Is Entrepreneurial Success Predictable?An Empirical Analysis of the Character-Based

Approach∗

Marco Caliendo†

DIW Berlin

Alexander S. Kritikos‡

University Viadrina

Working Paper

This draft: February 28, 2006

Abstract

This paper empirically analyses whether entrepreneurial success is predictablebefore a potential founder has launched his business. Thereby we focus on thecharacter-based approach, distinguishing between personal and learned skills. Aunique data set is used consisting of 414 founders who were screened by two differ-ent methods, a one-day assessment centre (AC) and a standardised questionnaire.Results are partly unexpected and below expectations: First, there is no correlationbetween the AC data and the questionnaire. Second, the predictive power of the ACdata is better than the one of the questionnaire but still not very high. Interestingly,for those subgroups where the AC data have a low performance, the questionnairedoes better. Third, both methods are poor predictors when success is measured interms of hired employees.

Keywords: Entrepreneurship, Psychological Assessment, Character-Based Ap-proach, Success PredictionJEL Classification: M13, J23, C13

∗The authors thank Katrin Kahle and Denitsa Vigenina for valuable research assistance in the fieldand in the data preparation. Moreover, the authors are very grateful to Gunda Laasch-Wrobel. Withouther support to understand the methods of an assessment centre and her assistance in transferring theobservations of the assessment centre into scaled variables, this paper would not have been possible. Theauthors would also like to thank Hajo Streitberger and Thorsten Muller for giving us access to the data ofEnigma and for helping us to contact the entrepreneurs who started their businesses after participating inthe business incubator of Enigma. Last but not least, we would like to thank the 414 former participantsof this incubator who agreed to become a “research subject” and for their willingness to be once moreinterviewed again, even though entrepreneurs usually have no time to spare. Alexander Kritikos is alsograteful for the financial support of the European Union within the Equal II project.

†Marco Caliendo is Senior Research Associate at the German Institute for Economic Research (DIW)in Berlin and Research Fellow of the IZA, Bonn and the IAB, Nuremberg, e-mail: mcaliendo@diw.de

‡Corresponding author: Alexander Kritikos is Assistant Professor at the University of Viadrina inFrankfurt/Oder and Research Fellow of the IAB, Nuremberg. Mailing address: Alexander S. Kritikos,Fakultat fur Wirtschaftswissenschaften, Große Scharrnstr. 59, Europa-Universitat Viadrina, 15230 Frank-furt (Oder), Germany, e-mail: kritikos@euv-ffo.de

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1 Introduction

In recent years, an increasing number of persons in several high-income countries have

made the significant decision to become an entrepreneur or simply self-employed - often

because they lost their jobs as salaried employees.1 Many governments decided to foster

these transformation processes by offering financial support in terms of monthly lump

sum payments, free access to vocational training or subsidised loan products.2 The Ger-

man government spent nearly 3 billion euros alone in 2004 on start-up aids for formerly

unemployed people (about thirty times more than ten years earlier) in order to ease the

transition process in the first months of their entrepreneurial activities.

In as much as public spending is dramatically increased for this purpose, the question

became more and more crucial whether a method exists, to predict - with at least some

probability - the eventual success of such entrepreneurial activities. And there are many

stakeholders - founders of the new business units, private and public banks, governmental

agencies and non-governmental organisations - who would be heavily interested in this

information, if it could be generated.

Several approaches have been tested. In particular banks have designed scoring models

focusing on branches, branch indices, and the core capabilities of the applicants, and report

correct predictions for around 50 percent of the analyzed cases.3 Branch indices proved to

be inconsistent predictors. Many entrepreneurs were able to successfully start businesses

in branches which were redlined, while others failed in branches where investments seemed

to yield a safe return. These observations showed that the personality of the entrepreneur

might have an impact on the eventual success of a firm, in particular when the firm is run

by the entrepreneur alone.

In this context, a psychological approach has been developed which focuses on per-

sonality characteristics of an entrepreneur. First empirical evidence based on a validated

questionnaire showed that there are differences in these characteristics between entrepre-

neurs and employed persons (see e.g. King, 1985). And there are also several psychologists

(see e.g. Muller, 1999b) who assume that the personal skills of a person are given traits

which do not change over time and which are therefore good for prediction.

1For details see the GEM-Report (2005).2See for an excellent overview over support measures in various European Countries, Siewertsen and

Evers (2005).3See Kreditanstalt fur Wiederaufbau (2004) for details.

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In this paper, we investigate whether it is possible, to predict the prospective develop-

ment of a business based on the personal characteristics of its founder. To do this, we need

first to identify those personal factors which are assumed to be crucial to entrepreneurial

development. In particular, research in the psychological sciences has suggested several

factors. We present in Section 2 a brief review of the main personal factors which are

assumed to have an impact on the later success of a potential founder.

Once these factors are recognised, the second task is to detect the skill levels of poten-

tial entrepreneurs. The most commonly used method is the standardised questionnaire.

An alternative to this is the design of assessment centers (ACs) where potential founders

are given certain tasks while psychologists evaluate their performance.

We use data from a business incubator – the start-up center “Enigma” in the city

of Hamburg – which applied both methods simultaneously, a questionnaire based on the

German version of King (1985, see also Muller, 1999a) and a one day assessment center.

They screen individuals when they start to plan their own business. We combined these

data with a second, short questionnaire which only asks for actual employment status and

the size of the business of the former founders. Accordingly, in Section 3, we describe the

two assessment methodologies applied by this incubator.

In Section 4 we present and analyse a unique data set consisting of 414 business-

founders who were assessed at Enigma Hamburg. Thereby we analyse the correlations

between both sets of variables and test the influence of these variables on two distinctive

outcome variables relating to the success of the start-up.

Section 5 concludes the findings, unfolding that the results of both screening methods

are below expectations. Firstly, we found that there is almost no correlation between the

questionnaire data and the observed behaviour during the assessment. Secondly, taking

each instrument for itself, the predictive quality of the AC data are better than of the

questionnaire, but still far from good forecast. Making use of the AC data, we found that

personal skills (when put together to one factor) have a significant impact on the later

success as an entrepreneur. However, this observation does not hold for all subgroups.

