Jenny Baker
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Diversity, Equity, and Inclusion: Where Do SIOP Members Stand? Evidence From the 2019 SIOP Salary Survey

Rachel W. Smith, Georgia Southern University; H. Kristl Davison, Appalachian State University; Nhung Hendy, Towson University; Anna L. Hulett, Booz Allen Hamilton; Chantale Wilson Antonik, Modern Hire; Mark W. Cawman, Azusa Pacific University; and Laura K. McAliley, R3 Government Solutions

Diversity, equity, and inclusion (DEI) have received growing interest among I-O psychologists and HR practitioners for various reasons. First, there is a business case for promoting DEI in organizations, including increased innovation, employee engagement, and profits (Bourke et al., 2017). Second, advancing DEI is the right thing to do under the social justice case as organizations move beyond legal compliance to avoid discrimination. However, SIOP members may wonder how SIOP itself compares on DEI issues. Using the 2019 SIOP Salary Survey data, we highlight how far SIOP has come to increase gender representation and identify how to achieve racial diversity and pay equity along gender and racial lines.

The structure of this article is divided into three studies examining the 2019 survey data. First, we quantified the gender pay gap among SIOP members and examined various predictors of the gender pay gap, including employer type, education, experience, and job title. Second, we examined the possibility of a double jeopardy effect of gender and ethnicity among SIOP members and the benefit of obtaining a professional license and/or certification on pay. Finally, we explored teleworking and the extent to which gender and racial inequality existed in teleworking among SIOP members.

Our analyses utilized data from the SIOP Salary Survey conducted in 2019, which asked SIOP members about their income in 2018. An email invitation was sent to all SIOP members (i.e., no Student Affiliates) with active email addresses. Of the 4,362 invitations sent, there were 1,605 respondents (a 36.8% response rate). After cleaning the data and limiting the sample to full-time employees, the final sample was 1,403 participants. The sample was evenly split in terms of gender (52% female), the majority identified as White (80.9%), and the average age was 42.25 years (SD = 11.28).1

Study 1: Are We Closing the Gap? Current State of the Gender Pay Gap With SIOP Members

Women have made significant strides in education, work experience, and representation across occupations to improve gender equality in the workplace, dramatically narrowing the gender pay gap over time (Blau & Khan, 2016). For example, women earned 85% of what men earned in 2018, compared to 64% in 1980 (Graf et al., 2019). The 2016 SIOP Salary Survey results observed a gender pay gap of 89.7%, suggesting that the pay gap within our field was continuing to close compared to prior SIOP salary surveys (Richard et al., 2018). Based on trends from prior surveys, we expected that (a) the gender pay gap continued to narrow, with a higher female-to-male income ratio compared to years prior; (b) differences in the pay gap continue to be significant; and (c) the lowest gender pay gaps would occur during early career and at lower level job titles compared to later career and higher level job titles. We examined several predictors of the gender pay gap, including education, experience, and employer type, to compare with the 2016 Salary Survey results (see Richard et al., 2018).

For the first time in the history of the SIOP salary survey, women surpassed men in representation, with 711 selecting female (52%, coded as 1) and 658 selecting male (48%, coded as 0). Contrary to trends in recent years, the gender pay gap widened compared to prior administrations, with a female-to-male base income ratio of 86.8%. This pay gap was significant (t = 6.19, df = 1,367, p < .01), and percentile breakdowns showed female-to-male income ratios ranging from 78.5% to 89.6% (Table 1).2 Income disparities were lowest at the 10th and 25th percentiles with the highest income disparities at the 90th percentile. This trend had also been found in the 2016 Salary Survey; however, at that time, the average female income for the 10th percentile was higher than the male income, which was not the case in the 2019 Salary Survey. The differences across all percentiles were much higher than previously, suggesting the gender pay gap is widening both across the board and within percentile bands.

