Description
On this explanation pertaining to new firm performance and the replacement of founder ceos.
New Firm Performance and the
Replacement of Founder-CEOs
Jing Chen*
Copenhagen Business School
Peter Thompson**
Emory University
May 2013
We study the replacement of founder-CEOs by non-owner managers in a sample of 4,172
Danish single-founder startups. Founder replacement is more likely among the worst-
and best-performing firms. Firms with founder replacement were more likely to fail, but
the survivors grew considerably faster. We also analyze pre-founding and post-turnover
earnings and occupational choices of founders who rescinded operating control of their
firms. Founders with pre-founding income in the tails of the distribution are more likely
to be replaced. Our results are consistent with the notion that founder-CEO replacement
is driven in part by mismatches between business quality and founder ability.
JEL Classification Codes: D21, D22, J62, L26, M13
Keywords: Entrepreneurship, business creation, founder turnover, CEO turnover.
* CBS - Department of Innovation and Organizational Economics, Kilevej 14, 2000 Frederiksberg,
Denmark. Email: [email protected]. ** Goizueta Business School, Emory University, 1300 Clifton
Road, Atlanta, GA 30322, USA. Email: [email protected]. We appreciate helpful com-
ments from: Christian Catalini, Alberto Galasso, Mirjam Van Praag, and participants in the 2012
Summer Residence Week for Entrepreneurship Economists at Oxford University, the Roundtable
for Engineering Entrepreneurship Research at Georgia Tech, and in seminars at the University of
Amsterdam, the University of Lugano, Syracuse, Emory. Berkeley, and Carnegie Mellon.
1
1. Introduction
How does firm performance influence the likelihood that founder-CEOs of new businesses
cede operating control to a specialist manager? What implications does the departure of
the founder have for subsequent business performance? And what happens to founders
after they have left? This paper reports the results of an analysis of the causes and con-
sequences of founder-CEO replacement among a cohort of new businesses founded in
Denmark. Our empirical work uses the matched employer-employee database maintained
by Statistics Denmark, and draws samples from the universe of new businesses created in
Denmark during 1999 and 2000. These firms are generally small, they are almost invari-
ably not financed by venture capital, and they are generally not at risk of an IPO event.
We know surprisingly little about founder turnover
1
among such firms.
A growing literature has provided us with substantial insight into the causes and conse-
quences of founder turnover in other types of firms. This literature, which has focused on
high-growth and VC-backed firms
2
, finds that founders are most likely to be replaced by
a manager after a period of rapid expansion [Boecker and Karichalil (2002)], or upon at-
tainment of critical milestones such as completion of product development or securing
new rounds of outside funding [Wasserman (2003)]. More important, perhaps, is that
founder turnover is typically motivated by an expectation among stakeholders that the
firm is likely in the near future to undergo an episode of transformational growth. In-
deed, a significant portion of the literature explicitly samples on such expectations by
studying only firms that are preparing for an IPO [Nelson (2003), Jain and Tabak
(2008), Pollack, Fund and Baker (2009)].
The extant research lacks findings that we can confidently extrapolate to the firms that
1
We will use the term of convenience founder turnover interchangeably to describe an event
where the founder of a firm, who had been managing the firm as his or her primary occupation,
rescinds operating control while maintaining ownership, Replacements will be referred to for con-
venience as managers.
2
Of course, executive turnover in established, typically public, firms has been extensively studied
for many years [e.g., Hofer (1980), Chen and Hambrick (2012)]. There is also an active literature
on succession in family-owned businesses, but the concerns of this literature are rather different
from those in which executive control is passed outside the founder’s family. See Handler (1994)
for a review.
2
are the focus of the present paper. First, founders of VC-financed firms appear to be re-
placed at markedly higher rates than firms that are not subject to significant control by
outside equity investors [Hellman and Puri (2002), Boeker and Wiltbank (2005)]. Second,
few of the firms in our sample are ever likely to undergo transformations of the sort that
appear to motivate many founder turnover events at VC-financed firms. Third, VC-
financed firms are not random draws from the firm population. They are more likely to
grow substantially, they are better financed, and their founders and employees on aver-
age have more human capital. Correlations between variables estimated from a sample
drawn from the upper tail of the quality distribution may not hold in other segments of
the population, especially when there are possibilities of non-monotonic relationships in
the population as a whole.
It is well-known that VC-financed firms account for a tiny fraction of business launches
3
but they play a much larger role in economic growth, employment creation and innova-
tion. As a result, academic effort to understand the dynamics of control in VC-financed
firms is appropriately disproportionate to their numbers. Nonetheless, given their numer-
ical importance, we should also be concerned with the control dynamics of the typical
small business. Patterns of founder turnover among representative businesses can also
provide insight into the forces governing business transfers.
4
They are also key to under-
standing the expected opportunity costs of business creation, as well as the dual phe-
nomena of serial and portfolio entrepreneurs.
In this paper we study founder turnover in a sample of 4,172 Danish businesses created
in 1999 and 2000. To ensure that the departure of the founder indicates a meaningful
change in control, the sample consists only of firms that were created by a single found-
er, and where the founder worked as his primary job in the business. The firms and
founders we study represent all firms founded in Denmark that meet these criteria. Only
18 percent of them are classified as high-tech, while wholesale, retail, construction and
3
For example, in the United States, 626,771 new businesses were created in 2008, only 3,276 of
which received VC financing (Bureau of labor Statistics athttp://www.bls.gov/bdm
/entrepreneurship/bdm_chart1.htm; and NCVA (2009).
4
Founders may sell businesses because they are poorly matched to the needs of the business, or
because they have reasons to cash out their equity position. Looking at founder turnover in busi-
nesses that are not transferred, allows us to gain insights about the first of these two motivations.
3
manufacturing are well-represented; this is a much broader sample than has previously
been used to study founder turnover. Our data are drawn from Denmark’s national
matched employer-employee dataset, so we are also in a unique position to explore what
happens to the founders after they have left the firm, and who replaces them. The data
allow us to distinguish business turnover from founder turnover and, in this paper, we
focus on the causes and consequences of events in which the founder retires from active
management but continues to retain controlling ownership. That is, our focus is on CEO
succession in small firms, rather than business transfers.
To guide our empirical work, we develop a simple model of firm creation, performance
and founder turnover. The goal of founder turnover among VC-backed firms is generally
held to be resolving mismatches between the managerial skills of the founder and the
expected demands of the transforming enterprise. We also develop a framework based on
mismatches between business and founder, although our model focuses on current rather
than anticipated mismatches. We suppose that firm performance is a function of the
ability of the founder and the quality of the business idea, both of which are draws from
stable population distributions. Ability and quality are complements in production so
that there can arise mismatches of sufficient magnitude to justify replacing the founder
with an manager who is better suited to the needs of the business.
5
Our model predicts that founder turnover is most likely in the tails of the founder ability
distribution and in the tails of the firm performance distribution. We find evidence for
both predictions. Also consistent with the model, we find that when low- (high-) ability
founders are replaced, the incoming managers tend to have higher (lower) ability. The
model also predicts that, while good early performance is on average associated with
good future performance, this effect is stronger in firms that had no turnover; we see this
pattern in the data, too.
Our model says nothing about failure rates post-turnover. What we find in the data is
that, although turnover is associated with higher rates of growth among surviving firms,
5
Our theoretical framework is related to the models of business transfers developed by Holmes
and Schmitz (1990, 1995), and to the model in Braguinsky et al. (2012), which explores the effect
of the cost of evaluating ideas on founder turnover. We shall comment further on the relationship
between our theoretical framework and these models in the next section.
4
it is also associated with higher firm exit rates. This finding is consistent with Holmes
and Schmitz’ (1995) earlier implementation of the notion of founder-business mismatch-
ing.
The layout of the remainder of the paper is as follows. Section 2 develops our mismatch-
ing model of founder turnover, and compares it with related models. Section 3 describes
our data. In Section 4, we explore the relationship between firm performance and found-
er turnover. Section 5 focuses on turnover and ability. Section 6 concludes.
2. Theoretical Framework
There are two periods. In the first period, a founder with ability q has an idea of quality
q
1
. Ability is general and determines both the success with which the founder can man-
age a firm that implements the idea, and his earnings in alternative employment. We
assume that founder ability is innate, fixed over time and known to the founder. Idea
quality incorporates a variety of factors influencing performance, including the quality of
the underlying idea or technology, its adaptation and fit to the market, and the public’s
state of knowledge about the firm’s product. Some of these factors may be regarded as
fixed, while others evolve over time as the business model is refined. Upon entry, the
founder knows ?, but he only knows that q
1
is a random draw from the distribution
1
( | ). F q q The realization of q
1
is observed after entry. The business idea is refined and
adapted to the market, yielding a new quality q
2
in the second period, which is drawn
from
( )
2 1
| . G q q
In each period, t = 1, 2, output is given by
1
t t
t t
y q
a a
q
-
= , (1)
for some [0, 1].
t
a Î Although q and q are assumed to be complements in both periods,
we allow for the relative importance of ability and idea quality to vary from one period
to the next.
Profit in period 1 is given by
1 1
1
1 1
q w
a a
p q q
-
= - . (2)
The only cost is the opportunity cost of the founder’s time, which is assumed to be line-
5
arly increasing in ability. For concreteness, we shall suppose this alternative use of the
agent's time is in wage work.
At the end of the first period, q
2
is observed and a decision is then made whether to con-
tract with an outside manager to replace the founder, to continue without change of
leadership, or to exit. If a manager with ability q
?
is hired, she must be paid a wage wq
?
and a transition cost, c, must be paid. This cost reflects both direct costs of any disrup-
tion associated with a change in leadership, as well as any premium necessary to resolve
agency problems arising from the separation of ownership and control [Fama and Jensen
(1983), Jensen and Meckling (1976)].
6
The reward to recruiting a manager is that her
ability can be chosen optimally in light of the realization of q
2
, and the founder is re-
leased to earn wq elsewhere while remaining as the residual claimant on the firm’s earn-
ings. The founder is replaced by a manager if
( )
2 2 2 2
1 1
2 2
arg max q w c q w
a a a a
q
q q q q
- -
- - > -
?
? ?
, (3)
and if the term on the left hand side of (3) is positive. The profit-maximizing ability of
the manager is given by
( )
2
1/(1 )
2 2
/ w q
a
q a
-
=
?
, (4)
so maximized profits in period 2 are
( )
2 2
1 *
2 2 2
max , q w Aq c
a a
p q q
-
= - - , (5)
where
2 2
/(1 )
2 2
(1 )( / ) . A w
a a
a a
-
= - If
*
2
0, p < the firm exits.
The two terms on the RHS of (5) are plotted separately in Figure 1 as functions of q
2
and holding q constant. The concave function
2 2
( , ) q p q indicates profits when the founder
continues, and the straight line
2 2
( ) q p? depicts profits when the founder is replaced. In
the case illustrated in the figure, there are four distinct regions defined by the boundary
values 0 :
E
q q q < £ £ if
2
[0, ],
E
q q Î the firm exits at the end of period 1; if
2
[ , ]
E
q q q Î
or if
2
, q q ³
the founder is replaced as CEO; and if
2
( , ), q q q Î the founder continues to
6
He (2008), for example, documents that founders have lower incentive compensation and lower
total compensation than managers.
6
operate the firm. When
2
[ , ]
E
q q q Î , the founder is replaced because the opportunity cost
of his time does not justify continued commitment to the business model; he is replaced
by a manager who is less able than the founder. In contrast, when
2
q q ³ the founder is
replaced by manager with higher ability.
Figure 1 also illustrates the effect of an increase in the founder’s ability, from q to . q
¢
This reduces the profitability of the founder continuing with the firm when the quality of
the idea is low, and increases it when the idea is good. It is easy to verify that the inter-
section of
2 2
( , ) q p q and
2 2
( , ) q p q
¢
lies between q and , q and hence that the effect of an
increase in q is to raise both q and . q The interval [ , ]
E
q q will vanish if the founder’s
ability declines sufficiently so that .
E
q q < There are, therefore, two distinct cases. In
one case, when founders have limited ability, the period 2 outcomes fall into three dis-
tinct groups, with replacement of founders occurring only at higher values of q. In the
second case, founders are replaced in firms with business models drawn from the tails of
the quality distribution among all surviving firms. Which of these cases arises depends
on the founder’s ability, as well as other parameters of the model. For example, an in-
crease in the transition cost, c, shifts
2 2
( ) q p? down, which may eliminate the interval
[ , ];
E
q q
that is, if the increase in c is large enough, replacement of founders occurs only
at high values of q
2
and in each case the replacement has higher ability than the founder.
Clearly, the minimum value of c that eliminates the interval [ , ]
E
q q
depends on the
founder’s ability.
