A Study of Interracial Differences in Turnover Intentions: The Mitigating Role of Pro-Diversity and Justice-Oriented Management

Abstract

Enhancing racial justice, equity, diversity, and inclusion are the core values of public administration and critical to the functions of public-sector strategic human resources management. However, very limited empirical research has delved into the interracial differences in public sector employees’ turnover intentions and its mitigating factors. Using the 2006–2017 Federal Employee Viewpoint Survey data, the present study aims to contribute toward filling this gap in the literature. The theoretical arguments and empirical findings of this study show that when compared with White employees, Federal Black, Indigenous, and Employees of Color (BIEOC) are significantly more likely to intend to leave their current organizations. However, the likelihood of turnover intentions of Federal employees, particularly, BIEOC can be reduced through institutional interventions anchored in pro-diversity management (e.g., commitment to fostering a racially representative workforce), distributive justice in employment outcomes (e.g., in pay and promotions) and procedural justice in organizational processes (e.g., anti-discrimination practices).

Keywords

Black Indigenous and Employees of Color White employees racial differences turnover intentions distributive justice procedural justice pro-diversity management

Introduction: An Interracial Focus on Turnover Intentions

Enhancing racial justice, equity, diversity, and inclusion (JEDI) of Black, Indigenous, and Employees of Color (BIEOC) are the core values of public administration (PA) and critical to the functions of public-sector strategic human resources management (Choi, 2011a; Chordiya, 2019, 2020; Fay et al., 2020; Guy & McCandless, 2012; McCandless & Zavattaro, 2020; Naylor, 2020; Protonentis et al., 2021; Riccucci, 2009; Starke et al., 2018; Stazyk et al., 2017; Svara & Brunet, 2020). However, organizational efforts to enhance racial diversity are often limited to recruitment functions. Successful recruitment of BIEOC does not ensure their retention (McKay & Avery, 2005; McKay et al., 2007; Scanlon et al., 2018; Theus, 2018). Therefore, the present study focuses on interracial differences in employees’ intentions to quit their current organizations (Hanisch & Hulin, 1990; Wynen et al., 2013).

The intention to quit or turnover intention is “the cognitive process of thinking of quitting, planning on leaving a job, and the desire to leave the job” (Lambert & Hogan, 2009, p. 98; Wynen et al., 2013). While it is distinct from actual turnover, turnover intention is an important outcome variable as it is considered to be an immediate precursor and a good predictor of actual turnover (Hanisch & Hulin, 1990; Wynen et al., 2013). From a managerial perspective, turnover intention deserves critical attention since employees who may be thinking of quitting may still be persuaded to stay, for example, by making favorable changes in the work environment (Wynen et al., 2013). As such, the present examines not only the interracial differences in employee turnover intentions but also the mitigating factors.

The general management literature shows support for the role of pro-diversity climates in reducing turnover intentions of BIEOC (McKay & Avery, 2005; McKay et al., 2007). However, very limited empirical research in public personnel management has focused on the contexts of BIEOC and inquired into the interracial differences in employee turnover intentions and its mitigating factors. (e.g., Choi, 2011a; Pitts et al., 2011).

Within PA scholarship, there have been numerous calls to elevate the empirical research focus on social equity issues (e.g., Blessett et al., 2019; Choi, 2011a; Chordiya, 2019, 2020; Guy & McCandless, 2012; Johnson, 2012; Naylor, 2020; Pitts, 2011; Pitts et al., 2011; Protonentis et al., 2021; Wooldridge & Gooden, 2009). The present study makes a salient contribution to the PA literature through systematic empirical analysis of Federal Employee Viewpoint Survey (FEVS) data to highlight the pattern of interracial differences in turnover intentions. It also examines three factors that have the potential to mitigate employee turnover intentions, particularly for BIEOC. These are—pro-diversity management (e.g., commitment to fostering a racially representative workforce), distributive justice in employment outcomes (e.g., in pay and promotions) and procedural justice in organizational processes (e.g., anti-discrimination human resource practices).

The following sections describe the theoretical context and foundations that inform the hypotheses of this study. After presenting the hypotheses, the article describes the data, measurements, and the estimation approach. Subsequent sections present empirical findings, followed by a discussion of the findings, the study limitations, and conclusion.

Interracial Differences in Turnover Intentions: A Modern Discrimination Perspective

In the pre-Civil Rights era, racial prejudice and segregation were openly espoused and had legal enforcement (see Quillian, 2006; Sue, 2010; Zinn, 2003). With the Civil Rights Act of 1964, the legally enforced segregation on the basis of race ended, the overt forms of racially discriminatory practices were legally prohibited, and White people increasingly repudiated blatant forms of racial prejudice and discrimination (Quillian, 2006). In the post-Civil Rights era, even though, overt expressions of prejudice and discrimination have declined sharply, racial discrimination and inequities constitute an enduring characteristic of the United States (see Banaji & Greenwald, 2013; Gooden, 2015; Heckler & Ronquillo, 2019; Kendi, 2019; Quillian, 2006; Sue, 2010). For instance, in 2018, of the total charges received by the U.S. Equal Employment Opportunity Commission (USEEOC, 2019), race-based discrimination charges from private and government sectors constituted 32.2% (USEEOC, 2019).

The present study is situated within the context of a broader literature on workplace discrimination. This literature recognizes the pervasive nature of systemic racial discrimination in organizations (Cheung et al., 2016; Gooden, 2015, 2020; Heckler & Ronquillo, 2019; Kendi, 2019; Marchiondo et al., 2018; McCandless & Zavattaro, 2020; Sue, 2018). Among the most distinct features of the contemporary manifestations of racial discrimination are that it is insidious in nature, it is pervasive, intersectional, durable, and mutating (Banaji & Greenwald, 2013; Cheung et al., 2016; Chugh et al., 2005; Gooden, 2020; Harro, 2018; Kendi, 2019; Marchiondo et al., 2018; Quillian, 2006; Sue, 2010, 2018; Volpone & Avery, 2013). It does not need an active support and, unless interrupted, it will continue to go on because it has a life of its own (Harro, 2018, p. 50; Gooden, 2020; McCandless & Zavattaro, 2020).

