Stay or Leave? Race,Education,and Changing Returns to the External Labor Market Strategy,1976

Abstract

Since the 1970s, firm-internal opportunities for advancement have waned, and more employees have switched employers to build their career. The author compares the effect of staying and leaving one’s employer and how each career avenue reproduces or alleviates race-based earnings inequality. Using the Panel Study of Income Dynamics 1976–2009, the author finds that racial differences among women are unaffected by external mobility. Among men, the effect of switching depends on education: Since the 1970s, the Black–White gap first widened and then narrowed among male high school graduates. In contrast, the race gap first narrowed and then widened among male college graduates.

Keywords

job mobility income inequality race education intersectionality

Since the 1970s, jobs in the United States and most other industrialized nations have become less secure (Cappelli, 1999; Hollister, 2011; Kalleberg, 2011; Osterman, 1999). Many employees can no longer expect to work for the same company for the next 15 to 20 years. As traditional job ladders within big corporations become less common and advancement within companies becomes increasingly difficult, more people—voluntarily or involuntarily—switch employers. With externalized job mobility, employees increasingly build their career by moving between employers instead of climbing firm-internal career ladders.

Scholars disagree over how this shift has affected the racial earnings gap. Some argue that the market-based boundaryless career (Arthur & Rousseau, 1996) grants access to new external opportunities and allows employees to circumvent racialized barriers to firm-internal advancement. This may have prevented the race gap from widening further. Others counter that externalized mobility causes racial disparities to widen as it increases the importance of inequality-reproducing processes in the hiring process (e.g., Dovidio & Gaertner, 2000; McPherson, Smith-Lovin, & Cook, 2001; Petersen, Saporta, & Seidel, 2000; Ridgeway, 1991). My study engages this debate by examining whether externalization of job mobility is associated with a widening of Black–White inequality since the 1970s or whether externalization prevented differences from becoming even more pronounced.

I advance the literature in three ways: First, this article contributes to our understanding of race-based earnings disparities by connecting studies on hiring discrimination (e.g., Fernandez, Castilla, & Moore, 2000; Reskin & Roos, 1990) with studies on racial differences in firm-internal career advancement (e.g., Baldi & McBrier, 1997; Smith, 2005; Wilson & Roscigno, 2010; Wilson, Sakura-Lemessy, & West, 1999). By doing so, this article compares the effect of staying and leaving and how each career avenue reproduces or alleviates race-based earnings inequality.

Second, few studies have examined how outcomes of staying and leaving have changed since the 1970s. I use the 1976–2009 waves of the Panel Study of Income Dynamics (PSID) to examine whether the effect of switching employers has changed over time. Results demonstrate that the earnings advantage of starting with a new employer increased since the 1970s and that not all employees benefit equally. This article provides a new perspective on how macro- (e.g., externalization) and microprocesses (e.g., uncertainty in hiring situations) interact over time and affect aggregate earnings differences.

Third, this article builds on recent intersectionality research (e.g., Browne & Misra, 2003; McCall, 2001) and expands our understanding of how racial differences in mobility outcomes are mediated by gender and education. I find that externalization has little effect on racial differences among women. Among men, the effect differs dramatically by employees’ education. For male high school graduates, external job mobility is associated with a widening of the racial earnings gap until the late 1980s, when racial differences begin to narrow again. Running in the opposite direction, racial disparities among male college graduates initially narrowed but then widened in the late 1990s and early 2000s. When disaggregating voluntary and involuntary decisions to leave an employer, the results demonstrate that racial disparities among leavers cannot be explained by the circumstances under which respondents left their previous employer.

Similar to the effect of externalization on gender earnings disparities (Kronberg, 2013), this article suggests that the effect of firm-external job mobility on aggregate racial disparities is shaped by politically mediated opportunity structures (S. Collins, 1996; Sites & Parks, 2011; Stainback, Robinson, & Tomaskovic-Devey, 2005). When social and political pressures, such as equal employment laws and social movement organizations, are strong, racial disparities narrow quickly among leavers. When political opportunity structures are weaker (e.g., among lower educated employees or since the late 1990s), it appears that other inequality-reproducing mechanisms move to the forefront, causing the race gap to widen quickly.

Racial Trends Among Men and Women Since the 1970s

Trends in Black–White earnings disparities since the 1970s differ greatly among men and women. Following the passage of the Civil Rights Act in 1964, Black women’s earnings were almost at parity with White women’s earnings, but then racial differences reemerged and widened steadily to around 19% in the early 2000s (Dozier, 2010; Pettit & Ewert, 2009). The race gap among men never fully closed after the Civil Rights Movement, and Black men earned about 14% less than White men in the 1970s. This gap widened further to about 25% in the early 1980s and then dropped to 4% to 6% in the late 1990s (Gottschalk & Danziger, 2005; Semyonov & Lewin-Epstein, 2009; Western & Pettit, 2005).

Divergent trends in earnings are due to different mechanisms among men and women (Acker, 2006; Browne & Misra, 2003; P. Collins, 1999; Crenshaw, 1989; McCall, 2001). For instance, restructuring and downsizing has been particularly prevalent in predominantly male industries like manufacturing (Baumoal, Blinder, & Wolff, 2003). At the same time, many female-dominated jobs were created in lower paying service industries (Kasarda, 1995; Lorence, 1992), while many male-dominated jobs were created in the highly paid information technology and financial sectors (Neumark & Reed, 2004). Hence, although industrial restructuring is one of the most important contributors to racial earnings inequality among men, it can only partially explain the racial gap for women. Instead, racial disparities among women are mostly shaped by family structure, neighborhoods, and social networks (Browne, 2000). In summary, evidence suggests that the effect of externalized job mobility on racial disparities depends on employees’ gender such that the effect may be greater for men than women. To account for these gender differences, I will examine racial disparities separately among men and women.

Racial Trends Among High School and College Graduates Since the 1970s

Racial earnings disparities are also affected by different mechanisms depending on employees’ education. Racial differences among lower educated employees have been shaped by deindustrialization, stagnant federal minimum wage, and declining federal welfare programs, while racial differences among college graduates have been strongly affected by equal employment opportunity (EEO) and affirmative action laws. Moreover, one of the most important factors contributing to the differences between educational groups is skill-based technological change. The introduction of computers to workplaces eliminated many lower skilled, routinized jobs, which were primarily held by high school graduates. At the same time, computerization increased demand for cognitive and interpersonal skills, which are perceived to be held by college graduates. This changing demand for skills contributed to the college/high school wage premium that grew from 30% in the 1970s to about 50% in the late 1990s (e.g., Autor, Katz, & Kearney, 2008; Cunha, Karahan, & Soares, 2011; Freeman, 1994).

