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 00:00:08,983 --> 00:00:09,883 Jenny Arnez: Hello everybody. 4 00:00:09,893 --> 00:00:11,983 We're so glad you joined us today. 5 00:00:12,483 --> 00:00:13,873 My name is Jenny Arnez. 6 00:00:13,893 --> 00:00:20,753 I'm from TestGenius and you are on the Testing Testing 1, 2, 3 podcast. 7 00:00:21,393 --> 00:00:23,873 With me today is my co host, Mike Callen. 8 00:00:23,883 --> 00:00:27,743 He's the president of TestGenius and Mike, do you want to say anything? 9 00:00:27,743 --> 00:00:28,233 Mike Callen: Greetings. 10 00:00:28,233 --> 00:00:29,793 Glad that you're here joining us. 11 00:00:29,793 --> 00:00:32,603 And we're excited about today's podcast. 12 00:00:32,883 --> 00:00:36,863 We've got a great guest with us and frankly we're all 13 00:00:36,873 --> 00:00:38,003 going to be learning stuff. 14 00:00:38,013 --> 00:00:42,463 This is new- new territory for us outside of our scope. 15 00:00:42,553 --> 00:00:45,493 And so it's going to be a fun couple sessions. 16 00:00:46,573 --> 00:00:47,033 Jenny Arnez: That's right. 17 00:00:47,033 --> 00:00:51,593 Today we are recording our first session of two with Dr. 18 00:00:51,593 --> 00:00:52,913 Brian Marentette. 19 00:00:53,093 --> 00:00:55,853 He's from Berkshire Associates. 20 00:00:55,963 --> 00:00:58,123 He's the Director of People Insights there. 21 00:00:58,653 --> 00:01:01,583 Brian's going to, well, he's going to be teaching us. 22 00:01:01,613 --> 00:01:06,963 This first episode is just about pay equity basics, and so Mike and I will be 23 00:01:06,963 --> 00:01:11,873 asking Brian lots of questions, and we hope this is a beneficial time for you. 24 00:01:12,343 --> 00:01:14,993 Brian, would you like to tell us a few things about yourself? 25 00:01:15,503 --> 00:01:16,063 Brian Marentette, PhD: Sure. 26 00:01:16,073 --> 00:01:17,033 Thank you for having me. 27 00:01:17,033 --> 00:01:18,583 First of all, it's an honor to be here. 28 00:01:18,903 --> 00:01:20,863 I am a former member of the Biddle team. 29 00:01:20,913 --> 00:01:25,183 Through two different stints with Biddle but recently we were acquired and 30 00:01:25,183 --> 00:01:27,153 joined the Berkshire Associates group. 31 00:01:27,153 --> 00:01:29,073 So I know the Biddle folks well, and it's nice to be back. 32 00:01:29,408 --> 00:01:32,068 Speaking with you, but yeah, Brian Marentette here. 33 00:01:32,068 --> 00:01:36,738 I'm an IO psychologist that specializes in a number of different areas across 34 00:01:36,758 --> 00:01:42,718 the HR space and EEO compliance doing sort of TestGenius type work as well 35 00:01:42,718 --> 00:01:47,268 with testing and validation, but also other statistical analyses surrounding 36 00:01:47,398 --> 00:01:49,398 personnel decisions in the workplace. 37 00:01:49,408 --> 00:01:52,318 So hiring, promotion, termination, and compensation, 38 00:01:52,768 --> 00:01:53,808 which is what we'll talk about. 39 00:01:54,028 --> 00:01:54,768 today with pay equity. 40 00:01:55,738 --> 00:01:56,198 Nice to be here. 41 00:01:56,198 --> 00:01:56,788 Wonderful. 42 00:01:56,878 --> 00:01:57,448 Jenny Arnez: Wonderful. 43 00:01:57,458 --> 00:01:58,058 Thank you. 44 00:01:58,488 --> 00:02:02,678 So my thought for today is that we would do the reporter questions, the what, 45 00:02:02,688 --> 00:02:04,978 who, why, when, and how of pay equity. 46 00:02:05,458 --> 00:02:07,778 And why don't we just start with a definition? 47 00:02:07,788 --> 00:02:11,098 What exactly does pay equity mean? 48 00:02:11,818 --> 00:02:12,608 Brian Marentette, PhD: Yeah, great. 49 00:02:12,608 --> 00:02:14,838 So it's a term that's thrown around a lot. 50 00:02:15,018 --> 00:02:17,838 And there's probably several definitions out there. 51 00:02:18,848 --> 00:02:23,818 Fundamental common thing that you'll see is equal pay for equal work. 52 00:02:24,398 --> 00:02:27,478 That doesn't mean same pay for same work. 53 00:02:27,978 --> 00:02:32,803 It's a relative term in that it's considering individual's attributes, 54 00:02:32,853 --> 00:02:36,153 their characteristics that they're bringing to the table, the characteristics 55 00:02:36,153 --> 00:02:40,703 of the work that they're performing and then considering pay after we 56 00:02:40,873 --> 00:02:43,593 take those factors into account. 57 00:02:46,843 --> 00:02:48,933 Jenny Arnez: So can you elaborate a little bit more? 58 00:02:48,933 --> 00:02:50,823 You said it doesn't mean same. 59 00:02:50,933 --> 00:02:54,793 So that kind of brings a question mark in my mind. 60 00:02:55,033 --> 00:02:55,723 Brian Marentette, PhD: Sure. 61 00:02:55,783 --> 00:02:59,053 So assume Mike and I are doing the same job. 62 00:02:59,363 --> 00:02:59,763 All right. 63 00:02:59,783 --> 00:03:02,143 We're both we're not going to be the president of test genius, but we're 64 00:03:02,143 --> 00:03:04,553 a consultant doing the same job. 65 00:03:04,653 --> 00:03:09,183 And I was just hired a year ago, fresh out of grad school. 66 00:03:09,583 --> 00:03:14,393 And Mike was hired 10 years ago and he has had several other jobs as well. 67 00:03:14,393 --> 00:03:17,833 In the consulting space, he brings a different level of expertise and 68 00:03:17,833 --> 00:03:20,273 experience knowledge to the table. 69 00:03:20,653 --> 00:03:25,303 If we were paid the same pay for the same work, that would 70 00:03:25,303 --> 00:03:26,483 mean we're making the same. 71 00:03:27,063 --> 00:03:29,873 Amount of money, regardless of any of those other factors. 72 00:03:30,353 --> 00:03:35,693 And that's where the pay equity concept comes into play is that we 73 00:03:35,693 --> 00:03:37,073 should not really be paid the same. 74 00:03:37,073 --> 00:03:38,363 That's actually not equitable. 75 00:03:38,413 --> 00:03:42,273 I don't think Mike would be happy if he's making the same amount as me as a newbie. 76 00:03:42,683 --> 00:03:48,033 And so he has compensated more and you'd see that in raw numbers. 77 00:03:48,063 --> 00:03:51,153 And it would appear that there's a disparity there, but behind the 78 00:03:51,153 --> 00:03:54,513 scenes, the equity piece would explain that there's other factors. 79 00:03:55,008 --> 00:03:55,988 That contribute to that. 80 00:03:55,988 --> 00:03:57,678 So once we factor those things in. 81 00:03:58,683 --> 00:04:04,883 We actually are making equal pay for the equal work that we're performing. 82 00:04:04,883 --> 00:04:07,623 Now, my work isn't going to be equal to Mike's. 83 00:04:08,023 --> 00:04:10,703 And that's where some of the more complicated statistical 84 00:04:10,723 --> 00:04:11,833 analyses come into play. 85 00:04:12,263 --> 00:04:15,863 But the concept there is that pay equity factors in these other 86 00:04:16,573 --> 00:04:19,963 relevant pieces of information that might influence somebody's pay. 87 00:04:20,243 --> 00:04:23,183 Mike Callen: Does it make sense to ask now, or if not, 88 00:04:23,183 --> 00:04:24,573 we can push it off till later. 