Personality Changes Predict Early Career Outcomes: Discovery and Replication in 12-Year Longitudinal Studies

Abstract

In this research, we examined whether personality changes from adolescence to young adulthood predicted five early career outcomes: degree attainment, income, occupational prestige, career satisfaction, and job satisfaction. The study used two representative samples of Icelandic youth (Sample 1: n = 485, Sample 2: n = 1,290) and measured personality traits over 12 years (ages ~17 to 29 years). Results revealed that certain patterns of personality growth predicted career outcomes over and above adolescent trait levels and crystallized ability. Across both samples, the strongest effects were found for growth in emotional stability (income and career satisfaction), conscientiousness (career satisfaction), and extraversion (career satisfaction and job satisfaction). Initial trait levels also predicted career success, highlighting the long-term predictive power of personality. Overall, our findings show that personality has important effects on early career outcomes—both through stable trait levels and how people change over time. We discuss implications for public policy, for theoretical principles of personality development, and for young people making career decisions.

Keywords

career success personality development longitudinal replication preregistered

Personality traits reflect relatively enduring patterns of thoughts, feelings, and behaviors that predict success in a variety of occupations. Research linking personality to career success has traditionally viewed personality as a stable set of variables that do not change meaningfully in adulthood (Barrick & Mount, 1991; Judge & Kammeyer-Mueller, 2007). However, stability does not preclude the possibility of change, as personality traits change characteristically at different periods of the life span (Roberts, Walton, & Viechtbauer, 2006). Young adulthood, in particular, is marked by formative developmental changes associated with personality maturation (Roberts & Mroczek, 2008). Yet not all young people change in the same way (Denissen, Luhmann, Chung, & Bleidorn, 2019; Schwaba & Bleidorn, 2018). It is not yet known whether individual differences in personality change during young adulthood meaningfully predict career success over and above adolescent personality.

Understanding the career-related consequences of personality changes during young adulthood has several important implications. First, young people face a variety of career decisions with long-term consequences. When personality assessments are used to inform career decision-making, young people (and their parents, teachers, and counselors) can benefit from knowing that personality can change over time in meaningful ways. Second, there is wide interest in developing public policy and applied interventions to help young people develop and enhance personality and other socioemotional skills (Bleidorn et al., 2019; Heckman & Kautz, 2012). When should such interventions occur to maximize their effects? We believe that young adulthood is often overlooked, even though research has shown that it is a key period for personality development (Roberts et al., 2006; Specht, Egloff, & Schmukle, 2011). Third, information about the predictive power of personality changes can inform theoretical principles of personality development (Wrzus & Roberts, 2016). If changing in certain ways is associated with career outcomes, this may help us understand why personality traits change across the life span.

In the present research, we tested whether long-term personality changes, such as becoming more conscientious during young adulthood, predict early career outcomes over and above adolescent trait levels and crystallized ability. We examined this question using two longitudinal samples spanning late adolescence (~17 years old) to young adulthood (~29 years old). Our study extends existing research in several ways. First, this is the first study to assess the predictive power of personality changes for a broad range of career outcomes across more than a decade of young adulthood. Second, we used two samples with Big Five personality data collected across three and five time points. Given our novel focus on personality changes as predictors, it was important to include a replication sample and data from more than two time points. Third, we incorporated five indicators of early career success: degree attainment, occupational prestige, income, career satisfaction, and job satisfaction. These variables cover both objective and subjective success (Heslin, 2005). Fourth, we used nationally representative samples drawn from Iceland’s student population to ensure that outcomes were collected from people working in diverse career fields.

Personality Development and Career Success

The Big Five framework is the most widely used model for assessing personality (Costa, McCrae, & Löckenhoff, 2019). Prior longitudinal studies have demonstrated that adolescent levels of three Big Five traits—emotional stability, extraversion, and conscientiousness—tend to be positively associated with future job satisfaction, income, and occupational attainment (Judge, Higgins, Thoresen, & Barrick, 1999; Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007; Sutin, Costa, Miech, & Eaton, 2009). This means that adolescents who are more emotionally stable, extraverted, and conscientious tend to achieve greater success in their future careers. But what about adolescents who score lower on these traits? Can their personality traits change during young adulthood in ways that promote career success? Although few studies have examined work-related correlates of personality change, research suggests that personality changes may be particularly impactful during young adulthood.

Statement of Relevance

We typically think of personality traits as enduring patterns of thoughts, feelings, and behaviors. Yet stability does not preclude change. This research revealed that there are real-world career benefits associated with personality growth during a critical juncture in life—the transition to employment. The study was based on two longitudinal samples of Icelandic youth tracked from late adolescence to young adulthood. We found that young people who developed higher levels of personality traits, such as conscientiousness and emotional stability, tended to be more successful in some aspects of their early careers. These findings demonstrate the potential paybacks of policy actions and educational initiatives aimed at promoting personality-based skills in young people. Young people can also benefit by knowing that their current personality is not fixed and can change over time. Such changes, in and of themselves, contain critical information about the predictive power of personality for career outcomes.

Here, we highlight two common findings about personality development. First, personality is least stable during adolescence but increases thereafter (Roberts & Mroczek, 2008). This means that between-person differences in personality tend to stabilize throughout young adulthood. Second, on average, people become more conscientious, emotionally stable, and agreeable during young adulthood (i.e., the maturity principle; Roberts et al., 2006). These mean-level increases often occur in relation to career transitions that incentivize personality maturity (Bleidorn, Hopwood, & Lucas, 2018). For example, when beginning full-time work, people find that several personality-based skills, such as time management, emotion regulation, and getting along with coworkers, become increasingly important (Golle et al., 2019; Lodi-Smith & Roberts, 2007). Becoming more conscientious and emotionally stable may therefore have advantages for young adults adapting to work. Indeed, cross-cultural research shows that early job entry is associated with accelerated personality maturation (Bleidorn et al., 2013).

