Racial Disparities in Occupational Distribution Among Black and White Adults with Similar Educational Levels: Analysis of Middle-Aged and Older Individuals in the Health and Retirement Study

Shervin Assari1,2,3,4*, Hossein Zare5,6, Amanda Sonnega7

1Department of Urban Public Health, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA.

2Department of Family Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA.

3Department of Internal Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA.

4Marginalization-Related-Diminished Returns (MDRs) Center, Los Angeles, CA, USA.

5Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.

6School of Business, University of Maryland Global Campus (UMGC), Adelphi, MD, 20774, USA.

7Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.


Background: Occupational classes play a significant role in influencing both individual and population health, serving as a vital conduit through which higher education can lead to better health outcomes. However, the pathway from education to corresponding occupational classes does not apply uniformly across different racial and ethnic groups, hindered by factors such as social stratification, labor market discrimination, and job segregation.

Aims: This study seeks to investigate the relationship between educational attainment and occupational classes among Black, Latino, and White middle-aged and older adults, with a focus on their transition into retirement.

Methods: Using cross-sectional data from the Health and Retirement Study (HRS), this research examines the impact of race/ethnicity, educational attainment, occupational classes, and timing of retirement among middle-aged and older adults. The analysis includes a sample of 7,096 individuals identified as White, Black, or Latino. Through logistic regression, we assess the additive and multiplicative effects of race/ethnicity and education on six defined occupational classes: 1. Managerial and specialty operations, 2. Professional Specialty, 3. Sales, 4. Clerical/administrative support, 5. Services, and 6. Manual labor.

Results: Participants were Black (n = 1,143) or White (n =5,953). This included Latino (N =459) or non-Latino (n = 6,634). Our analysis reveals a skewed distribution of Black and Latino adults in manual and service occupations, in stark contrast to White adults who were more commonly found in clerical/administrative and managerial positions. Educational attainment did not equate to similar occupational outcomes across racial groups. Key findings include: Firstly, Black individuals with a college degree or higher were less likely to occupy clerical and administrative positions compared to their White counterparts. Secondly, holding a General Educational Development (GED) credential or some college education was generally linked to reduced likelihood of being in managerial roles; however, this inverse relationship was less evident among Black middle-aged and older adults than White ones. Thirdly, having a GED reduced the chances of working in sales roles, while having a college degree increased such chances. An interaction between race and some college education revealed that the impact of some college education on sales roles was more significant for Black adults than for White ones. We did not observe any interaction between ethnicity (Latino) and educational attainment on occupational classes. Given the stability of occupational classes, these findings could also apply to the last occupation held prior to retirement.

Conclusion: This study highlights significant racial disparities in occupational classes among individuals with comparable levels of education, underscoring the profound implications for health and wellbeing disparities. Future research should explore strategies to alleviate labor market discrimination and job segregation as ways to close these occupational gaps. Additionally, the influence of social stratification, job segregation, and historical legacies, such as the repercussions of the Jim Crow era, on these disparities merits further investigation. Addressing these issues is crucial for enhancing the health and wellbeing of all populations.


Background

Occupation stands as a pivotal social determinant of health1, exerting a profound influence on individuals' well-being and a wide range of health outcomes2. Occupation serves as a mechanism through which education safeguards individual health3. Individuals with higher education often find employment in high-paying, low-stress occupations, making occupation a conduit through which the benefits of education extend to health outcomes4. Higher educational attainment typically opens doors to occupations characterized by greater job control, financial stability, and access to health-promoting resources5-7. Professionals with advanced education levels are more likely to secure jobs offering comprehensive healthcare benefits, safer working conditions, and opportunities for career growth8. The nature of one's occupation not only determines exposure to various physical and psychosocial stressors9 but also significantly influences access to resources, income, benefits, and health insurance, collectively influencing the ability to maintain good health10. Thus, understanding the intricate relationship between occupation, education, and health is paramount for developing targeted interventions that address health disparities and promote equitable access to optimal health outcomes across diverse populations11.

Significant and persistent racial disparities in occupational opportunities are deeply entrenched in US society, with racial and ethnic minority individuals consistently finding themselves consigned to occupations characterized by challenging conditions and limited upward mobility.12, 13 This phenomenon is particularly pronounced among Black and Latino populations, who, despite significant investments in the fight against racial discrimination, such as anti-discriminatory laws, continue to face disproportionately adverse circumstances in the US labor market12, 14, 15. Fifty years after Martin Luther King Jr.'s “I have a dream” speech16, a web of factors still contributes to the persistence of racialized occupations in the United States17, due to the interplay of labor market discrimination, residential segregation, social stratification, and job segregation18. The result is known to be differential effects of education on income and financial wellbeing by race and ethnicity19.

