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https://doi.org/10.1177/00910260211061824
Public Personnel Management
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DOI: 10.1177/00910260211061824
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Original Article
A Study of Interracial
Differences in Turnover
Intentions: The Mitigating
Role of Pro-Diversity and
Justice-Oriented Management
Rashmi Chordiya1
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
1Seattle University, WA, USA
Corresponding Author:
Rashmi Chordiya, Assistant Professor, The Institute of Public Service, Seattle University, 901 12th
Avenue, Seattle, WA 98122, USA.
Email: chordiyarash@seattleu.edu
1061824PPMXXX10.1177/00910260211061824Public Personnel Management XX(X)Chordiya
research-article2021
2 Public Personnel Management 00(0)
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, orga-
nizational 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 pre-
cursor 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 turn-
over intentions but also the mitigating factors.
The general management literature shows support for the role of pro-diversity cli-
mates in reducing turnover intentions of BIEOC (McKay & Avery, 2005; McKay
et al., 2007). However, very limited empirical research in public personnel manage-
ment has focused on the contexts of BIEOC and inquired into the interracial differ-
ences 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 inter-
racial 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 representa-
tive workforce), distributive justice in employment outcomes (e.g., in pay and promo-
tions) 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
Chordiya 3
data, measurements, and the estimation approach. Subsequent sections present empiri-
cal 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 preju-
dice 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 dis-
crimination 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 manifesta-
tions of racial discrimination are that it is insidious in nature, it is pervasive, intersec-
tional, 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 manifesta-
tions of racial discrimination in the post-Civil Rights era, and its implications for orga-
nizations 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 consid-
ered 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, for-
mal, 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
4 Public Personnel Management 00(0)
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 govern-
ment 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 dis-
crimination. 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 work-
place” (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, hard-
working, technically oriented, good at math and science, but also as passive, noncon-
frontational, and anti-social (Asian American and Pacific Islander Work Group Report,
2008). The African American workgroup report notes that African Americans experi-
ences 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 inequal-
ities 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, over-
looked, and are mostly untouched and unprotected by law (Acker, 2006; Hebl et al.,
2002; Shen & Dhanani, 2018). However, like traditional discrimination, modern dis-
crimination 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
Chordiya 5
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 distribu-
tive and procedural justice-oriented management can be instrumental in lowering turn-
over 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 allot-
ment 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, dis-
tributive 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 experi-
ence an emotional impact (e.g., anger, guilt). Perceived unfairness in outcomes is also
likely to influence their cognitions (e.g., dissatisfaction about inequitable inputs/out-
puts 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 behav-
ioral responses (Cohen-Charash & Spector, 2001). Therefore, the study proposes that
6 Public Personnel Management 00(0)
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-Cha-
rash & Spector, 2001; Naylor, 2020). Organizational procedures represent the mecha-
nisms by which an organization allocates resources. Procedures are considered fair
when they are perceived to be consistent, accurate, unbiased, and ethical, have mecha-
nisms 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 organiza-
tions, 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 dis-
tributive justice practices focus on eliminating discrimination in procedures and eco-
nomic 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 expe-
rience 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
Chordiya 7
turnover intentions such that, when it is considered, compared with White employ-
ees, 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 employ-
ees, 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 manage-
ment entails embracing the understanding that a diverse workforce enriches organiza-
tional 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 hierar-
chy. 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 unproduc-
tive 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 out-
comes 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-diver-
sity management will be negatively associated with employees’ turnover intentions.
Interracial difference in the moderating role of pro-diversity management on turnover inten-
tions. 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-diver-
sity 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
8 Public Personnel Management 00(0)
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 cultivat-
ing access to mentoring opportunities, coaching, supervisory support, and genuinely
valuing employees of marginalized social backgrounds. These HRM functions poten-
tially 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 manage-
ment 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,
Chordiya 9
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 iden-
tifying 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 indepen-
dence 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 individ-
ual (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 impor-
tant advantages of including survey responses representative across years and agen-
cies, 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 mini-
mize 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.
