1 00:00:00,240 --> 00:00:07,200 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,080 --> 00:00:11,920 Speaker 2: Just over five years ago, the onset of the COVID 3 00:00:12,000 --> 00:00:16,080 Speaker 2: nineteen pandemic brought the world to a halt. Schools were closed, 4 00:00:16,320 --> 00:00:20,000 Speaker 2: offices were empty, and without commuters, the number of people 5 00:00:20,040 --> 00:00:25,160 Speaker 2: taking public transit plummeted it. New York's Metropolitan Transportation Authority 6 00:00:25,320 --> 00:00:28,280 Speaker 2: has been knocked down before, but its current leader says 7 00:00:28,320 --> 00:00:32,239 Speaker 2: nothing matches the current pandemic by orders of magnitude. We're 8 00:00:32,240 --> 00:00:34,720 Speaker 2: back up to more than a million writers on the 9 00:00:34,720 --> 00:00:36,960 Speaker 2: subway and a daily basis, but that's still down some 10 00:00:37,159 --> 00:00:39,040 Speaker 2: eighty percent year on year. 11 00:00:39,120 --> 00:00:42,159 Speaker 3: House the fair box is like, you know, a huge 12 00:00:42,159 --> 00:00:46,600 Speaker 3: part of the revenue for these systems. So they needed 13 00:00:46,600 --> 00:00:48,400 Speaker 3: to get a bailout from the federal government. 14 00:00:48,800 --> 00:00:52,559 Speaker 2: Street Taylor covers transportation and public finance for Bloomberg, and 15 00:00:52,640 --> 00:00:55,880 Speaker 2: she says over the last half decade, the federal government 16 00:00:55,920 --> 00:01:00,000 Speaker 2: supplied roughly seventy billion dollars in aid to transit agency 17 00:01:00,240 --> 00:01:04,040 Speaker 2: across the US. A that's helped keep them afloat, but 18 00:01:04,200 --> 00:01:07,319 Speaker 2: ridership in the US across the board hasn't gotten back 19 00:01:07,360 --> 00:01:10,640 Speaker 2: to pre pandemic norms. It's been a struggle, and that 20 00:01:10,680 --> 00:01:14,280 Speaker 2: federal aid money is running out, so free Along with 21 00:01:14,280 --> 00:01:17,280 Speaker 2: Bloomberg Data reporter Aaron Gordon, they have been tracking what 22 00:01:17,360 --> 00:01:20,200 Speaker 2: could come next for those agencies, and they say the 23 00:01:20,240 --> 00:01:24,560 Speaker 2: prospect of riders not returning has some transit advocates sounding 24 00:01:24,560 --> 00:01:27,400 Speaker 2: the alarm about a potential death spiral. 25 00:01:27,640 --> 00:01:32,080 Speaker 1: A death spiral is when a transit agency has to 26 00:01:32,360 --> 00:01:35,319 Speaker 1: cut service to save money, which makes it less useful 27 00:01:35,319 --> 00:01:37,640 Speaker 1: to people who then find other ways to get around 28 00:01:38,040 --> 00:01:40,959 Speaker 1: or don't take trips entirely because they can't, which then 29 00:01:41,040 --> 00:01:44,880 Speaker 1: leads to less fair revenue for the transit agency, and 30 00:01:44,920 --> 00:01:47,400 Speaker 1: then they have to cut more service because revenue's down, 31 00:01:47,800 --> 00:01:49,160 Speaker 1: and that's the death spiral. 32 00:01:52,320 --> 00:01:54,200 Speaker 2: I'm David Gera and this is the big take from 33 00:01:54,240 --> 00:01:58,080 Speaker 2: Bloomberg News today. On the show, as federal funding dries up, 34 00:01:58,320 --> 00:02:01,360 Speaker 2: we dig into how mass transit actions across the US 35 00:02:01,760 --> 00:02:04,840 Speaker 2: or trying to avoid a so called death spiral. What 36 00:02:04,880 --> 00:02:08,600 Speaker 2: that vicious cycle of low ridership, lower revenue, and cuts 37 00:02:08,600 --> 00:02:17,919 Speaker 2: to services could mean for America's major cities. The world 38 00:02:18,000 --> 00:02:21,200 Speaker 2: shut down during the early days of the COVID nineteen pandemic, 39 00:02:21,560 --> 00:02:24,120 Speaker 2: and mass transit in the US was no exception. 