Interestingly, we found for the questionnaire data to have some predictive power exactly

in those subgroups where the AC data failed. An additional, severe limitation is that the

predictive power of both methods is actually partly negative in terms of hired employees.

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2 Previous Research

There are three theoretical approaches which are used as heuristics for the empirical

research on the key factors of entrepreneurial success at the time of a business foundation:

i) a character-based (rather psychological) approach, focusing on personal skills of the

founders4; ii) a rather business-oriented approach, concentrating on criteria of the firm;

and iii) a (rather economically-oriented) environmental approach, focusing on the local,

branch-specific, and macroeconomic conditions around the firm.

Our paper focuses on the predictability of entrepreneurial success. Predictability pre-

sumes that, firstly, those variables are identified which are deemed to be crucial for the

later entrepreneurial development, and, secondly, that these variables are stable over time.

In the last years psychological research has analysed such variables in great detail and has

controversially discussed its predictive power. As our paper aims to analyse the impact

of such personal traits on the business development, we will restrict the survey on the

character-based approach.5

Previous studies marked several personal characteristics as given dispositions of any

individual in a population which should be able to explain the past and predict the future

development of a business formation. These include several motivational factors, among

them “achievement motivation” and the “internal locus of control”, several cognitive skills

such as “combinatorial thinking” and “risk-taking behaviour”, affective personality traits,

including for example, the “skills of reflection”, as well as several social skills, among them

the personal factors of “empathy” and of the “ability to assert oneself”.6 In the following,

we will focus the review on those factors for which we have empirical data and which we

4In this context it has to be emphasised that personal skills have to be differentiated from learnedskills which are also necessary for a successful foundation of a business. As Backes-Gellner and Lazear(2003), Lazear (2004) and Wagner (2003) showed it is also of central importance that founders havedeveloped a fairly broad basis of knowledge. However, it is further assumed that every person is able toachieve a sufficient basis of the necessary knowledge at any time which is why the actual level of existingknowledge at the time a person is assessed is expected to have no predictive power.

5This is not to say that the other approaches have no predictive power. With respect to the businessoriented approach, see, e.g., Stichcombe (1965), Williamson (1985), Picot et al. (1989), Bruderl et al.(1996). A recent empirical analysis of Blanchflower and Oswald (1998) showed that in particular theamount of available capital has some predictive power among the business-oriented variables. For theeconomic approach, see e.g. Porter (1981), Stoner and Frey (1982), Hannan and Freeman (1977), Bruderland Schussler (1990), Preisendorfer (2002). Its predictive power was shortly noticed in the introduction.

6The research on these psychological components is already numerous. For extensive discussions onthe impact of these and further factors on the entrepreneurial success, see e.g. McClelland (1961, 1985,1987), Klandt (1984), Warneryd (1988), Brandstatter (1997), Schuler (1998), Muller (2000) or Rauchand Frese (2000).

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are able to test with respect to their predictive power.

“Achievement motivation” expresses the motivation and ability of a potential business-

founder to search for more efficient solutions than those given in his/her actual environ-

ment (see McClelland, 1961, as well as Lumkin and Dess (1996)). If a person believes

that he/she has a business idea which enables him/her to do things faster, in a better

way, or with less effort than his/her actual competitor(s) in the market under the given

conditions, and if the same person is actively trying to realise his/her business idea, it is

said that the “achievement motivation” of this person corresponds to the prerequisites of

becoming a successful entrepreneur. Within the present framework, the factor “achieve-

ment motivation” will be measured by making use of Test 1 of the standardised tests and

a corresponding variable from the assessment centre.7

“Locus of control” (drawing back on the approaches of Rotter, 1966, and Furnham,

1986) measures generalised expectations for internal versus external control of reinforce-

ment. People with an internal locus of control believe that they are able to determine

their future development through their own actions. In contrast, persons with an external

locus of control believe that their own behaviour does not have any impact on their future

outcome, and that success and failure (of a project) is determined randomly or through

the external environment which is, thus, beyond their control. Accordingly, it is assumed

that persons with an internal locus of control have higher success rates as entrepreneurs

than individuals with an external locus of control.8 In the present study, this factor will

be tested by Test 2 of the questionnaire.9

“Combinatorial thinking” expresses the cognitive ability to act in a complex or linked

environment. In the context of entrepreneurial research, these skills enable an individual

not only to understand a problem (for which he/she might need “skills of reflection”) and

7In a cross cultural study McClelland (1987) found first empirical evidence that the entrepreneurialsuccess is - at least ex post - influenced through the variable ’achievement motivation’.

8In this context, it should be highlighted that with respect to “locus of control”, the psychologicalapproach is in direct contradiction to the economic approach. If locus of control matters, as suggestedhere, we should expect the entrepreneur to be in control of his later success, at least if he/she has ahigh internal locus of control sufficiently to adjust his/her business strategy in an optimal way to thegiven environment. The economic approach suggests that the environment determines the later successof an entrepreneur, which again is - at least according to the psychological approach - only possible ifthe entrepreneur has an external locus of control. In that case, however, the entrepreneur is expected tohave a lower success probability.

9First empirical evidence of the impact of the factor ’locus of control’ is given by King (1985) whodesigned the original questionnaire which is also used in this paper in its German version and Bonnetand Furnham (1991). King (1985) and Muller (1999b) found that entrepreneurs have a higher internallocus of control than employees.

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to know how to solve it, but to act in the sense that he/she is able to transfer his/her

knowledge into specific actions. More specifically, the person knows how to break down

larger problems into sub-problems, derive subtasks or sub-goals, and is able to anticipate

obstacles and to re-evaluate alternatives. In this sense, these skills are different from pure

intellectual power, as they describe the entity of understanding and acting during the

entrepreneurial process (see also Conrad et al., 1998). It is assumed that the better the

skills of “combinatorial thinking” the higher the probability that the business will start

successfully. In the present study, we will analyse this variable with AC data (the variable

has the same name) and with Test 3.

