Particularism and racial mobility into privileged occupations

Accepted Manuscript Particularism and racial mobility into privileged occupations George Wilson, Nick Petersen, Ryan Smith, David Maume PII:

S0049-089X(17)30626-9

DOI:

https://doi.org/10.1016/j.ssresearch.2018.10.015

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Social Science Research

Received Date: 30 July 2017 Revised Date:

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Accepted Date: 31 October 2018

Please cite this article as: Wilson, G., Petersen, N., Smith, R., Maume, D., Particularism and racial mobility into privileged occupations, Social Science Research (2018), doi: https://doi.org/10.1016/ j.ssresearch.2018.10.015. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Particularism and Racial Mobility into Privileged Occupations

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George Wilson University of Miami Nick Petersen University of Miami

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Ryan Smith Baruch College

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David Maume University of Cincinnati

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Word count: 9,648

Address all correspondence to George Wilson, Department of Sociology, University of Miami, Coral Gables, Florida 33124 E-mail [email protected]

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ABSTRACT Particularism and Racial Mobility into Privileged Occupations We assess whether the “particularistic mobility thesis”, the predominant theory used to explain

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African American /White differences in mobility dynamics into occupationally privileged positions in the American labor market is applicable across a greater range of occupational destinations than previously considered, and, if so, whether it captures a racialized “glass

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ceiling”. Findings from a 2009-2014 Panel Study of Income Dynamics sample of men support broadening the scope of theory. Specifically, across four white-collar and blue-collar privileged

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destinations, African Americans, relative to, Whites, have low rates of mobility and are restricted to relying on a circumscribed and formal mobility route that is structured by a traditional range of stratification-based causal factors, i.e., background socio-economic status, human capital and job/labor market characteristics. In addition, a racialized glass ceiling in mobility prospects

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emerges across destinations based on two criteria—income and supervisory authority. We discuss how the application of theory in this broader context enhances our understanding of racebased access to occupational privilege in contemporary America and sheds light on the

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immediate and longer-term patterns of racial stratification in the American labor market.

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Particularism and Racial-Based Mobility into Privileged Occupations In recent years, sociologists have begun to document that in the new millennium, African

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American men, relative to White men, continue to be disadvantaged in prospects for achieving upward mobility into “privileged” (Kaufman 2010) managerial and professional positions (e.g., Day 2015; Wilson and Maume 2014; Wilson, Roscigno and Huffman 2015). This disadvantage

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experienced by African Americans, in fact, emerges as deep-rooted: it is found across industries, economic sectors, firms of varying sizes, geographic regions, across stages of the work-career,

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and, extends to when African Americans and Whites share identical job origins (Wilson and Maume 2014; Smith 2012; Kaufman 2010). Further, the roots of African American disadvantage lie in the mobility process itself: lower rates of African American mobility result from a discriminatorily-induced formal and narrow route, .e.g., having to “load up” on human capital

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such as education and tenure with present employer as a prerequisite to mobility, while Whites have greater access to privileged positions because of more mobility options, that is, utilizing either the formal route taken by African Americans or an informal one based on social networks

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(Wilson and Maume 2013; Wingfield 2010; Smith 2012, 2005).

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While these studies have made invaluable contributions, our understanding of the contemporary dynamics of African American/White men’s mobility into occupational privilege remains incomplete. First, studies have failed to tap into crucial stratification “fault lines” (Tilly 1998) within privileged occupational categories. “Managers”, for example, have been treated in an undifferentiated manner though they encompass traditional executive/managerial “white

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personnel recruitment policy on a broad basis (Leicht and Fennel 2001) and “first-line”

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collar” slots whose responsibility/supervisory tasks involve establishing firm direction and

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supervisory positions of blue collar workers who exert authority over a narrow range of day-today activities with little opportunity to help structure firm direction or recruitment policy, and

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constitutes a form of “elite blue collar labor” (Kalleberg 2010). Similarly, “professionals” have been treated monolithically, not distinguishing between, for example, traditional highly educated and credentialed white collar labor (e.g. engineers, accountants, lawyers) and the proliferating

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number of highly skilled/technical workers in the growing biotechnology, financial and

healthcare industries at the upper-end of the new service sector economy (Kalleberg 2010; Leicht

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and Fennell 2001). Sociologists, in fact, increasingly have differentiated between these two categories of professionals in conceptualizing the contemporary American class structure with, for example, skilled/technical workers constituting a fundamental component of the new “creative” (Florida 2002; Doneghan 2008) or “technical” (McCann 2007) class based on their unique knowledge/skill set, non-traditional credentialing, and, high rates of labor mobility

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(Kalleberg 2010; Doneghan 2008).

Second, studies of men have not adequately examined “hierarchical” mobility dynamics,

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including, most conspicuously, whether they are the basis of a racialized “glass ceiling”. In the context of occupational attainment, the racialized glass ceiling connotes the idea that lack of

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minority access increases at successively higher levels of the occupational structure (Smith 2012; Cotter, Mermson, Ovadio and Vanneman 2001). Accordingly, the notion of the racial glass ceiling is “relational” (Tilly 1998) based on stratification criteria encompassing, most conspicuously, income as well as opportunities to control/ direct a firm’s human and fiscal

“white collar” versus non-privileged “blue collar” occupations but also horizontally, that is, both

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criteria--as has been noted (see Kalleberg 2010)—not only vertically between privileged, e.g.

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resources (Maume 1999; Elliot and Smith 2001). Further, the notion of relational invokes this

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across .e.g., managers versus professionals, and, within, e.g., white collar professionals versus skilled/technical professionals, privileged occupational categories.

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The handful of studies examining the racialized glass ceiling in occupational mobility among men have been limited in scope: they rely almost exclusively on data from the pre-2000 period and restrict analyses to managers which constitutes only one segment of the privileged

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and conflates income and authority attainment, analytically separate dimensions of stratification. Overall, these studies find that African Americans, relative to Whites, have limited access to

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management operationalized as a monolithic category relative to a broad category of nonmanagerial white- and blue-collar destinations (e.g., Maume 1999). In addition, contextual factors emerge as significant: African Americans have particular difficulty gaining access to managerial destinations that entail decision-making responsibility/authority over Whites, instead

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being restricted to exercising authority over fellow African Americans, which is less remunerative and limits opportunities to influence firm policy regarding the most important business direction/daily practices and personnel issues (e.g., Smith 2012, 2005; Elliott and Smith

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2001).

