1 00:00:00,200 --> 00:00:03,080 Speaker 1: All right. Let's say you're a successful white colored professional 2 00:00:03,160 --> 00:00:06,240 Speaker 1: in your thirties or forties, You have one or two kids, 3 00:00:06,440 --> 00:00:08,720 Speaker 1: take a few years off to take care of them, 4 00:00:08,760 --> 00:00:11,160 Speaker 1: and when they're finally settled in school, you decided it's 5 00:00:11,200 --> 00:00:13,520 Speaker 1: time to find a job again. And that's when things 6 00:00:13,560 --> 00:00:16,760 Speaker 1: start to get really hard. For many women and an 7 00:00:16,800 --> 00:00:21,279 Speaker 1: increasing number of men, this has been their reality. You 8 00:00:21,400 --> 00:00:23,759 Speaker 1: can't get a job again if you have such a 9 00:00:23,840 --> 00:00:27,520 Speaker 1: long gap on your resume. But that's starting to change 10 00:00:27,520 --> 00:00:31,040 Speaker 1: in the US, where the lowest unemployment rate in almost 11 00:00:31,080 --> 00:00:34,800 Speaker 1: seventeen years is beginning to bite. With the labor market 12 00:00:34,800 --> 00:00:38,800 Speaker 1: getting tighter, companies are looking at potential workers they might 13 00:00:38,920 --> 00:00:43,199 Speaker 1: previously not have considered. Today on benchmark, we'll talk with 14 00:00:43,240 --> 00:00:46,640 Speaker 1: someone who's made it her mission to make sure that happens. 15 00:00:55,440 --> 00:00:59,120 Speaker 1: I'm Scott Landman, economics editor with Bloomberg News in Washington, 16 00:00:59,560 --> 00:01:02,880 Speaker 1: and I'm Annual Moss, an economics writer at Bloomberg View 17 00:01:03,080 --> 00:01:06,039 Speaker 1: in New York. Before we get to our guest who's 18 00:01:06,080 --> 00:01:08,800 Speaker 1: an expert in this subject, let's bring in my colleague, 19 00:01:08,800 --> 00:01:11,760 Speaker 1: Craig Torres, a reporter for Bloomberg in Washington who covers 20 00:01:11,800 --> 00:01:15,040 Speaker 1: the U. S economy, and Federal Reserve, and recently wrote 21 00:01:15,080 --> 00:01:17,920 Speaker 1: about this issue. Craig, thanks for being here. Glad to 22 00:01:17,959 --> 00:01:21,640 Speaker 1: be here, so Craig. We often say that the labor 23 00:01:21,720 --> 00:01:25,160 Speaker 1: market is tight, with the jobless rate at just about 24 00:01:25,240 --> 00:01:29,240 Speaker 1: four But that doesn't mean anyone can get a job, right. 25 00:01:29,640 --> 00:01:34,800 Speaker 1: It means that companies have found the easy supply, let's 26 00:01:34,800 --> 00:01:38,080 Speaker 1: put it that way, and that the hard supply the 27 00:01:38,120 --> 00:01:41,399 Speaker 1: people they need to train, the people they need to 28 00:01:41,440 --> 00:01:45,000 Speaker 1: look for, who could all provide something to them in 29 00:01:45,040 --> 00:01:47,400 Speaker 1: the in the form of labor. They got to start 30 00:01:47,440 --> 00:01:50,760 Speaker 1: going out and looking for them or having programs to 31 00:01:50,840 --> 00:01:55,440 Speaker 1: draw them in. And is this entirely about supply? Is 32 00:01:55,480 --> 00:01:59,280 Speaker 1: it also about societies acceptance that, you know what, it's 33 00:01:59,320 --> 00:02:02,640 Speaker 1: okay for the guy to take some time out and 34 00:02:02,680 --> 00:02:05,680 Speaker 1: look after his kids. Once upon a time that would 35 00:02:05,680 --> 00:02:08,880 Speaker 1: have been considered the sole purview of the mother. What 36 00:02:08,960 --> 00:02:12,760 Speaker 1: we're talking about here is a bias about people who 37 00:02:12,800 --> 00:02:14,880 Speaker 1: have stepped out of the labor force. I think the 38 00:02:14,919 --> 00:02:19,480 Speaker 1: bias is probably softer for women. They say, well, maybe 39 00:02:20,040 --> 00:02:22,400 Speaker 1: you know you were stay at home mom, But still 40 00:02:22,440 --> 00:02:26,440 Speaker 1: does mothers face obstacles when they try and come back in. 41 00:02:26,600 --> 00:02:30,280 Speaker 1: There's age bias at work. There's the concern that their 42 00:02:30,280 --> 00:02:33,760 Speaker 1: skills have withered. What we're finding, what I wrote about, 43 00:02:33,880 --> 00:02:37,880 Speaker 1: is companies are are when these people do come back in, 44 00:02:38,480 --> 00:02:41,079 Speaker 1: they find that's not true. Many of these people sort 45 00:02:41,120 --> 00:02:45,000 Speaker 1: of stuck outside the labor market are highly qualified. We'll 46 00:02:45,200 --> 00:02:47,080 Speaker 1: find out a little bit more about how much this 47 00:02:47,200 --> 00:02:51,320 Speaker 1: is happening today. Talk with somebody who's truly an expert. 48 00:02:51,720 --> 00:02:54,919 Speaker 1: Carol Fishman Cohen is founder of a company called I 49 00:02:55,240 --> 00:02:58,840 Speaker 1: Read Launch, which consults with dozens of big companies, including 50 00:02:58,880 --> 00:03:02,079 Speaker 1: IBM and more than Haley on programs to bring back 51 00:03:02,160 --> 00:03:05,080 Speaker 1: qualified people who have spent time out of the workforce. 52 00:03:05,520 --> 00:03:08,440 Speaker 1: She's based in Boston and joins us from our one 53 00:03:08,440 --> 00:03:13,120 Speaker 1: oh six point one FM AM radio studio there. Carol, 54 00:03:13,240 --> 00:03:16,440 Speaker 1: Welcome to Benchmark. Thanks for having me. Carol, Could you 55 00:03:16,680 --> 00:03:19,040 Speaker 1: please first tell us a little bit about your own 56 00:03:19,160 --> 00:03:22,400 Speaker 1: story and why you left your job years ago, or 57 00:03:22,440 --> 00:03:24,880 Speaker 1: how it wasn't really your choice, and how you got 58 00:03:24,880 --> 00:03:29,280 Speaker 1: a job again. I was working in investment banking in 59 00:03:29,320 --> 00:03:32,600 Speaker 1: the late eighties here in Boston for Drexel Burnham Lambert 60 00:03:32,680 --> 00:03:35,800 Speaker 1: in their Boston corporate finance group, and I was on 61 00:03:35,880 --> 00:03:40,920 Speaker 1: maternity leave with my first child in February of Drexel collapsed, 62 00:03:41,720 --> 00:03:46,080 Speaker 1: so I had no company to return to. That began 63 00:03:46,200 --> 00:03:49,160 Speaker 1: my career break. I ended up having three more children 64 00:03:49,200 --> 00:03:52,560 Speaker 1: in quick succession. Did some project work during that five 65 00:03:52,640 --> 00:03:55,480 Speaker 1: year period I was having more children, and then I 66 00:03:55,560 --> 00:03:58,560 Speaker 1: was completely out of the workforce for the following six 67 00:03:58,640 --> 00:04:02,480 Speaker 1: years home with them, And in two thousand and one 68 00:04:02,840 --> 00:04:06,080 Speaker 1: I returned to work at Bain Capital in their high 69 00:04:06,480 --> 00:04:10,600 Speaker 1: debt management group, in part because there were extractile people 70 00:04:10,640 --> 00:04:13,520 Speaker 1: working there. But you know, in two thousand and one, 71 00:04:13,600 --> 00:04:16,520 Speaker 1: no one was talking about this topic. H There were 72 00:04:16,520 --> 00:04:20,280 Speaker 1: no programs, there was no media attention, and I didn't 73 00:04:20,279 --> 