 

Table 1

Study 1: Descriptive Statistics and T-Tests by Gender

 

Female

 

Male

 

 

 

N

Median

Mean

(SD)

 

N

Median

Mean

(SD)

Female to male median income ratio

t-test (df) of female vs. male mean

 

Base income

711

108,500

120,322

(59,575)

 

658

125,000

147,540

(105,055)

86.8%

6.19** (1367)

Base income percentiles:

 

 

 

 

 

 

 

 

 

90th

 

190,800

 

 

 

243,000

 

78.5%

 

75th

 

144,000

 

 

 

170,000

 

84.7%

 

50th

 

108,500

 

 

 

125,000

 

86.8%

 

25th

 

81,500

 

 

 

90,916

 

89.6%

 

10th

 

61,360

 

 

 

70,000

 

87.7%

 

                           

** p < .01.

 

Next, we examined variables that might impact the current gender pay gap, including education level, experience, and employer type.3 Women earned less than men whether they held master’s degrees (t = 2.40, df =276, p < .05) or doctorates (t = 5.03, df =1053, p < .01; Table 2). However, this gap was slightly smaller for master’s degree holders (93.5% vs. 91.9%). Contrary to our expectations, the gender pay gap was not smaller in early career, and female-to-male income ratios fluctuated over the years with the pay disparity disappearing closer to midcareer at 10–14 years but re-emerging and widening for respondents with more experience (Table 3 and Figure 1).

 

Table 2

Study 1: Descriptive Statistics and T-Tests for Gender Across Educational Levels

 

Female

 

Male

 

 

Education

N

Median

Mean

(SD)

 

N

Median

Mean

(SD)

Female to male median income ratio

t-test (df) of female vs. male mean

Master’s degree

172

85,088

92,388

(38,891)

 

106

91,000

108,868

(59,186)

93.5%

2.40** (276)

Doctorate

525

119,409

129,815

(62,482)

 

530

130,000

156,243

(109,129)

91.9%

5.03** (1053)

Note. Results for the ABD/working on doctorate category are not shown due to small sample size.

** p < .01.

 

Table 3

Study 1: Descriptive Statistics and T-Tests for Gender Across Experience

 

Female

 

 

 

Male

 

 

Experience range

N

Median

Mean

(SD)

 

 

 

N

Median

Mean

(SD)

Female to male median income ratio

t-test (df) of female vs. male mean

0-4

212

81,000

86,642

(36,819)

 

 

 

150

92,250

97,939

(43,355)

87.8%

2.51* (360)

5-9

169

107,500

115,966

(48,154)

 

 

 

116

111,250

122,711

(56,989)

96.6%

.96 (283)

10-14

94

129,200

135,141

(51,129)

 

 

 

87

128,000

136,929

(49,716)

100.1%

.39 (179)

15-19

70

135,000

145,983

(59,385)

 

 

 

75

147,000

162,449

(68,737)

91.8%

1.73† (143)

20-24

51

144,000

163,028

(76,272)

 

 

 

65

153,000

206,226

(184,840)

94.1%

1.36 (114)

25-29

28

150,000

157,408

(53,207)

 

 

 

35

186,000

231,044

(180,164)

80.6%

2.06* (61)

30+

32

128,500

152,489

(82,405)

 

 

 

70

163,600

200,554

(130,168)

78.5%

2.39* (100)

p < .10; * p < .05.

 

 

Figure 1

SIOP Salary Survey Gender Female-to-Male Income Ratios by Experience

 

 

 

 

 

 

An examination of employer type (Table 4) found similar trends to the previous survey in that government and not-for-profit organizations had the lowest gender pay gaps (99.1% and 91.7% female-to-male income ratios, respectively), and private sector and university/collegiate institutions had higher pay gaps (82.7% and 83.3% female-to-male income ratios, respectively).

 

Table 4

Study 1: Descriptive Statistics and T-Tests for Gender Across Employer Type

 

Female

 

 

 

Male

 

 

Employer type

N

Median

Mean

(SD)

 

 

 

N

Median

Mean

(SD)

Female to male median income ratio

t-test (df) of female vs. male mean

Government

56

106,034

110,208

(36,878)

 

 

 

49

107,000

109,595

(36,204)

99.1%

-.03 (103)

Not-for-profit organization

51

105,414

105,715

(44,359)

 

 

 

39

115,000

137,695

(71,751)

91.7%

2.54* (88)

Private sector, for-profit

organization

366

112,500

126,462

(63,191)

 

 

 

318

136,000

165,058

(131,748)

82.7%

5.30** (682)

University or college

233

100,000

115,374

(58,318)

 

 

 

248

120,000

133,781

(70,327)

83.3%

3.18** (479)

* p < .05; ** p < .01.