This study addresses these major shortcomings in the sociological literature on African

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American/White men’s prospects for upward mobility into occupational privilege in the new millennium. In this vein, we utilize a recent sample of African American and White men from the Panel Study of Income Dynamics (PSID) to examine whether the dominant theoretical perspective--the “particularistic mobility thesis”---that documents racial inequities in mobility

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of privileged occupational destinations than previously considered, i.e., traditional white collar

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can be broadened in scope in two major ways. Specifically, is it applicable across a greater range

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managerial, traditional white collar professional, white collar skilled/technical and blue collar supervisory (“core” dynamics), and, if so, does it capture a racialized glass ceiling

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(“hierarchical” dynamics)? We believe that assessing the merits of this theory to address these two shortcomings produces the most comprehensive account to-date of African American/White men’s contemporary mobility prospects into occupational privilege, and, as such, significantly

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enhances our understanding of both immediate and longer-term patterns of racial stratification in

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the American labor market.

THE PARTICULARISTIC MOBILITY THESIS Core Dynamics

Sociologists have long maintained that traditional stratification-based causal factors operate uniformly across racial groups to structure access to privileged occupations in a single

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system of workplace attainment. In this vein, they explain access to occupational privilege for African Americans and Whites alike in terms of stratification factors encompassing background socioeconomic status through parent- and peer-driven aspirations for mobility and accompanying

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socio-economic rewards (status attainment theory—e.g., Featherman and Hauser 1978; Blau and Duncan 1967), human capital endowments such as education and labor force experience that

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constitute basic credentialing and experiential prerequisites to occupational mobility (human capital theory—e.g., Arrow 1972; Becker 1957), and, placement in niches of a differentiated labor market such as the “peripheral” sector which offers a disproportionate number of entrylevel and non-unionized positions as well as relatively unfavorable economic returns to human

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capital investments versus the “core” sector which offers relative access to internal labor markets

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and unionized jobs that are associated with opportunities for mobility and career status (dual labor market theory—e.g., Bridges and Villemez 1994; Hodson and Kaufman 1982).

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The “particularistic mobility thesis” challenges the notion that traditional stratificationrelevant factors operate uniformly across racial groups to structure mobility into occupational privilege. In so doing, the particularistic mobility thesis has emerged in recent decades as “the

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predominant theoretical explanation for racial/ethnic differences in …promotion dynamics in the American labor market” (Day 2015: 410). Distilling recent case studies and survey based

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analyses of promotion practices into privileged positions among White managers to elucidate race-specific determinants of occupational mobility, this perspectives constitutes an application of general theories of contemporary discrimination--such as “color blind racism” (Bonilla-Silva 2003; Bonilla-Silva, Embrick and Lewis 2004) and “laissez-faire racism” (Bobo, Kluegel and

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Smith 1997)--to the issue of mobility within the institutional context of work. Accordingly, in workplaces replete with avowed racial ideologies of meritocracy and “fair play” it identifies discriminatory mechanisms that while situational, institutional and ostensibly non-racial in

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character produce African American disadvantage in mobility prospects. Racial inequality in mobility dynamics, as such, emerges in employment-related decisions that are not discriminatory

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in intent but serve to disproportionately exclude African Americans from occupational privilege (Smith 2005; Tomaskovic-Devey and Stainback 2007). To date, the particularistic mobility thesis has received support from over twenty studies

that examine the causal roots of African American/White inequality in access to generic

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Maume 2014, Smith 2012, 2005: Tomaskovic-Devey, Thomas and Johnson 2005; Elliot and

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managerial and professional occupational categories (e.g. Berrey 2016; Day 2015; Wilson and

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Smith 2001; Wilson, Sakura-Lemessy and West 1999; Wilson 1997; Collins 1997; Mueller, Parcel and Tanaka 1989; Tsui and O’Reilly 1989; Braddock and McPartland 1987). It maintains

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that one institutionally-based practice disproportionately excludes African Americans from occupational privilege: promotion decisions are infused with “particularistic” (Kluegel 1978) considerations, that is, key decision-makers rely on a range of informal characteristics that are

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vaguely defined and difficult to directly measure such as perceived loyalty, sound judgment, and leadership potential (e.g., Wilson et. al 1999; Mueller, Parcel and Tanaka 1989; Kluegel 1978).

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Studies that have borne out the particularistic mobility thesis identify institutionallybased discriminatory practices in workplaces with avowed ideologies of race neutrality that make it relatively difficult for African Americans to demonstrate the informal characteristics that are prerequisites for promotion into occupational privilege. For example, Wilson et. al (1999) and

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Collins (1997) argue that African Americans disproportionally rely on segregated job networks and perform “racialized” job tasks, that is, they are restricted to performing functions to satisfy the consumer needs of African American customers/clients, both of which results in information

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bias, a form of “statistical discrimination” (Tomaskovic-Devey and Skaggs 1999) in which employers view the indirectly observable credentials of African Americans, such as school

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performance and character evaluations as less credible than similar credentials for Whites. In addition, Tsui and O’Reilly (1989) as well as Elliott and Smith (2001) highlight the detrimental consequences of minority concentration in segregated jobs, arguing that restricting African American superiors to co-racial work groups results in difficulties translating the accumulation

Further, Berrey (2016) as well as Braddock and McPartland (1987) identify African American

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communicate personal characteristics such as leadership qualities and job/firm commitment.1

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of human capital, supervisory experience and privileged job titles into opportunities to

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underrepresentation in, and the segregated operation of, management-executive and professional traineeship and internship programs that makes it difficult to demonstrate the crucial personal

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qualities necessary for promotion into top positions. Finally, the particularistic mobility thesis maintains that African Americans’ relative inability to demonstrate the informal means necessary for promotion into occupational privilege

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produces a unique set of mobility dynamics: they are restricted to a relatively formal and

deterministic route to occupational privilege in which the stratification-based causal factors

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emerge as more important underpinnings of mobility than for Whites. For example, African Americans with selected background socio-economic status characteristics, namely, relative privilege, have decidedly more intra-group opportunities to gain access to key decision-makers while White access tends to be is more diffuse and generalized across background status (e.g.,

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Smith 2012, 2005). Similarly, African Americans, relative to Whites, compensate for the dearth of opportunities to demonstrate personal characteristics by reliance on accumulating forms of human capital—such as educational attainment, labor force experience, and manifestations of job

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commitment—to reach privileged occupations (e.g., Day 2015; Wilson et. al 1999). In addition, access to key decision-makers by job/labor market characteristics is more selective among

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African Americans than Whites: levels of intra-group segregation experienced by African Americans are decidedly lower along measures of relative privilege such as working in unionized slots and in the public sector while Whites have no similar deep-rooted handicap across positions (e.g., Smith 2005).2

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that African Americans’ restriction to a relatively formal and deterministic route to mobility

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Overall, based on this discussion, we believe there is a strong basis for hypothesizing

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results--in terms of access to privileged occupational destinations—(H1) relatively low rates of mobility that are (H2) predicated on relying more than Whites a range of traditional

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individualistic characteristics encompassing a high background socio-economic status, the accumulation of significant human capital credential and, location in a favorable job/labor market segment position.

