SOLUTION: BHAN 820 Ashford Social and Environmental Determinants of Health Discussion

Social Science Research 63 (2017) 1e18
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Discovering pockets of complexity: Socioeconomic status,
stress exposure, and the nuances of the health gradient
Scott Schieman*, Jonathan Koltai
Department of Sociology, 725 Spadina Ave, University of Toronto, Toronto, ON M5S 2J4, Canada
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 9 September 2015
Received in revised form 2 September 2016
Accepted 29 September 2016
Available online 30 September 2016
One of the most pervasive statements about stratification and health identifies the strong
inverse relationshipdor gradientdbetween socioeconomic status (SES) and poor health.
We elaborate on the ways that the SES-based gradient in stress exposure contributes to
nuances in the SES-health association. In analyses of the 2008 National Study of the
Changing Workforce, we find some evidence that the inverse association between SES and
health outcomes is finely gradeddbut several ‘pockets of complexity’ emerge. First, education and income have different associations with health and well-being. Second, those
associations depend on the outcome being assessed. Education is more influential for
predicting anxiety and poor health than for depression or life dissatisfaction, while income
is more influential for predicting depression and, to a lesser extent, life dissatisfaction.
Third, different patterns of explanation or suppression reflect resource advantage or stress
of higher status dynamics. Some impactful stressors that people encounterdespecially job
pressure and work-family conflictdare not neatly graded in ways that corroborate the
conventional SES-health narrative. Instead, these mask the size of the overall health differences between lower versus higher SES groups. Our mapping of the SES gradient in
stressors extends that story and complicates the conventional view of the association
between SES and health/well-being.
© 2016 Elsevier Inc. All rights reserved.
1. Background
One of the most pervasive statements about social stratification and health identifies the inverse relationshipdor gradientdbetween socioeconomic status (SES) and a range of unfavorable health and well-being outcomes. Many researchers
have described this gradient as “robust,” “well-documented,” “established,” “longstanding,” and “one of the most consistent”
findings in sociology or epidemiology (Antonovsky, 1979; Backlund et al., 1999; Kessler, 1979, 1982; Kessler and Cleary, 1980;
Kunst et al., 1998; Lynch and Kaplan, 2000; Marmot, 2004; McLeod and Kessler, 1990; Muntaner et al., 2013; Sorlie et al., 1995;
Syme and Berkman, 1976; Walsemann et al., 2013; Wheaton, 1978). In her review of four decades of stress research, Thoits
(2010) summarizes the pattern as follows: “Persons with low education, income, or occupational prestige have the highest
rates of morbidity, disability, mortality, psychological distress, and mental disorder compared to those in more advantaged
socioeconomic positions” (p. 544). Citing the persistence of this inverse association over time, Link and Phelan and their
associates have theorized about the ways that SES represents a “fundamental cause” of population health inequalities (Link,
2008; Link and Phelan, 1995; Phelan et al., 2010).
* Corresponding author.
E-mail address: (S. Schieman).
0049-089X/© 2016 Elsevier Inc. All rights reserved.
S. Schieman, J. Koltai / Social Science Research 63 (2017) 1e18
Much of this literature has focused on education and income as two core dimensions of SES and stratification. Education
fosters access to valued labor market skills and credentials that signal work role abilities, and these are often associated with
greater workplace resources and material rewards (Mirowsky and Ross, 2003b; Muntaner et al., 2010; Ross and Wu, 1995).
Likewise, personal income is a key status marker in systems of stratification and an indicator of material resources (Lynch and
Kaplan, 2000; Muntaner et al., 2013). One of the most common patterns in the literature involves what could be characterized
as the “haves versus have-nots” hypothesisdthat is, individuals with the highest levels of education and income tend
experience better well-being than their peers with less education and income. In fact, when it comes to specific outcomesdespecially mortality and the diseases most strongly linked to premature deathdpeople who are advantaged socioeconomically tend to have the most favorable odds (Adler and Ostrove, 1999; Adler and Steward, 2010; Marmot and
Sapolsky, 2014). Despite this, some scholars have called for more attention to the details of the specific relationships between education or income and various forms of well-being (see Walsemann et al., 2013).
