes it easier to approach potential customers and investors, negotiate with
local officials, and even to recruit high-skilled labor.
The main advantage of this data source is that, in comparison to other datasets on
returnees (e.g. Dai and Liu 2009; Li et al 2012), Innofund data is has relatively detailed
biographical information about the individual entrepreneur. No previous study of returnee
entrepreneurs had information about the entrepreneur, using the use information about a firm’s
legal representative in lieu of it. In contrast, Innofund applications ask for one individual to be
listed as the entrepreneur, regardless of whether or not she is the firm’s legal representative. In
addition, previous studies have tended to collect information whether this individual is legally
considered as a returnee (in China an individual can apply to be certified as a returnee if she has
spent at least 12 months abroad). In contrast, Innofund application has a biographical
information for each entrepreneur that allows us to identify whether she studied or worked
15
abroad. We can use this information to perform a variety of robustness checks on our returnee
variables.
Lastly, we expect that our data is as accurate as other datasets that are based on
information the firms report to government offices. Specifically, each 40-60 page application
that each firm completes includes information about its ownership structure, statistic on R&D
performance, and several measures key indicators of a firm’s financial performance. The
financial statement included in the application reporting a firm’s assets and revenues for both the
year of the application as well as for two years prior to the application, has to be audited by an
accounting firm. Our interviews with Innofund administrators and firms that have applied for
the grant suggest that firms make pain-taking efforts to prepare the application form. We found
that some firms hired outside consultants for help fill out the application. Innofund
administrators told us that they are not concerned about fraud in applications for the grant
because of the high stakes involved in applying for the project.
We employed a team of 5 trained undergraduate students at Tsinghua University to hand-
code these rich data. Hand-coding was necessary because each firm used a different format to
provide biographical information about its entrepreneur; for example, some listed information by
date while others as a short bio sketch. We spent more than 15 hours in training sessions with
students, repeatedly going over problematic data coding cases.
VARIABLES
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We use a firm’s revenue as a proxy of its performance. Revenueln is measured as a natural
logarithm of sum of the total revenue the firm reported in the year of grant application and 1.
While it is important to recognize that firm performance is multidimensional in nature
(Chakravarthy 1986; Venkatraman and Ramanujam 1986), revenue is an ideal measure in our
context for both theoretical and substantive reasons. Theoretically, most startups start their lives
with zero revenue base; this makes revenue a unique and useful way to capture a young firm’s
growth (Eisenhardt and Schoonhoven 1990). Substantively, growing revenue is a common goal
of many entrepreneurs (Eesley and Roberts 2012). Endowed with little resources and legitimacy,
startup firms need revenue to gain financial independence and to signal customer endorsement to
stakeholders (Schoonhoven, Eisenhardt and Lyman 1990).
To test hypothesis 1, we construct a dummy variable returnee entrepreneur equal to 1 if
the entrepreneur studied or worked abroad and 0 otherwise. Because our theoretical focus is on
the time returnees spent away from China, we consider as returnee entrepreneurs those who have
spent time away from China either due to study or due to work. This definition is consistent with
the existent literature (Dai and Liu 2009; Obukhova 2009; Li et al. 2012). Even though fine-
grained information such as the length of overseas tenure is more desirable to capture a
returnee’s loss of embeddedness in the home country, such information is not systematically
available from the Innofund application material.
To test hypothesis 2, we construct a dummy variable - College in Beijing- equal to 1 if
the entrepreneur attended college in Beijing and 0 otherwise. To test hypothesis 3, we construct
a continuous variable Alumni Sizeln measured as the natural logarithm of the sum of the current
undergraduate enrollment of the entrepreneur’s alma mater in Beijing and 1, if the college was
17
located in Beijing and 0 otherwise. As Chinese universities rarely publish information on their
alumni size, we use current undergraduate enrollment as a proxy for (relative) alumni size.
Firm performance may be subject to several other influences aside from those arise from
the entrepreneur’s local social networks. Specifically, at the firm level, we control for Assetsln,t-1,
as the natural logarithm of firm asset in a year preceding the year for which performance data
were collected; R&D Expenditureln, as the natural logarithm of the R&D expenditure of the firm
at time t-1; Employeesln, measured as the natural logarithm of the number of employees; Venture
Age, as number of years since founding; Investorsln, as the natural logarithm of the number of
investors; and TMT Sizeln as the natural logarithm of the number of individuals listed in the
application section on top management team.