Not all college graduates benefited from the college wage premium, however. Between 1970 and 1990, highly educated Black employees sustained the greatest earnings losses compared with other racial and educational groups (Chay & Lee, 2000). Similarly, Grodsky and Pager (2001) and Huffman (2004) demonstrate that within-occupation racial earnings gaps are greater in higher status occupations than lower status occupations. Hence, the relative Black–White earnings gap is greater among college graduates than high school graduates.

Externalization of job mobility may be closely related to the increasing college wage premium. When hiring an external applicant, employers are more likely to rely on formal credentials, such as college degrees, to assess an employee’s fit or suitability. As more firms hire external applicants (instead of promoting internally), having a college degree should become increasingly important for career advancement. Hence, those with general, transferable education (i.e., college graduates) should be able to access increasingly better jobs, whereas those who lack general education are increasingly penalized (Cappelli, 1999). Greater demand for and emphasis on educational credentials might also reduce the importance of ascribed characteristics such as race among highly educated workers. Thus, while research shows that racial disparities are most pronounced in occupations with higher average income, externalized job mobility may have kept that gap from opening even further. In summary, evidence suggests that the effect of externalized job mobility on racial disparities depends on employees’ education, and I will examine the effect of race separately by employees’ education.

Changes in Job Attainment Since the 1970s

I distinguish between two types of job mobility: Firm-internal mobility describes employee movement from one job to another within the same organization. These promotions or transfers are based on performance and bureaucratic rules such as seniority (Doeringer & Piore 1971; Grimshaw, Ward, Rubery, & Beynon, 2001). Firm-external mobility describes employee movement from one company to the next via a hiring process. Labor economists perceive firm-external mobility to be more market-driven and performance-based because it relies less on bureaucratic rules, such as seniority. Instead, firm-external mobility focuses more on employees’ credentials (Lazear & Oyer, 2004).

In the past 40 years, increasing global competition, quickly advancing technology, and deregulation led to an extended period of organizational restructuring. Opening internal positions to external applicants, downsizing, delayering of organizational hierarchies, and the adoption of nonstandard and contingent work arrangements resulted in a gradual decline of lifetime commitment between companies and their employees (Davis, Diekmann, & Tiensley, 1994; Grimshaw et al., 2001; McGovern, Hope-Haily, & Stiles, 1998). Consequently, employees increasingly access jobs by changing employers, instead of being promoted internally (Bidwell, Briscoe, Fernandez-Mateo, & Sterling, 2013; Cappelli, 1999; Farber, 2008; Hollister, 2011; Neumark, Polsky, & Hansen, 1999; Osterman, 1999).

External Job Mobility and the Black–White Earnings Gap

Scholars debate over whether externalization of job mobility reduces or exacerbates existing labor market inequality. Literature rooted in neoclassical economic theory, such as Becker’s (1971) theory of discrimination and the boundaryless career approach (Arthur & Rousseau, 1996), emphasizes the equalizing effect of the market. In contrast, theories focused on the inequality-reproducing nature of informal labor market processes predict adverse effects on racial equality in wages (e.g., Dovidio & Gaertner, 2000; Petersen et al., 2000). I assume these inequality-alleviating and inequality-exaggerating processes occur simultaneously—as some processes become more salient than others, the net effect of switching employers on the race gap becomes positive or negative. Next, I discuss these two theoretical approaches and their empirical predictions. I group theoretical approaches based on their empirical predictions without testing each theory separately, which is beyond the scope of this article.

Closing the Racial Earnings Gap

Firm-internal criteria for advancement are not automatically race neutral. Instead, organizational rules and practices often create race-specific barriers to upward mobility (Acker, 2006; Williams, Muller, & Kilanski, 2012). For instance, past work shows that mobility paths in work organizations are more narrow and circumscribed for minorities (e.g., Baldi & McBrier, 1997; Smith, 2005; Wilson & Roscigno, 2010; Wilson et al., 1999). Promotion odds are strongly affected by the number of non-White employees. Likewise, minorities are often relegated to less visible, dead-end positions that are poorly connected to important promotion opportunities (e.g., S. Collins, 1996; Kanter, 1977). This results in fewer promotions for Black employees.

Arthur and Rousseau (1996) argue that a boundaryless career between organizations enable these employees to leave dead-end positions and find better positions in other firms. Externalization of job mobility might afford minorities more job opportunities outside of their current company, allowing them to circumvent firm-internal barriers to advancement and thereby reduce racial earnings inequality. Similarly, Becker’s taste theory of discrimination (1971) predicts that the more competitive nature of firm-external mobility reduces employers’ discrimination, as a taste for discrimination becomes a competitive disadvantage when firms compete for the best employees. Thus, the boundaryless career literature predicts that externalization of job mobility gives more agency to employees by providing access to firm-external opportunities at any time.

Historically, supply- and demand-side mechanisms have been strongly mediated by political factors such as EEO and affirmative action laws (S. Collins, 1996; Stainback et al., 2005). As the representation of African Americans was particularly low in professional and managerial jobs (Semyonov, Haberfeld, Cohen, & Lewin-Epstein, 2000; Stainback & Tomaskovic-Devey, 2012), EEO legislation created a demand for highly educated Black employees—at least initially (e.g., Chay, 1998; W. Collins 2003; Stainback et al., 2005). Neumark and Stock (2006) show that racial discrimination laws resulted in greater earnings increases among Black employees. Arguably, the enforcement of EEO laws is especially important during the hiring process, as hiring leaves more opportunities to discriminate (Petersen & Saporta, 2004). If equal employment laws create a greater demand for Black professionals, this group may be able to negotiate higher starting salaries when switching employers. In summary, theories focusing on the equalizing role of the market argue that firm-external mobility allows minorities to circumvent race-specific barriers in firm-internal career ladders. Thus, externalization of job mobility should be associated with narrowing racial disparities.

Widening of the Racial Earnings Gap

While neoclassical theories often stress the equalizing role of the market (e.g., Becker, 1971; Lazear & Oyer, 2004), other research points to inequality-reproducing mechanisms in the hiring process such as the importance of social networks and vulnerability to discrimination. A growing body of literature highlights the importance of social capital for successful career attainment in the external labor market (e.g., McPherson et al., 2001; Petersen et al., 2000). Social capital describes the number and kinds of potential contacts individuals use to access different resources such as information (Granovetter, 1995). Better access to information not only increases the likelihood of applying successfully, but it is also associated with higher starting wages (e.g., Brodt, 1994; Fernandez et al., 2000; Seidel, Polzer, & Stewart, 2000).

The increasing prevalence of external job mobility and thus more frequent reliance on social networks may lead to widening racial inequality as “race and ethnicity is clearly the biggest divide in social networks today in the United States” (McPherson et al., 2001, p. 420). Continuing segregation of social networks limits the number and kinds of potential contacts Black employees can draw on. This disadvantages Black employees in the process of finding and applying for positions on the external labor market (Petersen et al., 2000).

