1 00:00:11,960 --> 00:00:14,000 Speaker 1: If you're listening to this episode on the date it 2 00:00:14,080 --> 00:00:18,480 Speaker 1: comes out marcht one, it is Equal Payday. This date, 3 00:00:18,560 --> 00:00:22,520 Speaker 1: three months into symbolizes on average, how far into the 4 00:00:22,560 --> 00:00:25,560 Speaker 1: year women must work to earn what men earned in 5 00:00:25,680 --> 00:00:29,560 Speaker 1: the previous year, meaning that just today we've caught up 6 00:00:29,640 --> 00:00:31,800 Speaker 1: on average to what men made by the end of 7 00:00:31,800 --> 00:00:35,720 Speaker 1: twenty nineteen. Today's guest, Katecha Roy, is going to break 8 00:00:35,800 --> 00:00:39,080 Speaker 1: this all down for us. She's a gender economist and 9 00:00:39,120 --> 00:00:42,280 Speaker 1: the CEO and founder of Pipeline, a company that leverages 10 00:00:42,360 --> 00:00:46,959 Speaker 1: artificial intelligence to identify and drive economic gains through gender equity. 11 00:00:47,640 --> 00:00:52,239 Speaker 1: They demonstrate the connection between gender parity and economic opportunity 12 00:00:52,880 --> 00:00:57,120 Speaker 1: and preview it's huge. She shares what happened when she 13 00:00:57,160 --> 00:00:59,760 Speaker 1: found out she was being paid less than her male counterparts. 14 00:00:59,760 --> 00:01:03,040 Speaker 1: She explains the Lily Ledbetter act and implications of the 15 00:01:03,160 --> 00:01:06,319 Speaker 1: lack of gender equity in newsrooms, the steps we need 16 00:01:06,360 --> 00:01:16,760 Speaker 1: to take to finally close the gap, and more. Good Morning, 17 00:01:17,120 --> 00:01:19,959 Speaker 1: Good morning, I'm so excited to have you on the 18 00:01:19,959 --> 00:01:23,440 Speaker 1: show today, just to take the listeners at home back. 19 00:01:23,680 --> 00:01:28,040 Speaker 1: You and I met at the International Women in Media 20 00:01:28,120 --> 00:01:32,240 Speaker 1: Foundations Courage and Journalism Awards here in l A h 21 00:01:32,280 --> 00:01:35,200 Speaker 1: and you were telling me about this incredible article that 22 00:01:35,240 --> 00:01:38,280 Speaker 1: you had written for Fast Company, which I then immediately 23 00:01:38,280 --> 00:01:41,040 Speaker 1: went home and read titled There's a gender crisis in 24 00:01:41,120 --> 00:01:45,120 Speaker 1: media and It's threatening our democracy. And after reading it, 25 00:01:45,160 --> 00:01:47,600 Speaker 1: I wanted to put a little asterisk and say, and 26 00:01:47,720 --> 00:01:53,200 Speaker 1: our economy. The data points that you were able to 27 00:01:53,640 --> 00:01:57,800 Speaker 1: weave throughout that article to really explain what this stuff 28 00:01:57,840 --> 00:02:00,560 Speaker 1: looks like. The complexity of how we are or are 29 00:02:00,600 --> 00:02:07,360 Speaker 1: not represented and paid in media and in so many 30 00:02:07,400 --> 00:02:13,000 Speaker 1: other arenas was really staggering to me. The piece talks 31 00:02:13,080 --> 00:02:17,919 Speaker 1: about the lack of gender equity and newsrooms and obviously 32 00:02:17,960 --> 00:02:20,040 Speaker 1: then goes into much more of the data that I'm 33 00:02:20,040 --> 00:02:23,760 Speaker 1: talking about. What what was the impetus for writing this 34 00:02:24,480 --> 00:02:27,560 Speaker 1: in September of twenty nineteen, and and what were some 35 00:02:27,600 --> 00:02:30,360 Speaker 1: of the most shocking things that you found in researching 36 00:02:30,400 --> 00:02:33,800 Speaker 1: the piece? Yeah, you know the impetus. Whenever I write 37 00:02:33,840 --> 00:02:37,680 Speaker 1: an article, I am interested in providing insights to people, 38 00:02:37,760 --> 00:02:40,760 Speaker 1: So not just data, but data plus insights that makes 39 00:02:40,760 --> 00:02:44,320 Speaker 1: you maybe think about something differently or have an AHA moment. 40 00:02:45,080 --> 00:02:48,800 Speaker 1: And I often refer to myself as a data stitcher 41 00:02:48,840 --> 00:02:51,560 Speaker 1: and a storyteller, Right, So those two things together are 42 00:02:51,560 --> 00:02:54,119 Speaker 1: really important. And one of the things that I had 43 00:02:54,200 --> 00:02:58,440 Speaker 1: noticed in the news was that female reporters had had 44 00:02:58,480 --> 00:03:02,840 Speaker 1: a lot to do with the coverage of actually two topics. 45 00:03:03,200 --> 00:03:06,520 Speaker 1: One was Harvey Weinstein and Me Too, and the other 46 00:03:06,600 --> 00:03:10,880 Speaker 1: was Jeffrey Epstein. That they were really catalysts in the 47 00:03:11,000 --> 00:03:14,919 Speaker 1: coverage for um, those two stories and to some extent 48 00:03:15,080 --> 00:03:20,280 Speaker 1: kept them alive when maybe they weren't as paid attention to. 49 00:03:20,840 --> 00:03:23,800 Speaker 1: And what a difference that had. And so the female perspective, 50 00:03:23,880 --> 00:03:26,600 Speaker 1: the lived experience, what impact might that have? So that 51 00:03:26,680 --> 00:03:32,760 Speaker 1: was the original thought behind the article. And um, whenever 52 00:03:32,840 --> 00:03:35,760 Speaker 1: I because our voices and as the article talks about, 53 00:03:35,800 --> 00:03:38,320 Speaker 1: our voices are often not heard and the perspective is 54 00:03:38,360 --> 00:03:42,320 Speaker 1: not heard, and which means that people tune out, which 55 00:03:42,360 --> 00:03:45,000 Speaker 1: is which is what we found when when I actually 56 00:03:45,040 --> 00:03:48,160 Speaker 1: did the research, and what are they tuning out to? 57 00:03:49,280 --> 00:03:53,160 Speaker 1: They're not listening because they don't feel represented, their voices don't, um, 58 00:03:53,320 --> 00:03:57,480 Speaker 1: are not in the newsroom as much. And uh so, 59 00:03:57,480 --> 00:04:01,120 Speaker 1: so when I started the hypothesis, whenever I write an article, 60 00:04:01,360 --> 00:04:04,200 Speaker 1: I have a hypothesis which is just basically like an 61 00:04:04,240 --> 00:04:07,320 Speaker 1: idea that I'm testing out to say, Okay, what impact 62 00:04:07,360 --> 00:04:09,960 Speaker 1: do female voices have on the media. So that's where 63 00:04:09,960 --> 00:04:12,000 Speaker 1: I started with that article, and then I do a 64 00:04:12,000 --> 00:04:14,080 Speaker 1: bunch of research to figure out, well, what actually is 65 00:04:14,080 --> 00:04:17,359 Speaker 1: that impact? And what I found was that, in actual fact, 66 00:04:17,720 --> 00:04:22,200 Speaker 1: it has an impact on our democracy because the First 67 00:04:22,200 --> 00:04:26,120 Speaker 1: Amendment is key to our democracy, right, A free press 68 00:04:26,160 --> 00:04:29,760 Speaker 1: matters and facts matter, and so in order for us 69 00:04:29,800 --> 00:04:32,359 Speaker 1: to believe what the media is saying, they need to 70 00:04:32,400 --> 00:04:35,560 Speaker 1: actually represent us. And you can even just see that 71 00:04:35,680 --> 00:04:37,840 Speaker 1: in some of the you know, the Super Bowl, right 72 00:04:37,920 --> 00:04:40,200 Speaker 1: and some of the coverage of the halftime show and 73 00:04:40,680 --> 00:04:43,000 Speaker 1: what women were wearing and what they you know, what 74 00:04:43,080 --> 00:04:45,839 Speaker 1: Shakira and j Lo war and what the you know 75 00:04:45,880 --> 00:04:48,239 Speaker 1: what people said about that and what they believed about 76 00:04:48,240 --> 00:04:51,640 Speaker 1: that but didn't say the year before about Adam Levine. 77 00:04:51,640 --> 00:04:54,320 Speaker 1: I mean, I think those are those things matter quite 78 00:04:54,320 --> 00:04:58,560 Speaker 1: a bit. Yeah, I I was so struck by that, 79 00:04:58,680 --> 00:05:01,960 Speaker 1: and I thought, how int testing that no one ever 80 00:05:02,040 --> 00:05:06,159 Speaker 1: talks about what the team cheerleaders on the sidelines are wearing, 81 00:05:06,480 --> 00:05:10,440 Speaker 1: which is often far less than Jlo or Shakira had on. 82 00:05:11,480 --> 00:05:17,240 Speaker 1: But those women are part of these male cultures and 83 00:05:17,279 --> 00:05:20,840 Speaker 1: they fit into them, you know, cheerleading, the cheerleaders are 84 00:05:20,920 --> 00:05:23,880 Speaker 1: hired by the teams they are, they are sort of 85 00:05:24,040 --> 00:05:29,560 Speaker 1: looked at as this decorative sideline entertainment for men. And 86 00:05:29,680 --> 00:05:35,800 Speaker 1: Jlo and Shakira came out in this incredibly empowered and 87 00:05:36,440 --> 00:05:41,640 Speaker 1: in your face with political messaging and culture and and 88 00:05:41,720 --> 00:05:44,600 Speaker 1: they were really they were up there for themselves. And 89 00:05:44,640 --> 00:05:48,359 Speaker 1: it upset a lot of people who are not upset 90 00:05:48,760 --> 00:05:52,159 Speaker 1: again by the women on the sidelines wearing you know, 91 00:05:52,160 --> 00:05:56,719 Speaker 1: booty shorts and push up bras. And I was so 92 00:05:57,240 --> 00:06:02,840 Speaker 1: struck by that. And to your point, it was a 93 00:06:02,880 --> 00:06:08,599 Speaker 1: lot of men arguing about this stuff. I I also 94 00:06:08,680 --> 00:06:12,600 Speaker 1: can't help but think when when you talk about representation 95 00:06:13,440 --> 00:06:16,320 Speaker 1: and how people often will tune out when they don't 96 00:06:16,360 --> 00:06:20,000 Speaker 1: feel represented. In terms of the examples you gave, the 97 00:06:20,240 --> 00:06:25,080 Speaker 1: Weinstein story, the Epstein story, we've seen institutions, and now 98 00:06:25,160 --> 00:06:28,120 Speaker 1: we know from the reporting that came out, how many 99 00:06:28,160 --> 00:06:32,320 Speaker 1: institutions swept those stories under the rug, protected Harvey Weinstein, 100 00:06:32,360 --> 00:06:35,920 Speaker 1: protected Jeffrey Epstein. How many men in power ignored what 101 00:06:36,000 --> 00:06:39,520 Speaker 1: those men were doing. And you see how the Boys 102 00:06:39,560 --> 00:06:42,919 Speaker 1: Club really threatens the safety of women and girls everywhere. 103 00:06:43,839 --> 00:06:49,120 Speaker 1: And it is interesting that it takes us having power 104 00:06:49,240 --> 00:06:54,039 Speaker 1: in newsrooms. Two really hit hard on those stories to 105 00:06:54,120 --> 00:06:56,520 Speaker 1: make sure light is being shined on them, to make 106 00:06:56,560 --> 00:07:00,360 Speaker 1: sure that there is some sort of retribe U shan 107 00:07:01,839 --> 00:07:06,360 Speaker 1: accountability and women being believed the first time. Yeah, that's 108 00:07:06,360 --> 00:07:08,080 Speaker 1: the other part of it, right, And I would say 109 00:07:08,080 --> 00:07:11,160 Speaker 1: it's men, but also women have been complicit in the 110 00:07:11,240 --> 00:07:13,200 Speaker 1: cover ups as well. I mean we saw that definitely 111 00:07:13,200 --> 00:07:16,640 Speaker 1: with Harvey Weinstein. But that you know that that women 112 00:07:16,960 --> 00:07:20,440 Speaker 1: the first time they speak up, the one person who 113 00:07:20,480 --> 00:07:24,040 Speaker 1: speaks up is also believed. That's that that also tends 114 00:07:24,080 --> 00:07:26,360 Speaker 1: to be something that we see is it has to 115 00:07:26,400 --> 00:07:28,880 Speaker 1: be a collective that speaks up in order for us 116 00:07:28,880 --> 00:07:31,880 Speaker 1: to believe women. We don't believe women the first time. 117 00:07:32,440 --> 00:07:36,240 Speaker 1: And if we did, then we might, um, you know, 118 00:07:36,280 --> 00:07:39,600 Speaker 1: we might actually have a different outcome, right, just if 119 00:07:39,640 --> 00:07:41,920 Speaker 1: you think about all the women who didn't have to 120 00:07:41,960 --> 00:07:47,560 Speaker 1: experience that, well, yeah, think about the Cosby case. How 121 00:07:47,800 --> 00:07:51,960 Speaker 1: many women had to come forward and people still didn't 122 00:07:52,000 --> 00:07:56,440 Speaker 1: want to believe it. It's it's interesting. I was really 123 00:07:56,480 --> 00:07:59,440 Speaker 1: surprised by this piece of information. To to take it 124 00:07:59,480 --> 00:08:02,880 Speaker 1: back to the article. You talk about how even the 125 00:08:02,960 --> 00:08:07,000 Speaker 1: women out number men in journalism programs and colleges, they 126 00:08:07,080 --> 00:08:11,640 Speaker 1: become the minority voice almost immediately after entering the workforce. 127 00:08:12,560 --> 00:08:16,240 Speaker 1: You cited some statistics. On average, women represent forty one 128 00:08:16,680 --> 00:08:20,480 Speaker 1: seven percent of newsroom employees. They produce only thirty seven 129 00:08:20,520 --> 00:08:26,080 Speaker 1: percent of reports. Men snag of all newswire bylines published 130 00:08:26,080 --> 00:08:29,440 Speaker 1: by the Associated Press and Reuters an account for sixty 131 00:08:30,120 --> 00:08:34,480 Speaker 1: of primetime news anchors or correspondence, and write sixty of 132 00:08:34,520 --> 00:08:39,320 Speaker 1: all online news. But more women are going to journalism school. 133 00:08:39,400 --> 00:08:42,480 Speaker 1: So how is this happening? And it's actually something that 134 00:08:42,520 --> 00:08:45,280 Speaker 1: we see happen more broadly in the workforce, which is 135 00:08:45,320 --> 00:08:48,320 Speaker 1: that women start out in equal numbers or even in 136 00:08:48,400 --> 00:08:51,600 Speaker 1: higher numbers, and healthcare as an industry as an example 137 00:08:51,640 --> 00:08:54,920 Speaker 1: of that, and then there's a falloff in their participation. 138 00:08:55,440 --> 00:08:58,880 Speaker 1: So what happens is that they get to the workforce 139 00:08:59,240 --> 00:09:02,559 Speaker 1: with the belief that it's equitable and they'll be able 140 00:09:02,720 --> 00:09:07,320 Speaker 1: to compete on their merits and their qualifications, and they 141 00:09:07,400 --> 00:09:10,480 Speaker 1: soon find out that that's not actually true, and so 142 00:09:10,640 --> 00:09:15,000 Speaker 1: oftentimes they'll leave and go. We see a migration happen often, 143 00:09:15,080 --> 00:09:19,680 Speaker 1: which is that women will tend to migrate to professions 144 00:09:19,800 --> 00:09:23,520 Speaker 1: or organizations that are more female dominated with the belief 145 00:09:23,640 --> 00:09:26,360 Speaker 1: that they'll have better career opportunities, and that's some of 146 00:09:26,400 --> 00:09:28,880 Speaker 1: what we see. And and so you see that some 147 00:09:28,960 --> 00:09:31,320 Speaker 1: of the statistics that I talked about in the article. 148 00:09:31,800 --> 00:09:34,600 Speaker 1: You know, men having the most bylines. They're also the 149 00:09:34,640 --> 00:09:37,840 Speaker 1: majority of all sources. There are the majority of all 150 00:09:38,440 --> 00:09:42,240 Speaker 1: chief editors. So they actually you know, essentially in a newsroom, 151 00:09:42,559 --> 00:09:45,880 Speaker 1: I mean, I'm oversimplifying it, but just quite simply, you're 152 00:09:45,920 --> 00:09:48,640 Speaker 1: doing a pitch, right, You've got a pitch for a story, 153 00:09:48,800 --> 00:09:51,000 Speaker 1: and at the end of the day, your editor is 154 00:09:51,040 --> 00:09:55,000 Speaker 1: deciding what stories are going to get covered. Well, if 155 00:09:55,040 --> 00:10:00,520 Speaker 1: women aren't equitably represented as editors, those stories don't covered. 