The concept of modern discrimination was developed to make clear the manifestations of racial discrimination in the post-Civil Rights era, and its implications for organizations and target individuals or groups (Cheung et al., 2016; Marchiondo et al., 2018). Modern racial discrimination is potentially unconscious and representative of subtle, low-intensity behaviors that segregate or pose an impediment for those considered as “others.” It is often nonverbal in nature but can become evident through verbal and paraverbal (e.g., intonation, volume) behavior (Cheung et al., 2016; Marchiondo et al., 2018). It is distinct from traditional forms of discrimination that are overt, formal, and severe in nature (Cheung et al., 2016). Modern racial discrimination most distinctly manifests in forms such as interpersonal and subtle discrimination (see Cheung et al., 2016 for a discussion on dominant typologies of discrimination). Interpersonal discrimination refers to mistreatment in informal interactions such as negative verbal and nonverbal behaviors such demeaning remarks and inappropriate jokes (Hebl et al., 2002; Shen & Dhanani, 2018). Subtle discrimination includes “actions that are ambiguous in intent to harm, difficult to detect, low in intensity, and often unintentional but are nevertheless deleterious to target employees” (K. P. Jones et al. 2016, p. 1589).

Existing research shows that compared with White employees, BIEOC are more likely to experience workplace discrimination (see Greenhaus et al., 1990; J. R. Jones et al., 2009; Lewis & Gunn, 2007; Zurbrügg & Miner, 2016). In the Federal government context, which is the focus of the present study, reports by the U.S. Merit Systems Protection Board (USMSPB, 2008) and EEOC (EEOC) offer evidence of racial discrimination. In the 2008 report to the President and the Congress of the United States, Neil A. G. McPhie, the chairperson of U.S. MSPB, notes that “although differences in Federal employee opinion (about their jobs, agencies, and working conditions) across lines of ethnicity and race have diminished, (racial) minority employees remained more likely to report experiencing unfair treatment or discrimination in the workplace” (p. 1). Similarly, multiple Federal sector reports published by EEOC, bring to light concerns related to conscious and unconscious racial discrimination, stereotypes, and prejudices faced by African American, Hispanic, Asian, Native Hawaiian, and other Pacific Islander Federal employees that impede their career advancement in the Federal government (African American Workgroup Report, 2013; Asian American and Pacific Islander Work Group Report, 2008; Federal Sector Reports, n.d.; Hispanic Work Group Report, 2008).

It is important to note that the manifestations and experiences of discrimination may vary depending on the specific racial minority group. For instance, the Asian American and Pacific Islander (AAPI) workgroup report note the challenges to career advancement due to “model minority” stereotypes about AAPI people as quiet, hardworking, technically oriented, good at math and science, but also as passive, nonconfrontational, and anti-social (Asian American and Pacific Islander Work Group Report, 2008). The African American workgroup report notes that African Americans experiences of race-based obstacles include unconscious bias and perceptions against African Americans in Federal government employment decisions. They also include the lack of adequate mentoring and networking opportunities for higher level and management positions, and insufficient training and development assignments perpetuating inequalities in skills and opportunities for career advancements (African American Workgroup Report, 2013).

Whether it the “model minority” stereotypes experienced by AAPI or the subtle manifestations of discriminations such as inadequate mentoring, networking, and career development opportunities experienced by African/Black American employees, these modern forms of racial discrimination are often invisibilized, normalized, overlooked, and are mostly untouched and unprotected by law (Acker, 2006; Hebl et al., 2002; Shen & Dhanani, 2018). However, like traditional discrimination, modern discrimination is detrimental to target’s psychological and physical well-being. Consequently, as a way of resisting and/or coping with the emotional toll of these everyday experiences of workplace discrimination, BIEOC employees may display workplace withdrawal behaviors including intentions to quit their current organizations (Avery et al., 2007; Banaji & Greenwald, 2013; Gooden, 2020; K. P. Jones et al., 2016; Podsakoff et al., 2007; Singletary, 2009; Volpone & Avery, 2013). Therefore, the present study proposes:

Hypothesis 1 (H1): There will be interracial differences in turnover intentions of Federal employees such that compared with White employees, the intentions to leave current organizations will be higher for BIEOC.

The Mitigating Factors: The Role of Pro-Diversity and Justice-Oriented Management

In the above section, based on existing workplace discrimination literature, arguments are presented for Hypothesis 1, which suggests the existence of higher likelihood of turnover intentions among BIEOC compared with White employees. This section describes how institutional efforts stemming from values of pro-diversity and distributive and procedural justice-oriented management can be instrumental in lowering turnover intentions for all employees, and particularly, for BIEOC (Cheung et al., 2016; Choi, 2011b; Naylor, 2020; Sabharwal, 2014, 2015).

The Role of Distributive and Procedural Justice on Lowering Turnover Intentions

Distributive justice and turnover intentions

The core notion of justice involves an allotment of something to persons (e.g., duties, goods, offices, opportunities, penalties, punishments, privileges, roles, status) (Cohen-Charash & Spector, 2001; Frankena, 1962). Distributive justice involves a notion of comparative allotment (Adams, 1965; Cohen-Charash & Spector, 2001; Frankena, 1962). In an organizational context, distributive justice occurs when individuals are compensated based on their contributions or inputs (Colquitt et al., 2001; Cropanzano & Ambrose, 2002). It is largely equated with people’s reactions to economic allocations such as fairness in pay, rewards, and promotions (Choi, 2011b; Cropanzano & Ambrose, 2002; Naylor, 2020). The basis of judgments about distributive justice is often the comparison of one’s outcome/input ratios with those of others (Adams, 1965; Choi, 2011b; Colquitt et al., 2001).