Additionally, the more ambiguous nature of the hiring process might render minorities more vulnerable to discrimination in the hiring process than the promotion process (Petersen & Saporta, 2004). The social-cognitive approach to inequality (e.g., Bielby, 2000; Reskin, 2000; Ridgeway, 2011; Ridgeway & Correll, 2004) argues that individuals tend to carry (conscious and unconscious) stereotypes at all times. Once activated, these stereotypes or status expectation states affect individuals’ cognition and decision making such that White employees are perceived to be more competent and more deserving of rewards than Black employees (Biernat & Kobrynowicz, 1997; Ridgeway, 1991). Dovidio and Gaertner (2000) show that cognition and behavior is especially biased in ambiguous situations as individuals need to use their own discretion. In an experiment, participants were more likely to favor White over Black applicants, when the applicants’ qualifications were neither outstanding nor insufficient, hence making the hiring situation more ambiguous.

The likelihood for racial discrimination in hiring choices could render Black leavers more vulnerable to discrimination than Black stayers, as the problem of incomplete information is more prevalent during the hiring process than in the promotion process. Presumably, hiring managers have less information about external applicants than about internal incumbents, who already work for the organization (Bidwell, 2011; Chan, 1996; Petersen & Saporta, 2004). With greater ambiguity in the hiring process, the social-cognitive approach predicts that firm-external mobility is associated with greater racial disparities.

Finally, while broader political and social pressures in the 1960s and 1970s improved opportunities for minorities, these political pressures subsequently weakened (S. Collins, 1996). With weakening enforcement of EEO laws and the focus of social movements on other dimensions of inequality such as lesbian, gay, bisexual, and transgender (LGBT) rights, job mobility increasingly occurs in an environment in which jobs are resegregated (Stainback & Tomaskovic-Devey, 2012), and Black employees, particularly in prestigious jobs, are more likely to experience job dismissal and occupational downward mobility (Wilson, 2005; Wilson & Roscigno, 2010). As politically mediated opportunity structures for minority employees are waning (S. Collins, 1996; Stainback et al., 2005), inequality-reproducing mechanisms may become more salient. In summary, literature pointing to the inequality-reproducing effect of informal labor market mechanisms argues that firm-external mobility is strongly characterized by these informal mechanisms and race barriers. Thus, externalization of job mobility should be associated with a widening of the race gap.

Alternatively, there might be no association between externalized job mobility and racial disparities. Either inequality-reproducing and inequality-alleviating mechanisms cancel each other out or racial disparities are independent of job mobility. We also might find an association between external job mobility and racial disparities among some groups but not others. For instance, as men and women are more predominant in different types of jobs, there might be an effect among men but not among women (Dozier, 2010).¹

Data

To examine how firm-external job mobility has affected racial disparities since the 1970s, I use the PSID, which is an ongoing, representative sample of households in the United States (Hill, 1992). The panel’s data collection began in 1968 with a nationally representative sample of approximately 4,800 households. Respondents were interviewed every year until 1997, when interviews were conducted on a biennial schedule. My analysis of racial differences among men starts in 1976, which is the first year tenure is recorded in sufficient detail (Brown & Light, 1992). The analyses among women begin in 1979, as this was the first year in which employment data of wives and female partners became consistently available. I exclude self-employed respondents in order to contrast the effect of staying versus leaving an employer on earned income. My sample only includes non-Hispanic Whites and Blacks, because they are the only two racial/ethnic groups for which there are sufficient data between 1976 and 2009.² Finally, I limit my sample to the working-age population (18–65 year olds).

One drawback of the PSID is that employers are not uniquely identified. Therefore, I rely on changes in tenure to identify employer changes. This is problematic because respondents sometimes misremember their tenure (Brown & Light, 1992), especially when they have been working for the same employer for longer periods of time. While other studies have recognized this problem, they still use these data successfully to examine firm-external job mobility (e.g, Brown & Light, 1992; DiPrete, deGraaf, Luijkx, Tahlin, & Blossfeld, 1997; Mouw & Kalleberg, 2010; Sicherman & Galor, 1990). This shortcoming is outweighed by several advantages of using the PSID over other similar longitudinal panel studies such as the National Longitudinal Study of Youth (NLSY) or the Study of Income and Program Participation (SIPP). Unlike the NLSY, the PSID interviews multiple cohorts at a time, and unlike the SIPP, the PSID started data collection relatively early and has not discontinued the core sample since then. These features allow me to analyze historical developments, without confounding them with age and cohort effects.

As most longitudinal studies, the PSID is affected by nonrandom panel attrition over time. Lower educated, low-income, and Black respondents are more likely to stop participating over time (Fitzgerald, Gottschalk, & Moffit, 1998). Despite the attrition, income and mobility estimates using the PSID are comparable with estimates obtained using the Current Population Survey and better than other panel surveys such as the NLSY or the SIPP (Gouskova, Andreski, & Schoeni, 2010; Schoeni, Stafford, McGonagle, & Andreski, 2013; Zabel, 1998). More problematic is the growing percentage of lower educated Black men who have dropped out of the labor force altogether since the 1980s (e.g., due to incarceration). As Black men with the lowest earnings potential leave the workforce (voluntarily or involuntarily), the racial gap among high school-educated men has closed even though racial barriers may have stayed the same (Juhn, 2003; Neal, 2004). I will discuss this further in the Results section.

Measures

Dependent Variable

Similar to previous research (e.g., Dreher & Cox, 2000; Fuller, 2008; Lam & Dreher, 2004; Mincer, 1986; Topel & Ward, 1992), I use the natural logarithm of respondents’ hourly income from salary and wages at their most current main job in 2009 dollars to assess earnings. I exclude respondents in years in which they are unemployed or self-employed (as they have no income from employment). When individuals reenter employment, they reenter my analyses and their earnings are compared with the most recent year in which they were employed.³ Finally, I exclude outliers with either extremely low earnings (less than $5 per hour) or extremely high earnings (over $200 per hour), as outliers might bias the estimates. Appendix A provides an overview of all variables in the analysis, and further descriptive statistics by gender and education are provided in the Appendixes B and C.

Key Explanatory Variables

Similar to previous job mobility research (e.g., Brett & Stroh, 1997; Brown & Light, 1992; Dreher & Cox, 2000; Mouw & Kalleberg, 2010), the leaver (vs. stayer) variable indicates that individuals started with a new employer since the previous interview. I code individuals as leavers when their tenure with the current main employer is less than the time elapsed since their last interview (Brown & Light, 1992).⁴ If respondents just entered the panel or when the PSID conducted interviews biennially after 1997, I categorize respondents as leavers when their tenure in a given year is less than 12 months. When respondents’ tenure is internally inconsistent (e.g., if tenure increases or decreases by more than 12 months within one year), I code them as missing in these years.

