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Portfolio Entrepreneurs’ Behavior and Performance: A
Resource Redeployment Perspective
Simone Santamaria
To cite this article:
Simone Santamaria (2021) Portfolio Entrepreneurs’ Behavior and Performance: A Resource Redeployment Perspective.
Management Science
Published online in Articles in Advance 11 Mar 2021
. https://doi.org/10.1287/mnsc.2020.3929
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MANAGEMENT SCIENCE
Articles in Advance, pp. 1–22
ISSN 0025-1909 (print), ISSN 1526-5501 (online)
http://pubsonline.informs.org/journal/mnsc
Portfolio Entrepreneurs’ Behavior and Performance:
A Resource Redeployment Perspective
Simone Santamariaa
a
Department of Strategy and Policy, National University of Singapore Business School, National University of Singapore, Singapore 119245
https://orcid.org/0000-0001-5632-7051 (SS)
Contact: bizsim@nus.edu.sg,
Received: July 22, 2017
Revised: August 2, 2018; September 26, 2019;
April 3, 2020; July 30, 220, September 4, 2020;
November 13, 2020
Accepted: November 15, 2020
Published Online in Articles in Advance:
March 11, 2021
https://doi.org/10.1287/mnsc.2020.3929
Copyright: © 2021 INFORMS
Abstract. Prior research suggests portfolio entrepreneurs—businesspeople who run more
than one firm simultaneously—launch more successful ventures than their single-business
counterparts. However, their ventures are less likely to survive. In an attempt to reconcile
this paradox, this paper presents a framework in which portfolio entrepreneurs’ main
advantage is not a superior ability to select the best business opportunities ex ante, but
rather the ability to redeploy human and capital resources across businesses ex post, which
reduces the sunkenness of their investments in new projects. This redeployment option
facilitates their exit from new businesses that fail initial market tests. Thus, portfolio
entrepreneurs’ heterogeneous termination decisions explain a greater portion of new firm
performance differential than ex ante opportunity selection. We test these ideas using a
longitudinal data set of more than 5,700 entrepreneurs and find consistent evidence.
Portfolio companies do not show systematically higher performance at the time of entry; a
performance difference emerges only over time, as the selection effect and resource redeployment occur.
History: Accepted by Ashish Arora, entrepreneurship and innovation.
Supplemental Material: Data is available at https://doi.org/10.1287/mnsc.2020.3929.
Keywords: portfolio entrepreneurs • resource redeployment • new venture exit and performance
over-time differences in new firm performance even
when the quality of the businesses they start is similar.
To formalize this intuition, I propose a stylized
model of resource redeployment that translates a
conceptualization based on Lieberman et al. (2017) to
the context of entrepreneurial ventures. In the model,
we study the behavior of single-business and portfolio entrepreneurs who start new companies with
similar economic potential, encounter positive or negative market signals and then must decide whether and
when to exit. Our model suggests that because they can
redeploy resources, portfolio entrepreneurs are faster
to exit companies that receive negative early market
signals and this behavior generates a selection effect
over time—only the most successful portfolio companies stay alive, and a performance difference between the two groups of entrepreneurs appears. We
provide empirical evidence consistent with the model
using a cohort of 5,740 similar companies founded in
the same year by single-business (84% of the sample)
and portfolio entrepreneurs (16% of the sample). Our
empirical analysis shows that portfolio entrepreneurs
exhibit a higher probability of exit and exit earlier
from poorly performing companies, even after controlling for initial firm performance. This behavior
entails a selection mechanism that raises the relative
quality of the surviving portfolio companies over time.
1. Introduction
The growing literature devoted to entrepreneurial
performance has recently turned to entrepreneurs who
create and manage multiple firms simultaneously,
known as portfolio entrepreneurs (Westhead et al.
2005, Lechner and Leyronas 2009, Baert et al. 2016).
The extant research reveals mixed findings on the
performance of these entrepreneurs’ new ventures.
Although their firms generate more revenue and
grow faster than those of single-business entrepreneurs (Westhead et al. 2005), they are also less likely
to survive (Gottschalk et al. 2014). The goal of this
paper is to reconcile these previous contradictory
findings by introducing a new explanation based on
resource redeployment (Helfat and Eisenhardt 2004,
Levinthal and Wu 2010, Sakhartov and Folta 2014,
Lieberman et al. 2017, Belenzon et al. 2019). We argue
that a portfolio entrepreneur’s main advantage is
not a superior ability to select the best business opportunities ex ante, but rather the ability to redeploy
human and capital resources across multiple businesses ex post, which reduces the sunkenness of investments in new projects. This redeployment option
facilitates an exit from new businesses that fail initial market tests and increases responsiveness to negative market feedback. This differential exit behavior
between these two types of entrepreneurs leads to
1
2
Notably, however, the portfolio companies do not
show systematically higher performance at the time of
entry; the performance difference only emerges over
time, as the selection effect occurs. Finally, we find
evidence of the intertemporal redeployment of resources from failing to more successful companies
within the same portfolio by looking at the variation of
portfolio asset value and number of employees following new firm exit. These combined results suggest
that portfolio entrepreneurs are not systematically
more skilled owners who select better opportunities
ex ante; instead, they simply make different exit decisions ex post.
This paper advances prior literature on commitment
and experimentation in entrepreneurship (Sarasvathy
et al. 2013, Kerr et al. 2014, Belenzon et al. 2019, Gans
et al. 2019) by investigating how a portfolio approach
that enables resource redeployment across companies might help entrepreneurs make more efficient
exit decisions, which in turn determines entrepreneurial performance (Li and Chi 2013, Guler 2018, Yu
2019). In doing so, we emphasize the importance of
resource redeployment as part of an exit strategy,
which may encourage dynamic experimentation over
time (Belenzon et al. 2019). Consistent with the literature that treats survival as an outcome that is not always
desirable (Gimeno et al. 1997, Arora and Nandkumar
2011), an earlier exit appears to be advantageous for
portfolio entrepreneurs. Indeed, their heterogeneous
termination decision explains a greater portion of new
firm performance differential than ex ante opportunity
selection or heterogeneous management of successful
companies (Guler 2018). In this sense, this study helps
to conciliate prior contradictory findings on portfolio
entrepreneurs’ new firm performance (Westhead et al.
2005, Gottschalk et al. 2014). Finally, from an empirical perspective, we extend strategy literature on
resource redeployment by providing a novel, distinctive context for testing theories of intertemporal
economies of scope and their implications (Helfat and
Eisenhardt 2004, Levinthal and Wu 2010, Lieberman
et al. 2017).
2. Literature
Portfolio entrepreneurs own and manage multiple
firms at the same time, often in an explicit attempt to
seize additional opportunities related to the original
business idea (Lechner and Leyronas 2009, Iacobucci
and Rosa 2010), which may require the involvement
of other investors or partners. Interestingly, Westhead
et al. (2005) discovered that new firms created by
portfolio entrepreneurs systematically perform better
than other firms; however, the authors did not identify
the mechanisms that lead to this performance gap.
Extant research typically includes portfolio entrepreneurship as one type of successful entrepreneurial
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
Management Science, Articles in Advance, pp. 1–22, © 2021 INFORMS
experience, such that superior performance is presented as a byproduct of the founder’s entrepreneurial
skill or experience.
We acknowledge the importance of this framework, but we argue that looking at portfolio entrepreneurship exclusively in terms of experience may
be limiting. Moreover, the inconclusive prior results
suggest the need for an alternative framework. For
example, Gottschalk et al. (2014) could not replicate
the results of Westhead et al. (2005) when they considered the survival of new firms as a performance
variable. Instead, they found that firms owned by
portfolio entrepreneurs do not achieve a better survival rate; under some definitions of portfolio entrepreneurship, these companies even appear more
likely to die. No theory of portfolio entrepreneurship
has addressed this puzzle. In an attempt to establish
such a theory, we argue that the ability to select better
business opportunities ex ante because of their superior entrepreneurial experience or skill is not the
only advantage portfolio entrepreneurs enjoy when
launching new businesses. Rather, their ability to
redeploy resources across businesses ex post plays a
key role in their success. We posit that the option to
redeploy resources across businesses within the same
portfolio reduces the sunkenness of an entrepreneur’s
investments in a new business (Lieberman et al. 2017).
Entrepreneurs and organizations largely differ in
their ability and willingness to terminate underperforming ventures (Guler 2018). In many cases,
survival is a sign of stubbornness or a lack of better
options rather than an indicator of success. Gimeno
et al. (1997) find that the entrepreneur’s opportunity cost is fundamental for estimating a businessspecific performance threshold. Entrepreneurs with
high human capital in innovation-based industries,
for example, often prefer to fail quickly rather than
merely survive for an extended period while running
a poorly profitable business (Arora and Nandkumar
2011). In this context, the flexibility provided by resource redeployment may reduce portfolio entrepreneurs’ commitment to any specific business, such
that they exit faster from underperforming ventures
than single-business entrepreneurs do. Furthermore,
the faster reaction of portfolio entrepreneurs should
influence the average performance of portfolio firms.
Because portfolio entrepreneurs shut down their
underperforming companies earlier, their surviving
companies may perform better over time than companies owned by single-business entrepreneurs.
