Webinar Recording – How to Conduct a Pay Equity Analysis


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Pay equity is a top concern for HR professionals in 2018. In the past couple of years, state and local legislators have ramped up requirements for employers to address the pay gap between men and women, and between white and minority employees. Additionally, workers themselves now are well aware of how much they’re worth in the market, as they have more access to salary information than ever before. All of this means that businesses have to make it priority to pay employees fairly and consistently, without discrimination on the basis of gender or race.

In this webinar, hear from PayScale on how you can conduct a pay equity audit and take appropriate action to remove biases from your compensation system.

  • Key trends around gender and race pay equity
  • How to analyze data to understand potential pay disparities in your organization using PayScale MarketPay
  • How to communicate the findings of your pay equity analysis to different stakeholder groups
  • Steps organizations can take to close the gender and/or race pay gap

See how PayScale can help you stay on top of pay equity issues.

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Related Resources

Pay Equity Action Plan: Why Fair and Equal Aren’t the Same Thing

Explore the Gender Pay Gap in Detail


Jingcong: Hi everyone. Thank you so much for joining us today for our webinar, “How to Conduct a Pay Equity Analysis.” My name is Jingcong, and I’m a Content Marketing Manager at PayScale, and I will be your host today. Before we get started, just a few housekeeping items. First of all, everyone will receive a copy of the presentation afterwards in about one or two business days. Look out for an email from PayScale that will have the information. If you want to follow along or download the slides you can do so too through the handouts console in your tool right now. If you have any questions during the presentation please don’t hesitate to use the chat function to ask them. We will have about 10 minutes at the end for Q&A and try to get through as many questions as we can. And with that we can get started. I’m excited to introduce to you our three speakers today. First up, we have Brian Webber. Brian Webber is a Senior People Business Partner at PayScale. Brian’s been with PayScale for about a couple of years and he is our in-house compensation expert. He manages the compensation program and trains managers on how to communicate pay decisions with employees. As an HR professional his career has been dedicated to developing people-centric organizations, fostering transparent conversations between employees and managers, and building more inclusive workplaces. Hi, welcome Brian.

Brian: Hi, welcome. Thank you J.C for hosting us.

Jingcong: Thank you. Next up we have Chris Martin here. Chris is the Director of Research at PayScale, and he leads a data analytics team here that does research into all areas related to things like gender pay gap, pay equity, employee engagement and so on. And Chris hails from the great state of Utah and he has an MA economics at the University of Washington. Hi, Chris.

Chris: Hello.

Jingcong: Welcome. And then we also have Jennifer Farris joining us today. Jennifer we’ll be showing you guys how you can conduct pay equity analysis and pay skills platform. Jennifer is a CCP and a Solution Engineer at PayScale, so she helps PayScale customers and prospects understand the key functionalities and features of our platform. And prior to this role, Jennifer was managing a professional services team at PayScale where she was she was managing, developing, retaining a team of 14 people and helping PayScale expand its service offerings. Hi, Jennifer.

Jennifer: Hi there, good afternoon.

Jingcong: All right. Thank you. So first up we will have Brian Webber kinda talk about some of the key trends that make this topic top of mind right now.

Brian: All right. Thank you, J.C. Before we get the research on pay equity how to conduct gender pay audits, we wanna quickly recap what’s happened recently and on the pay equity front in the market. Pay equity is a hot concern right now for HR professional everywhere in the last couple of years state and local legislatures have ramped up requirements on employers to address the pay gap between men and women and between white and minority employees. The legal landscape is putting pressure on organizations to shift their approach on how to pay employees. There is now laws and rules at the state and local levels that are putting more responsibility on employers to show that they are not openly or unconsciously discriminating against workers than previous federal laws .For instance in 2016 the California pay our California Fair Pay Act. There are also similar in title adopted substantially similar standards requiring that employers use similar work as a composite skill effort and responsibility holding that legally liable if hate differs among white men and others performing substantially similar job. In other words, California employers have to explain and justify the entire wage differential.

Going further down the road, Massachusetts had their pay equity act scheduled to take effect. I believe it took effect July 1st this year, and that seeks to close the gender pay gap and make it unlawful for employers to pay men and women different rates for comparable work. Oregon’s version of the pay parity laws comes into effect in January of next year, and requires that work of comparable character be equally rewarded. In addition, multiple states and cities including Washington, Oregon, California choose your Connecticut Delaware and more so those are just the ones that were top of mind are banning employers from asking workers what their salary history is during the recruiting process. The decision on that is to sort of eliminate the historical pay gap to future employee employment.

So in addition to the legislators, workers themselves are now very much aware of the gender pay gap, more so in years past and have more information about salaries than ever before. Finally, employers themselves are shifting their thinking. They recognize that they can better attract, motivate and retain talent when they talk about their values such as supporting equal pay and equal access to opportunities. So they are choosing to be open with employees about how decisions are made and what their current gaps are. Companies such Salesforce have done gender racial chaos and have disclosed their results with employees and the public. This sort of behavior will prompt more organizations to follow suit. All of these even that remedying the pay disparity between men and women and various ethnicity groups has to become a priority for businesses. So next up Chris is gonna go ahead and start talking about some of the research that he and his team have done about the gender pay gap.