In addition to measuring job mobility dichotomously (stay vs. leave), I also conduct analyses in which I distinguish between voluntary and involuntary leavers, to assess whether the effect of leaving is driven by reasons for the departure. I code employer changes as voluntary when respondents quit their job. Job mobility occurred involuntarily when respondents were fired, laid off, or when their company folded.⁵

I categorize respondents’ first mentioned race into two groups: Black and White. The variable White is coded 1 for non-Hispanic Whites and 0 for non-Hispanic Blacks. Finally, I will conduct all analyses separately for men and women and for employees with a high school degree or less (up to 12 years of education) and those with a college or graduate degree (16+ years of education).

Control Variables

I control for differences in human capital, demographic, and job characteristics as well as geographic and industry effects. The first set of control variables consists of human capital measures: Years of education, years of full-time employment since the age of 18, and tenure with the current main employer in log months. I also control for whether respondents were unemployed in the previous year and whether respondents changed industry since the last interview. Previous unemployment and entry into a new industry are associated with loss of human and social capital and often result in earnings losses (Arulampalam, 2001; Gangl, 2006; Sullivan, 2010).

The second set includes additional demographic controls: marital status, number of children under 18 in the household, age, and birth cohort. Including age and cohort dummies accounts for the age- and cohort-specific effects of job mobility on employment outcomes. Employer changes affect careers more positively when mobility occurs in the earlier career stages than in later career stages (e.g., Lam, Ng, & Feldman, 2012; Topel & Ward, 1992). This may explain some of the racial differences as Black employees tend to switch employers less often in early career stages but more often in later career stages when compared with Whites (e.g., Alon & Tienda, 2005; Fuller, 2008; Oettinger, 1996).

The third set of variables control for occupational and job characteristics. To distinguish the direct effect of individuals’ education from the kinds of jobs employees worked in, I control for whether individuals work in a bad (vs. good) job. Good jobs are characterized by higher pay, greater job security, and better career opportunities. In contrast, bad jobs provide few opportunities for upward mobility and are often poorly paid (Doeringer & Piore, 1971; Hudson, 2006; Kalleberg, 2011; Kalleberg, Reskin, & Hudson, 2000). As good jobs are associated with greater entry barriers for minorities, greater earnings, and less frequent job changes, I am controlling for whether individuals work in a good or bad job.⁶

My measure of job quality is based on the instrument developed by Kalleberg et al. (2000) and Hudson (2006). Jobs are good when employers provide health insurance and pension plans and when earnings are above 120% of the federal poverty threshold. I use the annual March Supplements of the Current Population Survey via the Integrated Public Use Microdata Series (King et al., 2010) to compute the percentage of employees covered by employer’s health insurance, percentage covered by employer pension plans, and percentage of employees with an income above 120% of the federal poverty thresholds for each occupation-by-industry cell.^7–9 Similar to Hudson, I average these three percentages to construct a continuous measure of job quality. The final measure ranges from 0 (very good job) to 1 (very bad job). Different measures of job quality led to the same substantive results, which speaks to the measure’s validity.¹⁰ In analyses not shown here, I also tested whether the effect of being a leaver and the effect of being White is impacted by job quality. These interactions failed to reach conventional levels of significance.¹¹

Additionally, I also control for the following occupational and job characteristics: Percent female and percent Black control for the average percentage of female and Black employees in each occupation-by-industry cell.¹² I calculated percent female and Black using the annual March Supplements of the Current Population Survey via the Integrated Public Use Microdata Series (see notes 8 and 9 for further details). I also control for part-time status, average hours worked per week, presence of a worker’s union, and location in the private (vs. public) sector as all of these factors influence mobility patterns and earnings.

The final set of control variables accounts for geographic- (Northeast, South, Midwest, and West) and industry-specific variations in income and mobility patterns.¹³ I also control for business cycles by including the annual unemployment rate by state and a recession dummy.¹⁴

Analytic Strategy

Because my data are clustered, with repeated individual observations nested in individuals, I use growth curve models to estimate earnings.¹⁵ These models partition earnings variation into within-individual and between-individual components and therefore take into account that repeated observations within each person are correlated. Growth curve models are particularly well suited to estimate within-individual growth over time and how growth varies systematically between individuals, for example, between Black and White employees (Raudenbush & Bryk, 2002; Singer & Willet, 2003). These models are also less sensitive to unequal spacing of observations and when respondents participate in the panel for only a few waves (Raudenbush, 2002; Raudenbush & Bryk, 2002). As the number of observations per individual in my data vary between 3 and 24, and observations are spaced unequally (e.g., due to transitions in and out of unemployment), growth curve models are the most appropriate analysis method.

The repeated observations model (Level 1) predicts earnings for person i at time t. As I am interested in earnings growth between 1976 and 2009, a key variable is time.¹⁶ When controlling for elapsed time, the overall intercept ( $π 0$ ) indicates average hourly earnings in 1976, and the time and time-squared variables describe how average earnings have grown since 1976 ( $π 1 and π 2$ ).¹⁷ In addition to time, the repeated-observations model includes all other time-varying predictors such as whether respondents started with a new employer (leaver).¹⁸ To examine whether earnings growth differs for stayers and leavers, I introduce an interaction between leaver and time. The main effect for leaver ( $π 3$ ) gauges the average earnings gap between stayers and leavers in 1976, whereas the interaction effect between leaver and time ( $π 4$ ) indicates whether the effect of leaving has changed since 1976.

Earnings it = π 0 + π 1 (time) it + π 2 (time 2) it + π 3 (leaver) it + π 4 (leaver * time) it + \dots + ɛ it

(1)

To gauge between-person variation, the person-level model (Level 2) introduces the time-constant variables race and birth cohort. Level 1 slopes and intercepts now become a function of time-invariant employee characteristics. For instance, Equation 2 shows that average earnings in 1976 ( $π 0$ ) are a function of average earnings for Black employees ( $β 00$ ) plus the differences between Black and White employees in 1976 (β₀₁).

π 0 = β 00 + β 01 (White) i + \dots + u 0 i

(2)

π 1 = β 10 + β 11 (White) i + u 1 i

(3)

Similarly, Equation 3 shows that earnings growth $(π 1)$ comprises average earnings growth among Black employees $(β 10)$ plus the difference between Black and White growth ( $β 11$ ). When substituting the person-level equations (Equations 2 and 3) in Equation 1, the result is the combined Equation 4, which demonstrates that all interactions between race, time, and job mobility are essentially cross-level interactions (Raudenbush & Bryk, 2002). I introduce the cross-level interactions to examine whether starting with a new employer affects Black and White employees differently and whether this changes over time.