Portfolio entrepreneurs can take advantage of two
resource-sharing mechanisms. Strategy and business
group literature has traditionally focused on the first
mechanism: intratemporal economies of scope, also
known as synergy (Lieberman et al. 2017). This
mechanism involves the contemporaneous sharing
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
Management Science, Articles in Advance, pp. 1–22, © 2021 INFORMS
of resources between companies belonging to the same
group. The most common resources of this type are
intangible ones like information, brand/reputation
(Capron and Hulland 1999), and knowledge or capabilities (Capron and Mitchell 1998). Other resources
like skilled workers or physical assets, in contrast,
cannot be deployed among multiple businesses at
the same time (Levinthal and Wu 2010). These resources give rise to a second type of resource-sharing
mechanism: intertemporal economies of scope or resource redeployment. This mechanism refers to the
redeployment of resources between businesses over
time as resources are withdrawn from one business
and transferred to another (Helfat and Eisenhardt
2004, Lieberman et al. 2017). In this latter case, the
advantage provided to entrepreneurs is not directly
reflected in firm performance but instead in the form
of intertemporal flexibility. Entrepreneurship literature has not yet explored redeployment as a valuable exit option that can facilitate rapid divestment,
although a few qualitative studies indicate its relevance. For example, in their detailed case study of
a business portfolio, Baert et al. (2016, p. 362) find
strong evidence of human and capital resource redeployments following business exit: According to
the entrepreneur interviewed, the “process of reabsorbing resources from failed ventures back into the
portfolio” is an important exit strategy. Lechner and
Leyronas (2009, p. 654) find similar results in their indepth study of three French portfolio entrepreneurs:
An entrepreneur openly stated that “having a group
of companies facilitates the divestment of activities,
which increases the flexibility of the company and
reduces risk.”
3. Context, Sample, and Empirical Puzzle
To provide empirical evidence about our ideas, we
use a novel sample of 5,740 Italian firms and data
collected from the business register of Italian chambers
3
of commerce, UnionCamere. This database is publicly
available and contains official data about all Italian
companies. To construct our sample of focal firms, we
selected a starting year1 (2006) and extracted firms
founded in that year from the database. Then, we
collected balance sheet data about these firms from
2007 through 2011 (5 years), along with information
about all the firms that a founder owned or in which
he or she had previously owned majority stakes—the
indicator of a portfolio entrepreneur. Figure 1 provides a visual representation of the sampling process.
We compare focal firms founded by single-business
entrepreneurs with those founded by portfolio entrepreneurs. Because all the focal firms were created
in the same year, it is easy to make comparisons in
terms of their survival rate, exit timing, and performance.
3.1. Portfolio Entrepreneurs
We use the term entrepreneur to refer to the founder
and main shareholder of a focal firm. They founded
companies in 2006 that they control by owning a
majority of equity (minimum 51% shares) or serving
as chief executive officer (CEO). All the companies in
our sample are owned by individuals and not other
companies; we exclude any subsidiaries of established companies. Following established approaches
in the literature (Westhead et al. 2005), we define
Portfolio as a binary variable equal to 1 if the entrepreneur who launched the focal firm owns or is the
CEO of other companies at the same time.2 In contrast,
this variable is 0 if the entrepreneur runs only the focal
firm. In the sample, the average portfolio size is quite
small (50% of portfolio entrepreneurs own just two
companies, and 75% own three or fewer).
Finally, to address other issues that might distort
this classification, we removed all the entrepreneurs
who started a focal firm that can be classified as a
holding/management company or alleged shell corporation (Appendix B). Among the remaining 5,740
Figure 1. (Color online) Data Gathering Process
Notes. The figure reports our data collection process. We selected a starting year (2006) and collected a sample of firms founded that year (Focal
Firms). Then, we collected data about their founders and all the other firms in which they own majority stakes (Portfolio).
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
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Management Science, Articles in Advance, pp. 1–22, © 2021 INFORMS
Figure 2. (Color online) Survival Curves by Entrepreneur
Type
Note. The graph displays the focal firm survival curve for portfolio
and single-business focal companies.
entrepreneurs, we classified 84% as single business
and 16% as portfolio. Of the single-business entrepreneurs, about 20% had previous entrepreneurial
experience. Because our analysis focuses on portfolio
entrepreneurs rather than large business groups, most
firms in our sample are small or medium-sized enterprises with an average of 23 employees per business.
3.2. Nonparametric Analysis
Two interesting patterns emerge from simple comparison of survival and performance of focal firms
launched by single-business and portfolio entrepreneurs. In terms of survival rate, 68% of the focal
firms started by single-business entrepreneurs were
in business for the entire five-year period. This rate
drops to 60% for focal firms started by portfolio
entrepreneurs. Figure 2 provides a visual representation of the survival curves for the two groups of
entrepreneurs over time, using the Kaplan-Meier
methodology. Clearly, focal firms founded by portfolio entrepreneurs are more likely to exit, and exit
earlier, compared with focal firms launched by singlebusiness entrepreneurs.
Next, we compare focal firm performance between
the two groups of entrepreneurs. Table 1 reports the
results for the t test assessing the revenue differences
over time. Interestingly, portfolio firms do not differ
much from single-business firms in the early years;
the mean revenue difference between the two groups
is not statistically different in 2007 (t = −1.03, p = 0.30),
suggesting initial homogenous venture quality (Arora
and Nandkumar 2011). However, a performance difference emerges between the two groups in the following years.
These combined patterns of survival and revenue
differences present an interesting empirical puzzle. If
portfolio entrepreneurs are simply more skilled entrepreneurs and select better opportunities, why are
their companies less likely to survive? Similarly, why
does the revenue difference between single-business
and portfolio ventures manifest only later in time? We
argue that a theoretical framework to explain these
results lies in portfolio entrepreneurs’ main advantage being the ability to redeploy human and capital
resources across multiple businesses ex post.
4. Theoretical Model and Propositions
Our simple theoretical model aims to explain this
empirical puzzle. It formalizes the problem faced by
entrepreneurs who start a new venture, encounter
market signals, and must decide whether and when
to exit. We compare the new venture termination
Table 1. Two-Sample t Test for Portfolio and Single-Business Entrepreneurs’ Focal Firm
Revenue over Time
Difference (1) − (2)
Year
Observations
Mean revenue
Single business (1)
Portfolio (2)
2007
5,740
5,328
11.82
(3.10)
11.96
(3.26)
11.94
(3.66)
12.18
(3.61)
−0.12
2008
11.84
(3.19)
12.00
(3.32)
2009
4,922
11.71
(3.67)
11.60
(3.71)
12.33
(3.30)
−0.73***
2010
4,538
11.62
(4.02)
11.50
(4.08)
12.33
(3.58)
−0.83***
2011
4,169
11.48
(4.34)
11.35
(4.41)
12.26
(3.78)
−0.90***
−0.21*
Notes. The table reports the mean and the standard deviation (in parentheses) of focal firms’ revenue
from 2007 to 2011. Columns (1) and (2) report the mean and standard deviation separately for the
companies owned by single-business and portfolio entrepreneurs. The last column reports the difference
in the mean revenue between the two groups and the t test of the difference.
***p < 0.01; **p < 0.05; *p < 0.1.
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decision of a single business to that of a portfolio
entrepreneur when the main advantage of the latter is
the option to redeploy resources from the new venture to an established portfolio. The model develops
three main propositions that are empirically tested:
even when facing identical business opportunities,
(1) the single-business entrepreneur is slower to exit
ventures producing negative signals, which (2) generates a selection effect over time. (3) Both effects are
moderated by the portfolio entrepreneur’s redeployment potential relative to that of the single-business
entrepreneur. We also discuss how the main results
are affected when entrepreneurs select heterogeneous
opportunities before entry. Appendix A provides a
more detailed version of the model with formal statements and mathematical proofs of all propositions.
4.1. Timing and Exit Decision
The framework is a variation of the optimal stopping
problem with a finite time horizon (Ross 1983, Ferguson
2007). For simplicity, we focus on a case with just three
periods (t = 0, 1, 2), but the core intuition can be easily
extended to a case with a longer timeframe. At time t = 0,
the entrepreneur is facing an opportunity. To seize
this opportunity, she can make an investment F to
start a business. If the entrepreneur does not invest,
the game ends, and the resulting payoff can be normalized to 0. If the entrepreneur invests, at t = 1 she
observes the cash flow generated by the business and
chooses an action: stay in the business for an additional period or exit. With an exit decision, the entrepreneur can recover a fraction of the investment F
by divesting the activity. With a stay decision, the
game moves to time 2. At time t = 2, the entrepreneur
observes an additional cash flow and the game ends.
For simplicity, we assume two types of opportunities: good (G) and bad (B). Agents do not observe
opportunity types, but they know that the probability
of getting a good opportunity is α0 , and the probability of getting a bad opportunity is 1 – α0 . Good
opportunities generate positive cash flow normalized
to 1 (positive signal) with probability p and null cash
flow (negative signal) with probability (1 – p). Bad
projects generate a cash flow equal to 0 (negative
signal) with probability 1. For simplicity, we assume
that a good opportunity expected payoff in the first
or second period is sufficient to cover the fixed
costs of the venture; thus, p > F. Thus, entrepreneurs are always willing to continue if they know
the opportunity is good. Initially, we assume similar
opportunities—same p and α0 —for all entrepreneurs.
4.1.1. Single-Business Entrepreneur. Considering our
assumptions, when the single-business entrepreneur
observes positive cash flow in t = 1, the posterior
probability that the opportunity is good becomes 1,
and thus the entrepreneur keeps the business alive
until the end of the game. In contrast, a cash flow
equal to 0 in t = 1 is less informative about the quality
of the business: The opportunity might be bad (B) and
the entrepreneur is wasting resources, or the opportunity is good (G) and the entrepreneur has been
unlucky for one period. The relevant problem is thus
to decide whether to maintain the venture in period 2
when it has failed to produce positive cash flow in
period 1. Assuming the single-business entrepreneur
can recover a fraction y < 1 of the investment F by
divesting the activity, we can represent the exit problem mathematically as
max{yF, α1 p}.
(1)
The parameter α1 represents the updated beliefs
according to Bayes’s rule. If the entrepreneur observes a cash-flow of 0 in period 1, the posterior
probability that the opportunity is good in the next
period is
α1 Pr(X 0 | G )Pr ( G)
(1 − p)α0
. (2)
Pr(X 0 )
(1 − p)α0 + (1 − α0 )
Using Equation (1), we derive that it is optimal for
the single-business entrepreneur to exit in period 1
after observing a negative signal when
yF > α1 p.
(3)
The entrepreneur chooses to exit in period 1 when
the expected value of obtaining a good opportunity
payoff in period 2 is lower than the value recovered
through divestment.