However, there are individual differences in personality change, as some people become less conscientious and emotionally stable with age (Schwaba & Bleidorn, 2018). Variability in how traits change raises the possibility that certain changes may be associated with career success. In the present research, our general hypothesis was that personality changes (slopes) would be positively related to career outcomes. In other words, we expected that young adults whose personality levels increased more than average would achieve greater success in various aspects of their early careers, even after controlling for adolescent personality. Such a finding would have important implications for how personality is conceptualized in applied settings. Personality traits can be viewed as enduring dispositions that can change in adaptive ways, rather than as fixed characteristics.

Method

Sample

We used two longitudinal samples of Icelandic youth spanning approximately 12 years, from late adolescence to young adulthood. Both samples are representative of Iceland’s total student population in terms of gender, educational track, and location (i.e., urban, suburban, and rural; Einarsdóttir & Rounds, 2007; Statistics Iceland, 2011). Personality measures were included at each wave, and career outcomes were collected at the most recent wave for each sample. Table S1 in the Supplemental Material available online presents descriptive information about the samples.

Sample 1 (n = 485) was assessed over five waves, beginning in 2006 during the final year of compulsory education (age: M = 15.3 years, SD = 0.5; 47% female). Forty schools were randomly chosen from a list provided by the Ministry of Education of all compulsory-education schools offering 10th grade in Iceland. Administrators of 21 schools from six of the eight geographic regions in Iceland accepted the request for participation, informed parents, and offered parents the chance to decline participation. Participants were contacted again 2, 6, 8, and 11 years later through e-mail invitations (and phone calls to nonresponders). At Wave 5 (n = 207), 79% of respondents reported working as their primary status, 2% were unemployed, 16% were enrolled in school, and 3% were on disability or parental leave.

Sample 2 (n = 1,290) was assessed over three waves, beginning in 2006 when all participants were enrolled in upper-secondary education (51% female). All 31 upper-secondary schools in Iceland were initially contacted in 2006, and 22 schools from all eight geographic regions agreed to participate. At each school, career counselors selected groups of students to represent the student body in terms of fields of study. Compared with participants in the first sample, participants in Sample 2 were slightly older at Wave 1 (mean age = 17.3 years), and there was more variability in age¹ (SD = 1.4 years; range: 14–21 years). Participants were contacted again 6 and 12 years later through e-mail invitations and phone calls. At Wave 3 (n = 578, mean age = 29.3 years), 83% reporting working as their primary status, 2% were unemployed, 8% were enrolled in school, and 7% were on parental leave or disability.

Measures

The primary measures included personality and five career outcomes.² A measure of crystallized ability was also available in one of the samples. Crystallized ability is a strong predictor of career and academic achievement and shows moderate correlations with certain personality traits (Lubinski, 2000). We therefore used crystallized ability as a control variable in the supplemental analyses (reported below and in the Supplemental Material).

Personality

Big Five personality traits were measured using the Icelandic version of the NEO Five-Factor Inventory (Jónsson & Bergþórsson, 2004). The measure contains 60 total items, 12 for each Big Five trait, which participants rated on 5-point scales (1 = strongly disagree, 5 = strongly agree). Table 1 presents the means, standard deviations, and alpha reliabilities for the personality scales. The sample means are similar to those among the general Icelandic population (Jónsson & Bergþórsson, 2004).

Table 1.

Means, Standard Deviations, and Alpha Reliabilities of Big Five Personality Traits

Sample and trait	Wave 1			Wave 2			Wave 3			Wave 4			Wave 5
Sample and trait	M	SD	α	M	SD	α	M	SD	α	M	SD	α	M	SD	α
Sample 1
Emotional stability	38.57	8.41	.73	38.34	9.19	.88	39.16	8.74	.87	38.34	8.38	.85	38.67	9.58	.90
Extraversion	43.77	6.05	.74	44.51	5.74	.75	43.16	6.13	.79	42.15	6.20	.80	41.62	6.71	.82
Openness	37.10	6.08	.65	37.04	6.48	.71	38.64	6.87	.76	39.03	6.75	.76	40.11	6.90	.75
Agreeableness	41.36	5.78	.68	42.68	5.92	.74	43.49	6.18	.78	44.53	5.58	.72	45.22	5.77	.76
Conscientiousness	41.62	7.51	.85	41.68	7.94	.88	42.73	7.88	.88	42.85	7.56	.86	43.90	7.46	.87
Sample 2
Emotional stability	37.89	8.45	.85	39.42	8.41	.86	39.29	9.04	.87
Extraversion	43.01	6.14	.76	43.23	6.38	.82	41.80	6.94	.84
Openness	38.70	6.42	.67	39.31	6.78	.72	39.74	6.89	.74
Agreeableness	42.28	5.65	.68	44.98	5.75	.75	46.50	5.91	.77
Conscientiousness	41.29	7.60	.85	44.30	7.52	.88	45.13	7.23	.87

Note: There were 12 items for each Big Five scale in both samples.

Crystallized ability

Standardized test scores from the final year of compulsory education (age 15) were used to measure crystallized ability in Sample 1. (Test scores were not available for Sample 2.) We used the average test score from math, Icelandic, and English. Exam results are reported on a standardized scale of 0 to 60, and the overall (national) mean is 30 (SD = 10). In Sample 1, the means for math, Icelandic, and English were 30.1 (SD = 9.24), 30.5 (SD = 8.95), and 30.3 (SD = 9.29), respectively. These results support the representativeness of Sample 1 in terms of academic achievement.