Earnings it = β 00 + β 01 (White) i + β 10 (time) it + β 11 (time * White) it + \dots + β 30 (leaver) it + β 31 (leaver * White) it + β 40 (time * leaver) i + β 41 (time * leaver * White) it + \dots

(4)

Results

Racial Disparities Since the 1970s by Gender and Education

Tables 1 and 2 show the multivariate growth curve estimates for women and men, respectively.¹⁹ In each table, the first column shows the basic model for respondents with a high school degree or less, while the second column shows the results for college graduates. I then add the interaction between race and job mobility in the third and fourth columns to examine whether the effect of leaving depends on race. Regardless of gender, racial differences were significant in the late 1970s among high school graduates (Model 1a and 3a: β₀₀₁) but not among college graduates (Model 2a and 4a: β₀₀₁). Controlling for individual, job, and regional characteristics, racial disparities did not change significantly since the 1970s, except among female high school graduates—where racial differences increased significantly (Model 1a: β₀₁₁).

Table 1.

Growth Curve Estimates: Effect of Race and Job Mobility on Income, Women 1979–2009.

	Model 1a High School	Model 2a BA/Grad	Model 1b High School	Model 2b BA/Grad
β₀₀₀ intercept	2.25 (41.43)***	3.77 (11.66)***	2.25 (41.33)***	3.77 (11.65)***
β₀₁₀ time	−0.21 (−5.16)***	−1.21 (−5.57)***	−0.21 (−5.06)***	−1.22 (−5.57)***
β₀₂₀ time-squared	0.04 (6.84)***	0.01 (0.32)	0.04 (6.42)***	0.01 (0.63)
Job Mobility
β₀₃₀ leaver	0.05 (4.79)***	0.02 (0.82)	0.05 (3.60)***	0.00 (0.04)
β₀₄₀ leaver × time	−0.06 (−4.52)***	0.01 (0.38)	−0.07 (−3.08)**	0.08 (0.97)
β₀₅₀ leaver × time2	0.02 (4.74)***	0.03 (2.51)*	0.03 (2.99)**	−0.01 (−0.33)
Race
β₀₀₁ White	0.04 (2.73)**	0.03 (0.57)	0.04 (2.64)**	0.02 (0.46)
β₀₁₁ White × time	0.06 (3.04)**	0.04 (0.72)	0.05 (2.48)*	0.06 (0.89)
β₀₂₁ White × time2	−0.01 (−1.88)	−0.01 (−0.31)	−0.01 (−1.55)	−0.01 (−0.63)
Job Mobility by Race
β₀₃₁ leaver × White			−0.01 (−0.63)	0.02 (0.36)
β₀₄₁ leaver × White × time			0.01 (0.44)	−0.09 (−0.92)
β₀₅₁ leaver × White × time2			0.00 (−0.16)	0.05 (1.53)
Controls included?	yes	yes	yes	yes
Variance Components
Level-1 variance (σ²)	0.03	0.05	0.03	0.05
Intercept (τ₀₀)	0.13	0.16	0.13	0.16
Intraclass correlation (ρ)	0.83	0.78	0.83	0.78
N person-years	24,244	9,503	24,244	9,503
N persons	3,228	1,223	3,228	1,223
LL	2,469	−1,763	2,470	−1,759

Note. t values in parentheses, dependent variable is the natural log of hourly earnings from salaries and wages in 2009 dollars.

*p < .05. **p < .01. ***p < .001 (two-tailed).

Table 2.

Growth Curve Estimates: Effect of Race and Job Mobility on Income, Men 1976–2009.

	Model 3a High School	Model 4a BA/Grad	Model 3b High School	Model 4b BA/Grad
β₀₀₀ intercept	2.14 (46.57)***	3.77 (12.65)***	2.14 (46.45)***	3.82 (12.78)***
β₀₁₀ time	−0.18 (−5.68)***	−1.26 (−5.39)***	−0.17 (−5.37)***	−1.32 (−5.60)***
β₀₂₀ time-squared	0.06 (10.59)***	0.04 (2.27)*	0.05 (9.58)***	0.06 (2.98)**
Job Mobility
β₀₃₀ leaver	0.04 (4.36)***	0.06 (2.49)*	0.07 (4.34)***	−0.13 (−1.74)
β₀₄₀ leaver × time	−0.05 (−3.75)***	−0.05 (−1.66)	−0.11 (−4.48)***	0.18 (1.86)
β₀₅₀ leaver × time2	0.02 (3.99)***	0.03 (3.86)***	0.04 (4.44)***	−0.04 (−1.31)
Race
β₀₀₁ White	0.13 (8.79)***	0.11 (1.89)	0.14 (8.64)***	0.05 (0.87)
β₀₁₁ White × time	0.01 (0.57)	0.04 (0.64)	−0.01 (−0.28)	0.11 (1.48)
β₀₂₁ White × time2	−0.01 (−1.07)	0.00 (−0.18)	0.00 (−0.16)	−0.02 (−0.99)
Job Mobility by Race
β₀₃₁ leaver × White			−0.04 (−1.90)	0.20 (2.67)**
β₀₄₁ leaver × White × time			0.08 (2.83)**	−0.25 (−2.50)*
β₀₅₁ leaver × White × time2			−0.03 (−2.75)**	0.08 (2.70)**
Controls included?	yes	yes	yes	yes
Variance Components
Level-1 variance (σ²)	0.03	0.04	0.03	0.04
Intercept (τ₀₀)	0.13	0.17	0.13	0.17
Intraclass correlation (ρ)	0.82	0.81	0.82	0.81
N person-years	28,987	12,892	28,987	12,892
N persons	3,664	1,347	3,664	1,347
LL	1,989	−1,613	1,993	−1,607

Note. t values in parentheses, dependent variable is the natural log of hourly earnings from salaries and wages in 2009 dollars.

*p < .05. **p < .01. ***p < .001 (two-tailed).

Figure 1 illustrates the Black–White earnings gap (as percentage of average White earnings), controlled for individual, job, and regional characteristics. The predicted race gap in Figure 1 is based on a growth curve model similar to Models 1a–4a, but instead of modeling time using a continuous quadratic function, I entered biennial time dummies to allow for nonlinear effect of race over time. Consistent with the literature (e.g., Morris & Western, 1999), the Black–White gap in Figure 1 is greater among men (represented by the black lines) than among women (represented by the gray lines). In the early 1980s, Black men earned between 11% and 14% less than White employees. Starting in the late 1980s, earnings disparities continuously narrowed among male high school graduates, while they widened quickly among male college graduates. Although these trends fail to reach conventional levels of significance, patterns are consistent with previous studies (Chay & Lee, 2000; Grodsky & Pager, 2001; Huffman, 2004). Similarly, although only significant among high school graduates, the trends among female employees fit with the literature, such that the gap was very small in the 1970s and then increased over time (Dozier, 2010; Pettit & Ewert, 2009).²⁰

Figure 1.

