1 00:00:00,640 --> 00:00:04,090 Podcast-Intro: Welcome to Testing, Testing 123, a podcast 2 00:00:04,110 --> 00:00:08,420 brought to you by TestGenius. 3 00:00:09,416 --> 00:00:09,896 Jenny Arnez: everyone. 4 00:00:09,906 --> 00:00:14,196 My name is Jenny Arnez and you are watching the Testing, Testing, 5 00:00:14,196 --> 00:00:17,206 1-2-3 podcast by TestGenius. 6 00:00:17,906 --> 00:00:19,096 I'm your host today. 7 00:00:19,236 --> 00:00:20,806 I'm joined with Mike Callen. 8 00:00:20,816 --> 00:00:24,576 He's my co host and President of TestGenius. 9 00:00:25,126 --> 00:00:27,436 Also back with us today is Dr. 10 00:00:27,436 --> 00:00:28,556 Brian Marentette. 11 00:00:28,926 --> 00:00:33,066 Brian is the Director of People Insights at Berkshire Associates. 12 00:00:33,416 --> 00:00:38,516 And we are starting our part two of our conversation with Brian about pay equity. 13 00:00:38,876 --> 00:00:41,216 Guys, do you mind if I just go ahead and just jump right in? 14 00:00:42,196 --> 00:00:42,636 Brian Marentette, PhD: Let's do it 15 00:00:42,896 --> 00:00:48,216 Jenny Arnez: So Brian, We talked a lot about pay equity analysis 16 00:00:48,576 --> 00:00:50,956 last time, some basic principles. 17 00:00:50,956 --> 00:00:53,856 And as I thought about this, I have a couple questions. 18 00:00:54,476 --> 00:01:00,506 If there seems to be perhaps two different ways that, that one might conduct a pay 19 00:01:00,506 --> 00:01:02,666 equity analysis in their organization. 20 00:01:02,666 --> 00:01:05,436 They could either do it software driven. 21 00:01:06,236 --> 00:01:09,246 Right, or they could hire a third party consultant. 22 00:01:09,266 --> 00:01:10,776 Can you talk a little bit about that? 23 00:01:11,766 --> 00:01:16,056 Brian Marentette, PhD: Sure, so I am on the consultant side of things, so I will 24 00:01:16,056 --> 00:01:21,896 try not to be too biased in my discussion of this, but we too use software. 25 00:01:21,986 --> 00:01:23,846 We all have to use software at the end of the day. 26 00:01:24,236 --> 00:01:27,356 And ultimately those pieces of software are really just tools 27 00:01:27,366 --> 00:01:29,056 that allow us to run the analysis. 28 00:01:29,396 --> 00:01:33,666 Now, within the last several years several companies have made the software 29 00:01:33,686 --> 00:01:38,486 more user friendly, more available to, HR analysts and compensation 30 00:01:38,486 --> 00:01:42,796 analysts to be able to do some of these pay equity types of analyses. 31 00:01:43,371 --> 00:01:44,941 by themselves, which is great. 32 00:01:44,981 --> 00:01:49,271 The more that you can introduce this concept to the world and have more 33 00:01:49,271 --> 00:01:51,421 of it going on, the better, right? 34 00:01:51,421 --> 00:01:55,501 We're all looking to address pay equity and make it more equitable and fair. 35 00:01:56,021 --> 00:01:58,261 Now there's obviously some caveats there. 36 00:01:58,261 --> 00:02:03,971 What, what comes with, Software typically is limited support, right? 37 00:02:03,991 --> 00:02:06,861 You do off offer customer service and different technical 38 00:02:06,861 --> 00:02:08,811 support resources and things. 39 00:02:08,811 --> 00:02:12,521 But the level of support that, we provide in on the consulting 40 00:02:12,521 --> 00:02:13,951 side is pretty extensive. 41 00:02:14,021 --> 00:02:19,691 We have gone through years of schooling and years of research and experience 42 00:02:19,991 --> 00:02:23,891 on how to do these analyses and how to overcome obstacles and challenges 43 00:02:23,891 --> 00:02:28,281 and resolve data issues that, if you're just using software and have 44 00:02:28,281 --> 00:02:32,291 not been trained in this space are going to be brick walls for you. 45 00:02:32,381 --> 00:02:34,251 It's going to be extremely difficult. 46 00:02:34,721 --> 00:02:39,311 So to an experienced analyst that has been doing pay equity work that. 47 00:02:39,521 --> 00:02:44,571 Maybe understands the fundamental steps that are involved and has 48 00:02:44,571 --> 00:02:46,091 the ability to work with the data. 49 00:02:46,241 --> 00:02:49,661 Certainly, they could be using software and doing it effectively. 50 00:02:50,021 --> 00:02:53,841 And that's great to somebody that is new to the space has not 51 00:02:53,891 --> 00:02:58,061 been trained in statistics or, understands how to set up statistical 52 00:02:58,071 --> 00:03:00,181 models for analyzing compensation. 53 00:03:00,651 --> 00:03:04,061 Generally, that's where, consulting and the service side 54 00:03:04,491 --> 00:03:05,851 is going to be more beneficial. 55 00:03:06,151 --> 00:03:10,351 There's a lot of complexity and nuance in some of the data that we 56 00:03:10,351 --> 00:03:16,411 use and how we set up the analysis that again, can be confusing. 57 00:03:16,751 --> 00:03:20,531 Multiple routes that you can take and how do you decide which way 58 00:03:20,531 --> 00:03:22,041 to set this up and how to run it? 59 00:03:22,071 --> 00:03:24,641 And then most importantly on the backend. 60 00:03:25,031 --> 00:03:27,361 Once you run everything, you get your results. 61 00:03:27,601 --> 00:03:28,471 What does it all mean? 62 00:03:28,571 --> 00:03:32,061 How do you interpret these findings? 