Quality of Life Research

, Volume 18, Issue 2, pp 179–189

The relative contributions of race/ethnicity, socioeconomic status, health, and social relationships to life satisfaction in the United States

Authors

    • Department of PsychologyNorthern Arizona University
  • Carrie J. Donoho
    • Department of PsychologyNorthern Arizona University
  • Heidi A. Wayment
    • Department of PsychologyNorthern Arizona University
Article

DOI: 10.1007/s11136-008-9426-2

Cite this article as:
Barger, S.D., Donoho, C.J. & Wayment, H.A. Qual Life Res (2009) 18: 179. doi:10.1007/s11136-008-9426-2

Abstract

Purpose

To evaluate racial/ethnic disparities in life satisfaction and the relative contributions of socioeconomic status (SES; education, income, employment status, wealth), health, and social relationships (social ties, emotional support) to well-being within and across racial/ethnic groups.

Methods

In two cross-sectional, representative samples of U.S. adults (the 2001 National Health Interview Survey and the 2007 Behavioral Risk Factor Surveillance System; combined n > 350,000), we compared life satisfaction across Whites, Hispanics, and Blacks. We also evaluated the extent to which SES, health, and social relationships ‘explained’ racial/ethnic group differences and compared the magnitude of variation explained by life satisfaction determinants across and within these groups.

Results

Relative to Whites, both Blacks and Hispanics were less likely to be very satisfied. Blacks were somewhat more likely to report being dissatisfied. These differences were reduced or eliminated with adjustment for SES, health, and social relationships. Together, SES and health explained 12–15% of the variation in life satisfaction, whereas social relationships explained an additional 10–12% of the variance.

Conclusions

Racial/ethnic life satisfaction disparities exist for Blacks and Hispanics, and these differences are largest when comparing those reporting being ‘satisfied’ to ‘very satisfied’ versus ‘dissatisfied’ to ‘satisfied.’ SES, health, and social relationships were consistently associated with life satisfaction, with emotional support having the strongest association with life satisfaction.

Keywords

Quality of lifeHealth status disparitiesHispanicsBlacksSocial supportSocioeconomic status

Introduction

Subjective well-being is a lynchpin of population health. For example, the World Health Organization (WHO) defines well-being as an integral part of health [1] and the number one public health priority in the United States (U.S.) is to increase the number and quality of years of life [2]. Thus, subjective well-being is a critical component of psychological and health assessments, and a rigorous understanding of well-being determinants is a precondition for policy interventions to promote health and well-being [3, 4].

Well-being surveillance at the population level is essential to more precisely identify levels of health status in the U.S., as well as to monitor the conditions contributing to or detracting from one’s well-being [5]. Despite a maturing literature regarding this dimension of life quality, studies of nationally representative samples are urgently needed because much well-being research is conducted in “…small, accidental samples of respondents” [4]. One undesirable consequence of convenience sampling is the paucity of work on well-being among minorities, particularly Hispanics. In 2006, there were an estimated 44.3 million Hispanics in the U.S., comprising 14.8% of the population [6], yet, the well-being literature provides almost no data regarding this largest U.S. minority population. The evaluation of Hispanic well-being is essential to improve U.S. public health efforts to identify and reduce health and well-being disparities [2], i.e., unnecessary, avoidable, and unjust health differences [7], and to accurately characterize well-being determinants for policy decisions [3]. We use life satisfaction as an integrative measure of well-being. Both constructs encompass pleasure, engagement, and other positive emotions [4]. Life satisfaction is also considered as a marker of quality of life in Healthy People 2010 [2].

Well-being predictors

Health status, as denoted by perceived health and physical disability, is an important predictor of well-being [8, 9]. To better understand the contribution of health to well-being, it is important to compare the relative contribution of health when simultaneously considering other well-being correlates, such as income, education, and unemployment. Although studies have addressed the relative importance of life satisfaction predictors among racial/ethnic groups [9, 10], we are aware of none that have done so with representative samples that include Hispanics. Relative comparisons of life satisfaction predictors help to prioritize research resources. Further, representative multi-ethnic within-country samples can more precisely test hypotheses that cultural factors moderate well-being [4]. We, therefore, compared the relative predictive strength of health and socioeconomic status (SES) variables for life satisfaction across diverse White, Black, and Hispanic population-based samples.