7
Figure 2 provides a more complete depiction of the model’s implications. Firms that are
closed down are those with low-quality ideas. If the founder does not want to continue to
operate the firm, continuation requires a new CEO. However, payment of the transition
cost cannot be justified in low-quality firms. The boundary separating firms that are dis-
continued from those that are continued with the founder at the helm is given by the
pairs
2
{ , } q q satisfying
2
1/(1 )
2
, q w
a
q
-
= showing that low-ability founders may continue to
operate a business that a high-ability founder would close down. Founder turnover in
7
When 0, c > it is always the case that . q q < In the limiting case of 0, c = the function
2 2
( ) q p?
is then tangential to
2 2
( , ) q p q for any value of q. and . q q ? In this case, every founder is re-
placed, because it is always efficient to secure a perfect match between managerial ability and
idea quality.
7
continuing firms occurs in two distinct groups. First, there is a group of firms with high-
quality ideas but that were founded by agents of sufficiently low ability to merit re-
placement with a more able manager. The boundary between this group and the group
of founders that continue to operate their own firm is defined by the function
( ) q q that
provides the larger of the solutions to the equation
2 2
1
2 2
. Aq c q w
a a
q q
-
- = - As the tran-
sition cost rises, ( ) q q unambiguously shifts upwards. The second group consists of firms
with able founders but whose firms are of middling quality. The quality of the idea is
high enough to prefer continuation over exit, but not high enough to merit continuation
of the founder. The replacement manager has lower ability than the founder. The
boundary between this group and those that exit is a horizontal line at
1
2
. q cA
-
= The
boundary between this group and the group of founders that continue to operate their
own firm is given by the function ( ) q q that provides the smaller of the solutions to
2 2
1
2 2
Aq c q w
a a
q q
-
- = - . An increase in the transition cost shifts ( ) q q downwards. Figure
2 shows that ( ) q q and ( ) q q are both positively sloped, which is consistent with the dis-
cussion around Figure 1.
2.1 Baseline simulations
The empirical implications of our theoretical framework depend critically on the joint
distribution of q
2
and q (and, by virtue of the correlation between q
1
and q
2
, on the joint
distribution of initial idea quality and founder ability). For example, the implication that
founder turnover occurs both when q
2
is high (relative to q) and when it is low requires
that we observe in any sample instances where contrasting values of q
2
and q coexist.
Similarly, the transition cost cannot be so high that it eliminates the interval [ , ].
E
q q Be-
cause this may not be the case, and despite the simplicity of the theoretical framework,
there are no general empirical predictions about the relationships between performance
in the two periods, founder ability and turnover. We will therefore make use of some
numerical simulations to explore the possibilities of the model.
However, it is useful to consider some reasonable ways in which we can limit the varied
possibilities. We do so in two ways. The first imposes a constraint on the transition cost,
c, such that the model predicts an aggregate rate of turnover that approximates the rate
(about 12 percent) that we observe in our data. The second makes use of a simple free-
8
entry condition to help us constrain the relationship between ability and the quality of
the idea. We want to admit entrepreneurs of all abilities. This will be the case if all
agents, regardless of ability, are indifferent between entrepreneurship and wage work in
period 1. If the quality of ideas and agent ability are independent of each other, then
high-ability agents, who bear a higher opportunity cost of becoming an entrepreneur, will
find business creation less attractive than low-ability agents. For our purposes, it suffices
to suppose that ability and agents care only about expected first-period returns. In the
simulations to follow we therefore impose the constraint
8
1 1
1 1
1
, E q w
a a
q
- -
é ù
=
ê ú
ë û
(6)
which ensures that all agents, regardless of ability, are indifferent between creating a
business and wage employment.
In our baseline model, we assume that in both periods ability and the quality of the ide-
as are equally important. That is, we set
1 2
0.5. a a = = We assume that
1
1 a
q
-
has a
lognormal distribution, with unit mean and standard deviation of 0.5. Consistent with
(6), we then assume that, for agent i,
1
1
1i
q
a -
is a draw from a lognormal distribution with
mean
1
1
.
i
w
a
q
-
Finally, consistent with the assumption that
2 1
( | ) G q q is decreasing in q
1
,
we assume that
2
1
2i
q
a -
is a draw from a lognormal distribution with mean
1
1
1
.
i
q
a -
The
wage, w, is normalized to 1. Finally, we set 0.35, c = so that our baseline produces an
aggregate rate of founder turnover of about 12 percent, consistent with evidence we shall
show later.
Figure 3, which plots 5,000 random draws using the baseline parameters, depicts out-
comes that occupy all four segments of Figure 2. Although founders of exiting firms on
average have relatively low ability, this is incidental and driven by the correlation be-
tween idea quality and founder ability. Indeed, once one conditions on idea quality, low-
ability founders are less likely to close down the firm. The simulations yield observations
8
A forward-looking agent will also consider the option value that entering gives by providing
choices in the second period. In period 2, the founder may choose to remain in control of the
business, hire a manager if doing so increases profits, or exit. Even if expected second-period earn-
ings from continuation are the same as expected first-period earnings, the replacement and exit
options imply for a forward looking agent that
1 1
1
1 1
. E q w
a a
q
- -
<
é ù
ê ú
ë û
9
in the two sets of firms with sufficiently good ideas but poor matches between idea and
founder ability to merit replacement with a more able manager. Although founders of all
abilities may suffer a mismatch sufficiently severe to induce their replacement with a
manager, it is clear that the likelihood of replacement is greater in the tails of the ability
distribution.
Although in our empirical work, we will use pre-founding earnings as a crude proxy for
founder ability, we will conduct much of our analysis using revenues as a measure of firm
performance. However, because founder turnover occurs as a result of a mismatch be-
tween ability and business quality, it is not obvious that we should also expect to see
more frequent founder turnover in the tails of the firm performance distribution. To see
whether this is the case in our baseline simulations, Figure 4 plots first- and second-
period revenues of surviving firms by founder status. The two distinct groups of founders
that experience turnover are also clearly evident when we look at first-period perfor-
mance instead of founder ability, and they are indeed drawn more frequently from the
tails of the performance distribution. There is a group with above-average performance
in the first-period whose founders are replaced with less able managers. There is also a
group of firms whose founders are replaced with more able managers. This group consists
of firms with low-ability founders, but their poor ability is offset by ideas that in the
second-period are of sufficiently high quality to justify payment of the transition cost
(firms with the worst ideas in the second period are closed down). Although t hese firms
have a mean first-period performance that is not much different from the population av-
erage, they are overrepresented in the lower tail of the first-period performance distribu-
tion.
A distinctive feature of Figure 4 concerns the effect that founder turnover has on the
relationship between first-period performance and second-period performance. Perfor-
mance among the group of surviving firms with no turnover tends to be more consistent
from one period to the next than in the case of firms that experienced turnover. Firms in
which founders are replaced by less-able managers see on average a decline in their per-
formance, while the arrival of a more capable manager induces on average an improve-
ment in performance. Among surviving firms with no turnover, therefore, the relation-
ship between first- and second-period performance is strongly positive. In contrast, the
10
marginal ‘effect’ of first-period performance on second-period performance among firms
that experienced turnover is much weaker in absolute value, and may even be negative.
2.2 Alternative Scenarios
Do these implications of the model persist in the face of significant changes in parameter
values? Figures 5 through 7, which replicate Figure 4 for five alternative scenarios, sug-
gest that the possibilities depicted so far are, in fact, quite obust. In Figure 5, the rela-
tive importance of idea quality and ability is altered across the two periods. In the upper
panel, ability is most important in the first period
1 2
( 0.8, 0.2), a a = = while in the low-
er panel it is more important in the second
1 2
( 0.2, 0.8). a a = = The results reported in
the baseline are not sensitive to these significant changes in the production technology.
Figure 6 sharply alters the correlations between idea quality and ability. In the upper
panel, the standard deviation of the distribution of
2
q is reduced by 80 percent, to 0.1,
while maintaining its mean equal to the realized value of
1
q ; the lower panel similarly
increases the correlation between q and q
1
by reducing the variance of the distribution of
1
. q
The three groups of surviving firms become even more distinct than in the baseline,
but the original pattern is preserved. Finally, in Figure 7 the cost of transitioning to a
manager is reduced sharply, so that the fraction of firms that experience turnover is in-
creased from about 12 percent to 35 percent. Again, the baseline results are robust to
this change.
9
2.3 Related models
There are, of course, different ways one may specify a model of mismatching. In this
subsection we briefly compare our theoretical framework with three related models. The
best known of these is Holmes and Schmitz (1990), in which agents vary in their ability
to produce ideas, although all are equally gifted at managing. Agents with low innova-
tive ability prefer to buy and manage existing businesses, while agents with high ability
prefer to specialize in business creation (selling or closing each business they create). The
9
Our data do not afford us an opportunity to explore the effects of changes in the transition cost.
However, Braguinsky et al. (2012) explore the effects of a likely decline in the cost of evaluating
ideas in Japanese biotech firms, brought about by institutional reform. They find that a reduction
in evaluation cost was associated with a large increase in the rate of founder turnover.
11
outcomes for founders with intermediate ability depend on the quality of the business
idea: they will close the worst businesses and manage the best. A key determinant of the
boundaries between the choices is the cost of transferring a business. Figure 8 illustrates.
Clearly, only the better ideas produced by the more able innovators experience founder
turnover. As managerial ability is fixed, the model predicts that business turnover is as-
sociated with high initial and subsequent firm performance, but it does not predict any
change in performance after turnover.
In Braguinsky et al. (2012), entrepreneurs with known ability found businesses with un-
known quality. As in our model, business quality and founder ability are complements in
production. The quality remains unknown unless the founder chooses to pay an evalua-
tion cost, c. High-ability agents know that they are at risk of wasting their time with a
poor idea, so they choose to evaluate the idea, subsequently closing down businesses that
are not good enough. Low-ability agents know that they may not be good enough man-
gers if their business quality is good; they also pay the evaluation cost and either close
their business or transfer control to a professional manager. Agents with intermediate
ability do not pay the evaluation cost; they continue to manage a business of unknown
quality (see Figure 9). The model unambiguously predicts that only the better businesses
founded by low-ability managers are at risk of founder turnover, and that turnover elic-
its an improvement in performance. Auxiliary assumptions of the model associate better
initial firm performance (i.e., the combination of business quality and founder ability)
with turnover.
Holmes and Schmitz (1995) do not have explicit variations in skill. In this model, output
is given by
t t t
y q m = + , where q is business quality and m is match quality. Both q and
m evolve over time, each consisting of permanent and transitory components. The per-
manent component of business quality, b, is fixed when a business is first founded, while
the permanent component of match quality, m, is fixed each time a new manager takes
control of the business. Founders (and subsequent managers) who experience a sufficient
decline in match quality dispose of their businesses, either by closing them (if business
quality is sufficiently low) or by transferring control to a new manager (if quality is suf-
ficiently high). Figure 10 illustrates, with a sample path depicting one possible evolution
of business and match qualities. A business begins at point a, and suffers a steady de-
12
cline in match quality. At b the business quality is sufficiently high to enable a sale. The
new manager begins with the same business quality but a better match quality, at c, but
then suffers a decline in both business quality and match quality until exit at d. Holmes
and Schmitz show that the boundary between closing and selling a business shifts down
when the permanent business quality, b, increases. Hence, better quality businesses are
more likely to be sold. However the match quality may occasionally be worse immediate-
ly after a sale, so despite better average performance after business turnover, there can
be an increased risk of quick failure.
10
It is not possible to relate founder ability to the
likelihood of business turnover in this model unless we impose some functional relation-
ship between ability and the pair { , }. m q Doing so, however, would be unfair to the
Holmes and Schmitz model, although it strikes us that their model could be made con-
sistent with turnover occurring in the tails of the firm performance distribution.
3. Data and Sample Construction
Our empirical analysis focuses on all new business founded in Denmark in 1999 and
2000. We construct our samples out of three databases maintained by Statistics Den-
mark: the Entrepreneur Database, the Firm Database, and the Integrated Database for
Labor Market Research (IDA).
The Entrepreneur Database is the primary source for identifying new businesses (part-
nerships or sole proprietorships) registered in Denmark each year, and provides unique
identifiers for each firm, each plant, and one individual identified in the registration doc-
uments. The Entrepreneur Database spans the period 1996 to 2006. However, accounting
information is not collected for all industries until 1999, so this is the earliest cohort we
study.
There are several limitations to the Entrepreneur Database. First, since only one person
associated with the new business is recorded, the database provides no information for
identifying possible co-founders. Second, some of those who appear in the database may
not be the “real” founder. For example, there are cases in which founders used their
10
Figure 5 depicts the case where the m is common across matches. If m is lower after business
transfer, the boundaries shift to the right, increasing the risk of business failure.
13
spouse’s name to register a business. Third, it is unclear from the Entrepreneur Database
whether or not the identified entrepreneur actually works at their startup. Although it is
possible that entrepreneurs do not operate businesses themselves, this study focuses on
those who were actually working for their own businesses. In order to identify these en-
trepreneurs and possible co-founders, the data obtained from the Entrepreneur Database
are combined with information provided in the Firm Database.