4.1.2. Portfolio Entrepreneur. Considering the exit de-
cision of a portfolio entrepreneur launching the same
venture at t = 0, and facing the same problem, her
main advantage is the option to redeploy resources
from a closing business to the established portfolio. 3
Building on Lieberman et al. (2017), we model resource redeployability as the option to recover the
full sunk investment F in case of exit.4 This assumption is obviously extreme but provides a clear benchmark. In reality, we would expect that only part of the
initial investment F can be recovered. However, because the transaction costs associated with internal
redeployment are generally lower than those imposed by external factor markets, it is reasonable to
assume this fraction is larger than y. Facing a similar
exit problem as a single-business entrepreneur, the
portfolio entrepreneur has the same reaction to a
positive market signal in period 1: the posterior probability that the opportunity is good becomes 1 and the
entrepreneur keeps the business alive until the end of
the game. But when the portfolio entrepreneur is facing
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
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a negative signal, indeed it is optimal to exit in period 1 when
F > α1 p.
single-business entrepreneurs in terms of exit timing
(Proposition 1) and performance over time (Proposition 2).
(4)
Comparing Conditions (3) and (4) leads us to propose
the following.
Proposition 1. The portfolio entrepreneur is quicker to
exit a company producing negative signals than the singlebusiness entrepreneur.
4.2. Selection and Conditional
Performance over Time
Because of this early exit, the performance of the
surviving venture in period 2 experiences a selection
effect favoring portfolio performance. We can illustrate this effect by studying the expected venture
performance (cash flow) for the two types of ventures
in period 2 conditional on survival in period 1. Focusing on cases where a negative signal in period 1 is
enough to prompt exit from the portfolio entrepreneur but not from the single-business entrepreneur,5
the expected performance of the portfolio venture
at t = 2 (conditional on survival) is p. Conversely, the
expected performance of the single-business venture
at t = 2 (conditional on survival) is α0 p. Intuitively, if
the portfolio entrepreneur exits ventures producing
negative signals in period 1, only the good ventures
survive in period 2. On the other hand, the positive
probability that the single-business entrepreneur keeps
alive bad ventures reduces expected performance in
period 2. We expect no performance difference between single-business and portfolio ventures in period 1 if all the ventures have the same quality and
probability of producing positive cash flow. Based on
these arguments, we propose the following.
Proposition 2. Because of a selection effect, a performance
difference between new ventures started by single-business
and portfolio entrepreneurs appears over time, even if the
initial venture performance is similar.
4.3. Investment Sunkenness
The differential exit behavior between single-business
and portfolio entrepreneurs crucially depends on the
parameter y. The size of y, in turn, can be influenced by
the difficulty of reselling the company’s resources on
the market in the case of divestment. We call this
investment liquidation difficulty sunkenness and argue that if single-business entrepreneur can easily
resell their business assets after exit (i.e., parameter y
is high), the relative advantage of portfolio entrepreneur disappears.
Proposition 3. The greater the investment sunkenness
(lower y), the larger the difference between portfolio and
4.4. Entry Thresholds and
Heterogeneous Opportunities
Appendix A discusses the case in which we relax the
assumption that all entrepreneurs enter with the
same opportunity. Instead, we assume parameter α0
to be randomly drawn from a continuous distribution
with values ranging between (0,1) before entry. In this
case, the single-business entrepreneur shows a higher
entry threshold that pushes expected cash-flow in period 1
above that of the portfolio entrepreneur. Interestingly, this
result goes against conventional wisdom that assumes
portfolio entrepreneur’s greater skills in selecting opportunities (which should result in higher expected
cash-flow for the portfolio venture in period 1).
5. Propositions Test
The empirical tests of our three propositions follow
in succession, and we end the section providing
empirical evidence of the key mechanism—resource
redeployment.
5.1. Dependent Variables
The Active binary variable takes a value of 1 if the focal
firm survives for the entire five-year period of observation and 0 otherwise. We make no distinction
between firms that voluntarily dissolve and those that
enter bankruptcy. The Survival Time variable reflects
the number of years the focal firm survived. If the
firm is still active at the end of the five-year period
(2006–2011), it takes a value of 5. Finally, for Revenue,
we take the log(Revenue + 1) of the focal firm for a
given year t.
To assess how single-business and portfolio entrepreneurs react to negative market signals preceding
exit, we look at focal firm performance decline before
exit as a measure of the intensity of negative market
signals. This variable, coded as Revenue Exit – Revenue
t0, is the difference between the value of the company
the year before its exit and the initial value (2007).
5.2. Independent Variable
Our main independent variable Portfolio defines the
focal-firm founder’s status. It is a binary variable
equal to 1 if the entrepreneur who launched the focal
firm owns or is the CEO of other companies at the
same time. Section 3.1 provides a more detailed explanation of this variable.
5.3. Control Variables
We control for several characteristics of the focal firm:
for Revenue t0 measured as the revenue of the firm
one year after entry, or log(Revenue + 1) in 2007, and
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for Asset t0 measured as the asset value of the firm
one year after entry, or log(Asset + 1) in 2007. This
operationalization is in line with prior literature that
suggests initial size is a good proxy for startup quality
(Arora and Nandkumar 2011). We also include Regional Dummies to indicate the geographic location of
the firm according to Italy’s second Nomenclature of
Territorial Units for Statistics (NUTS) administrative
level (Region). For Sector Dummies, we use the twodigit Statistical Classification of Economic Activities
in the European Community (NACE) classification.
Appendix C contains the list of regions and sectors.
We also control for several variables at the founder
level. We control for CEO, equal to 1 if the entrepreneur
is also the CEO of the focal company and 0 otherwise; Age
of the entrepreneur, according to five classes (Class 1 is
between 20 and 30 years of age; Class 2, between 30 and
40 years; Class 3, between 40 and 50 years; Class 4, between 50 and 60 years; and Class 5, older than 60 years);
and Experienced, a binary variable that takes a value
of 1 if the entrepreneur has previous entrepreneurial
experience and exited from at least one company
before founding the 2006 focal firm, and 0 otherwise.
5.4. Moderating Variable: Sunkenness
We follow a procedure proposed by Kim and Kung
(2017) to construct an index of investment sunkenness
(irreversibility) at the industry level. This index refers
to the difficulty of reselling corporate physical assets
on the market in the case of divestment. Using the
Bureau of Economic Analysis (BEA) capital flow table, this Redeployability Index measures the extent to
which assets in a particular industry have alternative uses in any other industries on a scale from 0 to 1.
The greater the number of alternative uses, the easier
the industry assets are to liquidate on the market.
Using the same BEA data, we compute6 a measure
of Sunkenness as the inverse of their index: Sunkenness = 1 – Redeployability Index.
5.5. Descriptive Statistics
Table 2 reports descriptive statistics for all the variables, distinguishing between focal firms launched by
single-business and portfolio entrepreneurs. These
firms are comparable in terms of revenue generated
in the initial year (Revenue t0). Consistent with previous literature (Belenzon et al. 2019), portfolio ventures have a larger asset value (Asset t0). As expected,
however, focal companies launched by portfolio entrepreneurs have a lower survival rate.
Portfolio entrepreneurs tend to be older, less likely
to be CEO of the focal company, and more experienced than single-business entrepreneurs. Therefore,
it is important to control for all these confounding variables in our empirical analysis of the main
propositions.
Table 2. Descriptive Statistics (Focal Firms)
Variable
Description
All
Single
business (1)
Portfolio (2)
Difference
(1) − (2)
Revenue t
Log(1 + revenue focal firm) in year t
11.73
(3.72)
11.67
(3.82)
12.08
(3.82)
−0.35***
Revenue t0
Log(1 + revenue focal firm) in the initial year
11.84
(3.19)
11.82
(3.10)
11.94
(3.66)
−0.12
Asset t0
Log(1 + asset focal firm) in the initial year
12.26
(1.60)
12.18
(1.51)
12.67
(1.94)
−0.49***
Revenue Exit–Revenue t0
Revenue year before exit – Revenue t0
=1 if focal firm is active
−1.05
(3.42)
0.68
(0.466)
−0.36
(3.41)
0.60
(0.48)
−0.68**
Active
−0.94
(3.40)
0.67
(0.470)
Survival time
Number of years focal firm is active
4.32
(1.26)
4.34
(1.23)
4.08
(1.39)
0.25***
Sunkenness
Index of difficulty of reselling focal firm assets
0.27
(0.14)
0.27
(0.13)
0.24
(0.15)
0.03***
Portfolio
=1 if entrepreneur has a portfolio of companies in t0
Age
Entrepreneur’s age class
0.156
(0.363)
3.531
(1.193)
0
(0)
3.50
(1.23)
1
(0)
3.73
(1.04)
CEO
=1 if entrepreneur is CEO of focal firm
0.61
(0.488)
0.62
(0.48)
0.54
(0.49)
0.07***
Experienced
=1 if entrepreneur started other companies
0.238
(0.433)
0.18
(0.38)
0.54
(0.49)
−0.35***
0.08***
−0.23***
Notes. The table reports the mean and the standard deviation (in parentheses) of the main variables. Columns (1) and (2) report the mean and
standard deviation separately for single-business and portfolio entrepreneurs. The last column reports the difference between the two groups
and the t test of the difference
***p < 0.01; **p < 0.05; *p < 0.1.
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5.6. Econometric Specifications
In testing Proposition 1, we compare the focal firm
exit decision of portfolio and single-business entrepreneurs. The econometric specifications are as follows:
Activei β0 + β1 Portfolioj + β2 Agej
In testing Proposition 2, we compare portfolio and
single-business focal firm revenue over time. The
econometric specification is as follows:
Revenueit β0 + β1 Portfolioj + β2 Agej + β3 Experiencedj
+ β4 CEOj + β5 Revenue t0i + β6 Asset t0i
+ β7 Regioni + β8 Sectori + εij .
