Abstract
The human personality predicts a wide range of activities and occupational choices—from musical sophistication to entrepreneurial careers. However, which method should be applied if information on personality traits is used for prediction and advice? In psychological research, group profiles are widely employed. In this contribution, we examine the performance of profiles using the example of career prediction and advice, involving a comparison of average trait scores of successful entrepreneurs with the traits of potential entrepreneurs. Based on a simple theoretical model estimated with GSOEP data and analyzed with Monte Carlo methods, we show, for the first time, that the choice of the comparison method matters substantially. We reveal that under certain conditions the performance of average profiles is inferior to the tossing of a coin. Alternative methods, such as directly estimating success probabilities, deliver better performance and are more robust.
This is a preview of subscription content, access via your institution.
Notes
Similar approaches, as discussed here, are also used to provide advice, for instance, to young adolescent individuals seeking guidance on occupational choice.
For a review of the recent literature on entrepreneurshiprelated personality characteristics, see Kerr et al. (2017).
For an overview of studies analyzing the effect of risk attitudes on entrepreneurial activities, see also Kerr et al. (2017).
This kind of fit measure is widely used in psychology, and not only in entrepreneurship; see, inter alia, Chapman and Goldberg (2011).
Thus, this simplifying assumption can be relaxed by using a vector of personality variables instead of univariate Γ.
There are two possible interpretations of π with respect to risk consistent with the model setting. First, entrepreneurship is not associated with any risk such that π is the deterministic relative income (relative to alternative income). Second, entrepreneurship is associated with risk and π is the average relative income but the client is riskneutral and only cares about averages.
Typically, even if π can be measured without any problem, we would only have reliable historical data on it for a subset of individuals because it is a counterfactual for those who were never entrepreneurs.
Note that \(p_{\text {COIN}}=\mathbb {E}[\mathcal {S}_{\text {\textit {COIN}}}]=\mathbb {P}(\textbf {a}_{\text {\textit {COIN}}}= 1\wedge \textbf {t}= 1)+\mathbb {P}(\textbf {a}_{\text {\textit {COIN}}}= 0\wedge \textbf {t}= 0)\). The coin completely ignores historical and client data such that \(\mathbb {P}(\textbf {a}_{\text {\textit {COIN}}}=a)\) and \(\mathbb {P}(\textbf {t}=t)\) are independent. Hence, we get
For instance, Δ(ε,ε^{′}) = 1/2 implies that changing the similarity criterion by a certain amount would change the expected recommendation success rate by 50 percentage points in the same setting indicating that approach performance is unstable.
Selfemployment and wage work can take place at the same time or at different time points.
Note that the relatively low number of observations is due to the fact that we need sufficient information about individuals who generated incomes from selfemployment and wage work. This is also why we had to use the willingness to take risk as information instead of the Big Five. We show below that this choice does not affect the reasoning of our approach.
The code is provided upon request.
References
Anscombe, F., & Glynn, W. (1983). Distribution of Kurtosis statistic for normal statistics. Biometrika, 70, 227–234.
Astebro, T., & Chen, J. (2014). The entrepreneurial earnings puzzle. Journal of Business Venturing, 29, 88–105.
Begley, T., & Boyd, D. (1987). Psychological characteristics associated with performance in entrepreneurial firms and smaller businesses. Journal of Business Venturing, 22, 147–173.
Benz, M., & Frey, B. (2008a). Being independent is a great thing. Economica, 75, 362–383.
Benz, M., & Frey, B. (2008b). The value of doing what you like. Journal of Economic Behavior and Organization, 68, 445–455.
Bernardo, A., & Welch, I. (2001). On the evolution of overconfidence and entrepreneurs. Journal of Economics & Management Strategy, 10, 301–330.
Blanchflower, D. (2000). Selfemployment in OECD countries. Labour Economics, 7, 471–505.
Bono, J., Boles, T., Judge, T., Lauver, K. (2002). The role of personality in task and relationship conflict. Journal of Personality, 70, 311–344.
Borghans, L., Duckworth, A., Heckman, J., ter Weel, B. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43, 972–1059.
Bosma, N., Hessels, J., Schutjens, V., Van Praag, M., Verheul, I. (2012). Entrepreneurship and role models. Journal of Economic Psychology, 33, 410–424.
