1 00:00:00,640 --> 00:00:04,160 Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics, 2 00:00:04,360 --> 00:00:07,160 Speaker 1: where we discussed the top political headlines with some of 3 00:00:07,160 --> 00:00:10,600 Speaker 1: today's best minds. We're on vacation, but that doesn't mean 4 00:00:10,760 --> 00:00:12,960 Speaker 1: we don't have a great show for you today. Michael 5 00:00:13,080 --> 00:00:17,840 Speaker 1: Rohattan on his documentary Drop Dead City. It's an amazing 6 00:00:18,000 --> 00:00:21,279 Speaker 1: portrait of New York City in the seventies when it 7 00:00:21,360 --> 00:00:23,959 Speaker 1: ran out of money. But first we have the Groundwork 8 00:00:24,120 --> 00:00:30,080 Speaker 1: Collective's own Lindsey Owens on the Groundwork Collectives blockbuster reporting 9 00:00:30,440 --> 00:00:33,880 Speaker 1: on Instacart's dynamic pricing scam. 10 00:00:34,400 --> 00:00:36,520 Speaker 2: Welcome to Fast Politics. 11 00:00:36,600 --> 00:00:39,960 Speaker 1: Thanks for having me explain to us what the Groundwork 12 00:00:40,000 --> 00:00:42,760 Speaker 1: Collective is and how you got involved. 13 00:00:43,040 --> 00:00:46,320 Speaker 3: So Groundwork Collaborative is a think tank based in Washington, 14 00:00:46,400 --> 00:00:50,000 Speaker 3: d C. We're focused on building an economy that works 15 00:00:50,000 --> 00:00:52,560 Speaker 3: for all of us. The last five years or so, 16 00:00:52,960 --> 00:00:57,160 Speaker 3: that has meant we have been laser focused on high prices, 17 00:00:57,480 --> 00:01:00,640 Speaker 3: cost of living, affordability. This is the time issue that 18 00:01:00,720 --> 00:01:03,960 Speaker 3: Americans care about, and every poll that we have fielded 19 00:01:04,000 --> 00:01:07,040 Speaker 3: for five years now Americans are laser focused on the 20 00:01:07,080 --> 00:01:09,760 Speaker 3: price of groceries, the price of housing, the price of healthcare, 21 00:01:10,040 --> 00:01:13,440 Speaker 3: the fact that inflation was rising. Now we have Trump's tariffs, 22 00:01:13,560 --> 00:01:17,000 Speaker 3: and so we spend most of our time thinking about 23 00:01:17,200 --> 00:01:20,319 Speaker 3: why our price is high, who is to blame, what 24 00:01:20,360 --> 00:01:21,200 Speaker 3: we can do about it? 25 00:01:21,480 --> 00:01:26,440 Speaker 2: Okay, so this gets us to instacard discuss. 26 00:01:26,720 --> 00:01:30,640 Speaker 3: Yeah, so, look, Americans are really frustrated with grocery prices. 27 00:01:30,840 --> 00:01:35,280 Speaker 3: If you ask Americans which prices vex them the most, 28 00:01:35,440 --> 00:01:38,880 Speaker 3: they say grocery prices. It's not super surprising because you 29 00:01:38,920 --> 00:01:42,160 Speaker 3: buy a lot of groceries, right, You don't buy one grocery, 30 00:01:42,200 --> 00:01:44,040 Speaker 3: you don't buy one card of groceries. You go to 31 00:01:44,080 --> 00:01:45,920 Speaker 3: the grocery store a couple times a week. You go 32 00:01:45,920 --> 00:01:48,840 Speaker 3: to the grocery store all year long. And grocery prices 33 00:01:48,880 --> 00:01:52,760 Speaker 3: have been high, sometimes outpacing inflation for years now. 34 00:01:52,920 --> 00:01:54,440 Speaker 4: And there are a lot of reasons for this. 35 00:01:54,720 --> 00:01:59,920 Speaker 3: There are reasons related to not enough competition in agricultural market. 36 00:02:00,320 --> 00:02:04,000 Speaker 3: There are reasons related to the president's tariffs. You know, 37 00:02:04,000 --> 00:02:07,000 Speaker 3: we did a report at Groundwork looking at why Thanksgiving 38 00:02:07,040 --> 00:02:09,920 Speaker 3: prices were high. To put food on the table for 39 00:02:09,960 --> 00:02:12,600 Speaker 3: Thanksgiving one of the culprits. People buy a lot of 40 00:02:12,680 --> 00:02:16,440 Speaker 3: cans at Thanksgiving. They buy canned pumpkin candy, cranberry sauce, 41 00:02:16,520 --> 00:02:20,520 Speaker 3: cand green beans. Steel tariffs mean that even cheaper items 42 00:02:20,560 --> 00:02:23,440 Speaker 3: like can goods are up. But there's another reason why 43 00:02:23,480 --> 00:02:28,600 Speaker 3: grocery prices are high, and that is because increasingly grocers 44 00:02:28,840 --> 00:02:34,040 Speaker 3: and grocery delivery platforms like Instacart are running tests on 45 00:02:34,400 --> 00:02:38,359 Speaker 3: American shoppers to determine exactly how much they can get 46 00:02:38,360 --> 00:02:40,919 Speaker 3: away with charging them and to squeeze it out of them. 47 00:02:41,200 --> 00:02:43,720 Speaker 3: And we knew Instacart was doing this because this has 48 00:02:43,760 --> 00:02:47,520 Speaker 3: been a part of Instacart's business development and business model 49 00:02:47,600 --> 