“Empathy” describes the ability to put oneself in the place of another person. In the

context of business-founders, this mean that they should be able to put themselves in the

place of potential clients, and potential suppliers of preliminary products (see e.g. Bierhoff

and Muller, 1993). It is believed that a business-founder who has sufficient empathy,

will produce more client-focused products which again will have a positive impact on

his/her sales and profits. In the present study, we will analyse this factor by making

use of the categorical variable “self-assertion/empathy” which was extracted from the AC

observations.

The final personal factor, the “ability to assert oneself”, relates to the previous one in

a complementary way. With respect to business-founders and entrepreneurs, this factor

means that a person who has a sufficiently high level of self-assertion will be able to

demand the prices he bargained with clients and suppliers. This factor reflects, however,

not only the negotiating skills of founders but should be seen in a more general way, in the

sense that the total performance of a founder towards his clients and suppliers matters.

Thus, it is assumed that if the “ability to assert oneself” is sufficiently high (Winslow and

Solomon (1987) defined the optimal level of self-assertion as “mildly sociopathic”), the

founder will be better able to realise the planned profits. In the present study, the impact

of self-assertion is analysed by making use of Test 4. More importantly, the “ability

to assert oneself” is connected with the factor “empathy”. It is assumed that if both

factors are well-balanced, the future development of an entrepreneur will be improved.

Accordingly, for the AC data both factors are measured by making use of one categorical

variable (self-assertion/empathy).

Figure 1 displays the available variables from the standardised tests and the psycholog-

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Figure 1: Overview of the Set of Variables and Expected Correlations

ical assessments. In addition to the already mentioned variables, we have four additional

variables which were gathered by the psychologists during the presentation of the busi-

ness idea. All variables were evaluated against the contextual background of whether the

applicant had certain learned skills. These variables relate to a certain extent to the ap-

proach of Lazear, as they were i) the branch where the business should be started (Basic

Competences), ii) the financial background needed to start the business (Presentation

Finance), iii) the clients who have to be found to sell the planned product (Presentation

Clients), and iv) further financial needs in subsequent years if the business should de-

velop as planned. As we will see in the next sections, the variables measuring personal

traits and those variables measuring learnable skills were correlated. In psychological sci-

ences, it is usual in this case (also in order to overcome the problem of multicollinearity

among the initial variables) to compute latent variables by factor analysis.10 The factor

“Skills” was built from the variables “achievement motivation”, “self-assertion/empathy”,

and “combinatorial thinking”. The factor “Knowledge” was extracted from the variables

“presentation: clientele” and “presentation: finance”.

Besides the mentioned variables, psychological research has listed several other vari-

ables whose impact we were not able to evaluate in the present research.11 Among these,

10See, e.g., Nunnally and Bernstein (1994)11A further motivational factor is the “willingness to act independently of others” (see e.g. Alderfer,

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we would like to mention the factor “risk attitude”, which is deemed to be crucial for

the development of a business. With respect to this factor, it is assumed that successful

entrepreneurs will choose business opportunities which bear a medium risk. Chell et al.

(1991) as well as Klandt (1996) assert that it would be wrong to expect that risk-seeking

entrepreneurs would have a higher success probability. Business-founders should always

try to reduce their risks as much as possible, without becoming too risk-averse. The

risk of a business opportunity should therefore be of medium range. As it is difficult to

measure whether a person is willing to bear low, medium or high risks without a proper

benchmark, Enigma Hamburg decided to drop this variable when they designed their

assessment methodology.12

Putting this puzzle together, among the factors which were identified in the psycholog-

ical research as being crucial for the development of a business-founder, we will make use

of five variables which seem to have a clear and plausible impact on the success of an entre-

preneur. Each of these were measured by making use of the data gathered by “Enigma”

in Hamburg. With respect to the five variables, this overview allows for the following

hypotheses. If properly assessed, a business-founder should have a higher probability of

becoming a successful entrepreneur,

- the higher his/her “achievement motivation” is,

- the more internal his/her “locus of control” is,

- the better his/her “combinatorial thinking” skills are, and

- if his/her ability “to assert oneself” and to behave “empathically” are well balanced.

On a more general level, all variables listed in the character-based approach can be

grouped together as personal skills, which should be correlated, at least to some degree.

This may allow for the following second, more general hypothesis, that, if the single

factors measuring the personal skills of an assessed person are correlated, the probability

of becoming a successful entrepreneur should be higher, the higher the observed total

1972) which was found to be significantly different for entrepreneurs and employees (see e.g. Hackman andOldham, 1976). “Emotional stability”, “creativity” and the “skills of reflection” are affective personaltraits which are assumed to have an impact on the development of a business, as well (see e.g. Brandstatter(1997), Goebel (1991)).

12Nevertheless, it has to be emphasised that it is also assumed that the risk taking behaviour ofentrepreneurs should be higher than of employees. The empirical studies of King (1985) and of Muller(1999b) support this hypothesis.

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personal skills of a founder are. We will return to this hypothesis, when we will discuss

the variables gathered from the assessment centre and the questionnaires.

At the end of this section we should also review very shortly the discussion of the

stability and, thus, of the predictive power of these personal traits - which keeps on going

for more than twenty years (see inter alia MacMillan and Katz (1992) or Brandstatter

(1997)). Several psychologists propose that there is a “given set” of personal traits which

determines the development of a person as an entrepreneur which is why these traits

should be used as predictors. The contradicting position was based on the hypothesis of

socialisation, according to which personal traits are influenced by the experience a person

gains when working as a self-employed. In that case, the parameter value of certain

personal traits would not contain any predictive power.

