Peter Van Der Zwan

CASBEC Roy Thurik
email: thurik@few.eur.nl

Isabel Grilo
email: isabel.grilo@cec.eu.int

The entrepreneurial ladder and its determinants

Keywords: entrepreneurship, determinants, nascents, ordered multinomial logit, Europe orderedlogit pzw rth igr v17 H10, J23, L26, M13, R12 entrepreneurship, determinants, nascent entrepreneurship, ordered multinomial logit, Europe

We test a new model where the entrepreneurial decision is described as a process of successive engagement levels, i.e., as an entrepreneurial ladder. Five levels are distinguished using nearly 12,000 observations from the 2004 "Flash Eurobarometer survey on Entrepreneurship" covering the 25 European Union member states and the United States. The most surprising of the many results is that perception of lack of financial support is no obstacle for moving to a higher entrepreneurial engagement level whereas perceived administrative complexity is a significant obstacle. We also show that the effect of age on the probability of moving forward in the entrepreneurial process becomes negative after a certain age implying that if entrepreneurial engagements are not taken early enough in life they may well never be taken.

Introduction

The theory of occupational choice has dominated the investigations of the entrepreneurship (selfemployment) decision (Parker, 2004; Grilo and Thurik, 2005a). It views agents as (expected)-utility maximisers taking an occupational choice decision -to become employees or entrepreneurs -on the grounds of the utility associated with the returns accruing from these two types of activity. Rooted in the work of Knight (1921) this theory sees entrepreneurship as a state which one can adopt or not. This 'static' view has been updated by a more 'dynamic' one acknowledging that setting up a business is a process which consists of several stages (Reynolds, 1997). This view led to a wave of research of the determinants of so-called nascent entrepreneurs (Davidsson, 2006). Nascent entrepreneurs are people who are taking certain steps to become self-employed but are not yet officially established. The work of the Global Entrepreneurship Monitor (GEM) is inspired by this view (Reynolds, et al., 2005). Grilo and Thurik (2005a and 2006) introduce the concept of engagement levels to discriminate between the various stages of setting up or closing down a business. They apply a simple multinomial logit model to analyze the determinants of the various stages. The engagement levels in the present paper are analyzed in an ordered context, in the sense that each level can be associated with an increasing level of involvement in the entrepreneurial process. The idea behind this approach is that entrepreneurship can be described as a process one becomes involved in and where different engagement levels can be distinguished, each having a not necessarily identical set of determinants. (Potential) entrepreneurs climb the entrepreneurial ladder. In the present paper we analyze five of these naturally ordered engagement levels. Nearly 12,000 observations are used from the 2004 "Flash Eurobarometer survey on Entrepreneurship" covering all the 25 European Union member states and the United States to analyze whether an ordered regression model with five engagement levels gives an adequate description of the entrepreneurial process and to what extent the available covariates are determinants of this process. In other words, we analyze whether these covariates have a significant influence on moving people up the entrepreneurial process.

The contribution of the present paper is that, first, while in earlier studies only a multinomial logit model has been used, here we extend this framework to an ordered context. Hence, we investigate whether there is a natural ordering of the dependent variable supporting the view of entrepreneurship as a process. Secondly, we determine which variables 'drive' (potential) entrepreneurs through this process.

Data

In the 2004 "Flash Eurobarometer survey on Entrepreneurship"1 the following question was used to construct the dependent variable: "Have you started a business recently or are you taking steps to start one?" The following options for answering were given:

(1) "It never came to your mind."

(2) "No, but you are thinking about it." (2a) "No, you thought about it or had already taken steps to start a business but gave up."

(3) "Yes, you are currently taking steps to start a new business." (4) "Yes, you have started or have taken over a business in the last three years and are still active." (5) "Yes, you started or took over a business more than three years ago and are still active." (5a) "No, you once started a business, but currently you are no longer an entrepreneur."