89 00:04:24,833 --> 00:04:31,583 But one of the things that I always wonder about when I hear this term has to do with 90 00:04:31,673 --> 00:04:33,933 job performance ratings, for instance. 91 00:04:33,993 --> 00:04:40,623 How are different people performing, unlike the example you used, assuming 92 00:04:40,623 --> 00:04:43,693 we're working in the same role, it's been the same amount of time. 93 00:04:44,213 --> 00:04:50,293 How does that come into play as well in terms of determining pay equity 94 00:04:50,293 --> 00:04:54,803 or in terms of the analysis for pay equity, or is that something that 95 00:04:54,803 --> 00:04:58,028 occurs outside or is it not even something that gets considered? 96 00:04:58,568 --> 00:04:59,568 Brian Marentette, PhD: No, good question. 97 00:04:59,568 --> 00:05:03,088 That is, that comes up with almost every single client that we work with. 98 00:05:03,728 --> 00:05:09,368 Many organizations out there claim to be a pay per for pay for performance model. 99 00:05:09,408 --> 00:05:12,488 So they pay people more for better performance. 100 00:05:12,868 --> 00:05:16,838 And oftentimes that is quantified through performance ratings. 101 00:05:17,288 --> 00:05:21,728 That is a legitimate factor that could be considered in somebody's compensation 102 00:05:22,228 --> 00:05:25,358 I will tell you more often than not 103 00:05:26,208 --> 00:05:31,528 regardless of how strong a client pushes, me to use those ratings, they actually 104 00:05:32,048 --> 00:05:34,138 have a very weak relationship to pay. 105 00:05:34,758 --> 00:05:39,228 There might be some sort of individual cases where your star performer indeed 106 00:05:39,238 --> 00:05:42,508 has the highest ratings across the board and they are making the most, 107 00:05:42,798 --> 00:05:46,578 your lowest performer might have the lowest ratings and the lowest 108 00:05:46,578 --> 00:05:48,908 pay, but all the noise in the middle 109 00:05:49,478 --> 00:05:51,388 doesn't usually track very cleanly. 110 00:05:51,838 --> 00:05:53,918 But performance would be one of those factors that could be 111 00:05:53,918 --> 00:05:55,708 an influencing pay certainly. 112 00:05:57,128 --> 00:05:58,338 Mike Callen: And that's an interesting point. 113 00:05:58,358 --> 00:06:02,058 We hear very often when there's conversations either internally or 114 00:06:02,058 --> 00:06:06,918 externally about job performance ratings very often it's a gift. 115 00:06:07,128 --> 00:06:10,108 That everybody gets one time a year, somebody says you're 116 00:06:10,108 --> 00:06:14,178 spectacular and that could really throw a wrench into these things. 117 00:06:14,188 --> 00:06:18,198 So there are other metrics available as well. 118 00:06:18,208 --> 00:06:21,638 If you're doing an assembly job, you're making widgets. 119 00:06:21,638 --> 00:06:28,103 If you you could be, you could look at the attendance or lateness or, 120 00:06:28,273 --> 00:06:33,183 maybe there's a whole host of other, uh, metrics that might be available. 121 00:06:33,193 --> 00:06:36,193 Do those ever get factored into the work that you're doing or 122 00:06:36,733 --> 00:06:38,533 get considered as criteria? 123 00:06:38,713 --> 00:06:39,963 Brian Marentette, PhD: In some cases, yes. 124 00:06:39,993 --> 00:06:44,653 In situations where you have those objective quantifiable outputs. 125 00:06:45,843 --> 00:06:51,483 Yes, they often do factor in and that's usually on like variable compensation, so 126 00:06:51,483 --> 00:06:54,913 non base pay commissions, incentives yeah. 127 00:06:55,353 --> 00:06:58,943 So in, these days, there's not a lot of jobs that actually make widgets 128 00:06:58,943 --> 00:07:02,303 where people can quantify the number of products they're producing and 129 00:07:02,303 --> 00:07:04,283 things, but in some cases attorneys. 130 00:07:04,753 --> 00:07:09,193 Oftentimes we'll see that obviously billable hours impacts the some of 131 00:07:09,193 --> 00:07:11,103 the bonus structures that they have. 132 00:07:11,513 --> 00:07:16,053 And it's really pretty formulaic in those cases where if you hit certain 133 00:07:16,203 --> 00:07:19,523 targets, you get the incentives of course. 134 00:07:19,573 --> 00:07:19,893 Yeah. 135 00:07:20,183 --> 00:07:22,253 But those need to be factored in when you're looking at 136 00:07:22,253 --> 00:07:23,633 some of those non base pay. 137 00:07:24,783 --> 00:07:26,723 Incentives and commissions and bonuses. 138 00:07:26,723 --> 00:07:27,013 Yeah 139 00:07:27,543 --> 00:07:28,023 Mike Callen: I know. 140 00:07:28,133 --> 00:07:33,373 I have a friend who is an apprentice electrician and he is almost ready 141 00:07:33,373 --> 00:07:35,493 to become a journeyman electrician. 142 00:07:35,983 --> 00:07:43,403 And obviously they have structures that are based around merit, which 143 00:07:43,403 --> 00:07:47,593 is really associated with the steps that you increased through your 144 00:07:47,593 --> 00:07:50,963 education, the amount of years that you put into each program. 145 00:07:51,203 --> 00:07:54,743 And there's really a natural cadence from apprentice to journeyman to 146 00:07:54,743 --> 00:07:56,483 supervisor, those kinds of things. 147 00:07:56,873 --> 00:08:01,333 Is that a realm that you prefer to operate in? 148 00:08:01,333 --> 00:08:04,303 Is it, are things more clean there? 149 00:08:04,323 --> 00:08:08,793 Or do you even need to do compensation analysis in a realm like that? 150 00:08:08,823 --> 00:08:14,813 Or, do you prefer to do it in some of the areas that are more subjective? 151 00:08:14,813 --> 00:08:16,933 Or do you think it's more necessary in those areas? 152 00:08:17,693 --> 00:08:18,613 Brian Marentette, PhD: Great question. 153 00:08:18,613 --> 00:08:23,583 And yes, most of the compensation pay equity work is done in environments 154 00:08:23,583 --> 00:08:25,053 that are not so structured. 155 00:08:25,778 --> 00:08:27,708 It's particularly like unionized roles. 156 00:08:27,748 --> 00:08:29,858 We have organizations with unionized workforces. 157 00:08:29,898 --> 00:08:34,468 We oftentimes don't even analyze the union employees because they 158 00:08:34,468 --> 00:08:36,078 are tied to that rigid structure. 159 00:08:36,578 --> 00:08:38,348 And there's actually very little you can do about it. 160 00:08:38,718 --> 00:08:43,508 And when you see disparities they're often, at a job title level, as soon as 161 00:08:43,508 --> 00:08:49,043 we control for the grade that they're in or that step that they're in, it wipes it 162 00:08:49,043 --> 00:08:53,253 out because that usually explains almost all the variability in somebody's pay. 163 00:08:53,543 --> 00:08:57,343 It's tied to these steps and levels that they're on so it is the subjective 164 00:08:57,473 --> 00:09:03,653 pay decisions that are most prone to bias potentially or just general, 165 00:09:03,763 --> 00:09:09,563 poor practices that produce unintended consequences that might appear as 166 00:09:09,563 --> 00:09:12,033 bias that are caused by other factors. 