Chris: Oh wrong direction. Hello everyone. So as Brian mentioned, the landscape is changing a lot. Obviously one of the primary concerns the gender pay analyses is that we are protecting our organizations from potential legal action. What I’m gonna do with this bit is paint a picture of how labor force participation differs for different minority groups. That includes women but it also includes people of color. This is mostly research that we’ve done here at PayScale, but not exclusively. So I’ll be citing some research from Fairygodboss which is done, they look at promotional rates for minority groups. The point here is that this gives you some evidence that supports the reality of disparities based on gender and ethnicity or race. And this is something that should inform your approach to your own internal pay equity processes, and if you need to get buy in from people this evidence should convince that this problem exists at a large level throughout the labor force in the US, and you are responsible for making sure it doesn’t exist at your organization,not only to protect your company from potential legislation, or legal action, but also because it is the right thing to do.

So the first thing that we’ll look at, this is a report that we do every year, it’s called the Gender Pay Gap. And this, our numbers here reflect basically the same numbers that we get from the government numbers. But basically we have two different ways of looking at how much women earn compared to men. So the first is this idea called the uncontrolled gender pay gap. So what we do is we take the wages of all women in the pay scale data set, and we take the middle woman, so we call this the median pay for women, and then we compare that to the median pay for men. So if we lined up all men in the PayScale data set from the least paid to the most paid. And so we look at the difference in median earnings, and this doesn’t account for various things that could justifiably explain those differences. That’s why we call it the Uncontrolled Pay Gap or the Raw Gender Pay Gap is sometimes the word that we use. So when we look at that number we see 70 to 80 cents on the dollar, or women earn, the typical woman earns 22% less than the typical man. Oh, I’m, is in the wrong, but I’m here. Sorry.

Next we’ll look at the Controlled Gender Pay Gap. So the Controlled Gender Pay Gap controls for the things that we call compensable factors or other characteristics about the work or the employee that could explain why they earn more or less than someone else. So some of these factors might include education. So if you go to school and get a Masters degree, you expect that you’ll earn more than a similar person who doesn’t have the Masters degree. As you get more experience, you expect to earn more. As you take on more management responsibilities, you expect that to be reflected in your compensation, and a bunch of other reasons that justify why one person might earn more or less as another person. Another factor that comes in here to a certain job groups just pay more or less than others. So when we control for all of these observable characteristics, we then look at what we call the Residual Gender Pay Gap, or how much of the difference between female earnings and male earnings is leftover once we control for all these other things that make sense. And here we hear the gender pay gap shrinks to a much smaller number, to about 2%, with women earning slightly less than men. So both of these numbers matter. The fact is is that a lot of the Gender Pay Gap is driven by differences in job groups. So we call this Occupational Segregation. Women get funneled into different occupations than men and those occupations tend to pay less. And then the other thing is that there is, well, there’s some various other factors. We’ll talk about a few of those in upcoming slides. One point that I wanna add here is that the Gender Pay Gap actually increases as we move up the ladder. So when we start, look at individual contributors or people without management responsibility, women earn 1.7 cents less than men, or 1.7% less than men. But when we get up to executives, this gap rises to 5.6%. Not only does the Gender Pay Gap rise as we move up the ladder, representation up the ladder tends to skew male.

So what are we looking at with this graph? This is something we did for our gender pay report on equal pay day this year. So using data from 2016 to 2018, about 2 million profiles. So what we’ve done here is we split men and women. So the red dots are men and the yellow dots are women in this graph. And then we split them into three different age bands. So people under the age of 30 are early career, we’re calling people 30 to 44 mid career, and people above 45, late career. And then vertically were showing what job level these individuals are at. So the top all are our executive jobs, director jobs, manager jobs and individual contributor jobs. So the way we will look at this graph is we’ll start the last and then we’ll move over to the right. What we see is that very few people are at director, executive roles early in their career before they hit 30, which makes sense. And there’s not a great difference between men and women, although it looks like men ares slightly more likely to be managers early in their career than women are. When we move to mid-career, we start to see this discrepancy appear. We start to see a discrepancy appear. So specifically, look it in the middle of the graph there, into, there are more red dots in yellow. That means that men are more likely to be women, or men are more likely to be managers than women in mid-career, so at age 30 to 44. If you go up you see that they’re also slightly more likely to be directors and slightly more likely to be executives. When we moved to late career, things become much more dramatic. So at the bottom of that, at the bottom right you can see that there’s a lot more yellow dots than red dots. That means that women, later in their career, after age, beyond age 45, are much more likely to be individual contributors, to not have any management or executive responsibilities. And men are much more likely than women to be at any advanced level in the company. So they’re much more likely to be managers, much more likely to be directors and much more likely to be executives.

