1 00:00:02,279 --> 00:00:05,160 Speaker 1: More brands and businesses are becoming vocal in the movement 2 00:00:05,200 --> 00:00:07,720 Speaker 1: for racial justice and equity. Some are taking it a 3 00:00:07,760 --> 00:00:11,240 Speaker 1: step further, pledging donations and changes in their own practices 4 00:00:11,280 --> 00:00:14,760 Speaker 1: to address workforce and equity. Adidas, which also owns Reebok, 5 00:00:14,840 --> 00:00:17,160 Speaker 1: is pledging to increase the number of black and Latino 6 00:00:17,200 --> 00:00:19,000 Speaker 1: employees it hires by the end of next year. 7 00:00:19,040 --> 00:00:22,000 Speaker 2: Bank of America is pledging to address racial and economic 8 00:00:22,120 --> 00:00:25,480 Speaker 2: inequalities by partnering with community colleges and universities. 9 00:00:25,520 --> 00:00:29,160 Speaker 3: Microsoft CEO Satiandadella has now outlined some plans to double 10 00:00:29,200 --> 00:00:32,159 Speaker 3: the number of black managers and senior leaders in the 11 00:00:32,280 --> 00:00:34,200 Speaker 3: US of that company over the next five years. 12 00:00:35,200 --> 00:00:38,440 Speaker 4: In twenty twenty, after the murder of George Floyd in 13 00:00:38,520 --> 00:00:43,920 Speaker 4: Minneapolis led to protests across America, many US corporations pledged 14 00:00:43,920 --> 00:00:48,440 Speaker 4: to address racial imbalances in their workplaces. They promised to 15 00:00:48,560 --> 00:00:52,120 Speaker 4: hire and to promote more black people and others from 16 00:00:52,240 --> 00:00:56,920 Speaker 4: underrepresented groups. Now three years later, we're able to see 17 00:00:56,920 --> 00:01:00,720 Speaker 4: some of the early results. Bloomberg's Jeff green and Rebecca 18 00:01:00,760 --> 00:01:03,600 Speaker 4: Greenfield are here to talk about a new analysis by 19 00:01:03,640 --> 00:01:06,880 Speaker 4: a big team here in our newsroom. It shows for 20 00:01:06,920 --> 00:01:09,479 Speaker 4: the first time. How some of the most prominent US 21 00:01:09,560 --> 00:01:13,680 Speaker 4: companies did in making good on those promises. Where they've 22 00:01:13,680 --> 00:01:14,880 Speaker 4: made gains. 23 00:01:14,880 --> 00:01:19,440 Speaker 5: Across eighty eight companies, ninety four percent of those net 24 00:01:19,520 --> 00:01:22,319 Speaker 5: new people were people of color. 25 00:01:23,000 --> 00:01:24,640 Speaker 3: It's the kind of number when you see it, you 26 00:01:24,680 --> 00:01:26,440 Speaker 3: go back and you check all your data because you 27 00:01:26,480 --> 00:01:29,199 Speaker 3: figure you must have done something wrong. And we've done 28 00:01:29,200 --> 00:01:31,280 Speaker 3: that again and again, and it's correct. 29 00:01:31,920 --> 00:01:33,800 Speaker 4: And where they still fall short. 30 00:01:34,280 --> 00:01:36,760 Speaker 5: More than a quarter of companies and our data set 31 00:01:36,840 --> 00:01:40,600 Speaker 5: had fewer black executives and twenty twenty one than twenty twenties. 32 00:01:41,120 --> 00:01:43,360 Speaker 3: And the question we're talking about right now is did 33 00:01:43,400 --> 00:01:47,600 Speaker 3: it continue. 34 00:01:49,800 --> 00:01:53,920 Speaker 4: I'm west Ksova today on the big take workplace inequality 35 00:01:54,360 --> 00:02:06,160 Speaker 4: by the numbers. Jeff, you've been following these numbers for 36 00:02:06,400 --> 00:02:09,720 Speaker 4: three years, now, what got you started on this project? 37 00:02:10,680 --> 00:02:12,720 Speaker 3: We're trying to find a way to measure diversity, and 38 00:02:12,760 --> 00:02:15,360 Speaker 3: as Becca knows, it's really frustrating the lack of data 39 00:02:15,400 --> 00:02:17,919 Speaker 3: that's available that you can compare apples to apples. It 40 00:02:17,960 --> 00:02:21,360 Speaker 3: just doesn't exist except in this one database, which happens 41 00:02:21,360 --> 00:02:23,760 Speaker 3: to be secret and protected by the Supreme Court, so 42 00:02:23,800 --> 00:02:25,640 Speaker 3: you can't force companies to show it to you. 43 00:02:26,240 --> 00:02:27,399 Speaker 4: And what is this database? 44 00:02:27,880 --> 00:02:32,600 Speaker 3: Every year, the US Equal Employment Opportunity Commission requires companies 45 00:02:32,600 --> 00:02:35,040 Speaker 3: with more than one hundred workers to report the race 46 00:02:35,600 --> 00:02:40,480 Speaker 3: and gender for nine positions executive down to like service worker. 47 00:02:40,840 --> 00:02:43,480 Speaker 3: So basically every role, by every way you could think 48 00:02:43,520 --> 00:02:46,639 Speaker 3: of slicing and dicing it in the company. That information 49 00:02:46,800 --> 00:02:50,400 Speaker 3: is totally private unless the company volunteers to disclose it. 50 00:02:51,080 --> 00:02:52,920 Speaker 4: What does the government do with this information? 51 00:02:53,680 --> 00:02:56,120 Speaker 3: They aggregate it. They make a huge database of the 52 00:02:56,160 --> 00:02:58,440 Speaker 3: whole country, so you can track what's happening by like 53 00:02:58,560 --> 00:03:02,440 Speaker 3: city and region, non mislead and they also can specifically 54 00:03:02,520 --> 00:03:05,320 Speaker 3: use it to find really bad violators of like if 55 00:03:05,560 --> 00:03:07,280 Speaker 3: if your numbers are really wacky, they're going to come 56 00:03:07,320 --> 00:03:08,720 Speaker 3: and they're going to ask questions. 57 00:03:09,400 --> 00:03:12,240 Speaker 4: So when you went off trying to figure out, well, 58 00:03:12,280 --> 00:03:15,760 Speaker 4: how diverse is the American workforce at these big companies, 59 00:03:16,200 --> 00:03:17,120 Speaker 4: what did you have to do? 60 00:03:17,680 --> 00:03:21,560 Speaker 3: Well, we were aware of the data because Facebook and Google, 61 00:03:21,960 --> 00:03:24,000 Speaker 3: the tech companies, were under scrutiny. 62 00:03:24,320 --> 00:03:27,040 Speaker 6: Black professionals make up just five percent of the tech 63 00:03:27,120 --> 00:03:31,160 Speaker 6: workforce and three percent of executive positions. From twenty fourteen 64 00:03:31,200 --> 00:03:36,600 Speaker 6: to twenty twenty, black representation increased by only one percentage point. 65 00:03:36,040 --> 00:03:38,160 Speaker 3: And the banks were under pressure from the New York 66 00:03:38,200 --> 00:03:41,880 Speaker 3: City Controller to show they were hiring more fairly, especially 67 00:03:41,880 --> 00:03:45,640 Speaker 3: for women, and so they were convinced to release this 68 00:03:45,720 --> 00:03:47,440 Speaker 3: data to get these people off their back. So we 69 00:03:47,520 --> 00:03:49,760 Speaker 3: knew they existed. When we started this project, there were 70 00:03:49,800 --> 00:03:53,000 Speaker 3: about a dozen companies that had in some way released 71 00:03:53,000 --> 00:03:55,040 Speaker 3: this data to the public, so we knew it was possible. 72 00:03:55,640 --> 00:03:58,920 Speaker 4: And Jeff, what made you want to try to get 73 00:03:58,960 --> 00:04:01,240 Speaker 4: this information for his many companies as you could? 74 00:04:01,840 --> 00:04:04,160 Speaker 3: It was really the frustration of trying to figure out 75 00:04:04,200 --> 00:04:06,440 Speaker 3: what was really happening, because every company would report this 76 00:04:06,560 --> 00:04:08,840 Speaker 3: data in a different way, based on what made them 77 00:04:08,840 --> 00:04:11,280 Speaker 3: look good or how they felt comfortable. Some would give 78 00:04:11,760 --> 00:04:13,840 Speaker 3: just people of color, some would break it out by 79 00:04:13,840 --> 00:04:17,240 Speaker 3: different races, some would put all underrepresented groups together. And 80 00:04:17,279 --> 00:04:19,680 Speaker 3: it made it next to impossible to put two companies 81 00:04:19,720 --> 00:04:21,680 Speaker 3: next to each other and see how they're doing. So 82 00:04:22,040 --> 00:04:24,680 Speaker 3: this was the only way I could think of, you know, 83 00:04:24,760 --> 00:04:27,679 Speaker 3: apples to apples comparison, And there's a a federal law 84 00:04:28,120 --> 00:04:30,240 Speaker 3: that requires you to do this, and it says exactly 85 00:04:30,240 --> 00:04:31,880 Speaker 3: how to do it, and each company has to do 86 00:04:31,920 --> 00:04:34,640 Speaker 3: it the same way. So it is the only set 87 00:04:34,640 --> 00:04:37,719 Speaker 3: of data that I'm aware of at a federal level 88 00:04:37,760 --> 00:04:39,400 Speaker 3: that we could go to. So I just I just 89 00:04:39,480 --> 00:04:41,680 Speaker 3: kept making that case, and the companies just kept telling 90 00:04:41,720 --> 00:04:43,400 Speaker 3: me I was nuts and it's unreasonable. 91 00:04:44,720 --> 00:04:46,719 Speaker 5: Reporters like Jeff and I had been aware of this 92 00:04:46,839 --> 00:04:50,799 Speaker 5: data for a little while because companies were under pressure 93 00:04:50,920 --> 00:04:54,720 Speaker 5: to do better specifically with gender, specifically tech companies and banks. 94 00:04:55,080 --> 00:04:58,520 Speaker 5: But then there was a turning point, which was the 95 00:04:58,520 --> 00:05:01,400 Speaker 5: summer of twenty twenty when George Flood was murdered and 96 00:05:01,440 --> 00:05:05,760 Speaker 5: there were huge protests and a lot of big companies 97 00:05:06,080 --> 00:05:09,640 Speaker 5: basically said we are going to do better, and we're 98 00:05:09,680 --> 00:05:14,560 Speaker 5: going to hire and promote more black people specifically, but 99 00:05:14,640 --> 00:05:17,960 Speaker 5: then broadly more people of color in general. We know 100 00:05:18,120 --> 00:05:21,840 Speaker 5: that most workplaces are lopsided, that at the top are 101 00:05:21,880 --> 00:05:26,479 Speaker 5: mostly white men, and disproportionately so like, of course, you know, 102 00:05:26,480 --> 00:05:28,800 Speaker 5: white people are our larger share of the population, but 103 00:05:29,400 --> 00:05:33,000 Speaker 5: they still hold even more power and have more money 104 00:05:33,040 --> 00:05:35,719 Speaker 5: than other groups within the country. So that was when 105 00:05:35,720 --> 00:05:37,760 Speaker 5: we decided to say, okay, we want to look at this, 106 00:05:38,400 --> 00:05:41,360 Speaker 5: and that's when we systematically decided to ask the S 107 00:05:41,400 --> 00:05:43,520 Speaker 5: and P one hundred, will you give us these EO 108 00:05:43,600 --> 00:05:44,200 Speaker 5: one forms. 109 00:05:44,680 --> 00:05:46,719 Speaker 3: I would say that the murder of George Floyd definitely 110 00:05:46,800 --> 00:05:50,440 Speaker 3: broke this open as like a plausible request. Companies were 111 00:05:50,440 --> 00:05:53,559 Speaker 3: not so much suddenly happy to comply, but they were 112 00:05:54,000 --> 00:05:56,839 Speaker 3: reluctant to be on a list of companies that didn't comply. 113 00:05:57,440 --> 00:06:02,359 Speaker 3: Being singled out is not cooperating in racial equity was 114 00:06:02,400 --> 00:06:03,320 Speaker 3: a bad look. 115 00:06:04,080 --> 00:06:05,920 Speaker 4: So you went to all these companies, you asked for 116 00:06:05,960 --> 00:06:08,200 Speaker 4: the data. You went back and forth, and then you 117 00:06:08,360 --> 00:06:12,320 Speaker 4: got the numbers. Which companies gave you data of the 118 00:06:12,720 --> 00:06:15,480 Speaker 4: S and P one hundred, there were one hundred largest companies. 119 00:06:16,080 --> 00:06:17,960 Speaker 3: The first time we did it, it was twenty five. 120 00:06:18,320 --> 00:06:20,479 Speaker 3: The next time we did it was thirty seven, the 121 00:06:20,520 --> 00:06:22,840 Speaker 3: time after that it was somewhere in the seventies, and 122 00:06:23,000 --> 00:06:26,320 Speaker 3: now it's ninety six. Every time we went there were 123 00:06:26,320 --> 00:06:28,600 Speaker 3: more companies who were willing to comply, and we weren't 124 00:06:28,600 --> 00:06:31,239 Speaker 3: the only ones asking, but it became a very common question, 125 00:06:31,320 --> 00:06:33,440 Speaker 3: if you're serious, where's your EEO one? 126 00:06:34,000 --> 00:06:37,320 Speaker 5: And at first companies were trying to give us numbers 127 00:06:37,400 --> 00:06:40,040 Speaker 5: in different forms, you know, to make themselves look a 128 00:06:40,080 --> 00:06:42,760 Speaker 5: little better, or you know, they thought it was unfair 129 00:06:42,760 --> 00:06:45,760 Speaker 5: because the forum is very specific and doesn't work perfectly 130 00:06:45,839 --> 00:06:50,080 Speaker 5: for every company. Not every company has crafts workers, for example. 131 00:06:50,600 --> 00:06:53,760 Speaker 5: But over time, you know, we held farm and said, no, 132 00:06:53,800 --> 00:06:55,839 Speaker 5: we need the EEO one forum. That's the only way 133 00:06:56,040 --> 00:06:59,520 Speaker 5: to compare companies to each other. And now it's much 134 00:06:59,600 --> 00:07:02,479 Speaker 5: quicker to get that form. They're pretty familiar with. That's 135 00:07:02,520 --> 00:07:05,680 Speaker 5: what we want, that's what matters. Just give it to us. 136 00:07:06,000 --> 00:07:08,520 Speaker 4: And so what years do these data cover. 137 00:07:09,040 --> 00:07:12,240 Speaker 3: It's twenty twenty to twenty twenty one. Unfortunately, because of 138 00:07:12,280 --> 00:07:15,480 Speaker 3: COVID everything was slowed down. They haven't even submitted their 139 00:07:15,480 --> 00:07:17,600 Speaker 3: twenty twenty two data, so we don't know what that 140 00:07:17,640 --> 00:07:19,280 Speaker 3: looks like. They don't have to have that turned in 141 00:07:19,320 --> 00:07:22,600 Speaker 3: until December. So this is the most current data available 142 00:07:22,680 --> 00:07:24,000 Speaker 3: for these large companies. 143 00:07:25,720 --> 00:07:29,120 Speaker 4: And so essentially you have the numbers for right after 144 00:07:29,600 --> 00:07:32,280 Speaker 4: a lot of these companies made this big pledge, right 145 00:07:32,320 --> 00:07:35,280 Speaker 4: at the time when there was the most scrutiny on them. 146 00:07:35,600 --> 00:07:38,080 Speaker 4: And why don't we start talking about the numbers themselves? 147 00:07:38,520 --> 00:07:41,600 Speaker 4: What did you find in all this trove of data? 148 00:07:42,320 --> 00:07:45,920 Speaker 5: Jeff and some other really amazing reporters at Bloomberg decided 149 00:07:45,960 --> 00:07:50,760 Speaker 5: to look at where did things change on the margins, 150 00:07:50,800 --> 00:07:53,240 Speaker 5: because that's really where you're going to see changes Basically, 151 00:07:53,320 --> 00:07:57,080 Speaker 5: all companies can really do is hire people, promote people, 152 00:07:57,360 --> 00:07:59,600 Speaker 5: people retire, and people are laid off. So they looked 153 00:07:59,600 --> 00:08:03,760 Speaker 5: at these net worker changes. So that's gains and losses 154 00:08:03,960 --> 00:08:08,360 Speaker 5: across eighty eight companies. Ninety four percent of those net 155 00:08:08,440 --> 00:08:13,800 Speaker 5: new people were people of color, which is really astounding, 156 00:08:13,880 --> 00:08:16,360 Speaker 5: Like that is a big change. 157 00:08:16,880 --> 00:08:18,880 Speaker 4: So Jeff, let's break that down a little bit. When 158 00:08:18,920 --> 00:08:22,760 Speaker 4: you say ninety four percent of net new employees, what 159 00:08:22,800 --> 00:08:23,400 Speaker 4: does that mean. 160 00:08:24,360 --> 00:08:28,120 Speaker 3: So there's about nine million employees working at these companies. 161 00:08:28,600 --> 00:08:32,120 Speaker 3: They added about three hundred thousand new workers over the 162 00:08:32,160 --> 00:08:35,079 Speaker 3: time period we studied, and of those only about twenty 163 00:08:35,080 --> 00:08:37,280 Speaker 3: thousand were white. It's the kind of number when you 164 00:08:37,320 --> 00:08:39,000 Speaker 3: see it, you go back and you check all your 165 00:08:39,080 --> 00:08:41,360 Speaker 3: data because you figure you must have done something wrong. 166 00:08:41,720 --> 00:08:44,920 Speaker 3: And we've done that again and again and it's correct. 167 00:08:45,120 --> 00:08:47,600 Speaker 5: And I also want to say that we break it 168 00:08:47,679 --> 00:08:51,720 Speaker 5: out by different racial and ethnic groups, Asian, Hispanic, and black. 169 00:08:52,000 --> 00:08:54,199 Speaker 5: You know, they grew more than their share of the population. 170 00:08:54,440 --> 00:08:57,360 Speaker 5: So it's a disproportionate gain, is what we call that. 171 00:08:58,160 --> 00:09:01,600 Speaker 4: And so that top line numberley's something that these companies 172 00:09:01,640 --> 00:09:04,360 Speaker 4: would want to boast about. But then you dug more 173 00:09:04,400 --> 00:09:07,160 Speaker 4: deeply into the data and started to divide it up, 174 00:09:07,400 --> 00:09:11,040 Speaker 4: and there the picture becomes a little bit more complicated. 175 00:09:11,080 --> 00:09:13,320 Speaker 4: Can you describe what that looks like, Jeff. 176 00:09:13,920 --> 00:09:16,560 Speaker 3: What we found was that in the power positions like 177 00:09:16,600 --> 00:09:20,840 Speaker 3: executives and managers and professionals, there were fewer gains for 178 00:09:20,920 --> 00:09:25,000 Speaker 3: the underrepresented workers than in the broader sort of less skilled, 179 00:09:25,040 --> 00:09:28,120 Speaker 3: lower paying jobs. But still across the board they did better. 180 00:09:28,160 --> 00:09:30,680 Speaker 3: It's just as you would expect. It seems to be 181 00:09:30,760 --> 00:09:33,400 Speaker 3: harder for the companies to put people of color into 182 00:09:33,440 --> 00:09:35,280 Speaker 3: the power jobs than it is for them to get 183 00:09:35,320 --> 00:09:37,520 Speaker 3: them in the door, but at least across the board 184 00:09:37,559 --> 00:09:39,000 Speaker 3: they seem to show gains. 185 00:09:39,400 --> 00:09:42,600 Speaker 5: Also when you dig into specific companies. Of course, not 186 00:09:42,920 --> 00:09:46,960 Speaker 5: every company did everything across the board, but there were 187 00:09:47,000 --> 00:09:50,040 Speaker 5: some really big companies that did do interesting and notable things. 188 00:09:50,080 --> 00:09:53,600 Speaker 5: So one company that we single out is Amazon, which 189 00:09:53,800 --> 00:09:56,040 Speaker 5: is a big company with a lot of employees. It 190 00:09:56,080 --> 00:09:59,440 Speaker 5: made a lot of hires in twenty twenty one. Of course, 191 00:09:59,520 --> 00:10:03,280 Speaker 5: a lot of the gains among black and Hispanic workers. Specifically, 192 00:10:03,320 --> 00:10:05,680 Speaker 5: we're at the lowest level, so you know that's where 193 00:10:05,720 --> 00:10:09,440 Speaker 5: house jobs. But when you dig deeper, they also hired 194 00:10:09,760 --> 00:10:13,840 Speaker 5: a lot of people at the higher levels too. Another 195 00:10:13,960 --> 00:10:16,480 Speaker 5: company that stood out to us was Nike, and Nike's 196 00:10:16,520 --> 00:10:19,440 Speaker 5: much smaller company, but tends to have pretty high paying 197 00:10:19,480 --> 00:10:22,240 Speaker 5: them all paying jobs and pretty much for every single 198 00:10:22,400 --> 00:10:25,480 Speaker 5: racial and ethnic group, especially at the high paying level. 199 00:10:25,559 --> 00:10:30,280 Speaker 5: So that's executive manager, professional. Every race and ethnicity saw 200 00:10:30,360 --> 00:10:32,760 Speaker 5: some gains. So those are interesting. 201 00:10:32,400 --> 00:10:36,440 Speaker 4: Trends, Jeff, you also talked about some other companies. What 202 00:10:36,600 --> 00:10:38,920 Speaker 4: are some of the ones where it stood out to 203 00:10:38,960 --> 00:10:39,720 Speaker 4: you in the numbers. 204 00:10:40,520 --> 00:10:43,160 Speaker 3: CBS was interesting. They brought in a lot more people 205 00:10:43,240 --> 00:10:45,400 Speaker 3: for some of the similar reasons. I mean, this is COVID, 206 00:10:45,840 --> 00:10:47,800 Speaker 3: so you're seeing a lot of a COVID effect here. 207 00:10:47,800 --> 00:10:52,160 Speaker 3: But even interesting, home depot and lows shrink during this 208 00:10:52,240 --> 00:10:56,160 Speaker 3: time period, but their diversity improved. This is a big 209 00:10:56,280 --> 00:10:58,480 Speaker 3: question that we have is what happens when you lay 210 00:10:58,520 --> 00:11:02,400 Speaker 3: people off? Does diversity so in companies like that prove 211 00:11:02,480 --> 00:11:05,080 Speaker 3: the point. We could see it right there in the data. 212 00:11:05,480 --> 00:11:08,360 Speaker 3: They cut workers, but somehow they managed to maintain their 213 00:11:08,360 --> 00:11:10,920 Speaker 3: diversity and improve it slightly, which is a really relevant 214 00:11:11,000 --> 00:11:11,800 Speaker 3: question right now. 215 00:11:12,360 --> 00:11:15,240 Speaker 4: And does that suggest that even though they knew they 216 00:11:15,280 --> 00:11:18,840 Speaker 4: needed to cut, they were very specifically trying to maintain 217 00:11:18,920 --> 00:11:22,239 Speaker 4: diversity even though they had to shrink their overall numbers. 218 00:11:22,600 --> 00:11:24,960 Speaker 3: That is the premise. We've talked to some people not 219 00:11:25,080 --> 00:11:27,160 Speaker 3: in the s and p. One hundred about how they shrank, 220 00:11:27,200 --> 00:11:30,160 Speaker 3: and everyone that we talked to said, we've already recognized 221 00:11:30,200 --> 00:11:32,880 Speaker 3: that last in, first out. The idea that you fire 222 00:11:32,880 --> 00:11:35,440 Speaker 3: the newest workers in a downturn is the way to go. 223 00:11:35,520 --> 00:11:37,080 Speaker 3: It just doesn't work. So that gets rid of all 224 00:11:37,120 --> 00:11:39,400 Speaker 3: your diversity. Because your newest workers tend to be your 225 00:11:39,400 --> 00:11:43,439 Speaker 3: most diverse. It does appear that that's what was happening. 226 00:11:43,520 --> 00:11:46,080 Speaker 3: I mean, the problem is we don't know because no 227 00:11:46,080 --> 00:11:48,920 Speaker 3: one will talk to us about what their numbers actually say. 228 00:11:49,640 --> 00:11:53,080 Speaker 4: After the break why some companies are hesitant to point 229 00:11:53,160 --> 00:12:03,640 Speaker 4: out the progress they've made. Zecha, We've been talking about 230 00:12:03,640 --> 00:12:06,200 Speaker 4: some of the success stories, but you also found that 231 00:12:06,320 --> 00:12:08,720 Speaker 4: some companies moved in the other direction. 232 00:12:09,559 --> 00:12:12,280 Speaker 5: Yeah, so, I mean progress is uneven when you dig 233 00:12:12,320 --> 00:12:15,520 Speaker 5: into specific companies, not everyone is going to look the same. 234 00:12:15,880 --> 00:12:18,360 Speaker 5: More than a quarter of companies and our data set 235 00:12:18,400 --> 00:12:22,160 Speaker 5: had fewer black executives and twenty twenty one than twenty twenty, 236 00:12:22,200 --> 00:12:25,320 Speaker 5: So that's the opposite of what you want to see. 237 00:12:25,600 --> 00:12:28,280 Speaker 5: And there were just a smattering of companies that moved 238 00:12:28,400 --> 00:12:31,520 Speaker 5: in the opposite direction, so they lost the share of 239 00:12:31,600 --> 00:12:33,280 Speaker 5: people from underrepresented groups. 240 00:12:33,480 --> 00:12:35,240 Speaker 3: Yeah, and the key thing here is, even where we 241 00:12:35,520 --> 00:12:40,079 Speaker 3: saw a shared decline, we're talking points of percentage points, 242 00:12:40,120 --> 00:12:42,599 Speaker 3: we really didn't see anybody who went like in a 243 00:12:42,760 --> 00:12:46,160 Speaker 3: serious opposite direction. What this mostly does is show like 244 00:12:46,280 --> 00:12:51,360 Speaker 3: areas where companies are deficient, like not having any black executives. Directionally, 245 00:12:52,000 --> 00:12:54,960 Speaker 3: the momentum was clearly in the favor of people of 246 00:12:55,000 --> 00:12:56,079 Speaker 3: color in this year. 247 00:12:56,679 --> 00:13:00,440 Speaker 4: And bego, what are the companies that didn't increase the 248 00:13:00,520 --> 00:13:03,760 Speaker 4: number of underrepresented employees say about. 249 00:13:03,559 --> 00:13:07,800 Speaker 5: That outside Generally, companies that had what you might consider 250 00:13:07,840 --> 00:13:10,400 Speaker 5: good or bad or you know whatever their EO one 251 00:13:10,480 --> 00:13:13,520 Speaker 5: forum looked like didn't really want to talk specifics with us. 252 00:13:13,559 --> 00:13:16,240 Speaker 5: They didn't want to get into how they did it. 253 00:13:16,600 --> 00:13:20,600 Speaker 5: A lot of companies sent we are coming into diversity language, 254 00:13:20,760 --> 00:13:23,000 Speaker 5: or they would, you know, send like here, you know, 255 00:13:23,040 --> 00:13:24,720 Speaker 5: here's what we've said in the past about it, but 256 00:13:24,760 --> 00:13:27,400 Speaker 5: really known was going to dig in deep with us 257 00:13:27,440 --> 00:13:30,680 Speaker 5: on what they did, which really surprised us. Actually, you'd 258 00:13:30,720 --> 00:13:32,800 Speaker 5: think that, you know, especially the ones who made big gains, 259 00:13:32,840 --> 00:13:34,920 Speaker 5: would want to get into it and say here's how 260 00:13:34,960 --> 00:13:38,240 Speaker 5: we did it, but they didn't want to. One reason 261 00:13:38,320 --> 00:13:41,240 Speaker 5: we think that is is because it very quickly has 262 00:13:41,240 --> 00:13:44,160 Speaker 5: become very unpopular to talk about diversity in the current 263 00:13:44,280 --> 00:13:45,280 Speaker 5: cultural conversation. 264 00:13:45,840 --> 00:13:49,400 Speaker 3: So corporate diversity, equity, and inclusion programs are coming under 265 00:13:49,440 --> 00:13:53,880 Speaker 3: fire now as conservative groups are pushing against the workplace policies. 266 00:13:54,000 --> 00:13:57,720 Speaker 2: Legal threats against companies promoting these initiatives have only ramped 267 00:13:57,800 --> 00:13:59,959 Speaker 2: up since the Supreme Court ruling that determined if from 268 00:14:00,320 --> 00:14:02,400 Speaker 2: action was unconstitutional. 269 00:14:03,280 --> 00:14:05,840 Speaker 5: Things have really changed since twenty twenty. You know, there 270 00:14:06,000 --> 00:14:08,800 Speaker 5: was at that time a big push people thought it 271 00:14:08,840 --> 00:14:13,360 Speaker 5: was important. Now it's not that way. There's a big 272 00:14:13,520 --> 00:14:17,439 Speaker 5: anti ESG pushbacks that ESG as companies deciding that they 273 00:14:17,480 --> 00:14:21,920 Speaker 5: care about social and environmental issues in the way they 274 00:14:22,040 --> 00:14:24,400 Speaker 5: run their companies. A lot of people are saying that's 275 00:14:24,440 --> 00:14:28,360 Speaker 5: not important, corporate anti woke backlash. Basically a lot of 276 00:14:28,400 --> 00:14:32,520 Speaker 5: people saying, why are we focusing on these things? Companies 277 00:14:32,520 --> 00:14:36,280 Speaker 5: should only be thinking about their bottom line. US Supreme 278 00:14:36,280 --> 00:14:40,720 Speaker 5: Court reversed affirmative action and education, which some people think 279 00:14:40,840 --> 00:14:43,360 Speaker 5: could be applied to companies. They're very worried about legal 280 00:14:43,440 --> 00:14:45,760 Speaker 5: risk for that, so they have more reasons not to 281 00:14:45,800 --> 00:14:48,360 Speaker 5: talk about it now than they do too talk about it. 282 00:14:50,320 --> 00:14:52,760 Speaker 3: I think if this data came out in twenty twenty one, 283 00:14:52,800 --> 00:14:54,840 Speaker 3: they'd be dancing in the streets and companies would be 284 00:14:54,920 --> 00:14:57,280 Speaker 3: lining up to talk to us. But in twenty twenty 285 00:14:57,320 --> 00:15:00,480 Speaker 3: three it could be a lawsuit. So they're being more cautious. 286 00:15:00,520 --> 00:15:03,320 Speaker 3: They're being advised by their lawyers to just be careful 287 00:15:03,440 --> 00:15:06,200 Speaker 3: what you say and how you portray it, and that 288 00:15:06,320 --> 00:15:09,400 Speaker 3: really is kind of unfortunate, considering this is pretty much 289 00:15:09,400 --> 00:15:11,600 Speaker 3: shows what they can do when they're pressed. 290 00:15:12,360 --> 00:15:14,800 Speaker 4: And what is it that they're afraid of being sued for. 291 00:15:15,600 --> 00:15:18,120 Speaker 3: I mean the fact that a disproportionate number of the 292 00:15:18,120 --> 00:15:20,600 Speaker 3: people they hired were people of color. The groups that 293 00:15:20,720 --> 00:15:23,360 Speaker 3: are suing over this would say, hey, this shows very 294 00:15:23,360 --> 00:15:26,000 Speaker 3: clear bias in your hiring and therefore we should be 295 00:15:26,000 --> 00:15:28,160 Speaker 3: able to sue you on behalf of white workers who 296 00:15:28,200 --> 00:15:30,360 Speaker 3: didn't get their share of the jobs that were available. 297 00:15:31,120 --> 00:15:34,440 Speaker 5: I also think we need to clarify that when they're 298 00:15:34,600 --> 00:15:39,520 Speaker 5: the worker losses were, yes, disproportionately white people, but we 299 00:15:39,560 --> 00:15:43,320 Speaker 5: don't know what that is. That can be yes, layoffs, 300 00:15:43,560 --> 00:15:48,239 Speaker 5: it can be firings, it can be retirements. The demographics 301 00:15:48,280 --> 00:15:52,160 Speaker 5: of the US are shifting that many more people coming 302 00:15:52,240 --> 00:15:56,960 Speaker 5: into the workforce aren't white. So this is going to 303 00:15:57,040 --> 00:16:01,800 Speaker 5: happen naturally, but it's not necessarily discriminatory. There are lots 304 00:16:01,840 --> 00:16:04,680 Speaker 5: of reasons why the losses might look one way or another. 305 00:16:04,800 --> 00:16:07,320 Speaker 5: Or you know, a company closing down a warehouse in 306 00:16:07,400 --> 00:16:10,800 Speaker 5: a disproportionately white part of the country for what you know, 307 00:16:10,840 --> 00:16:12,720 Speaker 5: maybe the population is shrinking and they don't want a 308 00:16:12,720 --> 00:16:15,080 Speaker 5: warehouse there and opening it up in a more diverse 309 00:16:15,120 --> 00:16:17,200 Speaker 5: part of the country. So there's a lot of reasons 310 00:16:17,240 --> 00:16:19,640 Speaker 5: these changes are happening. I think we do need to 311 00:16:19,640 --> 00:16:23,120 Speaker 5: make that clear. It's not necessarily straight up discrimination. 312 00:16:23,800 --> 00:16:26,560 Speaker 3: That's the really important point, because the workforce at the 313 00:16:26,600 --> 00:16:30,200 Speaker 3: moment is kind of split, where white workers are older 314 00:16:30,240 --> 00:16:33,400 Speaker 3: and retiring and the black and Hispanic workers are the 315 00:16:33,440 --> 00:16:36,600 Speaker 3: youngest workers and entering the workforce. So if you want 316 00:16:36,600 --> 00:16:38,560 Speaker 3: to leave the workforce and you're white, you can probably 317 00:16:38,600 --> 00:16:41,480 Speaker 3: afford it. But for people of color, they're just getting 318 00:16:41,480 --> 00:16:44,040 Speaker 3: started and thus less able to leave the workforce. When 319 00:16:44,080 --> 00:16:46,800 Speaker 3: there were bios and such as well, there are a 320 00:16:46,800 --> 00:16:49,440 Speaker 3: lot of dynamics in play that could explain this. It's 321 00:16:49,480 --> 00:16:51,560 Speaker 3: just we wish the companies would give us some sense 322 00:16:51,560 --> 00:16:53,040 Speaker 3: of what they think is causing. 323 00:16:52,760 --> 00:16:56,200 Speaker 4: It beca One of the things you write is that 324 00:16:56,280 --> 00:16:58,440 Speaker 4: for a lot of years, companies said they have a 325 00:16:58,480 --> 00:17:03,120 Speaker 4: hard time finding qualified minority applicants because there isn't a pipeline. 326 00:17:03,160 --> 00:17:07,560 Speaker 4: And yet this shows suddenly that pipeline appears when they 327 00:17:07,560 --> 00:17:08,159 Speaker 4: have to do it. 328 00:17:08,880 --> 00:17:11,679 Speaker 5: Yeah, I mean, pipeline is something that people like me 329 00:17:11,720 --> 00:17:14,359 Speaker 5: and Jeff have been in this world for a long time. 330 00:17:14,880 --> 00:17:15,960 Speaker 5: Was a big buzzword. 331 00:17:16,480 --> 00:17:18,439 Speaker 4: And what exactly is a pipeline? Because you hear that 332 00:17:18,480 --> 00:17:19,880 Speaker 4: all the time, But what do they mean by that? 333 00:17:20,400 --> 00:17:24,679 Speaker 5: Well, we don't have enough people with X degree that 334 00:17:24,760 --> 00:17:27,680 Speaker 5: will lead them to this hire and paying job, whether 335 00:17:27,800 --> 00:17:31,639 Speaker 5: that's you know, a tech job or management position or 336 00:17:31,840 --> 00:17:34,439 Speaker 5: CEO position. We just don't have the people with the 337 00:17:34,480 --> 00:17:36,840 Speaker 5: skills and the talent we need to fill the job. 338 00:17:36,880 --> 00:17:40,159 Speaker 5: So it's not our fault. Actually, it's the structural problem, 339 00:17:40,160 --> 00:17:42,320 Speaker 5: which we you know, can do things to change, but 340 00:17:42,359 --> 00:17:45,320 Speaker 5: it's so long term you'll never see it. And I 341 00:17:45,359 --> 00:17:48,600 Speaker 5: think a lot of people have said that's not true. One, 342 00:17:48,640 --> 00:17:52,440 Speaker 5: there's more creative ways to hire and train people. There's 343 00:17:52,480 --> 00:17:55,560 Speaker 5: been a push away from requiring college degrees for every 344 00:17:55,560 --> 00:17:57,920 Speaker 5: single job. That might not need them. But also there 345 00:17:57,920 --> 00:18:01,560 Speaker 5: are reasons that people along the way of their career leave. 346 00:18:01,680 --> 00:18:04,240 Speaker 5: And I reported on gender in the workplace for a 347 00:18:04,359 --> 00:18:06,399 Speaker 5: very long time and that was a big one for 348 00:18:06,520 --> 00:18:08,920 Speaker 5: women specifically, there were a lot of them in entry 349 00:18:08,960 --> 00:18:12,320 Speaker 5: level jobs. Why are they not climbing up this ladder 350 00:18:12,359 --> 00:18:15,600 Speaker 5: to mix our metaphors, you know, they're discriminated against. The 351 00:18:15,600 --> 00:18:18,479 Speaker 5: workplace isn't very open and welcoming to them. They have 352 00:18:18,560 --> 00:18:20,760 Speaker 5: children and find it very difficult to stay in. So 353 00:18:21,080 --> 00:18:23,320 Speaker 5: you know, that's just the gender component, but there are 354 00:18:23,320 --> 00:18:28,280 Speaker 5: similar reasons for people who are underrepresented. They don't feel welcome, 355 00:18:28,320 --> 00:18:31,560 Speaker 5: it's very difficult, or you know, there's certain in group 356 00:18:31,680 --> 00:18:35,120 Speaker 5: skills that lead to your success. So I think it's 357 00:18:35,320 --> 00:18:37,480 Speaker 5: a much more complicated picture, and it shows when you 358 00:18:37,560 --> 00:18:40,879 Speaker 5: kind of get rid of some of these excuses. You 359 00:18:40,880 --> 00:18:43,120 Speaker 5: can not only build out your pipeline, as Jeff said, 360 00:18:43,200 --> 00:18:45,080 Speaker 5: but yet it's the higher paying levels. Do you have 361 00:18:45,160 --> 00:18:46,560 Speaker 5: more people in these jobs? 362 00:18:46,920 --> 00:18:50,000 Speaker 3: Also in this timeframe, suddenly work from home was possible, 363 00:18:50,359 --> 00:18:52,679 Speaker 3: and if you can hire people wherever they are, you 364 00:18:52,680 --> 00:18:55,400 Speaker 3: can hire from a much more diverse, you know, pipeline 365 00:18:55,440 --> 00:18:57,760 Speaker 3: of people. Then you can hire when they have to 366 00:18:57,800 --> 00:19:00,680 Speaker 3: live in the city where you're based. In the middle 367 00:19:00,680 --> 00:19:02,959 Speaker 3: of America might not have a diverse workforce, but if 368 00:19:02,960 --> 00:19:06,560 Speaker 3: they can suddenly hire from Georgia and Texas, it works great. 369 00:19:06,840 --> 00:19:19,840 Speaker 4: When we come back, Will companies keep up their diversity efforts, Jeff, 370 00:19:19,920 --> 00:19:22,960 Speaker 4: As you said, these numbers stop at twenty twenty one, 371 00:19:23,040 --> 00:19:25,800 Speaker 4: and a lot has happened since then. I guess one 372 00:19:25,800 --> 00:19:28,640 Speaker 4: of the big question marks is has this flurry of 373 00:19:28,760 --> 00:19:32,800 Speaker 4: new hiring continued or was it a one time thing 374 00:19:33,040 --> 00:19:35,239 Speaker 4: and it's tapered off and people are just going back 375 00:19:35,280 --> 00:19:36,200 Speaker 4: to their old habits. 376 00:19:36,520 --> 00:19:38,679 Speaker 3: We don't know for sure, but there are reasons to 377 00:19:38,720 --> 00:19:41,639 Speaker 3: be optimistic that maybe some of this will continue because 378 00:19:42,080 --> 00:19:46,280 Speaker 3: people are talking less publicly about diversity topics, but when 379 00:19:46,320 --> 00:19:49,280 Speaker 3: we dig down, it does appear they're still sending that 380 00:19:49,320 --> 00:19:53,119 Speaker 3: message to potential employees. Because companies are kind of stuck. 381 00:19:53,200 --> 00:19:55,680 Speaker 3: They know it's dangerous to sort of raise their hand 382 00:19:55,720 --> 00:19:58,560 Speaker 3: and call attention to themselves. But at the same time, 383 00:19:58,600 --> 00:20:01,520 Speaker 3: what Becka said, the demographics of the country are shifting, 384 00:20:01,720 --> 00:20:03,680 Speaker 3: and you've got to hire from the people who exist. 385 00:20:04,000 --> 00:20:06,439 Speaker 3: You can't be thinking about the workers of the past. 386 00:20:06,480 --> 00:20:08,639 Speaker 3: So to some degree, this is built in as a 387 00:20:08,680 --> 00:20:10,800 Speaker 3: requirement for these companies to be competitive. 388 00:20:11,280 --> 00:20:13,600 Speaker 5: It's very hard to tell what has happened since then. 389 00:20:13,760 --> 00:20:15,639 Speaker 5: I think, you know, twenty twenty one, big year of 390 00:20:15,720 --> 00:20:19,040 Speaker 5: hiring economy very different now. A lot of the companies 391 00:20:19,240 --> 00:20:21,679 Speaker 5: in our set have done layoffs, and we would love 392 00:20:21,720 --> 00:20:24,040 Speaker 5: to know what those look like. There have been big 393 00:20:24,080 --> 00:20:28,119 Speaker 5: cultural shifts. We don't know if those have, you know, 394 00:20:28,320 --> 00:20:32,160 Speaker 5: seeped down internally into hiring managers and companies. I think, 395 00:20:32,280 --> 00:20:34,600 Speaker 5: like Jeff said and our reporting, like companies and the 396 00:20:34,640 --> 00:20:37,120 Speaker 5: people hiring and the people work at them do think 397 00:20:37,119 --> 00:20:39,800 Speaker 5: it's important. They think it's good for business to have 398 00:20:39,880 --> 00:20:44,080 Speaker 5: different kinds of people reach different customers, not make missteps 399 00:20:44,080 --> 00:20:46,600 Speaker 5: where you might get called out in the broader world. 400 00:20:46,840 --> 00:20:49,959 Speaker 5: We don't know if that cultural backlash has resulted in 401 00:20:50,080 --> 00:20:53,320 Speaker 5: different hiring changes, but it certainly could have. The trend 402 00:20:53,359 --> 00:20:56,200 Speaker 5: is going to just demographically happen in some way, shape 403 00:20:56,280 --> 00:20:58,480 Speaker 5: or form. We know that. But what's interesting to us 404 00:20:58,560 --> 00:21:00,000 Speaker 5: is if the push will continue. 405 00:21:00,040 --> 00:21:04,040 Speaker 4: Yeah, Jeff, you've been working on this data for a 406 00:21:04,119 --> 00:21:06,560 Speaker 4: number of years. You're waiting for new data to come. 407 00:21:07,160 --> 00:21:09,840 Speaker 4: Where does the story go from here? What's your next step? 408 00:21:10,520 --> 00:21:12,680 Speaker 3: Well, I mean the question we're talking about right now, 409 00:21:12,720 --> 00:21:15,640 Speaker 3: is did it continue. We'll get a really good sense 410 00:21:15,680 --> 00:21:18,120 Speaker 3: of at some time, maybe mid next year, like how 411 00:21:18,160 --> 00:21:20,600 Speaker 3: big of a deal was it when when people started 412 00:21:20,640 --> 00:21:24,280 Speaker 3: to cut back, did they maintain these gains? An important 413 00:21:24,280 --> 00:21:28,080 Speaker 3: thing to keep in mind is these are big shares 414 00:21:28,280 --> 00:21:31,720 Speaker 3: of networkers, but they are very small incremental changes to 415 00:21:31,800 --> 00:21:35,080 Speaker 3: these nine million people. I mean, we're talking two percentage points. 416 00:21:35,520 --> 00:21:37,800 Speaker 3: We're not talking, you know, fifty percentage points. So there's 417 00:21:37,800 --> 00:21:40,320 Speaker 3: something for everyone here. If you gained only two percentage 418 00:21:40,320 --> 00:21:43,080 Speaker 3: points across all these people, that's like, not very much happened. 419 00:21:43,119 --> 00:21:44,919 Speaker 3: But if ninety four percent of the people who did 420 00:21:45,040 --> 00:21:47,440 Speaker 3: hire are people of color, that's awesome. It's a very 421 00:21:47,520 --> 00:21:48,680 Speaker 3: nuanced picture. 422 00:21:49,280 --> 00:21:53,480 Speaker 5: The big picture is that these companies still a glock side. 423 00:21:53,880 --> 00:21:57,920 Speaker 5: It's very hard to make these changes. And like Jeff said, 424 00:21:58,080 --> 00:22:00,560 Speaker 5: in the overall picture of at a two percentage shift, 425 00:22:00,560 --> 00:22:03,520 Speaker 5: which sounds small, but then a lot's happening at the margins. 426 00:22:04,119 --> 00:22:06,679 Speaker 3: Okay, So at the lower level jobs, there's already a 427 00:22:06,720 --> 00:22:08,760 Speaker 3: majority of people of color at these companies. So the 428 00:22:08,800 --> 00:22:12,640 Speaker 3: bases there, that pipeline we're talking about exists inside these 429 00:22:12,640 --> 00:22:15,919 Speaker 3: companies already. It's just now like Amazon did, can you 430 00:22:15,960 --> 00:22:16,520 Speaker 3: move them up? 431 00:22:17,160 --> 00:22:19,399 Speaker 4: So I guess that's the big question to look for 432 00:22:19,440 --> 00:22:21,520 Speaker 4: in the future is did all these people who they 433 00:22:21,640 --> 00:22:25,200 Speaker 4: hired then start to occupy the middle ranks and ultimately 434 00:22:25,240 --> 00:22:27,639 Speaker 4: the upper ranks of a company, which is traditionally how 435 00:22:27,680 --> 00:22:29,280 Speaker 4: people rise exactly. 436 00:22:29,800 --> 00:22:32,639 Speaker 3: It is like really important to keep the focus on 437 00:22:32,680 --> 00:22:36,640 Speaker 3: the fact that this is like this really great comprehensive 438 00:22:36,680 --> 00:22:39,120 Speaker 3: set of data that was not available three years ago. 439 00:22:39,280 --> 00:22:41,359 Speaker 3: And thanks to all the pressure that we're put on 440 00:22:41,400 --> 00:22:44,520 Speaker 3: these companies, even if it's abating, the precedent is there 441 00:22:44,720 --> 00:22:47,040 Speaker 3: and it's going to be difficult potentially for them to 442 00:22:47,080 --> 00:22:49,399 Speaker 3: stop releasing this data. So we're going to have this 443 00:22:49,520 --> 00:22:52,280 Speaker 3: ability to watch this now that we've never had in 444 00:22:52,280 --> 00:22:55,320 Speaker 3: the history of corporate America, to see like, how is 445 00:22:55,359 --> 00:22:57,679 Speaker 3: this going forward and what is this going to look like? 446 00:22:57,960 --> 00:22:59,880 Speaker 3: And I mean, if anything, that's like the big win here. 447 00:23:00,000 --> 00:23:02,000 Speaker 3: I mean, even if this is just a snapshot, we 448 00:23:02,080 --> 00:23:04,800 Speaker 3: now have an ability to kind of watch this that 449 00:23:04,840 --> 00:23:06,600 Speaker 3: we didn't have three years ago. 450 00:23:07,680 --> 00:23:10,480 Speaker 4: You mentioned that these companies are feeling political pressure. Have 451 00:23:10,560 --> 00:23:12,280 Speaker 4: you had any indication that some of them are going 452 00:23:12,320 --> 00:23:13,639 Speaker 4: to stop giving you this data? 453 00:23:14,480 --> 00:23:16,680 Speaker 3: Not so far, And that's what we're waiting to see 454 00:23:16,720 --> 00:23:18,800 Speaker 3: in terms of twenty twenty two. Did some of these 455 00:23:18,840 --> 00:23:20,840 Speaker 3: reports not show up? But the trend has been in 456 00:23:20,880 --> 00:23:23,600 Speaker 3: the other direction obviously, so now we'll wait and see. 457 00:23:23,640 --> 00:23:25,080 Speaker 3: That is like one of the big questions you have 458 00:23:25,200 --> 00:23:27,640 Speaker 3: to be answered is will there be eighty eight? Will 459 00:23:27,640 --> 00:23:30,760 Speaker 3: there be ninety six? Will there be seventy five? We 460 00:23:30,880 --> 00:23:33,879 Speaker 3: will find out next year sometime beca. 461 00:23:33,880 --> 00:23:36,480 Speaker 4: We've been talking about employees, but what about corporate boards. 462 00:23:36,520 --> 00:23:40,120 Speaker 4: There's been a lot of effort in recent years to 463 00:23:40,160 --> 00:23:42,280 Speaker 4: try to increase diversity on boards. 464 00:23:42,800 --> 00:23:47,720 Speaker 5: Broadly, boards are much easier to make more diverse than 465 00:23:47,760 --> 00:23:50,720 Speaker 5: an entire huge workforce. First of all, you can just 466 00:23:50,760 --> 00:23:53,040 Speaker 5: add people to boards, which is what a lot of 467 00:23:53,040 --> 00:23:56,679 Speaker 5: companies have done, and they've made huge gains and gender 468 00:23:56,920 --> 00:23:58,520 Speaker 5: and race and ethnicity. 469 00:23:59,080 --> 00:24:03,080 Speaker 3: The same exact trend was definitely seen in the boardroom 470 00:24:03,119 --> 00:24:06,040 Speaker 3: black directors, and many companies didn't have a single black 471 00:24:06,080 --> 00:24:10,000 Speaker 3: director there. Last year. We're about eleven percent from which 472 00:24:10,040 --> 00:24:13,639 Speaker 3: is almost double where they started, which means black directors 473 00:24:13,880 --> 00:24:16,639 Speaker 3: on boards in the S and P five hundred are 474 00:24:16,640 --> 00:24:20,080 Speaker 3: now almost at parody with the population, which is astounding. 475 00:24:20,600 --> 00:24:23,480 Speaker 3: In two years, they doubled this is a proxy for 476 00:24:23,520 --> 00:24:26,320 Speaker 3: how much pressure these companies felt, and in the most 477 00:24:26,400 --> 00:24:28,479 Speaker 3: visible place where you can see them, the boardroom, they 478 00:24:28,560 --> 00:24:31,600 Speaker 3: really responded. So that's an indication that what we were 479 00:24:31,640 --> 00:24:35,199 Speaker 3: seeing was specifically in reaction to the environment that they 480 00:24:35,240 --> 00:24:36,240 Speaker 3: lived in at that time. 481 00:24:37,280 --> 00:24:39,840 Speaker 4: Jeff Becca, thanks so much for coming on the show. 482 00:24:40,080 --> 00:24:41,880 Speaker 3: Thanks Wes, it's been really great to get to talk 483 00:24:41,920 --> 00:24:42,400 Speaker 3: about this. 484 00:24:43,600 --> 00:24:45,520 Speaker 4: Thanks for listening to us here at The Big Take. 485 00:24:45,680 --> 00:24:48,760 Speaker 4: It's a daily podcast from Bloomberg and iHeartRadio. For more 486 00:24:48,800 --> 00:24:52,840 Speaker 4: shows from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or 487 00:24:52,880 --> 00:24:55,840 Speaker 4: wherever you listen, and we'd love to hear from you. 488 00:24:55,920 --> 00:24:59,200 Speaker 4: Email us questions or comments to Big Take at Bloomberg 489 00:24:59,240 --> 00:25:02,600 Speaker 4: dot net. The supervising producer of The Big Take is 490 00:25:02,720 --> 00:25:07,040 Speaker 4: Vicky Virgolina. Our senior producer is Katherine Fink. Our producers 491 00:25:07,080 --> 00:25:11,480 Speaker 4: are Moe Barrow and Michael Falero. Kilde Garcia is our engineer. 492 00:25:11,880 --> 00:25:15,800 Speaker 4: Our original music was composed by Leo Sidrin. I'm West Kasova. 493 00:25:16,119 --> 00:25:18,440 Speaker 4: We'll be back tomorrow with another Big Take.