1 00:00:00,280 --> 00:00:05,280 Speaker 1: Hi, everybody, it's Chuck and I am introducing this week's 2 00:00:05,360 --> 00:00:08,559 Speaker 1: Stuff you Should Know Selects episode. This is from the 3 00:00:08,640 --> 00:00:13,320 Speaker 1: Vault from two zero zero nine two nine November twelve, 4 00:00:13,840 --> 00:00:17,240 Speaker 1: and it's called How Population Works. And I picked this 5 00:00:17,280 --> 00:00:21,120 Speaker 1: one because I remember this being a super cool episode 6 00:00:21,160 --> 00:00:23,760 Speaker 1: because it was one of those where I thought, population, 7 00:00:24,239 --> 00:00:26,920 Speaker 1: what does that even mean? And how can we make 8 00:00:26,960 --> 00:00:31,159 Speaker 1: a show out a full show about this? And it 9 00:00:31,200 --> 00:00:32,879 Speaker 1: turned out to be great. Um, Josh, I think it 10 00:00:32,880 --> 00:00:35,680 Speaker 1: was his pick initially, and it's just really really cool. 11 00:00:35,720 --> 00:00:38,360 Speaker 1: So if you don't even know what, uh what how 12 00:00:38,400 --> 00:00:41,040 Speaker 1: Population Works might mean, give it a listen. I think 13 00:00:41,040 --> 00:00:50,080 Speaker 1: you'll be pretty intrigued. Welcome to Stuff you Should Know 14 00:00:50,720 --> 00:00:59,120 Speaker 1: from House Stuff Works dot com. Punky chucking, pocky chucking, 15 00:00:59,120 --> 00:01:02,200 Speaker 1: puckey chucking. That's right, CHUCKK And welcome to the podcast. 16 00:01:02,280 --> 00:01:06,280 Speaker 1: I'm Josh Clark. Clearly Chuck Bryant's here, and uh, let's 17 00:01:06,640 --> 00:01:09,160 Speaker 1: talk about punkin Chunking. I guess you just kind of 18 00:01:09,200 --> 00:01:12,040 Speaker 1: forced our hand, Chuck. Yes, the road to punkin Chunking 19 00:01:12,080 --> 00:01:15,679 Speaker 1: and punkin Chunking. So that's on. That's on Science Channel 20 00:01:15,760 --> 00:01:19,280 Speaker 1: a p m. Eastern time on Thanksgiving night. Yeah, you 21 00:01:19,319 --> 00:01:23,440 Speaker 1: can see some pumpkins get chunk punkins. Get chunk Punkins. Yeah, 22 00:01:23,520 --> 00:01:26,840 Speaker 1: okay again. Science Channel. The Road to Punkin Chunkin starts 23 00:01:26,840 --> 00:01:30,319 Speaker 1: at eight pm Eastern Time. Punkin chunk In Itself starts 24 00:01:30,319 --> 00:01:36,720 Speaker 1: at nine Thanksgiving night. Yes, Science Channel on with the show. Yeah, chuck, Um, 25 00:01:36,760 --> 00:01:41,039 Speaker 1: have you ever belonged to a population? No? Man, I'm like, 26 00:01:41,160 --> 00:01:44,280 Speaker 1: I'm totally independent. Screw population. You're like that guy who 27 00:01:44,480 --> 00:01:46,919 Speaker 1: lives in the commune, right right, Yeah, Well the joke's 28 00:01:46,959 --> 00:01:51,200 Speaker 1: on him, because a commune constitutes a population. That's right. Um, 29 00:01:51,640 --> 00:01:54,120 Speaker 1: this sounds kind of boring, and you would think it is. 30 00:01:54,880 --> 00:01:57,920 Speaker 1: Do how population works. It actually started to pick up. 31 00:01:58,000 --> 00:01:59,680 Speaker 1: Actually didn't know what it was even gonna be. When 32 00:01:59,720 --> 00:02:02,120 Speaker 1: I saw how population works, I was like, what, you know, 33 00:02:02,160 --> 00:02:04,640 Speaker 1: it's awesome. This was my idea this article was. I 34 00:02:04,720 --> 00:02:07,800 Speaker 1: pinched it. Why didn't they let you write it? I 35 00:02:07,840 --> 00:02:10,600 Speaker 1: don't know, jerks, I know, but the grabst did a 36 00:02:10,600 --> 00:02:13,640 Speaker 1: good job with it. Oh yeah, the grabs is always good. Yeah, class, 37 00:02:13,639 --> 00:02:17,400 Speaker 1: that's ed grab Anowsky. By the way, right, So, um, 38 00:02:17,680 --> 00:02:25,120 Speaker 1: human beings marriage, human beings tend to um congregate, Yes, 39 00:02:25,760 --> 00:02:29,960 Speaker 1: we segregate. Interestingly, that is an excellent. You just blew 40 00:02:30,000 --> 00:02:34,080 Speaker 1: my mind, good lord chuck. UM. Well, let's get back 41 00:02:34,120 --> 00:02:36,040 Speaker 1: to what I was saying, unless you want to go 42 00:02:36,080 --> 00:02:39,280 Speaker 1: on the segregation. We'll get on that later. UM. Humans 43 00:02:40,080 --> 00:02:44,440 Speaker 1: congregate and segregate. But let's talk about congregation and that UM. 44 00:02:44,560 --> 00:02:49,000 Speaker 1: Most of the time, I would say, our early early ancestors, UH, 45 00:02:49,080 --> 00:02:53,040 Speaker 1: and probably even other species congregate because they're safety and 46 00:02:53,120 --> 00:02:56,639 Speaker 1: numbers and it helps like with farming, collecting water and 47 00:02:56,680 --> 00:03:00,000 Speaker 1: food power numbers. But even before farming, UM a hundred 48 00:03:00,160 --> 00:03:04,359 Speaker 1: hunter gatherers lived in bands. I think thirty was about tops. 49 00:03:04,360 --> 00:03:06,680 Speaker 1: They figured out somewhere along the way that groups of 50 00:03:06,760 --> 00:03:09,760 Speaker 1: thirty or groups of more than thirty there tended to 51 00:03:09,760 --> 00:03:13,440 Speaker 1: be a lot more hostility and UM inner group problems. 52 00:03:13,480 --> 00:03:14,760 Speaker 1: Have you ever tried to kill a mass it on 53 00:03:14,840 --> 00:03:19,840 Speaker 1: by yourself? That's another good point too. There's cooperation mastered 54 00:03:20,000 --> 00:03:25,440 Speaker 1: on UM. There's UM. If if let's say, if you 55 00:03:25,520 --> 00:03:28,400 Speaker 1: are farming and your crop fails, well you're not standing 56 00:03:28,440 --> 00:03:33,440 Speaker 1: there like, well I'm in trouble. You can say, hey, neighbor, UM, 57 00:03:33,520 --> 00:03:37,080 Speaker 1: I'll totally give you favors of some variety if you 58 00:03:37,200 --> 00:03:39,240 Speaker 1: will let me have some of your grain. I can 59 00:03:39,400 --> 00:03:43,120 Speaker 1: a chicken. Let's say, sure you can trade. There's a 60 00:03:43,160 --> 00:03:46,720 Speaker 1: lot of reasons people live together. So it's my theory 61 00:03:46,760 --> 00:03:51,160 Speaker 1: that people aggregate together naturally. Yes, And then there are 62 00:03:51,200 --> 00:03:54,160 Speaker 1: people out there who get their jollies by studying these 63 00:03:54,160 --> 00:03:57,640 Speaker 1: groups of people. They're called demographer demographers. So we have 64 00:03:57,920 --> 00:04:02,120 Speaker 1: populations natural or otherwise. And let's a natural population, uh 65 00:04:02,160 --> 00:04:06,720 Speaker 1: today are people who live in a certain state Georgians. 