Without engagement levels (2a) and (5a) we expect the process to be naturally ordered in terms of involvement in the entrepreneurial process. We will abbreviate the remaining five stages as "Never thought about it", "Thinking about it", "Taking Steps", "Young business" and "Old business", respectively. Other than demographic variables such as gender (male=1), age, education level (age when finished full time education) and whether parents are self-employed (one or both of the parents are self-employed=1), the set of explanatory variables used includes four perceptions of 'obstacles', a rough measure of risk tolerance, internal and external locus of control and country specific effects. We refer to the usual literature of the determinants of entrepreneurship for justifying the use of these variables [START_REF] Parker | The Economics of Self-Employment and Entrepreneurship[END_REF][START_REF] Davidsson | Nascent entrepreneurship: empirical studies and developments[END_REF]Grilo and Thurik, 2005a, 2005band 2005c). 2 The perception variables include the perception by respondents of: lack of available financial support, of complex administrative procedures, of lack of sufficient information on starting an own busines s, and of an unfavorable economic climate. These variables as well as the risk tolerance variable are captured, respectively, using the question "Do you strongly agree, agree, disagree or strongly disagree with the following statements?":

• "It is difficult to start one's own business due to a lack of available financial support."

• "It is difficult to start one's own business due to the complex administrative procedures."

• "It is difficult to obtain sufficient information on how to start a business."

• "The current economic climate is not favorable to start one's own business."

• "One should not start a business if there is a risk it might fail."

For the four 'obstacle' statements a dummy variable is constructed which equals 1 in the case of 'strongly agree' or 'agree'. For the 'risk tolerance' statement a dummy variable is constructed which equals 1 if 'disagree' or 'strongly disagree' has been chosen for the fifth statement.

Internal locus of control measures whether an individual believes that (s)he can influence events through own ability, effort or skills. On the other side, external locus of control measures whether an individual believes that external forces determine the outcome. Respondents can choose between five answers on the following question "When one runs a business, what do you think most determines its success (maximum of two answers)?":

• "The director's personality."

• "The general management of the business."

• "The overall economy."

• "The political context."

• "Outside entities."

The dummy internal success factors equals 1 if one or both of the first two possibilities is mentioned, whereas external success factors equals 1 if one or two of the last three possibilities is mentioned.

Country specific effects are controlled for using country dummies where the US serve as base.

Ordered logit model

The ordered logit model builds upon a latent continuous variable, * i y , which is modeled using the linear regression

i i i X y + = * ,
< = . if ; 1 , 2, = for , if ; if 1 * 1 * 1 1 * i J j i j i i y J j y j y Y J K Hence, for 1 , 2, = J j K
, each probability of belonging to engagement level j for individual i is given by Pr

) ( ) ( = ) = ( 1 i j i j i X F X F j Y with ) ( F the cumulative logistic distribution function. For 1 = j we have Pr ) ( = 1) = ( 1 i i X F Y and for J j = this probability equals ) ( 1 1 i J X F . Note that ) , , ( = 1 n X X X K
does not contain a row of ones for identification purposes.

The above model can be extended to the heteroskedastic case by taking the variance of i to be

2 2 2 ) ( exp 3 1 = ) ( i i z E
(with i z a vector of observed variables without constant term) so that ) ( exp / i i z is now a homoskedastic error term. In the remainder we use the notation .

) ( exp = i i z The probability Pr ) = ( j Y i in the heteroskedastic case equals i i j i i j X F X F 1 .

Model evaluation

The estimation results of both the homoskedastic and heteroskedastic ordered logit model with five engagement levels are shown in Table 1. 3 The magnitude of the coefficients and their significance do not differ much between the two models (only 'education squared' is insignificant in the heteroskedastic formulation). Threshold estimates are of different magnitude in both models but their absolute differences are comparable.

Variables that have a significant influence on the variance of the disturbance term in the heteroskedastic regression are gender (coefficient is 0.108), age (0.018), self-employed parents (0.218), education (-0.007), preference for self-employment (-0.161) (all at a one per cent significance level) and economic climate (0.066) and lack of insufficient info (0.059) (both at five per cent). 4 Economic interpretation of the heteroskedastic results is somewhat difficult. For instance, one could say that men and older people, ceteris paribus, generate a higher variance of the disturbance term i in the latent regression. In these cases, there is a higher uncertainty in the (latent) value * i y and hence, there is more uncertainty about the specific engagement level of the entrepreneurial process an individual belongs to.

