Academic Entrepreneur’s Human Capital
Depreciation
Kathrin Müller ∗This version: August, 2009

Abstract
Human capital is known to be one of the most important predictors of a person’s earnings. With regard to entrepreneurial success,founders’ human capital is an important determinant of firm’s employment growth as well. This paper investigates if the depreciation of afounder’s academic knowledge affects a start-up’s employment growth.
The depreciation of academic knowledge is investigated by quantifying
the effect of the time period which elapses after the founder has left
university until the start-up is founded on firm’s employment growth.
Using quantile regressions, human capital depreciation is found to beof crucial importance for both ordinary academic start-ups and academic spin-offs, the founders of the latter suffering even more fromhuman capital depreciation.

Keywords human capital depreciation, employment growth, academic entrepreneurship
JEL Classification: J23, J24, L25, L26∗

Centre for European Economic Research (ZEW), P.O. Box 103443, 68034
Mannheim, Germany. Phone: +49 621 1235-385, Fax: +49 621 1235-170. E-mail:kathrin.mueller@zew.de
Acknowledgments: The author would like to thank Martin Biewen, Robin Douhan,
Christian Rammer and the participants of the IFN Seminar in Stockholm and the ZEWBrown Bag in Mannheim for valuable comments and discussions.

1Introduction

Setting up one’s own business is a far-reaching step in a person’s vita. Several
risks, financial and personal, must be born by the start-up’s founder(s). As
manifold as founders’ motives for starting a business, so are founders’ choices
for the point in time at which the company should be set up. The age of thefounders highly varies and entrepreneurs bring different levels of experienceand qualification into the business. Its consequences for firm performanceare clear. Usually, higher human capital of a firm’s founder(s) is related
to better firm performance. In empirical studies founders’ human capital ismostly proxied by their educational degree or professional experience, only
seldomly both are analyzed at the same time. Professional experience is typically assumed to enhance start-up performance, the more the merrier. By
contrast Müller (2006), who analyzed employment growth of academic spinoffs, found as a by-product of her study a significant negative sign (but smallin size) of her control variable “job experience”, measured by the difference
between the year of foundation and the year in which the last founder has left
academia. Furthermore, academic spin-offs are mostly not founded directly
after university has been left, which is usually assumed in the vast majority of the literature, but with a substantial “time-lag” (Müller, 2008). The
purpose of this paper is to give a better understanding about what happens
during this time and about the consequences of the length of the time-lags. I
propose an explanation that the time which elapses between leaving the university and founding the start-up has strong implications on the total humancapital endowment the founders bring into business. The academic knowledge is exposed to serious depreciation, while the professional skills and theindustry experience are being acquired. Using quantile regressions, I detectthat a typical firm has the best growth prospects if the start-up is founded
3-5 years after the founder has left university. For academic spin-offs, i.e.
those academic start-ups which are involved in the commercialization of new
research results, the depreciation of academic knowledge has much stronger
implications for future employment growth. The time which elapses after
university has been left is of higher importance for academic spin-offs. A
further key issue of this study is to investigate the importance and effects ofboth firm-specific and founder-specific determinants of young firms’ employment growth along the whole distribution of growth rates. For this purpose1

quantile regressions are an adequate instrument. Also the validity of the famous “Gibrat’s Law” for employment growth in firms, which has been tested
almost exclusively in the conditional mean framework, is reexamined in thequantile regression framework. The paper is structured as follows: the next
section reviews the literature on (young) firm’s employment growth in detail.
Section 3 develops the hypothesis about academic founders’ human capitaldepreciation. Results of the empirical analysis are presented in section 4.
Finally, the conclusions of this paper are drawn in section 5.

2

Literature review

The determining factors of new firms’ employment growth analyzed by empirical studies up to now can be classified into three main groups: founderspecific factors, firm-specific factors and the external characteristics. Founders’human capital endowment is one of the most prominent founder-specific factors. Firm size, firm age, firm’s innovation activities, legal form and internationalization are usually classified as firm-specific factors. External characteristics are the surrounding business conditions which depend on the industry,
the region and the regulatory framework.
Size and age
One of the most famous theoretical concepts concerning the growth of a firm
traces back to Gibrat (1931) who presents one of the first formal models of the
dynamics of firm size (Sutton, 1998). His “Law of Proportional Effects” which
is mostly interpreted as the proposition that firms grow proportionally andindependent of their size became generally known as Gibrat’s Law. Gibrat’s
Law is also included in numerous theories about firm growth, such as in thestochastic theory of Simon and Bonini (1958), who assumed Gibrat’s Law
to apply for firms above the minimum efficient size level, and in a model ofcapital adjustment made by Lucas (1967), who supposed firm’s employment,
output and capital to follow the Law of Proportional Effects.
On the other hand, models of passive and active learning as those by
Jovanovic (1982) and Ericson and Pakes (1995) oppose the theories following
Gibrat’s Law - at least in the short run. In Jovanovic’s model firm growth2

is driven by firm’s (cost) efficiency. Firms do not know their efficiency exante and learn about it only after they have entered the market. Since
the older firms have already learned about their efficiency and are not inneed of further growth, small and young firms grow faster. The testable andexcessively tested (see below) hypotheses that firm growth decreases with age
when firm size is held constant can hence be directly drawn from Jovanovic’s
model.
As young firms usually start below their minimum efficient size, models of
optimum firm size predict young and small firms to grow fast in order toobtain their minimum efficient size. Growth is absolutely essential if firms
operate in industries with relevant economies of scale. Additionally, start-ups
with a smaller initial size will have a greater need to grow (Stam et al., 2007;
Niefert, 2005).
A bulk of empirical studies concerning firms’ employment growth has investigated the validity of Gibrat’s Law. As most studies reveal that firms’
growth decreases with size and age, Gibrat’s Law is mainly rejected for the
U.S. (Sutton, 1998), but also for most countries in Europe (Audretsch et al.,1999). Mansfield’s (1962) conjecture, that the early empirical rejection of
Gibrat’s Law is a statistical artifact, because it is driven by a “sample censoring” problem, which is caused by a higher likelihood of exit by small firms
with low growth rates, was addressed in literature in the later 1980s. Using
techniques which account for sample selection and the presence of heterogeneity Hall (1987), Evans (1987a,b), Dunne et al. (1989) and Dunne andHughes (1994) also found that firms’ growth rates are decreasing in size, i.e.
a robust rejection of the Law of Proportional Effects. This is at least true
for small or young firms. For Germany, Wagner (1992) can reject Gibrat’s
Law for most groups of manufacturing firms in Lower Saxony. Likewise Almus and Nerlinger (1999, 2000) show for young German manufacturing firmsthat the initial firm size is an important predictor for the future size and theemployment growth. These results apply for firms operating in either high,medium or low technological sectors in equal measure. By contrast, the evidence for service sectors is ambiguous, since in some cases either growth
rates are independent of firm size for a subsample of the firms investigated(Audretsch et al., 2004) or Gibrat’s Law is clearly rejected (Petrunia, 2008).
Studies investing Gibrat’s Law not in the conditional mean framework but
using quantile regressions are extremely rare. To the best of my knowledge
3only Lotti et al. (2003) have examined the influence of a firm’s employment
in the beginning period on employment in the following periods within theframework of conditional quantiles.1 Using a sample of new manufacturingfirms in Italy they find Gibrat’s Law to be invalid in the early years following
entry.
The violation of Gibrats Law seems to become less severe for larger firms(Evans, 1987a). Hall (1987) even finds Gibrat’s Law accepted for the larger
firms in her sample. Recent research (Lotti et al., 2008) regards Gibrat’s Law
to be rejected ex ante, i.e. in the early years of a firm’s life-cycle, but detects aconvergence toward Gibrat-like behavior ex post, i.e. the firm’s employment
growth follows Gibrat’s Law after the firm has been fully developed. Becauseof that Gibrat’s Law is seen as a long-run regularity.
Most of the cited studies above do not only investigate the influence offirm size, but concurrently also the influence of firm age on employment
growth and find that the growth decreases not only with firm size, but alsowith firm age.
Not least because of the bulk of investigations and the mostly consistent
results, the correlation of firm size and age with firm growth has become oneof “stylized results of entry” (see Geroski, 1995).
Innovation activities, legal form and internationalization
The influence of innovation activities on employment growth is widely investigated in the literature. Most firm-level analyses for Europe, which are
not as often made as on the aggregate level, are based on the Community
Innovation Survey (CIS), a harmonized innovation survey on the European
level. These studies concentrate on mostly matured small and medium establishments with more than 10 employees. Studies investigating the effectof innovation activities on employment growth of extremely young firms arerather rare (see Niefert (2005); Calvo (2006); Almus (2002)).
The effect of a firm’s innovation activity on its labor demand is a priori
unclear. For process innovations direct supply-side effects in form of laborsaving productivity gains allow a firm to produce the same output with less1

Two other studies by Reichstein et al. (2006) and Fotopoulos and Louri (2004) investigated Gibrat’s Law for growth in sales and growth in total assets respectively.