63 00:03:32,061 --> 00:03:33,471 What is it really telling you? 64 00:03:33,581 --> 00:03:38,101 Cause it's easy to just set something up with a dataset, hit run, get your results, 65 00:03:38,781 --> 00:03:42,241 and then start putting money towards potential problem areas that you've got. 66 00:03:42,241 --> 00:03:46,816 And in our experience, we've seen a few clients that have gone from using 67 00:03:46,816 --> 00:03:49,051 software internally to then coming to us. 68 00:03:49,841 --> 00:03:53,911 When they've got a claim or they're being audited by the department of labor Saying 69 00:03:54,121 --> 00:03:56,651 we're being told we have pay equity problems and we've been doing it for 70 00:03:56,651 --> 00:04:01,141 years and we don't see anything in our results Okay, let us give it a shot and 71 00:04:01,141 --> 00:04:03,501 we run it and then we see a completely different story than what they've told 72 00:04:03,501 --> 00:04:09,021 us so yeah to the experienced user and analyst of course using software would 73 00:04:09,031 --> 00:04:14,281 be a viable route for everybody else They might find more benefit in a Consultant 74 00:04:14,291 --> 00:04:16,601 relationships that guides them through it. 75 00:04:18,451 --> 00:04:21,951 Jenny Arnez: The personal example that immediately popped into my head 76 00:04:21,961 --> 00:04:24,801 was trying to do my income taxes. 77 00:04:25,221 --> 00:04:26,801 Do I use this software? 78 00:04:27,081 --> 00:04:30,361 And I go, Oh, I have no idea what that question means. 79 00:04:30,541 --> 00:04:34,701 Or do I go to an accountant who understands the law? 80 00:04:35,121 --> 00:04:35,891 Brian Marentette, PhD: Yeah, exactly. 81 00:04:35,941 --> 00:04:36,251 Yeah. 82 00:04:36,321 --> 00:04:40,691 And I, for years, I was able to do my own taxes because like 83 00:04:40,691 --> 00:04:42,171 back in grad school, I was broke. 84 00:04:42,221 --> 00:04:42,971 I didn't make any money. 85 00:04:42,971 --> 00:04:44,461 I didn't have anything to really do. 86 00:04:44,461 --> 00:04:46,771 So yeah, like a smaller employer. 87 00:04:46,771 --> 00:04:50,606 And we had this example earlier, which was, if you have 10 employees, 88 00:04:50,606 --> 00:04:52,046 can you really be doing pay equity? 89 00:04:52,336 --> 00:04:52,936 Sure. 90 00:04:53,176 --> 00:04:53,776 And guess what? 91 00:04:53,781 --> 00:04:54,796 You don't need software. 92 00:04:54,886 --> 00:04:56,806 You won't even open a software program. 93 00:04:56,836 --> 00:05:01,766 You might look at Excel once to see what everybody's making, but in that situation, 94 00:05:01,766 --> 00:05:05,096 you're gonna be doing what's called like a cohort analysis, which is literally 95 00:05:05,096 --> 00:05:06,361 looking at each person and saying. 96 00:05:07,086 --> 00:05:08,086 What are they making? 97 00:05:08,316 --> 00:05:08,496 Okay. 98 00:05:08,496 --> 00:05:10,516 How does that job compare to this other person? 99 00:05:10,646 --> 00:05:12,786 Because all 10 of your employees are probably doing different things. 100 00:05:13,216 --> 00:05:15,786 Do we value that work that much more than this? 101 00:05:15,796 --> 00:05:18,256 And does that person have that much more experience that we should 102 00:05:18,256 --> 00:05:19,936 be paying them that much higher? 103 00:05:20,326 --> 00:05:23,306 And it's really like a pretty manual review. 104 00:05:23,876 --> 00:05:25,936 That's considered a pay equity analysis. 105 00:05:26,076 --> 00:05:29,016 Now it's on a very small sample or small head count, but yeah. 106 00:05:29,606 --> 00:05:34,746 When you get to be a much more complicated organization, I can promise you, Companies 107 00:05:34,756 --> 00:05:39,696 like Google or Microsoft, they are not doing their pay equity analysis through 108 00:05:39,696 --> 00:05:43,896 an HR analyst, internally on software, things get much more complicated. 109 00:05:45,466 --> 00:05:45,566 I'm 110 00:05:45,566 --> 00:05:46,006 Jenny Arnez: sure. 111 00:05:46,056 --> 00:05:51,556 I would think too, that having a third party consultant involved means 112 00:05:51,626 --> 00:05:54,406 a less of a likely that you're going to be biased in your own findings. 113 00:05:56,806 --> 00:05:57,426 Brian Marentette, PhD: Potentially. 114 00:05:57,536 --> 00:05:57,946 Yeah. 115 00:05:57,996 --> 00:06:01,226 And I think, yeah, there's a potential there for somebody 116 00:06:01,226 --> 00:06:04,026 internally to maybe steer away, even 117 00:06:05,011 --> 00:06:09,541 unintentionally steering away from setting an analysis up that might produce, 118 00:06:10,061 --> 00:06:12,301 disparity or a red flag in their findings. 119 00:06:12,781 --> 00:06:15,806 But I think the, Probably one of the larger benefits of having 120 00:06:15,806 --> 00:06:20,256 it done externally is the ties into the transparency piece. 121 00:06:20,326 --> 00:06:24,376 Hey, we're having our books audited by an external firm. 122 00:06:24,876 --> 00:06:27,616 They're coming in, they're running the analysis, they're telling us 123 00:06:27,616 --> 00:06:30,416 what the problems are, they're telling us how much we need to 124 00:06:30,926 --> 00:06:32,716 adjust salaries to resolve them. 125 00:06:32,716 --> 00:06:35,756 It's not done internally. 126 00:06:36,096 --> 00:06:40,151 This is all done externally and they have no vested interest in how we do it. 127 00:06:40,301 --> 00:06:40,761 We operate. 128 00:06:40,811 --> 00:06:40,991 So 129 00:06:43,241 --> 00:06:47,671 Mike Callen: is it potentially a legal strategy to have a third party 130 00:06:48,131 --> 00:06:54,631 handle this issue like they might handle any other potentially legally 131 00:06:55,211 --> 00:06:59,701 legal minefield issue to keep things at arm's length and have some sort 132 00:06:59,701 --> 00:07:05,211 of attorney client privilege or some sort of similar type of protection. 