Just the numbers you can see on the slide here, by mid career men are 70% more likely to be in executive roles than women. By late career, men are 142% more likely to be in VP or C-suite roles. So this is one of the factors that drives the Gender Pay Gap. So that uncontrolled pay gap number, women earning 78 cents on the dollar, this is a piece of it. And it’s a little tricky because we can show what is causing this, is that men are more likely to be in leadership roles than women. The question is, is this discrimination? So legally it’s kind of a tricky question here, and certainly nothing in this webinar constitutes legal advice, but what we’re seeing are certain jurisdictions are raising the bar for employers. So under the EEOC requirements, if you can just show that you have more men leadership roles, you are probably okay. But under MEPA, the Massachusetts Equal Pay Act, for example, the bar is raised and if your organization doesn’t promote women at the same rate that it promotes men, you may find yourself running afoul of that legislation. And I think that that is certainly the trend legally, is that things are gonna become more and more strict and we’re gonna be looking at more axes in ways that organizations might be perpetuating discrimination against women and minority groups. So this should kind of sort of be in your bonnet. You wanna be in a position where you’re ahead of any legislation that comes. MEPA, again, which is the Massachusetts Equal Pay Act, one of the things that they did is they created a safe harbor for employers as well. If you conduct an equal analyses and are acting in good faith on that analysis to try to correct whatever injustices you, or inequities you identified, in this safe harbor you’re not exposing yourself to liability. So you’re kind of shielded from the Act’s negative ramifications for a few years. And if you continue doing this work, the idea is that your organization is a good actor. So it’s worth doing these analyses, A, because it is the right thing to do, B, because this is kind of the march of time, is that we’re seeing that companies are gonna become increasingly responsible for addressing these issues.

The next piece of research I’m gonna look at is, based on who your manager is, how likely are you to get promoted? So this is research coming from Fairygodboss with a couple partnering organizations. And the sample size here isn’t huge, but the results are certainly aligned with the trends that we’ve seen more broadly and with human psychology. So the idea is that they asked this question of, “What was the gender of the person who gave you your last promotion?” And what we saw was that women are much more likely to have been promoted by another woman than man. And men are much more likely to have been promoted by another man than women are. So that’s what this graph is showing us. Women are similarly more likely to say that they’re paid less for doing the same job. So we have evidence of this at the society level, and women are getting, that message is resonating with women. So they’re echoing that, not the men. Similarly 55% of women said that they have, said someone has paid more than them for doing the same job versus 49% of men. So both men and women feel like they’re getting the short end of the stick at some point, but women are more likely to express that. Next piece of research I’m gonna show, the next two are really interesting. This is more recent analysis that we’ve done here PayScale, the new analysis. So the first one is research that we did on employee referrals. So we surveyed tens of thousands of people last year and then we conducted this analysis to see whether or not rates of people receiving referrals when they get a job offer differ by minority status. And what we found is that women of any race are less likely to have received a referral than their white male counterparts. And this is, white women are 12% less likely to have received a referral, women of color are 35% less likely to have received a referral.

What we also saw in his research is that certain types of referral… So we asked a question which is, “When you first entered your employment with your current organization, did you receive a referral or not?” And if people said they had received a referral, we asked them further questions about the type of referral they received. So, for example, we wanted to distinguish between a referral from a friend or family member from a referral from someone that you had previously done business with, the idea being that those referrals send a different sort of signal to the organization. And sure enough, what we found is that the type of referral you receive impacts your pay differently. Some actually harm your pay versus not receiving a referral. So friends and family members, for example, the employer may not see that as an actual endorsement of your professional skills, but rather just a way that you got in the pipeline and you might already be sold on the organization. So they get away with paying slightly less. But the one type referral that really boosted pay for individuals was a referral from a former colleague, so someone that you had done business with before, either a co-worker or a former colleague or client is the way that we worded the question. So what we saw is that people who received that type of referral and they entered into the organization get paid significantly more, but the pay boost for men is much more than the pay boost for women. So men can expect to receive a pay of about $8,200 versus someone who didn’t receive a referral, but a woman should only expect a $3,700 salary increase. This fits into a really large body of research that academics have done, that we’ve contributed to as well, that show that the way that women negotiate for salary is different from the way that men negotiate for salary. And when a woman negotiates for salary, she is perceived differently even if she’s saying the exact same things, in the exact same way as a man. And so these discrepancies show up in a variety of different ways.

