Next Article in Journal
Sales and Advertising Channels of New Psychoactive Substances (NPS): Internet, Social Networks, and Smartphone Apps
Previous Article in Journal
On the Developmental Timing of Stress: Delineating Sex-Specific Effects of Stress across Development on Adult Behavior
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Education and Income Predict Future Emotional Well-Being of Whites but Not Blacks: A Ten-Year Cohort

Department of Psychology, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
Center for Research on Ethnicity, Culture and Health, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
Author to whom correspondence should be addressed.
Brain Sci. 2018, 8(7), 122;
Original submission received: 8 June 2018 / Revised: 21 June 2018 / Accepted: 28 June 2018 / Published: 29 June 2018


Background: The Minorities’ Diminished Return (MDR) theory is defined as systematically smaller effects of socioeconomic status (SES) on the health and well-being of minority groups when compared to Whites. To extend the existing literature on the MDR theory as applied to the change of mental well-being over time, we investigated Black-White differences in the effects of baseline education and income on subsequent changes in positive and negative affect over a ten-year period. Methods: The Midlife in the United States (MIDUS) is a 10-year longitudinal study of American adults. This analysis followed 3731 adults who were either Whites (n = 3596) or Blacks (n = 135) for 10 years. Education and income, as measured at baseline and 10 years later, were the independent variables. Negative and positive affect, measured at baseline and over ten years of follow up, were the dependent variables. Covariates were age, gender, and physical health (body mass index, self-rated health, and chronic medical conditions), measured at baseline. Race was the focal moderator. We ran multi-group structural equation modeling in the overall sample, with race defining the groups. Results: High education at baseline was associated with an increase in income over the 10-year follow up period for Whites but not Blacks. An increase in income during the follow up period was associated with an increase in the positive affect over time for Whites but not Blacks. Conclusion: The MDR theory is also relevant to the effects of baseline education attainment on subsequent changes in income and then in turn on positive affect over time. The relative disadvantage of Blacks in comparison to Whites in receiving mental health gains from SES may reflect structural racism and discrimination in the United States. There is a need for additional research on specific societal barriers that minimize Blacks’ mental health gains from their SES resources, such as education and income. There is also a need for policies and programs that help Blacks to leverage their SES resources.

1. Background

Positive and negative affect are major components of human emotional experiences. There is a body of research that discusses how socioeconomic status (SES), negative and positive affect, and health are linked [1]. Negative affect impacts all age groups, genders, races, and ethnicities [2] and is central to depression, a debilitating illness which affects 1 in 20 Americans and is the leading cause of disability worldwide [2]. While positive affect is particularly essential for a healthy sense of well-being [3,4,5], negative and positive affect are both under the influence of SES (education and income) [6,7,8]. Negative affect disproportionately affects individuals with low SES (low education and low income) [1,2], while individuals with high SES are more likely to report positive affect [9,10]. However, there might be a relative disadvantage for Blacks when compared to Whites, regarding how SES indicators impact positive and negative affect [11].
The Minorities’ Diminished Return (MDR) theory [12,13] can be defined as smaller protective effects of SES on a wide range of tangible outcomes for Black and other minority populations, when compared to Whites [14]. In line with the MDR theory, education has shown stronger effects in influencing income [15,16], drinking behaviors [17], smoking [18], diet [19], chronic disease [20], body mass index [21], self-rated health [22], and mortality [23,24,25,26] for Whites than for Blacks. Income has also shown stronger effects on impulse control [27], obesity [28], oral health [29], chronic disease [30], and mental well-being [31,32] of Whites than Blacks. Education also generates less income for Blacks than for Whites [15,16].
Research evidence suggests that MDR theory also holds for the effects of SES on affect [20,33,34,35]. Studies have documented an increased risk of depression [20] and suicidality [33] for high SES Blacks. In a national sample, high income Black boys had higher risk of lifetime, 12-month, and past month major depressive disorder (MDD) than their low SES counterparts [34]. In a nationally representative sample of adults, high income Black men had a higher risk of MDD than their low income counterparts [35]. In a 25-year follow up period of a nationally representative sample, most educated Black men showed an increase in depressive symptoms over time, a pattern that could not be seen in Black women, White men, or White women [20]. These findings contradict the mainstream findings that SES translates to positive health outcomes [1].
The MDR theory attributes such unequal gains of equal resources to the qualitative differences that exist in the lives of Whites and minority groups, such as Blacks. Such differences hinder Blacks’ mental health gains, even for those who have invested to enhance their SES and class. As the United States (U.S.) society treats Blacks and other minorities worse than Whites, high SES minority populations, particularly high SES Blacks, do not gain access to the same opportunity structure, and SES does not promote their health conditions as it does for Whites. As a result, high SES Blacks’ everyday lives are heavily affected by continuous prejudice and frequent discrimination [36,37,38]. Such racism at multi-levels deteriorates Blacks’ health gains from the new resources that become available to them [36,39,40].

2. Aims

The current study was conducted to examine Black-White differences in the effects of baseline education on change in income and then subsequently change in positive and negative affect over time among American adults. We hypothesized that high education attainment at baseline would be associated with a higher income 10 years later for Whites than Blacks. We also hypothesized that high income at year 10 would be associated with higher positive affect and lower negative affect for Whites but not Blacks.

3. Methods

3.1. Design

Data used in this longitudinal study came from the first 10 years of follow up of the Midlife in the United States (MIDUS), a longitudinal study of American adults. Data were collected from 1995 to 2004, and the study was carried out by the MacArthur Midlife Research Network (MMRN). MIDUS is a national cohort study of over 7000 American adults that were aged between 25 and 74 years with the primary purpose of understanding psychosocial processes that contribute to age-related decline in physical and mental health over time [41,42,43,44,45]. MIDUS is funded by the National Institute on Aging (NIA).

3.2. Ethical Considerations

The MIDUS study protocol was approved by the University of Wisconsin-Madison (UWM) Institutional Review Board (IRB). Written informed consent was received for all MIDUS participants. Participants received monetary incentives for their participation in both Wave 1 and Wave 2 of the study (USD 20 and USD 60 for completions of MIDUS 1 and MIDUS 2 surveys, respectively).