In the present study, we therefore conduct analyses that extend beyond the “haves versus have-nots” scenario in the effort
to distinguish between the ways that education and income are related to different indicators of health and subjective wellbeing. Our motivation evolves from three observations across diverse literatures. First, the strength of the gradient varies
according to the particular dimension of SES and the health or well-being outcome being investigated. In social epidemiological studies, many employ a single socioeconomic indicator and often compare health across only a few categories such as
“poor” versus “non-poor” or low versus high education (Braveman et al., 2005). Others have differentiated between a social
stratification approach that identifies markers of SES (e.g., education and income) as focal predictors of well-being, while
others pursue a social class approach that focuses on conflict and exploitation between group (e.g., employers, managers, and
workers) (see Muntaner et al., 2013).1 Second, claims about the inverse relationship between SES and some outcomesdespecially distressdare not consistently supported by uniformly negative linear patterns (Kessler, 1982; Mirowsky
and Ross, 2003a; Turner et al., 1995); the lack of consistent differences across the middle range of the SES spectrum are
often ignored or downplayed. Third, there is insufficient attention to the demands of work and its link to role
strainsdespecially overwork, job pressure, role blurring, and work-family conflict. Many of the early studies that are often
cited as the basis for the inverse SES-distress association, for example, did not include these conditions because they had not
yet emerged as prominently in the lives of Americans; these conditions have, however, become more common in recent years
(Cha and Weeden, 2014; Maume and Purcell, 2007; Nomaguchi, 2009; Voydanoff, 2007; Winslow, 2005). As we detail below,
the increasingly closer coupling of these stress exposures with higher levels of SES has implications for the SES-health/wellbeing gradient. In the effort to elaborate on the current characterization of this gradient, we document the patterning of these
particular stressors across levels of SES and then take those conditions into account in our analysis of the relationship between SES and distress.2 As comparison points alongside the indicators of distress, we include two other classic indicators of
health and subjective well-being: self-rated health and life satisfaction.
2. Pockets of complexity
A fundamental proposition is that the inverse association between SES and poor health is “finely graded” such that each
step up in the basic indicators of SES (e.g., education and income) yield incremental reductions in risk (Link et al., 2013). This
claim originates from analyses of SES-based patterns in morbidity and mortality, especially from the famous Whitehall
studies (Smith et al., 1990; Marmot et al., 1984; Reid et al., 1974), and it has greatly influenced ways that scholars frame the
SES-health relationship. For example, Smith and Egger (1992) assert that there are “finely stratified mortality differences
running from the top to the bottom of the social hierarchy” (p. 1080). In their review of the literature, Everson et al. (2002) find
that a “clear, graded association” between educational attainment or income and self-reported depressive symptoms could be
observed across several study populations. These characterizations of the gradient imply that as levels of SES increase, levels
of distress or the risk of poor subjective well-being should decrease. One possible consequence of this view is as follows: If the
SES-health/well-being association follows this finely graded pattern, then we might consistently observe statistically significant differences across the entire SES spectrum such that each one-unit increase in an indicator of SES results in the same
(or similar) decrease in levels of distress or the probability of reporting poor self-rated health and dissatisfaction with life. In
our analyses, we test this proposition with what we label the finely graded hypothesis. One primary concern emerges, however,
One challenge in summarizing this literature involves the numerous ways that researchers across disciplines conceptualize and measure SES. Even
before the discussion of these dimensions, we first encounter the labels in the epidemiologic literature, such as “social class,” “social stratification,” “social
inequality,” “social status,” and “socioeconomic status.” Lynch and Kaplan (2000: 14) suggest that these indicate “the social and economic factors that
influence what position(s) individuals and groups hold within the structure of society, i.e., what social and economic factors are the best indicators of
location in the social structure that may have influences on health.” Furthermore, in distinguishing between the stratification/class distinctions that define
this literature, Muntaner et al. (2013:222) conclude: “Social stratification and social class remain complementary and fertile traditions in the sociology of
mental health because of the key problems they address (e.g., the causes of mental health disparities), the strength of their theoretical foundations (e.g.,
neo-Weberian and neo-Marxian ideas about fundamental social divisions), and the explanatory power of the concepts and mechanisms that both continue
to generate.”