Degree attainment

Information about participants’ educational attainment from 2006 to 2016 was obtained using archival data from Statistics Iceland, the national institution overseeing Iceland’s educational registry. The registry contains information about all Iceland citizens’ educational status each year. For this study, we focused on the highest obtained educational degree, coded into one of five ordinal categories (1 = compulsory education or less, 2 = vocational degree, 3 = upper-secondary degree/matriculation exam, 4 = undergraduate degree, 5 = graduate degree). In Sample 1, 3% had a graduate degree, 24% had an undergraduate degree, 35% had an upper-secondary degree/matriculation exam, 11% had a vocational degree, and 27% had a compulsory-education degree. In Sample 2, 12% had a graduate degree, 31% had an undergraduate degree, 31% had an upper-secondary degree, 11% had a vocational degree, and 15% had a compulsory-education degree.

Occupational prestige

Occupational prestige reflects the status associated with different jobs in society (Heslin, 2005). Occupational-prestige ratings were assigned to self-reported job titles using a two-step process. First, self-reported job titles were coded into occupations using the Standard Occupational Classification taxonomy of the Occupational Information Network (O*NET; Office of Management and Budget, 2018). All job titles were coded twice, and there was an 83% agreement rate in Sample 1 and a 79% agreement rate in Sample 2. Second, prestige ratings were assigned to occupations using O*NET’s Achievement and Recognition work-value dimensions, the two values most associated with status and prestige (range = 0−100). This index of occupational prestige has been used previously (e.g., Spengler, Damian, & Roberts, 2018) and led to a wide distribution of prestige scores, ranging from 3 (“Cleaners of vehicles and equipment”) to 95 (“Surgeons”). For analyses, we included prestige data only from each participant’s most recently reported job title. Table S2 in the Supplemental Material presents all occupations from both samples sorted by prestige. The mean prestige ratings were 49.73 (SD = 20.64) in Sample 1 and 53.02 (SD = 20.88) in Sample 2.

Income

Income was measured via a self-report question asking for usual monthly wages before tax. There were 11 response categories (1 = 200,000 Króna or less, 11 = over 1.5 million Króna). In U.S. currency, the low-end category represents approximately $1,900 or less each month; the high-end category represents approximately $14,200 or more each month. The mean income ratings were 3.90 (SD = 2.15) in Sample 1 and 4.29 (SD = 2.14) in Sample 2.

Career satisfaction

Career satisfaction was measured with a translated version of the five-item scale from Greenhaus, Parasuraman, and Wormley (1990). The items were measured on a 5-point scale (1 = strongly disagree, 5 = strongly agree) and are presented in the Supplemental Material (example item: “I am satisfied with the success I have achieved in my career”). The alpha reliabilities of the scale were .86 in Sample 1 and .89 in Sample 2. The means were 3.55 (SD = 0.77) in Sample 1 and 3.48 (SD = 0.88) in Sample 2.

Job satisfaction

Job satisfaction was measured with five translated items from Brayfield and Rothe’s Revised Job Satisfaction Blank (1951). The items were measured with a 5-point scale (1 = strongly disagree, 5 = strongly agree) and are presented in the Supplemental Material (example item: “I feel fairly well satisfied with my present job”). The alpha reliabilities of the job-satisfaction scale were .91 in Sample 1 and .88 in Sample 2. The means were 3.97 (SD = 0.86) in Sample 1 and 3.95 (SD = 0.87) in Sample 2.

Analysis

The analytic plan for this study was preregistered as part of a larger project on OSF (https://osf.io/tz7wj/). Analyses were conducted in three major steps. First, we analyzed missing data, statistical power, and measurement invariance of the personality scales across time. Second, we examined patterns of mean-level personality development. The mean-level changes provide the basis for the third set of analyses, which tested personality growth as a predictor of career success.

Missing data, statistical power, and measurement invariance

Attrition analyses were conducted to examine differences between participants who dropped out of the study and those who reported career outcomes (at the most recent wave). Little’s (1988) missing completely at random (MCAR) test was significant (p < .01), indicating that the probability of missing career outcomes was related to other measured variables. Follow-up tests revealed that gender, educational attainment, and crystallized ability predicted missing data. In both samples, participants who dropped out completed less education—Sample 1: d = −0.64, 95% confidence interval (CI) = [−0.82, −0.45]; Sample 2: d = −0.53, 95% CI = [−0.65, −0.42]. There were also significant gender differences—Sample 1: χ²(1, N = 485) = 5.45, p < .05; Sample 2: χ²(1, N = 1,290) = 23.23, p < .01—indicating that males were more likely to drop out. In addition, Sample 1 participants who dropped out had lower crystallized-ability scores at age 15 (d = −0.35, 95% CI = [−0.54, −0.17]), and Sample 2 participants who dropped out scored lower on openness (d = −0.17, 95% CI = [−0.28, −0.05]) and agreeableness (d = −0.24, 95% CI = [−0.35, −0.12]) at Wave 1.

On the basis of these results, we included gender, degree attainment, and crystallized ability (in Sample 1) as auxiliary variables in all applicable analyses (Collins, Schafer, & Kam, 2001). Full-information maximum likelihood (FIML) technique was used for all analyses using Mplus Version 8 (Muthén & Muthén, 2017). The FIML technique is recommended for treating missing data in longitudinal modeling because it provides more efficient and less biased parameter estimates under missing-at-random situations, when missing data is related to other measured variables. The inclusion of auxiliary variables in FIML improves estimation accuracy by statistically accounting for possible causes of missingness that are not already included in models as predictors, covariates, or outcomes (Collins et al., 2001).