Social relationships are also important determinants of life satisfaction [4, 11, 12], and forming and maintaining social relationships are theorized to be a fundamental human motive [13]. Social relationships are central to theoretical models of SES [14], and adequate testing of these models requires the assessment of a variety of social relationship markers. One established social relationship indicator, marital status, is a robust life satisfaction correlate [8]. However, qualitative and quantitative markers of social relationships, such as emotional support and social integration (“…active engagement in a wide range of social activities or relationships” [15]) are theoretically important, but have received less attention in the context of life satisfaction [10, 16]. Emotional support and social integration should be potent predictors of well-being because they are theorized to buffer stressful experiences and to promote positive psychological states, respectively [15]. Simultaneously modeling marital status, emotional support, and social integration will help clarify the potential mechanisms through which being married confers greater well-being. Finally, variation in the predictive strength of social relationships across racial/ethnic groups may determine whether the benefits of social relationships vary across sociocultural contexts [17].

To address these gaps in the literature, we sought to advance our understanding of well-being by evaluating markers of important life satisfaction domains across two very large, nationally representative samples of non-Hispanic White, non-Hispanic Black, and Hispanic U.S. residents. Specifically, the present study: (1) evaluated racial/ethnic disparities in life satisfaction; (2) partitioned the relative contribution of health, SES, and social relationships to these disparities; and (3) compared the relative strength of association among life satisfaction predictors, both within and across racial/ethnic groups.

Methods

Data sources

We analyzed two U.S. population-based public health surveys, the 2001 National Health Interview Survey (NHIS) and the 2007 Behavioral Risk Factor Surveillance System (BRFSS). Although the focus of these surveys was not life satisfaction, a life satisfaction assessment was available for these years. Informed consent was obtained from all participants.

National Health Interview Survey

The NHIS is the primary U.S. health surveillance instrument for a broad range of health conditions [18]. The survey uses stratified, multistage cluster sampling to obtain a probability sample of U.S. households in the 50 states and the District of Columbia [19]. Households are contacted and randomly chosen adults are interviewed in person in their residence using computer-assisted interviewing technology. Black and Hispanic participants are oversampled to provide reliable estimates for these populations. The 2001 survey included 33,326 adult respondents (73.8% conditional response rate; [20]). Of those respondents, 32,121 participants described themselves as Hispanic (n = 5,615; 29.4% interviewed in Spanish), non-Hispanic Black (n = 4,622), or non-Hispanic White (n = 21,884). Life satisfaction data were missing for 1.6, 2.4, and 1.8% of these groups, respectively (n = 31,537). Missing values on covariates in fully adjusted multivariate models reduced the sample to 92.7% of those with life satisfaction data (final n = 29,243).

Behavioral Risk Factor Surveillance System

The BRFSS is an annual telephone survey of over 350,000 adults (18+ years of age) in the 50 United States, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, and Guam. It is a population-based stratified probability sample that provides the primary source of information on health-related behaviors in the U.S. [21]. The median cooperation rate across the 50 states in 2007 was 72% [22]. Of the 430,912 participants, 403,137 described themselves as Hispanic (n = 31,310; 41.1% interviewed in Spanish), non-Hispanic Black (n = 33,216), or non-Hispanic White (n = 338,611). Life satisfaction data were missing for 6.7, 7.1, and 4.0% of these groups, respectively, leaving 385,163 participants (95.5% of the three racial/ethnic groups). Missing data on covariates in fully adjusted multivariate models reduced the sample to 85.4% of those with life satisfaction data (final n = 329,004).

Census-based weights for respondents were calculated to adjust for nonresponse and to ensure representativeness. Our analyses incorporated the weights, sample strata, and clusters, and, therefore, estimate parameters for the adult civilian, non-institutionalized population of the United States [20]. We used Stata 10.0 (Stata Corp., College Station, TX) for all of the analyses.

Measures

There are modest differences in item wording across surveys and, generally, the BRFSS has a less comprehensive set of health, social ties, and SES variables (e.g., fewer chronic disease indicators, no measures of wealth or social contacts). Nonetheless, similar associations across samples and variable definitions increase generalizability and provide greater confidence in the associations. Items available only in one survey are identified in brackets.

Life satisfaction

In both surveys, life satisfaction was measured with the question “In general, how satisfied are you with your life?” The response options included very satisfied, satisfied, dissatisfied, or very dissatisfied. Because the very dissatisfied category was reported by 1% of participants in both samples, we combined it with the dissatisfied category.