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Hierarchical Dynamics

In terms of hierarchical dynamics, we assess whether the particularistic mobility thesis

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captures a racialized glass ceiling in mobility prospects along two dimensions of socio-economic rewards, namely, opportunities to exercise job authority and income. Access to job authority represents opportunities to profoundly influence racial the dynamics of stratification in firms: those with authority play a pivotal role in setting and implementing personnel policy regarding hiring, firing and promotion, help to determine firm commitment to diversity and inclusive

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policy initiative as well as establish race-based norms of inclusion/exclusion regarding the workplace-based racial division of labor (Stainback and Tomaskovic-Devey 2012; Smith 2002).

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Income, of course, is a scarce reward coveted by all racial groups: it is a crucial component of wealth possession and all the advantages that accrue from wealth on an intra- and inter-

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generational basis (Conley 1999; Oliver and Shapiro 1995). The expectation that African Americans experience a glass ceiling in positions with

higher authority and more generous income levels is premised on an extension of particularistic dynamics. Along these lines, several studies with an organizational focus—when synthesized—

jobs (Wingfield 2010; Wilson et. al 1999), and, (2) the perceived need to maintain favorable

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firms that link the presumed “natural” association between racial and specific kinds of non-elite

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maintain that factors such as: (1) the long-standing institutional inertia/cultural proscriptions in

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relations with well-heeled and longstanding profit-generating customers/clients and a stable workforce (Collins 1997) to remain competitive and ensure survival, result in African

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Americans’ networks being “positionally delineated” (McDonald 2011) across levels of occupational privilege. Specifically, formal and informal interactions more often take place with immediate and first-line supervisors who are not crucial gatekeepers of jobs offering high levels

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of supervisory responsibility and income (McDonald 2011; Small 2009). Whites, in contrast, have more expanded networks across levels of occupational privilege that encompass key

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decision-makers at the white collar managerial and professional level as well as those particularly highly valued White professionals whose input regarding promotions are sought by key decision-makers (McDonald 2011; Wingfield 2010; Fernandez 1981). In sum, we conclude there is a firm basis for maintaining that, among African Americans,

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opportunities to communicate requisite informal promotion-relevant characteristics vary across privileged occupational destinations; they are, we suspect, inversely related to the amount of supervisory responsibility and income offered by positions. Accordingly, in privileged

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destinations that have greater levels of authority and income attainment, African Americans, relative to Whites, should experience (H3) larger gaps in the incidence of mobility, and, (H4) a

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more pronounced formalized mobility process predicted on reliance upon traditional stratification-based causal factors.

DATA AND STATISTICAL MODEL

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Study of Income Dynamics (PSID) to assess racial gaps in the incidence and determinants of

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Data were combined for 6 years—2009, 2010, 2011, 2012, 2013 and 2014—from the Panel

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mobility into four privileged occupational destinations (for a description of the PSID data set see Hill 1992). Combining the data over this 6 year period is necessary to ensure adequate sample

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sizes for all analyses undertaken. The sample population consists of all non-self-employed White and African American men who are heads of household and between the ages of 18 and 70 who worked either part-or full-time at the time of their interview.3 The application of this criteria

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resulted in a sample of African American and (N=925) and White men (N= 1646). The model used in the multivariate analyses is operationalized as follows:

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Dependent Variable: All four privileged occupational destinations are based on the 2010 broadbased census-occupational categories. The first is white collar “Managers” and consists of men in “Management in Business and Financial Occupations” positions contained within the broader “Managerial” census-based category. The second is white collar “Professionals” and consists of

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men in “Professional and Related Occupations” positions within the broader “Professionals” census-based category. The third is “Skilled/Technical” and consists of men in “Skilled/Technical Specialists” within the broader “Professionals” census-based category, and,

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finally, the fourth is “Blue Collar Supervisors” consisting of men in “First Line Supervisor” positions in the following broader census-based occupational categories, “Service Occupations”,

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“Sales and Office Occupations”, “Food Service”, “Natural Resources, Construction and Maintenance Occupations” and “Production”, “Installation, Maintenance and Repair”, Transportation and Material Moving Occupations”. We situate these privileged occupational destinations in a hierarchy based on the 2010

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level of supervisory authority of jobs within each occupational destination. Doing so, produces a

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U.S. Census for (A) mean income data for jobs within each occupational destination, and, (B)

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hierarchy in which white collar managers and professionals are higher than blue collar supervisors and skilled/technical workers. Specifically: 4

White Collar Managers (“Managers”)

$92,247

White Collar Professionals (“Professionals”)

$86,368

Elite Blue Collar (“Blue Collar Supervisors”)

$73,345

White Collar Skilled/Technical (“Skilled/Technical”)

$70,822

Authority5

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Income

Higher None

Lower

None

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Occupational Category

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Independent Variables:

Race: Race is coded as 1 for Whites and 0 for African Americans.

Background Socioeconomic Status: Background socioeconomic status is measured by two variables: the first is mother's education, which refers to years of schooling completed by respondent's mother. Mother’s education is highly correlated with father’s occupation in the PSID

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(Hill 1982) and is routinely used in analyses of the PSID because of the high rate of single parent families among PSID African Americans. The second variable is family structure when respondent was growing up. This variable is measured as whether respondent had both parents in their

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household until the age of 16 (1= yes; 0= no). Significantly, variation in family structure is a critical

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indicator of forms of social and cultural capital children can use to attain occupational mobility (Hout 2015; Ahituv and Lerman 2007). Human Capital Credentials: Several human capital attributes are included in the statistical model. The first is level of educational attainment, represented by two dummy variables: “post-college

returns to educational attainment. Second, time with employer is measured in months. Third,

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reference category. This variable is coded categorically to allow for the possibility of non-linear

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degree,” and “college degree.” Respondents with a high school degree or less serve as the

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consistent with prior research (e.g. Mueller and Price 1992), attendance at work is used as an indicator of job commitment. Specifically, job commitment is operationalized as the number of

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job absences among respondents in the year prior to their PSID interview. Job absences are reverse coded so that higher scores reflect greater commitment (i.e., fewer absences) and lower scores reflect lesser commitment (i.e., more absences). Finally, labor force experience is

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measured by number of years since age 18 respondent has worked full time.

Job/Labor Market Characteristics: Several job/labor-market characteristics are included in the

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statistical model. Union status is measured by 1 = yes, 0 = no. Further, economic sector is coded 1= public and 0= private. In addition, respondents are coded 1 if they were employed in the “core” (Beck and Horan 1978) segment of the private labor market and 0 if they were employed in the “peripheral” segment of the private labor market: the assignment to sector is based on

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criteria identical to that by Beck and Horan (1978).