Caliendo, M., Fossen, F., Kritikos, A. (2009). Risk attitudes of Nascent entrepreneurs. Small Business Economics, 32, 153–167.
Caliendo, M., Fossen, F., Kritikos, A. (2010). The impact of risk attitudes on entrepreneurial survival. Journal of Economic Behavior and Organization, 76, 45–63.
Caliendo, M., Fossen, F., Kritikos, A. (2014a). Personality characteristics and the decision to become and stay selfemployed. Small Business Economics, 42, 787–814.
Caliendo, M., Kritikos, A., Künn, S., Loersch, C., Schröder, H., Schütz, H. (2014b). Evaluation der Programme Gründercoaching Deutschland. IZA Research Report 61.
Chapman, B., & Goldberg, L. (2011). Replicability and 40year predictive power of childhood ARC types. Journal of Personality and Social Psychology, 101, 593–606.
Costa, P., McCrae, R., Holland, J. (1984). Personality and vocational interests in an adult sample. Journal of Applied Psychology, 69, 390–400.
Cronbach, L., & Gleser, G. (1953). Assessing the similarity between profiles. Psychological Bulletin, 50, 456–473.
D’Agostino, R. (1970). Transformation to normality of the null distribution of G1. Biometrika, 57, 679–681.
Fischer, R., & Boer, D. (2014). Motivational basis of personality traits: a metaanalysis of valuepersonality correlations. Journal of Personality, 83, 491–510.
Gartner, W. (1989). ‘Who is an entrepreneur?’ Is the wrong question. Entrepreneurship: Theory and Practice, 12, 47–68.
Green, D., Müllensiefen, D., Lamb, M., Rentfrow, P. (2015). Personalty predicts musical sophistication. Journal of Research in Personality, 58, 154–158.
Heckman, J., Stixrud, J., Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labour market outcomes and social behavior. Journal of Labor Economics, 24, 411–482.
Heller, D., Ferris, D., Brown, D., Watson, D. (2009). The influence of work personality on job satisfaction: incremental validity and mediation effects. Journal of Personality, 77, 1051–1084.
Helmers, C., & Rogers, M. (2010). Innovation and the survival of new firms in the UK. Review of Industrial Organization, 36, 227–248.
Holland, J. (1997). Making vocational choices: a theory of vocational personalities and work environments (3rd edn.) Odessa: Psychological Assessment Resources.
Holmes, T., & Schmitz, J. (1990). A theory of entrepreneurship and its application to the study of business transfers. Journal of Political Economy, 98, 265–294.
Hundley, G. (2001). Why and when are the selfemployed more satisfied with their work? Industrial Relations, 40, 293–316.
Jokela, M. (2009). Personality predicts migration within and between U.S. States. Journal of Research in Personality, 43, 79–83.
Kalleberg, A. L., & Leicht, K. T. (1991). Gender and organizational performance: determinants of small business survival and success. Academy of Management Journal, 34, 136–161.
Kerr, W., Nanda, R., RhodesKropf, M. (2014). Entrepreneurship as experimentation. Journal of Economic Perspectives, 28, 25–48.
Kerr, S., Kerr, W., Xu, T. (2017). Personality traits of entrepreneurs: a review of recent literature. Harvard Business School Working Paper, 18–047.
Kihlstrom, R., & Laffont, J.J. (1979). A general equilibrium entrepreneurial theory of firm formation based on risk aversion. Journal of Political Economy, 87, 719– 748.
Koellinger, P., Minniti, M., Schade, C. (2007). I think I can, I think I can. Journal of Economic Psychology, 28, 502– 527.
Kösters, S., & Obschonka, M. (2011). Public business advice in the founding process: an empirical evaluation of subjective and economic effects. Environment and Planning C: Government and Policy, 29, 123–138.
Levine, R., & Rubinstein, Y. (2017). Smart and illicit: who becomes an entrepreneur and do they earn more? Quaterly Journal of Economics, 132, 963–1018.
Magnusson, D., & Törestad, B. (1993). A holistic view of personality: a model revisited. Annual Review of Psychology, 44, 427–452.
Manso, G. (2016). Experimentation and the returns to entrepreneurship. Review of Financial Studies, 29, 2319–2340.
Mardia, K. (1974). Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies. Sankhy A, 36, 115–128.