Demographic variables

Gender and age (dummy codes for six age categories) were used as covariates. The respondents reported a racial category (e.g., White, Black/African American) and whether or not they were of Hispanic ethnicity [23]. Participants were categorized into non-Hispanic White, non-Hispanic Black, and Hispanic (henceforth referred to as White, Black, or Hispanic, respectively). Racial and ethnic classifications encompass a number of social categories related to ancestral origins, language, history, and customs [24], but the present classification structure reflects the minimum administrative standards of the U.S. Government [23].

Socioeconomic status

Socioeconomic status (SES) was assessed by education (dummy variables representing <high school; high school graduate or equivalent; some college; college graduate; postgraduate degree [NHIS]), household income (> or <$20,000 per year [NHIS]; eight categories ranging from <$10,000 to >$75,000 per year [BRFSS]), employment status, and wealth. Employment was represented by dummy codes representing retired, unemployed, and never worked [NHIS] versus working (the referent group in both surveys). The BRFSS analyses included dummy categories for student, homemaker, and unable to work. Wealth was coded dichotomously (own home vs. rent/other [NHIS]).

Health status

Health was assessed by the presence or absence of any reported disability (“Are you limited in any way in any activities because of physical, mental, or emotional problems?” [BRFSS]; any limitations reported for physical activities, such as carrying groceries, grasping small objects, walking a quarter of a mile, etc. [NHIS]). Participants rated their health as poor, fair, good, very good, or excellent. Finally, we created a summary measure of reported chronic disease diagnoses (hypertension, diabetes, heart attacks, coronary heart disease, cancer [NHIS], and other heart disease [NHIS], summed and grouped into none, 1, or 2 or more).

Social relationships

These resources were assessed by marital status (married/cohabiting or not), emotional support (How often do you get the social and emotional support you need? Would you say always, usually, sometimes, rarely, or never?), and social integration. Social integration [NHIS only] was the sum of six questions covering the 2-week prevalence of contacts with friends or relatives over the phone or in person, as well as whether they attended a group social activity or a religious service. Although they are not perfect indicators, these social relationship markers capture elements of the general human motive to have stable, lasting, and positive social interactions with others [13].

Analytic strategy

We evaluated well-being disparities by comparing levels of life satisfaction for White, Hispanic, and Black participants. Next, we examined, in turn, the relative contributions of SES, health status, and social relationships to racial/ethnic disparities in life satisfaction. This strategy characterizes the relative importance of these domains for life satisfaction and addresses possible mediating factors for between-group variation in life satisfaction.

We then repeated these multivariate analyses within each of the three racial/ethnic groups using ordinary least squares regression. We report R2 effect size estimates to characterize the variance in life satisfaction accounted for by the blocks of SES, health, and social relationship variables. Variance was estimated with Taylor series linearization [25] to accommodate the clustered sampling design.

Results

An overview of the demographic characteristics for each sample is presented in Table 1.
Table 1

Percentages and means for the sociodemographic characteristics of the 2001 National Health Interview Survey (NHIS) and the 2007 Behavioral Risk Factor Surveillance System (BRFSS)

 

2001 National Health Interview Survey

2007 Behavioral Risk Factor Surveillance System

White (n = 21,884)

Black (n = 4,622)

Hispanic (n = 5,615)

Total (n = 32,121)

White (n = 338,611)

Black (n = 33,216)

Hispanic (n = 31,310)

Total (n = 403,137)

Age

    18–24

11.8

16.3

19.0

13.2

9.5

12.9

16.8

11.0

    25–34

16.3

20.8

24.8

17.8

15.9

20.1

27.1

18.1

    35–44

21.2

23.3

23.4

21.7

19.4

22.1

23.3

20.3

    45–54

19.6

17.9

15.1

18.9

20.0

18.6

15.1

19.1

    55–64

12.8

10.0

8.5

12.0

15.6

13.4

9.1

14.3

    ≥65

18.2

11.7

9.2

16.4

19.5

12.9

8.5

17.1

Gender

    Female

51.9

55.5

50.8

52.2

52.0

54.0

49.6

51.8

    Male

48.1

44.4

49.2

47.8

48.0

46.0

50.4

48.2

Education

    <High school

12.7

24.3

43.7

17.6

6.8

13.5

32.3

11.5

    High school or GED

30.4

30.3

23.1

29.6

28.6

33.9

29.4

29.3

    Some college

30.0

30.4

21.3

29.1

27.1

28.9

20.3

26.2

    College graduate (or higher)a

17.2

10.0

7.2

15.2

37.3

23.3

17.1

32.7

    Postgraduatea

8.9

3.9

2.4

7.6

    Missing

0.8

1.1

2.3

1.0

0.2

0.5

0.7

0.3

Annual income (US $)

