Resilient entrepreneurs? — revisiting the relationship between the Big Five and self-employment

Runst, Petrik; Thomä, Jörg Article — Published Version Resilient entrepreneurs? — revisiting the relationship between the Big Five and self-employment Small Business Economics Provided in Cooperation with: Springer Nature Suggested Citation: Runst, Petrik; Thomä, Jörg (2022) : Resilient entrepreneurs? — revisiting the relationship between the Big Five and self-employment, Small Business Economics, ISSN 1573-0913, Springer US, New York, NY, Vol. 61, Iss. 1, pp. 417-443, https://doi.org/10.1007/s11187-022-00686-7 This Version is available at: https://hdl.handle.net/10419/311459 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.: (0123456789) 1 3 https://doi.org/10.1007/s11187-022-00686-7 Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself‑employment PetrikRunst · JörgThomä Accepted: 2 September 2022 / © The Author(s) 2022, corrected publication 2022 our understanding of self-employment decisions. Previous studies emphasize a positive influence of the personality traits extraversion and openness on entrepreneurship. The present paper shows that the interaction of personality traits is also important. A resilient personality type that combines high values in the aforementioned traits with higher levels of conscientiousness, agreeableness and emotional stability has a positive impact on the likelihood to become selfemployed. We also show that a resilient personality type explains self-employment decisions beyond what can already be explained by profiling, another personcentred Big Five approach. As a practical implication, advice from career or start-up consultants should not be based on profiling alone. Otherwise, too many entrepreneurs may be discouraged from their entrepreneurial endeavours. Generally speaking, selfemployment decisions should incorporate personality aspects only as one among many relevant factors. Keywords Entrepreneurship· Self-employment· Big Five· Personality· Prototypes· Profiles JEL Classification D91· L26· M13 1 Introduction Based on the well-established literature on the broad Big Five personality traits (Digman, 1990; John etal., 1991, 2008; McCrae & Costa, 2008), a number of Abstract The Big Five personality traits and their influence on entrepreneurial action have been repeatedly studied using a trait-based approach. The present paper partly deviates from this perspective by analysing the role of personality prototypes in relation to entrepreneurship. This person-centred approach suggests that combinations of Big Five traits form individual personalities. By using data from the German Socio-Economic Panel (SOEP), we show that at least three prototypes can be identified, one of which — the resilient type — can be hypothesized to significantly increase the likelihood of entrepreneurial action. Our regression results provide evidence of a positive impact of this prototype on the likelihood of and transitioning into self-employment but not the likelihood of exit. We also show that the prototyping approach explains individual self-employment decisions over and above what can already be explained by the profiling approach, another person-centred Big Five approach. The paper concludes with implications for policy and research. Plain English Summary Identifying entrepreneurial personalities — an improved person-centred approach to the Big Five personality traits advances P.Runst(*)· J.Thomä Institute forSmall Business Economics attheGeorgAugust-University Göttingen, Heinrich-Düker-Weg 6, 37073Göttingen, Germany e-mail: petrik.r[email protected]ttingen.de Published online: 28 September 2022 Small Bus Econ (2023) 61:417–443 1 3 Vol:. (1234567890) researchers have analysed the effects of such traits on entrepreneurship. Two basic approaches can be distinguished here. First, by using the trait-oriented approach (i.e. the Big Five traits are examined separately from each other), openness to experience or extraversion has been repeatedly found to exert a positive influence on the decision to start a business (Brandstätter, 2011; Shane etal., 2010; Zhao & Seibert, 2006), while agreeableness is found to increase the exit probability from self-employment (Caliendo etal., 2014). The relationship between the Big Five traits and more narrow traits — such as locus of control (LOC) or risk tolerance – has also been examined, showing that additional personality aspects besides the Big Five traits are relevant for predicting entrepreneurial decisions (Caliendo etal., 2014; Leutner etal., 2014). Second, other entrepreneurship studies have taken a person-oriented approach to the Big Five Inventory. These are based on the observation that a combination of high levels of extraversion, conscientiousness, emotional stability, and openness and low levels of agreeableness is a good predictor of entrepreneurial activity. This particular configuration of Big Five traits has become known as the entrepreneurial personality profile (Schmitt-Rodermund, E. 2004; Obschonka etal., 2013; Obschonka & Stuetzer, 2017). In studies following this line of research, a hypothetical benchmark is generated that reflects the mentioned trait configuration. In a next step, the squared distance between an individual’s actual Big Five traits and this reference profile is calculated. According to Obschonka and Stuetzer (2017), the entrepreneurial personality profile is a robust predictor of self-employment decisions at both the individual and regional level. However, there is a debate about the practical implications of this profiling approach for the design of entrepreneurship education or business consulting. For example, Konon and Kritikos (2019) argue that while personality profiles based on hypothetical reference personalities may yield well-fitting regression lines, they are unsuccessful in making real predictions of future self-employment decisions, as the focus on a single profile cannot fully account for the stark heterogeneity of individuals who are prone to entrepreneurial activity. In the last two decades, another person-oriented Big Five approach has emerged in the psychology literature, with findings that remain, with two exceptions (Caliendo etal., 2022a; Runst & Thomä 2022), unexploited by small business and entrepreneurship research. Instead of treating the Big Five traits as five independent motivators of human action, this approach posits that traits are synergistic with each other, in the sense that stable and empirically discernable interdependencies exist between the Big Five traits. Starting from this assumption, distinct types of individual personalities are measured, known as personality prototypes (e.g. Asendorpf etal., 2001; Boehm etal., 2002; Herzberg & Roth, 2006; Specht etal., 2014; Gerlach etal., 2018). By working with trait configurations within individuals instead of single traits, prototyping is somewhat related