4

labor. The employment growth might thus be lower. But if the firm passes
on cost advantages to customers through price reductions, positive (indirect)
demand-side effects arise in form of increased demand. If the demand-side
effects compensate for the negative effect of labor-saving productivity gains,the effect of process innovations on employment growth can even be positive.
Similarly, two opposing effects of product innovation and market novelties
on labor demand can be distinguished. On the one hand the introduction of
new products stimulates new demand for a firm’s products and thus increases
firm’s labor demand. On the other hand new products, especially products
new to the market, can open up a temporary monopoly. Under profit maximizing assumptions a firm will exploit its monopoly power, i.e. raising theprice above marginal costs by output reduction, and reducing labor demand(Smolny, 1998; Blechinger et al., 1998). The theoretical model by Katsoulacos (1986) derives a positive total effect of product innovations and a total
negative effect of process innovations.
When measuring innovation activities, most empirical studies distinguishbetween input and output measures. Innovation input is often defined as
conducting R&D. The implementation of process or product innovations andmarket novelties are used as direct output measures. Patents can be seen as
intermediate innovation output as they are often claimed to be only a flawedmeasure of innovation output (Acs et al., 2002)
R&D is often found to be positively correlated with employment growth(Blechinger et al., 1998; Regev, 1998). Furthermore, most empirical studies
detect a positive effect of product innovations on labor demand (Van Reenen,
1997; Blechinger et al., 1998; Smolny, 1998, 2002; Greenan and Guellec,
2000; Jaumandreu, 2003; Peters, 2004; Lachenmaier and Rottmann, 2007;
Harrison et al., 2008). Evidence for process innovations is not as clear-cut.
Some studies find a positive effect for process innovations, too (Smolny, 1998,
2002; Lachenmaier and Rottmann, 2007), while others find either no effect(Van Reenen, 1997; Jaumandreu, 2003) or a negative effect (Peters, 2004;
Harrison et al., 2008).
The evidence for young firms is similar. Niefert (2005) found that patenting has a positive effect on the employment growth, Calvo (2006) stated thatboth process and product innovations have a strong positive influence on theemployment growth in young Spanish firms.
5Concerning the legal form, firms with limited liability are expected toshow up higher growth potentials. Owners of those firms are not liable with
their own fortune. Therefore incentives for taking riskier projects, whichyield higher returns on investment, are higher for firms with limited liabilities
conditional on surviving (Stiglitz and Weiss, 1981). This relation is supported
in studies by Harhoff et al. (1998) and Davidsson et al. (2002) as well as instudies by Engel (2002) and Almus and Nerlinger (1999) for newly foundedfirms.
Another line of the literature on firm growth addresses the export-growth
relationship. Theoretically, exporting improves firm performance because
serving a larger market allows a firm to exploit economies of scale and to copewith domestic demand variations. But a firm which serves foreign markets
has to bear additional cost. Therefore, only healthy firms will engage inexporting (Wagner, 2002). Two facts turned out to be of importance: First,
growth rates are higher for exporting firms ex-ante, i.e. successful firms are
more likely to become exporters. Second, benefits from exporting can beseen in terms of employment growth and the likelihood of survival, but notin terms of productivity growth (Bernard and Jensen, 1999). Using matching
techniques, Wagner (2002) shows that there is a causal effect from exportingon firm performance in terms of employment growth. For young high-techfirms, Bürgel et al. (2004) support these results for sales growth, but not for
employment growth.
Founders’ human capital
The human capital endowment of a firm’s founders is seen as an importantfactor influencing the growth path of that firm. Founders’ human capital
affects firm success by means of founders’ productivity, particularly by developing a promising business plan, i.e. to direct investment towards thoseareas of business activity that will generate the highest returns. Greater
human capital increases founders’ productivity in terms of organizing andmanagerial efficiency and acts as a positive signal for the firms’s prospective stakeholders (investors, customers and suppliers). These parties usually
have imperfect information about the firms’ potentials and will benchmark
the firms by means of observable characteristics they pull together with firm
success. Therefore, firm success should be higher for the founders with a rich
6

human capital endowment (Brüderl and Preisendörfer, 1992; Bosma et al.,2004).
A formalized model of human capital’s impact on a firm’s labor demand
was made by van Praag and Cramer (2001). In equilibrium labor demandof a firm is positively influenced by the individual’s entrepreneurial talent.
The estimation of a therefrom derived structural empirical model with Dutch
data confirms the predictions of their theoretical model.
Following Becker (1995), human capital is traditionally distinguished ingeneral human capital and specific human capital. In entrepreneurship research general human capital is usually measured in terms of schooling andwork experience as it is done in traditional labor economics. Specific humancapital is mostly approximated by industry-specific knowledge and prior selfemployment experience. The higher the human capital endowment of thefounders, the higher the survival probabilities are found to be. Concerning work experience, a concave relationship has been found (Brüderl andPreisendörfer, 1992). This concavity might be due to age-effects. Founders
with a very long working-experience have mostly reached a high age in which
flexibility as well as physical and mental fitness are limited.
Almus (2002) found that new enterprises of persons with a very highhuman capital endowment are more likely to become fast growing firms.
For new technology-based firms Almus and Nerlinger (1999) showed thathuman capital measured by a technical degree of the founder(s) is positively
correlated with the firm’s employment growth. Similarly, Moog (2004) showsthat founders with a university degree realize higher employment growth bothfor employees in general and for the highly qualified employees.
The influence of different components of founders’ human capital on
the growth of new technology-based firms is also investigated in detail by
Colombo and Grilli (2005), Bosma et al. (2004) and Koeller and Lechler(2006). They found that especially the nature of founders’ education as wellas prior work experience - most notably experience in the same industry - arethe key determinants of new firm growth. The most important conclusiondrawn from their analysis is that founders’ human capital is not just a proxy
for the founders’ personal wealth, but also for their capabilities.
The influence of the composition of founders’ human capital on employment growth in academic spin-offs was investigated by Müller (2006). She
7found that the human capital composition, i.e. specialization versus beinga generalist, is irrelevant for academic spin-offs’ employment growth. However, founding in a team causes higher employment growth in the future.
This corresponds with earlier literature (Eisenhardt and Schoonhoven, 1990;
Reynolds, 1993). In this line of literature, forming a team is seen as a way
for compensating individual deficits of one team member by the strengths ofother team members.

3

The depreciation of academic knowledge

The possibility of depreciation of human capital has almost exclusively been
investigated for employees since it has become common to decompose net
investments in human capital as a predictor of a person’s earnings into grossinvestments and depreciation. Depreciation rates in times of career interruptions are estimated by assuming that gross investments are zero during
career interruptions.
Depreciation rates of either voluntary (mostly family-related) or involuntary (unemployment, sick leave) career interruptions have been frequently
examined in terms of forgone earnings. The most common thing to do is
to use an adapted and extended version of Mincer’s (1974) earning function(e.g. Mincer and Polachek, 1974, 1978; Mincer and Ofek, 1978; Beblo and
Wolf, 2000; Görlich and de Grip, 2007).
Already Mincer and Polachek (1974) noticed that depreciation of humancapital’s earning power may occur not only in periods of nonparticipation
at the labor market, but at other times as well. Only a few studies (Groot,
1998; Arrazola and de Hevia, 2004) address the question of human capital depreciation during the times of employment, which has been usually specifiedin earning functions of earlier work, but has not yet been estimated explicitly. Non-linear methods enable Groot as well as Arrazola and de Hevia toestimate the rate of human capital depreciation without the use of careerinterruption spells.
Another study of Neumann and Weiss (1995) deals with human capitaldepreciation by investigating the shape of worker’s experience-earning profiles. They find different peaks for people working in high-tech and low-tech8

oriented industries. Furthermore, for highly-educated people, experienceearning profiles become steeper, i.e. the peak falls faster with increasing
education. This procedure implicitly assumes that human capital depreciation due to workers’ aging and the obsolescence of knowledge is also present
during the participation at the labor market.
Human capital depreciation has thus proved to be present for employees.
Since the human capital endowment of a firm’s founders has been shown to
be a major determinant of new firm’s success, the concept of human capitaldepreciation needs to be implemented and investigated by entrepreneurshipresearch, too. This study is an attempt in doing so.
The investigation in this paper follows the theory of heterogeneous human capital. Some parts of the human capital might even depreciate if there
are no career interruptions. Particularly with regard to academic knowledge,
depreciation might become severe once university is left, since scientific techniques fall into oblivion if they are not being used continuously. Furthermore,scientific techniques might become obsolete and their value might decrease
if one does not keep pace with scientific progress. I distinguish two maintypes of human capital which are relevant for employment growth in academic start-ups. Human capital of academic start-up founders is determinedby the stock of their academic knowledge and the stock of their professional
experience.
During the time in academia academic knowledge is accumulated. At the
moment when the academic institution is left, the stock of a person’s academic knowledge is assumed to be highest. Instantaneously, the depreciationof a person’s academic knowledge begins, because the skills fall into oblivion if they are not used and they might become obsolete as time passes by.
Hence, the academic knowledge A of an academic firm founder is a decreasing
function in t, the time which elapses after leaving university and founding
the start-up. One can thus assume that A(t) > 0 and ∂A(t)< 0 ∀t.
∂tWhile academic knowledge depreciates, professional experience P is gained.
Professional experience of new firm founders is essential as both, knowledge
about the industry and the organization of a firm, is acquired. Possibly,
the prospective founder could even gain management experience during thattime. In this context, professional experience P is expected to be accumulated with positive but decreasing returns over time.
92