156 00:10:01,559 --> 00:10:05,360 Speaker 1: It's it seems to me, it reminds me of the 157 00:10:05,480 --> 00:10:07,400 Speaker 1: data that comes out of schools where you find that 158 00:10:07,480 --> 00:10:10,560 Speaker 1: teachers call on boys more often, and it's not that 159 00:10:10,600 --> 00:10:14,000 Speaker 1: girls don't have the answer, and and it feels like 160 00:10:14,080 --> 00:10:17,680 Speaker 1: a a sort of morphing of that which begins at 161 00:10:17,720 --> 00:10:20,360 Speaker 1: a very young age for us. And then in the newsroom, 162 00:10:20,360 --> 00:10:22,839 Speaker 1: to your point, if all the editors are men, whether 163 00:10:22,880 --> 00:10:26,520 Speaker 1: it's conscious or unconscious bias, they're more likely to give 164 00:10:27,280 --> 00:10:29,480 Speaker 1: the go ahead to the pitches they're hearing from men 165 00:10:29,520 --> 00:10:32,440 Speaker 1: than the pitches they're hearing from women, Right, and also 166 00:10:32,520 --> 00:10:36,600 Speaker 1: because they attribute confidence and competence more often to men 167 00:10:36,679 --> 00:10:38,800 Speaker 1: than to women. I mean, if you think about the 168 00:10:38,840 --> 00:10:41,600 Speaker 1: system and the structure and how we think about how 169 00:10:41,720 --> 00:10:45,200 Speaker 1: people behave and what we value, you know, we assign 170 00:10:45,320 --> 00:10:48,280 Speaker 1: values to whether or not you have a deeper voice, 171 00:10:48,440 --> 00:10:51,640 Speaker 1: or whether or not you're when you talk, you're you 172 00:10:52,280 --> 00:10:55,240 Speaker 1: go up at the end of your when you're and 173 00:10:55,400 --> 00:10:59,000 Speaker 1: you know, so that sounds like a question. We we 174 00:10:59,000 --> 00:11:01,520 Speaker 1: we assign value you to that of whether or not 175 00:11:01,640 --> 00:11:04,079 Speaker 1: you're confident and therefore whether or not you know what 176 00:11:04,120 --> 00:11:06,960 Speaker 1: you're talking about, which is not it's not actually true 177 00:11:07,000 --> 00:11:10,400 Speaker 1: when you take gender away. And an example of that 178 00:11:10,400 --> 00:11:13,200 Speaker 1: would be, for instance, like there have been like blind 179 00:11:13,240 --> 00:11:16,880 Speaker 1: tests of coders programmers met in tech and what they've 180 00:11:16,920 --> 00:11:19,640 Speaker 1: found is that women are actually better programmers than men 181 00:11:19,880 --> 00:11:22,640 Speaker 1: as long as you don't know that they're women. So 182 00:11:22,679 --> 00:11:26,079 Speaker 1: that that's the sort of value that we assigned to gender. 183 00:11:26,120 --> 00:11:27,600 Speaker 1: And then of course, to bring it back from an 184 00:11:27,600 --> 00:11:31,360 Speaker 1: economic perspective, women are half of our talent pool, and 185 00:11:31,360 --> 00:11:34,440 Speaker 1: they're the majority of all of our college graduates, and 186 00:11:34,520 --> 00:11:37,319 Speaker 1: now they make up the majority of all college graduates 187 00:11:37,320 --> 00:11:40,400 Speaker 1: in the labor force, and that's a huge economic cost 188 00:11:40,600 --> 00:11:43,760 Speaker 1: to companies because you're essentially leaving out half of your 189 00:11:43,760 --> 00:11:47,320 Speaker 1: talent pool. Wow. You also made a point in the 190 00:11:47,360 --> 00:11:51,920 Speaker 1: piece that professional opportunities for female news anchors diminished with age, 191 00:11:51,960 --> 00:11:56,720 Speaker 1: whereas for male news anchors their opportunities increase with age. 192 00:11:57,440 --> 00:12:00,280 Speaker 1: Why do you think that is and how do we 193 00:12:00,320 --> 00:12:03,480 Speaker 1: begin to combat that. So the way that we begin 194 00:12:03,520 --> 00:12:06,480 Speaker 1: to combat that is to have more women over the 195 00:12:06,480 --> 00:12:09,480 Speaker 1: age of forty five who look over the age of 196 00:12:09,480 --> 00:12:14,760 Speaker 1: forty five on television, right, So that's that's one. The 197 00:12:14,800 --> 00:12:18,360 Speaker 1: other piece is, you know, and that we see this 198 00:12:18,360 --> 00:12:22,360 Speaker 1: this age happen where women who are over the age 199 00:12:22,360 --> 00:12:26,480 Speaker 1: of forty five tend to have diminished career opportunities. That's 200 00:12:26,520 --> 00:12:30,840 Speaker 1: then also true in the newsroom, where you know, men 201 00:12:31,000 --> 00:12:33,360 Speaker 1: who are over the age of forty five, they have 202 00:12:33,400 --> 00:12:35,520 Speaker 1: sort of have graying hair, they look a little bit 203 00:12:35,520 --> 00:12:39,040 Speaker 1: more distinguished, they look a little bit more authoritative. And 204 00:12:39,080 --> 00:12:41,480 Speaker 1: that's the again, the value that we assigned to that. 205 00:12:41,600 --> 00:12:45,360 Speaker 1: So women's voices and particularly older women's voices, which, by 206 00:12:45,360 --> 00:12:47,600 Speaker 1: the way, forty five is not that old. I'm forty six, 207 00:12:48,040 --> 00:12:52,240 Speaker 1: so so you know, but it's but that we we 208 00:12:52,320 --> 00:12:54,880 Speaker 1: assigned value to that. And then and then for women, 209 00:12:55,000 --> 00:12:57,240 Speaker 1: even if they are over a certain age, we expect 210 00:12:57,240 --> 00:12:59,040 Speaker 1: them to look a certain way. So if you think 211 00:12:59,080 --> 00:13:03,040 Speaker 1: about how women look on the media, you know, typically 212 00:13:03,120 --> 00:13:06,559 Speaker 1: like sheath dresses there don't have gray hair. It also 213 00:13:06,640 --> 00:13:11,959 Speaker 1: matters that you look older as well. Yeah, I can't 214 00:13:11,960 --> 00:13:17,080 Speaker 1: help but think about the internet hubbub after Keanu Reeves 215 00:13:17,080 --> 00:13:20,600 Speaker 1: and his girlfriend walked a red carpet together and he's 216 00:13:20,640 --> 00:13:24,520 Speaker 1: fifty five and she's forty six, and and happens to 217 00:13:24,559 --> 00:13:29,559 Speaker 1: have gray hair and it's beautiful, and people went ballistic 218 00:13:30,160 --> 00:13:34,360 Speaker 1: about the fact that he was dating someone quote age appropriate, 219 00:13:34,640 --> 00:13:38,320 Speaker 1: and and that his girlfriend has gray hair, and there 220 00:13:38,400 --> 00:13:41,800 Speaker 1: was all this really intense commentary about it, and I 221 00:13:42,360 --> 00:13:44,280 Speaker 1: just I felt like raising my hand and going, hey, guys, 222 00:13:44,360 --> 00:13:48,040 Speaker 1: she's still nine years younger than him. You know, she's 223 00:13:48,040 --> 00:13:51,679 Speaker 1: still nearly a decade younger than him. What are we 224 00:13:51,720 --> 00:13:55,800 Speaker 1: talking about? Why does this matter? But you know, you 225 00:13:55,920 --> 00:14:00,280 Speaker 1: realize that in a way out in the land scape 226 00:14:00,280 --> 00:14:03,800 Speaker 1: of media representation, it's kind of radical to see a 227 00:14:03,880 --> 00:14:07,280 Speaker 1: woman in her forties with a mane of silver hair, 228 00:14:08,679 --> 00:14:11,000 Speaker 1: just saying yeah, this is what my hair looks like. 229 00:14:11,040 --> 00:14:14,320 Speaker 1: Why do we care about it? It is that is 230 00:14:14,400 --> 00:14:16,480 Speaker 1: that is so true, because gray hair is definitely a 231 00:14:16,520 --> 00:14:21,120 Speaker 1: marker for women, and so the bravery to actually have 232 00:14:21,440 --> 00:14:25,960 Speaker 1: gray hair is I think pretty brave. You know, just 233 00:14:26,000 --> 00:14:28,640 Speaker 1: the judgments we make about how old you are and 234 00:14:28,720 --> 00:14:31,240 Speaker 1: what that what how old you are means to how 235 00:14:31,280 --> 00:14:36,200 Speaker 1: competent you are or how worthy you are? Yeah, worthy, 236 00:14:36,240 --> 00:14:39,960 Speaker 1: you are right. It's so interesting and I'm really I'm 237 00:14:40,000 --> 00:14:43,760 Speaker 1: excited to get into more the nitty critian statistics, but 238 00:14:44,000 --> 00:14:46,880 Speaker 1: I I do like to always start with people at 239 00:14:46,920 --> 00:14:48,760 Speaker 1: the beginning, and I feel like I'm letting the train 240 00:14:48,840 --> 00:14:52,360 Speaker 1: run away before I get those questions, those questions asked. Um, 241 00:14:52,440 --> 00:14:55,360 Speaker 1: So I do want to go back a bit you 242 00:14:55,360 --> 00:14:58,400 Speaker 1: You've talked about how you were so greatly influenced by 243 00:14:58,600 --> 00:15:03,280 Speaker 1: your parents journey to freedom, and can you tell our 244 00:15:03,320 --> 00:15:06,160 Speaker 1: listeners a little bit about that, how you grew up 245 00:15:06,200 --> 00:15:09,840 Speaker 1: where they came from. Sure, so, I'm the daughter of 246 00:15:09,880 --> 00:15:14,520 Speaker 1: an immigrant and a refugee. My mother was born on Guernsey, 247 00:15:14,640 --> 00:15:18,080 Speaker 1: which is one of the Channel aisles of England, so 248 00:15:18,120 --> 00:15:21,880 Speaker 1: it's closer to France than to mainland England. And she 249 00:15:21,960 --> 00:15:24,600 Speaker 1: was born in January of nineteen thirty nine, and so 250 00:15:24,640 --> 00:15:28,560 Speaker 1: when France fell to the German forces in nineteen forty, 251 00:15:28,680 --> 00:15:32,800 Speaker 1: Prime Minister Churchill doubted his ability to defend the Channel aisles, 252 00:15:32,840 --> 00:15:36,280 Speaker 1: so he evacuated them. And my mom was eighteen months 253 00:15:36,320 --> 00:15:38,840 Speaker 1: at the time, and the first ship off of Guernsey 254 00:15:38,880 --> 00:15:41,040 Speaker 1: was five thousand children. And my mom was on that 255 00:15:41,080 --> 00:15:45,000 Speaker 1: ship and she was separated from her mother, and she 256 00:15:45,080 --> 00:15:47,600 Speaker 1: had four siblings. They were all on the ship and 257 00:15:47,640 --> 00:15:50,880 Speaker 1: then put into an orphanage and lived in an orphanage, 258 00:15:50,920 --> 00:15:56,160 Speaker 1: and then was adopted a year later. That's yeah. So 259 00:15:56,200 --> 00:16:00,240 Speaker 1: if if anyone is seeing the Guernsey Potato peel in 260 00:16:00,320 --> 00:16:03,040 Speaker 1: literary society and they talked about the boy who was 261 00:16:03,080 --> 00:16:05,680 Speaker 1: taken on the ship, that is what my mom did. 262 00:16:05,720 --> 00:16:10,120 Speaker 1: She just never came back that she was sorry, No, 263 00:16:10,280 --> 00:16:12,760 Speaker 1: I'm just I mean, you saw my face. My jaw 264 00:16:12,840 --> 00:16:17,560 Speaker 1: just dropped. I I can't believe that they took all 265 00:16:17,600 --> 00:16:21,480 Speaker 1: those kids away from their parents. Why why weren't the 266 00:16:21,640 --> 00:16:25,240 Speaker 1: parents allowed to go with them? That I don't know. 267 00:16:25,480 --> 00:16:28,640 Speaker 1: I just know that the decision was that children should 268 00:16:28,680 --> 00:16:31,600 Speaker 1: go first, that the children needed to get off the 269 00:16:31,600 --> 00:16:34,160 Speaker 1: island first. Maybe they thought that they were the most vulnerable. 270 00:16:34,320 --> 00:16:36,880 Speaker 1: I don't think that they think about it, you know, 271 00:16:36,960 --> 00:16:39,360 Speaker 1: in terms of the way they do today in terms 272 00:16:39,360 --> 00:16:43,240 Speaker 1: of you know, of those that piece. But yeah, and 273 00:16:43,280 --> 00:16:49,080 Speaker 1: that it would be safest for to evacuate the children. Wow. 274 00:16:49,320 --> 00:16:53,680 Speaker 1: So when she was adopted, what path did her life take? 275 00:16:54,920 --> 00:16:58,640 Speaker 1: So she was adopted by a lovely couple. They were actually, 276 00:16:58,720 --> 00:17:01,800 Speaker 1: you know, very good to her for the most part. 277 00:17:01,880 --> 00:17:03,960 Speaker 1: They and I'll tell you what I mean by for 278 00:17:04,000 --> 00:17:08,520 Speaker 1: the most part, But my grandmother, my mom had a 279 00:17:08,600 --> 00:17:11,360 Speaker 1: lazy eye, and then she had developed a stutter, obviously 280 00:17:11,400 --> 00:17:13,679 Speaker 1: from the trauma of being taken away from her family 281 00:17:13,720 --> 00:17:16,960 Speaker 1: and put into an orphanage. And so they, you know, 282 00:17:17,160 --> 00:17:21,280 Speaker 1: they took care of her and helped her develop, etcetera. 283 00:17:21,320 --> 00:17:24,040 Speaker 1: But the interesting thing was my mom always wanted to 284 00:17:24,040 --> 00:17:27,480 Speaker 1: go to Oxford to study the classics. That was her dream. 285 00:17:28,080 --> 00:17:32,480 Speaker 1: And when she was of that age, my grandfather said 286 00:17:32,520 --> 00:17:35,520 Speaker 1: to her, this was in the fifties, Well, girls don't 287 00:17:35,520 --> 00:17:38,800 Speaker 1: get an education like, they don't go to they don't 288 00:17:38,840 --> 00:17:42,040 Speaker 1: get a college education. They go to secretary school or 289 00:17:42,119 --> 00:17:45,480 Speaker 1: nursing school. So my my mom went to secretary school, 290 00:17:45,480 --> 00:17:48,360 Speaker 1: which she was very ill suited for. And I think 291 00:17:48,400 --> 00:17:51,520 Speaker 1: that really was a key part of her her catalyzed 292 00:17:51,520 --> 00:17:54,600 Speaker 1: action to actually move to the States. So she wanted 293 00:17:54,640 --> 00:17:57,000 Speaker 1: to do it when she was eighteen, and then when 294 00:17:57,000 --> 00:17:59,920 Speaker 1: she was twenty one, she was emancipated, so she left 295 00:18:00,000 --> 00:18:03,080 Speaker 1: and came to the US in nineteen sixty. And is 296 00:18:03,119 --> 00:18:05,760 Speaker 1: that where she met your dad? She yeah, she met 297 00:18:05,760 --> 00:18:10,200 Speaker 1: my dad in Chicago. And so I'm also the daughter 298 00:18:10,680 --> 00:18:13,880 Speaker 1: and actually sister of refugees. So my father and three 299 00:18:13,880 --> 00:18:16,840 Speaker 1: eldest sisters escaped from Hungary after the fall of the 300 00:18:17,720 --> 00:18:22,080 Speaker 1: revolution and they lived in a refuge So it's actually 301 00:18:22,160 --> 00:18:24,920 Speaker 1: very interesting. My mom, I just moved her to live 302 00:18:24,960 --> 00:18:26,439 Speaker 1: with us. So we were going through a lot of 303 00:18:26,440 --> 00:18:29,800 Speaker 1: old papers, and my father is deceased, he died eleven 304 00:18:29,840 --> 00:18:32,960 Speaker 1: years ago, and some of them were him writing about 305 00:18:33,000 --> 00:18:35,560 Speaker 1: that escape and part of what they and I didn't 306 00:18:35,600 --> 00:18:37,800 Speaker 1: know this part. I just learned it a few weeks ago. 307 00:18:38,160 --> 00:18:39,679 Speaker 1: Part of what they actually had to do when my 308 00:18:39,760 --> 00:18:42,480 Speaker 1: three eldest sisters were with them is actually cross a 309 00:18:42,560 --> 00:18:47,000 Speaker 1: minefield to get into Austria. So my sisters were seven, 310 00:18:47,080 --> 00:18:50,480 Speaker 1: eight and three at the time, and it was the Austria. 311 00:18:50,600 --> 00:18:54,480 Speaker 1: It was the border of Austria and Hungary where the 312 00:18:54,560 --> 00:18:59,320 Speaker 1: Soviet tanks patrolled that border. And as they were as 313 00:18:59,320 --> 00:19:02,840 Speaker 1: they were about to cross the Soviet tank came by, 314 00:19:02,960 --> 00:19:05,040 Speaker 1: and my sister talks about this, my oldest sister, how 315 00:19:05,119 --> 00:19:08,879 Speaker 1: they had to lay in them on the ground to 316 00:19:08,960 --> 00:19:11,639 Speaker 1: actually hide. They were with some Hungarian freedom fighters to 317 00:19:11,640 --> 00:19:15,800 Speaker 1: actually hide from the Soviet soldiers. And then yeah, right, 318 00:19:16,320 --> 00:19:19,480 Speaker 1: and and my my youngest of my three eldest sisters, 319 00:19:19,520 --> 00:19:21,760 Speaker 1: they had to drug her because she was three, and 320 00:19:21,800 --> 00:19:24,919 Speaker 1: they she couldn't they couldn't risk her crying. And then 321 00:19:24,960 --> 00:19:28,320 Speaker 1: they made it into Austria. They lived in a refugee 322 00:19:28,359 --> 00:19:32,199 Speaker 1: camp for just under two months, so this is October 323 00:19:32,680 --> 00:19:36,760 Speaker 1: of nineteen fifty six, so you cold, snowy, you know, 324 00:19:36,960 --> 00:19:41,840 Speaker 1: they not bringing anything really with them. And then President Eisenhower, 325 00:19:42,720 --> 00:19:47,119 Speaker 1: who was supportive of the Hungarian fight for freedom and democracy, 326 00:19:47,320 --> 00:19:50,240 Speaker 1: sent Air Force one to bring twenty one Hungarian refugees 327 00:19:50,280 --> 00:19:53,520 Speaker 1: to the US on Christmas Day. And they were on 328 00:19:53,560 --> 00:19:59,360 Speaker 1: that plane. WHOA, that's wild. Sorry, I don't know why, 329 00:19:59,600 --> 00:20:05,520 Speaker 1: just literally person hears wow. And so the image that 330 00:20:05,680 --> 00:20:09,080 Speaker 1: I always have, which I normally do cry, so the 331 00:20:09,119 --> 00:20:13,320 Speaker 1: image that I always have is this because my my 332 00:20:13,400 --> 00:20:15,360 Speaker 1: dad was alive, we talked talked about it a lot, 333 00:20:15,400 --> 00:20:17,639 Speaker 1: and we were very aware of what our history was 334 00:20:17,680 --> 00:20:21,159 Speaker 1: and our responsibility was because of that. But he talked 335 00:20:21,160 --> 00:20:24,280 Speaker 1: about how his and he was thirty four at the 336 00:20:24,280 --> 00:20:27,600 Speaker 1: time when he made that decision, that his decision was 337 00:20:28,119 --> 00:20:30,600 Speaker 1: that it was better to risk his daughter's lives in 338 00:20:30,640 --> 00:20:33,840 Speaker 1: pursuit of freedom than to commit their futures to living 339 00:20:33,920 --> 00:20:37,840 Speaker 1: under communist rule. So when I think about that, I 340 00:20:37,880 --> 00:20:42,480 Speaker 1: think about the decision that he made, going from essentially 341 00:20:42,600 --> 00:20:44,800 Speaker 1: risking your life and running for your life crossing a 342 00:20:44,840 --> 00:20:48,119 Speaker 1: minefield with your daughters to watching them climb the stairs 343 00:20:48,119 --> 00:20:52,040 Speaker 1: of Air Force one to freedom. It's it's just, um, 344 00:20:52,080 --> 00:20:56,000 Speaker 1: I mean incredible and and and from that this the 345 00:20:56,040 --> 00:20:59,680 Speaker 1: President Eisenhower, one person in a position of power, that 346 00:20:59,800 --> 00:21:04,240 Speaker 1: tremendous impact that he had on my life, on my 347 00:21:04,359 --> 00:21:07,280 Speaker 1: sisters lives and my brother, on my family, and the 348 00:21:07,320 --> 00:21:10,640 Speaker 1: opportunities that we had, just because one person who had 349 00:21:10,720 --> 00:21:15,200 Speaker 1: power spoke up and one person who got to walk 350 00:21:15,200 --> 00:21:17,960 Speaker 1: through a doorway decided to hold it open behind them. 351 00:21:18,000 --> 00:21:24,040 Speaker 1: That's right, that's right. That is one of the more 352 00:21:24,080 --> 00:21:29,840 Speaker 1: incredible things I've ever heard. Thank you, thank you. Yeah, 353 00:21:29,920 --> 00:21:32,440 Speaker 1: it's um. We actually have a it was the Air 354 00:21:32,480 --> 00:21:35,120 Speaker 1: Force One at the time was column by the Columbine three, 355 00:21:35,760 --> 00:21:38,600 Speaker 1: and we actually have a picture of my family in 356 00:21:38,640 --> 00:21:41,120 Speaker 1: front of Air Force one when they arrived in America. 