Though we have found that the heteroskedastic model is statistically superior to the homoskedastic formulation, we proceed with the interpretation of the homoskedastic model as no important differences are present in the estimation results of the variables and thresholds (apart from 'education squared'). 3 We also ran regressions with 1) all engagement levels, 2) only without engagement level (2a) and 3) only without engagement level (5a). In turns out that all diagnostics are in favor of the model we use. 4 We used a simple likelihood ratio principle to test for the significance of in the heteroskedastic specification ) ( exp = i i z . This test statistic, which compares the restricted log-likelihood value (when = 0) with the unrestricted one, is asymptotically 2 distributed under the null hypothesis with 7 degrees of freedom (number of restrictions imposed). Note that we did not include a constant in z i , again due to an identification problem. The resulting value of the test statistic (261.40) is far above the five per cent critical value of a 2 distribution with 7 degrees of freedom (14.07) and hence, we reject the null hypothesis of = 0 finding statistically sufficient evidence that the heteroskedastic ordered logit model is preferred to the homoskedastic ordered model. A crucial assumption underlying the ordered logit model is the 'parallel regression assumption' (same coefficient vector for each engagement level j). If one includes J engagement levels in the ordered logit model, the test we use (Wald test proposed by Brant, 1990) investigates the equality of the coefficients for all J-1 binary logit regressions for k explanatory variables. 5 The coefficient vectors of these J-1 logit regressions are denoted as j , j=1,…, J-1. The null hypothesis of the Wald test assumes J-1 parameter equalities across k variables and hence -as Kim (2003) indicates -we cannot expect this assumption to be true, particularly not in large samples. In our homoskedastic model the 'parallel regression assumption' for all variables is violated. One can also check the violation of the 'parallel regression assumption' for each variable separately: only for male, age, age squared, self employed parents and preference for self-employment, the null hypothesis of equal parameter estimates is rejected at one per cent (country dummies are again not considered here). See Table 2 (left hand column). For the variables that do not 'pass the test', it is therefore relevant to look at the results of the binary logit regressions. In Table 2 the estimates of the coefficient vectors j are displayed together with their standard errors as well as marginal effects (not for country dummies). 6 With these marginal effects in mind, one can investigate how impacts of variables change (and the significance of these impacts) with increasing level of involvement. 7 Outcomes are discussed in our section on interpretation.

While testing the 'parallel regression assumption' homoskedasticity is assumed. So, rejection of the 'parallel regression assumption' may be a consequence of not permitting a non-linear function of the latent variable, i.e., a heteroskedastic specification of the error variance. A similar argumentation can be given the other way around: rejecting the homoskedastic specification may be caused by the fact that the 'parallel regression assumption' is not true, i.e., a non-linear specification might be better, while this test is performed under the assumption of equal j s.

Allowing for a heteroskedastic specification we test the 'parallel regression assumption' to investigate what the 'real' cause is of rejecting the left side model in Table 1. For each heteroskedastic binary regression we have

)) exp( / ( ) Pr( * j i j i i z x F j Y = =
. The estimates of * j and * j as well as marginal effects are displayed in Table 3 (without country dummies and constant). The Wald statistic only points at rejection of the 'parallel regression assumption' at a one per cent significance level in the case of preference for self-employment. However, it sometimes gives negative values. The results for gender, age, age squared, self-employed parents and administrative complexities tend to show less spread across the four binary regressions than the results of homoskedastic binary regression given in Table 2. As said above, for these five variables the 'parallel regression assumption' is violated in the homoskedastic case while the coefficients are significant. It is tempting to conclude that rejection of the 'parallel regression assumption' in the homoskedastic model is due to not allowing for a heteroskedastic formulation. 8

Interpretation

Interpretation of the ordered logit model is best done using the log odds ratios log(Pr(

