The Gender Pay Gap for Behavior Analysis Faculty: A 2024 Update

Abstract

We examined publicly available faculty salaries for men and women faculty members at 39 university programs affiliated with the Association for Behavior Analysis International (ABAI), either through Accreditation, Recognition, or Applying for Recognition status. Overall, 61.9% of the 181 faculty members were presumed women. Collectively, the mean salary for women was $95,345.16 (SD = $34,279.91) and the mean salary for men was $106,267.80 (SD = $41,513.61), though additional analyses revealed gender did not significantly predict salary at any rank. A multiple linear regression model revealed that program modality and rank were statistically significant predictors of salary differences. After adjusting for inflation, the average differences between actual earned salaries and inflation-adjusted salaries ranged from $11,950 to $31,718, suggesting that faculty wages have not kept pace with the rate of inflation.

In 2019, Li and colleagues published a report examining 103 faculty salaries for the 2016 to 2017 academic year and found an alarming discrepancy in pay. Across all ranks, the average salary difference between women and men was $20,116. This pay difference indicates that women, on average, earned 89% of the average salary of men. Additionally, the highest salary reported was earned by a man, and the lowest salary reported was earned by a woman. This reiterated the findings from the 2017 American Psychological Association (APA) workforce report on psychology faculty salaries (Christidis et al., 2017), where women faculty were underrepresented at the full professor level, and on average, women made less than men (typically, women earned 95% to 99% of the average salary of men at respective ranks). The last APA workforce report was published in the subsequent year (2018 to 2019), and although there was an improvement in starting salaries for new assistant professors (the median salary was the same for women and men) and assistant professors (women made 99% of the salaries of men), there was a greater gap for associate level and full professor level (97% and 95%, respectively) (Christidis et al., 2019).

Within applied behavior analysis (ABA) service delivery, Vance and Saini (2023) and Baires et al. (2023) identified gendered inequities among female and male practitioners. Baires et al. (2023) surveyed practitioners (N = 236) who worked with children and found that women, on average, made 94% of the average salary reported by men, although this difference of $5,108 was not statistically significant. Vance and Saini (2023) surveyed practitioners (N = 600) working in North America, and while the difference in salary accounting for gender alone was not statistically significant, there was a significant difference when accounting for gender and other variables such as race, certification, and years of experience. On average, female practitioners made 91% of the salaries of their male counterparts, which was a difference of $6,184. The Association of Professional Behavior Analysts (2015) previously published employment surveys from 2008 to 2015 using the Behavior Analyst Certification Board’s mass e-mail service. In their last report published in 2015, the average full-time BCBA annual income was between $65,000 and $75,000, which would be equivalent to $89,221 to $102,947 in 2025 (Bureau of Labor Statistics,  (n.d.). The data from both Baires et al. (2023) and Vance and Saini (2023) suggest salaries have not kept up with inflation: the reported mean salaries from Baires et al. (2023) were $75,599 and $80,707 for women and men respectively, and the reported mean salaries from Vance and Saini (2023)’s were $61,513 for women, $67,697 for men, and $52,491 for nonbinary participants.

The gender wage gap continues to persist in private, public, and nonprofit sectors in the United States despite increased gender representation and increased opportunities for hiring and promotion (Bishu & Alkadry, 2017). However, equal access to positions of power or decision-making in the workplace remained disparate across gender and management gender composition (i.e., the proportion of men versus women in leadership roles). Recent research within medicine and healthcare reiterates this. Hoff and Lee (2021) conducted a systematic review of gender-based pay research in medicine and found female doctors earned less than their male counterparts despite similar demographic and work profiles and increasing representation of female doctors. Similarly, Slama and colleagues (2023) reviewed the gender gap in surgeon salaries and found that the average female surgeon earns $0.78 per $1.00 made by her male colleague and that this disparity was more pronounced in Black and Hispanic women. They also found that the gender pay gap persists across surgical subspecialties and countries across three continents after controlling for confounds such as years of experience and caseload.