Literature reviews of the SES-health relationship (e.g., Adler et al., 1994) consider arguments related to ‘stress exposure’ by referring to the early sociology of mental health research on SES and life events (Dohrenwend and Dohrenwend, 1970; 1973; McLeod and Kessler, 1990).
S. Schieman, J. Koltai / Social Science Research 63 (2017) 1e18
when the observed patterns deviate from linearity; patterns that fail to conform to the finely graded scenario might enhance
insights about the “pockets of complexity” in the association between social stratification and well-being.3
Marmot and Sapolsky (2014) have encouraged greater attention to potential nuances: “Variation in the magnitude of the
gradient suggests that not taking [sic] the health gradient as a given. There is not a simple one-to-one relation between rank
in the social hierarchy and health” (pp. 368). Some scholars have indeed already elaborated on the nuances within the finely
graded SES-health association. For instance, House and Williams (2000:96) described their understanding of shape of the
association between socioeconomic position and health as follows:
“Despite some evidence for gradient effects of socioeconomic position on health, it is also important to note the many
studies indicating that the relationship of socioeconomic position, especially as indexed by income, to health is monotonic
but not a linear gradient. Although increasingly higher levels of socioeconomic position may be associated with increasingly better outcomes, there are also substantially diminishing returns of higher socioeconomic position to health. For
example, studies have found diminishing and even non-existent relationships between income and mortality (Wolfson
et al., 1993; Backlund et al., 1996; Chapman and Hariharan, 1996; McDonough et al., 1997) or morbidity (House et al.,
1990; 1994; Mirowsky and Hu, 1996) at higher levels of income (e.g., above the median)” (italics added for emphasis).
Mirowsky and Ross (2003b) also propose that the relationship between income and health follows a “law of diminishing
returns” because “each additional dollar makes the biggest difference to individuals who get the fewest dollars” (pp. 8).
Likewise, Bracke et al. (2014) articulate a portrait of diminishing returns of education for depressive symptoms in a European
sample, noting the influence of education-job mismatch as a key factor. While this notion of ‘diminishing returns’ does not
negate that the inverse SES-poor health association is finely graded, it does add this important qualifier: “up to a point.”
At this juncture, it is essential to underscore that wherever variations in health outcomes occur along the full spectrum of
SES, the attempts to identify factors that give rise to such variation ultimately contribute to our collective understanding of
the social determinants of health and subjective well-being. Attention therefore turns to the social conditions that might
explain nonlinearities in the association: That is, why do the benefits of SES for health and well-being seem to diminish at
higher levels of SES? Some research points toward role-related stress exposure. For example, after documenting nonlinearities
in the association between education and mortality among women, Backlund et al. (1999) speculate that while higher levels
of education provide more rewards and resources, it also can increase exposure to some stressors (e.g., job demands). This
status-based stress exposure argument is central to our thesis.
3. Stress exposure and the nuances of the gradient
One of our primary objectives involves the mapping of the SES-based gradient in stress exposure alongside the SES-health
gradient. As Link et al. (2013) observe: “Critical to any explanation of health gradients is a firm understanding of what is most
important about positional location in initiating the cascade of intervening social, psychological, and biological mechanisms
that lead to disease and death” (p. 193). Likewise, some scholars cite the importance of social hierarchy in the SES-health
association (Marmot, 2004; Marmot and Sapolsky, 2014; Sapolsky, 2005; Wilkinson, 2005). In line with these ideas, a long
tradition of research identifies the ways that job conditionsdas a salient and enduring feature of positional locationdcontribute to the association between SES and well-being (Kohn and Schooler, 1982; Ross and Van Willigen, 1997;
Tausig and Fenwick, 2011). Siegrist and Marmot (2004:1468) suggest that “it is likely that adverse working conditions
contribute in a significant way to the explanation of social inequalities in health.” Tausig (2013: 444) sharpens that point by
drawing an explicit connection from education through work conditions, asserting: “Educational attainment affects jobrelated distress by sorting workers into jobs with different levels of stressful characteristics.”