Therefore, P (t) > 0 with ∂P∂t(t) > 0 and ∂ ∂tP2(t) < 0 ∀t.
Concerning how the total human capital HC of a founder is influenced by
the stock of academic knowledge and professional experience, let us consider
two extreme cases: Either academic knowledge and professional experience
complement each other or they are perfect substitutes.
If they are perfect complements HC(t) = min [αA(t), βP (t)], where α > 0
denotes the weight assigned to academic knowledge and β > 0 denotes theweight assigned to professional experience. That means,(HC(t) =

αA(t) if αA(t) ≤ βP (t)βP (t) if αA(t) > βP (t).

As long as one assumes the professional experience to increase over time( ∂P∂t(t)> 0) and the academic knowledge to depreciate continuously after

leaving university ( ∂A(t)< 0), total human capital peaks at αA(t) = βP (t).
∂t
The other way around, if academic knowledge and professional experience
are perfect substitutes, i.e. HC = αA(t) + βP (t), the first order condition
for a maximum is given by

∂HC
∂t

= α

∂A(t)
∂P (t)
+β
=0
∂t
∂t

⇔α

∂A(t)
∂P (t)
= −β
.
∂t
∂t

That is, total human capital of prospective entrepreneurs peaks when
the weighted marginal products are equal, provided that the second order2

condition ∂ ∂tHC
< 0 is fulfilled.
2∂ 2 A(t)∂ 2 P (t)∂ 2 HC
=
α
+
β
∂t2
∂t2
∂t2
2
2
Since ∂ ∂tP2(t) < 0 by assumption and ∂ ∂tHC2

would ensure that a maximum exists.

10< 0 if −∂ 2 A(t)∂t2∂ 2 P (t)∂t22< αβ , ∂ ∂tA(t)
<0
2

2

Having ∂ ∂tA(t)< 0 seems to be rather plausible at least for small ts since2
one can possibly expect the depreciation of founder’s academic knowledge tobe disproportionately high at least at the beginning while the depreciation
might become less severe after some years have passed by (for illustration see
middle graph in Figure 1). This is reasonable since some basic skills whichhave been learned at university remain present even after decades.
Figure 1 illustrates how total human capital is composed of academic knowledge and professional experience. The upper graph depicts how professional
experience might be accumulated after university has been left. Similarly, themiddle graph illustrates how the stock of academic knowledge might evolve
over time once university is left.
Consequently, as the time that elapses between leaving the university
and starting a venture affects the two components of entrepreneur’s humancapital in opposite directions, founding is neither best directly after leaving the university nor after a very long time. The graph at the bottom of
Figure 1 illustrates the combined effect of the time after leaving academia,
but before founding, on the total human capital endowment of the (prospective) entrepreneur. The picture on the left illustrates the development of theentrepreneurial total human capital if academic knowledge and professional
experience are perfect complements. The picture on the right depicts thedevelopment if academic knowledge and professional experience are perfect
substitutes and if the second order condition is fulfilled. At the beginning
total human capital is increasing. At some point in time human capital peaks
and decreases thereafter. Because total human capital of the founders is directly linked to employment growth, those founders who have started theirfirms close to the peak of their total human capital endowment should reveal
the best growth prospects.
The hypothesis to be tested in the next part of this paper states thefollowing:
Hypothesis 1a: The influence of the time which elapses after a founder
has left the academic institution on firm’s employment growth is inverse ushaped.

11

professional experience
academic knowledge

time after leaving academia

total human capital − substitutes

total human capital − complements

time after leaving academia

time after leaving academia

time after leaving academia

Figure 1: Graphical illustration - Founder’s human capital development after
leaving university
Note: Graphical illustration of the development of entrepreneur’s human capital after
leaving university if professional experience and academic knowledge are perfectcomplements (left) or perfect substitutes (right).
Source: Author’s illustration.

12

For academic spin-offs, i.e. those start-ups which have greatly contributed
to the commercialization of research results, obsolescence of academic knowledge is of higher importance. If the business idea rests upon new research
results or newly developed scientific methods (so-called “academic spin-offs”)
technology changes and catching-up processes require starting the venture
earlier. Speaking in terms of the graph at the bottom of Figure 1: For
spin-offs the human capital peak is located to the left of ordinary academic
start-up’s human capital peak. Therefore the hypothesis is supplemented asfollows:
Hypothesis 1b: The influence of the time which elapses after a founder
has left her academic institution on firm’s employment growth is inverse ushaped and differs between academic start-ups and academic spinoffs.

4

Empirical Analysis

4.1

Growth model and estimation method

Employment growth of academic start-ups is modeled following an exponential growth path as suggested by Evans (1987a,b) and adopted by a numberof other studies investigating the growth of young firms (e.g. Almus and Nerlinger, 1999; Almus, 2002). The number of employees (including the owners)
S2008 at the beginning of 2008 is determined as following:

S2008 = [exp(Xβ) · G(A2008 , ST )]A2008 (ST )e.

(1)

ST denotes the number of employees in the founding year, A2008 the ageof the start-up in 2008 and e is the error term. G(·) is a function of age
A2008 and initial size ST . Firm size in 2008 is further determined by variables
contained in vector X. The vector X contains founder- and firm-specificvariables, particularly the time which elapsed after the last founder has left
academia, and relevant firm-specific variables2 .

2

See section 4.2 for a detailed description of the variables included.

13

Taking logarithms and rearranging yields for the annual growth ratey=ln(S2008 ) − ln(ST )= Xβ + ln(G(A2008 , ST )) + u.
A2008

(2)

Equation 2 is estimated using quantile regressions. This estimation technique of modeling the different conditional quantiles of a specific distribution was introduced by Koenker and Bassett (1978). Traditional estimation
techniques focus on the estimation of the conditional mean of the response
variable. But focusing solely on the “average” firm is not always appropriate.
Frequently cited in this context are Mosteller and Turkey (1977, p. 266):“What the regression curve does is give a grand summary for the averages ofthe distribution corresponding to the set of x’s. [. . . ] Just as the mean gives
an incomplete picture of a single distribution, so the regression curve gives acorrespondingly incomplete picture for a set of distributions.”
But investigating young firm’s growth we are especially interested in the determinants of especially high growth as well as in the causes of being at thelower tail of the growth distribution. Quantile regressions therefore provide
a possibility to get a more complete picture in analyzing the driving factorsof employment growth along the entire conditional distribution.
Additionally, quantile regression techniques provide some advantages compared to mean-oriented regression techniques. Firstly, the inference in thequantile regression framework is robust to distributional assumptions. In
fact, inferential statistics can be obtained distribution-free. Likewise quantile regressions are especially suitable for heteroscedastic data. Secondly, forhighly-skewed or heavy-tailed distributions the mean does not only give an incomplete picture of the distribution, but is likewise challenging to interpret.
As highly growing firms are a real-world phenomenon and not necessarily
data errors, there is no room for removing those observations as it is often
done in the conditional-mean framework. A third advantage of quantile regressions is their monotone equivariance which allows to measure the impactof a covariate both in relative and absolute terms using one model.
Analyses which model the conditional median or other quantiles often
turn out to be more appropriate as they show up an inherent robustness tooutliers.

14

The parameters in the quantile regression framework are estimated by minimizing the weighted sum of absolute residuals. The growth model is thenwritten asyi=

xi βθ + ln(Gθ (Ai2008 , SiT )) + uθiwithQθ (yi |xi , Ai2008 , SiT ) = xi βθ + ln(Gθ (◦)).