The last piece of research that I wanna show here is from recent package that we did, we called it our Raise Anatomy Package. This question was looking at how people receive raises when they ask for them. So we asked a couple of questions here. One was, “At your current organization, have you ever asked for a raise? And then if you had asked for a raise did you receive it? And you haven’t asked for a raise, why did you not? Why haven’t you asked for a raise?” One of the interesting results, or a more provocative results from this analysis, is that we found, we looked at four different groups. So we looked at white men, white women, men of color and women of color. What we found is that there is no statistical difference in the four groups of how frequently they ask for raises when we accounted for first observable differences like industry, occupation, job level, etc. So when we account for these things, there’s no difference between these groups, how often they, or their likelihood of having asked for a raise from their current organization, but we see the rate at which we receive those raises are significantly different. And particularly, women of color men of color are much less likely to receive a raise after they ask for a raise. Women of color are 19% less likely than white men and men of color are 25% less likely to receive raise than a white man. And this is after controlling for the whole suite of other factors, like men of color also tend to be in different jobs than white men, similarly with women of color. So we’re controlling for those differences, and then looking at what residual part is just due to the gender and racial identity of the employee.

So all of this kind of paints this broad picture that we are contributing to, as researchers at PayScale, but the conversation is certainly ongoing. And that the broad question is how did the labor market differ for minority groups versus white men in particular? And what we’re seeing is that, you know, asking women to negotiate more for raises or asking people of color to ask for raises is not gonna be enough to address the systemic issues, or even, you know, trying to funnel women into STEM jobs. Other research that we’ve seen has shown that women, like it’s not only a pipeline problem of getting women into these educational programs that once they get into the STEM field or STEM occupations, they are less likely to stay in them.

So we have our work cut out for us as a society, and a piece of this work needs to happen at the employer level. And so there’s these two justifications that I hope I’ve made the case for in this, you know, 15 or 20 minutes here. One is that there’s a legal requirement for you to be compliant with the law and those laws are changing and they’re becoming more stringent. By being ahead of the law and doing these gender pay or gender and racial equity analyses, you’re putting your company in a good position to be compliant with whatever law comes forth. The second thing is that this is something we’re becoming more aware ofas a society, and it is the right thing to do, to really give everyone equal opportunities in the workplace. It’s good for society, but it’s also good business, because there’s so much talent available in these minority pools, but they’re the fact that the labor market behaves differently with respect to them means that their labor force participation rates, for example, tend to be lower. So it’s something else that we should be concerned about from a business perspective.

Brian: All right. Thanks Chris for shedding light on the data. Really appreciate that. And you sort of dovetail nicely into what I’m gonna talk about next and how to conduct a Gender Pay Audit. And as a practitioner, I really recommend that organizations conduct these as a proactive measure and that you do them on a regular cadence. So for me as a HR professional, I’m doing these on an annual basis. And this is, I think, a best practice, but also it’s due in part to some of the legislative changes that we’ve been seeing. You may need to be confident in your pay system and how it delivers equal pay for men and women, and workers of different ethnicities because of those so you can protect against any compliance issues with these law if you’re operating in these states. But as HR professionals know, a lot of these states, whether it be California, Massachusetts, Washington or Oregon, as they implement laws, you can be sure to start seeing those go across the country over the matter of probably the next 10 years. So an audit involves examining your own pay data for evidence of Gender Pay Gap and making recommendations to senior management about ways to lower gender barriers and about, yeah. And then ways that lower gender barriers in recruitment, hiring practices, pay and promotion practices before they arise broader organizational concern. Basically you wanna be addressing these before it becomes a complaint from an employee or hopefully you never see a complaint from the EEOC or the Department of Labor, because that is not fun. This audit would be separate from your normal analysis you do as a part of your typical annual pay increase cycle, which looks at pay grades, ranges, job levels, where workers all within the range in their position, or how they fall within the Compa-ratio for that position.

What you want to do is look at the pay differentials based on gender and race. If you have workers, and most states consider doing separate analysis in those jurisdictions with their own Pay Equity Laws, especially in California and Massachusetts, Washington and Puerto Rico actually have some specific laws on the books there, because those locations have those unique qualifiers as to what you need to be measuring against. It’s a little bit different in Washington than it is in California, than it is in Massachusetts. So becoming familiar with those laws and what you need to be measuring is going to be critically important. The other thing that you want to do very, very early on, it’s not the first thing, is make sure that you involve legal counsel in this process so that your legal counsel can give guidance on the appropriate action items after you get the results. Ideally, you wanna be doing this analysis under their guidance. The reason being is that it can be protected from discovery. So MEPA has some nice safe harbor rules in it, but not all these laws have that. So having your analysis and, or your audit being conducted under the guidance of your, either your in-house counsel or maybe you aren’t large enough where you have in-house counsel, and you have an employment attorney that you would use for sticky situations, talk to them about this early on or before you start. And then once you have your results, if there are unjustified gender racial wage gap, in most cases you’re obligated to do something about it. Having the knowledge and not doing something to rectify it puts your organization at legal risk.