 

Last, hierarchical regression was used to test the effects of the education and experience variables on the gender pay gap. In Step 1 (R2 = .25, p < .01), base income was significantly predicted by years of experience (β = .42, p < .01) and education (β = .21, p < .01). The addition of gender in Step 2 was also significant (β = -.08, p < .01; ΔR2 = .01, p < .01), indicating that the gender wage gap persisted even when controlling for education and experience. 

In sum, pay inequities remain despite gender representation among SIOP membership rapidly growing in recent decades. Common assumptions about the causes of the gender wage gap are that women face a compensation penalty by leaving the workplace to care for children (Sigle-Rushton & Waldfogel, 2007) or that women are less likely to negotiate their salaries (Babcock & Laschever, 2009). However, follow-up analyses found no significant gender differences in salary negotiation. Indeed, other situational factors may explain the wage gap, such as women facing more work interruptions, shorter hours, or dual career issues such as sacrificing their career advancement for a partner’s career (Blau & Kahn, 2016). Further examination of these and other factors in the context of I-O psychology-related careers is needed to identify patterns regarding factors contributing towards the persistent pay gap in our field. In the next study of the 2019 Salary Survey we examine whether licensing and certification ameliorate wage gaps based on gender as well as race.

Study 2: Will Getting a Professional License and/or Certification Overcome Pay Inequity?

As noted in Study 1, the gender wage gap persists nearly 6 decades after the Equal Pay Act was passed. Moreover, women who are minorities are doubly disadvantaged, according to the double jeopardy hypothesis (Bradley & Healy, 2008). In 2018 U.S. income data, Black women, Native American women, and Latinas earned $0.62, $0.57, and $0.54, respectively, for every dollar a White man made (Connley, 2020).

Whether possessing a professional certification or license can help close these pay gaps is an important question. Of the many available human resources (HR) certifications, the two most popular sponsored by the Society for Human Resource Management (SHRM) are the SHRM-Certified Professional (SHRM-CP) and SHRM-Senior Certified Professional (SHRM-SCP) certifications. The two most popular sponsored by the Human Resource Certification Institute (HRCI) are the Professional in Human Resources (PHR) and Senior Professional in Human Resources (SPHR) certifications. Hundreds of thousands of people have obtained these certifications as of January 2019 (HR Certification Institute, 2019). Despite the growing importance of HR certification as a legitimate credential for those in I-O psychology and HRM professions, there lacks empirical evidence justifying the value of HR certification (Lengnick-Hall & Aguinis, 2012).

Licensing for I-O psychologists is a controversial topic. Although not mandated to practice I-O psychology, proponents view licensure as lending legitimacy to the profession. In addition, licensing provides curriculum standardization in I-O psychology. Critics of licensing argue that licensing is likely unenforceable and that such a requirement may suppress free competition in the marketplace. In addition, the lack of data justifying that licensing protects the public from harm caused by nonlicensed I-O psychologists explains the slow growth in I-O psychology licensing relative to HR certification (Latham, 2017).

In this set of analyses, we examine whether obtaining an HR certification and/or professional license can help to overcome pay inequity based on the double jeopardy hypothesis (Bradley & Healy, 2008). Human capital theory states that employees’ knowledge, skills, and abilities contribute to the firm’s profit (Becker, 1964). Obtaining a professional license and/or certification requires an investment of time and money that should generate a positive return on investment based on this theory. However, compared to obtaining a required occupational license (e.g., certified public accountant), obtaining an I-O-relevant certification is voluntary and, therefore, may not guarantee a positive return on investment. Thus, we expect pay inequity by sex and race to persist even after obtaining a certification and/or license.

Table 5 shows the mean effect size of pay differences by sex and race.4 Of the 4 racial subgroups examined in this research, the White–Black difference was the largest (d = .37), followed by White–Asian (d =.28), and White–Hispanic (d = .10), all favoring White participants. The effect size magnitude was similar for base pay and total pay. Additionally, Asian women earned the least base pay and total pay, followed by Black women, Hispanic women, and White women (Figure 2).