(3)

Qθ denotes the θth conditional quantile of firm’s annual logarithmic change
in employment (yi ). The θth regression quantile, 0 < θ < 1, is the solution of
the following minimization problem, which can be solved by linear programming methods.

min
β

X

θ

|yi − xi β − ln(Gθ (Ai2008 , SiT ))| +i:yi ≥xi β+ln(Gθ (Ai2008 ,SiT ))X(1 − θ) |yi − xi β − ln(Gθ (Ai2008 , SiT ))|.

i:yi <xi β+ln(Gθ (Ai2008 ,SiT ))Interpreting coefficients estimated by quantile regressions is as easy interpreting OLS coefficients. The coefficient βθ represents the change in y atthe θth conditional quantile due to a marginal change (zero-one change fordummy variables) in the corresponding regressor. For logarithmic transformations of dependent and independent variables the same interpretation rules
in terms of semi-elasticities and elasticities apply in the quantile regression
framework. Additionally, for quantile regressions the monotonic equivarianceproperty allows to easily calculate the effect of a regressor on the dependent
variable in absolute terms even when regressors or the independent variable
enter the model in logarithmic terms. This does not hold for OLS regressions.
Detailled information on quantile regression techniques can be found in
Hao and Naimann (2007), Koenker and Bassett (2001) or Buchinsky (1998).

15

4.2

Database and Descriptive Statistics

The following empirical analysis is based on a data-set of more than 10,000
German start-ups in research- and knowledge-intensive industries founded
between 2001 and 2006. For constructing this data-set a computer-assisted
telephone survey was conducted in the first quarter of 2008. The stratified
random sample3 was drawn from the Mannheim Foundation Panel, which
is build upon information of Germany’s largest credit rating agency Creditreform and covers data on all start-up companies in Germany.
The conceptual design of this survey is based on an earlier survey of theCentre for European Economic Research (ZEW), the ZEW Spin-Off Survey
2001, which was conducted in order to estimate the yearly number of academic spin-offs in Germany in the period between 1996-2000 and the corecharacteristics of these spin-offs4 .
The new survey covers a wide range of founder-related and firm-related
information. For the purpose of learning about founders’ academic background, information about founders’ highest formal educational degree was
retrieved during the interview.
For academic founders the year was recorded, when they had left academia.
With this information the spell from leaving academia up to the point intime when the establishment of the firm has taken place can be calculated5 .
Furthermore, firm-level information concerning the year of establishment,
financial and other retrieved support, employment, turnover, innovation activities and academic networking was collected.
As the purpose of this paper is to investigate the human capital depreciation of academic founders only academic start-ups (start-ups of either
students, graduates or academic researchers) are included in the empiricalanalysis. Out of 10.126 start-ups surveyed, 4.303 firms could be identified asacademic start-ups with non-missing values for the variables under investigation. Those start-ups were used for the quantile regressions presented in
section 4.3. Using only academic start-ups for the analysis implies that thefounders of those start-ups have a similar educational level. This fact allows
me to distinguish the depreciating effects on human capital of the time which
3

Stratification criteria are the year of establishment, industry and type of region
For further information about that data-set see Egeln et al. (2003); Müller (2008).
5
In case of establishments in teams the year when the last founder left academia wasrecorded.
4

16

elapses after leaving academia and their influence on employment growth.
Following the hypothesis that the effect of academic knowledge depreciation differs between ordinary academic start-ups and academic spin-offs, itis necessary to define which start-ups can be classified as academic spin-offs.
Academic spin-offs are only those academic start-ups which substantiallycontribute to technology transfer from academia. This has been classified
according to the founders’ self-assessment. During the interview foundersof academic start-ups were asked about the relevance of own, newly generated research results and the relevance of new scientific methods for thecreation of the firm. If either own research or the acquisition of new scientific methods has been indispensable for venture creation the contribution totechnology transfer can be assessed as high enough to refer to these ventures
as to academic spin-offs6 . This approach of defining academic spin-offs was
first adopted by Egeln et al. (2003) following Mansfield’s (1995) method in
identifying technology transfer from academic research concerning the development of new products and processes. Using this approach the sample of
4.303 academic start-ups contains 301 academic spin-offs.
According to the growth model presented in section 4.1 employment
growth is calculated as annual logarithmic change in employment between
2008 and the founding year. Average growth is 14 percent, median growthis 10 percent. The distribution of employment growth is positively skewed.
Various measures, e.g. the Kolmogorov-Smirnov test, indicate that employment growth deviates from the normal distribution (see Table 1).
Besides the description of the variables below, a summary table including
a short description and the descriptive statistics of all variables included in
the analysis is provided in the appendix.
The central variable which is investigated in this paper, the time thatelapses between leaving academia and starting a venture, is measured in categories. This is done for the following reason: The founders might have still
been in academia when the firm was founded but they might have alreadyleft university before the survey was carried out. That applies for around 9
percent of all start-ups in the sample. For those firms a negative time-lag
can be calculated. But the founders of further 8 percent of the start-ups have6

Earlier work has classified this type of academic start-ups as transfer spin-offs (seeEgeln et al., 2003; Müller, 2008).

17

Table 1: Non-normality of the annual logarithmic change in employment
employment growth
MeanStandard deviation
Skewness
Kurtosis

0.141
0.210
1.385
8.025

Q5
Q10
Q25
MedianQ75
Q90
Q95-0.096
0.000
0.000
0.099
0.231
0.393
0.536

Shapiro-Francia test
Kolmogorov-Smirnov test

0.912
0.177

*** (a)*** (a)******Notes: * p < 0.10, ** p < 0.05, *** p < 0.01;(a) Rejection of normality based on skewness and kurtosis test for normality.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

been still in academia, when the survey was conducted. For those firms weknow that their time-lag is negative, but we do not know the exact value
so far.7 Omitting these observations would cause a severe bias. Building
categories for different length of time-lags solves this problem, because allobservations can be used. No valuable information is lost since the depreciation of academic knowledge will occur not until leaving university. Therefore
I focus on positive time-lags. For the analysis six categories of the time-lag
between leaving academia and firm formation are built and included in theanalysis using dummies. The category of a time-lag from zero up to two
years was used as reference category. In order to capture different effectsof human capital depreciation between academic spin-offs and ordinary academic start-ups, interactions of all categories with a dummy, measuring if
the start-up can be classified as a spin-off, are included in the regressions.
Most academic start-ups have an time-lag of 11-20 years (see Table 2). This
is not surprising since the average age of founders in Germany is around 40
7

An example will help to understand that censoring problem: If the founder was stillin academia when the firm was founded due to the survey design two possible cases mightoccur: (1) If the founder has left university in 2006 and has already established his businessin 2004 the founder’s corresponding time-lag can be calculated and is -2 years. (2) For
those founders who have established their business, e.g. in 2006, and have still been inacademia when the survey was conducted (in the beginning of 2008) it is only known thatthe time-lag is negative.

18

years. A non-negligible fraction of academic start-ups is either founded whileat least one of the founders still has been in academia (17 percent) or shortly
after leaving academia (10 percent).
Table 2: Time-lag since academia was left

Still in academia
Time-lag: 0-2 years
Time-lag: 3-5 years
Time-lag: 6-10 years
Time-lag: 11-20 years
Time-lag: +20 years

Mean

Std. dev.

0.170
0.101
0.115
0.179
0.260
0.174

(0.375)(0.302)(0.319)(0.384)(0.439)(0.379)Source: ZEW Spin-Off Survey 2008, author’s calculations.

In developing the hypotheses I assumed that after leaving academia foundersof academic start-ups accumulate professional experience as well as industry
experience, but are exposed to a depreciation of their academic knowledge.
If founders experienced unemployment during that time not as much professional experience could have been acquired as supposed by their time-lag.
Even a depreciation of their human capital gained by means of professional
experience could have taken place. For this reason a further dummy variable is included in the analysis which indicates if one of the founders wasconfronted with a longer unemployment spell.
In order to control for the well documented relationship between size, age
and employment growth and to test for the validity of Gibrat’s Law for young
firms in the research- and knowledge-intensive industries, initial firm size andage are included in the analysis. Initial employment enters the equations inlogarithms and its square term. Age is captured by a set of dummy variables. This approach offers the most flexible functional form concerning how
age influences employment growth. Furthermore, the dummies can capture
possible ”year-effects” of economic cycles during that time.
A large set of further independent variables was included into the regressions which have been detected by theoretical and empirical studies to havesignificant influence on firm’s employment growth.
Those are dummy variables measuring if the firm is active in exporting,if establishment has taken place in a team of founders and if the legal formof the company involves a limited liability of firm’s owners.
19