Centuries after the abolition of slavery, legal segregation continues20-24. Jim Crow and segregation continued to differentially provide opportunities for racial and ethnic groups25-29. Still, centuries later, the United States grapples with the legacy of systemic racism, and its effects persist in structural and institutional racism in the labor market30-34. These aspects of racism alter employment benefits for racial and ethnic groups35. Black and Latino individuals often find themselves clustered in occupations that offer lower wages and simultaneously subject them to harsher working conditions36-38. The origins of this phenomenon can be traced back to the legacy of slavery and the subsequent Jim Crow era, where discriminatory practices were deeply embedded in the fabric of society25, 26, 28. While significant strides have been made in dismantling overtly discriminatory policies, subtle yet pervasive barriers persist, perpetuating the unequal distribution of opportunities across racial lines29.

Key contributors to racialized occupations are the enduring issues of residential and job segregation39-42. Despite enforcing anti-discriminatory laws, the legacy of Jim Crow and redlining has shaped the value of the housing market and has remained in a United States that is largely divided along racial lines43. This spatial separation has profound implications for access to employment opportunities44-46. Such segregation creates an environment where certain communities have access to subpar jobs, setting the stage for living in a disadvantaged life for Black and Latino people despite having education and employment47, 48.

The phenomenon of social stratification, which has continued for centuries in the US, still amplifies the disparities in the outcomes of being in the job market for racial and ethnic groups, even when they have the same educational credentials49-51. Societal structures also contribute to the perpetuation of stereotypes and prejudiced beliefs that, in turn, influence hiring decisions and career advancement opportunities52-55. The impact of these biases is particularly maximum in sectors where traditionally White individuals have been in power, creating a challenging environment for Black and Latino individuals to break through the glass ceiling4, 56.

Job segregation, a well-described phenomenon in the US, has resulted in the unequal concentration of specific occupations for certain racial or ethnic groups12, 13. This segregation results in low-paying jobs being available for Black and Latino individuals and limited job benefits and career advancement opportunities for them12, 13. Due to such segregation, Black and Latino individuals frequently find themselves confined to occupation sectors that offer limited upward mobility and financial stability, perpetuating a cycle of inequality57-59.

Aims

The primary aim of this study is to explore the connection between educational attainment and occupational classifications among middle-aged and older adults from various racial and ethnic backgrounds, drawing on data from the Health and Retirement Study (HRS)60-64. Specifically, we aim to examine how educational attainment differently influences occupational outcomes across racial and ethnic groups. We hypothesize that the benefits of education on occupational status are less pronounced for Black and Latino individuals when compared to their non-Latino White peers. By analyzing longitudinal data from the HRS, which provides detailed accounts of middle-aged adults moving into retirement, we seek to uncover trends that shed light on how racialized job markets affect the advantages usually linked with higher education. Our findings could offer further understanding of the intricate dynamics between race, ethnicity, education, and occupational class, both generally and in the context of retirement. This investigation is intended to lay the groundwork for policy suggestions aimed at mitigating racial and ethnic inequities in the workforce.

Methods

Design and Setting

Data were obtained from the first 15 waves of the Health and Retirement Study (HRS)61 conducted from 1992 to 2020. We used the RAND HRS data 202065 that were publicly released in March 2023. The HRS is a state-of-the-art longitudinal study of retirement transitions in the United States, with biannual repeated measurements. The study recruited and followed a nationally representative sample of middle-aged and older adults (aged 50–59 years at baseline). The HRS study collected extensive data on various aspects of participants, including demographic, socioeconomic, social, psychological, economic, employment, and health data, as well as health behaviors and health service utilization. HRS data has also measured a wide range of data related to retirement including time of retirement64. Data was collected through telephone or face-to-face interviews, and proxy interviews were used for participants who were unavailable. Detailed information on the HRS design, measures, sample, and sampling can be found elsewhere, and a brief overview is provided here.

Sample and Sampling

The HRS used a national area probability sample to recruit participants aged 50 to 59 at baseline. For the current analysis, only the core (primary) sample recruited in 1992 was included to offer the longest follow-up period. All our HRS participants were born between 1931 and 1941, and the sample reflects all middle-aged and older adults aged 50–59 residing in US households in the year 1992 (baseline = wave 1).

Inclusion & Exclusion (Analytical Sample)

The analytical sample for this study comprised HRS participants who identified as non-Latino White, Latino White, or Black, excluding individuals from other racial groups from the analysis. Eligibility for the analysis extended to all participants from the HRS core sample who were not retired at the start of the study, without consideration of follow-up duration, mortality timing, or retirement status. Participants were aged between 51 and 61 years at the initial recruitment in 1992, resulting in a sample of 7,096 working middle-aged and older adults. Although the HRS collected data from both participants and their partners or spouses, this study solely utilized data from the participants.