Finally, job trajectory variables are utilized to assess the influence that different types of career moves have on mobility prospects, distinguishing, specifically, between intra- and inter-

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firm movement as well as intra- and inter-industry movement between present and previous job. Similarly constructed trajectory variables across industries and jobs are widely used in related

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mobility studies (e.g. see Wilson and Roscigno 2010: McBrier and Wilson 2004) because it provides an indication of, for example, the extent to which labor mobility and scope of job networks are available/used in attaining occupational mobility. Specifically, as conceptualized in prior work, staying is tantamount to being “stuck” and having relatively few networks to viably

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opportunities are relatively abundant (McBrier and Wilson 2004). Accordingly, we utilize two

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search for employment on a relatively broad basis; not staying suggests network-driven mobility

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dummy variables to distinguishing between respondents who changed firms and industries or remained with the same firm and industry between present and previous job. “Firm stay” is

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coded 1 if respondent changed firms or 0 if respondent did not change firms between present and previous job; “industry stay” is coded 1 if respondent changed three-digit industry categories or 0 if respondent did not change industry categories between present and previous job.

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Controls: Based on its unique—and negative—historical track record regarding inter-group relations (Farley and Allen 1987) and the distribution of socio-economic resources by race across

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geographic regions (Farley 2004; Jaynes and Williams 1987), we include the South as a dummy variable relative to other regions in the U.S. In addition, age (years) is included as a control. Finally, in the context of our cross-sectional design we control for occupational origins at the time each respondent was first included in the PSID as a part- or full-time worker. In this vein,

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coding is based on the 2010 census broad-based three digit occupational codes: we operationalized this variables as 3= Privileged Occupations—those contained in our dependent variable, 2=Non-Privileged Occupations—“Service Occupations”, and, “Sales and Office

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Occupations”. 1=Non-Privileged Occupations--“Natural Resources, Construction and Maintenance Occupations”, “Transportation and Material Moving Occupations”, and,

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“Installation, Maintenance and Repair Occupations.”6 Inclusion of this control, we believe, helps to partial out origin occupational differences in the work-career of sample members.

ANALYTIC STRATEGY

among African American and White men. This multivariate technique is used for evaluating

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theoretical lines—the incidence and determinants of mobility into four privileged occupations

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We use multinomial logistic regression as our primary multivariate technique to assess—along

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categorical dependent variables that are treated as distinct and unordered, and where the issue of interest is to identify the factors that contribute to being in a particular privileged occupational

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category relative to a base category for an individual with characteristics of x1. The probabilities associated with each category are defined as:

1 + Σeβjxi for j= 1,2 .., J.

1_____

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Prob (Y=0) =

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Prob(Y=j)=eβjxi

1 + Σeβjxi

The output from the multinomial logit analyses includes one less vector of coefficients than levels of the dependent variable, with estimates representing the log odds of being in a particular

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category relative to a base category. We, specifically, generate odds ratios associated with each coefficient by computing the antilog of the beta parameter.

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Our multivariate analyses proceeds as follows. First, we assess “core” dynamics by reporting in Table 2 estimates of the incidence (Model 1) and determinants (Model 2) of African

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American/White men’s differences in mobility relative to non-mobile individuals as the base category. Significantly, all studies examining the particularistic mobility thesis to-date have utilized non-mobile individuals as the base category (e.g. Wilson and Maume 2014; Smith 2005). This practice, in fact, is consonant with the notion that identifying the causal factors that

analysis, African American/White differences in determinants are assessed by interpreting a

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research (Hout 2015; Blau and Duncan 1967 Lipset and Bendix 1954). Overall, of note in this

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structure mobility versus remaining non-mobile is a long-recognized principle aim of mobility

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series of race-based interactions with all main effects in the statistical model. We, second, evaluate “hierarchical”—and, thus, “glass ceiling”—dynamics in the incidence (Figure 1) and

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determinants (Table 3) of men’s mobility by calculating predicted probabilities for the main effects of race and predicted probabilities across all levels of the race-based interaction terms for the privileged occupational destinations that constitute our dependent variable.7 This assessment

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allows for—particularly with reference to determinants—an assessment of mobility prospects into privileged occupational destinations, relative to, each other, and identify the values along

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independent variables that structure mobility.

RESULTS

Descriptive Analyses

Table 1 reports descriptive statistics for all variables in the analysis.

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(Table 1 Here)

Several findings are worthy of comment. First, findings provide initial evidence that--with respect to the incidence of mobility—the scope of the particularistic mobility thesis can be

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broadened: African Americans, relative to Whites, experience low rates of upward movement

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into all four privileged occupational destinations and a racialized glass ceiling appears to operate on the basis of both supervisory authority and income. Specifically, the racial gap favoring Whites in mobility into managerial positions is significant at the .001 level, the gap is significant at the 01. level for entry into professional positions, and it is significant the .05 level for entry

emerge: specifically, Whites have a higher background SES standing, and, in the labor market,

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Whites in the PSID sample are similar along the majority of variables, though several differences

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into both blue collar supervisory and skilled/technical positions. Second, African Americans and

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African Americans are more likely to be employed in the public sector while Whites are more

Multivariate Analyses Core Dynamics

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likely to be employed in the core sector within the private labor market.

Table 2 reports results from multinomial logistic regressions for the incidence and determinants

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men, relative to, the occupationally non-mobile.

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of upward mobility into four privileged occupational categories for African American and White

(Table 2 Here)

Incidence of Mobility

Findings provide additional support for broadening the scope of the particularistic mobility thesis: African Americans experience lower rates of movement into all four occupational

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destinations than Whites (H1). Specifically, relative to African Americans, Whites are 28 percent (P<.001) more likely to move into managerial positions, 20 percent (P<.01) more likely to move into professional positions, 16 percent (P<.05) more likely to move into blue collar supervisory

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positions, and 14 percent (P< .05) more likely to move into skilled/technical positions. Determinants of Mobility

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Creating race-based interaction terms with all independent variables in the model allows us to assess whether the process of attaining mobility is the same across racial groups when they have similar values along traditional stratification-relevant factors.

destinations for African American men—as measured by the number, directionality and

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mobility thesis. Specifically, the process of mobility into each of the four privileged occupational

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Findings utilizing this analytic strategy support broadening the scope of the particularistic

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significance levels of significant race-based interactions—is relatively formalized and structured by each of the three categories of traditional stratification-relevant causal factors, namely,

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background status, human capital credentials, and job/labor market characteristics (H2). Regarding mobility into managerial positions, one half—6 of the 12 race-based

interactions with predictors—exert statistically significant differences across racial groups, they

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operate in the direction predicted by the particularistic mobility thesis, that is, serving to make the mobility process more formal for African Americans, and the overwhelming majority—4 of

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the 6—significant variables exert robust (P<.001) statistical effects while the remaining two exert a moderate (P<.01) statistical effect. Specifically, mobility into management for African Americans, relative to Whites, increases by 1.24 times the marginal odds of not being mobile if coming from an intact family as well as by, respectively, 1.20 and 1.23 times the marginal odds

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of not being mobile with a post-college degree as well as working in a unionized job. Finally, mobility into managerial positions for African Americans, relative to Whites, increases by 1.27, 1.26 and 1.22 times the marginal odds of not being mobile, respectively, by working in the

job.

