1 00:00:04,760 --> 00:00:08,080 Speaker 1: Welcome to the Bloomberg P and L Podcast. I'm pim Fox. 2 00:00:08,119 --> 00:00:11,200 Speaker 1: Along with my co host Lisa Abramowitz. Each day we 3 00:00:11,280 --> 00:00:14,480 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:14,520 --> 00:00:16,880 Speaker 1: you and your money, whether at the grocery store or 5 00:00:16,920 --> 00:00:20,680 Speaker 1: the trading floor. Find the Bloomberg p L Podcast on iTunes, 6 00:00:20,840 --> 00:00:29,319 Speaker 1: SoundCloud and at Bloomberg dot com pim Fox. It takes 7 00:00:29,360 --> 00:00:31,840 Speaker 1: a lot to survive in the jungle, in particular the 8 00:00:31,880 --> 00:00:34,080 Speaker 1: E t F jungle. I want to bring in my 9 00:00:34,159 --> 00:00:37,000 Speaker 1: colleague Eric Valtunes. He has an E t F analyst 10 00:00:37,040 --> 00:00:41,320 Speaker 1: for Bloomberg Intelligence, and he has great ideas. I love 11 00:00:41,400 --> 00:00:44,680 Speaker 1: talking with Eric. I'm very glad you could join us. Uh. 12 00:00:45,040 --> 00:00:48,320 Speaker 1: You know, we were talking yesterday about a couple of 13 00:00:48,440 --> 00:00:51,440 Speaker 1: moves that Fidelity has has made, uh and black Rock 14 00:00:51,520 --> 00:00:57,840 Speaker 1: has made to cut expenses as passive management takes a hold, 15 00:00:58,080 --> 00:01:02,840 Speaker 1: how much our investors prioritizing the cheapness of funds over 16 00:01:03,240 --> 00:01:07,679 Speaker 1: what they actually offer right now, it's it's everything, uh 17 00:01:07,319 --> 00:01:09,600 Speaker 1: that you know. The phrase I use his expense ratio 18 00:01:09,680 --> 00:01:12,520 Speaker 1: is the new past performance chart. I think this really 19 00:01:12,560 --> 00:01:16,319 Speaker 1: gets down to trust. I think investors just trust a 20 00:01:16,480 --> 00:01:18,880 Speaker 1: fee more than a pretty chart. Right now. And I 21 00:01:18,880 --> 00:01:21,400 Speaker 1: don't know if it's from decades of seeing the chart 22 00:01:21,600 --> 00:01:23,720 Speaker 1: buying into it. It's not working out because a lot 23 00:01:23,760 --> 00:01:25,880 Speaker 1: of times investors will buy in it after the chart 24 00:01:25,920 --> 00:01:28,320 Speaker 1: goes up and you know, it's hard to maintain our performance. 25 00:01:29,080 --> 00:01:31,720 Speaker 1: Or it's just this spread of information that's been going 26 00:01:31,760 --> 00:01:34,640 Speaker 1: on for about ten years that the Internet really helped 27 00:01:34,640 --> 00:01:36,720 Speaker 1: to get out there, which is how important fees are 28 00:01:37,240 --> 00:01:40,600 Speaker 1: in your in your final performance. Fees are a big 29 00:01:40,640 --> 00:01:43,679 Speaker 1: contributor and the one thing you can control. So give 30 00:01:43,760 --> 00:01:45,520 Speaker 1: us a sense of just how much more money has 31 00:01:45,520 --> 00:01:47,840 Speaker 1: gone to the cheapest fees versus the most expensive. I'll 32 00:01:47,840 --> 00:01:50,760 Speaker 1: throw out a few stats here. So last year, six 33 00:01:50,760 --> 00:01:53,440 Speaker 1: billion dollars went into ETFs. Over half of that money 34 00:01:53,480 --> 00:01:56,800 Speaker 1: went to products charging nine basis points or less UM. 35 00:01:56,880 --> 00:01:59,960 Speaker 1: So that's once that so basically free, basically free. Yeah, 36 00:02:00,040 --> 00:02:02,640 Speaker 1: I mean if you look at UM I've did this 37 00:02:02,880 --> 00:02:05,400 Speaker 1: study where I you know, put everything into different buckets. 38 00:02:05,680 --> 00:02:08,359 Speaker 1: It's going in active mutual funds. Funds that charge less 39 00:02:08,360 --> 00:02:11,440 Speaker 1: than forty basis points that are active saw inflows d 40 00:02:11,560 --> 00:02:14,600 Speaker 1: F A Vanguard or two big issuers in that area. 41 00:02:15,080 --> 00:02:17,880 Speaker 1: Index funds that charge less than twenty saw more inflows 42 00:02:17,880 --> 00:02:20,840 Speaker 1: than once charged forty So even within index funds, which 43 00:02:20,840 --> 00:02:23,480 Speaker 1: are already passive, there's the cost migration. So I think 44 00:02:23,520 --> 00:02:25,960 Speaker 1: when you look at the we put the flow every 45 00:02:26,080 --> 00:02:29,280 Speaker 1: mutual fund, share class and e T F on a 46 00:02:29,280 --> 00:02:32,920 Speaker 1: big spreadsheet, divided into buckets by expense ratios, and we 47 00:02:33,000 --> 00:02:35,800 Speaker 1: found that by the vast majority of money goes to 48 00:02:35,919 --> 00:02:38,880 Speaker 1: zero to ten basis points, then the next is ten 49 00:02:38,919 --> 00:02:42,040 Speaker 1: to twenty, and it cascades down to about forty. Then 50 00:02:42,080 --> 00:02:45,839 Speaker 1: at forty six it starts going down until plus ninety 51 00:02:45,919 --> 00:02:49,799 Speaker 1: is the biggest outflow. So there's definitely a big correlation 52 00:02:49,919 --> 00:02:52,200 Speaker 1: between the how much you charge and the flows you're seeing. 53 00:02:53,040 --> 00:02:55,640 Speaker 1: Are the e t F issuers as well as the 54 00:02:55,680 --> 00:02:58,600 Speaker 1: brokerage firms. Are they victims of their own success? And 55 00:02:58,680 --> 00:03:01,120 Speaker 1: how many people does it to run a business that 56 00:03:01,240 --> 00:03:04,240 Speaker 1: is going in this direction? Right? Good question. I have 57 00:03:04,280 --> 00:03:06,359 Speaker 1: a piece out today that looks at the fact that 58 00:03:06,720 --> 00:03:09,080 Speaker 1: you need human beings to do this. You also need 59 00:03:09,160 --> 00:03:12,200 Speaker 1: human beings to analyze ETFs. It's not all robots, but 60 00:03:12,639 --> 00:03:16,320 Speaker 1: I do think it's gonna definitely put some pressure on 61 00:03:16,800 --> 00:03:20,160 Speaker 1: the operations and the human element of running an asset manager. 