Measures

Predictors

Educational attainment. We used a 5-level categorical variable: (a) less than high school graduate, (b) high-school graduate, (c) General Educational Development (GED) (d) some college, and (e) college graduate or more. Educational attainment was self-reported at baseline in 1992.

Outcomes

Occupational classes. Using Census 1980, the HRS has generated 17 occupational classes that are as follows: 01. managerial specialty operators, 02. professional specialty operations/technical support, 03. sales, 04. clerical/administrative support, 05. service: private household/cleaning/building svc, 06. service: protection, 07. service: food preparation, 08. health services, 09. personal services, 10. farming/forestry/fishing, 11. mechanics/repair, 12. Construction trade/extractors, 13. precision production, 14. operators: machine, 15. operators: transport, etc., 16. operators: handlers, etc., and 17. member of the armed forces. We reduced these classes to the six following groups: 1. managerial and specialty operations, 2. professional specialty, 3. sales, 4. clerical/admin supp, 5. services, and 6. manual, as shown in Box 1.

Box 1: Six occupational classes used as outcomes in this study

 

 

New Occupational Class Used in this Analysis

 

 

 

Managerial

and specialty

 operations

Professional Specialty

Sales

Clerical/ admin supp

Services

Manual

All

 

 

 

 

 

Original Occupational Class with 17 Categories Based on 1980 Census

01. managerial specialty operators

1,079

0

0

0

0

0

1,079

02. professional specialty operations/technical support

0

1,087

0

0

0

0

1,087

03. sales

0

0

719

0

0

0

719

04. clerical/administrative support

0

0

0

1,067

0

0

1,067

05. service: private household/cleaning/building svc

0

0

0

0

106

0

106

06. service: protection

0

0

0

0

126

0

126

07. service: food preparation

0

0

0

0

207

0

207

08. health services

0

0

0

0

159

0

159

09. personal services

0

0

0

0

455

0

455

10. farming/forestry/fishing

0

0

0

0

0

235

235

11. mechanics/repair

0

0

0

0

0

284

284

12. Construction trade/extractors

0

0

0

0

0

268

268

13. precision production

0

0

0

0

0

252

252

14. operators: machine

0

0

0

0

0

470

470

15. operators: transport, etc

0

0

0

0

0

396

396

16. operators: handlers, etc.,

0

0

0

0

0

186

186

17. member of the armed forces

NA

NA

NA

NA

NA

NA

NA

 

All

1,079

1,087

719

1,067

1,053

2,091

7,096

Retirement Time (Time of Transition to Retirement). In this study, we determined the transition to retirement using the variable retirement status measured at each wave. Participants were asked to indicate their retirement status as not retired, completely retired, or partly retired66, 67. By comparing the retirement status across waves, we calculated the year of transition to retirement for those who transitioned from being employed to being retired. This variable was utilized in a sensitivity analysis concerning the final occupation held before retirement.

Controls

Age was measured in years (continuous variable). Gender was treated as a dichotomous variable.

Data Analysis

Data were analyzed using SPSS 25.0 (IBM Corporation, Armonk, NY, US). Univariate analyses included reporting means (standard deviation [SD]) and frequencies/relative frequencies (n and %). Racial and ethnic groups were compared using chi-square or Analysis of Variance (ANOVA). We also used Pearson correlation to investigate the association between all study variables. Multivariable models involved logistic regression analysis with educational attainment as the predictor variable, occupational class as the outcome, and race and ethnicity as moderators, while controlling for factors gender as a confounder. Models were tested without and with interaction terms. Model 1 did not include education x race or ethnicity interaction terms. Model 2 included such interactions to assess the significance of racial and ethnic differences in the relationships between educational attainment and occupational classes. Several models were examined, one for each occupational class. Given the stability of occupational classes over the follow up period, our sensitivity analysis showed similar findings for the last occupation held prior to retirement.

Ethics statement

The HRS study protocol was approved by the University of Michigan Institutional Review Board. All HRS participants signed written consent. The data were collected, restored, managed, and analyzed in a fully anonymous fashion. As we used fully de-identified publicly available data, this study was non-human subject research, according to the National Institute of Health (NIH) definition.

Results

As shown by Table 1, 7,096 individuals entered our analysis from which 55.0% were male and 45% were female. From this number, 16.1% (n = 1,143) were Black and 83.9% (n = 5,953) were White. Also, 6.5% (n =459) were Latino and 93.5% (n = 6,634) were non-Latino. Using the Census 1980 variable, the highest frequency of occupation classes was managerial specialty operator (n = 1079; 15.2%) and professional specialty operator/technical support (n= 1087; 15.3%). This table also shows that Black and Latino middle-age and older adults are more represented in operator and service occupational classes and White middle-age and older adults are more represented in clerical admin and managerial occupational classes.