While there is little doubt that lower SES groups experience greater exposure to some severe forms of stress (e.g., financial
strain) that contribute to poorer health, evidence indicates that exposure to some chronic stressors in the work role are
elevated across levels of SES (Mirowsky and Ross, 2003a; Schieman, 2013; Schieman et al., 2009). The stress of higher status
hypothesis proposesdand prior evidence supportsdthe contention that workers with more education and income disproportionately encounter four conditions associated with greater work demands: (1) job pressure, (2) overwork, (3) role
blurring, and (4) work-family conflict (Cha and Weeden, 2014; Hakanen et al., 2011; Jacobs and Gerson, 2004; Schieman,
2013; Schieman and Glavin, 2008, 2011; Schieman et al., 2006; Schieman et al., 2009; Tausig and Fenwick, 2001, 2011).
This SES-based stress exposure gradient might contribute to pockets of complexity in the SES-health/well-being relationship.
In research on work and stress, job pressure represents a quintessential demand (Diestel and Schmidt 2009; Kristensen
et al., 2004; Schieman 2013; Tausig and Fenwick 2011)done that is a prominent feature of contemporary working life
(Galinsky et al., 2005; Maume and Purcell, 2007; Moen et al., 2013). Research demonstrates that job pressure increases time
and energy commitments and is associated with overwork, exhaustion, burnout, and distress (Cha and Weeden, 2014;
Demerouti et al., 2001; Hakanen et al., 2008; Kristensen et al., 2004; Schieman and Glavin, 2011; Schieman and Young,
2013). In analyses of the National Study of the Changing Workforce, Voydanoff (2005) observes that job pressure is associated with more work-to-family conflictda pervasive stressor that also undermines well-being (Bellavia and Frone, 2005).
We credit Blair Wheaton for the phrase “pockets of complexity” and his insights about them.
S. Schieman, J. Koltai / Social Science Research 63 (2017) 1e18
Alongside job pressures and overwork, we identify a third condition that Voydanoff (2007) labels a “boundary-spanning
demand”dthe frequency that workers send and receive work-related communications outside of regular work hours. The
proliferation of communication technologies has facilitated the ways that work may be completed “any time and any place”
(Chesley 2005)da dynamic that heightens work-family border permeability (Glavin and Schieman, 2012). Work contact can
increase the fluidity of borders and promote the risk of engaging in work tasks simultaneously with other activities (at home).
While research suggests that work contact can have positive and negative consequences for individuals (Gajendran and
Harrison, 2007), others emphasize its association with work-family role blurring and conflict (Allen et al., 2014; Schieman
and Glavin, 2008; Schieman and Young, 2010a; 2010b).
As Hodson (2004: 221) explains, these concerns have “deep roots in the academic literature, dating back to Coser’s Greedy
Institutions (1974), which chronicles the limitless demands large corporations place on the lives and commitments of their
members, especially their relatively well-paid managerial and professional staffs.” Collectively, these ideas reflect what BlairLoy (2003) characterizes as a “schema of work devotion,” in which higher status work positions increasingly require a singleminded allegiance to one’s company or firm, and an immense commitment of time, energy and emotion. In a study of an
international firm, Wharton and Blair-Loy (2002) found that professionals and managers encounter an ‘overtime culture’ that
demands long hoursdand these dynamics are linked to job pressure, role blurring, and work-family conflict (Glavin and
Schieman, 2012; Nomaguchi, 2009; Schieman, 2013).
If we follow Marmot’s (2004:14) recommendation to investigate the health effects of “the circumstances in which people
live and work” then we must acknowledge the role conditions that might elaborate on the nuances of the gradientdand, by
extension, when findings reveal nuances in the gradient we should then revisit theories about SES and health. To be clear, the
stress of higher status hypothesis does not deny the existence of a relationship between SES and health or the plight of
workers with more severe disadvantages, but instead seeks to articulate the pockets of complexity in the gradient. The core of
the stress of higher status hypothesis focuses on the consequences of the SES-based stress exposure gradient in predicting
that individua …
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