Heterogeneous returns to personality: the role of occupational choice


We analyze the role of personality in occupational choice and wages using data from Germany for the years 1992 to 2009. Characterizing personality by use of seven complementary measures (Big Five personality traits, locus of control, and a measure of reciprocity), the empirical findings show that personal characteristics are important determinants of occupational choice. Associated with that, identical personality traits are differently rewarded across occupations. Hence, breaking down the analysis on the level of occupational groups provides more detailed results of returns to personality. By evaluating different personality profiles, we additionally estimate the influence of personality as a whole. The estimates establish occupation-specific patterns of significant returns to particular personality profiles. These findings underline the importance to consider the occupational distribution when analyzing returns to personality due to its heterogeneous valuation.

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  1. 1.

    While the measures of the Big Five inventory provide a concept to capture all superior facets of personality that are intrinsic to a person, reciprocity, and locus of control serve as measures of behavior or attitude related to outcomes. The Big Five traits can be characterized as follows: the first facet, conscientiousness, relates to whether a person is reliable, organized, and responsible. The second, extraversion, corresponds to an enthusiastic, outgoing attitude, while the third, agreeableness, relates to a kind and compassionate attitude. Neuroticism, being the fourth, instead is defined with respect to being unstable, worrying, and anxious, and finally, the fifth, openness to experience, refers to imaginative, original individuals with wide interests. Reciprocity aims at measuring the propensity to symmetrically react to friendly or hostile behavior, whereas locus of control captures the attitude of how self-determined (internal) or heteronomous (external) a person regards her own life.

  2. 2.

    Although cognitive and non-cognitive skills partly are substitutes, they are seen here as complements, since we can assume that cognitive ability is a very important requirement for job entry, which works as a hurdle before non-cognitive skills are considered.

  3. 3.

    Judge et al. (1999) relate these occupational personality measures with the concept of the Big Five and reveal significant correlations of e.g., openness to experience and the artistic type.

  4. 4.

    The Guilford-Zimmerman Temperament Survey (see Guilford et al. 1976) distinguishes the ten facets activity level, restraint, sociability, domination, emotional stability, objectivity, friendliness, thoughtfulness, personal relation skills, and masculinity. Sociability, friendliness, and emotional stability correspond to extraversion, agreeableness, and neuroticism (reversely defined) of the Big Five inventory.

  5. 5.

    Stevens et al. (1993) summarize evidence on this indirect link. They report results of several studies emphasizing different strategies and outcomes of salary negotiation for men and women. As a possible link, Stevens et al. (1993) name gender differences in self-efficacy, a concept that directly relates to other measures like locus of control or self-esteem.

  6. 6.

    NLSYW stands for National Longitudinal Survey of Young Women, whereas, NCDS stands for National Child Development Study.

  7. 7.

    BIBB denotes the Bundesinstitut für Berufsbildung, IAB the Institut für Arbeitsmarkt- und Berufsforschung der Bundesagentur für Arbeit.

  8. 8.

    See Appendix 8 for a more detailed discussion on the construction of our empirical measures for the personality traits. Moreover, we provide a commented STATA do-file, enabling reproducibility of the data, as an online resource.

  9. 9.

    Since individuals are not required to be at least 30 years when choosing their occupation, we rely on rank-order stability only when measuring the impact of personality on occupational choice.

  10. 10.

    The group of agricultural employees is not considered separately. Due to the small number they are counted in the group of laborers.

  11. 11.

    Gross labor income includes overtime premiums, but no special payments like e.g., leave pay.

  12. 12.

    P values of testing the hypothesis of equal means are displayed in detail in Table 5 in Appendix 9.

  13. 13.

    Note that for overlapping domains for all measures of non-cognitive skills, an individual can sort into more than one possible occupation.

  14. 14.

    There is a longstanding tradition to use a multinomial logit model to estimate occupational choice. Early general applications of using a multinomial logit to estimate occupational attainment were provided by Boskin (1974) and Schmidt and Strauss (1975). The method has also been applied to issues of occupational mobility, for example, of different ethnic minorities like in Kossoudji (1988) using the 1976 Survey of Income and Education or Chiswick and Miller (2009) using US Census data from 2000. Cobb-Clark and Tan (2011) likewise employ a multinomial logit model to the Australian HILDA data in order to estimate gender specific occupational attainment. Besides, Constant and Zimmermann (2003) make use of a multinomial logit model for German SOEP data to estimate the influence of parents occupation on occupational choice of children.