00:02:51,240 Speaker 3: for years now. In twenty twenty two, then CEO of 50 00:02:51,280 --> 00:02:57,560 Speaker 3: Instacart acquired an AI pricing tech company called Eversite. Eversite Technology, 51 00:02:57,919 --> 00:03:01,000 Speaker 3: the thing that they offer is the ab to build 52 00:03:01,040 --> 00:03:05,440 Speaker 3: out these large scale experiments that shoppers don't realize they're 53 00:03:05,480 --> 00:03:10,440 Speaker 3: in on to help companies fine tune and calibrate their pricing, 54 00:03:10,600 --> 00:03:13,680 Speaker 3: to really figure out the maximum amount you'll pay for 55 00:03:13,760 --> 00:03:16,560 Speaker 3: something before you pull it out of your cart or 56 00:03:17,040 --> 00:03:20,720 Speaker 3: log off Instacart altogether walk into the store and buy 57 00:03:20,720 --> 00:03:23,680 Speaker 3: your groceries another way. And so we knew this was happening. 58 00:03:23,800 --> 00:03:25,800 Speaker 3: It wasn't exactly a secret, it was sort of hiding 59 00:03:25,840 --> 00:03:28,720 Speaker 3: in plain sight from our perspective, and we wanted to 60 00:03:28,880 --> 00:03:32,480 Speaker 3: understand exactly how much it could be costing shoppers and families, 61 00:03:32,520 --> 00:03:37,760 Speaker 3: And so we turned instacarts experiment back around on the company. 62 00:03:37,960 --> 00:03:40,360 Speaker 3: And that's really how this project gets started. 63 00:03:40,680 --> 00:03:43,800 Speaker 1: You turn the experiment background in the company and what 64 00:03:43,920 --> 00:03:44,560 Speaker 1: do you discover. 65 00:03:44,960 --> 00:03:47,600 Speaker 3: Yeah, so we partnered with Consumer Reports and a news 66 00:03:47,600 --> 00:03:51,120 Speaker 3: outlet called more Perfect Union to recruit more than four 67 00:03:51,200 --> 00:03:55,880 Speaker 3: hundred volunteer secret shoppers. We have them log onto the 68 00:03:55,880 --> 00:03:59,960 Speaker 3: instacart app. We have them put the exact same twenty items, 69 00:04:00,280 --> 00:04:03,960 Speaker 3: same twenty grocery items in their carts at the exact 70 00:04:04,040 --> 00:04:07,280 Speaker 3: same time from the exact same pickup location. So these 71 00:04:07,280 --> 00:04:11,160 Speaker 3: were Safe Way stores and Target Stores and North Canton, Ohio, 72 00:04:11,240 --> 00:04:13,360 Speaker 3: and Seattle, Washington, and Washington, d C. 73 00:04:13,720 --> 00:04:16,360 Speaker 4: And Saint Paul, Minnesota. And then once all of. 74 00:04:16,279 --> 00:04:18,960 Speaker 3: The groceries are in their instacarts, we had them take 75 00:04:19,000 --> 00:04:23,840 Speaker 3: screenshots of the prices and we collected all of those screenshots. 76 00:04:24,120 --> 00:04:27,040 Speaker 3: The team at the Groundwork Collaborative entered the data by hand. Right, 77 00:04:27,160 --> 00:04:28,800 Speaker 3: you have to sort of, you know, look at the 78 00:04:28,839 --> 00:04:31,200 Speaker 3: screen shots, look at the prices, enter the data and 79 00:04:31,240 --> 00:04:34,680 Speaker 3: we analyzed the data. And what we found is seventy 80 00:04:34,760 --> 00:04:38,240 Speaker 3: five percent of the items in the carts were offered 81 00:04:38,240 --> 00:04:42,920 Speaker 3: at different prices to different customers, and that one hundred 82 00:04:42,960 --> 00:04:46,080 Speaker 3: percent of the shoppers in our study ended up in 83 00:04:46,120 --> 00:04:49,280 Speaker 3: some condition of the experiment at some point in the study, 84 00:04:49,600 --> 00:04:53,040 Speaker 3: and that the price differences weren't small. This wasn't sort 85 00:04:53,080 --> 00:04:56,800 Speaker 3: of like teeny tiny ab testing where they're trying to 86 00:04:56,800 --> 00:04:58,680 Speaker 3: figure out if you like the blue can or the 87 00:04:58,760 --> 00:05:01,560 Speaker 3: yellow can, or if you're more likely to buy something 88 00:05:02,080 --> 00:05:04,680 Speaker 3: with what we call charm pricing, right, something at three 89 00:05:04,839 --> 00:05:07,520 Speaker 3: ninety nine versus something at four dollars in one cent. 90 00:05:07,720 --> 00:05:10,640 Speaker 3: The price differences in our study were as high as 91 00:05:10,720 --> 00:05:13,880 Speaker 3: twenty three percent. So you and I picking out the 92 00:05:13,920 --> 00:05:17,120 Speaker 3: exact same box of wheat thens from Target and you 93 00:05:17,240 --> 00:05:20,680 Speaker 3: being charged twenty three percent more than me. Now again, 94 00:05:20,800 --> 00:05:23,599 Speaker 3: because you don't buy only one box of wheat THENDS 95 00:05:23,600 --> 00:05:25,440 Speaker 3: when you go grocery shopping, you buy a whole basket 96 00:05:25,440 --> 00:05:29,039 Speaker 3: of groceries. We look at the pricing variation across the 97 00:05:29,200 --> 00:05:34,280 Speaker 3: entire basket, and then we use instacart's own analysis of 98 00:05:34,400 --> 00:05:36,840 Speaker 3: how much a family of four a household of four 99 00:05:36,920 --> 00:05:40,000 Speaker 3: spends on groceries in a given year, and we estimate 100 00:05:40,040 --> 00:05:43,280 Speaker 3: that this experiment, I'm calling it the Instacart tax for 101 00:05:43,560 --> 00:05:47,640 Speaker 3: simplicity could cost a family as much as twelve hundred 102 00:05:47,640 --> 00:05:52,400 Speaker 3: dollars a year. So this is really a significant amount 103 00:05:52,600 --> 00:05:55,440 Speaker 3: coming from this Instacart experiment. 