The Firm Database consists of annual employment information on all workers (full-time
and part-time) at all firms operating in Denmark. The variable of particular interest is
their position in the firm, through which active founders of each new business are identi-
fied in the following way. First, founders are those whose positions are classified as self-
employed, employers, or business tax payers. Second, in businesses with fewer than four
employees, all people whose positions are top managers are defined as founders.
11
Third,
if nobody in a business with fewer than four employees is identified as a founder based
on the previous two criteria, all employees are considered as cofounders. The fourth
group of founders consists of those who registered the business and were working for the
business as their primary occupation, but did not meet the first three criteria for being a
founder.
Because we are interested in founder turnover as a mechanism for transfer of control, we
restrict our sample to firms in which there appears to be just a single founder. This leads
us, in particular, to remove from the sample firms with two to four employees where we
identified more than one top manager. We link the remaining businesses to the IDA,
where more detailed information such as industry classification and employee de-
mographics is available. The IDA is an employer-employee matched database, which
provides mainly employment information at firm, establishment, and employee levels.
The IDA enables us to track over time the turnover of founders, changes in firm status,
and occupational choices of individuals each year until 2008. However, because of incom-
plete accounting and earnings data in the most recent years, we will follow firms only
through 2004 and individuals through 2005.
Firm identifiers in the IDA are attached to a specific owner, so they change whenever
11
This way to identify founders was first used by Sørensen (2007).
14
there is a change of ownership. This design makes it possible to distinguish three firm-
level events in each year. Survival is assumed if the same firm identifier from the previ-
ous year appears in the current period. An ownership change occurs if the same estab-
lishments of the firm from the previous year are identified in the current period except
that they are associated with a different firm identifier. A business exit is identified if all
the establishments of the firm can no longer be found in the data.
In our analysis, the original founder of a startup must meet the following criteria: (1) He
or she formed a startup in 1999 or 2000 as a sole proprietor; (2) The person was current-
ly working at the new venture; and (3) The new business could be identified in the Firm
Database and the IDA database, indicating that at least one person was working in the
business as his or her primary job.
12
Using the unique person and firm identifiers, we can
track these founders’ employment in the following year to identify whether or not the
founder was still working at the startup, and whether he was still an employer. A found-
er was staying at the startup if he could be identified in the firm in the following year,
whether as an employee or as an employer. Otherwise, the founder is assumed to have
left the original firm because of turnover, business exit, or ownership change. In a few
cases, the founder might stay at the startup after a change of ownership, but in each of
these cases a new firm identifier is assigned to the startup.
Table 1 provides tabulations of changes in the status of startups and their founders from
the founding year to 2005. Four outcomes are possible in each year: the firm exits, the
firm is acquired or the firm continues, either with or without the founder remaining in
operational control. Of 4,172 firms in the sample, a total of 533 (12.8 percent) of them
eventually experienced founder turnover. Turnover was twice as common as ownership
change (6.5 percent), but much less common than firm exit (50.6 percent). The hazard of
turnover declines with firm age, declining from 5.11 percent in the first year, to 2.61 per-
cent at age five.
13
12
This person must include the founder, so our sample includes single-person firms. We have
repeated our analyses after restricting the sample to including firms with at least two individuals.
The results, available from the authors, are very similar to those reported here.
13
The declining hazard of turnover is consistent with gradual learning about match quality [Jo-
vanovic (1979)], which we have not attempted to model.
15
4. Firm Performance and Founder Turnover
In this section, we evaluate the relationships between startup performance and subse-
quent rates of founder turnover, and between founder turnover and subsequent firm per-
formance.
4.1 Startup performance and the subsequent likelihood of founder turnover
To assess the effect of initial performance on subsequent rates of founder turnover, we
remove from the baseline sample 2,388 startups (918 from the 1999 cohort, and 1,470
from the 2000 cohort) that exited or were acquired before any occurrence of a founder
turnover during the observation window between the founding year and 2005. Table 2
provides descriptive summary statistics for the remaining 1,784 startups and their found-
ers. There are no large differences in means between founders that continued and found-
ers who were replaced. However, the latter group are somewhat younger and less likely
to have had vocational training; they are also more likely to come from the tails of the
earning distribution (either less than high school or at least college); they are also some-
what less likely to have founded businesses in construction, and more likely to be en-
gaged in high-tech activities.
We first estimate the following logit model,
2
1 1 2 1 , 1 ,
ln ( )
1
t
t t i i t i t
t
p
logsale logsale X Z
p
a b b g d e
- - -
æ ö
÷ ç
÷
ç = + + + + +
÷
ç
÷
÷ ç -
è ø
(7)
where p
t
is the probability that the founder leaves a continuing firm in period t,
is a
vector of time-invariant variables related to founder demographics (gender, marital sta-
tus, age, and education) and startup founding conditions (founding year and industry),
and
,
is a vector of potentially time-varying variables, such as firm age, founder age,
marital status, and highest completed education.
The key independent variable is the logarithm of sales in the previous period, along with
its quadratic to allow for the possibility of a non-monotonic effect as predicted in Section
2.
14
Columns (1) and (2) of Table 3 present the results. In column (1), we include con-
14
For six observations with zero sales, we added one DKK prior to taking the logarithm.
16
trols only for firm age and founding year. The negative coefficient on firm age reflects
the declining hazard of founder turnover already noted in the summary statistics, while
the cohort dummy is insignificant. In column (2) we add some founder characteristics.
The probability of turnover declines with founder age, it is lower for founders with col-
lege education, and it is lower for men and married founders. Both columns show a U-
shaped relationship between the log of sales in the previous period and the likelihood of
founder turnover in the current period, indicating that a founder is more likely to rescind
operating control of the firm if it is located the tails of the firm performance distribution
among all surviving firms. Columns (3) and (4) replace sales in the previous period with
sales in the startups’ first year of operation. The results continue to show the U-shaped
relationship, and the same effects of founder characteristics and firm age on the likeli-
hood of turnover. The minimum of the U-shaped relationship between prior performance
and the probability of turnover occurs at revenue levels of DKK580,000 to DKK800,000,
equal to 15 to 37 percent of the sample mean. This is also well within the sample range.
Consistent with the predictions of our simple mismatching model, therefore, we find that
turnover is more common in the tails of the observed earnings distribution than in the
middle.
4.2 Founder turnover and the subsequent performance of startups
To examine the relationship between founder-CEO replacement and subsequent firm per-
formance, we use a subset of startups in the baseline sample and limit the observation
window to the interval between the founding year and 2004. The startups included in
this subsample meet two criteria. First, we eliminate firms in which the founder was re-
placed after 2002. That is, firms included in the sample either experienced founder turn-
over by 2002, or never had founder turnover during the observation period. Second, we
remove startups that failed or were acquired between the initial founding year and 2002
but prior to any observed founder turnover event. However, we retain in the subsample
startups that experienced founder turnover and then exited or were acquired. Table 4
illustrates how we construct the subsample.
Starting with the 1,588 startups in the 1999 cohort, we removed 709 firms that exited or
went through ownership change by the end of 2002 and before any occurrence of founder
17
turnover, and 36 firms that experienced founder turnover in 2003 or 2004. For the 2,584
startups of the 2000 cohort, we removed 995 and then 83 of them for the same reasons.
We are left with a sample of 2,349 startups, 843 of which come from the 1999 cohort,
and 1,506 from the 2000 cohort.
Table 5 summarizes the number of startups that survived, exited, or experienced owner-
ship change during the observation period. Among the 843 startups founded in 1999, 142
of them had founder turnover before 2002. Among them, 50 survived to 2004, 56 exited
or changed owners before 2002 but after founder turnover, and 36 exited after 2002.
Among the 1,506 startups founded in 2000, 232 had founder turnover before 2002. Of
these, 82 survived the entire observation period, 70 exited or changed owners before
2002, and 80 exited after 2002.
Table 6 provides the descriptive summary of this subsample (the distributions of firms in
each column across industries is very similar to those shown in Table 2 and are not re-
ported). The summary statistics show two interesting contrasts between firms that expe-
rienced turnover and those that did not. Turnover by 2002 is strongly associated with
lower odds of survival to 2004. Only 35 percent of firms with turnover survived, com-
pared with 74 percent of firms with no turnover. However, conditional on survival, the
growth rate of sales and of employment is much greater among firm that experienced
turnover. For example although there is little difference between the two groups in ini-
tial sales revenue, average 2004 sales are 55 percent higher among the turnover group.
We estimate the following model
,2004 1 ,0 2
ln( ) ln( )
i i i
sales sales turn a b b = + +
3 ,0 ,2004
ln( )
i i i i
sales turn X b g e + ´ + + , (8)
where turn
i
equals one if the startup experienced founder turnover between its founding
year and 2002, and zero otherwise, and the vector
includes controls for cohort and in-
dustry.
Table 7 presents OLS estimates of (8). In the first three columns, we focus on startups
that survived until 2004. Column (1) shows that both initial sales and founder turnover
have a positive impact on startups’ sales in 2004. Interacting the two factors in column
18
(2), we further find that the marginal effect of founder turnover on future sales is de-
creasing with the initial performance of startups (or, equivalently, that the marginal ef-
fect of initial performance is lower for firms that had turnover). This is consistent with
the model in Section 2. However, this positive effect of turnover is reduced to zero only
at about the 95
th
percentile in the distribution of initial sales. In column (3), we replace
the log of initial sales with three categorical variables that indicate membership in the
bottom quartile, the upper quartile, or the middle of the distribution of the log of initial
sales. The result confirms a positive correlation between initial and future performance.
Meanwhile, founder turnover has a positive association with a startup’s future perfor-
mance for the first two categories, but no association for the third category.
In columns (4) to (6), we estimate Tobit regressions including all firms in the subsample.
For startups that exited or went through ownership change before 2004, we recode the
logarithm of their sales as zero [cf. Dunne, Roberts and Samuelson (1989)], and make
zero our threshold for inclusion in the linear portion of the model. The inclusion of exit-
ing firms changes the results. Turnover is now associated with lower future performance
for all firms, although as in the first three columns, the effect of turnover is worse for
firms with good initial performance.
The contrast between the OLS regressions for surviving firms and the Tobit estimates
for the full sample suggests that turnover has disparate effects on survival and perfor-
mance conditional on survival. To confirm this, in Table 8 we report the result of logit
regressions where the dependent variable equals one if the firm survived to 2004 and zero
otherwise. Column (1), which include all firms in our sample, clearly indicates lower sur-
vival rates for firms that experienced founder turnover, regardless of initial size. There is,
however, a potential endogeneity problem in these two columns. It is possible the found-
ers leave firms because they are already in the process of exiting, but our data do not
record exits until after they have left. In column (2), we therefore remove from the sam-
ple 100 firms that had failed after founder departure but by 2002; these firms must have
failed very soon after the founder left. Doing so does not alter the evidence that turnover
events are associated with higher failure rates.
This section has generated three central findings. First, founder turnover is more likely
when firm performance is in the tails of the revenue distribution. Second, turnover is as-
19
sociated with higher future sales among surviving firms. Third, turnover is associated
with higher failure rates. We will draw some conclusions from these findings in Section 6.
5. Ability and Founder Turnover
In this section, we evaluate the relationships between founder ability and the rate of
founder turnover, as well as the relationship between the ability of founders and the
managers that replace them. To measure ability, we use average earnings during 1996-
1998. This measure predates the founding of the firm, so it is not contaminated by mis-
matches between ability and business quality. However, it remains a crude proxy. In
particular, founders may have elected to establish businesses because of mismatches in
prior employment, as suggested by Åstebro, Chen and Thompson (2011).
5.1 Founder ability and turnover
The model predicts that departing founders tend to come from the tails of the ability
distribution. To examine this prediction in our sample, we collected data on average
gross income between 1996 and 1998 for 1,784 founders. Figure 11 plots the income dis-
tributions separately for 533 founders that had given up control of their firm by 2005,
and 1,251 founders that did not. The distribution for founders that experienced turnover
exhibits more skew than those that did not, and has fatter tails.
We also ran “pre-program” quantile regressions to examine the relationship between
founder ability and turnover. The regressions take the form
0
0 1 it it i t it
AveInc Turnover X Year a a b g e = + + + + , (9)
where
0
it
AveInc is average gross income for 1996-8, and
are demographic controls, in-
cluding age, gender, college degree, and average tenure in pre-founding wage employ-
ment. We estimate (9) at various percentiles of the pre-founding income. The model sug-
gests that the coefficient on Turnover, a
1
, will be negative at low levels of income and
positive at high levels, reflecting a U-shaped relationship between turnover and ability.
As Figure 12 illustrates, this is exactly what we find, whether or not we include the de-
mographic controls in the quantile regressions: Up to the 50
th
percentile, departing
20
founders tended to have lower pre-founding income, while at the highest percentiles they
tended to have higher income.