Innovative activities of the start-ups are captured by three dummy variables measuring as well innovative input and output. Two of them show if
the start-up is conducting continuous or occasional R&D, the other dummy
variable indicates if the start-up has introduced novelties to the market.
In the past determinants of employment growth of young firms have been
investigated almost solely in the conditional mean framework, mostly using
OLS or discrete response models (for growth categories), sometimes withprocedures controlling for potential selection biases. Therefore this study
not only contributes to the investigation of the effect of entrepreneur’s humancapital depreciation on firm’s employment growth, but also to the assessmentof the effect of variables which have been frequently used in growth regression
on the median of the distribution of employment growth. Furthermore, in aquantile regression framework the influence of these variables on noncentral
positions, i.e. on different quantiles, of the growth distribution can be shown.
Beyond that, if those variables proof to be significant in the analysis presented
in this model of human capital depreciation robustness of the whole model
will be indicated.
Financial constraints are of high relevance especially for young firms(Oliveira and Fortunato, 2006; Cabral and Mata, 2003; Westhead and Storey,1997). If budget constraints are softened by state subsidies firms are found tohave higher rates of employment growth (Becchetti and Trovato, 2002). The
impact of external public support on young firm’s employment growth is also
addressed in my analysis. Public subsidies in terms of grants or soft loans
are expected to relax financial constraints the firm faces. Public funding
is differentiated into public funding from the Federal Employment Agency
and public funding from other public agencies such as special credit institutions (Kreditanstalt für Wiederaufbau (KfW) and Landesförderbanken), federal and state ministries, municipalities, the EU or the Chamber of Industry
and Commerce. Public funding from the Federal Employment Agency (Bundesagentur für Arbeit - BA) is granted to unemployed persons who start theirown business. The volume of financial support from the BA is rather low as
it primarily aims to ease the transition into self-employment and to ensure
the founder’s cost of living in the first months. The dummy might thus not
only capture a potential relaxation of financial constraints but poor growthprospects of start-ups aimed to get out of dire straits. Around 10 percent

20

of the academic start-ups in the sample received funding from the BA. On
the other hand, financial funding from other public agencies, which has been
granted to about 16 percent of the academic start-ups, is not driven by labor market programs but industrial policy. Usually these programs intend
to foster competitiveness and economic growth by supporting start-ups. For
that reason funding amounts are considerably larger and thus more likely tobe able to relax financial constraints.
Another way of public, though non-financial support is housing of new ventures in science parks or business incubators. 6 percent of all academic startups in the sample have in fact been hosted by a science park or business incubator. This infrastructural support might also relax financial constraints,back new firm’s business success and therefore enhance firm’s labor demand.
Networking effects, which have been shown to exert influence on employment growth in the analysis of Stam et al. (2007), are accounted for by
including dummy variables which indicate different types of regular contacts
to academia. Additionally, the depreciation of academic knowledge mightproceed not as fast if the founders stay in contact with academia.
External characteristics which are not investigated in this paper are controlled for by industry dummies. As the analysis is restricted to academic
start-ups in research- and knowledge-intensive industries external characteristics might not differ too much between the firms. In contrast to otherstudies this analysis is not restricted to manufacturing firms only (as it is
the case in Almus and Nerlinger, 1999). According to their NACE 4 digit
codes, start-ups in the research- and knowledge-intensive industries are subdivided into high-technology industries, technological services and knowledgeintensive services8 . High-technology industries are those industrial sectors
which exhibit an average R&D-intensity above 3.5%, e.g. manufacturing ofpharmaceutical and chemical products. 24 percent of the firms in the sample belong to the high-technology industries. 35 percent of the academic
start-ups investigated belong to the technological services (telecommunications, software etc.). This group serves as reference category. Most firms (41percent) belong to the knowledge-intensive services, e.g. consultancy.
Furthermore, using a dummy variable, I control for the possibility thata start-up is not growth-oriented. It is necessary to control for that motive,
8

A classification list based on NACE codes can be provided on request by the author.

21

since it is frequently monitored that founders prefer to stay small. For example, Storey (1994) detected that about 50 percent of UK founders start theirfirms with no intention to grow. In our sample this is true for 34 percent of
all start-ups investigated.

4.3

Estimation Results

Equation 3 was estimated for 19 quantiles (0.05, 0.10, 0.15 etc.) simultaneously. Inference is based on bootstrapped standard errors using 500
replications. Results for the median regression are displayed in Table 3 andcompared to OLS regression of the same model. Both for the OLS regression
and for the median regression the hypothesis of human capital depreciationof academic founders once university is left is confirmed. The impact of thetime-lag after university has been left is in fact u-shaped with a peak of human capital endowment roughly around 3-5 years. Employment growth isaround 4 percent higher if an academic start-up is founded with a professional experience of 3-5 years instead of an experience of 0-2 years (referencegroup). When the time-lag is 6-10 years employment growth is 3 percent
lower at the median. When interpreting these results, one has to consider
that the time-lag might not only comprise the depreciation and accumulation of human capital but might also capture the age and financing optionsof the founders, which unfortunately cannot be included in the analysis as
such information has not been part of the survey.
Furthermore, the coefficients of the interaction effects show that the relationship between human capital and employment growth is different when
academic spin-offs are examined. With a founding time-lag of 3-5 years
employment growth is 1.7 percent (= 0.040 + (−0.057)) lower than witha founding time-lag of 0-2 years. After 6-10 years employment growth iseven 6.6 percent lower. However, starting the venture while at least one of
the founders is still in academia is not advantageous for academic spin-offs.
Common effort of all founders of academic spin-offs seems to be needed in
the early stages of product and firm’s development. This result is in line withan observation of Doutriaux (1987). He finds that manufacturing firms growless if the academic is still employed by the university.

22

Table 3: OLS and Median Regression on employment growth
OLS

Q50
se

coeff.

secoeff.

0.005
0.033***
0.025**
0.015-0.025**-0.041**0.001-0.053*-0.074***-0.031
0.015(0.011)(0.012)(0.011)(0.010)(0.011)(0.020)(0.023)(0.029)(0.028)(0.028)(0.037)0.007
0.040***
0.030**
0.006-0.024**-0.034*-0.020-0.057*-0.096***0.029-0.019(0.011)(0.014)(0.012)(0.011)(0.011)(0.019)(0.030)(0.031)(0.028)(0.032)(0.043)-0.070***0.000
0.015**
0.036***
0.057***
0.106***
0.126***(0.010)(0.003)(0.007)(0.007)(0.008)(0.009)(0.012)-0.014-0.011***0.013*
0.026***
0.040***
0.070***
0.049***(0.010)(0.003)(0.007)(0.008)(0.009)(0.012)(0.014)0.024**
0.001
0.033***(0.009)(0.010)(0.008)0.016*-0.0020.036***(0.009)(0.009)(0.008)

Science park
Public funding (BA)
Public funding (non-BA)

0.029**-0.031***0.031***(0.013)(0.010)(0.008)0.029**-0.038***0.033***(0.013)(0.010)(0.009)Limitation of liability
Exports
Team
No-expansion strategy
UnemployedContacts to academia
Joint research contacts
Contract research contacts
Customer contacts
Continuing education contacts

0.058***
0.060***
0.010-0.061***-0.072***(0.008)(0.006)(0.008)(0.006)(0.019)0.053***
0.057***
0.004-0.056***-0.027(0.008)(0.007)(0.008)(0.007)(0.019)-0.0140.000
0.015
0.046***(0.015)(0.022)(0.013)(0.016)-0.001-0.0000.018*
0.045***(0.015)(0.018)(0.011)(0.014)High-technology industries
Knowledge-intensive services

0.009
0.009

(0.008)(0.007)0.018**
0.008(0.009)(0.007)

Constant

0.068***(0.011)0.031**(0.012)

Observations
R2 / Pseudo R2

4303
0.190

Time-lag since academia was left
Still in science
Time-lag: 3-5 years
Time-lag: 6-10 years
Time-lag: 11-20 years
Time-lag: +20 years(Spin-off)*(still in science)(Spin-off)*(0-2 years)(Spin-off)*(3-5 years)(Spin-off)*(6-10 years)(Spin-off)*(11-20 years)(Spin-off)*(+20 years)
Size and foundation yearln(size)ln(size)2
Founded in 2002
Founded in 2003
Founded in 2004
Founded in 2005
Founded in 2006
Innovation activities
Continuous R&D
Occasional R&D
Market novelties

4303
0.113

Notes: * p < 0.10, ** p < 0.05, *** p < 0.01, robust (OLS) or bootstrapped (Q50) standard errors inparentheses.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

23

Table 4 shows the regression results for the most, however due to shortage of space, only selected quantiles. It can be seen that human capitaldepreciation is of relevance, especially for fast-growing academic spin-offs.
Coefficients for the time-lag dummies are for most instances insignificant for
non-growing firms (5 percent - 25 percent quantile) while the coefficients
increase over the quantiles.
However, for growing firms differences are mostly not statistically significant except for the difference between the 40 percent quantile and the 75
percent quantile and the difference between the 40 percent quantile and the80 percent quantile for a time-lag of 3-5 years. Additionally, significant differences for the coefficient (Spin-off )*(6-10 years) can be observed between
the 40 percent and 70 percent quantile and between the 40 percent and 50
percent quantile. Human capital depreciation seems to have no effect ona firm’s employment growth in the lower quantiles, in which firms are not
growing. Almost all coefficients measuring the human capital depreciation
are insignificant at the 5, 10 and 25 percent quantiles.
Interesting insights can also be obtained from the estimated effects ofthe control variables. Over the whole distribution a significant impact offirm size and firm age on a firm’s employment growth can be observed. Following learning theories of firm growth (Jovanovic, 1982), capital adjustment theories of firm growth (Lucas, 1967, 1978), stochastic theories of firm
growth (Simon and Bonini, 1958) or evolutionary theories of firm growth∂lny(Neslon and Winter, 1982), Gibrat’s Law can be rejected if gs = ∂lnS=
T