Widespread gender pay gaps are often met with skepticism rooted in sexist stereotypes which center women’s behavior and de-center the historically marginalizing circumstances in which they behave (Friman, 2021). For example, Leibbrandt and List (2015) found in a natural field experiment of nearly 2,500 job seekers, women avoid negotiating their salaries significantly more frequently than men unless prompted to do so. This was echoed in a translational study conducted by Maitra and colleagues (2021), where they also found that women were more risk-averse than men. It should also be noted that men typically achieve significantly better economic outcomes for first offers and for negotiating (Mazei et al., 2015). More recently, Kray et al. (2024) found that women report negotiating their salaries more often than men in a sample of 3374. They also found that participants exposed to textbook excerpts reinforcing gender stereotypes about the wage gap, such as “women don’t ask [for higher wages]” were more likely to endorse these stereotypes. The gender pay gap is likely influenced by a differential history of punishment according to gender. Shan et al. (2019) found that men negotiated more and had better outcomes in individualistic cultures and posited that women expressing self-interest and assertiveness were likely to face backlash for aggressive negotiations due to cultural values of male assertiveness and female passiveness. This is reiterated in Recalde and Vesterlund (2023), where they emphasize the challenges of encouraging women to negotiate like men due to different societal expectations. In addition to awareness of this issue, both individual and organizational policies have been recommended to rectify the gender pay gap. As such, wages should be evaluated periodically to assess the changes in the pay gap to determine worsening or improving conditions.

Koedel and Pham (2023) conducted a cross-sectional analysis comparing their 2016 dataset of faculty from 40 highly ranked public universities to their updated 2021 dataset; 81.4% of the 2016 wages were recovered in the 2021 sample, with missing wage data attributed to factors such as leaving academia, retirements, faculty leaves, and sabbaticals. They found that the gender wage gap had reduced from 3.6% to 1.2%. It was noted that female faculty were disproportionately awarded atypically large raises. Humphries and colleagues (2023) compared 297 faculty wages for the 2020–2021 academic year within one institution in Louisiana and found that the gender wage gap was minimal, with men earning 2.6% more than women faculty. It should be noted that this varied by discipline, as female faculty in business earned 7.3% more than men, and female faculty teaching in the pharmacy school earned 2% on average more than their male counterparts.

While the general, ongoing trend of increasingly gender-equitable pay gives cause for celebration, behavior analysts should not grow complacent or shy away from the call to investigate and intervene upon the wage gap and other gendered inequities. With the attacks on diversity, equity, and inclusion initiatives and practices in higher education (HR Specialist, 2024; Lu, 2023; U.S. Department of Education, (2024), achieving pay equity will require concerted efforts from behavior analysts, especially senior faculty and industry leaders whose behaviors are most likely to change (or reinforce) cultural contingencies of the status quo. Li et al. (2019) called for readers and ABAI to act upon their evidence of gendered pay inequality in behavior analysis, but no study to date has evaluated subsequent progress on this front. The purpose of this study is to update Li et al. (2019) on gender representation and faculty wages at ABAI-affiliated programs, as well as evaluate the salary landscape relevant to cost of living and program modality for behavior analytic faculty.