167 00:09:13,113 --> 00:09:14,433 It's pretty deep into it but yeah, 168 00:09:15,853 --> 00:09:19,863 Mike Callen: So is it an effect of the way that those unionized 169 00:09:19,863 --> 00:09:21,083 environments are organized. 170 00:09:22,803 --> 00:09:28,613 It's pretty clear that there is very little inequity that exists because 171 00:09:28,613 --> 00:09:31,553 it's happens to be very much structured. 172 00:09:31,553 --> 00:09:34,323 And so people don't tend to have to look at that? 173 00:09:34,323 --> 00:09:38,933 Or is there some other nuance that directs you in terms of, you know what 174 00:09:39,353 --> 00:09:39,893 what you might 175 00:09:39,893 --> 00:09:41,903 need to do or for whom you do the service? 176 00:09:42,193 --> 00:09:42,563 Brian Marentette, PhD: Right. 177 00:09:43,063 --> 00:09:47,513 There's very little opportunity for their, for inequity to exist when you 178 00:09:47,523 --> 00:09:54,673 have such a rigid step type of system And if you're finding any differences, 179 00:09:55,353 --> 00:09:58,163 it's going to reflect people that are deviating from whatever has been. 180 00:09:58,183 --> 00:10:01,633 So there shouldn't be any opportunities for inequity because 181 00:10:01,633 --> 00:10:03,253 it's so structured like that. 182 00:10:03,763 --> 00:10:05,233 And that's why we will leave it alone. 183 00:10:05,623 --> 00:10:09,863 I will say from a different perspective there's kind of two lenses you 184 00:10:09,863 --> 00:10:10,843 can look at pay equity through. 185 00:10:10,853 --> 00:10:12,013 One is a legal lens. 186 00:10:12,013 --> 00:10:16,933 Are they violating any federal or state or local laws that might prohibit 187 00:10:17,363 --> 00:10:20,083 disparities or, apparent bias in pay? 188 00:10:20,553 --> 00:10:23,873 But the other is more of a D,E, & I lens or a more of a diversity lens 189 00:10:23,873 --> 00:10:29,683 where let's say we look at that job of electricians and we're not controlling for 190 00:10:29,683 --> 00:10:35,693 those different steps and we see a huge disparity in pay between, men and women. 191 00:10:36,443 --> 00:10:37,273 Now what does that mean? 192 00:10:37,283 --> 00:10:42,653 Does it mean we have pay bias against women or does it mean that we have 193 00:10:42,793 --> 00:10:47,443 only, got our female employees stuck down in these lower steps? 194 00:10:47,443 --> 00:10:50,253 They haven't moved out of those lower grades and steps 195 00:10:50,253 --> 00:10:51,333 to get up to higher levels. 196 00:10:51,783 --> 00:10:52,063 Sure. 197 00:10:52,063 --> 00:10:53,603 We can explain away the difference. 198 00:10:53,613 --> 00:10:56,433 We can statistically tell you, ah, it's not an issue. 199 00:10:56,613 --> 00:10:59,873 Once you factor in those steps, but then you then have to self reflect 200 00:10:59,873 --> 00:11:02,693 as an organization and say why do we have, all of our females are all 201 00:11:02,693 --> 00:11:08,193 of our Hispanic or black minority employees stuck in these lower levels. 202 00:11:08,583 --> 00:11:09,863 And then it becomes a separate issue. 203 00:11:09,873 --> 00:11:11,163 It's not really a pay equity issue. 204 00:11:11,173 --> 00:11:15,623 It's more of that career progression, glass ceiling type of a problem, 205 00:11:15,623 --> 00:11:19,113 which is a, another benefit of doing pay equity work is that it 206 00:11:19,113 --> 00:11:20,703 can spotlight some of those issues. 207 00:11:21,383 --> 00:11:23,023 Mike Callen: Another associated problem. 208 00:11:23,353 --> 00:11:24,783 So there's two things I want to ask. 209 00:11:24,783 --> 00:11:28,143 I'm going to throw this out here because I don't want to forget the comment that 210 00:11:28,143 --> 00:11:34,483 you just made about, identifying some of these issues that could really potentially 211 00:11:34,503 --> 00:11:37,203 point toward a systemic issue, right? 212 00:11:37,293 --> 00:11:41,393 If you find that something's happening over here on the compensation 213 00:11:41,393 --> 00:11:46,213 side you should probably look elsewhere within your organization. 214 00:11:46,213 --> 00:11:47,733 And the second part, I'm just going to throw this out. 215 00:11:47,773 --> 00:11:49,813 So I don't, I'll go ahead and answer that. 216 00:11:49,813 --> 00:11:50,813 No I'll type myself enough. 217 00:11:50,853 --> 00:11:51,143 Go ahead. 218 00:11:51,153 --> 00:11:52,863 Brian Marentette, PhD: Yes, no, you're spot on. 219 00:11:52,933 --> 00:11:59,233 It ties into a tangentially related concept, which is the pay gap problem. 220 00:11:59,673 --> 00:11:59,953 Okay. 221 00:11:59,953 --> 00:12:00,443 Which is. 222 00:12:00,988 --> 00:12:02,658 And I can define that before Jenny has to ask me, 223 00:12:05,668 --> 00:12:11,398 which is really just looking at like an unadjusted sort of like ratio of, 224 00:12:11,868 --> 00:12:14,348 let's say male to female compensation. 225 00:12:14,348 --> 00:12:17,558 We're not factoring in any of these relevant pieces of information that 226 00:12:17,558 --> 00:12:20,478 might come into play performance, education, whatever it might be. 227 00:12:20,868 --> 00:12:24,248 You'll hear about the pay gap statistic in the United States. 228 00:12:24,248 --> 00:12:28,688 It's published by the Census Bureau when they run their census every so often. 229 00:12:29,008 --> 00:12:32,148 And the most recent figure is like 83 cents on the dollar, right? 230 00:12:32,148 --> 00:12:36,068 Females earn 83 cents on the dollar compared to their male counterparts. 231 00:12:36,368 --> 00:12:38,728 And that leads a lot of people to think, Oh, we have a huge pay 232 00:12:38,728 --> 00:12:41,178 equity problem in the United States. 233 00:12:41,218 --> 00:12:43,118 And it's not necessarily true. 234 00:12:43,128 --> 00:12:45,888 It's not really a pay equity problem. 235 00:12:45,888 --> 00:12:51,078 It's more of a labor market availability or a talent acquisition problem. 236 00:12:51,418 --> 00:12:53,088 You've got your highest paying jobs. 237 00:12:53,743 --> 00:12:59,123 And we know from census data that there are more male employees 238 00:12:59,223 --> 00:13:02,533 available out there in the labor market for these higher paying roles. 239 00:13:02,753 --> 00:13:05,623 And the females are much more available out there in the 240 00:13:05,623 --> 00:13:07,443 lower paying bands and things. 241 00:13:07,503 --> 00:13:10,218 When you just look at an organization, and say, what's the 242 00:13:10,218 --> 00:13:12,578 average male versus female pay? 243 00:13:12,668 --> 00:13:17,348 Of course you're gonna see this 83% or 17% gap, right? 244 00:13:17,348 --> 00:13:20,048 Females making 83% of the male counterparts. 245 00:13:20,408 --> 00:13:26,938 And so through a pay equity analysis, you get a look at your like, job title level. 