to profiling. However, the two empirical approaches start from opposite ends. Profiling uses a single combination of traits that has been empirically shown to be associated with entrepreneurial activity. Thus, profiling starts from the predictive end of the empirical process, which can be argued is like putting the cart before the horse. On the other hand, prototyping is based on frequently occurring configurations of traits in the overall population of individuals, not only entrepreneurs. Only after stable personality types — i.e. discernable combinations of traits that reflect the heterogeneous nature of individual personalities — have been identified will their effects on entrepreneurship or any other phenomenon be examined. Profiling assumes that if a trait has been shown to exert an effect on entrepreneurial behaviour, the impact of this trait will be the same when combined with certain manifestations of other traits. However, this assumption may not always be correct. For example, Caliendo etal. (2014) find that the trait agreeableness does not affect entry or self-employment, whereas studies on the entrepreneurial personality profile assume a negative impact (Obschonka & Stuetzer, 2017; Obschonka et al., 2013). Nevertheless, the level of agreeableness may also positively affect entry and exit when it occurs in conjunction with higher levels of other traits. In fact, our empirical results suggest that higher agreeableness increases entry probabilities when combined with high values of extraversion but does not affect entry when cooccurring with low levels of extraversion. The possibility of such conditional effects is easily overlooked when entrepreneurial personality profiles are based on average effect sizes of the five trait variables in a regression analysis instead of considering the P.Runst, J.Thomä 418 1 3 Vol.: (0123456789) variety of possible trait combinations. The existence of such conditional (or interaction) effects suggests that the mutual interplay of traits matters. However, instead of examining the myriad of all theoretically possible combinations between all Big Five traits, prototyping starts from the distinct configurations that actually exist in the general population with some frequency and regularity, i.e. personality prototypes. In other words, while the profiling approach — with its focus on one specific combination of traits — can be understood as a first important step towards measuring distinct types of entrepreneurial personalities, the prototype approach likely represents a further step in this direction as it takes account of the heterogeneous nature of entrepreneurship-prone personalities. The contribution of this paper is twofold. First, we apply the prototype approach to studying the relationship between personality and self-employment decisions. In this way, our study complements the findings of Caliendo etal. (2022a) about the effects of personality prototypes on hiring decisions of early-stage entrepreneurs and the results of Runst and Thomä (2022) on the self-selection of small business owners into different modes of firm-level innovation, contingent on the Big Five personality prototype. To test and demonstrate the validity of our approach, we empirically derive personality prototypes from a large longitudinal dataset using latent profile analysis and a cluster analysis. Second, we investigate whether the prototype approach can expand upon the explanatory power of the profiling method. Our results have relevant practical implications in the context of entrepreneurship education and business consulting, as they suggest that the use of personality profiles should be combined with a focus on specific entrepreneurial personality prototypes to increase the effectiveness of measures and services. In doing so, we aim to respond to Konon and Kritikos (2019), who identify a need for research regarding ‘what kind of metric methods should be used that take the heterogeneity among individuals better into account [than the profiling approach]’ (p. 14). 2 Conceptual background 2.1 Big Five personality traits and entrepreneurship The Big Five Inventory (John etal., 1991, 2008) represents the most widely used measure of personality traits, and it has been employed extensively in the field of personality psychology and beyond. It contains the following five elements. The trait extraversion measures the extent to which an individual enjoys social interaction and possesses the corresponding social skills. Extraverted individuals are outgoing and communicate frequently. An individual with a high level of agreeableness tends to shy away from conflicts and has a more forgiving attitude towards others. Such an individual prefers cooperation to competition in social relationships and he or she is careful in his/her choice of words to avoid affronting others. A highly conscientious person is diligent in his or her tasks and has a higher achievement orientation. Due to their high level of conscientiousness, such persons are always planning ahead, prefer efficiency and pay close attention to details. The trait of emotional stability (opposite: neuroticism) is related to having fewer mood swings, less anxiety and fewer instances of feeling sad, hopeless or guilty. An individual with high levels of emotional stability is also more resilient in the face of setbacks and less vulnerable to psychological stressors. Finally, a person who is open to experience displays interest in novelty, variety and creativity. Higher levels of openness are associated with not liking routines and repetitive tasks, as well as higher degrees of active imagination. It is theoretically plausible to draw a connection between these five traits and entrepreneurial action (e.g. Brandstätter, 2011; Caliendo etal, 2014; Zhao etal., 2010). For example, starting a business involves new ways of doing things, serving a market that either has not existed before or satisfying demand in a better way than before. Individuals who are open to experience are more likely to recognize such business opportunities as well as acting upon them, as entrepreneurs with creativity and a willingness to propel innovative changes. Similarly, entrepreneurial action is highly social in nature (Sarasvathy, 2001; 2009) and should therefore be more appealing to extraverted individuals who are more likely to communicate, create and maintain social connections with different types of external stakeholders necessary for business formation and success. While the preference for routine and repetition actions in conscientious individuals could easily reduce the likelihood of starting a business, conscientious business owners’ attention of to detail, strong work motivation and efficiency should certainly increase entrepreneurial performance Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 419 1 3 Vol:. (1234567890) at the growth stage of new ventures. A higher