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Entrepreneurial persistence beyond survival: Measurement and determinants


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Entrepreneurial persistence is demonstrated by an entrepreneur’s continued positive maintenance of entrepreneurial motivation and constantly renewed active engagement in a new business venture despite counterforces or enticing alternatives. It thus is a crucial factor for entrepreneurs when pursuing and exploiting their business opportunities and in realizing potential economic gains and benefits. Using rich data on a representative sample of German business founders, we investigated the determinants of entrepreneurial persistence. Next to observed survival, we also constructed a hybrid persistence measure capturing the motivational dimension of persistence. We analyzed the influence of individual-level (human capital and personality) and business-related characteristics on both measures as well as their relative importance. We found that the two indicators emphasize different aspects of persistence. For the survival indicator, the predictive power was concentrated in business characteristics and human capital, while for hybrid persistence the dominant factors were business characteristics and personality. Finally, we showed that results were heterogeneous across subgroups. In particular, formerly unemployed founders did not differ in survival chances, but they were more likely to lack a high psychological commitment to their business ventures.
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Journal of Small Business Management
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Entrepreneurial persistence beyond survival:
Measurement and determinants
Marco Caliendo, Maximilian Goethner & Martin Weißenberger
To cite this article: Marco Caliendo, Maximilian Goethner & Martin Weißenberger (2020)
Entrepreneurial persistence beyond survival: Measurement and determinants, Journal of Small
Business Management, 58:3, 617-647, DOI: 10.1080/00472778.2019.1666532
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Entrepreneurial persistence beyond survival: Measurement
and determinants
Marco Caliendo
, Maximilian Goethner
, and Martin Weißenberger
Department of Economics, University of Potsdam, Germany;
School of Economics and Business
Administration, Friedrich Schiller University Jena, Germany;
University of Potsdam, Germany
Entrepreneurial persistence is demonstrated by an entrepreneurs
continued positive maintenance of entrepreneurial motivation
and constantly renewed active engagement in a new business
venture despite counterforces or enticing alternatives. It thus is
a crucial factor for entrepreneurs when pursuing and exploiting
their business opportunities and in realizing potential economic
gains and benefits. Using rich data on a representative sample of
German business founders, we investigated the determinants of
entrepreneurial persistence. Next to observed survival,wealso
constructed a hybrid persistence measure capturing the motiva-
tional dimension of persistence. We analyzed the influence of
individual-level (human capital and personality) and business-
related characteristics on both measures as well as their relative
importance. We found that the two indicators emphasize differ-
ent aspects of persistence. For the survival indicator, the predic-
tive power was concentrated in business characteristics and
human capital, while for hybrid persistence the dominant factors
were business characteristics and personality. Finally, we showed
that results were heterogeneous across subgroups. In particular,
formerly unemployed founders did not differ in survival chances,
but they were more likely to lack a high psychological commit-
ment to their business ventures.
Entrepreneurship; startups;
persistence; survival
Entrepreneurship has been recognized as vital to increasing productivity,
spurring innovation, and enhancing employment opportunities (Audretsch,
Keilbach, & Lehmann, 2006; Fritsch, 2008; Koellinger & Thurik, 2012).
However, to realize the economic benefits of their entrepreneurial activity,
individuals not only must choose to become entrepreneurs, but also must
persist with their business venture (Patel & Thatcher, 2014). Persistence can
be considered as a prerequisite to exploit the business potential of a given
venture and, consequently, its chances of success. Entrepreneurial persistence
entails two distinct components: First, the motivation and decision to
CONTACT Marco Caliendo Chair of Empirical Economics, University of Potsdam,
August-Bebel-Str. 89, Potsdam 14482, Germany
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
Supplemental data for this article can be accessed on the publisher's website.
2020, VOL. 58, NO. 3, 617647
© 2019 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives
License (, which permits non-commercial re-use, distribution, and reproduction
in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
continue to actively pursue a previously selected entrepreneurial opportunity;
and, second, doing so in the face of adversity or attractive alternatives
(Gimeno, Folta, Cooper, & Woo, 1997; Holland, 2011; Holland &
Shepherd, 2013). Accordingly, an entrepreneurs persistence decision is fun-
damentally different from the initial startup decision. An entrepreneur makes
the decision to start a new business at a single point in time and under
conditions that are likely to be favorable for the creation of the new venture.
By contrast, the decision to persist with the new venture has to be repeatedly
made, and is often most salient if the environment is changing and condi-
tions are challenging (Holland & Garrett, 2015). The venturing effort may
prove more difficult, expensive, or time consuming than originally expected.
Governmental regulations may delay development or the market may prove
to be much less interested in ones product/service/technology than initially
hoped. Furthermore, conflicts with business partners may arise. Persistence is
therefore an important ingredient for pursuing an entrepreneurial endeavor
despite uncertainties, challenges, and setbacks (Adomako, Danso, Uddin, &
Damoah, 2016; Cardon & Kirk, 2015).
While some early work has considered persistence as a trait (for example,
Baum & Locke, 2004), the more recent literature suggests that entrepreneur-
ial persistence is a function of individual, business-related, and contextual
factors (DeTienne, Shepherd, & De Castro, 2008; Holland & Shepherd, 2013).
For instance, studies have found that individual dispositions derived from
personality factors (for example, Caliendo, Fossen, & Kritikos, 2014; Patel &
Thatcher, 2014), and competencies, skills, and knowledge all strongly relate
to persistence with a newly founded business (for example, Freeland &
Keister, 2016; Gimeno et al., 1997). DeTienne et al. (2008) show that entre-
preneurs are more likely to persist when personal investment is high, even
with underperforming firms. Other studies emphasize the predictive role of
firm- and opportunity-related factors such as startup capital (for example,
Brüderl & Preisendörfer, 1998; DeTienne et al., 2008) and industry sector
(Fritsch, Brixy, & Falck, 2006) or the regional economic conditions (Gimeno
et al., 1997; Millán, Congregado, & Román, 2012).
Despite these prior efforts to understand the determinants of the per-
sistence decision, we still lack a thorough understanding of why some
individuals choose to stay in entrepreneurship when faced with unexpected
obstacles and challenges while others do not, and whether differences exist
across distinct subgroups of entrepreneurs. In particular, not much is
known about the relative importance of the multitude of persistence pre-
dictors identified in previous studies.Moreover,giventhecomplexnature
of the concept of entrepreneurial persistence, a diverse variety of persis-
tence measures are established in the literature, which makes a direct
comparison of previous results challenging and possibly reflects the source
of ambiguous findings for particular covariates. Previous persistence
variables can roughly be grouped into three different types of measures.
Whilemanystudiesusebusinesssurvival as a proxy for entrepreneurial
persistence, others apply more subjective measures to capture the motiva-
tional commitment to the business venture. Finally, some studies combine
survival and subjective persistence to obtain hybrid measures.
Using data from representative samples of regular and formerly unem-
ployed entrepreneurs in Germany (Caliendo, Hogenacker, Künn, & Wießner,
2015; Caliendo, Künn, & Weißenberger, 2019a), we contribute to the litera-
ture on the determinants of entrepreneurial persistence in three important
ways. First, we examined survival and a constructed hybrid measure as
different types of persistence from one single dataset. In particular, the data
contain indicators of entrepreneurial persistence in terms of observed survi-
val as well as a subjective measure capturing the motivational dimension of
persistence (that is, strong commitment to the business despite a hypothetical
offer of a similar job in paid employment). Thus, we could directly compare
results between the commonly applied survival indicator with findings using
the individual-level hybrid measure of entrepreneurial persistence, which
more directly reflects the psychological commitment part of entrepreneurial
persistence. Second, we had access to a rich list of predictors of entrepre-
neurial persistence covering a multitude of individual-level, business-related,
and contextual characteristics. We drew from research that proposes found-
ing/founder effects to explain variance in new venture performance (Baum &
Locke, 2004; Boeker, 1989; Stinchcombe, 1965) to identify a set of relevant
persistence predictors. Hence, we focused on human capital and personality
traits of the entrepreneur as well as business characteristics while controlling
for other determinants of the persistence decision such as sociodemographic
characteristics, intergenerational transmissions, startup motives, and the
regional economic context. This enabled an in-depth analysis of predictors
of entrepreneurial persistence and for testing the robustness of results when
including other relevant determinants while also minimizing potential threats
of omitted variable bias. Furthermore, the availability of this extensive vari-
able list enabled a more holistic approach to investigate the relative impor-
tance for entrepreneurial persistence between covariate groups. Third, we
took account of the fact that entrepreneurs are heterogeneous (Alvarez &
Busenitz, 2001). We provided a separate analysis for the subgroups of for-
merly unemployed and regular (nonunemployed) founders for the following
reasons. Unemployed founders represent a substantial share of all founders
in Germany, partly due to a series of active labor market policies promoting
self-employment (Caliendo & Kritikos, 2010; Caliendo et al., 2015), and they
are different from the general populationof founders in terms of avail-
ability of or access to human, social, and financial capital (Caliendo et al.,
2015,2019a). They are more likely to be necessity founders with lower
business attachment and, thus, their persistence is likely to depend on
different factors compared to regular founders.