) j Y i /Pr( i j i X j Y = )) >
. So, for each engagement level j, a positive coefficient implies 5 To illustrate these binary regressions, suppose one has three engagement levels, so J = 3. One can now perform two separate binomial logit regressions: Pr(Y i = 1) versus Pr(Y i > 1) and Pr(Y i 2) versus Pr(Y i = 3). For each binary regression a different coefficient vector is estimated. When these coefficient vectors do not significantly differ from each other, there is no reason to reject the 'parallel regression assumption'. 6 The computation of the marginal effects is done as follows: for each observation a marginal effect is calculated and the sample averages of these values are displayed in Table 2 for all variables. The p-values of these effects are comparable to p-values of the coefficients of the binary regressions in the same table. 7 If the 'parallel regression assumption' is not violated for a variable, this does not necessarily imply that the marginal effects in Table 2 are statistically the same across all binary regressions. 8 Furthermore, we investigated the redundancy of the variables in the heteroskedastic specification (testing 0 * = j for each j) with a likelihood ratio test statistic (7 degrees of freedom, 0.05 critical value is 14.07). The four test statistics given in Table 3 (79.42; 69.08; 58.20; 51.22) are all in excess of 14.07. We did the same for the entire model (46 degrees of freedom, 0.05 critical value is 62.83). The four test statistics given in Table 3 (3343.66; 2034.88; 1776.52; 1351.76) are all in excess of 62.83. 6 that an increase in the corresponding variable, while keeping all other variables equal, leads to a situation where an individual is more likely to move to an engagement level higher than j than to stay in j.

The estimates of the thresholds show that the first is relatively far away from the second (the confidence intervals do not even overlap). It seems difficult to switch from "Thinking about it" to "Taking steps". Once in the entrepreneurial process, the step from "Taking steps" to "Young business" is relatively easily made. This gap again is smaller than the one from "Young business" to "Old business".9

Demographic variables: gender, age, education

Table 1 reveals that the gender coefficient is significantly different from zero: men have a higher probability than women of moving to a higher level of entrepreneurial involvement. Note that for this gender variable the 'parallel regression assumption' has been violated, because of a different coefficient in each binary regression (see Table 2). Furthermore we see in Table 2 that the effect of gender on the probability of being in engagement level j+1 versus j decreases as j increases. So, the effect of gender becomes weaker (it plays a less important role) when higher levels of engagement are attained.

As can be seen from Table 1 age and education are significantly present in the ordered regression. Because of the violation of the 'parallel regression assumption' for the age variable we take a further look at Table 2. Taking into account the squared term we can calculate the turning points at which the effect of age becomes negative for each binary regression. It turns out that these turning points vary between 36 years old for the switch from "Never thought about it" to higher levels of involvement and 51 years old in the last binary regression which confronts any level of engagement below having a business for at least 3 years versus the highest involvement level of being an owner for at least 3 years. These turning points increase steadily as the switch portrayed in the binary regression corresponds to higher levels of entrepreneurial involvement. 10 These results seem to suggest that the 'jump' into any form of entrepreneurial involvement, even the mildest "Thinking about it", is more likely to be made until the mid-thirties with age playing against it as one gets older than that. Without making a case of the precision of this specific age, what this result implies as a message for those who design measures or incentives to help people consider an entrepreneurial carrier, is that the chances of success in triggering such a change of mind decrease after a certain age. In the same vain, using the information conveyed by the turning points implicit in the other binary regressions, every move towards higher levels of entrepreneurial engagement is less likely after a certain age.11 These results, eventually complemented by additional research, are useful for policy makers in determining target groups depending on the type of measures envisaged to prompt an entrepreneurial response from the population.

For education, on the other hand, the 'parallel regression assumption' has not been violated (the coefficient stays the same across all engagement levels). Furthermore, despite the negative sign of 'education squared' in Table 1 the effect of education remains positive in the relevant range.12

Self-employment preference and self-employed parents

Preference for self-employment is significantly present in the ordered regression. This coefficient does not change as one becomes more active in the entrepreneurial world. The marginal effect of this variable, however, decreases heavily in moving forward in the entrepreneurial process, while this variable seems to be very important in the switching behavior as can be seen from the large marginal effects across all binary regressions. Having self-employed parents also significantly increases the probability of moving to higher engagement levels, as the (large) significant marginal effects in Table 2 reveal.

Obstacle variables

The perception of lack of financial support does not affect the probability of being in any of the stages of the entrepreneurial process. It does not seem to discourage respondents in setting up a business and becoming entrepreneur. The same holds true for the lack of sufficient information. Also, the fact of perceiving an unfavorable economic climate does not play a role in switching through the whole entrepreneurial system, although in the last two binary regressions concerning levels of high involvement, this variable does have a significant effect.