    <$10,000

2.4

7.6

9.6

4.1

    $10,000–15,000

3.0

6.2

9.1

4.3

    $15,000–19,999

4.4

9.2

11.2

6.0

    <$20,000b

15.6

29.2

28.1

18.7

    $20,000–24,999

6.2

9.4

11.8

7.4

    >$20,000b

78.5

63.9

65.7

75.3

    $25,000–34,999

9.2

13.0

12.7

10.2

    $35,000–49,999

13.6

13.6

11.9

13.4

    $50,000–74,999

16.9

12.2

8.8

15.1

    ≥$75,000

31.9

14.9

12.3

27.1

    Missing

5.9

6.9

6.2

6.0

12.5

13.9

12.4

12.6

Own home

    Yes

76.4

49.2

49.3

29.7

    No

23.6

50.8

50.7

70.3

Employment status

    Employed

66.4

63.8

67.0

66.2

60.9

57.6

61.4

60.6

    Retired

16.7

9.8

6.9

14.7

18.7

13.6

7.4

16.4

    Never worked

2.8

6.0

10.7

4.1

    Unemployed

13.9

19.9

15.0

14.8

3.8

8.8

6.1

4.7

    Student

3.8

5.9

5.1

4.2

    Homemaker

8.0

3.6

13.6

8.4

    Unable to work

4.5

9.8

5.5

5.2

    Missing

0.2

0.5

0.3

0.2

0.3

0.7

0.9

0.5

Self-rated health

    Poor

2.8

4.7

3.4

3.1

4.1

5.7

5.2

4.4

    Fair

8.1

13.1

9.4

8.8

9.6

15.4

22.3

12.3

    Good

23.6

27.6

26.8

24.5

28.3

34.6

35.7

30.2

    Very good

33.2

27.4

31.5

32.3

36.0

26.4

20.3

32.5

    Excellent

32.1

27.0

28.8

31.1

21.6

17.3

16.1

20.3

    Missing

0.1

0.2

0.0

0.1

0.3

0.5

0.4

0.4

Disability

    No

64.2

68.1

78.1

66.3

78.7

79.3

84.2

79.6

    Yes

35.4

31.5

21.7

33.4

19.7

18.3

12.5

18.4

    Missing

0.3

0.4

0.2

0.3

1.6

2.5

3.3

2.0

Chronic diseases

    0

63.9

62.8

75.4

65.0

66.2

58.6

73.2

66.5

    1

22.7

23.7

16.5

22.1

23.0

26.7

18.4

22.7

    ≥2

13.4

13.5

8.1

12.9

10.8

14.7

8.5

10.8

Marital status

    Married/cohabitating

66.4

45.1

64.3

63.7

67.6

56.7

64.4

64.5

    Other

33.5

54.9

35.7

36.3

32.1

42.8

35.3

35.2

    Missing

0.3

0.5

0.3

0.3

Social relationships

Mean (SE)

Social ties

4.53 (0.012)

4.64 (0.029)

4.42 (0.029)

4.53 (0.011)

Emotional support

4.24 (0.008)

4.16 (0.018)

4.19 (0.019)

4.23 (0.007)

4.24 (0.003)

3.98 (0.015)

3.92 (0.019)

4.17 (0.004)

Life satisfaction

1.40 (0.005)

1.28 (0.011)

1.34 (0.010)

1.38 (0.045)

1.42 (0.002)

1.28 (0.007)

1.33 (0.007)

1.39 (0.002)

aCollege graduate and above was a combined category in the BRFSS

bIncome was coded as greater than or less than US $20,000 in the NHIS

– denotes an item not included in the survey. SE = standard error. All standard errors for demographic variables were less than or equal to 0.02. The variable percentages may not sum exactly to 100 due to rounding