level of emotional stability can be advantageous in terms of resilience when the entrepreneur confronts challenges, stressful situations or obstacles that need to be overcome in the process of setting up and maintaining a business. Finally, agreeableness could be expected to be negatively related to entrepreneurship, as agreeable individuals are often less inclined to be sufficiently strong willed in the face of opposing viewpoints and arguments, acquiescing too quickly, which thereby undermines creative change processes in the context of entrepreneurship. On the other hand, as entrepreneurship is a social process (Sarasvathy, 2001; 2009), a low value of agreeableness can exacerbate social conflict and deter potential partners from cooperating with the prospective entrepreneur. A number of empirical studies have followed a trait-oriented approach to examine the relationship between the Big Five and entrepreneurial action. Accordingly, they have established robust links between single traits and entrepreneurship. Higher levels of extraversion and openness and — to a lesser extent — emotional stability and conscientiousness are reliable predictors of entrepreneurial intention and performance (e.g. Ciavarella etal., 2004; Zhao & Seibert, 2006; Zhao etal., 2010; Brandstätter, 2011; Caliendo etal., 2014). Some evidence suggests that higher levels of agreeableness increase the likelihood of exit (Caliendo et al., 2014). Some entrepreneurship studies have also related narrow traits such as achievement orientation, locus of control and risk tolerance to the Big Five traits, showing that both broad and narrow traits have explanatory power for predicting entrepreneurial decisions (Caliendo etal., 2014; Leutner etal., 2014). Finally, by taking a person-oriented approach to the Big Five, the profiling literature has repeatedly found that the specific combination of high levels of extraversion, openness, conscientiousness and emotional stability and low levels of agreeableness — i.e. the entrepreneurial personality profile — is positively associated with the decision to enter self-employment (Obschonka & Stuetzer, 2017; Obschonka et al., 2013; Schmitt-Rodermund, 2004). 2.2 The prototype approach As has been noted, previous entrepreneurship research on person-oriented investigations into the Big Five has focused on the profiling approach. However, in the last two decades, a second body of literature concerning person-oriented Big Five analyses has unfolded within the field of psychology (Asendorpf etal., 2001; Boehm etal., 2002; Fruyt etal., 2002; Schnabel etal., 2002; Herzberg & Roth, 2006; Meeus etal., 2011; Specht etal., 2014; Gerlach etal., 2018), which holds relevant implications for entrepreneurship and small business research but — apart from Runst and Thomä (2021) — remains largely untapped. This literature does not deal with separate individual traits but rather examines the statistical clustering or co-occurrence of traits in the general population of individuals. In other words, are there certain combinations of Big Five traits — labelled as prototypes — that are more likely to manifest themselves within the personality of individuals? The number of identified prototypes varies between three (Asendorpf et al., 2001; Meeus et al., 2011), four (Specht, 2014, Gerlach etal., 2018), and five (Kerber et al., 2021) whereas the empirical evidence tends towards the first number. However, regardless of which solution was found, a particular personality type — labelled as the ‘resilient type’ (Asendorpf etal, 2001), also called the ‘role model’ (Gerlach etal., 2018) — has been clearly identified in all of these studies (for a literature review, see Kerber etal., 2021). This prototype is characterized by high values in all Big Five traits. According to Asendorpf etal., (2001, p. 175), the resilient type refers to a person’s ability ‘to respond flexibly, rather than rigidly to changing situational demands, particularly stressful situations.’ In three-type solutions, the other two prototypes that have been identified are ‘over-controllers’ (i.e. high values of conscientiousness but lower values of openness and extraversion) and ‘under-controllers’ (i.e. low values in all traits, including emotional stability). Fourand five-type solutions differ from threetype solutions in terms of the identification of underand over-controllers or certain subgroups thereof (Gerlach etal., 2018). In this context, the degree of self-control refers to an individual’s ‘tendency to contain versus express emotional and motivational impulses (strong control vs. weak control)’ (Asendorpf etal., 2001, p. 175). The prototype approach is inherently based on the idea that there are certain synergies between separate Big Five traits. For example, Runst and Thomä (2021) provide empirical evidence that small business P.Runst, J.Thomä 420 1 3 Vol.: (0123456789) owners’ personality traits complement each other in the context of firm-level innovation. According to their results, a small firm is more likely to successfully implement an informal mode of innovation, which places a special emphasis on interactive learning and cooperative relationships when the owner is of the resilient type. Such synergies can also be expected in the context of entrepreneurship. For example, while extraversion and openness have widely been found to positively affect the probability of entry into self-employment, in terms of new venture performance, the founder’s degrees of conscientiousness and emotional stability should play a complementary role as high degrees of achievement motivation and a pronounced ability to cope with stress should also be important for the success of entrepreneurs (Zhao etal., 2010). Perhaps the best example of such synergies is the ambiguous role of an entrepreneur’s degree of agreeableness. As noted above, in the profiling literature, a negative role is assigned to the trait of agreeableness in terms of entrepreneurial action. This perspective speaks to a conception of the entrepreneur as the lone maverick that pursues his/her vision of innovation and changes quite ruthlessly and overcomes obstacles in the form of resisting voices by not deferring to others in the face of conflict. Interestingly, this caricature of a visionary dynamic change agent is at odds with what qualitative research tells us that the process of entrepreneurship actually looks like (Sarasvathy, 2001, 2008). In fact, the entrepreneurial process has been described as a social one, embedded within and reliant upon a viable network of customers, suppliers etc. Instead of the lone maverick, Sarasvathy (2001, 2008) metaphorically describes the entrepreneur as a quilt maker, stitching various stakeholders and their ideas together into a joint fabrication of opportunity, generating a community of co-conspirators in the process. Such a conception of the entrepreneur would not suffer from high levels of agreeableness. In fact, such a personality trait would benefit the entrepreneurial process, as the other members of the emerging new venture’s network would be more willing to engage and trust an agreeable entrepreneur given that he/ she would be more likely to incorporate their various views and interests. Hence, agreeableness may exert different effects on entrepreneurship, depending on the context and the interplay with other Big Five traits involved. Indeed, the ability of the prototype approach to consider these heterogeneities among individuals and condense them into certain dominant personality types reflects precisely its strength. 