The fact that a respondent perceives it to be difficult to start a business due to complex administrative procedures has a negative impact on the probability of being in the more 'active' levels of entrepreneurship (see the significant negative coefficient estimate in Table 1 and the significant negative marginal effects in Table 2). Furthermore, if one is more risk tolerant, one is more likely to move to a higher engagement level in the entrepreneurial system than staying in the present engagement level.

Internal and external locus of control

Finally, internal and external success factors do not seem to be relevant in the context of the present setup. Hence, the fact that an individual believes that he or she can influence events through his/her own ability or skills does not have a significant influence of being in one of the five stages of the entrepreneurial process. The same can be concluded for the acknowledgement that external factors influence events.

Country dummies

Parameter estimates of the country dummies are insignificant in case of Denmark, Greece, Netherlands, United Kingdom, Latvia, Poland and Slovenia (at the ten per cent significance level). Furthermore, the coefficients are largest for Belgium, Spain, France, Portugal and Malta (all negative) and Estonia and Slovakia (both positive).

Conclusion

We start from the assumption that the decision to become entrepreneur should be modeled as a process rather than as a binary choice. We discriminate between five stages of entrepreneurship (engagement levels). These stages are successive so that 'climbing the entrepreneurial ladder' becomes the obvious metaphor. For each stage, 2004 survey data are available at the individual level for all the 25 EU member states and the US. We analyze these engagement levels using an ordered logit model to investigate the influence of various explanatory variables on moving through the various stages of the process, i.e., on climbing the ladder.

The estimation results of the ordered logit threshold levels reveal that it is difficult to switch from "Thinking about starting a business" to "Taking steps to start a business". Once in the entrepreneurial process, the step from "Taking steps" to "Having a young business" is made more easily. This gap is smaller than the one from "Having a young business" to "Having an old business".

We have shown that t he effects of gender and education are positive and significant while those of age are positive up to a certain age, at which point they turn negative. Moreover, on the basis of a set of binary regressions it is shown that the turning point at which the effect of age turns negative increases with higher levels of entrepreneurial involvement. Men move more easily through the process than women while the effect of this variable decreases with the level of entrepreneurial involvement. Furthermore, the better educated people move more easily through the process. Also, if one has a preference for self-employment, one is more likely to move to a higher engagement level than to stay in the current one. While the perception of lack of financial support, of insufficient information and of an unfavorable economic climate do not have a significant impact (this last variable has significant effects in the switching from "Taking steps" to "Young business" and from "Young business" to "Old business"), a respondent's perception that it is difficult to start a business due to complex administrative procedures has a negative impact on switching to higher engagement levels. Besides, more risk tolerant people find it easier to move upward through the various stages than people who are less risk tolerant. [START_REF]perception of lack of financial support as well as the unambiguous influences of the perception of administrative complexities, preference for self-employment and risk tolerance are in line with findings in earlier studies using different probit models but also based on the[END_REF] In this conclusion we want to stress the policy implications of two findings. First, we found that beyond the age of 36 years the probability of at least thinking about embracing an entrepreneurial carrier decreases. Together with the phenomenon of the aging European societies, this finding gives a sense of urgency to policies aimed at turning potential entrepreneurs into active ones. Second, our finding that administrative complexities have a negative effect on the probability of moving forward in the entrepreneurial process lends support to the many public efforts to cut re d tape and adopt better regulation approaches. : coefficient and marginal effect significant at 0.01; ** : at 0.05; * : at 0.10.

### : 'parallel regression assumption' violated for this variable at 0.01; ## : at 0.05; # : at 0.10. ^^^: significant at 0.01 in homoskedastic logit regression (see Table 1).

  Following this literature we also apply quadratic terms for age and education next to the linear ones.

									where	i	=	1,	K	,	. n	For example, * i y can be thought of as an
	unobserved willingness to be(come) an entrepreneur. The disturbance terms, i , are uncorrelated and for
	the ordered logit model it holds that all i follow a logistic distribution with mean zero and variance
	equal to	2	/3	. i X is a	k	×	1	vector of explanatory variables for individual i with corresponding
	coefficient vector (	1 k ) which is the same across all observations i and engagement levels j . ×
	In contrast with this latent variable we observe the variable i Y (the engagement level which
	individual i belongs to) with outcomes i y , where	y i	J = K and J is the number of engagement , 1,
	levels. Next, * i y is related with i y by means of	J	1	unobserved thresholds levels	1	,	, K	J	1	:
	2

Table 1 . Estimation results ordered logit model (estimates of coefficient vector and threshold levels with corresponding standard errors).