357 00:21:43,080 --> 00:21:45,960 Speaker 1: So where where did they land? What? What is that? 358 00:21:46,400 --> 00:21:48,400 Speaker 1: What happens when you get on that plane? And then 359 00:21:48,440 --> 00:21:52,200 Speaker 1: where are you going? I mean an Air Force based typically, 360 00:21:52,520 --> 00:21:55,919 Speaker 1: And so they had Camp Kilner was the refugee camp 361 00:21:56,480 --> 00:22:00,760 Speaker 1: for mostly for the Hungarian refugees. It was in New Jersey, uh, 362 00:22:00,800 --> 00:22:03,400 Speaker 1: and so they stayed there. They arrived here on Christmas 363 00:22:03,480 --> 00:22:06,560 Speaker 1: Day and there's a really cool excerpt. I don't have 364 00:22:06,640 --> 00:22:08,639 Speaker 1: it with me, but the New York Times covered it 365 00:22:08,880 --> 00:22:11,080 Speaker 1: and they talked about what if essentially, what if Santa 366 00:22:11,119 --> 00:22:14,520 Speaker 1: Claus is, you know, not a reindeer with a sleigh, 367 00:22:14,520 --> 00:22:18,760 Speaker 1: but a plane with a pilot. Anyway, it was. And 368 00:22:18,840 --> 00:22:21,040 Speaker 1: so they arrived. They were at Camp Kilner in New 369 00:22:21,160 --> 00:22:23,560 Speaker 1: Jersey for ten days and then they took the train 370 00:22:23,640 --> 00:22:26,760 Speaker 1: to Chicago. They had some family that lived in Chicago, 371 00:22:27,400 --> 00:22:30,600 Speaker 1: so they got to settle with family. Yeah, and in 372 00:22:30,640 --> 00:22:34,560 Speaker 1: the suburbs of Chicago. And did you grow up in Chicago. 373 00:22:35,119 --> 00:22:38,080 Speaker 1: I didn't. I grew up in California. So two of 374 00:22:38,119 --> 00:22:41,160 Speaker 1: my siblings were born in Chicago. And then my parents 375 00:22:41,240 --> 00:22:45,240 Speaker 1: moved out to California and actually land well, they landed 376 00:22:45,240 --> 00:22:47,040 Speaker 1: in pal Watto for a year and then moved up 377 00:22:47,080 --> 00:22:50,520 Speaker 1: to Napa Valley before it was really well known. And 378 00:22:50,600 --> 00:22:53,240 Speaker 1: this is sort of in the days when Robert Mandavi 379 00:22:53,440 --> 00:22:58,320 Speaker 1: was actually starting out, and so they lived in They 380 00:22:58,320 --> 00:23:01,920 Speaker 1: actually had a winery up on Mount Veter, which is 381 00:23:01,960 --> 00:23:04,359 Speaker 1: now part of the Hes collection, and so they landed 382 00:23:04,400 --> 00:23:06,360 Speaker 1: there and that's where I was born. On the winery. 383 00:23:07,400 --> 00:23:09,840 Speaker 1: We called it the ranch. But on the ranch, that's 384 00:23:09,880 --> 00:23:13,440 Speaker 1: so cool. What what was it like to grow up there? 385 00:23:13,440 --> 00:23:15,919 Speaker 1: What were you into as a kid? So I was 386 00:23:15,960 --> 00:23:18,520 Speaker 1: there for five years and then they sold it and 387 00:23:18,600 --> 00:23:20,520 Speaker 1: my dad was an entrepreneur, so then he was kind 388 00:23:20,520 --> 00:23:23,200 Speaker 1: of on to the next thing. But growing up there, 389 00:23:23,320 --> 00:23:25,199 Speaker 1: I mean the first five years, I mean I was 390 00:23:25,359 --> 00:23:28,280 Speaker 1: riding horses as soon as I could sit up. Um, 391 00:23:28,320 --> 00:23:30,879 Speaker 1: so there's pictures of me sitting in front of my 392 00:23:30,920 --> 00:23:33,720 Speaker 1: mom or my dad, and then and then I graduated 393 00:23:33,760 --> 00:23:36,760 Speaker 1: to sitting. I have my own horse, sitting on the horse, 394 00:23:36,800 --> 00:23:38,800 Speaker 1: and my dad had the reins, and then eventually I 395 00:23:38,840 --> 00:23:44,080 Speaker 1: could actually ride my own horse myself, which is interesting. 396 00:23:44,119 --> 00:23:47,520 Speaker 1: Before I was five, so and we you know, we 397 00:23:47,520 --> 00:23:49,919 Speaker 1: were all part of planting the vineyards. That was very 398 00:23:50,000 --> 00:23:54,040 Speaker 1: much a family affair in terms of uh the ranch 399 00:23:54,119 --> 00:23:57,639 Speaker 1: and cultivating the ranch. And they also ran cattle because 400 00:23:57,680 --> 00:24:01,040 Speaker 1: there's a quicker turn on cattle then there obviously is 401 00:24:01,080 --> 00:24:03,840 Speaker 1: on wine. So they did that and then we he 402 00:24:03,920 --> 00:24:07,119 Speaker 1: sold it and we we lived on the other side 403 00:24:07,119 --> 00:24:10,159 Speaker 1: of Napa, so kind of between Silverado Country Club and 404 00:24:10,440 --> 00:24:13,840 Speaker 1: Napa Valley Country Club and I think and in the country. 405 00:24:13,920 --> 00:24:16,439 Speaker 1: And it was interesting. I mean, it was a different 406 00:24:16,440 --> 00:24:19,000 Speaker 1: way to grow up when we lived on the ranch. 407 00:24:19,080 --> 00:24:22,120 Speaker 1: My brother used to ride his horse to school, so 408 00:24:23,560 --> 00:24:25,399 Speaker 1: there was a school up the road that he wrote 409 00:24:25,440 --> 00:24:27,600 Speaker 1: his horse to And yeah, I mean, I think it 410 00:24:27,680 --> 00:24:31,240 Speaker 1: was just a very grounded kind of way to grow 411 00:24:31,320 --> 00:24:34,399 Speaker 1: up in some ways, like you're very much, like you know, 412 00:24:34,920 --> 00:24:39,640 Speaker 1: expected to be part of the family and and from 413 00:24:39,640 --> 00:24:43,240 Speaker 1: a young age. I mean you talk about how, especially 414 00:24:43,240 --> 00:24:46,560 Speaker 1: with your father, there was a lot of consciousness around 415 00:24:46,680 --> 00:24:50,760 Speaker 1: what the opportunity to come here meant and and then 416 00:24:50,760 --> 00:24:55,320 Speaker 1: other people didn't get it. With your mom and dad 417 00:24:55,840 --> 00:24:59,360 Speaker 1: both given their incredible stories to to wind up here. 418 00:25:00,760 --> 00:25:03,280 Speaker 1: Was that always part of the conversation. Was it something 419 00:25:03,280 --> 00:25:05,360 Speaker 1: that you talked about when you were a little bit 420 00:25:05,359 --> 00:25:08,240 Speaker 1: older or or looking back, do you think that there 421 00:25:08,240 --> 00:25:12,360 Speaker 1: were always values they were instilling in you guys because 422 00:25:12,359 --> 00:25:16,800 Speaker 1: of those experiences that they carried. No, they were, Yeah, 423 00:25:16,840 --> 00:25:20,479 Speaker 1: they were always instilling it in us from the very 424 00:25:20,520 --> 00:25:24,199 Speaker 1: youngest age. The you know what, first of all, just 425 00:25:24,280 --> 00:25:27,960 Speaker 1: the opportunity to be an American. My dad was very 426 00:25:28,000 --> 00:25:31,000 Speaker 1: proud to be an American, as was my mom. But 427 00:25:31,040 --> 00:25:33,840 Speaker 1: my father in particular, we had a lot of you know, 428 00:25:33,880 --> 00:25:36,240 Speaker 1: because they both had to take citizen tests. You know, 429 00:25:36,280 --> 00:25:39,960 Speaker 1: we had conversations about the electoral college, and there were 430 00:25:40,000 --> 00:25:42,760 Speaker 1: a lot of rigorous political debates in my family. I 431 00:25:42,760 --> 00:25:46,320 Speaker 1: grew up in a pretty bipartisan family. But also this 432 00:25:46,760 --> 00:25:51,959 Speaker 1: belief uh in hard work, in not getting sidelined by 433 00:25:52,000 --> 00:25:56,120 Speaker 1: what people think of you, by getting back up when 434 00:25:56,160 --> 00:25:59,639 Speaker 1: things are hard, that that it's when things are hard 435 00:26:00,000 --> 00:26:02,160 Speaker 1: it you get back up and do something. They didn't 436 00:26:02,200 --> 00:26:05,800 Speaker 1: have a lot of tolerance for sort of drama or 437 00:26:06,960 --> 00:26:10,000 Speaker 1: just like it was like I think you know that 438 00:26:10,000 --> 00:26:12,479 Speaker 1: that the two things that I often talk about is 439 00:26:12,960 --> 00:26:15,040 Speaker 1: one is they taught me never to give up no 440 00:26:15,080 --> 00:26:18,080 Speaker 1: matter what, and the other was to always do my best. 441 00:26:18,560 --> 00:26:21,359 Speaker 1: And the always doing my best part taught me to 442 00:26:21,440 --> 00:26:23,879 Speaker 1: walk through a lot of fear even today. You know, 443 00:26:23,960 --> 00:26:26,240 Speaker 1: it's not like I'm absent from fear today. I have 444 00:26:26,320 --> 00:26:29,200 Speaker 1: it today. It's just what I choose to do with it, 445 00:26:29,680 --> 00:26:33,680 Speaker 1: and what what how that impacts what I do and 446 00:26:33,800 --> 00:26:37,400 Speaker 1: so and always knowing that what my parents had survived 447 00:26:37,400 --> 00:26:42,480 Speaker 1: in order for us to have opportunity was really um 448 00:26:42,600 --> 00:26:45,760 Speaker 1: very much the values that we had and just being 449 00:26:45,800 --> 00:26:49,320 Speaker 1: pretty tough. I mean even now when you obviously my 450 00:26:49,359 --> 00:26:51,400 Speaker 1: dad sees when you talk to my mom, she's like, 451 00:26:51,600 --> 00:26:53,439 Speaker 1: you know, you don't leave people in the middle of 452 00:26:53,440 --> 00:26:56,239 Speaker 1: a battlefield. You get back up, Like that's what you do, 453 00:26:56,320 --> 00:27:01,000 Speaker 1: It's your responsibility. And I think everything within the UM 454 00:27:01,000 --> 00:27:03,320 Speaker 1: there's a lot of perspective in terms of what they 455 00:27:03,359 --> 00:27:06,560 Speaker 1: had done and what they had survived that you know, uh, 456 00:27:06,720 --> 00:27:10,560 Speaker 1: struggling with finishing a math test or something in high 457 00:27:10,560 --> 00:27:14,080 Speaker 1: school just didn't quite seem you know, it's like as 458 00:27:14,119 --> 00:27:16,480 Speaker 1: big of a deal, like you could soldier through that, 459 00:27:16,480 --> 00:27:20,480 Speaker 1: that was fine, and and so, yeah, that's really beautiful. 460 00:27:30,920 --> 00:27:32,840 Speaker 1: When you were little, did you know what you wanted 461 00:27:32,880 --> 00:27:38,199 Speaker 1: to be. Were you always kind of bent towards politics 462 00:27:38,240 --> 00:27:42,920 Speaker 1: and injustice and data or was it a different path 463 00:27:43,040 --> 00:27:45,760 Speaker 1: back then? Well, I mean when I was little little, 464 00:27:45,800 --> 00:27:48,000 Speaker 1: I wanted to be a veterinarian, and then I wanted 465 00:27:48,040 --> 00:27:52,760 Speaker 1: to be a lawyer. But I, um, I think for like, 466 00:27:52,840 --> 00:27:56,760 Speaker 1: math was my favorite subject in in high school for sure, 467 00:27:57,000 --> 00:27:59,680 Speaker 1: and growing up I loved math. That was by far 468 00:27:59,800 --> 00:28:03,280 Speaker 1: my favorite subject. I was always a pretty vocal kid, 469 00:28:04,000 --> 00:28:08,080 Speaker 1: so I would set my brown aries, you know, pretty 470 00:28:08,080 --> 00:28:10,800 Speaker 1: strongly when I was a child's which I think to 471 00:28:10,880 --> 00:28:13,879 Speaker 1: some extent probably made me harder to parent but better 472 00:28:13,920 --> 00:28:18,040 Speaker 1: as an adult. But I I didn't. I thought I 473 00:28:18,040 --> 00:28:20,640 Speaker 1: wanted to be a lawyer, and I was a paral 474 00:28:20,760 --> 00:28:23,240 Speaker 1: legal right out of college, and then thought, you know, 475 00:28:23,320 --> 00:28:25,720 Speaker 1: I'll be a great lawyer, but I'll be completely miserable. 476 00:28:25,760 --> 00:28:28,959 Speaker 1: So I don't want to do this. So so I didn't. 477 00:28:29,040 --> 00:28:32,200 Speaker 1: And then I started learning to program actually and that 478 00:28:32,480 --> 00:28:35,000 Speaker 1: and that because I had a math background, it made 479 00:28:35,040 --> 00:28:36,960 Speaker 1: a lot of sense to me in terms of logic 480 00:28:37,119 --> 00:28:40,120 Speaker 1: and how things fit together. And this was, you know, 481 00:28:40,200 --> 00:28:44,160 Speaker 1: back in two thousand two, so this is before you know, 482 00:28:44,200 --> 00:28:48,360 Speaker 1: before kind of programming as a thing today. And I think, 483 00:28:49,080 --> 00:28:51,360 Speaker 1: uh so, yeah, I mean I think that, and yeah, 484 00:28:51,400 --> 00:28:53,680 Speaker 1: I mean that was sort of where I started. And 485 00:28:53,800 --> 00:28:56,280 Speaker 1: I think had the faith that if I did my 486 00:28:57,400 --> 00:29:00,160 Speaker 1: worked hard and you know, always did my best that 487 00:29:00,200 --> 00:29:02,320 Speaker 1: I'd be okay. I just didn't know where the journey 488 00:29:02,400 --> 00:29:03,960 Speaker 1: might lead. I think that was the other thing that 489 00:29:04,040 --> 00:29:07,400 Speaker 1: my parents gave me was this faith in myself and 490 00:29:07,440 --> 00:29:09,760 Speaker 1: in the belief in the journey that you might not 491 00:29:09,840 --> 00:29:12,320 Speaker 1: know where it ends up, but if you're willing to jump, 492 00:29:12,480 --> 00:29:17,160 Speaker 1: like the ledge will appear. I love that. When when 493 00:29:17,200 --> 00:29:20,400 Speaker 1: you were at us F, perhaps when you were on 494 00:29:20,440 --> 00:29:22,760 Speaker 1: this journey thinking you might be a lawyer, you studied 495 00:29:23,000 --> 00:29:27,120 Speaker 1: political science. I did with your legal studies emphasis. Do 496 00:29:27,200 --> 00:29:31,400 Speaker 1: you think that studying political science has continued to influence 497 00:29:31,400 --> 00:29:34,760 Speaker 1: the work that you do today? Yes, for sure. I 498 00:29:34,800 --> 00:29:39,320 Speaker 1: mean I love politics, you know. I I was an 499 00:29:39,320 --> 00:29:42,440 Speaker 1: intern in d C during college and a lot of 500 00:29:42,440 --> 00:29:44,360 Speaker 1: time up on the hill, lived there for five years 501 00:29:44,400 --> 00:29:49,120 Speaker 1: after college. I think that justice coupled with economic impact 502 00:29:49,360 --> 00:29:54,480 Speaker 1: is really where my passion is because it changes the conversation. 