62 00:03:20,639 --> 00:03:22,320 Speaker 1: We've already seen that. I mean, you wrote a story 63 00:03:22,360 --> 00:03:25,480 Speaker 1: just yesterday calling, uh, you know, with the situation of 64 00:03:25,560 --> 00:03:27,680 Speaker 1: pressure cooker for asset managers, and I think that's a 65 00:03:27,680 --> 00:03:31,560 Speaker 1: pretty good term. So they're probably gonna have to do 66 00:03:31,639 --> 00:03:34,440 Speaker 1: some things you saw Janice. I think this is the biggest, 67 00:03:34,440 --> 00:03:38,360 Speaker 1: biggest example, Janice got together with Henderson. So I think 68 00:03:38,360 --> 00:03:40,320 Speaker 1: you're gonna see more and more companies hooking up with 69 00:03:40,360 --> 00:03:43,080 Speaker 1: other companies to get big enough so they can lower 70 00:03:43,120 --> 00:03:45,200 Speaker 1: their fees and get scale going. And that will be 71 00:03:45,280 --> 00:03:47,320 Speaker 1: the new game for the next decade. All right, So 72 00:03:47,600 --> 00:03:51,400 Speaker 1: you see this migration toward passive, it doesn't this sort 73 00:03:51,440 --> 00:03:55,040 Speaker 1: of raise alarms that people are going at the wrong time. 74 00:03:55,080 --> 00:03:57,640 Speaker 1: I mean, the more people go into passive, the more 75 00:03:57,640 --> 00:04:01,960 Speaker 1: opportunities there ought to be for active investors to outperform. Now, 76 00:04:02,760 --> 00:04:05,200 Speaker 1: yes and no. So the fact that everybody's going into 77 00:04:05,200 --> 00:04:09,560 Speaker 1: passive has helped lift assets in the same indexes. So 78 00:04:09,680 --> 00:04:11,800 Speaker 1: right now, it's actually helping that there's this sort of 79 00:04:12,000 --> 00:04:13,560 Speaker 1: call we'll call it a mini bubble. Right a lot 80 00:04:13,560 --> 00:04:15,800 Speaker 1: of people are going into passive. It's lifting up stocks 81 00:04:15,840 --> 00:04:17,799 Speaker 1: in the SMP. In other words, it's a self fulfilling 82 00:04:17,800 --> 00:04:20,279 Speaker 1: prophecy because the more people go in, the more stocks rise, 83 00:04:20,320 --> 00:04:22,480 Speaker 1: and then they see good performance and they keep going in. 84 00:04:23,320 --> 00:04:26,839 Speaker 1: You talk to really smart managers that know their factors 85 00:04:26,880 --> 00:04:29,320 Speaker 1: back and forth. They claim that I'm just gonna sit 86 00:04:29,360 --> 00:04:32,400 Speaker 1: in value and wait till this little mini passive bubble 87 00:04:32,400 --> 00:04:34,920 Speaker 1: pops and I'll then I'll be the hero. The question 88 00:04:35,000 --> 00:04:37,640 Speaker 1: is when, because remember this isn't just the move to passive, 89 00:04:37,640 --> 00:04:40,080 Speaker 1: it's a cost migration. Just so happens that the cheapest 90 00:04:40,080 --> 00:04:43,800 Speaker 1: stuff is you know, index based, so passive is drawing 91 00:04:43,839 --> 00:04:46,039 Speaker 1: those assets. The quint is when will this all play out? 92 00:04:46,120 --> 00:04:49,960 Speaker 1: Right now, passive investments only own about twelve percent of 93 00:04:50,000 --> 00:04:53,279 Speaker 1: the stock market, so the majority is still in other things, 94 00:04:53,360 --> 00:04:56,440 Speaker 1: mostly active that so this pendulum could swing a lot further. 95 00:04:56,640 --> 00:04:58,960 Speaker 1: So if you're waiting, how long do you wait until 96 00:04:58,960 --> 00:05:01,440 Speaker 1: this happens? The second thing a smart Beta. You just 97 00:05:01,440 --> 00:05:03,400 Speaker 1: made the sales pitch for smart Beta rob or not? 98 00:05:03,560 --> 00:05:05,839 Speaker 1: He says, why would you want to buy an index 99 00:05:05,839 --> 00:05:08,200 Speaker 1: where they reward price and they give more waiting to 100 00:05:08,279 --> 00:05:11,080 Speaker 1: stuff that's bigger and pricier. You should come with me. 101 00:05:11,240 --> 00:05:14,560 Speaker 1: I actually put higher weightings and value things that are cheaper. 102 00:05:14,960 --> 00:05:17,080 Speaker 1: So smart Beta has funneled some money for people who 103 00:05:17,080 --> 00:05:19,680 Speaker 1: are investing based on what you just said, but still 104 00:05:19,760 --> 00:05:23,520 Speaker 1: using passive products to try to reward stocks that are 105 00:05:23,560 --> 00:05:26,039 Speaker 1: trading cheaper or or in a momentum spurt. That's the 106 00:05:26,040 --> 00:05:28,200 Speaker 1: whole smart beta sales pitch. You know, as you talk, 107 00:05:28,320 --> 00:05:31,359 Speaker 1: I just keep thinking, how much does passive have to 108 00:05:31,400 --> 00:05:34,520 Speaker 1: own before they've become a dominant force in a market? 109 00:05:34,560 --> 00:05:36,280 Speaker 1: And I think this is one big question a lot 110 00:05:36,320 --> 00:05:39,280 Speaker 1: of people are analyzing. There's not really a good answer 111 00:05:39,360 --> 00:05:42,320 Speaker 1: to it. You might say that there is, but well, 112 00:05:42,920 --> 00:05:45,000 Speaker 1: well we had an event. We had John Vogel he 113 00:05:45,040 --> 00:05:47,920 Speaker 1: said it could be nine before it's a problem. Wow. 114 00:05:48,520 --> 00:05:51,880 Speaker 1: Thanks very much. Eric Balcunis, he is an expert when 115 00:05:51,920 --> 00:05:54,040 Speaker 1: it comes to E t F. He's our senior et 116 00:05:54,480 --> 00:05:58,240 Speaker 1: F analyst for Bloomberg Intelligence and you can follow him 117 00:05:58,320 --> 00:06:13,680 Speaker 1: on Twitter at Eric Valtunist. Well, when we want to 118 00:06:13,720 --> 00:06:16,599 Speaker 1: know more about municipal bonds, we call on one person, 119 00:06:16,680 --> 00:06:19,560 Speaker 1: Joe Maisak. He is editor of Bloomberg Briefs for the 120 00:06:19,640 --> 00:06:22,520 Speaker 1: municipal market and he joins us. Now, Joe, thanks very 121 00:06:22,600 --> 00:06:25,920 Speaker 1: much for being here. Let's talk about municipal bonds and 122 00:06:26,080 --> 00:06:29,200 Speaker 1: what may or may not happen to the municipal bond market. 