my reseach question is: How does achieving a bachelor’s degree (higher Education) assist with job fulfillment? Your paper should have an introduction paragraph that Includes your thesis sentence, and

Th e B it te rs w eet T ra d e-o ffs B etw een Jo b R eso u rc e s, D em an d s, a n d S tr e ss.

Jo u rn al o f A pplie d P sy ch olo g y . A dva n ce o n lin e pub lic a tio n . h ttp ://d x.d oi. o rg /1 0.1 037/a p l0 000904 RESEARCH REPORT Does Educational Attainment Promote Job Satisfaction? The Bittersweet Trade-offs Between Job Resources, Demands, and Stress Brittany C. Solomon 1, Boris N. Nikolaev 2, and Dean A. Shepherd 1 1Department of Management & Organization, Mendoza College of Business, University of Notre Dame 2Department of Entrepreneurship, Hankamer School of Business, Baylor University Education is considered one of the most critical human capital investments. But does formal educational attainment “pay off ”in terms of job satisfaction? To answer this question, in Study 1 we use a meta- analytic technique to examine the correlation between educational attainment and job satisfaction ( k=74, N=134,924) and find an effect size close to zero. We then build on the job demands-resources (JD-R) model and research that distinguishes between working conditions and perceived stress to theorizethat educational attainment involves notable trade-offs. In Study 2 we develop and test a multipath, two-stage mediation model using a nationally representative sample to explore this idea. We find that, while better-educated individuals enjoy greater job resources (income, job autonomy, and job variety),they also tend to incur greater job demands (work hours, task pressure, job intensity, and time urgency).

On average, these demands are associated with increased job stress and decreased job satisfaction, largely offsetting the positive gains associated with greater resources. Given that the net relationship between education and job satisfaction emerges as weakly negative, we highlight that important trade-offs underlie the education –job satisfaction link. In supplemental analyses, we identify boundary conditions based on gender and self-employment status (such that being female exacerbates, and being self-employed attenuates, the negative association between education and job satisfaction). Finally, we discuss the practical implications for individuals and organizations, as well as alternative explanations for the education –jobsatisfactionlink. Keywords: education, job satisfaction, job demands-resources (JD-R) model, stress Supplemental materials: https://doi.org/10.1037/apl0000904.supp According to Aristotle, “the roots of education are bitter, but the fruit is sweet. ”Indeed, education is considered one of the most critical investments in human capital. Higher educational attainment can lead to more attractive job opportunities, greater labor force fl exibility, and more rewarding jobs ( Becker, 1964 ;Dickson & Harmon, 2011 ;Ng et al., 2005 ;Oreopoulos & Salvanes, 2011 ). Despite the potential for education to yield many bene fits, some studies point in the opposite direction. For example, educational attainment has been negatively associated with organizational com- mitment ( Angle & Perry, 1981 ;Morris & Sherman, 1981 ), job involvement ( Lounsbury & Hoopes, 1986 ), and organizational identi fication ( Gould & Werbel, 1983 ). Furthermore, higher levels of education and overquali fication (which is often based on educa- tion; McKee-Ryan & Harvey, 2011 ) can lead to burnout, turnover intentions, job search behavior, and voluntary turnover ( Erdogan & Bauer, 2009 ;Maslach et al., 2001 ;Maynard & Parfyonova, 2013 ). Of course, there are many indicators of what constitutes a “better ” (or “worse ”) job. We focus on job satisfaction because it is arguably the most studied construct related to “how people think about and relate to their work and jobs ”(Judge et al., 2017 , p. 357). Currently, it is not clear that more formally educated employees are more satis fied at work. To determine what we could glean about the relationship between the attainment of institutional education (hereafter, simply “educa- tion ”) and job satisfaction, we performed a meta-analytic technique on 74 independent samples since the year 2000 (Study 1). The link between education and job satisfaction had an effect size close to zero, but we reasoned there is likely more to the story than could be detected by a simple (albeit powerful) test of this correlation.

Following Kluger and Tikochinsky ( 2001 , p. 419), we conducted a second study to highlight how “additional factors must be taken into account to understand the (commonsense) phenomenon-under study. ”Speci fically, we drew on the job demands-resources (JD-R) model ( Demerouti et al., 2001 ) to theorize that education is associ- ated with trade-offs that may help explain our meta-analytic finding (Study 2). We then tested a multipath, two-stage mediation model using a nationally representative, publicly available data set (see Appendix A), which improved the study ’s generalizability, trans- parency, and reproducibility (see Barnes et al., 2018 ). We found that better-educated individuals enjoy greater job resources (income, job Brittany C. Solomon https://orcid.org/0000-0002-0462-7535 Correspondence concerning this ar ticle should be addre ssed to Brittany C. Solomon, Department of Management & Organization, Mendoza College of Business, University of No tre Dame, Notre Dame, IN, United States. Email: [email protected] Journal of Applied Psychology © 2021 American Psychological Association ISSN: 0021-9010 https://doi.org/10.1037/apl0000904 1 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. autonomy, and job variety) and incur greater job demands (work hours, task pressure, job intensity, and time urgency), which explain job stress and job satisfaction in the hypothesized directions. The scripts we used for all analyses and data for Study 1 can be found on the Open Science Framework. 1 Study 1 To analyze the relationship between education and job satisfac- tion based on the extant literature, we first conducted a review to identify published articles related to job satisfaction since the year 2000. Speci fically, we used a Boolean search of the keywords “job satisfaction ”OR “work satisfaction ”OR “employee satisfaction ”in the following journals: Journal of Applied Psychology, Academy of Management Journal, Organizational Behavior and Human Deci- sion Processes, Personnel Psychology, Journal of Management, Journal of Organizational Behavior, Journal of Occupational and Organizational Psychology, Journal of Vocational Behavior, Jour- nal of Business and Psychology, Journal of Business Venturing, and Entrepreneurship Theory and Practice .2This review yielded 381 articles. Of those, 295 did not include education. Another 22 did not provide codeable information. Thus, our review covered 64 articles and 72 independent samples. We also included samples from two nationally representative data sets. 3Thus, our analysis is based on 65 manuscripts ( k=74 and N=134,924). Appendix B lists each study and provides coding, reliability, sample size, and effect size information. We note that none of the reviewed articles examined the direct (or indirect) relationship between education and job satisfaction; education was exclusively a control. According to Bernerth and Aguinis (2016) , education is the fourth most common covariate (at 23%) in job satisfaction studies.

Because the effect of education varies across different groups (e.g., based on gender, race, etc.), and is thus likely to vary across studies, we used a random-effects model ( Hunter & Schmidt, 2004 ) and also corrected for observed correlations of the sampling error and measurement unreliability. We used the metafor software package in R ( Viechtbauer, 2010 ). As reported in Table 1 , the sample-size-weighted mean observed correlation corrected for unre- liability ( ˆ ¯ ρ ) spanned around zero ( ˆ ¯ ρ = .010, p=.58, 95% con fi- dence interval (CI) =[ .046, .026]). The Q test-statistic for homogeneity ( Q=283.32) had a p-value of .00, suggesting signi fi- cant heterogeneity between studies. The presence of heterogeneity can also be inferred from the I2, implying that close to 71% of the variability in the effect-size estimates is due to differences between studies. To explore this heterogeneity, we performed group analyses by examining whether there was a signi ficant difference between studies that used a single-item (global) versus multi-item (facet) job satisfaction measure and between studies that used a multi-item (global) versus multi-item (facet) job satisfaction measure. These tests ( Qb=.01, p=.92 and Qb=.75, p=.39, respectively) indi- cated that differences between these groups were statistically nonsigni ficant. Overall, the effect size for the relationship between education and job satisfaction neared zero. 4Because education was a covariate (vs. primary variable of interest) in the reviewed studies, we do not expect publication bias due to the “file drawer problem ”to be of concern. Study 2 In Study 2, we also expect to find an effect size close to zero. But, importantly, our aim is to further investigate the nature of the education –job satisfaction link by illuminating potential trade- offs associated with investments in education. Drawing on the JD-R model, we theorize that, relative to less-educated employees, the highly educated are more apt to attain jobs that provide them with greater resources but also involve greater demands. These working conditions tend to be associated with job stress and satisfaction (i.e., primary and secondary appraisals, respectively; Lazarus & Folkman, 1984 ,1987 ). As such, we expect that resources decrease stress and increase job satisfaction, while demands increase stress and decrease job satisfaction (see Figure 1 ). While the educated may bene fit in many ways, we test this trade-off story to provide one explanation for the near-zero effect that emerged in Study 1. In supplemental analyses, we explore gender and self-employment status as boundary conditions. Table 1 Education and Job Satisfaction: Meta-Analytic Findings Variable kN ¯r SDr ˆ ¯ ρ SDp 80% CV 90% CI % % ARTV Education 74 134,924 0.009 0.016 0.010 0.018 [ .034, 0.013] [ 0.046, 0.026] 39% Note .k=number of correlations meta-analyzed; N=total sample size; ¯ r =sample-size-weighted mean observed correlation; SDr =sample-size-weighted standard deviation of the observed correlations; ˆ ¯ ρ =sample-size-weighted mean observed correlation corrected for unreliability; SDp=standard deviation ˆ ¯ ρ ; 80% CV =80% credibility interval around ˆ ¯ ρ ; 90% CI =90% con fidence interval around ˆ ¯ ρ ; % ARTV =percent variance due to corrected artifacts. All analyses were conducted using random-effects meta-analyses based on the Hunter-Schmidt method (2004) to correct for observed correlations for sampling error and measurement unreliability. For single-item job satisfaction measures, we followed Wanous and Reichers (1996) and used α=.7. Because education was a demographic variable, and no reliability information was reported, we followed Ng et al. (2005) and used α=1. 1osf.io/ucyz2 . 2We initially searched for words in the title, abstract, and subject. This search often returned several thousand articles, most of which were irrelevantfor our purposes. Therefore, we narrowed the search to the title and abstract.For comparison with Study 2, and to keep the search manageable, we searched for articles published since 2000.3We also included the sample used in Study 2 and a sample from the British Household Panel Survey (from a prior version of this manuscript; fordetails, see Appendix B). The results did not change when excluded.4While Ng et al. (2005) found a weak, but positive relationship between education and job satisfaction, their meta-analysis consisted of studiespublished prior to 2004. Differences between their findings and ours (including the gender analysis in Study 2) may be due to changes in the economy (and a better educated female workforce), suggesting the impor- tance of considering the time period studied in future work on the education-job satisfaction link. 2 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Education and Job Satisfaction via Job Resources and Stress We first theorize that job resources help explain the education –job satisfaction link. Resources involve the rewards derived from one ’s work and the nature of the work itself ( Demerouti et al., 2001 ). Here, we focus on income, job autonomy, and job variety. All three resources are prominent in the JD-R model ( Bakker & Demerouti, 2007 ,2017 ;Demerouti et al., 2001 ) and its precur- sors (e.g., Hackman & Oldham, 1975 ;Karasek, 1979 ). Most notably, the potential to earn more money continues to be one of the top reasons people attend college ( Eagan et al., 2017 ). And higher education requires self-direction and involves acquiring a range of knowledge and critical thinking skills ( Arnold & King, 1997 ;Bowen, 1997 ). Thus, it should not be surprising that education is positively associated with income (e.g., Ng & Feldman, 2009 ;Ng et al., 2005 ), as well as job autonomy (i.e., discretion and control) and variety ( Oreopoulos & Salvanes, 2011 ;Ross & Reskin, 1992 ;Seybolt, 1976 ). Whether valued in their own right or because they enable the acquisition or protection of other resources ( Bakker & Demerouti, 2007 ;Hobfoll, 1989 ), income, autonomy, and variety likely operate as first-stage mediators between education and job satisfaction (with reduced job stress linking these resources with satisfaction).