2.3 The resilient type and entrepreneurial action As already mentioned, one trait configuration that has been consistently identified in the prototype literature is the resilient type. It refers to individuals who are ‘able to resourcefully adapt to changing situations and circumstances, to tend to show a diverse repertoire of behavioral reactions and to be able to have a good and objective representation of the “goodness of fit” of their behavior to the situations/people they encounter. This good adjustment may result in high levels of self-confidence and a higher possibility to experience positive affect’ (Kerber etal, 2021, p. 3). Such a personality type can be expected to be likely to engage in entrepreneurial action. For example, Runst and Thomä (2021) show that small firms with owners whose personality resembles the resilient type are more likely to successfully implement a non-R&D-based mode of innovation. Hence, we hypothesize that individuals of the resilient type are more likely to enter and remain self-employed, as they will create and maintain the necessary social ties (extraversion, agreeableness), diligently plan and execute required actions (conscientiousness), remain calm in the face of adversity (emotional stability) and display an open attitude toward novelty and change (openness). On the other hand, over-controllers have been described as constrained and inhibited in their behaviour, limited in their emotional expressivity and overly cautious in decision-making (Kerber et al., 2021). The social nature of entrepreneurship should render it less likely for such an individual to enter into self-employment.1 In addition, entrepreneurial action requires the capacity to make decisions under stressful and uncertain situations, meaning that a certain degree of emotional stability is needed for entrepreneurs to succeed (Zhao et al., 2010). On the other hand, under-controllers display high time discounting 1 To prevent possible misunderstandings, it should be noted that the label ‘over-controlled’ must not be confused with, and is different from an individual’s desire for independence and personal control, which is a key motivation to enter selfemployment. Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 421 1 3 Vol:. (1234567890) and are therefore often unable to delay gratification to receive larger gains in the future. Moreover, they tend to be ‘relatively unattached to social standards or customs’ (Kerber etal., 2021, p. 2). For example, the inability to delay gratification has been connected to various negative economic or social outcomes (see DellaVigna, 2009), such as lower scores on standardized tests (Mischel et al., 1989), lower educational attainment (Ayduk et al., 2000), higher body mass indexes (Schlam et al., 2013) and lower savings (Ashraf etal., 2006). In terms of social interactions, the results of Runst and Thomä (2021) imply that small business owners of the under-controlled personality type have a low likelihood of implementing a mode of learning and innovation at the firm level that builds on interactive learning and cooperation with external partners. In a similar manner, we expect under-controlled individuals to be less inclined to entrepreneurial action. 3 Data andmethods 3.1 German SOEP We use data from the Socio-Economic Panel (SOEP) for 2005 to 2019.2 The SOEP is a large and representative annual longitudinal household survey among individuals throughout Germany that has been used in entrepreneurship research on the Big Five (e.g. Caliendo et al., 2014) as well as psychology research on Big Five personality traits (e.g. Specht etal., 2014).3 The SOEP contains repeated questions on work, health, and well-being as well as additional non-repeated modules. Starting in 2005, the survey also includes a fifteen-item Big Five Inventory (BFI) at regular intervals (i.e. the five survey years 2005, 2009, 2013, 2017, and 2019). It has been shown that small item scales such as the BFI-15 retain significant levels of reliability and validity compared with longer versions such as the BFI-44 (Rammstedt & John, 2007). About 11,700 individuals fully answered all personality questions on the Big Five traits in 2005. As the survey has increased in sample size since then, there are about 14,900 complete Big Five observations in 2019. The dataset also provides information on the survey respondents, such as age, citizenship, educational and vocational degrees. This enables us to use information on self-employment status and transition as a measure for entrepreneurial action. There are ten LOC items in the SOEP (for the years 2005, 2010, and 2015). A factor analysis confirms that they can be reduced to one single factor. A positive loading on the LOC factor corresponds to an internal LOC orientation (i.e. the person believes in their own self-efficacy), and a negative loading relates to an external LOC (i.e. the person believes their life remains largely unaffected by his or her choices). The corresponding factor score — which we use in our regressions — represents an optimally weighted linear combination of these values. Following Caliendo et al. (2014), the following analysis is limited to individuals between the ages of 19 and 59 to ‘to avoid possible confounding effects due to early retirement decisions’ (ibid, p. 795). Moreover, invalidity pensioners, students (including vocational education and training), farmers, family workers, civil servants and military members are removed from the sample. Apart from that, we do not include observations from the SOEP ‘Refugee Samples’ 2016 and 2017 in our analysis to ensure sample consistency over time, and because there are marked personality differences between the specific group of refugees and the general population in Germany (Brücker etal., 2016), which could otherwise distort the prototyping results. 3.2 Methods 3.2.1 Overview Our empirical analysis proceeds in three main steps. First, entrepreneurial profiles are derived from the SOEP survey data based on the individual manifestations of the Big Five survey items (see Sect.3.2.2). Second, we generate personality prototypes. To ensure that the results of the prototype identification are valid, we derive them in two different ways, first via a latent profile analysis (LPA, see Sect.3.2.3) and additionally — for the purpose of robustness testing — by applying a cluster analysis (see Sect. 3.2.4). On this basis, a longitudinal data set is created for the 2005–2019 period by replacing missing values in 2 ‘Socio-Economic Panel (SOEP), data for years 1984–2019, SOEP-Core v36, EU Edition, 2021, 10.5684/soep.core.v36eu.’