40 00:02:24,760 --> 00:02:29,160 Speaker 3: So public transit agencies weren't exempt from the rule that 41 00:02:29,240 --> 00:02:32,360 Speaker 3: everything was just going to start clearing out. People weren't 42 00:02:32,400 --> 00:02:36,399 Speaker 3: traveling as much, so Ridership just kind of took a hit. 43 00:02:36,960 --> 00:02:39,720 Speaker 2: Street Tailor teamed up with Aaron Gordon, who was also 44 00:02:39,840 --> 00:02:43,120 Speaker 2: spent years covering transit. He says the funding the federal 45 00:02:43,160 --> 00:02:46,680 Speaker 2: government supplied during the pandemic helped agencies bridge the gap. 46 00:02:47,080 --> 00:02:52,040 Speaker 1: It was a very rare instance in US history where 47 00:02:52,520 --> 00:02:56,360 Speaker 1: the federal government provided money to transit, not just to 48 00:02:56,400 --> 00:02:59,880 Speaker 1: do big, expensive, long term projects, but to run so 49 00:03:00,000 --> 00:03:02,680 Speaker 1: servius on a day to day basis. Typically, the federal 50 00:03:02,680 --> 00:03:06,639 Speaker 1: government had never done this before, and it was absolutely essential. 51 00:03:06,840 --> 00:03:09,760 Speaker 2: So here we are five years hence, is all of 52 00:03:09,760 --> 00:03:12,400 Speaker 2: that money gone? Is the seventy billion gone completely? Or 53 00:03:12,440 --> 00:03:15,000 Speaker 2: are we at a point where these transit agencies are 54 00:03:15,040 --> 00:03:16,800 Speaker 2: beginning to reckon with the fact that it is going 55 00:03:16,880 --> 00:03:17,560 Speaker 2: to disappear soon. 56 00:03:17,840 --> 00:03:20,040 Speaker 1: It depends a little bit on which agency you're talking about, 57 00:03:20,040 --> 00:03:22,400 Speaker 1: whether they spent all their money or not. A select 58 00:03:22,400 --> 00:03:25,240 Speaker 1: few agencies kind of squirreled some away expecting that they 59 00:03:25,240 --> 00:03:28,240 Speaker 1: would need it, and by the mid twenty twenties, but 60 00:03:28,320 --> 00:03:30,840 Speaker 1: for the most part, it's all pretty much gone and 61 00:03:31,000 --> 00:03:32,800 Speaker 1: agencies are trying to figure out what they're going to 62 00:03:32,800 --> 00:03:33,400 Speaker 1: do now. 63 00:03:33,560 --> 00:03:36,400 Speaker 2: A lot of companies have brought workers back to the office. 64 00:03:36,680 --> 00:03:39,320 Speaker 2: I'll say, personally, riding the train day in and day out, 65 00:03:39,360 --> 00:03:42,200 Speaker 2: I have my frustrations with delays and them being crowded. 66 00:03:42,400 --> 00:03:45,400 Speaker 2: When it comes to ridership, are we back to that 67 00:03:45,480 --> 00:03:47,560 Speaker 2: level that we saw pre COVID or are we close 68 00:03:47,600 --> 00:03:47,920 Speaker 2: to it? 69 00:03:48,080 --> 00:03:51,120 Speaker 3: People are taking the train again, and ridership has ticked 70 00:03:51,200 --> 00:03:55,400 Speaker 3: up since the pandemic, but what hasn't recovered is people 71 00:03:55,680 --> 00:03:57,760 Speaker 3: going in as frequently as they used to. 72 00:03:58,240 --> 00:04:03,240 Speaker 1: In the US, most trans agencies