246 00:13:26,968 --> 00:13:31,118 Are we paying people the same for, equal pay for equal work after we 247 00:13:31,118 --> 00:13:32,528 consider these relevant factors? 248 00:13:32,878 --> 00:13:36,183 But then you can also look at that broader sense and see. 249 00:13:37,063 --> 00:13:37,303 Okay. 250 00:13:37,303 --> 00:13:39,293 Do we have more of a talent acquisition problem? 251 00:13:39,353 --> 00:13:42,723 Because we've got these pockets where if we take job title out and we 252 00:13:42,723 --> 00:13:45,983 look at average pay, there's a huge gap over in this one business unit. 253 00:13:46,383 --> 00:13:50,453 We're not bringing in any female or minority employees into any 254 00:13:50,453 --> 00:13:51,673 of the high (level positions).. 255 00:13:51,673 --> 00:13:55,593 And that conversation changes to again, the TA team. 256 00:13:55,593 --> 00:13:56,793 It's not a comp issue. 257 00:13:56,803 --> 00:13:58,073 It's not a dollar problem. 258 00:13:58,073 --> 00:14:00,653 It's are we getting people in the door? 259 00:14:00,663 --> 00:14:02,243 Are we not getting enough applicants? 260 00:14:02,263 --> 00:14:03,843 Are we not recruiting appropriately? 261 00:14:04,263 --> 00:14:08,813 So there's a lot of questions that get opened by these pay equity analysis. 262 00:14:08,853 --> 00:14:12,863 Mike Callen: So if it's a an availability issue, then does that 263 00:14:12,883 --> 00:14:15,063 enter the narrative in your report? 264 00:14:15,083 --> 00:14:20,103 I think that would be a really important aspect, especially use that basic 265 00:14:20,103 --> 00:14:26,113 example of national equity between males and females and a gap of 17 266 00:14:26,123 --> 00:14:34,268 cents, and you're explaining it based upon, of an availability issue, is that 267 00:14:34,268 --> 00:14:36,608 something that you folks ferret out? 268 00:14:36,608 --> 00:14:40,808 Or do you leave that to the employer to really try to help to present that in 269 00:14:40,808 --> 00:14:43,463 terms of taking action moving forward. 270 00:14:43,683 --> 00:14:45,143 Brian Marentette, PhD: Yeah great question. 271 00:14:45,143 --> 00:14:48,563 And that would be an kind of an add on to a pay equity analysis, 272 00:14:48,563 --> 00:14:51,973 because with pay equity data, we don't, we're not necessarily 273 00:14:51,973 --> 00:14:54,273 looking for market availability. 274 00:14:54,273 --> 00:14:58,093 We don't typically include that in a pay equity study. 275 00:14:58,413 --> 00:15:01,963 It is something we might do as a follow up or other clients 276 00:15:01,963 --> 00:15:03,603 would say that's great to know. 277 00:15:03,603 --> 00:15:05,073 We'll go and drill down on that. 278 00:15:06,243 --> 00:15:07,103 We'll take it from here. 279 00:15:07,773 --> 00:15:10,623 Mike Callen: The other point that I was going to ask you about was 280 00:15:10,623 --> 00:15:13,653 something that you mentioned a little bit earlier was regarding, 281 00:15:13,903 --> 00:15:18,633 sometimes it's an issue of legality and sometimes it's an issue of DE& I. 282 00:15:18,893 --> 00:15:23,743 And so there are certain classes of employers who are under the 283 00:15:23,753 --> 00:15:28,153 auspices of certain federal agencies for certain reasons. 284 00:15:28,363 --> 00:15:32,573 And then there's other ones who maybe aren't under the auspices of those 285 00:15:33,568 --> 00:15:39,008 oversight agencies, but they are wanting to be very transparent and 286 00:15:39,028 --> 00:15:40,678 they're wanting to do the right thing. 287 00:15:40,908 --> 00:15:46,278 And therefore, they're spending time, energy and money on having these studies 288 00:15:46,278 --> 00:15:49,248 done so that they can be equitable. 289 00:15:49,518 --> 00:15:52,708 I think that probably a lot of people. 290 00:15:52,908 --> 00:15:57,268 In our space and maybe even protect, particularly on the talent 291 00:15:57,268 --> 00:16:01,358 acquisition side, our side of HR, they don't really understand 292 00:16:01,388 --> 00:16:03,058 what that is or what that means. 293 00:16:03,318 --> 00:16:06,838 But that's, you grew up in that space, the consulting and the affirmative action 294 00:16:06,838 --> 00:16:09,488 and the EEO and the pay equity analysis. 295 00:16:09,508 --> 00:16:14,668 That all really has everything to do with those two spaces. 296 00:16:14,728 --> 00:16:19,288 Can you spend a couple of minutes and define those two arenas for us? 297 00:16:20,223 --> 00:16:20,823 Brian Marentette, PhD: Sure. 298 00:16:20,953 --> 00:16:26,313 So within the, I'll call it the EEO compliance realm, because even if you're 299 00:16:26,313 --> 00:16:31,013 not a, like a government contractor, for example, just about every employer 300 00:16:31,013 --> 00:16:34,053 is subject to Title VII laws, right? 301 00:16:34,053 --> 00:16:37,793 From the Civil Rights Act that prohibits discrimination based on protected 302 00:16:37,793 --> 00:16:41,543 characteristics, age, race, religion, age isn't necessarily part of that, 303 00:16:41,543 --> 00:16:42,493 it's the Age Discrimination Act. 304 00:16:43,118 --> 00:16:46,898 Age, race, religion, gender disability, veteran status. 305 00:16:47,368 --> 00:16:49,788 So these are all protected characteristics, right? 306 00:16:49,788 --> 00:16:56,268 If there's any differences in pay that are large enough to flag as being significant 307 00:16:56,378 --> 00:16:57,878 and they fall along any of those lines. 308 00:16:57,878 --> 00:16:58,208 Yes. 309 00:16:58,228 --> 00:17:05,643 Employers are liable really for a potential pay discrimination suit for 310 00:17:05,643 --> 00:17:11,483 those employers that are focused really just on legal compliance, there's that pay 311 00:17:11,483 --> 00:17:13,323 equity answers those questions as well. 312 00:17:13,323 --> 00:17:18,623 It's the same analysis that you would do for legal compliance versus a proactive 313 00:17:18,723 --> 00:17:22,493 look where people just want to know, Hey, do we have any pay potential 314 00:17:22,493 --> 00:17:23,793 pay problems that are out there? 315 00:17:24,163 --> 00:17:29,573 The difference on the proactive approach is that, a a legal standard 316 00:17:29,573 --> 00:17:31,093 is going to be very specific. 317 00:17:31,183 --> 00:17:35,803 We're looking at employees that are really doing they're called similarly situated 318 00:17:36,173 --> 00:17:39,963 technical term in the field, meaning, basically they're doing the same job. 319 00:17:40,693 --> 00:17:41,973 You can look beyond that though. 320 00:17:42,073 --> 00:17:47,283 You can see for, Employees that are doing similar work, are we paying 321 00:17:47,283 --> 00:17:51,353 differently for employees doing similar work and do those jobs 322 00:17:51,413 --> 00:17:54,583 predominantly, employ female versus male. 323 00:17:55,258 --> 00:17:57,128 And, there's a case study out there. 