Given the emphasis on outcomes, distributive justice is associated with cognitive, affective, and behavioral reactions to specific outcomes. When an individual perceives an outcome (e.g., pay, awards, and promotions) to be unfair, they are likely to experience an emotional impact (e.g., anger, guilt). Perceived unfairness in outcomes is also likely to influence their cognitions (e.g., dissatisfaction about inequitable inputs/outputs ratio as compared with others), and ultimately their behavior (e.g., withdrawal behaviors such as turnover intentions) (Adams, 1965; Cohen-Charash & Spector, 2001; Weiss et al., 1999). However, perceived fairness in the distribution of outcomes is likely to have a favorable impact on individual’s emotions, cognitions, and behavioral responses (Cohen-Charash & Spector, 2001). Therefore, the study proposes that overall (i.e., when all Federal employees are considered) perceptions of distributive justice will be negatively associated with employees’ turnover intentions.

Procedural justice and turnover intentions

Procedural justice refers to perceptions of fairness of procedures or a means for allocating outcomes (Choi, 2011b; Cohen-Charash & Spector, 2001; Naylor, 2020). Organizational procedures represent the mechanisms by which an organization allocates resources. Procedures are considered fair when they are perceived to be consistent, accurate, unbiased, and ethical, have mechanisms to correct flawed decisions and, consider opinions of various groups affected by the decision (Choi, 2011b; Colquitt et al., 2001; Leventhal, 1980).

Procedural justice, like distributive justice, is associated with positive cognitive, emotional, and behavioral reactions toward the organization (e.g., Choi, 2011b; Cohen-Charash & Spector, 2001; Martin & Bennett, 1996). When employees perceive procedural justice in organizations, they have positive evaluations of their organizations, producing favorable behaviors such as lower turnover intentions (Choi, 2011b). Therefore, this study hypothesizes that overall (i.e., when all Federal employees are considered) perceptions of procedural justice will be negatively associated with employees’ turnover intentions.

Interracial difference in the moderating role of distributive and procedural justice on turnover intentions

The most commonly reported scenarios of workplace discrimination show up in hiring and promotion decisions (e.g., Kessler et al., 1999). Procedural and distributive justice practices focus on eliminating discrimination in procedures and economic outcomes (e.g., pay and promotion) (Cohen-Charash & Spector, 2001). They have potential to overcome organizational biases in procedures and outcomes, foster fairness in distribution of organizational rewards and decision-making (Choi, 2011b; Cohen-Charash & Spector, 2001; Parker et al., 1997; Sabharwal, 2015; USMSPB, 2008). Compared with White employees, BIEOC who otherwise predominantly experience race-based injustice may attach a higher value to distributive and procedural justice and respond through more positive attitudinal consequences including lower turnover intentions (USMSPB, 2008). Therefore, this study proposes that there will be interracial differences in the moderating role of distributive and procedural justice on the relationship between employees’ racial identity and turnover intentions such that, when it is considered, compared with White employees, BIEOC will exhibit lower turnover intentions.

Hypothesis 2a (H2a): Overall (i.e., when all Federal employees are considered) improved perceptions of distributive justice will be negatively associated with employees’ turnover intentions.

Hypothesis 2b (H2b): Overall (i.e., when all Federal employees are considered) improved perceptions of procedural justice will be negatively associated with employees’ turnover intentions.

Hypothesis 2c (H2c): There will be interracial differences in the moderating role of distributive justice on the relationship between employees’ racial identity and turnover intentions such that, when it is considered, compared with White employees, BIEOC will exhibit lower turnover intentions.

Hypothesis 2d (H2d): There will be interracial differences in the moderating role of procedural justice on the relationship between employees’ racial identity and turnover intentions such that, when it is considered, compared with White employees, BIEOC will exhibit lower turnover intentions.

The Role of Pro-Diversity Management on Turnover Intentions

Racial diversity is an organizational reality. Operationalizing a pro-diversity management entails embracing the understanding that a diverse workforce enriches organizational learning and change processes and contributes toward organizational success (Groeneveld & Verbeek, 2012; Hays-Thomas, 2016; D. A. Thomas & Ely, 1996). Pro-diversity management measures entails going beyond entry-level hiring of BIEOC. It involves ensuring representation of BIEOC at all levels of the organizational hierarchy. It implies cultivating a safe work environment free from the racial hostilities and harassment and fostering the vitality of retention-focused inclusive behaviors (Groeneveld & Verbeek, 2012; Hays-Thomas, 2016; Riccucci, 2002).

When unmanaged or poorly managed, diversity could potentially lead to unproductive conflicts and negative outcomes (Hays-Thomas, 2016). A proactive diversity management approach has potential to alleviate unproductive conflicts that may arise when people of different backgrounds work together. When practiced effectively, pro-diversity management may play a vital role to achieve positive organizational outcomes including improved interpersonal relationships among individuals from diverse racial backgrounds and reduced turnover intentions (Choi, 2009; Hays-Thomas, 2016; R. R. Thomas, 1990; D. A. Thomas & Ely, 1996). Therefore, it is expected that overall (i.e., when all Federal employees are considered) improved perceptions of pro-diversity management will be negatively associated with employees’ turnover intentions.

Interracial difference in the moderating role of pro-diversity management on turnover intentions

Past research suggests that pro-diversity climate is the key to reducing turnover attitudes of BIEOC (McKay & Avery, 2005; McKay et al., 2007). Social exchange theory helps explain the effect of perceived organizational support through pro-diversity management programs on BIEOC retention (Ko & Hur, 2014; Lee & Hong, 2011). In an organizational context, the fundamental principle underlying social exchange theory is the reciprocal relationship between an organization and its employees. An organization potentially establishes a high-quality exchange relationship with an employee if they perceive their organization cares for them and benefit from positive actions directed toward them. Consequently, the employee will reciprocate through positive work behaviors and attitudes toward the organization (Ko & Hur, 2014; Lee & Hong, 2011; Settoon et al., 1996).

Pro-diversity management is a key element for creating an inclusive culture for traditionally marginalized BIEOC. Through pro-diversity management, organizations can promote an environment where employees of marginalized racial backgrounds can equally access opportunities to advance their careers, feel safe and open about their identities, express their opinions, and communicate freely with other group members (Choi, 2009; Ferdman et al., 2010; Pitts, 2009; Riccucci, 2002; The U.S. Office of Personnel Management, 2015).