are hovering somewhere around 73 00:04:03,320 --> 00:04:07,000 Speaker 1: seventy five percent of pre COVID ridership, which if you're 74 00:04:07,040 --> 00:04:09,840 Speaker 1: a commuter on a train, you know, especially like Tuesday 75 00:04:09,880 --> 00:04:12,840 Speaker 1: to Thursday, which are you know, typically the highest ridership days, 76 00:04:13,360 --> 00:04:16,440 Speaker 1: the train probably feels more or less pre COVID, you know, 77 00:04:16,520 --> 00:04:19,200 Speaker 1: like maybe it's not quite as crowded as it was. 78 00:04:19,279 --> 00:04:21,160 Speaker 1: You know, hard to get this, it's still hard to 79 00:04:21,160 --> 00:04:22,760 Speaker 1: get to see there's still tons of people on the train. 80 00:04:23,120 --> 00:04:24,840 Speaker 1: It feels like things are more or less back to 81 00:04:24,920 --> 00:04:28,280 Speaker 1: normal overall all washes out, So like most transidencies are 82 00:04:28,320 --> 00:04:32,320 Speaker 1: about seventy five percent of ridership, which is basically where 83 00:04:32,400 --> 00:04:34,720 Speaker 1: some of the most of the estimates were, you know, 84 00:04:34,720 --> 00:04:36,520 Speaker 1: in like twenty twenty two of where this would all 85 00:04:36,600 --> 00:04:40,799 Speaker 1: shake out. This is a problem for transit agencies because 86 00:04:40,920 --> 00:04:45,360 Speaker 1: if they rely on fair revenue to help balance the 87 00:04:45,360 --> 00:04:48,400 Speaker 1: budget every year, that's a really significant hit. 88 00:04:48,720 --> 00:04:49,400 Speaker 3: If we took a. 89 00:04:49,400 --> 00:04:52,720 Speaker 2: Tour through major cities in the United States, or most 90 00:04:52,760 --> 00:04:54,400 Speaker 2: of them or all of them fair dependent, is that 91 00:04:54,440 --> 00:04:56,040 Speaker 2: the kind of economic model that they use. 92 00:04:56,240 --> 00:04:59,200 Speaker 1: It varies by transit agency. Some are more fair dependent 93 00:04:59,279 --> 00:05:03,360 Speaker 1: than others. A lot of the transit agencies, for like 94 00:05:03,520 --> 00:05:06,400 Speaker 1: mid size American cities that you wouldn't really think of 95 00:05:06,480 --> 00:05:10,359 Speaker 1: as being very transit dependent, but have transit systems that 96 00:05:10,400 --> 00:05:14,960 Speaker 1: are very important to the cities. I'm thinking like Boston, Philadelphia, 97 00:05:15,400 --> 00:05:20,360 Speaker 1: San Francisco, Atlanta, cities where car dependency is still very 98 00:05:20,440 --> 00:05:23,080 Speaker 1: much the norm, but there's also a large population that 99 00:05:23,200 --> 00:05:26,920 Speaker 1: uses the transit system to commute every day. Those systems 100 00:05:27,000 --> 00:05:31,799 Speaker 1: tend to be extremely fair reliant. New York City gets 101 00:05:31,839 --> 00:05:34,400 Speaker 1: a ton of revenue, like billions and billions of dollars 102 00:05:34,480 --> 00:05:37,240 Speaker 1: a year from the subway fares, but they also get 103 00:05:37,480 --> 00:05:39,400 Speaker 1: a lot of tax revenue too. So it ends up 104 00:05:39,440 --> 00:05:42,479 Speaker 1: being about fifty to fifty more or less. So it 105 00:05:42,520 --> 00:05:45,120 Speaker 1: depends on the system just how much they rely on 106 00:05:45,160 --> 00:05:47,320 Speaker 1: fair revenues. But I would say even for the systems 107 00:05:47,320 --> 00:05:51,359 Speaker 1: where it's maybe a smaller chunk of their overall annual revenues, 108 00:05:51,680 --> 