10 Public Personnel Management 00(0)
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 replace-
ment for each year and each agency from the PCSTS data set resulting in 5,800 obser-
vations 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 (Cam-
eron & Trivedi, 2010, Chapter 4; Stata.com, n.d.). Finally, marginal effects were esti-
mated 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, dis-
tributive 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 demo-
graphic measure included in the FEVS asking employees to identify as (racial) “minor-
ity” 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
11
Table 1. T-test Mean Comparisons of Key Study Variables for White Employees Compared with BIEOC.
Turnover intentions
White employees BIEOC Combined Minimum–
maximumM (SD)M (SD)M (SD)
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
(continued)
12
Turnover intentions
White employees BIEOC Combined Minimum–
maximumM (SD)M (SD)M (SD)
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 1. (continued)
Chordiya 13
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 reli-
ability 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.
Control variables. This study controls for variables such as job satisfaction, pay satis-
faction, 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 gener-
ated for all agencies and all years included in this study. In addition, the model con-
trolled 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 agen-
cies 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 inten-
tions, 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 find-
ings 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 simula-
tions 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
14 Public Personnel Management 00(0)
Table 2. Findings From 1,000 Monte Carlo Simulations of the Probit Regression Model.
Variables
Column A Column B
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)
(continued)
Chordiya 15
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” docu-
ments 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
Variables
Column A Column B
Coefficients
Marginal effects
(dy/dx)
P(Y = 1)
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.
aBIEOC stands for Black, Indigenous, and Employees of Color.
***p < .001.
Table 2. (continued)
16 Public Personnel Management 00(0)
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 aver-
age, 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 consid-
ered, findings indicate a direct positive relationship between pro-diversity manage-
ment and turnover intentions (βpro-diversity management = 0.046 at p<.001). The estimated
M.E. indicate that on average, with an increase in pro-diversity management, the pre-
dicted 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) (βdiversity 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-diver-
sity management has a positive direct effect on turnover intentions of Federal employ-
ees 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 man-
agers. 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 sys-
temic 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
Chordiya 17
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 examina-
tion can potentially lead to better identification of problem areas and to develop prom-
ising 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 prac-
tices 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 interven-
tions indicate that, when these effects are considered (compared with White employ-
ees); BIEOC will exhibit lower turnover intentions. Furthermore, when the empirical
analysis focused on the overall sample of Federal employees,’ distributive and proce-
dural justice had a lowering effect on turnover intentions. However, contrary to a theo-
retical 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-diver-
sity 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 marginal-
ized groups through pro-diversity management programs may be perceived more posi-
tively 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, dis-
play 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 mea-
sures 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 effec-
tive 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 employ-
ees). Specifically, findings indicate support for the effectiveness of pro-diversity
18 Public Personnel Management 00(0)
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 develop-
ment opportunities for BIEOC, practices of supervisory commitment to workforce
representative of all social identities, and cultivation of an environment where employ-
ees of diverse backgrounds are able to work well together.
To circle back to the distributive justice findings—they suggest that turnover inten-
tions 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 recogni-
tions. 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 consid-
ered in this study include addressing and eliminating the fear of reprisals for reporting
a wrongdoing, eliminating arbitrary actions, personal favoritism, and coercion for par-
tisan 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 prac-
tice, 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 compe-
tence 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 orga-
nizational 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.
Chordiya 19
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 empir-
ical 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 intersec-
tional 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 com-
mon source bias because it uses a single survey to measure both outcome and explana-
tory variables (Favero & Bullock, 2015). Furthermore, although necessary steps were
taken to examine the face validity, discriminant validity, and reliability of the measure-
ments 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 mea-
sure 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 expla-
nations, including controls of job satisfaction, organizational satisfaction, pay satisfac-
tion, 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 organiza-
tional JEDI by offering theoretical and empirically supported insights into structural
interventions that can reduce the turnover intentions of government employees in gen-
eral 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
20 Public Personnel Management 00(0)
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 imple-
mentation of pro-diversity management programs, enhanced fairness in the distribu-
tion of outcomes (i.e., distributive justice), and fairness in organizational procedures
(i.e., procedural justice).
Acknowledgments
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 sub-
mitted 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 https://orcid.org/0000-0001-9122-7865
Supplemental Material
Supplemental material for this article is available online.
Note
1. To protect individuals’ anonymity, the information on categories of racial groups is not
publicly available for most years of Federal Employee Viewpoint Survey data.
Chordiya 21
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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.