324 00:17:57,428 --> 00:18:01,158 That's a good example where an organization had a daycare on site. 325 00:18:01,538 --> 00:18:05,158 It was run predominantly by female employees, right? 326 00:18:05,248 --> 00:18:10,008 And they had a parking garage and a valet service with parking car attendants, 327 00:18:10,648 --> 00:18:13,698 predominantly male occupant that role. 328 00:18:14,038 --> 00:18:15,288 And guess what? 329 00:18:15,388 --> 00:18:20,098 The parking car attendants, their base salary was like 5 more than the female. 330 00:18:21,508 --> 00:18:23,348 Daycare caregivers. 331 00:18:23,828 --> 00:18:26,808 And, you got to ask yourself as an organization, okay, you're paying 332 00:18:26,818 --> 00:18:29,148 fairly within these two jobs. 333 00:18:29,158 --> 00:18:30,398 There's no discrimination going on. 334 00:18:30,398 --> 00:18:31,748 We're not paying unequally. 335 00:18:32,188 --> 00:18:35,618 But then what's the value that we place on those two jobs that. 336 00:18:36,188 --> 00:18:41,388 Arguably, the daycare providers are even doing more complex work, more higher risk. 337 00:18:41,398 --> 00:18:42,858 They've got the lives of children there. 338 00:18:43,148 --> 00:18:43,278 Mike Callen: But 339 00:18:43,278 --> 00:18:44,498 certainly more hazardous, 340 00:18:44,498 --> 00:18:44,838 right? 341 00:18:44,938 --> 00:18:45,598 Brian Marentette, PhD: Hazardous, 342 00:18:45,758 --> 00:18:46,648 more stressful. 343 00:18:47,278 --> 00:18:48,218 I have three young kids. 344 00:18:48,218 --> 00:18:48,788 I know that. 345 00:18:48,958 --> 00:18:49,948 I've done both jobs. 346 00:18:49,948 --> 00:18:53,658 Actually, I was a valet and I did caregiving for my kids. 347 00:18:53,658 --> 00:18:56,668 And I would say this is a much harder job being a daycare provider. 348 00:18:56,668 --> 00:19:01,128 But looking at it from a legal lens, Find any problems because within 349 00:19:01,128 --> 00:19:02,808 these jobs they're paying equally. 350 00:19:03,158 --> 00:19:07,458 But when you look at then across similar types of jobs, so like that band of jobs, 351 00:19:07,458 --> 00:19:11,008 that's, just support work throughout the organization you're placing more 352 00:19:11,008 --> 00:19:15,538 value on these male dominated roles, and that's where some of those subtle 353 00:19:15,538 --> 00:19:18,178 biases that maybe existed decades ago. 354 00:19:18,698 --> 00:19:20,218 Have just become the norm. 355 00:19:20,328 --> 00:19:23,438 And of course we pay our parking lot attendance, 25 an hour. 356 00:19:23,438 --> 00:19:25,288 And our daycare providers, 20 an hour. 357 00:19:25,328 --> 00:19:27,558 So we've always paid them and it's never been a problem. 358 00:19:28,018 --> 00:19:31,248 You don't know that until you run something, like a proactive pay 359 00:19:31,248 --> 00:19:35,028 equity analysis that goes beyond the legal standard to really root out 360 00:19:35,388 --> 00:19:40,018 those areas where as an organization, have you made sort of biased 361 00:19:40,018 --> 00:19:44,653 decisions in the past that are no longer even a thought until they're 362 00:19:44,653 --> 00:19:46,013 surfaced in one of these analyses. 363 00:19:48,223 --> 00:19:50,043 Jenny Arnez: So let me jump in here really quickly. 364 00:19:50,043 --> 00:19:54,073 And I, you've used the term several times. 365 00:19:54,073 --> 00:19:56,353 I've heard pay equity analysis. 366 00:19:57,683 --> 00:20:03,393 Let's say I'm an employer maybe I'm a VP of HR or HR manager. 367 00:20:03,403 --> 00:20:09,783 At what point do I consider having a consultant like yourself come in and 368 00:20:09,783 --> 00:20:12,293 conduct an analysis of our pay structure? 369 00:20:12,918 --> 00:20:14,598 Brian Marentette, PhD: What would be a trigger you mean to? 370 00:20:14,598 --> 00:20:17,808 Jenny Arnez: Perhaps maybe talk about proactive versus reactive. 371 00:20:18,048 --> 00:20:18,338 Brian Marentette, PhD: Yeah. 372 00:20:18,338 --> 00:20:18,608 Okay. 373 00:20:18,898 --> 00:20:19,288 Sure 374 00:20:22,468 --> 00:20:26,748 Mike Callen: Sorry the aspect of whether you end up in the legal side 375 00:20:26,748 --> 00:20:32,018 of the scrutiny versus just the public opinion side of you know the DEI side 376 00:20:32,018 --> 00:20:35,943 of things You know, I think that all factors into that as well, depending 377 00:20:35,943 --> 00:20:38,343 on which side of commerce you're on. 378 00:20:38,733 --> 00:20:39,063 Brian Marentette, PhD: Yeah. 379 00:20:39,513 --> 00:20:39,903 Yeah. 380 00:20:40,323 --> 00:20:40,633 Okay. 381 00:20:40,643 --> 00:20:48,764 Might trigger a legal review would be, you can still do proactive, legally oriented 382 00:20:48,874 --> 00:20:53,354 pay equity analyses, but that really is going to be coming from your council that 383 00:20:53,354 --> 00:20:58,524 might think Hey, since that there's some up there and, there's been complaints, but 384 00:21:00,594 --> 00:21:05,154 maybe California has some fair pay activity here. 385 00:21:05,914 --> 00:21:07,734 Any individual employee can kind of. 386 00:21:08,139 --> 00:21:10,339 file a claim and say, Hey, I think I'm being discriminated 387 00:21:10,339 --> 00:21:12,179 against based on gender. 388 00:21:12,509 --> 00:21:18,219 And so from a legal sense, yeah, that there's no necessarily 389 00:21:18,219 --> 00:21:19,139 like a trigger for that. 390 00:21:19,139 --> 00:21:22,449 If you get a claim, you have to respond to that with a reactive analysis 391 00:21:22,449 --> 00:21:26,539 to support your legal counsel and rooting out what the real story is. 392 00:21:26,539 --> 00:21:27,579 And is there an issue here? 393 00:21:27,689 --> 00:21:30,689 And do we need to settle and how much should we settle for? 394 00:21:30,689 --> 00:21:34,079 And what's the back pay and That's a whole nother world, right? 395 00:21:34,119 --> 00:21:36,539 That gets activated when there's a lawsuit that's filed. 396 00:21:36,879 --> 00:21:41,709 But as an HR vp these days, I think almost every organization needs to 397 00:21:41,709 --> 00:21:46,559 be doing at least annual pay equity analysis for a number of reasons. 398 00:21:46,839 --> 00:21:51,949 First if they haven't been doing them, they've probably got some pay disparities 399 00:21:52,039 --> 00:21:53,179 that they're just not aware of. 400 00:21:53,179 --> 00:21:55,805 And until you resolve those you're just. 401 00:21:56,534 --> 00:21:58,184 You're not treating your employees fairly. 402 00:21:58,694 --> 00:22:02,874 But the other thing with the transparency era, now the state by state seems 403 00:22:02,874 --> 00:22:06,744 like almost every month, another state is coming out with more, regulations 404 00:22:06,744 --> 00:22:07,744 coming out all over the place. 405 00:22:08,074 --> 00:22:12,924 There's going to be more questions about pay and what I'm making. 406 00:22:12,924 --> 00:22:16,314 And, even if it's not within the same job, like my comparison earlier with the 407 00:22:16,314 --> 00:22:21,114 daycare and parking car tenants somebody can easily point to another employee. 