There is some empirical evidence in favour of the hypothesis that traits are stable

over time: Muller (1999a) developed, as mentioned earlier, a German version of the

questionnaire of King (1985). Both questionnaires were tested with self-employed people

and salaried employees, showing more or less the same significant differences for the

American and the German samples. Self-employed people reveal always higher parameter

values for all five characteristics. Muller (1999b) replicated the same questionnaire with

persons who were planning to launch a business. Persons with the ambition of becoming

self-employed showed exactly the same parameter values as the persons who were already

entrepreneurs and had, thus, the same differences with salaried employees. From this

Muller (1999b) concludes that personal traits are indeed stable over time and could be

used as predictor of later success of an entrepreneur. Moreover, he suggests that the

questionnaire itself could be used as an instrument to predict the development of a person

as entrepreneur.13

3 The Business Incubator “Enigma” in Hamburg

The source of our data is a “business incubator” which was developed 1997 by an organi-

sation named “Enigma” located in Hamburg and which was financed through a specially

13In this context, it should be mentioned, that Blanchflower and Oswald (1998) also analysed thepredictive power of test scores, however not some months before the business was launched but duringthe childhood. They found a small negative correlation between those children who were anxious foracceptance (at the age of 7 years) and the later probability to run an own business. These authors concludethat “psychology apparently does not play a key role in determining who becomes an entrepreneur”.

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designed program of the ministry of labour (aiming for “new labour market policies”). In

the meantime, the project (of business incubation) was replicated in four more cities. The

main target group of Enigmas’s incubator service are people who lost (or are threatened

with losing) their jobs as employees, who are planning to start their own business, and

who ask to be strongly supported in their efforts to become an entrepreneur. For this

specific target group Enigma has created an “integrated concept” – the incubator service.

This concept – lasting over six months – combines the transfer of knowledge with the

training of skills and with structured feedback on the first actions of these founders as

entrepreneurs.14

Since the incubator services are offered free of charge for persons who start their own

business out of unemployment (this is a prerequisite for getting access to the above-

mentioned special funds), these persons are then directed to an assessment centre lasting

one day.15 The assessment centre (AC) was directed by two trained psychologists accom-

panied by two laypersons. The purpose of the screening process is twofold. On one hand,

the incubator team should be informed about the skills and the level of pre-existing knowl-

edge (with respect to the founding of a business) of the candidates at the date of their

entry into the incubator. On the other hand, certain barriers were set as standards which

the candidates had to pass in order to get access to the extensive incubator services.

As the AC focuses on evaluating the skills, and basic entrepreneurial knowledge of

the applicants as well as their business ideas, it uses three independent tools for the

evaluation: the standardised questionnaire designed by Muller (1999a), the presentation

of the business idea by the applicant, and a number of structured exercises where each

applicant is assigned to a certain role.

4 Empirical Analysis

In the present study, we make use of data on 414 applicants who passed the above-

described AC, were invited to join, and started to plan and found their own business

in the incubator of Enigma. The participants launched their businesses between 2001

and beginning of 2004. In order to make sure that we do not have to deal with any

14Besides the incubator services, Enigma is also offering standard seminars for founders who need (orguess to need) only support to increase the necessary know-how for their entrepreneurial activities.

15For more details on the design of this incubator, see Kritikos and Wiessner (2004).

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unobserved heterogeneity regarding the support of the individuals, we restrict our analysis

to applicants who made use of the same kind of incubator service.

In addition to the data from the assessment center, we collected information which

records the actual performance of these business-founders. The aim was to identify how

many founders managed to set up a business, took a position as salaried employee or

returned to unemployment. Founders who have started their own business were also

asked whether they employ other persons. The data for this analysis was gathered through

phone interviews carried out in the first quarter of 2005.

Table 1 contains some summary statistics of the available variables, which we will de-

scribe in Section 4.1. The first column refers to the whole sample of participants, whereas

columns 2 through 5 differentiate the sample by age and gender. The differentiation by

age and gender is driven by the expectation that effects will be different for men and

women and for younger and older people. Hence, we will conduct the following analyses

for the whole group as well as for the four subsamples (men, women, young and old in-

dividuals). One shortcoming of the data is that we do not know the actual age of the

individuals, but only whether they are above or below 30 years old. As can be seen in the

table, older individuals and men are over-represented in our sample.

We will start the empirical analysis in Section 4.2 with an examination of the stan-

dardised test variables and the assessment of the psychologists. Thereby, we hope to show

whether the intended results of the standardised tests correspond to the psychological as-

sessments. In a second step (Section 4.3), we test the predictive power of the different

variables (tests and assessments) on two distinctive outcome variables.

4.1 Set of Variables and Some Descriptives

Table 1 contains a description and some summary statistics of the available information.

We will briefly describe each variable and its distribution in the data. We start with the

standardised tests as described in Section 2. Four test variables were used, with a scaling

from 0 to 5, where 5 indicates the best and 0 the worst result possible. In the context

of the variables we discussed in Section 2, the first variable measures the applicant’s

“achievement motivation”, where we have an average of 4 in the sample. The second

shows to what extent the applicant has an “internal locus of control”, whereas the third

test reflects the applicant’s “combinatorial thinking” skills. Finally, the fourth test is a

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Table 1: Description of the Variables and Summary Statistics

Age GenderVariables All < 30 > 30 Men WomenNumber of Observations 414 198 216 259 155Age (1 = over 30 years) 0.52 – – 0.56 0.45Gender (1 = Men) 0.63 0.57 0.68 – –Standardised Test (Measure of the applicant’s ...)

