1 00:00:02,120 --> 00:00:04,600 Speaker 1: Welcome back to a Numbers Game with Ryan Urduski. Thank 2 00:00:04,640 --> 00:00:06,600 Speaker 1: you all for being here again this week on this 3 00:00:06,720 --> 00:00:09,720 Speaker 1: Thursday's episode, once again on twice a week. So please 4 00:00:09,880 --> 00:00:12,120 Speaker 1: like and subscribe to You're on both episodes. You get 5 00:00:12,119 --> 00:00:14,200 Speaker 1: both episodes every week. It means a lot to us. 6 00:00:14,560 --> 00:00:16,239 Speaker 1: It was a very busy week, and I want to 7 00:00:16,239 --> 00:00:18,320 Speaker 1: start off by thanking everyone who sent me a message 8 00:00:18,320 --> 00:00:21,000 Speaker 1: on social media, but wishing me a happy birthday. That 9 00:00:21,120 --> 00:00:23,560 Speaker 1: meant a lot. It was really really great. If I 10 00:00:23,640 --> 00:00:26,279 Speaker 1: have the if I can, I just want to go 11 00:00:26,320 --> 00:00:29,480 Speaker 1: on a little jant for a second. On the birthday, 12 00:00:29,880 --> 00:00:33,120 Speaker 1: I went to go there's something called luckyseats dot com 13 00:00:33,159 --> 00:00:35,519 Speaker 1: in New York week you can win lottery tickets for 14 00:00:35,600 --> 00:00:37,800 Speaker 1: cheap Broadway tickets. So I did. I won them, and 15 00:00:37,840 --> 00:00:42,000 Speaker 1: I went to see Sunset Boulevard with Nicole sharon'ser Shirvanser, 16 00:00:42,040 --> 00:00:44,080 Speaker 1: I think it's her last name. She's from the Pussycat 17 00:00:44,080 --> 00:00:46,040 Speaker 1: Dolls and America's got talent. I'm sure you might have 18 00:00:46,080 --> 00:00:49,400 Speaker 1: seen her picture or her song somewhere anyway. So she's 19 00:00:49,400 --> 00:00:52,120 Speaker 1: starring in Sunset Boulevard. She's getting all this praise, so 20 00:00:52,120 --> 00:00:53,800 Speaker 1: we go to see it. I've seen the original with 21 00:00:53,920 --> 00:00:57,680 Speaker 1: Glenn Close, you know, a while ago, and everyone's saying 22 00:00:57,680 --> 00:00:59,840 Speaker 1: this is like the show to see. I go to 23 00:00:59,880 --> 00:01:03,760 Speaker 1: see it. It was a flaming pile of garbage. It 24 00:01:03,880 --> 00:01:07,440 Speaker 1: was horrible. In my opinion, it was one of the 25 00:01:07,480 --> 00:01:11,160 Speaker 1: top five worst things I've ever seen. The show is 26 00:01:11,400 --> 00:01:13,560 Speaker 1: from a Billy Waller movie from the fifties. It's not 27 00:01:13,600 --> 00:01:15,720 Speaker 1: a musical the original movie. It was made to a 28 00:01:15,800 --> 00:01:19,000 Speaker 1: music by Angeloa Weber, starring Glenn Close on Broadway Pilot 29 00:01:19,000 --> 00:01:23,080 Speaker 1: pone started in London. It's about an aging silent film 30 00:01:23,120 --> 00:01:25,559 Speaker 1: actress in the nineteen fifties and forties rather who wants 31 00:01:25,600 --> 00:01:27,920 Speaker 1: to make a comeback and she's kind of going insane 32 00:01:28,840 --> 00:01:31,679 Speaker 1: because she's believing that she's as famous as she used 33 00:01:31,680 --> 00:01:34,840 Speaker 1: to be. And it worked for Glenn Close because Glenn 34 00:01:34,840 --> 00:01:36,360 Speaker 1: Close kind of looks a little crazy once in a while, 35 00:01:36,360 --> 00:01:38,959 Speaker 1: like one hundred and one downations Glenn Close, and she 36 00:01:39,040 --> 00:01:42,679 Speaker 1: looks like an older woman, an older actress. Nicole shearings 37 00:01:42,720 --> 00:01:46,959 Speaker 1: Aer who started in this version, looks like a very young, 38 00:01:47,200 --> 00:01:49,880 Speaker 1: good looking woman. Although she's probably of the correct age 39 00:01:49,880 --> 00:01:52,600 Speaker 1: for the time, she doesn't look the age anymore. It 40 00:01:52,640 --> 00:01:55,279 Speaker 1: was in a black Box theater. It's everything bad about 41 00:01:55,280 --> 00:01:59,360 Speaker 1: like new age theater. Like people are in track suits, 42 00:01:59,440 --> 00:02:02,760 Speaker 1: there's no time, I'm coordination, there's no costumes, there's no sets, 43 00:02:03,720 --> 00:02:06,240 Speaker 1: and there's no point. Do you believe that she's actually 44 00:02:06,280 --> 00:02:10,040 Speaker 1: Norman Desmond? It was horrible. Okay, sorry, I had to 45 00:02:10,040 --> 00:02:12,000 Speaker 1: say that to somebody, and no one wants to hear 46 00:02:12,040 --> 00:02:14,560 Speaker 1: me ran about theater, and you're my captive audience, so 47 00:02:14,639 --> 00:02:17,040 Speaker 1: you get to hear about it. Now to the news 48 00:02:17,080 --> 00:02:19,120 Speaker 1: of the actual week. I want to go back and 49 00:02:19,160 --> 00:02:21,440 Speaker 1: focus on what we talked about last week and earlier 50 00:02:21,440 --> 00:02:25,080 Speaker 1: this week because it's important and it's still something that 51 00:02:25,120 --> 00:02:27,240 Speaker 1: people are talking about it and the news broke. So 52 00:02:27,280 --> 00:02:31,000 Speaker 1: first of all, the Canadian election, the Liberal Party, as expected, 53 00:02:31,080 --> 00:02:35,119 Speaker 1: made the big comeback, biggest comeback in Canadian politics, and 54 00:02:35,160 --> 00:02:38,600 Speaker 1: when their fourth consecutive government, they beat the Conservative Party 55 00:02:38,680 --> 00:02:41,080 Speaker 1: by forty three point seven percent of the vote to 56 00:02:41,120 --> 00:02:42,880 Speaker 1: forty one point seven percent of the vote, winning by 57 00:02:42,880 --> 00:02:45,480 Speaker 1: two points. They got one hundred and sixty nine seats 58 00:02:45,880 --> 00:02:48,160 Speaker 1: they call them writings in Canada instead of seats, but 59 00:02:48,160 --> 00:02:51,320 Speaker 1: one hundred and sixty nine writings able to form a 60 00:02:51,320 --> 00:02:54,720 Speaker 1: minority government, probably with the support of either the Progressive 61 00:02:54,760 --> 00:02:57,400 Speaker 1: New Democratic Party or the French Canadian Party of the 62 00:02:57,400 --> 00:02:59,680 Speaker 1: Block Quebecs. So we'll see what goes on, how they 63 00:02:59,720 --> 00:03:03,240 Speaker 1: end up government. But they have minority government. The casual 64 00:03:03,360 --> 00:03:05,359 Speaker 1: viewer in America who doesn't really spend a lot of 65 00:03:05,400 --> 00:03:09,679 Speaker 1: time listening to a Canadian news or Canadian politics, which 66 00:03:09,720 --> 00:03:11,280 Speaker 1: is most of us, no shame, but it is what 67 00:03:11,320 --> 00:03:14,920 Speaker 1: it is. They sat there and they heard about it, 68 00:03:14,919 --> 00:03:16,320 Speaker 1: and on social media they were like, well, I guess 69 00:03:16,320 --> 00:03:19,480 Speaker 1: Pierre Pola there, the leader of the Conservative Party who 70 00:03:19,560 --> 00:03:22,400 Speaker 1: ended up pilightly losing his own seat in the parliament, 71 00:03:23,000 --> 00:03:25,840 Speaker 1: his own writing that actually flipped to the Liberal Party. 72 00:03:26,280 --> 00:03:28,200 Speaker 1: He must have done a terrible job. And just like 73 00:03:28,240 --> 00:03:31,120 Speaker 1: the party lost and they just they weren't they were 74 00:03:31,200 --> 00:03:34,760 Speaker 1: insufficiently conservative. I want to actually go a little deeper 75 00:03:34,920 --> 00:03:39,040 Speaker 1: for the American audience who may not know what actually happened, 76 00:03:39,040 --> 00:03:41,960 Speaker 1: and really give you a good understanding. First of all, 77 00:03:42,000 --> 00:03:46,200 Speaker 1: the Canadian Conservative Party had their best performances nineteen eighty eight. 