Table 1: Demographic characteristics of our participants overall and by race and ethnicity (n = 7,096)

 

All

 

White

n =5,953

 

Black

n =1,143

 

Non-Latino

n =6,634

 

Latino

n =459

 

 

n

%

N

%

n

%

n

%

N

%

Sex

 

 

 

 

 

 

 

 

 

 

 Female

3,196

45.0

2,572

43.2

624

54.6

3,003

45.3

192

41.8

 Male

3,900

55.0

3,381

56.8

519

45.4

3,631

54.7

267

58.2

Race

 

 

 

 

 

 

 

 

 

 

 White

5,953

83.9

4251

71.4

754

66.0

5502

82.9

448

97.6

 Black

1,143

16.1

1702

28.6

389

34.0

1132

17.1

11

2.4

Latino

 

 

 

 

 

 

 

 

 

 

 No

6,634

93.5

5502

92.4

1132

99.0

 

 

 

 

 Yes

459

6.5

448

7.5

11

1.0

 

 

 

 

Education

 

 

 

 

 

 

 

 

 

 

 1.Less than high-school

1,515

21.4

1088

18.3

427

37.4

1268

19.1

244

53.2

 2.General Educational Development (GED)

362

5.1

315

5.3

47

4.1

339

5.1

23

5.0

 3.High-school graduate

2,353

33.2

2030

34.1

323

28.3

2264

34.1

89

19.4

 4.Some college

1,440

20.3

1238

20.8

202

17.7

1369

20.6

71

15.5

 5.College and above

1,426

20.1

1282

21.5

144

12.6

1394

21.0

32

7.0

Census Occupational Classes

 

 

 

 

 

 

 

 

 

 

 01.Managerial specialty operator

1,079

15.2

995

16.7

84

7.3

1042

15.7

37

8.1

 02.Prof specialty operator/tech sup

1,087

15.3

947

15.9

140

12.2

1057

15.9

30

6.5

 03.Sales

719

10.1

669

11.2

50

4.4

680

10.3

38

8.3

 04.Clerical/admin supp

1,067

15.0

946

15.9

121

10.6

1014

15.3

53

11.5

 05.Services:prv/clean/building svc

106

1.5

46

.8

60

5.2

93

1.4

13

2.8

 06. Services protection

126

1.8

94

1.6

32

2.8

122

1.8

4

.9

 07. Services food prep

207

2.9

157

2.6

50

4.4

187

2.8

19

4.1

 08.Health Services

159

2.2

83

1.4

76

6.6

149

2.2

10

2.2

 09.Personal Services

455

6.4

314

5.3

141

12.3

406

6.1

49

10.7

 10.Farming/forestry/fishing

235

3.3

197

3.3

38

3.3

205

3.1

29

6.3

 11.Mechanics/repair

284

4.0

256

4.3

28

2.4

266

4.0

18

3.9

 12.Constr trade/extractors

268

3.8

225

3.8

43

3.8

248

3.7

20

4.4

 13.Precision production

252

3.6

218

3.7

34

3.0

230

3.5

22

4.8

 14.Operators: machine

470

6.6

360

6.0

110

9.6

411

6.2

59

12.9

 15.Operators: transport, etc

396

5.6

317

5.3

79

6.9

358

5.4

38

8.3

 16.Operators: handlers, etc

186

2.6

129

2.2

57

5.0

166

2.5

20

4.4

Managerial

 

 

 

 

 

 

 

 

 

 

 No

6017

84.8

4958

83.3

1059

92.7

5592

84.3

422

91.9

 Yes

1079

15.2

995

16.7

84

7.3

1042

15.7

37

8.1

Occupation Professional Specialty

 

 

 

 

 

 

 

 

 

 

 No

6009

84.7

5006

84.1

1003

87.8

5577

84.1

429

93.5

 Yes

1087

15.3

947

15.9

140

12.2

1057

15.9

30

6.5

Sales

 

 

 

 

 

 

 

 

 

 

 No

6377

89.9

5284

88.8

1093

95.6

5954

89.7

421

91.7

 Yes

719

10.1

669

11.2

50

4.4

680

10.3

38

8.3

Clerical Admin

 

 

 

 

 

 

 

 

 

 

 No

6029

85.0

5007

84.1

1022

89.4

5620

84.7

406

88.5

 Yes

1067

15.0

946

15.9

121

10.6

1014

15.3

53

11.5

Service

 

 

 

 

 

 

 