3.3. Data Collection

MIDUS data collection used a multimodal strategy that was composed of a telephone interview, a computer-assisted personal interview (CAPI), a computer-assisted telephone interview (CATI), a mailed questionnaire, and a face-to-face interview. First, the study employed an initial 30-min phone interview. This was followed by self-administered questionnaires that were mailed to the participants [41,42,43,44,45].
Wave 1 data collection in MIDUS was conducted in 1995 and 1996. The follow-up data collection in MIDUS was conducted 10 years later in 2004 and 2005. Mailings with an accompanying brochure were sent to all Wave 1 MIDUS participants, in order to remind the participants about their participation and to increase their expectation that an interviewer would contact them for the initial telephone survey in the near future. The telephone survey was completed as part of Wave 1. After a phone interview, which lasted 30 min on average, participants received two mailed self-administered questionnaires [41,42,43,44,45].

3.4. Participants and Sampling

To enroll a random sample of adults, MIDUS used random digit dialing (RDD), a sampling technique commonly that is used for telephone surveys. RDD is conducted by generating telephone numbers at random. The sampling frame was a national RDD, which allowed all telephone numbers within the continental United States to be selected. MIDUS oversampled individuals in five cities (because of geographic-specific agenda), resulting in a baseline RDD sample of 4244 individuals [41,42,43,44,45].

3.5. Analytical Sample

The analytical sample in this study was 3731 White and Black individuals who completed the 10-year follow up duration. The remaining individuals were excluded due to either their racial category not being of interest in this study (not White or Black) or not completing the 10-year follow up assessment.

3.6. Follow-Up Data

From a total number of 7108 individuals who were enrolled at baseline (i.e., individuals who completed the phone survey at MIDUS Wave 1), follow up data were gathered for 4963 individuals (70%) at MIDUS Wave 2 ten years later. As a result, MIDUS sample had a 75% overall retention rate (adjusted for mortality). Major causes for loss to follow up were refusal, inability to be contacted, too ill to be interviewed, or deceased [41,42,43,44,45].

3.7. Measures

Demographic variables. Age (years), gender (male, female), and race (Black, White) were collected at baseline (in 1995). Age was treated as a continuous measure. Gender (men = 0 [reference group] and women = 1) and self-identified race (Whites = 0 [reference group], Blacks = 1) were operationalized as dichotomous variables
Educational Attainment. The main SES indicator in this study was educational attainment, which was measured as: (1) less than high school; (2) high school graduate or equivalent; (3) some college; or, (4) college graduate or more. Education was operationalized as a continuous measure.
Physical Health. Three proxy variables that reflect physical health were included in the current study: body mass index (BMI), self-rated health (SRH), and chronic medical conditions (CMC). SRH was a 10 level variable ranging from 1 (worst) to 10 (best). All of the health measures were conceptualized as continuous variables. While a high score for SRH was indicative of good physical health, a higher score for CMC and BMI was reflective of poor health.
Positive Affect. Using the Mroczek and Kolarz (1998) scale [46], positive affect during the past 30 days was measured, using the following feelings: “cheerful”, “in good spirits”, “extremely happy”, “calm and peaceful”, “satisfied”, and “full of life”. Responses were on a Likert scale ranging from 1 (all of the time) to 5 (none of the time) [46]. Mean positive affect scores were computed, with possible scores ranging from 1 to 5. Higher scores reflected more positive affect. Internal consistency (reliability) was very good (α = 0.91 for all, 0.91 for Whites, 0.92 for Blacks) [47,48,49].
Negative Affect. Using the same measure by Mroczek and Kolarz (1998) scale [46], negative affect during the past 30 days was measured using the following feelings: “so sad”, “nervous”, “restless or fidgety”, “hopeless”, “worthless”, and “everything was an effort”. Response items were on a Likert scale, ranging from 1 (all of the time) to 5 (none of the time) [46]. An average score was calculated that reflected negative affect, with scores ranging from 1 to 5. [47,48,49]. Higher scores were reflective of more negative affect. Internal consistency (reliability measure) was high for all (α = 0.86), for Whites (α = 0.86), and for Blacks (α = 0.87). This measure is widely used to assess affect [50,51].

3.8. Statistical Analysis

SPSS 22.0 (SPSS Inc., Chicago, IL, USA) and AMOS 22.0 [52,53] were used to conduct the data analysis. Frequency (%) and mean (SD) were reported to describe the sample at the baseline and 10 years later. Pearson’s correlation was used to calculate the bivariate correlations in the overall sample.
A multi-group structural equation model (SEM) was used for multivariable analysis [54]. In our models, the groups were defined based on race. Education and income measured at baseline were the independent variables. Negative and positive affect measured at baseline and over ten years of follow up were the dependent variables. Covariates included age, gender, and health (body mass index, self-rated health, and chronic medical conditions) measured at baseline. Income measured at ten years of follow up was the mediator. Race was the focal moderator. To handle the missing data, Full Information Maximum Likelihood (FIML) was used. The final SEM model did not include any constrains or co-variances for the errors.
The model fit was assessed using the conventional fit statistics that included a non-significant chi-square test (p > 0.05), a comparative fit index (CFI) larger than 0.95, a root mean squared error of approximation (RMSEA) of less than 0.06, and an X2 to degrees of freedom ratio of less than 4 [55,56,57,58]. We reported standardized regression coefficients, with associated standard errors (SE) and p values for each path.

4. Results

4.1. Descriptive Statistics

This study included 3731 adults who were either Whites (n = 3596) or Blacks (n = 135) for 10 years.
Table 1 provides a summary of the descriptive statistics for the overall sample, as well as for racial groups. Blacks had lower SES (education and income) than Whites. Blacks also had higher negative affect at baseline and 10 years later than Whites (Table 1).

4.2. Bivariate Correlations

Table 2 summarizes the results of bivariate correlations. Education and income were associated with positive and negative emotions at baseline and 10 years later in the overall sample (Table 2).