So when we go to getting the data, the first step in analysis is making sure that you have all the data that you need, and that information is up-to-date and accurate. Obviously it’s gonna be difficult to, once you start this analysis, to keep it live, so it’s gonna be this, a snapshot in time most often depending on the tool that you’re utilizing. Basic employment status and historic information is going to be important. Demographic information on gender race and other categories is going to be critical and job title level, overtime exemption or FLSA status,full-time or part-time status is also going to be critical information that you gather, and geographic work location and business unit information is going to be very important as well so that you can control those different measures. Quantity and quality of work, so work performance ratings, those will be helpful. However they aren’t an absolute necessity, but they can provide good context as to why there may be a pay difference for someone.

Now, pay range and compensation data, this is also going to be needed. So whether you’re using job-based ranges or if you have pay grades, this is gonna be critical information, but you can slice and dice the information that way as well. To do an analysis, your HR team will need to provide timely and accurate information on these key field. You need systems that can collect and store this data to support an ongoing process of gender pay audits. It is quite a task to take on the first time, but once you do it and you have a proper way to store this information, it’s much easier to replicate year after year after year. But building it can take some time. You’ll need to determine who is best the best person to compile the data and do this statistical analysis. Not every HR professional feels comfortable with doing the statistical analysis. So maybe there’s someone on your finance team that can help you or there is a data analyst that you feel comfortable utilizing or there might be an outside firm that can do that for you as well. These professionals need to have experience with regression analysis and the use of statistical software. So you need more than Excel here. Wouldn’t you say, Chris?

Chris: Yeah. I’ll talk a little bit later about some of… So I have partnered with Brian on our own internal gender equity analyses. I’ve spent a lot of time reading kinda what industry standard is there and I have thoughts on it, and so I’ll be sharing those a little bit later.

Brian: So data privacy and security matter. And this is really important in that whoever you are going to be working with or whoever is going to be conducting it needs to be professionally and emotionally prepared to see this information. As HR professionals, we see this information day to day so it’s no big deal to us. However, if you’re gonna be working with a data analyst or someone that isn’t used to seeing this information, you need to prepare them for what they’re about to see because when you see pay data on colleagues, it’s a little bit different than just seeing pay data in general. Then you also need to do your best to sanitize the information and make it as anonymous as you possibly can so that you don’t put that person into a compromised position. That goes into cleaning up your data. So ways to do this are that you want to group things in multiple different ways. So a few possible ways to do this is look at the jobs in a department one at a time or a business unit one at a time. You wanna look at all the employees from one position or job grade at a time, and then look at employees within the same job family at a time to see if there is sort of a systemic issue there. So when you group workers together, another possible way to do this is by location. This is important because when you have one location in Washington, and one location in Massachusetts there’s going to be different pay equity laws that you take into account. For each location there maybe pay differential based on the cost of living as well, and it’s a good idea to examine gender pay gaps for different locations separately. So you’re gonna wanna run your analysis in a similar fashion, but with the different considerations.

It can also be useful to have performance rating information, as I mentioned, on hand so that you can reference an employee’s performance as you look where someone is at in the range compared to other employers. Now that, I could go off on a tangent on performance ratings and how there’s bias built in there. That’s a whole different webinar topic, but that’s gonna be something that you’re going to want to be auditing on a different level. You also wanna look at your company’s control and under-controlled Gender Pay Gaps. Both of these measures are useful, and not all intentional pay discrepancies are discriminatory. There are justifiable reasons for paying one worker more than another in the same job, in the same job groups or job family or job level. Legitimate reasons for paying differently can include the amount of education someone has, maybe their years of experience, past performance rating or that they’re bringing different skills or proficiency with those skills to the job. Now when you analyze the data, you wanna make sure that you have accurate job descriptions for your benchmarks of the positions, making sure that you slot the position in the appropriate grade, and if the job description is flawed, then your benchmark will be flawed. If an employee were to file a claim with the EEOC or Department of Labor, one of the first things that they’re gonna look at is the job description that you have for the position the employee is in and compare it to what the employee says that they do day-to-day. Is this employee being asked to do something that is actually aligned with the job description or is it substantially different? Are they being given tasks that are stretched opportunities or are they being asked to take on managerial responsibilities that are not being compensated for? Those are things that you really want to make sure that you have built into the job description, and that you are educating your managers on the difference between stretch opportunities and what a different position is. And that’s another process as well.

So you wanna pay attention to make sure that you’ve got relevant compensable factors in your job descriptions. PayScale data does take into account skills and how they might demand that pay boost for certain roles. This data helps to ground your decision when you choose to increase a salary for a job with particular skills. Let’s look at an example. Say you’re looking at mobile developers in your workforce. Some employees have higher pay than others, and let’s say, several male employees have experience in manufacturing while other females in the role have experience in technology consulting. You can go back to the job description and the easiest route you’re benchmarking is that that you should pay a premium if someone in the business has experience for manufacturing sector if they have more than six years experience. So the pay difference in this scenario was due to a compensable factor, which is industry experience rather than gender. So that’s defensible and not discriminatory. Again, you wanna review your job descriptions every year. It’s not a fun part of our job, but it’s something that is going to protect you in the long run.