133 00:07:05,211 --> 00:07:05,481 Okay. 134 00:07:05,721 --> 00:07:06,511 Brian Marentette, PhD: Yeah, absolutely. 135 00:07:06,511 --> 00:07:09,161 Great question there on the point with legal counsel and privilege 136 00:07:09,161 --> 00:07:13,021 because a lot of what, pay equity analysis, even if you're not doing 137 00:07:13,021 --> 00:07:17,841 it for legal purposes, even if you're on more of a proactive side of the 138 00:07:18,651 --> 00:07:21,691 house, you're going to be surfacing. 139 00:07:22,056 --> 00:07:25,436 What we refer to as statistically significant differences. 140 00:07:25,436 --> 00:07:28,046 When we do these pay equity analyses, we're looking for differences that 141 00:07:28,046 --> 00:07:31,866 are large enough to be meaningful, not just due to random chance. 142 00:07:32,136 --> 00:07:35,056 So of course there's going to be differences along, gender or race 143 00:07:35,056 --> 00:07:38,816 lines and things, but are they big enough to really be meaningful? 144 00:07:38,816 --> 00:07:42,076 Do we know that they're not just due to random occurrence and. 145 00:07:42,411 --> 00:07:46,751 Through that exercise, you are producing results that could be used 146 00:07:46,751 --> 00:07:48,931 against you in the court of law. 147 00:07:49,031 --> 00:07:52,891 If you're not doing the work under attorney client privilege could be sued. 148 00:07:52,951 --> 00:07:55,921 Any employer could be sued by an individual employee or a class 149 00:07:55,921 --> 00:08:00,761 action, a group of employees and whatever analyses you've done 150 00:08:00,761 --> 00:08:02,821 internally, if they are not protected. 151 00:08:03,976 --> 00:08:06,116 They can say, Oh, let's bring those in as evidence. 152 00:08:06,156 --> 00:08:07,506 You found disparities. 153 00:08:07,506 --> 00:08:09,156 You didn't address all of them. 154 00:08:09,156 --> 00:08:13,166 And you let that work, go on you're in a bad position there as an employer. 155 00:08:13,836 --> 00:08:17,486 Mike Callen: Do you get hired by attorneys then? 156 00:08:17,586 --> 00:08:22,556 How does that relationship get triggered such that some information 157 00:08:22,626 --> 00:08:26,456 is attorney client privileged or can in house counsel hire you? 158 00:08:26,456 --> 00:08:30,356 And now all of a sudden, that, that sort of veil is in place. 159 00:08:30,586 --> 00:08:32,186 I just don't, I don't know anything about that. 160 00:08:32,346 --> 00:08:33,276 Brian Marentette, PhD: I'm not an attorney. 161 00:08:33,276 --> 00:08:35,266 I don't pretend to be one on TV either. 162 00:08:35,666 --> 00:08:38,106 And or podcasts or anything. 163 00:08:38,126 --> 00:08:38,426 Yeah. 164 00:08:38,826 --> 00:08:41,156 But it, there's some gray area there. 165 00:08:41,156 --> 00:08:44,376 I'd say the safest route is using external 166 00:08:44,816 --> 00:08:50,546 legal counsel who coordinates through internal counsel, external engages us 167 00:08:51,106 --> 00:08:56,446 to do the analysis, advise external counsel so that they can advise the 168 00:08:56,446 --> 00:08:58,686 company on interesting legal system. 169 00:08:59,006 --> 00:09:00,136 That's the safest route. 170 00:09:00,966 --> 00:09:04,906 Mike Callen: I do remember being in a conversation with you and I don't 171 00:09:04,906 --> 00:09:06,356 know what it was a year ago, maybe. 172 00:09:06,786 --> 00:09:12,356 And so this is going back to the soft software versus the consultant driven. 173 00:09:12,786 --> 00:09:13,426 And I hope I. 174 00:09:14,231 --> 00:09:16,871 I characterize this question correctly. 175 00:09:17,161 --> 00:09:20,871 And if not, if I don't, hopefully you understand what I'm referring to. 176 00:09:21,481 --> 00:09:28,301 But I remember that you were suggesting that there can oftentimes be a 177 00:09:28,301 --> 00:09:35,201 tendency within The results that are given by the software program to have 178 00:09:35,201 --> 00:09:40,331 this big series of overcorrections to the left or, up and down within 179 00:09:40,591 --> 00:09:45,161 classifications or positions that actually end up causing more problems. 180 00:09:45,161 --> 00:09:49,561 And that's another reason why you might want to have a consultant engage 181 00:09:49,731 --> 00:09:53,061 and look at the results, whether they're software results from you. 182 00:09:53,231 --> 00:09:56,221 Your program or software results from some platform. 183 00:09:56,591 --> 00:09:58,911 Did I say that anywhere near correctly? 184 00:09:59,521 --> 00:10:00,281 Brian Marentette, PhD: Yeah, I think so. 185 00:10:00,291 --> 00:10:00,601 Yeah. 186 00:10:00,951 --> 00:10:01,141 Yeah. 187 00:10:01,741 --> 00:10:01,941 Yeah. 188 00:10:01,971 --> 00:10:03,381 No, you're on track. 189 00:10:03,401 --> 00:10:06,491 And it goes back to setting up your analysis. 190 00:10:06,571 --> 00:10:10,571 Of course, if you analyze everything by job title if you're looking at individual 191 00:10:10,571 --> 00:10:15,381 jobs, see a gap and it's controlling for relevant factors, you can feel pretty 192 00:10:15,381 --> 00:10:17,401 confident that you need to address that. 193 00:10:17,801 --> 00:10:20,101 Now, not every job title has enough people to be able to 194 00:10:20,101 --> 00:10:21,981 analyze them as a standalone group. 195 00:10:21,991 --> 00:10:25,531 You need lots of head counts really to run these like 30 196 00:10:25,541 --> 00:10:27,511 employees in a particular grouping. 197 00:10:27,511 --> 00:10:29,801 So if you don't hit that that group doesn't get analyzed. 198 00:10:29,841 --> 00:10:35,631 Now to address that people doing this on their own might start to aggregate groups. 