66 00:04:07,240 --> 00:04:10,320 Speaker 1: That's where we are, So that you have natural populations 67 00:04:10,320 --> 00:04:15,040 Speaker 1: in demographer's studying, right, um, and they look at things 68 00:04:15,160 --> 00:04:18,520 Speaker 1: like say, how many people in this natural population are 69 00:04:18,560 --> 00:04:23,080 Speaker 1: Republicans or Democrat? Or how many are Caucasian? Right? Or 70 00:04:23,120 --> 00:04:27,200 Speaker 1: how many have um, how many you live below the 71 00:04:27,240 --> 00:04:30,080 Speaker 1: poverty line? All kinds of things you can study right 72 00:04:30,160 --> 00:04:34,000 Speaker 1: by looking at a population and are are is this? 73 00:04:34,560 --> 00:04:37,520 Speaker 1: Are these groups segregated like you brought up? You know, 74 00:04:37,560 --> 00:04:40,839 Speaker 1: like if you study, uh, where different races are living? 75 00:04:41,000 --> 00:04:43,440 Speaker 1: Are they living mingling? If so, then that's probably a 76 00:04:43,520 --> 00:04:47,800 Speaker 1: fairly harmonious place hopefully. If not, why are they living apart? 77 00:04:47,920 --> 00:04:50,880 Speaker 1: How do we fix this? Because it's probably a problem. 78 00:04:51,760 --> 00:04:57,040 Speaker 1: Who knows? But yes, so demographers study populations natural or otherwise? Right, Yes, 79 00:04:58,160 --> 00:05:01,479 Speaker 1: The problem is is you. Very few people have the 80 00:05:01,520 --> 00:05:05,560 Speaker 1: ability to hover over the earth and use super binocular 81 00:05:05,680 --> 00:05:10,840 Speaker 1: vision to study populations by side. Very few people, Yeah, 82 00:05:10,920 --> 00:05:14,520 Speaker 1: like three or four I think tops. Uh so does 83 00:05:14,560 --> 00:05:17,440 Speaker 1: that count as a statistic I think so? Okay, Richard, 84 00:05:17,920 --> 00:05:20,800 Speaker 1: um So measuring populations after you can talk about how 85 00:05:20,839 --> 00:05:23,159 Speaker 1: do we actually determine this kind of thing? Yeah, that 86 00:05:23,240 --> 00:05:25,800 Speaker 1: was my That was That was a good segue. There's 87 00:05:25,800 --> 00:05:30,880 Speaker 1: a kind of ways. One is um by counting them 88 00:05:30,920 --> 00:05:34,359 Speaker 1: literally counting them, like counting every single person, right, and 89 00:05:34,400 --> 00:05:38,040 Speaker 1: that is called complete enumeration. Yeah. Remember we talked about 90 00:05:38,080 --> 00:05:40,599 Speaker 1: that poor guy who was killed or possibly kill himself 91 00:05:40,640 --> 00:05:43,240 Speaker 1: in Kentucky, the census taker, right. Oh, I didn't know 92 00:05:43,279 --> 00:05:45,920 Speaker 1: that suicide was a possibility there. I got a cryptic 93 00:05:45,960 --> 00:05:48,880 Speaker 1: email from somebody never followed up on that said that 94 00:05:49,040 --> 00:05:53,000 Speaker 1: he identified himself as a doctor and I think said 95 00:05:53,040 --> 00:05:56,760 Speaker 1: that he was part of the group that was the 96 00:05:56,880 --> 00:06:01,880 Speaker 1: medical examination team and said that they suspect, strongly suspected suicide. 97 00:06:02,360 --> 00:06:04,320 Speaker 1: My problem with it is is how do you bind 98 00:06:04,360 --> 00:06:08,480 Speaker 1: yourself in duct tape? How do you bind your own 99 00:06:08,520 --> 00:06:12,480 Speaker 1: risks and duct tape? I'll show you later, Okay, um 100 00:06:12,560 --> 00:06:15,840 Speaker 1: So my point is, Wow, he threw me off of 101 00:06:15,920 --> 00:06:20,440 Speaker 1: that one. My point is that he was called an enumerator. Yes, 102 00:06:20,760 --> 00:06:24,120 Speaker 1: literally counter, and that's the people who work for the census. 103 00:06:24,440 --> 00:06:27,240 Speaker 1: Whenever they had their their drive and they count and 104 00:06:27,240 --> 00:06:29,560 Speaker 1: that's one way to determine it. Well, let's talk about 105 00:06:29,560 --> 00:06:32,800 Speaker 1: the census. It's gone on every ten years since, right, 106 00:06:33,120 --> 00:06:34,919 Speaker 1: And the reason they do it every ten years because 107 00:06:34,960 --> 00:06:37,159 Speaker 1: it's real pain in the asked to count every person 108 00:06:37,200 --> 00:06:39,200 Speaker 1: in America. Yeah, the real reason they do it so 109 00:06:39,279 --> 00:06:43,560 Speaker 1: they can. Well, there's a lot of reasons, but that 110 00:06:43,760 --> 00:06:47,680 Speaker 1: is the reason why anyone's ever conducted a census. Yeah. Well, 111 00:06:47,680 --> 00:06:50,960 Speaker 1: plus they they determine the number of house representatives for 112 00:06:51,000 --> 00:06:53,240 Speaker 1: your state based on population stuff like that. Oh yeah, 113 00:06:53,240 --> 00:06:55,520 Speaker 1: there's that too. But you know, come on, Texas, did 114 00:06:55,560 --> 00:07:00,359 Speaker 1: you know that, um, that that the census inform nation 115 00:07:01,080 --> 00:07:04,159 Speaker 1: is kept is kept secret for seventy two years aside 116 00:07:04,160 --> 00:07:07,159 Speaker 1: from the numbers, I believe the public cannot see that 117 00:07:07,200 --> 00:07:09,400 Speaker 1: information for seventy two years, Right, what do I seventy 118 00:07:09,440 --> 00:07:12,000 Speaker 1: two that's odd? It is odd. I wonder if that 119 00:07:12,040 --> 00:07:15,160 Speaker 1: was the average lifespan at the time or something. Dude, 120 00:07:15,400 --> 00:07:18,520 Speaker 1: that's got to be it. I'll bet you're right. Okay. 121 00:07:18,960 --> 00:07:23,280 Speaker 1: The other way, Josh, is to uh do something called sampling, 122 00:07:23,560 --> 00:07:27,640 Speaker 1: and that is when um statticians use a mathematical formula 123 00:07:27,680 --> 00:07:32,640 Speaker 1: to determine the minimum number of people that must be counted, 124 00:07:32,760 --> 00:07:37,080 Speaker 1: and then they multiply that out and basically end up 125 00:07:37,080 --> 00:07:40,080 Speaker 1: getting a full population. And sometimes I did, I didn't 126 00:07:40,080 --> 00:07:43,200 Speaker 1: know this. That's even more accurate than an actual head count. 