15
5.2 Founder and manager abilities
The model also predicts two types of mismatching and subsequently two kinds of succes-
sions: high (low) ability founders replaced by low (high) ability new managers. To exam-
ine whether these two kinds of turnover exist in the data, we investigate the turnover
events that happened in 2001 among the 2000 cohort of founders. As before, we use av-
erage gross income from 1996 to 1998 as the measure of individual ability.
It is quite difficult and labor intensive to match incoming replacements with founder de-
partures, especially when the founder is replaced by an incumbent employee. We there-
fore look at a subset of our observations, consisting of 69 startups where founders trans-
ferred operating control in 2001, and at least one outsider joined the business as employ-
er or top manager. In 41 startups, we identify a single individual that joined the firm as
manager; in 20 cases, two outsiders joined in top managements positions, while in the
remaining eight three outsiders joined. If more than one individual joined the business as
part of a new management team members, we select the one with the highest average
prior income as the replacement of the original founder. Figure 13 plots 1996-8 average
income for departing founders against the 1996-8 average income for the incoming man-
ager. As anticipated by the model, most observations related to founders with low prior
income are located above the 45 degree line. We do not have many observations on high-
income founders, but those we have are located well below the 45 degree line, indicating
that they were replaced by managers with lower ability.
5.3 What happens to replaced founders?
What happens to the founders that are replaced? Among all founders, first-period per-
formance is positively correlated with ability. However, some founders are replaced be-
cause they have low ability but good ideas, so it is not clear that the positive correlation
between firm performance and ability survives among the subset of founders that are
15
The regression output on which Figure 12 is based is available from the authors.
21
replaced. Because future earnings depend on ability, the relationship between first-period
performance and future earnings is also a priori unclear. However, as Figure 14 indicates,
the positive relationship survives in our baseline analysis among founders selected for
replacement.
16
As future earnings are expected to increase in ability (the model assumes
a linear relationship), Figure 14 also suggests a positive relationship between the firm’s
pre-turnover revenues and post-turnover founder earnings.
The pattern observed in Figure 14 is also evident in the data. We constructed a subsam-
ple that consists of all the founders that rescinded operational control of their businesses,
and tracked their income for three years after turnover occurred. We estimate a linear
model, where the dependent variable is the log of gross income averaged over three years
after the founder left his startup at time t, and the key independent variable is the log of
the startup’s sales before turnover. In column (1) of Table 9, we use the firm’s initial
sales, while in column (2) we use sales in the year prior to turnover. Clearly, after con-
trolling for their characteristics, founders who achieved higher sales in the first year of
their startups also have higher average gross income in the three years after CEO turno-
ver. There is, however also strong evidence of mean reversion: each ten percent increase
in previous firm revenues is associated with only about a one percent increase in subse-
quent founder earnings.
We also explore whether success in the firm prior to turnover is associated with the like-
lihood of creating another business. Recall that our model assumes a correlation between
idea quality and founder ability such that all agents are indifferent between founding a
business and working as an employee; this is not a central assumption for the model but,
if it is reasonable, it implies that good performance in one business does not raise the
probability of new business creation. We estimate a logit model where the dependent
variable equals one if the founder was an employer in the year after he left his startup,
and the key regressor is pre-turnover sales. Columns (1) and (2) of Table 10 reveal no
significant relationship between a startup’s initial performance and the likelihood of the
founder being an employer again in the period after turnover. Adding a quadratic term
16
In scatter plots not shown, we find this relationship holds also for the parameter values used in
Figures 5 through 7. In several cases the effect is, as in Figure 5, much weaker among founders
that are replaced with more able CEOs.
22
in column (2) shows a non-linear relationship, suggesting that founders from both the
highest and lowest-performing businesses are more likely to be self-employed again after
turnover.
17
However, as shown in column (4) this relationship disappears if we look at a
startup’s performance immediately before founder turnover.
18
6. Conclusions
In this paper we analyzed the relationships between founder turnover, firm performance,
and founder ability and occupational choices in a sample of 4,172 firms drawn from the
Danish matched employer-employee dataset. Our interest was in studying the causes and
consequences of changing operating control of young firms, so we restricted our attention
to firms that had a single founder. Our sample comprises the universe of Danish firms
founded in 1999 or 2000 that met this criterion.
Our key findings were as follows. First, founder turnover among the firms in our sample
were more likely among the most and least able founders, and among the worst- and
best-performing firms. Second, turnover was not unambiguously associated with better
subsequent performance; on the one hand, firms that replaced the founder with a man-
ager were more likely to fail, while on the other hand the surviving firms among them
grew faster. We also found that the marginal ‘effect’ of initial sales on future sales
among surviving firms was much stronger among firms that did not experience founder
turnover. This finding is consistent with the evidence we found that low- (high-) ability
founders tend to be replaced with managers who have higher (lower) ability. We also
analyzed subsequent earnings and occupational choices of founders who rescinded operat-
ing control of their firms. Although subsequent founder income was unsurprisingly found
to be increasing in the performance of their firms, those that left good firms were no
17
This is consistent with the model of labor-market mismatching in Astebro, Chen and Thomp-
son (2011), in which entrants into entrepreneurship are more likely to come from the tails of the
ability and earnings distributions.
18
We also examined occupational choice for three years after founders rescinded control of their
businesses. OLS regressions in which the dependent variable is the total number of years that
founders were an employer during this three-year period also yielded no significant relationship
between the dependent variable and startups’ performance in the initial year or in the year im-
mediately prior to founder turnover.
23
more likely than others to found another business.
Our results are consistent with the notion that mismatches between business quality and
founder ability matter, although not necessarily in the precise form of the theoretical
framework we developed in this paper. Åstebro, Chen and Thompson (2011) assumed
that mismatches between an agent’s ability and the quality of the firm that employed
him might drive him into self-employment or entrepreneurship. That model, like the
theoretical framework developed in this paper, predicted that mismatches were most
likely in the tails of the ability distribution. However, it assumed that business creation
serves the purpose of resolving mismatches. The present paper suggests that mismatches
may be important also after business creation.
Our model predicts that turnover occurs more frequently in the tails of the initial per-
formance distribution, that the marginal ‘effect’ of initial performance on future perfor-
mance is greater among non-turnover firms, and it also predicts the empirical results
about the future earnings and occupational choices of departing founders. However, the
model has no uncertainty at the time of founder-CEO replacement, and hence has noth-
ing to say about the much higher failure rates of firms that experienced turnover. The
disparate empirical results about firm failure and the growth of surviving firms recall
Holmes and Schmitz’ (1995) model of business turnover. In their model, firm perfor-
mance depends on the sum of business quality and match quality. In contrast to the pre-
sent paper, there is no variation in ability so all agents have the same outside option,
and new CEOs only observe the quality of their match with a business after they have
purchased the firm. Their model predicts that the best business ideas are most likely to
be transferred to new owners, so among surviving firms business transfers are on average
associated with good performance. However, because transfers may create a poor match,
businesses that are sold are at greater risk of subsequent exit.
19
Our results on growth
and survival of firms resonate with these predictions of their model.
Both our theoretical framework and our empirical analysis have focused on the turnover
19
Pollock, Fund and Baker (2009) find that VC-financed firms that are preparing for an IPO are
more likely to experience founder turnover if their firm operates in an uncertain industry envi-
ronment. This could also explain our results on survival, although our regressions do have indus-
try fixed effects.
24
of operating control while the founder retains ownership. In our sample, turnover of op-
erating control is more common than business turnover, but both are important features
of the data. Our paper leaves unanswered the interesting question of how owners decide
between selling a business and hiring a CEO. The Danish matched employer-employee
dataset may also be rich enough to allow us to compare some characteristics of outgoing
founders and incoming CEOs, and assess whether founder-CEO pairs differ from each
other in ways consistent with mismatching models. These are questions we intend to ad-
dress in future work.
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TABLE 1
Status Change of Startups by Year
Years after Founding
1 2 3 4 5 6*
Count
Surviving: founder stays 2,996 2,247 1,829 1,535 1,321
Surviving: founder turnover 213 127 82 54 40
Firm exit 898 550 291 193 140
Firm acquired 65 72 45 47 34
Total 4,172 2,996 2,247 1,829 1,535
Hazards (percentage)
Surviving: founder stay 71.81 75.00 81.40 83.93 86.06 87.16
Surviving: founder turnover 5.11 4.24 3.65 2.95 2.61 3.12
Firm exit 21.52 18.36 12.95 10.55 9.12 7.71
Firm acquired 1.56 2.40 2.00 2.57 2.21 2.02
Total 100 100 100 100 100 100
* Percentages for 1999 cohort only.
27
Table 2
Descriptive Summary of Subsample 1
All No Turnover Turnover
Age (mean years) 40.07 40.23 37.91
Male (%) 0.76 0.76 0.77
Married (%) 0.64 0.64 0.54
Education (%)
less than high school 16.84 16.55 21.01
exact high school 6.66 6.34 11.07
vocational training 50.91 51.75 39.21
some college 14.72 14.66 15.57
bachelor 1.13 1.02 2.63
Master 7.66 7.66 7.69
Phd 0.69 0.69 0.75
others 1.38 1.33 2.06
Founded in 1999 (%) 42.28 42.69 36.59
Initial sales (1,000 DKK) 2,149 2,135 2,347
Initial no. of full-time employees 1.87 1.85 2.09
Industry (%)
Agriculture 0.29 0.28 0.38
Manufacturing 7.25 7.34 6.00
Construction 17.57 17.86 13.51
Wholesale and retail 37.57 37.55 37.9
Transport, post, telecom 4.89 4.90 4.69
Low-tech businesses 6.93 6.93 6.94
Public Services 0.43 0.42 0.56
Personal Services 6.05 6.26 3.19
Kibs 0.44 0.34 1.88
High-tech 18.24 17.77 24.77
Others 0.34 0.35 0.19
Obs. 7,961 7,428 533
28
TABLE 3
The Effect of Startup Performance on the Probability of Founder-CEO Replacement
Dept Var: = 1 if founder replaced in year t
Logit Reegression
Using sales in year t-1 Using sales in founding year
(1) (2) (3) (4)
Log of sales -0.940
***
-0.966
***
-1.159
***
-1.270
***
(-5.08) (-5.10) (-3.30) (-3.43)
(Log of sales)
2
0.072
***
0.076
***
0.084
***
0.095
***
(5.71) (5.87) (3.36) (3.61)
Founder characteristics
Founder age
__
-0.017
***
__
-0.019
***
(-3.05) (-3.40)
College educated = 1
__
-0.235
*
__
-0.090
(-1.38) (-0.54)
Male = 1
__
-0.154
__
-0.104
(-1.28) (-0.86)
Married = 1
__
-0.331
***
__ -0.303
***
(-3.25) (-2.98)
Firm characteristics
Firm age -0.366
***
-0.340
***
-0.324
***
-0.288
***
(-10.39) (-9.47) (-9.68) (-8.46)
Cohort dummy (= 1 if
founding year = 2000)
0.130 0.10 3 0.095 0.069
(1.34) (1.04) (0.96) (0.69)
Controls for industry No Yes No Yes
Ave Log Likelihood
-0.239 -0.235 -0.241 -0.237
No. of observations
7,357 7,348 7,305 7,305
Sales in thousand DKK. z-scores are in parentheses. Significance levels: *** 0.01, ** 0.05, * 0.1.
29
Table 4
Firms Included in Subsample 2
Turnover by
2002
Turnover after
2002
No Turnover
Exited or acquired by 2002 ? × ×
Exited or acquired after 2002 ? × ?
Survived to 2004 ? × ?
Table 5
Survival, Exit, and Ownership Change after Founder Turnover
1999 Cohort
2000 Cohort
Turnover No Turnover
Turnover No Turnover
Total 142 701
232 1,274
Survived 50 545
82 912
Exited before 2002 56 0
70 0
Exited after 2002 36 156
80 362
Table 6
Descriptive Summary of Subsample 2
All No Turnover Turnover
Obs. 2,349 1,975 374
Survival in 2004 (%) 67.65 73.77 35.29
Founded in 1999 (%) 35.89 35.49 37.97
Initial sales (1,000 DKK) 2,008 1,962 2,253
Sales in 2004 (1,000 DKK) 5,168 4,929 7,639
Initial no. of full-time employees 1.68 1.62 2.00
No. of full-time employees in 2004 4.08 3.86 6.39
30
Table 7
Founder-CEO Replacement and Future Performance of Startups
Dept Var: log of sales in 2004
Surviving Firms
OLS
All Firms
Tobit
(1) (2) (3) (4) (5) (6)
Log.sale
0
0.437
***
0.453
***
__ 0.724
***
0.817
***
__
(18.8) (18.6) (5.92) (6.12)
Turnover 0.336
***
1.524
***
__ -5.00
***
-1.032 __
(4.15) (2.80) (-14.4) (-0.45)
Log.sale
0
*turnover __ -0.165
**
__ __ -0.552
*
__
(-2.21) (-1.73)
log.sale
0
percentile:
On this explanation pertaining to new firm performance and the replacement of founder ceos.