4.3. Multivariable Models

Our SEM showed very good fit. CMIN = 24.465; DF = 8; p = 0.002; CMIN/DF = 3.058; CFI = 0.998; RMSEA = 0.023 (90% CI = 0.013–0.034). Table 3 summarizes the path coefficients for the SEM. Figure 1a,b also show these paths for Whites and Blacks. As these models show, baseline education showed an effect on change in income over the next 10 years for Whites but not Blacks. Change in income, in turn, predicted an increase in positive emotions for Whites but not Blacks (Table 3).

5. Discussion

The results showed an effect of high education attainment at baseline on an increase in income over the 10-year follow up period for Whites but not for Blacks. The study also showed an association between an increase in income and an increase in the positive affect for Whites but not Blacks. These results indicate that the MDR theory also holds for economic and mental health return of education attainment over time.
Our findings support the MDR theory [12,13,21], defined as the systemically smaller economic and health effects of same SES indicators for Blacks and other minorities when compared to Whites [21,24,31,33,36,59,60,61,62,63,64]. Two studies in particular have shown that education generates more economic return for Whites than Blacks [15,16]. The impact of educational attainment on changing drinking patterns [17], BMI, insomnia, physical activity [17], depression [20], suicidal behaviors [33], and mortality [24] are all shown to be smaller for Blacks than for Whites. Similar results are seen in the transgenerational effects of parental education on child outcomes [21,22,27,65].
The results also support Link and Phelan’s (1995) Fundamental Cause Theory, suggesting that SES is a fundamental and root cause of a wide range of outcomes, including mental health [66,67,68]. Link and Phelan also introduce racism as a fundamental cause [69]. Our findings are also in line with the life course epidemiology approach, suggesting that risk factors and resources have long-term effects on population health decades later [70,71,72,73].
Not only do Blacks gain less than do Whites from SES, high SES may operate as a risk factor for poor mental health outcomes for Blacks, particularly in Black males [14,20,21,33,34,39,60,74,75,76]. To provide examples, high education and income are shown to be risk factors of MDD, symptoms of depression, and poor self-rated mental health for Black youth and adults, particularly males [32,34]. It is not clear why we could not replicate MDR theory for negative affect in the current sample.
The findings reported here should not be interpreted as Blacks are less capable of turning their SES resources to tangible outcomes. This interpretation would be blaming the victim of a system that oppresses them. Minorities’ Diminished Return is not a function of minorities’ culture or laziness, but a consequence of legacy of slavery and remaining systemic racism. Racism is still a core element of the social structure and function in the U.S. Across institutions and levels of society, there are deeply rooted inequalities that hinder Blacks’ progress, and reduce their ability to transform their resources into tangible gains [77,78,79]. As long as race and skin color strongly influence how individuals and groups are treated in our society and how people access opportunity structure, true equality between races is not achievable. Without a drastic change to the structure and function of the U.S. system, America will continue to fail the middle-class and high SES Blacks who have paid the cost to climb the social ladder. Society is charging Blacks and other racial and ethnic minority groups an extra cost for upward social mobility, which diminishes the physical and mental health status of Blacks who have successfully climbed the social ladder [39,60,80,81,82,83,84]. Historically, economic and public policies in the U.S. were designed and implemented to maximize the gain of the majority (Whites) even to the cost of ignoring specific needs of marginalized social groups. For example, U.S. culture overemphasizes the individual’s responsibility and bootstrapping, and it is systemically against safety nets, welfare, and universal coverage of health. High aspirations, motivations, and ambitions do not increase health outcomes for Blacks and other minority groups as compared to Whites. That is, educational attainment does not generate economic rewards such as prestigious and high paying jobs and wealth. Education and class that serve Whites, particularly White men, do not change purchasing power and wealth for Blacks, particularly Black men [85,86,87].

5.1. Implications for Policies and Programs

There is a need to reduce qualitative differences in the lives of Blacks and Whites so SES can generate similar economic and mental health gain, regardless of race. Without a systemic change, minorities will continue to remain at a systemic disadvantage relative to Whites. As mentioned before [12,13], policy solutions must not be limited to equalizing access and merely enhancing minorities’ SES. Instead, policies must address societal and structural barriers that disproportionately limit minorities’ abilities to translate their resources to health. Policy makers should be aware that some universal policies may have a larger impact on Whites than minorities, and may have the unintended effect of increasing the racial gap in health. For such high risk policies, evaluations are needed to explore racial variation in the reach and impact of the policy, and to ensure that minority groups are not left behind in gaining from the resources.
These results suggest that we need to reduce discrimination and inequalities in the education system, as well as in the labor market. There is a need to increase investment in quality of education of inner cities and in minority-majority schools. Policies should be in place to reduce both the discrimination of Black students in school and the differential hiring chances of racial groups. The results are also in support of affirmative action policies, simply because educational attainment is not enough for equalizing the well-being of Blacks and Whites. Blacks and other minority groups may require additional assistance leveraging their available SES resources, like education and employment. Policies should not permit education, which is a potential equalizer, to become a source of inequalities in employment, income, and life conditions across groups.

5.2. Limitations and Future Directions for Research

Our study had a few limitations. One main weakness includes the small sample size for Blacks (n = 135) as compared to Whites (n = 3596), which limits the power for statistically detecting the significant main effects for education or potential effect modification by gender. The inability to examine interactions by gender, for Blacks, is a major limitation since the literature has shown SES to be a risk factor for poor mental health of Black men. SES may impact mental health of Black males and Black females differently. Second, educational attainment was conceptualized and operationalized as a fixed variable. However, similar to other psychosocial constructs, educational attainment is subject to change over a 10-year period. Third, our study missed to control for all the potential confounders and covariates. Future research should test if the effects of other SES indicators, such as income, marital status, occupation, employment, home ownership, and wealth are also different for Whites and Blacks. This study neither measured multi-generational transition of SES nor childhood SES, which should be examined in future research on MDR. In addition, the SES level is not limited to the individual level, but to the family and community levels as well. Available resources across multiple levels, within the individual, in the family, and in the neighborhood and community should also be investigated in the future research. Contextual factors other than SES (e.g., neighborhood racial composition) are needed to be included as explanatory mechanisms that reduce Blacks’ health gains from individual-level SES. Research is needed on the role of quality of sleep and associated suicidality [88]. This is particularly important given the role of sleep on affect [89], and also given the racial differences in the protective effects of SES on sleep across racial groups [36,90]. Future research should go beyond the narrow definition of minority status based on race and it should consider the role of other marginalized identities. Research should also test the role of intersectionality of race, ethnicity, gender, sexual orientation, nativity, place, and class. Despite these limitations, the current study makes a unique contribution to the literature on the MDR theory [13,21,60], and shows that it also applies to the effects of educational attainment on changes in positive affect over ten years. Some strengths of our study included recruitment of a nationally representative sample, a large sample size, a longitudinal study design, and a 10-year follow up.

