Gardner, Ryan, and Snoeyink (Reference Gardner, Ryan and Snoeyink2018) emphasize the need to assess human capital and market factors that may contribute to gender differences in income and suggest that such data are not readily available. As members of the Institutional Research Committee, we thought it important to provide some evidence addressing the focal article's main points using what data are available. Specifically, we conducted ad hoc analyses using data from the 2016 SIOP Income and Employment Survey, with the intent of providing additional context related to employment and compensation for industrial and organizational (I-O) psychologists. Our sample included only respondents who indicated that they worked full time and who provided their gender (n = 1,069). These analyses answer Gardner et al.’s call to examine factors that explain the income gap between men and women within the field.
We note that our Winter 2018 TIP article (Richard et al., Reference Richard, Wright, Thomas, Wiggins, DuVernet, Parker and Davison2018), which was based on 2016 SIOP Income and Employment survey data, focused on gender differences in base income as reported by members. Briefly, the results indicated that men do earn more than women in the I-O psychology field. The mean reported income for male respondents was $138,873 and $117,985 for female respondents (t(1,056) = 4.093, p < .001). This income gap exists both in academic and applied settings. In this article, we seek to expand on these findings by focusing on variables that were not thoroughly explored in the TIP article.
Employment-Related Variables
Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) indicated the need to explore other factors related to pay, as they may contribute to the observed gender differences in income. In response, we first examined whether women were more likely to work part time. Across the entire sample of respondents (academics and practitioners), only 43 individuals reported working part time (20 male and 23 female), and the gender difference was not statistically significant (X 2(1) = .29, ns). Additionally, there was no significant gender difference in the amount of weeks respondents worked during 2015 (M males = 47.8; M females = 47.0; t(1076) = 0.22, ns). The practitioner survey respondents were asked how many hours they worked per week, on average, in 2015. We found no gender differences in average hours worked per week for the practitioner subsample (t(700.3) = –0.915, ns).
Although we had limited information about job type for our practitioner subsample, we did find differences in gender representation across the four most commonly reported industry types (X 2(4) = 12.89, p < .05); women were more likely to work in nonprofit sectors, whereas men were more likely to work in consulting and private sector organizations. Women make up 44.98% of those in consulting organizations, 58.66% of those in private sector organizations, 59.02% of those in not-for-profits, and 50.53% of those in government organizations.
Looking at our academic subsample, we found that men were more likely to be tenured than women. About 63% of male academic respondents reported being tenured, whereas about 51% of female academic respondents reported being tenured (X 2(1) = 4.17, p < .05). Of the 172 academic respondents who reported being tenured, about 62% were male. These differences may partially be explained by gender differences in years spent at the respondents’ current universities. Male academics reported having worked for their current employer for an average of 13.6 years, whereas female academics reported having worked for their current employers for an average of 10.89 years (t(270) = 2.26, p < .05).
Other Forms of Income
To bolster the analyses conducted for the TIP article and in answer to Gardner et al.’s (Reference Gardner, Ryan and Snoeyink2018) call for information on other factors that may contribute to gender differences in income, we revisited the income data. Specifically, we examined respondent reports of the raises and bonus/incentive pay they received in 2015. Splitting the sample by career type (i.e., academic and practitioner respondents), we found no statistically significant gender differences in raises in 2015. Of the practitioners, 71% of men and 76% of women received raises in 2015 (X 2(1) = 2.10, ns), and the mean salary increase was 6.08% for men and 6.37% for women (t(459), = 0.41, ns). Of the academic respondents, 70% of men and 68% of women received raises (X 2(1) = 0.13, ns), and the mean salary increase was 4.55% for men and 5.40% for women (t(181) = 0.60, ns).