199 00:10:36,266 --> 00:10:37,256 Of jobs together. 200 00:10:37,256 --> 00:10:41,876 They might even run it company wide and maybe try to control for things like the 201 00:10:41,876 --> 00:10:46,426 department that somebody is in or, the job group that they're in or your job level. 202 00:10:46,426 --> 00:10:50,016 And so you get these big, we call them like big models, right? 203 00:10:50,016 --> 00:10:53,596 There are hundreds, maybe thousands of people in them and you run it and 204 00:10:53,596 --> 00:10:57,536 you find, gosh, we have a gap of 1. 205 00:10:57,536 --> 00:10:58,456 2 million. 206 00:10:58,456 --> 00:11:00,406 Females are impacted by 1. 207 00:11:00,406 --> 00:11:01,576 2 million. 208 00:11:01,856 --> 00:11:02,346 Now. 209 00:11:03,076 --> 00:11:04,706 They didn't set that up correctly. 210 00:11:04,716 --> 00:11:05,946 And so they take 1. 211 00:11:05,986 --> 00:11:09,396 2 million to close that gap and they distribute it to these females. 212 00:11:10,076 --> 00:11:12,726 And then they run it again and it looks good. 213 00:11:12,896 --> 00:11:16,386 But what they didn't realize is that for all these jobs that are within 214 00:11:16,386 --> 00:11:19,416 that, everybody's pretty comparable. 215 00:11:19,626 --> 00:11:23,006 But they didn't really control for the job that somebody's doing. 216 00:11:23,346 --> 00:11:27,646 And so they give all this money to people in all these jobs that are female. 217 00:11:27,826 --> 00:11:29,706 And then now when you look at it by job title. 218 00:11:30,711 --> 00:11:33,621 Females are, you get that huge overcorrection, right? 219 00:11:33,621 --> 00:11:36,671 So in all these jobs, now females are making more than their male counterparts. 220 00:11:37,221 --> 00:11:41,791 And you've basically created a pain, another problem, inequity. 221 00:11:41,851 --> 00:11:44,781 So yeah, you went from probably having not very much of an issue to 222 00:11:44,781 --> 00:11:47,601 now you actually have a major issue that's swung the other direction. 223 00:11:48,961 --> 00:11:53,741 And so that's where the complexity, can lead people down the wrong path 224 00:11:53,741 --> 00:11:55,051 without them really even realizing it. 225 00:11:55,111 --> 00:11:58,531 You can run all these analyses in a software program and it'll 226 00:11:58,531 --> 00:11:59,791 tell you how much money you need. 227 00:12:00,646 --> 00:12:04,316 Get that bad result to go away, but that bad result is due 228 00:12:04,316 --> 00:12:07,166 to a faulty analysis, really. 229 00:12:07,746 --> 00:12:10,306 And again, that's in our experience when we get called in. 230 00:12:10,306 --> 00:12:13,746 It's because that exact thing has happened. 231 00:12:14,466 --> 00:12:15,016 Mike Callen: Interesting. 232 00:12:15,436 --> 00:12:15,846 Thank you. 233 00:12:17,526 --> 00:12:20,116 Jenny Arnez: So let's talk about best practices for a minute. 234 00:12:20,591 --> 00:12:27,501 Can you share with us what, what should an employer pay attention to when having 235 00:12:27,531 --> 00:12:32,511 a pay equity analysis done for them by an outside consultant or internally? 236 00:12:33,221 --> 00:12:33,621 Brian Marentette, PhD: Yeah. 237 00:12:34,071 --> 00:12:34,181 Yeah. 238 00:12:34,221 --> 00:12:34,481 Yeah. 239 00:12:34,481 --> 00:12:34,661 Yeah. 240 00:12:34,881 --> 00:12:39,281 So there's, three primary factors that they should be looking at. 241 00:12:39,281 --> 00:12:43,111 One is going to be the statistical model that, that they're 242 00:12:43,241 --> 00:12:44,721 using, that they're running. 243 00:12:45,091 --> 00:12:48,291 Even if it's a consultant coming in The goal of the pay equity 244 00:12:48,291 --> 00:12:51,901 analysis is to really reflect how they make pay decisions. 245 00:12:52,071 --> 00:12:56,906 So ensuring that your statistical model reflects the factors 246 00:12:56,906 --> 00:12:58,206 that you use to set pay. 247 00:12:58,266 --> 00:13:02,726 So tenure, maybe the time of the job, maybe performance, or if you have prior 248 00:13:02,726 --> 00:13:08,506 experience all those things that you use internally as a comp team to set pay 249 00:13:08,766 --> 00:13:10,726 need to be factored into your analysis. 250 00:13:10,746 --> 00:13:15,301 If you leave some of those out you're really, you've got an incomplete picture. 251 00:13:15,301 --> 00:13:20,646 And that can also lead to that problem of, kind of like, flagging false positives. 252 00:13:21,081 --> 00:13:23,441 Really of, red hotspots in your results. 253 00:13:23,831 --> 00:13:28,671 And so making sure that you are, you understand your pay decisions, how 254 00:13:28,681 --> 00:13:32,591 do you set pay and then making sure you've got data that reflects that. 255 00:13:32,611 --> 00:13:36,901 And if you don't have data in your HRS, that ties to some of those things like 256 00:13:36,901 --> 00:13:42,321 prior experience be ready to go, pull your sleeves up when you find problem areas. 257 00:13:42,921 --> 00:13:44,891 And you think it might be due to prior experience. 258 00:13:45,441 --> 00:13:49,951 You got to go look and maybe pull resumes potentially and see if prior 259 00:13:49,951 --> 00:13:52,071 experience plays a factor there. 260 00:13:52,441 --> 00:13:57,771 So that's one piece is making sure you understand your compensation practice 261 00:13:57,781 --> 00:14:03,131 so that the analysis that's intended to account for that can really do that. 262 00:14:03,521 --> 00:14:08,941 The other thing is knowing your pay variables. 263 00:14:08,941 --> 00:14:10,481 Like what do you want to analyze? 