. 3 For general information on the SOEP, see Giesselmann etal. (2019); Goebel etal. (2019); Schröder etal. (2020). P.Runst, J.Thomä 422 1 3 Vol.: (0123456789) years without the Big Five module with values from the last available year.4 The five personality dimensions are extracted from fifteen survey items by generating factor scores for each trait. As an example, factor loadings for 2005 are presented in the Appendix (Table5), and they conform to well-known patterns (e.g. Hahn etal., 2012; Lang etal., 2011).5 The factor scores are used in the LPA/cluster analysis as metric Big Five variables to generate the personality prototypes. Third, the profile and prototype variables both serve as variables in a regression analysis (see Sect.3.2.5) on the determinants of different entrepreneurial actions (i.e. the selfemployment status, the probability of entry/exit and the number of entries). 3.2.2 Entrepreneurial profile We follow Obschonka etal. (2013) and Obschonka and Stuetzer (2017) by defining an entrepreneurial reference profile of the highest possible values on the traits’ original scales of extraversion, conscientiousness, emotional stability and openness and the lowest possible value on the agreeableness scale ( Yk ). Their reference profile is derived from the empirically established links between the single Big Five traits and entrepreneurship activity. In other words, when regressing self-employment decisions on the Big Five traits, a positive and significant coefficient leads to a high reference value, and correspondingly, a negative coefficient leads to a low reference value. We then calculate each individual’s semblance to the entrepreneurial profile by summing up the squared distances between the actual trait value ( Xitk ) and its corresponding reference value ( Yk ), where index k refers to trait one to five. Entrepreneurial Profile = 5 ∑ k=1 (Xitk −Yk) 2 3.2.3 Prototyping: LPA We follow Specht etal. (2014), Asendorpf etal. (2001), and Asendorpf (2015) in performing a latent profile analysis (LPA) based on the derived factor scores on the Big Five traits, separately for each year in 2005, 2009, 2013, 2017 and 2019. As Specht etal. (2014) state, the aim of this typological approach ‘is to identify a preferably parsimonious number of personality types that allow for broad categorizations of individuals’ (p. 5). To determine the number of prototypes (k) in the model, we first run multiple LPAs, using two to five types. The Akaike information criterion (AIC) and Schwarz’s Bayesian information criterion (BIC) provide a statistic that can be used to assess the model’s fit, with lower values indicating a better fit. However, as is typical with these criteria, the AIC and BIC continuously decline when the number of prototypes k in the model rises. Masyn (2013) states in this regard that ‘because none of the information criteria are guaranteed to arrive at a single lowest value corresponding to a k -class model with k < k_max, these indices may have their smallest value at the k_max-class model’ (p. 572). We therefore perform a split-sample cross-validation procedure. First, the sample is randomly partitioned into two equally sized subsamples, subsample A (the calibration dataset) and B (the validation dataset). As a next step, an LPA is conducted based on subsample A, and all model parameters are retained. Subsequently, we turn to subsample B, whereby first, the retained model parameters are used for predicting whether an individual belongs to a certain prototype (i.e. the constrained prediction). Second, the LPA is performed without fixing the parameters (i.e. the unconstrained prediction). Finally, we compare the constrained and unconstrained predictions. As Masyn (2013) writes, ‘if the parameter estimates obtained from the k -class model fit to subsample A, then provide an acceptable fit when used as fixed parameter values for a k-class model applied to subsample B, then the model validates well and the selection of the k-class model is supported’ (p. 572–573). As the subsample selection is random, we repeat this process twenty times, separately for each survey year that contains BF items (2005, 2009, 2013, 2017 and 2019). The average share of incorrect predictions remains identical when moving from two to three prototypes (8.7% see Fig.1). Thus, the three-type solution yields more descriptive variety without losing predictive accuracy. When moving from a threeto a four-prototype model however, we observe a sharp increase in the average share of incorrect 4 A number of studies have shown that Big Five personalities are relatively constant and stable over time (e.g. Specht etal., 2011; Lucas and Donnellan 2011; Cobb-Clark and Schurer 2011; Specht etal., 2014). 5 Factor loadings for the other years are not reported but follow the same pattern. The corresponding results are available upon request. Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 423 1 3 Vol:. (1234567890) predictions across all years (8.7 to 44.9%). Moving from four to five types further lowers predictive accuracy (48.3% incorrect predictions). We therefore conclude that a model containing three prototypes fits the data best. Each individual in the sample is assigned to one prototype only, based on its highest-class probability. 3.2.4 Robustness test: cluster analysis In addition to LPA, clustering methods have also been used in previous research to identify personality prototypes (see e.g. Herzberg & Roth, 2006; Specht etal, 2014; Runst & Thomä, 2021). We resort to cluster analysis as it is well suited to check the robustness of the LPA results. Following Herzberg and Roth (2006), our clustering procedure comprises two steps. First, Ward’s hierarchical clustering is used to decide on the number of clusters to be formed. In this method, ‘the distance between two clusters is the sum of squares between the two clusters summed over all variables’ (Hair etal., 1998, p. 496). On this basis, the increase in within-cluster sum of squares is minimized over the stages of the clustering procedure. To determine the optimal number of clusters, we employ dendrograms showing the hierarchical relationship between the individual’s manifestations of the Big Five factors scores and apply two common cluster-stopping rules (Calinski/Harabasz pseudo-F index and Duda-Hart index). Across all survey years, as in the case of the LPA, the results speak in favour of a three-cluster-solution, although it should be noted that in some years, a four-cluster solution would have also been possible. However, in order to ensure consistency and comparability over the years regarding the LPA results, and because the particularly relevant group of the ‘resilient type’ clearly emerges in both solutions, we opted for three-prototype clusters. As a second step, we then conduct a k-means cluster analysis for each of the relevant survey years, where the cluster centroids of the Ward solution serve as initial seed points of the non-hierarchical clustering procedure. In this way, the benefits of hierarchical clustering in determining the number of clusters are combined with the advantages of non-hierarchical cluster analysis in fine-tuning ‘the results by allowing the switching of cluster membership’ (Hair et al., 1998, p. 498). 