00:05:54,080 Speaker 1: a twenty five percent reduction in that chunk is still 109 00:05:54,080 --> 00:05:55,000 Speaker 1: significant for them. 110 00:05:55,320 --> 00:05:57,280 Speaker 2: So if folks aren't riding the train the way they 111 00:05:57,360 --> 00:06:00,120 Speaker 2: used to, what are they doing differently? I imagine some 112 00:06:00,160 --> 00:06:02,560 Speaker 2: are just staying at home working from home. But are 113 00:06:02,760 --> 00:06:04,120 Speaker 2: there's some folks like I know you're a bike rider. 114 00:06:04,160 --> 00:06:06,440 Speaker 2: They're folks like you're riding bikes in now or taking 115 00:06:06,480 --> 00:06:08,800 Speaker 2: their cars through. How is that shift manifested itself? 116 00:06:08,920 --> 00:06:12,159 Speaker 1: I don't know. If we've seen a huge shift in 117 00:06:12,400 --> 00:06:15,960 Speaker 1: transportation action. Call this mode shift the mode you're using 118 00:06:16,000 --> 00:06:18,279 Speaker 1: to get where you're going. What we have found is 119 00:06:18,400 --> 00:06:21,640 Speaker 1: much more significantly. If people moved away from the city 120 00:06:21,720 --> 00:06:24,719 Speaker 1: during the pandemic thinking remote work would be the future, 121 00:06:24,760 --> 00:06:26,880 Speaker 1: and they got called back to the office and they 122 00:06:26,960 --> 00:06:28,800 Speaker 1: live further away from their office than they used to, 123 00:06:29,200 --> 00:06:33,279 Speaker 1: that probably means they're driving now. But broadly, the mode 124 00:06:33,400 --> 00:06:36,279 Speaker 1: choice pre and post pandemic is looking very similar. 125 00:06:36,520 --> 00:06:39,599 Speaker 3: A lot of these transit agencies don't think that ridership 126 00:06:39,640 --> 00:06:42,720 Speaker 3: will ever look the same as it used to before 127 00:06:42,760 --> 00:06:49,080 Speaker 3: the pandemic came. So is ridership inching back to what 128 00:06:49,160 --> 00:06:52,760 Speaker 3: we might consider normal, sure, But for these transit agencies 129 00:06:52,800 --> 00:06:57,680 Speaker 3: who are so used to certain levels of ridership for 130 00:06:57,880 --> 00:07:01,800 Speaker 3: decades before the pandemic came, they aren't seeing what they 131 00:07:01,839 --> 00:07:03,760 Speaker 3: need to see at the fairbox. 132 00:07:04,440 --> 00:07:09,960 Speaker 2: How would you characterize the financial situation of major transportation 133 00:07:10,000 --> 00:07:13,000 Speaker 2: agencies across the countries? Is there nuance here or are 134 00:07:13,040 --> 00:07:14,560 Speaker 2: some doing better than others? Where it's there kind of 135 00:07:14,560 --> 00:07:17,120 Speaker 2: a blanket sense you can give us of how they're 136 00:07:17,120 --> 00:07:18,280 Speaker 2: doing financially. 137 00:07:18,040 --> 00:07:22,960 Speaker 1: I would say generally there are two categories. As with 138 00:07:23,120 --> 00:07:27,000 Speaker 1: most things US transit related, there's New York and then 139 00:07:27,000 --> 00:07:30,240 Speaker 1: there's everywhere else. You have to remember that basically one 140 00:07:30,320 --> 00:07:33,840 Speaker 1: third of all public transit trips in the US occur 141 00:07:33,960 --> 00:07:37,480 Speaker 1: in the New York metro area. So even though it 142 00:07:37,560 --> 00:07:41,080 Speaker 1: feels disrespectful of the rest of the country to talk 143 00:07:41,160 --> 00:07:43,680 Speaker 1: New York and everywhere else, it really is