503 00:29:55,000 --> 00:29:58,400 Speaker 1: You know, when we talk about the economics of gender equity, 504 00:29:58,680 --> 00:30:02,920 Speaker 1: women are not the alreaty case we are. It is 505 00:30:03,040 --> 00:30:07,880 Speaker 1: about economic opportunity and how that impacts everyone. Right, you 506 00:30:07,920 --> 00:30:11,960 Speaker 1: know that this there's often this idea that, um, diversity, 507 00:30:12,000 --> 00:30:13,360 Speaker 1: Well I don't you know what they'll say, Well, I 508 00:30:13,400 --> 00:30:15,680 Speaker 1: really do want diversity but I don't want to have 509 00:30:15,800 --> 00:30:20,080 Speaker 1: to compromise on qualifications, which is, by the way, yeah, 510 00:30:20,080 --> 00:30:23,520 Speaker 1: that's right, which is a little insulting. And if you 511 00:30:23,560 --> 00:30:27,560 Speaker 1: look at women being the majority of all college graduates 512 00:30:27,600 --> 00:30:30,920 Speaker 1: is even more is more insulting. But it's a false 513 00:30:31,000 --> 00:30:33,880 Speaker 1: narrative that you have to choose between the two, which 514 00:30:33,920 --> 00:30:37,480 Speaker 1: is where I believe that social justice plus economic impact 515 00:30:37,520 --> 00:30:41,720 Speaker 1: matters so much, because it's not just in us versus them. 516 00:30:41,800 --> 00:30:46,239 Speaker 1: It is fundamentally about economic opportunity for everyone. And you 517 00:30:46,280 --> 00:30:50,360 Speaker 1: can't hold back a segment of the population and believe 518 00:30:50,520 --> 00:30:54,560 Speaker 1: and and then have positive economic outcomes for everyone from 519 00:30:54,600 --> 00:30:57,520 Speaker 1: that there it just doesn't like the math doesn't work. 520 00:30:58,080 --> 00:31:02,440 Speaker 1: And so and to your point, there is this really odd, 521 00:31:02,720 --> 00:31:10,120 Speaker 1: illogical belief that equity parody is a charity case and 522 00:31:10,240 --> 00:31:13,280 Speaker 1: a rising tide lifts all ships. If we're holding down 523 00:31:13,400 --> 00:31:17,800 Speaker 1: half of the nation, we're holding down our economy essentially 524 00:31:18,160 --> 00:31:22,360 Speaker 1: potentially by half. You know, it's it's logical, and and 525 00:31:22,480 --> 00:31:25,800 Speaker 1: it's interesting. I in some of the work that I 526 00:31:25,840 --> 00:31:30,240 Speaker 1: get to do, a lot of my learning over the 527 00:31:30,280 --> 00:31:35,720 Speaker 1: past decade plus has been about the u n's development goals, 528 00:31:35,760 --> 00:31:38,760 Speaker 1: and gender parody is goal five. But if we achieved 529 00:31:38,800 --> 00:31:41,480 Speaker 1: goal five, we would achieve half of the rest of 530 00:31:41,520 --> 00:31:47,120 Speaker 1: the goals immediately because gender parity fixes so many other problems. 531 00:31:47,640 --> 00:31:50,680 Speaker 1: And you know, you have talked about how closing the 532 00:31:50,720 --> 00:31:54,640 Speaker 1: gender pay gap could add five hundred and twelve billion 533 00:31:54,760 --> 00:31:58,800 Speaker 1: dollars to our g d P. This isn't a charity case. 534 00:31:58,840 --> 00:32:01,760 Speaker 1: This is about everybody doing better. And it's it's so 535 00:32:01,840 --> 00:32:05,320 Speaker 1: strange to me that it's not something we want to do, 536 00:32:05,520 --> 00:32:09,800 Speaker 1: that it's something people feel begrudgingly obligated to push for. 537 00:32:11,000 --> 00:32:15,479 Speaker 1: I don't I don't understand that. I think that comes 538 00:32:15,600 --> 00:32:19,320 Speaker 1: from a couple of places. One is that it comes 539 00:32:19,320 --> 00:32:23,000 Speaker 1: from this us versus them. It comes from this false 540 00:32:23,040 --> 00:32:26,120 Speaker 1: belief in the fixed economic pie if you will, that 541 00:32:26,200 --> 00:32:29,680 Speaker 1: the economy is fixed, which it's not. And so if 542 00:32:29,720 --> 00:32:32,080 Speaker 1: you get so, I mean, you know, to like simplify it, right, 543 00:32:32,120 --> 00:32:34,600 Speaker 1: if you get two slices of pie, I'm not gonna 544 00:32:34,680 --> 00:32:37,640 Speaker 1: get any which is just it's not true. It's actually 545 00:32:37,720 --> 00:32:41,640 Speaker 1: that we're constricting that through not through not having equity. 546 00:32:42,160 --> 00:32:45,160 Speaker 1: And that's that's the other reason why we look at 547 00:32:45,160 --> 00:32:48,240 Speaker 1: through the lens of the economics of gender equity is 548 00:32:48,280 --> 00:32:51,120 Speaker 1: that men are also held back from the lack of 549 00:32:51,160 --> 00:32:54,240 Speaker 1: gender and equity. You know, gender equity as we view 550 00:32:54,280 --> 00:32:56,600 Speaker 1: it is not a synonym for women's rights. Women are 551 00:32:56,640 --> 00:32:58,840 Speaker 1: half the conversation and men or the other half. And 552 00:32:58,880 --> 00:33:01,959 Speaker 1: the reason is that men are also held back by 553 00:33:02,000 --> 00:33:04,840 Speaker 1: gender and equity. We just don't talk about it a lot, right, 554 00:33:04,880 --> 00:33:06,960 Speaker 1: So there's a lot of narrative around what it means 555 00:33:07,000 --> 00:33:10,960 Speaker 1: to be a man in this world, about having emotions, 556 00:33:11,000 --> 00:33:13,880 Speaker 1: about being vulnerable. And when you talk about boys, like 557 00:33:13,960 --> 00:33:17,440 Speaker 1: boys and sports, that's where most boys learn what sport, 558 00:33:17,520 --> 00:33:20,440 Speaker 1: what it means to be a man, and that has 559 00:33:20,440 --> 00:33:23,920 Speaker 1: a direct tie to their economic opportunity. And you know, 560 00:33:23,960 --> 00:33:26,240 Speaker 1: you look at the data and forty percent of all 561 00:33:26,240 --> 00:33:28,440 Speaker 1: working dads would like to stay home with their kids. 562 00:33:29,080 --> 00:33:32,600 Speaker 1: Well why don't they? Right, it's because of identity who 563 00:33:32,600 --> 00:33:34,320 Speaker 1: they are, like, what it means to be a man 564 00:33:34,480 --> 00:33:37,520 Speaker 1: in isolation? Who will I talk to? And I we 565 00:33:37,800 --> 00:33:40,480 Speaker 1: my families live this struggle. My husband has been home 566 00:33:40,520 --> 00:33:44,400 Speaker 1: for twelve years with our kids, and it is he 567 00:33:44,520 --> 00:33:46,160 Speaker 1: doesn't he's not going to go to the mom and 568 00:33:46,200 --> 00:33:48,000 Speaker 1: me groups. He said, I don't, you know, I don't 569 00:33:48,000 --> 00:33:52,000 Speaker 1: feel comfortable. So it can be a fairly isolating thing, 570 00:33:52,200 --> 00:33:54,360 Speaker 1: even when that's what you want to do, and we 571 00:33:54,400 --> 00:33:56,400 Speaker 1: see that. You know, if you look at campaign issues, 572 00:33:56,440 --> 00:33:59,000 Speaker 1: it's a mental health issue as well. Like mental health 573 00:33:59,160 --> 00:34:01,920 Speaker 1: is a men's you far more than it is a 574 00:34:01,920 --> 00:34:05,360 Speaker 1: women's issue. And so those are things that we also 575 00:34:05,440 --> 00:34:08,600 Speaker 1: need to address, you know, to get to gender equity. 576 00:34:10,160 --> 00:34:13,319 Speaker 1: When did you begin noticing that the landscape was not 577 00:34:13,360 --> 00:34:15,760 Speaker 1: an equal playing field when you started out in your career, 578 00:34:15,880 --> 00:34:18,600 Speaker 1: When did those alarm bells start to go off? You know, 579 00:34:19,080 --> 00:34:22,080 Speaker 1: it's interesting. I am the youngest of six kids, so 580 00:34:22,160 --> 00:34:26,520 Speaker 1: I saw the economic barriers to women impact my sisters 581 00:34:26,520 --> 00:34:30,080 Speaker 1: and their children. So things like in my lifetime, women 582 00:34:30,520 --> 00:34:33,200 Speaker 1: couldn't get a credit card without a male cosigner, they 583 00:34:33,200 --> 00:34:35,680 Speaker 1: couldn't get a bank loan without a male cosigner, they 584 00:34:35,680 --> 00:34:39,320 Speaker 1: couldn't get housing without a male cosigner. Those were laws 585 00:34:39,360 --> 00:34:41,759 Speaker 1: that were all in effects in my lifetime. So I 586 00:34:41,880 --> 00:34:44,600 Speaker 1: watched that impact my sisters, and I remember at a 587 00:34:44,600 --> 00:34:47,040 Speaker 1: really young age thinking I'm never doing that. I think 588 00:34:47,080 --> 00:34:50,200 Speaker 1: I was like five, and then and then when I 589 00:34:50,239 --> 00:34:52,720 Speaker 1: got into the you know, I was a polycy major, 590 00:34:52,920 --> 00:34:54,920 Speaker 1: So you study a lot of women's rights, you know, 591 00:34:55,000 --> 00:34:58,719 Speaker 1: far beyond just the suffrage movement, and did. I did 592 00:34:58,760 --> 00:35:00,719 Speaker 1: a lot of work. And then it's point early in 593 00:35:00,719 --> 00:35:02,759 Speaker 1: my career thought, I don't just don't know if this 594 00:35:02,840 --> 00:35:05,359 Speaker 1: is all that applicable to the workplace anymore. I just 595 00:35:05,400 --> 00:35:08,640 Speaker 1: don't see it. And so I kind of switched from 596 00:35:08,680 --> 00:35:11,719 Speaker 1: building my career. And I certainly had instances where you know, 597 00:35:11,719 --> 00:35:17,000 Speaker 1: I was called aggressive, uh kataka's intimidating, I don't, you know, 598 00:35:17,040 --> 00:35:20,640 Speaker 1: those very kind of typical things that we say to women. 599 00:35:20,960 --> 00:35:23,440 Speaker 1: But the key, the thing that happened to me that 600 00:35:23,560 --> 00:35:25,759 Speaker 1: was this wake up moment was when I was on 601 00:35:25,800 --> 00:35:29,920 Speaker 1: eternity leave with my daughter, my boss was fired or 602 00:35:30,040 --> 00:35:32,440 Speaker 1: she was optimized, which is a fancy word for fired. 603 00:35:32,920 --> 00:35:36,239 Speaker 1: And I I had a team that I was managing. 604 00:35:36,680 --> 00:35:39,200 Speaker 1: When I got back, I now had two teams that 605 00:35:39,320 --> 00:35:42,000 Speaker 1: I was They asked me to manage another team the 606 00:35:42,040 --> 00:35:43,839 Speaker 1: day after I got back, and then two weeks later 607 00:35:43,840 --> 00:35:45,799 Speaker 1: asked me to manage a third team. Which is great, 608 00:35:45,880 --> 00:35:50,359 Speaker 1: like perfect time to have that opportunity, new mom, breadwinner mom, etcetera. 609 00:35:50,840 --> 00:35:54,360 Speaker 1: And but they didn't offer me any additional pay. And 610 00:35:54,520 --> 00:35:58,480 Speaker 1: my mail colleague was a pay grade higher than I was, 611 00:35:58,719 --> 00:36:02,640 Speaker 1: and he also received of additional compensation for that new team, 612 00:36:02,719 --> 00:36:04,520 Speaker 1: and people say, well, how did you know? And I said, well, 613 00:36:04,520 --> 00:36:06,799 Speaker 1: I asked him because I assumed that they were paying 614 00:36:06,880 --> 00:36:08,480 Speaker 1: him more. I just went and said, hey, what are 615 00:36:08,480 --> 00:36:11,200 Speaker 1: they giving you for that extra team? And he's and 616 00:36:11,280 --> 00:36:13,239 Speaker 1: he told me. And so I went to my new 617 00:36:13,280 --> 00:36:16,720 Speaker 1: manager and HR and said, well this, I'm very excited 618 00:36:16,719 --> 00:36:18,560 Speaker 1: about this opportunity. How do you want to make me 619 00:36:18,680 --> 00:36:21,759 Speaker 1: whole on my pay so that it's equitable? And there 620 00:36:21,840 --> 00:36:24,480 Speaker 1: was nothing. And this is where my background is a 621 00:36:24,520 --> 00:36:28,160 Speaker 1: policy major and a paralegal helped me because I was 622 00:36:28,239 --> 00:36:30,879 Speaker 1: really good at legal research, and I thought that this 623 00:36:30,920 --> 00:36:33,719 Speaker 1: has got to be illegal, like this has got to 624 00:36:33,880 --> 00:36:37,680 Speaker 1: this cannot be legal. So I found the Lily lad 625 00:36:37,680 --> 00:36:41,480 Speaker 1: Better Fair Pay Act called HR and said, this is 626 00:36:41,480 --> 00:36:43,520 Speaker 1: a Lily lead Better issue. Every time you pay me, 627 00:36:43,560 --> 00:36:46,000 Speaker 1: the statute of limitation starts over. What do you want 628 00:36:46,000 --> 00:36:51,440 Speaker 1: to do about it? And they were like okay, So 629 00:36:51,520 --> 00:36:54,200 Speaker 1: to their credit, they increase my pay, increase my level 630 00:36:54,239 --> 00:36:56,920 Speaker 1: and gave me back pay. And that was really anyway, 631 00:36:56,920 --> 00:36:59,160 Speaker 1: that was really the first moment that I realized it 632 00:36:59,200 --> 00:37:01,359 Speaker 1: was an equable, which is incredible and I just want 633 00:37:01,360 --> 00:37:04,160 Speaker 1: to like slow clap you over here. But the thing 634 00:37:04,200 --> 00:37:06,200 Speaker 1: that it makes me think about is what about the 635 00:37:06,200 --> 00:37:08,840 Speaker 1: women who don't know that. What about the women who 636 00:37:09,120 --> 00:37:12,759 Speaker 1: haven't been trained as paralegals, who who don't have access 637 00:37:13,400 --> 00:37:16,880 Speaker 1: to that kind of information, who are in essence being 638 00:37:16,920 --> 00:37:20,520 Speaker 1: shrunk in their positions because they don't know that they 639 00:37:20,560 --> 00:37:24,960 Speaker 1: can advocate in this way. It makes me crazy. And 640 00:37:26,239 --> 00:37:28,560 Speaker 1: I would like if if you wouldn't mind, could you 641 00:37:28,680 --> 00:37:33,799 Speaker 1: tell people at home listening more about the Lily Lad 642 00:37:33,800 --> 00:37:36,480 Speaker 1: Better Fair Pay Act, how it works, what it really means, 643 00:37:36,560 --> 00:37:39,160 Speaker 1: how they might do some research on it and figure 644 00:37:39,200 --> 00:37:42,479 Speaker 1: out what they're standing is in their own job. Sure, yes, 645 00:37:42,600 --> 00:37:44,880 Speaker 1: And I will also address the other issue that you 646 00:37:44,880 --> 00:37:47,520 Speaker 1: talked about, the why you know this is essentially why 647 00:37:47,560 --> 00:37:50,239 Speaker 1: did why did I have to speak up? Right? I mean, 648 00:37:50,280 --> 00:37:52,919 Speaker 1: that's that's I think that's another piece. But the Lily 649 00:37:53,000 --> 00:37:55,040 Speaker 1: Lad Better for a Pay Act was actually the first 650 00:37:55,960 --> 00:37:59,560 Speaker 1: piece of legislation that President Obama signed into law in 651 00:37:59,640 --> 00:38:03,800 Speaker 1: Januar ray of nine, and what it did was change 652 00:38:03,880 --> 00:38:07,120 Speaker 1: the statute limitations for equal pay. So it used to 653 00:38:07,160 --> 00:38:11,640 Speaker 1: be that the statuted limitations for equal pay started when 654 00:38:11,920 --> 00:38:15,680 Speaker 1: the equal pay began, right. So the issue with that 655 00:38:16,040 --> 00:38:18,960 Speaker 1: is that if you don't know, you're being paid equitably. 656 00:38:19,200 --> 00:38:22,839 Speaker 1: How can you really address that within the statued limitations? Right? 657 00:38:22,880 --> 00:38:25,239 Speaker 1: Then this what Lily lad Better was a good year 658 00:38:25,360 --> 00:38:28,600 Speaker 1: employee who got to essentially the back story, got a 659 00:38:28,640 --> 00:38:31,440 Speaker 1: tip off that she was earning substantially less than her 660 00:38:31,480 --> 00:38:34,440 Speaker 1: male colleagues, tried to sue UM, and because of the 661 00:38:34,480 --> 00:38:39,600 Speaker 1: statute of limitations, UH was essentially blocked from bringing her case. 662 00:38:40,000 --> 00:38:42,800 Speaker 1: And so what it did was move it from the 663 00:38:42,840 --> 00:38:47,120 Speaker 1: time at which the inequitable pay started to the time 664 00:38:47,160 --> 00:38:50,920 Speaker 1: it was paid, so essentially it starts over with every 665 00:38:50,960 --> 00:38:55,840 Speaker 1: single paycheck. And that, yeah, that is a huge difference, 666 00:38:55,840 --> 00:38:58,719 Speaker 1: which is obviously what I used, I think to the 667 00:38:58,800 --> 00:39:02,080 Speaker 1: other piece of found You know what do women do? 668 00:39:02,320 --> 00:39:04,800 Speaker 1: I think this is actually the problem with our system. 