 

Table 5

Study 2: Effect Size of Sex and Race Differences in Pay

Base pay 

Median (USD $)

Std

N

Cohen’s d

Male

125,000

107,497

619

 

Female

109,900

67,678

660

Male–Female = .17

White

120,000

88,391

1,131

 

Black

92,000

62,204

35

White–Black = .37

Asian

92,640

106,561

77

White–Asian = .28

Hispanic

110,250

114,721

42

Hispanic–White = .10

Total pay

 

 

 

 

Male

145,197

176,040

620

 

Female

121,000

167,292

662

Male–Female = .14

White

136,000

148,021

1,133

 

Black

97,000

94,591

35

White–Black = .31

Asian

102,750

110,315

77

White–Asian = .25

Hispanic

115,500

537,849

42

Hispanic–White = .05

 

Figure 2

Median Pay by Sex and Race

HR certification was positively associated with base pay (r = .09, p < .01) and total pay (r = .09, p <.01). HR certification was associated with a $14,000 increase in base pay and $11,000 increase in total pay after controlling for sex, race, place of employment, job experience and degree type (β = .13, t = 2.03, p < .05; β = .16 t = 1.95, p = .05, respectively).5 No significant gain in base pay or total pay was found for obtaining a professional license after controlling for the above variables. Some gender and racial pay gaps persisted after controlling for job experience, degree type, employer type, and certification/professional license. Specifically, the White–Asian difference in total pay was d = .32, whereas the same effect size for base pay was d = .31, favoring White participants. Hispanic–White difference in total pay was d = .34, favoring Hispanic participants. All other racial subgroup differences in pay (e.g., Black–White) became nonsignificant after controlling for the above variables.

Our results suggest that HR certification can help to reduce the pay inequity among SIOP members, but gaps persist even after obtaining a professional license and/or HR certification. Despite recent gains in female representation within SIOP, these findings are disconcerting and worthy of future research. In our final study, we examine whether telework affects the pay inequities documented in our first two studies.

Study 3: Exploring the Nature of Telework in SIOP Members

The term “telework” includes any work arrangement where one conducts work outside of their organization’s primary physical location (Biron & van Veldhoven, 2016). Facilitated by technological developments, 51 million (32%) employed Americans teleworked in some capacity in 2019 (U.S. Department of Commerce, 2020). The onset of the COVID-19 pandemic in early 2020 exponentially increased the number of employees teleworking. In the first 2 months of the pandemic in the US, the percentage of employed Americans teleworking nearly doubled to 62%, the majority (59%) of whom indicated they preferred to continue teleworking once pandemic restrictions are lifted (Brenan, 2020). Though the present research explores data collected prior to the COVID-19 pandemic, our findings are even more relevant in today’s increasingly virtual world.

Teleworkers experience benefits such as increased autonomy, job satisfaction, job performance, and supervisor relationship quality (Gajendran & Harrison, 2007; Gajendran et al., 2015). Organizations experience multiple benefits of telework including increased employee performance and productivity, increased recruiting appeal and retention, and real estate cost savings (Khanna & New, 2008; SHRM, 2010). Yet, extant research suggests that demographic variables such as gender and race impact one’s experiences in both the traditional physical workplace (e.g., Kabat-Farr & Cortina, 2012) and the remote workplace. For example, women and men may experience different benefits from teleworking (Sullivan & Lewis, 2001), and significant racial disparities exist in rates of telework (U.S. Department of Commerce, 2020). We explore the nature of telework and potential differences in telework regarding gender and race based on the 2019 SIOP Salary Survey data.

Of the sample, 40.4% worked remotely to some degree.6 Those that teleworked spent an average of 68.39% of their week teleworking (SD = 31.63%). Teleworkers worked 45.39 hours per week on average (SD = 7.50), which was not significantly different from non-teleworkers (M = 45.34, SD = 7.10; t(1350) = -0.14, p = .45). The majority of teleworking SIOP members worked in the private sector (58.8%), followed by those in academia (29.5%). The majority of teleworkers had one or more children (56.9%).

Regarding gender and telework, more women worked remotely compared to men (Mwomen = 0.44, Mmen = 0.37, t(1350) = -2.75, p < .001). The interaction of gender by telework did not significantly predict base income (β = -1750.58, p = .445). There was not a significant difference regarding the likelihood of teleworking by race (Mwhite = 0.41, Mnon-white = 0.40, t(1347) = .45, p = .347). Additionally, the interaction of race by telework did not significantly predict base income (β = -1978.94, p = .394). Thus, teleworking does not appear to exacerbate or attenuate pay inequities in our sample of SIOP members.