Life satisfaction in the U.S. by race/ethnicity

Whites had higher life satisfaction relative to Blacks and Hispanics (see Fig. 1). This pattern was largely explained by racial/ethnic differences in the very satisfied and satisfied categories, with a greater proportion of Whites in the very satisfied category. Within-group life satisfaction was consistent across the two surveys (Fig. 1).
https://static-content.springer.com/image/art%3A10.1007%2Fs11136-008-9426-2/MediaObjects/11136_2008_9426_Fig1_HTML.gif
Fig. 1

Life satisfaction by race/ethnicity: United States, 2001 and 2007. NHIS = National Health Interview Survey; BRFSS = Behavioral Risk Factor Surveillance System. The proportions may not sum to 1.0 due to rounding

To characterize the extent of poor life satisfaction across racial/ethnic groups, we used the person-level weights in the BRFSS to estimate the number of adults in the U.S. who are dissatisfied with their lives. We estimate that, in 2007, 1.6 million Blacks (95% confidence interval [CI] 1.5–1.8 million), 7.7 million Whites (95% CI 7.4–7.9 million), and 1.6 million Hispanics (95% CI 1.5–1.8 million) were dissatisfied or very dissatisfied with their lives.

Racial/ethnic disparities in life satisfaction

To contextualize the regression analyses, correlations among the key predictor variables are presented in Table 2. These estimates reveal independence among predictors and moderately higher life satisfaction–income correlations than that reported previously (r = 0.22–0.25 vs. 0.13) [26].
Table 2

Population-based correlations among life satisfaction, sociodemographic, health, and social relationship variables

 

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

1. Life satisfaction

0.00

0.15

0.25

–0.15

−0.20

0.31

−0.09

0.18

0.39

2. Male

0.01

0.00

0.08

−0.03

−0.03

0.02

0.02

0.05

−0.02

3. Education

0.16

0.02

0.49

−0.14

−0.07

0.29

−0.11

0.12

0.16

4. Family incomea

0.22

0.07

0.39

−0.26

−0.19

0.37

−0.18

0.35

0.22

5. Unemployed

−0.14

−0.15

−0.15

−0.24

0.12

−0.12

0.05

−0.13

−0.09

6. Own home

0.14

0.00

0.11

0.40

−0.06

7. Disability

−0.18

−0.10

−0.13

−0.16

0.22

0.05

−0.39

0.27

−0.07

−0.12

8. Self-rated health

0.29

0.04

0.28

0.29

−0.26

0.05

−0.43

−0.39

0.09

0.21

9. Chronic diseases

−0.09

−0.01

−0.12

−0.14

0.16

0.08

0.36

−0.43

−0.03

−0.06

10. Married

0.18

0.06

0.09

0.36

−0.05

0.24

−0.03

0.05

0.00

0.13

11. Emotional support

0.39

0.01

0.08

0.14

−0.07

0.10

−0.13

0.16

−0.04

0.12

12. Frequency of social contacts

0.25

−0.08

0.19

0.15

−0.08

0.08

−0.14

0.21

−0.09

0.06

0.22

Estimates are based upon Hispanic, non-Hispanic Black, and non-Hispanic White participants in the 2001 National Health Interview Survey (NHIS: below diagonal) and the 2007 Behavioral Risk Factor Surveillance System (BRFSS: above diagonal). Sample sizes range from 20,071–32,121 in the NHIS to 333,350–403,137 in the BRFSS. Unemployment compares currently employed to unemployed, but not retired, students, etc. Unemployed correlations are based upon 20,071–25,295 (NHIS) and 217,403–380,229 (BRFSS) observations. Social contacts and home ownership were not assessed in the BRFSS

aThe NHIS variable was last year’s family income in 11 levels ranging from $0–$4,999 to ≥$75,000 (n = 24,078). This measure was not used in the primary analyses because a substantial number of participants (23.5%) did not report income in these graded categories. The correlation with the binary family income variable was smaller (r = 0.15)

Racial/ethnic disparities were evaluated using two sets of regressions, each including a binary variable; one variable compared Hispanics with Whites, the other Blacks with Whites. To more precisely characterize patterns of disparities, we compared dissatisfied and very satisfied ratings with the satisfied category using multinomial logit models. Our measure of association was the exponentiated regression coefficient or odds ratio (OR), which reflects differences in the likelihood of being in a particular life satisfaction category. For example, when comparing the likelihood of being dissatisfied (relative to satisfied), a ratio of 1.0 would reflect no group differences, whereas a ratio of 1.33 would reflect a 33% greater likelihood of being dissatisfied, presuming that the confidence intervals did not include 1.0. Odds are on a log scale, where the distance from 1.0 to 2.0 is the same as from 0.5 to 1.0. Below, we report NHIS and BRFSS ORs, respectively, with 95% CIs in brackets.