 1

		Homoskedastic	Heteroskedastic
		coeff.	std.err.	coeff.	std.err.
	Gender	0.547 ***	0.041	0.806 ***	0.096
	Age	0.134 ***	0.007	0.317 ***	0.030
	(Age/100) squared	-16.864 ***	0.841	-44.869 ***	4.300
	Education	0.068 ***	0.013	0.115 ***	0.030
	(Education/100) squared	-7.264 ***	2.534	-9.264	5.983
	Self-employed parents	0.398 ***	0.046	0.464 ***	0.104
	Lack financial support	-0.019	0.053	-0.100	0.097
	Administrative complex.	-0.192 ***	0.047	-0.306 ***	0.088
	Insufficient info	0.052	0.044	0.008	0.087
	Risk tolerance	0.169 ***	0.043	0.254 ***	0.081
	Economic climate	0.029	0.046	-0.056	0.090
	Preference self-employment	1.756 ***	0.045	3.539 ***	0.251
	Internal success factors	-0.030	0.049	-0.062	0.091
	External success factors	-0.064	0.055	-0.100	0.105
	Belgium	-0.725 ***	0.133	-1.403 ***	0.259
	Denmark	-0.029	0.157	-0.218	0.303
	Germany	0.216 *	0.117	0.260	0.228
	Greece	0.172	0.112	0.194	0.212
	Spain	-0.918 ***	0.129	-1.846 ***	0.259
	France	-0.874 ***	0.129	-1.680 ***	0.270
	Ireland	-0.491 ***	0.144	-0.940 ***	0.265
	Italy	-0.546 ***	0.116	-1.176 ***	0.234
	Luxembourg	-0.572 ***	0.156	-1.217 ***	0.284
	Netherlands	0.157	0.124	0.308	0.244
	Austria	0.319 **	0.160	0.360	0.301
	Portugal	-0.584 ***	0.124	-1.383 ***	0.244
	Finland	0.369 **	0.154	0.562 *	0.288
	United Kingdom	-0.023	0.122	-0.002	0.232
	Czech Republic	0.334 ***	0.125	0.634 **	0.247
	Estonia	0.700 ***	0.148	1.114 ***	0.306
	Cyprus	-0.394 ***	0.147	-0.861 ***	0.258
	Latvia	0.009	0.140	-0.057	0.267
	Lithuania	0.339 **	0.139	0.559 **	0.268
	Hungary	0.237 *	0.128	0.207	0.237
	Malta	-0.620 ***	0.171	-1.182 ***	0.318
	Poland	0.015	0.118	-0.070	0.207
	Sweden	-0.359 **	0.156	-0.787 ***	0.293
	Slovakia	0.746 ***	0.140	1.373 ***	0.297
	Slovenia	0.230	0.142	0.373	0.266
	Threshold 1	4.876 ***	0.239	9.302 ***	0.711
	Threshold 2	6.492 ***	0.243	12.469 ***	0.913
	Threshold 3	6.855 ***	0.244	13.220 ***	0.967
	Threshold 4	7.355 ***	0.245	14.309 ***	1.046
	Number of observations	11751		11751
	Log-likelihood	-10927.83		-10666.40
	LR statistic	3349.30 ( 2 , 39 df.)	3872.16 ( 2 , 46 df.)
	Akaike inform. crit.	1.867		1.824
	Bayesian inform. crit.	1.894		1.855
	McFadden R 2	0.133		0.154

*** : significant at 0.01; ** : at 0.05; * : at 0.10.

Table 2 . Results from four homoskedastic binary logit regressions (estimates of coefficient vectors j , together with average marginal effects).