Indeed, while extrinsic rewards can diminish intrinsic motivation ( Deci et al., 1999 ), the job satisfaction of the highly educated may still be “bought ”via higher income. Pay satisfaction is a core component of job satisfaction ( Smith et al., 1969 ), and those with hi gher pay report being more satis fied ( Judge, Piccolo, et al., 2010 ). Earning more also enhances opportunities for work recovery ( Leana & Meuris, 2015 ;Saxbe et al., 2011 ), including more “pleasant ” off-job activities ( Bennett et al., 2018 ;Demerouti et al., 2009 ) that may improve work engagement ( Demerouti et al., 2012 ;ten Brummelhuis & Bakker, 2012 ) and help manage stressors and strain ( Geurts & Sonnentag, 2006 ; Sonnentag & Fritz, 2007 ). Job autonomy and variety may also yield higher job satisfaction due to having freedom [when to work, how to work, and what to do at work ( Karasek, 1979 ;Morgeson & Humphrey, 2006 )] and using a range of capabilities ( Fried & Ferris, 1987 ), respectively. Such resources create a sense of accomplishment and meaningfulness ( Hackman & Oldham, 1980 ). Autonomy and variety are also associ- ated with increased work engagement ( Christian et al., 2011 ;Mauno et al., 2007 ) and decreased burnout ( Demerouti et al., 2001 ;Hakanen et al., 2011 ). These insights are relevant because engaged employees are less likely to experience work stress ( Bakker et al., 2014 ,p.391) and tend to report higher job satisfaction (e.g., Rich et al., 2010 ). In contrast, burnout is inextricably linked to stress ( Pines & Keinan, 2005 ) and undermines job satisfaction ( Schaufeli & Buunk, 2002 ). Thus, it follows that autonomy and variety tend to lower stress and improve job satisfaction ( Fried & Ferris, 1987 ;Humphrey et al., 2007 ). Altogether, we expect:

Hypothesis 1 :(H1a ) Education is positively associated with job resources (i.e., income, job autonomy, and job variety), ( H1b ) job resources are negatively associated with job stress, and ( H1c ) education is indirectly and positively associated with job satisfaction, as mediated by job resources and job stress. Education and Job (Dis)Satisfaction via Job Demands and Stress So far, our logic is consistent with the dominant narrative regarding educational investment —attaining higher education should yield a more satisfying job. But, from a JD-R perspective, it is also important to consider the role of job demands. These working conditions generally require sustained physical and/or psychological effort and may be costly ( Demerouti et al., 2001 ). Thus, as the second step in our theorizing, we argue that job demands operate as a countervailing mechanism to the resources pathway between education and job satisfaction.

We focus on hours worked and qualitative demands that re flect task pressure, job intensity, and time urgency, all of which are prominent in the literature (e.g., Crawford et al., 2010 ;Kristensen et al., 2004 ). Valuable insights have been gained from distinguishing between challenge and hindrance demands (e.g., Podsakoff et al., 2007 ). However, the same working conditions do not have similar meanings for all employees ( Mazzola & Disselhorst, 2019 ;Webster et al., 2011 ). Thus, we do not make the challenge –hindrance distinc- tion here, but we do differentiate between demands and perceived job stress. Thus, consistent with Bliese et al. (2017) , we separate aspects of the job from the subjective reactions to those working conditions.

Importantly, the highly educated tend to attain jobs in which they incur high-pressure, intense, and time-sensitive work ( Hakanen et al., Figure 1 Conceptual Model Linking Education, Job Stress, and Job Satisfaction Through Job Demands and Job Resources + Education Job Resources Job Stress + + + Job Demands Job Satisfaction + THE EDUCATION –JOB SATISFACTION LINK 3 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 2011 ;Judge, Klinger, et al., 2010 ;Wilk & Cappelli, 2003 ). Such job demands can become stressful ( Cavanaugh et al., 2000 ) and costly if employees cannot adequately recover from their work ( Bakker & Demerouti, 2007 ;Bennett et al., 2018 ;Sonnentag & Fritz, 2015 ). Relative to the less-educated, highly educated employees report more work-related rumination, anxiety, and stress ( Moen et al., 2013 ; Perko et al., 2017 ;Smith, 2001 ). When stress is too high, it threatens the attainment of personal goals and, thus, can reduce job satisfaction ( Begley & Czajka, 1993 ; Hendrix et al., 1985 ;Sullivan & Bhagat, 1992 ). As such, greater job demands incurred by the highly educated may also help explain the education –job satisfaction link. For example, hours worked can lead to increased stress ( Parker & DeCotiis, 1983 ;Perlow, 1999 )and decreased job satisfaction ( Clark & Oswald, 1996 ). Other demands may operate similarly: Negative experiences can result from having too much to accomplish with too little time [i.e., time pressure/work intensity ( Schaubroeck et al., 1989 )]. Some research indicates that similar demands inherently imply greater stress ( Motowidlo et al., 1986 ;Parker & DeCotiis, 1983 ) and predict lower job satisfaction ( Verhofstadt et al., 2007 ;cf. Judge et al., 2000 ;Judge, Klinger, et al., 2010 ;Ng & Feldman, 2009 ;Ng et al., 2005 ). In sum, education may undermine job satisfaction via increased job demands and perceived stress. We do not imply that the highly educated are generally worse off, but, concurrent with H1a –c, we expect: Hypothesis 2 :(H2a ) Education is positively associated with job demands (i.e., hours worked and qualitative demands), ( H2b ) job demands are positively associated with job stress, and ( H2c ) education is indirectly and negatively associated with job satis- faction, as mediated by job demands and job stress. Sample We tested our model using data from the Household, Income, and Labour Dynamics in Australia (HILDA) survey —a nationally representative panel study of Australian households. The Australian Government funds the HILDA survey through the Department of Social Services. The survey collects information on many aspects of life, such as economic and personal wellbeing, labor markets, and family life. Like other major household panels, the coverage is broad and includes a core set of topics that appear in every wave and others that appear less frequently (see https://melbourneinstitute.unimelb .edu.au/hilda/for-data-users ). Based on people residing in private dwellings in Australia, the initial sample was selected in 2001 by identifying a sample of 488 Census Collection Districts and select- ing a representative number of households within each district. New respondents received a “New Entrant Brochure ”5explaining the survey. Data were collected through self-report surveys and in- person interviews, usually at the home of the respondent. Phone interviews were a last resort. Interviews varied in length from wave to wave but rarely exceeded 83 min per household. Because there is little within-person variation in education and 2005 was the first year in which most variables used to create our indices were available, we used only 2005 data for our analyses (which are cross-sectional). 6 Our sample included 16,958 full- and part-time (wage- and self-) employed individuals, ages 18 –65 ( M =35, SD =13; 50% male). Respondents were compensated with a $25 (Australian dollar; AUD) check at this wave. For more information, see Watson and Wooden (2012) . Measures Education reflects the number of years of education completed. We imputed these values from variables that measure respondents ’ highest educational level, age left school, and the highest year of school completed ( Summer field et al., 2016 ). For example, we assigned 12 years of education to a respondent who completed secondary education and 16 years to someone with a college degree.

We did not measure the actual time spent obtaining a degree because it can vary with the number of degrees or time spent studying that did not lead to a degree. This approach is common in the economics of education literature ( Card, 1999 ) and among studies that use the HILDA survey (e.g., Nikolaev, 2016 ;Shields et al., 2009 ). We use several measures that use Likert scales. Job satisfaction was assessed with “All things considered, how satis fied are you with your job, ”which is a reliable and valid proxy for global job satisfaction (e.g., Wanous et al., 1997 ), from 0 ( totally dissatis fied) to 10 ( totally satis fied). This item was strongly correlated ( r=.85) with a facet-level index of job satisfaction based on “the work itself, ”“total pay, ”“hours worked, ”“job security, ”and “flexibility. ” Job stress, job autonomy, job variety, and qualitative demands were latent measures based on multiple items assessed using Likert- type scales from 1 ( strongly disagree )to7( strongly agree ). See Appendix C for evidence of content, convergent, discriminant, and nomological validities. We conceptualized job stress as a reaction to various working conditions with “My job is more stressful than I had ever imagined ”and “I fear that the amount of stress in my job will make me physically ill ”(α=.80). These items have been used in prior work (e.g., Hessels et al., 2017 ;Wu, 2016 ), are similar to the scale developed by Motowidlo et al. (1986) , and capture experi- ences of stress (rather than any categorical demand/stressor). Within the JD-R literature, job resources may help achieve work goals, reduce physiological and psychological costs that stem from job demands, or stimulate personal growth ( Demerouti et al., 2001 ). Thus, we captured job autonomy with “I have a lot of freedom to decide when I do my work, ”“I have a lot to say about what happens at my job, ”and “I have a lot of freedom to decide how I do my own work ”(α=.82). We captured job variety with “My job requires me to learn new things, ”“ I use many of my skills and abilities in my current job, ”and “My job provides me with a variety of interesting things to do ”(α=.74). These items are prominent in prior studies (e.g., Crawford et al., 2010 ) and consistent with our theorizing as it relates to education. Measures of qualitative demands often include pressure to complete tasks, job intensity, and time urgency (e.g., Crawford et al., 2010 ). We used the following items to capture job 5https://melbourneinstitute.uni melb.edu.au/__data/assets/pdf_ file/ 0008/3115484/BrochureW19M.pdf" >https://melbourneinstitute.unimelb .edu.au/__data/assets/pdf_ file/0008/3115484/BrochureW19M.pdf . 6As Kennedy (2008) describes, when at all possible, it is best to approach data analyses with ordinary least squares regression applied to cross- sectional data. Moreover, the intraclass correlation coef ficient strongly indicates that the data can be reliably aggregated to the person level withoutlosing important variation (ICC(1) =.96; p<.00; Bliese, 1998 , p. 359). With this in mind, we analyzed data at the person level in a single year so as to simplify the analysis and not induce contamination (i.e., bias) in ourempirical estimates. 4 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. demands that match this conceptualization, originate from earlier related research (e.g., Karasek, 1979 ;Karasek et al., 1998 ), and are similar to items used in recent research (e.g., Xie et al., 2008 ): “ I don ’t have enough time to do everything at work, ”“ My job requires me to work intensely, ”and “My job requires me to work fast ”(α=.72). 7 To measure income , we used the logarithmic transformation of respondents ’labor earnings (wages and salaries from all employ- ment) for the fiscal year. Hours worked reflects total time spent on work each week (including paid or unpaid overtime).

