1 00:00:00,160 --> 00:00:02,480 Speaker 1: This episode is brought to you by square Space. Start 2 00:00:02,480 --> 00:00:05,480 Speaker 1: building your website today at square space dot com. Inter 3 00:00:05,640 --> 00:00:08,639 Speaker 1: offer code stuff at checkout to get ten percent off 4 00:00:08,920 --> 00:00:13,720 Speaker 1: square Space. Set your website apart. Welcome to Stuff you 5 00:00:13,720 --> 00:00:22,520 Speaker 1: Should Know from House Stuff Works dot com. Hey, and 6 00:00:22,560 --> 00:00:25,479 Speaker 1: welcome to the podcast. I'm Josh Clark. There's Charles W 7 00:00:25,640 --> 00:00:28,600 Speaker 1: Chuck Bryant, and Jerry. So it's stuff you should Know. 8 00:00:29,560 --> 00:00:34,000 Speaker 1: Oh is this coming out on Equal pay Day? I 9 00:00:34,040 --> 00:00:37,720 Speaker 1: don't think so. Well. Equal pay Day may have come 10 00:00:37,760 --> 00:00:39,920 Speaker 1: and gone by now, but it was April twelve, two 11 00:00:39,920 --> 00:00:44,400 Speaker 1: thousand sixteen, that's right. And do you remember when we 12 00:00:44,479 --> 00:00:48,080 Speaker 1: did our this Day in History weirdo series or video 13 00:00:48,120 --> 00:00:50,560 Speaker 1: series that was pretty great? It was a little odd. 14 00:00:50,600 --> 00:00:53,680 Speaker 1: It was Lynch I in I was, but one of 15 00:00:53,720 --> 00:00:56,960 Speaker 1: the one of the ones that we did was on 16 00:00:57,440 --> 00:01:01,320 Speaker 1: um uh Lily Ladbetter. Yeah, and I think it was 17 00:01:01,360 --> 00:01:06,440 Speaker 1: on Equal pay Day. I think I remember it. Um. 18 00:01:06,480 --> 00:01:08,520 Speaker 1: But Equal pay Day, for those of you not in 19 00:01:08,560 --> 00:01:12,959 Speaker 1: the know, is a day in the United States UM 20 00:01:13,000 --> 00:01:17,480 Speaker 1: where working women who work full time, year round would 21 00:01:17,480 --> 00:01:21,360 Speaker 1: have to work until to get the equal amount of 22 00:01:21,360 --> 00:01:24,800 Speaker 1: pay that their male counterparts got for the year before. Right, 23 00:01:24,920 --> 00:01:28,720 Speaker 1: So it is two thousand sixteen. So take our pay 24 00:01:28,800 --> 00:01:31,800 Speaker 1: for two thousand fifteen, and we'll don't take our pay. 25 00:01:31,880 --> 00:01:34,800 Speaker 1: We want to keep that. But take a man's pay, 26 00:01:34,840 --> 00:01:38,400 Speaker 1: a real man's pay from a woman in the same 27 00:01:38,440 --> 00:01:41,600 Speaker 1: position would have to work. I mean it changes every year, right, 28 00:01:42,240 --> 00:01:46,440 Speaker 1: the date, Uh, if the amount of pay changes, are 29 00:01:46,480 --> 00:01:49,880 Speaker 1: the paid disparity changes, right, But the significance of that 30 00:01:49,960 --> 00:01:51,920 Speaker 1: date would be like a woman has to work until 31 00:01:51,960 --> 00:01:55,680 Speaker 1: April whatever to the following year to make as much 32 00:01:55,680 --> 00:01:57,840 Speaker 1: as the man made in the previous year. Right. I 33 00:01:57,840 --> 00:01:59,640 Speaker 1: think I just basically said the same thing you said, 34 00:02:00,160 --> 00:02:02,680 Speaker 1: just in a weird way. And this is this is 35 00:02:02,720 --> 00:02:05,920 Speaker 1: nothing new. Actually, this idea that there is a disparity, 36 00:02:06,720 --> 00:02:10,680 Speaker 1: a pay gap, if you will, between men and women 37 00:02:10,800 --> 00:02:13,840 Speaker 1: in the United States, and actually it's around the world, um. 38 00:02:13,880 --> 00:02:17,360 Speaker 1: And the idea behind it is that women's work is 39 00:02:17,440 --> 00:02:22,399 Speaker 1: just inherently less valued than men's work. Um. And there's 40 00:02:22,440 --> 00:02:25,639 Speaker 1: a lot of debate over this. Barack Obama in the 41 00:02:25,720 --> 00:02:29,000 Speaker 1: State of the Union address in I don't remember the year, 42 00:02:29,800 --> 00:02:32,800 Speaker 1: said it might have been his last one where he said, um, 43 00:02:33,080 --> 00:02:37,720 Speaker 1: women make of what men make, and that's not okay. 44 00:02:38,040 --> 00:02:40,640 Speaker 1: We need to do something about that. Kind of sounded 45 00:02:40,720 --> 00:02:45,000 Speaker 1: like Obama, thanks man, thanks look at you like I 46 00:02:45,040 --> 00:02:46,840 Speaker 1: looked up for a minute. I was like, your President 47 00:02:46,880 --> 00:02:49,240 Speaker 1: Obama in the room. You really did look up. You 48 00:02:49,240 --> 00:02:51,200 Speaker 1: guys couldn't see it, but Chuck really did look up. 49 00:02:51,280 --> 00:02:54,280 Speaker 1: That was good. Uh. This Uh, the National Committee on 50 00:02:54,320 --> 00:02:59,200 Speaker 1: Pay Equity is who um, who began holding equal pay 51 00:02:59,280 --> 00:03:04,440 Speaker 1: day right in but UM apparently was it in a 52 00:03:04,560 --> 00:03:07,840 Speaker 1: nineteen fifty is when they started kind of gathering data 53 00:03:07,880 --> 00:03:10,919 Speaker 1: on this. Yeah. I think the Bureau of Labor Statistics 54 00:03:10,919 --> 00:03:13,120 Speaker 1: really started looking at it hard in the fifties. But 55 00:03:13,520 --> 00:03:17,400 Speaker 1: the paid disparity goes back way further than that, basically 56 00:03:17,400 --> 00:03:20,080 Speaker 1: to the beginning of the country. Yeah, so when people 57 00:03:20,080 --> 00:03:23,120 Speaker 1: started working and earning money, and when women were even 58 00:03:23,120 --> 00:03:25,920 Speaker 1: allowed to work. We need to do an episode just 59 00:03:26,040 --> 00:03:28,799 Speaker 1: on wage labor in the origin of it, the history 60 00:03:28,840 --> 00:03:33,239 Speaker 1: of it. Yeah, it's super fascinating interesting. So go back 61 00:03:33,240 --> 00:03:35,600 Speaker 1: in time, some things used to be a lot worse. 62 00:03:35,760 --> 00:03:39,600 Speaker 1: Believe it or not. UM. Women earned thirty cents on 63 00:03:39,640 --> 00:03:43,680 Speaker 1: the dollar in the early at the dawn of America, UM, 64 00:03:43,680 --> 00:03:47,800 Speaker 1: when we were farming mainly UM. During the Industrial Revolution, 65 00:03:47,840 --> 00:03:51,320 Speaker 1: things got a little bit better about fifty UM up 66 00:03:51,360 --> 00:03:57,200 Speaker 1: to UM. But here's the distressing thing. Um, in nineteen 67 00:03:57,240 --> 00:04:01,280 Speaker 1: sixty three, Congress pass the Equal Pay Act, and since 68 00:04:01,360 --> 00:04:04,480 Speaker 1: then it was about six for a little while, but 69 00:04:04,520 --> 00:04:08,120 Speaker 1: in the eighties they climbed up to and then thirty 70 00:04:08,200 --> 00:04:13,600 Speaker 1: years later it's only climbed up four percent. It's basically stagnated. 71 00:04:14,560 --> 00:04:17,320 Speaker 1: It's very much leveled off. And the sad thing is 72 00:04:17,320 --> 00:04:20,880 Speaker 1: is what you're seeing then is as men's wages grow, 73 00:04:21,000 --> 00:04:25,880 Speaker 1: women's wages aren't growing. Then, Um, it's a it's and 74 00:04:25,920 --> 00:04:29,479 Speaker 1: it's a big deal. It's a big problem because, um, 75 00:04:29,640 --> 00:04:32,839 Speaker 1: women are losing out on a tremendous amount of money 76 00:04:32,960 --> 00:04:36,640 Speaker 1: over their lifetimes. Um, just for what seems to be 77 00:04:36,960 --> 00:04:40,520 Speaker 1: gender discrimination. And I want to say now before anybody 78 00:04:40,560 --> 00:04:43,279 Speaker 1: just loses their mind, there are a lot of theories 79 00:04:43,279 --> 00:04:46,400 Speaker 1: behind this, and gender discrimination is one of them, but 80 00:04:46,520 --> 00:04:50,680 Speaker 1: none of them are actually proven necessarily. No, And like 81 00:04:50,839 --> 00:04:53,400 Speaker 1: most things, I think it's probably a combination of many, 82 00:04:53,440 --> 00:04:57,080 Speaker 1: many things, most likely. But as I was saying, whatever 83 00:04:57,120 --> 00:04:59,480 Speaker 1: the cause, women are losing out on a lot of money. So, 84 00:04:59,560 --> 00:05:03,160 Speaker 1: for examp, will check in two thousand eleven, five year 85 00:05:03,160 --> 00:05:05,960 Speaker 1: old woman earned about five grand less per year than 86 00:05:06,000 --> 00:05:09,560 Speaker 1: her male counterpart. And this is just woman who worked 87 00:05:09,920 --> 00:05:13,200 Speaker 1: full time, year round, man who worked full time, year round, 88 00:05:13,400 --> 00:05:17,320 Speaker 1: age five and in five grand, you're like, yeah, that's 89 00:05:17,440 --> 00:05:19,200 Speaker 1: that's a lot. You can see a lot of movies 90 00:05:19,720 --> 00:05:22,200 Speaker 1: with five grand. But when you add it up over 91 00:05:22,720 --> 00:05:25,280 Speaker 1: the woman's career and by the time she makes us 92 00:05:25,279 --> 00:05:27,960 Speaker 1: the sixty five, she will have lost out on four 93 00:05:28,040 --> 00:05:30,760 Speaker 1: hundred and thirty thousand dollars in wages compared to her 94 00:05:30,880 --> 00:05:34,200 Speaker 1: male counterpart. Yeah. That if if that remains consistent throughout 95 00:05:34,200 --> 00:05:36,120 Speaker 1: her career, that's a lot of dough. That's a lot 96 00:05:36,120 --> 00:05:39,920 Speaker 1: of movies. Uh. And this is uh, these are white women. 