123 00:06:29,520 --> 00:06:32,720 Speaker 1: If the Fedow Reserve A deigns to increase interest rates 124 00:06:32,720 --> 00:06:36,960 Speaker 1: twenty five basis points in a couple of weeks, tons 125 00:06:37,120 --> 00:06:41,120 Speaker 1: of uh of actually good things follow on the hills 126 00:06:41,160 --> 00:06:45,800 Speaker 1: of of higher yields PIM. If we see the triple 127 00:06:45,880 --> 00:06:49,680 Speaker 1: a ten year ago to maybe four percent, I see 128 00:06:50,240 --> 00:06:54,719 Speaker 1: things like insurance rising. Uh, there will be the muni 129 00:06:54,800 --> 00:06:58,800 Speaker 1: junk bottle of revive swaps will come back. The auction 130 00:06:58,880 --> 00:07:03,480 Speaker 1: rate securities market, which has been sort of dead for um, 131 00:07:04,440 --> 00:07:09,560 Speaker 1: you know, since two thousand seven, that will probably revive. Um, 132 00:07:09,720 --> 00:07:13,640 Speaker 1: We're probably going to see a little help for public pensions. 133 00:07:13,760 --> 00:07:16,120 Speaker 1: And why don't I toss in a little bit uh 134 00:07:16,600 --> 00:07:20,000 Speaker 1: more stadium construction, uh to sort of go hand in 135 00:07:20,040 --> 00:07:23,480 Speaker 1: hand with muni junk. And right now the returns are 136 00:07:24,000 --> 00:07:27,680 Speaker 1: pretty good, right, muni bond returns in February, we're the 137 00:07:27,840 --> 00:07:33,120 Speaker 1: highest since two tho correct, Yes, and uh, you know 138 00:07:34,080 --> 00:07:36,880 Speaker 1: it's still you know, these are the yields have been 139 00:07:36,960 --> 00:07:42,800 Speaker 1: solo for so long. Uh, it's it's almost incredible how 140 00:07:42,880 --> 00:07:45,280 Speaker 1: flat the market has been about two and a half 141 00:07:45,320 --> 00:07:47,760 Speaker 1: percent and ten years. Okay, So one place that has 142 00:07:47,800 --> 00:07:51,240 Speaker 1: not been flat is with Puerto Rico municipal bonds. And 143 00:07:51,280 --> 00:07:53,280 Speaker 1: we did get some news this week with the new 144 00:07:53,320 --> 00:07:58,680 Speaker 1: governor ra Cio. We're proposing a fiscal plan that included, 145 00:07:59,400 --> 00:08:02,440 Speaker 1: uh it was it, coverage of about two thirds of 146 00:08:03,040 --> 00:08:06,760 Speaker 1: bond payments over the next I don't know, nine years 147 00:08:06,800 --> 00:08:09,640 Speaker 1: or so, a little little less than that. Uh, he 148 00:08:10,240 --> 00:08:14,000 Speaker 1: said about one point two billion. I think the control Board, 149 00:08:14,040 --> 00:08:18,600 Speaker 1: the oversight, the Federal Oversight Committee, Uh, you know, they 150 00:08:18,640 --> 00:08:23,360 Speaker 1: see him paying maybe eight million. The kind of surprising 151 00:08:23,440 --> 00:08:26,960 Speaker 1: thing is he also asked for a complete stay on 152 00:08:27,000 --> 00:08:29,760 Speaker 1: any litigation to be extended to the end of the year. 153 00:08:30,200 --> 00:08:34,040 Speaker 1: So bond holders are sort of up in arms about that. Yeah, 154 00:08:34,040 --> 00:08:37,720 Speaker 1: but they don't seem to care. I think they didn't 155 00:08:37,760 --> 00:08:41,520 Speaker 1: really respond much to this, did they. But the bond prices, Yeah, 156 00:08:41,559 --> 00:08:43,280 Speaker 1: the fact that, like, you know, all of a sudden, 157 00:08:43,320 --> 00:08:47,120 Speaker 1: the amount that's covered under this fiscal plan is completely 158 00:08:47,120 --> 00:08:51,120 Speaker 1: insufficient for what for to cover their losses, and people 159 00:08:51,160 --> 00:08:53,200 Speaker 1: are just sort of like, yeah, whatever, you know, it's 160 00:08:53,480 --> 00:08:57,000 Speaker 1: right now. They're going to enter the negotiation period with 161 00:08:57,040 --> 00:08:59,600 Speaker 1: the governor. And it's the surprising thing about the governors. 162 00:08:59,600 --> 00:09:03,280 Speaker 1: He can panned as the uh as the man who's 163 00:09:03,320 --> 00:09:07,040 Speaker 1: going to repay the debt, and now that he's in office, 164 00:09:07,840 --> 00:09:12,400 Speaker 1: repaying the debt is a lot less appetizing. Shall we say, 165 00:09:12,480 --> 00:09:14,200 Speaker 1: can we talk about some numbers, because I was just 166 00:09:14,200 --> 00:09:18,440 Speaker 1: looking at a comparison. California, which actually went re entered 167 00:09:18,520 --> 00:09:21,960 Speaker 1: the green muni market, uh this week with an issuance 168 00:09:22,280 --> 00:09:26,560 Speaker 1: California for a ten year averages two point six seven percent. 169 00:09:27,000 --> 00:09:30,920 Speaker 1: That's obviously triple tax free. New York State two point 170 00:09:31,000 --> 00:09:34,280 Speaker 1: three one percent, and then you've got these outliers Illinois 171 00:09:34,360 --> 00:09:38,760 Speaker 1: four point five seven percent. Tell us a little bit 172 00:09:38,760 --> 00:09:43,160 Speaker 1: about the volume of issuance, because supply also affects the price. 173 00:09:44,160 --> 00:09:47,599 Speaker 1: You know, this year we started off first year, I 174 00:09:47,640 --> 00:09:51,200 Speaker 1: think January was around thirty billion. We're probably up to 175 00:09:51,960 --> 00:09:56,280 Speaker 1: over the first two months maybe fifty five billion or so. 176 00:09:56,720 --> 00:09:59,960 Speaker 1: And this is off a little bit. Of course, last 177 00:10:00,040 --> 00:10:02,920 Speaker 1: you was a record pace. We had forward six billion, 178 00:10:03,240 --> 00:10:05,440 Speaker 1: So this year we're off a little bit. It's a 179 00:10:05,440 --> 00:10:09,960 Speaker 1: little slack, however, you know, you bring this up and 180 00:10:10,320 --> 00:10:12,959 Speaker 1: next week, all of a sudden, we see a whole 181 00:10:13,080 --> 00:10:16,720 Speaker 1: batch of state general obligation bonds, including two point four 182 00:10:16,760 --> 00:10:21,120 Speaker 1: billion from California. So the states seemed to be looking 183 00:10:21,120 --> 00:10:25,440 Speaker 1: to take advantage of maybe the last shot at rates 184 00:10:25,480 --> 00:10:30,520 Speaker 1: this low. One reason why mini bonds initially sold off 185 00:10:30,640 --> 00:10:33,880 Speaker 1: after President Trump was elected was because people were ratcheting 186 00:10:34,120 --> 00:10:37,280 Speaker 1: uh back their expectations for how high their taxes might 187 00:10:37,320 --> 00:10:41,199 Speaker 1: go and expect expecting some tax relief. This basically gives 188 00:10:41,360 --> 00:10:44,400 Speaker 1: less of a benefit to municipal bonds, which are tax free. 189 00:10:44,840 --> 00:10:48,000 Speaker 1: Now that we really have not seen a tax plan, 190 00:10:48,559 --> 00:10:51,280 Speaker 1: uh and we don't know how soon it will be implemented. 191 00:10:52,000 --> 00:10:53,640 Speaker 1: Is that part of what's bringing people back to the 192 00:10:53,640 --> 00:10:56,960 Speaker 1: mini market too. Well, they have to put their money 193 00:10:56,960 --> 00:11:02,320 Speaker 1: to work, and you have, uh, you know, tax concerns, um. 194 00:11:02,360 --> 00:11:06,240 Speaker 1: But you know, the whole tax picture I think has 195 00:11:06,280 --> 00:11:09,520 Speaker 1: sort of been put on the back burner for a while. 196 00:11:09,600 --> 00:11:12,839 Speaker 1: Like you say, it's was something that instead of coming 197 00:11:12,880 --> 00:11:15,560 Speaker 1: here in June, July or August, which is what the 198 00:11:15,559 --> 00:11:18,720 Speaker 1: administration has talked about, most people really think it's going 199 00:11:18,800 --> 00:11:20,960 Speaker 1: to be very late in the year, probably more like 200 00:11:21,200 --> 00:11:25,040 Speaker 1: next year. The administration has a lot uh to worry 201 00:11:25,040 --> 00:11:28,640 Speaker 1: about between now and then, especially the Healthcare Act. Thanks 202 00:11:28,720 --> 00:11:31,120 Speaker 1: very much for joining us. Joe Maisak. He is the 203 00:11:31,240 --> 00:11:35,880 Speaker 1: editor of Bloomberg Briefs on Municipal Markets. Always enjoyed listening 204 00:11:35,920 --> 00:11:37,800 Speaker 1: to you, and of course meanings We're gonna have to 205 00:11:37,800 --> 00:11:40,160 Speaker 1: wait and see what the foto reserved does. And maybe 206 00:11:40,200 --> 00:11:43,240 Speaker 1: this will increase the issuance of municipal bands well. And 207 00:11:43,280 --> 00:11:46,000 Speaker 1: this might actually help with the whole idea of infrastructure 208 00:11:46,040 --> 00:11:50,000 Speaker 1: spending uh, as President Trump has proposed, perhaps by a 209 00:11:50,080 --> 00:12:04,800 Speaker 1: sort of a back channel with private money pim fox. 210 00:12:04,880 --> 00:12:08,640 Speaker 1: There is so much data that is available today. There 211 00:12:08,640 --> 00:12:11,000 Speaker 1: are some good statistics about just how much the n 212 00:12:11,080 --> 00:12:15,079 Speaker 1: s A has, for example, compared to prior administrations during 213 00:12:15,120 --> 00:12:17,280 Speaker 1: the communist era, when people thought, you know, oh my gosh, 214 00:12:17,280 --> 00:12:19,320 Speaker 1: the government has such a handle on what everyone is doing. 215 00:12:19,760 --> 00:12:21,640 Speaker 1: But now we have so much data that we need 216 00:12:21,679 --> 00:12:26,280 Speaker 1: to rely on algorithms to sort through it and find patterns. Uh. 217 00:12:26,360 --> 00:12:30,960 Speaker 1: But are these algorithms really better at sifting through the data, 218 00:12:31,000 --> 00:12:34,240 Speaker 1: albeit the massive tropes that we have, and coming up 219 00:12:34,280 --> 00:12:37,160 Speaker 1: with conclusions and humans? I want to bring in Kathy 220 00:12:37,240 --> 00:12:42,080 Speaker 1: O'Neill Bloomberg View columnists h and also a mathematician who 221 00:12:42,080 --> 00:12:45,760 Speaker 1: has been a professor, hedge fund analyst and data scientist, 222 00:12:46,120 --> 00:12:48,560 Speaker 1: and she wrote a fabulous column about how in the 223 00:12:48,600 --> 00:12:53,080 Speaker 1: world of big data, more isn't always better. Kathy uh, 224 00:12:53,480 --> 00:12:56,720 Speaker 1: first of all. Just to put this into perspective, just 225 00:12:56,920 --> 00:13:02,880 Speaker 1: how prevalent are algorithms in decision making processes throughout the 226 00:13:02,920 --> 00:13:06,640 Speaker 1: economy at this point. How dependent are we upon them? 227 00:13:06,640 --> 00:13:09,400 Speaker 1: That's a great question, um. And people don't really realize 228 00:13:09,400 --> 00:13:11,520 Speaker 1: this because a lot of the algorithms are actually happening 229 00:13:11,520 --> 00:13:13,680 Speaker 1: behind the scenes and we can't even see them. But 230 00:13:13,720 --> 00:13:17,120 Speaker 1: it turns out algorithms are being used on basically every 231 00:13:17,160 --> 00:13:19,880 Speaker 1: important decision in somebody's life, every time they have an option, 232 00:13:19,920 --> 00:13:22,640 Speaker 1: and they're sort of competing with other people. So that 233 00:13:22,720 --> 00:13:25,600 Speaker 1: means you know, college admissions, getting a job, even while 234 00:13:25,640 --> 00:13:28,640 Speaker 1: you're on the job, how you're being evaluated, how much 235 00:13:28,679 --> 00:13:31,400 Speaker 1: you pay for insurance, you know, how much you you 236 00:13:31,559 --> 00:13:33,959 Speaker 1: what kind of ap are you get for credit? Um. 237 00:13:34,000 --> 00:13:36,640 Speaker 1: Even things like policing and how long you're going to 238 00:13:36,760 --> 00:13:40,480 Speaker 1: jail if you get if you if you're guilty. Um, 239 00:13:40,520 --> 00:13:43,280 Speaker 1: those are all determined by algorithms. Never mind all the 240 00:13:43,320 --> 00:13:45,920 Speaker 1: things that are happening online of course, and the political 241 00:13:46,000 --> 00:13:48,920 Speaker 1: micro targeting, all the ads you see on politics, which 242 00:13:49,160 --> 00:13:53,760 Speaker 1: or are all algorithmically defined. So yeah, it's it's absolutely everywhere. Um. 243 00:13:53,800 --> 00:13:57,880 Speaker 1: And what's especially concerning is the moments like when you're 244 00:13:57,880 --> 00:13:59,640 Speaker 1: trying to get a job and you send your resume 245 00:13:59,760 --> 00:14:03,720 Speaker 1: in and there are algorithms that filter your resume based 246 00:14:03,760 --> 00:14:06,120 Speaker 1: on you know, used to be just keyword searches, like 247 00:14:06,160 --> 00:14:08,480 Speaker 1: what kind of words did you say on your resume? 