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public sector and not staying in the same firm and same industry between present and previous

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Regarding movement into professional positions, nearly one-half—5 of the 12 race-based

interactions with predictors—exert statistically significant differences across racial groups, they operate in the direction predicted by the particularistic mobility thesis and the five significant interactions are almost evenly split between exerting robust and moderate statistical effects.

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increases by 1.20 and 1.17 times the marginal odds of not being mobile, respectively, with

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Specifically, mobility into professional positions for African Americans, relative to Whites,

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working in a unionized position and obtaining a post-college degree as well as by 1.03 times the marginal odds with unit increases in time spent with present employer. Further, mobility into

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professional positions for African Americans, relative to Whites, increases by 1.25 and 1.26 times the marginal odds of not being mobile, respectively, when not staying in the same firm and the same industry between present and previous job.

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Race-specific mobility routes along lines predicted by theory are present, but are less pronounced for access to the blue collar supervisory and professional/technical privileged

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destinations. Regarding movement into blue collar supervisory positions, one fourth—3 of the 12 race-based interactions with predictors—exert statistically significant differences across racial groups, they operate in the direction predicted by the particularistic mobility thesis with significant variables exerting robust, moderate and modest (P<.05) effects. Specifically, mobility

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into blue collar supervisory destinations for African Americans, relative to, Whites increases by 1.29 and 1.20 times the marginal odds of not being mobile with, respectively, a post-college degree and working in the public sector as well as 1.02 times the marginal odds with unit

EP

increases in time spent with present employer. Regarding movement into skilled/technical positions, one-fourth—3 of the 15 race-based interactions with predictors—exert statistically

AC C

significant differences across racial groups, they operate in the direction predicted by the particularistic mobility thesis and all assert modest statistical effects. Specifically, mobility into skilled/technical slots for African Americans, relative to Whites, increases by 1.15, 1.12 and 1.02 times the marginal odds of not being mobile if respondents come from an intact family, have a

Hierarchical Dynamics

Page

18

post-college degree and with unit increases in labor force experience.

ACCEPTED MANUSCRIPT

We next estimate predicted probabilities for the main effects of race in Model 1 of Table 2 as a way of assessing the incidence of mobility into privileged occupational categories, relative to,

RI PT

each other. These predicted probabilities, which we display in Figure 1, were calculated with all covariates held constant at group mean values, and, thus, they represent the probabilities of

“average” White and African American men being in one of the five occupational categories

SC

captured by the dependent variable.8 (Figure 1 Here)

M AN U

Incidence of Mobility

Findings from Figure 1 extend analyses and indicate that the particularistic mobility thesis captures the presence of a racialized glass ceiling (H3). In particular, racial disparities are largest in access to the destinations that offer the greatest amount of supervisory responsibility and

TE D

income (i.e., white collar managers and professionals). In particular, the predicted probability of reaching the Managers destination for African Americans is less than half that of Whites (16% to 36%) and is only slightly less than half as likely for African Americans in terms of access to the

EP

Professionals destination (20% to 37%); the gap in predicted probability of mobility for African

AC C

Americans is approximately two-thirds as high as Whites in the two lower privileged occupational destinations (13% to 21%—blue collar supervisory; 15% to 23% for Skilled/ Technical positions).

calculating predicted probabilities for significant race-based interactions in Model 2. Similar to

Page

We now assess whether there is a racialized glass ceiling in the determinants of mobility by

19

Determinants of Mobility

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Figure 1, the predicted probabilities for interaction effects--presented in Table 3--were calculated with covariates held constant at group mean values so they represent probabilities for “average”

RI PT

Whites and African Americans men: in this analysis we report predicted probabilities and significance levels for all values along both significant and non-significant interactions, but restrict our discussion to the interactions that are statistically significant. Overall, given the large

(Table 3 Here)

SC

number of estimates generated, we display this information in tabular form.

M AN U

The findings regarding the predicted probabilities indicate that the particularistic mobility thesis captures a racialized glass ceiling in the determinants of mobility into the four privileged occupational destinations (H4). In this vein, we emphasize, first, racial group differences in the number of statistically significant predicted probabilities of mobility with a

TE D

particular reference to the highest levels across the statistically significant interactions. In this vein, across all significant race-based interactions, a greater number of predicted probabilities at the highest levels are significant for African Americans than Whites—thereby, indicating that

EP

African Americans are more dependent on traditional measures of socio-economic attainment to reach privileged occupational destinations than are Whites. Further, these racial differences are

AC C

greatest in access to the managerial and professional occupational destinations. In particular, regarding access to managerial occupations, across all six significant race-

based interactions (“Degree”—Post-College Degree, “Yes”—Intact Family, “Public—Public

“Public—Public Sector, “Yes”—Firm Stay, “Yes”—Industry stay, “Unionized—Union Status)

Page

probabilities at the highest level (“Degree”—Post-College Degree, “Yes”—Intact Family,

20

Sector9, “Yes”—Firm Stay, “Yes”—Industry stay, “Unionized—Union Status) predicted

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are significant for African Americans while only one (“Unionized”—Union Status) is significant for Whites. Similarly, regarding access to professional occupations, across four of five

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significant race-based interactions (“Degree”—Post-College Degree, “No”—Firm Stay, “No”— Industry stay, “Unionized—Union Status) predicted probabilities at the highest level are significant for African Americans while only two (“Degree”—Post-College Degree,

SC

“Unionized”—Union Status) are significant for Whites. In terms of access to blue collar

supervisory occupations, across two of three significant race-based interactions (“Degree”—

M AN U

Post-College Degree, “Public—Public Sector) predicted probabilities at the highest level are significant for African Americans while one (“Degree”—Post-Collee Degree) is significant for Whites. Finally, in terms of access to skilled/technical occupations, across two of three significant race-based interactions (“Degree”—Post-College Degree, “Yes”—Intact Family) predicted probabilities at the highest level are significant for African Americans while only one

TE D

(“Degree”—Post-College Degree) is significant for Whites. Second, evidence of a glass ceiling effect regarding the level of formality in the mobility

EP

process along lines enunciated by the particularistic mobility thesis derives from a comparison of within-group variation across values in predicted probabilities among the race-based interactions.