that way 144 00:07:43,720 --> 00:07:46,960 Speaker 1: with transit. So the MTA is actually in pretty good 145 00:07:47,000 --> 00:07:51,480 Speaker 1: financial shape all told. Because of some new taxes on 146 00:07:51,520 --> 00:07:55,960 Speaker 1: the state level, and also congestion pricing, which has allowed 147 00:07:56,000 --> 00:08:00,080 Speaker 1: them to fully fund their long term capital plans. D 148 00:08:00,240 --> 00:08:03,800 Speaker 1: Transit is in pretty dire financial shape, and then the 149 00:08:03,840 --> 00:08:06,800 Speaker 1: rest of the country is, I would say, in a 150 00:08:06,840 --> 00:08:09,680 Speaker 1: similar boat to New Jersey Transit. Their ridership recovery has 151 00:08:09,680 --> 00:08:14,560 Speaker 1: been quite poor. They tend to rely on commuters for 152 00:08:14,880 --> 00:08:17,840 Speaker 1: most of their rides, whereas the MTA has a lot 153 00:08:17,840 --> 00:08:19,880 Speaker 1: of discretionary trips. You know, people take the subway for 154 00:08:19,920 --> 00:08:22,080 Speaker 1: all kinds of reasons in New York, whereas in most 155 00:08:22,120 --> 00:08:25,520 Speaker 1: other cities, the transportation systems were designed to get workers 156 00:08:25,520 --> 00:08:28,080 Speaker 1: to work, and they assumed if you're taking discretionary trips, 157 00:08:28,120 --> 00:08:30,760 Speaker 1: you'd probably be driving your own personal car. So they've 158 00:08:30,960 --> 00:08:34,800 Speaker 1: had a lot harder time getting people to take their 159 00:08:35,240 --> 00:08:38,600 Speaker 1: systems as commuters aren't returning to the office, you know, 160 00:08:38,760 --> 00:08:40,640 Speaker 1: maybe more than like three or four days a week. 161 00:08:40,960 --> 00:08:43,640 Speaker 1: And also in those other states, it's harder for the 162 00:08:43,679 --> 00:08:46,280 Speaker 1: transa agencies often to get financial support from the state 163 00:08:46,720 --> 00:08:49,400 Speaker 1: for various different reasons. But buying large transit just isn't 164 00:08:49,400 --> 00:08:50,679 Speaker 1: prioritized in the US. 165 00:08:52,320 --> 00:08:56,760 Speaker 3: A lot of these systems have been flashing warning signs 166 00:08:56,880 --> 00:09:01,160 Speaker 3: for years now, so the fact that some systems that 167 00:09:01,280 --> 00:09:05,160 Speaker 3: are mentioned in the story still haven't gotten it together 168 00:09:05,600 --> 00:09:08,120 Speaker 3: or gotten what they've needed or sorted it out. It's 169 00:09:08,400 --> 00:09:13,240 Speaker 3: super down to the wire now, so. 170 00:09:13,280 --> 00:09:16,160 Speaker 2: Ridership is down, and if it's not coming back, what 171 00:09:16,200 --> 00:09:29,200 Speaker 2: options do these transit agencies have left? That's next? Could 172 00:09:29,200 --> 00:09:31,800 Speaker 2: you just quickly take us kind of inside a boardroom 173 00:09:31,800 --> 00:09:33,960 Speaker 2: at one of these agencies having to make this kind 174 00:09:34,000 --> 00:09:37,199 Speaker 2: of decision. I imagine that the calculus is incredibly difficult 175 00:09:37,240 --> 00:09:39,839 Speaker 2: and tricky whether or not to cut service, and that 176 00:09:39,880 --> 00:09:42,240 Speaker 2: fear that it might kickstart that vicious cycle or that 177 00:09:42,360 --> 00:09:42,959 Speaker 2: death spiral. 