  15. 15.

    See Ham et al. (2009b), who apply the same test to justify validity if IIA.

  16. 16.

    Socio-economic variables are dummy variables for German citizenship, the presence of children younger than 16 years, being married, and age coded into three dummy variables for being 40–49 and 50–55 years with 30–39 years as the reference group. Regarding education, dummy variables for basic, lower secondary, and higher secondary education, possessing a vocational degree and having a university degree are included. The reference group for education is no educational degree. Level of education of parents is also included in the analysis, but here a coarser classification is applied: dummy variables for possessing a lower secondary, higher secondary degree, or a university degree are coded for mother and father, respectively. Parents’ occupation is regarded as well using the same classification as for the individuals included in the estimation. Hence, there are eight dummy variables for group of occupation for mother and father, respectively. Regional information contains local unemployment rates and GDP measured at the level of federal states and dummy variables for regions East, West, North, South, and city state. East comprises federal states Mecklenburg-Western Pomerania, Thuringia, Saxony, Saxony-Anhalt and Brandenburg. Region West corresponds to federal states North Rhine-Westphalia, Rhineland-Palatinate, and Hesse. The northern region stands for federal states Schleswig-Holstein and Lower Saxony. South (the reference region) comprises federal states Bavaria and Baden-Wuerttemberg. City states correspond to federal city states Berlin, Hamburg, and Bremen.

  17. 17.

    Labor market experience is measured in quadratic polynomials of years spent in full-time and part-time employment as well as of years in unemployment. Job characteristics included are dummy variables for working in the public sector and for being employed in a company with at least 200 employees. In addition, tenure and the required training of the position held are considered. Required training is a dummy variable equal to one if the employment requires having a diploma from a university or a university of applied sciences.

  18. 18.

    Laborers are the group with the lowest qualification requirements.

  19. 19.

    Table 6 in the Appendix displays all estimated coefficients of the personality variables.

  20. 20.

    All scores are standardized to have mean zero and standard deviation one to enable comparison across groups.

  21. 21.

    See Card (1999) for a summary of empirical findings (Table 6, p. 1849–1850).

  22. 22.

    We apply a back-of-the-envelope calculation, where marginal effects are multiplied with the average wage rate in the sample times 4.29 (average number of weeks per month) times 40 (assuming full-time employment of 40 h per week). Earnings are measured in prices of the year 2000.

  23. 23.

    We thank an anonymous referee, who suggested this evaluation of the results.

  24. 24.

    Referring to the notation in Sect. 4, we use \(\mathbf {Z}\) to denote background variables in the occupation equation and \(\mathbf {X}\) for background variables in the wage equation.

  25. 25.

    Swope et al. (2008) undertake a similar analysis to classify interaction of certain traits: Using the Meyer-Briggs-typology they describe relative frequencies of types built as joint appearance of four attitudes.

  26. 26.

    Using factor analysis to analyze questionnaire items aiming at measuring personality shows that factors load on more than one factor. This in turn leads to correlated factors. Correlation analysis for the Big Five personality traits within our sample reveals a negative relationship between neuroticism and the other four traits. Conscientiousness and agreeableness as well as extraversion are substantially positively correlated. The same applies for extraversion and openness to experience. These relationships can be found in other samples, too, see Biesanz and West (2004).

  27. 27.

    This personality profile accounts for around 11.5 % of the sample. Assuming a uniform distribution, each type would occur with a share of 3.125 %. A share of 11.5 % thus represents a more than threefold more probable occurrence of that specific type.

  28. 28.

    Marginal effects for all personality types are displayed in Appendix 9 in Table 8.

  29. 29.

    We apply a back-of-the-envelope calculation, where marginal effects are multiplied with the average wage rate in the sample times 4.29 (average number of weeks per month) times 40 (assuming full-time employment of 40 h per week). Earnings are measured in prices of the year 2000.

  30. 30.

    The theoretical model as well as the estimation equation is presented in Appendix 10.

  31. 31.