1. Achievement motivation 3.99 3.97 3.97 3.88 4.172. Internal locus of control 3.22 3.20 3.20 3.20 3.243. Combinatorial thinking 3.67 3.54 3.54 3.66 3.704. Self-assertion 1.82 1.74 1.74 1.84 1.77

Psychological EvaluationsBasic Competencesa 0.94 0.91 0.96 0.94 0.93Financial Needsb 0.41 0.41 0.42 0.45 0.35Presentation: Clientelec 1.94 1.51 2.33 1.99 1.86Presentation: Financed 2.13 2.11 2.15 2.13 2.14Achievement Motivatione 1.80 1.64 1.94 1.76 1.85Combinatorial Thinkingf 1.95 1.91 2.00 1.92 2.01Self-Assertion/Empathy (in %)

Weak self-assertion and weak empathy 0.24 0.32 0.16 0.24 0.23Weak self-assertion and strong empathy 0.36 0.42 0.31 0.37 0.34Strong self-assertion and weak empathy 0.27 0.20 0.33 0.25 0.30Equally self-assertive and empathic 0.12 0.05 0.19 0.13 0.11

Outcome VariablesEmployment Status (in %)

Self-employed 0.75 0.76 0.75 0.74 0.77Salaried worker 0.12 0.14 0.10 0.14 0.09Unemployed 0.10 0.05 0.15 0.11 0.09Education + other 0.03 0.06 – 0.02 0.05

New Employment (1 = Yes) 0.28 0.32 0.23 0.31 0.22Number of Employed Persons 3.57 4.30 2.65 3.78 3.08a 1 - if the applicant has earlier experience in the business area he wants to work

in, 0 - otherwiseb 1 - if the applicant had a clear financial plan for the initial phase, 0 - otherwisec Shows whether the applicant knew his future clientele: 1 - no, 2 - partly, 3 - very

welld Shows whether the applicant knew how to finance his business: 1 - no, 2 - partly,

3 - very welle 1 - weak, 2 - intermediate, 3 - strongf Measures the applicant’s combinatorial thinking ability: 1 - low ability, 2 - inter-

mediate ability, 3 - high ability

measure of the applicant’s “ability to assert oneself”.16 It is interesting to note that all

tests are fairly equally distributed among the four subgroups. The fourth test is the one

where applicants achieve the lowest test scores. All four test scores correspond perfectly

to previous empirical findings (see Muller, 1999b).

All other variables articulate the evaluation of the two psychologists (who were sup-

16The original questionnaire contains a fifth variable ‘Risk taking behaviour’. However, as the impactof this variable on the later success cannot be predicted in a clear way, the head of the AC decided todrop this variable.

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ported by the two laypersons) on different scales while they observed the performance of

the applicants during the presentation of their business ideas and during the exercises. We

have discussed most of these variables already in Section 2. Basic competences, mea-

sures whether the applicant either had earlier working experience in the branch where

he/she planned to start their business or had other long-term experiences beyond the

standard working life which allowed for the conclusion that he/she would be competent

in the field of the planned business. 94 percent of the participants in Enigma fulfill this

requirement. Presentation: Finance, shows whether the applicant knew how to fi-

nance his business in the initial stage and whether he/she will need to search for outside

finance in order to start the business. This variable is equally distributed among the four

subsamples at a value of roughly 2.1 on a choice set of 1(=no), 2(=partly) and 3(=very

well). Presentation: Clientele, indicates whether the applicant explained (in his

presentation) what kind of persons his future clientele could be and why they should buy

the planned product. Given the same choice set as before, the average assessment is 1.94

here. However, we have strong variation, and especially young people do worse than aver-

age (while older do better). Financial needs, tells us whether the applicant presented

an idea about how to finance the business concept during the following years, in case the

business should become successful. Only 41 percent of all applicants did so; the rate for

women is particularly low at 35 percent.

The second block of variables analysed during the AC is concerned with the traits

of the applicants which are assumed to be given (in contrast to the learned skills) and

which relate to the characteristics discussed in Section 2. Here the participants of the

AC had to conduct three different exercises where they were assigned to specific roles

during the exercise. The main task of the observers was not to analyse the outcomes

of the groups but to examine to what extent the participants were “equipped” with the

following four characteristics, which relate to the theoretical survey provided in Section 2:

achievement motivation, empathy, ability to assert one-self, and combinatorial thinking

skills. Achievement motivation was measured on a scale from 1 (weak) to 3 (strong)

and the average value was 1.8 (intermediate). Self-assertion and Empathy were

measured by one categorical variable. A value of 1 reflects weak self-assertion and weak

empathy (24% of the sample), of 2 reflects weak self-assertion and strong empathy (36%),

of 3 strong self-assertion and weak empathy (27%), and of 4 well-balanced self-assertion

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Table 2: Pairwise Correlation Coefficients

Test 1 Test 2 Test 3 Test 4Test 1 1.000Test 2 0.238∗ 1.000Test 3 0.175∗ 0.191∗ 1.000Test 4 0.084 1.000Achievement MotivationCombinatorial Thinking 0.086 0.086Self-AssertionPresentation: ClientPresentation: Finance 0.099∗

Ach.Mot. Comb. Assert Pres. Pres.Think. Client Finance

Achievement Motivation 1.000Combinatorial Thinking 0.370∗ 1.000Self-Assertion 0.533∗ 0.438∗ 1.000Presentation: Client 0.325∗ 0.113∗ 0.293∗ 1.000Presentation: Finance 0.205∗ 0.152∗ 0.282∗ 1.000

Printed if significant at the 10 %-level, * indicates significance at the 5 %-level.

and empathy (12%). Finally, Combinatorial thinking measures to what extent the

applicant was able to provide step-wise solutions to complex problems during the exercises.

4.2 Correlation Analysis - Standardised Tests versus Psycholog-ical Assessments

Table 2 contains pairwise correlation coefficients of the variables of interest, i.e., the four

standardised tests and the five psychological assessments. To increase the visibility of the

results we only included coefficients that are at least significant at the 10 percent level, a

star indicates significance at the 1 percent level.

A surprising result is that only the variables “combinatorial thinking” and “presenta-

tion: finance” are correlated with the standardised test variables, though on a low level

of around 0.09. Since the third test should measure the combinatorial thinking capabil-

ities of the individual and is also correlated with the psychological assessment, this is

an expected result. The combinations “Test 1 and Achievement Motivation” as well as

“Test 4 and Self Assertion” are, however, not correlated. Hence, standardised tests and

psychological assessments seem to measure different things. As discussed already and can

be seen from the lower part of the table, psychological assessments are correlated with

each other. We also checked the correlation of the variables for the subgroups discussed

above; the results can be found in Tables A.1 (men/women) and A.2 (Age below/above

30) in the Appendix. For individuals over 30 years old we find more positive correlations,

14

whereas for people younger than 30 years the correlations are actually negative. The dif-

ferentiation by gender shows that for women the combination “Test 2 and Combinatorial

Thinking” is still valid, though we do not find corresponding results for men.