Racial earnings gap by gender and education 1976–2009.

Earnings Differences Between Stayers and Leavers Since the 1970s

With regard to the effect of leaving, Tables 1 and 2 show that initially leavers earned more than stayers (Models 1a–4a: β₀₃₀) except among college-educated women. This gap widened significantly over time (Models 1a–4a: β₀₄₀ and β₀₅₀). To better illustrate these trends, Figure 2 shows the earnings advantage for leavers by gender and education.²¹ Overall, the effect of leaving depends more on education than on gender. Regardless of gender, the relative advantage of leaving is greater among college graduates than among high school graduates. Among high school graduates, the earnings advantage among leavers dropped slightly in the 1980s, then remained constant before slightly increasing again in the 2000s.

Figure 2.

Relative advantage of leaving by gender and education 1976–2009.

College-educated leavers also earn more than stayers, and this advantage increased slightly since the early 1980s and then particularly during the economic boom in the early 2000s, which may be related to the vast growth of jobs in high income sectors such as finance and information technology.²²

Association Between Firm-External Job Mobility and Racial Earnings Disparities

In the next step, I address my main hypotheses and examine whether earnings growth among leavers is equally shared by Black and White employees. For this purpose, I introduce an interaction between race and job mobility in the third and fourth columns in Tables 1 and 2. As discussed earlier, economic theories predict that firm-external mobility circumvents race-specific barriers within firms and thus narrows the race gap. In contrast, social capital theory and the social-cognitive approach predict racial gaps to be greater among leavers, resulting in an increase of the racial gap.

Table 1 shows no significant interaction effect between race and job mobility among women (Models 1b and 2b: β₀₃₁, β₀₄₁, and β₀₅₁), suggesting that the female Black–White gap is unaffected by externalization.²³ This is further confirmed by Figure 3, which shows the biennial racial gap among female stayers and leavers by education. Due to the small sample size, trends among college-educated leavers vary widely, but generally they seem to follow the same trends as college-educated stayers and employees who have high school degrees. Thus, racial disparities slightly increased regardless of job mobility, which suggests that the increasing Black–White gap among women is not attributable to the externalization of job mobility.

Figure 3.

Race gap among stayers and leavers by education, women 1979–2009.

In contrast, Models 3b and 4b in Table 2 reveal that the externalization of job mobility is strongly associated with the male Black–White gap, but in different directions depending on employees’ education. To illustrate this further, Figure 4 shows the predicted race gap based on the quadratic time trends estimated in Models 3b and 4b. Looking at employees with a high school degree (represented by gray lines), the race gap was initially smaller among leavers than among stayers (Model 3b: β₀₃₁). Over time, the Black–White gap among leavers increased from 10% to about 14% in the early 1990s. In the mid-1990s, this trend reversed and in 2009 Black leavers earned only 5% less than White leavers (Model 3b: β₀₄₁ and β₀₅₁). Thus, while externalization of job mobility initially had an inequality-increasing effect, it is associated with a narrowing gap since the mid-1990s.

Figure 4.

Smoothed race gap among stayers and leaver by education, men 1976–2009.

When looking at male college graduates, a different picture emerges (represented by black lines). Initially, racial disparities were greater among leavers than among those who stayed (Model 4b: β₀₃₁) and then the gap narrowed significantly from 23% in 1976 to 16% in the late 1980s. However, starting in the mid-1990s, trends reverse and Black–White earnings disparities widened quickly among college graduates. In the 2000s, the Black–White earnings gap exceeded 30% and grew noticeably faster than among college-educated workers who stayed with their employer (Model 4b: β₀₄ and β₀₅). Hence, firm-external mobility is associated with widening racial disparities among college-educated men since the late 1990s.

For more detail, Figure 5 shows the same gap as Figure 4, but instead of using a continuous measure of time, I used biennial time dummies to allow for nonlinear changes (as done in Figures 1 –3). The predicted biennial trends in Figure 5 fit the smoothed trends in Figure 4: Among high school-educated leavers, the gap increased initially and then dropped in the 1990s. Among college-educated leavers, the gap initially closed slightly, with a short spike in the late 1980s, then increased during the boom years in the early 2000s. Unlike Figure 4, Figure 5 shows a drop in disparities among college-educated leavers in 2007 and 2009. It is unclear whether this drop represents a substantive compression of wages among leavers caused by the economic recession.²⁴

Figure 5.

Biennial race gap among stayers and leavers by education, men 1976–2009.

Reasons for Entering the External Labor Market

Finally, I examine how the circumstances under which individuals leave their employer affect mobility outcomes. As different processes lead to voluntary and involuntary turnover (Breen, 1992; Hachen, 1990), voluntary job mobility is more likely to result in earnings growth than involuntary moves (Polsky, 1999; Topel & Ward, 1992; Usui, 2009). Taking into account the reason for leaving may explain some of the observed racial differences, as Black employees are more likely to be terminated involuntarily than White employees (Campbell, 1997; Elvira & Zatzick, 2002; Fairlie & Kletzer, 1998; Park & Sandefur, 2003; Wilson, 2005). To account for the circumstances under which respondents left, I split the leaver variable into voluntary and involuntary leavers. I code employer changes as voluntary when respondents quit their job. Job mobility occurs involuntarily when respondents are fired, laid off, or when their company dissolves.

Table 3 shows the effect of staying and leaving involuntarily among women and men. When assessing the effect of job mobility, note that the reference group are now voluntary leavers (earlier, stayers were the reference category). I switch the reference category, because I want to examine whether the effect of leaving willingly differs significantly from leaving unwillingly. Table 3 demonstrates that racial disparities among leavers cannot be explained by the circumstances under which respondents left their previous employer, regardless of gender and education. That is, for the female sample (Models 6 and 7), none of the interactions between job mobility and race is significant.

Table 3.

Growth Curve Estimates: Effects of Race and Job Mobility, Reason for Mobility.