    Estimating the relationship of personality and earnings assumes that there is no correlation with other factors influencing returns, as for example cost of effort. Estimates therefore can only provide gross effects, since these factors are unmeasurable and are likely correlated with personality traits. Besides, returns to non-cognitive skills may interfere with compensating wage differentials that measure wage premiums for specific (adverse) characteristics of the employment.

  32. 32.

    Piatek and Pinger (2010) proceed in a similar way when extracting locus of control from the SOEP questionnaire.

  33. 33.

    \(m(P_j)\) corresponds to \(E(u_j), j=1,\ldots ,8\) given that outcome \(j\) has been chosen and \(m(P_k) \frac{P_k}{(P_k-1)}\) corresponds to \(E(u_k), k=1,\ldots ,8\) and \(k\ne j\) given that outcome \(j\) has been chosen.


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We thank Robert M. Kunst and an anonymous referee, Deborah Cobb-Clark, Kristin Kleinjans, Christoph Schmidt, and Barbara Grave for helpful comments as well as discussants at the research seminar at the University of Magdeburg, the labor market and social policy workshop at ifo Institute Dresden 2011, the Doctoral Meeting Montpellier 2011, the Canadian Economics Association Conference 2011, the Statistische Woche 2011 of the Deutsche Statistische Gesellschaft, and the Society of Labor Economists Conference 2012. Financial support from the Stifterverband für die Deutsche Wissenschaft (Claussen-Simon-Stiftung) and Wissenschaftszentrum Sachsen-Anhalt Lutherstadt Wittenberg (WZW) in the course of the project “Analyse des Bestands und der ökonomischen Bedeutung kognitiver und nicht-kognitiver Fähigkeiten in Sachsen-Anhalt zur Identifikation (bildungs-)politischer Handlungsbedarfe” is gratefully acknowledged. The data used in this publication were made available by the German Socio Economic Panel Study (SOEP) at the German Institute for Economic Research (DIW), Berlin.

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Correspondence to Stephan L. Thomsen.



Construction of measures for non-cognitive skills

Measures of non-cognitive skills are created by extracting information from the questions answered in SOEP, see Gerlitz and Schupp (2005). The Big Five are conscientiousness, extraversion, agreeableness, openness to experience, and neuroticism. Each of the traits is measured with the help of three questions relating to this particular attitude. Reciprocity is assessed with six questions and locus of control with the help of ten question. Questions relating to non-cognitive skills are answered with 7-point Likert-scales ranging from 1 for “does not apply” to 7 for “does apply”.

Before defining the scales, we conducted a principal component analysis in order to find out which items really load onto the facets. Principal component analysis applies an eigenvalue-decomposition of the correlation or covariance matrix. The extracted eigenvectors describe a series of uncorrelated linear combinations of the variables that contain most of the variance. Thereby, the number of underlying factors is analyzed and it is controlled whether variables intending to assess these factors are indeed able to measure those. The items that have been shown to load onto the facets are then used to define our measures of non-cognitive skills.

In case of the Big Five, we assume five underlying traits for the 15 variables. Building averages of three items each to construct a measure for a personality trait is only appropriate if all three items can clearly be attributed to that trait. Applying the Kaiser-Guttman criterion, we consider as many factors as there are eigenvalues larger than one. This procedure reveals that the 15 items describing the Big Five have indeed five underlying factors and all items clearly load onto them. For locus of control, principal component analysis generated two eigenvalues larger than one and another one equal to one, pointing to two or even three underlying factors. However, there were only two items clearly loading onto the second factor. Therefore we extract only one factor which would be perceived to characterize external attitude. As the concept of locus of control is primarily seen as a one-dimensional concept ranging from external to internal (see Rotter 1975, for a discussion of unidimensionality-multidimensionality), we decided to build one scale.Footnote 32 It includes the two items that load on the second factor (internal locus of control), but discards four items that did not load unambiguously onto both factors. The final scale therefore includes six items and higher values are coded to a more external attitude.

Regarding the six variables aiming at measuring reciprocity, principal component analysis revealed two eigenvalues larger than one. These two underlying factors can be defined as positive and negative reciprocity. However, we decided to extract one single factor capturing the intensity of reciprocity since this is a more general trait which is of interest with respect to social cooperation apparent in professional environments.