6. Conclusions

The magnitude of the effect of baseline education on changing future income and its subsequent impact on increasing positive affect are not equal across Black and White Americans. An inequality exists in the economic and emotional return of educational attainment over time. Similar resources, like education and income, consistently result in lower economic and mental health gains for Black Americans when compared to those of White Americans. Multi-level solutions should be comprehensive and include policy solutions that go beyond merely equalizing the access of populations to SES resources and eliminate the inequality in societal barriers in the lives of minority populations. It is only then that racial minority groups can achieve comparable outcomes to the majority group in response to similar access to resources.

Author Contributions

S.A. was responsible for the design and analysis of the data and revisions. B.P. and M.K. contributed to the drafting and revising the manuscript. All authors approved the final draft.


The MIDUS was funded by the National Institute on Aging (NIA). Monetary incentives were given at both Wave 1 and Wave 2 (US $20 for completion of MIDUS 1 surveys and up to US $60 for completion of MIDUS 2 surveys). Funding: This research was supported by a grant from the National Institute on Aging (P01-AG020166) to conduct a longitudinal follow-up of the MIDUS investigation. The original study was supported by the John D. and Catherine T. MacArthur Foundation Research Network on Successful Midlife Development. SA is supported by the Heinz C. Prechter Bipolar Research Fund and the Richard Tam Foundation at the University of Michigan Depression Center.

Conflicts of Interest

Authors declare that they have no conflict of interest.