669 00:39:04,840 --> 00:39:06,640 Speaker 1: And by the way, it's why I create founded the 670 00:39:06,680 --> 00:39:10,960 Speaker 1: company that I founded, because we still put it on 671 00:39:11,120 --> 00:39:13,239 Speaker 1: We have an equal pay law. It was signed into 672 00:39:13,280 --> 00:39:16,160 Speaker 1: law by President Kennedy in nineteen sixty three and still 673 00:39:16,239 --> 00:39:18,680 Speaker 1: in the last ten years we've actually moved backward in 674 00:39:18,760 --> 00:39:24,240 Speaker 1: equal pay in aggregate, and so we actually we still 675 00:39:24,239 --> 00:39:27,680 Speaker 1: put it on the backs of women and people who 676 00:39:27,680 --> 00:39:31,360 Speaker 1: are not paid equitably to bring a case and the 677 00:39:31,400 --> 00:39:35,160 Speaker 1: power like one that's not an equitable system, and to generally, 678 00:39:35,200 --> 00:39:37,400 Speaker 1: they're not the people that are in power, right, they 679 00:39:37,440 --> 00:39:40,560 Speaker 1: are not the people that are making those decisions. And generally, 680 00:39:41,040 --> 00:39:43,160 Speaker 1: when you're not the one in power, you get nervous 681 00:39:43,160 --> 00:39:45,120 Speaker 1: to make waves. You don't want to lose your job. 682 00:39:46,080 --> 00:39:50,200 Speaker 1: Because to your point, we're paying women now in less 683 00:39:50,200 --> 00:39:53,440 Speaker 1: than we were paying them in. But do you want 684 00:39:53,480 --> 00:39:55,399 Speaker 1: to make less money or no money. It's a real 685 00:39:55,880 --> 00:40:00,759 Speaker 1: conundrum for It's scary, and that's that's exactly right, is 686 00:40:00,800 --> 00:40:03,960 Speaker 1: that we are basically putting it on women to put 687 00:40:04,000 --> 00:40:07,200 Speaker 1: their economic security on the line in order to receive 688 00:40:07,520 --> 00:40:12,480 Speaker 1: equitable pay. That's madness. It is now Virginia just became 689 00:40:12,480 --> 00:40:14,880 Speaker 1: the thirty eight state to ratify the e r A. 690 00:40:15,440 --> 00:40:19,440 Speaker 1: Can you walk our listeners through what the differences mean, 691 00:40:19,560 --> 00:40:23,200 Speaker 1: because there is to your point about Kennedy having signed 692 00:40:23,320 --> 00:40:28,399 Speaker 1: the equal pay Law. People assume that this is the law. 693 00:40:28,440 --> 00:40:30,360 Speaker 1: They assume it's the law of the land. They assume 694 00:40:30,400 --> 00:40:34,920 Speaker 1: there are federal protections of people surveyed believe that it's 695 00:40:34,920 --> 00:40:37,160 Speaker 1: illegal to pay men and women differently, But men and 696 00:40:37,160 --> 00:40:41,000 Speaker 1: women are paid differently in every industry in the country. 697 00:40:41,080 --> 00:40:44,760 Speaker 1: So can you can you explain to people how that happens, 698 00:40:44,760 --> 00:40:47,120 Speaker 1: and what the differences and what the e r A 699 00:40:47,239 --> 00:40:52,120 Speaker 1: being ratified might mean face means a long Supreme Court 700 00:40:52,120 --> 00:40:58,040 Speaker 1: battle to overturn a Supreme Court case that essentially said 701 00:40:58,080 --> 00:41:02,040 Speaker 1: that you could put at time limit on constitutional amendments. 702 00:41:02,120 --> 00:41:04,279 Speaker 1: That that is will be probably the next thing that 703 00:41:04,360 --> 00:41:06,359 Speaker 1: comes up. And I actually think the lawsuit has been 704 00:41:06,400 --> 00:41:09,600 Speaker 1: filed in a federal court because there was a statute 705 00:41:09,600 --> 00:41:13,160 Speaker 1: of limits, not a statue limitations, but a time expiration 706 00:41:13,480 --> 00:41:18,319 Speaker 1: on the e r A. So uh and actually the 707 00:41:18,400 --> 00:41:22,759 Speaker 1: in ninete of professions there is a gender pay gap, 708 00:41:22,960 --> 00:41:28,080 Speaker 1: so it's an almost all professions. The the e r 709 00:41:28,120 --> 00:41:33,800 Speaker 1: A essentially women are not fundamentally equal citizens under the 710 00:41:33,880 --> 00:41:37,640 Speaker 1: law in the United States. We don't have constitutional protections, 711 00:41:37,920 --> 00:41:41,319 Speaker 1: not by gender or by sex. Is how how the 712 00:41:41,360 --> 00:41:44,200 Speaker 1: e r A and so what the e r A 713 00:41:44,320 --> 00:41:48,880 Speaker 1: means in terms of equity is that every single not 714 00:41:49,200 --> 00:41:51,600 Speaker 1: piece by piece. So you talk about the Equal Pay Law, 715 00:41:51,680 --> 00:41:53,680 Speaker 1: you talk about the lu Lula Better Fair Pay Act, 716 00:41:53,719 --> 00:41:55,919 Speaker 1: you talk about the Civil Rights Act of nineteen sixty four, 717 00:41:55,920 --> 00:41:59,200 Speaker 1: which created the e O C. All those different pieces 718 00:41:59,239 --> 00:42:03,840 Speaker 1: they're like little picks at you know, they're they're little divots. 719 00:42:03,880 --> 00:42:07,640 Speaker 1: If you will into equality. But what the e r 720 00:42:07,680 --> 00:42:11,640 Speaker 1: A would fundamentally do is say that every single it 721 00:42:11,800 --> 00:42:16,440 Speaker 1: is illegal, in every single statute in the United States 722 00:42:16,880 --> 00:42:19,799 Speaker 1: for there to be any sex discrimination. So you're not 723 00:42:19,880 --> 00:42:24,359 Speaker 1: chipping at it little by little, You're essentially saying everywhere 724 00:42:24,440 --> 00:42:27,960 Speaker 1: it must be equitable. And that is that is huge 725 00:42:28,080 --> 00:42:31,040 Speaker 1: and and it is It is also not only an 726 00:42:31,080 --> 00:42:33,480 Speaker 1: issue of fairness. It is I know I say this 727 00:42:33,520 --> 00:42:35,200 Speaker 1: a lot, but it is truly. I mean, you look 728 00:42:35,239 --> 00:42:40,200 Speaker 1: at it, and households with with children in the US, 729 00:42:40,280 --> 00:42:43,680 Speaker 1: women are the breadwinners. Seventy one percent of households with children. 730 00:42:43,719 --> 00:42:46,279 Speaker 1: Women work, and they're part of the economic well being. 731 00:42:46,640 --> 00:42:51,920 Speaker 1: This is fundamentally about economic justice. Really more than that. 732 00:42:52,000 --> 00:42:54,200 Speaker 1: I mean, you look at um. I talked about this 733 00:42:54,239 --> 00:42:57,160 Speaker 1: in a more recent Fast Company article about the gender 734 00:42:57,200 --> 00:43:00,520 Speaker 1: jail gap. Right, so women, UM you had about criminal 735 00:43:00,600 --> 00:43:04,880 Speaker 1: justice and cash bail. Women are more likely to be 736 00:43:05,040 --> 00:43:09,000 Speaker 1: held um, less likely to be able to pay for bails, 737 00:43:09,040 --> 00:43:11,840 Speaker 1: and of them or moms, they're separated from their children 738 00:43:11,840 --> 00:43:15,040 Speaker 1: within this country, their citizens of the United States, and 739 00:43:15,120 --> 00:43:18,319 Speaker 1: so that that all of that has a tremendous, you know, 740 00:43:18,400 --> 00:43:20,680 Speaker 1: impact on our economy. Absolutely, and I think some of 741 00:43:20,719 --> 00:43:24,040 Speaker 1: those numbers are really important. You reference in in that 742 00:43:24,320 --> 00:43:29,560 Speaker 1: piece that women represented nine percent of jail admissions in 743 00:43:29,600 --> 00:43:33,040 Speaker 1: the US, and by that share has jumped to twenty 744 00:43:33,200 --> 00:43:38,160 Speaker 1: three percent. So this idea that women are becoming more 745 00:43:38,200 --> 00:43:41,319 Speaker 1: of a threat to civil society really interests me. You know, 746 00:43:41,360 --> 00:43:43,160 Speaker 1: you saw in the mid terms we elected over a 747 00:43:43,200 --> 00:43:46,640 Speaker 1: hundred women to Congress, which still doesn't create parity of 748 00:43:46,640 --> 00:43:50,920 Speaker 1: representation per population, but it was a big deal. And 749 00:43:51,440 --> 00:43:55,440 Speaker 1: immediately after one women were elected to Congress, we saw 750 00:43:55,480 --> 00:43:58,480 Speaker 1: the GOP try to pass the most aggressive rollbacks of 751 00:43:58,480 --> 00:44:02,000 Speaker 1: women's reproductive rights. And that doesn't just mean the abortion issue. 752 00:44:02,040 --> 00:44:06,280 Speaker 1: That means even access to birth control and healthcare, because 753 00:44:06,560 --> 00:44:10,520 Speaker 1: there is a real backlash to us gaining equal footing. 754 00:44:11,400 --> 00:44:13,919 Speaker 1: And I think talking about how the gender pay gap 755 00:44:14,040 --> 00:44:18,800 Speaker 1: influences bail in this instance is really important because most 756 00:44:18,840 --> 00:44:22,000 Speaker 1: of these women, per the data, are incarcerated due to 757 00:44:22,080 --> 00:44:25,280 Speaker 1: non violent offenses. They have not been convicted of a crime, 758 00:44:25,680 --> 00:44:28,600 Speaker 1: but because they can't afford cash bail, they're being forced 759 00:44:28,640 --> 00:44:32,799 Speaker 1: to be separated from their families until they have a 760 00:44:32,880 --> 00:44:37,560 Speaker 1: day in court, which is horrendous. And you can be held, 761 00:44:37,600 --> 00:44:39,440 Speaker 1: it's not like three months. You can be held for 762 00:44:39,880 --> 00:44:43,279 Speaker 1: years like this. This is it's it's unknown. And so 763 00:44:43,320 --> 00:44:45,920 Speaker 1: you have children that go into a foster system. You 764 00:44:46,040 --> 00:44:48,600 Speaker 1: have people who are people are less likely to vote 765 00:44:49,080 --> 00:44:52,359 Speaker 1: when they have been in the criminal justice system. Um, 766 00:44:52,400 --> 00:44:56,440 Speaker 1: you have children who are tremendously impacted by being separated 767 00:44:56,480 --> 00:45:00,600 Speaker 1: from their parents. It's a huge it's simply because they 768 00:45:00,640 --> 00:45:07,560 Speaker 1: can't afford bail. That's it. That's why it's it's it's yeah, 769 00:45:07,640 --> 00:45:10,520 Speaker 1: it doesn't, it doesn't make any sense. It's a horrendous system, 770 00:45:10,600 --> 00:45:15,640 Speaker 1: which again creates disadvantaged people who don't have money to 771 00:45:15,719 --> 00:45:18,520 Speaker 1: pay for it or access to I mean, it creates 772 00:45:18,560 --> 00:45:23,040 Speaker 1: a horrendous it's a horrendous example of classism. Harvey Weinstein 773 00:45:23,080 --> 00:45:24,960 Speaker 1: could pay a million dollars in bail and go home 774 00:45:25,000 --> 00:45:28,600 Speaker 1: to his fancy house. But a working mom who potentially 775 00:45:28,600 --> 00:45:32,320 Speaker 1: has not even committed a crime, who might very quickly 776 00:45:32,360 --> 00:45:35,320 Speaker 1: be found not guilty once she gets to go to trial, 777 00:45:35,920 --> 00:45:38,400 Speaker 1: has to sit in a jail cell until said trial. 778 00:45:39,840 --> 00:45:41,680 Speaker 1: I want to I want to talk on sort of 779 00:45:41,719 --> 00:45:44,440 Speaker 1: the other end. You know, we're talking criminal justice, but 780 00:45:45,400 --> 00:45:49,480 Speaker 1: as we've been referencing higher education and and you know, 781 00:45:49,520 --> 00:45:53,799 Speaker 1: women in the workforce. You wrote as well about the 782 00:45:53,840 --> 00:45:57,400 Speaker 1: fact that we assume higher education is a great equalizer, 783 00:45:58,360 --> 00:46:02,319 Speaker 1: and in fact it isn't because the wage gap makes 784 00:46:02,360 --> 00:46:06,800 Speaker 1: it harder for women to pay back their loans than men. Yes, 785 00:46:07,400 --> 00:46:09,480 Speaker 1: and they take out more loans than men because of 786 00:46:09,520 --> 00:46:11,759 Speaker 1: the wage gap. That is, they earn less when they're 787 00:46:11,760 --> 00:46:14,239 Speaker 1: in college, so it starts so they have to take 788 00:46:14,280 --> 00:46:17,000 Speaker 1: more out and then they are less able to pay 789 00:46:17,040 --> 00:46:20,560 Speaker 1: their student loans back. So the student loan issue is 790 00:46:20,600 --> 00:46:23,840 Speaker 1: actually a gender equity issue because while women are fifty 791 00:46:23,880 --> 00:46:27,359 Speaker 1: seven percent of all students, of all college graduates a 792 00:46:27,360 --> 00:46:31,920 Speaker 1: bachelor's degree and higher, they hold sixty seven percent of 793 00:46:31,960 --> 00:46:34,359 Speaker 1: all student loan debts. So that issue, you know, when 794 00:46:34,360 --> 00:46:36,840 Speaker 1: we hear it on the campaign trail, that is also 795 00:46:37,120 --> 00:46:41,360 Speaker 1: about gender equity because it's not, to your point, only 796 00:46:41,400 --> 00:46:44,560 Speaker 1: about less money coming into women's wallets. It's actually about 797 00:46:44,560 --> 00:46:49,200 Speaker 1: more money coming out, right, So we have less coming 798 00:46:49,200 --> 00:46:51,600 Speaker 1: into our wallets, and then we've got ten percent more 799 00:46:51,719 --> 00:46:54,600 Speaker 1: going out, so you start to start to carve that 800 00:46:54,680 --> 00:46:58,200 Speaker 1: out right, And now you know, essentially if you actually 801 00:46:58,600 --> 00:47:01,680 Speaker 1: did the gender wage gap based on the pink tax, 802 00:47:02,239 --> 00:47:05,760 Speaker 1: student loan debt and the wage gap, you would actually 803 00:47:05,760 --> 00:47:09,160 Speaker 1: find that we probably earned somewhere near of what men do, 804 00:47:09,400 --> 00:47:13,239 Speaker 1: just in terms of discretionary income. Of course, wow, that's 805 00:47:13,280 --> 00:47:16,200 Speaker 1: fascinating because the stats are horrible. I mean, we talked 806 00:47:16,200 --> 00:47:19,600 Speaker 1: about the fact that the gender pay gap has widened 807 00:47:19,600 --> 00:47:22,880 Speaker 1: in the last ten years since two thousand ten, and 808 00:47:23,520 --> 00:47:26,160 Speaker 1: women earn eighty cents for every dollar their male counterparts 809 00:47:26,160 --> 00:47:28,919 Speaker 1: are earning. And that's an average obviously across the US 810 00:47:28,960 --> 00:47:31,360 Speaker 1: for women, for women of color, it's far worse. The 811 00:47:31,440 --> 00:47:35,560 Speaker 1: pay gap is larger. Latina's, for example, earn on the dollar, 812 00:47:35,960 --> 00:47:38,759 Speaker 1: and more than half of Latina mothers sited from your 813 00:47:38,840 --> 00:47:42,120 Speaker 1: article are are the breadwinners in their family. And to 814 00:47:42,160 --> 00:47:45,960 Speaker 1: put it in perspective to your point about what that 815 00:47:46,040 --> 00:47:48,800 Speaker 1: means as far as the numbers over a lifetime, fifty 816 00:47:49,040 --> 00:47:51,400 Speaker 1: cents on the dollar means over a million dollars and 817 00:47:51,480 --> 00:47:55,520 Speaker 1: lost wages over the course of a Latina's full time 818 00:47:55,560 --> 00:47:59,600 Speaker 1: for to year career. Now, when we begin to think about, 819 00:47:59,640 --> 00:48:02,719 Speaker 1: to your point, how much more the loans cost, how 820 00:48:02,719 --> 00:48:05,919 Speaker 1: many more years of interest you're paying, how how much 821 00:48:05,960 --> 00:48:09,680 Speaker 1: longer it takes to pay those things off, that million 822 00:48:09,719 --> 00:48:12,880 Speaker 1: dollars becomes a million and a half. That million dollars 823 00:48:12,880 --> 00:48:18,040 Speaker 1: becomes maybe two. We we don't know. That's That's something 824 00:48:18,080 --> 00:48:21,799 Speaker 1: that I hope for everyone listening at home, whether you're 825 00:48:21,800 --> 00:48:24,160 Speaker 1: a man or a woman, gets you fired up, because 826 00:48:24,680 --> 00:48:28,560 Speaker 1: if if that woman is your sister or your friend, 827 00:48:28,680 --> 00:48:30,440 Speaker 1: you should be upset for her. If that woman is 828 00:48:30,480 --> 00:48:33,280 Speaker 1: your partner, you should also be upset for yourself because 829 00:48:33,400 --> 00:48:36,120 Speaker 1: guess what, that's your household that's earning that much less money. 830 00:48:36,800 --> 00:48:39,960 Speaker 1: You know. We we need to personalize these issues and 831 00:48:40,040 --> 00:48:44,759 Speaker 1: realize that these fights belong to all of us. And 832 00:48:45,040 --> 00:48:49,759 Speaker 1: I do hope, per your point, that when people see 833 00:48:49,880 --> 00:48:54,960 Speaker 1: candidates out there campaigning, they really take the time, regardless 834 00:48:55,080 --> 00:48:59,279 Speaker 1: of you know, partisan garbage out there, regardless of which 835 00:48:59,320 --> 00:49:03,200 Speaker 1: news channel you watch that encourages you to, you know, 836 00:49:03,360 --> 00:49:07,040 Speaker 1: hate somebody for whatever reason. I really hope that people 837 00:49:07,080 --> 00:49:09,759 Speaker 1: are listening to what the candidates are saying on these subjects, 838 00:49:09,800 --> 00:49:14,920 Speaker 1: because no matter what party they're a part of, you know, 839 00:49:15,000 --> 00:49:17,919 Speaker 1: even if it surprises you, I would hope that people 840 00:49:17,960 --> 00:49:21,400 Speaker 1: would vote for the folks out there who want to 841 00:49:21,520 --> 00:49:24,919 Speaker 1: change these systems, who want to change student loan debt, 842 00:49:24,960 --> 00:49:28,400 Speaker 1: who want to change parity in the workplace, because it 843 00:49:28,840 --> 00:49:31,120 Speaker 1: does affect all of us. It holds all of us back, 844 00:49:31,239 --> 00:49:37,520 Speaker 1: male female. It's it's really really important. And and then obviously, 845 00:49:37,600 --> 00:49:40,399 Speaker 1: you know, things get even more complex when we get 846 00:49:40,400 --> 00:49:42,560 Speaker 1: outside of sex and we get into race, and we 847 00:49:42,640 --> 00:49:45,360 Speaker 1: get into the l g B, t q A community, 848 00:49:45,440 --> 00:49:49,919 Speaker 1: and it really matters that we are looking at this 849 00:49:50,360 --> 00:49:57,800 Speaker 1: as you're explaining it in this holistic, interconnected way. M Yeah, definitely. 