New Firm Performance and the
Replacement of Founder-CEOs
Jing Chen*
Copenhagen Business School
Peter Thompson**
Emory University
May 2013
We study the replacement of founder-CEOs by non-owner managers in a sample of 4,172
Danish single-founder startups. Founder replacement is more likely among the worst-
and best-performing firms. Firms with founder replacement were more likely to fail, but
the survivors grew considerably faster. We also analyze pre-founding and post-turnover
earnings and occupational choices of founders who rescinded operating control of their
firms. Founders with pre-founding income in the tails of the distribution are more likely
to be replaced. Our results are consistent with the notion that founder-CEO replacement
is driven in part by mismatches between business quality and founder ability.
JEL Classification Codes: D21, D22, J62, L26, M13
Keywords: Entrepreneurship, business creation, founder turnover, CEO turnover.
* CBS - Department of Innovation and Organizational Economics, Kilevej 14, 2000 Frederiksberg,
Denmark. Email: [email protected]. ** Goizueta Business School, Emory University, 1300 Clifton
Road, Atlanta, GA 30322, USA. Email: [email protected]. We appreciate helpful com-
ments from: Christian Catalini, Alberto Galasso, Mirjam Van Praag, and participants in the 2012
Summer Residence Week for Entrepreneurship Economists at Oxford University, the Roundtable
for Engineering Entrepreneurship Research at Georgia Tech, and in seminars at the University of
Amsterdam, the University of Lugano, Syracuse, Emory. Berkeley, and Carnegie Mellon.
1
1. Introduction
How does firm performance influence the likelihood that founder-CEOs of new businesses
cede operating control to a specialist manager? What implications does the departure of
the founder have for subsequent business performance? And what happens to founders
after they have left? This paper reports the results of an analysis of the causes and con-
sequences of founder-CEO replacement among a cohort of new businesses founded in
Denmark. Our empirical work uses the matched employer-employee database maintained
by Statistics Denmark, and draws samples from the universe of new businesses created in
Denmark during 1999 and 2000. These firms are generally small, they are almost invari-
ably not financed by venture capital, and they are generally not at risk of an IPO event.
We know surprisingly little about founder turnover
1
among such firms.
A growing literature has provided us with substantial insight into the causes and conse-
quences of founder turnover in other types of firms. This literature, which has focused on
high-growth and VC-backed firms
2
, finds that founders are most likely to be replaced by
a manager after a period of rapid expansion [Boecker and Karichalil (2002)], or upon at-
tainment of critical milestones such as completion of product development or securing
new rounds of outside funding [Wasserman (2003)]. More important, perhaps, is that
founder turnover is typically motivated by an expectation among stakeholders that the
firm is likely in the near future to undergo an episode of transformational growth. In-
deed, a significant portion of the literature explicitly samples on such expectations by
studying only firms that are preparing for an IPO [Nelson (2003), Jain and Tabak
(2008), Pollack, Fund and Baker (2009)].
The extant research lacks findings that we can confidently extrapolate to the firms that
1
We will use the term of convenience founder turnover interchangeably to describe an event
where the founder of a firm, who had been managing the firm as his or her primary occupation,
rescinds operating control while maintaining ownership, Replacements will be referred to for con-
venience as managers.
2
Of course, executive turnover in established, typically public, firms has been extensively studied
for many years [e.g., Hofer (1980), Chen and Hambrick (2012)]. There is also an active literature
on succession in family-owned businesses, but the concerns of this literature are rather different
from those in which executive control is passed outside the founder’s family. See Handler (1994)
for a review.
2
are the focus of the present paper. First, founders of VC-financed firms appear to be re-
placed at markedly higher rates than firms that are not subject to significant control by
outside equity investors [Hellman and Puri (2002), Boeker and Wiltbank (2005)]. Second,
few of the firms in our sample are ever likely to undergo transformations of the sort that
appear to motivate many founder turnover events at VC-financed firms. Third, VC-
financed firms are not random draws from the firm population. They are more likely to
grow substantially, they are better financed, and their founders and employees on aver-
age have more human capital. Correlations between variables estimated from a sample
drawn from the upper tail of the quality distribution may not hold in other segments of
the population, especially when there are possibilities of non-monotonic relationships in
the population as a whole.
It is well-known that VC-financed firms account for a tiny fraction of business launches
3
but they play a much larger role in economic growth, employment creation and innova-
tion. As a result, academic effort to understand the dynamics of control in VC-financed
firms is appropriately disproportionate to their numbers. Nonetheless, given their numer-
ical importance, we should also be concerned with the control dynamics of the typical
small business. Patterns of founder turnover among representative businesses can also
provide insight into the forces governing business transfers.
4
They are also key to under-
standing the expected opportunity costs of business creation, as well as the dual phe-
nomena of serial and portfolio entrepreneurs.
In this paper we study founder turnover in a sample of 4,172 Danish businesses created
in 1999 and 2000. To ensure that the departure of the founder indicates a meaningful
change in control, the sample consists only of firms that were created by a single found-
er, and where the founder worked as his primary job in the business. The firms and
founders we study represent all firms founded in Denmark that meet these criteria. Only
18 percent of them are classified as high-tech, while wholesale, retail, construction and
3
For example, in the United States, 626,771 new businesses were created in 2008, only 3,276 of
which received VC financing (Bureau of labor Statistics athttp://www.bls.gov/bdm
/entrepreneurship/bdm_chart1.htm; and NCVA (2009).
4
Founders may sell businesses because they are poorly matched to the needs of the business, or
because they have reasons to cash out their equity position. Looking at founder turnover in busi-
nesses that are not transferred, allows us to gain insights about the first of these two motivations.
3
manufacturing are well-represented; this is a much broader sample than has previously
been used to study founder turnover. Our data are drawn from Denmark’s national
matched employer-employee dataset, so we are also in a unique position to explore what
happens to the founders after they have left the firm, and who replaces them. The data
allow us to distinguish business turnover from founder turnover and, in this paper, we
focus on the causes and consequences of events in which the founder retires from active
management but continues to retain controlling ownership. That is, our focus is on CEO
succession in small firms, rather than business transfers.
To guide our empirical work, we develop a simple model of firm creation, performance
and founder turnover. The goal of founder turnover among VC-backed firms is generally
held to be resolving mismatches between the managerial skills of the founder and the
expected demands of the transforming enterprise. We also develop a framework based on
mismatches between business and founder, although our model focuses on current rather
than anticipated mismatches. We suppose that firm performance is a function of the
ability of the founder and the quality of the business idea, both of which are draws from
stable population distributions. Ability and quality are complements in production so
that there can arise mismatches of sufficient magnitude to justify replacing the founder
with an manager who is better suited to the needs of the business.
5
Our model predicts that founder turnover is most likely in the tails of the founder ability
distribution and in the tails of the firm performance distribution. We find evidence for
both predictions. Also consistent with the model, we find that when low- (high-) ability
founders are replaced, the incoming managers tend to have higher (lower) ability. The
model also predicts that, while good early performance is on average associated with
good future performance, this effect is stronger in firms that had no turnover; we see this
pattern in the data, too.
Our model says nothing about failure rates post-turnover. What we find in the data is
that, although turnover is associated with higher rates of growth among surviving firms,
5
Our theoretical framework is related to the models of business transfers developed by Holmes
and Schmitz (1990, 1995), and to the model in Braguinsky et al. (2012), which explores the effect
of the cost of evaluating ideas on founder turnover. We shall comment further on the relationship
between our theoretical framework and these models in the next section.
4
it is also associated with higher firm exit rates. This finding is consistent with Holmes
and Schmitz’ (1995) earlier implementation of the notion of founder-business mismatch-
ing.
The layout of the remainder of the paper is as follows. Section 2 develops our mismatch-
ing model of founder turnover, and compares it with related models. Section 3 describes
our data. In Section 4, we explore the relationship between firm performance and found-
er turnover. Section 5 focuses on turnover and ability. Section 6 concludes.
2. Theoretical Framework
There are two periods. In the first period, a founder with ability q has an idea of quality
q
1
. Ability is general and determines both the success with which the founder can man-
age a firm that implements the idea, and his earnings in alternative employment. We
assume that founder ability is innate, fixed over time and known to the founder. Idea
quality incorporates a variety of factors influencing performance, including the quality of
the underlying idea or technology, its adaptation and fit to the market, and the public’s
state of knowledge about the firm’s product. Some of these factors may be regarded as
fixed, while others evolve over time as the business model is refined. Upon entry, the
founder knows ?, but he only knows that q
1
is a random draw from the distribution
1
( | ). F q q The realization of q
1
is observed after entry. The business idea is refined and
adapted to the market, yielding a new quality q
2
in the second period, which is drawn
from
( )
2 1
| . G q q
In each period, t = 1, 2, output is given by
1
t t
t t
y q
a a
q
-
= , (1)
for some [0, 1].
t
a Î Although q and q are assumed to be complements in both periods,
we allow for the relative importance of ability and idea quality to vary from one period
to the next.
Profit in period 1 is given by
1 1
1
1 1
q w
a a
p q q
-
= - . (2)
The only cost is the opportunity cost of the founder’s time, which is assumed to be line-
5
arly increasing in ability. For concreteness, we shall suppose this alternative use of the
agent's time is in wage work.
At the end of the first period, q
2
is observed and a decision is then made whether to con-
tract with an outside manager to replace the founder, to continue without change of
leadership, or to exit. If a manager with ability q
?
is hired, she must be paid a wage wq
?
and a transition cost, c, must be paid. This cost reflects both direct costs of any disrup-
tion associated with a change in leadership, as well as any premium necessary to resolve
agency problems arising from the separation of ownership and control [Fama and Jensen
(1983), Jensen and Meckling (1976)].
6
The reward to recruiting a manager is that her
ability can be chosen optimally in light of the realization of q
2
, and the founder is re-
leased to earn wq elsewhere while remaining as the residual claimant on the firm’s earn-
ings. The founder is replaced by a manager if
( )
2 2 2 2
1 1
2 2
arg max q w c q w
a a a a
q
q q q q
- -
- - > -
?
? ?
, (3)
and if the term on the left hand side of (3) is positive. The profit-maximizing ability of
the manager is given by
( )
2
1/(1 )
2 2
/ w q
a
q a
-
=
?
, (4)
so maximized profits in period 2 are
( )
2 2
1 *
2 2 2
max , q w Aq c
a a
p q q
-
= - - , (5)
where
2 2
/(1 )
2 2
(1 )( / ) . A w
a a
a a
-
= - If
*
2
0, p < the firm exits.
The two terms on the RHS of (5) are plotted separately in Figure 1 as functions of q
2
and holding q constant. The concave function
2 2
( , ) q p q indicates profits when the founder
continues, and the straight line
2 2
( ) q p? depicts profits when the founder is replaced. In
the case illustrated in the figure, there are four distinct regions defined by the boundary
values 0 :
E
q q q < £ £ if
2
[0, ],
E
q q Î the firm exits at the end of period 1; if
2
[ , ]
E
q q q Î
or if
2
, q q ³
the founder is replaced as CEO; and if
2
( , ), q q q Î the founder continues to
6
He (2008), for example, documents that founders have lower incentive compensation and lower
total compensation than managers.
6
operate the firm. When
2
[ , ]
E
q q q Î , the founder is replaced because the opportunity cost
of his time does not justify continued commitment to the business model; he is replaced
by a manager who is less able than the founder. In contrast, when
2
q q ³ the founder is
replaced by manager with higher ability.
Figure 1 also illustrates the effect of an increase in the founder’s ability, from q to . q
¢
This reduces the profitability of the founder continuing with the firm when the quality of
the idea is low, and increases it when the idea is good. It is easy to verify that the inter-
section of
2 2
( , ) q p q and
2 2
( , ) q p q
¢
lies between q and , q and hence that the effect of an
increase in q is to raise both q and . q The interval [ , ]
E
q q will vanish if the founder’s
ability declines sufficiently so that .
E
q q < There are, therefore, two distinct cases. In
one case, when founders have limited ability, the period 2 outcomes fall into three dis-
tinct groups, with replacement of founders occurring only at higher values of q. In the
second case, founders are replaced in firms with business models drawn from the tails of
the quality distribution among all surviving firms. Which of these cases arises depends
on the founder’s ability, as well as other parameters of the model. For example, an in-
crease in the transition cost, c, shifts
2 2
( ) q p? down, which may eliminate the interval
[ , ];
E
q q
that is, if the increase in c is large enough, replacement of founders occurs only
at high values of q
2
and in each case the replacement has higher ability than the founder.
Clearly, the minimum value of c that eliminates the interval [ , ]
E
q q
depends on the
founder’s ability.