We also include a number of controls for entrepreneur’s characteristics that could affect
venture performance. We measured Educational Level on a four point-scale coding PhD degree
as “3”, masters degree (including an MBA) as “2”, bachelors degree as “1” and less than a
bachelors degree as “0”. Male was coded as 1 if the founder was male and 0 otherwise.
Entrepreneur Age was coded based on the information about the founder’s birth date. To control
for the quality of the education that an entrepreneur received, we created an ordinal variable
School Ranking on scale of 1 to 10, where the value of 10 was assigned to universities that were
ranked among the top 10 in any of the three popular university rankings in China.6
Lastly, because it is plausible that new venture performance and the importance of local
social networks may vary across industries, we include a series of industry dummy variables for
6 These three rankings are conducted by China Academy of Management Science, NetBig.Com in Shenzhen and China University Alumni Association.
18
industry fixed effects. We also add a series of dummy variables in our analysis to control the
type of grant that each firm was applying for, as the applied grant might reflect some otherwise
unobservable firm level characteristics.
RESULTS
The descriptive results provide preliminary support for our hypotheses. Figure 1 shows
the distribution of revenue across different types of entrepreneurs. Given that firms in our
sample are early-stage technology ventures it is not surprising that the distribution of revenue is
skewed downward from a normal distribution.7 The figure also suggests two patterns that are
broadly consistent with our expectations: First, firms established by returnee entrepreneurs
perform worse than those established by homegrown entrepreneurs. Second, firms established
by entrepreneurs with no college experience in Beijing perform worse than those established by
entrepreneurs who attended college in Beijing.
Bivariate correlations presented in Table 1 corroborate these impressions. Consistent
with the existence of “returnee-liability,” we find that the correlations between a entrepreneur’s
returnee status and the firm’s financial performance are negative (-0.173).Consistent with our
expectation that local social networks improve performance, we find that the correlations
between entrepreneur’s attendance of college in Beijing and a firm’s performance are positive
(0.088). Similar positive correlations are also found between firm revenue and other measures of
local school ties.
7 It’s worthwhile of mention that the natural logarithm of revenue is characterized by a normal distribution.
19
We proceed to use ordinary least squares (OLS) regressions to test our hypotheses,
reporting the main results in Table 2. Our Hypothesis 1 stated that returnee entrepreneurs
perform no better than homegrown entrepreneurs. Consistent with this hypothesis, we find that
on average returnee entrepreneurs perform worse than the homegrown ones. Specifically, Model
1 shows that, net of entrepreneur’s and firm’s characteristics, firms established by returnee-
entrepreneurs have lower revenue than firms established by homegrown ones (B = -1.175, S.E.
= 0.574). We also find that larger firms, or firms with more employees and assets in the year
prior to the grant application, have better performance (B = 1.322, S.E. = .358 for Employeesln;
B= .307, S.E. = 0.047 for Assetsln,t-1). We also find that older firms are associated with a higher
level of revenue (B = 0.138, S.E. = .070 for Venture Age).
Hypothesis 2 stated that among returnee entrepreneurs, those who attended educational
institutions in the region where they start their new ventures are less disadvantaged, relative to
homegrown entrepreneurs, then returnees who did not attend educational institutions in that
region. Our results provide support for this hypothesis. Specifically, Model 2 suggests that firms
established by entrepreneurs who attended college in Beijing perform better than firms where the
entrepreneurs did not attend college in Beijing (B = .859; S.E. = 0.453). In Model 3 we add the
interaction term between a returnee entrepreneur and whether she attended college in Beijing. As
predicted by our hypothesis, this interaction effect is positive and statistically significant (B =
2.358; S.E. = 1.021), indicating that returnee entrepreneurs who went to college in Beijing
perform significantly better than returnee entrepreneurs who did not. Additional F-test indicate
that returnee entrepreneurs who went to college in Beijing perform no worse than homegrown
entrepreneurs.
20
Furthermore, the comparison between Models 2 and 3 suggests that social networks are
more important for returnee entrepreneurs than for homegrown ones. In contrast to Model 2
where the coefficient for college in Beijing is positive and statistically significant (B=0.859; S.E.