AC C

In this vein, a broader scope across values of the significant interactions indicates greater reliance on these traditional measures of attainment as a determinant of mobility. Overall, relative to Whites, the scope of variation across values among the significant interactions for African Americans is largest at the managerial and professional privileged destinations.

nearly twice the range among Whites, (e.g., Managers: Post-College, African American 6%

Page

professional destinations, the range in predicted probabilities of mobility is—at a minimum—

21

Specifically, across all values along the significant interactions for the managerial and

ACCEPTED MANUSCRIPT

(07% Degree—13% No Degree); White 3% (23% Degree, 26% No Degree). Conversely, for the Blue Collar Supervisory and Skilled/Technical destinations, across all values for the statistically

RI PT

significant interactions, the scope of predicted probabilities among African Americans is only approximately 10-20 percent greater, e.g. Blue Collar Supervisory—Post-College, African

SC

American 11% (21% Degree, 32% No-Degree) White 10% (Degree 38%, 28% No Degree).

DISCUSSION

M AN U

Findings from the PSID sample of men support broadening the scope of the particularistic mobility thesis, the predominant theoretical perspective used to identify inequity in African American/White men’s prospects for mobility into occupational privilege. Specifically, across an unprecedented range of privileged occupational destinations considered, African Americans,

TE D

relative to Whites, have low rates of mobility, rely disproportionally on a circumscribed and formal mobility route that is structured by a traditional range of stratification-based causal factors (core dynamics), and, gaps in these manifestations of racial inequity--based on income

EP

and supervisory authority--increase at higher occupational destinations, thus, evidencing a racialized glass ceiling (hierarchical dynamics).

AC C

Significantly, emerging from this study is the notion that this broad application of the

particularistic mobility thesis enhances our understanding of racial inequities in occupational mobility. First, regarding core dynamics, African American men experience racial inequality in mobility prospects across a wider range of stratification-relevant privileged occupational

Page

whether seeking white collar or blue collar privilege. These privileged destinations, in fact,

22

destinations than has been previously documented: African American men are disadvantaged

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exhibit considerable variation in socio-economic rewards and African Americans emerge as disadvantaged across all of them. Second, regarding hierarchical dynamics, the presence of a

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racialized glass ceiling in mobility indicates that inequality is not only pervasive but has a systematic and non-arbitrary character: its levels are a positive function of the amount of attendant opportunities to exercise supervisory responsibility and income attainment.

SC

Accordingly, we conclude that African American men experience heightened disadvantage in access to positons that provide the greatest opportunity to accumulate socio-economic resources

M AN U

to assist individuals and their families as well as provide opportunities—through supervisoryinduced influence on firm practices—to provide broader African Americans access to positions that offer the most rewards and influence.

In addition, we believe a broad rendering of the particularistic mobility thesis sheds light

TE D

on critical aspects of racial stratification in the American workplace. First, at the privileged level, it enhances our understanding of race based “social closure” (Weber 1968), i.e., the mechanisms that restrict the access of historically marginalized groups to privileged occupations (Weeden

EP

2002; Parkin 1970). In this vein, the restriction of African Americans to a relatively circumscribed and formal path to mobility emerges as broad as well as variable in force, both of

AC C

which constitute unique contributions of this study: previous work, for example, has paid scant attention to searching for variations in levels of closure across privileged destinations (Roscigno 2007). Second, it assists in resolving long-standing issues in racial stratification research. For example, it identifies the roots of the well-documented finding that African Americans receive

payoffs are, in part, a product of positional inequality induced by the broad swath of

Page

Whites (Jaynes and Williams 1989; Farley 2004). Apparently, the underpinning of unequal

23

lesser “payoffs” by way of earnings to socio-economic status to human capital investments than

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particularism: African Americans relatively low rates of mobility into occupational privilege produce long-term concentration in positions that offer rewards that are not commensurate with

CONCLUSION

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those occupied by similarly credentialed Whites.

SC

Unfortunately, we suspect that patterns of African American men’s disadvantage in mobility prospects into occupational privilege brought to light in this study will accelerate in the near

M AN U

future. In this vein, we invoke recent research in the sociology of work which suggests that the trend toward increasing de-bureaucratization of formal work rules/regulations and enhanced onsite managerial discretion —hallmarks of the “new restructured workplace” (Kalleberg 2010)— is heightening race-based segregation in work tasks and composition of work groups (see Wilson and Roscigno 2016). These forms of segregation, we believe, likely induce particularism

TE D

in promotion decisions, creating in the process both pronounced levels of African American men’s disadvantage in access to privileged occupations and an increasingly impenetrable glass ceiling. In addition, we suspect that continuing to reinforce and legitimate African American

EP

men’s disadvantage in mobility prospects will be an increasing inability to contest discriminatory

AC C

mobility outcomes rooted in the progressively narrowing scope of, and retreating enforcement from, federal and state anti-discrimination laws that have gathered momentum in the new millennium (see Dobbin and Kalev 2017; Berrey 2015; Dobbin 2009). To mitigate these racial inequities, we call for the implementation of broad-sweeping

lines enunciated in recent sociological research. Policies, for example, should establish

Page

equity-based formalization of stratification-relevant rules and regulations in the workplace along

24

policies that are strictly enforced by formal review boards with sanctioning power to ensure

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transparent guidelines for promotion that are grounded in objective criteria concerning, for example, the attainment of human capital credentials (Kalev 2009). Polices should also establish

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more permeable/flexible work-group boundaries that provide increasing access of African American workers to a broader range of decision-makers and facilitate integrated social networks (Castilla 2008). Further, firms should make a formal commitment to expanding meaningful

SC

African American decision-making in matters of recruitment and promotion, and, in the process, reverse the root cause of African Americans’ inability to communicate relevant informal traits to

M AN U

key decision-makers: the subtle and institutional forms of workplace segregation and isolation experienced by African Americans (Elliott and Smith 2002; Tsui, Xin and Egan 1995). We also identify a major limitation in the research design that may have affected our findings: the causal mechanisms posited by the particularistic mobility thesis as producing race-

TE D

specific mobility prospects are not directly examined. In fact, sociologists continue to emphasize the importance of more directly examining the mechanisms posited as producing racial inequities in the American workplace (Roscigno 2007; Reskin 2000). In this spirit, we are mindful that our

EP

causal mechanisms posited are inferred from patterns of significant and non-significant regression coefficients that measure objective characteristics such as human capital credentials,

AC C

job/labor market characteristics and background socioeconomic status: it is possible that unacknowledged causal factors could be driving these patterns. Accordingly, we must couch our conclusions about causal findings regarding the operation of particularism in cautionary terms. To address this limitation, we call for a “mixed methods” approach in future research that

robust nature of our findings regarding racial inequality in mobility prospects will stimulate this

Page

workplaces where the decision-making of employers can be observed first-hand. Hopefully, the

25

combines survey-based analyses with case studies of inequality-generating processes in specific

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work. We look forward to it, knowing it will enhance our understanding of a critical issue in current racial stratification research—the manner in which race structures prospects for access to

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26

AC C

EP

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M AN U

SC

RI PT

privileged positions in the American occupational structure.