178 00:09:43,679 --> 00:09:45,679 Speaker 1: There are only so many levers you can pull if 179 00:09:45,679 --> 00:09:49,080 Speaker 1: you're running one of these transit agencies. You can raise fares, 180 00:09:49,520 --> 00:09:52,600 Speaker 1: but that again is going to discourage some number of riders. 181 00:09:53,280 --> 00:09:57,960 Speaker 1: You can cut service, you can try and defer hiring, 182 00:09:58,440 --> 00:10:01,960 Speaker 1: or you know, put long term projects on hold. You 183 00:10:02,000 --> 00:10:05,680 Speaker 1: can try and do as little maintenance as possible, but 184 00:10:05,760 --> 00:10:08,839 Speaker 1: all of those have long term costs and ramifications as 185 00:10:08,880 --> 00:10:11,920 Speaker 1: well that could be even more severe than anything you're 186 00:10:11,960 --> 00:10:14,199 Speaker 1: doing in the short run. You know, we saw the 187 00:10:14,240 --> 00:10:17,520 Speaker 1: cost of deferred maintenance in New York City back in 188 00:10:17,559 --> 00:10:21,160 Speaker 1: twenty seventeen twenty eighteen when the subway seemed like was 189 00:10:21,200 --> 00:10:25,120 Speaker 1: collapsing every day, and you know, not literally, but every 190 00:10:25,160 --> 00:10:27,440 Speaker 1: day you would go down into the station, you weren't 191 00:10:27,480 --> 00:10:28,920 Speaker 1: sure if you were going to see, you know, the 192 00:10:28,920 --> 00:10:31,600 Speaker 1: platform five six people deep because the trains just couldn't 193 00:10:31,640 --> 00:10:35,000 Speaker 1: run because maintenance had been deferred during the Great Recession. 194 00:10:35,200 --> 00:10:37,920 Speaker 1: That was a huge reason for it. So I think 195 00:10:38,040 --> 00:10:41,600 Speaker 1: the people who run these systems are extremely cognizant of this. 196 00:10:41,760 --> 00:10:45,800 Speaker 1: They're not naive. They know that almost anything they do 197 00:10:45,920 --> 00:10:48,200 Speaker 1: to try and cut costs and balance the books is 198 00:10:48,320 --> 00:10:51,280 Speaker 1: going to have some type of long term costs or 199 00:10:51,400 --> 00:10:53,640 Speaker 1: ramifications that they'll have to deal with. Eventually. 200 00:10:53,920 --> 00:10:59,400 Speaker 3: When you cut service, like those drastic cuts service, people 201 00:10:59,800 --> 00:11:02,560 Speaker 3: find other ways to get around and they'll get set 202 00:11:02,600 --> 00:11:05,640 Speaker 3: in their routines and then they might never come back. 203 00:11:06,240 --> 00:11:09,160 Speaker 3: If they hit the roads and you know, they can 204 00:11:09,160 --> 00:11:11,400 Speaker 3: get in their cars and they figure that out, then 205 00:11:11,720 --> 00:11:14,760 Speaker 3: that could also add to congestion and more traffic and 206 00:11:14,840 --> 00:11:17,600 Speaker 3: it's just not good for anyone really. 207 00:11:18,360 --> 00:11:21,640 Speaker 1: But by law, most of these transit agencies have to 208 00:11:21,640 --> 00:11:25,640 Speaker 1: present balanced budgets every year. That's kind of the governing 209 00:11:25,760 --> 00:11:30,520 Speaker 1: ethos of these public authorities. They're not government agencies that can, 210 00:11:30,720 --> 00:11:33,440 Speaker 1: in theory run deficits. They have to present a balanced 211 00:11:33,440 --> 00:11:37,240 Speaker 1: budget every year, so one way or another, something has 212 00:11:37,280 --> 00:11:37,560 Speaker 1: to give. 213 00:11:38,679 --> 00:11:40,840 Speaker 2: And I wonder sort of how you see this playing out. 214 00:11:41,440 --> 00:11:43,240 Speaker 2: Can go back five years we had the government, federal 215 00:11:43,280 --> 00:11:46,160 Speaker 2: government stepping in during the pandemic. Is there a world 216 00:11:46,160 --> 00:11:49,200 Speaker 2: in which the federal government steps in again to help 217 00:11:49,240 --> 00:11:50,040 Speaker 2: out these systems. 218 00:11:50,240 --> 00:11:54,120 Speaker 1: No one we have spoken to for our stories has 219 00:11:54,200 --> 00:11:57,240 Speaker 1: any expectation that this administration is going to step in 220 00:11:57,280 --> 00:12:00,160 Speaker 1: and help public transit. If anything, the fear runs the 221 00:12:00,160 --> 00:12:04,280 Speaker 1: opposite direction, which is previously committed money for projects is 222 00:12:04,320 --> 00:12:07,640 Speaker 1: going to be clawed back or never delivered. Certainly, the 223 00:12:07,920 --> 00:12:13,080 Speaker 1: administration's posture towards congestion pricing in New York is lending 224 00:12:13,120 --> 00:12:16,520 Speaker 1: credence to those fears. Right, the administration is aggressively trying 225 00:12:16,559 --> 00:12:21,040 Speaker 1: to cancel that program, revoke authorization for it, which would 226 00:12:21,080 --> 00:12:24,600 Speaker 1: have tremendous ramifications on New York City's transportation lands. 227 00:12:24,679 --> 00:12:26,760 Speaker 2: This is this is a program that was designed to 228 00:12:26,840 --> 00:12:30,080 Speaker 2: raise revenues to improve the larger system here in the 229 00:12:30,120 --> 00:12:32,360 Speaker 2: New York area by raising money from people who drove 230 00:12:32,360 --> 00:12:33,800 Speaker 2: their cars into certain parts of the city. 231 00:12:33,880 --> 00:12:36,240 Speaker 1: That's right, and in a lot of ways. That was 232 00:12:36,400 --> 00:12:39,120 Speaker 1: a program designed so that New York City would not 233 00:12:39,280 --> 00:12:42,400 Speaker 1: rely as much on the federal government for money to 234 00:12:43,200 --> 00:12:44,080 Speaker 1: rebuild the system. 235 00:12:44,200 --> 00:12:49,520 Speaker 3: Essentially, if Trump gets his way and congestion pricing is mixed, 236 00:12:50,120 --> 00:12:52,560 Speaker 3: then MTA needs to come up with a plan B 237 00:12:53,480 --> 00:12:56,800 Speaker 3: for you know, I guess, how to fund their capital 238 00:12:57,080 --> 00:13:01,160 Speaker 3: project for the next however many years, and then they 239 00:13:01,160 --> 00:13:04,200 Speaker 3: have to wonder, you know, how many people will actually 240 00:13:04,720 --> 00:13:08,720 Speaker 3: want to ride the MTA if we're dealing with CenTra 241 00:13:08,800 --> 00:13:11,000 Speaker 3: old equipment and it's always breaking down. 242 00:13:11,280 --> 00:13:14,920 Speaker 1: Transagencies are used to dealing with administration changes, but what 243 00:13:14,960 --> 00:13:18,840 Speaker 1: they're not as used to dealing with is an historic 244 00:13:18,920 --> 00:13:22,160 Speaker 1: drop in ridership at the same time. And so how 245 00:13:22,160 --> 00:13:25,400 Speaker 1: they're going to navigate those two things is, I think 246 00:13:25,520 --> 00:13:28,240 Speaker 1: something that has very little historical precedence. 247 00:13:29,679 --> 00:13:32,280 Speaker 2: If you look at the demographics, who is hurt the 248 00:13:32,320 --> 00:13:35,920 Speaker 2: most when we see a system get into a death spiral? 