 

 

 

 No

6043

85.2

5259

88.3

784

68.6

5677

85.6

364

79.3

 Yes

1053

14.8

694

11.7

359

31.4

957

14.4

95

20.7

Operator

 

 

 

 

 

 

 

 

 

 

 No

5005

70.5

4251

71.4

754

66.0

4750

71.6

253

55.1

 Yes

2091

29.5

1702

28.6

389

34.0

1884

28.4

206

44.9

Table 2 shows that higher education was associated with lower odds of working in managerial occupational class, however, a significant interaction between Some college x Black suggested that the inverse association between some college and working in managerial occupational class is weaker for Black than White middle-age and older adults

Table 2: Logistic regression between educational attainment and managerial occupational class overall and by race/ethnicity

Model 1

 

OR

95% CI

p

Age Baseline

 

.994

.977

1.011

.507

Male

 

1.793

1.555

2.068

.000

Black

 

1.976

1.555

2.509

.000

Latino

 

.636

.446

.907

.013

Education (Ref = Some High School)

 

 

 

 

 

 2. General Educational Development (GED)

 

.175

.135

.228

.000

 3.High-school graduate

 

.330

.232

.471

.000

 4.Some college

 

.390

.328

.464

.000

 5.College and above

 

.731

.613

.872

.001

 

 

 

 

 

 

Model 2

 

 

 

 

 

Age Baseline

 

.994

.977

1.011

.472

Male

 

1.808

1.567

2.085

.000

Black

 

1.346

.876

2.068

.175

Latino

 

.879

.389

1.986

.756

Education (Ref = Some High School)

 

 

 

 

.000

 2.GED

 

.078

.036

.170

.000

 3.High-school graduate

 

.186

.043

.817

.026

 4.Some college

 

.173

.088

.341

.000

 5.College and above

 

.674

.382

1.187

.171

Education x Race

 

 

 

 

.036

 2.GED x Black

 

2.534

1.105

5.813

.028

 3.High-school graduate x Black

 

1.848

.403

8.484

.430

 4.Some college x Black

 

2.424

1.205

4.880

.013

 5.College and above x Black

 

1.099

.606

1.995

.755

Education x Ethnicity

 

 

 

 

.905

 2.GED x Latino

 

.708

.248

2.022

.519

 3.High-school graduate x Latino

 

.822

.149

4.524

.822

 4.Some college x Latino

 

.620

.200

1.924

.408

 5.College and above x Latino

 

.602

.203

1.782

.360

Table 3 shows that higher education was associated with lower odds of working in professional specialty class.

Table 3: Logistic regression between educational attainment and professional specialty class overall and by race/ethnicity

Model 1

 

OR

95% CI

p

Age Baseline

 

1.003

.984

1.023

.746

Male

 

.539

.461

.630

<.001

Black

 

.962

.767

1.206

.735

Latino

 

.752

.492

1.151

.189

Education (Ref = Some High School)

 

 

 

 

 

 2.GED

 

.018

.012

.027

<.001

 3.High-school graduate

 

.031

.017

.056

<.001

 4.Some college

 

.041

.033

.052

<.001

 5.College and above

 

.197

.166

.236

<.001

Model 2

 

 

 

 

 

Age Baseline

 

1.003

.984

1.023

.741

Male

 

.542

.464

.635

<.001

Black

 

.743

.521

1.061

.102

Latino

 

1.186

.583

2.416

.638

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

.007

.002

.019

<.001

 3.High-school graduate

 

.000

.000

.

.997

 4.Some college

 

.033

.018

.061

<.001

 5.College and above

 

.150

.092

.246

<.001

Education x Race

 

 

 

 

.186

 2.GED x Black

 

3.936

1.273

12.175

.017

 3.High-school graduate x Black

 

NA

 

 

 

 4.Some college x Black

 

1.285

.660

2.501

.461

 5.College and above x Black

 

1.382

.814

2.345

.231

Education x Ethnicity

 

 

 

 

.424

 2.GED x Latino

 

.132

.016

1.114

.063

 3.High-school graduate x Latino

 

.000

.000

.

.998

 4.Some college x Latino

 

.679

.172

2.678

.581

 5.College and above x Latino

 

.581

.211

1.595

.292

Table 4 shows that GED reduced and college and above increased the odds of working in sales occupational class. Some college x Black was significant suggesting that the association between Some college and working in sales occupational class was stronger for Black than White middle-age and older adults.