Now we began defining the variables measuring personality facets. Some of the variables of the Big Five traits are reversely coded which is regarded in the calculation of the average scores. Personality variables were set to missing in case that one or more of the items defining it was missing. In the next step we build seven measures, one for each of the non-cognitive skills that we extracted. For the Big Five inventory, each measure is the average value of the three variables that load onto the particular trait. For locus of control, we only use six out of ten variables to construct the average as has been explained above.

Finally, scales of all seven non-cognitive skills are standardized to have mean zero and unit variance. For the analysis of occupation-specific returns to non-cognitive skills, the scales are standardized within each group of occupation to have mean zero and unit variance. Thereby, consistent interpretation across occupational groups is possible: returns to non-cognitive skills can be interpreted as the percentage change in wage for a change of the score by one standard deviation.

Further Tables

Robustness check: selection correction

Given the possible selection bias of the occupation-specific sample due to correlated error terms of occupation and wage (\(Cov(u_{ij}, \varepsilon _i)\ne 0\)), we estimate an augmented wage equation which aims at correcting for the selection process. When conditioning on occupation, we have to account for the fact that the distribution of occupations is the result of sorting due to characteristics and preferences of individuals as well as labor demand factors that have a direct influence on wages, too. We apply a correction method proposed by Bourguignon et al. (2007), who put forward an extension to the approach of Dubin and McFadden (1984). While Dubin and McFadden (1984) assume linearity between \(\varepsilon \) (error term of the wage equation) and the Gumbel distributed original error terms from the multinomial logit (\(u_j\)), Bourguignon et al. (2007) propose a transformation of the \(u_j\) to normally distributed error terms \(u_j^*\) so that the linearity assumption holds for the \(u_j^*\) and \(\varepsilon \):

$$\begin{aligned} u_j^*=J(u_j)=\varPhi ^{-1}(G(u_j)), \quad j=1,\ldots ,8 \end{aligned}$$

with \(G(u)=\exp (-e^{-u})\) as the cumulative distribution function of the Gumbel distribution. Then for every \(j\), the expected values of \(\varepsilon \) and \(u_j^*\) are linearly related

$$\begin{aligned} E(\varepsilon | u_1, \ldots , u_8) = \sigma \sum \limits _{j=1,\ldots ,8} r_j^*u_j^* \end{aligned}$$

where \(r_j^*\) is the correlation between \(\varepsilon \) and \(u_j^*\). Bourguignon et al. (2007) then show that the outcome equation can consistently be estimated. In our approach, this means that the wage equation conditional on choosing occupation \(j\) can be written as

$$\begin{aligned}&{\mathrm {ln}}(\hbox {wage}_\mathrm{i}|{\mathrm {occ}}_\mathrm{i}=\hbox {j})=\alpha _i + \mathbf {X'}_i\mathbf {\beta } + \mathbf {n'}_i\mathbf {\gamma } \nonumber \\&\quad +\, \sigma \left[ r_j^* m(P_j) + \sum \limits _{k=1,\ldots ,8, k \ne j} r_k^*\cdot m(P_k) \frac{P_k}{(P_k-1)} \right] + w_j, \end{aligned}$$

where \(j=1,\ldots ,8\) and \(i=1,\ldots ,N\). Furthermore, \(w_j\) is a residual that is mean-independent of the regressors and \(m(P_j)\) is the integral

$$\begin{aligned} m(P_j)=\int J(v-\ln P_j)g(v)dv, \quad \forall j, j=1,\ldots ,8 \end{aligned}$$

with \(g(u)=\exp (-u-e^{-u})\) as the probability distribution function of the Gumbel distribution.Footnote 33 Overall, each wage equation has eight correction terms which are consistent estimators of conditional expected values of the residuals for each possible choice derived from the multinomial logit model.

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John, K., Thomsen, S.L. Heterogeneous returns to personality: the role of occupational choice. Empir Econ 47, 553–592 (2014). https://doi.org/10.1007/s00181-013-0756-8

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  • Occupational choice
  • Wage differentials
  • Big Five personality traits
  • Locus of control
  • Measures of reciprocity
  • SOEP

JEL Classification:

  • J24
  • J31
  • C35