= 0.453), in model 3 once the interaction term between returnee entrepreneur and school ties is
added, whether a founder attended local college or not does not have statistical significance any
longer, even though the coefficient sign remains positive (B=0.267; S.E. = 0.479). This reduction
in the magnitude of the coefficient suggests that social networks as measured by founders
attending a university in Beijing matters most to the returnee entrepreneurs who due to their time
abroad were not able to cultivate local social networks. It is likely that those homegrown
entrepreneurs who did not attend a university in Beijing had worked in Beijing prior to their
entrepreneurial decisions, which gave them an opportunity to develop local social network there.
Hypothesis 3 stated that among returnee entrepreneurs who attended an educational
institution in the region where they start their new ventures, those who attended educational
institutions with larger alumni networks are less disadvantaged, relative to homegrown
entrepreneurs, then those who attended educational institutions with smaller alumni networks.
Our results provide support for this hypothesis. Specifically, Model 4 examines the alumni
network size of each entrepreneur in Beijing and finds empirical evidences supporting our
hypothesis – the coefficient for the interaction term between returnee entrepreneur and the
natural logarithm of his alma mater’s undergraduate enrollment in Beijing is 0.239 and is
statistically significant at the .05 level (S.E. = .110), adding further evidence that school network
is a mechanism through which returnee entrepreneurs overcome their liability.
21
ROBUSTNESS CHECK
Our models are robust to a number of alternative specifications for both our dependent
and independent variables (see Table 3). Specifically, we repeated out analyses using the cube
root of profit as an alternative measure of firm performance. We use a cube root instead of a
logarithmic transformation, because many firms in our sample had reported negative profits,
rendering a logarithmic transformation of profit meaningless. As Models 1-4 in Table 3 indicate,
out results remain substantively identical to those using revenue as proxy for firm performances.
We also repeated our analysis using alternative specifications for our key independent
variables. First, we examined alternative definitions of a returnee. Specifically, we coded as
returnees only those individuals who studied abroad, excluding those who only worked abroad. It
is plausible that this second group of individuals might have more qualities that should be
associated with successful entrepreneurship (such as risk-taking and human capital) and thus
they might not suffer from return liability or need local social networks. Model 5 suggests that
these individuals are not exempt from returnee liability and that local school ties help their
venture performance: the coefficient for Returnee Entrepreneur is -1.544 and for the interaction
term Returnee Entrepreneur x Local School Tie is 2.573; they are statistically significant at 0.10
and 0.05 levels respectively.
Second, we examined alternative measurements of local school ties. Model 6 reruns the
analysis of Model 3 in Table 2 but broaden the definition of local school ties by coding an
entrepreneur as having local school ties if he had ever attended any higher education program in
Beijing. Model 7 uses an ordinal measure of school ties to capture potential tie strength
22
variations among alumni coming out of different programs. In the Chinese education system,
undergraduates of the same class spend four years together, taking the same classes and living in
the same dormitory; the requirements for graduate programs are much less rigid and thus ties
between graduate students of the same class could be much weaker than ties between the
undergraduate counterparts. Thus, we assigned a score of 2 to founders who attended colleges in
Beijing and 1 to founders who only attended graduate programs but not colleges in Beijing. For
the rest, we assigned a baseline score of 0.
Third, using the location and enrollment size of each entrepreneur’s undergraduate
program, Model 8 classifies entrepreneurs’ local alumni networks into 5 size categories and
rerun the analyses in Model 4 in Table 2. To be specific, we create an ordinal variable that varies
from 0 to 4, with 0 assigned to colleges that are located outside Beijing, and 1 to 4 to Beijing-
based colleges based on the quartile of their undergraduate enrollment among all the Beijing-
based colleges in our sample. In Model 9, we repeat the same exercise but at the cofounder team
level – once again, taken into consideration of potential school overlap among cofounders, and
reruns the analysis in Model 8 in Table 2. While the magnitudes of the coefficient vary across
regressions, all these analyses confirm our main hypothesis that school ties help returnee
entrepreneurs to overcome the liability that arises from their being uproot from the local
environment while being overseas.
DISCUSSION AND CONCLUSION
Our study has sought to address an important puzzle in out understanding of returnee
entreprneurs: why despite the fact that given the human and social capital returnees build
23
overseas is helpful in creation of new ventures in their home countries, returnee entrepreneurs
perform no better than homegrown ones (Obukhova 2009; Li et. al. 2012). While most research
has sought the solution to this puzzle in institutional conditions in returnees’ home countries (e.g.