 2

					Binary regression
		(1) vs. >(1)	<=(2) vs. >(2)	<=(3) vs. >(3)	<=(4) vs. (5)
		coeff.	effect	coeff.	effect	coeff.	effect	coeff.	effect
	Gender ###, ^^^0.509 *** 0.091	0.753 *** 0.077	0.819 *** 0.067	0.853 *** 0.049
	Age ###, ^^^0.104 *** 0.018	0.241 *** 0.025	0.306 *** 0.025	0.328 *** 0.019
	(Age/100) squared ###, ^^^-14.498 *** -2.548 -26.190 *** -2.687 -31.671 *** -2.615 -32.273 *** -1.908
	Education ^^^0 .068 *** 0.012	0.068 *** 0.007	0.079 *** 0.007	0.064 **	0.004
	(Education/100) squared ^^^-6.368 ** -1.119	-8.934 ** -0.917 -13.017 *** -1.075 -11.827 ** -0.699
	Self-employed parents ###, ^^^0.340 *** 0.061	0.608 *** 0.067	0.685 *** 0.061	0.684 *** 0.044
	Lack financial support	-0.003	-0.001	-0.069	-0.007	-0.023	-0.002	-0.063	-0.004
	Administr. complex. ##,^^^-0.143 *** -0.025	-0.283 *** -0.030	-0.338 *** -0.029	-0.270 *** -0.017
	Insufficient info ##	0.042	0.007	0.162 **	0.017	0.114	0.009	-0.005	0.000
	Risk tolerance ^^^0 .167 *** 0.029	0.200 *** 0.021	0.246 *** 0.020	0.200 **	0.012
	Economic climate ##	-0.003	-0.001	0.080	0.008	0.184 **	0.015	0.235 *** 0.013
	Preference for	.783 *** 0.348	1.758 *** 0.178	1.605 *** 0.130	1.654 *** 0.093
	self-employment ###,^^^1
	Internal success factors	-0.076	-0.013	0.061	0.006	0.082	0.007	0.076	0.005
	External success factors	-0.101 *	-0.018	0.042	0.004	-0.001	0.000	0.040	0.002
	Number of observations	11751	11751	11751	11751
	Log-likelihood	-6183.25	-3930.88	-3269.68	-2466.62
	LR statistic ( 2 , 39 df.)	3264.23	1965.79	1718.32	1300.54
	Akaike inform. crit.	1.059	0.676	0.563		0.427
	Bayesian inform. crit.	1.084	0.701	0.588		0.452
	McFadden R 2	0.209	0.200	0.208		0.209

***

http://europa.eu.int/comm/enterprise/enterprise_policy/survey/eurobarometer_intro.htm

These results support the use of the influential TEA (Total Entrepreneurial Activity) measure of GEM where nascent and young entrepreneurs are taken together[START_REF] Reynolds | Global entrepreneurship monitor: data collection design and implementation 1998-2003[END_REF].

For each binomial regression in Table2the turning point where the effect of age becomes negative is 36, 46, 48 and 51 years old. These numbers are similar to those obtained in the heteroskedastic binary regressions, except that the turning point of any level of engagement below having a business for at least 3 years versus the highest involvement level of being an owner for at least 3 years becomes 50 years in stead of 51.

[START_REF] Reynolds | Who starts new firms? -Preliminary explorations of firms-in-gestation[END_REF] using the concept of "nascent entrepreneurs" (those reporting two or more firm gestation behaviours) finds that age is the dominant factor affecting decisions to start a new firm and that this effect is non-monotonic attaining its peak for the age class 25 to 34.

The turning point for education resulting from the coefficients in Table1takes the value of 47 for the variable "age when finished full time education".

Government < H -Public Economics, J23 -Employment Determination; Job Creation; Labor Demand; Self-Employment < J2 -Time Allocation, Work Behavior, and Employment Determination/Creation < J -Labor and Demographic Economics, L25 -Firm Size and Performance < L2 -Firm Objectives, Organization, and Behavior < L -Industrial Organization, M13 -Entrepreneurship < M1 -Business Administration < M -Business Administration and Business Econ; attributed to the European Commission. For the first two authors the paper has been written in the framework of the research program SCALES which is carried out by EIM and is financed by the Dutch Ministry of Economic Affairs.

: coefficient and marginal effect significant at 0.01; ** : at 0.05; * : at 0.10. ^^^: significant at 0.01 in heterokedastic logit regression (see Table 1).