The human resource management (HRM) interventions under the umbrella of pro-diversity management functions include casting a wider net for recruitment of BIEOC employees, building individual and organizational competence for diversity, equity, and inclusion through learning and development opportunities, creating and cultivating access to mentoring opportunities, coaching, supervisory support, and genuinely valuing employees of marginalized social backgrounds. These HRM functions potentially serve as a means for leveling the playing field for employees of traditionally marginalized racial groups (Hays-Thomas, 2016; Sabharwal, 2014). Overall, these interventions could demonstrate intentional organizational value, care, and inclusion for traditionally marginalized BIEOC. Therefore, this study expects that compared with White employees, BIEOC may attach a higher value to pro-diversity management and respond with positive attitudes such as lower turnover intentions (USMSPB, 2008).

Hypothesis 3a (H3a): Overall (i.e., when all Federal employees are considered) improved perceptions of pro-diversity management will be negatively associated with employees’ turnover intentions.

Hypothesis 3b (H3b): There will be interracial differences in the moderating role of pro-diversity management on the relationship between employees’ racial identity and turnover intentions such that, when it is considered, compared with White employees, BIEOC will exhibit lower turnover intentions.

Data, Estimation Approach, and Measurements

So far, the article describes the theoretical basis and arguments for the hypotheses. This section describes the study data and the estimation approach used for empirical testing of the study hypotheses.

Data

The present study uses FEVS data for 10 time periods between the years 2006 and 2017 (see online Appendix A). These data are made available by the U.S. Office of Personnel Management (The U.S. Office of Personnel Management, n.d.). Data were not available for years 2007 and 2009. In 2006 and 2008 FEVS data, the indicators of employee’s racial identity included the following categories: White, Black or African American, Native Hawaiian or Other Pacific Islander, Asian, American Indian or Alaska Native, and Two or more races (Not Hispanic or Latino). For subsequent years (i.e., from 2010 to 2017), these racial identity indicators in FEVS were collapsed into two categories: racial minority and racial nonminority. For measurement consistency across years, this study included employees identifying as Black or African American, Native Hawaiian or Other Pacific Islander, Asian, American Indian or Alaska Native, and Two or more races (Not Hispanic or Latino) in one category of racial minorities. In this study, they are called as BIEOC (i.e., BIEOC = 1). White employees were coded as racial nonminorities (i.e., BIEOC = 0). Of the total employees considered in this study across years, the percentage of BIEOC ranged between 29.23 % and 34. 63%. (Please see the online Appendix A for year-wise percentages of employees identifying as BIEOC [i.e., employees of racial minority status] and White employees [i.e., employees of nonminority status]).

Estimation Approach

The outcome variable of interest is the employee’s intention to leave their current organization (i.e., turnover intention). In the FEVS, asked respondents the following question: “Are you considering leaving your organization within the next year and if so, why?” They were offered the following response options: (a) No; (b) Yes, to retire; (c) Yes, to take another job within the Federal Government; (d) Yes, to take another job outside the Federal Government; and (e) Yes, other. The “No” responses were coded as 0 and, the responses “Yes, to take another job within the Federal government,” “Yes, to take another job outside the Federal government” and, “Yes, other” were coded 1. Because this article focuses on turnover intentions for movements within the active labor markets, the responses under the “retirement” category were dropped from the analyses.

With respect to the estimation approach, the binary nature of the outcome variable makes it appropriate to use a probit regression model (Cameron & Trivedi, 2010, Chapter. 14). Because the FEVS data are grouped at the agency level, correlation may arise between observations within the same agency. To relax the condition of independence for observations within the same agency, robust standard errors are clustered at the agency level (Cameron & Trivedi, 2010, Chapters 10 and 14).

The FEVS data for the 10 years from 2006 to 2017 are organized as a pooled cross-sectional time-series (PCSTS) data. The cross-sectional data for each year is organized (or “stacked”) on top of one another and every observation has both a unit (i) and a time period (t) subscript. The PCSTS method allows accounting for both the individual (i) and time (t) dimensions of individual attitudes/behavior (Podestà, 2002; Tourangeau, 2003). In this study, PCSTS allows controls for agency fixed effects and year fixed effects. To control for agency and year fixed effects, dummy variables were generated for all agencies and all years included in this study. The final PCSTS data for 10 years included 3,736,328 observations.

Although the FEVS data are obtained systematically and PCSTS data offer important advantages of including survey responses representative across years and agencies, the large sample size of the data set can potentially result in small confidence intervals and statistically significant results (Brassey et al., 2017). Therefore, to minimize potential bias introduced by large sample size and to improve the quality of analysis and conclusions, the present study uses Monte Carlo (MC) simulations of probit regression model.

Random samples of smaller size are drawn from the larger PCSTS data to run 1,000 MC simulation trials of the probit regression model and to estimate the marginal effects of the key explanatory variables on P(Y = 1) (i.e., P [turnover intentions = 1]) (Cameron & Trivedi, 2010; Ji & Li, 2015 Chapter 4; Stata.com, n.d.). With a binary dependent variable (i.e., turnover intention coded as zero or one), the marginal effects provide a single number that expresses the effect of the explanatory variable on P(Y = 1) or, for this study, P (turnover intention = 1) (Cameron & Trivedi, 2010, Chapter 10; Long, 2016). The mean values of regression coefficient and marginal effect estimates derived from MC simulation trials are reported and used for analysis and interpretation of the findings. Stata 16.1 software version was used to run the program (StataCorp, 2019).