	Model 6 Women High School	Model 7 Women BA/Grad	Model 8 Men High School	Model 9 Men BA/Grad
Intercept	2.31 (41.72)***	3.69(11.47)***	2.23(46.39)***	3.78(12.52)***
Time	−0.29 (−6.32)***	−1.05(−4.66)***	−0.29(−7.08)***	−1.16(−4.66)***
Time-squared	0.07 (7.01)***	0.00(0.12)	0.09(9.15)***	0.02(0.62)
Job Mobility (ref: voluntary quit)
Stayer	−0.06 (−3.59)***	0.01 (0.09)	−0.08 (−4.48)***	0.09 (1.19)
Involuntary termination	−0.03 (−1.22)	−0.04 (−0.21)	−0.05 (−1.83)	−0.12 (−0.66)
Time × stayer	0.08 (3.18)**	−0.08 (−0.87)	0.12 (4.15)***	−0.16 (−1.56)
Time × involuntary termination	0.05 (1.03)	0.29 (0.98)	0.02 (0.37)	0.10 (0.44)
Time2 × stayer	−0.03 (−3.04)**	0.01 (0.25)	−0.04 (−4.11)***	0.04 (1.23)
Time2 × involuntary termination	−0.02 (−0.91)	−0.13 (−1.19)	−0.01 (−0.33)	−0.02 (−0.35)
Race
White	0.02 (1.33)	0.08 (1.16)	0.09 (3.82)***	0.24 (2.79)**
Time × White	0.08 (2.39)*	−0.06 (−0.57)	0.09 (2.65)**	−0.16 (−1.38)
Time2 × White	−0.01 (−1.25)	0.04 (1.23)	−0.03 (−2.93)**	0.07 (2.02)*
Job Mobility by Race
White × stayer	0.02 (0.81)	−0.05 (−0.74)	0.05 (2.17)*	−0.18 (−2.23)*
White × involuntary termination	0.02 (0.69)	−0.18 (−0.86)	0.03 (0.77)	0.05 (0.26)
Time × White × stayer	−0.02 (−0.73)	0.11 (1.11)	−0.10 (−2.90)**	0.26 (2.37)*
Time × White × involuntary termination	−0.06 (−0.91)	0.11 (0.32)	−0.04 (−0.63)	0.04 (0.18)
Time2 × White × stayer	0.00 (0.29)	−0.05 (−1.62)	0.03 (2.92)**	−0.09 (−2.71)**
Time2 × White × involuntary termination	0.01 (0.37)	0.00 (−0.02)	0.01 (0.69)	−0.01 (−0.18)
Control variables	yes	yes	yes	yes
Variance components
Level-1 variance (σ²)	0.03	0.04	0.03	0.04
Intercept (τ₀₀)	0.12	0.14	0.12	0.15
Intraclass Correlation (ρ)	0.81	0.76	0.82	0.80
N person-years	24,244	9,503	28,987	12,892
N persons	3,228	1,223	3,664	1,347
LL	2,489	−1,686	2,111	−1,524
Time × White × involuntary termination	−0.06 (−0.91)	0.11 (0.32)	−0.04 (−0.63)	0.04 (0.18)
Time2 × White × stayer	0.00 (0.29)	−0.05 (−1.62)	0.03 (2.92)**	−0.09 (−2.71)**
Time2 × White × involuntary termination	0.01 (0.37)	0.00 (−0.02)	0.01 (0.69)	−0.01 (−0.18)
Control variables	yes	yes	yes	yes
Variance components
Level-1 variance (σ²)	0.03	0.04	0.03	0.04
Intercept (τ₀₀)	0.12	0.14	0.12	0.15
Intraclass Correlation (ρ)	0.81	0.76	0.82	0.80
N person-years	24,244	9,503	28,987	12,892
N persons	3,228	1,223	3,664	1,347
LL	2,489	−1,686	2,111	−1,524

Note. t values in parentheses, dependent variable is the natural log of hourly earnings from salaries and wages in 2009 dollars.

*p < .05. **p < .01. ***p < .001 (two-tailed).

In the male sample (Models 8 and 9), I find a significant effect for staying, which is consistent with the findings in Table 2 (except the directions are reversed as the reference category is also reversed). Similar to the female sample, the effect of leaving on the male Black–White gap does not vary significantly by the reason for turnover, which is indicated by the nonsignificant and substantively small effect of leaving involuntarily. Therefore, differences in the reasons for departure cannot explain the Black–White disparities among leavers.

Discussion

This study examined how externalization of job mobility (i.e., more employer changes) is associated with racial earnings disparities between 1976 and 2009. As summarized in Table 4, two major patterns emerged. First, there is little evidence that externalization affected racial earnings differences among women. Second, among men the effect of external job mobility on racial earnings disparities depends on education: Following an inverse u-shaped pattern, racial disparities among high school-educated leavers initially increased and then narrowed in the early 1990s. Running in the opposite direction, racial earnings disparities among college-educated leavers initially narrowed but then widened quickly in the late 1990s and early 2000s, causing the gap to increase far beyond the 1970s level.

Table 4.

Overview: Effect of Leaving on Racial Earnings Gap by Gender and Education.

	Women	Men
High School Degree or Less	No effect	1970s-late 1980s: Widening Mid 1990s--2010s: Narrowing
College or Graduate Degree	No effect	1970s-late 1980s: Narrowing Late 1990s--2010s: Widening

While this article primarily aims to examine the association between externalized job mobility and racial disparities, the findings are suggestive of several explanations that should be tested in the future. It appears that there is no single smoking gun. If boundaryless careers, social capital differences, or vulnerability to discrimination alone narrow or widen the race gap among leavers, then these changes should be present for everyone who changes employers. Instead, the varied effect of external job mobility suggests that there may be several (gender- and class-specific) mechanisms intersecting with each other.

For instance, externalization is primarily associated with changes in racial disparities among men but has little effect on racial differences among women. The absence of externalization effects among Black and White women can be interpreted as a result of absent statistical power or as evidence for intersectionality. As shown in Figure 1, women earn less than men and the Black–White earnings gap is smaller among women when compared with racial disparities among men (e.g., Morris & Western, 1999). Smaller racial disparities among women might make it more difficult to detect changes in the gap. Additionally, Hollister and Smith (2014) demonstrate that women's tenure and employer attachment depends on whether women have children or not, which may apply more to White than Black women. Likewise, Looze (in press) finds that motivations for leaving an employer differ greatly for women with and without children. Thus, future research should examine whether the effect of externalized job mobility depends on motherhood status.²⁵

Alternatively, the null findings among women may also indicate that racial differences are shaped by different mechanisms among men and women, and that there is truly no association between the externalization of job mobility and racial earnings differences among women. This interpretation is supported by intersectionality research by McCall (2001) and Browne and Misra (2003), who found that industrial restructuring can only partially explain the race gap among women. Instead, family structure, neighborhoods, and social networks play a greater role. Firm-external job mobility is closely associated with declining manufacturing and delayering among managerial and professional employees. Women tend to be underrepresented in all of these fields, and thus the context of external job mobility changed less dramatically for Black and White women. Given that Figure 3 shows no association between race and external job mobility, my analyses tentatively suggest that the nonsignificant association between externalization and racial trends can be interpreted in light of intersectionality. Or put differently, there is no evidence that the externalization of job mobility contributed to the widening of the racial gap among women since the 1970s.