+ β3 Experiencedj + β4 CEOj
+ β5 Revenue t0i + β6 Asset t0i
+ β7 Regioni + β8 Sectori + εij , and
Hazard ratei β0 + β1 Portfolioj + β2 Agej
+ β3 Experiencedj + β4 CEOj
+ β5 Revenue t0i + β6 Asset t0i
We estimate this regression five times, once for each
year from 2007 to 2011, and incorporate all control
variables. We use these specifications to test the
moderating effect of Sunkenness (Proposition 3).
6. Results
+ β7 Regioni + β8 Sectori + εij .
The subscript i denotes variables at the focal firm
level, which we estimate using ordinary least squares
(OLS), probit, and logit regressions; and j denotes
variables at the entrepreneur level, for which we use a
Cox survival analysis.
As a measure of the intensity of negative market
signals, we focus on the sample of 979 firms that
exited and assess performance decline before exit. We
control for the Exit Year of each firm. The econometric
specification is as follows:
Revenue Exiti − Revenue t0i
β0 + β1 Portfolioj + β2 Agej + β3 Experiencedj
+ β4 CEOj + + β5 Asset t0i + β6 Regioni + β7 Sectori
+ β8 Exit Yeari + εij .
6.1. Exit Decision
Table 3 reports the effect of Portfolio entrepreneurship
on survival probability and hazard rate. The exit and
hazard rate of portfolio entrepreneurs’ focal firms is
10%–20% higher than those of single-business entrepreneurs. These results are consistent across all
functional specifications.
The analysis of our theoretical predictions regarding exit time and market signals are reported in Table 4.
Most companies experienced a decline in revenue
before ultimately exiting. However, the average drop
in revenue of single-business entrepreneurs’ companies is systematically higher than that of portfolio
entrepreneurs’ companies. The average decline in
the value of portfolio companies exiting in 2009,
for example, is close to zero, whereas that of single-
Table 3. Focal Firm Survival Probability and Hazard Rate
Variables
Portfolio
(1) OLS
(2) OLS
(3) Cox survival
(3) Cox survival
Active
Active
Hazard rate
Hazard rate
−0.0716***
(0.0176)
−0.0537***
(0.0187)
0.266***
(0.0591)
0.204***
(0.0651)
Age
0.0122**
(0.00549)
−0.0547***
(0.0206)
Experienced
−0.101***
(0.0159)
0.370***
(0.0549)
CEO
0.00236
(0.0135)
−0.00335
(0.0518)
Revenue t0
0.00887***
(0.00277)
−0.0266***
(0.00849)
Asset t0
0.0288***
(0.00603)
−0.128***
(0.0205)
Constant
Region dummies
Sector dummies
Observations
R2
0.681***
(0.00670)
No
No
5,740
0.003
0.568***
(0.112)
Yes
Yes
5,740
0.065
No
No
5,740
Yes
Yes
5,740
Notes. The table shows the effect of Portfolio entrepreneurship on focal firm survival probability and
hazard rate. The coefficients in models 1 and 2 refer to an OLS model. The coefficients in models 3 and 4
are estimated using a Cox model. Results are qualitatively similar using Logit and Probit specifications.
Robust standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
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Table 4. Negative Market Signal Preceding Focal Firm Exit
(1) OLS
(2) OLS
(3) OLS
Revenue Exit – Revenue t0
Revenue Exit – Revenue t0
Revenue Exit – Revenue t0
0.647**
(0.281)
0.702***
(0.229)
0.747***
(0.238)
−0.179*
(0.0888)
0.478**
(0.217)
−0.208**
(0.0905)
0.497*
(0.241)
CEO
0.610**
(0.217)
0.647***
(0.192)
Assets t0
−0.138*
(0.0772)
−0.188**
(0.0796)
Variables
Portfolio
Age
Experienced
Exit year 2010
−0.505*
(0.265)
−0.481
(0.322)
−0.520**
(0.244)
Exit year 2011
−0.696***
(0.235)
−0.504***
(0.187)
−0.639**
(0.251)
1.251
(0.963)
−0.649***
(0.193)
0.514
(1.126)
No
No
979
0.011
No
No
979
0.035
Yes
Yes
979
0.097
Constant
Region dummies
Sector dummies
Observations
R2
Notes. The table shows how Portfolio entrepreneurship affects the performance drop before focal firm
exit. Robust standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
business entrepreneurs is 50%. These findings are
consistent with Proposition 1: Single-business entrepreneurs are less reactive to negative market signals than portfolio entrepreneurs and thus need to
experience a stronger negative signal (larger performance decline) to exit.
6.2. Selection and Conditional Performance
Table 5 shows the performance difference between
portfolio and single-business entrepreneurs’ focal firms
over time. We confirm that portfolio ventures do not
start off as better ventures. Interestingly, when we
introduce the control variables, portfolio ventures
show systematically lower revenues than singlebusiness ventures at entry. This result is in line
with the theoretical model and the idea that portfolio entrepreneurs have a lower entry threshold. A
positive portfolio coefficient emerges only over time,
as the number of companies in the sample decreases,
suggesting a selection effect similar to the one derived in the theoretical model. These findings rule
out the alternative explanation that portfolio firms
are launched by generically better entrepreneurs. If a
portfolio firm’s quality were systematically higher,
we should observe a positive, significant Portfolio
coefficient at each point in time.
6.3. Investment Sunkenness
We use the same econometric specifications from the
analyses of Propositions 1 and 2 to test the moderating
effect on Sunkenness. Table 6 reports this effect on exit
probability and timing, and Table 7 reports its effect
on performance. The results provide strong evidence
in support of our main theoretical mechanism. In
industries characterized by assets that can be easily
sold on the market, the behavioral difference between
portfolio and single-business entrepreneurs, in terms
of exit timing and probability, is minimal and barely
significant. Conversely, this difference becomes stronger
in industries characterized by resources that are difficult
to liquidate. According to the estimates in Table 6, a
standard deviation increase in Sunkenness increases
the difference in survival rate between portfolio and
single-business entrepreneurs by 5% points and the
difference in hazard rate by 13% points. We observe a
similar effect in Table 7. The performance difference
between portfolio and single-business entrepreneurs’
focal firms becomes large and significant only in industries characterized by high levels of Sunkenness.
These results help rule out the alternative explanation
that portfolio entrepreneurs are generically more
skilled than single-business entrepreneurs and thus
select better opportunities. If they were relatively
more skilled, we would observe a large performance
difference in every industry.
7. Measuring Resource Redeployment
We capture redeployment as a positive variation in
Portfolio Resources, specifically Portfolio Assets and
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Table 5. Focal Firm Performance over Time
Variables
(1) OLS
(2) OLS
(3) OLS
(4) OLS
(5) OLS
Revenue t0
Revenue t1
Revenue t2
Revenue t3
Revenue t4
Portfolio
−0.304***
(0.107)
−0.0921
(0.118)
0.319**
(0.136)
0.340**
(0.154)
0.374**
(0.176)
Age
−0.0539*
(0.0280)
−0.303***
(0.0884)
−0.105***
(0.0328)
0.0942
(0.0968)
−0.198***
(0.0422)
0.0965
(0.124)
−0.287***
(0.0495)
0.286**
(0.141)
−0.350***
(0.0557)
0.225
(0.160)
0.0572
(0.0696)
0.0176
(0.0786)
0.0536
(0.100)
0.167
(0.115)
0.174
(0.131)
0.347***
(0.0305)
0.0920***
(0.0274)
0.0265
(0.0290)
0.00210
(0.0318)
0.629***
(0.0498)
0.785***
(0.0479)
0.779***
(0.0551)
0.839***
(0.0617)
4.948*
(2.989)
Yes
Yes
5,328
0.337
4.189*
(2.450)
Yes
Yes
4,922
0.179
5.147**
(2.173)
Yes
Yes
4,538
0.146
4.680**
(2.230)
Yes
Yes
4,169
0.143
Experienced
CEO
Revenue t0
Asset t0
Constant
Region dummies
Sector dummies
Observations
R2
1.297***
(0.0399)
−12.70***
(3.529)
Yes
Yes
5,740
0.445
Notes. Effect of Portfolio entrepreneurship on focal firm revenue. Robust standard errors in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
Portfolio Employees, when the entrepreneur’s focal
firm is closed. In contrast to previous literature on
business groups, we are not interested in measuring
resource circulation between companies over time
(Belenzon and Tsolmon 2016). Instead, we are interested in capturing redeployment following a
Table 6. Focal Firm Exit and Investment Sunkenness
Variables
(1) OLS
(2) OLS
(3) Cox Survival
(4) Cox Survival
Active
Active
Hazard rate
Hazard rate
Portfolio
0.0271
(0.0335)
0.0400
(0.0337)
−0.0392
(0.116)
−0.0798
(0.115)
Sunkenness
0.0310
(0.0488)
−0.0814
(0.0511)
−0.112
(0.181)
0.317*
(0.186)
Portfolio×Sunkenness
−0.400***
(0.118)
−0.381***
(0.117)
1.196***
(0.376)
1.113***
(0.365)
Age
0.0147***
(0.00549)
−0.0628***
(0.0200)
Experienced
−0.107***
(0.0158)
0.00755
(0.0135)
0.386***
(0.0521)
−0.0175
(0.0493)
Revenue t0
0.00933***
(0.00272)
−0.0271***
(0.00845)
Asset t0
0.0276***
(0.00590)
−0.123***
(0.0224)
CEO
Constant
Region dummies
Sector dummies
Observations
R2
0.672***
(0.0150)
No
No
5,740
0.005
0.161**
(0.0720)
Yes
No
5,740
0.041
No
No
5,740
Yes
No
5,740
Notes. The table shows the moderating effect of investment Sunkenness on Portfolio effect on focal firm
survival probability and hazard rate. Robust standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
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Table 7. Sunkenness and Focal Firm Performance over Time
Variables
(1) OLS
(2) OLS
(3) OLS
(4) OLS
(5) OLS
Revenue t0
Revenue t1
Revenue t2
Revenue t3
Revenue t4
Portfolio
0.0220
(0.211)
−0.0314
(0.206)
0.167
(0.227)
0.0229
(0.272)
0.0257
(0.295)
Sunkenness
−0.579**
(0.279)
−1.654**
(0.804)
−0.226
(0.336)
−0.285
(0.742)
−0.640
(0.412)
0.653
(0.787)
−1.182**
(0.489)
1.446
(0.944)
−1.406***
(0.544)
1.695*
(1.021)
Age
−0.0579**
(0.0286)
−0.0986***
(0.0328)
−0.195***
(0.0422)
−0.282***
(0.0497)
−0.340***
(0.0555)
Experienced
−0.330***
(0.0888)
0.0917
(0.0958)
0.136
(0.122)
0.339**
(0.138)
0.282*
(0.156)
CEO
0.107
(0.0707)
0.0344
(0.0777)
0.0473
(0.0994)
0.145
(0.114)
0.161
(0.129)
Asset t0
0.0220
(0.211)
0.345***
(0.0302)
−0.0314
(0.206)
0.0869***
(0.0272)
0.167
(0.227)
0.0209
(0.0281)
0.0229
(0.272)
0.00446
(0.0310)
0.0257
(0.295)
Constant
−3.388***
(0.481)
0.152
(0.474)
0.996*
(0.583)
2.227***
(0.641)
1.806**
(0.760)
Yes
No
5,328
0.326
Yes
No
4,922
0.163
Yes
No
4,538
0.127
Yes
No
4,169
0.127
Portfolio×Sunkenness
Revenue t0
Region dummies
Sector dummies
Observations
R2
Yes
No
5,740
0.412
Notes. The table shows the moderating effect of investment Sunkenness on Portfolio performance effect
from 2007 (t0) to 2011 (t4). Robust standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
specific event—focal firm exit. Portfolio Assets is our
first dependent variable measured as the sum of
the (log) assets of all nonfocal firms owned by an
entrepreneur in a given year. Similarly, Portfolio Employees is a dependent variable constructed as the sum
of the number of employees of all nonfocal firms
owned by the entrepreneur in a given year. Our independent variable, Focal Firm Exit, is a binary measure equal to 1 if entrepreneur i closes the 2006 focal
firm at time t and 0 otherwise.