Steps in MC simulation program: An overview

The MC simulation program involved the following steps. First, random samples of 10 responses were drawn without replacement for each year and each agency from the PCSTS data set resulting in 5,800 observations for each trial (UCLA: Statistical Consulting Group, n.d.-a). Second, a probit regression model was estimated. Third, the MC simulation command was used to run this program for 1,000 times (Stata.com, n.d.). The output for a 1,000 simulation runs returned 1,000 values of probit regression coefficients for each of the explanatory variables in the probit model. A mean value of 1,000 probit regression coefficients was calculated for each explanatory variable, respectively, and is reported in Table 2 (Cameron & Trivedi, 2010, Chapter 4; Stata.com, n.d.). Finally, marginal effects were estimated for each of the key explanatory variables on P(Y = 1) (i.e., P [turnover intentions = 1]). For this step, after fitting the probit model, 1,000 MC simulation trials were run to estimate the marginal effects. The findings reported in Table 2, document the mean of 1,000 observations for each explanatory variable’s marginal effect on P(Y = 1) (see Table 2).

Measurements

Explanatory variables

The preceding subsection “Estimation Approach” describes the items and the codes used for the measurement of outcome variable turnover intention. The main explanatory variables in this study include employee’s racial identity, distributive and procedural justice, and pro-diversity management practices. As noted in the above section on “data,” racial identity of an employee is represented by a demographic measure included in the FEVS asking employees to identify as (racial) “minority” or (racial) “non-minority.” The “yes” responses were coded as “1” and “no” responses were coded as “0.” In this study, the terminology of White employees is used to describe employees identifying as a “racial nonminority” and BIEOC is used for employees identifying as a “racial minority.”

Multi-item measures used for explanatory variables distributive justice, procedural justice, and pro-diversity management are described in Table 1. Because the items used for measuring these three constructs are ordinal variables measured on a 5-point Likert-type scale of strongly agree to strongly disagree, a polychoric correlation matrix is used for factor analysis and to estimate factor scores (UCLA: Statistical Consulting Group, n.d.-b). To meet face validity, all measures for these variables were based on respective definitions of the constructs and are consistent with measures used in past research using FEVS data (e.g., Cho & Sai, 2013; Choi, 2011b; Cohen-Charash & Spector, 2001; Colquitt et al., 2001; Pitts, 2009). Measures were also tested for reliability and discriminant validity. The Cronbach’s alpha values, eigenvalues for the factor component and the t-test means comparisons for White employees and BIEOC, for items measuring distributive justice, procedural justice, and diversity management are described in Table 1.

Table 1.

T-test Mean Comparisons of Key Study Variables for White Employees Compared with BIEOC.

Turnover intentions	White employees	BIEOC	Combined	Minimum–maximum
Turnover intentions	M (SD)	M (SD)	M (SD)	Minimum–maximum
Intention to leave current organization (turnover intention)	.288 (.452)	.323*** (.467)	.299 (.458)	0–1
Distributive justice Cronbach’s alpha = .83; Eigenvalue = 2.41; factor loadings = .64 and above
My performance appraisal is a fair reflection of my performance	3.76*** (1.11)	3.68 (1.18)	3.73 (1.13)	1–5
Promotions in my work unit are based on merit.	3.01*** (1.22)	2.91 (1.24)	2.98 (1.23)	1–5
Awards in my work unit depend on how well employees perform their jobs.	3.13*** (1.21)	3.08 (1.25)	3.11 (1.23)	1–5
Pay raises depend on how well employees perform their jobs.	2.56 (1.16)	2.71*** (1.19)	2.61 (2.61)	1–5
Procedural justice Cronbach’s alpha = .85; Eigenvalue= 2.09; factor loadings = .75 and above
I can disclose a suspected violation of any law, rule or regulation without fear of reprisal.	3.65*** (1.19)	3.50 (1.24)	3.60 (1.21)	1–5
Arbitrary action, personal favoritism and coercion for partisan political purposes are not tolerated.	3.45*** (1.22)	3.26 (1.25)	3.39 (1.23)	1–5
Prohibited personnel practices (e.g., illegally discriminating for or against any employee/applicant, obstructing a person’s right to compete for employment, knowingly violating veterans’ preference requirements) are not tolerated	3.84*** (1.06)	3.57 (1.17)	3.75 (1.10)	1–5
Pro-diversity management Cronbach’s alpha = .797; Eigenvalue= 1.77; factor loadings = .72 and above
Policies and programs promote diversity in the workplace (e.g., recruiting minorities and women, training in awareness of diversity issues, mentoring)	3.68*** (.982)	3.38 (1.15)	3.58 (1.05)	1–5
My supervisor/team leader is committed to a workforce representative of all segments of society.	3.88*** (1.00)	3.72 (1.12)	3.83 (1.04)	1–5
Managers/supervisors/team leaders work well with employees of different backgrounds.	3.76*** (.997)	3.53 (1.12)	3.68 (1.04)	1–5
Control variables
Job satisfaction: Considering everything, how satisfied are you with your job?	3.73*** (1.06)	3.71 (1.07)	3.72 (1.06)	1–5
Pay satisfaction: Considering everything, how satisfied are you with your pay?	3.56*** (1.13)	3.43 (1.18)	3.52 (1.15)	1–5
Organizational satisfaction: Considering everything, how satisfied are you with your organization?	3.51 (1.10)	3.52*** (1.11)	3.51 (1.11)	1–5

Note. All items, except for turnover intentions, were measured on a 5-point Likert-type type scale ranging from 5 for “strongly agree” or “very satisfied” responses and 1 for “strongly disagree” or “very dissatisfied” responses. P values indicate a statistically significant difference in means for BIEOC compared with White employees. BIEOC = Black, Indigenous, and Employees of Color.

***

p < .001.

Table 2.

Findings From 1,000 Monte Carlo Simulations of the Probit Regression Model.