βln(ST ) + 2 · βln(ST )2 ln(ST ) 6= 0, which is the partial derivative of the logarithmic growth rate with respect to firm size (see Evans, 1987b).
At sample median and for the regression results of the median regression gS = −0.277, which is significantly different from 0. The elasticity
of end-of-period firm size with respect to beginning-of-period firm age is£¤2008 )
2 ln(ST )ES = ∂ln(S
=
1
+
A
·
β
+
2
·
β
= 1 + A2008 · gS .
2008
ln(S
)
ln(S
)
T
T
∂ln(St )With a normalization of A2008 to 4 years, which is the median age in thesample, ES = −0.107, which is significantly different from one9 . Hence, thenull hypothesis that Gibrat’s Law holds is rejected for employment growthof academic start-ups. Gibrat’s Law fails to hold along almost the whole
9This is another test of Gibrat’s Law. Gibrat’s Law holds if a one percent increase ininitial size gives rise to an increase in “end-of-period employment” (employment in 2008)of one percent.

24

distribution. In fact it can be rejected for all regressions from the 25 percent
quantile up to the 70 percent quantile (see Table 6 in the appendix).
The year of foundation significantly influences annual employment growth.
Growth rates are highest for those firms founded in 2005, i.e. which have an
age of three years. Their growth-rate is 7 percentage points higher at themedian. With respect to the coefficient founded in 2006 OLS and Median
regression largely differ. Initial size is found to affect employment growtheven in the lower quantiles, while the year of foundation only matters if thequantiles also include growing firms (from the 40 percent quantile on).
Furthermore, coefficients composing the size-age-growth relationship vary
sizeably from quantile to quantile when coefficients are compared along the
growth distribution (see Figure 2). Differences of the coefficient ln(size) are
statistically significant between the 60 percent quantile and the higher quantiles. Moreover, almost all coefficients for the “2003-2006 year of foundationdummies” differ significantly from quantile to quantile. In addition, testsof equality of coefficients between the quantiles can be interpreted as a robust test of heteroscedasticity irrespective of the functional form of the heteroscedasticity (Cameron and Trivedi, 2009). Homoscedasticity of the data
can therefore be rejected. Initial size and age do not only affect the locationof the distribution of firms’ employment growth but also the scale.
Innovation activities in terms of continuous R&D and the introduction ofmarket novelties enhance median firm’s employment growth by about 2 and4 percent, respectively. But innovation activities can only foster employment
growth if the firm is reasonably healthy. For non-growing firms (5, 10 and 25
percent quantiles) all coefficients measuring innovation activities are insignificant. While the effect of market novelties does not differ significantly among
the quantiles, continuous R&D differs significantly between the 50 percent
quantile and the quantiles above.

25

26

-0.026-0.012-0.000-0.0000.000-0.0000.000-0.000
0.000
0.000

0.000
-0.0000.024**
0.000-0.024
0.033
0.024
0.000
-0.000-0.104***
0.003
0.003
-0.006-0.003-0.0100.001-0.002-0.010
0.005

0.005
0.006
0.019**

0.005
0.012*
0.038**-0.013*-0.046

0.012
0.013
0.007
0.026

-0.001-0.002-0.011

0.146

Science park
Public funding (BA)
Public funding (non BA)

Limitation of liability
Exports
Team
No-expansion strategy
UnemployedContacts to academia
Joint researchContract research
CustomerContinuing education

High-technology industries
Knowledge-intensive services
Constant

Pseudo R2
0.011

0.003
-0.0000.000

0.060***
0.007
0.000
0.006

0.006
0.006
0.003
-0.006-0.0030.059***-0.0000.018*

0.006-0.0000.006

0.004-0.006**-0.000-0.000-0.000-0.000-0.000-0.000-0.000-0.0000.000-0.006*-0.012-0.003-0.006-0.009-0.013
0.003

Q25

0.094

0.015**
0.004-0.001

0.015
0.007
0.018*
0.037**

0.043***
0.044***
0.007-0.030***-0.0140.044***-0.018***0.033***

0.012
0.002
0.029***-0.002-0.011***0.010
0.019***
0.025***
0.036***
0.019**

0.006
0.027**
0.017**
0.006-0.022***-0.028-0.030-0.028-0.053***-0.013-0.001Q40

0.113

0.018**
0.008
0.031**-0.001-0.0000.018*
0.045***

0.053***
0.057***
0.004-0.056***-0.0270.029**-0.038***0.033***

0.016*-0.0020.036***-0.014-0.011***0.013*
0.026***
0.040***
0.070***
0.049***

0.007
0.040***
0.030**
0.006-0.024**-0.034*-0.020-0.057*-0.096***0.029-0.019Q50

0.124

0.025***
0.022***
0.067***-0.013-0.0040.026*
0.044**

0.058***
0.058***-0.000-0.067***-0.058***0.036**-0.039***0.026***

0.030***
0.008
0.034***-0.041***-0.006*0.018**
0.041***
0.061***
0.113***
0.119***

0.003
0.047***
0.023*
0.006-0.034***-0.043**-0.015-0.081*-0.092***-0.001
0.014

Q60

0.141

0.021**
0.019**
0.108***-0.017-0.0080.024*
0.065***

0.057***
0.062***-0.007-0.060***-0.071***0.029**-0.0210.026***

0.043***
0.011
0.029***-0.058***-0.0030.017*
0.049***
0.081***
0.145***
0.173***

0.007
0.047***
0.026**
0.005-0.040***-0.056**0.018-0.083*-0.111***-0.018
0.027

Q70

0.150

0.024**
0.019**
0.128***-0.004-0.0260.024
0.051***

0.063***
0.058***-0.008-0.063***-0.079***0.027*-0.022*0.021**

0.038***
0.007
0.032***-0.074***0.001
0.017*
0.057***
0.091***
0.160***
0.193***

0.016
0.056***
0.036***
0.016-0.026*-0.062**0.022-0.064-0.098***-0.016
0.030

Q75

0.163

0.019*
0.020**
0.157***-0.015-0.044*0.038*
0.043***

0.063***
0.063***-0.006-0.074***-0.085***0.013-0.033**0.022*

0.042***
0.011
0.031***-0.080***0.002
0.020*
0.060***
0.094***
0.186***
0.219***

0.012
0.057***
0.035**
0.015-0.030*-0.0390.020-0.085*-0.101**-0.0350.028

Q80

Notes: * p < 0.10, ** p < 0.05, *** p < 0.01, based on bootstrapped standard errors with 500 replications. 4303 Observations.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

0.044

0.000
-0.000-0.002-0.0000.000
0.000-0.0000.000-0.000-0.0000.000-0.029-0.0000.000-0.006-0.0290.000-0.0070.001
0.005-0.004-0.039*-0.001-0.005-0.057-0.085**
0.012
0.046

Q10

Time-lag since academia was left
Still in science
Time-lag: 3-5 years
Time-lag: 6-10 years
Time-lag: 11-20 years
Time-lag: +20 years(Spin-off)*(still in science)(Spin-off)*(0-2 years)(Spin-off)*(3-5 years)(Spin-off)*(6-10 years)(Spin-off)*(11-20 years)(Spin-off)*(+20 years)
Size and foundation yearln(size)ln(size)2
Founded in 2002
Founded in 2003
Founded in 2004
Founded in 2005
Founded in 2006
Innovation activities
Continuous R&D
Occasional R&D
Market novelties

Q5

Table 4: Results of quantile regressions on employment growth, selected quantiles

0.201

0.010
0.013
0.219***-0.028-0.0260.047**
0.034

0.074***
0.056***-0.002-0.083***-0.128***0.026-0.038**0.023

0.036**
0.002
0.047***-0.105***0.008
0.029**
0.070***
0.124***
0.248***
0.363***

0.021
0.060***
0.042**
0.029*-0.021-0.0250.015-0.097-0.065-0.004
0.087

Q90

0.239

0.016
0.019
0.231***-0.019-0.0350.050
0.047

0.088***
0.065***-0.022-0.081***-0.119**0.038-0.038
0.023

0.033
0.027
0.059***-0.086***0.007
0.046**
0.084***
0.147***
0.297***
0.467***

0.024
0.070**
0.050*
0.038-0.011-0.056-0.003-0.077-0.074-0.040
0.040

Q95

0.04

0.05

ln(size)²
0.00

0.02

0.00
ln(size)
−0.05

−0.02

−0.10

.2

.4

.6

.8

−0.04

−0.15
0

1

0

.2

.4

Quantile

.8

1

.8

1

.8

1

(b) ln(size)2

0

.2

.4

.6

.8

−0.05

0.00

0.00

founded in 2002
0.02
0.04
0.06

founded in 2003
0.05
0.10

0.08

0.15

(a) ln(size)