Relative to Whites, Blacks were more likely to be dissatisfied (OR = 1.31 [1.22, 1.52] and 1.33 [1.20, 1.47]) and less likely to be very satisfied (OR = 0.68 [0.63, 0.74] and 0.65 [0.61, 0.68]). Black/White differences in dissatisfaction were eliminated with SES adjustment (OR = 0.92 [0.78, 1.09]) and 0.90 [0.81, 1.01]). For very satisfied, Black/White differences persisted following adjustment for SES, health, and social relationships covariates in the NHIS but not the BRFSS (OR = 0.87 [0.79, 0.96] and 0.99 [0.92, 1.05]).

Hispanics were equally likely to report being dissatisfied relative to Whites (OR = 1.0 [0.86, 1.16] and 0.88 [0.78, 1.0]), but were less likely to report being very satisfied (OR = 0.80 [0.74, 0.86] and 0.69 [0.65, 0.73]). A pattern of lower odds of being dissatisfied for Hispanics was observed with adjustment for SES (OR = 0.72 [0.60, 0.86] and 0.66 [0.57, 0.77]) and this Hispanic advantage persisted after adjusting for all covariates, although the confidence intervals approached 1.0 in the NHIS (OR = 0.82 [0.68, 1.00]). Comparing the odds for very satisfied versus satisfied, SES covariates removed Hispanic/White differences in both samples (OR = 1.09 [1.00, 1.18] and 1.02 [0.95, 1.09]), a pattern which persisted, except in BRFSS analyses that included health (OR = 1.11 [1.03, 1.19]) and, additionally, social relationship covariates (OR = 1.16 [1.07, 1.25]). Thus, moderate Black/White differences in life satisfaction were observed, but were attenuated after adjustment for potential confounders. Smaller initial Hispanic/White life satisfaction differences were observed and were eliminated or reversed in multivariate models.

The relative contributions of SES, health status, and social relationships

Across all participants, SES indicators accounted for 6–9% of the variance in life satisfaction judgments. Adding health variables to these equations increased the explained variance to 12–15%. Adding social relationship variables substantially increased the explained variance to up to 24–25% of the variance across both samples (see Table 3).
Table 3

Multivariate estimates of variance (R2) in life satisfaction accounted for by racial/ethnic disparities, socioeconomic status (SES), health status, and social relationships by national health survey

 

Age and gender

+SES

+Health

+Social relationships

Emotional support omitted

NHIS

BRFSS

NHIS

BRFSS

NHIS

BRFSS

NHIS

BRFSS

NHIS

BRFSS

Overall

0.2

0.2

6.2

9.1

12.7

14.8

25.5

24.5

17.5

15.9

Disparitiesa

    Hispanic vs. White

0.3

0.5

6.2

9.0

13.1

15.0

26.0

24.9

17.8

15.0

    Black vs. White

0.6

0.8

6.6

10.0

13.1

15.9

26.5

26.6

18.0

15.7

Within groups

    White

0.1

0.2

6.6

10.0

13.5

16.0

27.1

27.4

18.4

17.3

    Black

0.6

0.6

4.6

7.8

9.0

12.7

21.4

20.7

14.0

13.2

    Hispanic

0.8

0.1

4.7

4.5

10.8

9.4

19.3

15.7

14.3

13.2

NHIS = National Health Interview Survey (n = 29,243 in fully adjusted models). BRFSS = Behavioral Risk Factor Surveillance System (n = 329,004 in fully adjusted models). The baseline model included age and gender, while subsequent models added, in turn, socioeconomic status (SES; education, income, home ownership [NHIS only], employment status), health status (functional limitations, self-rated health, and diagnosed chronic diseases), and social ties (marital status, emotional support, and frequency of social contacts [NHIS only])

aDisparities analyses compare only Black/White or Hispanic/White participants using a binary race/ethnicity variable