Moreover, resource redeployment at the time of
closure can be moderated by the level of resource
relatedness between the focal firm and portfolio. We
construct a moderating variable, Relatedness across
firm sectors, using the pairwise similarity index developed by Lee and Lieberman (2010): Relatedness is
equal to 1 if two sectors i and j have identical patterns
of joint occurrence across all portfolios and 0 if they do
not co-occur in the same portfolio.7 The higher the
index, the more similar the two sectors are in terms of
resources. Finally, resource redeployment at the time
of closure can be moderated by the number of companies in the portfolio. Our second moderating variable, No. of Companies, is coded as the number of
nonfocal companies in an entrepreneur’s portfolio i
at the time of focal firm founding. We also control
for portfolio revenue, Portfolio Revenue, over time.
Descriptive statistics for all portfolio variables are
available in Table 8.
The econometric specification to find evidence of
redeployment is as follows:
Portfolio Resourcesit
β0 + β 1 Focal Firm Exitit
+ β2 Relatednessi × Focal Firm Exitit
+ β3 No. of Companiesi × Focal Firm Exitit
+ β4 Portfolio Revenueit + Yeart + ui + εit .
The dependent variables, Portfolio Assets and Portfolio
Employees, pertain to the portfolio and not the focal
firm founded in 2006. We introduce Relatedness across
firm sectors and No. of Companies in the portfolio as
moderating variables. In the fixed-effect panel regression, Year dummies capture the year’s fixed effects,
and u i is the portfolio (entrepreneur) fixed effect.
The results of asset redeployment reported in Table 9
show that portfolios of businesses whose owners
closed the focal firm in a given year experienced an
average increase in asset value of 10% that same year.
Moreover, Relatedness and the number of redeployment options (No. of Companies) both increase the
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Table 8. Descriptive Statistics (Portfolio)
Variables
Portfolio Assets
Portfolio
Employees
Portfolio Revenue
Focal Firm Exit
No. of Companies
Relatedness
Description
Observations Mean
Standard
deviation
Log(1 + portfolio assets)
Number of employees in the portfolio
3,257
3,257
33.38
66
Log(1 + portfolio revenue)
=1 if focal firm exited this year
Number of companies in the portfolio in
t0
Index of relatedness focal firm-portfolio
3,257
3,257
898
23.8
0.13
2.151
22.5
0.33
1.867
898
0.196
0.056
amount of assets redeployed. Results are less clear if
we use Portfolio Employees as the dependent variable,
as reported in Table 10. The coefficient of Focal Firm
Exit is positive, as expected, but not significant. This
finding suggests that redeployment of human resources follows different timing, additional evidence
of which is presented in the next section.
8. Additional Analyses
We performed a series of additional analyses to verify
the robustness of our results and the validity of our
proposed theoretical mechanism. First, we provide
additional evidence of human resources redeployment as a response to a negative shock involving a
portfolio entrepreneur’s focal firm. Second, to identify the mechanism that leads to increased performance of portfolio entrepreneurs over time (Proposition 2), we craft an artificial counterfactual. Third,
to support the external validity of our study, we
34.20
350
replicated our main results using a different cohort of
companies and entrepreneurs.
8.1. Additional Test for Human
Resource Redeployment
The results presented in Section 7 show a weak empirical correlation between the timing of focal firm
exit and an increase in the number of employees in the
portfolio. We thus look at the effect of a demand shock
involving the focal firm on the change in number of
employees in the portfolio. We run the analysis performed in Section 7 introducing a new independent
variable, Focal Firm Negative Shock, instead of Focal
Firm Exit. This new variable is a binary measure with
value 1 if the focal firm experiences at time t the largest
drop in sales (in the five-year period) and 0 otherwise.
As reported in Table 11, the number of employees in a
portfolio increases by an average of eight units following a focal firm negative shock.8
Table 9. Capital Resources Redeployment Following Focal Firm Exit
Variables
Focal Firm Exit
(1) Panel FE
(2) Panel FE
(3) Panel FE
Portfolio Asset
Portfolio Asset
Portfolio Asset
2.826***
(0.977)
2.826***
(0.928)
−7.673***
(2.276)
Relatedness×Focal Firm Exit
23.36**
(11.51)
No. of Companies×Focal Firm Exit
2.118***
(0.575)
Portfolio Revenue
Constant
Year dummies
Portfolio fixed effect
Observations
R2
Number of portfolios
0.743***
(0.0732)
0.728***
(0.0734)
33.01***
(0.130)
17.65***
(1.755)
17.75***
(1.724)
Yes
Yes
3,257
0.004
792
Yes
Yes
3,257
0.151
792
Yes
Yes
3,238
0.162
788
Notes. The table provides empirical evidence of asset redeployment following focal firm exit. The
dependent variable is the (log) assets of all firms owned by the entrepreneur, except for the focal firm.
The variable Focal Firm Exit has value 1 the year of focal firm exit, 0 otherwise. Robust standard errors are
in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
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Table 10. Human Resources Redeployment Following Focal Firm Exit
Variables
Focal Firm Exit
(1) Panel FE
(2) Panel FE
(3) Panel FE
Portfolio Employees
Portfolio Employees
Portfolio Employees
10.08
(11.56)
10.09
(11.55)
Relatedness × Focal Firm Exit
−62.56
(50.59)
−3.436
(4.061)
No. of Companies × Focal Firm Exit
Portfolio Revenue
Constant
29.05
(29.04)
0.253
(0.286)
0.228
(0.276)
56.33***
(9.319)
50.84***
(7.784)
50.92***
(7.932)
Yes
Yes
3,257
0.002
792
Yes
Yes
3,257
0.002
792
Year dummies
Portfolio fixed effect
Observations
R2
Number of portfolios
Yes
Yes
3,238
0.002
788
Notes. The table provides empirical evidence of human resource redeployment following focal firm exit.
The dependent variable is the total number of employees in all nonfocal firms owned by the
entrepreneur. The variable Focal Firm Exit has value 1 the year of focal firm exit, 0 otherwise.
Robust standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
8.2. Counterfactual Analysis
Via a counterfactual analysis, we confirm that the
differential exit behavior (Proposition 1) explains
most of the performance difference between portfolio and single-business entrepreneurs observed in
the sample (Proposition 2). That is, the core of our
theory is that single-business entrepreneurs continue
to operate companies even after a substantial loss in
value, but portfolio entrepreneurs do not. If portfolio
and single-business entrepreneurs were to adopt the
same exit rule, we would anticipate no performance
difference. To reproduce this notion empirically, we
force our single-business entrepreneurs to exit when
they observe a negative market signal similar to
the one triggering portfolio entrepreneurs’ exit. This
negative market signal is represented by the average
performance decline preceding portfolio entrepreneurs’ exit, which we estimated using the results from
Table 4 (β0 + β 1 coefficients in the regression9). This
number defines the portfolio exit rule. We replace
a single-business entrepreneur’s focal firm revenue
with a missing value if that firm experienced a drop in
revenue equal to or greater than the one defined by
the portfolio exit rule. This forces single-business
Table 11. Human Resources Redeployment After Negative Shock Involving Focal Firm
Variables
Focal Firm Negative Shock
(1) Panel FE
(2) Panel FE
(3) Panel FE
Portfolio Employees
Portfolio Employees
Portfolio Employees
11.35
(8.086)
7.983*
(4.382)
7.969*
(4.388)
Portfolio Revenue
Constant
Year dummies
Portfolio fixed effect
Observations
R2
Number of portfolios
0.180
(0.291)
66.17***
(1.275)
No
Yes
2,543
0.001
742
59.15***
(10.09)
Yes
Yes
2,543
0.002
742
54.92***
(9.701)
Yes
Yes
2,543
0.002
742
Notes. The table provides empirical evidence of human resource redeployment following a negative
shock involving the focal firm. The dependent variable is the total number of employees in all nonfocal
firms owned by the entrepreneur. The variable Focal Firm Negative Shock has value 1 the year of the focal
firm’s largest decline in sales, 0 otherwise. Robust standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
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entrepreneurs to behave like portfolio entrepreneurs.