Chen 2008; Obukhova 2011, 2012; Li et. al. 2012), the key contribution of this paper was to
offer theoretical rational for the relatively novel mechanism that accounts for returnee-liability –
returnee’s lack of social networks in the regions where they start their new ventures. To provide
empirical support for this mechanism, we focused on Chinese high-technology firms in Beijing.
Consistent with our theoretical expectations, we found that while on average ventures established
by returnee entrepreneurs underperform homegrown ones, returnee entrepreneurs who had
experiences in the region that were likely to lead to formation of local social network are less
disadvantaged, relative to homegrown entrepreneurs, than those who did not have these
experiences.
While local social networks are likely to be important for nascent entrepreneurs
regardless of institutional context (Sorenson and Audia 2000; Dahl and Sorenson 2011), it is
important to emphasize that we have reasons to expect that institutional characteristics of China
as a developing economy magnify the importance of local social networks that our research has
identified. Because market institutions in developed economies are not fully developed, access
to critical resources, such as information, capital or raw materials, can be complicated, non-
transparent or simply limited (Acemoglu, Johnson, and Robinson 2005). Because they can’t rely
on markets, entrepreneurs in developed economies need to maintain extensive social networks to
access these resources (Peng and Heath 1996; Batjargal and Liu 2004; Wang 2009; Taussig
2010). Thus, we might to expect that the in institutional context where local social networks are
less important for new venture success, homegrown entrepreneurs have a smaller advantage
24
compared to those whose tenure in the region was interrupted. More research outside of China is
needed to explore the relationship between institutional context and “returnee-liability.”
Our results have important lessons for the literature in international business. It is well-
documented that multinationals companies (MNCs) operating abroad face additional costs,
which can make them less competitive than homegrown firms, a phenomenon referred to as
“liability of foreignness” (Hymer 1960; Zaheer 1995). We know that “liability of foreignness”
is partially due to the institutional differences between MNCs home and host environments and
partially due to the MNC’s lack of familiarity with the environment in the host country (Henisz
and Delios 2004). Consistently with research on MNCs, the results of our study suggest that
local social networks of entrepreneurs, and also likely managers, can be a significant way to
reducing “liability of foreignness” that is due to MNC’s lack of familiarity with the environment
in the host country. Also, somewhat surprisingly, we show that “liability of foreignness” can
occur even among those who had previous exposure to the country, unless these individuals
maintain a viable social network in the host country that can help them to re-embed themselves
once they return.
Lastly, our study has important lessons for the literature on social networks and
entrepreneurship. One of the most enduring findings in this literature has been that most
entrepreneurs start their firm in regions that “spawned” them, the places where she had worked
and lived prior to establishing their new venture (Katona and Morgan 1952; Dahl and Sorenson
2009). This is puzzling since the region that had “spawned” an entrepreneur is likely to be less
favorable for starting a new venture (Sorenson and Audia 2000; Figueiredo et al. 2002). Dahl
and Sorenson (2012) proposed one solution to this puzzle: that starting a firm in the region that
“spawned” her allows the entrepreneur to benefits from her local knowledge and social
25
relationships, enhancing her venture’s performance. Our study points to another complimentary
reason: by starting a firm in a region geographically distant from the region that spawned her,
even it is a region where she has previously lived, the entrepreneur runs the risk of lacking
necessary local knowledge and social networks, potentially hurting her ventures performance.