264 00:14:10,941 --> 00:14:14,321 Those different types of pay, your base salary of courses is 265 00:14:14,321 --> 00:14:15,621 probably the place to start. 266 00:14:15,991 --> 00:14:19,701 That's the most important really it's hardest to change. 267 00:14:19,701 --> 00:14:25,041 It's the the largest sum of money, usually that's the place to start. 268 00:14:25,091 --> 00:14:27,981 But then looking at other forms of pay and do you know what 269 00:14:27,981 --> 00:14:29,801 influences those elements of pay. 270 00:14:29,801 --> 00:14:32,751 So if you want to look at like overtime do you really want to look at overtime? 271 00:14:32,751 --> 00:14:36,471 Is there any opportunity for discretion or bias to enter the 272 00:14:36,471 --> 00:14:38,101 equation of who's earning overtime? 273 00:14:38,551 --> 00:14:41,681 Government agencies might look at it, but from a, more of a pay equity 274 00:14:41,761 --> 00:14:45,531 standpoint, maybe over time is not as much of a concern potentially. 275 00:14:46,181 --> 00:14:52,201 Bonuses, if you have bonuses that are tied to a formulaic kind of 276 00:14:52,221 --> 00:14:55,201 bonus plan, okay it's going to be the result of the formula that you've 277 00:14:55,201 --> 00:14:57,531 instituted versus discretionary bonuses. 278 00:14:57,531 --> 00:15:02,551 Maybe we want to look really more at just those end of year discretionary lump sums. 279 00:15:02,971 --> 00:15:06,791 So that's the other thing is understanding your pay and which forms of pay you 280 00:15:06,791 --> 00:15:10,791 want to analyze and how should they be analyzed what factors influence them. 281 00:15:11,311 --> 00:15:17,071 And the last piece is the grouping of how do you want to group your employees? 282 00:15:17,081 --> 00:15:20,011 Like I mentioned with job title, that's the most 283 00:15:20,121 --> 00:15:21,651 straightforward, it's the easiest. 284 00:15:22,361 --> 00:15:26,361 To look at everybody within the same job should be making roughly the same amount. 285 00:15:26,771 --> 00:15:30,331 But then are there other what we would refer to as pay analysis groups that 286 00:15:30,331 --> 00:15:33,851 you could look at, and then how do you interpret those outcomes as well? 287 00:15:33,871 --> 00:15:37,591 So maybe you want to analyze all employees within a single pay grade, right? 288 00:15:37,591 --> 00:15:38,751 You have grades one through 12. 289 00:15:38,801 --> 00:15:42,401 Maybe we use pay grade as our means of grouping. 290 00:15:42,841 --> 00:15:43,011 That. 291 00:15:43,896 --> 00:15:45,596 legal concerns. 292 00:15:45,756 --> 00:15:50,656 It's outside of the Title VII Civil Rights Act framework, but it will tell 293 00:15:50,656 --> 00:15:56,406 you another look at whether you might have differences that, again, are 294 00:15:56,896 --> 00:16:00,716 falling on race or gender lines and that'll include larger groups of people. 295 00:16:00,816 --> 00:16:03,216 Almost all of your pay grades are going to have at least 30 people 296 00:16:03,716 --> 00:16:05,686 that you can use to run the analysis. 297 00:16:06,296 --> 00:16:08,246 So that's one set. 298 00:16:08,266 --> 00:16:11,146 I'd say that's the starting place for a pay equity analysis. 299 00:16:11,146 --> 00:16:14,566 Those are things you need to pay attention to of, how do you set up your model? 300 00:16:14,906 --> 00:16:16,076 What pay are you going to look at? 301 00:16:16,086 --> 00:16:17,836 How are you going to group your employees? 302 00:16:18,286 --> 00:16:20,876 Now within each of those, there's other more nuanced. 303 00:16:21,366 --> 00:16:23,896 Discussion points and things that should be worked out with you and the 304 00:16:23,936 --> 00:16:25,326 consultant or within the software. 305 00:16:25,326 --> 00:16:28,456 But those are the three big categories to pay attention to. 306 00:16:30,176 --> 00:16:33,476 Jenny Arnez: So on that third question, how do you want to group your employees? 307 00:16:33,516 --> 00:16:35,266 How do you even decide that? 308 00:16:36,176 --> 00:16:37,276 Brian Marentette, PhD: Oh, good question there. 309 00:16:37,586 --> 00:16:40,616 Questions that are being asked, what's the, what are you seeking 310 00:16:40,626 --> 00:16:41,986 out of this pay equity analysis? 311 00:16:41,986 --> 00:16:44,196 Is it strictly a legal review? 312 00:16:44,406 --> 00:16:46,026 Okay, we can limit it to job title. 313 00:16:46,436 --> 00:16:47,606 No need to look any further. 314 00:16:47,916 --> 00:16:50,326 That's where really the legal standard would stop. 315 00:16:50,736 --> 00:16:52,916 Typically more of a DNI lens. 316 00:16:53,136 --> 00:16:53,316 Okay. 317 00:16:53,316 --> 00:16:57,456 Maybe we go and run it by departments and see that I talked about the 318 00:16:57,456 --> 00:17:01,746 pay gap, let's look at it without controlling for levels, not factoring 319 00:17:01,746 --> 00:17:03,376 in what job level somebody's at. 320 00:17:03,376 --> 00:17:06,166 Just look at it by department, what departments are showing. 321 00:17:06,606 --> 00:17:10,646 the largest spread there, the largest gap after we factor in performance 322 00:17:10,646 --> 00:17:11,896 and some of these other variables. 323 00:17:12,196 --> 00:17:16,536 And so it's really incumbent upon the client or the end user of 324 00:17:16,536 --> 00:17:19,766 the pay equity analysis to define what do we want out of this? 325 00:17:19,806 --> 00:17:22,896 If it's really just making sure at a, job title. 326 00:17:22,896 --> 00:17:24,346 We have equal pay for equal work. 327 00:17:24,396 --> 00:17:24,986 That's great. 