Magnitude and consistency of individual life satisfaction predictors

In general, the largest life satisfaction predictors were unemployment, disability, self-rated health, and the three social relationship markers. These patterns were consistent across both surveys and within each racial/ethnic group (see Fig. 2). Emotional support had the largest association of any predictor. It accounted for 15% of the variance in bivariate analyses, but persisted in explaining >8% of the variance in fully adjusted models. Despite this consistency in the full sample, there were appreciable racial/ethnic differences in explanatory power for emotional support (approximately 9–10, 7, and 2.5–5% of the variance explained for Whites, Blacks, and Hispanics, respectively; Table 3). Similar gaps in explained variance were observed for the fully adjusted regressions, in which 27, 21, and 16–19% of the variance was explained for Whites, Blacks, and Hispanics, respectively. Across racial/ethnic groups and surveys, the incremental increase in explained variance for marital status was 0.3–1.2% (median = 0.7%) when added to models containing the social relationship variables.
https://static-content.springer.com/image/art%3A10.1007%2Fs11136-008-9426-2/MediaObjects/11136_2008_9426_Fig2_HTML.gif
Fig. 2

Partial regression coefficients for life satisfaction predictors by race/ethnicity and U.S. public health survey

Discussion

This study evaluated racial/ethnic disparities in life satisfaction, and explored the relative contributions of SES, health status, and social relationships to life satisfaction among two very large, diverse probability samples of U.S. adults. This is the first major evaluation of Hispanic life satisfaction in the U.S. and is the largest U.S. population-based life satisfaction study to date. We found that Blacks and Hispanics have lower life satisfaction than Whites, but controlling for SES and health status attenuated these differences for Blacks and eliminated them for Hispanics. We also found a modest Hispanic benefit for being very satisfied in multivariable models. Statistical control aside, these data clearly show that disparities exist for the two largest racial/ethnic groups in the U.S., and falsify claims that “…knowing someone’s race or ethnic group … gives little clue to the person’s psychological well-being” [27] (for more evidence of racial well-being disparities, see [9, 28, 29]). This study addresses a central goal of U.S. health policy [2] by identifying well-being disparities and by identifying plausible health and economic mechanisms through which these disparities may be reduced. Further, this information provides a foundation for improving quality of life, a major priority for both U.S. and international health policy.

The relative importance of life satisfaction predictors varied across self-reported race and ethnicity, accounting for the least variance among Hispanics, with increased explanatory power for Blacks and still more so for Whites. These differences became particularly marked for Hispanics in models lacking measures of wealth and social integration. The consistently higher explained life satisfaction variance among Whites could represent substantive cultural variation in the types of support relevant to well-being judgments [30] or reflect methodological variance, e.g., differential measurement error and/or interactions of ethnicity with other predictors. Disentangling these possibilities is an important direction for future research.

Although these racial/ethnic differences in well-being and its correlates are consistent with cultural moderation hypotheses [4], we had no direct culture measures and it is perilous to conflate ethnicity with culture [31]. In addition, our use of one “Hispanic” category likely masks variation in well-being across Hispanic national origin groups, variation which has been documented for SES markers and health risk [32]. Even though we statistically controlled for differences in education, income, employment status, and wealth, racial/ethnic imbalances in SES may persist [33]. Thus, disparities may reflect residual SES confounding rather than inadequate well-being models or unmeasured well-being correlates.

The robust association of life satisfaction with social relationships provides strong support for theories emphasizing social ties [1315]. The independent association of both functional (emotional support) and structural (number of social contacts) social relationships replicates previous work [10] and is consistent with studies showing the broader health relevance of these two dimensions [34, 35]. Moreover, these aspects of the social environment are potentially modifiable, revealing intervention targets (as well as resources to protect) when considering clinical or policy interventions [3]. The potency of social relationship predictors supports theoretical models of SES that emphasize active participation in society and social engagement as the ultimate resources conferred by high social status [36]. Relative to income or education, the social relationship markers in this study are more proximal indicators of this participatory capacity, and, thus, from this perspective, one would expect a stronger life satisfaction association for variables more directly assessing this resource. This interpretation is consistent with longitudinal studies showing that social contacts are substantially more important to life satisfaction than increases in income [16].