7
Figure 2 provides a more complete depiction of the model’s implications. Firms that are
closed down are those with low-quality ideas. If the founder does not want to continue to
operate the firm, continuation requires a new CEO. However, payment of the transition
cost cannot be justified in low-quality firms. The boundary separating firms that are dis-
continued from those that are continued with the founder at the helm is given by the
pairs
2
{ , } q q satisfying
2
1/(1 )
2
, q w
a
q
-
= showing that low-ability founders may continue to
operate a business that a high-ability founder would close down. Founder turnover in
7
When 0, c > it is always the case that . q q < In the limiting case of 0, c = the function
2 2
( ) q p?
is then tangential to
2 2
( , ) q p q for any value of q. and . q q ? In this case, every founder is re-
placed, because it is always efficient to secure a perfect match between managerial ability and
idea quality.
7
continuing firms occurs in two distinct groups. First, there is a group of firms with high-
quality ideas but that were founded by agents of sufficiently low ability to merit re-
placement with a more able manager. The boundary between this group and the group
of founders that continue to operate their own firm is defined by the function
( ) q q that
provides the larger of the solutions to the equation
2 2
1
2 2
. Aq c q w
a a
q q
-
- = - As the tran-
sition cost rises, ( ) q q unambiguously shifts upwards. The second group consists of firms
with able founders but whose firms are of middling quality. The quality of the idea is
high enough to prefer continuation over exit, but not high enough to merit continuation
of the founder. The replacement manager has lower ability than the founder. The
boundary between this group and those that exit is a horizontal line at
1
2
. q cA
-
= The
boundary between this group and the group of founders that continue to operate their
own firm is given by the function ( ) q q that provides the smaller of the solutions to
2 2
1
2 2
Aq c q w
a a
q q
-
- = - . An increase in the transition cost shifts ( ) q q downwards. Figure
2 shows that ( ) q q and ( ) q q are both positively sloped, which is consistent with the dis-
cussion around Figure 1.
2.1 Baseline simulations
The empirical implications of our theoretical framework depend critically on the joint
distribution of q
2
and q (and, by virtue of the correlation between q
1
and q
2
, on the joint
distribution of initial idea quality and founder ability). For example, the implication that
founder turnover occurs both when q
2
is high (relative to q) and when it is low requires
that we observe in any sample instances where contrasting values of q
2
and q coexist.
Similarly, the transition cost cannot be so high that it eliminates the interval [ , ].
E
q q Be-
cause this may not be the case, and despite the simplicity of the theoretical framework,
there are no general empirical predictions about the relationships between performance
in the two periods, founder ability and turnover. We will therefore make use of some
numerical simulations to explore the possibilities of the model.
However, it is useful to consider some reasonable ways in which we can limit the varied
possibilities. We do so in two ways. The first imposes a constraint on the transition cost,
c, such that the model predicts an aggregate rate of turnover that approximates the rate
(about 12 percent) that we observe in our data. The second makes use of a simple free-
8
entry condition to help us constrain the relationship between ability and the quality of
the idea. We want to admit entrepreneurs of all abilities. This will be the case if all
agents, regardless of ability, are indifferent between entrepreneurship and wage work in
period 1. If the quality of ideas and agent ability are independent of each other, then
high-ability agents, who bear a higher opportunity cost of becoming an entrepreneur, will
find business creation less attractive than low-ability agents. For our purposes, it suffices
to suppose that ability and agents care only about expected first-period returns. In the
simulations to follow we therefore impose the constraint
8
1 1
1 1
1
, E q w
a a
q
- -
é ù
=
ê ú
ë û
(6)
which ensures that all agents, regardless of ability, are indifferent between creating a
business and wage employment.
In our baseline model, we assume that in both periods ability and the quality of the ide-
as are equally important. That is, we set
1 2
0.5. a a = = We assume that
1
1 a
q
-
has a
lognormal distribution, with unit mean and standard deviation of 0.5. Consistent with
(6), we then assume that, for agent i,
1
1
1i
q
a -
is a draw from a lognormal distribution with
mean
1
1
.
i
w
a
q
-
Finally, consistent with the assumption that
2 1
( | ) G q q is decreasing in q
1
,
we assume that
2
1
2i
q
a -
is a draw from a lognormal distribution with mean
1
1
1
.
i
q
a -
The
wage, w, is normalized to 1. Finally, we set 0.35, c = so that our baseline produces an
aggregate rate of founder turnover of about 12 percent, consistent with evidence we shall
show later.
Figure 3, which plots 5,000 random draws using the baseline parameters, depicts out-
comes that occupy all four segments of Figure 2. Although founders of exiting firms on
average have relatively low ability, this is incidental and driven by the correlation be-
tween idea quality and founder ability. Indeed, once one conditions on idea quality, low-
ability founders are less likely to close down the firm. The simulations yield observations
8
A forward-looking agent will also consider the option value that entering gives by providing
choices in the second period. In period 2, the founder may choose to remain in control of the
business, hire a manager if doing so increases profits, or exit. Even if expected second-period earn-
ings from continuation are the same as expected first-period earnings, the replacement and exit
options imply for a forward looking agent that
1 1
1
1 1
. E q w
a a
q
- -
<
é ù
ê ú
ë û
9
in the two sets of firms with sufficiently good ideas but poor matches between idea and
founder ability to merit replacement with a more able manager. Although founders of all
abilities may suffer a mismatch sufficiently severe to induce their replacement with a
manager, it is clear that the likelihood of replacement is greater in the tails of the ability
distribution.
Although in our empirical work, we will use pre-founding earnings as a crude proxy for
founder ability, we will conduct much of our analysis using revenues as a measure of firm
performance. However, because founder turnover occurs as a result of a mismatch be-
tween ability and business quality, it is not obvious that we should also expect to see
more frequent founder turnover in the tails of the firm performance distribution. To see
whether this is the case in our baseline simulations, Figure 4 plots first- and second-
period revenues of surviving firms by founder status. The two distinct groups of founders
that experience turnover are also clearly evident when we look at first-period perfor-
mance instead of founder ability, and they are indeed drawn more frequently from the
tails of the performance distribution. There is a group with above-average performance
in the first-period whose founders are replaced with less able managers. There is also a
group of firms whose founders are replaced with more able managers. This group consists
of firms with low-ability founders, but their poor ability is offset by ideas that in the
second-period are of sufficiently high quality to justify payment of the transition cost
(firms with the worst ideas in the second period are closed down). Although t hese firms
have a mean first-period performance that is not much different from the population av-
erage, they are overrepresented in the lower tail of the first-period performance distribu-
tion.
A distinctive feature of Figure 4 concerns the effect that founder turnover has on the
relationship between first-period performance and second-period performance. Perfor-
mance among the group of surviving firms with no turnover tends to be more consistent
from one period to the next than in the case of firms that experienced turnover. Firms in
which founders are replaced by less-able managers see on average a decline in their per-
formance, while the arrival of a more capable manager induces on average an improve-
ment in performance. Among surviving firms with no turnover, therefore, the relation-
ship between first- and second-period performance is strongly positive. In contrast, the
10
marginal ‘effect’ of first-period performance on second-period performance among firms
that experienced turnover is much weaker in absolute value, and may even be negative.
2.2 Alternative Scenarios
Do these implications of the model persist in the face of significant changes in parameter
values? Figures 5 through 7, which replicate Figure 4 for five alternative scenarios, sug-
gest that the possibilities depicted so far are, in fact, quite obust. In Figure 5, the rela-
tive importance of idea quality and ability is altered across the two periods. In the upper
panel, ability is most important in the first period
1 2
( 0.8, 0.2), a a = = while in the low-
er panel it is more important in the second
1 2
( 0.2, 0.8). a a = = The results reported in
the baseline are not sensitive to these significant changes in the production technology.
Figure 6 sharply alters the correlations between idea quality and ability. In the upper
panel, the standard deviation of the distribution of
2
q is reduced by 80 percent, to 0.1,
while maintaining its mean equal to the realized value of
1
q ; the lower panel similarly
increases the correlation between q and q
1
by reducing the variance of the distribution of
1
. q
The three groups of surviving firms become even more distinct than in the baseline,
but the original pattern is preserved. Finally, in Figure 7 the cost of transitioning to a
manager is reduced sharply, so that the fraction of firms that experience turnover is in-
creased from about 12 percent to 35 percent. Again, the baseline results are robust to
this change.
9
2.3 Related models
There are, of course, different ways one may specify a model of mismatching. In this
subsection we briefly compare our theoretical framework with three related models. The
best known of these is Holmes and Schmitz (1990), in which agents vary in their ability
to produce ideas, although all are equally gifted at managing. Agents with low innova-
tive ability prefer to buy and manage existing businesses, while agents with high ability
prefer to specialize in business creation (selling or closing each business they create). The
9
Our data do not afford us an opportunity to explore the effects of changes in the transition cost.
However, Braguinsky et al. (2012) explore the effects of a likely decline in the cost of evaluating
ideas in Japanese biotech firms, brought about by institutional reform. They find that a reduction
in evaluation cost was associated with a large increase in the rate of founder turnover.
11
outcomes for founders with intermediate ability depend on the quality of the business
idea: they will close the worst businesses and manage the best. A key determinant of the
boundaries between the choices is the cost of transferring a business. Figure 8 illustrates.
Clearly, only the better ideas produced by the more able innovators experience founder
turnover. As managerial ability is fixed, the model predicts that business turnover is as-
sociated with high initial and subsequent firm performance, but it does not predict any
change in performance after turnover.
In Braguinsky et al. (2012), entrepreneurs with known ability found businesses with un-
known quality. As in our model, business quality and founder ability are complements in
production. The quality remains unknown unless the founder chooses to pay an evalua-
tion cost, c. High-ability agents know that they are at risk of wasting their time with a
poor idea, so they choose to evaluate the idea, subsequently closing down businesses that
are not good enough. Low-ability agents know that they may not be good enough man-
gers if their business quality is good; they also pay the evaluation cost and either close
their business or transfer control to a professional manager. Agents with intermediate
ability do not pay the evaluation cost; they continue to manage a business of unknown
quality (see Figure 9). The model unambiguously predicts that only the better businesses
founded by low-ability managers are at risk of founder turnover, and that turnover elic-
its an improvement in performance. Auxiliary assumptions of the model associate better
initial firm performance (i.e., the combination of business quality and founder ability)
with turnover.
Holmes and Schmitz (1995) do not have explicit variations in skill. In this model, output
is given by
t t t
y q m = + , where q is business quality and m is match quality. Both q and
m evolve over time, each consisting of permanent and transitory components. The per-
manent component of business quality, b, is fixed when a business is first founded, while
the permanent component of match quality, m, is fixed each time a new manager takes
control of the business. Founders (and subsequent managers) who experience a sufficient
decline in match quality dispose of their businesses, either by closing them (if business
quality is sufficiently low) or by transferring control to a new manager (if quality is suf-
ficiently high). Figure 10 illustrates, with a sample path depicting one possible evolution
of business and match qualities. A business begins at point a, and suffers a steady de-
12
cline in match quality. At b the business quality is sufficiently high to enable a sale. The
new manager begins with the same business quality but a better match quality, at c, but
then suffers a decline in both business quality and match quality until exit at d. Holmes
and Schmitz show that the boundary between closing and selling a business shifts down
when the permanent business quality, b, increases. Hence, better quality businesses are
more likely to be sold. However the match quality may occasionally be worse immediate-
ly after a sale, so despite better average performance after business turnover, there can
be an increased risk of quick failure.
10
It is not possible to relate founder ability to the
likelihood of business turnover in this model unless we impose some functional relation-
ship between ability and the pair { , }. m q Doing so, however, would be unfair to the
Holmes and Schmitz model, although it strikes us that their model could be made con-
sistent with turnover occurring in the tails of the firm performance distribution.
3. Data and Sample Construction
Our empirical analysis focuses on all new business founded in Denmark in 1999 and
2000. We construct our samples out of three databases maintained by Statistics Den-
mark: the Entrepreneur Database, the Firm Database, and the Integrated Database for
Labor Market Research (IDA).
The Entrepreneur Database is the primary source for identifying new businesses (part-
nerships or sole proprietorships) registered in Denmark each year, and provides unique
identifiers for each firm, each plant, and one individual identified in the registration doc-
uments. The Entrepreneur Database spans the period 1996 to 2006. However, accounting
information is not collected for all industries until 1999, so this is the earliest cohort we
study.
There are several limitations to the Entrepreneur Database. First, since only one person
associated with the new business is recorded, the database provides no information for
identifying possible co-founders. Second, some of those who appear in the database may
not be the “real” founder. For example, there are cases in which founders used their
10
Figure 5 depicts the case where the m is common across matches. If m is lower after business
transfer, the boundaries shift to the right, increasing the risk of business failure.
13
spouse’s name to register a business. Third, it is unclear from the Entrepreneur Database
whether or not the identified entrepreneur actually works at their startup. Although it is
possible that entrepreneurs do not operate businesses themselves, this study focuses on
those who were actually working for their own businesses. In order to identify these en-
trepreneurs and possible co-founders, the data obtained from the Entrepreneur Database
are combined with information provided in the Firm Database.