Finally, following prior research on job satisfaction, we controlled for gender (Bernerth & Aguinis, 2016 ;0 =male, 1 =female), age and age squared (Clark et al., 1996 ),marital status (Ng et al., 2005 ; 0 =not married, 1 =married), and self-employment status [Benz & Frey, 2008 ;0 =wage-employed, 1 =self-employed (i.e., “employee of one ’s own business ”or“employer/self-employed ”was selected)]. Consistent with other national surveys [ Global Entrepreneurship Monitor (GEM), 2016 ;Organisation for Economic Co-operation and Development [OECD], 2016 ], eight percent of participants were self-employed, about one-third of whom were women. The results are robust to the exclusion of these covariates and to the inclusion of the Big Five traits (which we include in an alternative analysis, as they are associated with job satisfaction [ Judge et al., 2002 ]; see Appendix D).

We report the descriptive statistics and bivariate correlations in Table 2 . The correlation between education and job satisfaction is negative but trivial in magnitude. Yet, as we theorized, the relation- ship is more nuanced. Below, we demonstrate that the highly educated tend to enjoy greater resources but also incur demands and the associated stress that accompany their jobs.

Analytical Approach We estimated a series of structural equation models in Stata 16 using the sem command, which relies on a maximum likelihood estimator ( Baron & Kenny, 1986 ;Preacher & Hayes, 2008 ). We used Satorra – Bentler standard errors to correct for potential nonnormality, and we used a bootstrapping technique to calculate bias-corrected con fidence intervals using 10,000 bootstrapped samples for the indirect, direct, and total mediation effects. These models indicated that our proposed mediators separately mediated the education –job satisfaction relationship (see Table 3 ). As expected, income, job autonomy, and job variety each mediated the positive indirect effect of education on job satisfaction. Also as expected, hours worked, qualitative demands, and job stress each mediated the negative indirect effect of education on job satisfaction. Given the support for these effects, we then proceeded to estimate a single omnibus model, including the covariates described. 8We necessarily included the direct effects from education to job stress and from education to all the resources and demands (i.e., first-stage mediators) to job satisfaction. Primary Results Prior to hypothesis testing, we conducted a con firmatory factor analysis to examine the factor structure of our multi-item variables.

We fit the data to a four-factor model in which items loaded onto their respective latent variables, which provided a reasonably acceptable fit:χ2(38) =5681.7, comparative fit index (CFI) =.92, standardized root-mean-square residual (SRMR) =.07, root mean square error of approximation (RMSEA) =.09. Our full model explains 28% and 25% of the variation in job stress and job satisfaction, respectively. Figure 2 reports direct effects for each pathway. 9Table 4 reports indirect, direct, and total effects of education on stress and satisfaction. We report unstandardized effects below ( p<.01 unless noted otherwise), and we include both unstandardized and standardized effects in Figure 2 . First, we found that education is positively associated with resources (H1a), which are negatively associated with stress Table 2 Descriptive Statistics and Correlations Variable Mean SD 123456789101112 1. Education 12.66 2.17 1.00 2. Job satisfaction 7.60 1.74 .06 1.00 3. Job stress 2.78 1.47 .09 .30 1.00 4. Qualitative demands 4.59 1.34 .15 .10 .42 1.00 5. Hours worked 35.33 14.74 .10 .03 .26 .25 1.00 6. Job autonomy 4.04 1.57 .13 .27 .05 .05 .16 1.00 7. Job variety 4.79 1.38 .20 .27 .12 .34 .29 .34 1.00 8. Income 10.18 1.11 .23 .00 .19 .19 .57 .20 .24 1.00 9. Gender .50 .50 .05 .02 .02 .02 .33 .09 .06 .23 1.00 10. Age 34.35 13.03 .06 .07 .10 .02 .16 .21 .09 .31 .01 1.00 11. Self-employed .08 .27 .03 .04 .01 .03 .13 .31 .07 .06 .09 .20 1.00 12. Married .61 .49 .12 .02 .09 .08 .19 .16 .11 .30 .03 .35 .13 1.00 Note .N=16,958. All correlations greater than .02 are signi ficant at p<.01 (two-tailed test). 7We dropped “My job is complex and dif ficult ”due to its low factor loading in our con firmatory factor analysis. 8In Appendix D, we present models (1) with no controls, (2) controlling for the Big Five personality traits, and (3) controlling for time sinceeducational attainment. Moreover, in a series of robustness tests (available upon request), we controlled for job tenure, occupation tenure, and occupa- tion type, used an alternative five-item (facet-like) measure of job satisfac- tion, examined future job satisfaction and different timing in our variables(using an expanded version of the HILDA data set across multiple waves), and replicated our indirect effects using Mplus 8.5. All results were similar or identical to those reported in the main text.9The χ2statistic was highly signi ficant ( p>χ2=0.00). However, the χ2 exact- fit test is extremely sensitive to discrepancies from expected values at increasing sample sizes (e.g., see Barrett, 2007 ). With a sample size greater than 10,000 observations (such as ours), the χ2test is almost always signi ficant ( Burnham & Anderson, 2002 ). THE EDUCATION –JOB SATISFACTION LINK 5 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. (H1b). Speci fically, education is positively associated with income ( B=.12), autonomy ( B=.09), and variety ( B=.12). Also as expected, autonomy ( B= .12) and variety ( B= .03) are nega- tively associated with job stress. However, income is positively associated with stress ( B=.06). Thus, the indirect effect of educa- tion on job stress (via resources only) is negative but quite small (indirect effect = .008, 95% CI [ .012, .004]). Next, our results indicated that education is indirectly and posi- tively associated with job satisfaction, as mediated by job resources and job stress. Speci fically, autonomy ( B=.27) and variety ( B=.48) are positively associated with job satisfaction, and stress is negatively associated with job satisfaction ( B= .47). Though, the magnitude of income ’s effect on job satisfaction nears zero ( B=.02, p=.21). Ultimately, the indirect net effect of education Table 3 Mediation Models for Separate Indirect and Total Effects of Education on Job Satisfaction Resources and Demands Education →Mediator Mediator →Job satisfaction Indirect effect [95% CI] Total effect [95% CI] Income .117 (.004) .029 (.013) .003 [.000, .006] .046 [ .058, .033] Job autonomy .085 (.005) .440 (.013) .037 [.033, .042] .046 [ .058, .033] Job variety .119 (.005) .525 (.016) .063 [.057, .069] .046 [ .058, .033] Hours worked .688 (.050) .003 (.001) .002 [ .003, .000] .046 [ .058, .033] Qualitative demands .071 (.004) .068 (.016) .005 [ .007, .002] .046 [ .058, .033] Job stress .061 (.005) .446 (.014) .027 [ .032, .023] .046 [ .058, .033] Note . The table reports the results of structural equation models linking education to job satisfaction through separate mediators, indirect mediation effects, and total mediation effects. Satorra-Bentler standard errors are reported in parentheses, and bias-corrected con fidence intervals based on bootstrapped standard errors with 10,000 replications are reported in brackets. Figure 2 Effects of Education on Job Satisfaction via Job Demands, Job Resources, and Job Stress .02 [.01, .03] (.04) Education Qualitative Demands Job Autonomy -.47[-.50, -.44] (-.32) Job Stress Job Variety .12[.11, .13] (.22) .07 [.07, .08](.16) .46[.44, .48] (.41) -.12[-.14, -.11](-.13) Hours Worked JOB DEMANDS JOB RESOURCES Job Satisfaction .48[.45, .50] (.33) .69 [.59, .78] (.10) .09[.08, .09] (.15) .02[.01, .02] (.19) -.03[-.05, -.02] (-.03) .27[.25, .29] (.19) -.003[-.004, -.001] (-.02) -.0303[-.0606, -.0001] (-.02) Income .12[.11, .12] (.23) .01[-.01, .04] (.01) .06 [.04, .07](.05) -.10[-.11, -.08](-.12) Sex Age Age Squared .15 [.10, .19](.04) -.03 [-.04, -.01](-.19) .41[.25, .56](.23) Married Self- Employed .01 [-.04, .07](.00) -.20 [-.29, -.12](-.03) Note .N=16,958. Unstandardized coef ficients are reported with 95% con fidence intervals in brackets and standardized coef ficients in parentheses. R2(job stress) =.28, R2(job satisfaction) =.25. All effects are statistically signi ficant at p<.01 unless the con fidence interval overlaps with zero. Consistent with our modeling approach, ellipses and rectangles indicate latent and observed variables, respectively. 6 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. on job satisfaction (via resources alone and via resources and job stress) is positive (indirect effect =.086, 95% CI [.079, .095]), providing support for H1c. These results indicate that the more highly educated experience higher job satisfaction. Our results suggest that this is, in part, because they have more intrinsically rewarding jobs (i.e., autonomy and variety) and thus less stressful working conditions. So far, our results support the narrative that higher education is associated with an array of job resources that can help improve employees ’job satisfaction. However, our second set of hypotheses highlights how higher education can also be associated with undesirable (perhaps unex- pected) outcomes. First, we found that education is positively associated with job demands (H2a), and job demands are positively associated with job stress (H2b). Speci fically, education is associ- ated with longer hours worked ( B=.69) and greater qualitative demands ( B=.07). In turn, hours worked ( B=.02) and qualitative demands ( B=.46) are positively associated with job stress. We also found that the indirect effect of education on job stress (via hours worked and qualitative demands) is positive overall (indirect effect =.045, 95% CI [.040, .050]). Next, our results indicate that education is indirectly and nega- tively associated with job satisfaction through job demands and job stress. Speci fically, hours worked ( B= .003) and qualitative demands ( B= .03) are negatively associated with job satisfaction. And, as we reported above, stress and job satisfaction are inversely related ( B= .47). Ultimately, the indirect net effect of education on job satisfaction (via demands alone and via demands and stress) is negative (indirect effect = .025, 95% CI [ .029, .022]; H2c). Thus, these findings suggest that those with higher education experience somewhat lower job satisfaction, in part, because of the greater job demands they encounter and thus more stressful working conditions.

Importantly, as suggested by the JD-R model, we interpret both sets of results in combination. Given that job stress is an important aspect of our model, we first note that education ’s total effect on stress, via both job resources and demands, is positive (total indirect effect =.056, 95% CI [.047, .066]). Thus, overall, highly educated employees experience greater job stress. Regarding job satisfaction, our results indicate that education ’s positive indirect effect via job resources and job stress (.086) is partially offset by education ’s negative indirect effect via job demands and job stress ( .025). Yet, even after accounting for all of these paths, we still found a negative direct association between education and job satisfaction ( B= .096, 95% CI [ .108, .084]). Ultimately, education ’s negative association with job satisfaction (i.e., the direct effect, the indirect effect via demands, and the indirect effect via demands and stress) offsets its positive association with job satisfaction (i.e., the indirect effects via resources and via both resources and stress), such that the total (net) effect of education on job satisfaction is negative, albeit quite small (total effect = .043, 95% CI [ .056, .031]). Although our analyses cannot provide evidence of causal effects, a positive total (net) relationship between education and job satis- faction did not emerge. Thus, while the highly educated may receive an array of positive returns on their educational investment, our fi ndings suggest that studying the direct relationship between education and job satisfaction on its own may be unfruitful or misleading in light of countervailing mechanisms.

Supplemental Analyses Next, we explored whether gender and self-employment status operate as moderators, altering var ious pathways between education and job resources, demands, and st ress. First, women still face work- place adversity ( Weyer, 2007 ) that can undermine the positive returns on their educational investment ( Heilman & Chen, 2003 ;Stevenson & Wolfers, 2009 ). This dynamic is particularly important given the reversal of the gender gap in education, with more women completing higher education than men ( Organisation for Economic Co-operation and Development [OECD], 2017 ). As such, we explored the notion that the education –job satisfaction link is negative and stronger for women. Using our HILDA survey sample, we conducted a group comparison analysis. We allowed the path coef ficients (structural paths) and the error variances to differ across the two groups (males and females). We also tested (using the postestimation command estat ginvariant in Stata 16) whether each path in our model is signi ficantly different between the two groups (or should be treated as equal).

As reported in Figure 3 , we found a signi ficantly stronger nega- tive direct association between education and job satisfaction for women than men. Our results also indicate that highly educated women are more likely to earn higher income and experience greater job variety than their male counterparts. But they report signi ficantly less autonomy, greater qualitative demands —which is associated with greater job stress —and more hours worked. Overall, the total effect of education on job stress (via resources and demands) is considerably stronger for women (total effect =.073, 95% Table 4 Indirect Effects, Direct Effects, and Total Effects of Education on Job Stress and Job Satisfaction Outcome Job stress Job satisfaction Pathway B 95% CI β B 95% CI β Indirect effect via job resources .008 .012, .004 .014 .086 .079, .095 .108 Indirect effect via job demands .045 .040, .050 .083 .025 .029, .022 .031 Direct effect .020 .011, .029 .036 .096 .108, .084 .119 Total effect .056 .047, .066 .105 .043 .056, .031 .054 Note . The table reports indirect effects, direct effects, and total effects of education on job stress and job satisfaction based on our primary structura l equation model. Indirect effects on job satisfaction include indirect effects via both job resources/job demands alone and via job resources/job demands and job stress. B =unstandardized coef ficient, 95% CI =bias-corrected 95% con fidence interval based on bootstrapped standard errors with 10,000 replications, β =standardized coef ficient. THE EDUCATION –JOB SATISFACTION LINK 7 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Figure 3 Effects of Education on Job Satisfaction by Gender. N (males) =8,398, N (females) =8,560 .02 [.01, .03] (.04) Education Qualitative Demands Job Autonomy -.43[-.47, -.39] (.28) Job Stress Job Variety .09[.08, .11] (.18) .05[.04, .07](.11) .40[.37, .43] (.37) -.10[-.12, -.08](-.11) Hours Worked JOB DEMANDS JOB RESOURCES Job Satisfaction .46 [.42, .50] (.31) .37[.25, .50] (.06) .10[.08, .11] (.17) .01[.01, .02] (.18) -.06[-.09, -.04] (-.06) .28 [.25, .31] (.20) -.002[-.004, .001] (-.01) -.06[-.10, -.02](-.04) Income .11[.10, .12] (.22) .02 [-.01, -.06] (.01) .07[.05, .08](.06) -.07[-.09, -.05] (-.09) .01 [-.01, .02] (.01) Education Qualitative Demands Job Autonomy -.51[-.55, -.47] (-.35) Job Stress Job Variety .15[.14, .17] (.27) .09[.08, .10](.20) .52[.48, .55](.44) -.13[-.15, -.11] (-.13) Hours Worked JOB DEMANDS JOB RESOURCES Job Satisfaction .50[.46, .53] (.35) 1.22[1.09, 1.34] (.19) .08[.07, .09] (.15) .02[.02, .02] (.22) -.02[-.04, .01](-.02) .25[.22, .29](.18) -.002 [-.005, .001] (-.02) .00[-.05, -.04](.00) Income .14[.13, .15] (.27) .06[.03, .08] (.05) -.12[-.14, -.10](-.15) PANEL B: FEMALES PANEL A: MALES .02[-.02, .05] (.01) Age Age Squared Married Self- Employed -.06[-.07, -.04] (-.43) .77[.55, .99](.44) .01[-.07, .09] (.00) -.22[-.32, -.12](-.04) Age Age Squared Married Self- Employed .00[-.02, .02] (-.01) .14[-.08, .36](.08) .02[-.06, .09](.00) -.15[-.29, -.01](-.02) Note . Solid lines represent signi ficantly different paths between groups ( p<.01). Unstandardized coef ficients are reported with 95% con fidence intervals in brackets and standardized coef ficients in parentheses. Males: R2 (job stress) =.26, R2(job satisfaction) =.23. Females: R2(job stress) =.30, R2(job satisfaction) =.26. All effects are signi ficant at p<.01 unless the con fidence interval overlaps with zero. Consistent with our modeling approach, ellipses and rectangles indicate latent and observed variables, respectively. 8 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. CI [.060, .087]) than for men (total effect =.041, 95% CI [.028, .054]). Similarly, with regard to job satisfaction, while education has a small negative effect for men (total effect = .021, 95% CI [ .040, .003]), the effect is much larger for women (total effect = .061, 95% CI [ .078, .044]). These results suggest that, compared to their male counterparts, highly educated women experience more stress at work and lower job satisfaction. These negative experiences may stem from empowerment messages that imply women are responsible for solving gender inequality at work ( Kim et al., 2018 ). Such messages may prompt highly educated women (vs. men) to shoulder greater responsibility in the household and in the labor market to adhere to gender role expectations while advancing their careers. Of course, we need future research to explore the many explanations that may underlie the differential effects of educational attainment for women vis-à-vis men.

Finally, relative to traditional occupations, self-employment of- fers considerable flexibility to organize one ’s work schedule, choose the content of one ’s work, and decide how to respond to job demands ( Nikolaev et al., 2020 ;Stephan, 2018 ). As such, we explored the notion that self-employment weakens the relationship between education and job satisfaction. To do so, we conducted a group comparison analysis between the self-employed and (wage-) employed using the same data and parameters described above for our gender analysis.

As reported in Figure 4 , we found that better-educated workers in self-employment (vs. wage-employment) report lower income, less autonomy, less variety, and slightly greater qualitative demands, but fewer hours worked. Quite notably, we found that the net association between education and job stress (via resources and demands) is positive and stronger for the wage-employed (total effect =.062, 95% CI [.052, .072]) while weaker with a near-zero effect for the self-employed (total effect =.007, 95% CI [ .021, .034]). Regarding job satisfaction, our results indicate that, while education has a near-zero net association with job satisfaction for the self-employed (total effect = .013, 95% CI [ .049, .023]), education has a negative net association for the wage-employed (total effect = .048, 95% CI [ .060, .034]). Altogether, com- pared to their wage-employed counterparts, those in self-employ- ment seem to be more insulated from the adverse effects of education on job stress and satisfaction. We contend that illuminat- ing this boundary condition is notable for the educated and orga- nizations that value (and want to retain) their educated employees.

But again, we cannot determine causality. Discussion Neither our meta-analysis in Study 1 nor the total effect that emerged in Study 2 indicated that the highly educated tend to report higher job satisfaction. Drawing on the JD-R model and distinguish- ing between working conditions and job stress, we theorized that the story is more nuanced. In Study 2, we found that, despite being associated with greater resources (and indirectly less stress and higher job satisfaction), education is also associated with greater demands (and indirectly more stress and lower job satisfaction).

Ultimately, our work suggests a trade-off story: The fruit of education may be described as sweet, but also somewhat bitter.

In terms of contributions, career success studies have largely investigated education ’s effect on extrinsic outcomes, such as income. Notably, Ng et al. ’s (2005) meta-analysis included education and job satisfaction. But given changes in the economy and an increasingly educated workforce ( Fry et al., 2018 ), we believe our meta-analysis, which is based on more recent empirical work, provides additional value. Importantly, Study 1 revealed a near-zero correlation and set the groundwork for our second contri- bution. Study 2 identi fied two countervailing pathways from edu- cation to job satisfaction that indicate the nuance of the relationship missed when looking at a simple main effect. Also, building on the JD-R model, we demonstrated how resources and demands operate as explanatory mechanisms. Indeed, we have offered one explana- tion. But the positive and negative pathways that emerged in our primary and supplemental analyses provide a basis for further theorizing on the impact of education.

Practical Implications We do not suggest avoiding higher education to achieve higher job satisfaction. Rather, while our indirect effects are relatively small, a realistic calculation of trade-offs between desirable working conditions and experiences of stress and job satisfaction may still help workers make decisions that suit their priorities or recalibrate their values. Leaders may also consider better ways to manage the greater demands encountered by their highly educated employees so that exploiting an organization ’s, arguably, greatest human capital does not back fire. For example, by removing incentives to adopt excessive work hours, organizations can avoid inadvertently pres- suring employees to incur stress that undermines job satisfaction.

Indeed, rede fining the ideal worker away from someone “totally dedicated to their [job] and always on call ”may improve organiza- tional outcomes ( Reid & Ramarajan, 2016 , p. 86). This rede finition of the ideal worker may bene fit the highly educated as they are susceptible to incurring demands and experiencing job stress in kind. Such progress may help attract and retain top talent.

Limitations and Future Directions Despite its advantages, our archival data set (Study 2) required us to rely on a single-item measure of job satisfaction and limited our use of established measures and relevant variables. For instance, perhaps a more robust measure of stress or assessing strain would better capture the negativity associated with demands and alter the net effect. Moreover, scholars have long identi fied two dimensions of job demands: challenges and hindrances ( Cavanaugh et al., 2000 ), which re flect ostensibly “good ”versus “bad ”stressors ( Lazarus, 1966 ;Selye, 1974 ). While these dimensions play distinct roles in employees ’experiences and outcomes ( Crawford et al., 2010 ;Podsakoff et al., 2007 ), we could not draw on this framework due to data availability.

However, in some contexts, making a priori distinctions between challenge and hindrance demands can be arbitrary. Indeed, recent studies highlight the role of idiosyncratic appraisals and how employees can perceive so-called challenge demands as hindrances and vice versa ( Bakker & Demerouti, 2017 ;Mazzola & Disselhorst, 2019 ;Searle & Auton, 2015 ;Webster et al., 2011 ). Thus, challenges (such as high workload) may only yield positive outcomes when appraised as opportunities versus threats ( González-Morales & Neves, 2015 ). Here, we assessed job stress as a phenomenological experience and general reaction to one ’s working conditions. In the future, investigation of the subjective appraisals of each job demand THE EDUCATION –JOB SATISFACTION LINK 9 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Figure 4 Effects of Education on Job Satisfaction by Employment Status. N (Self-Employed) =1,371, N (Employed) =15,587 -.01 [-.03, .02] (-.01) Education Qualitative Demands Job Autonomy -.35[-.45, -.25] (-.25) Job Stress Job Variety .08[.06, .11] (.19) .08[.05, .10](.17) .41[.34, .48] (.40) -.14[-.20, -.07] (-.14) Hours Worked JOB DEMANDS JOB RESOURCES Job Satisfaction .48 [.38, .58] (.31) -.19[-.56, .18] (-.02) .04[.02, .07] (.09) .02[.01, .02] (.27) -.13[-.20, -.06](-.12) .14 [.06, .22] (.10) -.003 [-.009, .001] (-.04)-.08[-.17, .01] (-.06) Income .04[.01, .07] (.07) .04 [-.02, .10] (.03) -.02[-.06, .03](-.02) -.05[-.09, -.02] (-.08) .02 [.01, .03] (.04) Education Qualitative Demands Job Autonomy -.48[-.51, -.45] (-.32) Job Stress Job Variety .13[.12, .13](.22) .07[.07, .08](.15) .46[.44, .49] (.41) -.14[-.15, -.12](-.13) Hours Worked JOB DEMANDS JOB RESOURCES Job Satisfaction .47 [.45, .50] (.33) .75[.66, .85] (.11) .08[.07, .09](.15) .01[.01, .02] (.18) -.03[-.04, -.01] (-.03) .29 [.27, .32] (.19) -.003[-.004, -.001] (-.02)-.02[-.05, .01] (-.01) Income .12[.12, .13] (.24) .07[.05, .08] (.06) -.10[-.11, -.08](-.12) PANEL B: EMPLOYED PANEL A: SELF-EMPLOYED .01[-.01, .04] (.01) Age Age Squared Married Sex -.01[-.06, .04] (-.09) .14[-.42, .71](.09) .33[.11, .56] (.08) .25[.09, .42](.08) Age Age Squared Married Sex -.03[-.04, -.02] (-.22) .48[.31, .64](.26) .00[-.06, .05] (.00) .14[.09, .19](.04) Note . Solid lines represent signi ficantly different paths between groups ( p<.01). Unstandardized coef ficients are reported with 95% con fidence intervals in brackets and standardized coef ficients in parentheses. Self-employed: R2(job stress) =.26, R2(job satisfaction) =.20. Employed: R2(job stress) =.28, R2(job satisfaction) =.25. All effects are signi ficant at p<.01 unless the con fidence interval overlaps with zero. Consistent with our modeling approach, ellipses and rectangles indicate latent and observed variables, respectively. 10 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. as challenging or hindering may provide additional insights into how the highly educated experience their jobs.

Also, we offered a set of indirect effects as an explanation for the null effect (or weak negative effect in Study 2) of education on job satisfaction. But there are additional potential explanations that may be illuminating based on theory [e.g., the Big-Fish-Little-Pond effect ( Huguet et al., 2009 )], empirics [e.g., Western-sample restric- tion of range ( Diener & Oishi, 2000 )], or the inclusion of moderators [e.g., worker age ( Truxillo et al., 2012 )] and alternative outcomes [e.g., life satisfaction ( Adams et al., 1996 ), job security ( Kraimer et al., 2005 ), and career mobility ( Baruch et al., 2016 )]. Nonethe- less, the current study serves to provide new insights into the relationship between education and job satisfaction and, hopefully, stimulate additional inquiry. For example, beyond job character- istics, we hope that future research explores whether an expectation- reality gap also underlies the education –job satisfaction link. Indeed, education generates higher job expectations, which appear more dif ficult to meet ( Jebb et al., 2018 ). Due to their investments, the highly educated may even have unrealistic expectations about how they fare relative to others. Thus, examining social comparison processes may be fruitful. Because childhood socioeconomic status affects educational attainment ( Bradley & Corwyn, 2002 ), account- ing for this variable would help clarify the extent to which education versus expectations is associated with job satisfaction. Also, our post hoc analyses revealed that being female exacerbates and being self-employed attenuates the negative education –job satisfaction link. These findings suggest the need for further theorizing (e.g., from a gendered [ Clark, 1997 ] and an entrepreneurship [ Carter, 2011 ] perspective, respectively) and empirical investigation of workers ’differential expectations. Furthermore, we were surprised to find a positive relationship between income and job stress (contrary to H1b). Future research may explore whether income is more likely to provide resources for nonwork life (a life resource vs. job resource) and thus reduce nonwork stress rather than work stress. In fact, exploratory findings suggest that education may increase job stress via income (indirect effect =.012, 95% CI [.010, .015]). Variation in one ’s work centrality or job involvement may also shed light on the effects of income in different (work vs. nonwork) domains.

Finally, we examined a snapshot in time. Prior work indicates that effects of job rewards remain positive over time, whereas job costs increasingly undermine job satisfaction ( Rusbult & Farrell, 1983 ) and negative (vs. positive) experiences are generally stronger, compound more quickly, and prevail ( Rozin & Royzman, 2001 ). Because job demands are inevitable, future work may seek to understand how, in the long run, the highly educated may better calibrate their job expectations or leverage their resources to better manage job stress and enhance job satisfaction. References * References marked with an asterisk indicate studies included in the meta- analysis in Study 1. Adams, G. A., King, L. A., & King, D. W. (1996). Relationships of job andfamily involvement, family social support, and work –family con flict with job and life satisfaction. Journal of Applied Psychology ,81(4), 411. * Allen, T. D. (2001). Family-Supportive Work Environments: The Role of Organizational Perceptions. Journal of Vocational Behavior ,58(3), 414 –435. Angle, H. L., & Perry, J. L. (1981). An empirical assessment of organiza- tional commitment and organizational effectiveness. Administrative Sci- ence Quarterly ,26,1–14. Arnold, K. D., & King, I. C. (1997). College student development and academic life: Psychological, intellectual, social, and moral issues . Taylor & Francis. Bakker, A. B., & Demerouti, E. (2007). The job demands –resources model: State of the art. Journal of Managerial Psychology ,22, 309 –328. Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory:

Taking stock and looking forward. Journal of Occupational Health Psychology ,22, 273 –285. Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD-R approach. Annual Review of Organizational Psychology and Organizational Behavior ,1, 389 –411. Barnes, C. M., Dang, C. T., Leavitt, K., Guarana, C. L., & Uhlmann, E. L.

(2018). Archival data in micro-organizational research: A toolkit for moving to a broader set of topics. Journal of Management ,44(4), 1453 –1478. Baron, R. M., & Kenny, D. A. (1986). The moderator –mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology , 51 , 1173 –1182. Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences ,42, 815 –824. Baruch, Y., Altman, Y., & Tung, R. L. (2016). Career mobility in a global era: Advances in managing expatriation and repatriation. The Academy of Management Annals ,10(1), 841 –889. Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education . University of Chicago Press. Begley, T. M., & Czajka, J. M. (1993). Panel analysis of the moderating effects of commitment on job satisfaction, intent to quit, and health following organizational change. Journal of Applied Psychology ,78, 552 –556. Bennett, A. A., Bakker, A. B., & Field, J. G. (2018). Recovery from work- related effort: A meta-analysis. Journal of Organizational Behavior ,39, 262 –275. Benz, M., & Frey, B. S. (2008). Being independent is a great thing:

Subjective evaluations of self-employment and hierarchy. Economica , 75 , 362 –383. Bernerth, J. B., & Aguinis, H. (2016). A critical review and best-practice recommendations for control variable usage. Personnel Psychology ,69, 229 –283. Bliese, P. D. (1998). Group size, ICC values, and group-level correlations: A simulation. Organizational Research Methods ,1(4), 355 –373. Bliese, P. D., Edwards, J. R., & Sonnentag, S. (2017). Stress and wellbeing at work: A century of empirical trends re flecting theoretical and societal in fluences. Journal of Applied Psychology ,102 , 389 –402. * Booth, J. E., Park, T.-Y., Zhu, L. L., Beauregard, T. A., Gu, F., & Emery, C.