408 00:22:21,424 --> 00:22:22,754 That's doing similar work. 409 00:22:22,824 --> 00:22:28,024 And if you don't understand how your pay practice is operating, 410 00:22:28,034 --> 00:22:28,994 you have guidelines that. 411 00:22:29,139 --> 00:22:34,104 Your team uses in making offers and your managers when they're making promotions 412 00:22:34,114 --> 00:22:35,514 should be following these guidelines. 413 00:22:35,994 --> 00:22:39,674 But it's, it can be a lot of little small decisions that lean in one 414 00:22:39,674 --> 00:22:45,104 direction against one group that over time start to compound and can produce 415 00:22:45,104 --> 00:22:49,194 these disparities that again, like typical adverse impact theory, it's, 416 00:22:49,264 --> 00:22:51,404 facially neutral practice that results 417 00:22:52,124 --> 00:22:52,544 outcome. 418 00:22:53,014 --> 00:22:56,554 And even if it's not a legal claim that you're responding to, just as an 419 00:22:56,564 --> 00:23:02,234 organization, you need to understand these metrics, you need to know what your pay 420 00:23:02,234 --> 00:23:04,984 practice is producing, for your employees. 421 00:23:06,199 --> 00:23:09,519 Jenny Arnez: Now, is that true for any organization? 422 00:23:09,529 --> 00:23:13,669 Let's say a small business that has 10 employees versus one that has 200. 423 00:23:14,139 --> 00:23:14,609 Brian Marentette, PhD: Yeah. 424 00:23:14,669 --> 00:23:16,059 Good question there too. 425 00:23:16,279 --> 00:23:20,629 If you have 10 employees, you can still do pay equity analysis. 426 00:23:21,149 --> 00:23:25,574 Now it might look a little different than how we might do it for 200, 2000. 427 00:23:25,574 --> 00:23:31,354 200, 000 the technique or the analysis that's employed will change, but you 428 00:23:31,374 --> 00:23:33,854 can still be doing pay equity work. 429 00:23:33,934 --> 00:23:38,134 And this actually might be a good time to differentiate two 430 00:23:38,484 --> 00:23:40,224 critical types of pay equity. 431 00:23:40,284 --> 00:23:45,704 One is going to be your internal pay equity, internal equity, comparing 432 00:23:46,094 --> 00:23:50,504 employees within your organization, maybe with, within the job title, one 433 00:23:50,504 --> 00:23:53,374 against another or like I said, maybe you're looking at broader groups in your 434 00:23:53,814 --> 00:23:57,354 looking at substantially similar work still all within your organization that's 435 00:23:57,394 --> 00:24:03,964 internal pay equity, often confused with external equity, which is how you pay 436 00:24:04,084 --> 00:24:06,694 relative to what the market might demand. 437 00:24:07,734 --> 00:24:08,094 All right. 438 00:24:08,094 --> 00:24:11,954 And there are a lot of organizations that will say, yeah, we do pay equity work. 439 00:24:11,984 --> 00:24:13,614 We've been doing it for years. 440 00:24:13,994 --> 00:24:16,254 We use XYZ company. 441 00:24:16,334 --> 00:24:17,404 They give us the survey data. 442 00:24:17,404 --> 00:24:18,834 They tell us what all of our positions are. 443 00:24:19,339 --> 00:24:22,159 You know what the average should be and we stick to that pretty closely 444 00:24:22,669 --> 00:24:24,649 And that's when we know like red flag. 445 00:24:24,749 --> 00:24:25,939 They haven't looked internally. 446 00:24:26,159 --> 00:24:31,429 Yeah, and the internal piece is where the focus Usually lies with 447 00:24:31,699 --> 00:24:35,369 differences that fall on race gender age, etc you know the external 448 00:24:35,419 --> 00:24:37,069 . Mike Callen: The real problem areas, right? 449 00:24:37,069 --> 00:24:41,689 because the market tends to take play take the market tends to fix 450 00:24:41,699 --> 00:24:46,909 those external problems on its own, because if organization A isn't paying 451 00:24:46,909 --> 00:24:51,249 enough and B is paying twice as much, people will start get up and they'll 452 00:24:51,249 --> 00:24:52,809 start flocking over to a different. 453 00:24:53,759 --> 00:24:54,859 Organization, right? 454 00:24:55,019 --> 00:24:57,189 Brian Marentette, PhD: And it's incumbent on the organization to 455 00:24:57,409 --> 00:24:59,039 create their own philosophy there. 456 00:24:59,039 --> 00:25:01,089 How do they want to pay compared to the market? 457 00:25:01,099 --> 00:25:04,299 Some nonprofits say, you know what, we're not looking for the best and 458 00:25:04,299 --> 00:25:07,569 the brightest, we're not going to pay in the 75th percentile we want 459 00:25:07,569 --> 00:25:11,499 people that are just going to do this work in an altruistic way, and we're 460 00:25:11,499 --> 00:25:13,119 willing to pay at the 40th percentile. 461 00:25:13,149 --> 00:25:15,839 So they're going to pay below the market rate for that job, knowing 462 00:25:15,889 --> 00:25:17,009 That's where they're going to pay. 463 00:25:17,029 --> 00:25:21,549 So externally, they're not very equitable to what other employers might be paying 464 00:25:21,549 --> 00:25:24,209 for the same work, but they don't care. 465 00:25:24,779 --> 00:25:28,289 As long as they're paying everybody at about the 40th percentile relative 466 00:25:28,299 --> 00:25:30,869 for their standing, then it's fair. 467 00:25:31,899 --> 00:25:36,279 Jenny Arnez: So Brian, how often should a pay equity analysis be 468 00:25:36,279 --> 00:25:37,899 conducted by an organization? 469 00:25:38,509 --> 00:25:41,379 Brian Marentette, PhD: Yeah, there is a lot of factors that, 470 00:25:42,519 --> 00:25:49,259 influence that answer, but really an annual analysis will do, usually. 471 00:25:49,579 --> 00:25:54,019 Most companies are not changing pay more than, once a year. 472 00:25:54,069 --> 00:25:56,079 So they have an annual merit cycle. 473 00:25:56,499 --> 00:26:00,029 They look at their, annual increases and adjustments they make them, 474 00:26:00,099 --> 00:26:02,539 and then it sits for another year and they review it again. 475 00:26:03,114 --> 00:26:04,034 And their pay planning. 476 00:26:04,424 --> 00:26:07,314 It makes sense to do it at some point before or after 477 00:26:07,314 --> 00:26:09,394 that decision, ideally before. 478 00:26:09,454 --> 00:26:12,174 So you can look to see if we're, where are we at? 479 00:26:12,604 --> 00:26:14,694 Are there any gaps that need to be addressed? 480 00:26:15,304 --> 00:26:16,934 And you can do that through that cycle. 481 00:26:17,234 --> 00:26:20,294 And if not, you look at it at some other point in the year, knowing that you've 482 00:26:20,294 --> 00:26:23,624 got several months to remedy any issues. 483 00:26:24,144 --> 00:26:29,564 The one Other factor there that would come into play as the flux of your workforce. 484 00:26:29,574 --> 00:26:33,244 So if you have a lot of turnover, a lot of hiring, a lot of acquisitions 485 00:26:33,274 --> 00:26:38,074 going on divestitures, you need to look at that much more frequently. 