328 00:17:25,326 --> 00:17:28,736 If you want to know some of those other broader questions do we 329 00:17:28,736 --> 00:17:30,106 have talent acquisition issues? 330 00:17:30,106 --> 00:17:34,826 Do we have an uneven distribution of men and women throughout our hierarchy 331 00:17:34,826 --> 00:17:38,476 or our pay pay grades, then we can look at broader lenses and that will 332 00:17:38,476 --> 00:17:41,056 certainly open up that can for you. 333 00:17:42,886 --> 00:17:43,776 Mike Callen: I have a question 334 00:17:43,776 --> 00:17:45,176 if 335 00:17:45,176 --> 00:17:45,796 it's okay. 336 00:17:46,236 --> 00:17:53,786 In the first episode, a little bit of an echo in the first episode, there was 337 00:17:54,376 --> 00:17:59,836 a scenario that you described, Brian, where there was a business that had an 338 00:17:59,836 --> 00:18:01,756 underground parking and a daycare center. 339 00:18:01,756 --> 00:18:05,516 There were valets and there were daycare workers. 340 00:18:05,816 --> 00:18:11,376 And you brought up a really interesting scenario where the valet Parkers were 341 00:18:11,376 --> 00:18:15,726 getting maybe $20 an hour and the daycare workers, or sorry, 25 and 342 00:18:15,726 --> 00:18:17,406 the daycare workers were getting 20. 343 00:18:17,826 --> 00:18:19,686 The valets were mostly men. 344 00:18:20,011 --> 00:18:25,201 The daycare workers were mostly women and you were contending that, 345 00:18:25,501 --> 00:18:29,891 the, maybe the more difficult job was the daycare center operator. 346 00:18:30,191 --> 00:18:35,321 And so they were clearly not similar job titles. 347 00:18:35,741 --> 00:18:36,661 Yet they. 348 00:18:38,121 --> 00:18:43,041 When you compare them and maybe you could make a case for one of them being, 349 00:18:43,051 --> 00:18:45,021 a more difficult, more challenging job. 350 00:18:45,071 --> 00:18:48,081 And the women who predominantly worked there were getting paid 351 00:18:48,081 --> 00:18:51,141 less versus the other one that was on the other end of the spectrum. 352 00:18:51,551 --> 00:18:55,331 That's clearly something that human has to be able to sort out. 353 00:18:55,771 --> 00:18:59,611 How do you even start diving into something like that? 354 00:18:59,611 --> 00:19:06,421 Because that seems it seems like finding pay equity issues within job 355 00:19:06,461 --> 00:19:08,731 titles is going to be relatively easy. 356 00:19:09,841 --> 00:19:14,911 Identifying these is going to be much more difficult and probably the bigger problem. 357 00:19:15,431 --> 00:19:15,951 Brian Marentette, PhD: Yeah. 358 00:19:16,181 --> 00:19:16,521 Yeah. 359 00:19:16,531 --> 00:19:20,701 So a lot of it from our perspective, like we can assist clients with 360 00:19:20,701 --> 00:19:25,751 that if they have what we refer to as a job architecture in place. 361 00:19:26,356 --> 00:19:30,536 Job architecture, meaning, they've got more than just job titles and 362 00:19:30,536 --> 00:19:34,606 like divisions, or departments, they might have job families. 363 00:19:34,686 --> 00:19:38,676 They might have job levels like individual contributor one, two, three. 364 00:19:39,236 --> 00:19:43,286 Supervisor one, two, three manager, one, two, three, et cetera. 365 00:19:43,626 --> 00:19:47,236 And when you have that, now you can start to look at Hey, we know 366 00:19:47,246 --> 00:19:53,906 this job family, IT and finance are our two highest paid job family. 367 00:19:53,906 --> 00:19:56,466 They have the largest market demand, highest salaries. 368 00:19:56,466 --> 00:20:02,026 We can maybe we can analyze them together and look for our our P1s, our 369 00:20:02,026 --> 00:20:04,686 supervisor ones compared to each other. 370 00:20:05,066 --> 00:20:06,876 And we just put them all in together and we analyze it. 371 00:20:07,846 --> 00:20:10,196 Just by job level with this grouping. 372 00:20:10,196 --> 00:20:14,416 And then you can start to see jobs that, again, if they're at the same 373 00:20:14,416 --> 00:20:19,026 level and they're in like similar complexity job families that's where 374 00:20:19,026 --> 00:20:20,536 you can start to highlight some of that. 375 00:20:20,576 --> 00:20:24,776 And again, from our seat as a consultant, we can look at the job architecture and 376 00:20:24,776 --> 00:20:28,406 say this is how you potentially could start combining things and let's run it. 377 00:20:28,436 --> 00:20:29,056 We'll see. 378 00:20:29,501 --> 00:20:33,171 How do things look if we see wildly disparate results, then it's okay, 379 00:20:33,171 --> 00:20:36,381 let's step that back and maybe break this out a little bit more. 380 00:20:36,681 --> 00:20:39,681 But it's an iterative process and working with the client on, okay, 381 00:20:39,691 --> 00:20:43,101 using the data you've got, how could we potentially run this? 382 00:20:43,121 --> 00:20:43,901 And let's give it a shot. 383 00:20:44,221 --> 00:20:46,551 Or you can leave it incumbent upon the organization. 384 00:20:46,551 --> 00:20:51,781 If they don't have that documented architecture in place, it's incumbent 385 00:20:51,781 --> 00:20:55,901 on them to say okay, we know these jobs are all within like an entry level 386 00:20:56,881 --> 00:20:57,891 contributor role. 387 00:20:57,891 --> 00:21:00,911 We could probably start looking at them together over here. 388 00:21:01,771 --> 00:21:01,871 Yeah. 389 00:21:02,061 --> 00:21:02,741 That sort of thing. 390 00:21:02,761 --> 00:21:03,651 It's a lot more work. 391 00:21:05,241 --> 00:21:08,531 Mike Callen: It occurs to me that with the, everybody's got a human capital 392 00:21:08,541 --> 00:21:10,701 management system now that's in place. 