−0.02

.6
Quantile

1

0

.2

.4

Quantile.6
Quantile

founded in 2005
0.10
0.20
0.00

−0.05

0.00

founded in 2004
0.05
0.10

0.30

0.15

0.40

(d) Founded in 2003

0.20(c) Founded in 2002

0

.2

.4

.6

.8

1

0

Quantile

.2

.4

.6
Quantile(f) Founded in 2005

0.00

founded in 2006
0.20
0.40

0.60

(e) Founded in 2004

0

.2

.4

.6

.8

1

Quantile(g) Founded in 2006

Figure 2: Variation in the coefficients of size and year of foundation
Note: Horizontal lines represent OLS estimates, solid lines and shaded areas represent
quantile regression estimates with 95% confidence intervals.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

27

Public support in terms of being hosted in a science park or receiving
public funds is found to spur employment growth substantially. However,the differentiation between funding from the Federal Employment Agency(public funding (BA)) and other public agencies (public funding (non BA))has been pointed out to be pretty important. For funding from the Federal
Employment Agency the effect of poor growth prospects of start-ups by the
unemployed prevails. One may assume that the small volume of BA-funding
does not ease financial constraints substantially. In contrast, other publicfunding seems to relax financial constraints and help firms to realize their
growth potentials. For these firms employment growth is about 3 percent
higher (median regression). The same is true for being located in a sciencepark (median growth is about 3 percent higher). Since the usage of infrastructure at low or zero cost mainly helps start-ups in very early stages, it is
not surprising that the regression results reveal the high effect of the variable science park at the 25 percent quantile (growth increases by 6 percent)compared to an important, but significantly smaller effect at the 75 percent
quantile (growth increases by 3 percent). But this effect should be interpreted
cautiously since the growth performance of start-ups hosted by a science park
can be driven by a “picking the winners” selection process of the managers
of science parks.
Positive effects of exporting activities on firm growth, which have beenfound in earlier empirical literature, can be attested by this study, too. The
median firm exhibits a 6 percent gain in employment growth when carryingout exporting activities. The effect of exporting on academic start-up’s employment growth is furthermore significantly increasing from the 30 percent
quantile to the 50 percent quantile (see Figure 3).
A limitation of liability exerts a positive stimulus on employment growth.
This effect is significantly growing from a 4 percent stimulus at the 25%
quantile up to a 9 percent stimulus at the 95% quantile (see Figure 3). On
the contrary, starting the venture in a team of founders is only relevant forfirms of the lower growth quantiles. The team dummy is only significant in
the regressions for the 5 and 10 percent quantiles.

28

0.15

0.10

limitation of liability
0.05
0.10

0.08
exports
0.04
0.06
0.02

0.00

0.00
0

.2

.4

.6

.8

1

0

.2

.4

Quantile

.6

.8

1

Quantile(a) Exporting(b) Limitation of liability

Figure 3: Variation in the coefficients exports and limitation of liability
Note: Horizontal lines represent OLS estimates, solid lines and shaded areas represent
quantile regression estimates with 95% confidence intervals.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

Controlling regressors for firm strategies which are not growth-oriented,
industry effects and longer unemployment spells during the time after graduation or working in research provide the expected effects. For firms having
a no-expansion strategy not only the location but also the scale of startup’s employment growth is found to be considerably affected. The coefficient
no-expansion strategy is steadily decreasing along the quantiles (see Figure−0.10

no−expansion strategy
−0.05

0.00

4).

0

.2

.4

.6

.8

1

Quantile

Figure 4: Variation in the coefficient no-expansion strategy
Note: Horizontal lines represent OLS estimates, solid lines and shaded areas represent
quantile regression estimates with 95% confidence intervals.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

Positive effects of academic networks can only be found in the cases where
academia is a firm’s customer or where firm’s employees receive advanced
training in academia. This reveals that not only the human capital of the29founders is important for academic start-up’s firm success but also the humancapital of its employees.
In order to test the robustness of the results, a set of further median regressions was conducted. In each case a different set of control variables was
excluded. The results remain stable (see Table 7 in the appendix). Somecoefficients get larger when excluding the variables limitation of liability, exports, team, no-expansion strategy and unemployed. This result is reasonable
since most of these coefficients have been shown to exert important influence on employment growth. When these variables are excluded the effect is
captured by the error term or other variables correlated with the excluded
variables.

5

Conclusions

This paper investigated the human capital depreciation of entrepreneur’s
academic knowledge. As the influence of a potential knowledge depreciation
on firms employment growth ought to be studied, the analysis was conductedon the firm level. Examining employment growth is also beneficial sincefurther insights in young firms’ labor demand are gained.
Using quantile regressions I find a rapid decline of academic knowledge
after an academic institution is left. Professional experience which is gainedat the same time overcompensates the losses in academic knowledge up toa certain point in time. The highest growth rates are found for new firms
with a founding time-lag of 3-5 years after academia has been left. Thus,
for launching an ordinary academic start-up it is best to acquire 3-5 years ofprofessional experience before starting an own venture. This result should betaken into consideration by policy makers when designing policy programs
which should encourage students to become self-employed. If substantial
technology transfer is involved in launching the start-up, as it is the case foracademic spin-offs, depreciation of academic knowledge is of a higher importance. The danger of obsolescence of academic knowledge and catching-up
of others makes it most advantageous to start the firm directly, i.e. with atime-lag of up to two years, after the university has been left. Starting theventure while at least one of the founder is still in academia is not advantageous for academic spin-offs since common effort of all founders is especially30needed in the early stages of product’s and firm’s development.
Furthermore, the determinants of academic start-up’s employment growth
are examined along the whole distribution of young firm’s employment growth.
I find that some factors, e.g. founding in a team of founders or having been
hosted in a science park, are of higher relevance for firms in the lower partof the growth distribution, while other prominent factors, such as exporting, continuous R&D and a limitation of liability, are most important for
high-growing firms.
Gibrat’s Law, stating that the size of a firm and its growth rate are
independent, can be rejected along wide parts of the distribution of young
firm’s employment growth. This result is a material contribution to theliterature of young firm’s growth since the validity of Gibrat’s Law was mainly
tested in the conditional mean framework.

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Appendix
Table 5: Definition and descriptive statistics of all variables used
Variable

Definition

Mean

Std. dev.

Employment growth

Annual logarithmic change in employment
between 2008 and the founding year

0.141

(0.210)0.170(0.375)0.101(0.302)0.115(0.319)0.179(0.384)0.260(0.439)0.174(0.379)0.021(0.143)0.013(0.112)0.007(0.083)0.009(0.095)0.011(0.103)0.010(0.097)

0.859
1.444
0.151
0.153
0.179
0.176
0.185
0.156

(0.840)(2.329)(0.358)(0.360)(0.384)(0.381)(0.388)(0.363)

0.220
0.128
0.299

(0.415)(0.334)(0.458)Time-lag since academia was left
Still in science
At the time of establishment one of thefounders was still in science (Dummy)Time-lag: 0-2 years
The (last) founder left academia 0-2 years
before founding (Reference Category)Time-lag: 3-5 years
The (last) founder left academia 3-5 years
before founding (Dummy)Time-lag: 6-10 years
The (last) founder left academia 6-10
years before founding (Dummy)Time-lag: 11-20 years
The (last) founder left academia 11-20
years before founding (Dummy)Time-lag: +20 years
The (last) founder left academia morethan 20 years before founding (Dummy)(Spin-off)*(still in science) Interaction: academic spin-off (Dummy)
still in science (Dummy)(Spin-off)*(0-2 years)
Interaction: academic spin-off (Dummy)with time-lag: 0-2 years (Dummy)(Spin-off)*(3-5 years)
Interaction: academic spin-off (Dummy)with time-lag: 3-5 years (Dummy)(Spin-off)*(6-10 years)
Interaction: academic spin-off (Dummy)with time-lag: 6-10 years (Dummy)(Spin-off)*(11-20 years)
Interaction: academic spin-off (Dummy)with time-lag: 11-20 years (Dummy)(Spin-off)*(+20 years)
Interaction: academic spin-off (Dummy)with time-lag: +20 years (Dummy)Size and year of foundationln(size)
Logarithm of initial employmentln(size)2
Logarithm of initial employment squaredFounded in 2001
Start-up was founded in 2001 (Dummy)
Founded in 2002
Start-up was founded in 2002 (Dummy)
Founded in 2003
Start-up was founded in 2003 (Dummy)
Founded in 2004
Start-up was founded in 2004 (Dummy)
Founded in 2005
Start-up was founded in 2005 (Dummy)
Founded in 2006
Start-up was founded in 2006 (Dummy)
Innovation activities
Continuous R&D
Occasional R&D
Market novelties

Firm conducts continuous R&D (Dummy)
Firm conducts occasional R&D (Dummy)
Introduction of products new to the market (Dummy)

Continued on next page. . .