97 00:05:39,920 --> 00:05:43,320 Speaker 1: It gets much worse if you go across the races. Um, 98 00:05:43,360 --> 00:05:48,080 Speaker 1: an African American woman, the figure dips to sixty percent 99 00:05:48,200 --> 00:05:52,000 Speaker 1: sixty point five. Hispanic females even worse. Uh. And by 100 00:05:52,000 --> 00:05:53,640 Speaker 1: the way, we can say male and female because we're 101 00:05:53,640 --> 00:05:58,039 Speaker 1: talking about studies here. Yeah, layoff, um fifty four percent 102 00:05:58,480 --> 00:06:02,520 Speaker 1: fifty four point six for Hispanic women, and only Asian 103 00:06:02,520 --> 00:06:06,599 Speaker 1: women did better than white women, uh, with eighty three 104 00:06:06,640 --> 00:06:10,720 Speaker 1: point five percent of what their white male counterparts made. Yeah. 105 00:06:10,760 --> 00:06:13,760 Speaker 1: And I openingly, if you look at the earners in 106 00:06:13,800 --> 00:06:16,880 Speaker 1: the United States, white men are not the top Asian 107 00:06:16,880 --> 00:06:19,880 Speaker 1: men are actually the top earners. They earn on average 108 00:06:19,880 --> 00:06:23,160 Speaker 1: in two thousand, fourteen hundred and thirteen point five percent 109 00:06:23,240 --> 00:06:28,159 Speaker 1: more than white men. Uh huh. Interesting. Um, And there's 110 00:06:28,200 --> 00:06:30,920 Speaker 1: I want to say something to also the equal pay 111 00:06:31,040 --> 00:06:33,880 Speaker 1: day where a woman would have to work too to 112 00:06:33,960 --> 00:06:36,560 Speaker 1: make the way. Just that's a white woman would have 113 00:06:36,600 --> 00:06:39,760 Speaker 1: to work to April twelve. If you are an African 114 00:06:39,760 --> 00:06:43,159 Speaker 1: American woman, you would have to work until June to 115 00:06:43,320 --> 00:06:46,400 Speaker 1: make the what the average or the median pay of 116 00:06:46,440 --> 00:06:49,000 Speaker 1: a white man for the year before Latino woman all 117 00:06:49,040 --> 00:06:51,960 Speaker 1: the way into October. Yeah, so it's like it almost 118 00:06:52,760 --> 00:06:55,480 Speaker 1: I mean it's almost like twice as long. Alright. So 119 00:06:55,560 --> 00:06:58,719 Speaker 1: here is um, here's how it's determined. And this is 120 00:06:58,720 --> 00:07:01,920 Speaker 1: one of the one the problems with trying to get 121 00:07:02,000 --> 00:07:04,360 Speaker 1: behind the reasons because it's one thing to to talk 122 00:07:04,360 --> 00:07:07,360 Speaker 1: about this stuff, but what unless you can make change 123 00:07:07,760 --> 00:07:10,840 Speaker 1: and help this out, then it's just talking about it, 124 00:07:10,880 --> 00:07:13,920 Speaker 1: like to make a difference, you need to really understand 125 00:07:13,920 --> 00:07:17,000 Speaker 1: the underlying problems. Oh yeah, yeah yeah. But I think 126 00:07:17,080 --> 00:07:19,640 Speaker 1: step one is what's being done now and has been 127 00:07:19,640 --> 00:07:23,600 Speaker 1: done since shining a spotlight on absolutely and I think 128 00:07:23,600 --> 00:07:26,720 Speaker 1: step one is being undertaken still that we're trying to 129 00:07:26,800 --> 00:07:30,680 Speaker 1: understand it. But yeah, it's definitely it's definitely not settled. Like, 130 00:07:30,960 --> 00:07:34,280 Speaker 1: here's the problem, right, So one of the problems and 131 00:07:34,360 --> 00:07:37,560 Speaker 1: trying to is data and getting really good data. Um. 132 00:07:37,640 --> 00:07:40,280 Speaker 1: The way they get the pay gap figure is they 133 00:07:41,000 --> 00:07:44,040 Speaker 1: take all the women working full time, year round, find 134 00:07:44,040 --> 00:07:47,200 Speaker 1: their median salary. It's not an average median is in 135 00:07:47,200 --> 00:07:50,360 Speaker 1: the middle, um. And then they take that same calculation 136 00:07:50,400 --> 00:07:53,280 Speaker 1: for men and they say, well, subtract us too, and 137 00:07:53,280 --> 00:07:57,560 Speaker 1: this is the wage gap. Okay, so that's actually the 138 00:07:57,560 --> 00:08:00,720 Speaker 1: the earnings ratio. Right, So when you see something, uh, 139 00:08:00,840 --> 00:08:04,760 Speaker 1: women make sevent of what men make, it's very frequently 140 00:08:04,760 --> 00:08:06,680 Speaker 1: in this article does it all over the place, it's 141 00:08:06,720 --> 00:08:09,040 Speaker 1: called the wage gap. That's not the wage gap. That's 142 00:08:09,040 --> 00:08:13,960 Speaker 1: the earnings ratio. The wage gap would be yeah, yeah, 143 00:08:13,960 --> 00:08:16,280 Speaker 1: you know what I mean. But it's it's confusing, like 144 00:08:16,280 --> 00:08:17,880 Speaker 1: because if you think about you're like, wow, the wage 145 00:08:17,880 --> 00:08:21,520 Speaker 1: cap is sev the wage gap is. The earnings ratio 146 00:08:21,600 --> 00:08:25,280 Speaker 1: is seventy. But people tend to use that bigger number 147 00:08:25,320 --> 00:08:29,080 Speaker 1: because it's more eye popping when you're a media mogul, right, 148 00:08:29,320 --> 00:08:32,920 Speaker 1: you know what I mean? Absolutely, uh So a lot 149 00:08:32,920 --> 00:08:35,480 Speaker 1: of people will say that that just that calculation is 150 00:08:35,520 --> 00:08:39,120 Speaker 1: too broad to draw a conclusion, and it probably is. 151 00:08:39,160 --> 00:08:42,720 Speaker 1: You need better data for sure. Um. And this article 152 00:08:42,760 --> 00:08:44,360 Speaker 1: even points out that, you know, you can't take that 153 00:08:44,400 --> 00:08:47,960 Speaker 1: one statistic and and say this is all you need 154 00:08:48,000 --> 00:08:51,160 Speaker 1: to know about pay inequality. Like, I don't think anyone's 155 00:08:51,200 --> 00:08:56,400 Speaker 1: saying that, no, well, dummies. So but the one thing 156 00:08:56,440 --> 00:08:59,720 Speaker 1: that does so unfortunately is it gives critics a chance 157 00:08:59,760 --> 00:09:04,679 Speaker 1: to holes in it and dismiss it outright. Um, you know, yeah, 158 00:09:04,720 --> 00:09:08,560 Speaker 1: because they're saying, okay, let's let's take a step back here. 159 00:09:08,920 --> 00:09:12,680 Speaker 1: You're taking the median salary for all of the men 160 00:09:13,120 --> 00:09:17,959 Speaker 1: who worked in the United States full time, and then 161 00:09:17,960 --> 00:09:21,040 Speaker 1: you're taking the median salary for all the women who 162 00:09:21,080 --> 00:09:23,959 Speaker 1: worked full time in the United States throughout the entirety 163 00:09:23,960 --> 00:09:28,640 Speaker 1: of two thousand and that's comparing apples to oranges, they say. 164 00:09:28,960 --> 00:09:31,400 Speaker 1: And one, there's a number of reasons why they say 165 00:09:31,480 --> 00:09:34,720 Speaker 1: you're actually comparing apples oranges. There's a lot of different 166 00:09:34,800 --> 00:09:37,080 Speaker 1: um jobs that are being done, there's a lot of 167 00:09:37,160 --> 00:09:41,920 Speaker 1: varying educational backgrounds, there's a lot of different experience backgrounds, 168 00:09:42,080 --> 00:09:44,400 Speaker 1: and you just can't compare the two. So let's not 169 00:09:44,480 --> 00:09:46,880 Speaker 1: talk about this again. Okay, right now? What this the 170 00:09:46,920 --> 00:09:50,040 Speaker 1: follow up should be. So let's drill down further and 171 00:09:50,120 --> 00:09:52,400 Speaker 1: get better data and talk about it even in a 172 00:09:52,440 --> 00:09:55,280 Speaker 1: more detailed way, which some people are doing, which we'll 173 00:09:55,280 --> 00:09:59,280 Speaker 1: get to. Um. This was fascinating to me about job 174 00:09:59,400 --> 00:10:04,960 Speaker 1: clustering or occupational clustering. I know about job clustering. Uh, 175 00:10:06,120 --> 00:10:11,480 Speaker 1: of all working women are employed in just twenty fields. Uh, 176 00:10:11,640 --> 00:10:14,000 Speaker 1: it's crazy. It is crazy. Um. If you look at 177 00:10:14,040 --> 00:10:17,720 Speaker 1: men um, about thirty to thirty work in the top 178 00:10:17,800 --> 00:10:21,800 Speaker 1: twenty occupations for males, which are you know, we're talking 179 00:10:22,360 --> 00:10:26,920 Speaker 1: a lot of times managerial and supervisional roles, but also 180 00:10:27,080 --> 00:10:29,720 Speaker 1: roles that are more physically demanding two and may pay 181 00:10:29,760 --> 00:10:32,520 Speaker 1: more yeah, or more dangerous sometimes, Like they make the 182 00:10:32,520 --> 00:10:36,400 Speaker 1: point that you'll probably find a man uh working as 183 00:10:36,480 --> 00:10:38,480 Speaker 1: like a long haul trucker, although we've heard from quite 184 00:10:38,480 --> 00:10:40,640 Speaker 1: a few women who who are doing it. Large March. 