Although we sought to use all available data, we also conducted power analyses to assess the appropriateness of our sample sizes to detect moderate correlations (r = .20) between personality traits and career outcomes. We chose an r of .20 on the basis of effect sizes from prior studies that used adolescent personality traits to predict future career success (Judge et al., 1999; Sutin et al., 2009). Sample sizes were used from the final wave in each data set so that power estimates would be the most conservative possible. Power analyses were conducted using G*Power 3.1 (Faul, Erdfelder, Buchner, & Lang, 2009), which revealed 83% (Sample 1; n = 207) and 99% (Sample 2; n = 578) power to detect correlations of .20 with CIs that do not include zero.

We tested for measurement invariance across time for each Big Five trait using the general procedure described by Widaman, Ferrer, and Conger (2010). We used the same procedure for both samples, establishing measurement invariance separately in each data set. Fit statistics for measurement-invariance tests are displayed in Tables S3 and S4 in the Supplemental Material. To summarize, all personality traits were fully or partly consistent with scalar invariance across time. The results indicated acceptable measurement properties for the personality scales across time points.

Mean-level personality development

We analyzed mean-level changes in personality traits in two ways. First, we compared mean scale scores at adjacent time points using the standardized mean difference, Cohen’s d. Second, we formalized the description of mean-level change using linear growth-curve models. We chose linear models over other alternatives (e.g., quadratic models) because linear models were most consistent with our research goal: to test whether increasing more than average in certain personality traits was associated with career success.

In the linear growth models, personality traits were modeled as a function of time (i.e., Wave 1 to Wave 5) with three types of latent variables: intercept, slope, and residuals. The intercept reflected the level of the trait at the first wave, whereas the slope represented the average rate of change over time (separate from random error). We fixed the paths to the slope such that the loadings reflected the actual time intervals multiplied by two.

After specifying the growth-curve models, we modeled the influence of gender on the intercept and slope variables. Gender is important to consider because there are small-to-moderate gender differences in certain personality traits, and men and women are disproportionally represented in occupational fields (Lippa, 2010). Gender was dummy-coded (women = 0, men = 1). The path coefficient for the link between gender and the intercept or slope variable illustrates the magnitude and direction of gender differences for that trait category.

Personality growth as a predictor of career outcomes

We next examined associations between personality changes and career outcomes. Figure 1 displays the basic path model that was used to test these associations. For each personality trait, we tested for associations between the intercept and slope with career outcomes while controlling for variance associated with gender. These models were applied separately for all five career outcomes. Degree attainment, prestige, and income were modeled as manifest variables, whereas career and job satisfaction were modeled as latent variables. Altogether, 50 models were estimated for personality traits (25 in each sample). In addition, separate models controlling for crystallized ability in Sample 1 were estimated and are reported in the Supplemental Material.

Fig. 1.

Path models for estimating associations between career outcomes and personality intercepts (levels) and slopes (changes) in Samples 1 and 2. Circles represent error terms, rectangles represent manifest variables, and ovals represent latent variables. Slope loadings are based on the actual time interval multiplied by 2. In Sample 1, the 3-year mean age difference between Wave 1 and Wave 2 is due to rounding (there was a 2.4-year gap between assessments).

Because the two samples included identical measures and participants of similar ages, we focused our interpretations on the overall pattern of results across samples, noting any relevant discrepancies. For all analyses, we report CIs as a measure of effect-size precision. Tables S5 and S6 in the Supplemental Material display correlation matrices for each sample among career outcomes and personality traits from Wave 1. Rank-order stability estimates are included in Table S7 of the Supplemental Material.

Results

Mean-level personality development

We first examined mean-level changes in personality traits using standardized difference scores (d values). Figure 2 displays the cumulative, mean-level changes in Big Five personality traits in each sample (see also Table S8 in the Supplemental Material). Results were consistent with the maturity principle (Roberts et al., 2006). In Sample 1, the largest mean-level increases were found in agreeableness (d = 0.67, 95% CI = [0.50, 0.83]), followed by openness (d = 0.47, 95% CI = [0.31, 0.64]) and conscientiousness (d = 0.30, 95% CI = [0.14, 0.47]). Emotional-stability levels remained constant, but extraversion levels decreased (d = −0.34, 95% CI = [−0.51, −0.18]). The patterns of change were similar in Sample 2, as mean-level increases occurred in agreeableness (d = 0.73, 95% CI = [0.63, 0.84]), conscientiousness (d = 0.51, 95% CI = [0.41, 0.61]), openness (d = 0.16, 95% CI = [0.06, 0.26]), and emotional stability (d = 0.16, 95% CI = [0.06, 0.26]), whereas extraversion levels again decreased (d = −0.19, 95% CI = [−0.29, −0.09]).

Fig. 2.

Mean-level changes in Big Five personality traits, separately for Samples 1 and 2. Cumulative d values represent standardized difference scores between mean levels at each subsequent time point. At Wave 1, Sample 1 contained 485 participants and Sample 2 contained 1,290.

Next, we formalized the description of mean-level changes using latent growth curves. Table 2 displays the results of the linear growth models for each personality trait. In Table 2, the mean slope values represent the average rate of change per 2 years in the metrics of the original personality scales. The σ² values represent the variance of the intercepts and slopes. There was statistically significant variance in the intercepts and slopes of all personality scales. The final step before introducing career outcomes was to test for gender differences in the intercepts and slopes of personality traits. Table 3 displays parameter estimates for gender differences in personality growth curves (standardized with respect to the personality scales). In Table 3, positive values indicate that men scored higher, and negative values indicate that men scored lower. The pattern of gender differences was generally consistent with past research (Lippa, 2010), and the models displayed good or acceptable fit—for Sample 1, root-mean-square error of approximation (RMSEA) = .04–.06, comparative fit index (CFI) = .94–.98; for Sample 2, RMSEA = .05–.10, CFI = .96–.99. We now turn to applied correlates of personality slope variability.