39

Table 5 – Continued
Variable

Definition

Mean

Std. dev.

Firm has been hosted by a science park or
business incubator (Dummy)
Public funding (BA)
Firm has received funding from the Federal Employment Agency (Dummy)
Public funding (non BA)
Firm has received funding from other public agencies (Dummy)
Other firm and founder specific variables
Limitation of liability
Legal form of the company involves limited liability for its owners (Dummy)
Exports
Firm sells its products or services (also)abroad (Dummy)
Team
Establishment has taken place in a teamof founders (Dummy)
No-expansion strategy
Firm is not growth-oriented (Dummy)
Unemployed
Founder(s) have had a longer unemployment spell between academia and firm
foundation (Dummy)
Contacts to academia
Joint research
Firm has regular contacts with academia
in the form of joint research (Dummy)
Contract research
Firm has regular contacts with academia
in the form of contract research (Dummy)
Customer
Firm has regular contacts with academia
in the form of customers (Dummy)
Cont. education
Firm has regular contacts with academia
in the form of continuing education offirm’s employees (Dummy)
Industries
High-technology industries Firm belongs to high-technology industries (average R&D intensity is above
3.5%) (Dummy)
Knowledge-intensive ser- Firm belongs to knowledge-intensive services
vice (Dummy)
Technological services
Firm belongs to technological service (Reference Category)

0.059(0.237)0.095(0.293)0.156(0.363)0.547(0.498)0.461(0.499)0.359(0.480)

0.344
0.012

(0.475)(0.109)0.049(0.216)0.021(0.143)0.070(0.255)0.048(0.214)0.241(0.428)0.405(0.491)0.353(0.478)Public support
Science park

Source: ZEW-Spin-Off Survey 2008, author’s calculations.

40

Table 6: Testing Gibrat’s Law along the conditional growth distribution

Quantile(1)∂ygS = ∂ln(St)

2008 )
ES = ∂ln(S∂ln(St )

Q05
Q10
Q15
Q20
Q25
Q30
Q35
Q40
Q45
Q50
Q55
Q60
Q65
Q70
Q75
Q80
Q85
Q90
Q95

-0.024-0.311-0.293-0.111-0.147-0.193-0.187-0.256-0.252-0.277-0.258-0.174-0.128-0.119-0.040-0.025
0.007
0.075
0.074

0.902
-0.243-0.171
0.557
0.410
0.227
0.251
-0.023-0.008-0.107-0.031
0.303
0.487
0.523
0.838
0.902
1.028
1.301
1.297

**
***
***
***
***
***
***
***
**
*

(2)

**
***
***
***
***
***
***
***
**
*

∂y
Notes: Wald-test for (1) H0 : ∂ln(S= βln(ST ) + 2 · βln(ST )2 ln(ST ) = 0 andT)£¤2008 )
2(2) H0 : ∂ln(S∂ln(St ) = 1 + A2008 · βln(ST ) + 2 · βln(ST ) ln(ST ) = 1

Stars denote rejection at the following significance levels:* p < 0.10, ** p < 0.05, *** p < 0.01
Source: ZEW Spin-Off Survey 2008, author’s calculations.

41

42

(0.010)(0.003)(0.007)(0.008)(0.009)(0.012)(0.014)(0.009)(0.010)(0.008)(0.008)(0.007)(0.008)(0.007)(0.018)(0.017)(0.021)(0.012)(0.014)-0.016-0.010***0.012
0.024***
0.038***
0.068***
0.048***
0.016*-0.006
0.036***
0.024*-0.037***0.031***
0.054***
0.056***
0.005-0.056***-0.0200.003-0.0000.014
0.047***
0.018**
0.010
0.033***
4303
0.111

Science park
Public funding (BA)4
Public funding (non BA)4

Limitation of liability
Exports
Team
No-expansion strategy
UnemployedContacts to academia
Joint researchContract research
CustomerContinuing education

High-technology industries5
Knowledge-intensive services1
Constant
Observations
Pseudo R2

0.025***
0.011
0.046***
4322
0.107

0.019
0.004
0.019*
0.045***

0.056***
0.062***
0.003-0.059***-0.032*0.039***-0.037***0.029***-0.013-0.011***
0.008
0.023***
0.037***
0.066***
0.042***

se(0.008)(0.007)(0.013)(0.013)(0.020)(0.011)(0.013)(0.008)(0.007)(0.007)(0.007)(0.019)(0.014)(0.010)(0.008)(0.010)(0.003)(0.007)(0.009)(0.009)(0.010)(0.014)(0.012)(0.016)(0.013)(0.010)(0.012)(0.017)(0.026)(0.030)(0.033)(0.029)(0.052)

Q50(2)-0.0000.038**
0.022*
0.002-0.029**-0.035**-0.028-0.053*-0.098***0.016-0.002coeff.

0.025***
0.011*
0.039***
4308
0.108

0.011
-0.0030.014
0.047***

0.055***
0.060***
0.004-0.060***-0.0300.019**-0.0000.033***-0.009-0.012***0.010
0.021**
0.035***
0.065***
0.039***

se(0.009)(0.007)(0.013)(0.015)(0.019)(0.011)(0.014)(0.009)(0.007)(0.008)(0.007)(0.021)(0.008)(0.009)(0.008)(0.010)(0.003)(0.007)(0.008)(0.009)(0.011)(0.014)(0.011)(0.014)(0.012)(0.011)(0.012)(0.020)(0.027)(0.031)(0.031)(0.028)(0.040)Q50(2)

0.000
0.032**
0.023*
0.001-0.028**-0.031-0.011-0.071**-0.099***0.021-0.016

coeff.

0.035***
0.025***
0.004
4321
0.073

-0.0020.022
0.018
0.055***

0.052***-0.040***0.040***

0.046***
0.020**
0.056***

0.031***-0.018***0.005
0.025***
0.036***
0.068***
0.043**

se(0.009)(0.009)(0.011)(0.017)(0.025)(0.014)(0.019)(0.012)(0.010)(0.009)(0.010)(0.010)(0.010)(0.012)(0.004)(0.007)(0.009)(0.012)(0.016)(0.019)(0.012)(0.015)(0.013)(0.011)(0.011)(0.026)(0.034)(0.042)(0.026)(0.045)(0.035)

Q50(4)-0.0040.060***
0.039***
0.011-0.032***-0.020-0.004-0.067-0.082***-0.028-0.004

coeff.

0.020**
0.009
0.030**
4303
0.111

0.054***
0.056***
0.004-0.057***-0.0250.036***-0.039***0.032***

0.021**
0.003
0.036***-0.014-0.011***0.014*
0.027***
0.038***
0.071***
0.048***

se(0.008)(0.007)(0.012)(0.008)(0.007)(0.008)(0.007)(0.020)(0.013)(0.010)(0.008)(0.010)(0.009)(0.007)(0.010)(0.003)(0.007)(0.008)(0.009)(0.012)(0.015)(0.011)(0.014)(0.012)(0.010)(0.012)(0.018)(0.035)(0.029)(0.030)(0.028)(0.041)

Q50(5)

0.011
0.042***
0.032***
0.009-0.020*-0.025-0.016-0.063**-0.091***0.020-0.023coeff.

Notes: * p < 0.10, ** p < 0.05, *** p < 0.01, bootstrapped standard errors (500 replications) in parentheses. Reference categories: (1) time-lag: 0-2 years,(2) founded in 2001, (3) no R& D (4) no public funding (5) technological services.
Source: ZEW Spin-Off Survey 2008, author’s calculations.

(0.009)(0.007)(0.012)(0.012)(0.010)(0.008)(0.010)(0.014)(0.011)(0.010)(0.011)

0.004
0.036**
0.025**
0.008-0.024**

Still in science
Time-lag: 3-5 years
Time-lag: 6-10 years
Time-lag: 11-20 years
Time-lag: +20 years(Spin-off)*(still in science)(Spin-off)*(0-2 years)(Spin-off)*(3-5 years)(Spin-off)*(6-10 years)(Spin-off)*(11-20 years)(Spin-off)*(+20 years)
Size and year of foundation2ln(size)ln(size)2
Founded in 2002
Founded in 2003
Founded in 2004
Founded in 2005
Founded in 2006
Innovation activities
Continuous R&D3
Occasional R&D3
Market novelties

Time-lag since academia was left1

Q50 (1)coeff.
se

Table 7: Robustness-checks: Median Regressions on employment growth