26
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Figure 1a: Comparison of Revenue between Returnee and Homegrown Entrepreneurs
01
02
03
04
0R
eve
nue(
Uni
t: M
illon
Yu
an)
HomeGrown Returnee
Figure 1b: Comparison of Revenue between Entrepreneurs with College Experience in Beijing and
Entrepreneurs without College Experience in Beijing
01
02
03
04
0R
eve
nue(
Uni
t: M
illon
Yu
an)
Non-BJ Beijing
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Table 1: Correlation Matrix and Descriptive Statistics for Key Variables (N = 515)
Key Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 Revenueln 1.000 2 Returnee Entrepreneur -0.173 1.000 3 College in Beijing 0.088 -0.014 1.000 4 College in BeijingTMT 0.094 -0.006 0.536 1.000 5 College in BeijingCofounder 0.123 -0.016 0.773 0.516 1.000 6 Alumni Sizeln 0.084 -0.010 0.994 0.532 0.768 1.000 7 Alumni Size ln,TMT 0.087 0.002 0.479 0.793 0.620 0.479 1.000 8 Alumni Size ln,Cofounder 0.122 -0.023 0.773 0.510 0.993 0.771 0.620 1.000 9 Assetln,t-1 0.548 -0.078 -0.005 0.040 -0.001 -0.007 0.033 0.002 1.000 10 R&D Expenditureln 0.375 -0.081 0.054 0.028 0.015 0.057 0.007 0.021 0.433 1.000 11 Employeesln 0.448 -0.151 0.011 0.007 -0.008 0.019 0.023 -0.001 0.428 0.578 1.000 12 Venture Age 0.376 -0.185 0.043 0.056 0.013 0.047 0.010 0.023 0.503 0.250 0.410 1.000 13 Investorsln 0.050 0.040 0.035 0.029 0.143 0.040 -0.017 0.147 -0.007 0.108 0.048 -0.011 1.000 14 TMT Sizeln 0.024 -0.069 -0.006 0.117 0.038 -0.013 0.079 0.032 0.029 0.104 0.061 -0.008 0.032 1.000 15 Male 0.058 0.073 0.068 0.004 0.050 0.067 0.063 0.060 0.023 0.068 0.082 -0.016 0.060 -0.024 1.000 16 Entrepreneur Age 0.097 -0.075 -0.054 -0.022 -0.072 -0.056 -0.031 -0.078 0.221 0.060 0.128 0.271 0.007 0.107 -0.049 1.000 17 Educational Level -0.089 0.404 0.010 -0.019 -0.060 0.023 -0.080 -0.061 -0.050 -0.002 -0.004 -0.128 0.050 -0.029 0.064 -0.142 1.000 18 School Ranking 0.021 0.099 0.481 0.251 0.349 0.500 0.228 0.366 -0.023 0.035 -0.007 -0.019 0.069 0.042 0.031 -0.045 0.119 1.000
Mean 12.512 0.247 0.318 0.619 0.439 2.934 6.516 4.112 11.34 4.382 3.615 4.243 1.400 1.449 0.889 42.80 1.658 4.445 Standard Deviation 5.666 0.431 0.466 0.486 0.497 4.325 4.501 4.688 6.898 1.357 0.849 3.110 0.423 0.370 0.314 8.664 0.830 3.818 Minimum 0 0 0 0 0 0 0 0 0 0 0.693 1 0.693 0 0 28 0 1 Maximum 19.281 1 1 1 1 10.19 10.86 10.62 19.58 8.294 6.171 21 3.828 2.564 1 72 3 10
33
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Table 2: The Effect of Attending College in Beijing on New Venture Performances by Returnee
and Homegrown Entrepreneurs (n = 515) Model 1 Model 2 Model3 Model 4 Model 5 Model 6 Model 7 Model 8 DV: Revenue, natural logarithm
H1: Returnee liability
H2: The role of entrepreneur’s local
school tie
H3: The role of entrepreneur’s
alumni network size
H4: The role ofteam’s local ties
H5: The role of team’s alumni network size
Returnee Entrepreneur -1.175** -1.811*** -1.760*** -1.851* -2.263*** -2.648** -2.194***(0.574) (0.674) (0.668) (0.967) (0.791) (1.077) (0.783)
College in Beijing 0.859* 0.267 (0.453) (0.479)
Returnee Entrepreneur x College in Beijing
2.358** (1.021)
Alumni Sizeln 0.024 (0.052)
Returnee Entrepreneur x Alumni Sizeln
0.239** (0.110)
College in BeijingTMT 0.442 (0.474)
Returnee Entrepreneur x College in BeijingTMT
1.088 (1.098)
College in BeijingCofounder 0.599 (0.428)
Returnee Entrepreneur x College in BeijingCofounder
2.551*** (0.977)
Alumni Sizeln,TMT 0.004 (0.050)
Returnee Entrepreneur x Alumni Sizeln,TMT
0.221* (0.120)