Finally, we run the regression we used to test Proposition 2 with a different dependent variable, HRevenue,
which represents the hypothetical value of the firm in
the counterfactual.10 The results reported in Table 12
confirm that heterogeneity in termination choice explains most of the performance differences observed
over time. If we exclude extremely underperforming
businesses that single-business entrepreneurs refuse
to close (instead of forcing them to exit), we find
barely any performance difference between portfolio
and single-business companies over time.
8.3. Replication
Finally, our study relies on a cohort of firms founded in
Italy in 2006. To check the robustness of the results, we
replicated the test of our main propositions using
a different cohort of 7,000 firms founded in Italy in 2003
and found support for the main propositions. The results
of this additional analysis are available on request.
9. Conclusion
One of the key advantages enjoyed by portfolio entrepreneurs is their ability to redeploy resources
across businesses, which facilitates exit and triggers a
faster reaction to negative market signals. This differential exit behavior between portfolio and singlebusiness entrepreneurs leads to over-time differences
in new firm performance even when the quality of the
businesses they start is similar. We formalize this
intuition using a stylized model and provide empirical evidence of our theoretical framework using a
longitudinal data set of more than 5,700 ventures
started by portfolio and single-business entrepreneurs.
This paper accordingly contributes to entrepreneurship and strategy literature in several respects.
First, our findings contribute to entrepreneurial entry
and exit research (Gimeno et al. 1997, Arora and
Nandkumar 2011, Guler 2018, Yu 2019) by showing
that the potential to redeploy resources shapes an
entrepreneur’s business continuation decision and
reaction to negative market feedback. We believe this
theoretical mechanism is an essential component of
entrepreneurial experimentation. Gans et al. (2019)
argue that, although flexibility is extremely important for early-stage ventures, entrepreneurs can learn
about the feasibility of an idea only by starting and
committing to an activity. Yet our results caution
that excessive commitment without redeployment
options can lead to poor exit decisions.
Second, this study addresses some crucial determinants of portfolio entrepreneurs’ superior performance.
Previous literature indicates that entrepreneurs who
run more businesses at the same time produce betterperforming companies (Westhead et al. 2005), but
without specifying the sources of this superior performance. Whereas most literature relies on entrepreneurs’ experiences or endowments as a main
Table 12. Counterfactual Analysis
Variables
(1) OLS
(2) OLS
(3) OLS
(4) OLS
(5) OLS
HRevenue t0
HRevenue t1
HRevenue t2
HRevenue t3
HRevenue t4
Portfolio
−0.304***
(0.107)
−0.0921
(0.118)
−0.0124
(0.0628)
−0.0359
(0.105)
−0.0544
(0.151)
Age
−0.0539*
(0.0280)
−0.105***
(0.0328)
−0.0339*
(0.0198)
−0.0558*
(0.0301)
−0.0381
(0.0399)
Experienced
−0.303***
(0.0884)
0.0572
(0.0696)
0.0942
(0.0968)
0.0176
(0.0786)
0.0904*
(0.0546)
−0.0245
(0.0478)
0.00954
(0.0843)
−0.0377
(0.0761)
−0.0603
(0.125)
−0.0550
(0.105)
0.347***
(0.0305)
0.0567***
(0.0120)
0.0203
(0.0147)
0.0307
(0.0222)
0.629***
(0.0498)
0.616***
(0.0276)
0.597***
(0.0323)
0.563***
(0.0487)
−12.70***
(3.529)
4.948*
(2.989)
6.029***
(0.509)
6.733***
(0.395)
7.284***
(0.874)
Yes
Yes
5,740
0.445
Yes
Yes
5,328
0.337
Yes
Yes
2,031
0.602
Yes
Yes
1,005
0.559
Yes
Yes
580
0.515
CEO
Revenue t0
Asset t0
Constant
Region dummies
Sector dummies
Observations
R2
1.297***
(0.0399)
Notes. The table shows the effect of Portfolio entrepreneurship on the revenue of the focal firm from 2007
(t0) to 2011 (t4) in the counterfactual scenario. In this scenario, we force all entrepreneurs to exit when
their focal firm experiences a performance decline consistent with the portfolio exit rule. We used the
estimates of the first regression in Table 4 to model the portfolio exit rule, companies are forced to exit if
they experience a performance drop greater than +0.15 in 2009, –0.35 in 2010, and –0.60 in 2011. Robust
standard errors are in parentheses.
***p < 0.01; **p < 0.05; *p < 0.1.
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explanatory variable (Sarasvathy et al. 2013, Gottschalk
et al. 2014), we suggest that a substantial part of the
performance differential stems from their different
termination decisions, which result from their ability
to redeploy resources. Consistent with the predictions of the formal model, our empirical analysis illustrates that portfolio entrepreneurs do not start off
with better opportunities ex ante but are more selective ex post. We would not observe such selection
effects if portfolio entrepreneurs were simply more
skilled or resourceful.
Third, departing from established literature on
business groups (Locorotondo et al. 2012), we highlight important but overlooked aspects of resource
redeployment. In that literature stream, the focal
question is how to optimize the allocation of resources
across extant businesses, without specifying redeployment as a divestment option that can increase
flexibility and support more efficient experimentation
over time. Our proposed framework can help explain
why business group affiliates tend to be more innovative than stand-alone companies (Mahmood and
Mitchell 2004). Recent works in business group literature have indeed begun exploring how internal
market exibility fosters experimentation and business
creation (Belenzon et al. 2019).
In terms of limitations of the current study, although the empirical analysis produced results consistent with the predictions of the theoretical model,
we cannot make strong causal claims. Intertemporal
redeployment of capital and human resources is a
good explanation for the observed behavioral and
performance differences between portfolio and singlebusiness entrepreneurs, but other mechanisms may
be in play. Differences in motivation between these
entrepreneurs are certainly present and play a role.
Moreover, in our study redeployment mainly occurs between existing businesses in the portfolio.
Some entrepreneurs might start new ventures to redeploy resources sequentially over time after they
have learned from previous mistakes. Solving these
issues is a complex task that requires more finegrained data and a strategy to deal with the endogeneity of portfolio creation. Finally, our analysis
involves just one country, Italy, which has a business
environment characterized by weak institutions and
unsophisticated financial systems (La Porta et al.
1998). Our results might be different in other institutional contexts. Specifically, as markets for corporate assets become more developed, the difference
between single-business and portfolio entrepreneurs
is likely to decline (Belenzon and Tsolmon 2016).
Ultimately, however, we believe our study provides a
useful theoretical framework and interesting correlations that can guide future research on entrepreneurial experimentation and resource redeployment.
Acknowledgments
The authors express their appreciation to the department
editor, Ashish Arora, an anonymous associate editor, and
three anonymous reviewers for their guidance and constructive comments to improve the paper. The authors thank
Alfonso Gambardella, Andrea Fosfuri, Mario Amore, as
well as seminar participants at Bocconi University, Boston
University, Strategy, Entrepreneurship & Innovation (SEI)
Doctoral Consortium, the Academy of Management Annual
Meeting, and Strategic Management Society Conference for
the helpful feedback. Support provided by Bocconi University and Cerved Group in the data collection process is
also gratefully acknowledged. All remaining errors are
the author’s.
Appendix A. Extended Model and Proofs
A.1. Timeline
There are two types of entrepreneurs: single-business and
portfolio entrepreneur. The game lasts three periods (t = 0,
1, 2).
• At time t = 0, the entrepreneur is facing an opportunity. To seize this opportunity, she can make an investment
F to start a business. If the entrepreneur does not invest, the
game ends, and the resulting payoff can be normalized to 0.
• If the entrepreneur invests, at t = 1, she observes the
cash-flow generated by the business and chooses an action:
stay in the business for an additional period or exit. With an
exit decision, the entrepreneur can recover part of the
investment F by divesting the activity. With a stay decision,
the game moves to time 2. The single-business entrepreneur
can recover a fraction y < 1 of F in case of exit. The portfolio
entrepreneur can recover the full F in case of exit.
• At time t = 2 the entrepreneur observes an additional
cash-flow and the game ends.
A.2. Main Assumptions
There are two types of opportunities, good (G) and bad (B).
Agents do not observe opportunity types, but they know
that the probability of getting a good opportunity is α0 ∈
(0,1) and the probability of getting a bad opportunity is 1 –
α0 . Good opportunities generate positive cash flow normalized to 1 (positive signal) with probability p ∈ (0,1) and
null cash flow (negative signal) with probability (1 – p). Bad
projects generate a cash flow equal to 0 (negative signal)
with probability 1. We assume that a good opportunity
expected payoff in the first or second period is sufficient to
cover the fixed costs of the venture; thus, p > F. Thus, entrepreneurs are always willing to continue the business if
they know the opportunity is good. Initially, we assume similar
opportunities—same p and α0 —for all entrepreneurs.
A.3. Decision Tree
We can represent the (postentry) decision tree for the singlebusiness entrepreneur as shown in Figure A.1.
Decisions are represented by squares. Event nodes are
represented by circles. Payoffs are inside the diamond
shapes. Probabilities of events and payoffs are reported
outside the shapes.
The decision tree for the portfolio entrepreneur is similar,
the only difference being the value recovered in case of
exit F > yF.
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Figure A.1. Decision Tree
Consider the case in which both single-business and
portfolio entrepreneurs launch the same business in t = 0
and observe a signal in t = 1. Because p > F, when the entrepreneur observes positive cash flow in t = 1, the posterior
probability that the opportunity is good becomes 1, and
thus the entrepreneur keeps the business alive until the end
of the game. In contrast, a cash flow equal to 0 in t = 1 is less
informative about the quality of the business. The relevant
problem is thus to decide whether to maintain the venture
in t = 2 when it has failed to produce positive cash-flow
in t = 1.