4.3 Analysing the Success of the Start-Ups

We analyse the predictive power of the variables with respect to two distinct outcomes.

In a first regression we check the influence of the variables on the employment status of

the individuals as recorded at the time of the interview. To be specific, we estimate a

multinomial logit model of the form

P (y1i = 1) =

exp(x′ijβ)

1 + exp(x′i2β) + . . . + exp(x′iMβ), j = 1, 2, . . . , M, (1)

where y1 can take on the values self-employed (y1 = 1), regular employed (y1 = 2)

or unemployed (y1 = 3). X is a vector of explanatory variables which we define further

below and the coefficients β are the ones we are interested in.

A further measure which we want to analyse relates to the success of the entrepreneur

in terms of employees hired. Therefore we construct an outcome variable which takes on

the value 1 if the self-employed has at least one employee at the time of the interview and

0 otherwise, i.e.

y3i =

1 if Employees ≥ 1

0 otherwise

(2)

Hence, we can use a binary logit model for estimation. For the estimation of both

outcome variables we use five sets of explanatory variables X. In the first specification we

only use the standardised test scores, whereas in the second specification we exclusively

use the psychological assessments. Specification three combines both sets of explanatory

variables, whereas we use the reduced variables from the factor and cluster analysis in

specification 4. Finally, in specification five we include the reduced variables from speci-

fication 4 and add two more explanatory variables. Table 3 summarises the strategy.

15

Table 3: Overview of the Different Specifications

Variables Spec. 1 Spec. 2 Spec. 3 Spec. 4 Spec. 5Standardised Tests

1. Achievement motivation X X2. Internal locus of control X X3. Combinatorial thinking X X4. Self-assertion X X

Cluster variable of Test-Scores X XPsychological EvaluationsAchievement Motivation X XCombinatorial Thinking X XSelf-Assertion/Empathy X XPresentation: Clientele X XPresentation: Finance X XFactor Analysis: Skills X XFactor Analysis: Knowledge X XOtherBasic Competences XFinancial Needs X

X indicates that the variable is included in the specification.Spec. 1: Consists of standardised test scores only.Spec. 2: Consists of psychological evaluations only.Spec. 3: Combines standardised tests and psychological evaluations.Spec. 4: Combines reduced forms of standardised tests and psychological evalua-tions.Spec. 5: Combines reduced forms of standardised tests and psychological evalua-tions and two additional explanatory variables.

Employment Status: Table 4 contains the estimation results of the multinomial logit

model for the whole sample. The coefficients have to be interpreted in relation to the

basecategory, which is unemployment in our case. That means, that a positive coefficient

in the upper half of the table indicates, that a variable has a positive influence on the

probability to be in self-employment (compared to unemployment). The results in the

lower part refer to the status regular employment. The only significant effect in specifi-

cation 1 relates to the fourth test, i.e., the variable ‘self-assertion’. A higher score in this

variable increases both the probability to be in self-employment and regular employment

(relative to unemployment). Additionally, men and individuals older than 30 years have a

higher probability to be in regular employment. Using only the psychological assessments

in specification 2, does not have any explanatory power for the model, i.e., none of the

five variables is significant at a conventional level. Older individuals have now a signif-

icant lower probability to be in self-employment and regular employment. Combining

both sets of variables in specification 3 confirms the positive influence of the test variable

‘self-assertion’ and reveals additionally a positive influence of the variable ‘combinatorial

16

thinking’. Since we have shown in section 4.2, that we the psychological assessments are

highly correlated between each other, we used factor analysis to reduce the information to

two factor ‘skills’ and ‘knowledge’. Since the same holds true for the standardised test, we

used cluster analysis to condense the information to a dummy variable dividing the groups

into two groups (with a high and low overall test score). Specification 4 shows, that the

factor ‘skills’ has a positive influence on the probability to be in self-employment and also

on the probability to be in regular employment. Finally, in specification 5 we added two

more explanatory variables, which are not significant. The negative influence of the age

dummy (both for self-employment and regular employment) remains significant over the

different specifications. Overall, we have to conclude that neither the standardised tests

nor the psychological assessments have a large predictive power for the estimated models.

Insert Table 4 about here

To test, if there are different effects for the subsamples under consideration, we re-

peated the estimations for the four subsamples. To avoid flooding the reader with tables,

we do not present the full results here. Instead, Table A.3 in the Appendix shows which

coefficients have been significant for the different groups.17 We will discuss these results

at the end of this section.

Hired Employees: Finally, we take a look at the success of the start-up in terms of the

number of hired employees. Clearly, the induced labour demand effects are one argument

for the subsidisation of start-ups (by formerly unemployed people). The descriptives in

Table 1 already have shown, that roughly 30% of the former Enigma clients already had

at least one employee at the time of the interview. Unfortunately, Table 5 shoes, that

none of the discussed variable has any influence whatsoever on this outcome variable.

Table A.4 in the Appendix - once again - contains the results for the subgroups.

Insert Table 5 about here

Results in the Subgroups: In as much as the factor “skills” seemed to have from a

general point of view if not a large but at least some predictive power, we have to emphasise

that the correlation does not hold for all subgroups which we analysed. “Skills” was only

17The results are available on request by the authors.

17

significant for men and for older persons, “combinatorial thinking” only for men and

younger persons. The questionnaire variable “self-assertion” was in turn only significant

for women and younger persons.

There are some more results from the subgroup analysis which should be highlighted.

Firstly, several variables showed to have an impact on the later development which had no

relevance in the overall analysis. Worth mentioning is that for women two test variables

out of the questionnaire had a positive correlation while among the skills observed in

the AC no variable showed to have any correlation with the later development. More

astonishingly, it has to be highlighted that one learned skill (“presentation clientele”) was

positively correlated with the success variables.

Secondly, and most importantly, for the subgroup of the entrepreneurs who employed

further persons in their firm both assessment methods made inconsistent predictions: as

in the analysis of the complete data set, the male subgroup showed no correlations, at all.