Chris: Okay, I’m gonna talk a little bit about the data analysis associated with Gender Pay Audits. And I’m gonna move through this a little bit quickly just because we are time constrained and I wanna make sure that we have time to get to the demo. So let me talk about what happens. Say that you were gonna to hire a consultant in, and they were to come in and do a Gender Equity Analysis for you. There are certain industry standards where they say, for example we’re gonna group individuals in similar jobs based on the legal requirements of things like the EEOC or California’s Fair Pay Act or MEPA. And then as long as we have 30 individuals in that job grouping inside, for each protected groups that we wanna look at, then we can do this analysis and say whether or not those, where the pay is different for those minority groups versus white men usually as the base group. So that’s kinda the industry standard. What they do is a business analysis that says within this job group based on presumable things like education, years of experience, years with company, or performance ratings, etc., tailored to your own company’s compensation philosophy, how do we expect individuals to be paid, and then they look at the difference in expected pay versus actual pay for that individual. And then you can identify people that are paid much lower than we anticipate. And then we look at the gender breakdown of people that are paid less than we anticipate. And that’s where we get gender pay disparities from a consultant’s standpoint. Is this something that you can do? I’m gonna say, probably not. I don’t think you should start there. It takes training. If you haven’t gone to school for statistics, you can run into trouble. However, I think that there are things that you can do that will identify the same individuals or like a similar or overlap of individuals that a compensation or an equal pay compensation, consultant would identify. And I think you can get to those things relatively simply.

So first thing I would say, just start with summary statistics. So this just says what is the gender breakdown for different groups in my organization? So if I look at grade or department or job level, that can explain a lot of the all out uncontrolled gap at your organization. Is the gender break down at your organization similar to the gender breakdown in the economy at large? So you can look at data from PayScale, you can look at data from the Census Bureau or the BLS. If you’re a government contractor, you probably have an affirmative action plan where you’ve done similar things like this so you can see whether or not your organization reflects to the broader labor market pool that you’re pulling from. The next I would say is that you have already controlled for different job groups and characteristics when you put people into different grades levels. As long as you’re doing that in a way that’s equitable. So that is something you’ll have to look at. There’s a case of some lawyers, some female lawyers brought a case against their firm because they made the case that women were put in, just to lower grades than similar to qualified men. So that’s something you’ll wanna check on. But as long as your grading system is not biased, you can use your grading system to look at how people’s compare, pay compares to market. And this in effect gives you a controlled pay gap for your organization using tools you already have in your wheel house without needing to learn about the statistical software. And my view is that if you look at the range penetration by grade, for example, and you look at the gender breakdown of range penetration by grade, you will identify the same individuals that are constantly told, will identify as potential to be problematic for being underpaid. And that gives you a clear action plan. These are the individuals whose pay we need to bring up for reasons of equity. So that’s what I recommend. The two that we’re talking about here are range penetration. So this is looking at the range you have for a job or grade depending on how you set up your compensation structure. How deep into that range is an individual.

Another measure is Compa-ratio, so the midpoint of that range. How does this individual’s pay compared to the midpoint of the range is the ratio. Both of those are really useful ways that you can basically get to a controlled pay gap without needing to do the statistical analysis. So there are different ways to skin a cat. Both are useful. I think my prior is, or my belief going into this, is that both of them will identify mostly the same individuals. And so you can get a lot of the way towards a gender equity analysis without needing to get anyone to do the actual statistics. Which brings me to my next point which is that with statistical analysis a little knowledge is a dangerous thing. So one point is that, I’m actually gonna skip this a little bit. You can read my notes here. But recently some people have put out guides for how to conduct the statistical analysis. And the fact is statistics is not something you can do out-of-the-box. You need someone who’s trained and who can look at your data with a trained eye and build a model that is specific to your organization. So if you just try to have something that canned out of the box, you run the risk of doing a bad job of the statistical analysis. So I think that you are better off, as a comp professional or professional, using the tools that you already know and that you’re already comfortable with, and then doing and audit at the summary statistic level just looking at gender representation, making sure that your grading and range structures are just and that they’re unbiased. And then once you’ve ensured those are, that is the case, use those tools that you already have in place to conduct your own gender pay analysis.

The other case that I’d make is that just because of the way that statistics works, you need a large organization for statistical analysis to be valid. Some people say greater than 200 employees, but that makes me uncomfortable. Part of it is that I’m spoiled here at PayScale where we have very large data sets. But there are reasons why with a small group of employees, you’re just not gonna get the sort of statistical power that you need to really identify the problem areas for your organization. That goes away if you’re looking at ranges and grades because you already know what you’re looking at there, you’re very comfortable and that can identify potential pain points for you in a way that is certainly justifiable from a data perspective.