Table 2.

Results of the Latent Growth Curves Modeling Intercepts and Slopes of Personality Traits

Sample and trait	Intercept				Slope				Fit indices
Sample and trait	M	SE (M)	σ²	SE (σ²)	M	SE (M)	σ²	SE (σ²)	RMSEA	χ²	CFI	TLI	SRMR
Sample 1
Emotional stability	38.59	0.37	37.92	4.98	0.00	0.09	0.79	0.28	.06	24.46	.97	0.97	.09
Extraversion	44.19	0.26	13.89	2.47	−0.44	0.08	0.79	0.18	.05	21.67	.96	0.96	.06
Openness	36.97	0.27	19.80	2.48	0.51	0.06	0.46	0.14	.07	31.61	.97	0.97	.17
Agreeableness	41.53	0.25	16.14	2.32	0.67	0.07	0.54	0.14	.05	21.55	.97	0.97	.15
Conscientiousness	41.51	0.34	34.54	4.29	0.39	0.09	1.48	0.28	.07	32.78	.94	0.94	.18
Sample 2
Emotional stability	38.08	0.24	36.05	4.60	0.24	0.06	0.91	0.28	.09	10.22	.98	0.94	.03
Extraversion	43.19	0.17	19.26	2.53	−0.20	0.04	0.40	0.16	.12	19.28	.97	0.90	.05
Openness	38.71	0.18	31.92	2.55	0.17	0.04	0.70	0.13	.00	0.19	1.00	1.00	.00
Agreeableness	42.37	0.16	20.51	2.29	0.73	0.04	0.47	0.13	.09	10.76	.98	0.94	.05
Conscientiousness	41.48	0.22	39.17	3.62	0.65	0.05	0.93	0.20	.14	26.24	.96	0.87	.07

Note: Slope values in boldface are statistically significant (p < .05). At Wave 1, Sample 1 contained 485 participants, and Sample 2 contained 1,290 participants. All models in Sample 1 had 10 degrees of freedom; all models in Sample 2 had 1 degree of freedom. RMSEA = root-mean-square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis index; SRMR = standardized root-mean-square residual.

Table 3.

Gender Differences in Intercepts and Slopes of Personality Traits

Sample and trait	Intercept		Slope		Fit indices
Sample and trait	β	95% CI	β	95% CI	RMSEA	$χ^{2}$	CFI	TLI	SRMR
Sample 1
Emotional stability	0.39	[0.28, 0.50]	−0.38	[−0.59, −0.16]	.05	26.71	.97	0.97	.07
Extraversion	−0.23	[−0.36, −0.09]	0.00	[−0.16, 0.16]	.04	25.27	.96	0.96	.06
Openness	0.01	[−0.10, 0.12]	0.16	[−0.02, 0.35]	.06	32.47	.97	0.97	.15
Agreeableness	−0.28	[−0.39, −0.16]	0.02	[−0.15, 0.19]	.04	22.46	.98	0.97	.12
Conscientiousness	−0.08	[−0.18, 0.03]	−0.20	[−0.34, −0.04]	.06	36.24	.94	0.93	.16
Sample 2
Emotional Stability	0.40	[0.32, 0.48]	−0.14	[−0.26, −0.01]	.07	13.35	.98	0.94	.02
Extraversion	−0.29	[−0.37, −0.21]	0.07	[−0.06, 0.20]	.08	18.56	.97	0.91	.05
Openness	−0.18	[−0.24, −0.11]	0.23	[0.14, 0.32]	.05	7.17	.99	0.98	.02
Agreeableness	−0.27	[−0.34, −0.21]	−0.14	[−0.24, −0.03]	.06	10.57	.99	0.96	.05
Conscientiousness	−0.18	[−0.25, −0.12]	−0.08	[−0.18, 0.01]	.10	26.23	.96	0.88	.05

Note: The coefficients illustrate the magnitude and direction of gender differences in the intercepts and slopes, standardized with respect to the personality scale but not with respect to gender. Negative coefficients indicate higher intercepts or more positive slopes among women compared with men, and positive coefficients indicate lower intercepts or less positive slopes among women compared with men. Boldface indicates statistically significant gender differences (p < .05). At Wave 1, Sample 1 contained 485 participants, and Sample 2 contained 1,290 participants. All models in Sample 1 had 13 degrees of freedom; all models in Sample 2 had 2 degrees of freedom. CI = confidence interval; RMSEA = root-mean-square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis index; SRMR = standardized root-mean-square residual.

Personality growth as a predictor of career outcomes

Our primary hypothesis was that personality growth would positively predict career outcomes over and above adolescent personality levels. Figure 1 displays the path model used to examine this hypothesis, corresponding to the results reported in Tables 4 and 5. In Tables 4 and 5, r(i) indicates correlations between career outcomes and the intercept of each growth curve (representing adolescent trait levels); r(s) indicates correlations between outcomes and the slope of each growth curve (representing personality changes). Positive r(s) correlations mean that individuals who increased more than average in a certain personality trait achieved greater success as young adults. Figures 3 and 4 display the results graphically for income and career satisfaction, respectively.

Table 4.