  1. Adler, N.E.; Boyce, T.; Chesney, M.A.; Cohen, S.; Folkman, S.; Kahn, R.L.; Syme, S.L. Socioeconomic status and health: The challenge of the gradient. Am. Psychol. 1994, 49, 15–24. [Google Scholar] [CrossRef]
  2. Pratt, L.A.; Brody, D.J. Depression in the United States Household Population, 2005–2006; NCHS Data Brief No. 7; Centers for Disease Control and Prevention: Atlanta, GA, USA, 2008.
  3. Fredrickson, B.; Joiner, T. Positive emotions trigger upwards spiral toward emotional well-being. Psychol. Sci. 2002, 13, 172–175. [Google Scholar] [CrossRef] [PubMed]
  4. Fredrickson, B.L. Cultivating positive emotions to optimize health and well-being. Prev. Treat. 2000, 3. [Google Scholar] [CrossRef]
  5. School of Education and Behavioural Sciences. Available online: (accessed on 7 July 2017).
  6. Gallo, L.C.; Matthews, K.A. Understanding the association between socioeconomic status and physical health: Do negative emotions play a role? Psychol. Bull. 2003, 129, 10–51. [Google Scholar] [CrossRef] [PubMed][Green Version]
  7. Gallo, L.C. The Reserve Capacity Model as a Framework for Understanding Psychosocial Factors in Health Disparities. Appl. Psychol. Health Well-Being 2009, 1, 62–72. [Google Scholar] [CrossRef]
  8. Gallo, L.C.; Bogart, L.M.; Vranceanu, A.; Matthews, K.A.J. Socioeconomic Status, Resources, Psychological Experiences, and Emotional Responses: A Test of the Reserve Capacity Model. J. Personal. Soc. Psychol. 2005, 88, 386–399. [Google Scholar]
  9. Jakoby, N. Socioeconomic status differences in negative emotions. Sociol. Res. Online 2016, 21, 1–10. [Google Scholar] [CrossRef]
  10. Holahan, C.J.; Moos, R.H. Life stressors, personal and social resources, and depression: A 4-year structural model. J. Abnorm. Psychol. 1991, 100, 31–38. [Google Scholar] [CrossRef]
  11. Williams, D.R.; Sternthal, M. Understanding racial-ethnic disparities in health: Sociological contributions. J. Health Soc. Behav. 2010, 51, S15–S27. [Google Scholar] [CrossRef] [PubMed]
  12. Assari, S. Unequal Gain of Equal Resources across Racial Groups. Int. J. Health Policy Manag. 2018, 7, 1–9. [Google Scholar] [CrossRef] [PubMed]
  13. Assari, S. Health Disparities due to Diminished Return among Black Americans: Public Policy Solutions. Soc. Issues Policy Rev. 2018, 12, 112–145. [Google Scholar] [CrossRef][Green Version]
  14. Fuller-Rowell, T.E.; Curtis, D.S.; Doan, S.N.; Coe, C.L. Racial disparities in the health benefits of educational attainment: A study of inflammatory trajectories among African American and white adults. Psychosom. Med. 2015, 77, 33–40. [Google Scholar] [CrossRef] [PubMed]
  15. Assari, S. Parental Education Better Helps White than Black Families Escape Poverty: National Survey of Children’s Health. Economies 2018, 6, 30. [Google Scholar] [CrossRef]
  16. Assari, S. Diminished Economic Return of Socioeconomic Status for Black Families. Soc. Sci. 2018, 7, 74. [Google Scholar] [CrossRef]
  17. Assari, S.; Lankarani, M.M. Education and Alcohol Consumption among Older Americans; Black-White Differences. Front. Public Health 2016, 4, 67. [Google Scholar] [CrossRef] [PubMed]
  18. Assari, S.; Mistry, R. Educational Attainment and Smoking Status in a National Sample of American Adults; Evidence for the Blacks’ Diminished Return. Int. J. Environ. Res. Public Health 2018, 15, 763. [Google Scholar] [CrossRef] [PubMed]
  19. Assari, S.; Lankarani, M.M. Educational Attainment Promotes Fruit and Vegetable Intake for Whites but Not Blacks. J 2018, 1, 5. [Google Scholar] [CrossRef]
  20. Assari, S. Combined Racial and Gender Differences in the Long-Term Predictive Role of Education on Depressive Symptoms and Chronic Medical Conditions. J. Racial Ethn. Health Dispar. 2017, 4, 385–396. [Google Scholar] [CrossRef] [PubMed]
  21. Assari, S.; Thomas, A.; Caldwell, C.H.; Mincy, R.B. Blacks’ Diminished Health Return of Family Structure and Socioeconomic Status; 15 Years of Follow-up of a National Urban Sample of Youth. J. Urban Health 2018, 95, 21–35. [Google Scholar] [CrossRef] [PubMed]
  22. Assari, S.; Caldwell, C.H.; Mincy, R.B. Maternal Educational Attainment at Birth Promotes Future Self-Rated Health of White but Not Black Youth: A 15-Year Cohort of a National Sample. J. Clin. Med. 2018, 7, 93. [Google Scholar] [CrossRef] [PubMed]
  23. Hayward, M.D.; Hummer, R.A.; Sasson, I. Trends and group differences in the association between educational attainment and U.S. adult mortality: Implications for understanding education’s causal influence. Soc. Sci. Med. 2015, 127, 8–18. [Google Scholar] [CrossRef] [PubMed]
  24. Assari, S.; Lankarani, M.M. Race and Urbanity Alter the Protective Effect of Education but not Income on Mortality. Front. Public Health 2016, 4, 100. [Google Scholar] [CrossRef] [PubMed]
  25. Backlund, E.; Sorlie, P.D.; Johnson, N.J. A comparison of the relationships of education and income with mortality: The National Longitudinal Mortality Study. Soc. Sci. Med. 1999, 49, 1373–1384. [Google Scholar] [CrossRef]
  26. Everett, B.G.; Rehkopf, D.H.; Rogers, R.G. The Nonlinear Relationship between Education and Mortality: An Examination of Cohort, Race/Ethnic, and Gender Differences. Popul. Res. Policy Rev. 2013, 1, 32. [Google Scholar] [CrossRef] [PubMed]
  27. Assari, S.; Caldwell, C.H.; Mincy, R. Family Socioeconomic Status at Birth and Youth Impulsivity at Age 15; Blacks’ Diminished Return. Children 2018, 5, 58. [Google Scholar] [CrossRef] [PubMed]
  28. Assari, S. Family Income Reduces Risk of Obesity for White but Not Black Children. Children 2018, 5, 73. [Google Scholar] [CrossRef] [PubMed]
  29. Assari, S.; Hani, N. Household Income and Children’s Unmet Dental Care Need; Blacks’ Diminished Return. Dent. J. 2018, 6, 17. [Google Scholar] [CrossRef] [PubMed]