Mayer, J., & Skimmyhorn, W. (2017). Personality attributes that predict cadet performance at west point. Journal of Research in Personality, 66, 14–26.
Obschonka, M., & Stuetzer, M. (2017). Integrating psychological approaches to entrepreneurship: the entrepreneurial personality system (EPS). Small Business Economics, 49, 203–231.
Obschonka, M., Silbereisen, R., SchmittRodermund, E. (2010). Entrepreneurial intentions as development outcome. Journal of Vocational Behavior, 77, 63–72.
Obschonka, M., SchmittRodermund, E., Silbereisen, R., Goslin, S., Potter, J. (2013). The regional distribution and correlates of an entrepreneurshipprone personality profile in the United States, Germany, and the United Kingdom: a socioecological perspective. Journal of Personality and Social Psychology, 105, 104–122.
Quatraro, F., & Vivarelli, M. (2015). Drivers of entrepreneurship and postentry performance of newborn firms in developing countries. The World Bank Research Observer, 30, 277–305.
Rauch, A., & Frese, M. (2007). Let’s put the person back into entrepreneurship research. European Journal of Work and Organizational Psychology, 16, 353–385.
Rodionova, Z. (2015). The personality test that could one day decide your credit score. The Independent, 2. September.
SchmittRodermund, E. (2004a). Pathways to successful entrepreneurship: parenting, personality, entrepreneurial competence, and interest. Journal of Vocational Behavior, 65, 498–518.
SchmittRodermund, E. (2004b). Pathways to successful entrepreneurship: parenting, personality, entrepreneurial competence, and interests. Journal of Vocational Behavior, 65, 498–518.
Schumpeter, J. (1934). The theory of economic development. Cambridge: Harvard University Press.
Shaver, K., & Scott, L. (1991). Person, process, choice: the psychology of new venture creation. Entrepreneurship Theory & Practice, 16, 23–45.
Stewart, W., & Roth, P. (2001). Risk propensity differences between entrepreneurs and managers. Journal of Applied Psychology, 86, 145–153.
Stuetzer, M., Goethner, M., Cantner, U. (2012). Do balanced skills help nascent entrepreneurs to make progress in the venture creation process? Economics Letters, 117, 186–188.
Zhao, H., & Seibert, S. (2006). The big five personality dimensions and entrepreneurial status. Journal of Applied Psychology, 91, 259–271.
Zhao, H., Seibert, S., Lumpkin, G. (2010). The relationship of personality to entrepreneurial intentions and performance: a metaanalytic study. Journal of Management, 36, 381–404.
Acknowledgments
We are indebted two anonymous referees as well as to the participants of the Entrepreneurship Residence Week, in particular to Moren Levesque, Simon Parker, and Mirjam van Praag for their helpful comments.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix A: Proof of consistency
To establish consistency with the profilebased approach, we must essentially answer the following question: What happens to the distribution of the individual trait if we condition on entrepreneurial abilities? Let μ_{ΓE} denote the mean of Γ for entrepreneurs and let \(\mu _{\Gamma E^{\textsf {c}}}\) denote the mean of the personality trait for nonentrepreneurs. Similarly, denote the variance of personality trait, Γ, by \(\sigma _{\Gamma E}^{2}\), respectively \(\sigma _{\Gamma E^{\mathsf {c}}}^{2}\). It is straightforward to derive that
where κ = τ − μ_{π}, W(κ) = ϕ(κ)/[1 −Φ(κ)] > 0 where ϕ(⋅) is the density and Φ(κ) the distribution function of the standard normal distribution; V (κ) = −ϕ(κ)/Φ(κ) < 0; w(κ) = W(κ)[1 − W(κ)]; and v(κ) = V (κ)[1 − V (κ)].
As the correlation, ρ, determines how strong the connection is between the personality trait and entrepreneurial abilities, we focus on the role of this parameter. If trait and abilities are independent, the correlation between them is zero such that \(\mu _{\Gamma E}=\mu _{\Gamma E^{\textsf {c}}}=\mu _{\Gamma }\) and \(\sigma _{\Gamma E}^{2}=\sigma _{\Gamma E^{\mathsf {c}}}^{2}= 1\). In such a setting, we cannot construct a distinct personality profile of an entrepreneur. However, if traits and abilities depend on each other with nonzero correlation, there will be a personality profile of an entrepreneur given by μ_{ΓE}. To see this, note that ρ > 0 implies
such that there is a difference between the average trait of an entrepreneur and the average trait of a nonentrepreneur. The difference in Eq. A.3 increases in the correlation between trait and entrepreneurial abilities. Furthermore, the variance of the personality trait conditional on being an entrepreneur, \(\sigma _{\Gamma E}^{2}\), decreases if the correlation between trait and abilities increases, as can be clearly seen in Eq. A.2.