Methods

Publicly available salary data were collected from ABAI-Accredited, ABAI-Recognized, and Applying for Recognition universities based in the United States. Of the 30 ABAI-Accredited and 50 ABAI-Recognized/Applying universities, 21 and 18, respectively, had public salary information available for the 2023 to 2024 year. Two programs (programs 14 and 15 in Fig. 1) had both designations due to offering multiple programs and were designated only as ABAI-Accredited to prevent double-counting faculty (n = 21), as no distinction was provided regarding which faculty supported which designation. A total of 181 faculty salaries across 37 unique programs were included in this analysis. The authors recorded the university name, faculty name, rank, and salary for full-time faculty. Faculty members for each program were identified through the behavior analysis program webpage or by searching “[university name] behavior analysis faculty.” Faculty salary information was extracted from public salary databases which were found by searching “[university name] employee salaries.” We did not include visiting professors, lecturers, or adjunct faculty members or any pay reported above base salary (e.g., overload, stipends, or grants). We also recorded the state where the university is located. On the basis of the World Population Review’s Cost of Living Index (World Population Review, 2024), we created five cost-of-living categories (very low to very high) by grouping 10 states in each group, ordered by their cost-of-living indices. For example, the 10 states with the lowest indices were classified as very low cost of living and the 10 states with the highest indices were classified as very high cost of living. The categories for cost of living, states, number of programs, and number of faculty are listed in Table 1. Finally, we coded whether a program was on-campus, online, or both (including hybrid) by referencing the university modality from the BACB’s pass rates (BACB, 2025) or by searching through the program’s webpage.

Fig. 1.

Scatterplot of behavior analysis faculty across ABAI-affiliated programs. Note. Each data point represents the salary of one faculty, with open triangles denoting presumed female faculty members and closed circles denoting presumed male faculty members

Table 1.

Cost of living index by states and number of ABAI-affiliated programs

Cost of living category	Cost of living index range	States	Number of programs	Number of faculty
Very low	85.3–90.8	Alabama Arkansas Illinois Iowa Kansas Mississippi Missouri Nebraska Oklahoma Tennessee West Virginia	11	81
Low	91–94.1	Georgia Indiana Kentucky Louisiana Michigan Minnesota Ohio South Dakota Texas Wyoming	12	53
Medium	94.2–102.6	Delaware Florida Nevada New Mexico North Carolina North Dakota Pennsylvania South Carolina Utah Wisconsin	5	26
High	103.1–114.9	Arizona Colorado Connecticut Idaho Maine Montana New Jersey Rhode Island Vermont Virginia	2	4
Very high	115–179	Alaska California District of Columbia Hawaii Maryland Massachusetts New Hampshire New York Oregon Washington	6	17

We then determined the presumed gender of faculty members by first name as outlined in Li et al. (2019), which was derived from McSweeney and Swindell (1998). If the gender of faculty members could not be identified by first name, their full name was entered into a Google search, and gender was determined from the faculty member’s pronoun usage in publicly available biographies, LinkedIn account, or the university’s webpage (Rotta et al., 2021).

Intercoder agreement was collected for 25% of randomly selected faculty. The intercoder agreement was 97.10%. To further supplement the gender coding process, we compared both manual coding and confirmation by a gender identification tool, Gender-API, a database that sources publicly available records from multiple countries and estimates the probability of gender based on use within a designated country. We selected the United States as the search area. The comparison between manually coded data and Gender-API was 97.24%, with discrepancies originating from the Gender-API database (one name that was not found and four names that were miscoded on the basis of faculty personal information online). The average accuracy probability reported by Gender API was 96.12% (range 61–100%). For the disagreements, the primary researcher reviewed the differences to determine which dataset would be used in the analysis, which was the manually coded data.

Results

For the 2023 to 2024 year, salary data for 181 full-time faculty members were collected and analyzed. There were 20 on-campus programs, 3 online programs, and 14 programs that had both on-campus and online programs. Figure 1 displays the distribution of salaries for each faculty across individual programs according to rank. Of the 181 full-time faculty members, 112 were presumed women, and 69 were presumed men. Salary mean, median, and standard deviation according to rank and gender for all universities are shown in Table 2. Salary means, medians, and standard deviation for ABAI-accredited universities and ABAI-recognized universities are separately shown in Table 3 and Table 4, respectively. Collectively, the mean salary for women was $95,345.16 (SD = 34,279.91) and the mean salary for men was $106,267.80 (SD = 41,513.61). The higher standard deviation among men suggests greater variability in salary. Additionally, faculty employed at universities offering both online and on-campus programs had higher mean salaries compared to those at institutions offering only one modality.

Table 2.

Mean and median salaries of behavior analytic faculty at all ABAI-affiliated programs by gender and rank

Rank	Gender (# of faculty)	Mean salary	Median salary	SD
Assistant Professor	Women (46)	$77,868.73	$80,500.83	$18,231.14
	Men (21)	$71,472.35	$76,000.00	$18,483.89
Associate Professor	Women (37)	$94,095.56	$94,302.71	$27,424.28
	Men (20)	$99,312.15	$92.286.47	$23,432.51
Professor	Women (29)	$124,660.71	$119,228.00	$41,992.91
	Men (28)	$137,332.80	$133,329.10	$41,504.32

The total sample size was 181

Table 3.

Mean and median salaries of behavior analytic faculty at ABAI-accredited programs by gender and rank

Rank	Gender (# of faculty)	Mean salary	Median salary	SD
Assistant Professor	Women (29)	$77,916.08	$79,698.83	$18,721.42
	Men (15)	$69,681.54	$75,961.81	$16,934.29
Associate Professor	Women (26)	$93,716.78	$94,539.33	$32,281.94
	Men (11)	$99,899.76	$99,624.64	$25,462.11
Professor	Women (22)	$118,302.93	$116,745.00	$29,451.52
	Men (21)	$142,343.00	$143,942.00	$45,272.83

The total sample size was 124

Table 4.

Mean and median salaries of behavior analytic faculty at ABAI recognized and applying for recognition programs by gender and rank

Rank	Gender (# of faculty)	Mean salary	Median salary	SD
Assistant Professor	Women (17)	$77,313.92	$81,236.00	$17,914.36
	Men (6)	$75,949.38	$81,506.65	$23,021.45
Associate Professor	Women (11)	$94,990.85	$93,861.00	$10,048.12
	Men (9)	$98,593.95	$89,821.93	$22,194.98
Professor	Women (7)	$144,642.30	$119,228.00	$67,670.75
	Men (7)	$122,302.20	$119,794.70	$23,837.23

The total sample size was 57. This dataset did not include the two programs with both designations (n = 21)

Across all ABAI-affiliated programs combined, 46 women and 21 men held the position of assistant professor, with mean salaries of $77,688.60 and $71,472.35, respectively. At the position of associate professor, there were 37 women and 20 men, with mean salaries of $94,095.56, and $99,312.15. At the position of full professor, there were 29 women and 28 men, with mean salaries of $124,660.71 and $137,332.80, respectively (see Table 2). Overall, the mean salaries of full-time professors and associate professors for men were higher than women, whereas women had higher mean salaries as assistant professors. On average, men earned 5% more at the associate level and 10% more as full professors. However, women earned 8% more than men as assistant professors. The differences in the median were comparable (5% increase in salary for women in assistant professor positions and 12% increase in salary for men in full professor positions) except for the difference in men’s and women’s salaries at the associate level, which was women earning 2% more than men as associate professors. Figure 2 displays the female salaries are more densely distributed (SD = $34,280 vs. $45,514) around a lower average (mean = $95,345 vs. $106,268) compared to male salaries. On average, a female faculty member in behavior analysis makes $10,923 less than her male peers. A simple effects model stratifies gender pay disparities by rank: on average, women earn $6,396 more than men at the assistant rank but $5,217 less at the associate rank and $12,672 less at the full rank.

Fig. 2.

Note. The bounded vertical lines represent the ranges. The horizontal lines within the boxes represent the median, with one interquartile interval above and below the medians

Table 3 shows the mean and median salary for faculty from ABAI-accredited universities. Of the 123 total faculty, 76 were women and 47 were men. At the position of assistant professor, there were 28 women and 15 men, with mean salaries of $77,916.08 and $69,681.54, respectively. Men, however, earned higher mean and median salaries compared to women at both the associate professor and full professor positions. At the position of associate professor, there were 26 women and 11 men with mean salaries of $93,716.78 and $99,899.76. There were 21 men and 22 women occupying the full professor position. Men earned a higher mean salary of $142,343.00 and women earned a mean salary of $118,302.93. Women earned higher mean and median salaries as assistant professors in comparison to men.

In contrast, data extracted exclusively from ABAI-recognized universities revealed that women earned higher mean salaries as both professors and assistant professors (see Table 4). Of the 57 total faculty members in ABAI-recognized universities, 35 were women and 22 were men. At the position of assistant professor, there were 17 women and 6 men with mean salaries of $77,312.92 and $75,949.38, respectively. At the position of associate professor, there were 11 women and 9 men with mean salaries of $94,990.85 and $98,593.95. At the position of full professor, there were 7 women and 7 men with mean salaries of $144,642.30 and $122,302.20. While men earned higher mean salaries as associate professors, women earned higher mean salaries at the level of assistant and full professors. For ABAI-recognized or applying for recognition universities, median salaries were nearly identical for the assistant ($81,236 and $81,506 for women and men, respectively) and full professor positions ($119,288 and $119,794 for women and men, respectively) although men had slightly greater salaries in both. Women faculty had a higher median salary in the associate professor rank ($93,861 compared to $89,821).

We conducted multiple analyses to examine salary differences. Initial analyses using an independent samples t-test showed that men earned significantly higher salaries than women (p = .03). However, owing to the unequal sample sizes between female and male faculty, we then evaluated homogeneity of variances in salaries across gender. Levene’s test revealed a significant difference in variances (F = 5.73, p = .02). Analyses using a Welch’s unequal variance t-test showed that men did not earn significantly higher salaries than women (p = .07; 95% CI [−$22,711, $865]). To further examine salary differences while accounting for rank, we conducted a factorial ANOVA using heteroscedasticity-robust standard errors to account for the variance, which confirmed the difference between men’s salaries and women’s salaries was statistically insignificant (F = 2.71, p = .10; CI [−$39,997, $6,968]). There was also no statistically significant interaction between gender and rank at the assistant (F = 0.67, p = .42, 95% CI [−$9,082, $21,874]), associate (F = 0.40, p = .53, 95% CI [−$21,528, $11,095]), or full (F = 2.58, p = .11, 95% CI [−28,243, $2,899]) ranks. A one-way ANOVA revealed significant differences in salaries across ranks (F = 52.98, p < .001). Tukey’s post-hoc test indicates that associate professors earned on average $20,062.04 more than assistant professors, 95% CI [$7,357.00, $32,767.09], and full professors earned $34,959.65 more than associate professors, 95% CI [$21,752.24, $48,167.07].

Finally, multiple linear regression was conducted to evaluate gender, rank, cost of living, and program modality as predictors of salary (Table 5). Prior to interpreting the results of the multiple linear regression, key assumptions were evaluated. Linearity was assessed via residual versus fitted value plots and partial regression plots, which demonstrated no evident deviations from linearity. Because Levene’s test suggested a violation of homoscedasticity, heteroscedastic-robust standard errors were used to adjust for nonconstant variance. The assumption of normally distributed residuals was checked using both histogram and Q-Q plots, which indicated approximate normality. Multicollinearity was examined using variance inflation factors (VIFs); all VIF values were below the commonly accepted threshold of 10, suggesting no concerns with multicollinearity or redundancy among the predictors. These results support the appropriateness of the linear regression model for the data. The overall model was statistically significant F(4, 176) = 23.50, p < .001, indicating at least one predictor had a meaningful relation with salary, and explained 40% of the variance in salary (adjusted-R² = .40). Among the predictors, rank (p < .001) and program modality (p = .01) were statistically significant while gender (p = .24) and cost of living (p = .952) were not. Model comparisons indicated that this multiple linear regression model better fit the data (AIC = 4243.05, BIC = 4259.04) than a spline (nonlinear) regression model (AIC = 4250.71, BIC = 4273.10).

Table 5.

Regression coefficients of gender, rank, cost-of-living, and program modality on salary

Predictor	β (SE)	t	p
Gender	−5542.998 (4647.222)	−1.19	.235
Rank	27475.16 (2936.714)	9.36	.000*
Cost-of-living	138.0167 (2291.638)	0.06	.952
Program modality	6965.614 (2666.161)	2.77	.010*
Adjusted-R2	0.3982

Asterisks denote statistical significance at p ≤ .01

Discussion

In the present study, we analyzed publicly available salaries for 112 presumed female and 69 presumed male behavior analytic faculty across 21 ABAI-accredited and 18 ABAI-recognized programs in the United States. The total sample was 181 faculty salaries across 37 programs, in contrast to Li et al. (2019), which reported salaries for 103 faculty across 16 programs. Since data collection, two additional university programs have been accredited, and five additional universities are applying for recognition. The dataset from this paper represents approximately 66% of the 32 ABAI-accredited and 33% ABAI-recognized and applying for recognition programs as of 2025. It should be noted that 13 of the 55 recognized or applying for recognition universities are based outside of the United States. While this was a sizable sample compared to Li et al. (2019), our data were limited to publicly funded universities in the United States.

Regardless, our analysis offers cause for optimism with respect to the gender pay gap. Li and colleagues (2019) found that, at all ranks, the lowest salaries were earned by women, and the highest salaries were earned by men. In the present sample, the lowest salaries were still earned by women at the assistant and associate ranks, but the highest salaries were earned by women at all ranks. Additionally, female faculty comprise 61.88% of the present sample—a 9.45% increase from Li and colleagues’ previous statistic of 52.43%. Despite the growth in female representation among behavior analytic faculty, men remain overrepresented at all levels of academia. Men account for only 11% of BCBAs and BCBA-Ds (BACB, n.d.) but in our sample, men comprised 31.34%, 39.09%, and 49.12% of assistant, associate, and full professors, respectively. The increasing female representation at lower ranks suggests an improvement in gender representation over time, but considerable work lies ahead to ensure female behavior analysts can access and sustain careers in research and teaching.

Evidence on gendered pay gaps and interactions with rank is promising. A factorial ANOVA indicated an insignificant pay gap between male and female faculty of aggregated ranks (p = .10), suggesting the gender wage gap observed by Li et al. (2019) is improving. An average difference of $10,923 which favors men does exist in the dataset and warrants consideration, although this is likely contributed by the uneven sample sizes in this study. Visual inspection of the gender-rank interaction plot, specifically the intersecting lines between assistant and associate ranks, suggests that the gender pay gap, albeit statistically insignificant, favors women at the assistant rank, but this trend reverses and widens at higher ranks. These observations merely suggest areas for future research in gender pay equality as visual analysis of an interaction plot is not a substitute for statistical analysis. Notably, pay gaps still warrant institutional change regardless of statistical significance: our simple effects model indicates that the average woman earns $6,396 more than the average man at the assistant rank, but $5,217 less at the associate rank, and $12,672 less at the full rank. These findings echo Blau and Kahn’s 2017 report on the gender wage gap, where there are reliably larger gender gaps or more substantial gender pay gaps in higher-ranking positions, which suggest the “possibility of a glass ceiling among highly skilled women.”

The lack of gender disparity in the regression model is likely due to the overlap between gender and rank and the disproportionate number of women in assistant-level positions, which inherently have lower salaries compared to associate and full levels. While it is not surprising that rank predicts salary differences due to faculty promotions typically including a salary increase, the significance of program modality as a predictor was interesting. Faculty in programs offering hybrid or both online and in-person modes earn on average $14,157 more than in-person programs and $6,044.59 more than those in online programs (Fig. 3). This increase is likely attributed to differences in enrollment or institutional size, as the average enrollment size for institutions with both on-campus and online programs in this sample was 29,036, compared to 19,446 for institutions with only in-person programs, and 15,415 for institutions with only online programs. In examining the rank of professors in different programs, there was an equal number of full professors in programs that are on-campus only and programs with both on-campus and online programs; however, there were a greater number of associate level professors (21 versus 34, respectively), although this was also the case for assistant level professors (24 versus 39, respectively). Cost of living was not a significant predictor, but this was likely due to the overrepresentation of faculty in very low-cost-of-living states and the limited number of faculty and programs in high and very high-cost-of-living states.

Fig. 3.

Note. The bounded vertical lines represent the ranges. The horizontal lines within the boxes represent the median, with one interquartile interval above and below the medians

There are several limitations in this study. One limitation is the method used to determine presumed gender. While there has been a precedence for this method (Curiel et al., 2020; Gravina et al., 2019; Kranak et al., 2021; Li et al., 2019; McSweeney & Swindell, 1998; Nosik et al., 2019; Poling et al., 1983; Rotta et al., 2021), gender identity has become more nuanced than a binary distinction. One goal of this paper was to provide an update to Li et al. (2019) so that comparisons can be made between 2016 and 2024 salary data sourced in the same way. Because this study is reliant on accessing publicly available information, inferences were made on the basis of publicly available pronouns and conventional name use, which likely resulted in the erasure of gender-fluid and nonbinary faculty. Carpenter et al. (2025) published the first report on earning disparities experienced by nonbinary people using restricted access Canadian Census data and found that gender minority groups and cisgender women earned less than cisgender men. Additionally, nonbinary people assigned female-at-birth earned significantly less than all groups. In this paper, we used Gender-API to supplement the manual coding process; however, the database was also limited to binary categorization. Blevins and Mullen (2015) recommend using historical datasets to track naming practices over time, which may provide more nuanced data as census information is updated. For example, unisex names have increased in frequency, where some unisex names have consistent gender preference (e.g., in 2010, 90% of babies named Parker were assigned male-at-birth) and others have changed gender preference across time (e.g., Devon was more likely to be used equally among male- and female-at-birth babies in 1990, whereas in 2010, 70% of babies named Devon were assigned male-at-birth) (Barry III and Harper, 2014). While more sophisticated methods for archival research have yet to be developed, future researchers interested in this topic should consider e-mailing faculty directly to ensure data are socially valid (Oda & Stiehl, 2025). The other limitation of this study is the discrepancies in reporting among public salary databases. While some salary databases designated base salaries, others did not and may have contained additional pay for administrative service or overloads.

White-Lewis et al. (2023) found that gender-based discrimination and salary were driving factors in faculty’s decision to leave academia from a faculty retention and exit survey conducted across 37 institutions by the Collaborative on Academic Careers in Higher Education. One faculty member indicated they left for a non-academic job that paid 50% more than their academic salary. In Li et al. (2019), the average faculty salaries were $67,670 for assistant professors presumed women and $78,186 for assistant professors presumed men. Considering inflation, the averages should be $89,558 and $103,476, respectively. Table 6 displays the 2016 salaries from Li et al. (2019), inflation-adjusted salaries for 2024, and the actual 2024 salaries in this study. In all cases, 2024 faculty salaries fell considerably short of inflation-adjusted expectations.

Table 6.

Comparison of mean salaries of behavior analytic faculty in ABAI-affiliated programs

Rank	Presumed gender	Li et al. (2019) salaries (n)	Inflation-adjusted	Current salaries (n)	Difference
Assistant Professor	Women	$67,670 (16)	$89,558	$77,688 (46)	$11,870
	Men	$78,186 (12)	$103,476	$71,472 (21)	$32,001
Associate Professor	Women	$89,315 (25)	$118,204	$94,095 (37)	$24,109
	Men	$95,281 (11)	$126,100	$99,312 (20)	$26,788
Professor	Women	$107,516 (13)	$142,293	$124,660 (29)	$17,633
	Men	$126,006 (26)	$166,763	$137,332 (28)	$29,431

Women faculty also cited spousal employment much more often as a reason for leaving an institution or academia in general, which indicates a great need to support dual positions or resources to facilitate spousal employment if salary is not negotiable. In the medical field, predictors of leaving academia were hours spent on research, poor work life quality, lack of benefits and security (Ghahramanian et al., 2024), lack of recognition by administration, lack of support for scholarly work, lack of fair promotion processes, lack of institutional communities (Beltyukova & Graham, 2017), and unreasonable demands (Bramlage et al., 2021). It should be noted that these predictors were not isolated to pay but a greater emphasis on overall institutional practices and culture. Inequitable wages contribute to faculty’s perception of support as well as their quality of life. Promoting women and marginalized faculty to negotiate higher wages and benefits can be an initial step toward rectifying the wage gap, but ultimately, organizational interventions are required for greater impact (Recalde & Vesterlund, 2023). One strategy used by a large academic surgery department was a structured compensation plan, which did improve the gender pay gap between male and female surgeons but did not get rid of it completely (Morris et al., 2018). Similarly, Hayes et al. (2020) found that a structured compensation plan at the Mayo Clinic resulted in pay equity in 96% of physicians. Furthermore, salary audits can be another approach used to reduce gender pay gaps (Elia et al., 2023). Wage transparency can also contribute to reducing the wage gap. While our data set focused on publicly available salaries, it is unknown whether there is a gender wage gap among private universities and universities that do not publicize their salaries. We recommend that behavior analytic programs make this information available to their department as well as to the general public. This would enable existing faculty and new hires to be more informed during the negotiation process and minimize conflicting expectations. Additionally, program chairs should advocate for yearly salary audits to ensure fairness and equitable pay. Additional research is also needed to identify barriers in negotiation among behavior analytic faculty. For example, it may be possible that incoming faculty are unable to negotiate due to a recently implemented structured compensation plan; however, the senior faculty in the department are likely not subject to this plan due to being grandfathered in, potentially creating inequities.

More needs to be done to support faculty in behavior analytic programs. Carr (2024) reported that 10 universities contributed to 48% (n = 4,003) of 2023 first-time exam takers, with pass rates averaging 50.7% (range 14–78%), an “unacceptable” state of training programs. High-quality faculty deserve competitive compensation reflective of the rising cost of living and increasing workloads, especially considering that faculty not only consider leaving their institution but also leaving academia in general. While the gender wage gap has improved, the fight for pay equity must continue, especially in light of the erosion of real wages as reflected in inflation-adjusted salaries in Table 6. In 2019, we proposed that ABAI include standards on pay equity in the accreditation process. With accreditation now being required to apply for certification, having such a standard would significantly benefit women and nonbinary faculty. Pay equity and increasing support of women and nonbinary faculty are critical to retaining diverse and high-quality instructors and mentors, which ultimately increases the competency of future practitioners and clinical care. While the purpose of this paper is to provide an update to Li et al. (2019), we also hope our data inspires and aids women and marginalized faculty in pay negotiation and salary reviews.

Author Contributions

Conceptualization, methodology, data analysis, and the first draft of the manuscript were written by Anita Li. Material preparation, data collection, analysis, and writing were performed by Tien M. Pham and Jacob D. Desruisseaux. Jack F. Blake conducted statistical analysis and contributed to writing. All authors reviewed and edited previous versions of the manuscript and approved the final manuscript.

Funding

No funding was received for this study.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Compliance with Ethical Standards

This study was determined not to be human subject research per the standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Conflict of Interest

The authors have no conflict of interest to disclose.