3.2.5 Regression analysis We follow Caliendo etal., (2010, 2014) by estimating a logit model of the transitional probability of entry and exit conditional on the length of the pre-transition state. For the dependent variable ‘entry’, we therefore include the length of the employment or unemployment spell and drop all other individuals who are not employed or unemployed. For the dependent variable ‘exit’, we include the Fig. 1 Distance to the entrepreneurial profile, by prototype 0% 10% 20% 30% 40% 50% 60% 2345 s noitciderp tcerrocni fo erahS Number of prototypes 2005 2009 2013 2017 2019 No tes: We perform a split-sample cross-validation procedure. First, the sample is randomly partitioned in to two equally-sized subsamples, a subsample A (the calibration dataset) and B (the validation datase t). As a next step, an LPA is conducted based on subsample A and all model parameters are reta ined. Subsequently, we turn to subsample B, whereby first the retained model parameters are used for pr edicting whether an individual belongs to a certain prototype (i.e. the constrained prediction). Second , th e LPA is performed without fixing the parameters (i.e. the unconstrained prediction). Finally, we compar e the constrained and unconstrained predictions. As the subsample selection is random, we repe at this process twenty times. The graph depicts the share of incorrect predictions depending on the num ber of prototypes. P.Runst, J.Thomä 424 1 3 Vol.: (0123456789) profile and the entrepreneurial profile as our main explanatory variables (see Table10 in the Appendix). We find that the smaller the distance to the hypothetical resilient reference profile, the higher the likelihood of self-employment and entry, as well as the number of entries. There is no longer a statistically significant relationship between the entrepreneurial profile and the likelihood of entry, although the probability of being self-employment and the number of entries are still positively associated with it. As before, there is no relationship between the resilient profile and the likelihood of exit. The results of this robustness test thus confirm that personality prototypes contribute to explaining entrepreneurial action beyond profiling. 5 Conclusion There is an established body of literature on the relationship between Big Five personality traits and selfemployment. There is also highly relevant but hitherto underutilized literature from the field of psychology that moves from the trait-oriented approach to a person-oriented level of the Big Five, recognizing that there are stable and frequently occurring combinations of traits that exist in individuals in the general population (so-called personality prototypes). In this paper, we seek to bridge the gap between the prototyping and entrepreneurship literature by presenting evidence of a positive relationship between one particular prototype — i.e. the resilient type — and entrepreneurial activity. The results of our empirical analysis reveal the existence of three Big Five prototypes in the German SOEP data. While there is an on-going debate about methods and the correct number of types, all previous research recognizes the resilient type (high levels in all five traits), which we also find, and which we argue to play a particularly important role in entrepreneurial activity. In fact, we find a positive and moderate to strong relationship between the resilient type and the likelihood of being self-employed, as well as the likelihood of entering into self-employment. Our results also show that the resilient prototype explains entrepreneurial activity over and above what can already be explained by the ‘entrepreneurial profile’. This finding implies that the profiling approach is only a first step on the way from the standard trait-level theorizing to a person-oriented perspective on the Big Five in the context of entrepreneurship. Hence, the present paper expands upon the profiling approach by suggesting that there are potentially several other combinations of the Big Five that may be connected to entrepreneurship. Figure5 displays a histogram of the distance to the hypothetical entrepreneurial profile for the resilient type and an aggregate of the other two prototypes. Members of the resilient type are on average closer to the entrepreneurial profile. Nevertheless, the most important finding here pertains to the fact that a large share of the resilient type members does not resemble the entrepreneurial profile at all. At the same time, the regression results above suggest that being of the resilient type makes it considerably more likely to engage in entrepreneurship. Thus, we conclude that the entrepreneurial profile ignores a large number of individuals who exhibit certain combinations of traits, some of which predispose them to become entrepreneurs. In addition, neither our LPA nor cluster analysis identifies the combination of traits labelled as the entrepreneurial profile, which indicates that it does not represent a combination of traits that frequently exists in the general population. In fact, there is not a single individual in our dataset who has an agreeableness score less than one standard deviation below the mean and more than one standard deviation above the mean for each other trait scores. By contrast, prototyping generates combinations of traits that are much less extreme than the hypothetical entrepreneurial profile and are thus more likely to describe actually existing personality patterns. In fact, the size of the group of individuals who fall into the resilient category is non-trivial according to our results, whereas the number of individuals who closely resemble the entrepreneurial profile is quite small. In practice, this means that advice from career or business start-up advisors on personality should not be based on profiling alone. Otherwise, too many entrepreneurs might be discouraged from their entrepreneurial aspirations. In any case, it should be taken into account that the ‘right’ or ‘wrong’ personality is certainly not the one decisive factor for the start-up success of businesses, and that advice should never be given on the basis of personality alone. In this way, our