185 00:10:40,800 --> 00:10:43,480 Speaker 1: Have you not seen Big Adventure? Have you seen the 186 00:10:43,480 --> 00:10:45,200 Speaker 1: new one yet? Not yet? Is it good? I haven't 187 00:10:45,240 --> 00:10:47,080 Speaker 1: seen it yet. I can't wait. I'm kind of afraid to. 188 00:10:47,840 --> 00:10:50,920 Speaker 1: I did see Stitches the Clown though, and it's got 189 00:10:50,960 --> 00:10:53,440 Speaker 1: like a one star rating and usually on Netflix that 190 00:10:53,520 --> 00:10:55,800 Speaker 1: means like stay away for real, but for you it 191 00:10:55,880 --> 00:10:58,360 Speaker 1: means dig in. Well I read I read about it 192 00:10:58,400 --> 00:11:00,559 Speaker 1: online too. It may be kind of interested. I don't 193 00:11:00,559 --> 00:11:03,400 Speaker 1: think I know what that is. It's an Irish black 194 00:11:03,720 --> 00:11:08,240 Speaker 1: comedy horror movie. Um, about a clown that was murdered 195 00:11:08,280 --> 00:11:10,480 Speaker 1: that comes back from the dead to take his revenge 196 00:11:10,720 --> 00:11:14,840 Speaker 1: in the most gruesome and gory ways. Problem Taylor made 197 00:11:14,840 --> 00:11:17,800 Speaker 1: for Josh Clark. But it's it's hilarious too, Like it's 198 00:11:17,800 --> 00:11:21,880 Speaker 1: meant to be tongue in cheek to check that out. Um, 199 00:11:21,960 --> 00:11:25,080 Speaker 1: we're talking about how, uh some women are in the 200 00:11:25,080 --> 00:11:30,760 Speaker 1: trucking industry. That's right, but the cluster of occupations for women, um, 201 00:11:30,800 --> 00:11:34,640 Speaker 1: generally speaking pay less than the cluster of occupations uh 202 00:11:34,960 --> 00:11:38,800 Speaker 1: that men prefer. Yeah, here's the thing. When you take 203 00:11:38,880 --> 00:11:41,880 Speaker 1: a woman in the trucking industry salary and compared to 204 00:11:41,920 --> 00:11:44,400 Speaker 1: a man in the trucking industry salary, it's it's still 205 00:11:44,400 --> 00:11:47,760 Speaker 1: going to be less. And then conversely, when you look 206 00:11:47,800 --> 00:11:52,960 Speaker 1: at a man who who has entered a female dominated field, 207 00:11:53,160 --> 00:11:56,400 Speaker 1: say like nursing, they're still gonna probably make more than 208 00:11:56,440 --> 00:12:01,800 Speaker 1: their female counterparts. Yeah, elementary school cheach as human resources administrators. 209 00:12:01,800 --> 00:12:04,440 Speaker 1: The pay gap is only one per cent um, but 210 00:12:04,559 --> 00:12:07,440 Speaker 1: it's still there. No, exactly, it's still there. Like literally, 211 00:12:07,480 --> 00:12:13,200 Speaker 1: if they compared same experience, same uh, same job, working 212 00:12:13,240 --> 00:12:16,600 Speaker 1: the same amount of hours, almost always across the board, 213 00:12:16,600 --> 00:12:18,720 Speaker 1: men will still make more. Even like you said, if 214 00:12:18,760 --> 00:12:21,960 Speaker 1: it's nursing, which historically people might think is a job 215 00:12:22,000 --> 00:12:24,719 Speaker 1: that more women prefer. Uh. What you're talking about the 216 00:12:24,840 --> 00:12:29,000 Speaker 1: chuck is called occupational gender segregation, and that in and 217 00:12:29,040 --> 00:12:32,720 Speaker 1: of itself is a big problem here. It's saying, um, 218 00:12:33,040 --> 00:12:35,320 Speaker 1: but we here in the United States really tend to 219 00:12:35,400 --> 00:12:38,560 Speaker 1: value the work that women do less than the work 220 00:12:38,600 --> 00:12:42,000 Speaker 1: that men do. Just by saying these jobs that are 221 00:12:42,000 --> 00:12:47,520 Speaker 1: traditionally women women staffed are um just traditionally paid less. 222 00:12:47,559 --> 00:12:49,440 Speaker 1: That whole field is yeah, and it's and it's not 223 00:12:49,480 --> 00:12:52,240 Speaker 1: like the nineteen thirties and forties and fifties where it's 224 00:12:52,240 --> 00:12:54,000 Speaker 1: like you just go be a secretary now and we'll 225 00:12:54,000 --> 00:12:57,840 Speaker 1: take care of the business. But there's still it's still 226 00:12:57,840 --> 00:13:00,199 Speaker 1: there to a certain degree. I mean, there are more 227 00:13:00,240 --> 00:13:03,680 Speaker 1: women who work as secretaries and men um But I 228 00:13:03,760 --> 00:13:05,839 Speaker 1: just want to get back to that stat real quick. 229 00:13:05,880 --> 00:13:08,560 Speaker 1: When we were talking about the even when you compared 230 00:13:08,600 --> 00:13:10,719 Speaker 1: apples to apples, men still made more. I mean it's 231 00:13:10,800 --> 00:13:15,760 Speaker 1: it's like, you know, two to um five and thirty 232 00:13:15,800 --> 00:13:19,280 Speaker 1: four occupations tracked by the Bureau of Labor Statistics, and 233 00:13:19,480 --> 00:13:22,920 Speaker 1: seven out of the five thirty four paid women more 234 00:13:22,960 --> 00:13:27,679 Speaker 1: than men. Yeah, seven seven, Uh, and apparently the most lucrative. 235 00:13:27,679 --> 00:13:29,320 Speaker 1: If you're a woman, you want to make more than 236 00:13:29,320 --> 00:13:33,040 Speaker 1: a man, Uh, you should be a respiratory therapist, because 237 00:13:33,080 --> 00:13:36,160 Speaker 1: their salaries are six point four percent higher than men's 238 00:13:36,160 --> 00:13:39,520 Speaker 1: in their field. Should we take a break, yes, all right, 239 00:13:39,559 --> 00:14:01,760 Speaker 1: we'll be back with more staggering stats. So chuck, you said, um, 240 00:14:01,840 --> 00:14:04,520 Speaker 1: if you compare apples apples, it's a few percent. The 241 00:14:04,520 --> 00:14:07,640 Speaker 1: beer of Labor statistics. Um, they like to throw a 242 00:14:07,640 --> 00:14:10,800 Speaker 1: little wrench in the works. They surveyed like five hundred 243 00:14:10,840 --> 00:14:13,959 Speaker 1: and thirty four occupations, you said, um. And when they 244 00:14:14,000 --> 00:14:18,199 Speaker 1: do their statistics, they use weekly salaries rather than um 245 00:14:18,320 --> 00:14:20,880 Speaker 1: annual salaries, and they actually come up with a less 246 00:14:20,920 --> 00:14:23,480 Speaker 1: pay less of a pay gap. But one of the 247 00:14:23,520 --> 00:14:26,280 Speaker 1: things the internet has given us, one of the great things, 248 00:14:26,640 --> 00:14:29,880 Speaker 1: is crowdsourcing. And um, there are a lot of companies 249 00:14:29,920 --> 00:14:33,520 Speaker 1: that crewe data as almost like a byproduct or maybe 250 00:14:33,560 --> 00:14:36,000 Speaker 1: the real under the table product of what they're doing. 251 00:14:36,440 --> 00:14:39,040 Speaker 1: One of those companies is glass Door, where you can 252 00:14:39,080 --> 00:14:42,320 Speaker 1: go look for job listings but also rate your employer, 253 00:14:42,440 --> 00:14:48,000 Speaker 1: rate the company, and it's basically the masses UM reviewing employers. 