248 00:14:08,559 --> 00:14:11,000 Speaker 1: But now it's all sorts of other kinds of correlative 249 00:14:11,200 --> 00:14:14,120 Speaker 1: information about your resume and you will never know that. 250 00:14:14,240 --> 00:14:17,040 Speaker 1: Just to be clear, like, you'll either get a callback 251 00:14:17,160 --> 00:14:18,840 Speaker 1: or you won't. But if you don't get a callback, 252 00:14:18,880 --> 00:14:21,600 Speaker 1: you won't know why, and it might be because of 253 00:14:21,600 --> 00:14:23,880 Speaker 1: an algorithm. I wonder if you can give us an 254 00:14:23,880 --> 00:14:28,520 Speaker 1: example of an algorithm that's gone wrong. Yeah, well, I 255 00:14:28,520 --> 00:14:31,680 Speaker 1: actually wrote a book about this called Weapons of Mass Destruction. UM. 256 00:14:32,840 --> 00:14:35,120 Speaker 1: Very good book. I did read it. Oh, oh, thank 257 00:14:35,120 --> 00:14:38,280 Speaker 1: you so much, FIM. UM. So I would say, a 258 00:14:38,320 --> 00:14:42,520 Speaker 1: lot of algorithms go wrong, um for the people they 259 00:14:42,520 --> 00:14:45,320 Speaker 1: are targeted, but not necessarily for the people that build 260 00:14:45,320 --> 00:14:47,760 Speaker 1: the algorithms. So to be clear, UM, one of the 261 00:14:47,920 --> 00:14:51,120 Speaker 1: main points of the book is that, UM, what you 262 00:14:51,160 --> 00:14:53,520 Speaker 1: know It depends on this on your perspective on whether 263 00:14:53,560 --> 00:14:56,160 Speaker 1: something's going wrong. But one of the one of the 264 00:14:56,160 --> 00:14:58,760 Speaker 1: examples of my book, UM, it comes from the world 265 00:14:58,800 --> 00:15:03,480 Speaker 1: of education. UM. In their teacher assessment algorithms, and teachers 266 00:15:03,480 --> 00:15:06,400 Speaker 1: are basically being scored between zero and a hundred UM. 267 00:15:06,520 --> 00:15:09,640 Speaker 1: And I think that is probably the best example of 268 00:15:09,640 --> 00:15:12,920 Speaker 1: a terrible algorithm, because it's very inconsistent. I found a 269 00:15:12,920 --> 00:15:15,400 Speaker 1: teacher who got a ninety six one year and a 270 00:15:15,520 --> 00:15:19,040 Speaker 1: six another year UM. And another teacher got fired for 271 00:15:19,080 --> 00:15:21,480 Speaker 1: a bad score, even though she thinks that her score 272 00:15:21,560 --> 00:15:24,960 Speaker 1: was artificially low because other teachers cheating. UM. And there's 273 00:15:25,120 --> 00:15:27,560 Speaker 1: I mean, I think the critical point about these algorithms 274 00:15:27,640 --> 00:15:30,119 Speaker 1: isn't that they're bad, because of course there's bad algorithms 275 00:15:30,120 --> 00:15:32,560 Speaker 1: out there. The critical point is that they're being used 276 00:15:32,600 --> 00:15:36,760 Speaker 1: as if they're scientifically they have scientific authority. UM. So 277 00:15:36,800 --> 00:15:40,720 Speaker 1: people trust them in a kind of under an overexaggerated 278 00:15:40,760 --> 00:15:43,600 Speaker 1: way because of their mathematical nature. And so that's one 279 00:15:43,640 --> 00:15:45,440 Speaker 1: of the things I was trying to get go after 280 00:15:45,560 --> 00:15:48,600 Speaker 1: in my book. As a mathematician, I don't want people 281 00:15:48,600 --> 00:15:51,880 Speaker 1: to blindly trust mathematics. But the point of mathematics is 282 00:15:51,880 --> 00:15:54,720 Speaker 1: that it's actually supposed to clarify things, not obfuscate them. 283 00:15:54,840 --> 00:16:00,320 Speaker 1: Well to that point, how can companies and universe these 284 00:16:00,440 --> 00:16:04,320 Speaker 1: make sure that their algorithms are doing the right thing? 285 00:16:04,360 --> 00:16:06,320 Speaker 1: I mean, what's the check here on how to make 286 00:16:06,320 --> 00:16:08,720 Speaker 1: the algorithms better. The answer isn't necessarily for a human 287 00:16:08,760 --> 00:16:11,080 Speaker 1: being to be trying to sift through all the data themselves, 288 00:16:11,320 --> 00:16:15,280 Speaker 1: because at this point it's that's an unsustainable solution given 289 00:16:15,280 --> 00:16:17,760 Speaker 1: the amount of data that a lot of these algorithms 290 00:16:17,800 --> 00:16:22,320 Speaker 1: are tasked with the processing. Absolutely true, and I'm not 291 00:16:22,720 --> 00:16:25,560 Speaker 1: I am not anti algorithm whatsoever. What I'm trying to 292 00:16:26,080 --> 00:16:29,880 Speaker 1: um suggest um is that we create standards of evidence 293 00:16:30,640 --> 00:16:34,600 Speaker 1: that the algorithms are meaningful, that they have statistical meaning, 294 00:16:35,000 --> 00:16:38,680 Speaker 1: but they're also that they're fair, and they're they're legal. Um. 295 00:16:38,720 --> 00:16:41,280 Speaker 1: A lot of the algorithms I examined in my book 296 00:16:41,360 --> 00:16:45,160 Speaker 1: actually I think are probably illegal. But because regulators don't 297 00:16:45,200 --> 00:16:48,800 Speaker 1: know how to examine algorithms, um, they're you know, companies 298 00:16:48,840 --> 00:16:52,160 Speaker 1: are getting away with stuff. So in particular with the 299 00:16:52,440 --> 00:16:57,359 Speaker 1: algorithms for hiring, they're kind of replacing their HR divisions 300 00:16:57,440 --> 00:17:00,840 Speaker 1: with algorithms without making sure that all those all the 301 00:17:00,960 --> 00:17:05,440 Speaker 1: algorithms are actually you know, they reflect fair hiring practices, 302 00:17:05,480 --> 00:17:08,080 Speaker 1: which there's plenty of laws around that. So my point 303 00:17:08,200 --> 00:17:11,840 Speaker 1: is like we need to create evidence and to demand 304 00:17:11,880 --> 00:17:14,560 Speaker 1: evidence from the people that build these algorithms and use 305 00:17:14,600 --> 00:17:17,520 Speaker 1: all these algorithms that what they're doing is actually legal 306 00:17:17,560 --> 00:17:21,399 Speaker 1: and fair. A point to you, is it possible that 307 00:17:21,480 --> 00:17:25,240 Speaker 1: what's going on is algorithms are being used to cut 308 00:17:25,320 --> 00:17:29,560 Speaker 1: costs in a corporate setting, and that the reason it's 309 00:17:29,600 --> 00:17:33,400 Speaker 1: being done is because then there is no personal accountability 310 00:17:33,440 --> 00:17:36,440 Speaker 1: for the result, that is, you nailed it on the head, 311 00:17:36,480 --> 00:17:41,160 Speaker 1: Like I observe just many, many examples of these algorithms 312 00:17:41,200 --> 00:17:42,920 Speaker 1: that are what I call weapons of math destruction, and 313 00:17:42,920 --> 00:17:46,200 Speaker 1: they're very powerful, their secret and their destructive. And one 314 00:17:46,240 --> 00:17:48,800 Speaker 1: of the things I noticed sort of after writing the 315 00:17:48,840 --> 00:17:51,520 Speaker 1: book is that there's a kind of certain characteristics of 316 00:17:51,520 --> 00:17:54,040 Speaker 1: a situation that are ripe for these kinds of algorithms. 317 00:17:54,080 --> 00:17:56,359 Speaker 1: And it's exactly what you just said. It's when people 318 00:17:56,400 --> 00:17:59,840 Speaker 1: don't want to take personal responsibility for tricky decisions. Tricky 319 00:17:59,920 --> 00:18:01,840 Speaker 1: is asians like is this a good teacher or not? 320 00:18:02,000 --> 00:18:04,040 Speaker 1: Is this a good applicant or not? Those are tricky 321 00:18:04,359 --> 00:18:06,359 Speaker 1: and they know that it can go wrong, and they 322 00:18:06,359 --> 00:18:09,159 Speaker 1: would like to say it's not me, it's the algorithm. 323 00:18:09,160 --> 00:18:11,679 Speaker 1: And that's when the situation is right for for pretty 324 00:18:11,720 --> 00:18:14,919 Speaker 1: nasty things happen. Well, it certainly reminds me of the 325 00:18:14,920 --> 00:18:20,080 Speaker 1: Stanley Kubrick movie, Uh, Doctor Strange Love, because right, I 326 00:18:20,119 --> 00:18:22,160 Speaker 1: mean the doomsday machine. It just takes it takes over, 327 00:18:22,160 --> 00:18:24,320 Speaker 1: and there's no way to really get a human being 328 00:18:24,320 --> 00:18:27,320 Speaker 1: in between that and and the dire consequences. Thank you 329 00:18:27,440 --> 00:18:30,720 Speaker 1: very much. Cathy O'Neil is a Bloomberg View columnists and 330 00:18:31,160 --> 00:18:35,920 Speaker 1: you can follow her on Twitter at math Babe dot org. 331 00:18:36,240 --> 00:18:38,520 Speaker 1: Yes indeed, and she is also the author of the 332 00:18:38,560 --> 00:18:55,359 Speaker 1: book Weapons of Math Destruction. A lot has been said 333 00:18:55,440 --> 00:19:00,080 Speaker 1: about President Trump's immigration policies, particularly as they pertain to 334 00:19:00,240 --> 00:19:03,840 Speaker 1: Mexico and our other southern neighbors. I want to bring 335 00:19:03,880 --> 00:19:06,720 Speaker 1: in someone who could talk more about that. Hector Barretto, 336 00:19:06,800 --> 00:19:10,440 Speaker 1: who is chairman of the Latino Coalition and former US 337 00:19:10,480 --> 00:19:15,320 Speaker 1: Small Business Administrator under George W. Bush. Hector, I'm glad 338 00:19:15,359 --> 00:19:17,119 Speaker 1: you could join us. First, I want to start with 339 00:19:17,840 --> 00:19:22,960 Speaker 1: a question, how different are President Trump's proposed immigration policies 340 00:19:23,000 --> 00:19:27,680 Speaker 1: to the ones that were implemented under former President Barack Obama. 341 00:19:28,280 --> 00:19:31,840 Speaker 1: I think we're saying under Bush or who I worked for. UM, 342 00:19:31,920 --> 00:19:34,960 Speaker 1: you know, the policies are very different. Obviously, every administration 343 00:19:35,000 --> 00:19:36,800 Speaker 1: has a different take on it. We don't know all 344 00:19:36,800 --> 00:19:39,960 Speaker 1: the specifics about what the immigration policy is going to be, 345 00:19:40,320 --> 00:19:44,399 Speaker 1: especially going forward with regards to countries like Mexico. You know, 346 00:19:44,520 --> 00:19:47,240 Speaker 1: I worked for President Bush, and the first thing he 347 00:19:47,280 --> 00:19:50,480 Speaker 1: did when he uh got into office was he put 348 00:19:50,520 --> 00:19:53,960 Speaker 1: forth in an initiative with Mexico called the Partnership for Prosperity. 349 00:19:54,000 --> 00:19:56,520 Speaker 1: We worked on that for eight years while I was 350 00:19:56,960 --> 00:20:01,240 Speaker 1: in the administration. Uh. Less so in the last administration 351 00:20:01,280 --> 00:20:04,399 Speaker 1: in terms of things like the Partnership for Prosperity. Obviously, 352 00:20:04,480 --> 00:20:08,400 Speaker 1: there was never comprehensive immigration reform under the Obama administration, 353 00:20:08,640 --> 00:20:12,800 Speaker 1: and we don't know what the immigration reform proposals are 354 00:20:12,800 --> 00:20:15,399 Speaker 1: going to be uh in in this administration. Do you 355 00:20:15,400 --> 00:20:18,080 Speaker 1: think that immigration policies need to be reformed in the 356 00:20:18,200 --> 00:20:22,080 Speaker 1: last Absolutely? Well, I mean, you know, I think everybody 357 00:20:22,160 --> 00:20:28,159 Speaker 1: stipulates that our immigrations UH system are policies towards immigration 358 00:20:28,440 --> 00:20:31,159 Speaker 1: don't really work for anybody. They don't work for the 359 00:20:31,200 --> 00:20:33,440 Speaker 1: immigrants that are here, they don't work for the economy, 360 00:20:33,640 --> 00:20:36,160 Speaker 1: they don't really work for anybody. So, you know, everybody 361 00:20:36,200 --> 00:20:38,480 Speaker 1: basically says it's a broken system and we need to 362 00:20:38,520 --> 00:20:41,720 Speaker 1: fix it, but nobody can coalesce around what that looks 363 00:20:41,760 --> 00:20:44,200 Speaker 1: like and so that's why we keep kicking this can 364 00:20:44,240 --> 00:20:47,359 Speaker 1: down the road. Remember, we haven't had immigration reform in 365 00:20:47,400 --> 00:20:51,720 Speaker 1: the United States since nineteen six that was President Reagan. 366 00:20:51,960 --> 00:20:54,600 Speaker 1: So we've been thirty years dealing with a lot of 367 00:20:54,600 --> 00:20:56,919 Speaker 1: these issues. And I think a lot of people on 368 00:20:56,960 --> 00:20:59,960 Speaker 1: both sides, they all are very frustrated and and hope 369 00:21:00,040 --> 00:21:03,480 Speaker 1: bowl that we can get something done in this administration. Hector, 370 00:21:03,520 --> 00:21:05,920 Speaker 1: I want to just bring a little bit more detailed 371 00:21:05,960 --> 00:21:09,960 Speaker 1: to the conversation. You previously before your role in a 372 00:21:10,720 --> 00:21:14,560 Speaker 1: business on a large scale, you worked in your family's restaurant, uh, 373 00:21:14,760 --> 00:21:20,720 Speaker 1: import export business, construction accompany, and so on. In US, 374 00:21:20,800 --> 00:21:25,280 Speaker 1: Hispanic buying power was larger than the gross domestic product 375 00:21:25,720 --> 00:21:30,639 Speaker 1: of Mexico and it is growing substantially. Uh, we're talking 376 00:21:30,640 --> 00:21:36,240 Speaker 1: about a buying power of about thirteen point nine trillion dollars, right, 377 00:21:36,280 --> 00:21:42,800 Speaker 1: that's uh, that's the total. Is there a way for 378 00:21:42,880 --> 00:21:46,480 Speaker 1: this immigration plus a I don't know if you want 379 00:21:46,520 --> 00:21:51,400 Speaker 1: to call it immigration, but a nafter revamp, trade policy 380 00:21:51,440 --> 00:21:56,159 Speaker 1: revamp that could actually make that an even more a 381 00:21:56,200 --> 00:21:59,840 Speaker 1: stronger figure. How can it help the business about it? Look, 382 00:22:00,040 --> 00:22:02,480 Speaker 1: you know, I serve in a lot of different capacities. 383 00:22:02,480 --> 00:22:04,760 Speaker 1: I'm also on the board of the US Chamber of Commerce. 384 00:22:04,800 --> 00:22:08,200 Speaker 1: This has been a major, major focus and an initiative 385 00:22:08,240 --> 00:22:12,160 Speaker 1: for us for many, many years. And these things are connected. Uh, 386 00:22:12,240 --> 00:22:14,520 Speaker 1: you know you were talking about the family businesses. You know, 387 00:22:14,560 --> 00:22:17,560 Speaker 1: I'm the son of an immigrant. My father, Hector Bretto Sr. 388 00:22:17,880 --> 00:22:20,639 Speaker 1: Was an immigrant from Mexico in the fifties and he 389 00:22:20,720 --> 00:22:23,200 Speaker 1: was the founder of the U. S Hispanic Chamber of Commerce. 390 00:22:23,440 --> 00:22:27,520 Speaker 1: We've learned about these issues all my life in our family. 391 00:22:27,680 --> 00:22:31,000 Speaker 1: We know how important that they are, and they are interconnected. Look, 392 00:22:31,040 --> 00:22:33,399 Speaker 1: purchasing power in the United States. Just Hispanics in the 393 00:22:33,440 --> 00:22:36,600 Speaker 1: United States is one point five trillion dollars. My father 394 00:22:36,680 --> 00:22:38,920 Speaker 1: used to always say, you know, the Hispanics in the 395 00:22:39,000 --> 00:22:41,800 Speaker 1: United States have more purchasing power than all the the 396 00:22:41,800 --> 00:22:44,280 Speaker 1: the Hispanics in Mexico and a lot of countries in 397 00:22:44,359 --> 00:22:49,120 Speaker 1: Latin America. And and that is going to grow Hispanic business, 398 00:22:49,119 --> 00:22:52,359 Speaker 1: which I'm very passionate. About four million companies that are 399 00:22:52,359 --> 00:22:55,000 Speaker 1: generating close to seven hundred billion in revenues and they 400 00:22:55,000 --> 00:22:57,960 Speaker 1: could double every five years. And many of those have 401 00:22:58,119 --> 00:23:01,399 Speaker 1: connections and linkages back to Mexico and other parts of 402 00:23:01,480 --> 00:23:04,520 Speaker 1: Latin America. So there. You know, there are a lot 403 00:23:04,560 --> 00:23:06,480 Speaker 1: of reasons we should have immigration re form, and there's 404 00:23:06,480 --> 00:23:07,760 Speaker 1: a lot of reasons we had to look at some 405 00:23:07,800 --> 00:23:10,360 Speaker 1: of these trade deals. But the first reason is it's 406 00:23:10,400 --> 00:23:13,760 Speaker 1: in our self interest in this country. And so hopefully 407 00:23:13,840 --> 00:23:16,760 Speaker 1: we're gonna see some leadership coming out of Congress, coming 408 00:23:16,760 --> 00:23:19,080 Speaker 1: out of the White House, and it needs to be bipartisan. 409 00:23:19,119 --> 00:23:21,639 Speaker 1: There's no way, we've learned this over thirty years that 410 00:23:21,720 --> 00:23:24,480 Speaker 1: you can make this kind of change without having buy 411 00:23:24,520 --> 00:23:27,320 Speaker 1: in from both sides. You know, as you talk, it's 412 00:23:27,840 --> 00:23:32,879 Speaker 1: very optimistic view of things, coalition bringing together. It doesn't 413 00:23:32,920 --> 00:23:35,280 Speaker 1: really mesh with some of the rhetoric that we've heard. 414 00:23:35,320 --> 00:23:38,040 Speaker 1: We've heard about building the wall, we've heard about those 415 00:23:38,560 --> 00:23:41,520 Speaker 1: bad ombraise, We've heard about a lot of things from 416 00:23:41,520 --> 00:23:44,520 Speaker 1: President Trump to get out of this country. We've heard 417 00:23:44,560 --> 00:23:47,920 Speaker 1: about ice agents going and rounding people up. I mean, 418 00:23:48,119 --> 00:23:51,320 Speaker 1: are your members scared, Well, there's a lot of people 419 00:23:51,359 --> 00:23:53,880 Speaker 1: that are scared, not just in my community, but there's 420 00:23:53,920 --> 00:23:56,400 Speaker 1: a lot of members in my and my group who 421 00:23:56,400 --> 00:23:59,360 Speaker 1: are also very optimistic. And you know, business people tend 422 00:23:59,400 --> 00:24:02,040 Speaker 1: to be up a mystic. We see the glasses half full. 423 00:24:02,240 --> 00:24:04,680 Speaker 1: We wouldn't go into business if we didn't, if we didn't, 424 00:24:04,680 --> 00:24:06,600 Speaker 1: if we didn't, if we didn't, think that understood. But 425 00:24:06,640 --> 00:24:10,119 Speaker 1: do you think that as as such? Right now? Are 426 00:24:10,359 --> 00:24:15,200 Speaker 1: people worried about their businesses getting harmed because of exporting 427 00:24:15,800 --> 