850 00:49:57,800 --> 00:50:01,640 Speaker 1: And intersectionality is also something that is uh been talked 851 00:50:01,640 --> 00:50:05,279 Speaker 1: about more in the last decade, which is really good. 852 00:50:06,120 --> 00:50:08,560 Speaker 1: I have to actually have two reports that I haven't released, 853 00:50:08,640 --> 00:50:12,879 Speaker 1: but they will be released soon. One is actually retrospective, 854 00:50:12,880 --> 00:50:15,960 Speaker 1: which is gender equity over the last ten years, and 855 00:50:16,080 --> 00:50:19,440 Speaker 1: intersectionality is one of them that has become a lot 856 00:50:19,480 --> 00:50:23,960 Speaker 1: more important. So understanding gender plus age, gender plus race 857 00:50:23,960 --> 00:50:30,960 Speaker 1: and ethnicity, gender plus sexual orientation because typically women women 858 00:50:31,080 --> 00:50:34,320 Speaker 1: plus another diverse class like race and enicity, those women 859 00:50:34,360 --> 00:50:37,200 Speaker 1: tend to be farther behind, so we need to understand 860 00:50:37,320 --> 00:50:40,719 Speaker 1: what that impact is and solve for it. And then 861 00:50:40,760 --> 00:50:43,719 Speaker 1: also from a voting perspective, I wrote an article for 862 00:50:43,760 --> 00:50:45,960 Speaker 1: Fast Company and then I have another report that's coming 863 00:50:45,960 --> 00:50:49,000 Speaker 1: out that's a long form report because obviously articles are 864 00:50:49,000 --> 00:50:52,120 Speaker 1: a little bit shorter, which was about gender mainstreaming. And 865 00:50:52,200 --> 00:50:56,239 Speaker 1: so my article looked at I think five issues, but 866 00:50:56,400 --> 00:51:00,120 Speaker 1: the longer form report will be fifteen. And how you 867 00:51:00,200 --> 00:51:04,600 Speaker 1: can look at candidates platforms to see how they're committing 868 00:51:04,640 --> 00:51:07,359 Speaker 1: to gender equity and how you lens essentially all those 869 00:51:07,400 --> 00:51:12,560 Speaker 1: different issues from healthcare to immigration, to import taxes, etcetera, 870 00:51:12,640 --> 00:51:14,920 Speaker 1: through the lens of gender, and how you can actually 871 00:51:15,000 --> 00:51:19,200 Speaker 1: vote for that in the election. And what what is 872 00:51:19,239 --> 00:51:22,120 Speaker 1: sitting on the other side aside for US, which is 873 00:51:22,120 --> 00:51:26,000 Speaker 1: two trillion dollars and increased GDP in the US. If 874 00:51:26,040 --> 00:51:30,719 Speaker 1: we vote for candidates who commit to gender equity and 875 00:51:30,719 --> 00:51:36,200 Speaker 1: and lensing gender in every single policy, that's huge. I 876 00:51:36,239 --> 00:51:38,759 Speaker 1: can't wait to look at that stuff. Thank you in 877 00:51:38,800 --> 00:51:40,960 Speaker 1: advance or what you're about to put in the world. 878 00:51:41,960 --> 00:51:44,359 Speaker 1: You mentioned something as we were kind of going through 879 00:51:44,400 --> 00:51:48,120 Speaker 1: all of this, how many factors add up for us 880 00:51:48,160 --> 00:51:50,120 Speaker 1: and you know, we earn less and then we have 881 00:51:50,200 --> 00:51:52,239 Speaker 1: more going out and you and you said something about 882 00:51:52,239 --> 00:51:56,160 Speaker 1: the pink tax, which I think is the most insane thing. 883 00:51:56,800 --> 00:52:01,080 Speaker 1: That women's products cost seven per more than so called 884 00:52:01,120 --> 00:52:06,000 Speaker 1: men's products at least of the time. And you can 885 00:52:06,040 --> 00:52:08,480 Speaker 1: watch videos on this, guys, you know you can. You 886 00:52:08,520 --> 00:52:12,680 Speaker 1: can Google information about the pink tacks. One that I 887 00:52:12,719 --> 00:52:15,440 Speaker 1: saw that did such a good job of explaining it 888 00:52:15,440 --> 00:52:18,719 Speaker 1: showed how at the dry cleaner, a men's shirt costs 889 00:52:18,800 --> 00:52:21,880 Speaker 1: let's say five dollars and and the women's shirt to 890 00:52:22,040 --> 00:52:24,760 Speaker 1: dry clean costs six fifty and you go down every 891 00:52:24,800 --> 00:52:29,200 Speaker 1: single line item and it's more for us, which infuriates me. 892 00:52:29,239 --> 00:52:32,160 Speaker 1: Also because I'm like, my shirt, my my dress shirt 893 00:52:32,200 --> 00:52:34,040 Speaker 1: that I would wear under a suit is a lot 894 00:52:34,080 --> 00:52:36,319 Speaker 1: smaller than my dad's dress shirt that he wears under 895 00:52:36,360 --> 00:52:39,239 Speaker 1: his suit. I'm very pissed about this. But you know 896 00:52:39,320 --> 00:52:41,919 Speaker 1: that our razors cost more that that, all of our 897 00:52:41,960 --> 00:52:46,919 Speaker 1: products are more, shade and cream, everything are more expensive. 898 00:52:47,520 --> 00:52:52,960 Speaker 1: What do we do about this aside from buying men's razors? Well, 899 00:52:53,000 --> 00:52:56,759 Speaker 1: I do that the same, but but wherever I can, 900 00:52:56,800 --> 00:52:59,239 Speaker 1: because I'm like, it's cheaper. But there are actually two 901 00:52:59,280 --> 00:53:02,640 Speaker 1: things we can do about it, the pink tax. Overall, 902 00:53:02,719 --> 00:53:05,879 Speaker 1: there has been some movement in Congress, particularly with this 903 00:53:06,000 --> 00:53:09,840 Speaker 1: change in representation that we're still far from equitably represented 904 00:53:09,840 --> 00:53:13,280 Speaker 1: in the halls of Congress, around having a Consumer Rights 905 00:53:13,480 --> 00:53:18,440 Speaker 1: essentially Act past whereby you can't charge, making it illegal 906 00:53:18,560 --> 00:53:21,600 Speaker 1: to charge more for women's products. Than men's products. So 907 00:53:21,640 --> 00:53:25,160 Speaker 1: that's a consumer protection piece, which is sort of overall 908 00:53:25,200 --> 00:53:27,479 Speaker 1: for the pink tax. The second piece of it, which 909 00:53:27,560 --> 00:53:30,360 Speaker 1: is a subset, you know, a part of the pink 910 00:53:30,400 --> 00:53:35,319 Speaker 1: tax is our import taxes, so on average, and this 911 00:53:35,560 --> 00:53:39,600 Speaker 1: mostly comes into play for footwear and apparel. So the 912 00:53:40,320 --> 00:53:45,120 Speaker 1: majority I think it's seventy of the and I'm doing 913 00:53:45,120 --> 00:53:48,440 Speaker 1: this from memory, but about seventy five percent of the 914 00:53:48,480 --> 00:53:53,480 Speaker 1: import tax bringing the US households there is for is 915 00:53:53,520 --> 00:53:56,200 Speaker 1: for footwear and apparel, right, And most footwear and apparel 916 00:53:56,239 --> 00:53:58,200 Speaker 1: is not made here in the United States, right, It's 917 00:53:58,280 --> 00:54:02,120 Speaker 1: made in Asia, It's made in Southeast Asia. And I 918 00:54:02,160 --> 00:54:04,560 Speaker 1: think it's sixty but again I'd have to go look 919 00:54:04,600 --> 00:54:06,759 Speaker 1: at the numbers. But anyway, the majority of that is 920 00:54:06,800 --> 00:54:13,920 Speaker 1: actually born by women. So women actually pay more of 921 00:54:14,440 --> 00:54:18,880 Speaker 1: the import taxes. So on average, for the average import 922 00:54:18,960 --> 00:54:22,960 Speaker 1: tax for men's items is eleven point nine percent. That 923 00:54:23,080 --> 00:54:25,719 Speaker 1: is essentially the tax that's added over the cost of 924 00:54:25,800 --> 00:54:28,960 Speaker 1: the item once it's imported into the United States, and 925 00:54:29,080 --> 00:54:33,160 Speaker 1: for women's it's fifteen point one percent. And men sometimes 926 00:54:33,200 --> 00:54:36,880 Speaker 1: pay more, but women typically pay more than men. And 927 00:54:37,040 --> 00:54:40,080 Speaker 1: so so this is and this we can fix. We 928 00:54:40,239 --> 00:54:43,920 Speaker 1: do just lobby Congress to the reason why it exists 929 00:54:44,680 --> 00:54:47,719 Speaker 1: is that in the code in the tariff, in the 930 00:54:48,040 --> 00:54:52,200 Speaker 1: tax the tariff code, there are statistical categories how you 931 00:54:52,400 --> 00:54:55,840 Speaker 1: essentially calculate what tariff is assigned to each item, and 932 00:54:55,960 --> 00:55:01,640 Speaker 1: for footwear and apparel, gender is part of the statistical category. 933 00:55:02,200 --> 00:55:04,959 Speaker 1: So we can do one of two things. The first 934 00:55:05,000 --> 00:55:07,280 Speaker 1: thing is we can just remove gender from the code 935 00:55:07,600 --> 00:55:11,280 Speaker 1: so there's no difference shoes or shoes, shoes or shoes, 936 00:55:12,040 --> 00:55:15,000 Speaker 1: or we can lobby Congress to use the lower code 937 00:55:15,560 --> 00:55:19,719 Speaker 1: one or the other. That's wild when when we talk 938 00:55:19,760 --> 00:55:24,800 Speaker 1: about making change in government, Iceland comes to mind for 939 00:55:24,920 --> 00:55:29,160 Speaker 1: me because you know, they're famous for having had every 940 00:55:29,200 --> 00:55:32,560 Speaker 1: woman in the country walk out of work, every single one, 941 00:55:33,160 --> 00:55:36,040 Speaker 1: and then bam, they had equal pay. They had a 942 00:55:36,080 --> 00:55:39,959 Speaker 1: whole new model because the leaders realized that the women 943 00:55:40,000 --> 00:55:46,480 Speaker 1: were serious. And now Iceland is on lists about one 944 00:55:46,520 --> 00:55:49,200 Speaker 1: of the greatest places to work and to work for women. 945 00:55:49,840 --> 00:55:52,879 Speaker 1: Can can you explain how their model for equal pay 946 00:55:52,960 --> 00:55:56,840 Speaker 1: works and how we can try to pass true equal 947 00:55:56,880 --> 00:56:00,280 Speaker 1: pay legislation? And this goes back to the convert station 948 00:56:00,320 --> 00:56:04,080 Speaker 1: we were having before, which is that essentially what Iceland 949 00:56:04,120 --> 00:56:07,880 Speaker 1: did and it went into effect on January. One is 950 00:56:07,960 --> 00:56:12,879 Speaker 1: they shifted the burden from employees, so in this case 951 00:56:12,960 --> 00:56:16,719 Speaker 1: mostly women, but employees to bring a case that they 952 00:56:16,800 --> 00:56:21,839 Speaker 1: weren't paying being paid equitably to the employer, employers must 953 00:56:22,000 --> 00:56:25,960 Speaker 1: actually prove now that they are paying equitably and if 954 00:56:26,040 --> 00:56:30,040 Speaker 1: they don't, they face a fine. And and what's interesting 955 00:56:30,160 --> 00:56:32,880 Speaker 1: is we have sort of skirted some of this issue 956 00:56:33,080 --> 00:56:34,920 Speaker 1: in the US, we've kind of come up right to it. 957 00:56:35,080 --> 00:56:38,440 Speaker 1: So Kamala Harris when she was in the presidential election, 958 00:56:38,600 --> 00:56:42,840 Speaker 1: she talked about shifting to shifting the burden approved to 959 00:56:43,000 --> 00:56:47,360 Speaker 1: employers and find you know, finding them in the and 960 00:56:47,960 --> 00:56:50,960 Speaker 1: I interviewed for of the presidential candidates UM and they 961 00:56:51,080 --> 00:56:54,640 Speaker 1: also committed to that as well as did UM They 962 00:56:54,680 --> 00:56:56,960 Speaker 1: did in a recent I think Fortune article as well. 963 00:56:57,600 --> 00:56:59,880 Speaker 1: But that we've done that. The other thing that we 964 00:57:00,080 --> 00:57:04,960 Speaker 1: have done, which was uh legacy from President Obama, was 965 00:57:05,080 --> 00:57:07,960 Speaker 1: we actually, for the first time ever in twenty nineteen 966 00:57:08,040 --> 00:57:13,759 Speaker 1: so Toteen report, companies with a hundred employees or more 967 00:57:13,960 --> 00:57:19,280 Speaker 1: in the United States actually reported pay by gender to 968 00:57:19,520 --> 00:57:24,080 Speaker 1: the e o C. They added another component the issue 969 00:57:24,240 --> 00:57:27,240 Speaker 1: was they didn't release the data, the pattern data, which 970 00:57:27,280 --> 00:57:30,120 Speaker 1: they've always done for their demographic data, and they said 971 00:57:30,160 --> 00:57:32,640 Speaker 1: that they weren't going to collect it again. So you're 972 00:57:32,640 --> 00:57:37,160 Speaker 1: saying that they release other data that they collect, but 973 00:57:37,240 --> 00:57:40,520 Speaker 1: they weren't releasing data on pay and gender. That's right. 974 00:57:40,880 --> 00:57:43,480 Speaker 1: So the e o C was created and as part 975 00:57:43,520 --> 00:57:45,920 Speaker 1: of it was essentially part of Title seven of the 976 00:57:46,040 --> 00:57:49,280 Speaker 1: ninth Civil Rights Act of nineteen sixty four. They started 977 00:57:49,320 --> 00:57:53,000 Speaker 1: collecting that information, I believe in nineteen six and releasing 978 00:57:53,080 --> 00:57:55,640 Speaker 1: the pattern data. They don't release it to an employer 979 00:57:55,800 --> 00:57:58,640 Speaker 1: like the UK would, but they released the pattern data, 980 00:57:58,720 --> 00:58:01,680 Speaker 1: so there is precedent for them to release that data. 981 00:58:02,240 --> 00:58:06,080 Speaker 1: They chose, however, not to release that pattern data. Interesting, 982 00:58:06,800 --> 00:58:09,920 Speaker 1: isn't it interesting? I was on NPR that morning talking 983 00:58:10,000 --> 00:58:14,040 Speaker 1: about it and talking about the economics and economic importance 984 00:58:14,080 --> 00:58:16,960 Speaker 1: to your point about the five billion dollars that's sitting 985 00:58:17,040 --> 00:58:21,160 Speaker 1: on the table for us if we actually shift the 986 00:58:21,240 --> 00:58:24,720 Speaker 1: burden of proof from women in particular and people of 987 00:58:24,800 --> 00:58:30,480 Speaker 1: color to UM employers. So what can people listening at home, 988 00:58:31,240 --> 00:58:34,520 Speaker 1: both men and women in our audience and people who 989 00:58:34,640 --> 00:58:38,439 Speaker 1: identify as neither, what can we all do to help 990 00:58:39,240 --> 00:58:43,680 Speaker 1: push the gap shut? That's a good question. And we 991 00:58:43,760 --> 00:58:46,080 Speaker 1: didn't talk about pipeline, so all the time in a 992 00:58:46,160 --> 00:58:50,520 Speaker 1: second is okay. You know, I think the the things 993 00:58:50,640 --> 00:58:54,400 Speaker 1: that they can do is and we have an Equity 994 00:58:54,440 --> 00:58:56,800 Speaker 1: for All Report, which people can get on our website. 995 00:58:56,840 --> 00:58:59,760 Speaker 1: But you know, I think to really begin to shift 996 00:58:59,880 --> 00:59:03,200 Speaker 1: the conversation from one of an issue as an issue 997 00:59:03,240 --> 00:59:07,280 Speaker 1: of fairness, but actually as an issue of economic opportunity. Yes, 998 00:59:07,480 --> 00:59:09,560 Speaker 1: we've done a fairly good job in our Equity for 999 00:59:09,640 --> 00:59:12,720 Speaker 1: All Report. There's other resources as well, but to lens 1000 00:59:12,840 --> 00:59:16,920 Speaker 1: that and talk about that, that's one. They can contact 1001 00:59:17,000 --> 00:59:21,360 Speaker 1: their representatives in Congress, their senators, their rep in the 1002 00:59:21,480 --> 00:59:24,320 Speaker 1: House to advocate, you know, think for things like the 1003 00:59:24,520 --> 00:59:28,600 Speaker 1: Fair Paycheck Fairness Act. There's other pieces that they can do. 1004 00:59:28,720 --> 00:59:30,959 Speaker 1: They can lobby companies. I mean one of the great 1005 00:59:31,080 --> 00:59:35,120 Speaker 1: equalizers today is social media. We can use social media 1006 00:59:35,200 --> 00:59:39,000 Speaker 1: to catalyze toward action. I mean that's fundamentally me too write. 