(2018). Prosocial response to client-instigated victimization: The roles of forgiveness and workgroup con flict. Journal of Applied Psychology , 103 (5), 513. Bowen, H. R. (1997). Investment in learning . JHU Press. * Bowler, W. M., & Brass, D. J. (2006). Relational correlates of interpersonal citizenship behavior: A social network perspective. Journal of Applied Psychology ,91(1), 70. Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology ,53, 371 –399. * Burke, R. J., Astakhova, M. N., & Hang, H. (2015). Work passion through the lens of culture: Harmonious work passion, obsessive work passion, and work outcomes in Russia and China. Journal of Business and Psychology , 30 (3), 457 –471. Burnham,K.P.,&Anderson,D.R.(2002). Model selection and multi- model inference: A practical information-theoretic approach (2nd ed.). Springer. THE EDUCATION –JOB SATISFACTION LINK 11 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Card, D. (1999). The causal effect of education on earnings. In O. C.

Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3, Part A, pp. 1801 –1863). Elsevier. * Carless, S. A., & Arnup, J. L. (2011). A longitudinal study of the determinants and outcomes of career change. Journal of Vocational Behavior ,78(1), 80 –91. Carter, S. (2011). The rewards of entrepreneurship: Exploring the incomes, wealth, and economic well –being of entrepreneurial households. Entre- preneurship Theory and Practice ,35(1), 39 –55. Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W.

(2000). An empirical examination of self-reported work stress among U.S.

managers. Journal of Applied Psychology ,85,65 –74. * Chen, Z. X., & Aryee, S. (2007). Delegation and employee work outcomes:

An examination of the cultural context of mediating processes in China.

Academy of Management Journal ,50(1), 226 –238. Christian, M. S., Garza, A. S., & Slaughter, J. E. (2011). Work engagement:

A quantitative review and test of its relations with task and contextual performance. Personnel Psychology ,64,89 –136. Clark, A., Oswald, A., & Warr, P. (1996). Is job satisfaction U-shaped in age? Journal of Occupational and Organizational Psychology ,69,57 –81. Clark, A. E. (1997). Job satisfaction and gender: Why are women so happy at work? Labour Economics ,4(4), 341 –372. Clark, A. E., & Oswald, A. J. (1996). Satisfaction and comparison income.

Journal of Public Economics ,61, 359 –381. * Colbert, A. E., Bono, J. E., & Purvanova, R. K. (2016). Flourishing via workplace relationships: Moving beyond instrumental support. Academy of Management Journal ,59(4), 1199 –1223. Crawford, E. R., LePine, J. A., & Rich, B. L. (2010). Linking job demands and resources to employee engagement and burnout: A theoretical exten- sion and meta-analytic test. Journal of Applied Psychology ,95, 834 –848. * Davis, P. R., Trevor, C. O., & Feng, J. (2015). Creating a more quit-friendly national workforce? Individual layoff history and voluntary turnover.

Journal of Applied Psychology ,100 (5), 1434. *Davis-Blake, A., Broschak, J. P., & George, E. (2003). Happy together?

How using nonstandard workers affects exit, voice, and loyalty among standard employees. Academy of Management Journal ,46(4), 475 –485. * Debus, M. E., Probst, T. M., König, C. J., & Kleinmann, M. (2012). Catch me if I fall! Enacted uncertainty avoidance and the social safety net as country-level moderators in the job insecurity –job attitudes link. Journal of Applied Psychology ,97(3), 690. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin ,125 , 627 –668. Demerouti, E., Bakker, A. B., Geurts, S. A. E., & Taris, T. W. (2009). Daily recovery from work-related effort during non-work time. In S. Sonnentag, P. L. Perrewé, & D. C. Ganster (Eds.), Research in occupational stress and well-being: Vol. 7. Current perspectives on job-stress recovery (pp. 85 –123). JAI Press. Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands –resources model of burnout. Journal of Applied Psychology , 86 , 499 –512. Demerouti, E., Bakker, A. B., Sonnentag, S., & Fullagar, C. J. (2012). Work related flow and energy at work and at home: A study on the role of daily recovery. Journal of Organizational Behavior ,33, 276 –295. Dickson, M., & Harmon, C. (2011). Economic returns to education: What we know, what we don ’t know, and where we are going —some brief pointers. Economics of Education Review ,30, 1118 –1122. Diener, E., & Oishi, S. (2000). Money and happiness: Income and subjective well-being across nations. Culture and subjective well-being , 185 –218. * Donohue, R. (2007). Examining career persistence and career change intent using the career attitudes and strategies inventory. Journal of Vocational Behavior ,70(2), 259 –276. Eagan, M. K., Stolzenberg, E. B., Zimmerman, H. B., Aragon, M. C., Whang Sayson, H., & Rios-Aguilar, C. (2017). The American freshman: National norms fall 2016 . Higher Education Research Institute, UCLA; www.heri .ucla.edu * Erdogan, B., & Bauer, T. N. (2005). Enhancing career bene fits of employee proactive personality: The role of fit with jobs and organizations. Person- nel Psychology ,58(4), 859 –891. * Erdogan, B., & Bauer, T. N. (2009). Perceived overquali fication and its outcomes: The moderating role of empowerment. Journal of Applied Psychology ,94, 557 –565. Fried, Y., & Ferris, G. R. (1987). The validity of the job characteristics model: A review and meta-analysis. Personnel Psychology ,40, 287 –322. Fry, R., Igielnik, R., & Patten, E. (2018). How Millennials today compare with their grandparents 50 years ago [Web log] .https://www .pewresearch.org/fact- tank/2018/03/16/how-mille nnials-compare-with- their-grandparents/ Geurts, S. A. E., & Sonnentag, S. (2006). Recovery as an explanatory mechanism in the relation between acute stress reactions and chronic health impairment. Scandinavian Journal of Work, Environment & Health ,32, 482 –492. Global Entrepreneurship Monitor (GEM). (2016). Global entrepreneurship monitor 2015/16 global report .https://www.gemconsortium.org/report González-Morales, G. M., & Neves, P. (2015). When stressors make you work: Mechanisms linking challenge stressors to performance. Work & Stress ,29, 213 –229. Gould, S., & Werbel, J. D. (1983). Work involvement: A comparison of dual wage earner and single wage earner families. Journal of Applied Psychol- ogy ,68, 313 –319. * Goulet, L. R., & Singh, P. (2002). Career commitment: A reexamination and an extension. Journal of Vocational Behavior ,61(1), 73 –91. * Guerra, G., & Patuelli, R. (2016). The role of job satisfaction in transitions into self –employment. Entrepreneurship Theory & Practice ,40(3), 543 –571. Hackman, J. R., & Oldham, G. R. (1975). Development of the job diagnostic survey. Journal of Applied Psychology ,60, 159 –170. Hackman, R. J., & Oldham, G. R. (1980). Work redesign . Addison Wesley. Hakanen, J., Bakker, A. B., & Jokisaari, M. (2011). A 35-year follow-up study on burnout among Finnish employees. Journal of Occupational Health Psychology ,16, 345 –360. Heilman, M. E., & Chen, J. J. (2003). Entrepreneurship as a solution: The allure of self- employment for women and minorities. Human Resource Management Review ,13, 347 –364. Hendrix, W., Ovalle, N. K., & Troxler, R. G. (1985). Behavioral and physiological consequences of stress and its antecedent factors. Journal of Applied Psychology ,70, 188 –201. Hessels, J., Rietveld, C. A., & van der Zwan, P. (2017). Self-employment and work-related stress: The mediating role of job control and job demand.

Journal of Business Venturing ,32, 178 –196. * Higgins, M. C., & Thomas, D. A. (2001). Constellations and careers:

Toward understanding the effects of multiple developmental relationships.

Journal of Organizational Behavior: The International Journal of Indus- trial. Occupational and Organizational Psychology and Behavior ,22(3), 223 –247. * Hmieleski, K. M., & Sheppard, L. D. (2019). The Yin and Yang of entrepreneurship: Gender differences in the importance of communal and agentic characteristics for entrepreneurs ’subjective well-being and performance. Journal of Business Venturing ,34(4), 709 –730. Hobfoll, S. E. (1989). Conservation of resources: A new attempt at concep- tualizing stress. American Psychologist ,44, 513 –524. * Holtom, B., Goldberg, C. B., Allen, D. G., & Clark, M. A. (2017). How today ’s shocks predict tomorrow ’s leaving. Journal of Business and Psychology ,32(1), 59 –71. * Holtom, B. C., Lee, T. W., & Tidd, S. T. (2002). The relationship between work status congruence and work-related attitudes and behaviors. Journal of Applied Psychology ,87(5), 903. * Hoppe, A., Fujishiro, K., & Heaney, C. A. (2014). Workplace racial/ethnic similarity, job satisfaction, and lumbar back health among warehouse 12 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. workers: Asymmetric reactions across racial/ethnic groups. Journal of Organizational Behavior ,35(2), 172 –193. Huguet, P., Dumas, F., Marsh, H., Wheeler, L., Seaton, M., Nezlek, J., Suls, J., & Régner, I., Nezlek, J. (2009). Clarifying the role of social comparison in the big- fish–little-pond effect (BFLPE): An integrative study. Journal of Personality and Social Psychology ,97(1), 156 –170. Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: A metaanalytic summary and theoretical extension of the work design literature. Journal of Applied Psychology ,92, 1332 –1356. Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings . Sage Publications. * Janssen, O. (2001). Fairness perceptions as a moderator in the curvilinear relationships between job demands, and job performance and job satis- faction. Academy of Management Journal ,44(5), 1039 –1050. Jebb, A. T., Tay, L., Diener, E., & Oishi, S. (2018). Happiness, income satiation and turning points around the world. Nature Human Behaviour , 2 ,33 –38. Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction: The mediating role of job characteristics. Journal of Applied Psychology ,85(2), 237 –249. Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology ,87(3), 530 –541. * Judge, T. A., Ilies, R., & Zhang, Z. (2012). Genetic in fluences on core self- evaluations, job satisfaction, and work stress: A behavioral genetics mediated model. Organizational Behavior and Human Decision Pro- cesses ,117 (1), 208 –220. Judge, T. A., Klinger, R. L., & Simon, L. S. (2010). Time is on my side:

Time, general mental ability, human capital, and extrinsic career success.