104 00:05:55,720 --> 00:05:59,320 Speaker 2: I shop sometimes on Instacart. It seems really expansive. 105 00:05:59,680 --> 00:06:03,560 Speaker 1: Yeah, but is it really expensive or is it expensive 106 00:06:03,600 --> 00:06:05,360 Speaker 1: for some people and not for other people. 107 00:06:05,600 --> 00:06:06,599 Speaker 4: Yeah, it's a great question. 108 00:06:06,760 --> 00:06:09,960 Speaker 3: So there are certainly aspects of shopping on instacart that 109 00:06:10,000 --> 00:06:13,359 Speaker 3: are expensive for everyone. Right, you pay a premium for 110 00:06:13,400 --> 00:06:16,200 Speaker 3: the convenience of Instacart. You're going to pay a service fee, 111 00:06:16,279 --> 00:06:20,240 Speaker 3: a delivery fee, potentially a tip, right, all of those pieces. 112 00:06:20,480 --> 00:06:24,200 Speaker 3: We also know that instacart prices are sometimes just higher 113 00:06:24,200 --> 00:06:27,359 Speaker 3: than in store prices, right. So that's something that is 114 00:06:27,480 --> 00:06:30,840 Speaker 3: true but can be consistent across all shoppers. We also 115 00:06:30,920 --> 00:06:33,800 Speaker 3: know that there are some additional levees that Instacart charges 116 00:06:33,880 --> 00:06:37,320 Speaker 3: for certain items. Heavy items, it's harder to get those 117 00:06:37,360 --> 00:06:40,800 Speaker 3: delivered to you. Fragile items like a bouquet of fresh flowers. 118 00:06:41,080 --> 00:06:43,400 Speaker 3: Can't stick that in your trunk if you're a delivery driver. 119 00:06:43,880 --> 00:06:45,520 Speaker 3: So there are a whole host of ways in which 120 00:06:45,560 --> 00:06:49,520 Speaker 3: Instacart is expensive. This is a new additional way in 121 00:06:49,560 --> 00:06:52,080 Speaker 3: which instacart can be expensive, which is that on top 122 00:06:52,120 --> 00:06:55,120 Speaker 3: of all of those fees which we call drip pricing, right, 123 00:06:55,240 --> 00:06:58,279 Speaker 3: these little fees that get tacked on at different stages 124 00:06:58,320 --> 00:07:01,400 Speaker 3: of the transaction, on top of all of that, you're 125 00:07:01,480 --> 00:07:05,080 Speaker 3: also effectively a guinea pig in this experiment. And if 126 00:07:05,080 --> 00:07:09,400 Speaker 3: you're unlucky, if you're in the sort of gouge treatment 127 00:07:09,440 --> 00:07:12,600 Speaker 3: condition and not the discount treatment condition, you could also 128 00:07:12,640 --> 00:07:16,200 Speaker 3: be paying a premium as a result. So this piece 129 00:07:16,400 --> 00:07:19,760 Speaker 3: is variable. Some people are lucky, some people are not. 130 00:07:20,120 --> 00:07:22,880 Speaker 3: But for those who aren't, and again, you know, we 131 00:07:23,440 --> 00:07:26,000 Speaker 3: found that seventy five percent of items were offered at 132 00:07:26,080 --> 00:07:28,280 Speaker 3: variable prices. One hundred percent of people in our study 133 00:07:28,280 --> 00:07:30,880 Speaker 3: were subject to some variable pricing. You know, this can 134 00:07:30,920 --> 00:07:35,360 Speaker 3: be a substantial additional amount of pricing that's getting layered 135 00:07:35,360 --> 00:07:36,960 Speaker 3: in to your grocery bill. 136 00:07:37,200 --> 00:07:41,200 Speaker 2: So now explain to us why this is not okay? 137 00:07:41,640 --> 00:07:43,679 Speaker 4: Yeah, I think there are two answers to this question. 138 00:07:43,760 --> 00:07:46,600 Speaker 3: There's sort of morally why this isn't okay, and I 139 00:07:46,600 --> 00:07:48,640 Speaker 3: think there are some good answers to that. And then 140 00:07:48,720 --> 00:07:53,120 Speaker 3: there's legally whether this is okay? Does it pass legal muster? 