Alumni Sizeln, Cofounders 0.059 (0.046)
Returnee Entrepreneur x Alumni Sizeln, Cofounders
0.260** (0.104)
Assetln,t-1 0.307*** 0.304** 0.307*** 0.307*** 0.306*** 0.303*** 0.306*** 0.304***(0.047) (0.047) (0.047) (0.047) (0.047) (0.046) (0.047) (0.046)
R&D Expenditureln 0.190 0.176 0.166 0.165 0.184 0.174 0.170 0.175(0.259) (0.260) (0.255) (0.255) (0.252) (0.251) (0.250) (0.252)
Employeesln 1.322*** 1.387** 1.376*** 1.363*** 1.341*** 1.381*** 1.340*** 1.382***(0.358) (0.361) (0.356) (0.357) (0.358) (0.355) (0.356) (0.356)
Venture Age 0.138* 0.147** 0.129* 0.128* 0.130* 0.123* 0.133* 0.123*(0.070) (0.070) (0.069) (0.070) (0.070) (0.069) (0.070) (0.069)
Investorsln 0.532 0.521 0.502 0.499 0.505 0.287 0.508 0.303(0.480) (0.479) (0.479) (0.479) (0.479) (0.474) (0.485) (0.474)
TMT Sizeln -0.075 0.029 0.013 0.014 -0.147 -0.092 -0.096 -0.074(0.649) (0.666) (0.636) (0.638) (0.669) (0.612) (0.646) (0.616)
Male 0.448 0.301 0.343 0.357 0.460 0.366 0.450 0.317(0.725) (0.733) (0.724) (0.723) (0.727) (0.711) (0.737) (0.716)
Entrepreneur Age -0.025 -0.026 -0.023 -0.022 -0.025 -0.018 -0.024 -0.017(0.024) (0.024) (0.024) (0.024) (0.024) (0.024) (0.024) (0.024)
Educational Level -0.237 -0.436 -0.265 -0.257 -0.217 -0.141 -0.206 -0.138(0.287) (0.267) (0.286) (0.286) (0.285) (0.283) (0.284) (0.284)
School Ranking 0.044 -0.012 -0.009 -0.007 0.020 -0.012 0.029 -0.012(0.050) (0.056) (0.055) (0.057) (0.050) (0.051) (0.049) (0.051)
Industry Fixed Effects YES YES YES YES YES YES YES YESGrant Type Fixed Effects YES YES YES YES YES YES YES YES
Constant 4.144** 4.048** 4.087** 4.150** 3.950* 3.931** 4.108** 3.892**(1.993) (2.014) (1.978) (1.982) (2.012) (1.948) (1.998) (1.952)
Adjusted R2 0.381 0.378 0.389 0.387 0.384 0.397 0.387 0.395D.F. 20 20 22 22 22 22 22 22
Note: Robust standard errors are presented below the coefficients; * p<0.10, ** p<0.05, *** p<0.01 for two‐tailed tests.
34
34
Table 3: The Effect of Attending College in Beijing on New Venture Performances by Returnee and Homegrown Entrepreneurs, Robustness Check (N = 515)
DV: Profit, cube root DV: Revenue, natural logarithm Model 1 Model 2 Model3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Entrepreneur
attended college in Beijing
Entrepreneur’s alumni network size
Cofounder attended college in Beijing
Cofounders’ alumni network size
Returnee entrepreneur redefined: only entrepreneurs who studied abroad counted
School ties redefined: entrepreneur ever attended college or graduate program in Beijing
School ties redefined: Assigning weight to different programs in Beijing
Entrepreneur’s alumni network redefined: 5-group classification
Cofounders’ alumni network redefined: 5-group classification
Returnee Entrepreneur -15.162* -14.760* -17.993** -15.907* -1.544* -1.747** -1.881** -1.692** -1.801** (7.877) (7.761) (9.019) (8.964) (0.858) (0.775) (0.742) (0.676) (0.743) Local School Tie -5.124 4.075 0.483 0.521 0.249 (7.346) (6.398) (0.466) (0.467) (0.256) Returnee Entrepreneur x 28.899** 25.794** 2.573** 1.693* 1.137** Local School Tie (12.127) (11.618) (1.207) (0.994) (0.536) Alumni Network Size -0.545 0.502 0.027 0.147 (0.781) (0.676) (0.131) (0.137) Returnee Entrepreneur x 3.017** 2.283* 0.509* 0.592* Alumni Network Size (1.278) (1.212) (0.260) (0.316) All other controls YES YES YES YES YES YES YES YES YES Adjusted R2 0.540 0.540 0.543 0.541 0.383 0.388 0.390 0.385 0.387 D.F. 22 22 22 22 22 22 22 22 22
Note: Robust standard errors are presented below the coefficients; * p<0.10, ** p<0.05, *** p<0.01 for two-tailed tests.