The last thing that I’d say is that you, Brian mentioned this, that you should remove any PII from the data before you send it to an analyst, which is certainly good advice. But you should pause and take a hard look at your data before you publish any results because some combination of characteristics can still make an individual identifiable. So, for example, if you’re doing job family by department, you may be getting down to only a couple of individuals that are in that job family in that department. So someone who knows your organization well can look at it and say, like, “Oh, well, that’s clearly Jenny’s pay,” or, “Yeah. This person is clearly over or underpaid.” So just be careful when you’re showing data and look at it with this sense, which is, if you were an investigator and you knew your organization really well and you wanted to figure out who individual data points were, could you do it? And if so, don’t share the graph or chart or statistic that identifies an individual. So I’m gonna kick things over Jen and she’s going to do a demo of how you can start looking at some of that stuff in the MarketPay product.

Jennifer: Perfect. Thank you. And will one of you change the presenter over?

Jingcong: Yep, doing that now. Just one second.

Jennifer: Sure. Thank you very much.

Jingcong: Jen, are you getting this?

Jennifer: I am. Okay, let me know if you can see my blue dashboard here.

Brian: It’s on half the screen. If you wanna drag it over a little bit.

Jennifer: It’s showing me that it has the full screen here. Is that any better?

Brian: Okay, it’s good.

Jennifer: Okay. Perfect. All right. Let’s just jump right in. I know that we only have a few brief minutes to get to some good analysis here. So as context, this is PayScale’s MarketPay dashboard. The Market Pay tool is a service management platform that allows organizations to load in their employee data, their job data, their structure data along with their partner’s salary surveys to get that all into one place. You can also use PayScale’s market data within a platform if you want to expand your survey library. So when you log in, you know, you’re welcomed with some really nice, high level insights. I can quickly see things like server utilization, recent reports, market pricing activity, some statistics around that. And within MarketPay, you can do a variety of different comp-related things. So, you can market price your jobs, you can participate in surveys, you can model structures and merit matrices, and then of course there is reporting which is the main focus of this piece today.

So I’ll start in here. There are both tabular and visual reports within the system. And I’ll start in a tabular report and show you how we can change this to look at some equity analysis. So the report I’m going into here is a Compa-ratio report. I’m gonna make it a little bit larger for you. This is a standard report within this system. So we’re looking at employee name, department, job title, I have some structure information, employees pay data, etc. But the really nice feature of the MarketPay system is the ability to pull in other data elements when we’re looking at reporting. So this data elements tree is all of that data that we’ve loaded in from your HRIS that you’re able to pull into reports to run different analyses. So in this example, I’m going to pull and gender of course, that’s the highlight here of our conversation today, and then pull over ethnicity as well. I’m going to take out some data that maybe is less relevant to the work that we’re doing here.

Let’s bring over range penetration as well. Okay. Perfect. So when I go back to my report, the report will automatically update to include all of that data that I’ve just pulled in, so gender, ethnicity, etc. Can rearrange really easily here. But now if I want to pull in some insights, you know, going through and kind of looking one by one, even if I sorted of by job title, for example, not very useful. But I’m going to sort of break to bring some sum totals through. So maybe I want to sort first by job title and then gender, going to apply my break here. And now I can see really quickly, I have six female accountants. They’re paid on average about $54,000. Range penetration is about 24%. And I have five male accountants who’s base pay is a bit higher here so closer to $60,000, closer to 50% range penetration. It’s a really nice system that allows you to, you know, very easily change your data around. And, you know, Chris and Brian had talked about looking at a higher level. So maybe I want to break by department or grade or job family. I can really quickly change the ways that I am viewing my data. Let’s look at the Grade 3 here. So Grade 3 for females, average base pay is at around $37,000 for males, right around $36,500. Not bad. But sometimes we want to take a more visual picture, and the MarketPay system allows us to do that as well. So back in the report library we have a direct integration with Tableau that allows us to put a visualization, you know, onto the data and get a more comprehensive picture of what’s going on here.

Out of your Gender Pay Equity analysis report here, this is also a standard report within the system, and this is going to show us a dashboard that has a few different pieces of information on it. So let me, let me narrow our scope here. I’m gonna use one of our filters to just pull in my accounting AP and AR departments here. You can see there’s other filters, these are all configurable. But at a very high level view, we can quickly see how females are compared to males in terms of overall base salary amount. A little counterintuitive here. Females are actually in blue, males are in orange, and I can see that within these departments in my US locations, on average males are paid about 11% less than my females. I can then break it down a little bit further so I can look and see by grade what’s going on here. I can see in the Grade 4, females are paid 20% higher than males, but in the Grade 6 males are paid 11% higher than my females. And then of course I have my employee data grid which shows in a little bit more detail things like performance rating and tenure. And I can highlight a couple of grades, here, I’ll show Grade 6 and 7, my data grid will update to show me the jobs in the Grade 6 and 7 and I can take a more in-depth look that way. There are many other reports in here. I want to be respectful of y’all’s time. So I am going to pass it back to you, Chris and Brian, to wrap us up. I know we have just about seven minutes left. If you would like a further demonstration of the MarketPay product, there is a link on our website. You can get that scheduled.