141 00:07:53,480 --> 00:07:57,280 Speaker 3: And then the intersection of those two presents the question 142 00:07:57,360 --> 00:07:59,840 Speaker 3: that policymakers are asking right now, which is like, to 143 00:07:59,880 --> 00:08:02,640 Speaker 3: the extent that this is legal if we don't feel 144 00:08:02,680 --> 00:08:04,640 Speaker 3: like it should be, because we don't feel like it's fair, 145 00:08:04,800 --> 00:08:07,400 Speaker 3: we don't feel like it's right, should we be pursuing, 146 00:08:07,680 --> 00:08:10,520 Speaker 3: you know, new legislation something like that to curtail this. 147 00:08:10,920 --> 00:08:14,880 Speaker 3: So from the moral perspective, it feels unfair because we're 148 00:08:15,000 --> 00:08:16,600 Speaker 3: used to a public price. 149 00:08:16,880 --> 00:08:18,080 Speaker 4: We're used to a price tag. 150 00:08:18,200 --> 00:08:19,480 Speaker 3: If you and I are standing in line at the 151 00:08:19,520 --> 00:08:21,520 Speaker 3: grocery store check out together and we're both holding the 152 00:08:21,600 --> 00:08:24,080 Speaker 3: same box of cheerios, you and I expect to, say, 153 00:08:24,320 --> 00:08:26,680 Speaker 3: pay the same price for that box of cheerios. And 154 00:08:26,760 --> 00:08:29,080 Speaker 3: it doesn't feel right that if we stepped out of 155 00:08:29,120 --> 00:08:32,720 Speaker 3: line together and fired up our phones and got that 156 00:08:32,760 --> 00:08:34,880 Speaker 3: same box of cheerios on Instacart, that you and I 157 00:08:35,000 --> 00:08:38,400 Speaker 3: might pay different amounts because it's easier for them to 158 00:08:38,480 --> 00:08:42,719 Speaker 3: run these experiments in this online setting where we're atomized. 159 00:08:42,840 --> 00:08:44,480 Speaker 3: You and I were sitting on our couches at home. 160 00:08:44,679 --> 00:08:46,719 Speaker 3: I don't know I don't know you, I don't know 161 00:08:46,760 --> 00:08:49,520 Speaker 3: what you're buying. I don't know that you're getting charged 162 00:08:49,559 --> 00:08:52,080 Speaker 3: more than me, your time's very valuable. They've figured that 163 00:08:52,160 --> 00:08:55,400 Speaker 3: out right, and they're like, this woman doesn't have time 164 00:08:55,440 --> 00:08:57,160 Speaker 3: to go to the grocery store, like we're going to 165 00:08:57,240 --> 00:09:00,360 Speaker 3: go for gold with her, right, And that doesn't feel there. 166 00:09:00,720 --> 00:09:02,480 Speaker 3: I think the other thing that doesn't feel fair is 167 00:09:02,520 --> 00:09:05,680 Speaker 3: how deceptive this is. I think the backbone of a 168 00:09:05,720 --> 00:09:09,120 Speaker 3: healthy economy is fair and honest markets, and the fact 169 00:09:09,160 --> 00:09:11,480 Speaker 3: that we don't know that we're the guinea pigs in 170 00:09:11,520 --> 00:09:16,079 Speaker 3: this grand Instacart eversight AI experiment doesn't feel right. There's 171 00:09:16,120 --> 00:09:20,679 Speaker 3: a sort of transparency problem here. It's opaque, it's arguably deceptive. 172 00:09:20,960 --> 00:09:23,000 Speaker 3: The final thing that I would argue, in addition to 173 00:09:23,000 --> 00:09:25,200 Speaker 3: the fact that it's just costing us more right, these 174 00:09:25,200 --> 00:09:29,480 Speaker 3: companies are extracting all of our consumers surplus and we 175 00:09:29,520 --> 00:09:33,920 Speaker 3: can decide that, yeah, maybe that's okay in a capitalist economy, 176 00:09:33,920 --> 00:09:34,440 Speaker 3: but we don't have. 177 00:09:34,440 --> 00:09:34,920 Speaker 4: To like it. 178 00:09:35,400 --> 00:09:40,240 Speaker 3: The final thing for me that doesn't sit well is predictability. 179 00:09:40,440 --> 00:09:43,160 Speaker 3: It is really hard to budget for things that you 180 00:09:43,200 --> 00:09:45,439 Speaker 3: know you need, like groceries, if you have no idea 181 00:09:45,440 --> 00:09:47,320 Speaker 3: what it's going to cost from one week to the next. 182 00:09:47,520 --> 00:09:50,040 Speaker 3: Part of the reason that people find inflation so uncomfortable. 183 00:09:50,400 --> 00:09:55,679 Speaker 3: Is it adds that element of volatility to essential items 184 00:09:55,720 --> 00:09:57,960 Speaker 3: and we start to wonder where we have enough money 185 00:09:58,040 --> 00:10:00,800 Speaker 3: left when the music finally stops. And I think this 186 00:10:00,960 --> 00:10:05,240 Speaker 3: instacart experiment is adding volatility and uncertainty and unpredictability. So 187 00:10:05,360 --> 00:10:06,959 Speaker 3: I think those are all the reasons why I think 188 00:10:06,960 --> 00:10:09,440 Speaker 3: it doesn't sit well with people, and arguably I think 189 00:10:09,480 --> 00:10:12,040 Speaker 3: some of them, you know, do constitute a sort of 190 00:10:12,160 --> 00:10:15,600 Speaker 3: violation of economic norms of fairness that lawmakers should take 191 00:10:15,600 --> 00:10:17,839 Speaker 3: a look at. Whether or not it's legal, I think 192 00:10:17,920 --> 00:10:20,760 Speaker 3: depends upon a few things. My sense, and we have 193 00:10:20,840 --> 00:10:25,280 Speaker 3: been talking to legal scholars and attorneys general and people 194 00:10:25,360 --> 00:10:28,719 Speaker 3: like that, is, you know, we already have laws protecting 195 00:10:28,840 --> 00:10:32,440 Speaker 3: us against unfair and deceptive acts and practices. 