  30. Assari, S.; Moghani, M.L. Poverty Status and Childhood Asthma in White and Black Families: National Survey of Children’s Health. Healthcare 2018, 6, 62. [Google Scholar] [CrossRef] [PubMed]
  31. Assari, S. High Income Protects Whites but Not African Americans against Risk of Depression. Healthcare 2018, 6, 37. [Google Scholar] [CrossRef] [PubMed]
  32. Assari, S.; Lapeyrouse, L.M.; Neighbors, H.W. Income and Self-Rated Mental Health: Diminished Returns for High Income Black Americans. Behav. Sci. 2018, 8, 50. [Google Scholar] [CrossRef] [PubMed]
  33. Assari, S. Ethnic and Gender Differences in Additive Effects of Socio-economics, Psychiatric Disorders, and Subjective Religiosity on Suicidal Ideation among Blacks. Int. J. Prev. Med. 2015, 6, 53. [Google Scholar] [CrossRef] [PubMed]
  34. Assari, S.; Caldwell, C.H. High Risk of Depression in High-Income African American Boys. J. Racial Ethn. Health Dispar. 2017. [Google Scholar] [CrossRef] [PubMed]
  35. Assari, S. Social Determinants of Depression: The Intersections of Race, Gender, and Socioeconomic Status. Brain Sci. 2017, 7, 156. [Google Scholar] [CrossRef] [PubMed]
  36. Assari, S.; Nikahd, A.; Malekahmadi, M.R.; Lankarani, M.M.; Zamanian, H. Race by Gender Group Differences in the Protective Effects of Socioeconomic Factors Against Sustained Health Problems Across Five Domains. J. Racial Ethn. Health Dispar. 2016. [Google Scholar] [CrossRef] [PubMed]
  37. Ross, C.E.; Mirowsky, J. Refining the association between education and health: The effects of quantity, credential, and selectivity. Demography 1999, 36, 445–460. [Google Scholar] [CrossRef] [PubMed]
  38. Montez, J.K.; Hummer, R.A.; Hayward, M.D.; Woo, H.; Rogers, R.G. Trends in the educational gradient of US adult mortality from 1986 through 2006 by race, gender, and age group. Res. Aging 2011, 33, 145–171. [Google Scholar] [CrossRef] [PubMed]
  39. Hudson, D.L.; Bullard, K.M.; Neighbors, H.W.; Geronimus, A.T.; Yang, J.; Jackson, J.S. Are benefits conferred with greater socioeconomic position undermined by racial discrimination among African American men? J. Mens Health 2012, 9, 127–136. [Google Scholar] [CrossRef] [PubMed]
  40. Hudson, D.L. Race, Socioeconomic Position and Depression: The Mental Health Costs of Upward Mobility. Ph.D. Thesis, The University of Michigan, Ann Arbor, MI, USA, 2009. [Google Scholar]
  41. Radler, B.T.; Ryff, C.D. Who participates? Accounting for longitudinal retention in the MIDUS national study of health and well-being. J. Aging Health 2010, 22, 307–331. [Google Scholar] [CrossRef] [PubMed]
  42. Dienberg Love, G.; Seeman, T.E.; Weinstein, M.; Ryff, C.D. Bioindicators in the MIDUS national study: Protocol, measures, sample, and comparative context. J. Aging Health 2010, 22, 1059–1080. [Google Scholar] [CrossRef] [PubMed]
  43. Midlife in the United States (MIDUS). Available online: (accessed on 7 July 2017).
  44. National Institute on Aging Database of Longitudinal Studies. Available online: (accessed on 7 July 2017).
  45. Snowden, M.; Dhingra, S.S.; Keyes, C.L.; Anderson, L.A. Changes in mental well-being in the transition to late life: Findings from MIDUS I and II. Am. J. Public Health 2010, 100, 2385–2388. [Google Scholar] [CrossRef] [PubMed]
  46. Mroczek, D.K.; Kolarz, C.M. The effect of age on positive and negative affect: A developmental perspective on happiness. J. Personal. Soc. Psychol. 1998, 75, 1333–1349. [Google Scholar] [CrossRef]
  47. Grzywacz, J.G. Work-family spillover and health during midlife: Is managing conflict everything? Am. J. Health Promot. 2000, 14, 236–243. [Google Scholar] [CrossRef] [PubMed]
  48. Keyes, C.L.M. Subjective change and its consequences for emotional well-being. Motiv. Emot. 2000, 24, 67–83. [Google Scholar] [CrossRef]
  49. Walen, H.R.; Lachman, M.E. Social support and strain from partner, family, and friends: Costs and benefits for men and women in adulthood. J. Soc. Pers. Relatsh. 2000, 17, 5–30. [Google Scholar] [CrossRef]
  50. Mroczek, D.K. Age and Emotion in Adulthood. Curr. Dir. Psychol. Sci. 2001, 10, 87–90. [Google Scholar] [CrossRef]
  51. Brim, O.G.; Ryff, C.D.; Kessler, R.C. (Eds.) How Healthy Are We?: A National Study of Well-Being at Midlife; University of Chicago Press: Chicago, IL, USA, 2004. [Google Scholar]
  52. Allison, P.D. Structural Equation Modeling with Amos: Basic Concepts, Applications, and Programming, 2nd ed.; Taylor and Francis Group: New York, NY, USA, 2002. [Google Scholar]
  53. Arbuckle, J.L. Amos 18 User’s Guide; Amos Development Corporation: Chicago, IL USA, 2009. [Google Scholar]
  54. Kline, R.B. Principles and Practice of Structural Equation Modeling, 3rd ed.; Guilford Press: New York, NY, USA, 2011. [Google Scholar]
  55. Hu, G.; Bouchard, C.; Bray, G.A.; Greenway, F.L.; Johnson, W.D.; Newton, R.L.; Ravussin, E.; Ryan, D.H.; Katzmarzyk, P.T. Trunk versus extremity adiposity and cardiometabolic risk factors in white and African American adults. Diabetes Care 2011, 34, 1415–1418. [Google Scholar] [CrossRef] [PubMed]
  56. Dion, P.A. Interpreting structural equation modeling results: A reply to Martin and Cullen. J. Bus. Ethics 2008, 83, 365–368. [Google Scholar] [CrossRef]
  57. Lei, M.; Lomax, R.G. The effect of varying degrees of nonnormality in structural equation modeling. Struct. Equ. Model. 2005, 12, 1–27. [Google Scholar] [CrossRef]
  58. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics, 3rd ed.; Harper Collins: New York, NY, USA, 1996. [Google Scholar]
  59. Assari, S. The link between mental health and obesity: Role of individual and contextual factors. Int. J. Prev. Med. 2014, 5, 247–249. [Google Scholar] [PubMed]