paper complements the study of Konon and Kritikos (2019). This leads to the need for further research. As the research on personality prototypes is an on-going process and statistical tools continue to be developed and refined, it is likely that additional combinations of stable and frequently occurring combinations of Big Five traits will emerge in the future. This prospect provides a promising avenue for future Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 431 1 3 Vol:. (1234567890) research efforts on the present topic. While the entrepreneurial profile has been repeatedly shown to display a positive association with entrepreneurship, our findings suggest that this approach is too narrow and represents only a first step. There is at least one other configuration of Big Five traits that displays a propensity for entrepreneurial action, and it is likely there are more that remain to be discovered. Finally, another interesting area of entrepreneurship research could only be briefly touched upon in this paper: The relationship between the prototype approach to personality and the psychological concept of resilience. Recently, a number of papers have been published on the resilience of entrepreneurs or small firms, e.g. in terms of business survival (Chadwick etal. 2020) or regarding the effects of the COVID-19 pandemic (Belitski etal., 2022; Caliendo etal., 2022b; Hadjielias etal., 2022). The present paper provides initial indications that persons of the resilient prototype have a higher psychological resilience in the narrow sense, which is why it is to be expected that they are better able to cope with crises than persons of the other two personality prototypes. However, further research is needed to substantiate this hypothesis. Table 5 Factor loadings after factor analysis (SOEP wave 2005) Source: SOEP, own calculation. Inverting the neuroticism value scale yields the variable emotional stability. Extraversion Conscientiousness Neuroticism (emotional stability) Openness Agreeableness Thorough 0.13 0.66 − 0.02 0.05 0.11 Communicative 0.66 0.21 − 0.04 0.14 0.09 Too rough 0.05 − 0.09 0.15 0.15 − 0.48 Inventive 0.37 0.20 − 0.08 0.50 − 0.08 Worried − 0.02 0.11 0.50 0.05 0.09 Forgiving 0.16 0.15 − 0.01 0.10 0.39 Lazy − 0.06 − 0.45 0.06 0.16 − 0.18 Social 0.67 0.10 − 0.06 0.21 0.11 Artistic 0.21 0.07 0.03 0.41 0.15 Nervous − 0.06 − 0.07 0.63 0.04 − 0.04 Efficient 0.17 0.60 − 0.06 0.18 0.14 Reserved − 0.48 0.08 0.15 0.05 0.22 Friendly 0.15 0.28 − 0.01 0.12 0.58 Imaginative 0.32 0.04 0.01 0.52 0.09 Stress resilient 0.15 0.15 − 0.51 0.21 0.15 Table 6 Mean values for Big Five item ‘stress resistance’ The survey question reads ‘I deal well with stress’ and ranges from 1 (not well at all) to 7 (very well). LPA_under-controllers LPA_over-controllers LPA_resilients 2005 3.98 4.63 5.12 2009 4.10 4.53 5.06 2013 4.00 4.59 5.12 2017 4.05 4.77 5.35 2019 3.93 4.83 5.28 Appendix P.Runst, J.Thomä 432 1 3 Vol.: (0123456789) Table 7 Variable overview and descriptive statistics Source: The sample of the first model specification in Table4 (dep. var. self-employed, n = 111,559) has been used to calculate the descriptive statistics.See Table11 for a correlation matrix. Variable name Description Mean Standard deviation Self-employed Dummy for being self-employed 0.085 0.279 Entry Dummy for entry into self-employment 0.008 0.090 Number of entries Number for entries into self-employment 0.063 0.274 Exit Dummy for exit decision from self-employment 0.007 0.084 Extraversion Metric factor scores of the Big Five personality traits 0.000 1 Conscientiousness 0.000 1 Emotional stability 0.000 1 Openness 0.000 1 Agreeableness 0.000 1 LPA_resilients Prototypes derived from Latent profile analysis. Type membership (exclusive) as binary variable 0.154 0.361 LPA_over-controllers 0.567 0.496 LPA_under-controllers 0.290 0.454 Cluster_resilients Prototypes derived from cluster analysis. Type membership (exclusive) as binary variable 0.403 0.490 Cluster_over-controllers 0.306 0.461 Cluster_under-controllers 0.291 0.454 Entrepreneurial profile Distance to entrepreneurial profile − 395.230 128.296 LOC Locus of control (factor score after factor analysis) 0.017 0.816 Risk tolerance On a Likert scale (0–10) 4.831 2.143 Age In years 44.018 9.412 University Dummy for having a university degree 0.240 0.427 Vocational training Dummy for individuals who finished an apprenticeship 0.751 0.433 Full-time work Dummy for full-time work 0.594 0.491 Part-time work Dummy for part-time work 0.202 0.402 Female Dummy for females 0.544 0.498 Unemployed Dummy for individuals not in paid work 0.146 0.353 Foreigner Dummy for non-German nationality 0.079 0.270 Experience work Full-time work experience prior to the year of observation 15.090 10.776 Experience unemployed Years of unemployment experience prior to the year of observation 1.214 2.829 High school Dummy for individuals who received a diploma from a secondary school qualifying for university entrance 0.229 0.420 Disability Degree of disability in per cent 3.106 12.960 Father self-employed Dummy for having a father who was self-employed when the respondent was 15years old 0.093 0.290 Capital income Household income from asset flows, Euros per year 2265.428 18,308.130 North Dummy for individuals living in the North of Germany 0.163 0.369 East Dummy for individuals living in the East Germany 0.234 0.424 West Dummy for individuals living in the West Germany 0.330 0.470 South Dummy for individuals living in the South Germany 0.273 0.445 Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 433 1 3 Vol:. (1234567890) noisrevartxe:1F -suoitneicsnoc:2F ness -atslanoitome:3F bility ssennepo:4F F2: conscientiousnessF3: emotional stabilityF4: openness F5: agreeableness Fig. 2 Interaction effects after logit regression, predicted probabilities (Dep. var. self-employed) noisrevartxe:1F -suoitneicsnoc:2F ness -atslanoitome:3F bility ssennepo:4F F2: conscientiousness F3: emotional stabilityF4: openness F5: agreeableness Fig. 3 Interaction effects after logit regression, predicted probabilities (Dep. var. entry) P.Runst, J.Thomä 434 1 3 Vol.: (0123456789) noisrevartxe:1F -suoitneicsnoc:2F ness -atslanoitome:3F bility ssennepo:4F F2: conscientiousness F3: emotional stabilityF4: openness F5: agreeableness Fig. 4 Interaction effects after logit regression, predicted probabilities (Dep. var. exit) Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 435 1 3 Vol:. (1234567890) Table 8 Regression results, Big Five factor scores and entrepreneurship Robust standard errors, clustered on the individual level, are given in parentheses. Controls for each survey year are included. Specifications 1, 2, and 4 display marginal effects after logit regressions. Specification 3 displays OLS coefficients. p-values in parentheses: *p < 0.10; **p < 0.05; ***p < 0.01. a A visualization of the non-linear relationship between age and the probability of