Overall, our empirical results yielded the following findings. First, while
some factors (locus of control, startup capital) had a robust influence on
persistence, the importance of most other factors was sensitive to the choice
of the persistence measure (for example, unemployment and industry-
specific experience, Big Five personality traits). Second, for the survival
indicator, the relative importance of predictors was concentrated in business
characteristics and human capital, while for hybrid persistence the dominant
factors were business characteristics and personality. Third, our heterogene-
ity analysis enabled a detailed subgroup analysis and revealed that the
psychological commitment of unemployed founders was more strongly influ-
enced by personality compared to regular founders.
The remainder of this article is organized as follows. In the next section,
we review the literature on entrepreneurial persistence. Next, we introduce
our dataset, describe the construction of our persistence measures, and
present some descriptive statistics. We then describe our empirical strategy,
after that present the results. We conclude the article with a summary and
discussion of our results.
Literature review
Measurement of entrepreneurial persistence
The notion of individual persistence in the context of entrepreneurship
usually involves two aspects: First, the founders maintain their entrepreneur-
ial motivation, choosing to continue their effortful and active engagement in
their business ventures at a particular point in time; and, second, they do so
despite challenging conditions, impediments, counterforces, or attractive
alternatives (Gimeno et al., 1997; Holland, 2011; Holland & Shepherd,
Given the complexity of the concept, we found a varied range of
persistence measures applied in previous entrepreneurship studies on this
topic. Overall, we identified three distinct approaches in the literature to
measure entrepreneurial persistence.
First, the most common practice is to use the foundersobjectivesurvival in
self-employment or running a business as a proxy variable for persistence if
longitudinal data are available.
While survival and persistence are undoubtedly
Davidsson (2012) distinguishes this shorter-term perspective from a longer-term view, in which entrepreneurial
persistence captures reentries to the venture creation processes after previous efforts have been concluded.
Although, in principle, persistence can also be defined at the level of the business venture, we followed the large
majority of previous studies in the literature and considered persistence at the individual founders level.
See, for example, Block and Sandner (2009), Brüderl and Preisendörfer (1998), Brüderl and Ziegler (1992), Caliendo
et al. (2014), Ciavarella et al. (2004), Fritsch et al. (2006), Georgellis et al. (2007), Gimeno et al. (1997), Millán et al.
(2012), Oberschachtsiek (2012), Patel and Thatcher (2014), van Praag (2003), Zhu et al. (2011).
closely linked, they are not necessarily identical. The definition of persistence
usually involves a psychological commitment; that is, the motivation to actively
engage in and the decision to continue business activities irrespective of circum-
stances. For instance, founders might be observed operating their businesses
despite actively seeking alternative business opportunities, thus lacking full
commitment to their original business ventures. The difference between survival
and persistence can also be illustrated by founders who were predominantly
motivated to start a business due to push factors as a last resort (for example,
a lack of employment alternatives). These founders might show a persistent
survival of their businesses, albeit not mainly due to their motivational dedica-
tions or preferences, but rather because there remains a shortage of employment
opportunities. Second, as an alternative to survival, a cross-section of entrepre-
neurs are surveyed on subjective measures of persistence, often by presenting
hypothetical scenarios to them and asking them whether or not they would
continue operations under the described circumstances in the future (for exam-
ple, Holland & Garrett, 2015; DeTienne et al., 2008, applying conjoint analyses).
Purely subjective measures could be criticized because they solely rely on self-
reported assessments of artificial hypothetical scenarios and might differ from
actual behavior or attitudes displayed in reality. As a third option in the
literature, Davidsson (2012) and Freeland and Keister (2016) combine survival
measures with a subjective question about the founders projected active busi-
ness engagement in the near future to construct a hybrid persistence measure.
Determinants of entrepreneurial persistence
The entrepreneurship literature presents the prevailing view that entrepre-
neurial persistence is a function of a variety of predictors (DeTienne et al.,
2008; Holland & Shepherd, 2013). Both individual attributes of the entrepre-
neur and initial characteristics of the startup are among the most prominent
determinants of the pivotal strategic decision to persist or disengage. This is
also consistent with research positing that new ventures are imprinted at the
time of founding and that this has long-lasting effects on their strategy
(Boeker, 1989), structure (Stinchcombe, 1965), and performance (Cooper,
Gimeno-Gascon, & Woo, 1994). Driven by their values, motivations, goals,
and personalities, the founders determine the subsequent development of
startups because they shape the basic identity and configuration of the new
organizations (Baum & Locke, 2004; Boeker, 1989; Stinchcombe, 1965). The
founder effects most persistently and extensively studied by entrepreneurship
researchers include: (a) entrepreneur dispositions derived from personality
factors, and (b) individual competencies, skills, and knowledge (Cooper et al.,
See, for example, Cardon and Kirk (2015), DeTienne et al. (2015), DeTienne et al. (2008), Holland and Garrett
(2015), Holland and Shepherd (2013), Wu et al. (2007).
1994). The former reflect the influence of long-run stable individual traits
(Zhao & Seibert, 2006), whereas the latter reflect the impact of human capital
accumulated over time (Unger, Rauch, Frese, & Rosenbusch, 2011).
In the following, we elaborate on individual characteristics of the entrepre-
neur; that is, (a) human capital and (b) personality traits as well as (c) business
characteristics as determinants of entrepreneurial persistence. We further devel-
oped hypotheses for predicting our two distinct persistence measures, survival
and hybrid persistence. Our data also allowed us to control for other character-
istics (sociodemographic characteristics, intergenerational transmissions,
startup motives, and the regional economic context), although they were not
the focus of our interest. Table 1 provides an overview of previous findings.
Human capital
Human capital reflects knowledge and skills that individuals have acquired
through education, training, and on-the-job experience, which provide them
Table 1. Determinants of persistence in the empirical literature.
Covariate Sign of relation Literature references
(1) Human capital
Schooling þ=0a, b, c, h, i, k, n, o
Professional education þa, j, o
Unemployment experience þ=g, i, j, l, o
Industry-specific experience þb, c, e, j, l, n, o
Skills and knowledge
Strategy/leadership 0 c, h, j
Back office þh
Front office 0 j
Industry knowledge þh
(2) Personality
Big Five
Openness 0=d, e, k
Conscientiousness þ=0d, e, k
Extraversion 0 d, e, k
Agreeableness 0=d, e, k
Neuroticism þ=0d, e, k
Locus of control 0 d
Self-efficacy þp
Readiness to take risk Concave d
(3) Business characteristics
Startup capital þb, c, h, j, m, o, r
Business sector þ=b, c, f, g, h, l, n
Note: The table summarizes the findings of the literature review about the direction of the relationship
between covariates and entrepreneurial persistence. þdenotes a positive effect; denotes a negative
effect; 0 denotes no effect; and þ=,þ=0, and 0=denote ambiguous effects. Literature references by type
of persistence measure: Survival: a. Block and Sandner (2009), b. Brüderl and Preisendörfer (1998),
c. Brüderl and Ziegler (1992), d. Caliendo et al. (2014), e. Ciavarella et al. (2004), f. Fritsch et al. (2006),
g. Georgellis et al. (2007), h. Gimeno et al. (1997), i. Millán et al. (2012), j. Oberschachtsiek (2012), k. Patel
and Thatcher (2014), l. van Praag (2003), m. Zhu et al. (2011); Hybrid: n. Davidsson (2012), o. Freeland and
Keister (2016); Subjective: p. Cardon and Kirk (2015), q. DeTienne, McKelvie, and Chandler (2015),
r. DeTienne et al. (2008), s. Holland and Garrett (2015), t. Holland and Shepherd (2013), u. Wu,
Matthews, and Dagher (2007)
with increased cognitive abilities, leading to higher levels of productivity at work
(Becker, 1964). Entrepreneurship researchers have investigated the influence of
a variety of human capital factors for over three decades (Cooper et al., 1994;
Unger et al., 2011). This work has strongly focused on the ways in which
individualsemployment careers shape the knowledge and skills available to
them when they become entrepreneurs. Human capital may be influential in
shaping the predispositions and entrepreneurial outlook of individuals, with
some studies showing that different prior experiences contribute to different
perceptions about the market opportunities available from the same innovation
(Shane, 2000). Based on Unger et al. (2011)s meta-analysis of 70 studies, it also
appears that human capital has a significant relationship with venture perfor-
mance. Because human capital encompasses a diverse range of skills and knowl-
edge, it may lead to divergent influences on startup firms. Investments in general
education and work experience yield quite different performance impacts than
specific industry experience. For example, previous research provides some
support for a positive relationship between the level of education and self-
employment longevity (Freeland & Keister, 2016; Gimeno et al., 1997;Millán
et al., 2012), while there is also evidence that education has no effect on