254 00:14:48,120 --> 00:14:51,000 Speaker 1: You know, it's a pretty cool idea. UM and glass 255 00:14:51,040 --> 00:14:54,640 Speaker 1: Door did a study in two sixteens, just came out 256 00:14:54,680 --> 00:14:57,400 Speaker 1: this month as a matter of fact, and they surveyed 257 00:14:57,640 --> 00:15:01,240 Speaker 1: five hundred and five thousand employee The last few of 258 00:15:01,320 --> 00:15:04,440 Speaker 1: labor statistics thing I saw was like fifty people. This 259 00:15:04,520 --> 00:15:07,880 Speaker 1: is like half a million employees and their salary reports 260 00:15:08,200 --> 00:15:13,200 Speaker 1: and found that if you compared women who are equally 261 00:15:13,280 --> 00:15:18,440 Speaker 1: qualified in the same position at the same company as 262 00:15:18,480 --> 00:15:22,000 Speaker 1: a man, uh, they still made on average five percent 263 00:15:22,120 --> 00:15:26,520 Speaker 1: less and in among computer scientists, which I guess as 264 00:15:26,600 --> 00:15:30,200 Speaker 1: developers and stuff. Um, the pay gap was the worst 265 00:15:31,440 --> 00:15:34,280 Speaker 1: for a woman in the same position equally qualified at 266 00:15:34,280 --> 00:15:36,120 Speaker 1: the same company as a man. And this is like 267 00:15:36,320 --> 00:15:39,120 Speaker 1: brand new data that just came out. Well. One of 268 00:15:39,160 --> 00:15:42,920 Speaker 1: the reasons they say, um, this is ongoing is that, uh, 269 00:15:43,360 --> 00:15:45,640 Speaker 1: maybe women don't know that they're not making as much 270 00:15:45,680 --> 00:15:49,320 Speaker 1: as their male counterparts because we still are in a 271 00:15:50,280 --> 00:15:53,280 Speaker 1: I guess society, or at least a work environment most 272 00:15:53,320 --> 00:15:56,760 Speaker 1: times where you're not only not encouraged to talk about 273 00:15:56,760 --> 00:16:00,240 Speaker 1: your pay, but some companies like prohibited. Yeah, you can't 274 00:16:00,240 --> 00:16:03,120 Speaker 1: talk about your pay your fellow employees, right, And I mean, 275 00:16:03,240 --> 00:16:06,520 Speaker 1: I guess, as draconian as it is, you can kind 276 00:16:06,520 --> 00:16:09,160 Speaker 1: of understand where the company is coming from because they 277 00:16:09,160 --> 00:16:12,040 Speaker 1: want to get away paying their workforce as little as possible, 278 00:16:12,480 --> 00:16:15,600 Speaker 1: so they don't want their workforce comparing salaries and realizing 279 00:16:15,640 --> 00:16:19,520 Speaker 1: that people are being paid substantially different stuff. Right. Still, 280 00:16:19,840 --> 00:16:23,520 Speaker 1: it's mean to to say, like, no, you can't talk 281 00:16:23,560 --> 00:16:27,240 Speaker 1: about your salary that we pay you to this other person. 282 00:16:27,280 --> 00:16:29,560 Speaker 1: Be quiet, you know. And there's been a lot of 283 00:16:29,600 --> 00:16:32,080 Speaker 1: reform that we'll talk about it as far as that's concerned. 284 00:16:32,080 --> 00:16:35,000 Speaker 1: But that's a big thing is there appears to be 285 00:16:35,200 --> 00:16:38,120 Speaker 1: among a lot of women this idea and I'm sure 286 00:16:38,160 --> 00:16:40,240 Speaker 1: a lot of men too don't realize this as well, 287 00:16:40,640 --> 00:16:45,080 Speaker 1: that there there is a gender paid disparity. And part 288 00:16:45,080 --> 00:16:48,520 Speaker 1: of the reason why is because of a lack of transparency. Yeah. Um, 289 00:16:48,560 --> 00:16:52,040 Speaker 1: last year, um, last May, those woman named Lauren voss 290 00:16:52,040 --> 00:16:55,320 Speaker 1: Winkle got on Twitter and said, here's a new hashtag, 291 00:16:55,800 --> 00:16:59,200 Speaker 1: um talk pay t a l k P A y 292 00:16:59,560 --> 00:17:02,400 Speaker 1: and said eat out your job titles, how experienced you are, 293 00:17:03,200 --> 00:17:05,879 Speaker 1: where you've been in life, and what your salary is. Uh. 294 00:17:05,920 --> 00:17:08,520 Speaker 1: And then at talk pay ann came out, which is 295 00:17:08,560 --> 00:17:13,680 Speaker 1: a probably smarter way. It's it's anonymous UM and about Well, 296 00:17:13,720 --> 00:17:16,600 Speaker 1: there were a lot of tweets, but most of them were, 297 00:17:16,720 --> 00:17:19,600 Speaker 1: of course, people just you know, complaining, and I'm sure 298 00:17:19,600 --> 00:17:21,960 Speaker 1: there were plenty of rolls too, but just you know, 299 00:17:22,000 --> 00:17:26,000 Speaker 1: complaining about the situation, which is fine. But about actually 300 00:17:26,000 --> 00:17:29,760 Speaker 1: tweeted their actual salary. UH. And a lot of people say, 301 00:17:29,800 --> 00:17:31,320 Speaker 1: this is kind of one of the first steps is 302 00:17:31,359 --> 00:17:34,560 Speaker 1: at least getting the information out there. UH. So a 303 00:17:34,560 --> 00:17:37,000 Speaker 1: woman might say, wait a minute, you make four thousand 304 00:17:37,080 --> 00:17:40,440 Speaker 1: dollars more than me for no reason at all. And 305 00:17:40,680 --> 00:17:43,600 Speaker 1: there are websites to um where you can compare things. 306 00:17:43,600 --> 00:17:48,840 Speaker 1: There's one called uh pay scale and one called comparably. 307 00:17:49,840 --> 00:17:55,520 Speaker 1: It's a little awkward title. It's beautiful. It makes sense. 308 00:17:56,400 --> 00:17:59,800 Speaker 1: I believe you mentioned some of the things that are doing. Uh, 309 00:17:59,840 --> 00:18:03,800 Speaker 1: the the government is doing um. President Obama signed an 310 00:18:03,800 --> 00:18:07,080 Speaker 1: executive order in two thousand fourteen, so that's when, as 311 00:18:07,119 --> 00:18:12,080 Speaker 1: you stay the union address must have come UM. He 312 00:18:12,200 --> 00:18:16,600 Speaker 1: barred federal contractors from punishing an employee for comparing their salary, 313 00:18:17,600 --> 00:18:22,520 Speaker 1: and a memorandum on those same contractors to submit data 314 00:18:22,600 --> 00:18:26,439 Speaker 1: compensation data if there was any to see if there 315 00:18:26,480 --> 00:18:28,800 Speaker 1: was any wage discrimination going on. Right, So, if you 316 00:18:28,840 --> 00:18:31,240 Speaker 1: worked at a company that was a government contractor and 317 00:18:31,240 --> 00:18:34,399 Speaker 1: you're a woman who suddenly got an unexpected raise and pay, 318 00:18:34,480 --> 00:18:36,440 Speaker 1: you may have had something to do with that maybe. 319 00:18:36,520 --> 00:18:41,159 Speaker 1: So UM. California actually also is leaning the way on this. 320 00:18:41,280 --> 00:18:44,480 Speaker 1: They uh, what is his name, Jerry Brown, the governor 321 00:18:44,680 --> 00:18:49,360 Speaker 1: the second time around UM he signed into law this 322 00:18:49,359 --> 00:18:53,800 Speaker 1: this UM Act that prevents companies from punishing employees for 323 00:18:53,840 --> 00:18:58,520 Speaker 1: talking about their pay, their salaries UM. And it mandates 324 00:18:59,040 --> 00:19:02,720 Speaker 1: at first man dated same pay for the same work, 325 00:19:03,119 --> 00:19:07,320 Speaker 1: which is actually the language in UM the Civil Rights Act. 326 00:19:07,440 --> 00:19:09,800 Speaker 1: Title seven of the Civil Rights Act says you have 327 00:19:09,840 --> 00:19:11,880 Speaker 1: to pay people the same pay for the same work, 328 00:19:12,119 --> 00:19:16,840 Speaker 1: regardless of their UM gender, of their UM race, of 329 00:19:16,920 --> 00:19:19,880 Speaker 1: their religion, any of that. They still have to get 330 00:19:19,880 --> 00:19:22,600 Speaker 1: the same pay. Uh. And women are protected by that 331 00:19:22,720 --> 00:19:26,720 Speaker 1: as well. But you know that was nineteen sixty three, 332 00:19:26,880 --> 00:19:32,520 Speaker 1: I think. And the specificity of the language same work 333 00:19:32,560 --> 00:19:36,520 Speaker 1: for same pay is it's it's so specific that it's 334 00:19:36,560 --> 00:19:38,440 Speaker 1: just easy to get around you just say, well, it's 335 00:19:38,440 --> 00:19:40,159 Speaker 1: not the same work. I'm not going to give these 336 00:19:40,160 --> 00:19:45,040 Speaker 1: people the same title or whatever. Um with the California 337 00:19:45,080 --> 00:19:47,320 Speaker 1: legislation kind of opened that and said you have the 338 00:19:47,359 --> 00:19:52,040 Speaker 1: same pay for the substantially same work, you know. Yeah, 339 00:19:52,080 --> 00:19:55,280 Speaker 1: and the Equal Pay Act of nineteen sixty three specifically 340 00:19:55,320 --> 00:19:58,679 Speaker 1: says employers may not pay unequal way just to men 341 00:19:58,720 --> 00:20:02,240 Speaker 1: and women who perform job that requires substantially equal skill, 342 00:20:02,320 --> 00:20:05,560 Speaker 1: effort and responsibility, and that are performed under similar working 343 00:20:05,560 --> 00:20:09,760 Speaker 1: to conditions within the same establishment. And then it goes 344 00:20:09,800 --> 00:20:14,040 Speaker 1: down to break down what is defined as skill