Regarding bonus/incentive pay for both the practitioner and academic subsamples, there were no statistically significant gender differences in whether someone received a bonus/incentive. Sixty-nine percent (69%) of both male and female practitioners reported a bonus/incentive for 2015 (X 2(1) = 0.01, ns). Among academic respondents, 18% of men and 16% of women reported a bonus/incentive for 2015 (X 2(1) = 0.11, ns). However, there was a gender difference in the size of the bonus/incentive, with men on average receiving a larger bonus/incentive. For practitioners, men received an average bonus/incentive of $38,547, and women received an average bonus/incentive of $22,663 (t(316.5) = 2.69, p < .01). For academics, men received an average bonus/incentive of $12,006, and women received an average bonus/incentive of $4,830 (t(38.5) = 2.604, p = .01). Unique to the practitioner subsample, we found that, of those 26 individuals who reported receiving a sign-on or recruiting bonus, only seven (27%) were female (X 2(1) =6.99, p < .01). Although this is a small sample size, it may speak to the point made by Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) regarding gender differences in negotiation; however, more data are needed in order to fully explore this hypothesis.
We wholeheartedly agree with Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) that more research is needed to fully understand career outcome differences between male and female I-O psychologists, as well as how we might address and mitigate the differences that have been observed. We were limited by our dataset in the kinds of additional analyses and questions we could investigate; however, these results do provide some indication that the gender gap might be smaller in some aspects of income and employment than in others.
Still, we need to know more to be able to fully understand this complex issue. Future SIOP Income and Employment surveys could include additional questions regarding relevant employment-related data (e.g., area of specialization) that may be helpful in further addressing the issues raised in the focal article. However, as noted by Gardner et al. (Reference Gardner, Ryan and Snoeyink2018), there are limitations to our data, one of which is low response rates for the Income and Employment Survey. For example, for the 2016 survey, only 24.0% of members responded. We believe that if we want to know more about the differences that exist in our field, we as members of SIOP should all own part of the responsibility to make it happen. Providing responses through SIOP-sponsored data collection efforts like the Income and Employment Survey is one way to do that. We ask all SIOP members, regardless of membership type, to participate in our next survey, which will be conducted in 2019, in order to make sure that SIOP has sound data to address questions like those posed by Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) and other issues of importance to our membership.
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Gardner, Ryan, and Snoeyink (Reference Gardner, Ryan and Snoeyink2018) emphasize the need to assess human capital and market factors that may contribute to gender differences in income and suggest that such data are not readily available. As members of the Institutional Research Committee, we thought it important to provide some evidence addressing the focal article's main points using what data are available. Specifically, we conducted ad hoc analyses using data from the 2016 SIOP Income and Employment Survey, with the intent of providing additional context related to employment and compensation for industrial and organizational (I-O) psychologists. Our sample included only respondents who indicated that they worked full time and who provided their gender (n = 1,069). These analyses answer Gardner et al.’s call to examine factors that explain the income gap between men and women within the field.
We note that our Winter 2018 TIP article (Richard et al., Reference Richard, Wright, Thomas, Wiggins, DuVernet, Parker and Davison2018), which was based on 2016 SIOP Income and Employment survey data, focused on gender differences in base income as reported by members. Briefly, the results indicated that men do earn more than women in the I-O psychology field. The mean reported income for male respondents was $138,873 and $117,985 for female respondents (t(1,056) = 4.093, p < .001). This income gap exists both in academic and applied settings. In this article, we seek to expand on these findings by focusing on variables that were not thoroughly explored in the TIP article.
Employment-Related Variables
Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) indicated the need to explore other factors related to pay, as they may contribute to the observed gender differences in income. In response, we first examined whether women were more likely to work part time. Across the entire sample of respondents (academics and practitioners), only 43 individuals reported working part time (20 male and 23 female), and the gender difference was not statistically significant (X 2(1) = .29, ns). Additionally, there was no significant gender difference in the amount of weeks respondents worked during 2015 (M males = 47.8; M females = 47.0; t(1076) = 0.22, ns). The practitioner survey respondents were asked how many hours they worked per week, on average, in 2015. We found no gender differences in average hours worked per week for the practitioner subsample (t(700.3) = –0.915, ns).