00:04:22,320 Speaker 1: even know any other people who had gone back to 74 00:04:22,360 --> 00:04:25,520 Speaker 1: work after taking career breaks at that point. So I 75 00:04:25,560 --> 00:04:29,440 Speaker 1: felt isolated and without a game plan. But I made 76 00:04:29,480 --> 00:04:31,799 Speaker 1: some mistakes, but then I did get back to work 77 00:04:31,960 --> 00:04:34,400 Speaker 1: and I learned a lot too. Now, Carol, how long 78 00:04:34,440 --> 00:04:38,880 Speaker 1: did it take between you deciding after your fourth child, 79 00:04:39,000 --> 00:04:41,960 Speaker 1: I'm going to get back in and the opportunity at 80 00:04:42,080 --> 00:04:45,120 Speaker 1: Bain opening up? In other words, were you an example 81 00:04:45,200 --> 00:04:48,599 Speaker 1: of the phenomenon way describing or because you were in 82 00:04:48,680 --> 00:04:52,120 Speaker 1: financial markets it was a little easier. Well, I wouldn't 83 00:04:52,120 --> 00:04:55,440 Speaker 1: say it was easy at all, So I probably made 84 00:04:55,480 --> 00:04:58,400 Speaker 1: the decision. I'm thinking in year nine of my eleven 85 00:04:58,440 --> 00:05:02,440 Speaker 1: year career break. I had just taken on the two 86 00:05:02,520 --> 00:05:05,880 Speaker 1: year term of PTO president at my kids school, and 87 00:05:05,920 --> 00:05:09,640 Speaker 1: I remember thinking, when this term is over, I'm going 88 00:05:09,720 --> 00:05:11,640 Speaker 1: to go back to work. And I had no idea 89 00:05:11,680 --> 00:05:13,720 Speaker 1: how I was going to do it, but I just 90 00:05:13,800 --> 00:05:16,240 Speaker 1: kind of put that date in my mind. So that 91 00:05:16,279 --> 00:05:19,520 Speaker 1: was in nine, and then in two thousand I ended 92 00:05:19,600 --> 00:05:24,040 Speaker 1: up going to my fifteen year business school reunion and 93 00:05:24,440 --> 00:05:27,320 Speaker 1: I ran into someone there who I hadn't been in 94 00:05:27,400 --> 00:05:30,520 Speaker 1: touch with in years, and she had become a head hunter, 95 00:05:30,839 --> 00:05:33,719 Speaker 1: and so I told her that I had this finance 96 00:05:33,760 --> 00:05:35,360 Speaker 1: career and I was on career break and I was 97 00:05:35,400 --> 00:05:39,159 Speaker 1: thinking about going back. And about nine months later, she 98 00:05:39,440 --> 00:05:42,480 Speaker 1: came back to me with a job in the Boston 99 00:05:42,560 --> 00:05:46,280 Speaker 1: area that she said she felt was perfect for my 100 00:05:46,400 --> 00:05:49,640 Speaker 1: skill set. Now I I always get a laugh out 101 00:05:49,640 --> 00:05:52,160 Speaker 1: of that because I I think what skill set? What 102 00:05:52,240 --> 00:05:55,240 Speaker 1: she's talking about? The skill set that she remembered that 103 00:05:55,320 --> 00:05:57,920 Speaker 1: I had when we were sitting next to each other 104 00:05:57,960 --> 00:06:01,479 Speaker 1: as first years in nineteen eighty three. Uh So that's 105 00:06:01,520 --> 00:06:05,000 Speaker 1: when this whole idea of being frozen in time came 106 00:06:05,000 --> 00:06:07,680 Speaker 1: into my mind that people from the past, people with 107 00:06:07,720 --> 00:06:10,200 Speaker 1: whom you work or went to school, remember you as 108 00:06:10,200 --> 00:06:13,040 Speaker 1: you were. Uh, even if your sense of self has 109 00:06:13,080 --> 00:06:15,200 Speaker 1: diminished over time if you've been on career break, and 110 00:06:15,240 --> 00:06:17,479 Speaker 1: that was certainly the case with me. So that was 111 00:06:17,520 --> 00:06:20,680 Speaker 1: what really started it going. I didn't end up getting 112 00:06:20,720 --> 00:06:23,159 Speaker 1: that job, but it did get me back in job 113 00:06:23,200 --> 00:06:26,279 Speaker 1: search mode, and then I started getting in touch with people, 114 00:06:26,760 --> 00:06:31,040 Speaker 1: and people were very encouraging because they remembered me from before. 