78 00:03:46,680 --> 00:03:50,120 Speaker 1: They grew their coalition based upon the data of these 79 00:03:50,240 --> 00:03:56,360 Speaker 1: writings of these seats by fifteen to twenty points among minorities, immigrants, 80 00:03:56,520 --> 00:03:59,840 Speaker 1: young people, and blue collar voters in the area outside 81 00:03:59,840 --> 00:04:05,000 Speaker 1: of Michigan, that part of Canada which is very heavy manufacturing, free, 82 00:04:05,880 --> 00:04:09,200 Speaker 1: very blue collar. In the ethnic suburbs outside Toronto and Vancouver. 83 00:04:09,480 --> 00:04:11,200 Speaker 1: There was like a fifteen to twenty point swing in 84 00:04:11,240 --> 00:04:14,760 Speaker 1: some of these districts some of these writings. Overall, Conservatives 85 00:04:14,760 --> 00:04:17,760 Speaker 1: gained seven point six percent more of the popular vote 86 00:04:17,760 --> 00:04:21,679 Speaker 1: than they got last time. So what happened. Well, first 87 00:04:21,680 --> 00:04:24,880 Speaker 1: of all, baby boomers happened. Baby Boomer's number one issue 88 00:04:24,920 --> 00:04:27,760 Speaker 1: in Canada, according to the Exit poles, was stopping Trump. 89 00:04:28,160 --> 00:04:31,560 Speaker 1: Young people wanted the economy be better. Old people wanted 90 00:04:31,760 --> 00:04:34,760 Speaker 1: to stop Trump. It is like a whole nation of 91 00:04:34,880 --> 00:04:37,919 Speaker 1: Rachel Mattow viewers. It is problematic. That's why when you 92 00:04:38,520 --> 00:04:42,240 Speaker 1: say someone say at Canada as the fifty first state, no, 93 00:04:42,440 --> 00:04:47,200 Speaker 1: please do not keep them where they are. We don't don't, 94 00:04:47,240 --> 00:04:50,719 Speaker 1: we don't need that. Also, college educated voters moved to 95 00:04:50,760 --> 00:04:54,839 Speaker 1: the left. This is why Pierre Poulaverir's seat flipped because 96 00:04:54,920 --> 00:04:58,359 Speaker 1: he's in a very heavily college educated writing. He's in 97 00:04:58,400 --> 00:05:01,120 Speaker 1: a very college educated district districts that used to be 98 00:05:01,160 --> 00:05:04,839 Speaker 1: solidly conservative. It's the same thing we've seen in America 99 00:05:04,920 --> 00:05:07,200 Speaker 1: in the last election with Trump. It's what we're seeing 100 00:05:07,200 --> 00:05:11,640 Speaker 1: in Europe, this great realignment of young people, of ethnic minorities, 101 00:05:11,640 --> 00:05:15,919 Speaker 1: of blue collar people moving to the right, and older 102 00:05:16,000 --> 00:05:19,960 Speaker 1: voters in many cases moving to the left. And that's 103 00:05:20,200 --> 00:05:23,720 Speaker 1: what we saw in Canada. And this is the best 104 00:05:23,760 --> 00:05:26,920 Speaker 1: election for the Conservatives since nineteen eighty eight. It sets 105 00:05:27,000 --> 00:05:30,520 Speaker 1: them in a good place for the next election, certainly 106 00:05:30,560 --> 00:05:33,520 Speaker 1: depending on what's going on and who's ever in charge 107 00:05:33,520 --> 00:05:36,120 Speaker 1: of our country and their country and what the economy 108 00:05:36,120 --> 00:05:40,120 Speaker 1: looks like. But that is what happened. It wasn't a 109 00:05:40,120 --> 00:05:43,960 Speaker 1: complete disaster for the Conservatives. It was a big disappointment. 110 00:05:44,440 --> 00:05:48,640 Speaker 1: But if the third parties one last thing, the third 111 00:05:48,839 --> 00:05:52,679 Speaker 1: there's multiple progressive parties in Canada. There's the Green Party, 112 00:05:52,920 --> 00:05:56,560 Speaker 1: the Bau Quebec, the New Democratics, whatever. The New Democratics 113 00:05:56,600 --> 00:06:00,360 Speaker 1: lost almost all their support, their numbers plummeted, so it 114 00:06:00,480 --> 00:06:05,040 Speaker 1: wasn't so much as the center was holding for the Liberals. 115 00:06:05,640 --> 00:06:09,160 Speaker 1: The New Democrats, part of the block Quebecs, part of 116 00:06:09,400 --> 00:06:13,640 Speaker 1: the Green Party moved to the Liberals. So the far 117 00:06:13,760 --> 00:06:16,560 Speaker 1: right moved to the Liberals, and the centrist voters who 118 00:06:16,560 --> 00:06:18,800 Speaker 1: were part of the Liberal Party moved to the conservatives. 119 00:06:19,120 --> 00:06:22,680 Speaker 1: They're just happening more far left liberals in Canada than 120 00:06:22,680 --> 00:06:25,840 Speaker 1: there were centrist who moved. So that's what happened. Wasn't 121 00:06:25,839 --> 00:06:27,680 Speaker 1: a complete disaster with the conservative It was actually a 122 00:06:27,680 --> 00:06:30,240 Speaker 1: fairly strong showing. And we'll see what happens in that 123 00:06:30,240 --> 00:06:34,080 Speaker 1: country going forward. And on Monday Show, we talked about 124 00:06:34,080 --> 00:06:36,800 Speaker 1: Trump's first one hundred days in office. Now, this show 125 00:06:36,920 --> 00:06:40,120 Speaker 1: was taped ahead of the Monday Program, which we always do. 126 00:06:40,200 --> 00:06:41,880 Speaker 1: We were table at least a few days ahead of time, 127 00:06:41,960 --> 00:06:44,919 Speaker 1: or a day ahead of time in this case, and 128 00:06:45,000 --> 00:06:48,039 Speaker 1: we taped the episode on Friday. Well, of course on 129 00:06:48,160 --> 00:06:51,160 Speaker 1: Sunday what comes out, but a ton of new polling 130 00:06:51,520 --> 00:06:53,560 Speaker 1: it pulls up. We didn't mention that when I taped 131 00:06:53,560 --> 00:06:55,279 Speaker 1: down on a Friday because we didn't have them yet. 132 00:06:55,480 --> 00:06:57,440 Speaker 1: And the headlines from the Washington Post and the New 133 00:06:57,520 --> 00:07:01,160 Speaker 1: York Times and a bunch of other publication said Trump 134 00:07:01,240 --> 00:07:04,440 Speaker 1: has his worst performance in history. He's the worst performing 135 00:07:04,480 --> 00:07:08,000 Speaker 1: president of the modern era, worse than he did the 136 00:07:08,040 --> 00:07:12,160 Speaker 1: first term. Republicans are on a precipice for a doomsday disaster. 137 00:07:12,760 --> 00:07:16,520 Speaker 1: You've heard these headlines before they were regurgitated. So I 138 00:07:16,560 --> 00:07:18,960 Speaker 1: wanted to go over them, and I pulled all the data. 139 00:07:19,200 --> 00:07:21,520 Speaker 1: I put on my substack, the National Populist newsletter. It's 140 00:07:21,520 --> 00:07:23,320 Speaker 1: free there for everyone who wants to listen. You have 141 00:07:23,360 --> 00:07:25,040 Speaker 1: to subscribe. You could just go there and read it. 142 00:07:25,880 --> 00:07:27,960 Speaker 1: I pulled all the data and I want to go 143 00:07:28,000 --> 00:07:31,760 Speaker 1: over it. So from April, these were the major polls, 144 00:07:31,840 --> 00:07:33,880 Speaker 1: and I'll give you the poll number, the name of 145 00:07:33,920 --> 00:07:37,160 Speaker 1: the pollster, and the margin at which Trump's at. The 146 00:07:37,200 --> 00:07:40,240 Speaker 1: ABC Washington Post Pole had him at negative thirteen. New 147 00:07:40,280 --> 00:07:43,960 Speaker 1: York Times had a negative twelve, CNN had negative fourteen, 148 00:07:44,160 --> 00:07:47,960 Speaker 1: CBS was negative ten, The Economist was negative nine, Fox 149 00:07:48,000 --> 00:07:51,880 Speaker 1: News was negative eleven, Atlas Intel was negative six, Pew 150 00:07:52,000 --> 00:07:56,320 Speaker 1: Research was negative nineteen, Quinny Peck was negative twelve. Quantus 151 00:07:56,360 --> 00:07:59,960 Speaker 1: Insight was negative three. And so if you average that out, 152 00:08:00,520 --> 00:08:02,720 Speaker 1: you have about a negative eleven. That's where he is 153 00:08:02,760 --> 00:08:06,680 Speaker 1: on average, negative eleven. But there's two numbers there that 154 00:08:06,760 --> 00:08:10,240 Speaker 1: are much different than all the other numbers, Atlas Intel 155 00:08:10,560 --> 00:08:14,760 Speaker 1: and Quantus Insight. Alice Intel has meant negative six, Quantus 156 00:08:14,800 --> 00:08:18,119 Speaker 1: Insight has meant negative three. They're both taken a little 157 00:08:18,120 --> 00:08:21,480 Speaker 1: while earlier in April, but after Liberation Day, after the tariffs. 158 00:08:22,280 --> 00:08:25,360 Speaker 1: Alice Intel was the number one most accurate pollster of 159 00:08:25,360 --> 00:08:28,720 Speaker 1: twenty twenty four. Quantus was the third most accurates pollster 160 00:08:28,880 --> 00:08:33,040 Speaker 1: of twenty twenty four. So why are their numbers significantly 161 00:08:33,120 --> 00:08:36,679 Speaker 1: better for Trump than the New York Times, Sienna, New 162 00:08:36,760 --> 00:08:39,160 Speaker 1: York Times, Santa Poll, the CNN poll, the Washington Post pol. 163 00:08:39,200 --> 00:08:43,080 Speaker 1: Why are they different? And they actually match an NRCC 164 00:08:43,280 --> 00:08:47,480 Speaker 1: and that's a National Republican Congressional Committee internal poll which 165 00:08:47,520 --> 00:08:49,280 Speaker 1: had the president of negative seven. So they're in the 166 00:08:49,320 --> 00:08:52,040 Speaker 1: same They're all playing the same kind of realm. So 167 00:08:52,080 --> 00:08:56,080 Speaker 1: why are their polls much different than the mainstream media polls. 168 00:08:56,240 --> 00:08:59,520 Speaker 1: I went through the cross tabs to look the major 169 00:08:59,600 --> 00:09:05,440 Speaker 1: difference is there's a double digit difference among Republicans. First 170 00:09:05,520 --> 00:09:10,840 Speaker 1: of all, Quantus and Atlas their polls have Trump in 171 00:09:10,880 --> 00:09:15,560 Speaker 1: the mid eighties among Republicans. All the other pollsters have 172 00:09:15,720 --> 00:09:20,120 Speaker 1: him barely crossing seventy. And I am more inclined to 173 00:09:20,160 --> 00:09:24,040 Speaker 1: believe an Atlas poll or a Quantus poll showing that 174 00:09:24,080 --> 00:09:27,640 Speaker 1: Trump's approval among Republicans probably hasn't dipped all that much. 175 00:09:27,679 --> 00:09:30,320 Speaker 1: I mean, Republicans have been loiled for him for a 176 00:09:30,320 --> 00:09:33,439 Speaker 1: lot crazier things and a lot more ups and downs 177 00:09:33,720 --> 00:09:38,280 Speaker 1: than just the tariff anouncement. Also a double digit difference 178 00:09:38,360 --> 00:09:43,520 Speaker 1: among independents. Some of these polls have Trump's independent numbers 179 00:09:43,559 --> 00:09:46,720 Speaker 1: in horrific territory. I mean, I'm talking negative thirty five, 180 00:09:46,840 --> 00:09:50,320 Speaker 1: negative forty seven, negative thirty six, negative twenty five. Negative 181 00:09:50,320 --> 00:09:52,040 Speaker 1: twenty five is actually a good number. In some of 182 00:09:52,080 --> 00:09:55,800 Speaker 1: these polls, Alice Intel has met negative eighteen and Quantus 183 00:09:55,840 --> 00:09:58,440 Speaker 1: has been negative twelve. That's a much different place than 184 00:09:58,440 --> 00:10:01,280 Speaker 1: negative thirty seven. It's not great eight, but it's not 185 00:10:01,400 --> 00:10:05,800 Speaker 1: negative thirty seven. These double digit margins matter a lot, 186 00:10:05,840 --> 00:10:09,720 Speaker 1: and overall the average among Hispanics for these poles is 187 00:10:09,800 --> 00:10:13,800 Speaker 1: much higher. Hispanics saw this big realignment, like the when 188 00:10:13,800 --> 00:10:15,200 Speaker 1: we just saw on Canada we had a re alignment 189 00:10:15,200 --> 00:10:17,400 Speaker 1: in twenty twenty four, Hispanics moved to the right. Did 190 00:10:17,440 --> 00:10:18,640 Speaker 1: they all of a sudden move to the left of 191 00:10:18,679 --> 00:10:22,280 Speaker 1: the re alignment get erased? And the big, big, big 192 00:10:22,400 --> 00:10:25,520 Speaker 1: question mark that kind of raised on my spicy senses, 193 00:10:25,559 --> 00:10:28,800 Speaker 1: my pols or spidy senses, was this His support among 194 00:10:28,960 --> 00:10:35,199 Speaker 1: black voters is as high as it was post election. So, 195 00:10:35,240 --> 00:10:39,600 Speaker 1: in other words, all of the decline of support among 196 00:10:40,400 --> 00:10:44,920 Speaker 1: minorities really primarily comes from Latino. Could that be because 197 00:10:44,920 --> 00:10:48,920 Speaker 1: the mass depredations, maybe, But could it also be from 198 00:10:48,960 --> 00:10:53,280 Speaker 1: over sampling Latino progressives and Democrats and centrists. Yes, it could, 199 00:10:53,640 --> 00:10:55,520 Speaker 1: because that happens quite a bit. It happened throughout the 200 00:10:55,559 --> 00:10:58,280 Speaker 1: twenty twenty four election in many polls that said Trump 201 00:10:58,320 --> 00:11:00,840 Speaker 1: could not cross thirty five percent among life Tinos, when 202 00:11:00,840 --> 00:11:04,440 Speaker 1: he crossed the mid forties. I think that all those 203 00:11:04,440 --> 00:11:08,080 Speaker 1: things are feeding these negative pole members, and I am 204 00:11:08,120 --> 00:11:10,760 Speaker 1: more inclined to believe an antless intel poll which is 205 00:11:10,800 --> 00:11:13,559 Speaker 1: matching the NRCC pole, which is matching the quantities insight 206 00:11:13,640 --> 00:11:16,720 Speaker 1: pole over the sexy headlines of the New York Times 207 00:11:16,760 --> 00:11:19,200 Speaker 1: and the Washington Post and all these other outlets really 208 00:11:19,280 --> 00:11:23,880 Speaker 1: have been pushing forever. That's my personal opinion. Now we'll 209 00:11:23,880 --> 00:11:27,880 Speaker 1: see what happens. Tariffs have been destabilized at the market. 210 00:11:28,040 --> 00:11:31,480 Speaker 1: Our GDP numbers have just released as we're recording this 211 00:11:31,480 --> 00:11:34,400 Speaker 1: podcast on Wednesday, showing a shrinkage in the economy of 212 00:11:34,440 --> 00:11:38,640 Speaker 1: point three percent, which is concerning. It's not we're not 213 00:11:38,720 --> 00:11:41,199 Speaker 1: in a great place, but I don't believe we're in 214 00:11:41,240 --> 00:11:44,240 Speaker 1: a doomsday scenario. I don't think Trump's in a doomsday scenario, 215 00:11:44,480 --> 00:11:47,400 Speaker 1: and I think we have a while till the midterm 216 00:11:47,440 --> 00:11:49,960 Speaker 1: election of our polls really start to matter. But I 217 00:11:50,000 --> 00:11:53,240 Speaker 1: don't think that the prevailing narrative, like it's not in 218 00:11:53,280 --> 00:11:56,559 Speaker 1: the Canadian election, the prevailing narrative over Trump is not accurate. 219 00:11:57,120 --> 00:12:00,640 Speaker 1: One poster that I did not mention in in the 220 00:12:00,720 --> 00:12:03,720 Speaker 1: summary of all these polls is my buddy James Johnson. 221 00:12:03,760 --> 00:12:05,800 Speaker 1: He is a polster from the UK. He was one 222 00:12:05,800 --> 00:12:08,520 Speaker 1: of the most accurate polsters in twenty twenty four. He 223 00:12:08,559 --> 00:12:11,840 Speaker 1: has a much different opinion than the headlines and the 224 00:12:11,840 --> 00:12:14,360 Speaker 1: prevailing narrative over Trump, and he'll be with us next 225 00:12:14,440 --> 00:12:16,240 Speaker 1: so stay tuned to hear what he has to say. 226 00:12:19,040 --> 00:12:22,360 Speaker 1: James Johnson is the founder of j L Partners. He 227 00:12:22,400 --> 00:12:23,880 Speaker 1: has a lot of stuff that gets published in the 228 00:12:23,920 --> 00:12:26,200 Speaker 1: Daily Mail. He was once the polster for the Prime 229 00:12:26,240 --> 00:12:28,560 Speaker 1: Minister of England. He's a good buddy of mine. James, 230 00:12:28,679 --> 00:12:30,280 Speaker 1: thanks for being on this podcast. 231 00:12:30,800 --> 00:12:31,680 Speaker 2: Thanks for having me. Ron. 232 00:12:32,000 --> 00:12:34,440 Speaker 1: So you have a new poll out from it's in 233 00:12:34,480 --> 00:12:38,880 Speaker 1: the Daily Mail. What is your findings on Trump's approval rating? 234 00:12:39,000 --> 00:12:42,680 Speaker 3: Right, so, we do think that Trump's approval rating has 235 00:12:42,760 --> 00:12:45,960 Speaker 3: gone down. We've got a forty five percent before we 236 00:12:46,000 --> 00:12:48,520 Speaker 3: were actually about fifty percent for Trump. In fact, two 237 00:12:48,520 --> 00:12:51,720 Speaker 3: weeks ago we had him at his joint highest approval rating. 238 00:12:52,040 --> 00:12:55,480 Speaker 3: So we've generally shown better numbers for Trump than the average. 239 00:12:55,520 --> 00:12:57,040 Speaker 3: We do think he's come down a bit. We think 240 00:12:57,080 --> 00:13:00,720 Speaker 3: he's in the mid forties. We think, however, he's nowhere 241 00:13:00,760 --> 00:13:02,960 Speaker 3: near some of the more dire ratings that some of 242 00:13:02,960 --> 00:13:04,560 Speaker 3: the poles as are putting out. So we don't think 243 00:13:04,600 --> 00:13:06,920 Speaker 3: he's on thirty eight thirty nine percent. We think he's 244 00:13:06,960 --> 00:13:10,080 Speaker 3: in the mid forties. Actually, by Trump standards, that's really 245 00:13:10,160 --> 00:13:11,000 Speaker 3: not that bad. 246 00:13:11,200 --> 00:13:14,720 Speaker 1: Right, So okay, so first things first I did. I 247 00:13:14,760 --> 00:13:17,040 Speaker 1: looked at all the polls that had just recently come out, 248 00:13:17,520 --> 00:13:22,560 Speaker 1: and the interesting thing about them, where pollsters like you Atlas, 249 00:13:22,720 --> 00:13:26,600 Speaker 1: Intel and Quantus Insights, you, the three you're at three 250 00:13:26,679 --> 00:13:31,720 Speaker 1: poles are much more alike with each other than other polls. 251 00:13:32,520 --> 00:13:35,160 Speaker 1: What's your what's his unfavorable number? If it's favoral is forty. 252 00:13:34,960 --> 00:13:38,600 Speaker 2: Five fifty five? We do it without the donotes, okay, 253 00:13:38,640 --> 00:13:39,320 Speaker 2: so oh gotcha? 254 00:13:39,320 --> 00:13:41,800 Speaker 1: All right, so it's ten with no donuts, gotcha. Now. 255 00:13:41,840 --> 00:13:44,640 Speaker 1: The thing that I found interesting, and the reason I 256 00:13:44,679 --> 00:13:47,679 Speaker 1: think it was interesting, is that in the ABC Washington Post, 257 00:13:47,679 --> 00:13:50,320 Speaker 1: the New York Times, CNN, CBS, economists, you name it. 258 00:13:50,720 --> 00:13:55,200 Speaker 1: Trump's favorable numbers with all those pollsters among Republicans is 259 00:13:55,240 --> 00:14:00,400 Speaker 1: somewhere in the low seventies. The now for you guys, 260 00:14:00,440 --> 00:14:02,720 Speaker 1: for your pole, they just came, it's eighty five. And 261 00:14:02,760 --> 00:14:07,160 Speaker 1: that ten point margin among Republicans, and also you have 262 00:14:07,240 --> 00:14:12,920 Speaker 1: a isentially higher support among independents that makes up a 263 00:14:13,120 --> 00:14:16,679 Speaker 1: large chunk of the favorability rating. Do you think that 264 00:14:16,760 --> 00:14:19,120 Speaker 1: there's any possibly like one in four Republicans have an 265 00:14:19,200 --> 00:14:20,320 Speaker 1: unfavorable opinion of him. 266 00:14:21,320 --> 00:14:25,640 Speaker 3: No, No, okay, And if this is the thing, so, 267 00:14:25,680 --> 00:14:27,360 Speaker 3: I think a lot of pollsters are well. 268 00:14:27,360 --> 00:14:28,720 Speaker 2: I think it's a lot of reasons behind this. 269 00:14:29,120 --> 00:14:32,760 Speaker 3: But the fundamental problem with some of these polls is 270 00:14:32,800 --> 00:14:35,480 Speaker 3: that they are still getting too many Harris voters in 271 00:14:35,520 --> 00:14:38,320 Speaker 3: their samples. Now, that was a problem in twenty twenty four, 272 00:14:38,360 --> 00:14:39,800 Speaker 3: where people were getting too many people. 273 00:14:39,680 --> 00:14:40,760 Speaker 2: Intending to vote Harris. 274 00:14:41,000 --> 00:14:42,880 Speaker 3: But they've now got too many people who voted Harris 275 00:14:42,920 --> 00:14:45,480 Speaker 3: in twenty twenty four in their samples to debt to death. 276 00:14:46,120 --> 00:14:50,000 Speaker 3: And that's the reason you're getting these Republicans who actually 277 00:14:50,040 --> 00:14:53,440 Speaker 3: are not indicative of Republicans as a whole. They are 278 00:14:53,440 --> 00:14:56,440 Speaker 3: Republicans who actually perhaps opted for Harris in twenty four 279 00:14:56,880 --> 00:14:59,680 Speaker 3: or indeed didn't vote. So I think we're getting these 280 00:14:59,680 --> 00:15:03,120 Speaker 3: Poles who they did not do very well in twenty four, 281 00:15:03,520 --> 00:15:06,080 Speaker 3: and now they're putting out these bad approval ratings of Trump. 282 00:15:06,240 --> 00:15:08,360 Speaker 3: I have to say, right, I find it baffling. I 283 00:15:08,360 --> 00:15:10,360 Speaker 3: find it incredible if you just take the three posters 284 00:15:10,400 --> 00:15:12,640 Speaker 3: with the lowest Trump ratings that also had Poles in 285 00:15:12,680 --> 00:15:15,560 Speaker 3: twenty twenty four, let me read them out to you. ABC, 286 00:15:15,800 --> 00:15:18,680 Speaker 3: Washington Post, Dipsos. They've got thirty nine percent Trump approval. 287 00:15:19,000 --> 00:15:22,440 Speaker 3: They had Harris three points ahead in November in an 288 00:15:22,560 --> 00:15:25,840 Speaker 3: SRSS forty one percent Trump approval. They had Harris winning 289 00:15:25,880 --> 00:15:28,720 Speaker 3: by six points in Wisconsin, five points in Michigan. New 290 00:15:28,800 --> 00:15:31,440 Speaker 3: York Times Sienna often talks about as a gold standard 291 00:15:31,760 --> 00:15:34,840 Speaker 3: forty two percent Trump approval. They had Harris winning all 292 00:15:34,840 --> 00:15:37,840 Speaker 3: the swing states by Arizona. Now, I don't need to 293 00:15:37,880 --> 00:15:41,000 Speaker 3: point out to your esteemed listeners that's not what happened. 294 00:15:41,360 --> 00:15:44,000 Speaker 3: Trump won the popular vote anyone, all seven sin swing states. 295 00:15:44,040 --> 00:15:47,160 Speaker 3: So I find it slightly baffling that these media outlets 296 00:15:47,160 --> 00:15:50,280 Speaker 3: are still hiring the exact same polsters with the exact 297 00:15:50,280 --> 00:15:53,480 Speaker 3: same methodology that meant that they fell so far wide 298 00:15:53,480 --> 00:15:54,920 Speaker 3: of the mark in November. 299 00:15:55,080 --> 00:15:56,880 Speaker 1: Well, I mean the New York Times poll did a 300 00:15:56,920 --> 00:15:59,640 Speaker 1: little better than the other ones, as I believe they 301 00:15:59,640 --> 00:16:01,760 Speaker 1: were in version of error in most of the swing stations. 302 00:16:01,880 --> 00:16:04,680 Speaker 1: Is why I still listen to them. I like CNN 303 00:16:04,760 --> 00:16:07,040 Speaker 1: for the fact that they show cross tabs. I mean, 304 00:16:07,040 --> 00:16:09,280 Speaker 1: that's really all I have to go for them. I'm like, Okay, 305 00:16:09,280 --> 00:16:11,120 Speaker 1: at least I can see the I can see the work, 306 00:16:11,440 --> 00:16:13,280 Speaker 1: and a lot of other poles they don't show cross 307 00:16:13,280 --> 00:16:15,560 Speaker 1: steps or they show limited ones, even like Wuinnipiac, which 308 00:16:15,600 --> 00:16:17,960 Speaker 1: those limited ones. But so you were at a piece 309 00:16:18,000 --> 00:16:20,360 Speaker 1: with the Spectator where you went in on these polsters 310 00:16:20,440 --> 00:16:22,880 Speaker 1: and why that they were wrong? Is it the fact? 311 00:16:23,360 --> 00:16:25,800 Speaker 1: And this has been my long standing opinion, especially when 312 00:16:25,800 --> 00:16:29,400 Speaker 1: a him to polling senior citizens senior citizen poll in 313 00:16:29,640 --> 00:16:31,640 Speaker 1: many polls, like even the New York Times Sanaple which 314 00:16:31,680 --> 00:16:34,040 Speaker 1: you just mentioned, in the swing states in Wisconsin, Michigan, 315 00:16:34,040 --> 00:16:37,520 Speaker 1: and Pennsylvania, they had Harris not only winning seniors in 316 00:16:37,560 --> 00:16:39,840 Speaker 1: those states, but they went winning them by double digits. 317 00:16:39,880 --> 00:16:42,000 Speaker 1: I think in Michigan they had her winning by twenty 318 00:16:42,040 --> 00:16:45,360 Speaker 1: points senior citizens, senior citizens have moved to the left 319 00:16:45,440 --> 00:16:48,680 Speaker 1: as polier. Senior citizens who were I would say silent 320 00:16:48,720 --> 00:16:51,840 Speaker 1: generation or greatest generation have died off and baby boomers, 321 00:16:51,840 --> 00:16:54,440 Speaker 1: who are more progressive have become senior citizens. I said, 322 00:16:54,480 --> 00:16:57,200 Speaker 1: it's like, you know, it's like the archie Bunker was 323 00:16:57,200 --> 00:16:59,320 Speaker 1: a senior citizen. He's no longer alive. Meat loaf. The 324 00:16:59,400 --> 00:17:03,600 Speaker 1: liberal son alone is a senior citizen now. But among 325 00:17:03,680 --> 00:17:08,480 Speaker 1: senior citizens there is a there are there are older 326 00:17:08,760 --> 00:17:13,280 Speaker 1: liberals who are you know MSNBC viewers who would break 327 00:17:13,400 --> 00:17:15,520 Speaker 1: someone's neck to go answer a poll. They will do 328 00:17:15,600 --> 00:17:17,720 Speaker 1: anything to answer a pole. They cannot wait to tell 329 00:17:17,720 --> 00:17:20,840 Speaker 1: you how much they hate Trump, And that same kind 330 00:17:21,240 --> 00:17:26,960 Speaker 1: of participation is not is not there for conservative senior citizens. 331 00:17:27,000 --> 00:17:28,520 Speaker 1: Do you find that as well as that why we're 332 00:17:28,520 --> 00:17:30,960 Speaker 1: seeing these numbers that are sometimes very off in that 333 00:17:31,160 --> 00:17:32,160 Speaker 1: in that demographic. 334 00:17:33,080 --> 00:17:34,399 Speaker 2: Yeah, I think that's a key factor. 335 00:17:35,560 --> 00:17:38,480 Speaker 3: Our research during the twenty twenty four election showed that 336 00:17:38,520 --> 00:17:40,600 Speaker 3: the group that was most likely to pick up a 337 00:17:40,680 --> 00:17:46,720 Speaker 3: landline polling pool were older, white middle class women. Now, 338 00:17:46,880 --> 00:17:49,760 Speaker 3: I haven't got anything against older white middle class women. 339 00:17:49,760 --> 00:17:53,280 Speaker 3: They're an important group to poll, but they're not so 340 00:17:53,359 --> 00:17:56,119 Speaker 3: important that they should flood your your polling answers, and 341 00:17:56,160 --> 00:17:58,840 Speaker 3: that group is more likely to vote Democrat than a 342 00:17:58,840 --> 00:18:02,359 Speaker 3: Republican way. Is what was behind that and Seltzer's disastrous 343 00:18:02,400 --> 00:18:05,280 Speaker 3: Iowa pole. She only used landline polling, she picked up 344 00:18:05,320 --> 00:18:06,960 Speaker 3: more of those people. She thought that was the story 345 00:18:06,960 --> 00:18:08,080 Speaker 3: of the election. It was actually the. 346 00:18:08,000 --> 00:18:11,520 Speaker 2: Story of her polling buyers. So that is a big element. 347 00:18:12,000 --> 00:18:15,240 Speaker 3: The fundamental problem I think to that poll to others 348 00:18:15,960 --> 00:18:20,760 Speaker 3: is that they are using traditional methods in an information 349 00:18:20,880 --> 00:18:25,200 Speaker 3: ecosystem that isn't traditional anymore. For every voter, I sort 350 00:18:25,200 --> 00:18:27,560 Speaker 3: of think of them as having a bubble around their head, 351 00:18:27,880 --> 00:18:30,760 Speaker 3: and they've got media on one hand giving them some things. 352 00:18:30,760 --> 00:18:32,640 Speaker 3: They've got AI on the other giving them some things. 353 00:18:32,640 --> 00:18:35,320 Speaker 3: They've got social media. They've also got their friends and family. 354 00:18:35,320 --> 00:18:37,439 Speaker 3: They've also just got their own view of the world, 355 00:18:38,000 --> 00:18:42,000 Speaker 3: and polling has to sort of overlap with that bubble. 356 00:18:42,040 --> 00:18:45,360 Speaker 3: You can't just go people on landlines and only penetrate 357 00:18:45,400 --> 00:18:47,359 Speaker 3: a bit of that. You've got to a potion in 358 00:18:47,400 --> 00:18:48,760 Speaker 3: a different way. So one of the things that we 359 00:18:48,840 --> 00:18:52,399 Speaker 3: do alongside the traditional methods is in app polling. So 360 00:18:52,400 --> 00:18:54,200 Speaker 3: if you're playing a game on your phone or you're 361 00:18:54,240 --> 00:18:56,080 Speaker 3: online shopping, you get a notification, do you want to 362 00:18:56,080 --> 00:18:57,520 Speaker 3: get a discount or do you want to win fifty 363 00:18:57,560 --> 00:19:00,959 Speaker 3: thousand game points? Complete this short survey. It picks up 364 00:19:01,000 --> 00:19:05,200 Speaker 3: people who are not who are not answering those traditional things. 365 00:19:05,200 --> 00:19:07,760 Speaker 3: It picks up more disengaged people. It also picks up 366 00:19:07,800 --> 00:19:10,800 Speaker 3: busy people. Ryan a key part of the Trump coalition 367 00:19:11,280 --> 00:19:14,080 Speaker 3: were busy blue collar workers who weren't going to go 368 00:19:14,400 --> 00:19:16,680 Speaker 3: and answer a poll. It wasn't because they didn't distrust 369 00:19:16,680 --> 00:19:18,200 Speaker 3: the bowling. It was because they were working all day 370 00:19:18,200 --> 00:19:19,159 Speaker 3: and didn't have the time. 371 00:19:19,560 --> 00:19:23,480 Speaker 1: So problem when you have like the economists, which asks 372 00:19:23,760 --> 00:19:26,320 Speaker 1: the economists you go poles which literally ask one hundred 373 00:19:26,359 --> 00:19:29,159 Speaker 1: and eighty seven questions, so like you have to spend 374 00:19:29,600 --> 00:19:31,679 Speaker 1: an hour of your day. Now they pay people or 375 00:19:31,680 --> 00:19:34,040 Speaker 1: do those poles, which I think also feeds into the inaccuracy. 376 00:19:34,280 --> 00:19:37,080 Speaker 1: But you have to do one hundred and eighty sum questions. 377 00:19:37,960 --> 00:19:42,720 Speaker 1: So in your piece that you know that Trump's approvings 378 00:19:42,760 --> 00:19:46,119 Speaker 1: have gone down, how much of it is tariffs? And 379 00:19:46,280 --> 00:19:48,560 Speaker 1: what is the conversation tariffs are having a month? 380 00:19:48,680 --> 00:19:51,359 Speaker 3: So we asked people in that same poll, what is 381 00:19:51,440 --> 00:19:54,200 Speaker 3: the reason that you feel more negative towards Trump? And 382 00:19:54,240 --> 00:19:56,440 Speaker 3: we asked that of people, of everyone, but we've narrowed 383 00:19:56,480 --> 00:19:59,280 Speaker 3: it down in the analysis to Trump voters, now too 384 00:19:59,359 --> 00:20:01,760 Speaker 3: small a samples to say too much about. But the 385 00:20:01,920 --> 00:20:05,399 Speaker 3: general gist of those answers was that it was about tariffs. 386 00:20:05,400 --> 00:20:07,280 Speaker 3: And we actually did a word cloud and the most 387 00:20:07,320 --> 00:20:10,560 Speaker 3: mentioned word in the word cloud was the word tariffs. However, 388 00:20:11,000 --> 00:20:14,680 Speaker 3: I think it's more complicated than just people think tariffs 389 00:20:14,680 --> 00:20:17,920 Speaker 3: are bad. I don't think they do. When Trump first 390 00:20:17,960 --> 00:20:20,840 Speaker 3: announced his Liberation Day tariffs, we actually had Trump's approval 391 00:20:20,840 --> 00:20:24,399 Speaker 3: at fifty four percent the week after. They respected when 392 00:20:24,440 --> 00:20:27,320 Speaker 3: we dug into the numbers. They respected a sense of direction, 393 00:20:27,400 --> 00:20:29,720 Speaker 3: a sense of strength, a sense of this is what 394 00:20:29,760 --> 00:20:33,439 Speaker 3: he said he'd do. What we've actually seen happened since 395 00:20:33,920 --> 00:20:36,720 Speaker 3: is that since Trump softened and changed some of his 396 00:20:36,760 --> 00:20:39,760 Speaker 3: positions on tariffs, voters have lost a bit of that 397 00:20:39,800 --> 00:20:42,840 Speaker 3: benefit of the doubt. They're less saying he's strong, he's 398 00:20:42,880 --> 00:20:45,359 Speaker 3: getting things done, he's taking a strong stance on this. 399 00:20:45,440 --> 00:20:48,280 Speaker 3: They're more saying, well, I still trust Trump, I still 400 00:20:48,359 --> 00:20:50,679 Speaker 3: like him, but I'm not quite sure what the strategy is, 401 00:20:50,680 --> 00:20:53,080 Speaker 3: what's the plan here? And that's taken away a bit 402 00:20:53,119 --> 00:20:55,040 Speaker 3: of that respect, and it's made people worry a little 403 00:20:55,040 --> 00:20:57,119 Speaker 3: bit more about the impact on their four oh one 404 00:20:57,200 --> 00:20:57,959 Speaker 3: k and their wallets. 405 00:20:58,000 --> 00:20:59,440 Speaker 2: So, look, I don't think this is a. 406 00:21:00,080 --> 00:21:02,440 Speaker 1: That's exactly what I've been saying to everyone in Washington 407 00:21:02,600 --> 00:21:06,399 Speaker 1: is that I don't I who work in the media 408 00:21:06,480 --> 00:21:08,639 Speaker 1: a lot, and I know every I know a lot 409 00:21:08,640 --> 00:21:11,679 Speaker 1: of members of Congress and the Trump administration, and I 410 00:21:11,720 --> 00:21:16,000 Speaker 1: work for the Vice president. I don't know what what 411 00:21:16,080 --> 00:21:18,320 Speaker 1: the plan is. I don't know. I don't understand what 412 00:21:18,400 --> 00:21:21,600 Speaker 1: the end goal is with the tariffs, and it's very 413 00:21:21,680 --> 00:21:23,960 Speaker 1: very frustrating. I'm looking at your word bubble right now. 414 00:21:24,200 --> 00:21:27,080 Speaker 1: Tariffs is like not only the biggest word, it takes 415 00:21:27,160 --> 00:21:29,800 Speaker 1: upody I think at least probably forty percent of all 416 00:21:29,840 --> 00:21:33,000 Speaker 1: the space available. And then in the lower words, the 417 00:21:33,040 --> 00:21:35,080 Speaker 1: words that are are not as big as tariffs are 418 00:21:35,200 --> 00:21:39,840 Speaker 1: tariff tariffs spelled a different way with two r's instead 419 00:21:39,840 --> 00:21:44,560 Speaker 1: of the two f's, mosque elon doje. And then minor 420 00:21:44,680 --> 00:21:48,439 Speaker 1: lower is like deportations, which is very very very small, 421 00:21:49,640 --> 00:21:52,800 Speaker 1: but it's mostly China's. There is a small bubble. Ukraine 422 00:21:52,840 --> 00:21:56,000 Speaker 1: is a very small bubble, but it is almost entirely tariffs. 423 00:21:56,960 --> 00:21:59,600 Speaker 3: Yeah, and I think it's particularly this this this watering 424 00:21:59,640 --> 00:22:02,440 Speaker 3: down the people have wondered. Look from a political CON's 425 00:22:02,480 --> 00:22:05,200 Speaker 3: point of view, that's either a that's either an issue 426 00:22:05,240 --> 00:22:07,399 Speaker 3: of well, you know, perhaps the U turn, perhaps he 427 00:22:07,440 --> 00:22:10,040 Speaker 3: shouldn't have changed position, or perhaps you shouldn't have brought 428 00:22:10,040 --> 00:22:11,560 Speaker 3: it in the first place if it was always inevitably 429 00:22:11,600 --> 00:22:13,520 Speaker 3: that it was going to change position. It reminds me 430 00:22:13,520 --> 00:22:17,560 Speaker 3: of a British example, Ryan of Liz Truss. She was 431 00:22:17,800 --> 00:22:19,960 Speaker 3: Prime Minister in the UK for a very short period. 432 00:22:20,320 --> 00:22:22,560 Speaker 3: She ended up resigning. She went from pretty good approval 433 00:22:22,640 --> 00:22:26,159 Speaker 3: ratings to catechismically bad ones. And the reason is not 434 00:22:26,200 --> 00:22:28,400 Speaker 3: so much because of the economic policy that she took, 435 00:22:28,440 --> 00:22:30,080 Speaker 3: but the fact she reversed it massively. 436 00:22:30,800 --> 00:22:32,320 Speaker 2: She lost that hunch. Now. 437 00:22:32,480 --> 00:22:34,200 Speaker 3: I don't think we're seeing the same thing happen here. 438 00:22:34,240 --> 00:22:36,040 Speaker 3: I don't think we are seeing an abandonment of Trump. 439 00:22:36,080 --> 00:22:38,200 Speaker 3: I think this is people just going a bit like, okay, 440 00:22:38,280 --> 00:22:40,040 Speaker 3: I'm still with him, but what's the plan they want 441 00:22:40,080 --> 00:22:42,719 Speaker 3: that reassurance. I don't think it's an abandonment on trust levels. 442 00:22:42,960 --> 00:22:45,320 Speaker 3: But it's similar in that it's the reversal that's done 443 00:22:45,359 --> 00:22:47,960 Speaker 3: the damage rather than the policy itself, but just very quickly. 444 00:22:48,000 --> 00:22:49,240 Speaker 3: Ry So I just want to pick up on something 445 00:22:49,280 --> 00:22:52,520 Speaker 3: you said there. What this isn't It is not a 446 00:22:52,560 --> 00:22:56,520 Speaker 3: backlash against deportations. It is not a backlash against the 447 00:22:56,560 --> 00:22:59,520 Speaker 3: case of Garcia in Al Salvador. It is not really 448 00:22:59,560 --> 00:23:02,800 Speaker 3: even a bat against Elon Musk or Doche. It's not 449 00:23:02,880 --> 00:23:06,159 Speaker 3: a backlash against Trump's style. It's not a backlash on 450 00:23:06,240 --> 00:23:09,200 Speaker 3: Ukraine and Zelenski. It's a very particular thing that has 451 00:23:09,280 --> 00:23:11,399 Speaker 3: just made them doubt whether Trump's cat a strategy. On 452 00:23:11,520 --> 00:23:14,199 Speaker 3: the bulk of the Trump policy platform, his voters are 453 00:23:14,200 --> 00:23:15,920 Speaker 3: still really, really behind him. 454 00:23:16,160 --> 00:23:17,439 Speaker 1: Now, I want to bring up something else that you 455 00:23:17,480 --> 00:23:19,399 Speaker 1: put it in your poll. I'm looking at the cross 456 00:23:19,400 --> 00:23:22,520 Speaker 1: tims you've made about approval disapproval with the neutral there, 457 00:23:22,520 --> 00:23:25,679 Speaker 1: with the I don't know Trump's numbers, My Hispanics have 458 00:23:25,720 --> 00:23:28,520 Speaker 1: fallen substantially, and this has been in a lot of 459 00:23:28,560 --> 00:23:30,920 Speaker 1: different poles. That actually makes me question whether they're or 460 00:23:31,040 --> 00:23:35,320 Speaker 1: correct or not. Did the entire realignment that affected how 461 00:23:35,680 --> 00:23:38,160 Speaker 1: you know, this election? Last election went and his bag 462 00:23:38,200 --> 00:23:41,359 Speaker 1: supporting Trump has fallen apart because of deportations. 