The Firm Database consists of annual employment information on all workers (full-time
and part-time) at all firms operating in Denmark. The variable of particular interest is
their position in the firm, through which active founders of each new business are identi-
fied in the following way. First, founders are those whose positions are classified as self-
employed, employers, or business tax payers. Second, in businesses with fewer than four
employees, all people whose positions are top managers are defined as founders.
11
Third,
if nobody in a business with fewer than four employees is identified as a founder based
on the previous two criteria, all employees are considered as cofounders. The fourth
group of founders consists of those who registered the business and were working for the
business as their primary occupation, but did not meet the first three criteria for being a
founder.
Because we are interested in founder turnover as a mechanism for transfer of control, we
restrict our sample to firms in which there appears to be just a single founder. This leads
us, in particular, to remove from the sample firms with two to four employees where we
identified more than one top manager. We link the remaining businesses to the IDA,
where more detailed information such as industry classification and employee de-
mographics is available. The IDA is an employer-employee matched database, which
provides mainly employment information at firm, establishment, and employee levels.
The IDA enables us to track over time the turnover of founders, changes in firm status,
and occupational choices of individuals each year until 2008. However, because of incom-
plete accounting and earnings data in the most recent years, we will follow firms only
through 2004 and individuals through 2005.
Firm identifiers in the IDA are attached to a specific owner, so they change whenever
11
This way to identify founders was first used by Sørensen (2007).
14
there is a change of ownership. This design makes it possible to distinguish three firm-
level events in each year. Survival is assumed if the same firm identifier from the previ-
ous year appears in the current period. An ownership change occurs if the same estab-
lishments of the firm from the previous year are identified in the current period except
that they are associated with a different firm identifier. A business exit is identified if all
the establishments of the firm can no longer be found in the data.
In our analysis, the original founder of a startup must meet the following criteria: (1) He
or she formed a startup in 1999 or 2000 as a sole proprietor; (2) The person was current-
ly working at the new venture; and (3) The new business could be identified in the Firm
Database and the IDA database, indicating that at least one person was working in the
business as his or her primary job.
12
Using the unique person and firm identifiers, we can
track these founders’ employment in the following year to identify whether or not the
founder was still working at the startup, and whether he was still an employer. A found-
er was staying at the startup if he could be identified in the firm in the following year,
whether as an employee or as an employer. Otherwise, the founder is assumed to have
left the original firm because of turnover, business exit, or ownership change. In a few
cases, the founder might stay at the startup after a change of ownership, but in each of
these cases a new firm identifier is assigned to the startup.
Table 1 provides tabulations of changes in the status of startups and their founders from
the founding year to 2005. Four outcomes are possible in each year: the firm exits, the
firm is acquired or the firm continues, either with or without the founder remaining in
operational control. Of 4,172 firms in the sample, a total of 533 (12.8 percent) of them
eventually experienced founder turnover. Turnover was twice as common as ownership
change (6.5 percent), but much less common than firm exit (50.6 percent). The hazard of
turnover declines with firm age, declining from 5.11 percent in the first year, to 2.61 per-
cent at age five.
13
12
This person must include the founder, so our sample includes single-person firms. We have
repeated our analyses after restricting the sample to including firms with at least two individuals.
The results, available from the authors, are very similar to those reported here.
13
The declining hazard of turnover is consistent with gradual learning about match quality [Jo-
vanovic (1979)], which we have not attempted to model.
15
4. Firm Performance and Founder Turnover
In this section, we evaluate the relationships between startup performance and subse-
quent rates of founder turnover, and between founder turnover and subsequent firm per-
formance.
4.1 Startup performance and the subsequent likelihood of founder turnover
To assess the effect of initial performance on subsequent rates of founder turnover, we
remove from the baseline sample 2,388 startups (918 from the 1999 cohort, and 1,470
from the 2000 cohort) that exited or were acquired before any occurrence of a founder
turnover during the observation window between the founding year and 2005. Table 2
provides descriptive summary statistics for the remaining 1,784 startups and their found-
ers. There are no large differences in means between founders that continued and found-
ers who were replaced. However, the latter group are somewhat younger and less likely
to have had vocational training; they are also more likely to come from the tails of the
earning distribution (either less than high school or at least college); they are also some-
what less likely to have founded businesses in construction, and more likely to be en-
gaged in high-tech activities.
We first estimate the following logit model,
2
1 1 2 1 , 1 ,
ln ( )
1
t
t t i i t i t
t
p
logsale logsale X Z
p
a b b g d e
- - -
æ ö
÷ ç
÷
ç = + + + + +
÷
ç
÷
÷ ç -
è ø
(7)
where p
t
is the probability that the founder leaves a continuing firm in period t,
is a
vector of time-invariant variables related to founder demographics (gender, marital sta-
tus, age, and education) and startup founding conditions (founding year and industry),
and
,
is a vector of potentially time-varying variables, such as firm age, founder age,
marital status, and highest completed education.
The key independent variable is the logarithm of sales in the previous period, along with
its quadratic to allow for the possibility of a non-monotonic effect as predicted in Section
2.
14
Columns (1) and (2) of Table 3 present the results. In column (1), we include con-
14
For six observations with zero sales, we added one DKK prior to taking the logarithm.
16
trols only for firm age and founding year. The negative coefficient on firm age reflects
the declining hazard of founder turnover already noted in the summary statistics, while
the cohort dummy is insignificant. In column (2) we add some founder characteristics.
The probability of turnover declines with founder age, it is lower for founders with col-
lege education, and it is lower for men and married founders. Both columns show a U-
shaped relationship between the log of sales in the previous period and the likelihood of
founder turnover in the current period, indicating that a founder is more likely to rescind
operating control of the firm if it is located the tails of the firm performance distribution
among all surviving firms. Columns (3) and (4) replace sales in the previous period with
sales in the startups’ first year of operation. The results continue to show the U-shaped
relationship, and the same effects of founder characteristics and firm age on the likeli-
hood of turnover. The minimum of the U-shaped relationship between prior performance
and the probability of turnover occurs at revenue levels of DKK580,000 to DKK800,000,
equal to 15 to 37 percent of the sample mean. This is also well within the sample range.
Consistent with the predictions of our simple mismatching model, therefore, we find that
turnover is more common in the tails of the observed earnings distribution than in the
middle.
4.2 Founder turnover and the subsequent performance of startups
To examine the relationship between founder-CEO replacement and subsequent firm per-
formance, we use a subset of startups in the baseline sample and limit the observation
window to the interval between the founding year and 2004. The startups included in
this subsample meet two criteria. First, we eliminate firms in which the founder was re-
placed after 2002. That is, firms included in the sample either experienced founder turn-
over by 2002, or never had founder turnover during the observation period. Second, we
remove startups that failed or were acquired between the initial founding year and 2002
but prior to any observed founder turnover event. However, we retain in the subsample
startups that experienced founder turnover and then exited or were acquired. Table 4
illustrates how we construct the subsample.
Starting with the 1,588 startups in the 1999 cohort, we removed 709 firms that exited or
went through ownership change by the end of 2002 and before any occurrence of founder
17
turnover, and 36 firms that experienced founder turnover in 2003 or 2004. For the 2,584
startups of the 2000 cohort, we removed 995 and then 83 of them for the same reasons.
We are left with a sample of 2,349 startups, 843 of which come from the 1999 cohort,
and 1,506 from the 2000 cohort.
Table 5 summarizes the number of startups that survived, exited, or experienced owner-
ship change during the observation period. Among the 843 startups founded in 1999, 142
of them had founder turnover before 2002. Among them, 50 survived to 2004, 56 exited
or changed owners before 2002 but after founder turnover, and 36 exited after 2002.
Among the 1,506 startups founded in 2000, 232 had founder turnover before 2002. Of
these, 82 survived the entire observation period, 70 exited or changed owners before
2002, and 80 exited after 2002.
Table 6 provides the descriptive summary of this subsample (the distributions of firms in
each column across industries is very similar to those shown in Table 2 and are not re-
ported). The summary statistics show two interesting contrasts between firms that expe-
rienced turnover and those that did not. Turnover by 2002 is strongly associated with
lower odds of survival to 2004. Only 35 percent of firms with turnover survived, com-
pared with 74 percent of firms with no turnover. However, conditional on survival, the
growth rate of sales and of employment is much greater among firm that experienced
turnover. For example although there is little difference between the two groups in ini-
tial sales revenue, average 2004 sales are 55 percent higher among the turnover group.
We estimate the following model
,2004 1 ,0 2
ln( ) ln( )
i i i
sales sales turn a b b = + +
3 ,0 ,2004
ln( )
i i i i
sales turn X b g e + ´ + + , (8)
where turn
i
equals one if the startup experienced founder turnover between its founding
year and 2002, and zero otherwise, and the vector
includes controls for cohort and in-
dustry.
Table 7 presents OLS estimates of (8). In the first three columns, we focus on startups
that survived until 2004. Column (1) shows that both initial sales and founder turnover
have a positive impact on startups’ sales in 2004. Interacting the two factors in column
18
(2), we further find that the marginal effect of founder turnover on future sales is de-
creasing with the initial performance of startups (or, equivalently, that the marginal ef-
fect of initial performance is lower for firms that had turnover). This is consistent with
the model in Section 2. However, this positive effect of turnover is reduced to zero only
at about the 95
th
percentile in the distribution of initial sales. In column (3), we replace
the log of initial sales with three categorical variables that indicate membership in the
bottom quartile, the upper quartile, or the middle of the distribution of the log of initial
sales. The result confirms a positive correlation between initial and future performance.
Meanwhile, founder turnover has a positive association with a startup’s future perfor-
mance for the first two categories, but no association for the third category.
In columns (4) to (6), we estimate Tobit regressions including all firms in the subsample.
For startups that exited or went through ownership change before 2004, we recode the
logarithm of their sales as zero [cf. Dunne, Roberts and Samuelson (1989)], and make
zero our threshold for inclusion in the linear portion of the model. The inclusion of exit-
ing firms changes the results. Turnover is now associated with lower future performance
for all firms, although as in the first three columns, the effect of turnover is worse for
firms with good initial performance.
The contrast between the OLS regressions for surviving firms and the Tobit estimates
for the full sample suggests that turnover has disparate effects on survival and perfor-
mance conditional on survival. To confirm this, in Table 8 we report the result of logit
regressions where the dependent variable equals one if the firm survived to 2004 and zero
otherwise. Column (1), which include all firms in our sample, clearly indicates lower sur-
vival rates for firms that experienced founder turnover, regardless of initial size. There is,
however, a potential endogeneity problem in these two columns. It is possible the found-
ers leave firms because they are already in the process of exiting, but our data do not
record exits until after they have left. In column (2), we therefore remove from the sam-
ple 100 firms that had failed after founder departure but by 2002; these firms must have
failed very soon after the founder left. Doing so does not alter the evidence that turnover
events are associated with higher failure rates.
This section has generated three central findings. First, founder turnover is more likely
when firm performance is in the tails of the revenue distribution. Second, turnover is as-
19
sociated with higher future sales among surviving firms. Third, turnover is associated
with higher failure rates. We will draw some conclusions from these findings in Section 6.
5. Ability and Founder Turnover
In this section, we evaluate the relationships between founder ability and the rate of
founder turnover, as well as the relationship between the ability of founders and the
managers that replace them. To measure ability, we use average earnings during 1996-
1998. This measure predates the founding of the firm, so it is not contaminated by mis-
matches between ability and business quality. However, it remains a crude proxy. In
particular, founders may have elected to establish businesses because of mismatches in
prior employment, as suggested by Åstebro, Chen and Thompson (2011).
5.1 Founder ability and turnover
The model predicts that departing founders tend to come from the tails of the ability
distribution. To examine this prediction in our sample, we collected data on average
gross income between 1996 and 1998 for 1,784 founders. Figure 11 plots the income dis-
tributions separately for 533 founders that had given up control of their firm by 2005,
and 1,251 founders that did not. The distribution for founders that experienced turnover
exhibits more skew than those that did not, and has fatter tails.
We also ran “pre-program” quantile regressions to examine the relationship between
founder ability and turnover. The regressions take the form
0
0 1 it it i t it
AveInc Turnover X Year a a b g e = + + + + , (9)
where
0
it
AveInc is average gross income for 1996-8, and
are demographic controls, in-
cluding age, gender, college degree, and average tenure in pre-founding wage employ-
ment. We estimate (9) at various percentiles of the pre-founding income. The model sug-
gests that the coefficient on Turnover, a
1
, will be negative at low levels of income and
positive at high levels, reflecting a U-shaped relationship between turnover and ability.
As Figure 12 illustrates, this is exactly what we find, whether or not we include the de-
mographic controls in the quantile regressions: Up to the 50
th
percentile, departing
20
founders tended to have lower pre-founding income, while at the highest percentiles they
tended to have higher income.