persistence (for example, Davidsson, 2012; Georgellis, Sessions, & Tsitsianis,
2007; Patel & Thatcher, 2014). Block and Sandner (2009) demonstrate a positive
effect of education if entrepreneurs have been educated in the professional area
in which they start their venture. Furthermore, industry-specific experience
provides knowledge and information about rules and regulations that are
specific to the industry sector, customer and supplier networks, and employ-
ment practices. Several studies have found this kind of human capital to be
positively associated with entrepreneurial survival (for example, Ciavarella,
Buchholtz, Riordan, Gatewood, & Stokes, 2004; Davidsson, 2012;Freeland&
Keister, 2016). Likewise, skills related to labor market experience, management
experience, and previous entrepreneurial experience have a strong and positive
impact on persistence (for example, Georgellis et al., 2007; Gimeno et al., 1997;
Oberschachtsiek, 2012). On the other hand, unemployment experience may
imply skill obsolescence or reflect a lack of business acumen, which might
indicate a lower probability of survival. In line with these arguments, van
Praag (2003), Georgellis et al. (2007), and Millán et al. (2012) report that
individuals with previous unemployment experience are more likely to termi-
nate their current startup projects. This negative effect on survival seems to be
pronounced for longer unemployment spells. Oberschachtsiek (2012)found
that an unemployment duration of less than four months before starting
a business indeed positively relates to survival in self-employment. Taken
together, the literature offers an abundant basis for expecting a strong relation-
ship between human capital attributes and our survival measure of entrepre-
neurial persistence. Beyond the well-established link with survival, there are also
arguments proposing human capital as a determinant of an entrepreneurs
motivational dedication and preferences to continue business activity, as
reflected in our hybrid measure of entrepreneurial persistence. For example,
according to expectancy-value theory (see, for example, Vroom, 1964), the
motivation to commence a particular course of action is influenced by the
expectation that the action will lead to valued outcomes. Applied to the persis-
tence decision of an entrepreneur, human capital may influence the motivation
to persist by affecting expectancy (that is, the entrepreneurs belief in running
a successful business) and value (that is, the perceived desirability of the expected
performance of the new venture) (Holland, 2011; Holland & Shepherd, 2013). In
particular, prior knowledge and skills help the entrepreneur to define, under-
stand, and respond to the challenges and obstacles that they face while running
a startup. Overcomingthese challenges and increasingly believing in ones ability
to control events will increase ones own expectations of entrepreneurial success
(Urbig & Monsen, 2012). Additionally, a broader perspective and understanding
enable the entrepreneur to derive a wider range of possible development path-
ways for the new venture when facing adverse situations. This may result in the
entrepreneur perceiving the expected performance of the startup as more desir-
able. With higher expectancy and a more favorable appraisal of the expected
entrepreneurial outcomes, the entrepreneur shows a higher motivation to persist
(Holland, 2011; Holland & Shepherd, 2013). Overall, we propose:
Hypothesis 1: For both persistence measures, entrepreneurshuman capital is
a significant predictor of entrepreneurial persistence.
From an early stage, entrepreneurship scholars suggest that there might be
important relationships between individual personality traits and entrepreneur-
ship (McClelland, 1965). Within the vocational psychology literature, scholars
share a broad agreement that personality scores systematically vary across job
types and work environments (Zhao & Seibert, 2006, p. 260). Researchers
conjecture that peoples personalities affect what interests them, thus resulting
in differences in personality configurations across job types. The person-job fit
literature emphasizes that people seek to secure a good match between their
personal predispositions and their career choices (Kristof, 1996). Such predis-
positions include personality factors (which are generally viewed as innate and
stable over time) as well as more variable factors such as identity, values, and
beliefs (which may be partly culture dependent and may change over a persons
lifetime). Person-job fit theory suggests that some people are more likely to
choose entrepreneurship than others regardless of whether the perceived match
is necessarily true (Zhao & Seibert, 2006). We restrict the discussion below to the
personality characteristics available in our dataset. One of the most commonly
applied personality constructs is the Five Factor model of personality (Barrick,
Mount, & Gupta, 2003; Rauch & Frese, 2007; Schmitt-Rodermund, 2004,2007;
Zhao & Seibert, 2006), which establishes the five broad personality dimensions
of openness, conscientiousness, extraversion, agreeableness, and neuroticism
(the Big Five, McCrae & Costa, 2008;Costa&McCrae,1992;see,forexample,
John & Srivastava, 1999, for a detailed description of each factor). To date,
evidence on the relationship between the Big Five personality traits and persis-
tence in self-employment is rather ambiguous. Patel and Thatcher (2014)found
that less open and more neurotic individuals are more likely to persist in self-
employment, while Ciavarella et al. (2004) demonstrate the importance of
conscientiousness for long-term venture survival. Caliendo et al. (2014) report
a positive link between agreeableness and exit from self-employment, whereas
no significant relationship can be found for the other Big Five traits. Control
beliefs such as locus of control (Rotter, 1966) and self-efficacy (Bandura, 1997)
represent more specific personality constructs and they are key in theories on
vocational choice in general (Lent, Brown, & Hackett, 1994), as well as playing
a prominent role in entrepreneurship research in particular (for example, Rauch
& Frese, 2007). One basic result in past entrepreneurship studies is that inter-
individual differences in control beliefs (for example, higher levels of self-
efficacy or internal locus of control) are among those personal factors that
show the strongest effects on entrepreneurial success (Rauch & Frese, 2007)
and self-employment entry and exit decisions (Caliendo et al., 2014). Creating
and sustaining a business involves risky decisions with uncertain outcomes,
which implies a positive relationship with the willingness to take risks.
However, overly risky investments can lead to large losses and business failure.
Taken together, this implies an inverse u-shaped influence of risk tolerance on
entrepreneurial persistence (Chell, Harworth, & Brearley, 1991), which has also
found empirical support (Caliendo, Fossen, & Kritkos, 2010; Caliendo et al.,
2014). Given the ambiguous associations between personality traits and survival
found in previous entrepreneurship studies, we expect a stronger relationship
with our hybrid persistence measure as it additionally captures the motivational
component of the persistence decisions. Therefore, we predict:
Hypothesis 2: The relationship between entrepreneurspersonality traits and
entrepreneurial persistence is stronger for the hybrid persistence measure
relative to the survival measure.
Business-related determinants
Previousresearch has proposed a number of organizational characteristics of the
new venture that help to explain variance in the persistence decision of entre-
preneurs. Among these characteristics, the amount of financial resources
available at startup has been shown to increase the chances of a new venture
surviving and growing (Brüderl & Ziegler, 1992; Cooper et al., 1994), for
example by providing a buffer against random shocks such as market downturns
or managerial mistakes, and facilitating the pursuit of resource-intensive growth
strategies (Cooper et al., 1994). A number of studies underpin the positive
influence of a higher level of startup capital on an entrepreneurspersistence
decision (Freeland & Keister, 2016;Gimenoetal.,1997; Oberschachtsiek, 2012).
Industry affiliation also plays a significant role for explaining persistence differ-
ences (for example, Fritsch et al., 2006). Industries differ in competition inten-
sity, capital intensity, demand structure, and barriers to exit. In some industries,
switching to wage employment is less difficult due to local demand conditions.
Overall, the evidence is quite diverse and does not provide a consistent picture of
the relation between the chosen industry sector and the entrepreneursprob-
ability of persisting (for example, Davidsson, 2012;Georgellisetal.,2007;van
Praag, 2003). To our knowledge, the literature does not provide any arguments
suggesting differences in the relationship between business characteristics and
either of our persistence measures. As a result, we propose:
Hypothesis 3: For both persistence measures, business characteristics are sig-
nificant predictors of entrepreneurial persistence.
Other characteristics
To avoid omitted variable bias, later in the empirical analysis we also con-
trolled for other characteristics that had been proven to be important in
previous research (but were not in the focus of our interest). These variables
include sociodemographic characteristics (for example, age, based on the
findings by Block & Sandner, 2009;Gimenoetal.,1997; van Praag, 2003),
intergenerational transmissions (for reviews see, for example, Aldrich &
Kim, 2007;Parker,2009), startup motivations and the distinction between
opportunity and necessity entrepreneurs (for example, Caliendo, Kritikos, &
Stier, 2019b;Gimenoetal.,1997; Oberschachtsiek, 2012; Patel & Thatcher,
2014), as well as the macro environment in which an entrepreneur operates
(see,forexample,Audretsch,Keilbach,&Thurik,2000; Georgellis et al.,
2007; Millán et al., 2012; van Praag, 2003).
Data creation and estimation sample
We used data originally collected by Caliendo et al. (2015,2019a)onasampleof
male founders who started full-time businesses in the first quarter of 2009 in
Germany.The dataset comprised random samples of unemployed founders who
participated in the German startup subsidy program for unemployed individuals
(Gründungszuschuss), and regularfounders; that is, founders who were not
unemployed directly prior to startup and consequently did not receive the
subsidy (see Caliendo et al., 2015,2019a for details on data construction). The
startup subsidy could be legally claimed if the eligible unemployed individuals
met the following requirements: First, they had a remaining unemployment
benefit I entitlement
of at least another 90 days, which was then offset against
the subsidy receipt; and, second, they were required to provide a business and
financing plan to the employment agency that had been evaluated by
a competent external institution. The subsidy amount was equivalent to the
individuals last unemployment I benefit plus a lump sum of 300 euros to cover
social security costs during the first nine months, with an optional six-month
extension during which only the lump sum was paid. Finally, it should be
mentioned that subsidized startups out of unemployment constituted a large
share, about 40 percent to 60 percent, of all full-time startups in Germany
between 2006 and 2011 (depending on the underlying data source, see
Caliendo et al., 2015), which is why we included them in our analysis.
The business founders in our sample were surveyed twice. The first inter-
view was conducted about 19 months after startup (wave 1) and focused on
an extensive list of startup characteristics, sociodemographics, previous labor
market experiences, and intergenerational transmissions as well as the foun-
derslabor market status and, conditional on ongoing business activity with
their initial startup from the first quarter in 2009, their business performance.
In total, 1,478 (930) valid interviews were completed with male, formerly
unemployed (regular) founders (see Figure 1). Conducted with the same
individuals, the second interview (wave 2) extended the observation window
to 40 months after startup. Figure 1 shows that we had 827 (453) panel
observations on formerly unemployed (regular) founders in wave 2. Some of
the important variables for our analysis were only surveyed for a random
subsample due to budget constraints. This resulted in 653 observations for
our final estimation sample, of which 388 (265) were formerly subsidized
(regular) founders. An examination of selective sample attrition showed that
our estimation sample was similar to the original full sample. Most impor-
tantly, survival rates in wave 1 were not affected by significant sample
The estimation sample contained 495 founders who were still
In Germany, every individual who has been in employment subject to social security for at least one out of the
two previous years is eligible for unemployment benefit I. The amount of the benefit comprises 60 percent
(67 percent with children) of the last net wage and is basically paid for a period of 12 months, with the exception
of older individuals (see Caliendo & Hogenacker, 2012).
Meanwhile, a major reform of the program at the end of 2011 has substantially reduced entry numbers (see
Bellmann, Caliendo, & Tübbicke, 2018, for details).
See Table A.1 in the Appendix in the online supplement for details.
Figure 1. Data generation and sample restrictions. Note: For details, see Caliendo et al. (2015,2019a).
self-employed in wave 2 with the same business as at startup in 2009, divided
between 287 formerly subsidized and 208 regular founders.
Definition of persistence measures
In the literature review, we classified previous empirical studies on the topic
of persistence into the following three categories according to the persis-
tence measures used: survival, subjective measures, and hybrid measures
combining survival with subjective persistence indicators. In our dataset,
we captured the latter aspect by surveying the founders willingness to
remain self-employed while having the hypothetical option of performing
a 7-point Likert-type scale, all surviving founders were asked whether they
would terminate their current self-employment in the hypothetical case that
they were offered a similar job as a dependent employee. Because this
question was asked only in the second interview, we were unable to conduct
a full panel analysis, but used this information cross-sectional at the end of
our observation period instead. Based on the reverse scores, we constructed
a persistence index, whereby higher values indicated higher entrepreneurial
motivation to continue to actively pursue self-employment despite the
(hypothetical) presence of potentially attractive job alternatives. The dis-
tribution of this persistence index is depicted in Figure 2.Aclearand
distinctive majority were fully motivated and committed to continue their
self-employment and score the highest value on the index, which applied
across all subgroups. Based on this,weconstructedthefollowingtwo
Following the majority of studies using survival as a proxy variable for
entrepreneurial persistence, our first persistence measure was a binary survi-
val dummy indicating whether the founder was still self-employed and
actively operating the same business in wave 2 as at the original startup in
the first quarter of 2009; that is, 40 months after business formation:
Survival ¼1 if self -employed with the same business in wave 2;
¼0 if not self -employed with the same business in wave 2:
Hybrid persistence
For this measure, we combined survival and the willingness to remain self-
employed into one indicator. According to the hybrid measure, a highly
persistent founder is defined as someone who is still self-employed with the
same business and shows a strong commitment to their business activity:
1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
a. Pooled sample b. Unemployed founders c. Regular founders
Figure 2. Willingness to stay self-employed. Note: Respondents in the second wave were asked: Now, I would like to know how satisfied you are overall with
your professional self-employment. Assume you were offered a similar job as a dependent employment. Would you terminate your current self-employment and
accept the offer of the dependent employment? Please answer on the basis of a scale ranging from 1 does not apply at allto 7 applies completely.””
Hybrid persistence ¼1 if self -employed with the same business in wave 2
and persistence index 2f7g;
¼0 if not self-employed with the same business in
wave 2 or persistence index 2f1;2;3;4;5;6g:
In this sense, the hybrid measure differs from survival by imposing the
additional requirement of a high score on the subjective persistence index
to be considered as persistent.
Overall, both persistence measures emphasize
a different aspect of persistence, and, consequently, the examination of their
determinants has different implications depending on which measure is
applied. While the analysis of survival reveals which factors contribute to
the founders mere continuation of the business venture (compared to non-
survival), examining the hybrid measure also shows which variables contri-
bute to a high psychological commitment of the founder. Essentially, this
compares survival with a high commitment to nonsurvival or survival with
a stronger preference to abandon self-employment.
Selected descriptive statistics
Distribution of persistence measures
The top panel in Table 2 reports the mean values for our two persistence
measures. The survival indicator reveals that 75.8 percent of all founders were
still self-employed in wave 2. Comparing across subgroups, we found moder-
ately lower survival rates among formerly unemployed founders (74.0 percent,
column 2) compared to regular founders (78.5 percent, column 3). Moving to
our hybrid persistence indicator revealed that 35.5 percent of all founders
displayed high persistence in the full sample (column 1), where the share of
highly persistent formerly unemployed founders was significantly lower
(30.4 percent) than the respective share of regular founders (43.0 percent).
Control variables
Based on our review of the entrepreneurship literature, we arranged our 46
control variables into four blocks Xi;with i¼1;...;4. They comprised: (1)
human capital (12 variables), (2) personality (9 variables), (3) business
characteristics (8 variables), and (4) other characteristics (17 variables).
Taking into account that our sample comprised regular founders and
Our subjective component reflects the presence of strong persistence. Given the wording and design of the scale,
motivational persistence could alternatively be defined as scoring 5, 6, or 7 on the index. While a few results are
no longer significant at conventional levels for this alternative, the findings are qualitatively robust to this slight
change in the definition. Detailed estimation tables are available from the authors on request.
The fourth category comprises (4a) sociodemographic characteristics, (4b) intergenerational transmissions, (4c)
startup motives, as well as (4d) the current regional economic context at the time of the second interview.
Table 2. Descriptive statistics.
Pooled By former employment status
Number of observations 653 388 265
Survival (same business) 0.758 0.740 0.785
Hybrid persistence 0.355 0.304 0.430
(1) Human capital
Highest schooling certificate Upper secondary school 0.518 0.518 0.517
Professional education University education 0.325 0.332 0.313
Unemployment experience before startupa
0 or not specified 0.248 0.072 0.506
>02 0.332 0.381 0.260
>25 0.225 0.281 0.143
>5 0.194 0.265 0.091
Industry-specific experience
before startup
Due to former self-emp. 0.225 0.193 0.272
Due to dependent emp. 0.784 0.812 0.743
None 0.093 0.082 0.109
Skills and knowledgeb
Strategy and leadership 5.6 5.6 5.5
Back office 4.6 4.6 4.7
Front office 4.8 4.9 4.8
Industry knowledge 5.8 5.9 5.8
(2) Personality
Big Fiveb
Openness 4.8 4.9 4.8
Conscientiousness 5.9 6.0 5.8
Extraversion 5.6 5.6 5.4
Agreeableness 5.9 5.9 6.0
Neuroticism 3.8 3.8 3.8
Locus of controlb5.5 5.5 5.5
General self-efficacyb5.3 5.3 5.3
Readiness to take riskc6.2 6.3 6.1
(3) Business characteristics
Startup capital
None or not specified 0.161 0.160 0.162
<10,000 euros 0.349 0.379 0.306
10,000<50,000 euros 0.322 0.345 0.287
50,000 euros 0.149 0.108 0.208
Share of own equity at startup 0.575 0.589 0.556
Business sector
Manufacturing, construction 0.271 0.242 0.313
Retail 0.152 0.144 0.162
Information, financial, and IT services 0.164 0.183 0.136
Other services 0.315 0.320 0.309
Other sector 0.098 0.111 0.079
Note: Reported are shares and mean values. ***, **, * indicate significantly different means between
subgroups at the 1, 5, 10 percent level. aMeasured as share of working time, standardized by age 15.
bMeasured on a 7-point Likert-type scale ranging from 1 does not apply at allto 7 applies completely;
see Table A.2 in the Appendix (available online) for details. cMeasured on an 11-point Likert-type scale
ranging from 0 not at all willing to take risksto 10 very willing to take risks; see Table A.2 in the
Appendix (available online) for details.
formerly unemployed participants in a startup subsidy program, our list also
included a corresponding group dummy. Descriptive statistics for the main
variables are reported in Table 2, whereas statistics for the other variables are
available in Table A.3 in the Appendix (available online).
The founders in our estimation sample (column 1) were, on average,
42 years old. The majority had German citizenship (95 percent), were
married (65 percent), and had completed upper secondary school (52 per-
cent). About one in four founders had industry-specific experience due to
former self-employment, whereas 10 percent did not have any such experi-
ence prior to business formation. Close to 40 percent had at least one parent
who was currently or was self-employed in the past. The average startup
capital amounted to around 30,000 euros, and one-fourth of all businesses
were set up in the manufacturing or construction sector.
Comparing the subgroups of formerly unemployed and regular business
founders (column 2 versus column 3) showed that, as expected, formerly
unemployed founders had more unemployment experience and less indus-
try-specific experience prior to their new business formation. They also
suffered from shortages in intergenerational transmissions, in particular
with respect to parental self-employment. Necessity motives were more
pronounced among formerly unemployed business founders, who also
invested less capital in their new businesses at startup. Moreover, formerly
unemployed founders also operated in slightly less favorable regional eco-
nomic environments in terms of open vacancies and unemployment rates.
Empirical strategy
Our main goal for the empirical section was twofold: First, we examined the
main determinants of entrepreneurial persistence and their relative impor-
tance; and, second, we compared results across the two distinct persistence
measures to reveal differences and the sensitivity of findings to the choice of
persistence indicator.
For this purpose, we conducted a series of robust ordinary least squares
(OLS) estimations for each persistence measure.
In a first step, we regressed
persistence on each covariate block Xiseparately in the simple specifications
1 to 4, see Equation (1), and determined their individual coefficients vector ~
and goodness-of-fit measures, which indicated their joint explanatory power.
Because we did not condition on any other covariate blocks at this stage, the
results are labeled unconditional.
For details on the construction of selected control variables, see Table A.2 in the Appendix (available online).
The results are robust to applying a logit/probit approach and are presented in the Robustness analysis. We used
robust ordinary least squares (OLS) because the interpretation of R2measures is more straightforward than in
logit/probit approaches.
Persistence ¼~
u"i¼1;...;4 (1)
In a second step, we regressed persistence on all covariate blocks jointly (full
specification), see Equation (2) below, and determined the individual coeffi-
cients vector βiand the partial joint explanatory contribution for each
covariate block Xi. Because these findings relate to a full specification and
describe the results conditional on all other covariate blocks, refer to them as
Persistence ¼β0þX
The comparison of unconditional and conditional results for a particular
covariate block and a given persistence measure reveals how sensitive the
results are to the inclusion of other covariate blocks. As goodness-of-fit
measures, we chose the joint significance of all control variables in each
covariate block Xias well as the (partial) regression-R2
ifor this block, which
reflects the share of explained variance in persistence.
In the first two parts of the following empirical discussion, we describe
how we conducted the analysis for the full sample. In the third part, we
account for the heterogeneous nature of our sample and distinguish between
unemployed and regular founders to investigate heterogeneity across these
two subgroups. Finally, we present a brief robustness analysis in the fourth
Empirical results
Individual effects of covariates
We begin our analysis by comparing the detailed regression results between
the two persistence indicators and discuss the most notable similarities and
differences. Table 3 reports the regression results for the survival indicator in
columns 1 and 2 and the hybrid persistence measure in columns 3 and 4. For
each outcome variable, the first column contains the unconditional regres-
sion results ~
βfrom the simple specifications 1 to 4 (stacked over each other
into one column to save space), where only the respective covariate block Xi
is included; see Equation (1) above. The second column per outcome variable
reports the conditional results βfrom the full specification, which includes all
four covariate blocks jointly; see Equation (2).
Since the number of control variables varies across covariate blocks, we also calculated the adjusted R2
a, which is
better comparable across non-nested specifications because it adjusts the original R2for the number of included
control variables.
Table 3. Main regression results: Regression coefficients.
A. Survival
(same business)
B. Hybrid
unc. (~
cond. β
unc. (~
cond. β
(1) Human capital
Highest schooling certificate Upper secondary school 0.048 0.092 0.016 0.026
Professional education University education 0.0008 0.019 .025 .033
Unemployment experience before startupa
0 (ref.)
>02.025 0.002 .074 .023
>25.025 0.012 .096.033
>5.155 .127 .163 .074
Joint F-stat. 3.1 2.8 3.0 0.6
Industry-specific experience
before start-up
Due to former self-emp. 0.006 .001 0.105 0.075
Due to dependent emp. 0.141 0.129 .034 .033
None .006 0.01 0.016 0.044
Joint F-stat. 3.5 2.8 2.1 1.4
Skills and knowledge
Strategy and leadership .014 .007 0.018 .015
Back office 0.037 0.012 0.029 0.011
Front office 0.0004 .004 0.051 0.044
Industry knowledge 0.071 0.054 0.035 0.026
(2) Personality
Big Fiveb
Openness 0.0340.0330.031 0.026
Conscientiousness 0.004 .016 .002 .002
Extraversion .040 .038 0.009 .00002
Agreeableness .023 .008 .033.035
Neuroticism .017 .017 .038.040
Locus of controlb0.051 0.048 0.063 0.062
General self-efficacyb0.017 0.012 0.046 0.024
Readiness to take riskc.012 .035 .060 .085
Squared 0.0004 0.003 0.005 0.007
Joint F-stat. 0.3 0.4 0.8 1.7
(3) Business characteristics
Startup capital
None or not spec. (ref.)
<10,000 euros .033 0.015 0.007 0.096
10,000<50,000 euros 0.124 0.133 0.166 0.203
50,000 euros 0.209 0.191 0.259 0.233
Joint F-stat. 12.0 7.8 9.3 6.3
Share of own equity 0.062 0.007 0.073 .001
Business sector
Other sector (ref.)
construction 0.0920.114 .023 .058
Retail .096 .049 .067 .091
Information, financial,
and IT services .082 .074 .118 .173
Other services .087 .054 .071 .100
Joint F-stat. 7.6 6.3 1.0 1.7
Number of obs. 653 653 653 653
Human capital
A higher lifetime share of unemployment proved to be negatively associated
with objective persistence (that is, business survival). Its significant negative
effect on hybrid persistence was not robust to the inclusion of other covariate
blocks, and it did not affect the motivational persistence of surviving business
founders in any significant way. This comparison shows that while a higher
share of lifetime unemployment did have negative implications for survival,
presumably due to the greater lack of work experience, depreciation of
human capital, and smaller professional and business networks, it did not
affect hybrid persistence.
Furthermore, we found ambiguous effects of industry-specific experience.
First, previous self-employment had no significant effect on the survival indi-
cator of persistence. As our heterogeneity analysis below reveals, this finding was
the result of a negative effect for formerly unemployed and a positive effect for
regular founders, which together yielded a net effect in the full sample close to
zero. Second, industry-specific experience acquired through former dependent
employment had a robust positive impact on survival. However, the negative
(but insignificant) coefficient for hybrid persistence might indicate that founders
who have previously been employed might feel a strong desire to return to
dependent employment and, therefore, exhibit lower psychological commit-
ment to their businesses.
While the signs of the personality variables were relatively similar across both
persistence measures, with the exception of extraversion, the magnitudes and
significances of particular personality items differed. Locus of control had
Table 3. (Continued).
A. Survival
(same business)
B. Hybrid
unc. (~
cond. β
unc. (~
cond. β
Controls for other characteristics No Yes No Yes
Joint F-stat. 4.95 5.11
Joint p-value 0.000 0.000
Regression-R20.227 0.203
Note: Reported are robust ordinary least squares (OLS) coefficients. The unconditional (unc.) results ~
βrefer to
a specification where only the covariates from the respective covariate block are included, see Equation (1) in the
text; separate results of all covariates blocks are stacked in one column to save space. The conditional (cond.)
results βrefer to a full specification containing all covariates from all covariate blocks, see Equation (2) in the text.
For details on the definition and construction of the outcome variables, see subsection Definition of persistence
measures. ***, **, * indicate significantly different means between subgroups at the 1, 5, 10 percent level.
aMeasured as share of working time, standardized by age 15. bInitially measured on a 7-point Likert-type scale
from 1 does not apply at allto 7 applies completely; see Table A.2 in the Appendix (available online) for details,
and then standardized. cMeasured on an 11-point Likert-type scale from 0 not at all willing to take risksto 10
very willing to take risks; see Table A.2 in the Appendix (available online) for details.
a relatively robust positive impact of similar magnitude on both measures.
The comparison across the outcome variables revealed that the personality
traits openness and extraversion had a significant impact on business survi-
val, whereas motivational persistence depended more strongly on agreeable-
ness, neuroticism, and risk attitudes.
Business characteristics
Formerly unemployed founders did not show any significant difference in
persistence as indicated by business survival after 40 months. With respect to
the hybrid persistence measure, unemployed founders showed a relatively
large and highly significant negative gap in the unconditional specification,
albeit which substantially decreased in size and became insignificant once we
controlled for all covariate blocks in the full specification. The role of startup
capital was robust and unambiguous across both persistence measures.
A higher startup capital increased survival chances and hybrid persistence.
Relative importance of covariate blocks
After comparing the individual coefficients of all covariates for the two
persistence measures, we now determined the relative importance of the
four covariate blocks Xi(1) human capital, (2) personality, (3) business
characteristics as well as (4) other characteristics (including (4a) sociodemo-
graphic characteristics, (4b) intergenerational transmissions, (4c) startup
motives, (4d) the current regional economic context) relative to each
We assessed the relative importance as the share of the regression-R2
iof the
covariate block irelative to the full regression-R2in the full specification. Results
are reported in Table 4, where we again separated by survival (Panel A) and
hybrid persistence (Panel B).
We again distinguished between unconditional
regression results from the simple specifications 1 to 4, where only the respective
covariate block Xiwas included (compare to Equation (1)), and conditional
results from the full specification controlling for all other covariate blocks as well
(compare to Equation (2)).
All covariate blocks were individually significant at the 10 percent level in the
simple specifications. The explanatory contributions varied considerably,
however, with the highest unconditional contributions coming from human
capital (40.3 percent, column 1) and business characteristics (38.9 percent,
The results of a robustness check applying the adjusted R2
a, which was corrected for the number of variables in
each block, are similar to the standard regression-R2results reported here; see Table A.4 in the Appendix
(available online) for details.
column 3). Personality (column 2) displayed a moderate explanatory power
of around 15 percent, while the combined other characteristics explained
about 29.6 percent.
The values for the partial regression-R2in the full
specification controlling for all variables simultaneously were slightly lower
than in the unconditional regressions as expected since correlations between
covariates across blocks were now controlled for. Nevertheless, we found
a similar pattern across covariate blocks, with human capital and business
characteristics having the largest predictive power.
Hybrid persistence
For the hybrid indicator (Table 4, Panel B), the strong roles of human capital
and business characteristics were confirmed, but now personality was simi-
larly important, with unconditional R2shares around 30 percent for each of
Table 4. Main regression results: Explanatory contributions.
Full (1) (2) (3) (4)
A. Outcome: Survival (same business)
Unconditional contributions in the simple specification
Joint p-value 0.000 0.000 0.004 0.000 0.000
R20.228 0.092 0.033 0.088 0.067
Share of R2(in %) 100 40.3 14.4 38.9 29.6
Conditional contributions in the full specification
Joint p-value 0.000 0.000 0.013 0.000 0.000
R20.228 0.08 0.031 0.067 0.048
Share of R2(in %) 100 35.2 13.6 29.3 20.9
B. Outcome: Hybrid persistence
Unconditional contributions in the simple specification
Joint p-value 0.000 0.000 0.000 0.000 0.000
R20.203 0.066 0.063 0.061 0.088
Share of R2(in %) 100 32.5 30.9 29.9 43.5
Conditional contributions in the full specification
Joint p-value 0.000 0.264 0.000 0.000 0.000
R20.203 0.023 0.049 0.045 0.059
Share of R2(in %) 100 11.1 24.4 22.2 29.4
C. Control variables
(1) Human capital ✓✓
(2) Personality ✓✓
(3) Business characteristics ✓✓
(4) Other characteristics ✓✓
Number of control variables 46 12 9 8 17
Note: Reported are results from robust ordinary least squares (OLS) estimations. The reported results always
refer to the joint block of indicated control variables in Panel C only. The unconditional contributions stem
from regressions of the indicated outcome variable on only the indicated block of control variables (see
Equation (1) in the text), while the conditional contributions stem from regressions of the persistence
measure on the indicated block of control variables and all other blocks (full specification) (see Equation
(2) in the text). Detailed estimation results are reported in Table 3.
See Table A.5 in the Appendix (available online) for detailed information on the other characteristics.
The explanatory shares of the full specification R2did not add up to 100 percent across covariate blocks in either
case because correlations between covariates across (unconditional case) and within covariate blocks (uncondi-
tional and conditional case) were not controlled for.
these three blocks. The conditional contributions in the full specification
confirmed this observation with a notable difference. Human capital was no
longer significant, and its predictive power declined sharply to one-third of
its unconditional value. This reflects the finding from the detailed coefficient
results that some human capital variables in the full specification had oppos-
ing effects on survival and hybrid persistence and canceled out with respect
to the hybrid measure.
Summary and hypotheses
Overall, our results generally match previous evidence in the literature
(summarized in Table 1), but they also reveal that findings depend to
a certain extent on the choice of persistence measure applied. For the survival
indicator, the predictive power was concentrated in business characteristics
and human capital, while for hybrid persistence the dominant factors were
business characteristics and personality. We can therefore confirm all three
hypotheses from the subsection Determinants of entrepreneurial persistence.
Heterogeneity analysis among different types of entrepreneurs
In the second part of our empirical analysis, we conducted a heterogeneity
analysis to account for the fact that our full sample was comprised of both
formerly unemployed and regular (nonunemployed) founders. As seen in the
descriptive statistics, the share of necessity startups was significantly higher
among unemployed founders, who also suffered from a shortage of industry-
specific experience from former self-employment. They also set up smaller
businesses, whereby they might have exhibited a lower level of business
attachment and might have been more affected in their persistence by
external factors, such as the local labor market, compared to regular foun-
ders. Therefore, we split the sample by former employment status and reran
the estimations for both subgroups separately. The conditional explanatory
contributions from the full specification (compare to Equation (2)) for the
two persistence measures are reported in Table 5.
The separate results for unemployed and regular founders reported in
Table 5 showed in general higher overall regression-R2values for each
subsample, indicating a better model fit for the split sample. The dominant
roles of human capital and business characteristics for survival in the pooled
sample were confirmed for both unemployed and regular founders (Table 5,
Panel A). The most notable difference between the two groups concerned the
role of personality. For formerly unemployed founders personality had only
a moderate influence on survival, but carried the largest importance for the
The corresponding detailed regression results for the individual coefficients are presented in Table A.6 in the
Appendix (available online).
hybrid measure (Table 5, Panel B). For regular founders, hybrid persistence
was mainly determined by business-related characteristics.
Robustness analysis
Adjusted R2
Results of a robustness check applying the adjusted R2
a, which is corrected for the
number of variables in each block, were similar to the standard regression-R2
results reported here; see Table A.4 in the Appendix for details (available online).
Estimation method (logit versus OLS)
To analyze whether our results were robust to the chosen OLS estimation
method we alternatively applied logit regressions, and present the results in
Table 5. Heterogeneity results by former employment status.
Full (1) (2) (3) (4)
A. Outcome: Survival (same business)
Conditional contributions in the full specification: Unemployed founders
Joint p-value 0.000 0.001 0.000 0.000 0.018
R20.255 0.092 0.022 0.073 0.069
% of full spec. R2100 36.2 8.6 28.5 27.0
Conditional contributions in the full specification: Regular founders
Joint p-value 0.000 0.002 0.03 0.001 0.153
R20.36 0.118 0.067 0.129 0.072
% of full spec. R2100 32.7 18.7 35.8 20.0
Number of obs.
Subsidized 388 388 388 388 388
Regular 265 265 265 265 265
B. Outcome: Hybrid persistence
Conditional contributions in the full specification: Unemployed founders
Joint p-value 0.000 0.74 0.022 0.086 0.048
R20.187 0.025 0.054 0.038 0.062
% of full spec. R2100 13.5 29.0 20.2 33.3
Conditional contributions in the full specification: Regular founders
Joint p-value 0.000 0.435 0.091 0.008 0.332
R20.29 0.045 0.06 0.08 0.071
% of full spec. R2100 15.5 20.6 27.7 24.5
Number of obs.
Subsidized 388 388 388 388 388
Regular 265 265 265 265 265
C. Control variables
(1) Human capital ✓✓
(2) Personality ✓✓
(3) Business characteristics ✓✓
(4) Other characteristics ✓✓
Number of control variables 46 12 9 8 17
Note: Reported are results from robust ordinary least squares (OLS) estimations. The reported results always
refer to the joint block of indicated control variables in Panel C only. The conditional contributions stem
from regressions of the persistence measure on the indicated block of control variables and all other
blocks (full specification), see Equation (2) in the text. Detailed estimation results are reported in Table A.6
in the Appendix (available online).
Table A.7 in the Appendix (available online). We used McFaddens(1974)
pseudo-R2as goodness-of-fit measures, which are shown in Table A.4 for the
different specifications.