The cluster analysis of the test scores (which had nowhere else any explanatory power)

showed only for the subset of older persons a positive correlation. However, for the same

subset of older persons the test variable “locus of control” was negatively correlated, while

the same test variable (“locus of control‘”) was positively correlated for younger persons.

Worse, the factor “personal skills” had for younger persons a slightly negative and for

women a strongly negative impact. Of course, the latter findings have to be interpreted

very cautiously, as the number of observations was very low in these two subgroups.

5 Conclusions and Policy Recommendations

Most persons planning to found their own firm will probably ask themselves whether

they are going to succeed with their business and many of them are willing to spend

significant sums if they could get access to an instrument which allows a forecast of their

later development as entrepreneurs. The aim of this study is, therefore, to investigate the

predictive power of two different assessment methods at the time where people plan to

start an own business.

As we showed in Section 2, psychological research has developed a set of variables which

is believed to be crucial for the development as an entrepreneur and stable over time. The

variables can be grouped as “personal skills” and are composed of characteristics like the

18

motivational, affective, cognitive and social skills of a person. These variables differ from

learnable skills (recently discussed by Lazear (2004) as of being crucial for entrepreneurial

success, as well) insofar as the latter skills are not stable over time and should, therefore,

have no predictive power according to the psychological approach.

We use in this paper two different kind of assessments - a validated questionnaire

and results from a one-day assessment centre - in order to test the predictive power of a

subset of personal skills as well as of a subset of learnable skills. In order to evaluate the

impact of these variables on the development of a business-founder we briefly interviewed

414 persons who passed both assessment methods and founded their business in the

same business incubator of the start-up centre Enigma in Hamburg. Our expectations

were that i) each of the five variables which we were able to test - namely “achievement

motivation”, “internal locus of control”, “combinatorial thinking”, “self-assertion” and

“empathy” - should have a positive impact on the development of a business that ii) if

the variables should be correlated among each other the factor “personal skills” should

be a predictor for the later success of a person who is planning to found an own firm.

From a general point of view the results are below expectations. First of all, there is

no correlation between the questionnaire and the observed behaviour of the participants

during the assessment centre. Secondly, among the observed variables (of the assessment

centre) we found a correlation between the factor “personal skills” and the later success as

an entrepreneur. As for the test of the single variables only the “combinatorial thinking

skills” showed to have a positive impact on the entrepreneurial activities. For all other

variables we could not find any impact. As to the questionnaire, it is remarkable that

the cluster variable of the test scores had in the overall analysis no (and for men even

a slightly negative) predictive power. Nevertheless, among the four test variables the

variable “self-assertion” showed to be positively correlated.

In as much as the factor “personal skills” seemed to have from a general point of view

certain predictive power, we have to emphasise that the correlation does not hold for all

subgroups which we analysed. “Personal skills” was only significant for men and for older

persons, “combinatorial thinking” only for men and younger persons. The questionnaire

variable “self-assertion” was in turn only significant for women and younger persons.

There are some more results which should be highlighted. Firstly, persons who started

an own business but later on returned to a position of a salaried employee after having

19

been offered a - possibly more attractive - job had the same level of “personal skills” and

were different to those persons who are still running their own business only insofar as

the employees took higher values with respect to the factor “knowledge”. Secondly, in the

subgroup analysis, several variables showed to have an impact on the later development

which had no relevance in the overall analysis. Thirdly, and most importantly, for the

subgroup of the entrepreneurs who employed further persons in their firm (those who

are deemed to be the ’real entrepreneurs’) both assessment methods made inconsistent

predictions.

These findings allow drawing several conclusions. With respect to the questionnaire,

it seems that this kind of self-assessment has a lower predictive power than expected by

Muller (1999b). It is obvious that this test does not allow any discrimination between

persons who are willing to start an own business irrespective whether they are going to

succeed as entrepreneurs, will return to a salaried job or to unemployment. It seems that

the willingness of becoming an entrepreneur has a higher impact on this method than the

given “personal skills” of a person.18

The predictive power of a specially designed assessment centre is better than of the

questionnaire. However, this study makes clear that this method can only be used in

order to improve decision making processes as to whether a certain person will become

a successful entrepreneur and whether it pays to support the person with public money.

However, a mere prediction based only on one day of observing a person who is conducting

an assessment centre is not (yet) possible. Further, it became clear that the variables

which were selected for this assessment centre are only valid for those persons who want to

become self-employed, but who do not intent to employ any further persons. For potential

founders who aim to open up a larger business it seems that other skills then the observed

ones are crucial. Thus, a more differentiated screening method seems necessary.

From a more general point of view, this research leaves open whether psychological

sciences have identified the right variables to capture entrepreneurial behaviour, whether

the observed variables are indeed stable over time, whether intensive support measures like

an incubator service have an impact even on personal traits, or whether only the methods

of assessing the potential entrepreneurs need to be improved. To this end additional

research is needed.

18It cannot be excluded that persons who are answering to this questionnaire under the condition ofan assessment centre may anticipate the “right answer” in several cases.

20

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23

Tab

le4:

Mult

inom

ialLog

itE

stim

atio

nR

esult

s:E

mplo

ym

ent

Sta

tus

(Ref

eren

ceC

ateg

ory:

Unem

plo

yed)

Spec

.1

Spec

.2

Spec

.3

Spec

.4

Spec

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f-Em

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yed

Gen

der

(1=

Men

)0.

218

0.04

30.

129

0.09

10.

123

Age

(1=

>30

)−0

.413

−0.8

99∗

−0.8

15+

−0.5

2−0

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+St

anda

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ent

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ivat

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ance

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100∗∗

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627

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=>

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41∗

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ised

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Con

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ster

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234

7

Sign

ifica

nce

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ls:

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,**

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.

25

Tab

le5:

Log

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ne

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plo

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vs.

Non

e

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Spec

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261

0.27

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173

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(1=

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−0.3

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02−0

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Stan

dard

ised

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easu

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appl

ican

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ieve

men

tm

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n−0

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tern

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cus

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l0.