196 00:10:32,480 --> 00:10:33,480 Speaker 4: These are called you'd. 197 00:10:33,280 --> 00:10:36,400 Speaker 3: App laws, and I think there's maybe a case to 198 00:10:36,440 --> 00:10:41,560 Speaker 3: be made here that this particular experimental design violates those 199 00:10:41,800 --> 00:10:45,000 Speaker 3: DApp laws. And so I am actually interested to see 200 00:10:45,040 --> 00:10:48,040 Speaker 3: if we may see some legal action coming down the 201 00:10:48,080 --> 00:10:50,959 Speaker 3: pike here to sort of take this on and say, actually, 202 00:10:51,000 --> 00:10:53,679 Speaker 3: we think this is already something that a company like 203 00:10:53,720 --> 00:10:55,120 Speaker 3: in Stickhardt shouldn't be able to. 204 00:10:55,080 --> 00:10:55,600 Speaker 4: Do to us. 205 00:10:55,840 --> 00:11:00,120 Speaker 3: There have been further legal conversations and conversations with policy 206 00:11:00,160 --> 00:11:04,360 Speaker 3: makers and lawmakers about better understanding whether or not our 207 00:11:04,480 --> 00:11:07,880 Speaker 3: personal data is being used in the experiment. So is 208 00:11:07,920 --> 00:11:11,240 Speaker 3: the experiment random or is this that they've taken a 209 00:11:11,240 --> 00:11:13,679 Speaker 3: look at our shopping history, They've taken a look at 210 00:11:13,720 --> 00:11:16,880 Speaker 3: demographic information that they have about us because they've purchased 211 00:11:16,920 --> 00:11:20,079 Speaker 3: it from third party data sellers, and that's actually being 212 00:11:20,200 --> 00:11:22,800 Speaker 3: used to determine whether we're in the discount condition or 213 00:11:22,840 --> 00:11:25,960 Speaker 3: the guage condition. And I think if that is the case, 214 00:11:26,280 --> 00:11:29,280 Speaker 3: it's something that looks more like personalized pricing, what we 215 00:11:29,400 --> 00:11:32,800 Speaker 3: call surveillance pricing, where pricing is varying based on the 216 00:11:32,800 --> 00:11:36,280 Speaker 3: individual characteristics of the consumer. And there is actually a 217 00:11:36,400 --> 00:11:39,640 Speaker 3: nice kind of movement afoot to curtail the practice of 218 00:11:39,640 --> 00:11:43,160 Speaker 3: surveillance pricing, and New York State actually just took the 219 00:11:43,240 --> 00:11:46,840 Speaker 3: first action, and just last month in November, in New 220 00:11:46,920 --> 00:11:50,160 Speaker 3: York State, there is now a requirement that companies let 221 00:11:50,200 --> 00:11:53,440 Speaker 3: you know through disclosure if they are using your data 222 00:11:53,800 --> 00:11:57,080 Speaker 3: and their algorithm to set your prices. And so in 223 00:11:57,120 --> 00:11:59,520 Speaker 3: New York, now if you're shopping online, you'll get that 224 00:11:59,559 --> 00:12:02,600 Speaker 3: disclosure or if that's indeed happening. But in the US Congress, 225 00:12:02,880 --> 00:12:06,560 Speaker 3: Senator Reuben Diego last week introduced legislation to ban the 226 00:12:06,559 --> 00:12:10,200 Speaker 3: practice of surveillance pricing, and that follows action earlier this 227 00:12:10,320 --> 00:12:14,520 Speaker 3: year in the House of Representatives, Representative Greg Kassar offered 228 00:12:14,640 --> 00:12:18,320 Speaker 3: similar legislation. So there are some thorny questions here about 229 00:12:18,600 --> 00:12:20,360 Speaker 3: is this a deceptive issue? 230 00:12:20,720 --> 00:12:22,280 Speaker 4: Therefore maybe UDAP covers it. 231 00:12:22,960 --> 00:12:25,800 Speaker 3: Is this new terrain of surveillance pricing, therefore we need 232 00:12:25,840 --> 00:12:28,079 Speaker 3: sort of new laws to curtail it. 233 00:12:28,360 --> 00:12:34,400 Speaker 1: Can you talk about what else you guys are working on, 234 00:12:34,800 --> 00:12:38,480 Speaker 1: what other investigations you've done, what other investigations you're doing, 235 00:12:38,640 --> 00:12:42,360 Speaker 1: or what sort of what other focuses you guys have. 236 00:12:42,800 --> 00:12:45,080 Speaker 3: Yeah, So one of the things that we've been interested 237 00:12:45,160 --> 00:12:48,600 Speaker 3: in looking at is pricing around sports. 