Abstract
Based on a large, representative German household panel, we investigate to what extent the personality of individuals influences the entry decision into and the exit decision from self-employment. We reveal that some traits, such as openness to experience, extraversion, and risk tolerance affect entry, but different ones, such as agreeableness or different parameter values of risk tolerance, affect exit from self-employment. Only locus of control has a similar influence on the entry and exit decisions. The explanatory power of all observed traits among all observable variables amounts to 30 %, with risk tolerance, locus of control, and openness having the highest explanatory power.
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The approach can be traced back to Ajzen’s (1991) more general theory of planned behavior.
Thus, among the list of crucial personality characteristics highlighted by Gartner (1985), only the variable “need for achievement” is not included, which is a limitation of our study. In their meta-analytical research, Rauch and Frese (2007) show that this variable is related to entrepreneurial status.
Caliendo et al. (2012) also analyzed the variables positive and negative reciprocity but found that these two variables have almost no influence on entrepreneurial decisions.
There are a number of single variable studies where the effect of further specific personality characteristics on entrepreneurial entry or exit is studied. Chen et al. (1998) found differences between managers and entrepreneurs for the variable entrepreneurial self-efficacy, Stewart and Roth (2001) for risk attitudes, Müller and Gappisch (2005) for problem solving orientation, Koellinger et al. (2007) for overconfidence, and Caliendo and Kritikos (2008) for assertiveness. As we are not focusing on all personality variables related to entrepreneurial development in this study, we do not aim to review the complete literature with respect to the question where entrepreneurs differ from others.
The SOEP is similar to the PSID (Panel Study of Income Dynamics) in the US and the BHPS (British Household Panel Survey) in the UK. A stable set of core questions appears every year, covering the most essential areas, such as: population and demography; education, training, and qualification; labor market and occupational dynamics; earnings, income, and social security; housing; health; and basic orientation. Other questions—like the ones about personality variables which we will use and describe later on—are asked in rotating intervals. Respondents also provide biographical background information, like lifetime work and unemployment experience and parents’ occupation. For a detailed data description, see Wagner et al. (2007).
As shortly discussed in Sect. 2.1, the concept of entrepreneurship and of entry into self-employment are of course not exactly the same, as the former usually implies the risk-bearing of innovation, whereas the latter goes along with income risk but not necessarily with technological innovation.
According to Radl (2007), the actual average retirement age for men in Germany was 63 years in 2004.
The SOEP waves of 2004 and 2009 additionally included a measure of risk tolerance using lottery choices. This paper uses the question about the general willingness to take risks, as this is the only risk question also available in 2006 and 2008. Furthermore, the experiment by Dohmen et al. (2011) showed that this measure performs better than the lottery measure in predicting behavior.
Histograms for all personality variables are available from the authors on request.
Detailed results are available in Table S 1 in the Supplementary Appendix.
Only for the ten items intended to measure locus of control are the results from the factor analysis somewhat mixed. We therefore do not use two of these items (indicated in Table A1) for the construction of internal and external locus of control. The item “Inborn abilities are more important than any efforts one can make” loads on factor 9, which seems to represent internal locus of control, but we stick with the ex-ante concept and use it for external locus of control. We repeated the main estimations without using this item and obtained very similar results (available from the authors on request).
In fact, the correlation coefficients between the Big Five personality variables in the sample, as measured in 2005 and 2009, are 0.60 for openness, 0.53 for conscientiousness, 0.66 for extraversion, 0.55 for agreeableness, and 0.59 for neuroticism (all being significant at the 1 % level). Given these quite high correlations, it seems plausible that the deviations represent (random) noise in the survey response.
Section 4.4 further assesses the stability of personality traits for entrepreneurs.
A new result in this context is that individuals tending to external locus of control also trust other people less.
We conducted additional tests on the validity and on the internal consistency of the questionnaire, e.g., by analyzing correlations between the single items behind the personality variables, by analyzing correlations between the Big Five factors confirming hypotheses of Digman (1997), and also by relating personality variables to other information available in the SOEP such as the number of friends. All tests show that the questionnaire is valid and internally consistent. The tests are available from the authors on request. Moreover, general tests on the validity and on the reliability of the instrument measuring the Big Five factors are provided by Lang et al. (2011). They reported that the questionnaire used “is a reasonable, short instrument designed to measure the Big Five personality factors in large surveys”.
See Table S 10 in the Supplementary Appendix.
Table 7 in the “Appendix” provides the definitions of the variables.
To avoid endogeneity, work experience (in decades) and unemployment experience (in years) accumulate until the year before the observation year. We use retrospective information about a respondents’ employment history to recover the work and unemployment experience before the respondent enters the panel.
These are: age, prior working experience, and prior unemployment experience, the number of children, real income from interests, dividends, and rents as an indicator of wealth, and dummy variables indicating gender, educational degrees, disability, German nationality, marital status, geographical region, and whether the father was self-employed when the respondent was 15 years old. Year dummy variables are also included to control for the business cycle. The results with respect to the personality variables are almost exactly the same when we allow for region-specific time trends by including a full set of interaction terms between the regional and the year dummy variables (available from the authors on request).
If personality is fully described by the Big Five constructs and the specific personality characteristics are noisily measured linear combinations of these, the coefficients of the specific personality characteristics are expected to be zero and insignificant. Coefficients significantly different from zero indicate partial effects beyond what the Big Five model explains.
We test including additional squared terms for all the linearly significant personality variables. All these squared terms are insignificant and are thus dropped from the final specifications.
Table S 2 in the Supplementary Appendix provides the logit coefficients, including those of the control variables. In the transition models, only observations from 2000 to 2008 can be included, such that we have fewer observations than shown in Table 3 (which also includes observations from 2009).