effort, responsibilities, 345 00:20:14,520 --> 00:20:17,960 Speaker 1: working conditions, and establishment. So it definitely gets like way 346 00:20:17,960 --> 00:20:21,160 Speaker 1: more specific, which is good. And apparently the reason why 347 00:20:21,160 --> 00:20:23,720 Speaker 1: California is leading the way on this is because Silicon 348 00:20:23,840 --> 00:20:25,840 Speaker 1: Valley is one of the worst defenders out of all 349 00:20:25,880 --> 00:20:30,880 Speaker 1: of them. That's where that's California, that's sure. They also 350 00:20:30,920 --> 00:20:35,200 Speaker 1: signed a fifteen dollar minimum wage into law recently too. Yeah, 351 00:20:35,320 --> 00:20:38,240 Speaker 1: should we talk more about Lily lead Better, Yeah, because 352 00:20:38,280 --> 00:20:42,600 Speaker 1: I mean, if you want to talk uh wage equality law, 353 00:20:43,480 --> 00:20:47,280 Speaker 1: you've got to talk Lily lad Better. So she worked 354 00:20:47,320 --> 00:20:50,520 Speaker 1: for good Year Tire and over the years, I think 355 00:20:50,600 --> 00:20:53,600 Speaker 1: she worked there for nineteen years. She was given low 356 00:20:53,680 --> 00:20:57,679 Speaker 1: rankings and annual performance in salary reviews pretty consistently in 357 00:20:57,760 --> 00:21:02,199 Speaker 1: low raises compared to her fellow ployees. So she sued them, 358 00:21:02,280 --> 00:21:06,480 Speaker 1: and a jury initially said you win three point five 359 00:21:06,520 --> 00:21:09,520 Speaker 1: million dollars and then a district year was like, what, 360 00:21:09,720 --> 00:21:15,400 Speaker 1: we can't afford that, Uh, goodyear, I'm sorry. UM. District 361 00:21:15,520 --> 00:21:18,680 Speaker 1: judge later said, let's reduce that from three point five 362 00:21:18,720 --> 00:21:22,320 Speaker 1: million to three hundred and sixty thousand, and good year 363 00:21:22,400 --> 00:21:26,200 Speaker 1: was like still, well they did because they appealed UM. 364 00:21:26,280 --> 00:21:28,399 Speaker 1: Basically what they did was they cited a title a 365 00:21:28,480 --> 00:21:33,600 Speaker 1: Title seven provision. They said any discrimination complaint needs to 366 00:21:33,680 --> 00:21:37,600 Speaker 1: be made within a hundred eighty days of the of 367 00:21:37,680 --> 00:21:42,240 Speaker 1: the bad deed, basically of the conduct of yes of you. 368 00:21:42,400 --> 00:21:47,040 Speaker 1: They give you your first paycheck and it's discriminatory pay 369 00:21:47,200 --> 00:21:49,320 Speaker 1: then you have a hundred and eighty days. So this 370 00:21:49,400 --> 00:21:53,440 Speaker 1: lady worked for eighteen and a half years longer then 371 00:21:53,520 --> 00:21:58,119 Speaker 1: she could have submitted a Title seven complaint, which is 372 00:21:58,280 --> 00:22:02,160 Speaker 1: b s that's right. And they basically eventually said, well, 373 00:22:02,200 --> 00:22:05,400 Speaker 1: what you can do is you can only sue for 374 00:22:05,480 --> 00:22:10,760 Speaker 1: the last days worth of discriminatory conduct and then it 375 00:22:10,800 --> 00:22:13,080 Speaker 1: went to the Supreme Court of the United States and 376 00:22:13,160 --> 00:22:17,560 Speaker 1: she lost by five four vote. Uh, basically holding to 377 00:22:17,680 --> 00:22:22,080 Speaker 1: that claim that it was time sensitive. Yeah, it's just 378 00:22:22,800 --> 00:22:25,000 Speaker 1: I mean the idea that, like you get your first 379 00:22:25,000 --> 00:22:29,520 Speaker 1: paycheck and you just immediately go, well, this is discriminatory. 380 00:22:29,920 --> 00:22:32,720 Speaker 1: Even that stick. Right. This woman worked for nineteen years 381 00:22:32,720 --> 00:22:35,360 Speaker 1: without realizing it, and the reason why she finally did 382 00:22:35,400 --> 00:22:38,600 Speaker 1: come to understand it was because a coworker passed her 383 00:22:38,640 --> 00:22:43,040 Speaker 1: an anonymous note um telling her so at a retirement Basically, 384 00:22:43,440 --> 00:22:46,639 Speaker 1: so she had been played for a sap basically by 385 00:22:46,640 --> 00:22:48,840 Speaker 1: good year as far as the lower courts are concerned, 386 00:22:49,119 --> 00:22:53,399 Speaker 1: for nineteen years. But because nineteen years earlier, in nineteen 387 00:22:53,480 --> 00:22:56,720 Speaker 1: seventy nine, she didn't immediately recognize that she was getting 388 00:22:56,760 --> 00:23:00,639 Speaker 1: less pay. So, like you have to basically be inspector 389 00:23:00,680 --> 00:23:05,160 Speaker 1: Cluseau the first time you get paid to to get 390 00:23:05,200 --> 00:23:09,440 Speaker 1: a successful Title seven complaint cleared by the court. It's 391 00:23:09,480 --> 00:23:14,720 Speaker 1: just ridiculous. Uh. In descent, I love reading the Supreme Court, 392 00:23:14,800 --> 00:23:17,560 Speaker 1: like when they actually, you know, write why they find 393 00:23:17,560 --> 00:23:20,639 Speaker 1: it in favor or not. It's really interesting stuff. Uh. 394 00:23:20,720 --> 00:23:24,160 Speaker 1: Justice Ruth Bader Ginsberg in her descent called it out 395 00:23:24,200 --> 00:23:26,919 Speaker 1: of tune with the realities of wage discrimination and quote 396 00:23:27,240 --> 00:23:31,320 Speaker 1: a cramped interpretation of Title seven incompatible with the statutes 397 00:23:31,680 --> 00:23:35,120 Speaker 1: broad remedial purpose end quote. So basically, like the whole 398 00:23:35,160 --> 00:23:38,320 Speaker 1: purpose of this, like, you're just shirking the whole purpose 399 00:23:38,359 --> 00:23:40,200 Speaker 1: of the strant to begin with. Yeah, the purpose isn't 400 00:23:40,200 --> 00:23:44,160 Speaker 1: to protect corporations from lawsuits. It's so that you keep 401 00:23:44,200 --> 00:23:47,879 Speaker 1: employees from getting screwed over by their employers. So again 402 00:23:47,920 --> 00:23:50,879 Speaker 1: Obama came in with another thing signed into law of 403 00:23:50,880 --> 00:23:53,960 Speaker 1: the Lily Ledbetter Act, which said, we'll stick to the 404 00:23:54,040 --> 00:23:57,399 Speaker 1: hundred and eighty days thing. But that's from the last 405 00:23:57,520 --> 00:24:03,359 Speaker 1: discriminatory paycheck. So she could have found out at her retirement, um, 406 00:24:03,720 --> 00:24:06,480 Speaker 1: gotten her last paycheck and had six months to sue 407 00:24:06,560 --> 00:24:09,919 Speaker 1: for the whole shebang, her whole career basically, and probably 408 00:24:09,920 --> 00:24:12,720 Speaker 1: would have one had that law been an effect at 409 00:24:12,720 --> 00:24:15,360 Speaker 1: the time. All right, well, let's take another break and 410 00:24:15,480 --> 00:24:17,480 Speaker 1: um we'll talk a little bit more about this right 411 00:24:17,520 --> 00:24:39,479 Speaker 1: after this. All right, So, uh, what are the reasons 412 00:24:39,480 --> 00:24:43,960 Speaker 1: behind this? They're myriad, They're myriad, They're myriad, and um, 413 00:24:44,320 --> 00:24:48,960 Speaker 1: the thing is, critics of the gender pay gap say 414 00:24:49,400 --> 00:24:52,000 Speaker 1: this is not a discrimination, like employers are not saying 415 00:24:52,040 --> 00:24:55,000 Speaker 1: like you're a woman, I'm gonna just you know, treat 416 00:24:55,040 --> 00:24:58,080 Speaker 1: you poorly compared to your male counterparts. That doesn't happen. 417 00:24:58,320 --> 00:25:01,560 Speaker 1: It may happen, but it's not happening on a systemic scale. 418 00:25:01,880 --> 00:25:04,840 Speaker 1: But if you look at the explanations for this, and 419 00:25:05,000 --> 00:25:08,240 Speaker 1: economists have investigated this and thought long and hard about 420 00:25:08,240 --> 00:25:11,440 Speaker 1: it and argued over it and really drilled down into 421 00:25:11,440 --> 00:25:14,919 Speaker 1: it rather than stop talking about it, right, um, it 422 00:25:15,000 --> 00:25:19,920 Speaker 1: would be what's still going on is still ultimately gender discrimination. 