127 00:07:44,080 --> 00:07:46,080 Speaker 1: You see that margin of air, it's like plus or 128 00:07:46,080 --> 00:07:48,440 Speaker 1: minus four percent. Yeah, you gotta have a margin of 129 00:07:48,520 --> 00:07:51,280 Speaker 1: error there whenever you're sampling, right, because you're not actually 130 00:07:51,280 --> 00:07:54,360 Speaker 1: going around asking every single person in America are you 131 00:07:54,440 --> 00:07:57,320 Speaker 1: left handed? To determine how many people are left handed? 132 00:07:58,280 --> 00:07:59,920 Speaker 1: But let's say you have a population with a thou 133 00:08:00,160 --> 00:08:03,120 Speaker 1: in and some statistician has been like, you need a 134 00:08:03,200 --> 00:08:08,440 Speaker 1: hundred do it, but do your egghead voice, Yeah, you 135 00:08:08,480 --> 00:08:10,920 Speaker 1: need a hundred and fifty people. The hundred and fifty 136 00:08:10,920 --> 00:08:13,080 Speaker 1: people and that are left handed, and you can just 137 00:08:13,200 --> 00:08:15,960 Speaker 1: multiply that out to determine that there are, in fact 138 00:08:16,440 --> 00:08:19,000 Speaker 1: how many people, let's say, ten percent of the population 139 00:08:19,800 --> 00:08:24,480 Speaker 1: of the population. But your sample is perfect. Your sample 140 00:08:24,600 --> 00:08:29,600 Speaker 1: has to be a random sample to be an effective sample. Yeah, 141 00:08:29,600 --> 00:08:31,200 Speaker 1: and you know how they used to do that. Uh 142 00:08:31,280 --> 00:08:32,560 Speaker 1: huh at used to just pick it out of the 143 00:08:32,559 --> 00:08:35,679 Speaker 1: phone book. Oh, I know and call people I know. 144 00:08:35,800 --> 00:08:39,240 Speaker 1: And that makes sense to a certain extent. No, well 145 00:08:39,280 --> 00:08:40,920 Speaker 1: back then it made a little more sense. I would 146 00:08:40,920 --> 00:08:43,560 Speaker 1: think it made it made less sense, especially if you're 147 00:08:43,600 --> 00:08:46,120 Speaker 1: talking like nineteen fifty years. Well, it depends on what year. 148 00:08:46,400 --> 00:08:48,559 Speaker 1: I'd say in the nineteen eighties it was probably a 149 00:08:48,600 --> 00:08:51,640 Speaker 1: good way. But now there are cell phones. People in 150 00:08:51,640 --> 00:08:53,960 Speaker 1: college probably don't have a phone. Poor people who don't 151 00:08:53,960 --> 00:08:55,680 Speaker 1: have phones at all, people who don't have phone. Sure, 152 00:08:55,800 --> 00:08:58,080 Speaker 1: so that's not a very good way. Because what about 153 00:08:58,720 --> 00:09:02,760 Speaker 1: freight train writers of America? What's that they don't have funds? 154 00:09:02,800 --> 00:09:05,440 Speaker 1: Oh yeah, good point. Yeah, they're not allowed. I don't 155 00:09:05,440 --> 00:09:07,800 Speaker 1: think they want them. So sampling is a little harder 156 00:09:07,840 --> 00:09:11,640 Speaker 1: than it seems. Yeah, right, especially coming with a random 157 00:09:11,679 --> 00:09:15,600 Speaker 1: populationandom sample of the population. Um. But okay, so so 158 00:09:15,640 --> 00:09:20,560 Speaker 1: far we've talked about people and where they live. There's 159 00:09:20,600 --> 00:09:23,640 Speaker 1: other ways to define a population. There's other attributes that 160 00:09:23,679 --> 00:09:26,520 Speaker 1: people have that we use to lump into population. It's 161 00:09:26,559 --> 00:09:30,040 Speaker 1: not just a geography when people think um populations, it's 162 00:09:30,080 --> 00:09:34,760 Speaker 1: not just a city population or state. Yes, what acian 163 00:09:35,080 --> 00:09:37,640 Speaker 1: age you have a population of age or continent, a 164 00:09:37,640 --> 00:09:43,600 Speaker 1: demographic what else location of course, socio economic Well, population, 165 00:09:43,679 --> 00:09:45,760 Speaker 1: let's talk about age. Why would you even want to 166 00:09:45,800 --> 00:09:49,199 Speaker 1: know age? Who cares? People are old, people are young? Whatever? Right, Well, 167 00:09:49,240 --> 00:09:51,600 Speaker 1: there's a lot of factors like, um, take the baby 168 00:09:51,600 --> 00:09:53,960 Speaker 1: boom for instance, after World War Two, all these babies 169 00:09:53,960 --> 00:09:56,960 Speaker 1: were born, so there was a bulge in the population. 170 00:09:57,200 --> 00:09:59,400 Speaker 1: And I just like saying that we're bulged. You got 171 00:09:59,400 --> 00:10:03,400 Speaker 1: to do the air quote. So, um, what that will 172 00:10:03,400 --> 00:10:05,160 Speaker 1: show them then is wow, we got a bulge here. 173 00:10:05,200 --> 00:10:09,880 Speaker 1: So that means probably in to sixty years there's gonna 174 00:10:09,920 --> 00:10:13,240 Speaker 1: be some serious buying power. Let's start borrowing as much 175 00:10:13,280 --> 00:10:15,600 Speaker 1: money as we can right now. But it also means 176 00:10:15,679 --> 00:10:19,960 Speaker 1: in seventy plus years that they may be a medical 177 00:10:20,000 --> 00:10:23,080 Speaker 1: burden and a burden on social security. So let's start 178 00:10:23,080 --> 00:10:26,440 Speaker 1: borrowing as much money as we can right now. Same 179 00:10:27,840 --> 00:10:29,839 Speaker 1: same result there. I like that, and we'll get to 180 00:10:30,240 --> 00:10:33,840 Speaker 1: bulges again in a little bit. But let's move on. 181 00:10:33,880 --> 00:10:36,920 Speaker 1: Like you said, socioeconomic data, right, yeah, what what why 182 00:10:36,960 --> 00:10:38,679 Speaker 1: would they want to do this job? This one? I found? 183 00:10:38,720 --> 00:10:41,920 Speaker 1: I find this the most interesting of all data. You 184 00:10:41,960 --> 00:10:45,079 Speaker 1: can look at a bunch of people who are maybe 185 00:10:45,200 --> 00:10:49,640 Speaker 1: related geographically, um, but other than that, aren't related in 186 00:10:49,679 --> 00:10:53,280 Speaker 1: any other way. Uh. And all of them suddenly have 187 00:10:53,600 --> 00:10:57,240 Speaker 1: this horrible cancer and they're just so happens to be 188 00:10:57,400 --> 00:11:02,400 Speaker 1: some manufacturer there by what did you say, high tension wires, 189 00:11:03,360 --> 00:11:05,800 Speaker 1: which has been proven I think, to not actually have 190 00:11:05,840 --> 00:11:10,480 Speaker 1: any effect on people, not in my buddy. Um. So uh, now, 191 00:11:10,520 --> 00:11:13,240 Speaker 1: all of a sudden you have this information thanks to 192 00:11:13,360 --> 00:11:17,079 Speaker 1: your demographer friend who went and collected it, and um, 193 00:11:17,160 --> 00:11:20,880 Speaker 1: you can say, okay, paint factory, you guys better start 194 00:11:20,880 --> 00:11:26,280 Speaker 1: giving away some free paint, yeah, or we're gonna sue you. Race. 195 00:11:27,080 --> 00:11:30,520 Speaker 1: That's a little little more hinky, because technically there is 196 00:11:30,640 --> 00:11:33,640 Speaker 1: no such thing as any difference in different races. I 197 00:11:33,679 --> 00:11:37,720 Speaker 1: remember watching MTV years and years and years ago, and um, 198 00:11:37,840 --> 00:11:41,680 Speaker 1: the VJ was interviewing the Bastie Boys and he was like, 199 00:11:41,960 --> 00:11:44,679 Speaker 1: Mike D, I hear you're dating a black girl. You know, 200 00:11:44,760 --> 00:11:47,640 Speaker 1: what's it like dating somebody from a different race, Which 201 00:11:47,679 --> 00:11:49,800 Speaker 1: is just an anthenine question to begin with. But I 202 00:11:49,840 --> 00:11:52,920 Speaker 1: remember Mike D going there's only one race, the human race, 203 00:11:53,400 --> 00:11:57,880 Speaker 1: And I was like, huh. That was clearly before he 204 00:11:57,960 --> 00:12:02,400 Speaker 1: was down with the ione or no, was ad Rock sorry. Yeah, 205 00:12:02,480 --> 00:12:05,040 Speaker 1: add rocks down with the Ionia. They're divorced though, so 206 00:12:05,040 --> 00:12:08,040 Speaker 1: he's not down with her anymore. Ione. Uh So, Yeah, 207 00:12:08,160 --> 00:12:10,720 Speaker 1: race is a little hinky, but you can't actually determine 208 00:12:10,760 --> 00:12:14,400 Speaker 1: some um useful things when you study populations of race 209 00:12:15,000 --> 00:12:17,280 Speaker 1: because of like you know, it's important for people to 210 00:12:17,400 --> 00:12:20,600 Speaker 1: be involved in their culture. Yeah, and to hang onto 211 00:12:20,679 --> 00:12:24,360 Speaker 1: that for sure. I guess racial profiling again. I don't 212 00:12:24,360 --> 00:12:25,840 Speaker 1: know if I should say again or not, but it's 213 00:12:25,880 --> 00:12:30,000 Speaker 1: such a hot button issue that Yeah, I don't know. 214 00:12:30,880 --> 00:12:33,120 Speaker 1: We need to talk about it collectively. That's my answer 215 00:12:33,160 --> 00:12:35,480 Speaker 1: for everything. Everybody needs to get together and decide what 216 00:12:35,520 --> 00:12:37,360 Speaker 1: we want to do. Okay. Well, the other thing with race, 217 00:12:37,440 --> 00:12:39,600 Speaker 1: so is if there's a medical problem the specific to 218 00:12:39,679 --> 00:13:06,000 Speaker 1: that race that can help out that exactly true. It's alright, so, Chuck, 219 00:13:06,000 --> 00:13:10,800 Speaker 1: We've got all these different factors, attributes, variables. We've used 220 00:13:10,840 --> 00:13:15,480 Speaker 1: the word demographer several times. Um, so we know that 221 00:13:15,800 --> 00:13:19,079 Speaker 1: people study populations. One of the reasons why we study 222 00:13:19,120 --> 00:13:23,440 Speaker 1: populations is to see how big it's getting. And I 223 00:13:23,440 --> 00:13:27,440 Speaker 1: gotta tell you, buddy, the human population is kind of 224 00:13:27,480 --> 00:13:31,280 Speaker 1: exploded on this planet in the last several thousand years. Yeah, 225 00:13:31,280 --> 00:13:32,880 Speaker 1: but you know what they were reading these stats. There 226 00:13:32,880 --> 00:13:35,679 Speaker 1: were a lot more people here way back when than 227 00:13:35,720 --> 00:13:39,880 Speaker 1: I thought. Yeah again, favorite book of all Time fourteen 228 00:13:40,360 --> 00:13:45,040 Speaker 1: one UM Charles C. Man. Yes, he basically points out 229 00:13:45,080 --> 00:13:48,880 Speaker 1: that there is probably a hundred million people on the 230 00:13:48,960 --> 00:13:54,520 Speaker 1: North American or on the in the America's uh in Yeah, 231 00:13:54,640 --> 00:13:56,960 Speaker 1: which is a fifth of the world population. Is way 232 00:13:56,960 --> 00:14:00,240 Speaker 1: more than anyone thought. And the reason why is because 233 00:14:01,400 --> 00:14:06,079 Speaker 1: Columbus shows up. Smallpox just ravages both continents and by 234 00:14:06,080 --> 00:14:10,200 Speaker 1: the time the European settlers start coming for real, uh, 235 00:14:10,320 --> 00:14:13,160 Speaker 1: the places decimated. It seems like there's nobody there right. Well, 236 00:14:13,200 --> 00:14:14,920 Speaker 1: he had the whole genocide to things you ever know 237 00:14:14,960 --> 00:14:18,000 Speaker 1: about that Columbus I hear um his men used to 238 00:14:18,040 --> 00:14:20,880 Speaker 1: like sharpen their knives on like the skulls of live 239 00:14:21,360 --> 00:14:26,440 Speaker 1: um live natives. Well, there's the because genocide we talked 240 00:14:26,440 --> 00:14:30,520 Speaker 1: about later on um in the article. But there's uh 241 00:14:30,800 --> 00:14:34,160 Speaker 1: speculation that Columbus may have been responsible for like the 242 00:14:34,200 --> 00:14:38,120 Speaker 1: worst mass genocide in human history by completely wiping out 243 00:14:38,200 --> 00:14:42,800 Speaker 1: the the Tano Taino Indian people. And that was in Hispanielo, 244 00:14:42,840 --> 00:14:45,440 Speaker 1: which is modern day I think Haiti and Dominican Republic. 245 00:14:45,920 --> 00:14:47,840 Speaker 1: And they some people say there were only like five 246 00:14:48,240 --> 00:14:49,840 Speaker 1: thousand of them, and some people say there were as 247 00:14:49,840 --> 00:14:53,800 Speaker 1: many as fifteen million at the time that were decimated 248 00:14:53,840 --> 00:14:58,480 Speaker 1: to about two thousand. Decimated through violence or through disease. Yeah, 249 00:14:58,480 --> 00:15:00,800 Speaker 1: well through violence, because Columbus came over, set up a 250 00:15:00,800 --> 00:15:04,080 Speaker 1: camp in Hispaniola for about forty people, and then left, 251 00:15:04,840 --> 00:15:07,960 Speaker 1: came back on trip number two and found that the 252 00:15:08,720 --> 00:15:11,560 Speaker 1: Indian tribe there had killed all those people. So he 253 00:15:11,640 --> 00:15:15,760 Speaker 1: went on to kill crazy rampage basically and completely wiped 254 00:15:15,800 --> 00:15:18,240 Speaker 1: out the population. And they're saying it may have been 255 00:15:18,280 --> 00:15:23,280 Speaker 1: like double the size of the Holocaust. So Happy Columbus Day, everybody, seriously, 256 00:15:23,760 --> 00:15:25,840 Speaker 1: but we do mention that because genocide is is a 257 00:15:25,880 --> 00:15:29,440 Speaker 1: way that a population can change rapidly. Well, let's talk 258 00:15:29,440 --> 00:15:32,880 Speaker 1: about population growth. Yes, all right, so I guess about 259 00:15:32,880 --> 00:15:36,160 Speaker 1: ten thousand BC, they estimate that there's between one and 260 00:15:36,240 --> 00:15:39,840 Speaker 1: ten million humans. So we're starting to slowly grow because 261 00:15:39,880 --> 00:15:43,280 Speaker 1: by one thousand BC there's fifty million, and then by 262 00:15:43,320 --> 00:15:47,720 Speaker 1: six hundred ce UM we're at two millions. See that's 263 00:15:47,720 --> 00:15:49,960 Speaker 1: a lot more than I thought. Yeah, there would be 264 00:15:49,960 --> 00:15:52,320 Speaker 1: at the time. Yeah, I think there was about five 265 00:15:52,400 --> 00:15:57,680 Speaker 1: hundred million in the mid fifteen century. So um, let's go. 266 00:15:57,760 --> 00:16:00,760 Speaker 1: But let's say there's a five million in the mid 267 00:16:00,800 --> 00:16:05,120 Speaker 1: fifteenth century. The twentieth century, the industrial revolutions happened, There's 268 00:16:05,200 --> 00:16:09,280 Speaker 1: been great leaps in science and UM medicine. That's when 269 00:16:09,320 --> 00:16:12,240 Speaker 1: populations really grow is during those big booms. Yeah, because 270 00:16:12,240 --> 00:16:16,400 Speaker 1: it lends itself to fertility, higher and fertility and um 271 00:16:16,600 --> 00:16:20,880 Speaker 1: longer lifespans, good times breed kids. So the twentieth century 272 00:16:20,920 --> 00:16:24,320 Speaker 1: hits were at one point five billion people and then 273 00:16:24,760 --> 00:16:29,960 Speaker 1: this century the population of the world has quad druple. Yeah, 274 00:16:30,880 --> 00:16:33,040 Speaker 1: and with like six billions. I know that that. It 275 00:16:33,120 --> 00:16:35,040 Speaker 1: sounded like there should have been a drum roll there, 276 00:16:35,080 --> 00:16:37,520 Speaker 1: but maybe there was. By that Jerry might have put 277 00:16:37,520 --> 00:16:40,000 Speaker 1: one in there, our producer, Jerry, we'll find out later. Uh. 278 00:16:40,040 --> 00:16:43,520 Speaker 1: And Josh here projecting the U. S. CENTSUS Bureau projects 279 00:16:43,520 --> 00:16:47,600 Speaker 1: that by there will be ten billion people. Right. So 280 00:16:47,680 --> 00:16:50,760 Speaker 1: the reason for this is what we call the lenthusisant 281 00:16:50,800 --> 00:16:56,880 Speaker 1: growth model. Mouth. This was a eighteenth century clergyman, Thomas 282 00:16:57,200 --> 00:17:02,240 Speaker 1: huh he uh he actually, I guess inadvertently became one 283 00:17:02,240 --> 00:17:06,320 Speaker 1: of the great economic theorists, and he figured out that 284 00:17:06,440 --> 00:17:10,320 Speaker 1: population grows exponentially. Right, So if you have one million 285 00:17:10,320 --> 00:17:12,960 Speaker 1: people and they have enough kids double the population. But 286 00:17:13,040 --> 00:17:15,200 Speaker 1: the next generation you have four million people. So in 287 00:17:15,359 --> 00:17:17,680 Speaker 1: one full generation you've gone from one million to four 288 00:17:17,720 --> 00:17:23,320 Speaker 1: million people. Right. Yeah, that's that's big, especially when the 289 00:17:23,359 --> 00:17:27,000 Speaker 1: planet is finite in size and we don't have the 290 00:17:27,040 --> 00:17:30,120 Speaker 1: ability to go colonize other planets yet. Right. But it's 291 00:17:30,119 --> 00:17:34,240 Speaker 1: not necessarily that incremental and steady because of what we 292 00:17:34,280 --> 00:17:41,280 Speaker 1: talked about, which are bulges or spikes and bottlenecks like genocide. Right, yeah, 293 00:17:41,320 --> 00:17:45,239 Speaker 1: so it doesn't always grow steadily. And actually, Chuck, if 294 00:17:45,240 --> 00:17:48,640 Speaker 1: you heard of the replacement right now, the replacement rate 295 00:17:48,800 --> 00:17:52,800 Speaker 1: is it's how many kids a woman has to have 296 00:17:53,600 --> 00:17:57,720 Speaker 1: to have a high uh statistical probability of having a 297 00:17:57,800 --> 00:18:02,560 Speaker 1: daughter so that she in essence replaces herself. And right 298 00:18:02,560 --> 00:18:05,439 Speaker 1: now it's two point three three is the replacement rate worldwide, 299 00:18:06,000 --> 00:18:09,119 Speaker 1: and the point of it is to trend towards zero 300 00:18:09,160 --> 00:18:12,639 Speaker 1: population growth. Right, So for every woman who dies, she 301 00:18:12,760 --> 00:18:16,760 Speaker 1: has a daughter that can reproduce and and continue on 302 00:18:16,840 --> 00:18:19,440 Speaker 1: and continue on and continue on, So you have overall 303 00:18:19,480 --> 00:18:22,440 Speaker 1: as many people dying as they're being born, so there's 304 00:18:22,480 --> 00:18:27,440 Speaker 1: no strain, right, and there's also no um dearth. Well, 305 00:18:27,480 --> 00:18:30,159 Speaker 1: it's equilibrium that reading this reminded me of when we 306 00:18:30,160 --> 00:18:33,359 Speaker 1: did our Big Econ audio book. It's kind of population 307 00:18:33,440 --> 00:18:37,600 Speaker 1: kind of wants to seek equilibrium. I think, like, uh, 308 00:18:37,840 --> 00:18:41,200 Speaker 1: just like economics does and and and it doesn't always happen, 309 00:18:41,840 --> 00:18:46,080 Speaker 1: uh organically, I should say it probably rarely happens organically. 310 00:18:46,280 --> 00:18:48,840 Speaker 1: But let's think about um. Like you said, the baby boom, 311 00:18:49,440 --> 00:18:54,520 Speaker 1: post war success in in Europe and um, the US 312 00:18:54,560 --> 00:18:58,520 Speaker 1: and Canada, I guess lead to UM a huge boom 313 00:18:58,520 --> 00:19:01,119 Speaker 1: in the population. No, it he went to war to 314 00:19:01,440 --> 00:19:04,480 Speaker 1: grow the population. It was just an indirect effect. So 315 00:19:04,520 --> 00:19:06,760 Speaker 1: all of a sudden we had a population spike that 316 00:19:06,880 --> 00:19:10,399 Speaker 1: created a bulge. Bulge if you will, things can go 317 00:19:10,520 --> 00:19:13,520 Speaker 1: the other way to which is a bottleneck. Right. Yeah, 318 00:19:13,600 --> 00:19:16,400 Speaker 1: And that's well we and God, if I say genocide 319 00:19:16,400 --> 00:19:18,719 Speaker 1: one more time, we should do a podcast in genocide. 320 00:19:18,760 --> 00:19:20,479 Speaker 1: I wonder if there's a drinking game where every time 321 00:19:20,520 --> 00:19:26,720 Speaker 1: it's like genocide, gide drink um. Famine disease. Uh, something 322 00:19:26,760 --> 00:19:29,000 Speaker 1: called the plague I think wiped out like half the 323 00:19:29,000 --> 00:19:31,560 Speaker 1: world population at one point, or half the population of Europe. 324 00:19:31,600 --> 00:19:36,320 Speaker 1: They suspect that um in uh the fifth century. That 325 00:19:36,359 --> 00:19:40,320 Speaker 1: would be a c e. The plague of Justinian may 326 00:19:40,359 --> 00:19:43,919 Speaker 1: have killed as many as half the world's population, a 327 00:19:43,960 --> 00:19:47,359 Speaker 1: hundred million people. Unbelievable. Can you imagine walking around at 328 00:19:47,440 --> 00:19:50,639 Speaker 1: that time, like, holy crap, the entire half the world 329 00:19:50,720 --> 00:19:53,120 Speaker 1: is dead, just died in the last couple of years. 330 00:19:53,119 --> 00:19:55,440 Speaker 1: It's crazy. In the Black Death killed twenty to thirty 331 00:19:55,440 --> 00:20:00,320 Speaker 1: million Europeans, So so plagues can happen. There's also I 332 00:20:00,760 --> 00:20:04,040 Speaker 1: was talking to an evolutionary geneticist this is my way 333 00:20:04,640 --> 00:20:09,280 Speaker 1: um today recently, and he was talking about study he 334 00:20:10,320 --> 00:20:15,080 Speaker 1: authored where they found two evolutionary bottlenecks, one coming out 335 00:20:15,160 --> 00:20:18,880 Speaker 1: of Africa. Uh, they suggested a fifty thousand years ago 336 00:20:19,000 --> 00:20:22,680 Speaker 1: and another one that happened along the bearing Land Bridge, right. 337 00:20:23,080 --> 00:20:25,000 Speaker 1: And he wasn't saying like all of a sudden a 338 00:20:25,000 --> 00:20:29,080 Speaker 1: bunch of people died, but um, these bottlenecks turned up 339 00:20:29,119 --> 00:20:32,760 Speaker 1: because big groups of people separating in smaller groups of people, 340 00:20:33,240 --> 00:20:35,920 Speaker 1: which is which accounts for a loss of genetic diversity. 341 00:20:36,400 --> 00:20:39,240 Speaker 1: So you have the founders effect. Because, as he put it, 342 00:20:39,480 --> 00:20:41,920 Speaker 1: if you take um, if you go into a town 343 00:20:42,160 --> 00:20:44,760 Speaker 1: and grab the first fifteen people you meet and say, 344 00:20:44,840 --> 00:20:47,080 Speaker 1: let's go found a new town. That new town isn't 345 00:20:47,080 --> 00:20:49,760 Speaker 1: going to have a representative sample of all the surnames 346 00:20:49,760 --> 00:20:52,119 Speaker 1: in that town. If you do that enough time, some 347 00:20:52,160 --> 00:20:55,080 Speaker 1: surnames are going to be lost because people didn't reproduce 348 00:20:55,160 --> 00:20:58,160 Speaker 1: or whatever. The same thing happens with jenes and genetic diversity. 349 00:20:58,359 --> 00:21:02,480 Speaker 1: Look at you stuff? Thanks? Uh? Can I mention this 350 00:21:02,520 --> 00:21:05,800 Speaker 1: place in Hong Kong? Yeah, we're talking about well we 351 00:21:05,840 --> 00:21:10,120 Speaker 1: should mention. Population density is the number of humans per 352 00:21:10,240 --> 00:21:13,040 Speaker 1: unit area whatever unit you you know, you choose to 353 00:21:13,440 --> 00:21:16,639 Speaker 1: call it. And the highest ever is believed to have 354 00:21:16,760 --> 00:21:22,399 Speaker 1: been a place called Kowloon Walled City in Hong Kong, 355 00:21:22,920 --> 00:21:25,600 Speaker 1: and at one point evidently there were fifty thousand people 356 00:21:26,720 --> 00:21:28,960 Speaker 1: in a mega block, which is five hundred by six 357 00:21:29,400 --> 00:21:33,520 Speaker 1: fifty feet. Fifty thousand people stuffed in there, and apparently 358 00:21:33,520 --> 00:21:36,560 Speaker 1: it was a lawless district. The grabsters. She's kidding me, 359 00:21:37,480 --> 00:21:41,520 Speaker 1: fifty people could conceivably get along. Yeah, hands across America style? 360 00:21:41,680 --> 00:21:44,800 Speaker 1: Did you know that? Um? In Athens when Widespread Panic 361 00:21:44,840 --> 00:21:47,399 Speaker 1: played that free show, there was an estimated hundred thousand 362 00:21:47,440 --> 00:21:51,320 Speaker 1: people there, not one fight really, Yeah, that's because they 363 00:21:51,320 --> 00:21:55,320 Speaker 1: were all on dope. The dope. I wasn't there? Were 364 00:21:55,359 --> 00:21:58,240 Speaker 1: you there? Yeah? I never got into them. Although I 365 00:21:58,240 --> 00:22:01,400 Speaker 1: did hang out with that guy the bass play day schools. Yeah, 366 00:22:01,520 --> 00:22:03,080 Speaker 1: I hung out with him a couple of times, just 367 00:22:03,080 --> 00:22:06,840 Speaker 1: through friends. Anyway, Uh, that that park is no, I'm sorry. 368 00:22:07,000 --> 00:22:09,959 Speaker 1: It is now a park where the walled city used 369 00:22:10,000 --> 00:22:15,480 Speaker 1: to be. Yeah, which is the opposite of the highest population. Ironically, 370 00:22:15,480 --> 00:22:18,720 Speaker 1: it's just the park maybe the highest population of grass. 371 00:22:18,720 --> 00:22:44,400 Speaker 1: But that's it. So what do we got here, Josh? 372 00:22:44,440 --> 00:22:48,360 Speaker 1: We got um. Population control is something that we've referenced 373 00:22:48,359 --> 00:22:51,399 Speaker 1: before with our China One Child policy. Yeah, and we 374 00:22:51,440 --> 00:22:53,680 Speaker 1: talked about why you would want to control the population. 375 00:22:54,000 --> 00:22:57,000 Speaker 1: A huge group of people put a strain on resources. 376 00:22:57,320 --> 00:23:00,280 Speaker 1: When resources go away, you have resource conflicts like in 377 00:23:00,600 --> 00:23:05,679 Speaker 1: darfour again genocide. Right. Um. There's all sorts of problems 378 00:23:05,720 --> 00:23:09,880 Speaker 1: that come from too many people coming or living in 379 00:23:09,920 --> 00:23:12,800 Speaker 1: one place because of the strain that puts on resources 380 00:23:12,800 --> 00:23:17,280 Speaker 1: and resource allocation. Right. Um. And yeah, you can control 381 00:23:17,320 --> 00:23:23,840 Speaker 1: the population e g. You know, state mandated reproduction um China. Right, 382 00:23:24,240 --> 00:23:28,560 Speaker 1: and that actually works as as China shows. Um, although 383 00:23:28,760 --> 00:23:31,680 Speaker 1: much to the detriment of some people, Thank you, Chuck 384 00:23:32,080 --> 00:23:35,680 Speaker 1: for that. Look and not everyone thinks um, some people 385 00:23:35,680 --> 00:23:38,679 Speaker 1: think we should add more people though, well, yeah, there's Japan. 386 00:23:38,960 --> 00:23:42,320 Speaker 1: In other countries there's a problem of population declins. So 387 00:23:42,359 --> 00:23:46,320 Speaker 1: we talked about the strain um people put on an 388 00:23:46,359 --> 00:23:49,159 Speaker 1: area that's carrying capacity, which we've talked about before, and 389 00:23:49,280 --> 00:23:54,040 Speaker 1: that's also from Malthus, that eventually human population is going 390 00:23:54,080 --> 00:24:01,600 Speaker 1: to outstrip advances in technology or our resources and we're screwed. Right. Um, 391 00:24:01,680 --> 00:24:04,560 Speaker 1: on the other side, is shrinking, population shrinking? And what's 392 00:24:04,560 --> 00:24:07,440 Speaker 1: the problem with that, Well, you don't want the population 393 00:24:07,480 --> 00:24:10,720 Speaker 1: to shrink too much because you, uh, you need those 394 00:24:10,760 --> 00:24:13,800 Speaker 1: hands to go to work and to contribute to the 395 00:24:13,840 --> 00:24:17,240 Speaker 1: economy and to grow the grain and sow the flower 396 00:24:17,440 --> 00:24:20,800 Speaker 1: and all that good stuff. And apparently in Russia, Japan, 397 00:24:20,840 --> 00:24:23,879 Speaker 1: and Australia they all have like little incentive programs to 398 00:24:23,920 --> 00:24:26,280 Speaker 1: make little babies. Sure, how about that? Which is the 399 00:24:26,320 --> 00:24:29,640 Speaker 1: way to go? Remember John Fuller's famous quote, um, when 400 00:24:29,640 --> 00:24:32,800 Speaker 1: he was pitching an article about that program in Russia, 401 00:24:32,840 --> 00:24:35,680 Speaker 1: and he's talking about poutin giving away a TV. Oh yeah, 402 00:24:35,720 --> 00:24:39,200 Speaker 1: that's right, that's really funny. Yeah, um, the baby get 403 00:24:39,200 --> 00:24:41,560 Speaker 1: a TV. I think you'd be there and check the 404 00:24:41,600 --> 00:24:45,679 Speaker 1: reason why. Uh, some of these places are seeing a 405 00:24:45,720 --> 00:24:49,080 Speaker 1: population shrink and are having to I guess give incentives 406 00:24:49,160 --> 00:24:54,640 Speaker 1: to reproduce. Uh, started in about nineteen sixty birth control. 407 00:24:55,440 --> 00:24:58,120 Speaker 1: That's so crazy that it had an effect that much 408 00:24:58,119 --> 00:25:00,600 Speaker 1: of an effect, that pronounced of an effect. Well, it 409 00:25:00,600 --> 00:25:02,600 Speaker 1: would seem like it would though, I guess so because 410 00:25:02,640 --> 00:25:06,480 Speaker 1: it's called birth control. Sure you know before that it 411 00:25:06,560 --> 00:25:08,800 Speaker 1: was called have as many babies as you possibly can. 412 00:25:09,440 --> 00:25:13,440 Speaker 1: It was called no control. All right, So clearly there's 413 00:25:13,440 --> 00:25:17,040 Speaker 1: a lot of reasons to study people. Yeah, it's I 414 00:25:17,040 --> 00:25:18,359 Speaker 1: thought it would be. There's a lot of stuff to 415 00:25:18,400 --> 00:25:21,479 Speaker 1: study to you can find out whether or not we're 416 00:25:21,520 --> 00:25:25,760 Speaker 1: going to kill the planet, or whether um people need 417 00:25:25,800 --> 00:25:29,520 Speaker 1: to stop using contraceptives, or whether you know what your 418 00:25:29,560 --> 00:25:33,040 Speaker 1: chances are of putin giving you a free TV. Uh. 419 00:25:33,080 --> 00:25:36,119 Speaker 1: It's all in there. It's all demographers know everything, all 420 00:25:36,160 --> 00:25:40,199 Speaker 1: there for the taking. So when your frenzy friendly enumerator 421 00:25:40,240 --> 00:25:43,280 Speaker 1: comes knocking on your door, don't chase them off your 422 00:25:43,359 --> 00:25:46,520 Speaker 1: land with your dog or a gun. Let him in, 423 00:25:46,640 --> 00:25:49,040 Speaker 1: give them some lemonade, maybe some cookies. Yeah, we'll check 424 00:25:49,040 --> 00:25:51,560 Speaker 1: their lamb in at first. But before you let them in, 425 00:25:51,760 --> 00:25:54,359 Speaker 1: ceo a chuck pep going. And if you want to 426 00:25:54,359 --> 00:25:58,040 Speaker 1: know more about population, you could read Grabbingowski's great article 427 00:25:58,200 --> 00:26:00,480 Speaker 1: on the site. Just type in population in the handy 428 00:26:00,520 --> 00:26:03,480 Speaker 1: search part how stuff works dot com, which of course 429 00:26:03,560 --> 00:26:08,880 Speaker 1: leads us to the listener mail. Josh, I'm just gonna 430 00:26:08,920 --> 00:26:13,680 Speaker 1: call this your turn at listener mail, because I think 431 00:26:13,720 --> 00:26:16,479 Speaker 1: you have to do we'll talk about Yeah. I just 432 00:26:16,920 --> 00:26:20,560 Speaker 1: I don't necessarily have too much listener mail per se um. 433 00:26:20,600 --> 00:26:22,520 Speaker 1: But I just wanted to give a shout out to 434 00:26:22,600 --> 00:26:27,040 Speaker 1: a couple of fellow Toledo wins. One who's a longtime resident, 435 00:26:27,119 --> 00:26:30,600 Speaker 1: one who's a recent transplant. Christopher is holding the fort 436 00:26:30,640 --> 00:26:33,480 Speaker 1: down in Toledo for me. Keeping it real, he has 437 00:26:33,800 --> 00:26:39,280 Speaker 1: uh officially lobbied um the congresswoman from Toledo to get 438 00:26:39,320 --> 00:26:43,680 Speaker 1: me the key to the city. How awesome with it? Yeah, 439 00:26:43,800 --> 00:26:46,520 Speaker 1: so Marcy kept her. If you're listening, I would like 440 00:26:46,600 --> 00:26:48,320 Speaker 1: if you get a key to the city, we gotta 441 00:26:48,359 --> 00:26:50,240 Speaker 1: go for a ceremony and I at least want to 442 00:26:50,280 --> 00:26:52,600 Speaker 1: get like a key chain to the city, and you 443 00:26:52,640 --> 00:26:56,200 Speaker 1: can have the key. We'll see what we can do. Um. 444 00:26:56,280 --> 00:26:59,760 Speaker 1: So yeah, Christopher has officially petitioner. He's he suggested it 445 00:26:59,800 --> 00:27:02,200 Speaker 1: and third most famous Toledo in of all time after 446 00:27:02,280 --> 00:27:06,720 Speaker 1: Jamie Farr. Jamie Farr, Danny Thomas the Great Entertainer and 447 00:27:06,720 --> 00:27:08,879 Speaker 1: then me and I was like, I think you're for 448 00:27:08,920 --> 00:27:13,200 Speaker 1: getting Katie Holmes from Toledo and he's like, no, you 449 00:27:13,280 --> 00:27:18,639 Speaker 1: got her? Bek is it Kate Cruise now? So anyway, 450 00:27:18,720 --> 00:27:21,159 Speaker 1: thanks a lot for the effort, Christopher, even if it 451 00:27:21,200 --> 00:27:23,960 Speaker 1: doesn't come to fruition. If it does, you will get 452 00:27:24,000 --> 00:27:28,120 Speaker 1: a firm handshake and a free Friendlies Sunday of your choosing. 453 00:27:28,160 --> 00:27:30,960 Speaker 1: For me, Yeah, we'll be going to Friendlies if we 454 00:27:31,000 --> 00:27:33,440 Speaker 1: go to Toledo. But uh, and then I also want 455 00:27:33,480 --> 00:27:37,639 Speaker 1: to say hi to Colin, who is a recent transplant. 456 00:27:37,680 --> 00:27:41,440 Speaker 1: As I said, Um from Colorado, I believe, who moves 457 00:27:41,480 --> 00:27:45,200 Speaker 1: from Colorado to Toledo. He moved to Toledo to attend 458 00:27:45,240 --> 00:27:49,120 Speaker 1: Bowling Green State University. Joe Falcons, my brother went there, 459 00:27:49,880 --> 00:27:52,679 Speaker 1: and uh, Colin did so in an eight eight Dodge 460 00:27:52,800 --> 00:27:57,080 Speaker 1: Colt that's having a couple of problems. One, the rear 461 00:27:57,200 --> 00:28:00,680 Speaker 1: struts are completely detached and the acts sole is holding 462 00:28:00,760 --> 00:28:04,960 Speaker 1: on by a tread, he says, Um, and the mechanics 463 00:28:04,960 --> 00:28:07,160 Speaker 1: didn't want him to leave when he took it in 464 00:28:07,200 --> 00:28:09,800 Speaker 1: for service, so they're like, you're going to die in 465 00:28:09,840 --> 00:28:13,720 Speaker 1: this thing, Um. And the other problem is it has ants. 466 00:28:14,280 --> 00:28:16,560 Speaker 1: He says, I've never heard of a car having ants. 467 00:28:16,880 --> 00:28:19,239 Speaker 1: I had an incident car once. Really, you can't get 468 00:28:19,359 --> 00:28:20,840 Speaker 1: rid of them when they well, that's probably when you 469 00:28:20,840 --> 00:28:22,800 Speaker 1: were living in the car, which was probably always parked 470 00:28:22,920 --> 00:28:25,080 Speaker 1: on the ant hill. This is actually prior to that, 471 00:28:25,160 --> 00:28:27,560 Speaker 1: when I lived in the car. Um. But yeah, no, 472 00:28:27,720 --> 00:28:30,720 Speaker 1: it's a it's a it's a real problem. And Collin's 473 00:28:30,800 --> 00:28:32,879 Speaker 1: basically just put the bullet and said, well, I have 474 00:28:32,960 --> 00:28:35,200 Speaker 1: ants in my car now. He loves his AD eight 475 00:28:35,240 --> 00:28:37,800 Speaker 1: Dutch cole. He said, he loves solito. He's enjoying. He 476 00:28:37,840 --> 00:28:40,400 Speaker 1: went to Tony Paco's as I suggested, I gotta try 477 00:28:40,440 --> 00:28:42,000 Speaker 1: that one day. I also told him to go to 478 00:28:42,120 --> 00:28:46,200 Speaker 1: Rusty's Jazz Cafe because it's as authentic as it comes. 479 00:28:46,040 --> 00:28:50,320 Speaker 1: It's awesome. Um. So hey Christopher, hey Colin, you guys, 480 00:28:50,360 --> 00:28:53,800 Speaker 1: enjoy yourselves, be safe and toledown for the winner. Go 481 00:28:54,000 --> 00:28:57,480 Speaker 1: mud hens and uh, thanks for writing in. And if 482 00:28:57,520 --> 00:28:59,800 Speaker 1: you want to say hi to me or Chuck or 483 00:28:59,840 --> 00:29:03,120 Speaker 1: both of us Chuckers or Jerry you right, Chucker's Jerry 484 00:29:03,400 --> 00:29:07,600 Speaker 1: chuck Er, I I mean Chucker me chucking me. Um. 485 00:29:08,000 --> 00:29:11,320 Speaker 1: You can put that in an email to stuff podcast 486 00:29:11,720 --> 00:29:18,400 Speaker 1: at how stuff works dot com. For more on this 487 00:29:18,560 --> 00:29:21,080 Speaker 1: and thousands of other topics, is it how stuff works 488 00:29:21,080 --> 00:29:24,520 Speaker 1: dot com. Want more how stuff works, check out our 489 00:29:24,520 --> 00:29:26,960 Speaker 1: blogs on the house stuff works dot com home page