486 00:26:38,074 --> 00:26:41,834 So we do have a couple of clients that look at it every six months. 487 00:26:42,204 --> 00:26:44,274 Other ones that will, tap us every. 488 00:26:45,164 --> 00:26:48,694 A couple of times a year, three times a year, if they have several acquisitions 489 00:26:48,704 --> 00:26:52,184 going on and they need to see how do we look now that we've reshuffled and 490 00:26:52,334 --> 00:26:54,504 integrated some new employees in here. 491 00:26:56,254 --> 00:26:59,844 Jenny Arnez: So we are getting close to our 30 minutes length for our 492 00:26:59,844 --> 00:27:03,184 first episode, but would you mind if I asked you a couple more questions? 493 00:27:03,294 --> 00:27:03,834 Brian Marentette, PhD: Of course. 494 00:27:04,244 --> 00:27:04,464 Of course. 495 00:27:04,464 --> 00:27:04,904 I don't mind. 496 00:27:06,994 --> 00:27:08,374 Jenny Arnez: Thank you for clarifying that. 497 00:27:10,094 --> 00:27:12,904 So I heard you say pay transparency. 498 00:27:13,114 --> 00:27:13,434 Brian Marentette, PhD: Yeah. 499 00:27:14,284 --> 00:27:18,404 Jenny Arnez: And I think that's more of a technical term than I think that it is. 500 00:27:18,654 --> 00:27:19,544 Can you speak to that? 501 00:27:19,704 --> 00:27:20,344 Brian Marentette, PhD: Sure. 502 00:27:20,434 --> 00:27:25,124 So yeah, and pay transparency often gets lumped in with pay equity. 503 00:27:25,624 --> 00:27:30,434 But transparency is really more just that employers having to share more 504 00:27:30,454 --> 00:27:35,704 information about their pay practice, how they make pay decisions, even 505 00:27:35,704 --> 00:27:37,554 what they are paying employees. 506 00:27:37,944 --> 00:27:40,404 So there's certain states you have to share 507 00:27:40,554 --> 00:27:42,044 if an employee asks. 508 00:27:42,114 --> 00:27:43,774 A lot of states, you have to share the range. 509 00:27:43,824 --> 00:27:46,614 First of all, the range for the position that's being applied, it has 510 00:27:46,614 --> 00:27:48,234 to even be shared in the job posting. 511 00:27:48,684 --> 00:27:52,244 And so like states like Colorado, that has to be within your job posting. 512 00:27:52,244 --> 00:27:54,664 What is the salary range for this position? 513 00:27:55,154 --> 00:27:59,594 Other states and organizations, you have to provide the average within your 514 00:27:59,594 --> 00:28:03,084 job so that any employee can say what's the average pay within My job here. 515 00:28:03,084 --> 00:28:04,964 And am I below or above that average? 516 00:28:05,444 --> 00:28:10,504 And so the transparency era is that's really what everybody's 517 00:28:10,504 --> 00:28:15,164 referring to is just being more public with how decisions are made. 518 00:28:15,164 --> 00:28:19,044 What are the factors that, that go into, salary increases when it comes 519 00:28:19,044 --> 00:28:21,094 time for raises and things like that. 520 00:28:21,144 --> 00:28:23,014 But then also what are the data points. 521 00:28:23,464 --> 00:28:25,154 relative for the position that somebody might hold. 522 00:28:27,114 --> 00:28:27,384 Jenny Arnez: Great. 523 00:28:27,484 --> 00:28:27,904 Thank you. 524 00:28:28,384 --> 00:28:30,094 Mike, it looks like you have a question. 525 00:28:30,704 --> 00:28:33,354 Mike Callen: Yeah, and I don't know whether it's a session 526 00:28:33,354 --> 00:28:36,234 two question or not, and feel free to kick the can forward. 527 00:28:36,634 --> 00:28:42,794 But I'm curious, I've read, I won't say a lot but often enough to be curious about 528 00:28:43,184 --> 00:28:50,934 people being hired in because the market is tight right now at much higher pay 529 00:28:50,934 --> 00:28:57,664 rates, pay levels than Existing employees are getting paid for the same job that 530 00:28:57,664 --> 00:28:59,774 they have way more experience for. 531 00:29:00,104 --> 00:29:05,194 In other words, if you looked at really rehiring your own personnel, it 532 00:29:05,194 --> 00:29:07,294 would cost you a lot more to do that. 533 00:29:07,334 --> 00:29:12,424 And so that certainly is identifying an issue, whether or not it's 534 00:29:12,424 --> 00:29:15,644 a legal issue, it might just be a moral ethical issue, but. 535 00:29:16,279 --> 00:29:18,609 Is that something you want to talk about now or do you want to cover 536 00:29:18,609 --> 00:29:20,169 that in the next next session? 537 00:29:20,229 --> 00:29:21,439 Brian Marentette, PhD: I can briefly hit that. 538 00:29:21,489 --> 00:29:23,169 And it is a phenomenon that we see. 539 00:29:23,259 --> 00:29:27,819 We refer to that as pay compression, where in general, some of your newer 540 00:29:27,819 --> 00:29:31,719 employees are going to be making more than your more tenured employees. 541 00:29:31,719 --> 00:29:34,729 And typically when we're running these pay equity analyses, we 542 00:29:34,729 --> 00:29:37,489 try to account for the fact that. 543 00:29:38,209 --> 00:29:41,199 Employees that have been there longer should be making more. 544 00:29:41,589 --> 00:29:44,839 And so when you put that into the equation, you actually 545 00:29:44,839 --> 00:29:50,229 see the inverse and it's not a legal factor or a legal concern. 546 00:29:50,839 --> 00:29:52,239 These can pay however they want. 547 00:29:52,399 --> 00:29:56,579 However, if all of your recent hires that are making more fall into one 548 00:29:56,579 --> 00:30:00,779 particular group, that could be creating an equity issue along. 549 00:30:00,989 --> 00:30:04,249 One of the other demographic protected group so companies 550 00:30:04,249 --> 00:30:05,659 need to be mindful of that. 551 00:30:05,709 --> 00:30:10,379 Again, it's not going to be a race, gender equity issue. 552 00:30:11,079 --> 00:30:11,959 It's going to be. 553 00:30:12,309 --> 00:30:17,679 Probably just more of a, are our older, more tenured employees valued. 554 00:30:17,829 --> 00:30:18,129 Okay. 555 00:30:18,139 --> 00:30:21,909 Maybe we need to bring them up closer, start closing that spread between 556 00:30:21,909 --> 00:30:25,289 that group that we've just hired recently with a much higher rate. 557 00:30:25,289 --> 00:30:29,489 Mike Callen: It's bad water cooler talk, bad optics, right? 558 00:30:29,579 --> 00:30:31,114 This is what it creates is, it is. 559 00:30:32,204 --> 00:30:35,034 Brian Marentette, PhD: And that's the downside of some of the transparency 560 00:30:35,134 --> 00:30:38,734 work that's, that people are pushing is that now that those ranges are out 561 00:30:38,734 --> 00:30:43,564 there and Incoming applicants can see they ask for the moon and they have more 562 00:30:43,564 --> 00:30:46,014 leverage to push their salaries higher. 563 00:30:46,394 --> 00:30:49,314 But at the same time, existing employees have that. 564 00:30:49,384 --> 00:30:51,224 it as well. 565 00:30:51,504 --> 00:30:55,854 Now it's harder once you're in to negotiate your salary upward 566 00:30:55,964 --> 00:30:58,454 than it is at that offer stage. 