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Table 1: Descriptive Statistics For PSID Sample

M

SD

Afr Am. (N=925) M

SD

Dependent Variable

57% 10.8

Intact Family Mother’s Educ. Human Capital

AC C

Union Status Public Sector Core Sector Firm Stay Industry Stay

4.4 2.6 1.5

12.0% 6.8% 19.8 10.9 12.4

TE D

Job Market

12.1% 7.0% 19.5 11.3 12.4

8.7% 8.8% 10.2% 12.5%

6.66*** 4.38** 2.77* 2.56*

2.2

67% 11.53

4.5 2.5 1.3

12.1% 7.1% 19.4 11.4 2.5

2.3

7.43*** 6.14**

4.2 2.7 1.6

2.32 1.78 2.11 1.79 1.63

14.6% 32.2% 33.8% 34.2% 46.3%

11.8% 36.8% 26.8% 36.8% 44.3%

16.7% 25.6% 42.3% 33.9% 44.7%

2.14 3.35* 3.93** 1.83 2.14

11.2% X=38.6 X= 2.1

10.0% X=38.2 X=2.2

12.4% X=39.0 X= 2.1

2.22 2.07 2.12

EP

Post-College College Time with Employer Job Commitment Labor Force Experience

2.2

40% 9.2

T-Test

SD

SC

Independent Variables

3.3% 4.6% 6.8% 9.2%

M

M AN U

6.5% 6.8% 8.9% 11.6%

Into Managerial Into Professional Into Blue Coll. Sup. Into Skilled/Technical

White (N=1646)

RI PT

Total (N=2571)

*P< .05 **P<.01 ***P<.001

.6

.7

.6

Page

South Age Occ. Origins

27

Controls

ACCEPTED MANUSCRIPT

Table 2: Multinomial Logistic Regression For Determinants of Mobility Into Four Occupational Destinations

Odds Ratio

Coeff.

Odds Ratio

Coeff.

.28*** (.09) .26*** (.09)

1.29

.20** (.09) .17* (.08)

1.21

.03** (.01) .05 (.03)

1.03

.02* (.01) .14* (.08)

1.02

.24*** (.08) .05 (.03) .02* (.01) .01 (.01) .02* (.01)

1.25

.15* (.07) .08 (.06) .02 (.02) .01 (.01) .01 (.01)

1.15

.23*** (.08) .20*** (.06) .15* (.07) .20** (.07) .19* (.09)

1.24

.14* (.07) .16* (.08) .11 (.06) .13 (.09) .12 (.08)

1.14

-.02 (.02) .02 (.02) .03 (.02)

.98

-.01 (.01) .01 (.01) .01 (.01)

1.01

Intact Family Human Capital Post-College College Time with Employer Job Commitment Labor Force Experience Job/Labor Market Union Status

Industry Stay Controls South Age

Occupational Origins Interactions with Race Predictors

1.02 1.01

1.17

1.14

1.08 1.02 1.01

1.21

1.16

AC C

Core Sector Firm Stay

1.05

1.02

EP

Public Sector

1.05

1.15 1.21 1.19

1.02 1.03

Coeff.

.16* (.07) .15* (.07)

1.16

.14* (.07) .14* (.07)

1.14

01 (.01) .07 (.04)

1.01

.01 (.01) .09 (.05)

1.01

.16* (.08) .04 (.03) .01 (.01) -.01 (.01) .01 (.01)

1.16

.10 (.07) .09 (.05) -.01 (.01) .01 (.01) .01 (.01

1.10

.09 (.05) .20*** (.06) .18* (.09) .14 (.08) .17* (08)

1.09

.07 (.04) .16* (.08) .12 (.07) .13 (.08) .10 (.06)

1.07

-.03 (.02) .01 (.01) -.01 (.01)

.97

-.10 (.06) -.01 (.01) .01 (.01)

.90

M AN U

Background SES Mother’s Education

1.27

TE D

White (Model 2)

Odds Ratio

SC

Main Effects

.01

1.11 1.13 1.12

1.01 1.01

Skilled/Technical

RI PT

Coeff. Variable

Predictors Race White (Model 1)10

Blue Collar Sup.

1.15

1.07

1.04 1.01 .99 1.01

1.21 1.19 1.14 1.17

1.01 .99

Odds Ratio

1.14

1.09

1.09 .99 1.01 1.01

1.16 1.12 1.13 1.10

.99 1.01

28

Professional

Page

Managerial

ACCEPTED MANUSCRIPT

Job Commitment Labor Force Experience Job/Labor Market Union Status Public Sector Core Sector Firm Stay Industry Stay Controls South Age Occupational Origins

1.23

.03 (.02) -.01 (.01) .01 (.01)

1.03

1.01 1.01 1.01

1.26 1.11 1.27 1.22

1.17

.20** (.06) .14 (.08) .12 (.08) .24*** (.08) .25*** (.09)

1.20

-2.292 -305.12 .27

.99

1.01

1.05 1.03 1.01 .01

1.01

.28*** (.08) .06 (.04) .02* (.01) -.01 (.01) .01 (.01)

1.29

.03 (.02) .20*** (.06) .12 (.08) .12 (.07) .13 (.07)

1.03

-.05 (.03) .01 (.01) .01 (.01)

-3.748 -303.32 .25

1.14 1.12 1.25 1.26

.95

1.01 1.01

1.05

-.01 (.01) .15* (.07)

.01 (.01) .01 (.01) -.01 (.01) -1.821 -302.02 .23

1.06 1.02 .99

1.01

1.20 1.12 1.12 1.13

.99 1.01 .99

.99 1.15

.12* (.06) .04 (.03) .01 (.01) .01 (.01) .02* (.02)

1.12

.08 (.05) .11 (.06) .09 (.05) .10 (.07) .10 (.08)

1.08

-.04 (.03) .01 (.01) .01 (.01)

.96

1.04 1.01 1.01 1.02

1.11 1.09 1.10 1.10

1.01 1.01

-4.882 -307.48 .21

AC C

Constant Log-Likelihood Pseudo R2

.22*** (.06) .25*** (.06) .11 (.06) .26*** (.07) .21** (.09)

1.07

.17** (.07) .05 (.04) .03* (.02) .01 (.01) .01 (.01)

1.07

.01 (.01) .05 (.03)

RI PT

Time with Employer

1.21

1.01

SC

College

.20** (.07) .07 (.05) .01 (.01) .01 (.01) .01 (.01)

1.24

.01 (.01) .07 (.05)

M AN U

Human Capital Post-College

1.01

TE D

Intact Family

.01 (.01) .23*** (.06)

EP

Background SES Mother’s Education

Page

29

*P< .05 **P<.01 ***P<.001 .003333, .000666, .000066 = Bonfferroni Adjustment for Model 211 Standard Errors are in parentheses

ACCEPTED MANUSCRIPT

Figure 1. Predicted Probabilities for Main Effects of Race by Occupational Categories White Men 37%

36%

35% 30% 25% 20% 16%

23%

13%

15%

SC

15% 10%

M AN U

5% 0%

Professionals

Blue-Collar Supervisory

Skilled/Technical

30

AC C

EP

TE D

Managers

Page

20%

21%

RI PT

40%

African-American Men

ACCEPTED MANUSCRIPT

Table 3. Predicted Probabilities of Mobility Into Four Privileged Occupations

Post-College Degree Degree No Degree

White .26 .23

Afr. Am .13** .07

White .29* .26

Afr. Am .16* .11

College Degree Degree No Degree

.11 .09

.09 .07

.12 .10

.10 .09

Intact Family Yes No

.27 .24

.12* .08

.13 .11

Mother’s Education 1-11 12 13+

.10 .11 .12

.10 .08 .09

.10 .11 .13

Job Commitment 1-5 6+ 11+ 21+

.13 .12* .10 .08

.11 .07 .08 .07

Private

.28 .27

Firm Stay No Yes

.27 .25

Afr. Am .23

.14 .13

.11 .11

.14 .13

.12 .10

.10 .08

.09 .08

.07 .06

.37 .28

.32* .22

.09 .09 .11

.11 .13 .14

.11 .11 .12

.13 .14 .14

.10 .11 .12

.10 .10 .08 .06

.09 .09 .07 .05

.11 .12 .12 .13

.11 .11 .10 .12

.13 .13 .14 .15

.11 .12 .13 .14

.16* .12

.14 .13

.10 .11

.24 .27

.20* .22

.14 .13

.10 .11

.15* .11

.30 .27

.17* .12

.12 .11

.09 .08

.12 .11

.10 .09

TE D

M AN U

SC

RI PT

White .38* .30

EP

White .28* .28

Skilled/Technical

Afr. Am .22* .21

AC C

Public Sector Public

Blue-Collar Supervisory

31

Professionals

Page

Managers

ACCEPTED MANUSCRIPT

.29

.16*

.13

.09

.12

.10

.20

.10

.27

.12

.11

.08

.12

.10

Not Unionized

.28 .24

.12* .09

.27* .23

.13* .10

.10 .11

.14 .13

.11 .12

Time With Employer 1-5 6+ 11+ 21+

.08 .10 .08 .08

.07 .08 .07 .06

.15 .18 .22* .24

.06 .08 .11 .12*

.12 .13 .16* .18

.09 .09 .11 .13

.12 .13 .11 .12

.09 .10 .09 .10

Labor Force Experience 1-5 6+ 11+ 21+

.08 .07 .06 .06

.06 .06 .05 .05

.06 .08 .08 .07

.05 .07 .06 .06

.07 .07 .07 .09

.06 .05 .06 .07

.11 .13 .14 .17

.07 .09 .10 .12

.16 .13

.14 .12

.12 .11

.09 .07

.07 .06

.14 .11

.12 .10

No

.003333,

*P< .05 **P<.01 ***P<.001 .000666, .000066

M AN U

SC

.13 .12

TE D EP

Core Sector Yes

AC C

Union Status Unionized

.14 .12

=

Bonfferroni

Adjustment

for

Model

32

.13*

RI PT

.28

Page

Industry Stay No Yes

ACCEPTED MANUSCRIPT

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Kanter, Rosabeth. 1977. Men and Women of the Corporation. Cambridge: Harvard University Press..

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NOTES 1

This line of work draws from a rich theoretical tradition concerned with the effects of

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relative numbers of different racial groups in organizations and occupations/jobs (see Kantor 1977; Blalock 1967). 2

Dynamics associated with the particularistic mobility thesis also extend to socio-

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demographic factors, which are our control variables. Specifically, African Americans

face unique hardships in conveying personal characteristics because of higher levels of

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segregation in the workplace in the South than other regions of the U.S. (Wilson 1997), and, the bonds of workplace segregation are most likely to be reduced as they move into the middle of the work-career and as a product of age (Day 2015). 3

Sample size restrictions precluded examining mobility dynamics among women as well

as incorporating Latinos/Latinas into the sample.

Placement in this hierarchy receives support by Duncan SEI scores. Specifically, jobs

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contained within the four destination occupational categories had the following mean SEI

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scores: Managers—55.9, Professionals 53.6, Skilled/Technicians 44.3, Blue Collar Supervisors 39.3.

The PSID does not measure job authority at the individual level for any of the years in

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which we analyzed data. 6

This coding scheme is based on mean Duncan SEI scores for the specific jobs within

each occupational category.

across different values of the independent variables in multinomial logistic regression

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This mode of analysis is based on the recognition that interactions effects may differ

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(Long and Freese 2014). In terms of execution, we calculated marginal effects, holding other covariates constant at mean values. Figure 1 excludes predicted probabilities for non-mobile workers because of our

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primary interest in comparing each of the four privileged occupational categories with one another. Results for this group in terms of predicted probabilities of mobility into

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each privileged occupational destinations are available upon request.

As the public sector has long been an “occupational niche” (Wilson and Roscigno 2010)

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for African Americans, that is, it is the segment of the labor market in which they have achieved relative parity with Whites in the post-1965 civil rights era, we view this as the “higher” category of socio-economic attainment for African Americans. 10

These effects of race were entered with all main effects in the statistical model and

constitute model 1 of our hierarchical regression procedure. The specific coefficients for

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all main effects in model 1 are available upon request. We utilize the Bonferroni adjustment for standard errors as a multiple comparison

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correction. Specifically, we divide standard alpha level cut-offs (i.e., .05, .01, .001) by the number of interaction significance tests (15) in Model 2 of Table 2. We, however, do not

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utilize the Bonferroni adjusted standard errors in Model 1 of Table 2 because multiple

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group comparisons (i.e., race-based interactions) were not estimated in Model 1.