423 00:25:20,280 --> 00:25:24,560 Speaker 1: It's just not this nefarious handlebar mustache version of it 424 00:25:24,840 --> 00:25:28,040 Speaker 1: that people look for because everyone wants a smoking gun 425 00:25:28,160 --> 00:25:30,439 Speaker 1: or a gotcha moment, you know what I mean. But 426 00:25:30,560 --> 00:25:33,199 Speaker 1: when you look at the explanations and the reasons behind 427 00:25:33,960 --> 00:25:38,119 Speaker 1: purportedly the gender discrimination gap or the gender pay gap, 428 00:25:38,359 --> 00:25:41,720 Speaker 1: it is still discrimination based on gender most likely and 429 00:25:41,960 --> 00:25:45,359 Speaker 1: race too. Like we we shouldn't. We don't mean to 430 00:25:45,440 --> 00:25:48,720 Speaker 1: undermine that or diminish that at all, Like it's even 431 00:25:48,760 --> 00:25:53,840 Speaker 1: worse based on race. You know. Um, there was a 432 00:25:54,000 --> 00:25:58,639 Speaker 1: Freakonomics podcast episode. Mr Dubner did a great episode in 433 00:25:58,760 --> 00:26:01,960 Speaker 1: January of this year, um that I listened to and 434 00:26:02,000 --> 00:26:04,399 Speaker 1: he got um and you know, fre economics. It seems 435 00:26:04,440 --> 00:26:06,640 Speaker 1: like they kind of get to the real reasons behind things. 436 00:26:07,480 --> 00:26:09,840 Speaker 1: This was called the True Story of the Gender Pay 437 00:26:09,840 --> 00:26:12,240 Speaker 1: Gap UH, and he sat down with a few people. 438 00:26:12,240 --> 00:26:15,040 Speaker 1: One of them was Claudia Golden. She's a econ professor 439 00:26:15,080 --> 00:26:17,960 Speaker 1: at Harvard. So the long and short of it is, 440 00:26:18,000 --> 00:26:22,480 Speaker 1: after reading UH and listening to this, was that Claudia 441 00:26:22,520 --> 00:26:26,679 Speaker 1: Golden and others economists have tried to compare apples to 442 00:26:26,680 --> 00:26:31,119 Speaker 1: apples more and found that there are many, many reasons 443 00:26:31,160 --> 00:26:35,520 Speaker 1: why there is a pay gap um, but they can't 444 00:26:35,760 --> 00:26:39,480 Speaker 1: prove that discrimination is the leading cause, and they think 445 00:26:39,480 --> 00:26:43,240 Speaker 1: that it's probably one of the minor causes. Well, yeah, 446 00:26:43,240 --> 00:26:45,879 Speaker 1: it's most likely not a major cause at all. Like 447 00:26:45,920 --> 00:26:53,439 Speaker 1: when you look at that that mind boggling gap ratio um, 448 00:26:53,520 --> 00:26:57,320 Speaker 1: when you start comparing apples to apples, most of it vanishes, 449 00:26:58,280 --> 00:27:02,800 Speaker 1: but there is a mysterious three four five that can't 450 00:27:02,800 --> 00:27:06,960 Speaker 1: be accounted for by things like experience or educational background 451 00:27:07,520 --> 00:27:11,320 Speaker 1: or what have you. Well, yeah, and that's three. I 452 00:27:11,320 --> 00:27:14,240 Speaker 1: mean that's too much, you know, any percent is too much. 453 00:27:14,320 --> 00:27:16,880 Speaker 1: Like if there is, if if there is gender discrimination, 454 00:27:16,920 --> 00:27:24,600 Speaker 1: going zero exactly, Thank you, you just said it so succinctly. Um. So, 455 00:27:24,640 --> 00:27:27,679 Speaker 1: one of the theories that people say, um, is that 456 00:27:28,119 --> 00:27:31,679 Speaker 1: men are better at bargaining for their salary and asking 457 00:27:31,680 --> 00:27:34,720 Speaker 1: for raises and getting tough in that room and demanding 458 00:27:34,720 --> 00:27:38,440 Speaker 1: the raise and valuing their work more. In valuing their work. Um. 459 00:27:38,480 --> 00:27:40,879 Speaker 1: And there may be some truth to that. But um. 460 00:27:40,920 --> 00:27:44,639 Speaker 1: This economist from Harvard says, well, if that were true, 461 00:27:44,720 --> 00:27:47,480 Speaker 1: then you would be able to look at men and 462 00:27:47,520 --> 00:27:50,520 Speaker 1: women right out of college with the same degree getting 463 00:27:50,520 --> 00:27:53,800 Speaker 1: the same job. The men should be making more money. 464 00:27:53,840 --> 00:27:56,119 Speaker 1: And she said when you look at that, they actually 465 00:27:56,160 --> 00:27:59,880 Speaker 1: make almost the same thing. Right. The gender pay gap 466 00:28:00,080 --> 00:28:04,240 Speaker 1: is less earlier on in the career. Right. But even 467 00:28:04,280 --> 00:28:07,920 Speaker 1: if there is that small bit right there at the beginning, 468 00:28:08,320 --> 00:28:11,359 Speaker 1: if you start out lower, your raises over time and 469 00:28:11,359 --> 00:28:14,600 Speaker 1: your promotions over time are gonna be lower. Two. And 470 00:28:14,680 --> 00:28:18,080 Speaker 1: that can actually accrue and make the gap wider by 471 00:28:18,119 --> 00:28:20,720 Speaker 1: the end of the career, which is what you see, right. Yeah, 472 00:28:20,760 --> 00:28:23,560 Speaker 1: And what she contends. And this other woman and Marie 473 00:28:23,560 --> 00:28:26,199 Speaker 1: Slaughter who is a public policy scholar who wrote a 474 00:28:26,200 --> 00:28:30,280 Speaker 1: book called Unfinished Business, and Mr Dubner sat down with 475 00:28:30,320 --> 00:28:33,320 Speaker 1: her too, and they kind of peg it down to 476 00:28:33,440 --> 00:28:37,240 Speaker 1: it seems like two things. Um. And Mary Slaughter calls 477 00:28:37,320 --> 00:28:40,080 Speaker 1: one of them the care penalty. Um. And it's not 478 00:28:40,160 --> 00:28:42,280 Speaker 1: just having a baby and staying home with a baby. 479 00:28:42,360 --> 00:28:45,400 Speaker 1: It's women are more likely to care for their parents 480 00:28:45,400 --> 00:28:48,760 Speaker 1: when they get old. Um. Of a sibling that needs help, 481 00:28:49,240 --> 00:28:51,160 Speaker 1: like you know, they they have cancer and they need 482 00:28:51,200 --> 00:28:52,720 Speaker 1: to come live with you. And I need to take 483 00:28:52,720 --> 00:28:55,160 Speaker 1: time on work to care for my brother's sister, and 484 00:28:55,280 --> 00:28:59,560 Speaker 1: especially children when when you have kids. Um, there is 485 00:29:00,320 --> 00:29:03,560 Speaker 1: most assuredly a care penalty because they found that men 486 00:29:03,600 --> 00:29:07,360 Speaker 1: sometimes work even harder after they have a baby. Right, 487 00:29:07,400 --> 00:29:10,080 Speaker 1: they're staying away from home, maybe staying away from home, 488 00:29:10,160 --> 00:29:11,680 Speaker 1: or maybe it hits them like I need to work 489 00:29:11,680 --> 00:29:14,840 Speaker 1: harder to make more money now, whereas women falls to 490 00:29:14,960 --> 00:29:18,440 Speaker 1: more like you know, I want to care for this child. So, UM, 491 00:29:18,480 --> 00:29:22,880 Speaker 1: I'm gonna select a job that will pay less because 492 00:29:22,920 --> 00:29:26,480 Speaker 1: it's either part time or it offers more flexibility. Um. 493 00:29:26,520 --> 00:29:29,320 Speaker 1: And then I saw another interview with um Golden right 494 00:29:29,720 --> 00:29:33,280 Speaker 1: is their name? Um, and she pointed out that, uh, 495 00:29:33,360 --> 00:29:38,000 Speaker 1: if women are if women are more likely to get 496 00:29:38,000 --> 00:29:41,680 Speaker 1: a job that's closer to home that's more fulfilling, say 497 00:29:41,800 --> 00:29:44,640 Speaker 1: than um, something a man is looking for, then there's 498 00:29:44,640 --> 00:29:48,080 Speaker 1: going to be more competition for those jobs, which is 499 00:29:48,080 --> 00:29:52,000 Speaker 1: going to drive wages down just from supply and demand theory. Yeah, 500 00:29:52,040 --> 00:29:57,080 Speaker 1: she calls it temporal flexibility women. Men tend to favor 501 00:29:57,760 --> 00:30:02,120 Speaker 1: income growth way more than women do, whereas women tend 502 00:30:02,160 --> 00:30:04,760 Speaker 1: to favorite temporal flexibility more. I want to be near 503 00:30:04,800 --> 00:30:07,080 Speaker 1: the house so I'm not stuck in my car, so 504 00:30:07,120 --> 00:30:09,080 Speaker 1: I can be around my family more, so I can 505 00:30:09,120 --> 00:30:14,240 Speaker 1: be you know, a better member, a better family member 506 00:30:14,280 --> 00:30:18,160 Speaker 1: basically overall, which is you know, it seems like men 507 00:30:18,240 --> 00:30:20,280 Speaker 1: don't care as much about that, right and and I 508 00:30:20,320 --> 00:30:24,640 Speaker 1: mean the most overt example of this is childcare, where 509 00:30:25,040 --> 00:30:30,160 Speaker 1: especially in the US, um you're lucky to get X 510 00:30:30,280 --> 00:30:33,600 Speaker 1: number of days off as a father, as a as 511 00:30:33,640 --> 00:30:38,160 Speaker 1: a woman, you're you're you get a guaranteed three months off, 512 00:30:38,200 --> 00:30:43,480 Speaker 1: twelve weeks off by federal law. Men there's no guarantee whatsoever, 513 00:30:44,000 --> 00:30:47,600 Speaker 1: so you may get zero off. And that reflects this again, 514 00:30:47,640 --> 00:30:50,640 Speaker 1: this gender bias, if not outright gender discrimination, that does 515 00:30:50,680 --> 00:30:53,440 Speaker 1: tend to set some women back because even if they're 516 00:30:53,520 --> 00:30:56,320 Speaker 1: just out for three months or something, they're they're missing 517 00:30:56,360 --> 00:30:59,320 Speaker 1: out of the flow of their career. And there you 518 00:30:59,440 --> 00:31:01,800 Speaker 1: couldn't go on work trip, so we're gonna send uh, 519 00:31:01,840 --> 00:31:05,719 Speaker 1: you know exactly, So they're missing out on future promotions, 520 00:31:05,880 --> 00:31:09,440 Speaker 1: future raises UM and future experience and all of those 521 00:31:09,440 --> 00:31:16,240 Speaker 1: things count for future um income wages. Yeah, and I 522 00:31:16,280 --> 00:31:19,000 Speaker 1: want to rephrase what I just said that men don't 523 00:31:19,040 --> 00:31:21,959 Speaker 1: care as much about being a good family member. What 524 00:31:22,000 --> 00:31:24,400 Speaker 1: I meant to say is men might have a different 525 00:31:24,440 --> 00:31:28,360 Speaker 1: outlook on what is being a better family member, and 526 00:31:28,400 --> 00:31:30,760 Speaker 1: in that case, it's going out and working harder to 527 00:31:30,760 --> 00:31:33,160 Speaker 1: make more money. Oh yeah, does that make sense? Sure? 528 00:31:33,840 --> 00:31:35,480 Speaker 1: Because I get roughed up for being too hard on 529 00:31:35,480 --> 00:31:39,720 Speaker 1: white men, right, I do. We get emails all the time, Chuck, 530 00:31:39,760 --> 00:31:41,240 Speaker 1: why do you hate white men? I don't know. I 531 00:31:41,240 --> 00:31:43,400 Speaker 1: don't hate what I'm I am a white man, and 532 00:31:43,440 --> 00:31:46,200 Speaker 1: it's not self loathing. No. I love myself and I 533 00:31:46,240 --> 00:31:49,360 Speaker 1: love you, thanks man. Uh. And it's just kind of 534 00:31:49,360 --> 00:31:52,880 Speaker 1: warm in here. So temporal flexibility is one of the 535 00:31:52,880 --> 00:31:58,400 Speaker 1: big things. And um and actually Golden says that if 536 00:31:58,440 --> 00:32:03,200 Speaker 1: women were allowed to work flexible hours of their choosing, 537 00:32:03,760 --> 00:32:07,800 Speaker 1: then the she believes that the the gender pay gap 538 00:32:07,920 --> 00:32:11,720 Speaker 1: might actually vanish right well, and um a Marie Slaughter said, 539 00:32:11,720 --> 00:32:13,640 Speaker 1: if you if you take the other big one is 540 00:32:13,680 --> 00:32:16,200 Speaker 1: the care penalty. If you take that off the table 541 00:32:16,640 --> 00:32:20,040 Speaker 1: and you don't have any caregiving obligations at all. Uh, 542 00:32:20,040 --> 00:32:25,000 Speaker 1: it climbs to about but there's still that five right, 543 00:32:25,120 --> 00:32:27,480 Speaker 1: you know, Like, where is that coming from? I think 544 00:32:27,560 --> 00:32:30,320 Speaker 1: that's it. I think it's got to be discrimination. I 545 00:32:30,320 --> 00:32:32,560 Speaker 1: can't remember her name, but I was reading an interview 546 00:32:32,600 --> 00:32:36,400 Speaker 1: in the Atlantic with a Cornell economist who studies this, 547 00:32:37,000 --> 00:32:40,240 Speaker 1: and she was saying, um, that if you look at Europe, 548 00:32:40,520 --> 00:32:45,680 Speaker 1: there they're much more um even they're equal with their 549 00:32:45,920 --> 00:32:50,640 Speaker 1: um their paternity leaving their maternity to leave actually and um, 550 00:32:51,520 --> 00:32:55,040 Speaker 1: I don't know that it's it's old enough to have 551 00:32:55,280 --> 00:32:59,280 Speaker 1: like had a demonstrable effect. But um, I wonder if 552 00:32:59,320 --> 00:33:02,160 Speaker 1: if having something like that here in the US, where 553 00:33:02,400 --> 00:33:05,280 Speaker 1: I don't remember the country she cited some skin and 554 00:33:05,360 --> 00:33:10,240 Speaker 1: even country obviously. UM. But the the men are given 555 00:33:10,320 --> 00:33:13,240 Speaker 1: like a family is given an a lotted amount of 556 00:33:13,720 --> 00:33:18,160 Speaker 1: paternity or maternity time, and X amount of it has 557 00:33:18,200 --> 00:33:21,000 Speaker 1: to be paternity, and the dad can either use it 558 00:33:21,080 --> 00:33:24,200 Speaker 1: or not, but the mom can't take that on. So 559 00:33:24,240 --> 00:33:26,400 Speaker 1: it's kind of like you're gonna spend some time with 560 00:33:26,480 --> 00:33:29,400 Speaker 1: your kids or not. But to you, um, and so 561 00:33:29,440 --> 00:33:32,800 Speaker 1: there's a lot more guys taking that than there was before, 562 00:33:32,960 --> 00:33:36,280 Speaker 1: and so there's a lot more equality and caregiving. Which 563 00:33:36,480 --> 00:33:39,040 Speaker 1: if it's gonna be, you know which ways America is 564 00:33:39,040 --> 00:33:42,320 Speaker 1: gonna go. Is it gonna be women need to spend 565 00:33:42,400 --> 00:33:44,800 Speaker 1: less time at home and get into the workforce more 566 00:33:44,880 --> 00:33:47,280 Speaker 1: or men need to spend more time at home caring 567 00:33:47,280 --> 00:33:49,680 Speaker 1: for the kids and make the whole thing more of 568 00:33:49,720 --> 00:33:52,400 Speaker 1: an equal thing, you know, and then maybe that will 569 00:33:52,440 --> 00:33:56,080 Speaker 1: erase some of the pay gap for sure. They m 570 00:33:56,920 --> 00:33:59,120 Speaker 1: in the same fre Economics episode. They kind of close 571 00:33:59,200 --> 00:34:02,840 Speaker 1: with asking Ms Golden what her like, what kind of 572 00:34:02,920 --> 00:34:06,320 Speaker 1: legislation would like, how can you fix this? Like more laws, 573 00:34:06,360 --> 00:34:08,239 Speaker 1: more laws, And she had a pretty good idea. She 574 00:34:08,320 --> 00:34:10,799 Speaker 1: was like, you need to start at like the school level, 575 00:34:11,320 --> 00:34:13,560 Speaker 1: because you send your kids off to school, and what 576 00:34:13,640 --> 00:34:16,000 Speaker 1: happens is they get out at two or three o'clock 577 00:34:16,760 --> 00:34:19,200 Speaker 1: and unless you have you know, I want your kid 578 00:34:19,239 --> 00:34:21,480 Speaker 1: to be a latch key kid, someone's gonna need to 579 00:34:21,480 --> 00:34:24,000 Speaker 1: be home with them. Uh. And then there are a 580 00:34:24,000 --> 00:34:26,120 Speaker 1: lot of times out, like for a couple of months 581 00:34:26,160 --> 00:34:28,759 Speaker 1: during the summer, so someone needs to be there. Then, 582 00:34:29,280 --> 00:34:33,520 Speaker 1: so she argues for lengthenings, extending school days in school, 583 00:34:33,880 --> 00:34:38,279 Speaker 1: the school year such that it doesn't require that one 584 00:34:38,280 --> 00:34:41,080 Speaker 1: of the family members to have to like take off 585 00:34:41,080 --> 00:34:43,480 Speaker 1: work or take a part time job, yeah, instead of 586 00:34:43,480 --> 00:34:45,360 Speaker 1: a full time job. Well, I remember when I was 587 00:34:45,400 --> 00:34:48,239 Speaker 1: a little kid, my mom was home and until I 588 00:34:48,400 --> 00:34:52,600 Speaker 1: entered I guess kindergarten. Yeah, my mom quit teaching to 589 00:34:52,719 --> 00:34:55,000 Speaker 1: raise all three of her kids up until I was 590 00:34:55,080 --> 00:34:57,239 Speaker 1: like fifteen, and then she went back to work as 591 00:34:57,239 --> 00:35:00,000 Speaker 1: a and then so I mean, how much further back 592 00:35:00,120 --> 00:35:04,240 Speaker 1: can her in their careers where our moms staying home hugely, 593 00:35:04,360 --> 00:35:08,719 Speaker 1: You should not be penalized for raising two stellar sons. Yeah, 594 00:35:08,760 --> 00:35:11,239 Speaker 1: and I have to say in a stellar daughter, Um, 595 00:35:12,640 --> 00:35:15,360 Speaker 1: it's kind of personal. But like my parents got divorced, 596 00:35:15,840 --> 00:35:19,400 Speaker 1: and so divorce proceedings can get ugly, and a woman 597 00:35:19,480 --> 00:35:23,880 Speaker 1: in a divorce court says, well, I took off uh 598 00:35:23,920 --> 00:35:26,640 Speaker 1: like eighteen years to raise my children, and now I'm 599 00:35:26,680 --> 00:35:29,799 Speaker 1: going back to school at a much lower wage than 600 00:35:29,840 --> 00:35:32,680 Speaker 1: I would have because like that needs to be valued. 601 00:35:33,239 --> 00:35:37,400 Speaker 1: And uh, it's tough, man, you know it is. It 602 00:35:37,520 --> 00:35:40,640 Speaker 1: is tough. Like we said, if there's any percentage it's 603 00:35:40,680 --> 00:35:47,239 Speaker 1: based on gender discrimination or race discrimination, that's too much. Agreed. Uh, Yeah, 604 00:35:47,239 --> 00:35:49,920 Speaker 1: I don't know what to do what the answer is. 605 00:35:50,080 --> 00:35:52,279 Speaker 1: And I guess the reason why we can't say here's 606 00:35:52,320 --> 00:35:55,399 Speaker 1: the answer because we don't fully understand which of these 607 00:35:55,400 --> 00:35:57,800 Speaker 1: factors it is or what combination. You know, yet, we 608 00:35:57,840 --> 00:35:59,880 Speaker 1: should just go sit down with all our fellow podcasts 609 00:35:59,880 --> 00:36:01,520 Speaker 1: are and talk about what we all make right. We 610 00:36:01,640 --> 00:36:04,479 Speaker 1: should um, there's something else I know as to chuck 611 00:36:04,800 --> 00:36:08,920 Speaker 1: um if we keep going at the same rate. So 612 00:36:08,960 --> 00:36:11,759 Speaker 1: there's actually a huge jump from nineteen eight and nine 613 00:36:14,080 --> 00:36:16,400 Speaker 1: of the pay gap that had been there before vanished, 614 00:36:17,000 --> 00:36:19,240 Speaker 1: And it was because women that had entered the workforce 615 00:36:19,239 --> 00:36:22,040 Speaker 1: starting in the sixties gained the kind of experience to 616 00:36:22,080 --> 00:36:25,880 Speaker 1: where like their wages were reflecting those of men. That 617 00:36:26,000 --> 00:36:28,600 Speaker 1: was that's one theory behind it. But then after that 618 00:36:28,680 --> 00:36:31,840 Speaker 1: it's stagnated. It's been stagnant pretty much the last decade 619 00:36:31,920 --> 00:36:34,879 Speaker 1: or so UM and apparently at the pace that it's 620 00:36:34,920 --> 00:36:41,040 Speaker 1: going now is when um parody for white women will 621 00:36:41,080 --> 00:36:43,839 Speaker 1: be reached. It's very frequently called like women won't reach 622 00:36:43,960 --> 00:36:48,879 Speaker 1: gender or gender parody until that's white women. If you 623 00:36:49,000 --> 00:36:52,680 Speaker 1: are African American, it would be sometime towards the end 624 00:36:52,680 --> 00:36:55,560 Speaker 1: of the next next century for an African American woman 625 00:36:55,600 --> 00:36:59,319 Speaker 1: to reach parody with white men, and around the world 626 00:36:59,400 --> 00:37:02,719 Speaker 1: this is an issue as well. Apparently women around the 627 00:37:02,760 --> 00:37:07,240 Speaker 1: world make on average half of what men do around 628 00:37:07,280 --> 00:37:14,040 Speaker 1: the world half half Eddie, Yeah, half, and so the 629 00:37:14,120 --> 00:37:18,799 Speaker 1: average woman in the world by three um would eat 630 00:37:18,840 --> 00:37:22,440 Speaker 1: would reach pay parody. Uh. Well, I have one final 631 00:37:22,480 --> 00:37:24,480 Speaker 1: stat here, just depending on what state you live in, 632 00:37:24,760 --> 00:37:28,760 Speaker 1: it's gonna make a big difference. Washington, d C leads 633 00:37:28,800 --> 00:37:34,640 Speaker 1: the way as far as the smallest gender pay gap. Um, 634 00:37:34,680 --> 00:37:42,640 Speaker 1: they're earnings ratio. Women are at of men. Okay, Georgia. 635 00:37:42,680 --> 00:37:48,399 Speaker 1: Our own state of Georgia is on the list, and 636 00:37:48,520 --> 00:37:52,319 Speaker 1: if you are a woman living in Louisiana, you have 637 00:37:52,400 --> 00:37:55,160 Speaker 1: the distinction of living in the worst state in the 638 00:37:55,239 --> 00:38:01,719 Speaker 1: United States at the scant earnings ratio. Wow, pretty low 639 00:38:01,960 --> 00:38:07,399 Speaker 1: SOT pay gap there. Uh and huge thanks to uh 640 00:38:07,600 --> 00:38:10,440 Speaker 1: for I was able to lean on that fre Economics 641 00:38:10,440 --> 00:38:14,040 Speaker 1: episode so heavily. To Stephen Dubna, that was a really 642 00:38:14,040 --> 00:38:16,720 Speaker 1: good episode. Yeah, that was nice of him to send 643 00:38:16,760 --> 00:38:23,200 Speaker 1: you a gold thumb drive with it. That was very nice, guys, Stubbs. Yeah. 644 00:38:23,000 --> 00:38:26,160 Speaker 1: Uh Well, if you want to learn more about this 645 00:38:26,280 --> 00:38:29,239 Speaker 1: kind of stuff, you know, gender pay gaps and pay 646 00:38:29,320 --> 00:38:33,680 Speaker 1: disparity and wages, that kind of thing that tickles economists fancies. 647 00:38:33,920 --> 00:38:36,080 Speaker 1: You can type those words into the search bar at 648 00:38:36,080 --> 00:38:38,359 Speaker 1: how staff works dot com. Since I said search parts 649 00:38:38,400 --> 00:38:43,319 Speaker 1: time for listener. Now, I think it was a little 650 00:38:43,320 --> 00:38:47,319 Speaker 1: clumsy in that one, So apologies to people what I 651 00:38:47,400 --> 00:38:48,680 Speaker 1: just felt that was a little clumsy in that if 652 00:38:48,680 --> 00:38:51,520 Speaker 1: that's how I felt in the Tornadoes episode. Really, there 653 00:38:51,560 --> 00:38:53,280 Speaker 1: was a lot of points I wanted to get across 654 00:38:53,520 --> 00:38:56,000 Speaker 1: the right way, and that's usually when I find myself 655 00:38:56,560 --> 00:38:59,840 Speaker 1: saying things exactly the wrong way. Well, you corrected yourself 656 00:38:59,840 --> 00:39:02,920 Speaker 1: on that one thing, right, Well I tried. Um, all right, 657 00:39:02,960 --> 00:39:07,759 Speaker 1: I'm gonna call this, uh nostalgia immediate nostalgia. Oh yeah, 658 00:39:08,160 --> 00:39:11,960 Speaker 1: from Jason Tardy. O. Wait, hey, guys, I was recently 659 00:39:12,000 --> 00:39:14,759 Speaker 1: listening to The Nostalgia Show while going on a trail run. 660 00:39:15,440 --> 00:39:17,239 Speaker 1: I didn't get a chance to finish it, and later 661 00:39:17,600 --> 00:39:19,760 Speaker 1: I went to listen to the podcast again. I started 662 00:39:19,800 --> 00:39:22,640 Speaker 1: over from the beginning and I skipped ahead to find 663 00:39:22,640 --> 00:39:24,680 Speaker 1: where I left off, and I was hit with instant 664 00:39:24,760 --> 00:39:28,319 Speaker 1: nostalgia of that trail run. Certain phrases I heard you 665 00:39:28,360 --> 00:39:30,640 Speaker 1: say during my run, We're tied with images of the 666 00:39:30,640 --> 00:39:33,080 Speaker 1: beautiful trail every time I skipped ahead, I saw a 667 00:39:33,080 --> 00:39:36,000 Speaker 1: different image and could remember exactly where I was when 668 00:39:36,040 --> 00:39:38,239 Speaker 1: I had previously heard you say that phrase had brought 669 00:39:38,280 --> 00:39:41,160 Speaker 1: up warm feelings of happiness of the trail while listening 670 00:39:41,160 --> 00:39:46,560 Speaker 1: to you, guys, I don't know if that's nostalgia. Thanks 671 00:39:46,560 --> 00:39:48,239 Speaker 1: for everything you do. I wish you all the best. 672 00:39:48,239 --> 00:39:50,920 Speaker 1: You've been a great distraction while training for my marathon, 673 00:39:51,000 --> 00:39:53,720 Speaker 1: driving long hours on the road as a performance artist, 674 00:39:53,760 --> 00:39:56,880 Speaker 1: in keeping me sane during the craziness of having my 675 00:39:56,960 --> 00:39:59,520 Speaker 1: wife go through breast cancer while still keeping the house 676 00:39:59,520 --> 00:40:03,319 Speaker 1: clean with two kids. So, Jason Tardy of Auburn, Maine, 677 00:40:03,320 --> 00:40:05,840 Speaker 1: hats off to you, sir, and best of luck to 678 00:40:05,960 --> 00:40:09,959 Speaker 1: your wife and good luck hearing for those kids. Yeah, 679 00:40:10,000 --> 00:40:15,720 Speaker 1: best wishes, guys. Yeah, we will instantly nostalgize you whenever 680 00:40:15,760 --> 00:40:20,359 Speaker 1: you want nice. If you want to get in touch 681 00:40:20,400 --> 00:40:22,680 Speaker 1: with us, like Jason did, you can tweet to us 682 00:40:22,719 --> 00:40:25,319 Speaker 1: at s Y s K podcast. You can join us 683 00:40:25,360 --> 00:40:27,719 Speaker 1: on Facebook dot com, slash stuff you Should Know. You 684 00:40:27,719 --> 00:40:29,960 Speaker 1: can send us an email to stuff Podcasts at how 685 00:40:30,000 --> 00:40:32,359 Speaker 1: Stuff Works dot com and has always joined us at 686 00:40:32,400 --> 00:40:39,719 Speaker 1: home on the web Stuff you Should Know dot com 687 00:40:39,760 --> 00:40:42,200 Speaker 1: for more on this and thousands of other topics. Is 688 00:40:42,200 --> 00:40:50,600 Speaker 1: it how stuff works? Dot com