15
5.2 Founder and manager abilities
The model also predicts two types of mismatching and subsequently two kinds of succes-
sions: high (low) ability founders replaced by low (high) ability new managers. To exam-
ine whether these two kinds of turnover exist in the data, we investigate the turnover
events that happened in 2001 among the 2000 cohort of founders. As before, we use av-
erage gross income from 1996 to 1998 as the measure of individual ability.
It is quite difficult and labor intensive to match incoming replacements with founder de-
partures, especially when the founder is replaced by an incumbent employee. We there-
fore look at a subset of our observations, consisting of 69 startups where founders trans-
ferred operating control in 2001, and at least one outsider joined the business as employ-
er or top manager. In 41 startups, we identify a single individual that joined the firm as
manager; in 20 cases, two outsiders joined in top managements positions, while in the
remaining eight three outsiders joined. If more than one individual joined the business as
part of a new management team members, we select the one with the highest average
prior income as the replacement of the original founder. Figure 13 plots 1996-8 average
income for departing founders against the 1996-8 average income for the incoming man-
ager. As anticipated by the model, most observations related to founders with low prior
income are located above the 45 degree line. We do not have many observations on high-
income founders, but those we have are located well below the 45 degree line, indicating
that they were replaced by managers with lower ability.
5.3 What happens to replaced founders?
What happens to the founders that are replaced? Among all founders, first-period per-
formance is positively correlated with ability. However, some founders are replaced be-
cause they have low ability but good ideas, so it is not clear that the positive correlation
between firm performance and ability survives among the subset of founders that are
15
The regression output on which Figure 12 is based is available from the authors.
21
replaced. Because future earnings depend on ability, the relationship between first-period
performance and future earnings is also a priori unclear. However, as Figure 14 indicates,
the positive relationship survives in our baseline analysis among founders selected for
replacement.
16
As future earnings are expected to increase in ability (the model assumes
a linear relationship), Figure 14 also suggests a positive relationship between the firm’s
pre-turnover revenues and post-turnover founder earnings.
The pattern observed in Figure 14 is also evident in the data. We constructed a subsam-
ple that consists of all the founders that rescinded operational control of their businesses,
and tracked their income for three years after turnover occurred. We estimate a linear
model, where the dependent variable is the log of gross income averaged over three years
after the founder left his startup at time t, and the key independent variable is the log of
the startup’s sales before turnover. In column (1) of Table 9, we use the firm’s initial
sales, while in column (2) we use sales in the year prior to turnover. Clearly, after con-
trolling for their characteristics, founders who achieved higher sales in the first year of
their startups also have higher average gross income in the three years after CEO turno-
ver. There is, however also strong evidence of mean reversion: each ten percent increase
in previous firm revenues is associated with only about a one percent increase in subse-
quent founder earnings.
We also explore whether success in the firm prior to turnover is associated with the like-
lihood of creating another business. Recall that our model assumes a correlation between
idea quality and founder ability such that all agents are indifferent between founding a
business and working as an employee; this is not a central assumption for the model but,
if it is reasonable, it implies that good performance in one business does not raise the
probability of new business creation. We estimate a logit model where the dependent
variable equals one if the founder was an employer in the year after he left his startup,
and the key regressor is pre-turnover sales. Columns (1) and (2) of Table 10 reveal no
significant relationship between a startup’s initial performance and the likelihood of the
founder being an employer again in the period after turnover. Adding a quadratic term
16
In scatter plots not shown, we find this relationship holds also for the parameter values used in
Figures 5 through 7. In several cases the effect is, as in Figure 5, much weaker among founders
that are replaced with more able CEOs.
22
in column (2) shows a non-linear relationship, suggesting that founders from both the
highest and lowest-performing businesses are more likely to be self-employed again after
turnover.
17
However, as shown in column (4) this relationship disappears if we look at a
startup’s performance immediately before founder turnover.
18
6. Conclusions
In this paper we analyzed the relationships between founder turnover, firm performance,
and founder ability and occupational choices in a sample of 4,172 firms drawn from the
Danish matched employer-employee dataset. Our interest was in studying the causes and
consequences of changing operating control of young firms, so we restricted our attention
to firms that had a single founder. Our sample comprises the universe of Danish firms
founded in 1999 or 2000 that met this criterion.
Our key findings were as follows. First, founder turnover among the firms in our sample
were more likely among the most and least able founders, and among the worst- and
best-performing firms. Second, turnover was not unambiguously associated with better
subsequent performance; on the one hand, firms that replaced the founder with a man-
ager were more likely to fail, while on the other hand the surviving firms among them
grew faster. We also found that the marginal ‘effect’ of initial sales on future sales
among surviving firms was much stronger among firms that did not experience founder
turnover. This finding is consistent with the evidence we found that low- (high-) ability
founders tend to be replaced with managers who have higher (lower) ability. We also
analyzed subsequent earnings and occupational choices of founders who rescinded operat-
ing control of their firms. Although subsequent founder income was unsurprisingly found
to be increasing in the performance of their firms, those that left good firms were no
17
This is consistent with the model of labor-market mismatching in Astebro, Chen and Thomp-
son (2011), in which entrants into entrepreneurship are more likely to come from the tails of the
ability and earnings distributions.
18
We also examined occupational choice for three years after founders rescinded control of their
businesses. OLS regressions in which the dependent variable is the total number of years that
founders were an employer during this three-year period also yielded no significant relationship
between the dependent variable and startups’ performance in the initial year or in the year im-
mediately prior to founder turnover.
23
more likely than others to found another business.
Our results are consistent with the notion that mismatches between business quality and
founder ability matter, although not necessarily in the precise form of the theoretical
framework we developed in this paper. Åstebro, Chen and Thompson (2011) assumed
that mismatches between an agent’s ability and the quality of the firm that employed
him might drive him into self-employment or entrepreneurship. That model, like the
theoretical framework developed in this paper, predicted that mismatches were most
likely in the tails of the ability distribution. However, it assumed that business creation
serves the purpose of resolving mismatches. The present paper suggests that mismatches
may be important also after business creation.
Our model predicts that turnover occurs more frequently in the tails of the initial per-
formance distribution, that the marginal ‘effect’ of initial performance on future perfor-
mance is greater among non-turnover firms, and it also predicts the empirical results
about the future earnings and occupational choices of departing founders. However, the
model has no uncertainty at the time of founder-CEO replacement, and hence has noth-
ing to say about the much higher failure rates of firms that experienced turnover. The
disparate empirical results about firm failure and the growth of surviving firms recall
Holmes and Schmitz’ (1995) model of business turnover. In their model, firm perfor-
mance depends on the sum of business quality and match quality. In contrast to the pre-
sent paper, there is no variation in ability so all agents have the same outside option,
and new CEOs only observe the quality of their match with a business after they have
purchased the firm. Their model predicts that the best business ideas are most likely to
be transferred to new owners, so among surviving firms business transfers are on average
associated with good performance. However, because transfers may create a poor match,
businesses that are sold are at greater risk of subsequent exit.
19
Our results on growth
and survival of firms resonate with these predictions of their model.
Both our theoretical framework and our empirical analysis have focused on the turnover
19
Pollock, Fund and Baker (2009) find that VC-financed firms that are preparing for an IPO are
more likely to experience founder turnover if their firm operates in an uncertain industry envi-
ronment. This could also explain our results on survival, although our regressions do have indus-
try fixed effects.
24
of operating control while the founder retains ownership. In our sample, turnover of op-
erating control is more common than business turnover, but both are important features
of the data. Our paper leaves unanswered the interesting question of how owners decide
between selling a business and hiring a CEO. The Danish matched employer-employee
dataset may also be rich enough to allow us to compare some characteristics of outgoing
founders and incoming CEOs, and assess whether founder-CEO pairs differ from each
other in ways consistent with mismatching models. These are questions we intend to ad-
dress in future work.
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TABLE 1
Status Change of Startups by Year
Years after Founding
1 2 3 4 5 6*
Count
Surviving: founder stays 2,996 2,247 1,829 1,535 1,321
Surviving: founder turnover 213 127 82 54 40
Firm exit 898 550 291 193 140
Firm acquired 65 72 45 47 34
Total 4,172 2,996 2,247 1,829 1,535
Hazards (percentage)
Surviving: founder stay 71.81 75.00 81.40 83.93 86.06 87.16
Surviving: founder turnover 5.11 4.24 3.65 2.95 2.61 3.12
Firm exit 21.52 18.36 12.95 10.55 9.12 7.71
Firm acquired 1.56 2.40 2.00 2.57 2.21 2.02
Total 100 100 100 100 100 100
* Percentages for 1999 cohort only.
27
Table 2
Descriptive Summary of Subsample 1
All No Turnover Turnover
Age (mean years) 40.07 40.23 37.91
Male (%) 0.76 0.76 0.77
Married (%) 0.64 0.64 0.54
Education (%)
less than high school 16.84 16.55 21.01
exact high school 6.66 6.34 11.07
vocational training 50.91 51.75 39.21
some college 14.72 14.66 15.57
bachelor 1.13 1.02 2.63
Master 7.66 7.66 7.69
Phd 0.69 0.69 0.75
others 1.38 1.33 2.06
Founded in 1999 (%) 42.28 42.69 36.59
Initial sales (1,000 DKK) 2,149 2,135 2,347
Initial no. of full-time employees 1.87 1.85 2.09
Industry (%)
Agriculture 0.29 0.28 0.38
Manufacturing 7.25 7.34 6.00
Construction 17.57 17.86 13.51
Wholesale and retail 37.57 37.55 37.9
Transport, post, telecom 4.89 4.90 4.69
Low-tech businesses 6.93 6.93 6.94
Public Services 0.43 0.42 0.56
Personal Services 6.05 6.26 3.19
Kibs 0.44 0.34 1.88
High-tech 18.24 17.77 24.77
Others 0.34 0.35 0.19
Obs. 7,961 7,428 533
28
TABLE 3
The Effect of Startup Performance on the Probability of Founder-CEO Replacement
Dept Var: = 1 if founder replaced in year t
Logit Reegression
Using sales in year t-1 Using sales in founding year
(1) (2) (3) (4)
Log of sales -0.940
***
-0.966
***
-1.159
***
-1.270
***
(-5.08) (-5.10) (-3.30) (-3.43)
(Log of sales)
2
0.072
***
0.076
***
0.084
***
0.095
***
(5.71) (5.87) (3.36) (3.61)
Founder characteristics
Founder age
__
-0.017
***
__
-0.019
***
(-3.05) (-3.40)
College educated = 1
__
-0.235
*
__
-0.090
(-1.38) (-0.54)
Male = 1
__
-0.154
__
-0.104
(-1.28) (-0.86)
Married = 1
__
-0.331
***
__ -0.303
***
(-3.25) (-2.98)
Firm characteristics
Firm age -0.366
***
-0.340
***
-0.324
***
-0.288
***
(-10.39) (-9.47) (-9.68) (-8.46)
Cohort dummy (= 1 if
founding year = 2000)
0.130 0.10 3 0.095 0.069
(1.34) (1.04) (0.96) (0.69)
Controls for industry No Yes No Yes
Ave Log Likelihood
-0.239 -0.235 -0.241 -0.237
No. of observations
7,357 7,348 7,305 7,305
Sales in thousand DKK. z-scores are in parentheses. Significance levels: *** 0.01, ** 0.05, * 0.1.
29
Table 4
Firms Included in Subsample 2
Turnover by
2002
Turnover after
2002
No Turnover
Exited or acquired by 2002 ? × ×
Exited or acquired after 2002 ? × ?
Survived to 2004 ? × ?
Table 5
Survival, Exit, and Ownership Change after Founder Turnover
1999 Cohort
2000 Cohort
Turnover No Turnover
Turnover No Turnover
Total 142 701
232 1,274
Survived 50 545
82 912
Exited before 2002 56 0
70 0
Exited after 2002 36 156
80 362
Table 6
Descriptive Summary of Subsample 2
All No Turnover Turnover
Obs. 2,349 1,975 374
Survival in 2004 (%) 67.65 73.77 35.29
Founded in 1999 (%) 35.89 35.49 37.97
Initial sales (1,000 DKK) 2,008 1,962 2,253
Sales in 2004 (1,000 DKK) 5,168 4,929 7,639
Initial no. of full-time employees 1.68 1.62 2.00
No. of full-time employees in 2004 4.08 3.86 6.39
30
Table 7
Founder-CEO Replacement and Future Performance of Startups
Dept Var: log of sales in 2004
Surviving Firms
OLS
All Firms
Tobit
(1) (2) (3) (4) (5) (6)
Log.sale
0
0.437
***
0.453
***
__ 0.724
***
0.817
***
__
(18.8) (18.6) (5.92) (6.12)
Turnover 0.336
***
1.524
***
__ -5.00
***
-1.032 __
(4.15) (2.80) (-14.4) (-0.45)
Log.sale
0
*turnover __ -0.165
**
__ __ -0.552
*
__
(-2.21) (-1.73)
log.sale
0
percentile: