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Speaker 1: Welcome back to a numbers game. Happy Monday, everybody. I

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Speaker 1: am glad for you to be here with me for

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Speaker 1: one more week. So much happened with the New York

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Speaker 1: City election. I didn't guys, I didn't get you get

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Speaker 1: like to give my listeners the update on the Kansas speech,

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Speaker 1: So I gave for those who missed the episode. I

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Speaker 1: gave the keynote address the Bob Dolden Or in Kansas,

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Speaker 1: which is a very nice thing, usually reserved for a congressman.

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Speaker 1: I wasn't really sure why I was invited, but I

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Speaker 1: was honored to be there. I flew into Kansas and

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Speaker 1: had some barbecue at Joe's Barbecue in Kansas City, Missouri. Delicious, amazing,

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Speaker 1: I highly recommend. And then go to the speech and

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Speaker 1: I'm very nervous. I really didn't know why I was invited.

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Speaker 1: And I meet with the organizers the night before and

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Speaker 1: I say, why did you have me over a senator

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Speaker 1: or congressman whatever, And they say, oh, because you're so

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Speaker 1: edgy and funny. Just be edgy and funny, which is

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Speaker 1: not good guidelines for someone like me, because it's like, hey,

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Speaker 1: push every boundary you possibly can, like I'm a child

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Speaker 1: like you need to give me parameters, Like I work

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Speaker 1: very well within the lines, but like if I don't

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Speaker 1: see the lines, I you know, it's striving. Like I

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Speaker 1: drive like someone like a woman from New Jersey. I'm

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Speaker 1: all over the place. So anyway, I I'm very nervous.

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Speaker 1: I write my speech and then it dawns on me

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Speaker 1: like that right before the speech, nobody is going to

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Speaker 1: know who I am, Like, I guess if you're like

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Speaker 1: listen to me in my podcast, but I mean that's

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Speaker 1: not the whole audience. It's all donors and established from

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Speaker 1: people and elected officials. They definitely have no they're not

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Speaker 1: listening to my podcasts or not watching me or I'm

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Speaker 1: rooting my sub stack. Those are like the people like

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Speaker 1: those are not like the electeds. So I walk into

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Speaker 1: like the room. First of all, the MIC's not working,

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Speaker 1: which is or it's like working like kind of like

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Speaker 1: very shoddy. I go into the room. This very well dressed,

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Speaker 1: very elegant Asian, older Asian woman walks up to me, goes,

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Speaker 1: can we get a picture? And I go, oh, my god,

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Speaker 1: you know who I am? And she goes, no, I

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Speaker 1: don't know, but I get pictures with everybody and I

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Speaker 1: was like, oh my god, this is going to be bad.

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Speaker 1: So I meet some of the elected officials, the Senate president,

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Speaker 1: people running for governor. It was really nice whatever, but

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Speaker 1: once again, no one knew who I am. I'm having

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Speaker 1: to be introduced everybody for the first time and then

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Speaker 1: make the small talk and then talk about my pack

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Speaker 1: and nonprop with education. And I text my one friend

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Speaker 1: from who's a political political consultant from Kansas, and I'm like,

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Speaker 1: this is going to go horrible. He goes, no, No, it's

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Speaker 1: a Kansas audience. They give standing ovations to everybody. It's

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Speaker 1: just how they operate. So the state treasurer gets up,

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Speaker 1: he gets a standing ovation. The first speaker gets up,

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Speaker 1: he gets a standing ovation. The German ambassador's assistant god

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Speaker 1: a standing ovation. I'm like, okay, I'm cool. I'm you know,

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Speaker 1: it's going to be fine. Everything is gonna work out

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Speaker 1: fine for me. I go give the speech, and once

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Speaker 1: again I'm looking at the audience. They have no idea

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Speaker 1: who I am. So I say I'm the guy who

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Speaker 1: made the beeper a joke on CNN, and you hear

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Speaker 1: this like collective oh yeah in the audience. I'm like, oh, Okay, well,

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Speaker 1: this is going great. So I give the speech, and

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Speaker 1: I go on about how Republicans support immigration reduction, and

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Speaker 1: it's time that all the Kansas legislators, congressmen, and senators,

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Speaker 1: including those who live in Florida, because one Kansas senator

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Speaker 1: may have a residence, may spend most of his time

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Speaker 1: in Florida instead of Kansas, but all the Kansas congressmen

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Speaker 1: and senators should back immigration reduction. We can't let farmers

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Speaker 1: have low skilled workers flood our country in the name

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Speaker 1: of like picking crops. I just go off and then

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Speaker 1: I talk about AI policy and how Kansas needs to

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Speaker 1: regulate AI because they're taking jobs. And I said, if

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Speaker 1: you want to make sure you turn Trump supporters into

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Speaker 1: AOC supporters, make sure that our unemployment numbers reach twenty

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Speaker 1: percent in the country, especially why collar workers. And then

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Speaker 1: I go into education, how Kansas ranks thirty third in

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Speaker 1: the country. I lay it off with like a nice

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Speaker 1: thing about Bob Dole and the legacy of Bob Dole.

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Speaker 1: I could not over emphasize how few people clapped at

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Speaker 1: the end of my speech was so tepid. They absolutely

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Speaker 1: did not care. Oh this was the best part. Wait

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Speaker 1: one last thing. The only other famous Republican from Kansas.

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Speaker 1: I looked at my notes, this came back to me.

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Speaker 1: Only other famous Republican from Kansas that I know of

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Speaker 1: is Dwight Eisenhower. And I said in my speech, Dwight

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Speaker 1: Eisenhower had a much easier legacy to wrap up than

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Speaker 1: Bob Doles. He one d Day, he built the highways,

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Speaker 1: and he deported the illegal immigrants with Operation Wetback, which

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Speaker 1: was a real thing. You can look it up. Operation Wetback,

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Speaker 1: I said, Trump Show named Tom Holman the czar of

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Speaker 1: Operation wet Back two point zero. They did not like that.

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Speaker 1: Apparently that was not cool with them, to which one

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Speaker 1: evangelical lady walked up and said, I can't believe you

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Speaker 1: said the word wet back. It's like saying retard. I said,

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Speaker 1: retard's back. Okay, you could say retard now, okay, it's

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Speaker 1: Trump's amarror. So they also did not think that was funny.

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Speaker 1: I was. This was not my audience. This was not

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Speaker 1: a New York audience. This is not people who deal

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Speaker 1: with politically incorrect conversations. I heard that the Speaker of

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Speaker 1: the House was biting the inside of his cheeks when

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Speaker 1: I was started immigration reduction and slamming farmers for bringing

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Speaker 1: in cheap labor taxpayer expenses. So that was that. That

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Speaker 1: was Kansas. One woman, and I'm going to apologize for

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Speaker 1: my swearing. One state legislator walked up to me at

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Speaker 1: the end of my speech and said, so, were you

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Speaker 1: born without fox to give or did that come recently?

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Speaker 1: And I was just like, nope, born this way, just

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Speaker 1: ready for a barn burner anyway. Okay, that was I

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Speaker 1: was going to tell you my way more about Kansas,

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Speaker 1: but it's not worth it. But that was. It was

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Speaker 1: very funny. It was very my life in a nutshell,

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Speaker 1: where you end up in a very cool setting but

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Speaker 1: no one really knows why you're there. You don't know

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Speaker 1: why you're there, and kind of everything goes wrong because

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Speaker 1: you can't shut your mouth and just give like a

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Speaker 1: normal nice thing. You can't just play by the rules,

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Speaker 1: which is very how my life has always been. Okay,

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Speaker 1: So two things. One, this episode, the main point of it,

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Speaker 1: which I will get to in the second part, was

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Speaker 1: requested by more than one email person receiving an email

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Speaker 1: listener over email I get always asked me anything. Segment

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Speaker 1: emails now, which I love you can email me number

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Speaker 1: Ryan at numbers gamepodcast dot com and I try to

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Speaker 1: answer every email or get to it on the show.

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Speaker 1: Well more than one person asked the same question. So

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Speaker 1: this episode, the interview part and the second part will

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Speaker 1: be completely about the listener questions, which is about illegal

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Speaker 1: immigration when it comes to congressional redistricting and it comes

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Speaker 1: to reapportionment and getting and the sense is taking illegal

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Speaker 1: immigration information. I will get to that, but first, this

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Speaker 1: happened just as I'm about to tape, which is a

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Speaker 1: few days before it airs. Pew Research, which is one

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Speaker 1: of the premier nonprofits that analyzes elections, especially presidential elections,

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Speaker 1: came out with their post twenty twenty four analysis. Now,

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Speaker 1: I know we've gotten this from before, and I know

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Speaker 1: that we are half a year, more than half a

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Speaker 1: year since the twenty twenty four election, and people generally

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Speaker 1: don't care. They've moved on. I care, and I am

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Speaker 1: fascinated by this, and I want to talk about some

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Speaker 1: highlights in the Pew Research data about the twenty twenty

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Speaker 1: four election. So first, from twenty sixteen to twenty twenty four,

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Speaker 1: Trump coalition in twenty sixteen, eighty eight percent of all

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Speaker 1: people that voted for Donald Trump were white. By twenty

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Speaker 1: twenty four, that number had dropped to seventy eight percent.

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Speaker 1: His support among the number of voters in his coalition

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Speaker 1: that were Hispanic has essentially doubled, and those who are

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Speaker 1: black have essentially tripled, which is fascinating. Well, the Democrat

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Speaker 1: coalition from Clinton to kamalaw has actually gotten whiter, and

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Speaker 1: they've lost a significant portion of both their black and

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Speaker 1: Hispanic voter base, which isn't shocking. Of the people who

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Speaker 1: voted in the twenty twenty election right that voted for Trump,

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Speaker 1: eleven percent did not vote for Trump in twenty twenty four.

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Speaker 1: They didn't vote at all. Three percent voted for Kamala,

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Speaker 1: so it was a fourteen point drop off. Among people

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Speaker 1: who voted for Biden, five percent voted for Trump, number

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Speaker 1: three for Kamala. From Trump's Trump had a neck game

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Speaker 1: of two from Biden people, and fifteen percent of people

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Speaker 1: who voted for Biden in twenty twenty did not go

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Speaker 1: and vote for Kamala. Of the people who didn't vote

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Speaker 1: at all in twenty twentyteen percent of vote sorry didn't

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Speaker 1: vote in twenty twenty, but voted in twenty twenty four,

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Speaker 1: fourteen percent voted for Trump, twelve voted for Kamala, So

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Speaker 1: Trump got an advantage of boost and the raw vote

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Speaker 1: total of not only people who had voted for Biden

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Speaker 1: and then voted for Trump, but also people who didn't

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Speaker 1: vote at all and voted for Trump, which is part

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Speaker 1: of the reason why he won the popular vote among

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Speaker 1: those voters. Building on that, voters who didn't vote in

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Speaker 1: twenty sixteen but voted in twenty twenty favored Biden by

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Speaker 1: five points over Trump. People who didn't vote in twenty

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Speaker 1: twenty but voted in twenty twenty four have favored Trump

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Speaker 1: over Kama by twelve points. It's an immense group of

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Speaker 1: people who are inactive voters, in frequent voters, people who

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Speaker 1: are all of a sudden becoming very new and fresh

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Speaker 1: to the political scene. Very interesting, Okay. Trump's support among

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Speaker 1: his percentage of the vote that he received among certain

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Speaker 1: demographics from twenty sixteen to twenty twenty to twenty twenty four.

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Speaker 1: This is fascinating. Among men, Trump's growth of the male

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Speaker 1: vote has increased three points from twenty sixteen and went

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Speaker 1: from fifty two percent of the overall mail vote to

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Speaker 1: fifty five percent. Among the female vote, it is increased

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Speaker 1: by seven points from thirty nine to forty six. This

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Speaker 1: is what I'm about to say about the male female thing.

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Speaker 1: Just take it. I know there's a lot of numbers

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Speaker 1: coming at you, but take it in for a second.

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Speaker 1: White men, his numbers actually declined by three points among

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Speaker 1: white women. His numbers increased by four points among black men. Now,

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Speaker 1: that was from twenty sixteen to twenty twenty four. The

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Speaker 1: numbers from minorities are from twenty twenty to twenty twenty four.

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Speaker 1: Just asterisk. It's not that important, but it's worth analyzing.

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Speaker 1: From twenty twenty to twenty twenty four. Among black men,

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Speaker 1: his numbers increased by nine points among Black women, by

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Speaker 1: five among Hispanic men, eleven points among Hispanic women, thirteen points.

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Speaker 1: Trump's gain in the popular vote, and Trump's win of

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Speaker 1: the popular vote, was because women, especially non white women,

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Speaker 1: voted for him. That is the headline. Right. Of course,

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Speaker 1: the male vote counted, but Trump's growth among females with

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Speaker 1: white women and Latino women was larger than his gains

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Speaker 1: among Latino men and white men, and only black men's

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Speaker 1: were passed black women, which is not that surprising at

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Speaker 1: all anything else. Now, the white vote overall, Trump's numbers

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Speaker 1: improved by one point from twenty sixteen fifty four to

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Speaker 1: fifty five. Among Blacks, it went from six to fifteen.

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Speaker 1: That's a nine point gain. Among Latinos it went from

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Speaker 1: twenty eight to forty eight. That's a plus twenty point swing. Huge,

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Speaker 1: And among Asians it went from thirty to forty, so

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Speaker 1: that's plus ten pretty significant. It was. The Latino vote

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Speaker 1: is very signcoriing. He won Latino men by two points.

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Speaker 1: He went from losing them fifty seven to thirty nine,

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Speaker 1: losing them by eighteen points in twenty twenty to winning

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Speaker 1: them by two points fifty to forty eight and twenty

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Speaker 1: twenty four. Okay, Trump's base also became younger, which we

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Speaker 1: all knew, but it's fascinating to see the numbers among

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Speaker 1: all eighteen to twenty nine year olds. Trump went from

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Speaker 1: getting twenty eight percent of their support in twenty sixteen

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Speaker 1: to thirty nine percent in twenty twenty four. He went

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Speaker 1: from losing that by thirty points to gaining that, so

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Speaker 1: losing that by nineteen points. So he wasn't he wasn't

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Speaker 1: he wasn't, you know, losing them by huge numbers. The

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Speaker 1: same is true among thirty to forty nine year olds,

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Speaker 1: my demographic millennials. This is the group that builds Obama's coalition.

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Speaker 1: These are Obama voters. People forget he went from losing

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Speaker 1: them by eleven points to fifty one to forty in

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Speaker 1: twenty sixteen to losing them by two points. Millennials basically

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Speaker 1: broke even Trump, Harris, the Obama coalition is a solidly

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Speaker 1: mixed back now. The biggest group to sit there and

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Speaker 1: support Trump, we're gen xers. Fifty to sixty four year olds.

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Speaker 1: He went from winning them by six points to winning

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Speaker 1: them by fourteen points. And among people sixty five and

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Speaker 1: old older, he went from winning them by nine to

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Speaker 1: winning them by three. There was a move towards Democrats

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Speaker 1: with them. But remember I've said this to people before.

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Speaker 1: I'm kind of blue in the facement. People kind of

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Speaker 1: have a frozen time of when what a senior citizen

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Speaker 1: is like. They think, oh, I'm thinking of my grandma

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Speaker 1: from when I was a childhood, or my neighbor from

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Speaker 1: when I was a kid who was eighty twenty years ago. Well,

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Speaker 1: there's likely that person doesn't exist anymore. The person's probably

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Speaker 1: gone to their eternal rest. They're not alonger with us.

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Speaker 1: Remember Henry Fonda, who was very conservative, is not a

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Speaker 1: senior citizen. Jane Fonda is right. People who were anti

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Speaker 1: war activists in the sixties and seventies are now in

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Speaker 1: their seventies and eighties. Those are the people who are

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Speaker 1: Archie Bunker is dead, but Meathead is a senior citizen.

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Speaker 1: Think of it like that. People are wondering, how did

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Speaker 1: like the seniors become so left wing. They didn't just

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Speaker 1: become so left wing. Left wing people have aged into

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Speaker 1: being senior citizens, and that's what's important. Trump won men

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Speaker 1: under Trump won all men, but Trump won men under fifty,

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Speaker 1: which he had lost in twenty twenty. And his growth

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Speaker 1: among women between the ages of eighteen to forty nine,

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Speaker 1: a group that he lost by thirty six points in

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Speaker 1: twenty sixteen, he only lost by fourteen points in twenty

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Speaker 1: twenty four. This is the women who were supposed to

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Speaker 1: win for Kamala because of abortion. All they cared about

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Speaker 1: is abortion. Cared about abortion. Trump made eighteen point swing

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Speaker 1: among that demographic, people who were supposed to care about

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Speaker 1: abortion more than anything else in the world, all right,

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Speaker 1: which groups. When it comes to education, Trump made games

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Speaker 1: with college educated whites. He went from thirty eight to

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Speaker 1: forty three. The only demographic, though, to give Trump a

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Speaker 1: majority of support were non college educated white people. Trump

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Speaker 1: won sixty four percent of non college educated white people.

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Speaker 1: Oh sorry, he also won non college educated Hispanic people

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Speaker 1: as well. He won them by a single point, so

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Speaker 1: non college educated Hispanics and young college educated whites. Without

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Speaker 1: those two groups, Kamala Harris would have won. And I

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Speaker 1: think it's important to say those things out loud because

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Speaker 1: I have to hear and this will be in the

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Speaker 1: email from the listener later in the last segment of

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Speaker 1: the show, and no shame to them, but this is

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Speaker 1: what I hear all the time is how do we

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Speaker 1: win back white college educated women, or not white, just

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Speaker 1: college educated women in the suburbs. We hear this at nauseum.

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Speaker 1: What we should be talking about is how do we

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Speaker 1: deliver for the only people that make sure Republicans win

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Speaker 1: the White House? How do we deliver policy gains for

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Speaker 1: people who are delivering the White House and Congress and

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Speaker 1: Senate for Republicans, that is, non college educated whites and

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Speaker 1: non college educated Hispanics. If the policies we are advocating

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Speaker 1: do not fight for our voters. Why are we doing them?

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Speaker 1: And there's a lot of policies we do which hurt them,

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Speaker 1: hurt them. A lot high school voters without a high

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Speaker 1: school degree, which have backed Trump in twenty sixteen by

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Speaker 1: seven points, back Trump by twenty points this time, twenty

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Speaker 1: points this time. Huge swings by religion. Trump gained with

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Speaker 1: basically every religious group. Protestants he gained by six points

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Speaker 1: since twenty sixteen, Evangelical white Evangelicals by six points, Catholics

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Speaker 1: by three points, unaffiliated by four points, Black Protestants by

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Speaker 1: six points, Latino Catholics by ten points, non white Protestants

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Speaker 1: by fifteen points. They're all reflective in the other stuff. Okay,

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Speaker 1: last part of this entire autistic rambling that I've just

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Speaker 1: had on the twenty twenty four election. I promise this

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Speaker 1: is the probably the last time I reflect on it

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Speaker 1: and have a whole segment on it. Immigrants among people

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Speaker 1: who were born in the USA, Trump lost the American

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Speaker 1: native born American vote in the twenty twenty election by

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Speaker 1: three points, which is lower than the overall average. Because

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Speaker 1: the immigrant vote voted for Joe Biden by twenty one points.

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Speaker 1: The immigrant vote voted for Hillary Clinton by huge margins

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Speaker 1: in the CNN exit poll, which is different than Pew.

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Speaker 1: But immigrants delivered Democrats the popular vote and delivered Democrats

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Speaker 1: a lot of swing states. That is why you will

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Speaker 1: never hear the Democrats back away from it. They need

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Speaker 1: these voters. In the twenty twenty four election, Trump won

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Speaker 1: Americans people who were born in America, American born, natural

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Speaker 1: born Americans. He won them by two points fifty to

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Speaker 1: forty eight, which is about what he got in the

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Speaker 1: overall popular vote. Among naturalized citizens immigrants, Trump lost them

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Speaker 1: only by four. There was a seventeen point swing among

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Speaker 1: immigrants in this country, which is kray Z. I never

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Speaker 1: thought we would see this. I mean still, they voted

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Speaker 1: for Joe Biden. Other sorry, they voted for Kamala Harris.

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Speaker 1: So it's not like they were you know, Magahar right,

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Speaker 1: Trump free people. But the fact that they voted that

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Speaker 1: close and it wasn't a twenty point loss. So where

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Speaker 1: did this come from? Which immigrants made this swing? This

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Speaker 1: is going to surprise you. In twenty twenty, fifty six

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Speaker 1: percent of white immigrants i e. Australians, Canadians, New Zealanders,

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Speaker 1: Kiwis and Europeans voted for Joe Biden by a fifteen

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Speaker 1: point margin. White immigrants voted for Donald Trump by a

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Speaker 1: sixteen point margin. In twenty twenty four. There was a

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Speaker 1: thirty one point swing. It is the largest single swing

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Speaker 1: of any immigrant group, and actually of any group that

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Speaker 1: it's even larger swing than Hispanics. Thirty one point swing

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Speaker 1: among white immigrants from Canada Europe. I guess there's some

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Speaker 1: whites from South Africa, Canada, Europe, Eweis, and Australians wild.

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Speaker 1: Among Hispanic immigrants, Trump won. According to Pew Research, Trump

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Speaker 1: won Hispanic immigrants by three points. He lost them by nineteen,

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Speaker 1: so it was a twenty two point swing among Hispanic immigrants,

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Speaker 1: and among Asian immigrants he lost them only by five

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Speaker 1: after losing them by thirty last time, twenty five point

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Speaker 1: swing for Asian immigrants. There's no information on black immigrants.

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Speaker 1: I guess they don't have enough sample size to make

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Speaker 1: a accurate telling. Okay, why why did immigrants all of

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Speaker 1: a sudden become Republican? Obviously the economy plays a big

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Speaker 1: part of that, right, There's no rhyme or Reasoners say

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Speaker 1: that otherwise the economy was sour and they paid back

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Speaker 1: from it. But Trump campaign on mass deportations. There was

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Speaker 1: no hidden agenda that, oh Trump didn't really campaign immigration.

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Speaker 1: He came pade hard on immigration. I think, and this

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Speaker 1: is my belief, and there's a little bit of data

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Speaker 1: to back this up, but it's more my belief than

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Speaker 1: it is the data. And if there is, I can

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Speaker 1: go into this at a different time. I believe that

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Speaker 1: the Black Lives Matter riots in twenty twenty one and

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Speaker 1: the end of twenty twenty really change people's opinion on

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Speaker 1: a lot of things when it came to how the

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Speaker 1: Democratic Party governs, and then the COVID lockdowns that absolutely

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Speaker 1: affected people into sitting there and reanalyzing how Democrats act.

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Speaker 1: The education policies, not only of the transgender stuff in schools,

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Speaker 1: but closing specialized schools which so many Asian immigrants and

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Speaker 1: European immigrants benefit from. And then also the insane stuff

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Speaker 1: when it comes to the transgender thing. This is not

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Speaker 1: just for not just for Latino immigrants and Asian immigrants,

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Speaker 1: but also europeam Grants member. It's Europe which is at

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Speaker 1: the forefront at stopping transgender surgery. For minors. It's Sweden,

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Speaker 1: it's Norway, it's England. These are not you know, right wing,

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Speaker 1: hardcore countries. These are countries that Bernie Sanders wants the

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Speaker 1: model America after. They are at the forefront of all

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Speaker 1: these fights. So I think that that is fascinating and

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Speaker 1: I think it's worth telling, and I don't know if

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Speaker 1: it means something bigger going forward. If this was a

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Speaker 1: blip in the map, but a thirty one point swing

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Speaker 1: vote change among European immigrant white immigrants and a twelve

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Speaker 1: point change among Hispanic immigrants and an eleven point change

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Speaker 1: among Asian immigrants from twenty twenty twenty twenty four is

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Speaker 1: why that number among immigrants overall has changed significantly. Okay,

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Speaker 1: I'm not going to talk about that anymore. That's the

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Speaker 1: twenty twenty four election. That's the best data that there

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Speaker 1: is out there. We put a pin in this, we

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Speaker 1: put a bow in it. I'm landing the plane. That

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Speaker 1: is fascinating. It's the kind of the gold star. As

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Speaker 1: post election analysis goes, what the Democrats and Republicans campaign

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Speaker 1: on in the future will be based off of what

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Speaker 1: that information says. All right, So now go to the question,

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Speaker 1: the question that I received from listeners was about the

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Speaker 1: census in twenty thirty and the question was looking forward

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Speaker 1: into how the census is coming out and how congressional

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Speaker 1: districts are being up reapportion based on population. How is

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Speaker 1: the illegal immigrants affecting the number of seats different states

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Speaker 1: are receiving, different congressional districts are receiving. This matter is

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Speaker 1: not only for the House, but for the electoral college.

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Speaker 1: Right do you get the number of electoral College votes

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Speaker 1: that you get is based off of how many House

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Speaker 1: seats you have in any given state. Right after COVID

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Speaker 1: in twenty twenty two, a left wing organization called the

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Speaker 1: Brennan Center and they have this image of float on

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Speaker 1: social media. They show that based on current trends, states

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Speaker 1: like Tennessee, Arizona, and Georgia were all expected to gain

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Speaker 1: one seat. Florida and Texas we're supposed to gain four

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Speaker 1: seats each, while California, New York, and Illinois lost the

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Speaker 1: combined ten seats. Right this massive reshuffling, This came out

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Speaker 1: of Michael Lee, by the way, the Brennan Center, and

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Speaker 1: it was basically sitting there and saying COVID had so

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Speaker 1: affected the amount of blue state voters, leaving that in

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Speaker 1: dark blue states Illinois, California, Rhode Island, in Minnesota, and

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Speaker 1: New York and Oregon were slated to lose total of

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Speaker 1: twelve seats. The soul swing state Pennsylvania was supposed to

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Speaker 1: lose one, while red states like Idaho, Utah, Tennessee, Texas,

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Speaker 1: and Florida were supposed to gain ten. In swing states

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Speaker 1: North Carolina, Georgia, and Arizona, we're supposed to gain one apiece. Okay,

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Speaker 1: so you might have seen that kind of floating around.

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Speaker 1: There was another one, but another graph, another map given

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Speaker 1: by Michael Lee Year an updated map since COVID, showing

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Speaker 1: the population changes because obviously we do every decade, so

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Speaker 1: he's just updating it year by year. The Brennan Center

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Speaker 1: found that instead of losing two seats, Illinois was slated

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Speaker 1: to lose one, instead of losing three, New York was

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Speaker 1: going to lose two, and instead of losing five, California

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Speaker 1: was going to lose three. Tennessee and Georgia, which was

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Speaker 1: supposed to gain seats, are not going to gain seats anymore.

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Speaker 1: Red states are going to be losing more and more

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Speaker 1: leverage During the reshuffling. Why is that? Why are these

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Speaker 1: states not gaining as many seats? Answers immigration, immigration, and

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Speaker 1: the Obviously, the number of people leaving states has slowed

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Speaker 1: since COVID, though it has continued, but immigration is padding

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Speaker 1: the numbers for failed blue state governance. Let's take New

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Speaker 1: York City for example, between twenty twenty three and twenty

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Speaker 1: twenty four, d eighty one hundred and fifty eight Americans

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Speaker 1: left the Borough of Brooklyn for greener pastures. There was

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Speaker 1: a natural increase, which is birth's minus deaths, of fifteen thousand,

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Speaker 1: so that means that Brooklyn should have had a population

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Speaker 1: trunk of twelve thousand, seven hundred, but thirty seven thousand

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Speaker 1: immigrants moved to the borough in the meantime, so that

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Speaker 1: means they actually grew by twenty five thousand, despite the

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Speaker 1: fact that nearly thirty thousand New Yorkers said New York

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Speaker 1: camp be governed. We got to get out of here,

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Speaker 1: We got to leave. We gotta go to Florida and

429
00:25:25,760 --> 00:25:30,159
Speaker 1: North Carolina, Georgia, Connecticut, long, you know wherever. If you

430
00:25:30,160 --> 00:25:32,880
Speaker 1: look at New York City as a whole, in one

431
00:25:33,000 --> 00:25:36,680
Speaker 1: year time, ninety one thousand people left New York City,

432
00:25:37,040 --> 00:25:39,960
Speaker 1: there was a natural growth of thirty four thousand, six hundred.

433
00:25:40,240 --> 00:25:42,159
Speaker 1: That means there should have been a loss of fifty

434
00:25:42,200 --> 00:25:44,639
Speaker 1: six thousand over the course of a decade. That means

435
00:25:44,680 --> 00:25:47,840
Speaker 1: New York City should have lost one congressional seat. But

436
00:25:47,920 --> 00:25:51,000
Speaker 1: the number of people who immigrated to New York from

437
00:25:51,200 --> 00:25:54,080
Speaker 1: mostly the Third World, but across the globe was one

438
00:25:54,160 --> 00:25:58,399
Speaker 1: hundred and forty four thousand. So despite lockdowns and rising

439
00:25:58,440 --> 00:26:02,600
Speaker 1: crime in sky high taxi is, New York grew by

440
00:26:02,720 --> 00:26:07,199
Speaker 1: eighty seven thousand people because of immigration. That means New

441
00:26:07,280 --> 00:26:10,080
Speaker 1: York is on track not to not only not to

442
00:26:10,160 --> 00:26:13,520
Speaker 1: lose three seats, but they may not even lose two seats.

443
00:26:14,600 --> 00:26:17,280
Speaker 1: The same is true for Illinois and California. Now some

444
00:26:17,359 --> 00:26:20,159
Speaker 1: Red states experiences kind of boomed to Florida and Texas

445
00:26:20,160 --> 00:26:23,880
Speaker 1: have loads of immigrants, lots of illegals, but there's also

446
00:26:23,920 --> 00:26:25,840
Speaker 1: a lot of Americans moving there, which is why they're

447
00:26:25,840 --> 00:26:31,879
Speaker 1: going to gain four seats each. Immigration rewards failing Blue

448
00:26:31,920 --> 00:26:34,879
Speaker 1: states and robs red states of seats that they should

449
00:26:34,880 --> 00:26:38,840
Speaker 1: have gained. Tennessee and Georgia should gain a house seat each,

450
00:26:39,160 --> 00:26:42,080
Speaker 1: but they're not probably not going to. This is why

451
00:26:42,119 --> 00:26:46,639
Speaker 1: big Blue states fight so hard against deporting illegal immigrants,

452
00:26:47,119 --> 00:26:52,000
Speaker 1: their numbers are dependent on who's being counted by mass immigration.

453
00:26:52,920 --> 00:26:56,520
Speaker 1: There was researchers that went back to nineteen eighty to

454
00:26:56,520 --> 00:26:59,600
Speaker 1: see how congressional districts have been affected by illegal immigration.

455
00:27:00,080 --> 00:27:03,960
Speaker 1: They said this, under the hypothetical scenario of not counting

456
00:27:03,960 --> 00:27:07,080
Speaker 1: people who were not in the country illegally, two seats

457
00:27:07,119 --> 00:27:09,520
Speaker 1: would have switched. In nineteen eighty, California and New York

458
00:27:09,520 --> 00:27:11,560
Speaker 1: would have lost a seat, Indiana and Georgia would have

459
00:27:11,560 --> 00:27:14,840
Speaker 1: each gained one. To ninety, California would have lost two, seats,

460
00:27:14,880 --> 00:27:18,760
Speaker 1: Texas would have lost one, while Kentucky and Montana would

461
00:27:18,760 --> 00:27:22,160
Speaker 1: have gained one. In two thousand, California would have lost

462
00:27:22,200 --> 00:27:25,400
Speaker 1: three seats, Texas would have lost one, Indiana, Michigan, miss

463
00:27:25,480 --> 00:27:30,520
Speaker 1: it Being, Montana would all gain one. In twenty ten, Louisiana, Missouri, Montana, Ohio,

464
00:27:30,640 --> 00:27:33,480
Speaker 1: North Carolina to all gained a seat, California would have

465
00:27:33,520 --> 00:27:36,199
Speaker 1: lost three, Texas and Florida would have lost one. And

466
00:27:36,240 --> 00:27:39,080
Speaker 1: in twenty twenty, California and Texas would have each lost

467
00:27:39,080 --> 00:27:40,960
Speaker 1: a seat, and Ohio and New York would have each

468
00:27:40,960 --> 00:27:44,399
Speaker 1: gained one. So put that know those numbers together. Since

469
00:27:44,520 --> 00:27:50,440
Speaker 1: nineteen eighty, California has ten extra congressional seats that they

470
00:27:50,480 --> 00:27:54,320
Speaker 1: would have not had had it not been for illegal immigration.

471
00:27:55,320 --> 00:27:55,560
Speaker 2: Ten.

472
00:27:56,680 --> 00:28:00,000
Speaker 1: Think of what the California delegation has voted on since

473
00:28:00,080 --> 00:28:03,920
Speaker 1: the last I don't know, forty five years. I'm a

474
00:28:04,040 --> 00:28:09,560
Speaker 1: care femnisties, cash for clunkers, green new deals, all of them,

475
00:28:09,840 --> 00:28:13,280
Speaker 1: not that all of them were benefited from illegal immigration.

476
00:28:14,000 --> 00:28:21,840
Speaker 1: Illegal immigrants absolutely padded it. While you know, states like Ohio, Montana, Georgia, Kentucky, Massachusetts, Louisiana,

477
00:28:22,119 --> 00:28:26,000
Speaker 1: they all lost representation and you could see it in

478
00:28:26,160 --> 00:28:29,840
Speaker 1: how it how many votes it takes to win some

479
00:28:29,920 --> 00:28:34,000
Speaker 1: House seats some states, In some seats it takes. There's

480
00:28:34,080 --> 00:28:37,000
Speaker 1: three hundred and fifty three hundred and seventy five thousand

481
00:28:37,119 --> 00:28:41,560
Speaker 1: people who vote in Ohio's ninth congressional district, which is

482
00:28:41,600 --> 00:28:44,680
Speaker 1: I think Warren Davidson seat. There are three hundred and

483
00:28:44,680 --> 00:28:47,680
Speaker 1: seventy five thousandople who vote in that last presidential election

484
00:28:48,360 --> 00:28:51,240
Speaker 1: go to Nidia Vasquez. The seat in New York seventh District,

485
00:28:51,480 --> 00:28:54,280
Speaker 1: only two hundred and twenty one thousand people vote because

486
00:28:54,320 --> 00:28:56,239
Speaker 1: a lot of the people are illegal. One hundred and

487
00:28:56,280 --> 00:28:59,600
Speaker 1: fifty thousand extra people vote in this one district in

488
00:28:59,600 --> 00:29:02,360
Speaker 1: ad distrition with more Americans than a district with not

489
00:29:02,440 --> 00:29:06,720
Speaker 1: more Americans. So yeah, it's unfair representation. People are being robbed.

490
00:29:06,880 --> 00:29:09,000
Speaker 1: Your vote counts a lot more if you live in

491
00:29:09,080 --> 00:29:12,040
Speaker 1: Media at Native of Asquaz is Brooklyn based district. Then

492
00:29:12,080 --> 00:29:14,520
Speaker 1: if you live in the middle of Ohio because you're

493
00:29:14,560 --> 00:29:19,440
Speaker 1: around American citizens versus illegal immigration. What's the solution of that?

494
00:29:19,960 --> 00:29:22,320
Speaker 1: You know, how do we get forward on this? Well,

495
00:29:22,360 --> 00:29:24,560
Speaker 1: my guest this week has been writing about this and

496
00:29:24,600 --> 00:29:26,680
Speaker 1: talking with this for a very long time. He's an

497
00:29:26,680 --> 00:29:28,720
Speaker 1: expert on the issue. We're going to have him up

498
00:29:28,920 --> 00:29:34,480
Speaker 1: next with me today is RJ Holm, and he is

499
00:29:34,520 --> 00:29:37,720
Speaker 1: the president of the National Immigration Center for Enforcement. RJ

500
00:29:37,880 --> 00:29:40,680
Speaker 1: thanks for being here. You wrote a great article on

501
00:29:40,760 --> 00:29:43,080
Speaker 1: Fox News saying that illegal immigrants should no longer be

502
00:29:43,200 --> 00:29:46,480
Speaker 1: counted in the census data that counts congressional districts. Right first,

503
00:29:46,480 --> 00:29:52,200
Speaker 1: discussed with me how how desperate some Democrats are to

504
00:29:52,360 --> 00:29:55,160
Speaker 1: make sure illegals are counted. You brought up a needia

505
00:29:55,240 --> 00:29:57,920
Speaker 1: vat Native Asquez saying that she needs immigrants in her

506
00:29:57,960 --> 00:29:59,200
Speaker 1: district to keep her district.

507
00:30:00,120 --> 00:30:01,760
Speaker 2: Yeah. Well, first things for having me on Ryan, and

508
00:30:01,800 --> 00:30:04,080
Speaker 2: you pronounce my last name as Homan, which I love

509
00:30:04,200 --> 00:30:07,520
Speaker 2: every time. Was vinal great guy. But you know, let's

510
00:30:07,560 --> 00:30:10,040
Speaker 2: get something straight here. I mean, you know, congressional seats

511
00:30:10,080 --> 00:30:13,760
Speaker 2: and electoral College votes are supposed to represent the American

512
00:30:13,760 --> 00:30:18,200
Speaker 2: people citizens, you know, voters, not everybody who's physically present

513
00:30:18,200 --> 00:30:20,360
Speaker 2: on our soil. But you know right now, that's not

514
00:30:20,440 --> 00:30:24,400
Speaker 2: what's happening under current law. The census counts all persons,

515
00:30:24,400 --> 00:30:28,040
Speaker 2: so you're talking non citizens, even illegal aliens. I mean,

516
00:30:28,040 --> 00:30:31,240
Speaker 2: for the purpose of you know, abortioning congressional districts, and

517
00:30:31,360 --> 00:30:34,320
Speaker 2: it was very important too, is determining presidential electors. So

518
00:30:34,360 --> 00:30:38,120
Speaker 2: with this massive illegal alien population that Biden brought in

519
00:30:38,200 --> 00:30:43,000
Speaker 2: the country purposely, that party's effectively rewarded with more political power,

520
00:30:43,400 --> 00:30:46,600
Speaker 2: while states who actually enforced the law they're being punished.

521
00:30:46,640 --> 00:30:49,000
Speaker 2: I mean, and that is upside down, and it's happening

522
00:30:49,000 --> 00:30:52,440
Speaker 2: in plain sight. Back in twenty twenty, President Trump, you know,

523
00:30:52,480 --> 00:30:55,200
Speaker 2: tried to fix this. He issued an order that would

524
00:30:55,200 --> 00:30:58,560
Speaker 2: have excluded illegal aliens from the apportionment count. But on

525
00:30:58,640 --> 00:31:01,840
Speaker 2: day one, guess what happened. Buying reversed it, ensuring all

526
00:31:02,080 --> 00:31:06,720
Speaker 2: non citizens, regardless of their immigration status. So illegal aliens

527
00:31:06,760 --> 00:31:09,960
Speaker 2: and the mass migration he does through legal pathways are

528
00:31:10,000 --> 00:31:13,480
Speaker 2: counted the same as citizens when it comes to distributing power.

529
00:31:13,720 --> 00:31:16,320
Speaker 2: And I mean in practice, like states like California where

530
00:31:16,320 --> 00:31:19,760
Speaker 2: you have sanctuary policies under new some okay, you have

531
00:31:19,840 --> 00:31:23,320
Speaker 2: millions of non citizens, you'd get more seats in Congress

532
00:31:23,360 --> 00:31:26,480
Speaker 2: and more sway in electoral college than they otherwise would.

533
00:31:26,600 --> 00:31:29,000
Speaker 2: And say you follow the law, You're Ohio, you're Alabama,

534
00:31:29,120 --> 00:31:31,480
Speaker 2: what do you do? You lose that? This is a

535
00:31:31,600 --> 00:31:35,960
Speaker 2: corrosive matter, dilutes the votes of American citizens and warps

536
00:31:36,160 --> 00:31:40,240
Speaker 2: our entire representative system, and it creates a massive incentive

537
00:31:40,280 --> 00:31:43,240
Speaker 2: I think for open border states to keep flouting federal

538
00:31:43,280 --> 00:31:48,200
Speaker 2: immigration law California can't always be offset by people fleeing

539
00:31:48,440 --> 00:31:50,440
Speaker 2: when illegal aliens come in mass.

540
00:31:51,000 --> 00:31:53,560
Speaker 1: Right, So are there any bills in Congress that take

541
00:31:54,080 --> 00:31:54,800
Speaker 1: to take this on.

542
00:31:55,680 --> 00:31:59,040
Speaker 2: Well, I'm sound done here so consumed with reconciliation. But

543
00:31:59,120 --> 00:32:02,160
Speaker 2: last Congress I think it is still introduced. There is

544
00:32:02,200 --> 00:32:05,320
Speaker 2: a great solution. It's called the Equal Representation Act. That

545
00:32:05,480 --> 00:32:08,840
Speaker 2: was I think it was Warren Davidson and Bill Haggerty.

546
00:32:09,200 --> 00:32:11,480
Speaker 2: What they would do is two basic things. They'd add

547
00:32:11,480 --> 00:32:15,160
Speaker 2: a citizenship question to the census and require that only

548
00:32:15,240 --> 00:32:19,000
Speaker 2: citizens be counted for purposes of racial apportionment and electoral

549
00:32:19,120 --> 00:32:22,400
Speaker 2: vote allocation. I mean, that's it. There's no games, there's

550
00:32:22,440 --> 00:32:24,959
Speaker 2: no guess work for an approach like that. It's a

551
00:32:25,040 --> 00:32:29,800
Speaker 2: straightforward fix to restore fairness to our entire political system

552
00:32:29,880 --> 00:32:34,160
Speaker 2: because you know, let's be honest representation, Ryan, that's power,

553
00:32:34,480 --> 00:32:37,160
Speaker 2: and right now we're handing that power to people who

554
00:32:37,200 --> 00:32:40,920
Speaker 2: ain't even supposed to be here that they're making communities unsafe,

555
00:32:40,960 --> 00:32:43,520
Speaker 2: they're corrupting our system. And at the same time, we're

556
00:32:43,560 --> 00:32:47,760
Speaker 2: telling the American voter their vote means less. That's not democracy, man,

557
00:32:47,840 --> 00:32:48,920
Speaker 2: that's distortion.

558
00:32:49,360 --> 00:32:52,280
Speaker 1: Well, as have you ever pulled this issue? Because I

559
00:32:52,360 --> 00:32:54,200
Speaker 1: started looking at for polls on this and there were

560
00:32:54,240 --> 00:32:56,120
Speaker 1: not many. There was like a few in twenty nineteen,

561
00:32:56,560 --> 00:33:00,280
Speaker 1: and the America and the public were favoring idea of

562
00:33:00,360 --> 00:33:04,040
Speaker 1: citizenship being asked on the census. Was that if you

563
00:33:04,080 --> 00:33:05,560
Speaker 1: ever poll this question, just curious.

564
00:33:06,040 --> 00:33:07,560
Speaker 2: Well, first you got to make sure you pull the

565
00:33:07,640 --> 00:33:09,680
Speaker 2: right people, because will we have a very big poll

566
00:33:09,840 --> 00:33:13,120
Speaker 2: the presidential election and stuff, you know, illegal aliens are

567
00:33:13,160 --> 00:33:15,640
Speaker 2: impact again. But no, I think the American people. I

568
00:33:15,680 --> 00:33:17,680
Speaker 2: mean on both parties. I mean I can't. I don't

569
00:33:17,680 --> 00:33:19,400
Speaker 2: have data in front of me that would be a

570
00:33:19,440 --> 00:33:21,960
Speaker 2: great thing to kind of capture. I mean, if you

571
00:33:22,040 --> 00:33:24,880
Speaker 2: ask an American citizen, do you want your vote to

572
00:33:24,920 --> 00:33:28,840
Speaker 2: be diluted both for Congress, your congressional representation, and your

573
00:33:28,880 --> 00:33:31,880
Speaker 2: Electoral College votes, Everybody's going to say, hell no, that's

574
00:33:31,880 --> 00:33:33,400
Speaker 2: not what the founding fathers a tenant.

575
00:33:33,920 --> 00:33:36,920
Speaker 1: That's a good leading question. Have you written a little differently?

576
00:33:36,960 --> 00:33:42,000
Speaker 1: But yeah, so you mentioned resolution sorry ecleation in Congress.

577
00:33:42,280 --> 00:33:45,760
Speaker 1: We'll tell my listeners what is going on with Congress

578
00:33:45,880 --> 00:33:48,360
Speaker 1: right now over the right. First of all, reconciliation bill

579
00:33:48,520 --> 00:33:51,120
Speaker 1: is part of the big beautiful bill. It's the vote

580
00:33:51,120 --> 00:33:53,680
Speaker 1: that they need for fifty one votes in order to pass.

581
00:33:53,720 --> 00:33:56,600
Speaker 1: It's not the sixty voth threshold. Explain what is going

582
00:33:56,600 --> 00:34:01,600
Speaker 1: on with immigration specifically in reconciliation right now on the

583
00:34:01,680 --> 00:34:02,840
Speaker 1: Republican side.

584
00:34:03,240 --> 00:34:05,440
Speaker 2: Well, aside from being in Texas. That's part of why

585
00:34:05,480 --> 00:34:08,359
Speaker 2: I'm sweating right now, being engaged on Capitol Hill from

586
00:34:08,360 --> 00:34:11,520
Speaker 2: down here as well. Listen, you know Republicans did send

587
00:34:11,560 --> 00:34:15,839
Speaker 2: a decently strong bill to the Senate. But one thing

588
00:34:16,120 --> 00:34:18,799
Speaker 2: that pisses us off and rightfully so. President Trump was

589
00:34:18,800 --> 00:34:21,640
Speaker 2: elected on a key electoral mandate. It was loud and clear,

590
00:34:21,640 --> 00:34:24,799
Speaker 2: it's mass deportation, not the worst of the worst, not

591
00:34:24,920 --> 00:34:28,560
Speaker 2: just the worst. Everybody should be eligible for deportation, and

592
00:34:28,600 --> 00:34:32,120
Speaker 2: I should get the resources to do so. Congress passed

593
00:34:32,200 --> 00:34:36,080
Speaker 2: a resolution which gave the amounts of money for each function. Arey,

594
00:34:36,520 --> 00:34:41,439
Speaker 2: Homeland Security was a ninety billion judiciary, which is ice

595
00:34:41,560 --> 00:34:44,280
Speaker 2: was one hundred and ten. You know what, I Scott

596
00:34:44,719 --> 00:34:48,239
Speaker 2: seventy four billion in that House version. Okay, so they

597
00:34:48,680 --> 00:34:52,560
Speaker 2: treated a number that was a floor like a ceiling.

598
00:34:52,760 --> 00:34:55,319
Speaker 2: And now what's over in the Senate. You're starting to

599
00:34:55,360 --> 00:34:57,960
Speaker 2: see a scrub. It's called the bird bath. A lot

600
00:34:57,960 --> 00:35:02,120
Speaker 2: of the paid for mechanisms of medicare me eligibility, getting

601
00:35:02,360 --> 00:35:06,200
Speaker 2: rid of both immigrants and illegal aliens from any eligibility

602
00:35:06,480 --> 00:35:09,920
Speaker 2: that would help pay for enforcement. That stuff's getting scrobbed.

603
00:35:10,160 --> 00:35:11,680
Speaker 1: I want you before you go in further.

604
00:35:11,760 --> 00:35:12,040
Speaker 2: Okay.

605
00:35:12,120 --> 00:35:14,879
Speaker 1: What he says the bird bath is is that there's

606
00:35:14,920 --> 00:35:17,680
Speaker 1: something called the Senate parliamentarian, who is supposed to be

607
00:35:17,719 --> 00:35:22,360
Speaker 1: a nonpartisan figure who goes to the reconciliation bill and

608
00:35:22,400 --> 00:35:24,239
Speaker 1: says there and says, if anything is not to do

609
00:35:24,320 --> 00:35:26,960
Speaker 1: with budgetary items, it cannot be in the bill. So,

610
00:35:27,120 --> 00:35:30,520
Speaker 1: for instance, and I'm just making something up, an abortion

611
00:35:30,760 --> 00:35:33,160
Speaker 1: ban could not be in a reconciliation bill because it

612
00:35:33,160 --> 00:35:35,560
Speaker 1: has nothing to do with the budget. And the Senate

613
00:35:35,560 --> 00:35:38,880
Speaker 1: Parliamentarian's roles to go through and take things out that

614
00:35:38,920 --> 00:35:41,719
Speaker 1: have nothing to do with the budget. A number of

615
00:35:41,840 --> 00:35:45,640
Speaker 1: key Republican items have have been iced out by the

616
00:35:45,680 --> 00:35:49,719
Speaker 1: Senate Parliamentarian because of the Bird rule, named after Senator Bird,

617
00:35:49,760 --> 00:35:52,719
Speaker 1: who's been dead for quite a while now. The but

618
00:35:52,880 --> 00:35:55,520
Speaker 1: so you're saying is that a lot of immigration enforcement

619
00:35:55,520 --> 00:35:58,799
Speaker 1: has not made it through the Senate Parliamentarian.

620
00:35:59,239 --> 00:36:02,279
Speaker 2: Well, we don't. In terms of the immigration enforcement stuff. Listen,

621
00:36:02,280 --> 00:36:03,560
Speaker 2: I just had a lot of pay for is there

622
00:36:03,600 --> 00:36:06,080
Speaker 2: was a lot of fee increases, Okay. You know when

623
00:36:06,080 --> 00:36:08,120
Speaker 2: you put asylum at about two grand or something, you

624
00:36:08,200 --> 00:36:10,960
Speaker 2: use it to deter forms of you know, I call

625
00:36:11,000 --> 00:36:14,120
Speaker 2: it a legal legal juries under the guise of legality.

626
00:36:14,400 --> 00:36:17,960
Speaker 2: But the biggest thing here that's very irritating the parliamentarian

627
00:36:18,160 --> 00:36:22,479
Speaker 2: is an unelected, smoke filled back room figure. Senator Thune,

628
00:36:22,520 --> 00:36:24,640
Speaker 2: the Majority leader, kept her on as a show of

629
00:36:24,640 --> 00:36:27,960
Speaker 2: a good faith. She serves. Elizabeth MacDonald serves at the

630
00:36:27,960 --> 00:36:31,040
Speaker 2: pleasure of Senate Republicans. If she scrubs this whole damn thing,

631
00:36:31,280 --> 00:36:33,680
Speaker 2: it's on their plate. But what we're seeing though, in

632
00:36:33,760 --> 00:36:38,200
Speaker 2: terms of Medicare Medicaid tax credit eligibility, where they're allowing

633
00:36:38,280 --> 00:36:41,840
Speaker 2: legal aliens. Tell me why. I mean, it is still

634
00:36:41,960 --> 00:36:46,160
Speaker 2: budgetarian nature when you're getting rid of eligibility of someone

635
00:36:46,200 --> 00:36:49,040
Speaker 2: who shouldn't even freaking be here. Okay, that's going to

636
00:36:49,120 --> 00:36:51,960
Speaker 2: save the American taxpayer money, that's going to give the

637
00:36:52,000 --> 00:36:55,080
Speaker 2: program more integrity. How is that not compliant with the

638
00:36:55,120 --> 00:36:57,040
Speaker 2: Bird rule? And the birder will keep in mind the

639
00:36:57,040 --> 00:37:00,000
Speaker 2: reason they're doing reconciliation. You only need fifty one votes,

640
00:37:00,160 --> 00:37:03,120
Speaker 2: simple majority. If it is not compliant with Bird just

641
00:37:03,120 --> 00:37:05,920
Speaker 2: like everything else in the Senate, you need sixty votes,

642
00:37:06,160 --> 00:37:08,560
Speaker 2: which is as always a lost cost.

643
00:37:09,480 --> 00:37:12,440
Speaker 1: Yeah, and that's why I think partly why I think

644
00:37:12,480 --> 00:37:14,760
Speaker 1: the AI policy, which I talked about a different podcast,

645
00:37:14,920 --> 00:37:18,160
Speaker 1: that didn't make it through the Bird rule either on

646
00:37:19,080 --> 00:37:23,080
Speaker 1: AI technology. So is there anything making it through this

647
00:37:23,160 --> 00:37:26,040
Speaker 1: immigrat I mean you mentioned ice is so is border

648
00:37:26,080 --> 00:37:28,320
Speaker 1: wall funding getting through any of this other stuff?

649
00:37:29,120 --> 00:37:31,960
Speaker 2: Yeah? I mean, well, first, you know, President Trump wasn't

650
00:37:32,000 --> 00:37:35,200
Speaker 2: elected on building the border wall to port sub not all. Okay,

651
00:37:35,200 --> 00:37:37,640
Speaker 2: the border wall funding is fully there. It's forty five billion,

652
00:37:37,640 --> 00:37:39,359
Speaker 2: but the border is secure. A lot of that money

653
00:37:39,400 --> 00:37:40,759
Speaker 2: needs to go to ice. But let me tell you something.

654
00:37:40,760 --> 00:37:43,200
Speaker 2: The most egregious thing that's happening kind of in the

655
00:37:43,239 --> 00:37:47,360
Speaker 2: shadows of reconciliation is the DHS Appropriations Bill. Okay, the

656
00:37:47,400 --> 00:37:51,160
Speaker 2: traditional funding mechanism for fiscal year twenty six was marked

657
00:37:51,200 --> 00:37:54,520
Speaker 2: up and left the House Appropriations Committee the day before yesterday.

658
00:37:54,960 --> 00:37:57,839
Speaker 2: There were two amendments that were included in it by

659
00:37:57,960 --> 00:38:00,279
Speaker 2: Republicans allowed it, and they were the most egregious things

660
00:38:00,320 --> 00:38:04,480
Speaker 2: I've ever seen. First. One of them essentially reputs Biorcus's

661
00:38:04,800 --> 00:38:09,000
Speaker 2: enforcement priorities back. Is like a sense in the text

662
00:38:09,200 --> 00:38:12,480
Speaker 2: where it's only criminals terrorist threats to public safety. Hell,

663
00:38:12,560 --> 00:38:15,200
Speaker 2: it's even weaker than my works, because my orcists said

664
00:38:15,880 --> 00:38:21,520
Speaker 2: recent illegal entries also our priorities for removal.

665
00:38:23,280 --> 00:38:28,880
Speaker 1: Maorcist was President Biden's DHS secretary who let let immigration

666
00:38:29,120 --> 00:38:31,600
Speaker 1: flood the country. Just engage anyone who didn't know what

667
00:38:31,640 --> 00:38:32,960
Speaker 1: we're talking about. That's what we're talking about.

668
00:38:33,000 --> 00:38:36,680
Speaker 2: Go ahead, Yeah, invasion architect Okay, and his memos were

669
00:38:36,719 --> 00:38:39,680
Speaker 2: guided it all civil guidelines to enforcement. They called it

670
00:38:39,680 --> 00:38:42,919
Speaker 2: it essentially handcuffed Ice. So they kind of mimic that

671
00:38:43,040 --> 00:38:45,360
Speaker 2: as we're seeing, you know, as Ice is just getting

672
00:38:45,360 --> 00:38:48,880
Speaker 2: directed essentially by like TV flips about hey, worst of

673
00:38:48,960 --> 00:38:51,800
Speaker 2: the worst criminals. No, we got to go after freaking everybody,

674
00:38:52,040 --> 00:38:54,879
Speaker 2: especially where that money comes in. And second, what they

675
00:38:54,920 --> 00:38:57,600
Speaker 2: did too is is told Ice. They gave a directive

676
00:38:57,680 --> 00:39:00,600
Speaker 2: President Trump in that EO too said I can enforce

677
00:39:00,640 --> 00:39:04,160
Speaker 2: the law anywhere, whether it's a school, a church, anywhere.

678
00:39:04,280 --> 00:39:06,640
Speaker 2: ICE has to go arrest the person that they're going

679
00:39:06,640 --> 00:39:10,440
Speaker 2: to go after. What they did in this language that

680
00:39:10,480 --> 00:39:12,120
Speaker 2: they put in there is they said ICE has to

681
00:39:12,200 --> 00:39:18,080
Speaker 2: do a sensitivity assessment of wherever they're going to go raid.

682
00:39:18,320 --> 00:39:20,520
Speaker 2: So I still has to sit back and check their

683
00:39:20,560 --> 00:39:23,360
Speaker 2: feelings about is this a good place that we should

684
00:39:23,400 --> 00:39:26,279
Speaker 2: go to. It forced the damn rule of law and

685
00:39:26,440 --> 00:39:28,960
Speaker 2: act in accordance to the EO. Republicans are going to

686
00:39:29,040 --> 00:39:31,640
Speaker 2: pay for that, and that stuff better be stripped in rules.

687
00:39:32,360 --> 00:39:34,960
Speaker 1: Who what Republican proposed that amendment?

688
00:39:35,120 --> 00:39:39,439
Speaker 2: Mark Amma Day is a House Approached subcommittee chair who

689
00:39:39,800 --> 00:39:44,520
Speaker 2: did influenced it with Juan Siscamoni from Arizona.

690
00:39:44,040 --> 00:39:47,359
Speaker 1: Both from Arizona and Mark and Madi from from Mark,

691
00:39:47,400 --> 00:39:52,440
Speaker 1: Amadi from Nevada and Jan Sescimoni from Arizona. Yeah, that

692
00:39:52,640 --> 00:39:56,120
Speaker 1: sounds about right. Juan Siscimony is terrible in immigration and

693
00:39:56,120 --> 00:39:59,160
Speaker 1: Emodi has always been squished, so that is not surprised.

694
00:39:59,200 --> 00:40:01,440
Speaker 1: But Emodi, I was this Coney has a border district.

695
00:40:01,440 --> 00:40:02,520
Speaker 1: You think that he would care more?

696
00:40:04,320 --> 00:40:05,160
Speaker 2: Is there? Okay?

697
00:40:05,160 --> 00:40:06,560
Speaker 1: I have was one question I want to ask you.

698
00:40:06,640 --> 00:40:08,520
Speaker 1: This wasn't even my topics. I want to ask you.

699
00:40:08,760 --> 00:40:14,359
Speaker 1: I keep hearing that deportations are not that is that true?

700
00:40:14,400 --> 00:40:18,320
Speaker 1: I hear arrests are high. I hear I hear people

701
00:40:18,360 --> 00:40:22,800
Speaker 1: are more, there are more people in in sorry detention centers,

702
00:40:23,600 --> 00:40:26,920
Speaker 1: but the actual deportations are not happening. Is that true?

703
00:40:27,080 --> 00:40:29,240
Speaker 2: They're on the help percent true. I mean they're trying

704
00:40:29,320 --> 00:40:31,520
Speaker 2: to count to some extent the beginning a coast guard

705
00:40:31,520 --> 00:40:35,879
Speaker 2: at sea, repatriation, stuff like that. Listen, the American people.

706
00:40:36,040 --> 00:40:39,640
Speaker 1: Why the way, why why a deportation is it?

707
00:40:39,680 --> 00:40:42,600
Speaker 2: Is it because it's a budget issue? Well, yeah, it

708
00:40:42,640 --> 00:40:44,479
Speaker 2: is a budget issue. Listen. Ice is in the red,

709
00:40:44,520 --> 00:40:47,600
Speaker 2: Ice is struggling. But here's my point, the American people,

710
00:40:47,800 --> 00:40:50,920
Speaker 2: your listeners, I mean, we all understand that ICE is

711
00:40:50,960 --> 00:40:53,640
Speaker 2: operating on a very low budget, that the Biden administration,

712
00:40:54,120 --> 00:40:56,160
Speaker 2: you know, they kept them constrained for a reason. They're

713
00:40:56,160 --> 00:40:57,160
Speaker 2: abolishing it from within.

714
00:40:57,640 --> 00:41:01,600
Speaker 3: You don't need to pretend that if you're arresting, detaining

715
00:41:01,640 --> 00:41:04,440
Speaker 3: and deporting and mass you need to go to Congress

716
00:41:04,440 --> 00:41:07,600
Speaker 3: and say, guys, listen, the American people spoke loud and clear,

717
00:41:07,680 --> 00:41:08,800
Speaker 3: we're freaking struggling.

718
00:41:09,080 --> 00:41:11,640
Speaker 2: Give us the full one hundred and ten billion authorise

719
00:41:12,000 --> 00:41:14,880
Speaker 2: and then we could do it. Stop playing games. The

720
00:41:14,920 --> 00:41:18,839
Speaker 2: American people will see if mass deportation is either water

721
00:41:19,000 --> 00:41:19,440
Speaker 2: down or.

722
00:41:19,600 --> 00:41:23,320
Speaker 1: This abb So the Big betfl bill needs to pass

723
00:41:23,400 --> 00:41:27,239
Speaker 1: so we can get the deportation. Okayah, important, we want

724
00:41:27,280 --> 00:41:29,839
Speaker 1: to RJ. Where can people go to follow your work?

725
00:41:30,440 --> 00:41:34,400
Speaker 2: Yep, it's nice enforcement with one E dot org. I'm

726
00:41:34,440 --> 00:41:37,040
Speaker 2: also a fellow at the Heritage Foundation's Border Security and

727
00:41:37,040 --> 00:41:40,000
Speaker 2: Immigration Center. We'll lead the charge up there, also operate

728
00:41:40,080 --> 00:41:43,480
Speaker 2: the Mass Deportation and Border Security Coalition up on Capitol

729
00:41:43,520 --> 00:41:46,440
Speaker 2: Hill that drove passes to HR two. But nice enforcement,

730
00:41:46,760 --> 00:41:50,279
Speaker 2: the National Immigration Center for Enforcement ICE is nice. They

731
00:41:50,280 --> 00:41:52,400
Speaker 2: shouldn't be the line President Trump is'mpowered.

732
00:41:52,719 --> 00:41:56,000
Speaker 1: That is great, great branding RJ. Thank you for coming

733
00:41:56,000 --> 00:41:57,640
Speaker 1: on this podcast. I appreciate it.

734
00:41:57,520 --> 00:41:57,879
Speaker 2: Of course.

735
00:41:57,920 --> 00:42:00,279
Speaker 1: Thanks Ryan, Hey, we'll be right back after this. Yes,

736
00:42:03,560 --> 00:42:05,600
Speaker 1: and now for the ask Me Anything segment of the show.

737
00:42:05,680 --> 00:42:08,040
Speaker 1: I love getting these emails. They're very helpful. I'm trying

738
00:42:08,080 --> 00:42:10,520
Speaker 1: to build a show around your interest and what you

739
00:42:10,560 --> 00:42:12,960
Speaker 1: guys are talking about, So please email me Ryan at

740
00:42:13,040 --> 00:42:17,480
Speaker 1: Numbers Game podcast dot com, Ryan at numbers Plural gamepodcast

741
00:42:17,560 --> 00:42:20,160
Speaker 1: dot com. I'll take a question on basically anything and

742
00:42:20,200 --> 00:42:21,719
Speaker 1: we can go through it and I can try to

743
00:42:21,719 --> 00:42:25,239
Speaker 1: find the best, most accurate information about political questions you

744
00:42:25,400 --> 00:42:28,720
Speaker 1: always wanted to know about. This question comes from Patrick. Hey, Ryan,

745
00:42:28,800 --> 00:42:31,440
Speaker 1: do you agree with the belief that a major deciding

746
00:42:31,440 --> 00:42:34,120
Speaker 1: factor in Trump losing a lot of the suburban white

747
00:42:34,160 --> 00:42:37,160
Speaker 1: and older white voters was because Trump and his supporters

748
00:42:37,200 --> 00:42:39,799
Speaker 1: were coded as a low class and they didn't want

749
00:42:39,880 --> 00:42:42,160
Speaker 1: to be associated with him, even if they agree with

750
00:42:42,160 --> 00:42:44,840
Speaker 1: his policies. If so, is it possibly someone like JDA,

751
00:42:44,960 --> 00:42:48,440
Speaker 1: well spoken conservative populist that isn't always an attack mode

752
00:42:48,480 --> 00:42:51,120
Speaker 1: to win back some of the white suburbs that were

753
00:42:51,160 --> 00:42:53,960
Speaker 1: scared away from being seen as trashies for supporting Trump.

754
00:42:54,320 --> 00:42:58,080
Speaker 1: In short, does JD scare the hose. Thank you Patrick,

755
00:42:59,440 --> 00:43:04,840
Speaker 1: I have the listeners. Thank you Badrick for that question. Okay,

756
00:43:04,920 --> 00:43:08,560
Speaker 1: so I don't think it's all about scaring the hoes, right,

757
00:43:09,040 --> 00:43:12,000
Speaker 1: And I worked for JD. I know him, I'm a

758
00:43:12,040 --> 00:43:14,520
Speaker 1: fan of him politically, so this is not a criticism

759
00:43:14,520 --> 00:43:17,640
Speaker 1: of him. First and foremost, I think part of why

760
00:43:17,680 --> 00:43:20,880
Speaker 1: the suburbs have shifted is changing demographics. Look at the

761
00:43:20,920 --> 00:43:23,640
Speaker 1: suburbs of Atlanta. When George Bush won them in two

762
00:43:23,680 --> 00:43:26,440
Speaker 1: thousand and four, they were sixty six percent white. They

763
00:43:26,440 --> 00:43:28,840
Speaker 1: are now thirty three percent white. As we said in

764
00:43:28,920 --> 00:43:32,240
Speaker 1: earlier in the podcast, the only group of people voting

765
00:43:32,360 --> 00:43:37,080
Speaker 1: for a majority nationwide. But this is true in Georgia.

766
00:43:37,160 --> 00:43:40,040
Speaker 1: It's also true that white college voters, you know, vote

767
00:43:40,040 --> 00:43:42,680
Speaker 1: Republican in Georgia as well. But white voters are the

768
00:43:42,680 --> 00:43:47,600
Speaker 1: ones who actually give Trump his majorities Republican. They are majorities, right.

769
00:43:48,320 --> 00:43:51,040
Speaker 1: Those being going from sixty six to thirty three is

770
00:43:51,080 --> 00:43:55,000
Speaker 1: why the Georgia suburbs have moved so far to the left.

771
00:43:55,200 --> 00:43:57,960
Speaker 1: But that's so, that's that's a big part of it. Secondly,

772
00:43:58,000 --> 00:44:00,960
Speaker 1: when we're talking about the female votes, specifically the college

773
00:44:01,080 --> 00:44:04,160
Speaker 1: educated female vote, let's look at the examination the numbers

774
00:44:04,160 --> 00:44:07,200
Speaker 1: from Catalyst Right. Catalyst is the Democratic firm that also

775
00:44:07,200 --> 00:44:09,880
Speaker 1: looked at the twenty twenty four elections. There was a

776
00:44:10,040 --> 00:44:14,200
Speaker 1: six point difference amongst college educated women from Mitt Romney

777
00:44:14,200 --> 00:44:17,520
Speaker 1: in twenty twelve to Trump in twenty twenty four. That's it,

778
00:44:17,520 --> 00:44:19,920
Speaker 1: it was the six points, and six points means a lot.

779
00:44:20,080 --> 00:44:22,920
Speaker 1: Six points among an fifty percent of the population will

780
00:44:22,920 --> 00:44:27,400
Speaker 1: swing suburbs for sure, right, especially in certain regions of

781
00:44:27,440 --> 00:44:33,640
Speaker 1: the country, But that six points is reflective of other

782
00:44:33,840 --> 00:44:37,160
Speaker 1: college educated women around the world. We saw similar ships

783
00:44:37,200 --> 00:44:42,440
Speaker 1: in Canada, in Britain, in Europe. I think that I

784
00:44:42,560 --> 00:44:44,640
Speaker 1: don't know if I mean, maybe there's a one or

785
00:44:44,680 --> 00:44:46,719
Speaker 1: two points that would sit there and swing back because

786
00:44:46,800 --> 00:44:48,880
Speaker 1: JD is different. I think JD has a lot of

787
00:44:48,880 --> 00:44:51,560
Speaker 1: advantages that Trump doesn't have. I think that being young,

788
00:44:51,960 --> 00:44:54,319
Speaker 1: having a young family. I think his wife, Usha Vance

789
00:44:54,440 --> 00:44:57,239
Speaker 1: is a major asset to him that has not been

790
00:44:57,320 --> 00:44:59,880
Speaker 1: talked enough about. I think him being more into like

791
00:45:00,000 --> 00:45:03,440
Speaker 1: Su'll absolutely would not scare as many people. But I

792
00:45:03,480 --> 00:45:06,280
Speaker 1: think that the media apparatus and the social media apparatus

793
00:45:06,400 --> 00:45:09,440
Speaker 1: will make him scary. Right, They're going to make him

794
00:45:09,680 --> 00:45:13,160
Speaker 1: too divisive about a million different issues and he speaks,

795
00:45:13,440 --> 00:45:17,239
Speaker 1: He's spoken a lot, He's spoken very thoughtfully, and unfortunately,

796
00:45:17,239 --> 00:45:20,680
Speaker 1: we don't live in a society that rewards thoughtfulness. So

797
00:45:21,920 --> 00:45:24,920
Speaker 1: when those things are boiled down to talking points for

798
00:45:25,160 --> 00:45:29,000
Speaker 1: thirty second TikTok videos or thirty second Instagram videos, they

799
00:45:29,000 --> 00:45:32,560
Speaker 1: will probably be viewed as negative. Can he win some

800
00:45:32,600 --> 00:45:36,640
Speaker 1: of them back? Yeah, maybe, Kenny get back to Mitt

801
00:45:36,719 --> 00:45:39,080
Speaker 1: Romney numbers. I don't think so. But you don't have

802
00:45:39,160 --> 00:45:41,560
Speaker 1: to get back to Mitt Romney numbers in order to

803
00:45:41,640 --> 00:45:45,360
Speaker 1: win them. That's my take on that. I think the

804
00:45:45,400 --> 00:45:48,520
Speaker 1: Trump the JD. Trump coalition, whoever the nomini is in

805
00:45:48,560 --> 00:45:51,440
Speaker 1: twenty twenty eight, is built on the backs of the

806
00:45:51,480 --> 00:45:54,239
Speaker 1: working class, especially working class minorities that I've moved to

807
00:45:54,239 --> 00:45:57,759
Speaker 1: the right, but also more working class white voters as well.

808
00:45:57,760 --> 00:46:00,279
Speaker 1: There's a lot of ground to still gain. Consider a

809
00:46:00,280 --> 00:46:03,000
Speaker 1: lot of them. I think twenty five or about twenty

810
00:46:03,000 --> 00:46:06,160
Speaker 1: five to thirty percent of all Kamala Harris voters were

811
00:46:06,160 --> 00:46:08,880
Speaker 1: whites without a college degree. There is a lot more

812
00:46:09,320 --> 00:46:13,239
Speaker 1: of that fruit to squeeze juice out of. Anyway, that's

813
00:46:13,280 --> 00:46:15,320
Speaker 1: my opinion. Hope you guys like it. I hope you

814
00:46:15,320 --> 00:46:18,080
Speaker 1: guys like this podcast. Please like and subscribe on the

815
00:46:18,120 --> 00:46:20,840
Speaker 1: iHeartRadio app, Apple Podcasts, wherever you get your podcast. I

816
00:46:20,880 --> 00:46:22,759
Speaker 1: promise that every episode is can be this autistic. But

817
00:46:22,760 --> 00:46:24,560
Speaker 1: if you like the numbers and you like the data,

818
00:46:25,080 --> 00:46:27,480
Speaker 1: please give me a five star review. I really appreciate

819
00:46:27,480 --> 00:46:30,200
Speaker 1: it helps boost this podcast. Take five seconds of your time.

820
00:46:30,400 --> 00:46:32,359
Speaker 1: It's the Christiana skiff you guys didn't give me. I'm

821
00:46:32,400 --> 00:46:34,480
Speaker 1: just joking. Thank you guys so much. I will see

822
00:46:34,480 --> 00:46:35,280
Speaker 1: you guys on Thursday.