Recently, The eLearning Guild published the 2010 e-Learning Salary and Compensation report. Among other findings, one topic that generated much discussion in social networks online is the 14.5% gap in gender pay in e-Learning. Janet Clarey started a blog carnival with Cammy Bean, Julie Dirksen, and Kelly Garber to explore the subject.
The emotions expressed in the social learning discussions seem to range from confusion to anger as to why in today’s world we are still facing this issue. In response to that interest, and out of my own curiosity to find the data source of the difference, I drilled down further into the data to see if I could find a key conclusion or answer to the questions being asked in our e-Learning community. Sadly, after much analysis and exploration of the data, I could not identify one obvious potential root cause to isolate or explain the difference in data. The discrepancy in gender pay is pervasive across almost all job roles, states, degrees, and levels of experience.
As discussed in the report, the initial summary findings were that while e-Learning pay for women is 37% better than the national average as reported by The National Organization for Women for full-time regular employees, as a field across part-time vs. full-time status and employee vs. contractor designation categories, men on average earn 14.5% more than women. (See Figures 1 and 2.)
Figure 1. The U.S. gender gap in e-Learning ($)
Figure 2. The U.S. gender gap in e-Learning (%)
When broken down into an hourly rate, while the gap is even more prominent for part-time employees than full-time, men are still paid higher than women across all categories. (See Figure 3.) The gap is most significant with part-time regular employee hourly rates where men earn almost twice as much as women while working a similar number of hours per week.
Figure 3. The U.S. gender gap in
e-Learning by hourly rate.
The source of this data is from eLearning Guild members filtered for the year from January 1, 2009 to January 1, 2010. It represents more than 6,000 responses from across the United States. Women make up 56.7% of the survey respondents.
To investigate this further, I examined the data in greater detail to determine if any variables could be identified that might isolate the difference. My friend and colleague, Patti Shank, suggested it might be because more men tend to be developers, and developers receive higher pay than other e Learning roles. That hypothesis had potential, so I cross-referenced job roles with gender. Men are paid more than women in all job roles, with the one exception of technical writing. There are bright spots in this data though, where the gap is narrower between women in some job roles, such as Instructional Design and Research and Development.
For Figure 4, please bear in mind that the data is not statistically significant for the specific job roles with low response counts. The eLearning Guild generally does not publish data with counts this low or with this much detail, but for purposes of this discussion, it seems useful to have this information, whether statistically significant or not.
Figure 4. The U.S. gender gap by job role.
As shown in Figure 4, some job roles do have significantly more men working in them than women relative to the sample of data, and therefore provide some explanation for the difference in pay. As noted above, women represent 56.7% of the respondents. In any job where women in the job make up less than 57.6% of those in that role, that will explain some of the source of some incremental discrepancies. For example, in Executive Management, men represent 68.3% or 25% more than the average respondents of 43.3%men. This works out to an explanation of $209.95 of the overall $11,400 discrepancy in annual average difference in gender pay.
Executive Management Example:
25% difference = 55.25 more men than average respondents
Salary difference = $22,800 greater salary for men than women
55.25 * 22,800 = $1,259,700 more pay for men
$1,259,700 / 6,000 respondents = $209.95 explanation of discrepancy in gender pay
Therefore, some jobs that have more men in them are higher paying jobs and therefore may explain incremental amounts of the discrepancy, but combined together comprise a relatively small total and are not enough to explain the overall $11,400 average difference in pay. Also, based on the current pay for women in those roles, it appears the overall discrepancy would worsen if more women worked in those higher paying roles.
Patti’s hypothesis explained a small portion of the discrepancy, but not enough to cover the full amount, so I explored other potential variables. I evaluated gender against the geographic state to see if some regional areas were different than others. Yet again, men are paid more than women in almost every state, with the exceptions of Connecticut and Tennessee. Since states with small response counts are grouped together as “Other States,” the data in Figure 5 and all subsequent charts are statistically significant.
Figure 5. Gender gap by U.S. state
I then compared gender to education level. I questioned whether men have more advanced degrees with higher pay than women. (See Figure 6.)