Variables	Column A	Column B
Variables	Coefficients	Marginal effects(dy/dx)P(Y = 1)
Racial identity (BIEOC = 1)^a	.372*** (.016)	.095*** (.004)
Distributive justice	–.0588*** (.003)	–.015*** (.000)
Procedural justice	–.0269*** (.003)	–.0068*** (.000)
Pro-diversity management	.046*** (.003)	.011*** (.000)
Interaction effects
Racial Identity (BIEOC = 1) × Distributive Justice	–.0196*** (.005)	–.0049*** (.001)
Racial Identity (BIEOC = 1) × Procedural Justice	–.032*** (.005)	–.008*** (.001)
Racial Identity (BIEOC = 1) × Pro-diversity Management	–.029*** (.006)	–.007*** (.001)
Individual and organizational control variables
Job satisfaction	–.379*** (.002)	–.097*** (.000)
Pay satisfaction	–.0618*** (.001)	–.0158*** (.000)
Organizational satisfaction	–.168*** (.002)	–.043*** (.000)
Age-group squared	.005*** (.000)	.001*** (.000)
Years in government squared	–.0279*** (.000)	–.007*** (.000)
Female	–.046*** (.003)	–.011*** (.000)
Supervisor	.108*** (.004)	.027*** (.001)
Agency size	.400*** (.015)	.102*** (.003)
Proportion of employees of color per agency per year	.554*** (.126)	.142*** (.032)
Proportion of supervisors of color per agency per year	–.0418 (.105)	.010 (.027)
Controlled for agency fixed effects	Yes	Yes
Controlled for year fixed effects	Yes	Yes
Constant	.266*** (.050)
Number of trials	1,000
Number of randomly selected observations per trial	5,800

Note. Mean of regression coefficients of probit model with robust standard errors clustered at agency level (Column A) and mean of marginal effects for key regressors (Column B). BIEOC = Black, Indigenous, and Employees of Color. Outcome Variable: Turnover Intention. p values are based on Z-test for the estimated mean of probit regression coefficients and marginal effects for explanatory and control variables that were obtained from 1,000 Monte Carlo simulation runs. Standard errors are reported in parenthesis.

BIEOC stands for Black, Indigenous, and Employees of Color.

***

p < .001.

Control variables

This study controls for variables such as job satisfaction, pay satisfaction, and organizational satisfaction that could have a significant negative impact on employee turnover intentions (Cantarelli et al., 2015). The measures are described in Table 1. Other demographic controls include quadratic terms for employee’s age group variable and tenure in government, sex (female = 1), and supervisory status (yes = 1). To control for agency and year fixed effects, dummy variables were generated for all agencies and all years included in this study. In addition, the model controlled for agency characteristics such as proportion of BIEOC (that is, racially minority employees) per agency per year, proportion of BIEOC (that is, racially minority supervisors) per agency per year, and for agency size. To construct an “agency size” variable, the Partnership for Public Service (2019) classification of Federal agencies was used to categorize agencies into large (coded as 3), midsize (coded as 2), and small agencies (coded as 1).

Findings

This section focuses on the description of findings related to interracial differences in mean values for the main study variables as reported in Table 1 (i.e., turnover intentions, distributive justice, procedural justice, and pro-diversity management). It also describes the extent of empirical support for study hypotheses using the mean values for probit regression coefficient and marginal effects from 1,000 MC simulation trials as reported in Table 2.

Using pooled cross-sectional time-series (PCSTS) data, Table 1 presents the findings of the t-test mean comparisons between White employees and BIEOC for items measuring all key study variables. Table 1 findings indicate that, on average, compared with White employees, BIEOC expressed higher turnover intentions. The mean values of all items measuring procedural justice and pro-diversity management practices were significantly higher for White employees than BIEOC. For distributive justice, the mean values of all except one item (i.e., “pay raises depend on how well employees perform their jobs”) were significantly higher for White employees than BIEOC.

As noted in the “Data and Estimation Approach” section above, 1,000 MC simulations of the probit regression model were conducted using the large PCSTS data. Specifically, random samples of smaller sizes were drawn from the larger PCSTS data to run 1,000 MC simulation trials of the probit regression model and to estimate the marginal effects of the key explanatory variables on P(Y = 1) (i.e., P[turnover intentions = 1]) (Cameron & Trivedi, 2010; Ji & Li, 2015 Chapter 4; Stata.com, n.d.). Mean values were computed for each of the key variables for the outputs of regression coefficients and marginal effect estimates resulting from these 1,000 simulation trials. These mean values are reported in Table 2 in two separate columns. “Column A” documents mean values of probit regression coefficients and “Column B” documents mean values of marginal effects (M.E.) on P(Y = 1).

Table 2 findings reveal support for H1. The probability of exhibiting turnover intentions is significantly higher for BIEOC (β = 0.372 at p<.001). The M.E. indicate that on average as compared with a White employee, the predicted probability of exhibiting turnover intentions is greater by 9.5 percent points for a BIEOC.

As expected in H2a and H2b, overall, when all Federal employees were considered, an increase in distributive justice and procedural justice were found to have a direct negative effect on turnover intentions (β _{distributive justice}= -0.0588 at p<.001; β _{procedural justice}= −0.0269 at p<.001, see Table 2). The estimated M.E. indicate that on average, with an increase in distributive justice and procedural justice, the predicted probability of turnover intentions of Federal employees is lowered by 1.5 percent points (p<.001) and by 0.68 percent points, respectively.

Findings also reveal support for H2c and H2d. H2c and H2d propose that there will be interracial differences in the moderating role of distributive and procedural justice, respectively, on the relationship between employees’ racial identity and turnover intentions, such that, compared with White employees, BIEOC will exhibit lower turnover intentions. Both distributive justice and procedural justice were found to have a negative moderating effect on the relationship between turnover intentions and employees’ BIEOC racial identity (i.e., BIEOC = 1) (β _{distributive justice*BIEOC} = −0.0196 at p<.001; β _{procedural justice* BIOEC}= −0.032 at p<.001). The M.E. indicate that on average, with an increase in distributive justice and procedural justice, compared with White employees, the predicted probability of turnover intentions of BIEOC decreases by 0.49 percent points and 0.8 percent points, respectively (see Table 2).−

In contradiction to H3a, when an overall sample of all Federal employees is considered, findings indicate a direct positive relationship between pro-diversity management and turnover intentions (β_{pro-diversity management} = 0.046 at p<.001). The estimated M.E. indicate that on average, with an increase in pro-diversity management, the predicted probability of overall Federal employee turnover intentions increases by 1.1 percent points (p<.001).