different entrepreneurial decisions can be found in the Appendix (see Figure7). (1) (2) (3) (4) Self-employment Entry Number of Entries Exit Extraversion 0.011*** (0.000) 0.000 (0.242) 0.005*** (0.000) − 0.001 (0.829) Conscientiousness − 0.002* (0.093) 0.000 (0.208) − 0.003*** (0.003) 0.000 (0.964) Emotional stability − 0.004*** (0.000) 0.000 (0.918) 0.001 (0.538) − 0.000 (0.945) Openness 0.015*** (0.000) 0.002*** (0.000) 0.015*** (0.000) − 0.003 (0.340) Agreeableness 0.000 (0.715) 0.000 (0.912) 0.001 (0.300) 0.003 (0.386) LOC 0.018*** (0.000) 0.001** (0.032) 0.004*** (0.001) − 0.012*** (0.003) Risk tolerance 0.011*** (0.000) 0.001*** (0.000) 0.009*** (0.000) 0.002 (0.321) Age (not reported)a Age squared (not reported)a University degree 0.023*** (0.000) 0.004*** (0.000) 0.016*** (0.000) − 0.006 (0.433) Vocational training degree − 0.006*** (0.004) 0.001 (0.161) 0.001 (0.640) − 0.011 (0.171) Full-time employment 0.042*** (0.000) − 0.016*** (0.000) − 0.026*** (0.000) − 0.085*** (0.000) Part-time employment − 0.021*** (0.000) − 0.012*** (0.000) − 0.034*** (0.000) − 0.042*** (0.000) Female − 0.019*** (0.000) − 0.004*** (0.000) − 0.010*** (0.000) 0.026*** (0.000) Unemployed − 0.066*** (0.000) − 0.003*** (0.002) − 0.023*** (0.000) 0.055*** (0.000) Foreigner − 0.003 (0.471) 0.001 (0.347) 0.016*** (0.000) 0.005 (0.676) Experience work − 0.001*** (0.000) − 0.000 (0.894) − 0.002*** (0.000) − 0.000 (0.352) Experience unemployed − 0.001 (0.168) − 0.000*** (0.004) 0.001*** (0.001) 0.004*** (0.006) High school 0.035*** (0.000) 0.005*** (0.000) 0.035*** (0.000) − 0.022*** (0.005) Disability − 0.001*** (0.000) − 0.000** (0.026) − 0.000*** (0.001) 0.001*** (0.007) Father self-employed 0.038*** (0.000) 0.002* (0.072) 0.016*** (0.000) − 0.017* (0.077) North − 0.004 (0.169) − 0.000 (0.876) − 0.003 (0.249) − 0.005 (0.622) East 0.012*** (0.000) 0.001 (0.179) 0.009*** (0.000) − 0.014 (0.101) South 0.004** (0.034) 0.001 (0.172) 0.007*** (0.001) − 0.003 (0.680) Capital income 0.069*** (0.000) 0.002*** (0.000) 0.019*** (0.001) − 0.005 (0.638) N111,559 95,082 111,559 8928 Mean Y-outcome 0.085 0.008 0.063 0.007 P.Runst, J.Thomä 436 1 3 Vol.: (0123456789) Table 9 Regression results, cluster analysis’ prototypes and entrepreneurship Robust standard errors, clustered on the individual level, are given in parentheses. Controls for each survey year and the full set of covariates are included. Specifications 1, 2, and 4 display marginal effects after logit regressions. Specification 3 displays OLS coefficients. p-values in parentheses: *p < 0.10; **p < 0.05; ***p < 0.01. (1) (2) (3) (4) Self-employed Entry Number of entries Exit Resilients 0.016*** 0.002** 0.014*** 0.001 (0.000) (0.017) (0.000) (0.865) Over-controllers − 0.005** − 0.000 − 0.004** − 0.004 (0.026) (0.937) (0.026) (0.619) N111,559 95,071 111,559 8928 Mean Y-outcome 0.085 0.008 0.063 0.007 Table 10 Regression results, entrepreneurial profile and resilient profile Robust standard errors, clustered on the individual level, are given in parentheses. Controls for each survey year and the full set of covariates are included. Specifications 1, 2, and 4 display marginal effects after logit regressions. Specification 3 displays OLS coefficients. p-values in parentheses: * p < 0.10; ** p < 0.05; *** p < 0.01. (1) (2) (3) (4) Self-employed Entry Number of entries Exit Resilient profile 0.006*** 0.001*** 0.006*** 0.002 (0.000) (0.004) (0.000) (0.629) Entrepreneurial profile 0.005*** 0.000 0.003*** -0.004 (0.000) (0.200) (0.000) (0.264) N111,559 95,071 111,559 8928 Mean Y-outcome 0.085 0.008 0.063 0.007 Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 437 1 3 Vol:. (1234567890) Fig. 5 Share of incorrect predictions contingent on the number of prototypes. We perform a split-sample cross-validation procedure. First, the sample is randomly partitioned into two equally-sized subsamples, a subsample A (the calibration dataset) and B (the validation dataset). As a next step, an LPA is conducted based on subsample A and all model parameters are retained. Subsequently, we turn to subsample B, whereby first, the retained model parameters are used for predicting whether an individual belongs to a certain prototype (i.e. the constrained prediction). Second, the LPA is performed without fixing the parameters (i.e. the unconstrained prediction). Finally, we compare the constrained and unconstrained predictions. As the subsample selection is random, we repeat this process twenty times. The graph depicts the share of incorrect predictions depending on the number of prototypes. P.Runst, J.Thomä 438 1 3 Vol.: (0123456789) Table 11 Correlation matrix (self-employed sample) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 Selfemployed 1.00 2 Number of entries 0.40 1.00 3 Resilients 0.03 0.01 1.00 4 Over-controllers − 0.02 0.00 − 0.49 1.00 5 Under-controllers 0.00 − 0.01 − 0.27 − 0.71 1.00 6 EntreprProfile 0.09 0.06 0.14 0.00 − 0.11 1.00 7 Resilient Profile 0.08 0.05 0.32 0.22 − 0.51 0.68 1.00 8LOC 0.09 0.03 0.08 0.06 − 0.12 0.21 0.29 1.00 9 Risk 0.11 0.08 0.07 − 0.02 − 0.03 0.27 0.22 0.10 1.00 10 Age 0.07 0.01 0.03 0.01 − 0.03 0.00 0.01 − 0.04 − 0.07 1.00 11 University 0.13 0.07 − 0.06 0.03 0.01 0.08 0.08 0.13 0.02 0.06 1.00 12 Vocationaö − 0.06 − 0.03 0.04 0.00 − 0.04 0.02 0.00 0.00 − 0.02 0.09 − 0.43 1.00 13 Full_time 0.13 0.00 0.00 − 0.02 0.02 0.14 0.07 0.12 0.13 0.01 0.12 0.04 1.00 14 Part_time − 0.07 − 0.01 0.01 0.04 − 0.05 − 0.05 0.01 0.00 − 0.10 0.05 − 0.02 0.03 − 0.61 1.00 15 Female − 0.09 − 0.01 0.06 0.06 − 0.11 − 0.10 0.03 − 0.03 − 0.19 − 0.01 − 0.04 − 0.01 − 0.49 0.37 1.00 16 Unemployed − 0.09 − 0.01 − 0.01 − 0.02 0.03 − 0.11 − 0.08 − 0.14 − 0.04 − 0.07 − 0.10 − 0.07 − 0.42 − 0.11 0.17 1.00 17 Foreigner − 0.02 0.00 0.00 0.00 − 0.01 − 0.02 0.00 − 0.08 0.01 − 0.06 − 0.02 − 0.20 − 0.06 − 0.02 0.01 0.10 1.00 18 Experience Work 0.09 − 0.03 0.03 − 0.02 0.00 0.09 0.02 0.03 0.05 0.63 − 0.02 0.18 0.49 − 0.29 − 0.40 − 0.26 − 0.07 1.00 19 Experience Unemployment − 0.05 0.01 0.00 − 0.03 0.03 − 0.10 − 0.09 − 0.19 0.01 0.07 − 0.13 − 0.01 − 0.25 − 0.04 0.03 0.35 0.03 − 0.13 1.00 20 High School 0.13 0.08 − 0.07 0.02 0.04 0.06 0.05 0.14 − 0.01 − 0.03 0.55 − 0.35 0.07 0.00 − 0.01 − 0.09 − 0.11 − 0.10 − 0.14 1.00 21 Disability − 0.04 − 0.02 0.01 − 0.03 0.03 − 0.07 − 0.08 − 0.09 − 0.03 0.12 − 0.05 0.01 − 0.05 − 0.02 − 0.03 0.05 − 0.02 0.08 0.08 − 0.06 1.00 22 Father Selfemployed 0.03 0.02 − 0.02 − 0.01 0.02 0.01 − 0.01 0.00 0.02 − 0.20 0.02 − 0.07 0.01 − 0.02 − 0.01 0.01 − 0.01 − 0.13 − 0.05 0.02 − 0.02 1.00 23 Capital income 0.09 0.02 − 0.01 − 0.01 0.02 0.01 0.01 0.04 0.02 0.05 0.06 − 0.02 0.01 0.00 − 0.01 − 0.01 − 0.01 0.02 − 0.04 0.06 0.00 0.03 Resilient entrepreneurs? — revisiting therelationship betweentheBig Five andself-employment 439 1 3 Vol:. (1234567890) Fig. 6 Marginal and nonlinear effects of age (corresponds to Table3) Fig. 7 Marginal and nonlinear effects of age (corresponds to Table8) P.Runst, J.Thomä 440