567 00:30:58,454 --> 00:30:58,774 So 568 00:30:59,534 --> 00:31:02,704 Mike Callen: I wonder if that's why people use hiring bonuses. 569 00:31:03,004 --> 00:31:04,794 So maybe it doesn't get, 570 00:31:05,174 --> 00:31:05,894 Brian Marentette, PhD: that's a question. 571 00:31:05,964 --> 00:31:06,384 Yeah. 572 00:31:06,474 --> 00:31:07,184 Mike Callen: Let's factor it in. 573 00:31:07,354 --> 00:31:08,734 The part two question. 574 00:31:09,164 --> 00:31:10,664 Brian Marentette, PhD: Part two question for sure. 575 00:31:10,924 --> 00:31:11,184 Mike Callen: Yeah. 576 00:31:11,464 --> 00:31:12,904 Brian Marentette, PhD: How to manage your equity. 577 00:31:13,094 --> 00:31:13,564 Yeah. 578 00:31:13,964 --> 00:31:17,604 Mike Callen: Well, there's a cliffhanger for session two. 579 00:31:17,904 --> 00:31:21,744 If you're dying to understand the answer to that question, then be sure 580 00:31:21,744 --> 00:31:25,094 and join us on the next Next episode of this conversation with Brian. 581 00:31:25,204 --> 00:31:26,194 So cool. 582 00:31:26,194 --> 00:31:32,514 Jenny Arnez: Yeah, Brian as we wrap up our first session together I just I want I'm 583 00:31:32,514 --> 00:31:38,614 curious as to your why- I know you to be a very purposeful Intentional individual 584 00:31:38,994 --> 00:31:44,424 and you've dedicated well gosh what years of education in this space, right? 585 00:31:44,424 --> 00:31:48,564 So yeah, what's your personal motivation for this career? 586 00:31:49,594 --> 00:31:51,964 Brian Marentette, PhD: Gosh it's a field that I'm naturally drawn 587 00:31:51,964 --> 00:31:56,304 to and that it the field of IO Psychology, it's involves people. 588 00:31:56,354 --> 00:32:00,354 It's not quite HR per se, but touches HR obviously. 589 00:32:00,784 --> 00:32:03,924 But I'm drawn to that type of work for whatever reason. 590 00:32:04,254 --> 00:32:07,834 And then on the technical side of all the schooling that I went through, 591 00:32:08,184 --> 00:32:13,174 We learn quite a bit about statistics and analyses and how to quantify 592 00:32:13,194 --> 00:32:15,534 and use data to inform decisions. 593 00:32:15,534 --> 00:32:17,434 And I'm also drawn to that. 594 00:32:17,444 --> 00:32:20,244 I think the best decisions are made with data. 595 00:32:20,964 --> 00:32:24,884 So then taking those two things together and then looking at, a career that 596 00:32:24,884 --> 00:32:30,144 can be fulfilling and do some good in the world, preventing and correcting 597 00:32:30,654 --> 00:32:35,444 discrimination along race and gender, any line really, it's rewarding. 598 00:32:36,479 --> 00:32:38,419 Makes you feel like maybe you're doing something. 599 00:32:40,289 --> 00:32:44,839 Mike Callen: So I have the last question, if that's okay with you, Jenny. 600 00:32:45,179 --> 00:32:45,389 Brian Marentette, PhD: Sure. 601 00:32:46,119 --> 00:32:49,239 Mike Callen: I want to know, and I know everybody else wants to know this as well. 602 00:32:49,559 --> 00:32:54,889 Does being a pay equity expert make you a better parent of three toddlers? 603 00:32:54,954 --> 00:32:55,384 Brian Marentette, PhD: No. 604 00:32:57,514 --> 00:32:58,874 Mike Callen: No winning that battle. 605 00:32:59,244 --> 00:33:02,654 Brian Marentette, PhD: I always say it's tough to transition from work Brian 606 00:33:02,654 --> 00:33:08,394 to a dad within a matter of seconds as a work from home employee and the 607 00:33:08,394 --> 00:33:12,554 rigidity and the regimen that you must follow with pay equity work and data 608 00:33:12,554 --> 00:33:17,914 analysis to them, the chaos of trying to wrangle kids are starkly different 609 00:33:17,994 --> 00:33:22,524 and I enjoy both of them, but it takes a little bit of a re reconfiguration 610 00:33:22,524 --> 00:33:24,024 Mike Callen: hat shifting. 611 00:33:24,324 --> 00:33:24,924 Brian Marentette, PhD: Absolutely. 612 00:33:25,434 --> 00:33:26,004 Mike Callen: Yeah. 613 00:33:26,094 --> 00:33:26,364 Yeah. 614 00:33:26,494 --> 00:33:30,934 I'll bet you around the dinner table when you hear that's not fair, the answer 615 00:33:30,934 --> 00:33:33,244 from you is really original and special. 616 00:33:33,244 --> 00:33:35,164 Brian Marentette, PhD: So have you considers how old you are? 617 00:33:35,164 --> 00:33:37,534 You are and he is nine. 618 00:33:38,434 --> 00:33:40,174 He's had a lot more on experience. 619 00:33:40,894 --> 00:33:43,234 Mike Callen: He's been on this job for much longer than you. 620 00:33:43,254 --> 00:33:43,614 So 621 00:33:45,754 --> 00:33:46,564 so much, Brian. 622 00:33:46,564 --> 00:33:49,074 It's always a pleasure to to chat with you. 623 00:33:49,074 --> 00:33:52,924 And this is a really great time spending time on the podcast with you. 624 00:33:52,924 --> 00:33:56,974 And we want to thank you very much for taking time out of your day, to 625 00:33:56,984 --> 00:33:59,664 come and share with Jenny and I, and. 626 00:33:59,934 --> 00:34:02,774 And as well, anybody else who happens to listen to this, it's 627 00:34:02,774 --> 00:34:06,784 been incredibly informative and I am looking forward to part two as well. 628 00:34:06,814 --> 00:34:08,084 So thank you very much for that. 629 00:34:08,464 --> 00:34:08,984 Brian Marentette, PhD: Thank you both. 630 00:34:08,984 --> 00:34:11,974 Jenny Arnez: Brian, if somebody wants to get in touch with you, 631 00:34:11,974 --> 00:34:15,114 they want to know more about conducting an analysis for their own 632 00:34:15,114 --> 00:34:17,464 organization, how can they do that? 633 00:34:18,334 --> 00:34:20,194 Brian Marentette, PhD: My email would be the best if you can throw 634 00:34:20,194 --> 00:34:24,184 it in a window or a chat right there. 635 00:34:24,184 --> 00:34:24,649 Jenny Arnez: How about that? 636 00:34:27,009 --> 00:34:27,479 Perfect. 637 00:34:27,479 --> 00:34:28,509 We're 638 00:34:28,509 --> 00:34:29,059 fancy. 639 00:34:29,419 --> 00:34:30,739 We're fancy around here. 640 00:34:31,689 --> 00:34:32,219 Brian Marentette, PhD: I love it. 641 00:34:32,239 --> 00:34:32,549 Yup. 642 00:34:32,639 --> 00:34:32,859 Yeah. 643 00:34:32,859 --> 00:34:33,499 Shoot me an email. 644 00:34:33,539 --> 00:34:35,689 I'm happy to chat with people whenever. 645 00:34:36,044 --> 00:34:38,774 And sometimes offer some free free advice as well. 646 00:34:40,404 --> 00:34:41,234 Jenny Arnez: Wonderful. 647 00:34:41,394 --> 00:34:47,084 Thank you to our listeners and to our viewers as well for joining us today and 648 00:34:47,084 --> 00:34:51,574 make sure you tune in next time for part two with our conversation with Brian. 649 00:34:53,004 --> 00:34:54,024 Thank you very much. 650 00:34:56,434 --> 00:35:00,234 Podcast Outro: Thanks for tuning in to Testing Testing 123 brought to you by 651 00:35:00,254 --> 00:35:02,104 TestGenius and Biddle Consulting Group. 652 00:35:02,904 --> 00:35:05,884 Visit our website at testgenius.com for more information.