238 00:12:48,520 --> 00:12:50,560 Speaker 2: Which is also similar. Yeah. 239 00:12:50,760 --> 00:12:52,839 Speaker 3: Yeah, we're in the New York Times this morning and 240 00:12:52,920 --> 00:12:57,480 Speaker 3: the Athletic with some comments about our concerns about how 241 00:12:57,520 --> 00:13:01,920 Speaker 3: FIFA has allocated World Cup tickets, and you know, as 242 00:13:02,000 --> 00:13:04,839 Speaker 3: you know, they've sort of changed their tune a little 243 00:13:04,840 --> 00:13:08,320 Speaker 3: bit and they're making some additional tickets available at some 244 00:13:08,800 --> 00:13:11,600 Speaker 3: you know, firer and lower prices. But you know, when 245 00:13:11,600 --> 00:13:16,000 Speaker 3: you have scarce resources like seats in a stadium or 246 00:13:16,160 --> 00:13:19,440 Speaker 3: plane tickets. You often see dynamic pricing, right. This is 247 00:13:19,480 --> 00:13:22,840 Speaker 3: the idea that as the number of seats shrinks, the 248 00:13:22,840 --> 00:13:25,720 Speaker 3: price goes up. When there's two seats left, Like, the 249 00:13:25,760 --> 00:13:27,520 Speaker 3: price really goes up, right when you need a last 250 00:13:27,559 --> 00:13:29,560 Speaker 3: minute plane ticket, or you want the last seat at 251 00:13:29,600 --> 00:13:33,480 Speaker 3: a concert or a World Cup game, World Cup playoff game. 252 00:13:33,640 --> 00:13:35,960 Speaker 3: And you know what we talk about in the piece 253 00:13:36,000 --> 00:13:37,760 Speaker 3: and what we argue in the piece is there are 254 00:13:37,800 --> 00:13:40,560 Speaker 3: a lot of different ways to allocate scarce resources. You 255 00:13:40,640 --> 00:13:43,840 Speaker 3: can allocate them based on ability to pay, which is 256 00:13:43,880 --> 00:13:47,360 Speaker 3: what many people choose, many companies choose, and what FIFA 257 00:13:47,440 --> 00:13:50,559 Speaker 3: chows initially. But you can also allocate them based on lottery. 258 00:13:50,720 --> 00:13:53,680 Speaker 3: You can allocate based on queuing, first come, first serve. 259 00:13:53,800 --> 00:13:55,960 Speaker 3: I mean, I remember when I lived in Palo Alto 260 00:13:56,000 --> 00:13:56,760 Speaker 3: for a while. 261 00:13:57,120 --> 00:13:57,840 Speaker 4: A long time ago. 262 00:13:57,880 --> 00:14:00,640 Speaker 3: We'd watch these people line up and camp out and 263 00:14:00,679 --> 00:14:03,200 Speaker 3: sleep outside the Apple store, right, like the price of 264 00:14:03,200 --> 00:14:05,680 Speaker 3: the iPhone didn't change, but they wanted to be first 265 00:14:05,679 --> 00:14:07,440 Speaker 3: in line because they wanted to get the next batch. 266 00:14:07,559 --> 00:14:07,800 Speaker 4: Right. 267 00:14:07,880 --> 00:14:09,080 Speaker 3: So, there are a lot of different ways you can 268 00:14:09,120 --> 00:14:12,040 Speaker 3: allocate scarce resources. But what many companies do and what 269 00:14:12,080 --> 00:14:15,559 Speaker 3: FIFA decided to do is prioritize ability to pay, and 270 00:14:15,880 --> 00:14:18,840 Speaker 3: the result is you're going to have passionate fans watching 271 00:14:18,880 --> 00:14:22,440 Speaker 3: from their couch and hedge fund managers watching, you know, 272 00:14:22,480 --> 00:14:25,640 Speaker 3: from the sidelines. We've also been really interested in stadium 273 00:14:25,680 --> 00:14:29,080 Speaker 3: pricing concessions in stadiums. This is something that came up 274 00:14:29,280 --> 00:14:32,920 Speaker 3: with soon to be Mayor's or on Mandami's team recently 275 00:14:33,000 --> 00:14:34,680 Speaker 3: and there was there was a sort of I don't 276 00:14:34,680 --> 00:14:36,040 Speaker 3: know what you would call it. There was a Twitter 277 00:14:36,080 --> 00:14:40,240 Speaker 3: brew haha about to be except that that matters anymore. 278 00:14:40,560 --> 00:14:44,440 Speaker 3: But basically, you know, sort of our argument is that 279 00:14:44,720 --> 00:14:47,760 Speaker 3: you know, it's okay to prioritize the fans and pricing decisions, 280 00:14:47,800 --> 00:14:50,200 Speaker 3: and just because you have a captive audience doesn't mean 281 00:14:50,320 --> 00:14:54,320 Speaker 3: you need to gouge people. And also, our tax dollars 282 00:14:54,400 --> 00:14:58,920 Speaker 3: go to creating these stadiums, our labor goes to making 283 00:14:58,960 --> 00:15:04,240 Speaker 3: sure they're our ancillary businesses developed around the stadiums, and 284 00:15:04,400 --> 00:15:07,400 Speaker 3: it would be okay if there were rules saying you 285 00:15:07,480 --> 00:15:09,560 Speaker 3: got to charge the same amount for a pepsi inside 286 00:15:09,560 --> 00:15:12,040 Speaker 3: the stadium as you charge down the street. There are 287 00:15:12,080 --> 00:15:15,320 Speaker 3: actually places in the country that do that. The Portland 288 00:15:15,360 --> 00:15:17,600 Speaker 3: Airport is one of the only airports in the country 289 00:15:17,680 --> 00:15:21,360 Speaker 3: where you can get a bottle water and you know, 290 00:15:21,480 --> 00:15:24,000 Speaker 3: some p tog at a normal price. 291 00:15:24,320 --> 00:15:24,560 Speaker 5: Right. 292 00:15:25,040 --> 00:15:27,800 Speaker 3: We have something called street pricing, right, which is this 293 00:15:27,920 --> 00:15:31,080 Speaker 3: idea that prices outside and inside can be similar and 294 00:15:31,200 --> 00:15:33,760 Speaker 3: just because they've got you over a barrel doesn't mean 295 00:15:33,760 --> 00:15:36,280 Speaker 3: they get to shoot you. So we've been really focused 296 00:15:36,360 --> 00:15:40,760 Speaker 3: on understanding norms around pricing and thinking about different ways 297 00:15:41,240 --> 00:15:44,920 Speaker 3: in which lawmakers could regulate pricing. And by the way, 298 00:15:45,000 --> 00:15:48,560 Speaker 3: companies can take this on voluntarily. So you know, I 299 00:15:48,600 --> 00:15:52,600 Speaker 3: do think as pricing gets more volatile and more opaque, 300 00:15:52,640 --> 00:15:55,720 Speaker 3: there will be more kind of white hat companies who say, hey, 301 00:15:56,000 --> 00:15:58,720 Speaker 3: you come to Owens's store, like we're not going to 302 00:15:58,800 --> 00:16:01,920 Speaker 3: use electronic shelf label that where the price changes all 303 00:16:02,000 --> 00:16:03,280 Speaker 3: day long, Like if you walk it. 304 00:16:03,280 --> 00:16:04,840 Speaker 4: In the morning, the price will be four dollars. 305 00:16:04,960 --> 00:16:07,440 Speaker 3: If you come back in the afternoon because you forgot 306 00:16:07,480 --> 00:16:09,440 Speaker 3: something or you need another one, the price will still 307 00:16:09,480 --> 00:16:11,680 Speaker 3: be four dollars. And I think we will start seeing 308 00:16:11,720 --> 00:16:13,720 Speaker 3: some of that backlash. And you know we already have 309 00:16:14,000 --> 00:16:17,560 Speaker 3: in sports, we have the Savannah bananas, which is a 310 00:16:17,600 --> 00:16:18,880 Speaker 3: real sensation. 311 00:16:18,560 --> 00:16:20,560 Speaker 4: In the baseball sort of world. 312 00:16:20,640 --> 00:16:23,200 Speaker 3: And you know, Jesse Cole, who runs the Savanna Bananas, 313 00:16:23,200 --> 00:16:25,360 Speaker 3: has said, look like I'm in the business of creating 314 00:16:25,400 --> 00:16:29,080 Speaker 3: a positive experience, a memorable experience for fans and their families, 315 00:16:29,240 --> 00:16:31,400 Speaker 3: not in the business of charging you whatever I can 316 00:16:31,440 --> 00:16:33,880 Speaker 3: get away with charging you. So they use lottery systems, 317 00:16:33,880 --> 00:16:36,320 Speaker 3: they have all in pricing for concessions, all sorts of 318 00:16:36,360 --> 00:16:37,880 Speaker 3: things that they do a little differently. 319 00:16:38,000 --> 00:16:39,400 Speaker 4: I think it'll be great to see more of that. 320 00:16:39,760 --> 00:16:42,720 Speaker 1: So interesting. I hope you will come back with your 321 00:16:42,760 --> 00:16:43,680 Speaker 1: next investigation. 322 00:16:44,240 --> 00:16:45,640 Speaker 4: Yeah, happy too, that would be great. 323 00:16:50,240 --> 00:16:53,560 Speaker 1: We have exciting news over on our YouTube channel. The 324 00:16:53,680 --> 00:16:56,960 Speaker 1: second episode from our Project twenty twenty. 325 00:16:56,760 --> 00:16:58,520 Speaker 2: Nine series is out now. 326 00:16:58,720 --> 00:17:01,840 Speaker 1: It's a reimagining where we examined what went wrong with 327 00:17:01,920 --> 00:17:05,040 Speaker 1: democrats approach to politics and how we can correct it 328 00:17:05,080 --> 00:17:08,800 Speaker 1: and deliver changes to help people's lives. The first episode 329 00:17:08,920 --> 00:17:13,400 Speaker 1: dove into the very sexy topic of campaign finance reform, 330 00:17:13,600 --> 00:17:17,159 Speaker 1: and our second episode deals with an even sexier topic, 331 00:17:17,359 --> 00:17:23,480 Speaker 1: antitrust and regulation. We look at how antitrust and regulation 332 00:17:23,680 --> 00:17:28,920 Speaker 1: can protect American citizens and make America thrive in an 333 00:17:28,960 --> 00:17:33,840 Speaker 1: era of rampant corruption and predatory crony capitalism. We talk 334 00:17:34,200 --> 00:17:37,240 Speaker 1: to the smartest names in the field, like Lena Kahan, 335 00:17:37,720 --> 00:17:42,359 Speaker 1: Elvero Bedoya, Elizabeth Wilkins, and Doha Mecki. 336 00:17:42,600 --> 00:17:46,399 Speaker 2: Republicans were prepared for when they got the levers of power. 337 00:17:46,680 --> 00:17:49,920 Speaker 1: We need democrats to be too, So please head over 338 00:17:50,000 --> 00:17:53,600 Speaker 1: to YouTube and search Molli John Fast Project twenty twenty 339 00:17:53,680 --> 00:17:58,240 Speaker 1: nine or go to the Fast Politics YouTube channel and 340 00:17:58,320 --> 00:18:03,800 Speaker 1: find it there and help spread the word. Michael Rohantan 341 00:18:04,080 --> 00:18:08,439 Speaker 1: is the director of the documentary Drop Dead City. 342 00:18:08,800 --> 00:18:10,760 Speaker 2: Welcome to Fast Politics. 