00:24:20,119 Speaker 1: power to Mexico and a possible deterioration of the relationship 428 00:24:20,200 --> 00:24:23,200 Speaker 1: the US has with Mexico. Look, you probably know this 429 00:24:23,480 --> 00:24:26,800 Speaker 1: is that small business companies in the United States is up, 430 00:24:26,920 --> 00:24:29,640 Speaker 1: you know, almost at historic highs right now. And it's 431 00:24:29,640 --> 00:24:32,119 Speaker 1: not historic highs because they feel that their businesses are 432 00:24:32,119 --> 00:24:34,320 Speaker 1: going to be failing. They felt that over the last 433 00:24:34,400 --> 00:24:36,720 Speaker 1: eight years, they felt that were all these barriers. But 434 00:24:36,760 --> 00:24:39,400 Speaker 1: when they hear the administration saying, hey, look we get it. 435 00:24:39,640 --> 00:24:41,600 Speaker 1: We've got a lower regulations on you, we've got a 436 00:24:41,640 --> 00:24:44,000 Speaker 1: lower taxes on You've gotta make healthcare easier for you 437 00:24:44,040 --> 00:24:46,960 Speaker 1: to get. And yes, we've got to renegotiate trade deals 438 00:24:47,000 --> 00:24:49,480 Speaker 1: because you know what small businesses are the ones that 439 00:24:49,520 --> 00:24:53,520 Speaker 1: don't participate in trade deals. Were of all the companies 440 00:24:53,520 --> 00:24:56,000 Speaker 1: that do international trade, and we're less than thirty percent 441 00:24:56,040 --> 00:24:58,280 Speaker 1: of the trade dollars and that's not lost on those 442 00:24:58,280 --> 00:25:01,679 Speaker 1: twenty seven million small businesses. So you know, uh, we 443 00:25:01,760 --> 00:25:04,000 Speaker 1: hear a lot of things, and it goes back and forth. 444 00:25:04,320 --> 00:25:07,320 Speaker 1: We're trying to focus on the things that are actually happening, 445 00:25:07,320 --> 00:25:09,719 Speaker 1: the real things that are happening, not the stuff that 446 00:25:09,760 --> 00:25:12,560 Speaker 1: you know gets scandalized. And every day there's a there's 447 00:25:12,560 --> 00:25:14,840 Speaker 1: a new threat to to the world as we know it. 448 00:25:15,119 --> 00:25:17,280 Speaker 1: I mean, we're gonna be paying very close attention and 449 00:25:17,320 --> 00:25:19,560 Speaker 1: we're gonna be calling balls and strikes. We're not going 450 00:25:19,600 --> 00:25:22,320 Speaker 1: to agree with everything that comes out of Washington, d C. 451 00:25:22,880 --> 00:25:25,479 Speaker 1: But where we can, where we can help create that 452 00:25:25,640 --> 00:25:28,679 Speaker 1: environment so we can start more small businesses instead of 453 00:25:28,680 --> 00:25:31,359 Speaker 1: having them fail as they have over the last eight years. 454 00:25:31,640 --> 00:25:34,199 Speaker 1: We're very interested and focused on that. We have to 455 00:25:34,240 --> 00:25:36,159 Speaker 1: have a seat at the table. We have to be 456 00:25:36,240 --> 00:25:39,280 Speaker 1: speaking to the people that are in power. Not everybody 457 00:25:39,280 --> 00:25:41,960 Speaker 1: agrees and not ever. That's why we have elections. But 458 00:25:42,119 --> 00:25:45,359 Speaker 1: after the election is over, it would be irresponsible and 459 00:25:45,400 --> 00:25:48,200 Speaker 1: we think mal practice for us to just to check 460 00:25:48,200 --> 00:25:50,720 Speaker 1: out for the next four years. Hector, I want to 461 00:25:50,720 --> 00:25:52,840 Speaker 1: thank you very much for coming in and sharing your 462 00:25:52,880 --> 00:25:55,159 Speaker 1: thoughts with us. So this is a very important topic. 463 00:25:55,480 --> 00:25:59,159 Speaker 1: Hector Barretto is the chairman of the Latino Coalition. They 464 00:25:59,160 --> 00:26:04,080 Speaker 1: are based in uh Irvine, California, and in Washington, do 465 00:26:04,160 --> 00:26:05,959 Speaker 1: you see him? Well, of course, and I'm sure you're 466 00:26:05,960 --> 00:26:07,560 Speaker 1: there quite a bit these days. We are. We have 467 00:26:07,600 --> 00:26:10,880 Speaker 1: a major event next week, so thank you. Having'll give 468 00:26:10,880 --> 00:26:12,760 Speaker 1: you ten seconds tell us about it. We're having a 469 00:26:12,760 --> 00:26:16,680 Speaker 1: policy event Latino Coalition. Sp administrators coming over the White 470 00:26:16,680 --> 00:26:19,160 Speaker 1: House at a very high level will be participating. We've 471 00:26:19,160 --> 00:26:21,560 Speaker 1: got a half a dozen Congressmen. And if you're in 472 00:26:21,600 --> 00:26:24,800 Speaker 1: Washington and interested in attending a small business event with 473 00:26:24,880 --> 00:26:27,399 Speaker 1: the fastest growing segment of small business, you gotta be 474 00:26:27,400 --> 00:26:30,119 Speaker 1: at the Latino Coalition next Thursday in Washington, d C. 475 00:26:30,359 --> 00:26:32,119 Speaker 1: All right, see there, we give we give you the 476 00:26:32,160 --> 00:26:34,879 Speaker 1: little plug. That's good thing for you, you know, at 477 00:26:34,920 --> 00:26:37,840 Speaker 1: least excuse me, I was noting that. You know, Mexico 478 00:26:38,480 --> 00:26:41,280 Speaker 1: is no longer the top origin country among the most 479 00:26:41,359 --> 00:26:45,520 Speaker 1: recent immigrants to the United States. China and India have 480 00:26:45,680 --> 00:26:51,439 Speaker 1: overtaken Mexico as the most common countries of origin. Hector Burrado, 481 00:26:51,520 --> 00:26:54,800 Speaker 1: thank you very much once again, Chairman of the Latino Coalition. 482 00:27:01,000 --> 00:27:03,879 Speaker 1: Thanks for listening to the Bloomberg pien L podcast. You 483 00:27:03,920 --> 00:27:08,560 Speaker 1: can subscribe and listen to interviews at iTunes, SoundCloud or whatever. 484 00:27:08,840 --> 00:27:12,360 Speaker 1: Podcast platform you prefer. I'm pim Fox. I'm out there 485 00:27:12,359 --> 00:27:15,399 Speaker 1: on Twitter at pim Fox. I'm out there on Twitter 486 00:27:15,520 --> 00:27:18,479 Speaker 1: at Lisa Abramo. It's One before the Podcast. You can 487 00:27:18,520 --> 00:27:21,000 Speaker 1: always catch us worldwide on Bloomberg Radio