However, findings support H3b. It proposes that there will be interracial differences in the moderating role of pro-diversity management on the relationship between employees’ racial identity and turnover intentions, such that, compared with White employees, BIEOC will exhibit lower turnover intentions. Findings show a negative moderating effect on the relationship between turnover intentions and employees’ BIEOC racial identity (i.e., BIEOC = 1) (β_{diversity management *BIEOC} = −0.029 at p<.001). The M.E. indicate that on average, with an increase in pro-diversity management, compared with White employees, the predicted probability of turnover intentions of BIEOC decreases by 0.7 percent points. These findings suggest that while pro-diversity management has a positive direct effect on turnover intentions of Federal employees in general, its moderating effect on turnover intentions of BIEOC is negative as expected.

The following section discusses the implications and conclusions of these findings for future research and practice of public-sector management.

Discussion of Findings: Implications for Future Research and Managerial Practice

The findings of this study have important practical implications for public sector managers. They show that even in the context of the Federal government with espoused values of being a model employer, enhancing racial JEDI is a long winding road. After analyzing data from diverse Federal agencies and across 10 different time periods, this study highlights the pattern in interracial differences in employee turnover intentions. The findings reveal that compared with White employees, the predicted probability of BIEOC displaying turnover intentions is greater by 9.5 percent points (H1).

Consistent with the existing literature, this finding implies the existence of systemic barriers to retention of employees from marginalized racial groups and to improving JEDI within the Federal government (African American Workgroup Report, 2013; Asian American and Pacific Islander Work Group Report, 2008; Chordiya, 2019, 2020; Federal Sector Reports, n.d.; Hebl et al., 2002; Hispanic Work Group Report, 2008; Marchiondo et al., 2018; Shen & Dhanani, 2018). Public managers and leaders who are committed to addressing the issues related to higher turnover intentions among employees from racially minoritized and marginalized groups in their organizations should consider conducting further independent research and deep institutional scanning at macro, meso, and micro levels. Such research should aim at identifying and examining individual and institutional racial biases, organizational diversity climates, and the attitudes and experiences of BIEOC. A thorough examination can potentially lead to better identification of problem areas and to develop promising practices to improve outcomes for racial JEDI.

The present study offers empirically supported insights into a systemic level into some of these promising practices that are linked to racial JEDI. The promising practices examined in the present study are rooted in the values of distributive justice, procedural justice, and pro-diversity management. Findings related to the moderating effects of distributive and procedural justice and pro-diversity management interventions indicate that, when these effects are considered (compared with White employees); BIEOC will exhibit lower turnover intentions. Furthermore, when the empirical analysis focused on the overall sample of Federal employees,’ distributive and procedural justice had a lowering effect on turnover intentions. However, contrary to a theoretical proposition, when the analysis focused on the overall sample of all Federal employees, pro-diversity management had a positive effect on turnover intentions.

One of the potential explanations for the contradictory findings related to pro-diversity management could be based on the egocentric bias or the self-interest perspective (Cohen-Charash & Spector, 2001; Parker et al., 1997; Truxillo & Bauer, 1999). Past research has shown that in the public-sector context, compared with White employees; BIEOC exhibit higher preference for pro-diversity and social equity-oriented values (Ortega et al. 2012; Stazyk et al., 2017). The self-interest (or rather, a collective interest) perspective suggests that organizational efforts to enhance equity for racially marginalized groups through pro-diversity management programs may be perceived more positively by BIEOC considered to be primarily benefiting from its outcomes (Cohen-Charash & Spector, 2001; Parker et al., 1997; Truxillo & Bauer, 1999). Federal employees who may not perceive the direct benefits from pro-diversity management efforts and/or those who view these efforts as a challenge to their career opportunities may express less or unfavorable attitudes toward pro-diversity management efforts (including, display of higher turnover intentions) (Parker et al., 1997). This diversity management-related finding suggests a need for deeper investigation into less or unfavorable views toward pro-diversity management efforts as well as a need for measures to enhance the effectiveness of pro-diversity changes in the Federal government. These proactive measures may include intentional and evolving engagement in organizational learning and unlearning process related to critical JEDI topics (e.g., intersectionality and critical race theory), open-minded and constructive conversations about pro-diversity changes including nuanced and courageous conversations on racism and anti-racism, and effective management of conflicts emerging from racial diversity.

Although pro-diversity management did not have the proposed negative impact on turnover intentions of Federal employees in general, it had the predicted negative moderating effect on turnover intentions of BIEOC (compared with White employees). Specifically, findings indicate support for the effectiveness of pro-diversity management efforts in reducing turnover intentions of BIEOC. These include concrete programs and policies for recruitment and proportional representation of BIEOC, engagement in diversity-related learning and development, mentoring and development opportunities for BIEOC, practices of supervisory commitment to workforce representative of all social identities, and cultivation of an environment where employees of diverse backgrounds are able to work well together.

To circle back to the distributive justice findings—they suggest that turnover intentions of Federal employees, in general, and BIEOC, in particular, can be reduced by paying attention to fairness in performance appraisals and fairness in performance-related distributive outcomes such as pay raises, promotions, awards, and recognitions. Like distributive justice, findings also indicate support for a promising role of procedural justice in reducing turnover intentions of Federal employees in general, and BIEOC in particular. Practices rooted in the values of procedural justice considered in this study include addressing and eliminating the fear of reprisals for reporting a wrongdoing, eliminating arbitrary actions, personal favoritism, and coercion for partisan political purposes and practicing anti-discrimination and legal protection of civil rights in HRM functions related to employment opportunities.

It is important to note here that JEDI is not a one-size-fits-all practice and requires a holistic approach of organizational development and change management. In practice, for example, to enhance distributive justice, procedural justice, and pro-diversity management efforts, it is critical to have leadership commitment starting at the highest levels of the organization. Other important elements include holistic research and measurement plans to guide these efforts (e.g., by auditing the organization’s culture and climate for JEDI that include measures for distributive justice, procedural justice, and diversity management). These efforts would also necessitate including systematic educational and training opportunities to build individual and organizational competence for JEDI at all levels. Finally, ensuring that there is an alignment of management systems to promote practices for distributive justice, procedural justice, and diversity management while creating and maintaining channels for trust-building through organizational transparency, accountability, and follow-up (Cox, 2001, Chapter 2).