A personality or profileoriented approach has the following strategy. It takes the client’s personality trait, γ, and compares it to the typical trait, μ_{ΓE}, of an entrepreneur. If Γ and π are sufficiently correlated, the Γvalues of entrepreneurs will be concentrated in one place and Γvalues of nonentrepreneurs in another. Hence, similarity between the client’s γ and profile μ_{ΓE} is an indication that the client is an entrepreneur. If the correlation is weak, all Γvalues will be located in roughly one place independent from π such that similarity between the client’s trait, γ, and profile μ_{ΓE} has little meaning.
To show consistency with the variableoriented approach, let \({\Psi }\in \mathbb {R}\) denote a normally distributed variable with mean μ_{Ψ} and variance \(\sigma _{\Psi }^{2}\). Ψ is assumed to capture all factors affecting entrepreneurial abilities that are not related to the personality trait, represented by Γ, such that we can assume that Ψ and personality trait, Γ, are independent. The variableoriented approach is consistent with the following model of entrepreneurial abilities:
where a and b are nonzero constant scalars. For instance, let Γ represent extraversion (one of the Big Five personality traits). If a > 0, more extraversion will increase entrepreneurial abilities, which is in line with previous research (Costa et al. 1984; Zhao and Seibert 2006; Zhao et al. 2010). The difference between the variableoriented perspective and entrepreneurshipprone profiles is that in the model in Eq. A.4 there is no specific reference profile of an entrepreneur. The assumption underlying Eq. A.4 is that, given a > 0, the trait Γ simply positively relates to entrepreneurial abilities, i.e., a higher score in Γ is associated with higher abilities. The model in Eq. A.4 generates a joint distribution of personality trait and abilities that is consistent with our recommendation model. Notice that π in Eq. A.4 is normal (it is the sum of two normal independent variables) with the following parameters:
The covariance between π and personality trait Γ is given by
Furthermore, it can be demonstrated that π and Γ are jointly normal according to the model in Eq. A.4. The joint distribution of π in model (A.4) and personality trait Γ is bivariate normal if and only if Y = απ + βΓ is normal for any constant \(\alpha ,\beta \in \mathbb {R}\). It is obvious that Y is normal if either α = 0 or β = 0, as π and Γ are both normal. If α = β = 0, Y = 0 with probability 1, which corresponds to a normal distribution with mean and variance zero. Hence, we must demonstrate that Y is normal if α and β are both nonzero. Note that π and Γ are dependent and correlated. Furthermore, note that
where δ_{Γ} ≡ αa + β and δ_{Ψ} ≡ αb. Using independence and normality of Γ and π, the momentgenerating function of Y is given by
such that
Equation A.6 is the momentgenerating function of a normal distribution with mean δ_{Γ}μ_{Γ} + δ_{Ψ}μ_{Ψ} and variance \(\delta _{\Gamma }^{2}+\delta _{\Psi }^{2}\sigma _{\Psi }^{2}\). As Y is normal for any constant α and β, π and Γ must be bivariate normal. Without loss of generality, we can normalize a such that \(a^{2}= 1b^{2}\sigma _{\Psi }^{2}\) obtaining \(\sigma _{\Pi }^{2}= 1\) and ρ = σ(π,Γ). Hence, as our recommendation model, the model in Eq. A.4 generates a bivariate normal distribution for [Γ,π]^{⊤} with mean m and covariance Q.
Appendix B: Robustness
To check the robustness of the results obtained with GSOEP data, this appendix provides additional simulation results using 1,620 combinations of parameter values.