249 00:13:36,080 --> 00:13:39,720 Speaker 2: We see cuts across the system. Can you characterize who's 250 00:13:40,000 --> 00:13:41,800 Speaker 2: most likely to be adversely affected by that? 251 00:13:42,080 --> 00:13:44,320 Speaker 1: It's going to be the people who are transit dependent, 252 00:13:44,400 --> 00:13:46,960 Speaker 1: the people who need those systems because they don't have 253 00:13:47,000 --> 00:13:49,760 Speaker 1: another way to get around. And I think in this country. 254 00:13:49,800 --> 00:13:52,280 Speaker 1: The first thing that comes to mind when most people 255 00:13:52,320 --> 00:13:55,560 Speaker 1: here transit dependent is likely income based. You think of 256 00:13:55,559 --> 00:13:57,760 Speaker 1: someone who can't afford to take a car, but that's 257 00:13:57,760 --> 00:14:00,760 Speaker 1: not necessarily true. It could be people who can't drive 258 00:14:00,800 --> 00:14:02,840 Speaker 1: a car for medical reasons. It could be people who 259 00:14:02,880 --> 00:14:05,920 Speaker 1: can't drive a car because they're elderly and aren't comfortable 260 00:14:06,000 --> 00:14:08,680 Speaker 1: driving anymore, they don't feel safe doing it. It could 261 00:14:08,720 --> 00:14:12,280 Speaker 1: be people who choose not to drive a car for 262 00:14:12,480 --> 00:14:15,679 Speaker 1: various lifestyle reasons. They want to live a car free lifestyle. 263 00:14:16,160 --> 00:14:18,760 Speaker 1: But the reality is, in most places in the country, 264 00:14:18,800 --> 00:14:22,040 Speaker 1: if you want to be an active economic participant, you 265 00:14:22,160 --> 00:14:24,560 Speaker 1: have to be able to drive. So there are all 266 00:14:24,600 --> 00:14:27,280 Speaker 1: types of people who get affected when transit doesn't provide 267 00:14:27,320 --> 00:14:28,680 Speaker 1: the kind of service they need. 268 00:14:29,120 --> 00:14:33,040 Speaker 3: And that's going to be even more devastating for these 269 00:14:33,200 --> 00:14:37,760 Speaker 3: communities where people rely heavily on public transportation. There's a 270 00:14:37,800 --> 00:14:39,960 Speaker 3: lot at sake for not just these agencies who might 271 00:14:40,000 --> 00:14:42,920 Speaker 3: lose a lot of money, but for writers who are 272 00:14:42,920 --> 00:14:49,800 Speaker 3: depending on public transit and a robust system to get around. 273 00:14:52,640 --> 00:14:55,400 Speaker 2: This is the big take from Bloomberg News. I'm David Gera. 274 00:14:55,520 --> 00:14:58,520 Speaker 2: This episode is produced by David Fox and Rachel Lewis Krisky. 275 00:14:58,920 --> 00:15:01,280 Speaker 2: It was edited by Air and Edwards, Patty Hirsch, and 276 00:15:01,360 --> 00:15:04,080 Speaker 2: Tim Annette. He was fact checked by andrean A. Tapia 277 00:15:04,160 --> 00:15:06,920 Speaker 2: and Rachel Lewis Chrisky, and mixed and sound designed by 278 00:15:06,920 --> 00:15:10,680 Speaker 2: Alex Sagura. Our senior producer is Naomi Shaven. Our senior 279 00:15:10,800 --> 00:15:14,680 Speaker 2: editor is Elizabeth Ponso. Our deputy executive producer is Julia Weaver. 280 00:15:15,040 --> 00:15:18,360 Speaker 2: Our executive producer is Nicole beemster Boor. Sage Bauman is 281 00:15:18,400 --> 00:15:21,520 Speaker 2: Bloomberg's head of podcasts. If you liked this episode, make 282 00:15:21,560 --> 00:15:23,680 Speaker 2: sure to subscribe and review The Big Take wherever you 283 00:15:23,720 --> 00:15:26,880 Speaker 2: listen to podcasts. It helps people find the show. Thanks 284 00:15:26,880 --> 00:15:29,840 Speaker 2: for listening. We'll be back on Monday.