463 00:23:42,480 --> 00:23:43,800 Speaker 2: No, I don't think it has. 464 00:23:44,119 --> 00:23:46,560 Speaker 3: I think we'd be careful with those small, small cross 465 00:23:46,560 --> 00:23:49,560 Speaker 3: tab numbers. I think all polsters at this stage, remember, 466 00:23:50,080 --> 00:23:53,000 Speaker 3: are not trying to predict an election, so they therefore 467 00:23:53,080 --> 00:23:55,119 Speaker 3: are a little bit like you know that that that 468 00:23:55,160 --> 00:23:57,640 Speaker 3: they're spending a bit less money, frankly, And I think 469 00:23:57,640 --> 00:24:00,600 Speaker 3: that's also why we get a lot of these more 470 00:24:00,600 --> 00:24:03,120 Speaker 3: garbage poles coming out, because people are just actually investing 471 00:24:03,119 --> 00:24:05,240 Speaker 3: a lot less in it, because their reputation is riding 472 00:24:05,240 --> 00:24:07,520 Speaker 3: on it less. Right, So I'd always be I'd always 473 00:24:07,520 --> 00:24:10,280 Speaker 3: be cautious even with my own poll about cross tabs 474 00:24:10,280 --> 00:24:12,280 Speaker 3: at this cross breaks at that level at the stage 475 00:24:12,520 --> 00:24:15,800 Speaker 3: when I've carried on doing one to one voter interviews 476 00:24:16,640 --> 00:24:20,240 Speaker 3: with voters since since the election, and I was actually 477 00:24:20,320 --> 00:24:24,280 Speaker 3: doing Hispanic voter interviews in Nevada a couple of weeks ago, 478 00:24:24,600 --> 00:24:27,200 Speaker 3: and the general gist that I get is that actually, 479 00:24:28,320 --> 00:24:30,879 Speaker 3: although they do not want to go out and show 480 00:24:31,080 --> 00:24:33,119 Speaker 3: and talk to their friends and families saying they're in 481 00:24:33,160 --> 00:24:38,440 Speaker 3: support of deportations, actually their support for them has basically 482 00:24:38,480 --> 00:24:41,919 Speaker 3: remained pretty stable since the election. Yes, they're more nervous 483 00:24:41,920 --> 00:24:45,119 Speaker 3: about talking about it and telling the neighbors that they 484 00:24:45,160 --> 00:24:48,159 Speaker 3: support President Trump, but actually on the bulk of the 485 00:24:48,200 --> 00:24:50,480 Speaker 3: fairness issue, I think they're pretty much in line. The 486 00:24:50,520 --> 00:24:52,199 Speaker 3: message I got again and again and again in an 487 00:24:52,240 --> 00:24:54,000 Speaker 3: election I still get now is I did it the 488 00:24:54,080 --> 00:24:56,879 Speaker 3: right way. These people did it the wrong way. So 489 00:24:57,000 --> 00:24:59,359 Speaker 3: I don't think that's gone. Look, I don't think these Hispanics, 490 00:24:59,600 --> 00:25:02,600 Speaker 3: especially Hispanic men, who came over to Trump, I don't 491 00:25:02,600 --> 00:25:05,640 Speaker 3: think they became lifelong Republicans overnight. But I do think 492 00:25:05,680 --> 00:25:08,080 Speaker 3: they became swing voters for the first time in that election. 493 00:25:08,600 --> 00:25:10,879 Speaker 3: That doesn't mean the Republicans have goten for good. It 494 00:25:10,920 --> 00:25:12,560 Speaker 3: means they're up for grabs, But it also means they 495 00:25:12,600 --> 00:25:13,440 Speaker 3: haven't lost them either. 496 00:25:13,880 --> 00:25:15,480 Speaker 1: Got it Okay, Well that's I mean, that's good to 497 00:25:15,520 --> 00:25:18,040 Speaker 1: know the other part of the cross tabs, and I 498 00:25:18,119 --> 00:25:19,639 Speaker 1: know not to get to obsess them. But this is 499 00:25:19,640 --> 00:25:22,639 Speaker 1: not laying your cressos. This is in everybody's young people. 500 00:25:22,800 --> 00:25:25,640 Speaker 1: Young people. There was a research done by David Shore 501 00:25:25,680 --> 00:25:28,000 Speaker 1: that showed that young men between these of eighteen to 502 00:25:28,040 --> 00:25:30,800 Speaker 1: twenty one were the most Republican demographic. The Yale poll 503 00:25:30,920 --> 00:25:33,880 Speaker 1: came out right afterwards, showing that young men were plan 504 00:25:33,960 --> 00:25:36,639 Speaker 1: voting for Republicans in the twenty twenty six mid terms 505 00:25:36,760 --> 00:25:39,800 Speaker 1: at the greatest portion of any group. Your poll has 506 00:25:39,960 --> 00:25:42,920 Speaker 1: Trump falling. I think twenty points among young men. Other 507 00:25:42,960 --> 00:25:45,719 Speaker 1: polls actually a bit for much much lower, not young men, 508 00:25:45,800 --> 00:25:49,360 Speaker 1: young people as a whole. What is your opinion of 509 00:25:49,480 --> 00:25:51,440 Speaker 1: the youth vote and how that's changed, Because they would 510 00:25:51,520 --> 00:25:55,080 Speaker 1: seemingly be the least affected by tariffs in the stock 511 00:25:55,119 --> 00:25:56,959 Speaker 1: market because they don't have, you know, a lot of stocks, 512 00:25:56,960 --> 00:25:58,399 Speaker 1: and they're not, you know, their four to one k 513 00:25:58,520 --> 00:26:02,000 Speaker 1: plans are rather small to fit a boom orrogen X. 514 00:26:02,320 --> 00:26:02,840 Speaker 2: It's interesting. 515 00:26:02,880 --> 00:26:05,040 Speaker 3: I think that is right, But they are also a 516 00:26:05,080 --> 00:26:07,080 Speaker 3: group young men are also a group that are more 517 00:26:07,200 --> 00:26:10,920 Speaker 3: like he's been plugged into financial news, and I think 518 00:26:10,920 --> 00:26:13,280 Speaker 3: they're more what our research are shown, they're more likely 519 00:26:13,359 --> 00:26:16,280 Speaker 3: to be tracking things like cryptocurrency, things like stocks on 520 00:26:16,320 --> 00:26:18,600 Speaker 3: their phone than actually any group, even those who actually 521 00:26:18,640 --> 00:26:19,440 Speaker 3: have more stocks. 522 00:26:20,240 --> 00:26:21,080 Speaker 2: So it's interesting. 523 00:26:21,280 --> 00:26:23,040 Speaker 3: They do seem to be a bit more plugged into 524 00:26:23,040 --> 00:26:26,360 Speaker 3: that sort of information system that means that they see 525 00:26:26,400 --> 00:26:27,480 Speaker 3: those things. 526 00:26:27,520 --> 00:26:28,560 Speaker 2: So I think there's a bit of that. 527 00:26:28,880 --> 00:26:31,240 Speaker 3: Look again, I would just come back to the point 528 00:26:31,280 --> 00:26:33,680 Speaker 3: that I don't think there's any I don't think they're 529 00:26:33,720 --> 00:26:35,560 Speaker 3: lost for good by any stretch. I think we have 530 00:26:35,600 --> 00:26:38,800 Speaker 3: got a really interesting generational shift here, and I do 531 00:26:38,840 --> 00:26:41,720 Speaker 3: think young men both in America and in other countries 532 00:26:42,640 --> 00:26:45,480 Speaker 3: are having a real backlash to some of the stuff 533 00:26:45,480 --> 00:26:48,520 Speaker 3: that's have been out there about gender rights. I spoke 534 00:26:48,600 --> 00:26:52,840 Speaker 3: to a eighteen year old black man in Nevada as 535 00:26:52,840 --> 00:26:55,200 Speaker 3: a swing voter interview. He was a first time Trump voter. 536 00:26:55,359 --> 00:26:57,160 Speaker 3: I mean, he was a first time voter, but he 537 00:26:57,200 --> 00:26:59,600 Speaker 3: is voting trumpet for the first time of anyone in 538 00:26:59,640 --> 00:27:02,280 Speaker 3: his family and for him. 539 00:27:02,359 --> 00:27:03,880 Speaker 2: I asked him why, and he just came. 540 00:27:03,720 --> 00:27:07,479 Speaker 3: Back to the Democrats are pushing nonsense on gender and 541 00:27:07,520 --> 00:27:09,520 Speaker 3: they want you know, they're pushing all this stuff on trends, 542 00:27:09,560 --> 00:27:12,080 Speaker 3: and I'm more interested in the economy now. His view 543 00:27:12,600 --> 00:27:15,120 Speaker 3: was that Trump had to improve the economy for him 544 00:27:15,119 --> 00:27:18,200 Speaker 3: to carry on voting Republican. So he's not with Trump 545 00:27:18,200 --> 00:27:20,520 Speaker 3: for good, but he's giving Trump a chance, and he's 546 00:27:20,520 --> 00:27:23,320 Speaker 3: giving the Republican Party a chance. I do think though, 547 00:27:23,359 --> 00:27:25,640 Speaker 3: that if the Republicans need to be concerned about one thing, 548 00:27:26,160 --> 00:27:29,439 Speaker 3: it's how they get out these younger men, younger non 549 00:27:29,520 --> 00:27:31,800 Speaker 3: white voters who gave Trump a vote for the first time. 550 00:27:31,880 --> 00:27:33,840 Speaker 3: How do they get them out in twenty twenty six 551 00:27:34,119 --> 00:27:36,439 Speaker 3: Because these are not people who have traditionally voted a 552 00:27:36,440 --> 00:27:38,600 Speaker 3: lot in these elections, and that's the big challenge I think. 