Based on Akaike’s (1973) information criterion (AIC), a posteriori one would prefer the specification including the Big Five, risk tolerance, locus of control, reciprocity, and trust in the models of entry and of being self-employed, whereas reciprocity and trust would be dropped in the exit model. The Bayesian information criterion, BIC (Schwarz 1978), penalizes model complexity more than the AIC and favors more parsimonious specifications. Both criteria are provided in Table S 3 and Table S 4 in the Supplementary Appendix.
Among psychologists there is an ongoing discussion about the separate influence of risk tolerance. Some argue that it is a compound personality characteristic reflected by a specific combination of scores within the Big Five personality construct (see, e.g., Nicholson et al. 2005), others suggest that risk tolerance forms a separate dimension of personality outside of the Big Five (see, e.g., Paunonen and Ashton 2001). Our results are rather in favor of the latter interpretation.
Positive reciprocity is found to have a small, but significant, negative partial effect on the probability of self-employment. This effect is not robust, however: positive reciprocity becomes significant only when the Big Five are also included, but it is insignificant without these regressors, as shown in Table S 4 in the Supplementary Appendix. This explains the insignificance of positive reciprocity in the study of Caliendo et al. (2012), which did not include the Big Five.
See Table S 2 in the Supplementary Appendix.
As an additional variable, we considered optimism (available in 2005 and 2009). When we include “optimism” with a score from 1 (pessimistic) to 4 (optimistic) in our probability models of self-employment, entry, and exit, in addition to the other personality variables, its coefficients are insignificant in all models, so it could be dropped from the final specifications. The insignificance is consistent with the view that the concept of optimism as a personality characteristic is fully described by the Big Five dimensions.
Results are qualitatively similar when other pseudo-R 2 statistics (McKelvey and Zavoina’s R 2 or Efron’s R 2) are used (the results are available from the authors on request).
These are: the number of children, real income from interests, dividends, and rents as an indicator of wealth, years of prior unemployment experience, and dummy variables indicating gender, disability, German nationality, marital status, and geographical region.
The estimated logit coefficients of the personality variables in the corresponding models for self-employment status appear in Table S 3 in the Supplementary Appendix.
The pseudo-R 2 are not very large, even in the full models, as is typical in micro-data applications. Obviously, most of an individual’s circumstances that induce him or her to be, become, or give up self-employment, such as specific business opportunities, are unobserved. This does not invalidate the identification of the partial effects of the observed variables, many of which are shown to be significant.
Openness to experience accounts for about three-quarters of the explanatory power of the Big Five, and extraversion for most of the rest.
Previous research expected that a self-employed father explains much of what the personality variables are able to explain, because offspring of a self-employed father might develop a personality inclining towards entrepreneurship during childhood. In this context, Mungai and Velamuri (2011) discovered at what stage parental performance in self-employment may influence the offspring's entrepreneurial development.
In Table S 4 and Table S 5 in the Supplementary Appendix, we show that the main results are not sensitive to the order in which variables are added. Table S 6 and Table S 7 in the Supplementary Appendix show that using the single items from the questionnaire instead of the aggregated personality variables does not change the results with respect to the importance of the personality variables.
Table S 8 in the Supplementary Appendix shows logit coefficients from these separate regressions.
Positive and negative reciprocity are significant only when included jointly with the other personality variables, which is consistent with the observation reported before.
See Table S 9 (Spec. B3) in the Supplementary Appendix.
Psychologists argue that, in particular, personality traits covered by the Big Five approach are stable over lifetimes (see, inter alia, Caspi et al. 2005). Similarly, Borghans et al. (2008) concluded in their paper that traits are stable across situations and certain time periods. Furthermore, our results do not change notably when we exclude adults younger than 25 years of age from the sample.
See Table S 9 (Spec. B4 and B5) in the Supplementary Appendix.
See Table S 9 (Spec. B6 and B7) in the Supplementary Appendix. The personality constructs significant in the pooled estimation keep their signs in both subsamples. The coefficient of conscientiousness remains negative and becomes significant at the 10 % level in the sample with wage income below the median. Perhaps, in this group, the sub-component dutifulness, which may be more beneficial as an employee, dominates.
Some coefficients become statistically insignificant, however. The coefficient of patience remains positive and becomes significant in the model of entry from regular employment.
See Table S 9 (Spec. B8 and B9) in the Supplementary Appendix for these two robustness checks.
Full results of all robustness tests described in this section are available from the authors on request.
Recent research (Sahakian et al. 2008) has highlighted the importance of impulsivity for entrepreneurial status.
The influence of locus of control (as a potential sub-factor of conscientiousness) may also capture effects of the factor conscientiousness on survival, which were reported by Ciavarella et al. (2004).
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Acknowledgments
We would like to thank two anonymous referees, Silke Anger, David Audretsch, Rob Fairlie, Philipp Koellinger, Maria Minniti, Martin Obschonka, Rainer Silbereisen, Siri Terjesen, Roy Thurik, Mirjam van Praag, the seminar participants in Berlin, Bloomington, Erfurt, Jena, Lüneburg, Rotterdam, and Washington, D.C., the participants of the Workshop on Entrepreneurship Research at IZA in Bonn, of the ESEM conference in Malaga and of the Verein für Socialpolitik in Frankfurt/Main for their helpful and valuable comments.
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Caliendo, M., Fossen, F. & Kritikos, A.S. Personality characteristics and the decisions to become and stay self-employed. Small Bus Econ 42, 787–814 (2014). https://doi.org/10.1007/s11187-013-9514-8
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DOI: https://doi.org/10.1007/s11187-013-9514-8