Future SIOP salary surveys should collect additional telework data, such as whether telework is optional or a non-negotiable aspect of one’s job. This would allow us to examine Straw’s (1989) equality dimensions of equal access (equal opportunity to telework), equal chance (everyone teleworking is treated the same way), and equal share (equal representation regarding who is teleworking). More nuanced data would afford the opportunity to examine potential gender or racial differences regarding who can telework, the degree of equality in the treatment of teleworkers, and representation among gender and racial groups.

Conclusion

This research addressed three aspects of DEI within the 2019 SIOP Salary Survey. First, we examined the persistent gender pay gap among SIOP members. Compared to the 2016 results, the gender pay gap widened slightly in 2019. Results were consistent with research showing that the gender pay gap narrows during the initial years of women’s careers and widens with progressing years of experience (Leaker, 2008; Meara et al., 2017).

Second, we examined the possibility of a double jeopardy effect of gender and race/ethnicity among SIOP members. We found pay inequities in terms of race, favoring White participants, and these disparities were further exacerbated by gender. Additionally, we found that obtaining a HR certification and/or professional license was associated with an income gain among SIOP members but that gender and racial pay gaps persisted.

Finally, we explored teleworking among SIOP members and the extent to which gender and racial inequality existed in teleworking. Approximately 40% of surveyed members reported teleworking in 2018. We did not observe any racial differences among White and non-White SIOP members regarding teleworking access, but women were found to telework more than men. However, these differences did not translate into differences in base pay.

Certain limitations in the current research are worth noting. The small number of individuals who identified as a racial minority limited our analyses such that we had to group racial categories. This is consistent with the lack of racial diversity reported by SIOP, as only ~16% of SIOP members self-report as non-White (SIOP, 2019), reflecting a larger issue our profession must tackle regarding representation of individuals from diverse backgrounds. Additionally, these data were collected prior to COVID-19, highlighting the need for future research to examine these issues during and eventually after the pandemic. Notably, 80% of employees who have left the workforce during the pandemic are women (U.S. Department of Labor, Bureau of Labor Statistics, 2020). Of the women that left the workforce, 38% were Latina and 7% were Black, providing a snapshot into the potential gender and racial disparities occurring more broadly.

Altogether, our findings suggest that despite our field’s interest in DEI, SIOP members are not immune to pay inequities based on gender and race. Although other factors certainly influence or ameliorate these inequities (e.g., educational level, experience, employer type, certification and/or licensure), the gaps tended to persist in our sample even when controlling for such factors. Clearly, work remains to be done within our profession to achieve SIOP’s goals of promoting worker well-being.

 

Notes

1 Analyses revealed that this sample closely mirrors the characteristics of SIOP membership as a whole (see the full 2019 SIOP Salary Survey Report for more details: https://www.siop.org/Portals/84/PDFs/Surveys/SIOP_TI_Income-and-Employment_Report.pdf?ver=2020-09-22-152339-387)

2 Due to skew in the base pay data, a natural logarithmic transformation was conducted to normalize this variable. 

3 Education level was coded as 0 for a master’s degree (in any area), 1 for ABD or working on a doctorate, and 2 for a doctorate (in any area). Years of experience was computed based on years since earning highest degree. Employer type included government, not-for-profit, private sector for-profit, and university or college. To help preserve respondent anonymity, analyses with sample sizes < 20 per cell are not reported.

4  Due to the small sample of some racial subgroups, we combined participants who identified as Indian Asian, Chinese, and other Asian origins into the larger Asian group (N=78). The remaining groups are White (N = 1,133); Black (N = 35), and Hispanic (N = 42). In terms of sex, the final sample included 712 females and 658 males. Due to skewness of base pay and total pay data, a natural logarithmic transformation was conducted to normalize these two variables. 

5  We obtained the same results when conducting these analyses after removing the highest and lowest values of base pay and total pay. 

6  41% of this sample did not report race. Due to low numbers of participants in many race categories, race was dichotomized into White or non-White. Telework was also dichotomized, such that if participants indicated they teleworked in any capacity during their regular workweek, they were coded as teleworking.

 

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