553 00:27:38,840 --> 00:27:41,480 Speaker 1: Yeah, I mean that's the problem where you have with 554 00:27:43,880 --> 00:27:45,879 Speaker 1: the with Boomer's movies, a lt of people who are 555 00:27:45,920 --> 00:27:49,120 Speaker 1: always voting, voting consistently, you'll have That's why you're seeing 556 00:27:49,160 --> 00:27:51,640 Speaker 1: the special elections move ten twenty points and some seats 557 00:27:51,680 --> 00:27:53,520 Speaker 1: that they're really not competitive in it, and it might 558 00:27:53,560 --> 00:27:56,119 Speaker 1: be a very big problem for the midterms. I agree 559 00:27:56,160 --> 00:27:58,480 Speaker 1: with that sentiment. I don't really know the answer the 560 00:27:58,520 --> 00:27:59,920 Speaker 1: top of my head, but I think that that is 561 00:28:00,119 --> 00:28:03,440 Speaker 1: really important part. What is the most if you were 562 00:28:03,480 --> 00:28:07,080 Speaker 1: to sit there and talk to somebody about from like 563 00:28:07,119 --> 00:28:10,080 Speaker 1: the White House, about an administration about the whole tariffs, 564 00:28:10,480 --> 00:28:13,000 Speaker 1: would it be to double down on a plan or 565 00:28:13,080 --> 00:28:15,520 Speaker 1: to kind of walk back and announce a bunch of 566 00:28:15,560 --> 00:28:17,960 Speaker 1: free trade agreements and and just you know, call it 567 00:28:18,000 --> 00:28:20,080 Speaker 1: a day. What would be what would be the best 568 00:28:20,119 --> 00:28:22,879 Speaker 1: impactful for a polls? You know, because you have a 569 00:28:22,960 --> 00:28:26,000 Speaker 1: few months until they start really mattering ahead of the midterms, 570 00:28:26,000 --> 00:28:27,359 Speaker 1: But what would your opinion be? 571 00:28:28,640 --> 00:28:31,080 Speaker 3: Yeah, Look, my first I'm going to I promise I'm 572 00:28:31,080 --> 00:28:32,960 Speaker 3: going to answer your question, Ryan, But my first, my 573 00:28:33,040 --> 00:28:36,400 Speaker 3: instinctive answer to that is ignore us, ignore the pollsters. 574 00:28:36,440 --> 00:28:37,879 Speaker 2: Do what you think is right for the country. 575 00:28:37,960 --> 00:28:40,640 Speaker 3: Like you know, President Trump, if if he's going to 576 00:28:40,680 --> 00:28:42,959 Speaker 3: face what usually happens at the mid terms, which is 577 00:28:42,960 --> 00:28:46,680 Speaker 3: that the opposition wins, then he should use this two 578 00:28:46,720 --> 00:28:48,440 Speaker 3: years to do whatever the hell he wants to improve 579 00:28:48,480 --> 00:28:51,680 Speaker 3: the country, so that that would be That's my fundamental answer. 580 00:28:51,760 --> 00:28:53,840 Speaker 3: But if you're looking at how to message it best, 581 00:28:54,280 --> 00:28:56,920 Speaker 3: I think you know, and I think that it's probably 582 00:28:56,960 --> 00:28:59,960 Speaker 3: now showing that this is a plan that is paid 583 00:29:00,160 --> 00:29:03,160 Speaker 3: off and that it's resulting in this free trade deal, 584 00:29:03,200 --> 00:29:06,080 Speaker 3: this trade deal, tying it back to it. If they 585 00:29:06,160 --> 00:29:08,200 Speaker 3: can't do that, then I think it's probably best to 586 00:29:08,240 --> 00:29:10,000 Speaker 3: get off the issue and move on to something else. 587 00:29:10,800 --> 00:29:13,120 Speaker 1: James Andson. Where can people go to read your stuff, 588 00:29:13,160 --> 00:29:15,040 Speaker 1: read your polls, all your information? 589 00:29:16,000 --> 00:29:18,320 Speaker 3: So they go on Twitter x is the best place, 590 00:29:18,640 --> 00:29:22,120 Speaker 3: James Johnson two five two, And if you also look 591 00:29:22,160 --> 00:29:25,320 Speaker 3: on jail partners dot com that's where we upload all 592 00:29:25,360 --> 00:29:27,360 Speaker 3: of our latest polling and you can also get in 593 00:29:27,400 --> 00:29:29,480 Speaker 3: touch there if you're interested in any further information. 594 00:29:30,000 --> 00:29:32,400 Speaker 1: Cheers James Andson, Happy birthday. Thank you so much. You 595 00:29:32,440 --> 00:29:35,280 Speaker 1: do great jobs in polling and people should check it 596 00:29:35,280 --> 00:29:35,720 Speaker 1: out now. 597 00:29:36,360 --> 00:29:36,880 Speaker 2: Thanks so much. 598 00:29:36,960 --> 00:29:39,840 Speaker 1: Ron you're listening to It's The Numbers Game with Ryan Grodowski. 599 00:29:39,960 --> 00:29:45,440 Speaker 1: We'll be right back after this message. And now for 600 00:29:45,480 --> 00:29:48,280 Speaker 1: the Ask Me anything part of this show. Once again, 601 00:29:48,360 --> 00:29:51,520 Speaker 1: please send me a questions about literally anything that I 602 00:29:51,560 --> 00:29:54,040 Speaker 1: can answer. I'll do the research for you. Can ema 603 00:29:54,160 --> 00:29:58,440 Speaker 1: me Ryan at Numbers Game podcast dot com. That's plural numbers, 604 00:29:58,520 --> 00:30:01,640 Speaker 1: Ryan at Numbers Game Podcast, come my email. This question 605 00:30:01,680 --> 00:30:03,560 Speaker 1: this week was actually so simple. Do you have a 606 00:30:03,600 --> 00:30:06,360 Speaker 1: book recommendation for me? And I do so. This is 607 00:30:06,400 --> 00:30:09,760 Speaker 1: not a popular World War two book. It's not like huge, 608 00:30:09,760 --> 00:30:11,959 Speaker 1: but it's one of my favorites called Diary of a 609 00:30:12,000 --> 00:30:15,640 Speaker 1: Man in Despair by Friedrich Reich R. E. C. K. 610 00:30:16,440 --> 00:30:19,200 Speaker 1: It is a book about a German aristocrat watching the 611 00:30:19,280 --> 00:30:22,960 Speaker 1: rise of Nazis and losing his country. It is written 612 00:30:22,960 --> 00:30:26,560 Speaker 1: from a conservative perspective. It's beautiful, it's heartbreaking, it's his 613 00:30:26,680 --> 00:30:31,560 Speaker 1: own it's it's his diary, so it's it's it's very powerful, 614 00:30:31,600 --> 00:30:33,920 Speaker 1: beautiful book. Love it, love it, love it. Cannot recommend 615 00:30:33,960 --> 00:30:36,600 Speaker 1: it enough to people once again. Diary of a Man 616 00:30:36,680 --> 00:30:39,680 Speaker 1: in Despair by Frederick Reich quick and easy, and it's 617 00:30:39,680 --> 00:30:41,280 Speaker 1: also not super long. I'm the middle of reading a 618 00:30:41,320 --> 00:30:43,280 Speaker 1: book right now, which is like seven hundred pages, and 619 00:30:43,320 --> 00:30:46,240 Speaker 1: sometimes it's this is this book's a slog. It's a 620 00:30:46,240 --> 00:30:48,160 Speaker 1: good book, but it's a slog. So maybe i'll give 621 00:30:48,160 --> 00:30:50,400 Speaker 1: you a review when I'm finally. 622 00:30:50,080 --> 00:30:50,640 Speaker 2: Done with it. 623 00:30:51,240 --> 00:30:53,880 Speaker 1: Anyway, thank you all for listening this week. I really 624 00:30:53,880 --> 00:30:56,360 Speaker 1: appreciate all of you. Please like and subscribe at the 625 00:30:56,400 --> 00:30:59,840 Speaker 1: iHeartRadio app, Apple Podcasts, wherever you get your podcasts, and 626 00:31:00,080 --> 00:31:02,800 Speaker 1: please check us out next week. Thank you all.