  60. Assari, S.; Preiser, B.; Lankarani, M.M.; Caldwell, C.H. Subjective Socioeconomic Status Moderates the Association between Discrimination and Depression in African American Youth. Brain Sci. 2018, 8, 71. [Google Scholar] [CrossRef] [PubMed]
  61. Assari, S.; Lankarani, M.M.; Caldwell, C.H. Does Discrimination Explain High Risk of Depression among High-Income African American Men? Behav. Sci. 2018, 8, 40. [Google Scholar] [CrossRef] [PubMed]
  62. Assari, S. Socioeconomic Status and Self-Rated Oral Health; Diminished Return among Hispanic Whites. Dent. J. 2018, 6, 11. [Google Scholar] [CrossRef] [PubMed]
  63. Assari, S.; Caldwell, C.H. Social Determinants of Perceived Discrimination among Black Youth: Intersection of Ethnicity and Gender. Children 2018, 5, 24. [Google Scholar] [CrossRef] [PubMed]
  64. Assari, S. Life Expectancy Gain Due to Employment Status Depends on Race, Gender, Education, and Their Intersections. J. Racial Ethn. Health Dispar. 2018, 5, 375–386. [Google Scholar] [CrossRef] [PubMed]
  65. Azizi, M.; Mohamadian, F.; Ghajarieah, M.; Direkvand-Moghadam, A. The Effect of Individual Factors, Socioeconomic and Social Participation on Individual Happiness: A Cross-Sectional Study. J. Clin. Diagn. Res. 2017, 11, VC01–VC04. [Google Scholar] [CrossRef] [PubMed]
  66. Phelan, J.C.; Link, B.G.; Tehranifar, P. Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. J. Health Soc. Behav. 2010, 51, S28–S40. [Google Scholar] [CrossRef] [PubMed]
  67. Link, B.G.; Phelan, J. Social Conditions as Fundamental Causes of Health Inequalities. In Handbook of Medical Sociology; Prentice Hall: Upper Saddle River, NJ, USA, 2010; pp. 3–17. [Google Scholar]
  68. Link, B.; Phelan, J. Social conditions as fundamental causes of disease. J. Health Soc. Behav 1995, 36, 80–94. [Google Scholar] [CrossRef]
  69. Phelan, J.C.; Link, B.G. Is racism a fundamental cause of inequalities in health? Annu. Rev. Sociol. 2015, 41, 311–330. [Google Scholar] [CrossRef]
  70. Ben-Shlomo, Y.; Diana Kuh, D. A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. Int. J. Epidemiol. 2002, 31, 285–293. [Google Scholar] [CrossRef] [PubMed]
  71. Kuh, D.; Ben-Shlomo, Y.; Lynch, J.; Hallqvist, J.; Power, C. Life course epidemiology. J. Epidemiol. Commun. Health 2003, 57, 778. [Google Scholar] [CrossRef]
  72. Lynch, J.; Smith, G.D. A life course approach to chronic disease epidemiology. Annu. Rev. Public Health 2005, 26, 1–35. [Google Scholar] [CrossRef] [PubMed]
  73. Keyes, C.L. The Black-White paradox in health: Flourishing in the face of social inequality and discrimination. J. Personal. 2009, 77, 1677–1706. [Google Scholar] [CrossRef] [PubMed]
  74. Hudson, D.L.; Neighbors, H.W.; Geronimus, A.T.; Jackson, J.S. Racial Discrimination, John Henryism, and Depression among African Americans. J. Black Psychol. 2016, 42, 221–243. [Google Scholar] [CrossRef] [PubMed]
  75. Hudson, D.L.; Eaton, J.; Banks, A.; Sewell, W.; Neighbors, H. “Down in the Sewers”: Perceptions of Depression and Depression Care Among African American Men. Am. J. Mens Health 2018, 12, 126–137. [Google Scholar] [CrossRef] [PubMed]
  76. Fuller-Rowell, T.E.; Doan, S.N. The social costs of academic success across ethnic groups. Child Dev. 2010, 81, 1696–1713. [Google Scholar] [CrossRef] [PubMed]
  77. Zajacova, A.; Everett, B.G. The nonequivalent health of high school equivalents. Soc. Sci. Q. 2014, 95, 221–238. [Google Scholar] [CrossRef] [PubMed]
  78. Williams, D.R.; Mohammed, S.A.; Leavell, J.; Collins, C. Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Ann. N. Y. Acad. Sci. 2010, 1186, 69–101. [Google Scholar] [CrossRef] [PubMed][Green Version]
  79. Ferraro, K.F.; Kelley-Moore, J.A. Self-rated health and mortality among black and white adults: Examining the dynamic evaluation thesis. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 2001, 56, S195–S205. [Google Scholar] [CrossRef]
  80. Sellers, S.L.; Neighbors, H.W. Effects of Goal-Striving Stress on the Mental Health of Black Americans. J. Health Soc. Behav. 2008, 49, 92–103. [Google Scholar] [CrossRef] [PubMed][Green Version]
  81. Neighbors, H.W.; Sellers, S.L.; Zhang, R.; Jackson, J.S. Goal-striving stress and racial differences in mental health. Race Soc. Probl. 2011, 3, 51–62. [Google Scholar] [CrossRef]
  82. Sellers, S.L.; Neighbors, H.W.; Bonham, V.L. Goal-striving stress and the mental health of college-educated Black American Men: The protective effects of system-blame. Am. J. Orthopsychiatr. 2011, 81, 507. [Google Scholar] [CrossRef] [PubMed]
  83. Messersmith, E.E.; Schulenberg, J.E. Goal attainment, goal striving, and well-being during the transition to adulthood: A ten-year US national longitudinal study. New Dir. Child Adolesc. Dev. 2010, 2010, 27–40. [Google Scholar] [CrossRef] [PubMed]
  84. Assari, S.; Howard Caldwell, C. Income and Depression among Black; Gender by Ethnic Differences in a National Sample. J. Racial Ethn. Health Dispar. 2015, 2, 481–493. [Google Scholar] [CrossRef] [PubMed]
  85. Oliver, M.L.; Shapiro, T.M. Black Wealth/White Wealth; Routledge: New York, NY, USA, 1995. [Google Scholar]
  86. Oliver, M.L.; Shapiro, T.M. Black Wealth, White Wealth: A New Perspective on Racial Inequality; Taylor & Francis: New York, NY, USA, 2013. [Google Scholar]
  87. Shapiro, T.; Meschede, T.; Osoro, S. The Roots of the Widening Racial Wealth Gap: Explaining the Black-White Economic Divide; Research and Policy Brief; Institute on Assets and Social Policy, Brandeis University: Waltham, MA, USA, 2013. [Google Scholar]
  88. Pompili, M.; Innamorati, M.; Forte, A.; Longo, L.; Mazzetta, C.; Erbuto, D.; Lamis, D.A. Insomnia as a predictor of high-lethality suicide attempts. Int. J. Clin. Pract. 2013, 67, 1311–1316. [Google Scholar] [CrossRef] [PubMed]