Brian: Okay, great. Thanks, Jen. Yeah. Let’s switch the presenter back, and then I’ll finish up the presentation. It’s Slide 19. All right. Yeah. So interpreting the results. So what you want me has to do is are there any red flags that come up? And these maybe things like an employee having poor performance marks, but they’re at the top of the range or an employee with really good performance marks, has similar experience to their co-workers and similar skills, but they’re at the bottom of the range. These are things where you’re like, “Huh? What? That doesn’t make sense.” Well, those are red flags and those are things that you need to gain more context to. They could be indicators of a pay equity difference that you need to address. So you’ll wanna dig deeper to figure out why these employees are paid differently. And then, again, when you find these sorts of things, you wanna be bringing them to legal counsel and get guidance about what your exposure is as a company and what next steps ought to be. Once you’ve done that, you’re gonna wanna bring some of this information to your senior leadership team. And what you need to communicate to them is if there are systemic problems that you’ve identified based upon this analysis that you did, and make sure that the senior leadership team is aware them. Communicate to them what those disparities are and come with an informed solution as an HR professional. This could be things to look at as a good, better, best scenario. So good is that you need to comply with, like “This is what we need to do in order to comply with the law.” Better would be, “This is what we need to do in order to be consistent with our pay philosophy and what we’ve communicated out to employees about our pay brand.” And best would be, “Okay. This is what we wanna do as a, if we’re positioning ourselves as a leading edge company in the way that we pay. This is what’s the best thing to do,” and share that with them. And then let them make the decision.

And then communicating out to employees. So, once you’ve found, or if you found that there is a pay equity issue that you need to address, and you’ve developed a solution and you’re putting it into practice, what that solution is, and you want to communicate that out to employees, it’s important that you be very honest and letting them know, “Hey, this is what we found. This is the number of employees that were impacted. This is what we’re doing as an organization.” And that may be that you’re making the change effective when you found it. You may be in a scenario where you can go back and correct things for that fiscal year. If you say you find it in the middle of the fiscal year and your company is doing really well and you have executive buy-in and you want to go back to the beginning of fiscal year, that would be a best case scenario. And communicating that out to employees I think is really nice and being transparent about the findings that you had and the things that you’re doing. That way it doesn’t feel to them, where it often, where compensation decisions often feel as being very block boxy. So you’re being as transparent as you feel comfortable as the organization and letting them know about that because when you just make a decision and just make a change and you provide no context as to the reason why, then they’re gonna think, “Okay. Well, what else was there? Why are they doing more?” And maybe you weren’t able to do everything as the organization at the get go, right away. Maybe it’s something that you’re gonna need to address over one, two, three different pay cycles or fiscal years. Communicating what your plan and strategy is to accomplish that plan out to employees is gonna provide them the context to understand where you’re coming from as a company and how you’re treating as an employee, and how do you value them. So it I kind of rambled quickly there through the last little bit. I know that we do have some questions, and hopefully we can get one or two of them in before we go.

Jingcong: Okay. We have a question. It’s of around resolving any discrepancies you’ve already found. But, let’s say you have budgetary constraints. How do you handle resolving discrepancies and managing that budgetary constraint?

Brian: So I would say that there are, you have the budgetary constraint of like, “Okay. We need to hit our EBIT number.” But you need to weigh that against, “Okay. What’s the exposure that we have as a company if we don’t do anything? Are we in locations where we are not complying with the law?” Those would be locations where you’d want to fix things fastest and so you can comply with the law, because, there’s gonna be considerable dollar figures that you may be worried about if it comes out that you didn’t do anything, and that could be often worse than the budgetary concerns. And maybe you have to forego other things. So it’s getting that information to the executives that can make the decision on that, and then allow them to make that decision and then exit you off of that. Chris do you have any?

Chris: Yeah, yeah. The other thing I would say is that a good analysis, it sounds like the person that asked this question may already know which individuals pay needs adjusted. If you’re making a good faith effort, that really goes a long way. Everyone knows that there are budgetary constraints and you just can’t bring everyone up to the range, for example, if people are below. And so I think the key is making sure that there is some budget available. Maybe it’s conditional on other things and then putting out an idea where it’s doing the greatest goods to address the inequities first is a great way to start your plan. And it’s something else that you’re getting out of the analysis, is what are the worst pieces and how can we start with those?

Jingcong: Okay. Well, I want to respect everyone’s time today. Thank you, Chris. Thank you, Brian and Jennifer, for sharing all this great information. Everyone, we will be sending you the slide and the recording as well. I know there’s still a lot of questions we didn’t get to, but if you like please, you can follow up with Brian Webber. I put his LinkedIn information into the chat box. If you have specific questions, he can be a resource for you. Thank you, Brian. And thank you everyone for spending your time with us today. Good bye.