A.4. Exit Decision
Proposition 1 (Repeated). A portfolio entrepreneur is more
likely to exit after observing a negative signal in t = 1 than a singlebusiness entrepreneur.
Proof. It is optimal for the single-business entrepreneur to
exit in t = 1 after observing a negative signal when
yF > α1 p.
It is optimal for the portfolio entrepreneur to exit in t = 1
after observing a negative signal when
F > α1 p.
With α1 being the posterior probability, the opportunity is
good after observing a negative signal:
α1 Pr(X 0 | G )Pr( G)
(1 − p)α0
.
Pr(X 0 )
(1 − p)α0 + (1 − α0 )
Considering that entrepreneurs have the same p and α0 , and
y < 1, the portfolio entrepreneur condition is true for a larger
range of values than the single-business condition. Thus,
the portfolio entrepreneur is more likely to exit in t = 1 after
observing a negative signal than the single-business entrepreneur is.
A.5. Selection and Performance
Proposition 2 (Repeated). Because of a selection effect, the
portfolio venture expected cash flow in t = 2 is higher than that of the
single-business venture.
Proof. We are now interested in studying the performance or
cash-flow of ventures in t = 2. The cash-flow of ventures
launched by single-business and portfolio entrepreneurs
in t = 2 is conditional on survival in t = 1. Thus, we can identify
and discuss three main cases:
(1) When α1 p ≥ F, both portfolio and single-business entrepreneurs continue their business in t = 2 irrespective of the
market signal in t = 1. Thus, the expected venture cash flow
in t = 2, conditional on survival, is α0 p for everyone. Considering that entrepreneurs have the same p and α0 , there is
no cash flow difference in t = 2 between portfolio and singlebusiness ventures.
(2) When yF > α1 p, both portfolio and single-business
entrepreneurs exit when they face a negative market signal
in t = 1. Thus, the expected venture cash flow in t = 2,
conditional on survival, is p for everyone. That is, all entrepreneurs continue their business in t = 2 only if they observe a
positive signal in t = 1; thus, their opportunity is good (G).
Also in this case, there is no cash flow difference between
single-business and portfolio ventures.
(3) When F > α1 p ≥ yF, we have the most interesting case.
A negative signal in t = 1 is enough to prompt exit from the
portfolio entrepreneur but not from the single-business entrepreneur. Thus, the expected cash flow of the portfolio
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
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venture in t = 2, conditional on survival, is p. Conversely, the
expected cash-flow of the single-business venture in t = 2,
conditional on survival, is α0 p. Intuitively, if the portfolio
entrepreneur exits ventures producing negative signals in
t = 1, only the good ventures survive in t = 2. In this case, a
performance difference between ventures launched by portfolio and single-business entrepreneurs appears in t = 2
thanks to a selection effect.
When yF ≤ α1 p, the single-business entry decision is
α0 p(1 + p) + α0 (1 − p)p ≥ F. The minimum α0 that leads to
single-business entry defines the entry threshold value:
α0 ≥
The entry threshold value is inside the range defined by the
case condition if
yF
F
1−p
≤
or y ≤
.
p(1 − p + yF) 2p
2−F
A.6. Moderation
Proposition 3 (Repeated). The likelihood of observing a difference in exit behavior and venture performance between portfolio
and single-business entrepreneurs in t = 2 decreases as the
parameter y approaches 1.
Proof. Propositions 1 and 2 derive from the portfolio entrepreneur’s ability to recover a larger fraction of F in case of
exit. As described in the previous paragraphs, we observe a
difference in exit behavior and performance between singlebusiness and portfolio ventures in t = 2 only when F >
α1 p ≥ yF. This latter condition is less likely to hold as the
parameter y approaches 1.
F
.
2p
Case 2.
The condition yF > α1 p implies that
yF
.
p(1 − p + yF)
α0 <
When yF > α1 p, the single-business entry decision is
α0 p(1 + p) + (1 − α0 p)yF ≥ F. The minimum α0 that leads to
single-business entry defines the entry threshold value:
(1 − y)F
.
p(1 + p − yF)
α0 ≥
A.7. Entry Decisions and Thresholds
We now study the entry decisions of our entrepreneurs.
Entrepreneurs start a venture if the expected value of the
opportunity is larger than or equal to the fixed costs. We
can compute the expected value of an opportunity in t = 0
by solving backward the decision tree of our entrepreneurs. The minimum α0 that leads to entry defines the
entry threshold.
The entry threshold value is inside the range defined by the
case condition if
Lemma A.1. Because the portfolio entrepreneur can recover the
full F in case of a negative signal, she always enters.
Proof. The maximum α0 that leads to exit in case of negative
market signal defines the exit threshold value. Rearranging the exit condition F > α1 p, we can write portfolio exit
threshold as
Proof.
When α1 p ≥ F, the portfolio entry decision is α0 p(1 + p)+
α0 (1 − p)p ≥ F. Because α0 p > α1 p > F, the entry condition is
always satisfied.
When F > α1 p, the portfolio entry decision is α0 p(1 + p)+
(1 − α0 p)F ≥ F. Also in this case, the entry condition is always satisfied.
Lemma A.2. The single-business entrepreneur enters only if α0 is
sufficiently high. The entry threshold for the single-business entrepreneur is
F
1−p
⎪
⎧
⎪
α0 ≥ if y ≤
⎪
⎪
2p
2−F
⎪
⎪
⎪
⎪
⎨
.
⎪
⎪
⎪
⎪
⎪
⎪
(1 − y)F
1−p
⎪
⎪
⎩ α0 ≥
if y >
p(1 + p − yF)
2−F
Proof.
Case 1.
(1 − y)F
yF
1−p
<
or y >
.
p(1 + p − yF) p(1 − p + yF)
2−F
Lemma A.3. Portfolio entrepreneur exit threshold value is always
larger than single-business entry or exit threshold value.
α0 <
F
.
p(1 − p + F)
Rearranging the exit condition yF > α1 p, we can write singlebusiness exit threshold as
α0 <
Case 1. In case y >
yF
.
p(1 − p + yF)
1−p
2−F ,
we can write
F
yF
(1 − y)F
>
>
.
p(1 − p + F) p(1 − p + yF) p(1 + p − yF)
The condition is always verified for any y < 1.
Case 2. In case y ≤ 1−p
2−F , the entry threshold of the singlebusiness entrepreneur is
α0 ≥
The condition yF ≤ α1 p implies that
α0 ≥
yF
.
p(1 − p + yF)
F
.
2p
Portfolio exit threshold is larger than the above entry
threshold when
F
F
> .
p(1 − p + F) 2p
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Rearranging we can write the following:
p > F − 1.
The condition is always verified.
A.8. Heterogeneous Opportunities
In this section, we relax the assumption that all entrepreneurs start the venture with the same opportunity. Instead,
we assume that the parameter α0 is randomly drawn from
an unknown continuous distribution D(0,1) before entry.
Proposition A.1. When entrepreneurs draw heterogeneous opportunities before entry, the expected cash flow of single-business
ventures is higher than that of portfolio ventures in t = 1. Because of
Lemma A.3, Propositions 1–3 are still valid.
Proof.
Case 1. When y ≤
1−p
2−F .
In this case, the entry threshold for the single-business
F
(Lemma A.2). Thus, in t = 1, companies
entrepreneur is 2p
launched by portfolio entrepreneur have 0 < α0 < 1 ,
F
whereas those launched by single business have 2p
≤α0 < 1.
Because the portfolio entrepreneur launches ventures with
lower α0 , her expected cash flow is lower than that of single
business in t = 1.
The expected cash flow in t = 1 for the portfolio venture is
pE(α0 ).
The expected cash flow in t = 1 for the single-business
venture is
)
(
F
> pE(α0 ).
pE α0 |α0 ≥
2p
F
Figure A.2. Decision Path when α0 < p(1−p
+F)
In the second period, the expected cash flow is conditional
on survival. Indeed, exited ventures do not generate any
cash flow.
Starting from the portfolio, we can identify two main
cases of venture survival based on the α0 drawn by the
F
entrepreneur. First, if α0 < p(1−p
+F) , the venture survives
in t = 2 only in case of a positive cash flow in t = 1. In this case,
the expected cash flow, conditional on survival, is p. We can
visualize this path on the game tree in Figure A.2.
F
Second, if α0 ≥ p(1−p
+F) , the venture survives in t = 2 regardless of the feedback in t = 1. In this case, the surviving
opportunity can be good or bad. Thus, if the opportunity is
good the venture will generate cash-flow p in t = 2. If the
opportunity is bad, cash-flow will be zero. Thus, the expected cash-flow is α0 p. We can visualize this path on the
game tree in Figure A.3.
Combining both cases, we can represent portfolio expected cash flow in t = 2 conditional on survival as
(
)
F
Pr α0 <
p
p(1 − p + F)
) (
)
(
F
F
pE α0 |α0 ≥
.
+ Pr α0 ≥
p(1 − p + F)
p(1 − p + F)
Notice that because y ≤ 1−p
2−F , the single-business entrepreneur always continues the venture in t = 2. Thus, the expected cash flow in t = 2 is
)
(
F
.
pE α0 |α0 ≥
2p
Because of Lemma A.3, we can conclude that portfolio
ventures generate a higher expected cash-flow in t = 2,
conditional on survival.
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F
Figure A.3. Decision Path when α0 ≥ p(1−p
+F)
Case 2. When y > 1−p
2−F .
(1−y)F
The single-business entrepreneur enters only if α0 ≥ p(1+p−yF)
(Lemma A.2). Thus, in t = 1, companies launched by portfolio entrepreneurs have 0 < α0 < 1 , whereas those launched
(1−y)F
by single business have p(1+p−yF)
≤ α0 < 1. Because the portfolio entrepreneur launches ventures with lower α0 , her
expected cash flow is lower than that of single business
in t = 1.