B.1 Simulation setup
Let l = 1,…,L denote all parameter combinations. Let S^{l} denote the recommendation performance of an arbitrary approach given parameter combination l. We consider L = 1, 620 combinations. As before, for every parameter combination, we compute 10,000 simulations with sample sizes n(Θ) = 1, 000 and n(Ω) = 100. Given a sample of historical and client data, we apply three approaches to the same simulated data:

general average scores (GAS);

average scores with an optimized similarity criterion, given that m and Q are known (OAS); and

the probabilitybased approach (PBA).
Parameters, which are given in Table 3, are selected in a way such that a high number of different conditions is covered. Correlation between personality trait and entrepreneurial abilities ranges from weak, ρ = 0.1, to strong, ρ = 0.9.
𝜖 is the nonoptimized similarity criterion of average scores. Following the general logic of entrepreneurshipprone personality profiles, we assume that to receive a recommendation for entrepreneurship the client’s personality trait must be sufficiently similar to the profile, and that sufficient similarity promises good recommendation results. Hence, we use a rather strict (small) similarity criterion for average scores. However, we consider two different similarity criteria to examine the effect of changes in similarity criteria on average success rates (to test the second requirement). In particular, if 𝜖 = 0.01, we say that the similarity criterion is strict, whereas 𝜖 = 0.15 is interpreted as a tolerant similarity criterion.
To compute 𝜖^{∗} for the optimized version of average scores, we numerically maximize Eq. 6 for every parameter combination. Given the assumption on μ_{π} and τ, we cover a wide range of population shares of entrepreneurs, which is demonstrated in Fig. 3.
B.2 Performance analysis using 1,620 parameter combinations
Benchmarking success probabilities
To get an overview over average performance, we compute the simulation average, approximating \(\mathbb {E}[\mathcal {S}^{l}]\), for every parameter combination l and every approach.
In Fig. 4, we plot the distribution of average recommendation. Figure 4 reveals that the general averagescores approach (GAS) substantially underperforms compared to all other approaches.
In Fig. 5, we only show distributions of average success rates for a high correlation between personality trait and entrepreneurial abilities (ρ = 0.9). Still, even when correlation between personality trait and entrepreneurial abilities is high, general average scores underperform in comparison to all other approaches.
In contrast to general average scores, optimized average scores (OAS) exhibit high average success rates, which are slightly inferior to the upper boundary of recommendation performance represented by the probabilitybased approach (PBA). The results on relative performance are consistent with those obtained with the GSOEP calibrated model.
Testing requirements
To test the first requirement, in Fig. 6, we plot average recommendation success rates of (a) the optimized averagescores approach and (b) the general averagescores approach as a function of the parameter combinations index, l. Optimized average scores (Fig. 6a) always fulfill the first requirement. In case of general average scores (Fig. 6b), average success rates are smaller than 50%, the approach is inferior to the coin, in about 44% of all parameter combinations. More specific, only if the population share of entrepreneurs is low (about 19% on average, ranging between about 2% and 50%), general average scores outperform the coin with respect to average recommendation success rates.
To test the second requirement, let \(l_{\varepsilon ,\varepsilon ^{\prime }}=(l_{\varepsilon },l_{\varepsilon ^{\prime }})\) denote a pair of parameter combination where all parameters besides the similarity criterion are exactly the same. Our simulationbased measure of robustness, the simulation counterpart of Δ(ε,ε^{′}), is
In Fig. 7, we present robustness measures for average scores. Changing from a strict (𝜖 = 0.01) to a tolerant (𝜖 = 0.15) similarity criterion, or vice versa, changes average recommendation success rates by about 11 percentage points at maximum. The results become more striking when we compare the strict and the tolerant criterion to the optimized similarity criterion 𝜖^{∗}. The difference in average success rates between the strict and the optimized criterion is 95 percentage points at maximum, while the success rate difference between the tolerant and the optimized criterion is approx. 84 percentage points at maximum. The results indicate that average scores are not robust—mistakes of the adviser can generate high costs (e.g., a loss in average recommendation success rates of 95 percentage points).
Rights and permissions
About this article
Cite this article
Konon, A., Kritikos, A.S. Prediction based on entrepreneurshipprone personality profiles: sometimes worse than the toss of a coin. Small Bus Econ 53, 1–20 (2019). https://doi.org/10.1007/s1118701801118
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s1118701801118
Keywords
 Advice
 Personality
 Entrepreneurship
 Profiles
JEL Classification
 C15
 D81
 L26