Associations Between Objective Career Outcomes and Personality Levels and Changes

Sample and variable	Degree attainment				Occupational prestige				Income
Sample and variable	r(i)	95% CI	r(s)	95% CI	r(i)	95% CI	r(s)	95% CI	r(i)	95% CI	r(s)	95% CI
Sample 1
Gender (control)	−.30*	[−.39, −.20]			.10	[−.05, .24]			.32*	[.15, .48]
Crystallized ability^a	.46*	[.38, .53]			.22*	[.09, .34]			.14	[−.01, .28]
Emotional stability	.18*	[.06, .31]	.35*	[.11, .58]	.32*	[.17, .47]	−.05	[−.29, .19]	.18	[−.01, .36]	.39*	[.12, .66]
Extraversion	.07	[−.07, .20]	.33*	[.15, .51]	.03	[−.14, .20]	.08	[−.10, .27]	.06	[−.14, .25]	.29*	[.10, .48]
Openness	.03	[−.09, .15]	.11	[−.10, .31]	.03	[−.12, .19]	.10	[−.11, .30]	.02	[−.15, .19]	−.16	[−.38, .05]
Agreeableness	.15	[.03, .28]	.14	[−.05, .32]	.14	[−.02, .30]	−.21	[−.39, −.02]	−.13	[−.31, .06]	−.02	[−.22, .19]
Conscientiousness	.25*	[.14, .36]	−.01	[−.17, .15]	.28*	[.13, .42]	−.09	[−.27, .09]	.10	[−.08, .28]	.11	[−.08, .30]
Sample 2
Gender (control)	−.23*	[−.29, −.16]			.04	[−.06, .13]			.39*	[.30, .48]
Emotional stability	.15*	[.07, .23]	.06	[−.07, .19]	.17*	[.06, .27]	.12	[−.03, .26]	.07	[−.05, .19]	.26*	[.12, .40]
Extraversion	.16*	[.09, .24]	−.09	[−.23, .05]	.19*	[.09, .29]	.04	[−.10, .19]	.24*	[.12, .35]	.10	[−.04, .23]
Openness	.20*	[.14, .26]	.06	[−.04, .16]	.22*	[.14, .30]	−.03	[−.13, .07]	−.03	[−.12, .06]	−.10	[−.19, .00]
Agreeableness	.16*	[.09, .23]	.08	[−.03, .20]	−.02	[−.12, .08]	.03	[−.10, .15]	−.09	[−.19, .02]	.07	[−.05, .19]
Conscientiousness	.20*	[.13, .26]	−.10	[−.20, .01]	.17*	[.08, .25]	.00	[−.11, .11]	.18*	[.08, .27]	.11	[.00, .21]

Note: The r(i) values indicate correlations between career outcomes and personality intercepts; the r(s) values indicate correlations between career outcomes and personality slopes. Values in boldface but with no asterisk are statistically significant at the .05 level; values in boldface with an asterisk are statistically significant at the .005 level. In Sample 1, 485 individuals had data for degree attainment, 277 had data for occupational prestige, and 204 had data for income; in Sample 2, 1,290 individuals had data for degree attainment, 681 had data for occupational prestige, and 568 had data for income. CI = confidence interval.

Crystallized ability was available only in Sample 1.

Table 5.

Associations Between Subjective Career Outcomes and Personality Levels and Changes

Sample and variable	Career satisfaction				Job satisfaction
Sample and variable	r(i)	95% CI	r(s)	95% CI	r(i)	95% CI	r(s)	95% CI
Sample 1
Gender (control)	.06	[−.11, .24]			.20	[.03, .37]
Crystallized ability^a	.05	[−.11, .21]			.09	[−.07, .25]
Emotional stability	.39*	[.21, .58]	.20	[−.07, .47]	.30 *	[.12, .49]	−.05	[−.32, .21]
Extraversion	.07	[−.14, .28]	.38*	[.20, .57]	.09	[−.11, .30]	.27	[.08, .46]
Openness	−.17	[−.35, .01]	.11	[−.12, .33]	−.03	[−.21, .15]	−.03	[−.25, .20]
Agreeableness	.18	[−.02, .37]	.06	[−.16, .28]	.14	[−.05, .33]	.22	[.01, .42]
Conscientiousness	.08	[−.11, .28]	.25	[.06, .44]	.19	[.01, .37]	.10	[−.09, .29]
Sample 2
Gender (control)	.11	[.02, .20]			−.03	[−.13, .07]
Emotional stability	.14	[.02, .27]	.40*	[.23, .56]	.19 *	[.06, .31]	.23*	[.08, .38]
Extraversion	.25*	[.14, .37]	.33*	[.15, .51]	.26 *	[.14, .38]	.20	[.03, .37]
Openness	−.03	[−.13, .07]	.09	[−.02, .19]	.09	[.00, .18]	−.02	[−.12, .08]
Agreeableness	.02	[−.09, .14]	.15	[.01, .28]	.07	[−.05, .18]	.06	[−.07, .19]
Conscientiousness	.23*	[.13, .32]	.26*	[.13, .38]	.22*	[.13, .32]	.05	[−.06, .17]

Note: The r(i) values indicate correlations between career outcomes and personality intercepts; the r(s) values indicate correlations between career outcomes and personality slopes. Values in boldface but with no asterisk are statistically significant at the .05 level; values in boldface with an asterisk are statistically significant at the .005 level. In Samples 1 and 2, 205 and 577 individuals, respectively, had data for career and job satisfaction. CI = confidence interval.

Crystallized ability was available only in Sample 1.

Fig. 3.

Correlations between income and personality levels (intercepts) and changes (slopes). Error bars show 95% confidence intervals.

Fig. 4.

Correlations between career satisfaction and personality levels (intercepts) and changes (slopes). Error bars show 95% confidence intervals.