Table 4: Logistic regression between educational attainment and sales occupational class overall and by race/ethnicity

Model 1

 

OR

95% CI

p

Age Baseline

 

1.034

1.014

1.054

.001

Male

 

1.108

.943

1.300

.213

Black

 

2.530

1.876

3.411

<.001

Latino

 

.852

.599

1.212

.374

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

.683

.517

.902

.007

 3.High-school graduate

 

.732

.477

1.125

.155

 4.Some college

 

1.197

.964

1.486

.103

 5.College and above

 

1.385

1.097

1.749

.006

Model 2

 

 

 

 

 

Age Baseline

 

1.034

1.014

1.054

.001

Male

 

1.116

.950

1.311

.181

Black

 

1.209

.650

2.248

.548

Latino

 

1.275

.440

3.699

.655

Education (Ref = Some High School)

 

 

 

 

.010

 2.GED

 

.335

.149

.753

.008

 3.High-school graduate

 

.243

.031

1.922

.180

 4.Some college

 

.320

.131

.777

.012

 5.College and above

 

.884

.401

1.952

.761

Education x Race

 

 

 

 

.040

 2.GED x Black

 

2.248

.946

5.343

.067

 3.High-school graduate x Black

 

3.199

.385

26.588

.282

 4.Some college x Black

 

4.118

1.647

10.300

.002

 5.College and above x Black

 

1.615

.705

3.701

.257

Education x Ethnicity

 

 

 

 

.873

 2.GED x Latino

 

.554

.164

1.874

.342

 3.High-school graduate x Latino

 

.875

.138

5.556

.888

 4.Some college x Latino

 

.623

.174

2.232

.467

 5.College and above x Latino

 

.765

.212

2.766

.683

As shown by Table 5, there was a positive association between educational attainment and clerical and admin occupational class, meaning that highly educated people were more likely to work in clerical and admin occupational class. However, a statistical interaction between educational level of college and above x Black suggested that the effect of college and above on clerical and admin occupational class was weaker for Black than White middle-age and older adults. No interaction was found for Latino ethnicity.

Table 5: Logistic regression between educational attainment and clerical and admin occupational class overall and by race/ethnicity

Model 1

 

OR

95% CI

p

Age Baseline

 

1.000

.980

1.020

.997

Male

 

.139

.117

.164

<.001

Black

 

1.784

1.438

2.215

<.001

Latino

 

.971

.700

1.345

.858

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

.759

.547

1.053

.099

 3.High-school graduate

 

2.105

1.420

3.121

<.001

 4.Some college

 

4.018

3.158

5.114

<.001

 5.College and above

 

3.685

2.850

4.765

<.001

 

 

 

 

 

 

Model 2

 

 

 

 

 

Age Baseline

 

1.000

.980

1.020

.986

Male

 

.139

.117

.164

<.001

Black

 

3.198

1.265

8.080

.014

Latino

 

.855

.194

3.770

.836

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

.987

.339

2.875

.981

 3.High-school graduate

 

5.158

1.570

16.947

.007

 4.Some college

 

5.837

2.256

15.105

<.001

 5.College and above

 

9.463

3.606

24.834

<.001

Education x Race

 

 

 

 

.021

 2.GED x Black

 

.846

.274

2.613

.771

 3.High-school graduate x Black

 

.350

.099

1.247

.105

 4.Some college x Black

 

.671

.251

1.793

.426

 5.College and above x Black

 

.343

.126

.936

.037

Education x Ethnicity

 

 

 

 

.662

 2.GED x Latino

 

.755

.146

3.897

.737

 3.High-school graduate x Latino

 

1.735

.256

11.753

.572

 4.Some college x Latino

 

1.139

.234

5.539

.872

 5.College and above x Latino

 

1.444

.291

7.177

.653

As shown by Table 6, there was a positive association between educational attainment and service occupational class, meaning that highly educated people were more likely to work in service occupational class. No interaction was found for Latino ethnicity or Black race.

Table 6: Logistic regression between educational attainment and service occupational class overall and by race/ethnicity

Model 1

 

OR

95% CI

p

Age Baseline

 

1.024

1.005

1.044

.013

Male

 

.270

.232

.315

<.001

Black

 

.345

.293

.406

<.001

Latino

 

1.441

1.110

1.872

.006

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

12.698

8.873

18.171

<.001

 3.High-school graduate

 

9.391

6.082

14.500

<.001

 4.Some college

 

7.062

4.961

10.053

<.001

 5.College and above

 

3.498

2.388

5.123

<.001

 

 

 

 

 

 

Model 2

 

 

 

 

 

Age Baseline

 

1.025

1.006

1.045

.010

Male

 

.268

.230

.312

<.001

Black

 

.623

.266

1.458

.275

Latino

 

.000

.000

.