Limitations

Like all research efforts, the present study has its imperfections. The key explanatory variable of this study is the racial identity of an employee. By focusing on interracial differences of Federal employees using a large N, this study offers empirical evidence to advance a big-picture understanding of interracial differences turnover intentions and the mitigating role of pro-diversity and justice-oriented factors. This interracial focus of analysis uses a categorical approach for comparing turnover intentions and pro-diversity and justice-related perceptions of White employees with BIEOC. Thus, the present study is limited in offering a nuanced intra-racial and intersectional understanding of Federal employees’ turnover intentions and the mitigating pro-diversity and justice management factors when interracial differences intersect with other social identities based on gender, class, sexual orientation, disability, age, religion, and national origin.

It is important to note that this limitation continues to be a challenge and trade-off consideration not only for inter-racial analysis of differences but is also true for empirical intersectionality research that often foregrounds certain categories (e.g., race and gender) while excluding others (Al-Faham et al., 2019; Crenshaw, 1989; Fay et al., 2020; McDonald, 2015; McKay et al., 2007; Stazyk et al., 2017). One of the ways forward to address this limitation is to move toward smaller N qualitative research to test intersectionality’s explanatory value. Future research could build on the present study to dive deeper into smaller N, qualitative analysis of intersectional experiences and turnover attitudes of BIEOC in workplace for one or more intersecting categories. Another limitation of this study is related to the inadequacy of measures to distinguish between racially marginalized groups, as all BIEOC are included in one category of “racial minorities” in the FEVS data.¹ Multiple and distinct research studies will be needed to adequately address the unique challenges and promising practices to create equitable and inclusive workplaces for people of specific racial groups with intersectional identities.

Like similar studies using the FEVS data (e.g., Chordiya, 2019, 2020; Pitts, 2009; Pitts et al., 2011; Sabharwal, 2015), the findings of this study are vulnerable to common source bias because it uses a single survey to measure both outcome and explanatory variables (Favero & Bullock, 2015). Furthermore, although necessary steps were taken to examine the face validity, discriminant validity, and reliability of the measurements for key independent variables namely distributive justice, procedural justice, and pro-diversity management, these measures are limited to the extent that items were available to measure them in the FEVS data. Additional survey questions to measure these constructs or a research design using other qualitative methods would potentially yield a deeper analysis and conclusions. Another limitation is with respect to the omitted variable bias. Although this study accounts for various alternative explanations, including controls of job satisfaction, organizational satisfaction, pay satisfaction, individuals and demographic factors, agency and time fixed effects, and size of the agency, other specific contextual variables such as agency type (e.g., distributive, redistributive, regulatory, and constituent) may have been omitted.

Finally, the focus on the U.S. Federal employees may limit the generalizability of these findings for other state and local governmental contexts in the United States and internationally. Despite these limitations, this study advances research on organizational JEDI by offering theoretical and empirically supported insights into structural interventions that can reduce the turnover intentions of government employees in general and, particularly, employees from racially marginalized groups.

Conclusion

In this article, modern discrimination theories were utilized to argue that, compared with White employees, Federal BIEOC will exhibit higher likelihood of turnover intentions (Cheung et al., 2016; Marchiondo et al., 2018). Findings indicate, in context of the Federal government, when compared with White employees, the likelihood of turnover intentions is higher among BIEOC. Turnover intention represents a serious consequence of barriers to the career success of historically marginalized groups (Sabharwal, 2015). Higher turnover may also act as a barrier to leadership attainment: higher turnover means there are fewer BIEOC left in the organizations to get to those upper echelons of leadership. With theoretical arguments and empirical evidence, this study shows organizational leaders working toward lowering turnover intentions of their racially marginalized workforce need to pay special attention to effective implementation of pro-diversity management programs, enhanced fairness in the distribution of outcomes (i.e., distributive justice), and fairness in organizational procedures (i.e., procedural justice).

Supplemental Material

sj-docx-1-ppm-10.1177_00910260211061824 – Supplemental material for A Study of Interracial Differences in Turnover Intentions: The Mitigating Role of Pro-Diversity and Justice-Oriented Management

Supplemental material, sj-docx-1-ppm-10.1177_00910260211061824 for A Study of Interracial Differences in Turnover Intentions: The Mitigating Role of Pro-Diversity and Justice-Oriented Management by Rashmi Chordiya in Public Personnel Management

Footnotes

Acknowledgements

This article is made possible thanks to the generous and kind support of my mentors, teachers, friends, and family. I am grateful to my dear mentor, doctoral supervisor, colleague, and friend, Dr. Meghna Sabharwal for her support and invaluable advice on this project. I am thankful to Dr. James Harrington, Dr. R. Paul Battaglio, Dr. Doug Goodman, Dr. L. Douglas Kiel, and Dr. Donald F. Kettl for their helpful comments and feedback on the initial drafts of this article submitted for my PhD dissertation. I thank my dear friends and colleagues Dr. Nuri Heckler, Dr. Karo Solat for their valuable help and feedback on theory and data analysis of this research project. My heartfelt thanks to our daughter Saphira’s caregivers Ms. Ana Condur and Ms. Ashyia Wainright for their warm-hearted and kind support that gave me the peace of mind to experience the joy and creativity of a research process. I am forever grateful for my loving kind life partner Mr. Sahil Pujani who has always been my strongest supporter, cheerleader, and my source of inspiration, energy, and nourishment. I thank him for his particularly important role in completion of this project and for giving me the help that I needed to make the data analysis process efficient.

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.

ORCID iD

Rashmi Chordiya

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biography

Rashmi Chordiya is an assistant professor in the Institute of Public Service at Seattle University. Her research focuses on organizational culture and institutional practices for improving public management outcomes in diverse national contexts, and from the lens of social justice, equity, diversity, and inclusion.

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