Objective career outcomes

Table 4 displays associations between personality levels and changes with degree attainment, occupational prestige, and income. For degree attainment, adolescent levels of personality traits were stronger predictors than personality changes. Among personality intercepts, conscientiousness showed the strongest positive association with degree attainment (Sample 1: r = .25, 95% CI = [.14, .36]; Sample 2: r = .20, 95% CI = [.13, .26]), followed by emotional stability and agreeableness (rs = .15–.18). Two personality slopes were significantly associated with degree attainment in Sample 1: emotional stability (r = .35) and extraversion (r = .33). However, these two slope correlations were substantially weaker in Sample 2 (rs ≤ .06).

As with degree attainment, adolescent personality levels were stronger predictors of occupational prestige relative to personality changes. The strongest associations with occupational prestige were found with the intercepts of emotional stability (Sample 1: r = .32, 95% CI = [.17, .47]; Sample 2: r = .17, 95% CI = [.06, .27]) and conscientiousness (Sample 1: r = .28, 95% CI = [.13, .42]; Sample 2: r = .17, 95% CI = [.08, .25]). The intercepts of openness and extraversion were positively associated with prestige in Sample 2 (r = .22 for openness; r = .19 for extraversion), but not in Sample 1 (r = .03 for both traits).

For income, the results present a more compelling case for the importance of personality changes (see Fig. 3 for a graphical representation). In both samples, the slope of emotional stability showed the strongest positive association with income (Sample 1: r = .39, 95% CI = [.12, .66], Sample 2: r = .26, 95% CI = [.12, .40]). This means that participants who became more emotionally stable across the study were more likely to earn greater income as young adults. Income was also associated with the extraversion slope, although the effects were stronger in Sample 1 (r = .29, 95% CI = [.10, .48]) compared with Sample 2 (r = .10, 95% CI = [−.04, .23]). The conscientiousness slope was also weakly positively associated with income in both samples (Sample 1: r = .11, 95% CI = [−.08, .30], Sample 2: r = .11, 95% CI = [.00, .21]).

Subjective career outcomes

Table 5 displays associations between personality levels and changes with career and job satisfaction. The results for career satisfaction were particularly striking (see Fig. 4 for a graphical representation). Changes in three personality traits—emotional stability, extraversion, and conscientiousness—were the strongest predictors of career satisfaction in both samples. The slope correlations were notably strong for extraversion (Sample 1: r = .38, 95% CI = [.20, .57]; Sample 2: r = .33, 95% CI = [.15, .51]), emotional stability (Sample 1: r = .20, 95% CI = [−.07, .47]; Sample 2: r = .40, 95% CI = [.23, .56]), and conscientiousness (Sample 1: r = .25, 95% CI = [.06, .44]; Sample 2: r = .26, 95% CI = [.13, .38]).

Job satisfaction was positively associated with the slope of extraversion in both samples (Sample 1: r = .27, 95% CI = [.08, .46]; Sample 2: r = .20, 95% CI = [.03, .37]). The slopes of emotional stability (Sample 1: r = −.05, 95% CI = [−.32, .21]; Sample 2: r = .23, 95% CI = [.08, .38]) and agreeableness (Sample 1: r = .22, 95% CI = [.01, .42]; Sample 2: r = .06, 95% CI = [−.07, .19]) were significantly associated with job satisfaction in one sample, but the effect sizes differed substantially across samples.

Summarizing results across samples

In total, 50 statistical tests were conducted in each sample linking personality intercepts and slopes with career outcomes (with 10 tests per outcome variable). Although we focused on effect sizes when presenting results, it is also informative to evaluate the results using alpha levels that correct for family-wise error rates resulting from the 10 tests per outcome. Using a Bonferroni-corrected cutoff of p < .005, we found that 11 of 50 correlations were significant in Sample 1 and 21 of 50 were significant in Sample 2 (note the asterisks in Tables 4 and 5). We expect that the differences in significance rates across samples is due to Sample 2 being substantially larger than Sample 1. Nonetheless, in both samples, 5 of 25 correlations between personality slopes and career outcomes were significant at the corrected cutoff (p < .005).

Focusing on the magnitude of personality-change effects, there were five particularly robust slope–outcome correlations (rs ≥ .20 in both samples). These correlations were found with the slopes of emotional stability (income and career satisfaction), conscientiousness (career satisfaction), and extraversion (career and job satisfaction). Slope variance in these three traits also positively predicted other career outcomes, but with smaller combined effects across samples. In contrast, changes in openness and agreeableness did not consistently predict career outcomes. Follow-up models controlling for crystallized ability in Sample 1 are reported in the Supplemental Material (Tables S9 and S10). Controlling for crystallized ability had a negligible influence on all associations between personality slopes and career outcomes.

Discussion

The present research examined the importance of personality changes during young adulthood for predicting early career outcomes. The study was conducted with two representative samples of Icelandic youth with personality data spanning 12 years from ages approximately 17 to 29 years. Across both samples, personality changes were most important for predicting income, career satisfaction, and job satisfaction. In contrast, adolescent personality traits were consistently stronger predictors of degree attainment and occupational prestige relative to personality changes. These findings highlight the importance of personality development throughout childhood, adolescence, and young adulthood for promoting different aspects of career success.

Three key points stand out. First, as noted above, the results generally differed between objective and subjective career outcomes. Adolescent personality levels were stronger predictors of objective career success, whereas personality changes were more strongly related to subjective career success. Career satisfaction, in particular, was robustly associated with changes in emotional stability, conscientiousness, and extraversion. This suggests that personality growth plays an important role in determining how young people evaluate their early careers. Public policy and educational interventions designed to help young adults develop personality-based skills may be especially impactful for promoting intrinsic outcomes (Bleidorn et al., 2019). On the other hand, interventions that target childhood and adolescent personality development may be more impactful for promoting objective outcomes, including academic success and early job placement (Heckman & Kautz, 2012).