1007 00:59:39,040 --> 00:59:41,680 Speaker 1: That is what me Too was about was catalyzed. So 1008 00:59:41,760 --> 00:59:45,200 Speaker 1: we can use social media to catalyze toward action. Those 1009 00:59:45,240 --> 00:59:49,440 Speaker 1: are really specific things that we can we can do. 1010 00:59:49,840 --> 00:59:53,200 Speaker 1: In addition to if you're in a company, most companies 1011 00:59:53,280 --> 00:59:56,880 Speaker 1: now have women's employee resource groups or something that they're 1012 00:59:56,960 --> 01:00:00,080 Speaker 1: doing in terms of that and to really begin to 1013 01:00:01,240 --> 01:00:05,360 Speaker 1: bring up the issue of not just sponsorship, not just 1014 01:00:05,480 --> 01:00:08,040 Speaker 1: some of the things that are kind of typically brought up, 1015 01:00:08,080 --> 01:00:11,680 Speaker 1: but how are we actually moving the needle through the 1016 01:00:11,760 --> 01:00:15,480 Speaker 1: decisions that we're making, because how we do that is 1017 01:00:15,560 --> 01:00:18,200 Speaker 1: how we will actually make change. It's not just about 1018 01:00:18,760 --> 01:00:21,800 Speaker 1: a woman having a sponsor. It's how many women did 1019 01:00:21,880 --> 01:00:24,840 Speaker 1: you actually promote? How many women are actually in your 1020 01:00:24,920 --> 01:00:27,400 Speaker 1: succession plan to be the CEO or to be the 1021 01:00:27,520 --> 01:00:30,840 Speaker 1: next chief revenue officer, or to be the next CEO 1022 01:00:31,080 --> 01:00:35,600 Speaker 1: of your organization. How are you actually ensuring that there 1023 01:00:35,760 --> 01:00:38,920 Speaker 1: isn't bias in your performance reviews so that women actually 1024 01:00:39,120 --> 01:00:41,160 Speaker 1: end up being you know, because of that, they end 1025 01:00:41,240 --> 01:00:44,160 Speaker 1: up being underrated, and then they're not putting your succession 1026 01:00:44,160 --> 01:00:47,720 Speaker 1: plan and they're not paid equitably. It's all those decisions. 1027 01:00:47,800 --> 01:00:50,800 Speaker 1: And actually in the Fortune five hundred, which most you know, 1028 01:00:52,560 --> 01:00:56,200 Speaker 1: there's a thirty million employees in the Fortune five d 1029 01:00:57,240 --> 01:01:00,000 Speaker 1: and you know, so you're talking about millions of decisions 1030 01:01:00,160 --> 01:01:03,160 Speaker 1: that we can actually change every year. So really, employees 1031 01:01:03,360 --> 01:01:06,440 Speaker 1: advocating for real change through the decisions that they're actually 1032 01:01:06,480 --> 01:01:08,640 Speaker 1: being made, not through things that sort of make us 1033 01:01:08,680 --> 01:01:11,800 Speaker 1: feel good, make us feel like we're making change, but 1034 01:01:11,880 --> 01:01:14,840 Speaker 1: we're not actually making change. And that's why in the 1035 01:01:14,960 --> 01:01:17,760 Speaker 1: last ten years the gender pay gap having gotten bigger. 1036 01:01:18,120 --> 01:01:21,520 Speaker 1: Despite the fact that we talk about women having sponsors, 1037 01:01:21,640 --> 01:01:24,640 Speaker 1: we talk about I don't know, teaching women to negotiate, 1038 01:01:24,720 --> 01:01:27,960 Speaker 1: which is another false narrative. But because women do negotiate 1039 01:01:28,000 --> 01:01:30,800 Speaker 1: as much as men, etcetera. But we need to shift 1040 01:01:30,880 --> 01:01:34,200 Speaker 1: the conversation off of women being a charity case to 1041 01:01:34,800 --> 01:01:38,920 Speaker 1: essentially ensuring that the system itself is equitable and and 1042 01:01:39,160 --> 01:01:41,680 Speaker 1: people on the ground need to advocate for those decisions 1043 01:01:41,760 --> 01:01:45,240 Speaker 1: being equitable. Yes, now I want to talk about pipeline 1044 01:01:45,360 --> 01:01:48,560 Speaker 1: because you've built something to help with this, and and 1045 01:01:48,720 --> 01:01:52,040 Speaker 1: before we get into it, because by blind is a 1046 01:01:52,720 --> 01:01:55,640 Speaker 1: SAS platform. It uses data science as its foundation. In 1047 01:01:55,920 --> 01:01:59,960 Speaker 1: the worlds of tech, people throw sas around a lot. 1048 01:02:00,440 --> 01:02:02,320 Speaker 1: Can you tell listeners at home who are like, what 1049 01:02:02,600 --> 01:02:05,640 Speaker 1: the frick is a SASS platform? Tell us what that 1050 01:02:05,880 --> 01:02:10,520 Speaker 1: is first, and then let's get into what pipeline does. Yeah. Sure, 1051 01:02:10,640 --> 01:02:13,680 Speaker 1: So SAS is just it's another it's a fancy word 1052 01:02:13,760 --> 01:02:17,440 Speaker 1: for cloud. Cloud is just another It's essentially where the 1053 01:02:17,520 --> 01:02:23,160 Speaker 1: servers live. That's basically like all data is um stored 1054 01:02:23,240 --> 01:02:26,680 Speaker 1: on a server. It used to be that it was 1055 01:02:26,800 --> 01:02:30,080 Speaker 1: sort of like give an example like da Vita here 1056 01:02:30,320 --> 01:02:33,960 Speaker 1: in in in Denver, they would have their own servers, 1057 01:02:34,400 --> 01:02:37,160 Speaker 1: store their own data on their own servers. So about 1058 01:02:37,200 --> 01:02:40,080 Speaker 1: twenty years ago that there was a big move and 1059 01:02:40,200 --> 01:02:43,800 Speaker 1: still is continuing to move data from your native servers 1060 01:02:43,920 --> 01:02:47,840 Speaker 1: into the cloud, which is essentially shared servers. So why 1061 01:02:47,960 --> 01:02:51,000 Speaker 1: that matters is you can actually then access that data, 1062 01:02:51,480 --> 01:02:54,960 Speaker 1: you can lay additional platforms off of it and and 1063 01:02:55,080 --> 01:02:59,080 Speaker 1: make change. So that is just essentially where the data lives. 1064 01:02:59,200 --> 01:03:01,760 Speaker 1: That's an oversimple vacation, but for non tech people, it's 1065 01:03:01,760 --> 01:03:03,880 Speaker 1: where the data lives. The data living in the cloud 1066 01:03:03,960 --> 01:03:06,720 Speaker 1: makes it more accessible. Making it more accessible means you 1067 01:03:06,800 --> 01:03:10,280 Speaker 1: can put additional software against it. Putting additional software against 1068 01:03:10,320 --> 01:03:17,400 Speaker 1: it means that you can have new solutions. So pipeline pipeline. 1069 01:03:17,680 --> 01:03:20,479 Speaker 1: So the idea behind pipeline. So I talked a little 1070 01:03:20,480 --> 01:03:23,400 Speaker 1: bit by my experience fighting for equitable pay. I fought 1071 01:03:23,440 --> 01:03:27,120 Speaker 1: another time, I won both times, and I was on 1072 01:03:27,760 --> 01:03:30,160 Speaker 1: a radio and when I fought after after the first 1073 01:03:30,200 --> 01:03:34,760 Speaker 1: time I fought for equitable pay, my commitment was so 1074 01:03:34,960 --> 01:03:38,960 Speaker 1: Mika Brazynski's book No Your Value actually came out right 1075 01:03:39,240 --> 01:03:41,800 Speaker 1: after that. The first time I thought to be paid 1076 01:03:41,880 --> 01:03:43,960 Speaker 1: fought to be paid equitably, and I thought, oh my gosh, 1077 01:03:44,000 --> 01:03:46,680 Speaker 1: I'm not alone. And then I had inherited two teams 1078 01:03:46,760 --> 01:03:49,040 Speaker 1: and I saw all the pay and equities in those teams, 1079 01:03:49,480 --> 01:03:53,040 Speaker 1: and so my commitment was, if you reported to me, 1080 01:03:53,360 --> 01:03:55,520 Speaker 1: I was going to do everything in my power to 1081 01:03:55,680 --> 01:03:59,920 Speaker 1: ensure you were paid equitably. So I uh. And then 1082 01:04:00,160 --> 01:04:03,000 Speaker 1: I was on a radio show for Game Changing Women 1083 01:04:03,520 --> 01:04:05,880 Speaker 1: and the topic was negotiation and pay. And much of 1084 01:04:06,080 --> 01:04:09,440 Speaker 1: my corporate experience is actually reporting up through sales, so 1085 01:04:09,480 --> 01:04:11,840 Speaker 1: it's reporting up through the revenue part of the business. 1086 01:04:12,480 --> 01:04:14,720 Speaker 1: And the host asked us if we ever thought the 1087 01:04:14,760 --> 01:04:17,520 Speaker 1: pay gap would be closed in our lifetime, and I said, well, 1088 01:04:17,560 --> 01:04:19,600 Speaker 1: not until we make it an economic issue. And then 1089 01:04:19,600 --> 01:04:21,600 Speaker 1: I thought, oh, I think I can solve that. So 1090 01:04:21,720 --> 01:04:24,360 Speaker 1: that was really where the idea for pipeline came from, 1091 01:04:24,400 --> 01:04:27,760 Speaker 1: which was turning this idea from kind of the right 1092 01:04:27,880 --> 01:04:29,960 Speaker 1: thing to do to the smart thing to do, and 1093 01:04:30,120 --> 01:04:33,080 Speaker 1: how do you actually make that visible to people in 1094 01:04:33,160 --> 01:04:36,600 Speaker 1: an organization. So the first thing we did was a 1095 01:04:36,680 --> 01:04:40,160 Speaker 1: research study across four thousand companies in twenty nine countries, 1096 01:04:40,680 --> 01:04:42,960 Speaker 1: and what we found was that for every ten percent 1097 01:04:43,000 --> 01:04:45,920 Speaker 1: increase in gender equity, there's a one to two percent 1098 01:04:45,960 --> 01:04:51,720 Speaker 1: increase in revenue. So if as a CEO, you're the 1099 01:04:51,800 --> 01:04:56,800 Speaker 1: fiduciary of your company, you're responsible for essentially maximizing the 1100 01:04:56,880 --> 01:04:59,520 Speaker 1: return to your shareholders. This is a really key lever 1101 01:04:59,640 --> 01:05:03,360 Speaker 1: that you can pull in order to do that. So 1102 01:05:04,360 --> 01:05:07,560 Speaker 1: what pipeline does. So we talked about my you know, 1103 01:05:07,680 --> 01:05:09,720 Speaker 1: me fighting to be paid equitably and what do other 1104 01:05:09,760 --> 01:05:12,440 Speaker 1: women do if they're not paralegals. You know, even though 1105 01:05:12,520 --> 01:05:15,240 Speaker 1: my story is one of success in terms of fighting 1106 01:05:15,320 --> 01:05:18,600 Speaker 1: be pay equitably and winning, there is the question of 1107 01:05:18,680 --> 01:05:20,640 Speaker 1: why did I have to research my rights in order 1108 01:05:20,680 --> 01:05:23,360 Speaker 1: to be treated fairly? Right? If we shift the burden 1109 01:05:23,440 --> 01:05:27,680 Speaker 1: off of employees to employers, that that makes it really 1110 01:05:27,920 --> 01:05:30,240 Speaker 1: that makes a difference. So what that's what essentially what 1111 01:05:30,320 --> 01:05:35,439 Speaker 1: pipeline does. We intercept were recommendations engine were augmented decision making, 1112 01:05:35,560 --> 01:05:37,880 Speaker 1: so kind of like if you use ways or you 1113 01:05:38,280 --> 01:05:41,720 Speaker 1: use Google Maps to get somewhere, We're like, we're like that, 1114 01:05:42,000 --> 01:05:45,040 Speaker 1: but for your company and for equitable decision making within 1115 01:05:45,160 --> 01:05:49,840 Speaker 1: your company. And there are five key decisions in the 1116 01:05:49,920 --> 01:05:55,720 Speaker 1: corporate world that companies make across their talent, which is hiring, pay, 1117 01:05:56,480 --> 01:06:01,480 Speaker 1: performance potential, and promotion ums essentially like who gets into 1118 01:06:01,560 --> 01:06:03,960 Speaker 1: a company, how much do they get paid, how much 1119 01:06:04,000 --> 01:06:07,560 Speaker 1: opportunity do they have? And so we intercept those decisions 1120 01:06:07,680 --> 01:06:11,479 Speaker 1: and make recommendations and then track it over time. Very cool. 1121 01:06:12,080 --> 01:06:16,479 Speaker 1: So we make it. We make gender equity possible through 1122 01:06:16,560 --> 01:06:21,080 Speaker 1: the decisions that companies are already making. One just really 1123 01:06:21,160 --> 01:06:23,120 Speaker 1: quick example to try and make it maybe a little 1124 01:06:23,120 --> 01:06:26,880 Speaker 1: bit more tangible for people is that in corporations there's 1125 01:06:26,960 --> 01:06:30,120 Speaker 1: essentially three decisions you make across your talent every year, 1126 01:06:30,560 --> 01:06:33,640 Speaker 1: which is performance, potential and pay. So if you think 1127 01:06:33,680 --> 01:06:37,280 Speaker 1: about the Fortune five hundred, they have thirty million employees 1128 01:06:37,320 --> 01:06:41,480 Speaker 1: in the Fortune five hundred, that's ninety million opportunities for 1129 01:06:41,640 --> 01:06:45,160 Speaker 1: Fortune for for the Fortune fred companies to move towards 1130 01:06:45,160 --> 01:06:48,720 Speaker 1: gender equity every single year. That's what's possible with the 1131 01:06:48,720 --> 01:06:52,920 Speaker 1: pipeline platform. And to your point, if out there in 1132 01:06:52,960 --> 01:06:57,400 Speaker 1: the workforce we become equitable, profit margins go up. There's 1133 01:06:57,440 --> 01:07:01,160 Speaker 1: not a limit, there's no finite bucket of economy. We 1134 01:07:01,240 --> 01:07:06,000 Speaker 1: can actually grow the US economy for all people if 1135 01:07:06,080 --> 01:07:11,480 Speaker 1: we create parity that's right for everyone. It is fundamentally 1136 01:07:11,520 --> 01:07:14,720 Speaker 1: about economic when you grow a company. I mean just 1137 01:07:14,800 --> 01:07:17,400 Speaker 1: look at like a salesforce as an example. But you 1138 01:07:17,480 --> 01:07:20,560 Speaker 1: know when you when companies are growing, people get more opportunity, 1139 01:07:20,680 --> 01:07:24,000 Speaker 1: they earn more money, there's more opportunity within the ecosystem. 1140 01:07:24,520 --> 01:07:27,080 Speaker 1: That's what you can create, That's what we can create 1141 01:07:27,120 --> 01:07:30,080 Speaker 1: as a collective. So for folks listening who might be 1142 01:07:30,320 --> 01:07:33,439 Speaker 1: a manager at their job, how would you recommend they 1143 01:07:34,280 --> 01:07:38,160 Speaker 1: take action to make sure they're not accidentally falling into 1144 01:07:38,240 --> 01:07:41,920 Speaker 1: any of these biased patterns. Yeah, that's a good question. 1145 01:07:42,120 --> 01:07:44,280 Speaker 1: I mean one way, I mean quite simply, you could 1146 01:07:44,320 --> 01:07:50,320 Speaker 1: implement pipeline. That's right, I mean, that's anyway that that's one. 1147 01:07:50,440 --> 01:07:54,240 Speaker 1: I think that doing an audit of the decisions that 1148 01:07:54,360 --> 01:07:57,600 Speaker 1: you're making is really important. So who what are your 1149 01:07:57,640 --> 01:08:01,520 Speaker 1: performance ratings look like? What is your succession plan look like? 1150 01:08:01,680 --> 01:08:04,720 Speaker 1: So who's in line to replace you? How diverse is 1151 01:08:04,840 --> 01:08:08,400 Speaker 1: that group? Who's going to be promoted? How are they paid? 1152 01:08:08,920 --> 01:08:11,400 Speaker 1: Those are different audits that you can step back and 1153 01:08:11,560 --> 01:08:14,560 Speaker 1: look and see what you can do. The other thing 1154 01:08:14,800 --> 01:08:17,880 Speaker 1: that is maybe a little bit more happens every day 1155 01:08:18,479 --> 01:08:23,200 Speaker 1: is this idea of amplifying women's voices in meetings so 1156 01:08:24,160 --> 01:08:27,920 Speaker 1: making sure that there's equitable air time when you have meetings, 1157 01:08:28,000 --> 01:08:31,479 Speaker 1: because what the data shows is that men are more 1158 01:08:32,080 --> 01:08:35,400 Speaker 1: typically talk more in meetings than women. So ensuring that 1159 01:08:35,800 --> 01:08:38,880 Speaker 1: women are have equitable air time, that women are not 1160 01:08:39,000 --> 01:08:43,000 Speaker 1: being interrupted, that there's a strategy in place to combat 1161 01:08:43,080 --> 01:08:46,040 Speaker 1: that ahead of time. UM, making sure that all of 1162 01:08:46,120 --> 01:08:49,479 Speaker 1: your meetings have both women and men in it. Are 1163 01:08:49,560 --> 01:08:51,960 Speaker 1: other ways that you can solve for that. And to 1164 01:08:52,080 --> 01:08:55,720 Speaker 1: your point about the time, the opportunity to speak, you know, 1165 01:08:55,920 --> 01:09:00,880 Speaker 1: it's it's really important to be not is conscious of it, 1166 01:09:01,000 --> 01:09:04,640 Speaker 1: but to actually implement a plan. You know, in in 1167 01:09:05,400 --> 01:09:09,280 Speaker 1: your article you reference you know, the article about the 1168 01:09:09,360 --> 01:09:12,680 Speaker 1: issue of a gender gap in media, you referenced that 1169 01:09:12,920 --> 01:09:16,439 Speaker 1: in the early stages of the debates, the presidential debates, UM, 1170 01:09:16,640 --> 01:09:18,680 Speaker 1: there was one in which Elizabeth Warren was asked four 1171 01:09:18,800 --> 01:09:22,360 Speaker 1: questions and a lot of other candidates were only asked too, 1172 01:09:22,960 --> 01:09:26,800 Speaker 1: but that the men on stage interrupted her seventy percent 1173 01:09:26,920 --> 01:09:30,439 Speaker 1: of the time that she was talking, so she didn't 1174 01:09:30,439 --> 01:09:34,320 Speaker 1: actually get to answer four questions. And you saw back 1175 01:09:34,400 --> 01:09:37,759 Speaker 1: in sixteen, after a debate between Donald Trump and Hillary Clinton, 1176 01:09:38,200 --> 01:09:40,960 Speaker 1: he went ballistic in the press about how Hillary had 1177 01:09:41,040 --> 01:09:43,280 Speaker 1: used up all of his time, and they had given 1178 01:09:43,320 --> 01:09:46,280 Speaker 1: her more time to talk, when in fact the clock 1179 01:09:47,320 --> 01:09:51,519 Speaker 1: came out the numbers of timing, and he'd spoken for 1180 01:09:51,600 --> 01:09:53,760 Speaker 1: fifty one minutes and she had spoken for forty nine. 1181 01:09:54,040 --> 01:09:57,160 Speaker 1: So he had still dominated the conversation, but to him, 1182 01:09:58,160 --> 01:10:04,120 Speaker 1: getting close to parity in time and attention to him 1183 01:10:04,320 --> 01:10:06,759 Speaker 1: felt as though he was having all of his time stolen. 1184 01:10:07,720 --> 01:10:10,200 Speaker 1: And I think that's a really important thing to highlight 1185 01:10:10,560 --> 01:10:12,560 Speaker 1: for men. Not to say in any way, shape or 1186 01:10:12,600 --> 01:10:15,240 Speaker 1: form that all men behave like Donald Trump, but that 1187 01:10:16,280 --> 01:10:19,120 Speaker 1: when you are used to dominating the conversation, when the 1188 01:10:19,160 --> 01:10:22,240 Speaker 1: conversation becomes equitable, it can feel as though you're losing, 1189 01:10:23,200 --> 01:10:27,120 Speaker 1: but in fact you're not, because again, when things become 1190 01:10:27,160 --> 01:10:31,439 Speaker 1: more equitable, everyone takes more money home. So that's right, Yeah, 1191 01:10:31,520 --> 01:10:34,280 Speaker 1: that's right. Well, And this is why also it's important 1192 01:10:34,320 --> 01:10:38,519 Speaker 1: to talk about gender equity as fifty percent men and women, 1193 01:10:39,080 --> 01:10:44,920 Speaker 1: because that gender inequity impacts men too, because otherwise men 1194 01:10:45,040 --> 01:10:49,599 Speaker 1: feel like it's invasion, not inclusion. Yes, which is exactly 1195 01:10:49,640 --> 01:10:52,120 Speaker 1: what you're talking about. Oh my gosh, it's equitable. Now 1196 01:10:52,200 --> 01:10:54,360 Speaker 1: I have more time taken away from me, right, So 1197 01:10:54,560 --> 01:10:57,840 Speaker 1: that that makes the difference in terms of in terms 1198 01:10:57,880 --> 01:11:01,400 Speaker 1: of understanding what that feels like. Yes, it's really important. 1199 01:11:02,160 --> 01:11:04,519 Speaker 1: I know you mentioned that you have some kind of 1200 01:11:04,760 --> 01:11:08,080 Speaker 1: retrospectives in data about the last decade coming out. Now 1201 01:11:08,120 --> 01:11:11,360 Speaker 1: that we're in and it's a big year with the 1202 01:11:11,600 --> 01:11:16,040 Speaker 1: election and all things coming toward us, what are what 1203 01:11:16,160 --> 01:11:21,280 Speaker 1: are your hopes? What are your hopes? My hopes, Oh, 1204 01:11:21,680 --> 01:11:25,240 Speaker 1: that's a good question. My hopes more immediately, because it's 1205 01:11:25,240 --> 01:11:31,679 Speaker 1: a presidential election, is that you know, in we close 1206 01:11:31,800 --> 01:11:34,639 Speaker 1: the gap in the halls of Congress by four points, 1207 01:11:34,880 --> 01:11:38,600 Speaker 1: so we went from women being twenty of Congress to 1208 01:11:38,720 --> 01:11:43,840 Speaker 1: women being of Congress and that. But however, women are 1209 01:11:44,600 --> 01:11:47,160 Speaker 1: of the population. So we have twenty seven points left 1210 01:11:47,240 --> 01:11:51,200 Speaker 1: to go. So my hope is that people don't stop 1211 01:11:51,479 --> 01:11:54,439 Speaker 1: that they cattle, They take that momentum and continue to 1212 01:11:54,560 --> 01:11:57,599 Speaker 1: move it forward, and every year we're getting at least 1213 01:11:58,000 --> 01:12:01,040 Speaker 1: four more points of representation in Congress, right, so that 1214 01:12:02,040 --> 01:12:04,040 Speaker 1: it won't be the end of this decade, bit pretty 1215 01:12:04,080 --> 01:12:06,160 Speaker 1: early in the next decade that we can actually have 1216 01:12:06,280 --> 01:12:10,400 Speaker 1: parody in Congress. That's one, and that matters in terms 1217 01:12:10,479 --> 01:12:13,840 Speaker 1: of how laws are written, how they're how our lived 1218 01:12:13,920 --> 01:12:17,479 Speaker 1: experience is actually in those laws, because when we assume 1219 01:12:17,760 --> 01:12:21,640 Speaker 1: that the system is not biased. We essentially bake our 1220 01:12:21,680 --> 01:12:24,479 Speaker 1: biases into the system, right, just look at the cash 1221 01:12:24,520 --> 01:12:28,200 Speaker 1: bail system. That's a perfect example. So that's one. The 1222 01:12:28,280 --> 01:12:31,479 Speaker 1: other is that we have the opportunity to close the 1223 01:12:31,560 --> 01:12:35,360 Speaker 1: gap and girls education. Education not just in the US, 1224 01:12:35,439 --> 01:12:39,560 Speaker 1: but globally. Education is the foundation of all economic opportunity 1225 01:12:40,000 --> 01:12:44,280 Speaker 1: for countries. Every additional year of education there's a temper 1226 01:12:44,360 --> 01:12:47,519 Speaker 1: cent increase in GDP that is within our sites in 1227 01:12:47,600 --> 01:12:50,080 Speaker 1: twelve years if we work really hard for it. It's 1228 01:12:50,120 --> 01:12:53,840 Speaker 1: also connected to the U N goals. That's um something 1229 01:12:53,920 --> 01:12:55,640 Speaker 1: that else that I would like to see. And then 1230 01:12:55,920 --> 01:12:57,599 Speaker 1: the third I mean, there's lots of things I would 1231 01:12:57,600 --> 01:12:59,479 Speaker 1: like to see, but I'll just add one more. Yeah, 1232 01:12:59,520 --> 01:13:02,200 Speaker 1: So the last thing that I will say is that 1233 01:13:02,439 --> 01:13:06,040 Speaker 1: I would like to see in the next decade that 1234 01:13:06,760 --> 01:13:10,600 Speaker 1: at the very least the Equality Act is past and 1235 01:13:10,800 --> 01:13:15,280 Speaker 1: l g B t Q citizens have protections across the 1236 01:13:15,400 --> 01:13:19,000 Speaker 1: United States because in twenties states in the US you 1237 01:13:19,120 --> 01:13:21,760 Speaker 1: can be fired because of who you are or who 1238 01:13:21,840 --> 01:13:25,080 Speaker 1: you love. That should be illegal. I would also like 1239 01:13:25,200 --> 01:13:28,439 Speaker 1: to see that's a good start because it amends and 1240 01:13:28,560 --> 01:13:33,439 Speaker 1: it adds sexual orientation. Uh, and sexual identity to Title seven. 1241 01:13:33,640 --> 01:13:35,720 Speaker 1: But that's not an ending. I would also like to 1242 01:13:35,720 --> 01:13:39,720 Speaker 1: see a constitutional amendment protecting that here here I'm on 1243 01:13:39,840 --> 01:13:43,120 Speaker 1: board for all of that. The day that this is daring, 1244 01:13:43,880 --> 01:13:48,760 Speaker 1: March thirty one is quite important because it symbolizes how 1245 01:13:48,920 --> 01:13:52,400 Speaker 1: far into the year women have had to work to 1246 01:13:52,479 --> 01:13:56,240 Speaker 1: earn what men earned in the previous year. The statistic 1247 01:13:56,400 --> 01:13:58,800 Speaker 1: kind of speaks for itself. It's it's hard to know 1248 01:13:58,920 --> 01:14:01,240 Speaker 1: what question to ask about it because the questions that 1249 01:14:01,320 --> 01:14:05,000 Speaker 1: come to mind for me are inappropriate and slightly rage field. 1250 01:14:05,680 --> 01:14:09,880 Speaker 1: But you know, I'm sitting here like, what the fuck 1251 01:14:10,160 --> 01:14:13,959 Speaker 1: are you kidding? Anger into action is a good catalyst. 1252 01:14:14,120 --> 01:14:16,439 Speaker 1: Anger into action. I like that. I like that a 1253 01:14:16,520 --> 01:14:21,560 Speaker 1: lot when we when we qualify or i suppose, quantify 1254 01:14:22,160 --> 01:14:24,760 Speaker 1: the reality of the pay gap in that way and 1255 01:14:24,880 --> 01:14:31,479 Speaker 1: we think about our goals for what can what can 1256 01:14:31,560 --> 01:14:35,679 Speaker 1: we all? Do? You know you mentioned you've you've educated 1257 01:14:35,760 --> 01:14:38,679 Speaker 1: us a lot on the r A and what's currently 1258 01:14:38,760 --> 01:14:42,400 Speaker 1: at stake. Would you suggest that every woman who might 1259 01:14:42,479 --> 01:14:46,400 Speaker 1: be angry about the fact that only today has she 1260 01:14:46,520 --> 01:14:51,800 Speaker 1: potentially earned what men earned last year? Would you encourage 1261 01:14:52,000 --> 01:14:55,040 Speaker 1: all of us to call our congress people and our 1262 01:14:55,120 --> 01:15:00,160 Speaker 1: senators to demand that these laws are enacted. What what 1263 01:15:00,240 --> 01:15:02,200 Speaker 1: do you think? What do you think we can do 1264 01:15:02,400 --> 01:15:06,960 Speaker 1: that that anger into action? Yes, I do. I think 1265 01:15:07,439 --> 01:15:13,280 Speaker 1: I think we I believe that the four things that 1266 01:15:13,320 --> 01:15:16,560 Speaker 1: we should catalyze towards action. One is the Fair to 1267 01:15:17,240 --> 01:15:19,720 Speaker 1: Paycheck Fairness Act. It doesn't do enough, but it's a 1268 01:15:19,760 --> 01:15:25,000 Speaker 1: start um the Equality Act for sure, because of intersectionality, 1269 01:15:25,520 --> 01:15:29,640 Speaker 1: we need to also the ear A and changing the 1270 01:15:30,240 --> 01:15:33,400 Speaker 1: time expiration on the ear A. And then the third 1271 01:15:33,720 --> 01:15:38,519 Speaker 1: is a true equal pay law that shifts the burden 1272 01:15:39,120 --> 01:15:44,200 Speaker 1: of proof for equal pay from UH women and employees 1273 01:15:44,479 --> 01:15:48,720 Speaker 1: to UH companies. And I think it's interesting, you know, 1274 01:15:49,720 --> 01:15:52,240 Speaker 1: even being a policy. I mean, I did action, but 1275 01:15:52,280 --> 01:15:53,720 Speaker 1: I was never at a rally, but I did go 1276 01:15:53,920 --> 01:15:57,200 Speaker 1: to the UH to Washington, D C. For the first 1277 01:15:57,320 --> 01:16:00,720 Speaker 1: Women's March. I think about for equal pay Day, what 1278 01:16:00,840 --> 01:16:03,200 Speaker 1: if we did. What if we did something like that 1279 01:16:03,320 --> 01:16:06,400 Speaker 1: and we had four very clear demands and we catalyze 1280 01:16:06,439 --> 01:16:10,760 Speaker 1: people towards action that those four things they must they 1281 01:16:10,920 --> 01:16:14,400 Speaker 1: must do. And we must also talk to the presidential 1282 01:16:14,479 --> 01:16:18,280 Speaker 1: candidates about equal pay that that this is fundamentally an 1283 01:16:18,320 --> 01:16:20,720 Speaker 1: economic issue. And here are the four things that we 1284 01:16:20,960 --> 01:16:23,600 Speaker 1: expect and by the way, women are the majority of 1285 01:16:23,640 --> 01:16:27,559 Speaker 1: all voters in the United UM we should start voting 1286 01:16:27,640 --> 01:16:30,439 Speaker 1: like it. We should, well, we should start voting like it. 1287 01:16:30,520 --> 01:16:34,200 Speaker 1: But we should also demand action. And on Equal Pay 1288 01:16:34,280 --> 01:16:36,680 Speaker 1: Day we should we should make the I mean, what 1289 01:16:36,800 --> 01:16:39,000 Speaker 1: if we had a march for equal pay? What if 1290 01:16:39,680 --> 01:16:42,000 Speaker 1: on this Equal Pay Day, on March thirty one, we 1291 01:16:42,080 --> 01:16:45,599 Speaker 1: did what Iceland did and we demanded what the women 1292 01:16:45,640 --> 01:16:48,280 Speaker 1: in Iceland did, and we we demanded equal pay. That 1293 01:16:48,479 --> 01:16:51,080 Speaker 1: is possible. I'm in you tell me where to be. 1294 01:16:51,280 --> 01:16:54,920 Speaker 1: Let's go. Okay, all right, We're gonna start around a 1295 01:16:55,000 --> 01:16:58,760 Speaker 1: march across the US and and those four pieces must 1296 01:16:58,800 --> 01:17:03,240 Speaker 1: be passed. I love that plan. I have one final 1297 01:17:03,360 --> 01:17:05,000 Speaker 1: question for you, and it is my favorite thing to 1298 01:17:05,080 --> 01:17:08,360 Speaker 1: ask everyone. The the bod is called work in progress. 1299 01:17:09,200 --> 01:17:11,679 Speaker 1: And when you hear the phrase, what comes to mind 1300 01:17:11,800 --> 01:17:14,080 Speaker 1: for you as a work in progress in your life 1301 01:17:14,200 --> 01:17:16,559 Speaker 1: right now? Oh? I love that. I love that. By 1302 01:17:16,600 --> 01:17:19,600 Speaker 1: the way, I love the name and the whole that 1303 01:17:19,720 --> 01:17:22,160 Speaker 1: you are enough and you're a work in progress. That 1304 01:17:22,240 --> 01:17:26,679 Speaker 1: they are not mutually exclusive. I think um the work 1305 01:17:27,320 --> 01:17:31,200 Speaker 1: in progress for me, especially three years into pipeline, it's 1306 01:17:31,280 --> 01:17:34,480 Speaker 1: very different than it was when I first started. Pipeline 1307 01:17:35,479 --> 01:17:40,160 Speaker 1: is really the I had to talk about a little bit. 1308 01:17:40,160 --> 01:17:42,400 Speaker 1: But the willingness to jump and the ledge will appear, 1309 01:17:42,560 --> 01:17:46,599 Speaker 1: like the willingness to be vulnerable and to make mistakes 1310 01:17:46,720 --> 01:17:51,200 Speaker 1: and to walk through fear without the guarantee that you 1311 01:17:51,280 --> 01:17:54,760 Speaker 1: will be successful, but with the belief that even if 1312 01:17:54,840 --> 01:17:57,559 Speaker 1: you're not successful, that has nothing to do with your worth. 1313 01:17:58,200 --> 01:18:02,320 Speaker 1: And I think my experience as has been that when 1314 01:18:02,360 --> 01:18:04,439 Speaker 1: I'm willing to do that, which is a lot of 1315 01:18:04,560 --> 01:18:06,240 Speaker 1: my life, but when I'm willing to do that and 1316 01:18:06,520 --> 01:18:08,880 Speaker 1: what I often call be brave, like when I am 1317 01:18:08,920 --> 01:18:13,320 Speaker 1: willing to be brave, the the changes that we can 1318 01:18:13,479 --> 01:18:16,960 Speaker 1: make from understanding that we are a work in progress, 1319 01:18:17,040 --> 01:18:21,000 Speaker 1: the voices that we can use, the the impact that 1320 01:18:21,120 --> 01:18:24,160 Speaker 1: we can have is far beyond anything that we could 1321 01:18:24,160 --> 01:18:27,240 Speaker 1: have ever imagined. That is that is possible. And if 1322 01:18:27,360 --> 01:18:31,280 Speaker 1: we do that, I mean, from my perspective quite frankly, 1323 01:18:31,320 --> 01:18:35,160 Speaker 1: gender equity is within our grasp. If we are willing 1324 01:18:35,640 --> 01:18:37,559 Speaker 1: to jump in, the ledge will appear. If we are 1325 01:18:37,640 --> 01:18:42,840 Speaker 1: willing to use our voices, it is possible. Yes, all 1326 01:18:42,960 --> 01:18:47,759 Speaker 1: of the yes, thank you, thank you, thank you for today. 1327 01:18:47,880 --> 01:18:52,680 Speaker 1: This has been informative and inspiring and I just I 1328 01:18:52,800 --> 01:18:55,280 Speaker 1: feel so fired up, and I'm for that everybody at 1329 01:18:55,320 --> 01:18:57,880 Speaker 1: home does too, and I can't wait to see the 1330 01:18:57,960 --> 01:19:01,920 Speaker 1: next reports. Just thank you for everything you do well likewise, 1331 01:19:02,000 --> 01:19:04,599 Speaker 1: and thank you, thank you for raising voices and using 1332 01:19:04,640 --> 01:19:13,640 Speaker 1: your platform. I I'm so grateful. Thanks. This show is 1333 01:19:13,680 --> 01:19:17,840 Speaker 1: executive produced by me, Sophia Bush, and sim Sarna. Our 1334 01:19:17,880 --> 01:19:22,400 Speaker 1: supervising producer is Alison Bresnick. Our associate producer is Kate Linley. 1335 01:19:22,800 --> 01:19:26,320 Speaker 1: This episode was edited by Matt Sasaki and our music 1336 01:19:26,439 --> 01:19:28,920 Speaker 1: was written by Jack Garrett and produced by Mark Foster. 1337 01:19:29,280 --> 01:19:31,240 Speaker 1: This show is brought to you by Grilliant Anatomy