Below the pseudo-R2, the table shows an index
where the pseudo-R2achieved with the full model was normalized to 100 per-
cent. The row below this index provides the difference in the index between
two adjacent columns. This difference may be interpreted as an approxima-
tion of the share in the full models explanatory power that was provided by
the variables added in this column.
The results confirmed our findings and
our hypotheses.
Entrepreneurial persistence is the constantly renewed decision to commit to
apreviously selected business venture activity despite opposing forces and
enticing alternatives, and it is an essential prerequisite for entrepreneurs to
exploit their business potential and realize economic gains and benefits (Patel
& Thatcher, 2014). Based on a representative sample of German startups, in
this article, we add to the evidence on entrepreneurial persistence in three
important ways.
First, we identified the basic approaches to measure entrepreneurial per-
sistence that had typically been applied in the entrepreneurship literature,
and were able to construct two indicators survival and hybrid persistence
from one single dataset and compare results. Second, we compared the
relative importance of different predictors of entrepreneurial persistence.
Based on an extensive literature review, we incorporated a long list of
individual-level (human capital and personality) and business-related char-
acteristics, which were previously identified as individually important deter-
minants. Third, we took account of the fact that the population of
entrepreneurs was highly diverse and determinants of entrepreneurial persis-
tence might be heterogeneous between formerly unemployed and regular
(nonunemployed) founders.
Our empirical results generally encompassed previous findings, although
they revealed that the influence of most of the determinants was sensitive to
the choice of persistence measure. For the full sample, we found that human
capital and business-related characteristics had the highest explanatory con-
tribution to survival, while personality and business characteristics held
similar importance in explaining the hybrid measure. Our findings underline
the complex nature of entrepreneurial persistence. Both persistence measures
were inevitably approximations, and each one emphasized different aspects
Results were qualitatively similar when other pseudo-R2statistics (McKelvey and ZavoinasR2or EfronsR02) were
used (the results are available from the authors on request).
Full estimation results for these logit estimations are available on request from the authors.
of the construct. Survival indicators reflected the mere continuation of
a business venture and did not necessarily imply or capture the psychological
commitment to actively engage in the business and to invest physical and
psychological resources to advance the venture as implied by entrepreneurial
persistence. The hybrid measure in our setting combined survival with
a subjective measure of entrepreneurial commitment in the presence of
a hypothetical offer of similar paid employment. Therefore, it specifically
accentuated an entrepreneurs commitment despite the availability of (poten-
tially) attractive alternatives.
In the context of our German sample, which comprised formerly unem-
ployed founders participating in a startup subsidy program as well as regular
founders, the nature of our hybrid measure also allowed us to draw some
policy conclusions about the subsidy program. We found descriptive evi-
dence that 40 months after startup, the share of business owners with a high
commitment to their businesses was significantly lower among formerly
unemployed compared to regular founders, whereas we found only a small
difference in survival rates. This implies that among the group of formerly
unemployed founders, there was a higher share of (successfully surviving)
self-employed business owners with lower business attachment, who would
prefer dependent employment if those job opportunities were indeed avail-
able. This could be one contributing factor in explaining why unemployed
founders were shown to create fewer jobs, induce less innovation, and invest
less in their businesses, which can only insufficiently be explained by obser-
vable characteristics and endowments at business formation or (restricted)
access to capital in poststartup phases (see Caliendo et al., 2019a, for a more
detailed discussion) and, in turn, reinforces lower levels of entrepreneurial
persistence (Gimeno et al., 1997; Zhu, Chen, & Li, 2011). From a policy
perspective this needs to be considered when implementing (or redesigning)
startup subsidy programs for unemployed individuals. Additional soft sup-
port measures such as coaching, counseling, mentoring, or training (accom-
panying the subsidy) during the pre- or early startup phase (see, for example,
Rotger, Görtz, & Storey, 2012) might improve commitment and, henceforth,
business potential and long-term development.
be viewed as a prerequisite to exploit the potential of a given business
opportunity, high persistence does not necessarily lead to positive results
or outcomes (Holland & Shepherd, 2013). It rather depends on how
persistent business founders react to feedback, changing environments,
and adversity. On the one hand, there is evidence that persisting entrepre-
neurs with high resilience use their resourcefulness to adapt and improve
their business performances (Ayala & Manzano, 2014). On the other hand,
staying with a previously chosen, but failing, course of action is a sign of
a perilous escalation of commitment. In this case, founders overly commit
to their original strategies and react to negative feedback by investing too
much into and staying too long with the same plan (McCarthy,
Schoorman, & Cooper, 1993). This then results in an inefficient and
ineffective use of onesownandsocietys resources (DeTienne et al.,
2008). Thus, a deeper understanding of the link between entrepreneurial
persistence and entrepreneurial success is important, but beyond the scope
of this article.
We thank Stefan Tübbicke, Lutz Bellmann, the editors, and two anonymous reviewers for helpful
comments and suggestions. We further thank the Institute of Employment Research (IAB) for
cooperation and institutional support within the research project 1755. Caliendo is grateful for
financial support from the German Research Foundation (Deutsche Forschungsgemeinschaft,
DFG, project number: 407087322).
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... Entrepreneurial persistence includes two components: a) an entrepreneur's decision to continue their firm and b) the determination to do so "in the face of opposing motivational factors such as negative feedback about venture performance and alternative opportunities" (Holland and Shepherd, 2013, p. 138; see also Holland, 2011). The idea of the lone entrepreneur pushing through obstacles and refusing to give up paints a very romantic picture of entrepreneurship, and some research shows that persistence is related to positive firm outcomes, such as opportunity exploitation, performance, and firm growth (see e.g., Adomako, 2020;Caliendo et al., 2019). However, persistence has another side, particularly when coupled with very low (or under-) performance. ...
... 45). Since this seminal work, researchers have sought to understand why some entrepreneurs persist with underperforming firms (Chirico et al., 2020;Gimeno et al., 1997;Hsu et al., 2016;Wennberg et al., 2010); yet, we do not have a thorough understanding of the decision-making processes of such individuals (Caliendo et al., 2019). Meyer and Zucker (1989) added that permanently failing organizations persist because "[m]ost people are more concerned with [a rational or irrational decision towards] maintaining existing organizations than with maximizing organizational performance." ...
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We theorize that both highly rational entrepreneurs and entrepreneurs with a high need for cognitive closure (NFCC) are likely to put more emphasis on retrospective factors (period and degree of underperformance, personal investments) and less on prospective factors (risk of going into default, potential for growth, personal options) when deciding whether to persist with an underperforming venture. Our findings from three discrete choice experiments with three independent samples of entrepreneurs (a sample of 176 Australian entrepreneurs; a narrow-replication with 128 Australian entrepreneurs; and a quasi-replication with 157 United Kingdom entrepreneurs) consistently show that entrepreneurs who perceive themselves as rational do not always demonstrate rational behavior and entrepreneurs with a high NFCC put more emphasis on retrospective factors in persistence decisions. Important theoretical and practical contributions flowing from our study are shared in the concluding section.
... The results reveal that individual factors and entrepreneurship education have a direct positive impact on entrepreneurship intention (Boahemaah et al., 2020). Caliendo et al. (2020) analyzed the impact of personal level (human capital and personality) and the impact of business-related characteristics on these two indicators and their relative importance. It shows that the results are heterogeneous between groups. ...
... It shows that the results are heterogeneous between groups. In particular, the previously founders have no difference in survival opportunities, but they are more likely to lack a high psychological commitment to entrepreneurship (Caliendo et al., 2020). Through the experiment, the specific influencing factors of entrepreneurial motivation were predicted, and the experimental results were verified, which further shows that the entrepreneurial environment exerts a great impact on entrepreneurial motivation. ...
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... However, entrepreneurship is a process and should be investigated as such (McMullen and Dimov, 2013). Furthermore, entrepreneurship often takes time, requiring multiple starts, and continued effort in the face of obstacles and failures (Caliendo et al., 2020;Kiani et al., 2021;Mill an et al., 2014). Thus, persistence is an important foundational behavior for entrepreneurs. ...
... Entrepreneurial persistence is typically studied as persisting toward a venture (e.g. Holland and Shepherd, 2013), however, it has also been noted that entrepreneurial persistence transcends the venture and can be applied to the pursuit of an entrepreneurial career (Caliendo et al., 2020). For instance, Cardon and Kirk (2015) suggest entrepreneurial persistence is a behavior that is the result of an interaction of a trait and situation. ...
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... 1. Our focus is on psychological resilience at the level of the individual, but we use the phrase resilience and psychological resilience interchangeably throughout the paper. 2. A number of concepts are often associated with resilience such as hustle as a way of achieving positive outcomes (Fisher et al., 2020), grit (Mueller et al., 2017), persistence (Caliendo et al., 2020), and so forth but they tend to ignore the role of adversity or process in favor of emphasizing certain traits or attributes . 3. According to the American Psychological Association Dictionary of Psychology, "Distress" is the negative type of stress which is what researchers generally intend to mean by the word "Stress." ...