Journal of Applied Psychology ,95,92 –107. Judge, T. A., Piccolo, R. F., Podsakoff, N. P., Shaw, J. C., & Rich, B. L.

(2010). The relationship between pay and job satisfaction: A meta-analysis of the literature. Journal of Vocational Behavior ,77, 157 –167. Judge, T. A., Weiss, H. M., Kammeyer-Mueller, J. D., & Hulin, C. L. (2017).

Job attitudes, job satisfaction, and job affect: A century of continuity and of change. Journal of Applied Psychology ,102 (3), 356 –374. * Kammeyer-Mueller, J. D., Wanberg, C. R., Glomb, T. M., & Ahlburg, D.

(2005). The role of temporal shifts in turnover processes: It ’s about time. Journal of Applied Psychology ,90(4), 644. Karasek, R. A., Jr. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly ,24, 285 –308. Karasek, R., Brisson, C., Kawakami, N., Houtman, I., Bongers, P., & Amick, B. (1998). The job content questionnaire (JCQ): An instrument for internationally comparative assessments of psychosocial job characteris- tics. Journal of Occupational Health Psychology ,3, 322 –355. Kennedy, P. (2008). A guide to econometrics (2nd ed.). Blackwell. Kim, J. Y., Fitzsimons, G. M., & Kay, A. C. (2018). Lean in messages increase attributions of women ’s responsibility for gender inequality. Journal of Personality and Social Psychology ,115 (6), 974 –1001. * Kirkman, B. L., & Shapiro, D. L. (2001). The impact of cultural values on job satisfaction and organizational commitment in self-managing work teams: The mediating role of employee resistance. Academy of Manage- ment Journal ,44(3), 557 –569. Kluger, A. N., & Tikochinsky, J. (2001). The error of accepting the “ theoretical ”null hypothesis: The rise, fall, and resurrection of common- sense hypotheses in psychology. Psychological Bulletin ,127 (3), 408 –423. * Koch, M., Park, S., & Zahra, S. A. (2021). Career patterns in self- employment and career success. Journal of Business Venturing ,31, 105998. Kraimer, M. L., Wayne, S. J., Liden, R. C., & Sparrowe, R. T. (2005). The role of job security in understanding the relationship between employees ’ perceptions of temporary workers and employees ’performance. Journal of Applied Psychology ,90(2), 389. * Kreiner, G. E. (2006). Consequences of work-home segmentation or integration: A person-environment fit perspective. Journal of Organiza- tional Behavior: The International Journal of Industrial. Occupational and Organizational Psychology and Behavior ,27(4), 485 –507. Kristensen, T. S., Bjorner, J. B., Christensen, K. B., & Borg, V. (2004). The distinction between work pace and working hours in the measurement of quantitative demands at work. Work & Stress ,18, 305 –322. * Lankau, M. J., & Scandura, T. A. (2002). An investigation of personal learning in mentoring relationships: Content, antecedents, and conse- quences. Academy of Management Journal ,45(4), 779 –790. Lazarus, R. S. (1966). Psychological stress and the coping process . McGraw-Hill. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping . Springer.

Lazarus, R. S., & Folkman, S. (1987). Transactional theory and research on emotions and coping. European Journal of Personality ,1, 141 –169. * Leana, C., Appelbaum, E., & Shevchuk, I. (2009). Work process and quality of care in early childhood education: The role of job crafting.

Academy of Management Journal ,52(6), 1169 –1192. Leana, C. R., & Meuris, J. (2015). Living to work and working to live:

Income as a driver of organizational behavior. The Academy of Manage- ment Annals ,9,55 –95. * Li, N., Liang, J., & Crant, J. M. (2010). The role of proactive personality in job satisfaction and organizational citizenship behavior: A relational perspective. Journal of Applied Psychology ,95(2), 395. * Liao, H., Chuang, A., & Joshi, A. (2008). Perceived deep-level dissimilar- ity: Personality antecedents and impact on overall job attitude, helping, work withdrawal, and turnover. Organizational Behavior and Human Decision Processes ,106 (2), 106 –124. * Loi, R., Yang, J., & Diefendorff, J. M. (2009). Four-factor justice and daily job satisfaction: A multilevel investigation. Journal of Applied Psychol- ogy ,94(3), 770. Lounsbury, J. W., & Hoopes, L. L. (1986). A vacation from work: Changes in work and nonwork outcomes. Journal of Applied Psychology ,71, 392 –401. Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology ,52, 397 –422. * Masuda, A. D., McNall, L. A., Allen, T. D., & Nicklin, J. M. (2012).

Examining the constructs of work-to-family enrichment and positive spillover. Journal of Vocational Behavior ,80(1), 197 –210. Mauno, S., Kinnunen, U., & Ruokolainen, M. (2007). Job demands and resources as antecedents of work engagement: A longitudinal study.

Journal of Vocational Behavior ,70, 149 –171. Maynard, D. C., & Parfyonova, N. M. (2013). Perceived overquali fication and withdrawal behaviours: Examining the roles of job attitudes and work values. Journal of Occupational and Organizational Psychology ,86, 435 –455. Mazzola, J. J., & Disselhorst, R. (2019). Should we be “challenging ” employees?: A critical review and meta-analysis of the challenge-hindrance model of stress. Journal of Organizational Behavior ,40,949 –961. McKee-Ryan, F. M., & Harvey, J. (2011). “I have a job, but ::: ”A review of underemployment. Journal of Management ,37, 962 –996. Moen, P., Lam, J., Ammons, S., & Kelly, E. (2013). Time work by over- worked professionals: Strategies in response to the stress of higher status.

Work and Occupations ,40,79 –114. Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Question- naire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychol- ogy ,91, 1321 –1339. Morris, J. H., & Sherman, J. D. (1981). Generalizability of an organizational commitment model. Academy of Management Journal ,24, 512 –526. Motowidlo, S. J., Packard, J. S., & Manning, M. R. (1986). Occupational stress: Its causes and consequences for job performance. Journal of Applied Psychology ,71, 618 –629. THE EDUCATION –JOB SATISFACTION LINK 13 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. * Munyon, T. P., Madden, L. T., Madden, T. M., & Vigoda-Gadot, E. (2019).

(Dys)functional attachments?: How community embeddedness impacts workers during and after long-term unemployment. Journal of Vocational Behavior ,112 ,35 –50. Ng, T. W. H., Eby, L. T., Sorensen, K. L., & Feldman, D. C. (2005).

Predictors of objective and subjective career success: A meta-analysis.

Personnel Psychology ,58, 367 –408. Ng, T. W. H., & Feldman, D. C. (2009). Age, work experience, and the psychological contract. Journal of Organizational Behavior ,30, 1053 –1075. Nikolaev, B. (2016). Does other people ’s education make us less happy? Economics of Education Review ,52, 176 –191. Nikolaev, B., Boudreaux, C. J., & Wood, M. (2020). Entrepreneurship and subjective well- being: The mediating role of psychological functioning.

Entrepreneurship Theory and Practice ,44(3), 557 –586. * Nikolaev, B., Shir, N., & Wiklund, J. (2020). Dispositional positive and negative affect and self-employment transitions: The mediating role of job satisfaction. Entrepreneurship Theory and Practice ,44(3), 451 –474. * Obschonka, M., Silbereisen, R. K., & Wasilewski, J. (2012). Constellations of new demands concerning careers and jobs: Results from a two-country study on social and economic change. Journal of Vocational Behavior , 80 (1), 211 –223. Organisation for Economic Co-operation and Development. (2016).

Employment —self-employment rate —OECD data .https://data.oecd.org/ emp/self-employment-rate.htm Organization for Economic Co-operation and Development. (2017). Educa- tion at a glance 2017: OECD indicators .https://doi.org/10.1787/eag- 2017-en Oreopoulos, P., & Salvanes, K. G. (2011). Priceless: The nonpecuniary bene fits of schooling. The Journal of Economic Perspectives ,25, 159 –184. * Ozer, M. (2008). Personal and task-related moderators of leader-member exchange among software developers. Journal of Applied Psychology , 93 (5), 1174. Parker, D. F., & DeCotiis, T. A. (1983). Organizational determinants of job stress. Organizational Behavior & Human Performance ,32, 160 –177. Perko, K., Kinnunen, U., & Feldt, T. (2017). Long-term pro files of work- related rumination associated with leadership, job demands, and exhaus- tion: A three-wave study. Work & Stress ,31(4), 395 –420. Perlow, L. A. (1999). The time famine: Towards a sociology of work time.

Administrative Science Quarterly ,44,57 –81. Pines, A. M., & Keinan, G. (2005). Stress and burnout: The signi ficant difference. Personality and Individual Differences ,39, 625 –635. Podsakoff, N. P., LePine, J. A., & LePine, M. A. (2007). Differential challenge stressor- hindrance stressor relationships with job attitudes, turnover intentions, turnover, and withdrawal behavior: A meta-analysis.

Journal of Applied Psychology ,92, 438 –454. * Porath, C., Spreitzer, G., Gibson, C., & Garnett, F. G. (2012). Thriving at work: Toward its measurement, construct validation, and theoretical re finement. Journal of Organizational Behavior ,33(2), 250 –275. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.

Behavior Research Methods ,40(3), 879 –891. Reid, E., & Ramarajan, L. (2016). Managing the high-intensity workplace.