393 00:21:10,756 --> 00:21:15,866 Most every organization who's got any sizable number of employees. 394 00:21:16,156 --> 00:21:23,186 And it's, it occurs to me that potentially those could be used to add certain 395 00:21:23,186 --> 00:21:29,296 amounts of metadata to the structure so that you did, now you would group entry 396 00:21:29,296 --> 00:21:34,596 level service, entry level professional, and you could add areas of expertise 397 00:21:34,646 --> 00:21:39,616 or degrees of expertise or degrees of education or degrees of experience, 398 00:21:39,856 --> 00:21:41,226 and you could start looking at. 399 00:21:41,776 --> 00:21:46,956 All sorts of job titles along those sort of lines, rather than, 400 00:21:47,236 --> 00:21:51,646 some of the more traditional job, family, job title kinds of aspects. 401 00:21:51,766 --> 00:21:56,286 Is that anything, has anybody ever tried to really slice and dice HR differently, 402 00:21:56,506 --> 00:21:58,526 because everything's all database now? 403 00:21:58,981 --> 00:21:59,761 Or would that be helpful? 404 00:22:00,881 --> 00:22:01,901 Brian Marentette, PhD: Yeah, absolutely. 405 00:22:01,901 --> 00:22:08,101 I think some providers are doing that within the HRIS, they have some 406 00:22:08,101 --> 00:22:13,661 of that architecture built into it so that as you're structuring, as 407 00:22:13,741 --> 00:22:17,261 you're integrating into that database you have the ability to map jobs to 408 00:22:17,261 --> 00:22:18,761 certain levels and that sort of thing. 409 00:22:18,761 --> 00:22:23,151 And we're always advocates of data and management of data. 410 00:22:23,921 --> 00:22:24,261 Yeah. 411 00:22:24,281 --> 00:22:25,811 Organization database, 412 00:22:26,211 --> 00:22:31,811 the better and easier it will be for us as consultants or as your analysts to 413 00:22:31,811 --> 00:22:36,191 yeah, be able to parse out and explain things that otherwise require the human 414 00:22:36,191 --> 00:22:38,961 to go in and qualitatively review. 415 00:22:39,591 --> 00:22:40,801 Mike Callen: That was a bit of a rabbit trail. 416 00:22:40,801 --> 00:22:43,091 I'm sorry about that, but it just, it did seem to 417 00:22:43,451 --> 00:22:46,651 so maybe make sense and it could be something that could, maybe 418 00:22:46,681 --> 00:22:50,941 potentially solve some analysis issues or make some analyses 419 00:22:50,941 --> 00:22:53,981 easier over time and in your space. 420 00:22:54,251 --> 00:23:00,791 We're all here because of the computer and because of the database, 421 00:23:01,201 --> 00:23:04,606 all being ever present in HR. 422 00:23:04,606 --> 00:23:10,966 If that hadn't happened, back in the early nineties, it wouldn't have created 423 00:23:11,016 --> 00:23:15,776 all of these aspects of this cottage industry that have come about your work, 424 00:23:15,916 --> 00:23:19,846 even your service work, our software work, it's all there because of that. 425 00:23:19,896 --> 00:23:21,501 So it's a fun, exciting territory. 426 00:23:22,186 --> 00:23:22,526 Brian Marentette, PhD: Yeah. 427 00:23:23,286 --> 00:23:23,586 Yeah. 428 00:23:23,586 --> 00:23:27,076 I don't know how the world existed without computers, 429 00:23:28,116 --> 00:23:29,296 Mike Callen: reams of paper. 430 00:23:29,526 --> 00:23:29,906 Brian Marentette, PhD: Yah 431 00:23:30,546 --> 00:23:30,886 Jenny Arnez: Yes. 432 00:23:31,576 --> 00:23:34,696 I know that we're winding down our time and I know that you've got 433 00:23:34,996 --> 00:23:38,356 somewhere to go with your children and certainly want to support that. 434 00:23:38,756 --> 00:23:44,126 If I, or an HR professional wanted to learn more about pay equity 435 00:23:44,601 --> 00:23:50,671 just to be aware of what laws to pay attention to, just being able 436 00:23:50,671 --> 00:23:54,351 to recognize a fair pay structure. 437 00:23:54,701 --> 00:23:57,741 What resources would you suggest that they go to? 438 00:23:59,681 --> 00:24:05,246 Brian Marentette, PhD: Gosh there's a lot on the EEOC's website I don't have it 439 00:24:05,256 --> 00:24:10,946 handy to share to you, but there's really, gosh, at a federal level, it's going to 440 00:24:10,946 --> 00:24:14,686 be your civil rights act, 1964, Title VII. 441 00:24:15,656 --> 00:24:16,906 There's also the Equal Pay Act. 442 00:24:17,416 --> 00:24:23,166 You have a state level, a number of different states particularly 443 00:24:23,206 --> 00:24:25,466 California and Illinois currently. 444 00:24:26,096 --> 00:24:31,316 That require you as an employer to submit employee level compensation data 445 00:24:31,836 --> 00:24:33,956 to the States for regulatory purposes. 446 00:24:34,486 --> 00:24:37,876 The best place to go is going to be our website, frankly, and I 447 00:24:37,876 --> 00:24:41,426 will follow up with you to give you the pertinent information. 448 00:24:41,896 --> 00:24:46,286 There's just a lot of information out there and, there's some associations, 449 00:24:46,286 --> 00:24:50,356 obviously things like SHRM and World at Work, which is a compensation 450 00:24:50,356 --> 00:24:57,646 specific organization but really there's again, probably too much 451 00:24:57,646 --> 00:25:01,626 information out there when it comes to pay equity in that it's it's not like 452 00:25:01,626 --> 00:25:03,416 a relatively new field by any means. 453 00:25:03,416 --> 00:25:05,436 We've been looking at compensation for decades. 