Although we had limited information about job type for our practitioner subsample, we did find differences in gender representation across the four most commonly reported industry types (X 2(4) = 12.89, p < .05); women were more likely to work in nonprofit sectors, whereas men were more likely to work in consulting and private sector organizations. Women make up 44.98% of those in consulting organizations, 58.66% of those in private sector organizations, 59.02% of those in not-for-profits, and 50.53% of those in government organizations.
Looking at our academic subsample, we found that men were more likely to be tenured than women. About 63% of male academic respondents reported being tenured, whereas about 51% of female academic respondents reported being tenured (X 2(1) = 4.17, p < .05). Of the 172 academic respondents who reported being tenured, about 62% were male. These differences may partially be explained by gender differences in years spent at the respondents’ current universities. Male academics reported having worked for their current employer for an average of 13.6 years, whereas female academics reported having worked for their current employers for an average of 10.89 years (t(270) = 2.26, p < .05).
Other Forms of Income
To bolster the analyses conducted for the TIP article and in answer to Gardner et al.’s (Reference Gardner, Ryan and Snoeyink2018) call for information on other factors that may contribute to gender differences in income, we revisited the income data. Specifically, we examined respondent reports of the raises and bonus/incentive pay they received in 2015. Splitting the sample by career type (i.e., academic and practitioner respondents), we found no statistically significant gender differences in raises in 2015. Of the practitioners, 71% of men and 76% of women received raises in 2015 (X 2(1) = 2.10, ns), and the mean salary increase was 6.08% for men and 6.37% for women (t(459), = 0.41, ns). Of the academic respondents, 70% of men and 68% of women received raises (X 2(1) = 0.13, ns), and the mean salary increase was 4.55% for men and 5.40% for women (t(181) = 0.60, ns).
Regarding bonus/incentive pay for both the practitioner and academic subsamples, there were no statistically significant gender differences in whether someone received a bonus/incentive. Sixty-nine percent (69%) of both male and female practitioners reported a bonus/incentive for 2015 (X 2(1) = 0.01, ns). Among academic respondents, 18% of men and 16% of women reported a bonus/incentive for 2015 (X 2(1) = 0.11, ns). However, there was a gender difference in the size of the bonus/incentive, with men on average receiving a larger bonus/incentive. For practitioners, men received an average bonus/incentive of $38,547, and women received an average bonus/incentive of $22,663 (t(316.5) = 2.69, p < .01). For academics, men received an average bonus/incentive of $12,006, and women received an average bonus/incentive of $4,830 (t(38.5) = 2.604, p = .01). Unique to the practitioner subsample, we found that, of those 26 individuals who reported receiving a sign-on or recruiting bonus, only seven (27%) were female (X 2(1) =6.99, p < .01). Although this is a small sample size, it may speak to the point made by Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) regarding gender differences in negotiation; however, more data are needed in order to fully explore this hypothesis.
We wholeheartedly agree with Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) that more research is needed to fully understand career outcome differences between male and female I-O psychologists, as well as how we might address and mitigate the differences that have been observed. We were limited by our dataset in the kinds of additional analyses and questions we could investigate; however, these results do provide some indication that the gender gap might be smaller in some aspects of income and employment than in others.
Still, we need to know more to be able to fully understand this complex issue. Future SIOP Income and Employment surveys could include additional questions regarding relevant employment-related data (e.g., area of specialization) that may be helpful in further addressing the issues raised in the focal article. However, as noted by Gardner et al. (Reference Gardner, Ryan and Snoeyink2018), there are limitations to our data, one of which is low response rates for the Income and Employment Survey. For example, for the 2016 survey, only 24.0% of members responded. We believe that if we want to know more about the differences that exist in our field, we as members of SIOP should all own part of the responsibility to make it happen. Providing responses through SIOP-sponsored data collection efforts like the Income and Employment Survey is one way to do that. We ask all SIOP members, regardless of membership type, to participate in our next survey, which will be conducted in 2019, in order to make sure that SIOP has sound data to address questions like those posed by Gardner et al. (Reference Gardner, Ryan and Snoeyink2018) and other issues of importance to our membership.