A second key point concerns the use of personality assessments for career and educational decision-making. Young people face a number of consequential career decisions, such as whether to pursue further education or enter the labor market. When personality assessments factor into such decisions, young people can benefit by knowing that their current personality can change over time (Tasselli, Kilduff, & Landis, 2018). For example, developing higher levels of conscientiousness may be useful for individuals seeking well-paying and satisfying careers. A recent college graduate with low conscientiousness levels need not feel stuck, given that most people become more conscientious during young adulthood. However, simply wanting to change one’s personality is not enough to invoke meaningful change (Baranski, Gray, Morse, & Dunlop, 2020). People who want to change their personality are more successful when they actively change their behaviors to align with change goals (Hudson, Briley, Chopik, & Derringer, 2019).

Third, our findings inform theoretical understanding about personality development (Roberts & Mroczek, 2008). In both samples, most correlations between personality slopes and career outcomes were positive, even if not statistically significant. This provides a partial explanation for why mean levels of personality traits tend to increase during young adulthood. If developing higher trait levels is positively associated with favorable career outcomes, then there is a clear incentive for most individuals to change in this way (Wrzus & Roberts, 2016). Of course, individuals who already have high trait levels may not benefit from increasing even further, and developing lower trait levels can sometimes lead to better person–job fit (Denissen et al., 2018). Certain causal mechanisms may also be bidirectional. For example, becoming more emotionally stable may lead to greater income because regulating one’s emotions is a valued skill in many jobs. Yet at the same time, earning a comfortable income may reduce people’s overall anxiety. Achieving career success may therefore promote emotional stability, just as becoming more emotionally stable promotes career success.

Extraversion is an interesting exception to the mean-level trends. Although slope variance in extraversion positively predicted several career outcomes, mean extraversion levels decreased with age. This indicates that there were career advantages associated with decreasing less than average in extraversion. People who exhibited more positive extraversion slopes may have benefited from building stronger social networks or from more quickly advancing to leadership roles (Wilmot, Wanberg, Kammeyer-Mueller, & Ones, 2019). We also note that the extraversion scale in our study captured more social-vitality facets (i.e., gregariousness, activity, excitement seeking, positive emotion) than social dominance (i.e., assertiveness). Meta-analytic research has indicated that only social-dominance levels increase during young adulthood, whereas social-vitality levels decrease or remain constant (Roberts et al., 2006). This helps explain the mean-level decreases in extraversion observed in the current study. Future research is needed to examine career outcomes associated with facet-level changes in extraversion and other traits.

Limitations and future directions

The present research used two longitudinal samples, which increases our confidence in the robustness of the findings. Nonetheless, future studies are needed to replicate our analyses in other countries and contexts. Iceland has a notably small population with low wealth inequality. A measure of socioeconomic status was not available in our study, but we note that socioeconomic status predicts career success in many contexts (Damian, Su, Shanahan, Trautwein, & Roberts, 2015; Spengler et al., 2018). In addition, we found conflicting gender differences in degree attainment and income: Women earned higher degrees than men but were paid less on average. This may be due to large gender disparities across public and private sectors in Iceland. Proportionally, more Icelandic women work in public occupations that require advanced degrees yet often pay less. Future research is needed to better understand these disparities.

Another limitation is that because of study attrition, complete career outcomes were available for only about half the original participants. We included auxiliary variables with advanced missing-data techniques (i.e., FIML) to reduce the likelihood of bias stemming from missing data (Collins et al., 2001). Missing data may also have been more likely for participants who moved away from Iceland, although we were unable to test this formally. In addition, most career outcomes were collected at only a single time point, which prevented us from examining changes in career success (e.g., Wille, Hofmans, Feys, & De Fruyt, 2014). We also chose to examine each personality trait independently to reduce model complexity when estimating associations between personality slopes and career outcomes. Future studies can address this limitation by considering interactions among changes in multiple personality traits.

Conclusion

Our study revealed that there are economic and subjective career benefits associated with personality growth during the transition to employment. These findings support the potential of policy actions aimed at improving human welfare by helping young people develop personality-based skills (Bleidorn et al., 2019). Such initiatives may be effective at various periods throughout the early life span, not just during childhood and adolescence, as most mean-level personality development occurs during young adulthood. Young people can also benefit by knowing that their current personality is not fixed; personality traits can be expected to gradually change over time. Given our findings on the importance of personality growth for early career outcomes, we also hope to see more research investigating methods that will help people to change their personality in desirable ways.

Supplemental Material

sj-docx-1-pss-10.1177_0956797620957998 – Supplemental material for Personality Changes Predict Early Career Outcomes: Discovery and Replication in 12-Year Longitudinal Studies

Supplemental material, sj-docx-1-pss-10.1177_0956797620957998 for Personality Changes Predict Early Career Outcomes: Discovery and Replication in 12-Year Longitudinal Studies by Kevin Hoff, Sif Einarsdóttir, Chu Chu, Daniel Briley and James Rounds in Psychological Science

Footnotes

Acknowledgements

We are grateful to Daniel A. Newman and Rong Su for providing helpful feedback on an earlier version of the manuscript. We are also grateful to Arna Pétursdóttir for her ongoing management of the data collection and the data-set preparation for this study.

Transparency

Action Editor: Paul Jose

Editor: Patricia J. Bauer

Author Contributions

All of the authors contributed to planning the manuscript and engaged in conceptual discussions. S. Einarsdóttir led data-collection efforts with assistance from J. Rounds. K. A. Hoff performed statistical analyses, aided by C. Chu and D. A. Briley. K. A. Hoff drafted the manuscript, and all of the authors contributed critical feedback and revisions. All of the authors approved the final version of the manuscript for submission.

ORCID iD

Kevin A. Hoff

Supplemental Material

Additional supporting information can be found at

Notes

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