1 00:00:00,000 --> 00:00:04,160 Speaker 1: We've got another angle on today's labor report. Uh, and 2 00:00:04,519 --> 00:00:05,880 Speaker 1: you know what we saw, of course, you know the 3 00:00:05,920 --> 00:00:09,840 Speaker 1: numbers coming in hotter than expected, revisions, higher wages. We're 4 00:00:09,840 --> 00:00:12,760 Speaker 1: seeing that spike and wages continuing, which we know plays 5 00:00:13,039 --> 00:00:16,000 Speaker 1: into overall inflation in the economy. Our next guest has 6 00:00:16,000 --> 00:00:18,960 Speaker 1: some thoughts on why inflation is tougher actually on working women. 7 00:00:19,280 --> 00:00:21,239 Speaker 1: So great to have back with us, Kataka Roy. She's 8 00:00:21,280 --> 00:00:24,480 Speaker 1: CEO at Pipeline. They use AI to work with companies 9 00:00:24,480 --> 00:00:26,840 Speaker 1: on gender biases, so they pull in all the data 10 00:00:27,120 --> 00:00:30,160 Speaker 1: and she joins us on the phone from Paris. Kataka, 11 00:00:30,280 --> 00:00:33,120 Speaker 1: good to be talking with you again. Tell us a 12 00:00:33,120 --> 00:00:36,040 Speaker 1: little bit about your perspective on what's going on in 13 00:00:36,080 --> 00:00:42,000 Speaker 1: the labor market, especially when it comes to women. Yeah, Carol, 14 00:00:42,040 --> 00:00:44,000 Speaker 1: thank you, it's great to be here. You know what 15 00:00:44,040 --> 00:00:47,320 Speaker 1: we saw in today's jobs report is that across all 16 00:00:47,400 --> 00:00:51,400 Speaker 1: cohorts of women, they've actually lost jobs. And since the 17 00:00:51,440 --> 00:00:54,440 Speaker 1: beginning of the pandemic, we actually have one point eight 18 00:00:54,480 --> 00:00:58,080 Speaker 1: million women missing from the labor force. And if we 19 00:00:58,200 --> 00:01:01,200 Speaker 1: brought those one point eight million women back, we could 20 00:01:01,240 --> 00:01:06,280 Speaker 1: actually close uh, the the the the gap of workers 21 00:01:06,319 --> 00:01:10,160 Speaker 1: by almost a quarter. And so we could actually make 22 00:01:10,200 --> 00:01:14,000 Speaker 1: our jobs market a little less hot Kataka. To be fair, 23 00:01:14,120 --> 00:01:16,160 Speaker 1: I do think we're all trying to figure out why 24 00:01:16,280 --> 00:01:19,360 Speaker 1: so many women and men are staying away from the 25 00:01:19,440 --> 00:01:21,920 Speaker 1: labor force. We talked about the available labor pool, right, 26 00:01:21,920 --> 00:01:25,120 Speaker 1: this is so participation. You know, it's been down coming 27 00:01:25,120 --> 00:01:27,440 Speaker 1: off of the pandemic. What are the reasons that women 28 00:01:27,480 --> 00:01:30,880 Speaker 1: in particular are not coming back. Yeah, we have a 29 00:01:30,920 --> 00:01:34,039 Speaker 1: couple of reasons. You know. One is you know, certainly 30 00:01:34,120 --> 00:01:36,880 Speaker 1: Childcrison talked about and that is part of the solution, 31 00:01:36,920 --> 00:01:39,960 Speaker 1: but it's not the only solution. There are two pieces. 32 00:01:40,400 --> 00:01:43,000 Speaker 1: One is actually equity in the labor force, and so 33 00:01:43,080 --> 00:01:46,120 Speaker 1: this goes beyond pay but actually ensuring that they have 34 00:01:46,240 --> 00:01:50,360 Speaker 1: equity of opportunity. Just to give you one example, women 35 00:01:50,400 --> 00:01:54,000 Speaker 1: are fifty eight percent of college graduate percent of the 36 00:01:54,640 --> 00:01:58,160 Speaker 1: labor force, and yet only eight percent of the fortune 37 00:01:58,200 --> 00:02:01,640 Speaker 1: five d CEO. That's one. The other piece is skilling. 38 00:02:02,120 --> 00:02:05,920 Speaker 1: So we actually leaked forward five years in terms of 39 00:02:05,960 --> 00:02:09,320 Speaker 1: digital acceleration during the pandemic. So the jobs that existed 40 00:02:09,360 --> 00:02:12,240 Speaker 1: at the beginning of that pandemic, yeah, don't necessarily exist 41 00:02:12,360 --> 00:02:15,280 Speaker 1: right now. So we've got a mismatch between skills that 42 00:02:15,360 --> 00:02:19,680 Speaker 1: companies need and the actual available workers. And so if 43 00:02:19,720 --> 00:02:23,800 Speaker 1: we skilled folks and ensured equitable skilling that is, equitable 44 00:02:23,840 --> 00:02:27,360 Speaker 1: access to skilling, but also the ability to apply those skills, 45 00:02:27,440 --> 00:02:32,040 Speaker 1: we could close that gap exclosure quickly. May Kodak, I 46 00:02:32,040 --> 00:02:34,680 Speaker 1: guess you're My question would be, what would you recommend 47 00:02:34,720 --> 00:02:37,120 Speaker 1: to policymakers given the data that you have and what 48 00:02:37,160 --> 00:02:38,560 Speaker 1: you know, like, how do we fix this from a 49 00:02:38,560 --> 00:02:44,240 Speaker 1: policy perspective? Well, you know, certainly the pay transparency laws 50 00:02:44,320 --> 00:02:46,760 Speaker 1: that have both gone into effect and are slated to 51 00:02:46,800 --> 00:02:49,239 Speaker 1: go into effect in the first of the year step 52 00:02:49,400 --> 00:02:52,280 Speaker 1: in the right direction. Okay, Well, let's let's break down 53 00:02:52,320 --> 00:02:54,040 Speaker 1: what we're talking about here, because not everyone knows what 54 00:02:54,080 --> 00:02:56,040 Speaker 1: those mean. You're talking about you know, recently a New 55 00:02:56,080 --> 00:02:58,480 Speaker 1: York City about a month ago, jobs that were posted 56 00:02:58,520 --> 00:03:01,960 Speaker 1: for certain companies had to include some sort of pay range. 57 00:03:02,000 --> 00:03:06,239 Speaker 1: Is that what you're referring to? Exactly? So on jobs postings, 58 00:03:06,480 --> 00:03:09,000 Speaker 1: Colorado was the first state to do that in the 59 00:03:09,040 --> 00:03:13,120 Speaker 1: New York City. Uh is the is the second area 60 00:03:13,200 --> 00:03:14,960 Speaker 1: to do them that We've got three more states that 61 00:03:15,000 --> 00:03:17,600 Speaker 1: will go live with that on January feet So that's 62 00:03:17,639 --> 00:03:20,120 Speaker 1: exactly what I'm talking about. And so it's a step 63 00:03:20,160 --> 00:03:22,840 Speaker 1: in the right direction in terms of putting guard rails up. 64 00:03:23,120 --> 00:03:25,960 Speaker 1: But what we really need from polishy makers is true 65 00:03:26,000 --> 00:03:29,040 Speaker 1: equal pay, and we don't have that in the United States. 66 00:03:29,160 --> 00:03:33,120 Speaker 1: That is actually ensuring that we're paying people equitably and 67 00:03:33,160 --> 00:03:36,360 Speaker 1: that companies are proving that they're paying people equitably. And 68 00:03:36,400 --> 00:03:38,920 Speaker 1: that's not only an issue of fairness, it's actually an 69 00:03:38,920 --> 00:03:42,280 Speaker 1: issue of economic growth. Because if we closed the gender 70 00:03:42,320 --> 00:03:44,920 Speaker 1: pay gap in the United States, we could close the 71 00:03:44,960 --> 00:03:48,320 Speaker 1: gender we could we could add five twelve billion to 72 00:03:48,400 --> 00:03:53,520 Speaker 1: the U. S economy. And every American taxpayer subsidizes the 73 00:03:53,560 --> 00:03:57,960 Speaker 1: pay gaps because women in particular are more likely to 74 00:03:58,200 --> 00:04:02,120 Speaker 1: rely on social welfare programs. Kataka one thing I don't 75 00:04:02,200 --> 00:04:05,120 Speaker 1: kind of get. And if if we're relying on policy makers, 76 00:04:05,120 --> 00:04:07,480 Speaker 1: it may take a little while to get that parody right. 77 00:04:07,520 --> 00:04:09,360 Speaker 1: I feel like we've been waiting for a long long 78 00:04:09,400 --> 00:04:12,160 Speaker 1: time already, so you know that to me continues to 79 00:04:12,200 --> 00:04:14,800 Speaker 1: be a slow process. But what about the private sector, 80 00:04:14,840 --> 00:04:17,560 Speaker 1: And I'm thinking about corporations. We see E s G 81 00:04:17,720 --> 00:04:20,040 Speaker 1: policies put being put out in a big way, d 82 00:04:20,120 --> 00:04:22,599 Speaker 1: E I policies being put out in a big way. 83 00:04:22,680 --> 00:04:25,479 Speaker 1: So what's the onus actually on the private sector and 84 00:04:25,520 --> 00:04:29,160 Speaker 1: companies to really move more aggressively on party. You work 85 00:04:29,200 --> 00:04:31,919 Speaker 1: with companies, you look at their data points. What's the 86 00:04:31,960 --> 00:04:35,320 Speaker 1: conversations that you're having as to why or what you're 87 00:04:35,360 --> 00:04:38,599 Speaker 1: hearing is to why companies aren't being kind of better 88 00:04:38,640 --> 00:04:41,000 Speaker 1: when it comes to making sure there's parody, especially on 89 00:04:41,080 --> 00:04:44,640 Speaker 1: pay an opportunity for men and women. Yeah, it's a 90 00:04:44,680 --> 00:04:48,240 Speaker 1: great question. The the the the issue isn't awareness in 91 00:04:48,320 --> 00:04:52,720 Speaker 1: the execution. So about percent of companies put equity in 92 00:04:52,760 --> 00:04:55,719 Speaker 1: their top priorities. The only the issue has been only 93 00:04:56,520 --> 00:04:59,280 Speaker 1: of employees regularly see it shared and measured. So it's 94 00:04:59,320 --> 00:05:03,320 Speaker 1: really a different between employer branding and the employee experience 95 00:05:03,720 --> 00:05:06,440 Speaker 1: and and what pipeline the company that High Run is 96 00:05:06,480 --> 00:05:08,839 Speaker 1: designed to do is to actually close that gamp, which 97 00:05:08,839 --> 00:05:12,159 Speaker 1: is to get ahead of the decisions that companies were making, 98 00:05:12,320 --> 00:05:16,000 Speaker 1: but to actually insure that they Yeah, no, I listen 99 00:05:16,000 --> 00:05:17,880 Speaker 1: to what you say, Kada like this high idea, Like 100 00:05:17,960 --> 00:05:21,000 Speaker 1: they have the awareness that's pretty high, But it's the execution. 101 00:05:21,080 --> 00:05:23,160 Speaker 1: And I know this is what you help companies do, 102 00:05:23,520 --> 00:05:26,239 Speaker 1: but why aren't they executing if they see the data 103 00:05:26,279 --> 00:05:29,120 Speaker 1: they understand just got about thirty seconds left here. Why 104 00:05:29,120 --> 00:05:33,040 Speaker 1: aren't they doing it? I think in the past they 105 00:05:33,040 --> 00:05:35,880 Speaker 1: haven't had the tools and the systems to actually do that, 106 00:05:35,960 --> 00:05:38,520 Speaker 1: and now they do with companies like Pipeline, and we 107 00:05:38,600 --> 00:05:42,800 Speaker 1: actually now just in the last month, UM expanded our 108 00:05:42,839 --> 00:05:45,520 Speaker 1: offerings to companies that have two hundred or more employees 109 00:05:46,040 --> 00:05:48,720 Speaker 1: as well as they can get a free equity baseline report. 110 00:05:48,800 --> 00:05:53,160 Speaker 1: So if there's not even a cost to it right now, alright, 111 00:05:53,160 --> 00:05:55,000 Speaker 1: We're gonna leave it on that note. Kodaka. Always good 112 00:05:55,000 --> 00:05:57,520 Speaker 1: to catch up with you, Kardaka Roy. She is chief 113 00:05:57,560 --> 00:06:00,640 Speaker 1: executive officer founder of Pipeline. John Gus on the phone 114 00:06:00,680 --> 00:06:01,159 Speaker 1: from Paris