The expected cash flow in t = 1 for the portfolio venture is
pE(α0 ).
The expected cash flow in t = 1 for the single-business
venture is
)
(
(1 − y)F
> pE(α0 ).
pE α0 |α0 ≥
p(1 + p − yF)
In the second period, the expected cash flow is conditional
on survival.
We already identified portfolio expected cash-flow in
t = 2 conditional on survival as
)
(
F
p
Pr α0 <
p(1 − p + F)
(
) (
)
F
F
+ Pr α0 ≥
pE α0 |α0 ≥
.
p(1 − p + F)
p(1 − p + F)
Following the same logic, we can identify the single business expected cash flow in t = 2 conditional on survival. The
main differences are that the single business has a lower exit
threshold value and all probabilities are conditional on
(1−y)F
entry. Defining the entry threshold value as e p(1+p−yF)
, we
can write
(
)
yF
| α0 ≥ e p
Pr α0 <
p(1 − p + yF)
)
) (
(
yF
yF
| α0 ≥ e pE α0 |α0 ≥
.
+ Pr α0 ≥
p(1 − p + yF)
p(1 − p + yF)
We can rewrite the condition as
[ (
)
1
yF
Pr e ≤ α0 <
p
Pr(α0 ≥ e)
p(1 − p + yF)
) (
)]
(
yF
yF
pE α0 |α0 ≥
.
+ Pr α0 ≥
p(1 − p + yF)
p(1 − p + yF)
Notice that the probability the entrepreneur will continue
the venture, irrespective of the market signal is lower
for portfolio.
Indeed, given Pr(α0 ≥ e) < 1 :
)
(
)
(
1
yF
F
> Pr α0 ≥
.
Pr α0 ≥
Pr(α0 ≥ e)
p(1 − p + yF)
p(1 − p + F)
In addition, the expected cash flow produced in the previous case is higher for the portfolio:
(
)
(
)
F
yF
pE α0 |α0 ≥
> pE α0 |α0 ≥
.
p(1 − p + F)
p(1 − p + yF)
In summary, because of Lemma A.3, we can conclude
that portfolio ventures are less likely to survive and
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
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Management Science, Articles in Advance, pp. 1–22, © 2021 INFORMS
generate a higher expected cash flow in t = 2. Finally, notice
that y moderates the cash-flow difference between portfolio
and single-business entrepreneurs.
Appendix B. Shell Corporations
In some cases, entrepreneurs create different companies
(legal entities) for strategic goals that have nothing to do
with seizing a new business opportunity. Some companies
may have been designed to run unproductive activities such
as managing the resources of other companies (e.g., holding
companies). We accordingly removed from our sample all
focal firms belonging to the business administration, real
estate, and financial services sectors and all generic service
companies. Other companies may be shell corporations,
designed primarily to lower or avoid taxes. We followed the
guidelines provided by the Italian financial police (Guardia
di Finanza), which identify a company as a shell company
(Società di Comodo) if its expected operating revenues, estimated using the assets available, are much lower than its
actual revenues. The rules to compute the expected operating revenues of a company given its assets are detailed
and based on the intuition that companies with an extremely low level of revenue in comparison with their
available assets are more likely to be nonoperating or shell
corporations. We adopt these criteria and remove any alleged shell companies from the sample. The main results are
robust to alternative definitions of portfolio entrepreneurs;
however, they do not change even if we ignore all these
modifications and define a portfolio entrepreneur simply as
someone who owns more than one company. The results of
this alternative analysis are available upon request.
Appendix C. Geographic Location and Sector of
Focal Companies
Italian region
Abruzzo
Aosta Valley
Apulia
Basilicata
Calabria
Campania
Emilia Romagna
Lazio
Liguria
Lombardy
Marche
Molise
Piedmont
Sardinia
Sicily
Trentino South Tyrol
Tuscany
Umbria
Veneto
Venezia Giulia
Total
Frequency
Percentage
134
5
300
33
55
456
550
660
98
1,173
225
18
397
81
222
73
459
89
596
116
5,740
0.02
0.00
0.05
0.01
0.01
0.08
0.10
0.11
0.02
0.20
0.04
0.00
0.07
0.01
0.04
0.01
0.08
0.02
0.10
0.02
100.00
Appendix C. (Continued)
Frequency Percentage
Sector
Agriculture and fishing
Agriculture and fishing
Mining and quarrying
Total
Manufacturing
Manufacture of fabricated metal
products
Manufacture of machinery and
equipment
Manufacture of food products
Manufacture of other chemical
products
Manufacture of furniture
Manufacture of leather and related
products
Manufacture of electrical equipment
Manufacture of wood and products
of wood
Manufacture of rubber and plastic
products
Manufacture of computer, electronic,
and optical products
Manufacture of textiles
Other manufacturing
Manufacture of other transport
equipment
Manufacture of motor vehicles,
trailers, and semitrailers
Manufacture of paper and paper
products
Manufacture of machinery for
metallurgy
Manufacture of beverages
Manufacture of chemicals and
chemical products
Manufacture of basic pharmaceutical
products
Manufacture of coke and refined
petroleum products
Manufacture of tobacco products
Total
Services
Architectural and engineering
activities
Advertising and market research
Other professional and scientific
activities
Repair and installation of machinery
and equipment
Packaging activities
Printing and reproduction of
recorded media
Legal and accounting activities
Scientific research and development
Retail trade (except of motor
vehicles)
Construction
Other services
161
46
207
0.028
0.008
0.036
821
0.143
327
0.057
235
212
0.041
0.037
172
161
0.030
0.028
161
149
0.028
0.026
144
0.025
132
0.023
115
103
103
0.020
0.018
0.018
46
0.008
34
0.006
34
0.006
23
11
0.004
0.002
11
0.002
2
0.000
1
2,999
0.000
0.520
682
0.120
382
319
0.067
0.056
267
0.047
158
147
0.028
0.026
130
95
61
0.023
0.017
0.011
50
43
0.009
0.008
Santamaria: Portfolio Entrepreneurs’ Behavior and Performance
21
Management Science, Articles in Advance, pp. 1–22, © 2021 INFORMS
redeployment is always from the new venture to the established portfolio.
Appendix C. (Continued)
Frequency Percentage
Wholesale trade (except of motor
vehicles)
Business support service activities
Electric power generation,
transmission, and distribution
Specialized construction activities
Information service activities
Software publishing
Transporting and storage
Sports activities and amusement and
recreation activities
Prepress and premedia services
Human health activities
Wholesale and retail trade; repair of
motor vehicles and motorcycles
Veterinary activities
Civil engineering
Education
Telecommunications
Travel agency activities
Hotels and similar accommodation
Waste collection, treatment, and
disposal activities
Motion picture, video, and television
programming activities
Waste treatment and disposal
Employment activities
Services to buildings and landscape
activities
Gambling and betting activities
Repair of computers and personal
and household goods
Security and investigation activities
Total
Grand Total
26
0.005
21
17
0.004
0.003
17
17
11
11
11
0.003
0.003
0.002
0.002
0.002
11
11
11
0.002
0.002
0.002
5
5
5
5
5
5
5
0.001
0.001
0.001
0.001
0.001
0.001
0.001
2
0.000
2
2
1
0.000
0.000
0.000
1
1
0.000
0.000
1
2,543
5,740
0.000
0.443
1.000
Endnotes
1
We selected 2006 to maximize the information available related to
focal firm performance and other companies created by the same
entrepreneur and covered by the chambers of commerce. A European
business intelligence company helped us with the data collection
process. Italy is an appropriate setting to for the analysis because of its
high level of entrepreneurial activity and business ownership compared with other developed countries (E&Y 2013).
2
The status of an entrepreneur is dynamic and may vary over time
(e.g., a single-business entrepreneur in 2006 might start a new
company in 2008 and become a portfolio entrepreneur), which raises
some classification concerns. We classify entrepreneurs according to
their status at the moment of the founding of the focal firm in 2006
and exclude from the sample the small fraction of entrepreneurs
(around 10%) who changed their status during our study period.
However, to check the robustness of the findings, we also ran the
analysis with the full sample and a time-varying classification of
entrepreneurs. The key results do not change.
3
For simplicity, we abstract from the decision to become a portfolio
entrepreneur and simply take the existence of an established business
portfolio for the entrepreneur as given. We can assume that some
entrepreneurs are more capable when it comes to discovering
business opportunities than others.
4
In this model we focus exclusively on the newly created venture termination decision and thus the direction of resource
5
When portfolio and single-business entrepreneurs react to a period 1
negative signal in the same way (exit or continue), we do not observe
any performance difference in period 2. Appendix A provides a more
detailed discussion of these cases.
Similar to Kim and Kung (2017), we find that the industries with
the lowest Sunkenness are services such as social assistance and advertising, and industries such as mining and agriculture have
the highest.
6
7
To compute overall Relatedness between the focal firm and the
business portfolio, we compute the pairwise similarity index for the
focal firm’s sector and for all other sectors in the portfolio. We take the
highest index in the portfolio as the overall measure of Relatedness,
with the idea that portfolio entrepreneurs can redeploy all their resources into the most related company in the portfolio.
These results combined with the findings of Section 7 provide insights into the timing of different resource redeployments. The results
in Table 9 show how capital resource redeployment usually occurs in
the same year as new firm exit. The results obtained from Table 11
suggest that human resources redeployment precedes the exit event
and starts once the focal firm experiences a decline in sales.
8
9
For the baseline model, we used the estimates of the drop in performance from the first regression of Table 4, so single-business
companies are forced to exit if they experience a performance drop
greater than +0.15 in 2009, –0.35 in 2010, and –0.60 in 2011.
HRevenue is equal to a missing value (exit) if the firm experienced a
drop in value consistent with the portfolio exit rule. Otherwise,
HRevenue is equal to the actual value of the firm’s revenue (Revenue).
10
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