Figure 6. U.S. gender gap by education level.
Table 1 shows the distribution of advanced degrees. Each comparison of degrees needs to be adjusted against the source of 56.7% women in the data. For example, the female respondents in the survey have 2.9% more Master’s degrees ((1,709 women/2,866 total) – 56.7% = 2.9%) than the male respondents, and 5.1% fewer Doctorate degrees. While the average of $12,700 greater pay for Doctorate degrees could explain the discrepancy if there were more respondents that category, there are not enough men in that group to significantly impact the overall discrepancy. There are 5.1% more men than the average of 43.3% men with Doctorate degrees, and that only explains $46.42 of the annual $11,400 average difference in gender pay.
|
|
Percentage of Women with Degree |
Difference from Data Source |
|
Less Than Two Years Higher Education |
56.4% |
-.3% |
|
Associate’s Degree |
50.0% |
-6.7% |
|
Bachelor’s Degree |
54.8% |
-1.9% |
|
Master’s Degree |
59.6% |
+2.9% |
|
Doctorate |
51.6% |
-5.1% |
Years of experience in e-Learning did not explain the difference either (Figure 7).
Figure 7. Years in e-Learning by gender.
Finally, out of curiosity as to whether the problem seeped into supplemental pay too, I compared gender to bonus pay (Figure 8). The discrepancy in gender pay crosses over into supplemental pay too. As an aside, it was also intriguing to see that part-time and full-time men earn a comparable bonus, while part-time women earn a significantly lower bonus than full-time women.
Figure 8. The U.S. gender gap in pay extends to bonus pay as well.
While my more detailed investigation into the data did highlight small pockets of an explanation, it did not illuminate any large-scale sources of the discrepancy in gender pay. In fact, it confirmed that the issue is pervasive across almost all job roles, states, degrees, and years of experience. It is, most likely, a cultural bias, and it is a problem we have been facing for four generations of women in the workforce. However, the gap does appear to be closing over time nationally, and in the field of e-Learning, we are doing better than the national average.
If we want to change the situation, we in the e-Learning field are the epitome of change catalysts. We have first-hand experience in enacting growth and change in organizations. As an e-Learning community, we also represent all levels of management. We have both the power and capability to reverse this problem to eliminate gender from the equation of fair compensation within our field. We can create adjustments, and even do so within an existing budget pool for salary increases, to appropriately allocate and equalize salaries across teams based on performance, education, years of experience, and any other criteria that an organization uses to calculate salaries. It won’t happen overnight, but it can shift over time.
Use the examples in the last section of the 2010 Salary and Compensation Report to calculate the fair “average” salary for yourself or your employees without gender as a variable. If you already enjoy fair or above average compensation without a gender bias, then congratulate yourself. If not, chart a course to enact a small change for you or your team. That course could include first presenting your findings to the person in your organization responsible for determining the budget pool for salary increases, or your direct management. If your organization is large enough and has a dedicated compensation analyst, get them involved. Reach out to the decision makers and analytical folks in your organization who can influence a change. It is highly possible that the problem is pervasive across other fields and job roles within your organization and may even grow in scope. If everyone starts with making small incremental changes within their own team, we will see large scale results over time.
If you are an independent contractor, you might want to consider increasing your rate for new incoming clients or upon contract negotiations based on the formulas and tools provided in the report. A time-tested and excellent book on negotiating is Getting to Yes by Roger Fisher and William Ury. If your management or client is not responsive to your argument and you feel you are underpaid, whether you are male or female, you may want to evaluate if it is worth it for you to stay in the job or look elsewhere.
I greatly desire that future Guild salary and compensation reports will show the gender gap close a little bit each year until eventually it becomes an irrelevant variable in pay. In fact, let’s set a goal right now to close the gap in gender pay by 3% each year for 5 years until we establish equal pay in 2015.
Resources
National Organization for Women: http://www.now.org/issues/economic/factsheet.html
Fisher, R., Ury, W. (1991). Getting to Yes: Negotiating Agreement Without Giving In. Penguin Group. New York, NY.