The findings among male college graduates are consistent with S. Collins (1996) and Stainback et al. (2005), who show that race inequality is strongly affected by politically mediated opportunity structures. That is, Collins finds that many African Americans were able to enter highly paid managerial and professional jobs after the passage of the Civil Rights Act, which narrowed the earnings gap. Many of these Black professionals, however, were employed in racialized positions with little power, often serving racial niches and minority customers. As political pressure weakened and economic imperatives moved to the forefront of management in the 1980s, many of these racialized positions turned into dead-end jobs or were terminated altogether. This resulted in a reversal of the advances made by the Black middle class. Along these lines, Stainback et al. (2005) and Stainback and Tomaskovic-Devey (2012) show that workplace integration leveled out in the late 1980s and actually reversed in some professional and managerial jobs beginning in the 1990s. With waning political pressure to integrate workplaces, other inequality-reproducing mechanisms such as importance of social networks and discrimination in ambiguous situations may have moved to the forefront, causing the race gap to widen quickly among college-educated leavers, especially in the late 1990s and early 2000s.

Widening and then narrowing disparities among high school-educated men may also be explained by politically mediated opportunity structures—or the lack thereof. First, affirmative action laws primarily increased the representation of Black employees in professional and managerial occupations. Low-skilled African Americans benefited less from these laws because they were already overrepresented in low-skilled jobs (Semyonov et al., 2000). Instead, high school graduates increasingly faced adverse consequences of skill-based technological change and deindustrialization, which primarily eliminated lower skilled, routine jobs. As competition for the remaining low-skilled jobs increased, it is possible that inequality-reproducing mechanisms such as the importance of social networks and discrimination in the hiring process became more salient, resulting in a widening racial gap in the 1970s and 1980s.

The subsequent narrowing of racial disparities among male high school graduates since the 1990s may be due to increases in federal minimum wages (DiNardo, Fortin, & Lemieux, 1996), which would most likely affect low-educated Black employees. Alternatively, the closing gap may also be a result of selection bias. Due to rapidly increasing joblessness and incarceration rates among low-skilled Black men, the average earnings of Black high school graduates rose since the 1990s as those with the lowest earnings potentials dropped out of the labor force (Western & Pettit, 2005). Future research should examine how race-, class-, and gender-specific opportunity structures helped to translate job mobility into earnings among different groups of employees.

Conclusion

Over a decade ago, Morris and Western (1999) called for a closer investigation of how changing firm structures affect earnings inequality in the United States. This article is a step toward this goal. Using individual income data from the PSID between 1976 and 2009, I ask whether externalized job mobility increased Black–White inequality since the 1970s or whether it was one of the factors that prevented differences from being even more pronounced.

The evidence suggests that results depend on employees’ gender and education: Externalization of job mobility had little effect on racial differences among women. Among men, the effect depends on education; for high school graduates, the race gap initially widened before it narrowed again in the 1990s. Running in the opposite direction, racial differences among college-educated men initially closed but then rapidly opened in the late 1990s and early 2000s. Thus, externalizing job mobility might contribute to growing racial earnings differences especially among high-income jobs, as the gap is widening particularly fast among college-educated leavers while it seems to narrow among high school graduates.

My findings speak to how racial differences are shaped by the intersection of race, class, and gender (e.g., Acker, 2006; Browne & Misra, 2003; P. Collins 1999; Crenshaw, 1989; Grodsky & Pager, 2001; Massey & Denton, 1993; McCall, 2001; Thomas, 1993). External job mobility can alleviate racial disparities or exaggerate them, depending on existing opportunity structures. When politically mediated opportunity structures such as affirmative action laws are strong, racial disparities narrow particularly quickly among employees who start with a new employer and the boundaryless career truly allows Black employees to catch up. In absence of these opportunity structures (e.g., among low-skilled employees or when political pressure fades) and when earnings expand as rapidly as they did among college graduates in the early 2000s, other inequality-reproducing mechanisms come to the forefront, causing the racial gap to widen. These findings parallel the effect of externalization on the gender earnings gap (Kronberg, 2013).

This article primarily focuses on examining and establishing an association between externalized job mobility and trends in race-based inequality. Future research that explores the mechanisms by which externalized job mobility translates into racial disparities is critical. Focusing on mechanisms within employment organizations and how these are racialized and gendered appears to be a fruitful approach to understanding different outcomes of job mobility (e.g., Bidwell, 2011; Castilla, 2011, 2012; Petersen & Saporta, 2004; Tomaskovic-Devey, 2014). As the U.S. workforce grows more diverse, it becomes increasingly important to understand how job mobility affects different groups of employees in the long term.

Footnotes

Appendix A. Variable Overview

Appendix B. Descriptive Statistics,Women by Education

Appendix C. Descriptive Statistics,Men by Education

Appendix D. Growth Curve Estimates: Control Variables From Models 1b,2b,3b,and 4b

Acknowledgments

I thank Irene Browne, Arne Kalleberg, Richard Rubinson, Brian Sheppard, Lesley Watson, and the anonymous reviewers for great feedback and comments.

Author Note

An earlier version of this article was presented at the annual meeting of the American Sociological Association in 2010.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Anne-Kathrin Kronberg is a PhD candidate in sociology at Emory University. Her research focuses on job mobility, and race and gender inequality in work organizations. Her dissertation examines how entering a job via hire or promotion affects racial and gender differences in career outcomes. Her work appeared in Social Forces.

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Stay or Leave? Race,Education,and Changing Returns to the External Labor Market Strategy,1976–2009

Abstract

Keywords

Racial Trends Among Men and Women Since the 1970s

Racial Trends Among High School and College Graduates Since the 1970s

Changes in Job Attainment Since the 1970s

External Job Mobility and the Black–White Earnings Gap

Closing the Racial Earnings Gap

Widening of the Racial Earnings Gap

Data

Measures

Dependent Variable

Key Explanatory Variables

Control Variables

Analytic Strategy

Results

Racial Disparities Since the 1970s by Gender and Education

Earnings Differences Between Stayers and Leavers Since the 1970s

Association Between Firm-External Job Mobility and Racial Earnings Disparities

Reasons for Entering the External Labor Market

Discussion

Conclusion

Footnotes

Appendix A. Variable Overview

Appendix B. Descriptive Statistics,Women by Education

Appendix C. Descriptive Statistics,Men by Education

Appendix D. Growth Curve Estimates: Control Variables From Models 1b,2b,3b,and 4b

Acknowledgments

Author Note

Declaration of Conflicting Interests

Funding

Notes

Author Biography

References