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... Entrepreneurial persistence has been emphasized as an important factor in venture success because it enables business owners to overcome challenges they face in the entrepreneurial journey as they move their ventures forward (Ahsan et al., 2021;Davidsson and Gordon, 2016;Meek and Williams, 2018). Entrepreneurial persistence refers to the effortful actions that entrepreneurs put in despite failures, threats, or impediments either imagined or real (Caliendo et al., 2020;Gimeno et al., 1997). Because the process of founding and developing a venture is difficult and requires strong determination (Cardon and Kirk, 2015), entrepreneurs who are relentless in approach venture work may foster their sense of empowerment, which may spill over to their wage work (Sessions et al., 2021). ...
Hybrid entrepreneurs are individuals who are employees and entrepreneurs at the same time. In their quest to make their businesses successful, they commonly encounter challenges and adversities. This makes entrepreneurial persistence a key factor in the success of hybrid entrepreneurs. Drawing on social cognitive theory, the hybrid entrepreneurship context, and the perspective of person–environment fit, we developed a moderated mediation model in which person–venture fit, needs–venture supplies fit, and venture demands–abilities fit are associated with entrepreneurial persistence through entrepreneurial self-efficacy. We proposed that these indirect effects are conditional on wage work-to-entrepreneurship enrichment (WE enrichment) (the skills and experiences transferred to entrepreneurial work from wage work) and its converse, i.e., entrepreneurship-to-wage work enrichment (EW enrichment). Based on a sample of 279 hybrid entrepreneurs, we found support for the moderated mediation model, in which the positive effects of fit perceptions on entrepreneurial persistence via entrepreneurial self-efficacy were stronger for hybrid entrepreneurs reporting higher levels of WE enrichment. Theoretical and practical implications are discussed.
... Many pieces of literature note that entrepreneurship has played a critical role in increasing productivity, driving innovation, and opening new opportunities [25][26][27]. Even entrepreneurship is often a prerequisite for exploiting potential businesses in certain circumstances, thus providing excellent opportunities for success. ...
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This study aimed to investigate the effects of government support for the business survival of SME restaurants in Indonesia. In this study, we analyzed the impact of government support on the innovation of SME restaurants as well as the impact of entrepreneurial self-efficacy on innovation. Furthermore, this study analyzed the impact of entrepreneurial self-efficacy and innovation on business survival. A total of 120 owners or managers of SME restaurants participated in this study. The sample was collected based on a purposive method. To analyze the relationship among latent variables, we implemented structural equation modeling (SEM). The results show that government support has a positive impact on business survival through marketing and process innovation. In addition, the business survival of SMEs is affected by marketing innovation, process innovation, and entrepreneurial self-efficacy. In this study, the entrepreneurial factor had the highest impact on SMEs’ survival. This study established the body of knowledge related to the positive effect of government support on innovation in the perspective of small and medium-sized restaurants in the emerging market countries and developed a model of business survival of SMEs during pandemic crises by integrating external factors (government support) and an internal factor (entrepreneurial self-efficacy) through marketing and process innovation in the food processing industry.
The development of skills for entrepreneurship among young people has attracted interest at various levels, as a way of overcoming many problems that affect this group in the areas of economic development and job creation. This article assumes that participating in a youth association enables young people to develop a series of skills, in particular, their entrepreneurial capacities. This study pays attention to the contributions of the participation in youth associations for the promotion of entrepreneurship. The investigation based on a qualitative approach, through comparative case studies in Portugal. It was possible to verify that youth associations assume a dual role, on the one hand contributing to the personal, social and professional development of its leaders, members and participants, and on the other hand, as a promoter of social transformation, particularly at the local level.
Purpose This study aims to explore the dynamics of resilience in tourism and hospitality enterprises by investigating the influence of internal and external contextual factors (i.e. adaptive performance and institutional orientation) on the relationship between entrepreneurs’ resilience and business continuity indicators (i.e. perception of career insecurity and business exit intention). Design/methodology/approach In the Covid-19 pandemic context, quantitative data were collected using self-administrated questionnaires from entrepreneurs (founders of small-sized restaurants and travel agents in Egypt) using structural equation modeling. Findings The study reveals that entrepreneurs’ resilience under adversities directly correlates with business continuity indicators, with adaptive performance and institutional orientation functioning as mediators. Research limitations/implications The socio-demographic characteristics of entrepreneurs could be further investigated to observe the differences based on age, education and region. The type of business (i.e. restaurants and travel agents) could have an impact on the examined relationships. Therefore, further studies can use multi-group analysis to examine such differences between various sub-sectors of the hospitality business. Finally, the cross-sectional sample method used in this study is another limitation. In any study in which causality is inferred, longitudinal research confirms stronger inferences (Morgan & Hunt, 1994). Practical implications An instant implication is that entrepreneurs can take proactive actions to enhance their resilience. Entrepreneurs should seek to influence their own skills and abilities through various educational and training programs. For example, they can take advantage of business seminars, workshops and executive education courses. Entrepreneurs who have the chance of enhancing their skills in solving complex problems, identifying their strengths, managing their emotions are better able to adapt to unfavorable circumstances. Social implications The inhabited environment. Entrepreneurs should be institutionally oriented by building strong communications and networks with key actors and business-to-business customers. This would help entrepreneurs to understand the rules of the game, adapt to the environment, gain market legitimacy and accordingly acquire the social and financial support when hazards occur. Originality/value The extant literature lacks evidence about the internal and external contextual factors underlying the process of resilience in small and medium-sized enterprises and its outcomes. Research on entrepreneurship has rarely discussed the antecedents of business withdrawal. This study contributes to addressing this research gap.
From an active labor market policy perspective, start-up subsidies for unemployed individuals are very effective in improving long-term labor market outcomes for participants. From a business perspective, however, the assessment of these public programs is less clear since they might attract individuals with low entrepreneurial abilities and produce businesses with low survival rates and little contribution to job creation, economic growth, and innovation. In this paper, we use a rich data set to compare participants of a German start-up subsidy program for unemployed individuals to a group of regular founders who started from non-unemployment and did not receive the subsidy. The data allows us to analyze their business performance up until 40 months after business formation. We find that formerly subsidized founders lag behind not only in survival and job creation, but especially also in innovation activities. The gaps in these business outcomes are relatively constant or even widening over time. Hence, we do not see any indication of catching up in the longer run. While the gap in survival can be entirely explained by initial differences in observable start-up characteristics, the gap in business development remains and seems to be the result of restricted access to capital as well as differential business strategies and dynamics. Considering these conflicting results for the assessment of the subsidy program from an ALMP and business perspective, policy makers need to carefully weigh the costs and benefits of such a strategy to find the right policy mix.
The current German start‐up subsidy for unemployed individuals underwent a major reform in 2011 that altered key parameters of the program, leading to ambiguous ex ante predictions on the post‐reform effectiveness of the program, making a new evaluation necessary. In our descriptive analysis, we find that participants after the reform differ significantly from pre‐reform participants in terms of important characteristics and subsequent labor market performance. Our causal analysis reveals positive and sizable treatment effects on the treated regarding employment and income that are larger effects than what was estimated for the pre‐reform program. Potential reasons for this are discussed.
We draw upon recent developments in the identity and motivation literatures to address an intriguing puzzle in entrepreneurial gestation: Are entrepreneurs who start their businesses for non-pecuniary reasons more likely to persist with their venturing efforts until the businesses profitable birth? Using the latest data from the second phase of Panel Study of Entrepreneurial Dynamics (PSED II), we found that two non-pecuniary motivations, autonomy and passion for work, have significant impacts on entrepreneurial persistence, albeit in opposite directions “C the pursuit of autonomy impairs nascent entrepreneurs’ venturing efforts, whereas passion-driven nascent entrepreneurs are likely to continue their venturing efforts. By untangling the different identity focuses of these two non-pecuniary motivations, we caution entrepreneurs against the over-idealized dream of pursuing autonomy through entrepreneurship.
Purpose – The purpose of this paper is to examine the moderating effects of cognitive style dimensions on the relationship between entrepreneurs’ optimism and persistence. Design/methodology/approach – This theoretically derived research model is empirically validated using survey data from 198 small and medium-sized enterprises in Ghana. Findings – The study’s empirical findings are that the relationship between entrepreneurs’ optimism and entrepreneurial persistence is enhanced at higher levels of cognitive planning and creating styles. Somewhat interestingly, cognitive knowing style negatively moderates the relationship between optimism and entrepreneurial persistence. Research limitations/implications – The cross-sectional design of the study does not permit causal inferences to be made regarding the variables examined. Future studies may use longitudinal design to examine the causal links of the variables. Practical implications – The results of this paper can assist entrepreneurs and policy-makers in understanding the dynamics and processes involved in entrepreneurial decision making. The understanding of this issue can promote the development and maintenance of entrepreneurial ventures. Originality/value – The paper has a strong theoretical value as it relies on cognitive explanations of human behaviour, and seeks to advance the theoretical field by demonstrating the value of cognitive style within the domain of entrepreneurship.