454 00:25:05,766 --> 00:25:11,266 But in, in terms of how people go about doing pay equity analyses and, how 455 00:25:11,266 --> 00:25:14,901 they're approaching it, even companies that are based in Europe come over to 456 00:25:14,901 --> 00:25:17,011 the U S and offer pay equity services. 457 00:25:17,021 --> 00:25:20,781 It's fundamentally different than how we might operate as more of 458 00:25:20,781 --> 00:25:24,221 a U S centric, EEOC compliance. 459 00:25:24,711 --> 00:25:29,931 So I'd say, first of all, maybe consider contacting legal counsel to ask them 460 00:25:30,061 --> 00:25:33,031 what legal requirements they have given the state that they're in or how 461 00:25:33,031 --> 00:25:36,711 large their organization is, whether or not they have a contract with the 462 00:25:36,711 --> 00:25:38,191 federal government to do business. 463 00:25:38,641 --> 00:25:42,361 If they are, under the Department of Labor and the Office of Federal 464 00:25:42,361 --> 00:25:46,141 Contract Compliance Programs, the OFCCP there's a number of factors 465 00:25:46,141 --> 00:25:50,031 that might influence your decision of how you might go about doing a pay 466 00:25:50,031 --> 00:25:51,931 equity analysis given who you are. 467 00:25:52,411 --> 00:25:56,251 And of course, a conversation with me, I can work through that 468 00:25:56,251 --> 00:25:57,541 with you if you shoot me an email. 469 00:25:58,981 --> 00:26:02,351 Mike Callen: Does your education, training, BCGi, do they handle? 470 00:26:03,481 --> 00:26:05,421 Topic of, oh, okay. 471 00:26:05,511 --> 00:26:05,771 Okay. 472 00:26:05,771 --> 00:26:08,301 So there's another resource available as well. 473 00:26:08,661 --> 00:26:09,201 Brian Marentette, PhD: Absolutely. 474 00:26:09,201 --> 00:26:12,891 We do free webinars almost a couple of times a month. 475 00:26:12,991 --> 00:26:16,161 You can usually count on maybe once once a month or every other month, 476 00:26:16,171 --> 00:26:19,401 there'll be a topic related to pay equity or compensation either 477 00:26:19,401 --> 00:26:23,881 hosted by myself or one of our other team members, we have a number of 478 00:26:23,881 --> 00:26:25,881 articles, white papers, blog posts. 479 00:26:26,541 --> 00:26:27,941 Like I said, all on our website. 480 00:26:28,846 --> 00:26:33,506 Jenny Arnez: Okay we'll include in our show notes link to BCGI. 481 00:26:33,636 --> 00:26:35,426 Sounds like to the blog as well, right? 482 00:26:35,436 --> 00:26:41,266 For those articles and, um, I guess if they want to reach, I don't want to put 483 00:26:41,266 --> 00:26:42,956 your email address on the show notes. 484 00:26:42,956 --> 00:26:46,566 Otherwise you'll be, you'll get lots of spam bots reaching out to you, 485 00:26:46,616 --> 00:26:50,126 but we'll make sure that we link 486 00:26:50,856 --> 00:26:53,326 to the website so that they can get in touch with you there. 487 00:26:53,666 --> 00:26:53,926 Yeah. 488 00:26:54,896 --> 00:26:57,596 Brian, this has been a pleasure. 489 00:26:57,606 --> 00:27:00,776 It's been really fun to have you on here and to give us 490 00:27:02,881 --> 00:27:06,001 some basic information about what pay equity looks like 491 00:27:06,001 --> 00:27:07,651 and did a few deep dives. 492 00:27:07,741 --> 00:27:10,891 And so Mike, any other final thoughts you'd like to share? 493 00:27:11,881 --> 00:27:14,311 Mike Callen: No, I just, I really appreciate it as well. 494 00:27:14,361 --> 00:27:19,671 Sometimes when we have these podcasts, they're squarely in our space and this 495 00:27:19,671 --> 00:27:26,161 is really, squarely outside of our space within the same silo of HR, but it's 496 00:27:26,171 --> 00:27:31,991 really interesting to, to learn about this and, we've had the, great pleasure 497 00:27:31,991 --> 00:27:37,481 of knowing you for decades and working with you for a long amount of time. 498 00:27:37,731 --> 00:27:42,201 We just don't have an opportunity to really get together and sit down. 499 00:27:42,451 --> 00:27:44,291 And plumb the depths of your knowledge. 500 00:27:44,291 --> 00:27:47,901 And so it's been a really great time and look forward to, doing 501 00:27:47,901 --> 00:27:49,411 it again sometime down the road. 502 00:27:49,451 --> 00:27:52,261 So thank you very much for being here with us very much. 503 00:27:52,261 --> 00:27:53,811 Appreciate it. 504 00:27:53,811 --> 00:27:54,331 Brian Marentette, PhD: Thank you. 505 00:27:54,331 --> 00:27:58,311 I appreciate the kind words and it's been a pleasure speaking with both of you. 506 00:27:58,311 --> 00:28:01,221 I'm happy to come back anytime I was going to say, check me out on LinkedIn. 507 00:28:01,221 --> 00:28:02,461 Jenny, you are reading my mind. 508 00:28:04,071 --> 00:28:04,441 Jenny Arnez: Yeah. 509 00:28:04,441 --> 00:28:06,271 I noticed that you're pretty active on there. 510 00:28:06,271 --> 00:28:08,851 And so that's a great place to get in touch with Brian. 511 00:28:09,271 --> 00:28:09,871 And. 512 00:28:10,896 --> 00:28:12,176 Again, thank you so much. 513 00:28:12,176 --> 00:28:17,536 We hope today has been of value to you, our listeners, and our, to our viewers. 514 00:28:18,036 --> 00:28:19,946 Again, reach out if you have any questions. 515 00:28:20,596 --> 00:28:21,316 We're here to help you. 516 00:28:22,326 --> 00:28:22,896 Thanks again. 517 00:28:23,776 --> 00:28:24,646 Mike Callen: Thanks very much. 518 00:28:26,547 --> 00:28:30,347 Podcast Outro: Thanks for tuning in to Testing Testing 123 brought to you by 519 00:28:30,367 --> 00:28:32,217 TestGenius and Biddle Consulting Group. 520 00:28:33,017 --> 00:28:35,997 Visit our website at testgenius.com for more information.