115 00:06:31,600 --> 00:06:35,040 Speaker 1: And one of the most junior guys on an investment 116 00:06:35,040 --> 00:06:38,000 Speaker 1: banking team that I had worked with at Drexel became 117 00:06:38,040 --> 00:06:41,039 Speaker 1: a managing director at Bain Capital in the eleven years 118 00:06:41,080 --> 00:06:43,440 Speaker 1: while I was on career break, and he's the one 119 00:06:43,440 --> 00:06:45,640 Speaker 1: who opened the door for me to go interview there. 120 00:06:45,720 --> 00:06:47,680 Speaker 1: He said, I can't get you a job, but why 121 00:06:47,720 --> 00:06:49,839 Speaker 1: don't you come in and talk to a few people. 122 00:06:50,200 --> 00:06:53,159 Speaker 1: But that doesn't talk about or include the time that 123 00:06:53,240 --> 00:06:57,520 Speaker 1: I spent having informational interviews and informal conversations with people. 124 00:06:57,760 --> 00:07:00,919 Speaker 1: And also I was trying to re up my skill set. 125 00:07:00,960 --> 00:07:04,479 Speaker 1: I had to resubscribe to the Wall Street Journal. I 126 00:07:04,640 --> 00:07:07,400 Speaker 1: had hadn't read it for years, and read it cover 127 00:07:07,480 --> 00:07:09,920 Speaker 1: to cover for a good six months before I felt 128 00:07:09,960 --> 00:07:11,840 Speaker 1: like I had a handle on what was going on 129 00:07:11,880 --> 00:07:14,360 Speaker 1: in the business world. Again. I had to, you know, 130 00:07:14,400 --> 00:07:17,040 Speaker 1: look at my old business school cases and deals that 131 00:07:17,080 --> 00:07:21,160 Speaker 1: I worked on and and refresh my idea of how 132 00:07:21,160 --> 00:07:25,280 Speaker 1: to make certain calculations, and also understand the new market. Um, 133 00:07:25,280 --> 00:07:27,760 Speaker 1: what were the new financial products? What why were we 134 00:07:27,800 --> 00:07:30,960 Speaker 1: not using certain old ones anymore? And then there there 135 00:07:31,000 --> 00:07:33,400 Speaker 1: was so much consolidation in the market that I had 136 00:07:33,400 --> 00:07:36,480 Speaker 1: to even think about what companies exist now and what 137 00:07:36,600 --> 00:07:38,760 Speaker 1: don't exist, and which ones had their name change and 138 00:07:38,800 --> 00:07:41,120 Speaker 1: who had acquired who, So all of that was going 139 00:07:41,160 --> 00:07:44,320 Speaker 1: on at the same time. So that's a lot of 140 00:07:44,400 --> 00:07:48,200 Speaker 1: homework to do. And did this happen during that nine 141 00:07:48,240 --> 00:07:52,400 Speaker 1: month period between when you met your old classmate and 142 00:07:52,440 --> 00:07:55,680 Speaker 1: when they reached out with a potential opportunity or was 143 00:07:55,760 --> 00:08:00,240 Speaker 1: this after the potential opportunity kind of kicked him land 144 00:08:00,280 --> 00:08:04,920 Speaker 1: into high gia. I was starting to think about going back, 145 00:08:04,960 --> 00:08:07,320 Speaker 1: and I was new I wanted to go back into 146 00:08:07,360 --> 00:08:10,600 Speaker 1: something in finance, And actually that in itself was a mistake. 147 00:08:10,640 --> 00:08:13,520 Speaker 1: I didn't do a whole new career assessment for myself. 148 00:08:13,560 --> 00:08:16,400 Speaker 1: I just thought I'd come from financial analysis. I'm going 149 00:08:16,440 --> 00:08:19,600 Speaker 1: back into it. Uh And and later I realized I 150 00:08:19,600 --> 00:08:22,000 Speaker 1: should have done more of a full assessment, but that 151 00:08:22,080 --> 00:08:26,560 Speaker 1: probably started. It probably started maybe a year before I 152 00:08:26,600 --> 00:08:32,360 Speaker 1: got the job. Carol, Let's roll forward fifteen years. Why 153 00:08:32,440 --> 00:08:37,960 Speaker 1: are dozens of companies now looking for working moms, stay 154 00:08:38,000 --> 00:08:42,600 Speaker 1: at home dad's or simply those people who are forty 155 00:08:42,640 --> 00:08:45,280 Speaker 1: and older who haven't been able to get back in. 156 00:08:45,679 --> 00:08:49,160 Speaker 1: Why are every Why is everybody from Booze Allen to 157 00:08:49,320 --> 00:08:53,640 Speaker 1: Morgan Stanley to Barkley's going after this labor pool. You know, 158 00:08:53,640 --> 00:08:55,760 Speaker 1: it's so interesting to hear you talk about it in 159 00:08:55,840 --> 00:08:58,040 Speaker 1: that way, because you know, we've been in the space 160 00:08:58,280 --> 00:09:02,640 Speaker 1: UM for over ten years, toiling away talking about the 161 00:09:02,720 --> 00:09:06,559 Speaker 1: virtues of the return to work population, and it feels 162 00:09:06,600 --> 00:09:09,040 Speaker 1: like all of a sudden, there is a lot of 163 00:09:09,080 --> 00:09:12,720 Speaker 1: attention on this issue and companies across different industry sectors 164 00:09:12,760 --> 00:09:15,400 Speaker 1: are starting to engage with this pool. If you look 165 00:09:15,440 --> 00:09:19,280 Speaker 1: at the history, the first formal return to work internship 166 00:09:19,280 --> 00:09:22,160 Speaker 1: programs started in two thousand and eight with Gold and Sacks. 167 00:09:22,679 --> 00:09:25,560 Speaker 1: JP Morgan added one in two thousand thirteen, and then 168 00:09:25,720 --> 00:09:28,600 Speaker 1: UM three more financial services companies added them in two 169 00:09:28,640 --> 00:09:31,600 Speaker 1: thousand fourteen, and now we have even more. So I 170 00:09:31,640 --> 00:09:35,480 Speaker 1: think what happened was in the financial services sector there 171 00:09:35,559 --> 00:09:38,920 Speaker 1: was this sense of urgency around not enough women in 172 00:09:39,000 --> 00:09:42,679 Speaker 1: mid to senior level roles. And that happened because those 173 00:09:42,679 --> 00:09:45,959 Speaker 1: companies are so old and they have seen so many 174 00:09:46,000 --> 00:09:50,199 Speaker 1: generations at generation after generation of their employees go through 175 00:09:50,240 --> 00:09:52,800 Speaker 1: all these different life stages, and then they've seen women 176 00:09:53,040 --> 00:09:56,280 Speaker 1: peel off at in each one, and so suddenly there 177 00:09:56,280 --> 00:09:58,760 Speaker 1: became this urgency, we don't have enough women at mid 178 00:09:58,800 --> 00:10:02,000 Speaker 1: to senior level roles, and that is what triggered a 179 00:10:02,040 --> 00:10:05,200 Speaker 1: lot of these programs in that sector. And then once 180 00:10:05,280 --> 00:10:09,600 Speaker 1: the program started, even skeptical hiring managers saw who was 181 00:10:09,640 --> 00:10:11,640 Speaker 1: coming in the door in terms of the caliber of 182 00:10:11,640 --> 00:10:14,960 Speaker 1: who was participating, and then they got interested too, and 183 00:10:15,000 --> 00:10:18,560 Speaker 1: the programs started to expand. Carol, one thing that really 184 00:10:18,600 --> 00:10:23,080 Speaker 1: struck me is that even though these programs have multiplied, 185 00:10:23,400 --> 00:10:27,319 Speaker 1: as Craig said, companies are much more interested in the past. 186 00:10:27,360 --> 00:10:29,960 Speaker 1: I was struck by something that Craig had written in 187 00:10:30,000 --> 00:10:32,760 Speaker 1: his recent story that there were, you know, hundreds and 188 00:10:32,840 --> 00:10:35,800 Speaker 1: hundreds of applicants for maybe just a couple dozen or 189 00:10:35,800 --> 00:10:39,560 Speaker 1: a handful of spots at some of these companies. Is 190 00:10:39,600 --> 00:10:42,920 Speaker 1: that a sign that there still are a lot more 191 00:10:43,120 --> 00:10:46,280 Speaker 1: people on the outside trying to get into these programs, 192 00:10:46,280 --> 00:10:49,600 Speaker 1: and there is you know, an actual supply of these 193 00:10:49,679 --> 00:10:53,319 Speaker 1: kinds of jobs that are available. Well, what I would 194 00:10:53,320 --> 00:10:56,720 Speaker 1: say is we're only at the beginning of companies setting 195 00:10:56,800 --> 00:10:59,600 Speaker 1: up these programs and expanding them. So what I relaunched, 196 00:10:59,760 --> 00:11:03,000 Speaker 1: we work with the biggest companies in the world, in 197 00:11:03,160 --> 00:11:06,440 Speaker 1: part because of the potential to scale UM grow the 198 00:11:06,440 --> 00:11:11,480 Speaker 1: programs across divisions domestically and internationally UM. But really there 199 00:11:11,480 --> 00:11:14,280 Speaker 1: are only a handful of companies that are running these programs. 200 00:11:14,280 --> 00:11:16,160 Speaker 1: If you look, we we keep a running list. I 201 00:11:16,200 --> 00:11:20,000 Speaker 1: think we're up to about seventy two globally of paid 202 00:11:20,000 --> 00:11:23,960 Speaker 1: career reentry programs around the world, formal corporate programs. But 203 00:11:24,040 --> 00:11:27,200 Speaker 1: in terms of the supply, if you look at Bureau 204 00:11:27,200 --> 00:11:31,000 Speaker 1: of Labor Statistics micro data, and you look at educated 205 00:11:31,160 --> 00:11:34,560 Speaker 1: mothers of prime working age, so women between the ages 206 00:11:34,600 --> 00:11:37,720 Speaker 1: of fifty four with children under eighteen who have a 207 00:11:37,720 --> 00:11:41,280 Speaker 1: bachelor's degree or higher. If you look at that demographic, 208 00:11:41,360 --> 00:11:44,320 Speaker 1: about there are about two point six million of them 209 00:11:44,400 --> 00:11:47,760 Speaker 1: who are not in the labor force, and studies show 210 00:11:47,880 --> 00:11:53,080 Speaker 1: us roughly eight of them are interested in returning after 211 00:11:53,640 --> 00:11:58,160 Speaker 1: taking a career break. So I think we are only 212 00:11:58,240 --> 00:12:00,880 Speaker 1: at the tip of the iceberg in terms of tapping 213 00:12:00,920 --> 00:12:05,160 Speaker 1: this population. And remember that it's this is a dynamic number, 214 00:12:05,200 --> 00:12:08,160 Speaker 1: so there are constantly people leaving the workforce to join 215 00:12:08,240 --> 00:12:11,320 Speaker 1: this pool and then leaving the school to rejoin the workforce. 216 00:12:11,320 --> 00:12:14,160 Speaker 1: So we always have this fresh supply of roughly a 217 00:12:14,240 --> 00:12:17,600 Speaker 1: little over two million educated mothers of prime working age 218 00:12:17,640 --> 00:12:20,680 Speaker 1: who could participate in these programs. And that doesn't even 219 00:12:20,760 --> 00:12:23,600 Speaker 1: count the women and men who are taking career breaks 220 00:12:23,640 --> 00:12:26,320 Speaker 1: for reasons that have nothing to do with childcare, could 221 00:12:26,320 --> 00:12:30,120 Speaker 1: be elder care or other reasons. Carol Craig and others 222 00:12:30,160 --> 00:12:35,560 Speaker 1: here at Bloomberg have written about the skills mismatch more broadly, 223 00:12:35,600 --> 00:12:39,360 Speaker 1: and we've heard some CEOs complain about that. Now you've 224 00:12:39,400 --> 00:12:42,360 Speaker 1: talked about the impetus for some big companies to get 225 00:12:42,400 --> 00:12:47,520 Speaker 1: more women into executive ranks. How does that dovetail with 226 00:12:47,559 --> 00:12:50,800 Speaker 1: the skills mismatch that a lot of employees are starting 227 00:12:50,800 --> 00:12:56,040 Speaker 1: to complain increasingly volubly about. Are you talking mostly about 228 00:12:56,120 --> 00:13:00,880 Speaker 1: skills mismatch on the technical side or something more broad 229 00:13:00,920 --> 00:13:04,760 Speaker 1: than that. Technology would be one example and Craig feel 230 00:13:04,800 --> 00:13:06,840 Speaker 1: free to jump in here in a story of yours 231 00:13:06,840 --> 00:13:08,800 Speaker 1: that I edited a year or two ago. I believe 232 00:13:08,800 --> 00:13:11,760 Speaker 1: he was citing Boeing for example, but it certainly wasn't 233 00:13:11,760 --> 00:13:16,000 Speaker 1: limited to air space. I think what companies find, correct 234 00:13:16,040 --> 00:13:19,120 Speaker 1: me if I'm wrong, Carol, is that these people, the 235 00:13:19,160 --> 00:13:22,840 Speaker 1: reason they have internships is that these returners, as Carol 236 00:13:22,880 --> 00:13:25,800 Speaker 1: calls them, can learn on the job. And what they 237 00:13:25,840 --> 00:13:30,760 Speaker 1: find is, at least in finance, skills are still pretty sharp. 238 00:13:30,920 --> 00:13:33,760 Speaker 1: That some things have changed, but not a lot, and 239 00:13:33,800 --> 00:13:35,920 Speaker 1: it doesn't take that much time for these people to 240 00:13:35,920 --> 00:13:39,320 Speaker 1: get up to speed. Is that your experience, Carol, Yeah, 241 00:13:39,360 --> 00:13:41,520 Speaker 1: it's really interesting that you say this, because we've now 242 00:13:41,600 --> 00:13:46,479 Speaker 1: heard people say across industry sectors, like the fundamental principles 243 00:13:46,520 --> 00:13:51,560 Speaker 1: of multivarianable statistics analysis have not changed, only the tools 244 00:13:51,600 --> 00:13:55,200 Speaker 1: to interpret them, or you know, the fundamental ways that 245 00:13:55,320 --> 00:13:59,600 Speaker 1: you analyze risk and for certain financial products have not changed, 246 00:13:59,800 --> 00:14:03,200 Speaker 1: but only some of the software that you use in 247 00:14:03,320 --> 00:14:05,720 Speaker 1: order to do the analysis has changed. So we have 248 00:14:05,840 --> 00:14:09,120 Speaker 1: heard that. Let me just take the most extreme example, 249 00:14:09,200 --> 00:14:12,240 Speaker 1: and that would be people who are on long career 250 00:14:12,240 --> 00:14:16,320 Speaker 1: breaks from I T or engineering careers, because that's the 251 00:14:16,360 --> 00:14:19,680 Speaker 1: other area where we're doing a lot of work. And 252 00:14:19,800 --> 00:14:24,640 Speaker 1: at this point we have fourteen huge companies that will 253 00:14:24,640 --> 00:14:27,440 Speaker 1: all soon be eighteen that are part of an initiative 254 00:14:27,440 --> 00:14:29,800 Speaker 1: we co lead with the Society of Women Engineers called 255 00:14:29,800 --> 00:14:32,400 Speaker 1: the STEM re Entry Task Force, and all of those 256 00:14:32,440 --> 00:14:37,640 Speaker 1: companies are running paid re entry internship programs for returning 257 00:14:37,640 --> 00:14:40,480 Speaker 1: technical professionals. And these are people who've been out of 258 00:14:40,480 --> 00:14:43,400 Speaker 1: the workforce for years and years, so oftentimes they need 259 00:14:43,440 --> 00:14:46,120 Speaker 1: to take some kind of formal coursework in order to 260 00:14:46,520 --> 00:14:49,800 Speaker 1: have their skills updated to the point where they're viable candidates. 261 00:14:50,240 --> 00:14:53,200 Speaker 1: We have an unemployment right in this country of full 262 00:14:53,280 --> 00:14:59,440 Speaker 1: point one. What happens to the demographic phenomenon you're describing 263 00:15:00,400 --> 00:15:03,560 Speaker 1: next time the economy slows. Current expansion is the third 264 00:15:03,640 --> 00:15:05,840 Speaker 1: largest on record. It's going to end at some point. 265 00:15:06,280 --> 00:15:09,560 Speaker 1: So look, this is a high caliber labor pool that 266 00:15:09,680 --> 00:15:13,600 Speaker 1: companies looking for top talent should tap regardless of the 267 00:15:13,640 --> 00:15:17,080 Speaker 1: status of the economy. The mid career returned to work 268 00:15:17,120 --> 00:15:23,120 Speaker 1: internship programs at big companies are getting great results. Of 269 00:15:23,120 --> 00:15:26,640 Speaker 1: the participants in these programs are getting hired, So companies 270 00:15:26,680 --> 00:15:29,720 Speaker 1: should aim to attract the best candidates from the return 271 00:15:29,760 --> 00:15:33,680 Speaker 1: to workpool, just like any other talent pool. Carol, we're 272 00:15:33,680 --> 00:15:35,720 Speaker 1: out of time and we'll have to leave it there, 273 00:15:35,800 --> 00:15:38,440 Speaker 1: but this will be a really fascinating issue to see 274 00:15:38,440 --> 00:15:41,120 Speaker 1: how it develops in the coming years, especially if we 275 00:15:41,240 --> 00:15:44,640 Speaker 1: do have a downturn. Carol Fishman Cohen Craig, tourist, Thanks 276 00:15:44,640 --> 00:15:47,120 Speaker 1: so much for joining us today on Benchmark. Thank you 277 00:15:47,160 --> 00:15:53,480 Speaker 1: for having me. Benchmark will be back next week and 278 00:15:53,560 --> 00:15:55,840 Speaker 1: until then, you can find us on the Bloomberg terminal, 279 00:15:55,880 --> 00:15:58,680 Speaker 1: Bloomberg dot com, are Bloomberg app, as well as on 280 00:15:58,720 --> 00:16:02,440 Speaker 1: Apple Podcasts, pocket Cast, and Stitcher. While you're there, take 281 00:16:02,440 --> 00:16:04,320 Speaker 1: a minute to rate and review the show so more 282 00:16:04,360 --> 00:16:06,480 Speaker 1: listeners can find us and let us know what you 283 00:16:06,560 --> 00:16:08,960 Speaker 1: thought of the show. You can follow me on Twitter 284 00:16:09,080 --> 00:16:12,800 Speaker 1: at at Scott Landman. Craig is at at s Taurus. 285 00:16:12,920 --> 00:16:17,080 Speaker 1: Reporter Dan you are at most on the School Echo 286 00:16:17,560 --> 00:16:21,760 Speaker 1: and our guest Carol you are at. Benchmark is produced 287 00:16:21,760 --> 00:16:24,400 Speaker 1: by Sarah Patterson with the head of Bloomberg Podcast is 288 00:16:24,480 --> 00:16:27,520 Speaker 1: Francesco Levie. Thanks for listening. To see you next time.