.998

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

19.117

8.658

42.208

<.001

 3.High-school graduate

 

16.816

6.381

44.314

<.001

 4.Some college

 

12.571

5.644

28.000

<.001

 5.College and above

 

4.099

1.744

9.633

.001

Education x Race

 

 

 

 

.168

 2.GED x Black

 

.568

.233

1.384

.213

 3.High-school graduate x Black

 

.502

.170

1.488

.214

 4.Some college x Black

 

.447

.183

1.091

.077

 5.College and above x Black

 

.765

.294

1.990

.583

Education x Ethnicity

 

 

 

 

.289

 2.GED x Latino

 

NA

 

 

 

 3.High-school graduate x Latino

 

NA

 

 

 

 4.Some college x Latino

 

NA

 

 

 

 5.College and above x Latino

 

NA

 

 

 

As shown by Table 7, there was a positive association between educational attainment and operator occupational class, meaning that highly educated people were more likely to work in operator occupational class. No interaction was found for Latino ethnicity or Black race.

Table 7: Logistic regression between educational attainment and operator occupational class overall and by race/ethnicity

 

 

OR

95% CI

P

Model 1

 

 

 

 

Age Baseline

 

.966

.951

.981

<.001

Male

 

8.592

7.481

9.869

<.001

Black

 

.854

.722

1.011

.066

Latino

 

1.252

.989

1.585

.061

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

38.936

29.241

51.847

<.001

 3.High-school graduate

 

27.326

19.338

38.613

<.001

 4.Some college

 

15.675

11.931

20.592

<.001

 5.College and above

 

6.461

4.838

8.629

<.001

 

 

 

 

 

 

Model 2

 

 

 

 

 

Age Baseline

 

.966

.951

.981

<.001

Male

 

8.625

7.507

9.909

<.001

Black

 

.723

.301

1.735

.468

Latino

 

.718

.095

5.435

.748

Education (Ref = Some High School)

 

 

 

 

<.001

 2.GED

 

26.400

11.153

62.490

<.001

 3.High-school graduate

 

17.683

5.940

52.640

<.001

 4.Some college

 

17.289

7.223

41.382

<.001

 5.College and above

 

6.631

2.668

16.477

<.001

Education x Race

 

 

 

 

.017

 2.GED x Black

 

1.643

.659

4.099

.287

 3.High-school graduate x Black

 

1.545

.489

4.883

.459

 4.Some college x Black

 

.881

.352

2.206

.786

 5.College and above x Black

 

.945

.361

2.469

.908

Education x Race

 

 

 

 

.697

 2.GED x Latino

 

1.631

.210

12.674

.640

 3.High-school graduate x Latino

 

3.129

.327

29.906

.322

 4.Some college x Latino

 

1.449

.180

11.644

.727

 5.College and above x Latino

 

1.852

.224

15.325

.568

Discussion

The findings from our analysis of nationally representative data from the Health and Retirement Study (HRS) provide compelling evidence of pervasive racialized effects of educational attainment on occupational classes in the United States. Contrary to the expectation that higher education universally improves occupational outcomes, our study reveals distinct racial disparities, shedding light on the enduring impact of social stratification, racism in the labor market, and historical legacies such as the Jim Crow era on occupational classes of highly educated Black individuals. Given the stability of occupational classes, similar racial variation in the effects of educational attainment applied to the last occupation held prior to retirement.

Operator and service occupational classes saw an overrepresentation of Black and Latino middle-aged and older adults, whereas clerical/administrative and managerial occupational classes were more prevalent among their White counterparts. Our analysis brought to light significant racialized effects of educational attainment on occupational distribution. Despite achieving similar education levels, Black and Latino individuals were placed in distinct occupational classes compared to White individuals. To illustrate, a higher level of education was linked to lower odds of working in managerial occupational classes. Nevertheless, a noteworthy interaction between individuals with some college education and Black ethnicity indicated that the inverse correlation between some college education and employment in managerial occupational classes was less pronounced for Black middle-aged and older adults than their White counterparts. Furthermore, holding a GED decreased the likelihood of working in sales occupational classes, while possessing a college degree or above increased these odds. A significant interaction between individuals with some college education and Black ethnicity suggested a stronger association between some college education and employment in sales occupational classes for Black middle-aged and older adults compared to their White counterparts. Additionally, a statistical interaction between individuals with a college degree or above and Black ethnicity hinted at a weaker effect of having a college degree or above on clerical and administrative occupational classes for Black middle-aged and older adults in comparison to their White counterparts.

The observed disparities underscore a troubling pattern wherein educational attainment does not uniformly translate into improved occupational status for all racial and ethnic groups[68-70]. Black and Latino individuals, despite achieving comparable levels of education to their White counterparts, experience a distinct lack of upward mobility in occupational classes71. This phenomenon is indicative of deeply rooted systemic issues, including job segregation and discriminatory practices within the labor market72. The historical context of Jim Crow, with its entrenched racial biases, continues to cast a long shadow over contemporary employment dynamics, perpetuating an environment where racial and ethnic minorities face unique and persistent barriers to occupational advancement26, 28, 29.