Harvard Business Review ,94(6), 78 –85. Rich, B. L., LePine, J. A., & Crawford, E. R. (2010). Job engagement:

Antecedents and effects on job performance. Academy of Management Journal ,53, 617 –635. Ross, C. E., & Reskin, B. F. (1992). Education, control at work, and job satisfaction. Social Science Research ,21, 134 –148. Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review ,5, 296 –320. Rusbult, C. E., & Farrell, D. (1983). A longitudinal test of the investment model: The impact on job satisfaction, job commitment, and turnover of variations in rewards, costs, alternatives, and investments. Journal of Applied Psychology ,68, 429 –438. Saxbe, D. E., Repetti, R. L., & Graesch, A. P. (2011). Time spent in housework and leisure: Links with parents ’physiological recovery from work. Journal of Family Psychology ,25, 271 –281. Schaubroeck, J., Cotton, J. L., & Jennings, K. R. (1989). Antecedents and consequences of role stress: A covariance structure analysis. Journal of Organizational Behavior ,10,35 –58. Schaufeli, W. B., & Buunk, B. P. (2002) Burnout: An overview of 25 years of research and theorizing. M. J. Schabracq, J. A. M. Winnubst, C. L. Cooper (Eds.), Handbook of work and health psychology (pp. 383 –425). Wiley. * Schaumberg, R. L., & Flynn, F. J. (2017). Clarifying the link between job satisfaction and absenteeism: The role of guilt proneness. Journal of Applied Psychology ,102 (6), 982. * Schjoedt, L. (2009). Entrepreneurial job characteristics: An examination of their effect on entrepreneurial satisfaction. Entrepreneurship Theory and Practice ,33(3), 619 –644. * Schlett, C., & Ziegler, R. (2014). Job emotions and job cognitions as determinants of job satisfaction: The moderating role of individual differ- ences in need for affect. Journal of Vocational Behavior ,84(1), 74 –89. Searle, B. J., & Auton, J. C. (2015). The merits of measuring challenge and hindrance appraisals. Anxiety, Stress, & Coping ,28, 121 –143. Selye, H. (1974). Stress without distress . Lippincott. Seybolt, J. W. (1976). Work satisfaction as a function of the person-environ- ment interaction. Organizational Behavior & Human Performance ,17, 66 –75. * Shaffer, M. A., Joplin, J. R. W., Bell, M. P., Lau, T., & Oguz, C. (2000).

Gender discrimination and job-related outcomes: A cross-cultural com- parison of working women in the United States and China. Journal of Vocational Behavior ,57(3), 395 –427. * Shalley, C. E., Gilson, L. L., & Blum, T. C. (2000). Matching creativity requirements and the work environment: Effects on satisfaction and intentions to leave. Academy of Management Journal ,43(2), 215 –223. Shields, M. A., Price, S. W., & Wooden, M. (2009). Life satisfaction and the economic and social characteristics of neighbourhoods. Journal of Popu- lation Economics ,22, 421 –443. Smith, A. (2001). Perceptions of stress at work. Human Resource Manage- ment Journal ,11,74 –86. Smith, P. C., Kendall, L. M., & Hulin, C. L. (1969). The measurement of satisfaction in work and retirement . Rand McNally. * Son, J., & Ok, C. (2019). Hangover follows extroverts: Extraversion as a moderator in the curvilinear relationship between newcomers ’organiza- tional tenure and job satisfaction. Journal of Vocational Behavior ,110 , 72 –88. Sonnentag, S., & Fritz, C. (2007). The recovery experience questionnaire:

Development and validation of a measure for assessing recuperation and unwinding from work. Journal of Occupational Health Psychology ,12, 204 –221. Sonnentag, S., & Fritz, C. (2015). Recovery from job stress: The stressor- detachment model as an integrative framework. Journal of Organizational Behavior ,36, S72 –S103. * Steel, R. P., Rentsch, J. R., & Van Scotter, J. R. (2007). Timeframes and absence frameworks: A test of Steers and Rhodes ’(1978) model of attendance. Journal of Management ,33(2), 180 –195. Stephan, U. (2018). Entrepreneurs ’mental health and well-being: A review and research agenda. The Academy of Management Perspectives ,32, 290 –322. Stevenson, B., & Wolfers, J. (2009). The paradox of declining female happiness. American Economic Journal. Economic Policy ,1, 190 –225. Sullivan, S. E., & Bhagat, R. S. (1992). Organizational stress, job satisfac- tion, and job performance: Where do we go from here? Journal of Management ,18, 353 –374. Summer field, M., Freidlin, S., Hahn, M., La, N., Li, N., Macalalad, N., O ’Shea, M., Watson, N., Wilkins, R., & Wooden, M. (2016). HILDA user 14 SOLOMON, NIKOLAEV, AND SHEPHERD This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. manual —Release 15 . https://melbourneinstitute.unimelb.edu.au/data/ assets/pdf_ file/0007/2194342/HILDA_User_Manual_Release_15.0.pdf * Sy, T., Tram, S., & O ’Hara, L. A. (2006). Relation of employee and manager emotional intelligence to job satisfaction and performance.

Journal of Vocational Behavior ,68(3), 461 –473. * Takeuchi, R., Chen, G., & Lepak, D. P. (2009). Through the looking glass of a social system: Cross-level effects of high-performance work systems on employees ’attitudes. Personnel Psychology ,62(1), 1 –29. ten Brummelhuis, L. L., & Bakker, A. B. (2012). Staying engaged during the week: The effect of off-job activities on next day work engagement.

Journal of Occupational Health Psychology ,17, 445 –455. * Trevor, C. O. (2001). Interactions among actual ease-of-movement deter- minants and job satisfaction in the prediction of voluntary turnover.

Academy of Management Journal ,44(4), 621 –638. Truxillo, D. M., Cadiz, D. M., Rineer, J. R., Zaniboni, S., & Fraccaroli, F.

(2012). A lifespan perspective on job design: Fitting the job and the worker to promote job satisfaction, engagement, and performance. Organiza- tional Psychology Review ,2(4), 340 –360. * Tschopp, C., Grote, G., & Gerber, M. (2014). How career orientation shapes the job satisfaction –turnover intention link. Journal of Organiza- tional Behavior ,35(2), 151 –171. * Van Dyne, L., & Pierce, J. L. (2004). Psychological ownership and feelings of possession: Three field studies predicting employee attitudes and organizational citizenship behavior. Journal of Organizational Behavior: The International Journal of Industrial. Occupational and Organizational Psychology and Behavior ,25(4), 439 –459. * Van Hooft, E. A. J., Born, M. Ph., Taris, T. W., Van der Flier, H., & Blonk, R. W. B. (2004). Predictors of job search behavior among employed and unemployed people. Personnel Psychology ,57(1), 25 –59. * Venkataramani, V., Labianca, G. J., & Grosser, T. (2013). Positive and negative workplace relationships, social satisfaction, and organizational attachment. Journal of Applied Psychology ,98(6), 1028. * Vergauwe, J., Wille, B., Feys, M., De Fruyt, F., & Anseel, F. (2015). Fear of being exposed: The trait-relatedness of the impostor phenomenon and its relevance in the work context. Journal of Business and Psychology ,30(3), 565 –581. Verhofstadt, E., De Witte, H., & Omey, E. (2007). Higher educated workers:

Better jobs but less satis fied? International Journal of Manpower ,28(2), 135 –151. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software ,36(3), 1 –48. * Vigoda, E. (2000). Organizational Politics, Job Attitudes, and Work Out- comes: Exploration and Implications for the Public Sector. Journal of Vocational Behavior ,57(3), 326 –347. * Wanberg, C. R., & Banas, J. T. (2000). Predictors and outcomes of openness to changes in a reorganizing workplace. Journal of Applied Psychology ,85(1), 132. * Wanberg, C. R., Kanfer, R., & Banas, J. T. (2000). Predictors and outcomes of networking intensity among unemployed job seekers. Journal of Applied Psychology ,85(4), 491. * Wang, M., Zhan, Y., Mccune, E., & Truxillo, D. (2011). Understanding newcomers ’adaptability and work- related outcomes: Te sting the mediating roles of perceived P –Efit variables. Personnel Psychology ,64(1), 163 –189. * Wang, Y.-D., & Hsieh, H.-H. (2014). Employees ’reactions to psychologi- cal contract breach: A moderated mediation analysis. Journal of Voca- tional Behavior ,85(1), 57 –66. Wanous, J. P., & Reichers, A. E. (1996). Estimating the reliability of a single- item measure. Psychological Reports ,78(2), 631 –634. Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfac- tion: How good are single-item measures? Journal of Applied Psychology , 82 (2), 247. Watson, N., & Wooden, M. P. (2012). The HILDA survey: A case study in the design and development of a successful household panel survey.

Longitudinal and Life Course Studies ,3, 369 –381. * Wayne, J. H., Musisca, N., & Fleeson, W. (2004). Considering the role of personality in the work –family experience: Relationships of the big five to work –family con flict and facilitation. Journal of Vocational Behavior , 64 (1), 108 –130. Webster, J. R., Beehr, T. A., & Love, K. (2011). Extending the challenge- hindrance model of occupational stress: The role of appraisal. Journal of Vocational Behavior ,79, 505 –516. * Weller, I., Holtom, B. C., Matiaske, W., & Mellewigt, T. (2009). Level and time effects of recruitment sources on early voluntary turnover. Journal of Applied Psychology ,94(5), 1146. Weyer, B. (2007). Twenty years later: Explaining the persistence of theglass ceiling for women leaders. Women in Management Review ,22, 482 –496. * Wiese, B. S., & Salmela-Aro, K. (2008). Goal con flict and facilitation as predictors of work –family satisfaction and engagement. Journal of Voca- tional Behavior ,73(3), 490 –497. Wilk, S. L., & Cappelli, P. (2003). Understanding the determinants of employer use of selection methods. Personnel Psychology ,56, 103 –124. * Wright, T. A., Cropanzano, R., Bonett, D. G., & Diamond, W. J. (2009).

The role of employee psychological well-being in cardiovascular health: When the twain shall meet. Journal of Organizational Behavior: The International Journal of Industrial. Occupational and Organizational Psychology and Behavior ,30(2), 193 –208. Wu, C. H. (2016). Personality change via work: A job demand –control model of big- five personality changes. Journal of Vocational Behavior , 92 , 157 –166. Xie, J. L., Schaubroeck, J., & Lam, S. S. K. (2008). Theories of job stress and the role of traditional values: A longitudinal study in China. Journal of Applied Psychology ,93, 831 –848. *Zacher, H. (2015). Daily manifestations of career adaptability: Relation- ships with job and career outcomes. Journal of Vocational Behavior ,91, 76 –86. * Zimmerman, R. D., Swider, B. W., & Arthur, J. B. (2020). Does turnover destination matter? Differentiating antecedents of occupational changeversus organizational change. Journal of Vocational Behavior ,121 , 103470. Received May 19, 2019 Revision received January 30, 2021 Accepted February 4, 2021 ▪ THE EDUCATION –JOB SATISFACTION LINK 15 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.