013

0.01

2C

ombi

nato

rial

thin

king

−0.0

98−0

.09

Self-

asse

rtio

n0.

154

0.10

7A

chie

vem

ent

Mot

ivat

ion

−0.3

05−0

.294

Com

bina

tori

alT

hink

ing

−0.0

10

Self-

Ass

erti

on/E

mpa

thy

(Ref

.w

eak

self-

asse

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nan

dw

eak

empa

thy)

wea

kse

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ion

and

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ance

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Skill

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asic

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pete

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s29

529

828

628

626

1

Sign

ifica

nce

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ls:

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5%

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.

26

Tab

leA

.1:

Pai

rwis

eC

orre

lati

onC

oeffi

cien

ts–

Diff

eren

tiat

edby

Gen

der

Tes

t1

Tes

t2

Tes

t3

Tes

t4

Ach

.Mot

.C

omb.

Ass

ert

Pre

s.P

res.

Thi

nk.

Clie

ntFin

ance

Men

Tes

t1

1.00

0Tes

t2

0.27

5∗1.

000

Tes

t3

0.17

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162∗

1.00

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80.

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ch.

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ivat

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000

Com

b.T

hink

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sser

t0.

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7∗0.

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res.

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Pre

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ance

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enTes

t1

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t2

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000

Tes

t3

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240∗

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ch.

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ivat

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Com

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hink

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ert

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res.

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ance

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nted

ifsi

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cant

atth

e10

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el,*

indi

cate

ssi

gnifi

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eat

the

5%

-lev

el.

27

Tab

leA

.2:

Pai

rwis

eC

orre

lati

onC

oeffi

cien

ts–

Diff

eren

tiat

edby

Age

Tes

t1

Tes

t2

Tes

t3

Tes

t4

Eng

ag.

Com

b.A

sser

tP

res.

Pre

s.T

hink

.C

lient

Fin

ance

Age

<30

Tes

t1

1.00

0Tes

t2

0.20

1∗1.

000

Tes

t3

0.22

7∗0.

296∗

1.00

0Tes

t4

1.00

0A

ch.

Mot

ivat

.−0

.144∗

−0.1

62∗

1.00

0C

omb.

Thi

nk.

−0.1

42∗

−0.1

330.

395∗

1.00

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sser

t−0

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1∗0.

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res.

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nt0.

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4∗1.

000

Pre

s.Fin

ance

0.27

0∗1.

000

Age

>30

Tes

t1

1.00

0Tes

t2

0.26

9∗1.

000

Tes

t3

0.12

21.

000

Tes

t4

0.16

1∗1.

000

Ach

.M

otiv

at.

0.25

5∗0.

147∗

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81.

000

Com

b.T

hink

.0.

343∗

1.00

0A

sser

t0.

144∗

0.16

6∗0.

154∗

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7∗0.

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1.00

0P

res.

Clie

nt0.

259∗

0.11

90.

145∗

1.00

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res.

Fin

ance

0.18

6∗0.

298∗

0.23

3∗0.

365∗

1.00

0

Pri

nted

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cant

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e10

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el,*

indi

cate

ssi

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canc

eat

the

5%

-lev

el.

28

Table A.3: Multinomial Logit Estimation: Employment Status(a)

Gender AgeVariables Men Women < 30 > 30Standardised Test (Measure of the applicant´s ...)

Achievement motivation 0/0 +/+ 0/0 0/+Internal locus of control 0/0 0/0 0/0 0/0Combinatorial thinking 0/0 0/+ 0/0 0/0Self-assertion 0/0 +/0 +/+ 0/0

Achievement Motivation 0/0 0/0 0/0 0/0Combinatorial Thinking +/+ 0/0 +/+ 0/0Self-Assertion/Empathy (Ref. weak self-assertion and weak empathy)

weak self-assertion and strong empathy 0/0 0/0 0/0 0/0strong self-assertion and weak empathy +/0 0/0 0/0 +/0strong self-assertion and strong empathy 0/0 0/- 0/0 0/0

Presentation: Clientele 0/0 +/0 0/0 0/0Presentation: Finance 0/0 0/0 0/0 0/0Skills +/+ 0/0 0/0 +/0Knowledge -/0 0/0 0/0 0/0Cluster variable of Testscores 0/0 0/0 0/0 0/0Financial Needs 0/0 0/0 0/0 0/+Basic Competences 0/0 0/0 0/0 0/0

+ indicates a significant (at least on the 10% level) positive coefficient- indicates a significant (at least on the 10% level) negative coefficient0 indicates no significant influence(a) The coefficients from the multinomial logit model have to be interpreted in relation to the base category,

which is unemployment in our case. The first sign in each cell corresponds to self-employment, thesecond one to regular employment. For example, the combination (0/+) in the last column of line 1means, that the variable achievement motivation has no significant effect on the probability to be inself-employment (relative to unemployment) but increases the probability to be in regular employment(relative to unemployment).

29

Table A.4: Logit Estimation Results: At Least One Employee vs. None

Gender AgeVariables Men Women < 30 > 30Standardised Test (Measure of the applicant´s ...)

Achievement motivation 0 0 0 0Internal locus of control 0 0 + -Combinatorial thinking 0 0 0 0Self-assertion 0 0 0 0

Achievement Motivation 0 0 - 0Combinatorial Thinking 0 - 0 0Self-Assertion/Empathy (Ref. weak self-assertion and weak empathy)

weak self-assertion and strong empathy 0 0 0 0strong self-assertion and weak empathy 0 0 0 0strong self-assertion and strong empathy 0 0 0

Presentation: Clientele 0 0 0 0Presentation: Finance 0 0 0 0Skills 0 - - 0Knowledge 0 0 0 0Cluster variable of Testscores 0 - 0 +Financial Needs 0 0 0 0Basic Competences 0 0 0 0

+ indicates a significant (at least on the 10% level) positive coefficient- indicates a significant (at least on the 10% level) negative coefficient0 indicates no significant influence

30