Prior reviews emphasizing marriage as a key life satisfaction predictor have not considered the joint effects of other types of social ties [37, 38]. Our analyses confirm the importance of marital status, yet, suggest a subordinate role for this condition. Relative to being married, reporting emotional support accounted for roughly eight times more variance in life satisfaction, a pattern seen in research using depressed mood as an outcome [39]. The weak bivariate association between marital status and emotional support undermines the argument that being married confers social support, whereas the positive correlation between marital status and education, income, and home ownership suggest alternative mechanisms for the higher well-being enjoyed by those who are married. Continued attention to the joint contributions of multiple well-being indicators will clarify their relative importance and the potential mechanisms through which they influence well-being.

Our health variables were strongly and consistently associated with life satisfaction in both samples, but which is more important to well-being, subjective or objective health measures [37]? On the one hand, we found that reported chronic disease diagnoses were weakly related to life satisfaction relative to other health reports. However, the putatively less objective disability and self-rated health reports tap substantive health dimensions. For example, self-reported disabilities [40] and global health appraisals [41] predict mortality, and the latter health-related quality of life judgment exhibits discriminant validity from negative moods [42]. Chronic diseases were, in fact, associated with poorer well-being in our data, but that association was eliminated or substantially reduced when controlling for disability and subjective health appraisals (data not shown). Thus, the well-being impact of ill health is better captured by reported disability and global self-rated health.

This study has a number of strengths. We presented timely, population-based life satisfaction estimates for Whites, Hispanics, and Blacks using two different representative samples of U.S. adults. These surveys utilized rigorous sampling procedures and multiple assessment methods, i.e., both telephone and in-home computer-assisted interviews. The combined sample size in this report is commensurate with the sum of participants in all U.S. life satisfaction literature to date, and the Hispanic and Black participants in our study each exceeded the total sample for 33 years of aggregated data from the U.S. General Social Survey [9]. This study, thus, directly and decisively addresses population well-being [5]. In addition, we incorporated multiple markers of SES, health, and social relationships, and provided the first population-based analysis of well-being among Hispanics, a group that is estimated to comprise 15.5% of the population by 2010 [6]. We also partitioned the importance, in explained variance terms, of marital status relative to emotional support, and provided evidence prioritizing the relative importance of theoretically important life satisfaction predictors within and across racial/ethnic groups. These analyses help contextualize the relative influence of life satisfaction determinants and provide a common metric for comparison with other studies.

Despite these strengths, a number of cautions are appropriate. We measured life satisfaction with a single item assessed at one point in time. Including more sophisticated life satisfaction assessments obtained on multiple occasions is desirable. There are still unrepresented minority populations that deserve research attention, and space constraints precluded the examination of happiness and sadness, dimensions that capture somewhat independent aspects of well-being [43]. Contextual factors, such as neighborhood quality, receive less research attention but also predict well-being [44], as do moods [45], personality [46], and major life events [11]. Most important, our cross-sectional design cannot determine whether the observed associations are causal. In fact, existing evidence reveals a fascinating concoction of reciprocal influence among the variables considered here. For example, social integration predicts and results from well-being [16]; slightly lower life satisfaction has been observed among people who will divorce [47]; people in poor health are more likely to become unemployed [48]; and continuous employment can preserve psychological health, even among those with initially poorer health (at least among men [49]). Only by evaluating the prevalence of various health and socioeconomic conditions in combination with the effect sizes for each possible bidirectional association can we begin to address the relative importance of these pathways and their suitability for modification [11].

Conclusions

These data provide an inclusive picture of life satisfaction in the U.S., revealing reliable racial/ethnic disparities and suggesting order for the relative importance of a number of life satisfaction correlates. Having emotional support, a job, good health, no disabilities, diverse social networks, and a spouse are all strong and independent predictors of being satisfied with life. These patterns are particularly compelling given their consistency across two nationally representative samples and racial/ethnic groups. Our study informs health policy decision-making and provides a foundation for exploring psychological factors that may be selected or impaired by conditions such as disability, unemployment, or social isolation [50]. We hope that our work clarifies the relative importance of well-being determinants [3] in order to improve individual and population health in the U.S. and elsewhere.

Acknowledgments

The views expressed in this paper are the authors and neither those of the National Center for Health Statistics (NCHS) nor the Centers for Disease Control and Prevention (CDC). We are grateful to the NCHS, the CDC, and the survey participants for making this study possible.

Copyright information

© Springer Science+Business Media B.V. 2008