Social stratification might be the root cause of our findings49-51. The stratified nature of US society means differential access to the same jobs across racial and ethnic groups who have attained the same educational attainment19. These differential access to opportunity structures are usually added to the ingrained biases and discriminatory practices of the labor market and those who have the hiring decision. All these contribute to the perpetuation of occupational disparities to the disadvantage of highly educated Black and Latino individuals49-51. The lingering effects would be seen as differential returns of educational attainment by race/ethnicity68-70.

The implications of our study extend beyond the immediate occupational sphere, resonating with broader US societal issues73-75. The observed racialized effects on educational attainment and occupational classes have far-reaching consequences for wealth accumulation, retirement planning, and overall well-being, particularly among middle-aged and older adults19, 72, 76, 77. Addressing these disparities requires a multifaceted approach that considers historical context, institutional reform, and targeted policies aimed at dismantling systemic inequities78.

Surprisingly, our study did not uncover any ethnic variations in how educational attainment influences occupational classes. This finding is particularly noteworthy considering the prevalent belief that labor market discrimination disproportionately impacts Black individuals more than Latino individuals. Therefore, while factors such as segregation undoubtedly contribute to these dynamics, it suggests that additional obstacles may hinder the employability of highly educated Black individuals into high-paying, low-stress jobs. Nonetheless, our bivariate analysis did reveal significant main effects of ethnicity on occupational classes, which can primarily be attributed to differences in educational levels. This observation underscores the complex interplay between education, ethnicity, and occupational outcomes, highlighting the need for further exploration into the multifaceted barriers that contribute to disparities in the labor market.

Future research in this domain should delve deeper into the underlying mechanisms perpetuating racialized effects on educational attainment and occupational classes. Exploring the role of intersectionality79, considering factors such as gender, age, and geographic location, will provide a more nuanced understanding of the complexities at play. Longitudinal studies that follow individuals from diverse racial and ethnic backgrounds over extended periods can help uncover dynamic patterns and identify critical points for intervention. Additionally, investigations into the impact of specific policies and interventions on mitigating occupational disparities should be a priority80. A comprehensive examination of the evolving landscape of the labor market and its response to changing societal norms will contribute to the development of targeted strategies aimed at dismantling systemic barriers and fostering greater equity. Lastly, expanding the scope of research to include the perspectives and experiences of individuals within non-traditional and emerging occupational sectors will provide a more comprehensive understanding of the contemporary challenges faced by different racial and ethnic groups in the workforce.

Limitations

Despite the insights gained from our study, several limitations warrant consideration. First, while the HRS is a nationally representative dataset, we only included Black, White, and Latino individuals. As such, the generalizability of our findings may be limited by these factors. The dataset's reliance on self-reported measures of educational attainment and occupational classes may also introduce the possibility of recall bias and misclassification. This study overlooked the experiences of other minority populations such as Native American individuals. The study did not have data on segregation or discrimination. Finally, multiple factors may influence occupational choices such as preferences and culture. Thus, we invite readers to take caution in attributing the observed disparities solely to racialized effects, warranting further exploration of omitted variables. Acknowledging these limitations, our study makes a unique contribution to the existing knowledge on the intricate dynamic links between race/ethnicity, education, and occupation in the United States.

Conclusion

In conclusion, our study adds to the growing body of evidence highlighting racialized occupations in the United States that go beyond education levels. The differential returns of education on occupation classes among Black, Latino, and White individuals may be due to social stratification, job segregation, and discriminatory practices within the labor market. The US has introduced legislations to confront these issues head-on; however, we still see racial and ethnic disparities in the occupation of Black and Latino elites in the US when compared to their White counterparts.

Funding: The research reported herein was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement and Disability Research Consortium through the Michigan Retirement and Disability Research Center Award RDR23000008. The opinions and conclusions expressed are solely those of the author(s) and do not represent the opinions or policy of SSA or any agency of the Federal Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply endorsement, recommendation or favoring by the United States Government or any agency thereof. Part of Hossein Zare effort comes from the NIMHD U54MD000214.

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Article Info

Article Notes

  • Published on: March 12, 2024

Keywords

  • Racial Disparities
  • Health and Retirement Study (HRS)
  • Occupational classes

*Correspondence:

Dr. Shervin Assari,
Department of Urban Public Health, Charles R. Drew University of Medicine, Los Angeles, California, United States;
Email: assari@umich.edu

Copyright: ©2023 Assari S. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.