  89. Saunders, E.F.; Fernandez-Mendoza, J.; Kamali, M.; Assari, S.; McInnis, M.G. The effect of poor sleep quality on mood outcome differs between men and women: A longitudinal study of bipolar disorder. J. Affect. Disord. 2015, 180, 90–96. [Google Scholar] [CrossRef] [PubMed][Green Version]
  90. Assari, S.; Sonnega, A.; Pepin, R.; Leggett, A. Residual effects of restless sleep over depressive symptoms on chronic medical conditions: Race by gender differences. J. Racial Ethn. Health Dispar. 2017, 4, 59–69. [Google Scholar] [CrossRef] [PubMed]
Figure 1. (a) Summary of path coefficients in Whites. (b) Summary of path coefficients in Blacks. Self-Rated Health (SRH), Chronic Medical Conditions (CMC), Body Mass Index (BMI).
Figure 1. (a) Summary of path coefficients in Whites. (b) Summary of path coefficients in Blacks. Self-Rated Health (SRH), Chronic Medical Conditions (CMC), Body Mass Index (BMI).
Brainsci 08 00122 g001aBrainsci 08 00122 g001b
Table 1. Descriptive Statistics.
Table 1. Descriptive Statistics.
All n = 3731 Whites n = 3596 Blacks n = 135
Income1 (Personal) *27,511.1527,372.2727,686.5827,563.1322,810.0821,187.33
Income 2 (household) *42,525.0940,669.1442,661.1640,868.1038,423.4234,032.02
Self-Rated Health (SRH)*7.581.507.571.497.841.72
Chronic Medical Conditions (CMC)*2.312.352.312.342.492.66
Body Mass Index (BMI)*26.695.1926.595.0929.356.93
Positive Affect 1 *3.410.713.410.713.550.75
Positive Affect 23.430.703.430.703.550.79
Negative Affect 11.500.581.500.581.550.76
Negative Affect 2 *1.500.571.500.561.650.82
* p < 0.05 for comparison of Blacks and Whites; Independent sample t test.
Table 2. Summarizes the results of bivariate correlations. Education and income were associated with positive and negative affect at baseline and 10 years later in the overall sample. (Table 2).
Table 2. Summarizes the results of bivariate correlations. Education and income were associated with positive and negative affect at baseline and 10 years later in the overall sample. (Table 2).
Race (Black)10.05 **−0.01−0.04 *−0.03 *0.030.03 *0.010.10 **0.040.03 *0.020.05 **
Gender (Female) 1−0.02−0.09 **−0.38 **−0.16 **−0.010.14 **−0.10 **−0.03−0.010.09 **0.09 **
Age 1−0.11 **−0.12 **0.39 **0.03 *0.16 **0.01 **0.11 **0.14 **−0.12 **−0.10 **
Education 10.26 **0.06 **0.05 **−0.11 **−0.10 **0.020.03−0.07 **−0.11 **
Income 1 10.08 **0.09 **−0.17 **0.010.04 *0.07 **−0.11 **−0.15 **
Income 2 10.010.06 **0.04 *0.05 **0.10 **−0.08 **−0.10 **
Self-Rated Health (SRH) 1−0.39 **−0.25 **0.39 **0.32 **−0.34 **−0.27 **
Chronic Medical Conditions (CMC) 10.17 **−0.31 **−0.23 **0.39 **0.28 **
Body Mass Index (BMI) 1−0.05 **−0.06 **0.05 **0.08 **
Positive Affect 1 10.53 **−0.63 **−0.37 **
Positive Affect 2 1−0.38 **−0.61 **
Negative Affect 1 10.50 **
Negative Affect 2 1
Race (Black)10.05 **−0.01−0.04 *−0.03 *0.030.03 *0.010.10 **0.040.03 *0.020.05 **
Gender (Female) 1−0.02−0.09 **−0.38 **−0.16 **−0.010.14 **−0.10 **−0.03−0.010.09 **0.09 **
Age 1−0.11 **−0.12 **0.39 **0.03 *0.16 **0.01 **0.11 **0.14 **−0.12 **−0.10 **
Education 10.26 **0.06 **0.05 **−0.11 **−0.10 **0.020.03−0.07 **−0.11 **
Income 1 10.08 **0.09 **−0.17 **0.010.04 *0.07 **−0.11 **−0.15 **
Income 2 10.010.06 **0.04 *0.05 **0.10 **−0.08 **−0.10 **
Self-Rated Health (SRH) 1−0.39 **−0.25 **0.39 **0.32 **−0.34 **−0.27 **
Chronic Medical Conditions (CMC) 10.17 **−0.31 **−0.23 **0.39 **0.28 **
Body Mass Index (BMI) 1−0.05 **−0.06 **0.05 **0.08 **
Positive Affect 1 10.53 **−0.63 **−0.37 **
Positive Affect 2 1−0.38 **−0.61 **
Negative Affect 1 10.50 **
Negative Affect 2 1
* p < 0.05, ** p < 0.01.
Table 3. Summary of linear regression models in the overall sample and across races.
Table 3. Summary of linear regression models in the overall sample and across races.
Whites Blacks
EducationIncome 20.090.22<0.0010.031.500.765
GenderIncome 2−0.120.22<0.001−0.051.610.615
Income 1Income−
AgeIncome 20.400.01<0.0010.470.06<0.001
Self-Rated Health (SRH)Income 2−
Chronic Medical Conditions (CMC)Income
Body Mass Index (BMI)Income−
Positive Affect 1Positive Affect 20.440.02<0.0010.440.09<0.001
Negative Affect 1Negative Affect 20.390.02<0.0010.370.10<0.001
EducationPositive Affect−
Income 2Positive Affect 20.060.00<0.001−
Income 2Negative Affect 2−−
AgeNegative Affect 2−−
AgePositive Affect 20.070.00<0.0010.020.010.787
GenderNegative Affect
GenderPositive Affect
EducationNegative Affect 2−0.070.02<0.001−
Negative Affect 1Positive Affect 2−−
Body Mass Index (BMI)Positive Affect−
Body Mass Index (BMI)Negative Affect
Chronic Medical Conditions (CMC)Positive Affect 2−0.060.01<0.0010.180.020.016
Chronic Medical Conditions (CMC)Negative Affect 20.100.00<0.001−
Self-Rated Health (SRH)Positive Affect 20.100.01<0.0010.320.04<0.001
Self-Rated Health (SRH)Negative Affect 2−0.070.01<0.001−
Positive Affect 1Negative Affect 2−0.070.02<0.0010.020.100.785
Our SEM showed very good fit. CMIN = 24.465; DF = 8; p = 0.002; CMIN/DF = 3.058; CFI = 0.998; RMSEA = 0.023 (90% CI = 0.013–0.034).

Share and Cite

MDPI and ACS Style

Assari, S.; Preiser, B.; Kelly, M. Education and Income Predict Future Emotional Well-Being of Whites but Not Blacks: A Ten-Year Cohort. Brain Sci. 2018, 8, 122.

AMA Style

Assari S, Preiser B, Kelly M. Education and Income Predict Future Emotional Well-Being of Whites but Not Blacks: A Ten-Year Cohort. Brain Sciences. 2018; 8(7):122.

Chicago/Turabian Style

Assari, Shervin, Brianna Preiser, and Marisa Kelly. 2018. "Education and Income Predict Future Emotional Well-Being of Whites but Not Blacks: A Ten-Year Cohort" Brain Sciences 8, no. 7: 122.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop