1 00:00:00,600 --> 00:00:05,560 Speaker 1: Already, and this is the daily This is the Daily OS. Oh, 2 00:00:05,920 --> 00:00:15,720 Speaker 1: now it makes sense. Good morning and welcome to the 3 00:00:15,840 --> 00:00:18,599 Speaker 1: Daily ODS. It is Wednesday, the twenty eighth of February. 4 00:00:18,760 --> 00:00:21,000 Speaker 2: I'm Billy, I'm Zara. 5 00:00:20,600 --> 00:00:25,800 Speaker 1: Pandora, Cea, Folly, Jetstar and Collingwood Football. What do all 6 00:00:25,840 --> 00:00:28,360 Speaker 1: of these companies have in common? Zara? 7 00:00:28,360 --> 00:00:30,400 Speaker 2: And he guesses no, but this feels like one of 8 00:00:30,400 --> 00:00:32,919 Speaker 2: your Wednesday riddles and I never get there when they're 9 00:00:32,960 --> 00:00:34,400 Speaker 2: in the newsletter, so I don't think I'm going to 10 00:00:34,440 --> 00:00:35,159 Speaker 2: get this one. 11 00:00:35,400 --> 00:00:39,200 Speaker 1: Well, they all have a gender pay gap favoring men 12 00:00:39,400 --> 00:00:45,240 Speaker 1: of more than forty percent. Yesterday, Australia's biggest employers were 13 00:00:45,280 --> 00:00:48,959 Speaker 1: forced to publicly reveal their gender pay gap. We'll take 14 00:00:49,000 --> 00:00:51,960 Speaker 1: you through which companies had the biggest pay gaps and 15 00:00:52,120 --> 00:00:55,080 Speaker 1: what it all means in today's deep dive. But before 16 00:00:55,080 --> 00:00:59,920 Speaker 1: we get thoir, Zara, what is making headlines today? 17 00:01:00,280 --> 00:01:03,440 Speaker 2: New South Wales Police say they are quote very confident 18 00:01:03,560 --> 00:01:06,840 Speaker 2: they've located the bodies of Jessebaird and Luke Davies, who 19 00:01:06,880 --> 00:01:09,560 Speaker 2: were allegedly murdered by a New South Wales police officer 20 00:01:09,760 --> 00:01:13,720 Speaker 2: last week. The bodies were found in Bungonia, a small 21 00:01:13,760 --> 00:01:17,160 Speaker 2: town about two hundred kilometers southwest of Sydney. If you 22 00:01:17,200 --> 00:01:20,040 Speaker 2: want more information about that case, you can listen to 23 00:01:20,160 --> 00:01:23,360 Speaker 2: yesterday's podcast. We'll throw the link in today's show notes. 24 00:01:25,040 --> 00:01:28,600 Speaker 1: Queensland Supreme Court has found the state's COVID nineteen vaccine 25 00:01:28,640 --> 00:01:33,720 Speaker 1: mandate for public service and health workers was unlawful. Queensland's 26 00:01:33,720 --> 00:01:38,040 Speaker 1: frontline workers, including police and paramedics, were required to be 27 00:01:38,080 --> 00:01:42,360 Speaker 1: fully vaccinated against COVID by January twenty twenty two. A 28 00:01:42,400 --> 00:01:46,600 Speaker 1: group of frontline staff challenged that requirement overclaims it was discriminatory. 29 00:01:47,240 --> 00:01:49,840 Speaker 1: This week, a judge ruled the mandate was in breach 30 00:01:49,880 --> 00:01:53,320 Speaker 1: of the Human Rights Act, meaning the vaccine requirement for 31 00:01:53,440 --> 00:01:55,200 Speaker 1: frontline workers will be scrapped. 32 00:01:57,320 --> 00:02:00,720 Speaker 2: A proposal to ban pride flags in Tennessee classrooms is 33 00:02:00,760 --> 00:02:03,680 Speaker 2: a step closer to becoming law after a draft bill 34 00:02:03,720 --> 00:02:07,680 Speaker 2: passed the state government's lower house. Under the legislation, it 35 00:02:07,720 --> 00:02:10,520 Speaker 2: would become illegal to display the rainbow flag in the 36 00:02:10,560 --> 00:02:14,120 Speaker 2: state's public schools. The proposal is part of a push 37 00:02:14,160 --> 00:02:18,639 Speaker 2: to ban quote ideological flags which represent a political viewpoint, 38 00:02:18,800 --> 00:02:22,040 Speaker 2: from being displayed in schools. The bill passed the lower 39 00:02:22,040 --> 00:02:24,480 Speaker 2: house seventy votes to twenty four and will now be 40 00:02:24,520 --> 00:02:25,640 Speaker 2: debated in the Senate. 41 00:02:27,400 --> 00:02:30,440 Speaker 1: And today's good news, Students at a medical school in 42 00:02:30,480 --> 00:02:32,520 Speaker 1: New York have been told they won't have to pay 43 00:02:32,560 --> 00:02:36,399 Speaker 1: any tuition fees after ninety three year old doctor Ruth Gotsman, 44 00:02:36,639 --> 00:02:41,000 Speaker 1: chair of the collegees board, donated one billion US dollars, 45 00:02:41,160 --> 00:02:44,920 Speaker 1: which is around one point five billion Australian dollars. The 46 00:02:44,960 --> 00:02:48,200 Speaker 1: Albert Einstein College of Medicine said the money quote will 47 00:02:48,240 --> 00:02:51,080 Speaker 1: free up and lift our students to pursue projects and 48 00:02:51,120 --> 00:02:55,200 Speaker 1: ideas that might otherwise be prohibitive. Gotsman came into the 49 00:02:55,200 --> 00:02:59,359 Speaker 1: money after her husband, who invested with billionaire Warren Buffett, 50 00:02:59,600 --> 00:03:03,160 Speaker 1: left her a valuable stock portfolio when he died. It's 51 00:03:03,200 --> 00:03:06,280 Speaker 1: one of the largest donations ever received by a medical 52 00:03:06,280 --> 00:03:10,000 Speaker 1: school in the US. 53 00:03:11,400 --> 00:03:15,680 Speaker 2: Billy Yesterday, Australia's biggest employers were actually forced to reveal 54 00:03:16,040 --> 00:03:19,480 Speaker 2: their gender pay gaps. Can you first just begin by 55 00:03:19,520 --> 00:03:21,760 Speaker 2: taking us through why did they have to do that? 56 00:03:22,120 --> 00:03:25,320 Speaker 1: Yeah? It was really big news yesterday that nearly five 57 00:03:25,440 --> 00:03:29,799 Speaker 1: thousand of Australia's private companies were forced to reveal their 58 00:03:29,840 --> 00:03:33,000 Speaker 1: gender pay gaps. And I'll take you through first how 59 00:03:33,000 --> 00:03:36,920 Speaker 1: we got there? So last year, the federal government passed 60 00:03:36,920 --> 00:03:40,720 Speaker 1: a law that was all about improving gender equality in 61 00:03:40,840 --> 00:03:44,720 Speaker 1: our workplaces. They were responding to review. There were ten recommendations, 62 00:03:45,080 --> 00:03:47,840 Speaker 1: and they passed this law that acted on six of 63 00:03:47,880 --> 00:03:52,280 Speaker 1: those recommendations. And the one we're focusing on today is 64 00:03:52,320 --> 00:03:56,360 Speaker 1: the aspect that forced businesses with at least one hundred 65 00:03:56,440 --> 00:04:00,880 Speaker 1: workers to publish their gender pay gap. So what was 66 00:04:00,880 --> 00:04:04,040 Speaker 1: the purpose of this. It was to accelerate action to 67 00:04:04,240 --> 00:04:07,080 Speaker 1: close the gender pay gap. So the thinking being that 68 00:04:07,240 --> 00:04:10,640 Speaker 1: companies obviously wouldn't want to be named and shamed, and 69 00:04:10,760 --> 00:04:13,960 Speaker 1: knowing that this was coming out, they would perhaps action 70 00:04:14,160 --> 00:04:16,800 Speaker 1: to close the gender pay gap within their companies. 71 00:04:16,880 --> 00:04:19,240 Speaker 2: I just want to stop you their ability for one second, 72 00:04:19,360 --> 00:04:22,080 Speaker 2: because I do think that you've said gender pay gap 73 00:04:22,240 --> 00:04:26,560 Speaker 2: approximately eleven times in the opening thirty seconds of this podcast, 74 00:04:27,120 --> 00:04:28,680 Speaker 2: and I know that every time we talk about the 75 00:04:28,720 --> 00:04:31,720 Speaker 2: gender pay gap, we get this influx of messages from 76 00:04:31,760 --> 00:04:35,200 Speaker 2: people who are confused about what it actually means. Do 77 00:04:35,200 --> 00:04:37,000 Speaker 2: you mind just clearing that up for us from the 78 00:04:37,040 --> 00:04:37,320 Speaker 2: get go? 79 00:04:37,680 --> 00:04:40,120 Speaker 1: Yeah, it's an infamous one in the daily o's office 80 00:04:40,200 --> 00:04:44,839 Speaker 1: because every single time the comments are a nightmare and honestly, 81 00:04:44,880 --> 00:04:47,040 Speaker 1: fair enough because there are so many different ways that 82 00:04:47,279 --> 00:04:50,320 Speaker 1: it is measured. But from the outset, I think there's 83 00:04:50,360 --> 00:04:52,320 Speaker 1: one thing that we need to clear up. The gender 84 00:04:52,360 --> 00:04:55,840 Speaker 1: pay gap is not referring to men and women being 85 00:04:55,880 --> 00:04:59,520 Speaker 1: paid differently for doing the same job. That is illegal, 86 00:04:59,800 --> 00:05:02,640 Speaker 1: is absolutely illegal in Australia. And so what it is 87 00:05:02,760 --> 00:05:07,120 Speaker 1: generally referring to is pay across all different levels, across 88 00:05:07,160 --> 00:05:12,240 Speaker 1: all different industries. But what we're talking about today specifically 89 00:05:12,279 --> 00:05:15,800 Speaker 1: defines the gender pay gap as the median difference in 90 00:05:15,960 --> 00:05:19,640 Speaker 1: earnings between men and women. And the crucial word there 91 00:05:20,120 --> 00:05:23,640 Speaker 1: is median, and that's different to average. 92 00:05:23,760 --> 00:05:26,560 Speaker 2: Obviously we all know the median mode something that we 93 00:05:26,640 --> 00:05:27,400 Speaker 2: learned at school. 94 00:05:27,440 --> 00:05:29,000 Speaker 1: We are taking it. 95 00:05:29,160 --> 00:05:30,560 Speaker 2: I still don't have the answer. 96 00:05:30,800 --> 00:05:34,440 Speaker 1: We're taking you back to yeuten mas. So the median 97 00:05:34,600 --> 00:05:37,120 Speaker 1: number means it is the middle figure. So if you 98 00:05:37,200 --> 00:05:40,160 Speaker 1: line up all the earnings from the highest to lowest 99 00:05:40,400 --> 00:05:43,600 Speaker 1: for men and women, you take the middle number of 100 00:05:43,600 --> 00:05:47,320 Speaker 1: that list for each gender and compare that number in 101 00:05:47,440 --> 00:05:48,920 Speaker 1: terms of a percentage. 102 00:05:49,320 --> 00:05:52,440 Speaker 2: Okay, And you're saying it's that number that we are 103 00:05:52,480 --> 00:05:54,000 Speaker 2: assessing the gender pay gap on. 104 00:05:54,160 --> 00:05:56,960 Speaker 1: Exactly, okay, And there is a lot of confusion, and 105 00:05:57,000 --> 00:05:59,479 Speaker 1: that's because that's not always how it's done. But in 106 00:05:59,520 --> 00:06:01,800 Speaker 1: this SPECI case, it has been done like this to 107 00:06:01,800 --> 00:06:06,040 Speaker 1: not skew the data with extreme values on either end. 108 00:06:06,120 --> 00:06:09,719 Speaker 1: So a really high salary or even a really low salary. 109 00:06:09,279 --> 00:06:12,000 Speaker 2: So that would happen if you were to do an average, Yes, 110 00:06:12,320 --> 00:06:14,480 Speaker 2: that would skew it one way or another, exactly. 111 00:06:14,480 --> 00:06:17,200 Speaker 1: Okay, And just one more thing before we actually get 112 00:06:17,200 --> 00:06:21,359 Speaker 1: to the findings. This figure includes full time salaries, and 113 00:06:21,440 --> 00:06:25,039 Speaker 1: it also includes part time and casual salaries which have 114 00:06:25,160 --> 00:06:29,600 Speaker 1: been converted into annualized full time equivalent earnings. And one 115 00:06:29,640 --> 00:06:32,360 Speaker 1: more thing. On top of salary, it also includes things 116 00:06:32,400 --> 00:06:35,880 Speaker 1: like bonuses and extra payments such as commissions for sales. 117 00:06:35,920 --> 00:06:39,520 Speaker 1: It also includes superannuation, so it's a total remuneration, which 118 00:06:39,520 --> 00:06:41,240 Speaker 1: is always a hard way to say it is. 119 00:06:41,279 --> 00:06:43,440 Speaker 2: But I do think that it's important that it included 120 00:06:43,480 --> 00:06:45,760 Speaker 2: that because we so often hear about the fact that 121 00:06:45,800 --> 00:06:49,239 Speaker 2: women end up retiring with a lot less super banked away. 122 00:06:49,320 --> 00:06:52,000 Speaker 2: So I think that that's an important element to include. 123 00:06:52,920 --> 00:06:56,000 Speaker 2: So I think I understand what it is this gender 124 00:06:56,040 --> 00:06:58,360 Speaker 2: pay gap was assessed on. Can you take me through 125 00:06:58,560 --> 00:06:59,760 Speaker 2: what the findings were? 126 00:07:00,400 --> 00:07:02,800 Speaker 1: Well, it found that the issue of a gender pay 127 00:07:02,800 --> 00:07:07,039 Speaker 1: gap absolutely exists in Australia. We knew that exactly, We 128 00:07:07,120 --> 00:07:09,880 Speaker 1: absolutely knew that. And one thing to note is how 129 00:07:09,880 --> 00:07:12,800 Speaker 1: they've done it is they've done it with negative percentages 130 00:07:12,840 --> 00:07:16,840 Speaker 1: and positive percentages. If it's a negative percentage, that means 131 00:07:16,880 --> 00:07:20,160 Speaker 1: that it favors women, and if it's a positive percentage, 132 00:07:20,200 --> 00:07:23,360 Speaker 1: that means that it favors men. And what they've said 133 00:07:23,480 --> 00:07:27,320 Speaker 1: is that between negative five percent and positive five percent 134 00:07:27,640 --> 00:07:30,640 Speaker 1: is a normal range for companies which they should aim to. 135 00:07:31,000 --> 00:07:35,000 Speaker 2: Okay, So understanding that that's kind of where you're aiming 136 00:07:35,040 --> 00:07:37,000 Speaker 2: to be, what did it say? 137 00:07:37,600 --> 00:07:40,000 Speaker 1: So it found that the pay gap at more than 138 00:07:40,160 --> 00:07:43,960 Speaker 1: three in five companies was over five percent, so in 139 00:07:44,040 --> 00:07:46,720 Speaker 1: favor of men. And just a reminder, that's three and 140 00:07:46,840 --> 00:07:50,760 Speaker 1: five private companies with over one hundred employees that have 141 00:07:50,840 --> 00:07:55,280 Speaker 1: a pay gap of more than five percent. Following following, 142 00:07:55,520 --> 00:07:57,880 Speaker 1: and it also found that more than half of employers 143 00:07:58,240 --> 00:08:01,280 Speaker 1: had a pay gap that was higher than nine percent. 144 00:08:01,640 --> 00:08:05,920 Speaker 2: Okay, so just put that in a sentence, So nine percent, 145 00:08:06,440 --> 00:08:08,160 Speaker 2: what are we saying about that figure? 146 00:08:08,680 --> 00:08:12,440 Speaker 1: So, when these companies measured the median difference between the 147 00:08:12,480 --> 00:08:17,200 Speaker 1: pay of their male employees and their female employees, more 148 00:08:17,240 --> 00:08:20,760 Speaker 1: than half of them found that difference was more than 149 00:08:20,920 --> 00:08:21,640 Speaker 1: nine percent. 150 00:08:22,120 --> 00:08:25,480 Speaker 2: Okay, so that's a pretty significant percentage there. 151 00:08:25,560 --> 00:08:28,280 Speaker 1: Yeah, definitely is. And one more thing it found was 152 00:08:28,280 --> 00:08:32,520 Speaker 1: that companies with female leadership were more likely to have 153 00:08:32,760 --> 00:08:36,440 Speaker 1: a lower gender pay gap, closer to that negative five 154 00:08:36,520 --> 00:08:40,000 Speaker 1: to positive five percent range. And that might seem unsurprising, 155 00:08:40,000 --> 00:08:41,560 Speaker 1: but I still think it's definitely of note. 156 00:08:41,840 --> 00:08:45,520 Speaker 2: It is definitely of note. I want to understand a 157 00:08:45,559 --> 00:08:48,680 Speaker 2: bit more about the profile of these companies who were listed. 158 00:08:48,720 --> 00:08:52,120 Speaker 2: Obviously it was an extremely comprehensive list, but in a 159 00:08:52,120 --> 00:08:55,200 Speaker 2: lot of the coverage I've seen certain companies singled out. 160 00:08:55,520 --> 00:08:57,720 Speaker 2: Can you just run me through a couple a handful 161 00:08:57,760 --> 00:08:58,040 Speaker 2: of them? 162 00:08:58,400 --> 00:09:00,280 Speaker 1: Yeah, Like you said, it's a list of nearly five 163 00:09:00,320 --> 00:09:03,000 Speaker 1: thousand companies, so I'm obviously not going to go through 164 00:09:03,040 --> 00:09:03,800 Speaker 1: every single one. 165 00:09:03,920 --> 00:09:06,640 Speaker 2: I did control f the Daily Os, knowing full well 166 00:09:06,679 --> 00:09:09,600 Speaker 2: we only employ under twenty people, and I don't know 167 00:09:09,600 --> 00:09:11,240 Speaker 2: why I did it, but I also would have been 168 00:09:11,520 --> 00:09:12,520 Speaker 2: not there surprised. 169 00:09:13,000 --> 00:09:14,600 Speaker 1: You also would have been the one to submit that 170 00:09:14,800 --> 00:09:17,440 Speaker 1: I would be concerning if it was there. Okay, but 171 00:09:17,480 --> 00:09:19,319 Speaker 1: I will name a few companies, and the ones being 172 00:09:19,400 --> 00:09:22,320 Speaker 1: named are kind of just the ones that media Publications 173 00:09:22,320 --> 00:09:25,120 Speaker 1: thinks their audiences will recognize they're some of the biggest 174 00:09:25,160 --> 00:09:29,480 Speaker 1: companies in Australia. The companies are name now. They employ 175 00:09:29,559 --> 00:09:32,079 Speaker 1: more than two hundred and fifty workers, and they fell 176 00:09:32,120 --> 00:09:36,360 Speaker 1: within the top five percent of the highest gender pay gaps. 177 00:09:36,559 --> 00:09:39,320 Speaker 1: The first one I'll mention is Pandora. You might recognize 178 00:09:39,320 --> 00:09:42,240 Speaker 1: that name. It's a jewelry brand bracelet. Yeah, they had 179 00:09:42,240 --> 00:09:45,440 Speaker 1: a pay gap of fifty two point three percent. They 180 00:09:45,480 --> 00:09:48,360 Speaker 1: were actually the twenty seventh worst overall. 181 00:09:48,720 --> 00:09:49,840 Speaker 2: That is so interesting. 182 00:09:49,960 --> 00:09:52,880 Speaker 1: Yeah, And another surprising one was Valley Girl. I don't 183 00:09:52,920 --> 00:09:55,720 Speaker 1: know if you recognize that name. They do fast fashion 184 00:09:55,840 --> 00:09:59,040 Speaker 1: towards young girls in particular. They had a gap of 185 00:09:59,160 --> 00:10:02,600 Speaker 1: fifty one point nine percent, making them the twenty ninth 186 00:10:02,640 --> 00:10:03,920 Speaker 1: worst overall. 187 00:10:04,000 --> 00:10:07,760 Speaker 2: I think both of those are interesting because they're meant 188 00:10:07,800 --> 00:10:09,199 Speaker 2: to be targeting women, right. 189 00:10:09,200 --> 00:10:12,520 Speaker 1: Yeah, exactly. Another one was c Folly, the swimwear brand. 190 00:10:12,840 --> 00:10:15,680 Speaker 1: They also target females, and they had a pay gap 191 00:10:15,720 --> 00:10:19,160 Speaker 1: of forty four point five percent. Again I found that interesting. 192 00:10:19,200 --> 00:10:23,640 Speaker 1: The airline industry recorded some high pay gaps. Jetsar had 193 00:10:23,640 --> 00:10:26,959 Speaker 1: a gap of forty three point seven percent, Virgin had 194 00:10:26,960 --> 00:10:30,160 Speaker 1: one of forty one point seven percent and Quantis was 195 00:10:30,440 --> 00:10:33,280 Speaker 1: thirty seven percent, so pretty high, definitely higher than the 196 00:10:33,360 --> 00:10:37,440 Speaker 1: national average. Also the sports industry, at Collingwood Football Club, 197 00:10:37,480 --> 00:10:40,320 Speaker 1: for example, they had a pay gap of forty four percent, 198 00:10:40,720 --> 00:10:43,199 Speaker 1: So some pretty big companies that have a really high 199 00:10:43,280 --> 00:10:44,040 Speaker 1: gender pay gap. 200 00:10:44,360 --> 00:10:46,120 Speaker 2: Something that I thought was interesting that you said at 201 00:10:46,160 --> 00:10:49,400 Speaker 2: the beginning was the idea behind this was that with 202 00:10:49,760 --> 00:10:54,240 Speaker 2: increased scrutiny would hopefully come some movement on the gender 203 00:10:54,240 --> 00:10:56,640 Speaker 2: pay gap. So I think that it is important to say, 204 00:10:56,679 --> 00:10:58,520 Speaker 2: this is the first year this has been published. We 205 00:10:58,520 --> 00:11:00,800 Speaker 2: can hope that there will be improvement year on year 206 00:11:01,280 --> 00:11:03,880 Speaker 2: and that, you know, hopefully something productive will come out 207 00:11:03,920 --> 00:11:05,520 Speaker 2: of this. We're not talking about it just for the 208 00:11:05,520 --> 00:11:08,360 Speaker 2: sake of shaming. I think we're all hoping that these 209 00:11:08,360 --> 00:11:11,680 Speaker 2: gender pay gaps are rectified. But yeah, I do think 210 00:11:11,720 --> 00:11:14,040 Speaker 2: that it's interesting to look at that list and understand 211 00:11:14,080 --> 00:11:16,920 Speaker 2: a bit more about it. Were there any gender pay 212 00:11:16,960 --> 00:11:19,439 Speaker 2: gaps that went the other way, those that were favoring women. 213 00:11:19,520 --> 00:11:22,280 Speaker 2: Obviously it's usually the other way, but curious to know 214 00:11:22,480 --> 00:11:23,360 Speaker 2: if it went that way. 215 00:11:23,679 --> 00:11:25,960 Speaker 1: Yeah, they were obviously less of those, but there were 216 00:11:26,000 --> 00:11:29,640 Speaker 1: definitely some of Note one I thought was interesting was 217 00:11:29,679 --> 00:11:33,000 Speaker 1: Baker Mackenzie, the big law firm. They had a negative 218 00:11:33,080 --> 00:11:36,199 Speaker 1: pay gap, so a pay gap favoring women of nine 219 00:11:36,240 --> 00:11:38,120 Speaker 1: point five percent, which is pretty sid. 220 00:11:38,080 --> 00:11:41,160 Speaker 2: So that's outside of that healthy range that they were referring. 221 00:11:41,240 --> 00:11:41,840 Speaker 1: Yeah, exactly. 222 00:11:41,880 --> 00:11:42,200 Speaker 2: Wow. 223 00:11:42,400 --> 00:11:45,160 Speaker 1: There was also Astrozeneca. You may remember them from being 224 00:11:45,200 --> 00:11:48,920 Speaker 1: one of the big pharmaceutical companies who supplied COVID vaccines 225 00:11:48,960 --> 00:11:49,680 Speaker 1: in Australia. 226 00:11:49,760 --> 00:11:50,600 Speaker 2: How could we forget? 227 00:11:51,520 --> 00:11:54,400 Speaker 1: They had a negative pay gap of two point nine percent, 228 00:11:54,440 --> 00:11:57,280 Speaker 1: so that is within the healthy range. Another one was 229 00:11:57,280 --> 00:11:59,920 Speaker 1: black More's, the vitamin's company. They had a negative pay 230 00:12:00,080 --> 00:12:02,600 Speaker 1: gap of one point nine percent. There's just some of 231 00:12:02,640 --> 00:12:04,679 Speaker 1: the ones that I recognized when I went through that list, 232 00:12:04,720 --> 00:12:07,160 Speaker 1: but obviously that was a smaller list than the ones 233 00:12:07,200 --> 00:12:09,040 Speaker 1: that had a gender pay gap favoring men. 234 00:12:09,320 --> 00:12:11,480 Speaker 2: When I was reading our post about this on the feed, 235 00:12:11,520 --> 00:12:14,199 Speaker 2: I was very interested, and I will say a little 236 00:12:14,200 --> 00:12:17,319 Speaker 2: surprised to read that this number that the gender pay 237 00:12:17,320 --> 00:12:21,360 Speaker 2: gap was assessed on didn't include the salaries of CEOs. 238 00:12:21,679 --> 00:12:23,280 Speaker 2: I think that that's a big deal. 239 00:12:23,559 --> 00:12:26,520 Speaker 1: Yeah, this surprised me too, and I actually called the 240 00:12:26,640 --> 00:12:30,640 Speaker 1: WGEA who published this data, to make absolute certain that 241 00:12:30,760 --> 00:12:35,360 Speaker 1: I wasn't misinterpreting this data because like, it seemed slightly 242 00:12:35,360 --> 00:12:38,040 Speaker 1: odd to me that they wouldn't include the highest paid 243 00:12:38,080 --> 00:12:41,800 Speaker 1: employees of the company. But it's absolutely right. They confirmed 244 00:12:41,800 --> 00:12:44,400 Speaker 1: to me this data does not include the earnings of 245 00:12:44,520 --> 00:12:49,640 Speaker 1: CEOs or heads of business or casual managers. Okay, why, Well, 246 00:12:49,679 --> 00:12:52,520 Speaker 1: it's just because of how the legislation was planned out, 247 00:12:52,600 --> 00:12:55,560 Speaker 1: and the changes were always going to be made in increments, 248 00:12:55,880 --> 00:12:59,280 Speaker 1: and so from next year that information will be included. 249 00:13:00,040 --> 00:13:02,720 Speaker 2: Can imagine that we'll have a significant effect though, on 250 00:13:02,760 --> 00:13:03,440 Speaker 2: those numbers. 251 00:13:03,600 --> 00:13:06,360 Speaker 1: Well, that's what I immediately thought, right, surely that would 252 00:13:06,400 --> 00:13:09,600 Speaker 1: have a big impact on how big these gender pay 253 00:13:09,640 --> 00:13:13,080 Speaker 1: gaps are. For two reasons. We know that CEOs typically 254 00:13:13,120 --> 00:13:15,560 Speaker 1: will be earning a higher salary than the rest of 255 00:13:15,600 --> 00:13:20,280 Speaker 1: the employees. And also we know according to wgea nearly 256 00:13:20,360 --> 00:13:23,360 Speaker 1: eighty percent of CEOs in Australia are men. That is 257 00:13:23,400 --> 00:13:27,439 Speaker 1: a huge percent, eighty percent. I was shocked by that number, 258 00:13:28,160 --> 00:13:33,120 Speaker 1: but then I remember that this is based on median earnings, which, 259 00:13:33,240 --> 00:13:36,480 Speaker 1: like I said before, explicitly is designed to lessen the 260 00:13:36,559 --> 00:13:39,600 Speaker 1: impact of more extreme earnings such as the one of 261 00:13:39,640 --> 00:13:40,120 Speaker 1: the CEO. 262 00:13:40,520 --> 00:13:44,280 Speaker 2: Interesting, so they're not expecting it to radically shift these 263 00:13:44,360 --> 00:13:45,199 Speaker 2: dynamics that much. 264 00:13:45,280 --> 00:13:47,080 Speaker 1: Yeah, I don't think so. If this was a measure 265 00:13:47,160 --> 00:13:50,680 Speaker 1: on averages rather than medians, it would have a much 266 00:13:50,800 --> 00:13:54,520 Speaker 1: larger impact. But still, absolutely it's something worth noting and 267 00:13:54,600 --> 00:13:57,600 Speaker 1: keeping in mind when we are interpreting this data. I 268 00:13:57,640 --> 00:13:59,480 Speaker 1: hope that no one has turned off because the mass 269 00:13:59,520 --> 00:14:00,559 Speaker 1: is getting too much. 270 00:14:00,840 --> 00:14:02,920 Speaker 2: I mean, I'm nearly there. Okay, we've made it to 271 00:14:02,920 --> 00:14:06,520 Speaker 2: the end. What has been the feedback to this. Obviously, 272 00:14:06,640 --> 00:14:09,679 Speaker 2: it's the first time that we've seen this data because 273 00:14:09,720 --> 00:14:13,000 Speaker 2: the government's legislation only passed last year. What's the general 274 00:14:13,040 --> 00:14:13,679 Speaker 2: response been. 275 00:14:14,480 --> 00:14:17,160 Speaker 1: Generally, the sentiment from everyone is that things need to 276 00:14:17,200 --> 00:14:21,640 Speaker 1: improve and women broadly across the workforce are not being 277 00:14:21,760 --> 00:14:25,160 Speaker 1: valued equally to men and they're not getting promoted as 278 00:14:25,200 --> 00:14:29,240 Speaker 1: fast and that needs to change. Here's what the CEO 279 00:14:29,520 --> 00:14:32,640 Speaker 1: of WGEA, Mary Wooldridge said yesterday. 280 00:14:32,840 --> 00:14:35,760 Speaker 3: When you look at every industry across the board, the 281 00:14:35,840 --> 00:14:38,560 Speaker 3: gender pay gap is in favor of men. It is 282 00:14:38,600 --> 00:14:41,760 Speaker 3: a complex issue, and in that in terms of the profile, 283 00:14:42,480 --> 00:14:45,880 Speaker 3: even in the highest companies and in industries like construction, 284 00:14:46,040 --> 00:14:50,240 Speaker 3: professional services, the financial services industries, some gender pay gaps 285 00:14:50,240 --> 00:14:53,240 Speaker 3: are actually close to zero, while others may be at thirty, 286 00:14:53,320 --> 00:14:56,880 Speaker 3: forty and fifty percent, so it's doable. Change is possible. 287 00:14:57,200 --> 00:14:59,640 Speaker 3: It just takes that motivation and it takes that action. 288 00:15:00,040 --> 00:15:02,720 Speaker 1: And the Shadow Minister for Women Susan Lee, so she's 289 00:15:02,720 --> 00:15:06,280 Speaker 1: from the Coalition, she said this yesterday in a press conference. 290 00:15:06,680 --> 00:15:10,680 Speaker 4: We're not doing enough to enable women to get ahead, 291 00:15:11,280 --> 00:15:14,880 Speaker 4: to get promotions. We're not doing enough to support women 292 00:15:15,280 --> 00:15:20,360 Speaker 4: and this data forming an important baseline, will be something 293 00:15:20,400 --> 00:15:24,400 Speaker 4: that is measured year on year, and I'm going to 294 00:15:24,480 --> 00:15:29,360 Speaker 4: call out not by name, not by identity, but start 295 00:15:29,400 --> 00:15:33,160 Speaker 4: to call out the organizations in the main that are 296 00:15:33,240 --> 00:15:36,520 Speaker 4: not doing what they can to keep the gender pay 297 00:15:36,560 --> 00:15:37,880 Speaker 4: gap reducing. 298 00:15:38,440 --> 00:15:42,000 Speaker 1: There was some negativity though around the report and some 299 00:15:42,080 --> 00:15:47,160 Speaker 1: people said it's just not helpful data. Senator Matt Canavan 300 00:15:47,440 --> 00:15:50,360 Speaker 1: yesterday from the National Party, which is part of the coalition. 301 00:15:50,880 --> 00:15:54,280 Speaker 1: He did not mince his words when he tweeted quote, 302 00:15:54,360 --> 00:15:57,480 Speaker 1: the gender pay Gap report is useless data because it 303 00:15:57,480 --> 00:16:01,280 Speaker 1: does not even correct for basic differences like ours worked. 304 00:16:01,800 --> 00:16:04,440 Speaker 1: He went on, The gender pay Report is now the 305 00:16:05,080 --> 00:16:06,760 Speaker 1: Andrew Tait recruitment drive. 306 00:16:07,400 --> 00:16:09,840 Speaker 2: Andrew Tate being he is the. 307 00:16:09,720 --> 00:16:13,200 Speaker 1: Online influencer who has been banned from social media for 308 00:16:13,400 --> 00:16:17,440 Speaker 1: posting misogynistic views, and he's also currently facing several charges 309 00:16:17,480 --> 00:16:22,080 Speaker 1: in Romania, including for human trafficking and rape. So just 310 00:16:22,120 --> 00:16:24,200 Speaker 1: to be clear on what Matt Kinavan said, he said 311 00:16:24,200 --> 00:16:27,480 Speaker 1: that this report quote is now the annual Andrew Tait 312 00:16:27,600 --> 00:16:31,840 Speaker 1: recruitment drive. It just breeds resentment and division. Andrew Tate 313 00:16:31,960 --> 00:16:35,720 Speaker 1: is so popular because governments and corporates push a simplistic, 314 00:16:35,880 --> 00:16:40,360 Speaker 1: divisive and clearly incorrect gender narrative. So there was a 315 00:16:40,360 --> 00:16:43,560 Speaker 1: lot of conversation around that yesterday in question time. 316 00:16:43,960 --> 00:16:49,320 Speaker 2: Billy. I think it's a really, really important and interesting topic, 317 00:16:49,480 --> 00:16:53,200 Speaker 2: and I think that it is very easily misinterpreted. I 318 00:16:53,280 --> 00:16:56,160 Speaker 2: know that you know, when we get these sorts of reports, 319 00:16:56,240 --> 00:16:59,120 Speaker 2: or when we get figures kind of every other day, 320 00:16:59,160 --> 00:17:01,440 Speaker 2: it can be really difficult to understand the weight of them. 321 00:17:01,720 --> 00:17:03,560 Speaker 2: But I think this is a real moment in time 322 00:17:03,600 --> 00:17:07,040 Speaker 2: and hopefully the start of quite significant change when it 323 00:17:07,080 --> 00:17:11,040 Speaker 2: comes to the gender pay gap here in Australia. Thanks 324 00:17:11,080 --> 00:17:13,800 Speaker 2: for listening to today's episode of The Daily OS. If 325 00:17:13,800 --> 00:17:15,960 Speaker 2: you do have a spare couple of minutes, we would 326 00:17:16,040 --> 00:17:19,800 Speaker 2: love you to please fill out our podcast survey. We 327 00:17:19,840 --> 00:17:23,000 Speaker 2: are absolutely loving reading all of your responses and are 328 00:17:23,080 --> 00:17:25,400 Speaker 2: learning a lot about what your preferences are and how 329 00:17:25,440 --> 00:17:28,960 Speaker 2: you like to consume this podcast. We'll drop the link 330 00:17:29,040 --> 00:17:31,880 Speaker 2: in today's show notes. We will be back again tomorrow, 331 00:17:31,960 --> 00:17:36,600 Speaker 2: but until then, have a great day. 332 00:17:37,359 --> 00:17:39,639 Speaker 1: My name is Lily Madden and I'm a proud Arunda 333 00:17:39,880 --> 00:17:44,040 Speaker 1: bunge Lung Chalcotin woman from Gadigol Country. The Daily oz 334 00:17:44,119 --> 00:17:46,879 Speaker 1: acknowledges that this podcast is recorded on the lands of 335 00:17:46,880 --> 00:17:50,199 Speaker 1: the Gadighl people and pays respect to all Aboriginal and 336 00:17:50,240 --> 00:17:53,280 Speaker 1: Torres Strait Island and nations. We pay our respects to 337 00:17:53,359 --> 00:17:56,240 Speaker 1: the first peoples of these countries, both past and present.