343 00:18:10,800 --> 00:18:12,920 Speaker 5: Michael, thank you for having me talk. 344 00:18:12,760 --> 00:18:14,200 Speaker 2: To us about this movie. 345 00:18:14,400 --> 00:18:16,240 Speaker 1: You know, as someone who wrote a book about their mother, 346 00:18:16,600 --> 00:18:19,399 Speaker 1: this is a sort of interesting way to process the 347 00:18:19,640 --> 00:18:23,960 Speaker 1: familial connection, So talk to us about how you got here. 348 00:18:24,240 --> 00:18:26,960 Speaker 6: That's interesting that you frame it that way. That I 349 00:18:27,320 --> 00:18:30,399 Speaker 6: made the film with my friend Peter Yost, and I 350 00:18:30,480 --> 00:18:32,640 Speaker 6: sort of pitched it to him as an idea after 351 00:18:32,680 --> 00:18:36,080 Speaker 6: having spent time with my father and that cohort of 352 00:18:36,119 --> 00:18:40,440 Speaker 6: old lions, where I saw how still, you know, vibrant 353 00:18:40,600 --> 00:18:43,960 Speaker 6: they were with the whole energy of their experience. But 354 00:18:44,000 --> 00:18:45,760 Speaker 6: I didn't want to make a film about my father 355 00:18:45,840 --> 00:18:47,919 Speaker 6: that was really like that would have bothered me. There 356 00:18:47,920 --> 00:18:50,960 Speaker 6: are so many puff piece. Not what you did is 357 00:18:50,960 --> 00:18:52,359 Speaker 6: about you and your mother. 358 00:18:52,600 --> 00:18:55,040 Speaker 5: I didn't want to do a movie about me explain 359 00:18:55,200 --> 00:18:56,919 Speaker 5: to listeners who your father is. 360 00:18:57,160 --> 00:19:00,199 Speaker 6: So Felix Rohiton was my father, and he was one 361 00:19:00,240 --> 00:19:03,720 Speaker 6: of the principal architects of the rescue and was sort 362 00:19:03,720 --> 00:19:07,359 Speaker 6: of dropped into the crisis in nineteen seventy five at 363 00:19:07,400 --> 00:19:10,119 Speaker 6: the invitation of the Governor q Carry to sort of 364 00:19:10,160 --> 00:19:13,080 Speaker 6: see if he could sort out what was happening in 365 00:19:13,080 --> 00:19:14,040 Speaker 6: New York City. 366 00:19:14,080 --> 00:19:17,320 Speaker 1: The financial crisis. So New York City, just talk us 367 00:19:17,320 --> 00:19:20,320 Speaker 1: through the financial crisis. Where How did New York City 368 00:19:20,680 --> 00:19:25,000 Speaker 1: get to this moment in nineteen seventy five where it 369 00:19:25,119 --> 00:19:26,479 Speaker 1: went bankrupt. 370 00:19:26,080 --> 00:19:29,679 Speaker 6: In nineteen seventy five, New York had been spending past 371 00:19:29,840 --> 00:19:34,679 Speaker 6: its ability to earn. The combination of things drove New 372 00:19:34,720 --> 00:19:39,159 Speaker 6: York City to a sort of sudden bankruptcy, a sudden 373 00:19:39,400 --> 00:19:40,680 Speaker 6: brush with bankruptcy. 374 00:19:40,920 --> 00:19:43,880 Speaker 5: One was white flight rich New Yorkers leaving New York, 375 00:19:43,920 --> 00:19:45,639 Speaker 5: which had been years happening. 376 00:19:45,920 --> 00:19:49,400 Speaker 6: One was the arrival of poor people to New York 377 00:19:49,400 --> 00:19:52,000 Speaker 6: who needed services, also years happening. 378 00:19:52,160 --> 00:19:56,280 Speaker 5: One was terrible accounting at city Hall, also years happening. 379 00:19:56,520 --> 00:19:58,879 Speaker 5: One was interest rates that had gone up. 380 00:19:59,440 --> 00:20:02,960 Speaker 6: Sharply because of the oil crisis, not so long happening, 381 00:20:03,280 --> 00:20:06,480 Speaker 6: And one day the governor of New York at the time, 382 00:20:06,480 --> 00:20:11,880 Speaker 6: a Republican, Nelson Rockefeller, had been very generous in spending 383 00:20:11,960 --> 00:20:14,960 Speaker 6: money on public programs at the state level and also 384 00:20:15,080 --> 00:20:19,120 Speaker 6: in approving city budgets sent to him by John Lindsay, 385 00:20:19,119 --> 00:20:22,320 Speaker 6: who was the mayor before our period, before our story. 386 00:20:22,600 --> 00:20:25,920 Speaker 6: And so when Lindsay wanted to spend money on housing, 387 00:20:25,960 --> 00:20:29,040 Speaker 6: when he wanted to spend money on social services, the 388 00:20:29,080 --> 00:20:33,719 Speaker 6: governor would approve it, and the legislature would design bond 389 00:20:34,040 --> 00:20:38,359 Speaker 6: issuances that would kind of accommodate whatever the needs were. 390 00:20:38,840 --> 00:20:43,439 Speaker 6: And these were more and more far fetched bonds that 391 00:20:43,560 --> 00:20:47,080 Speaker 6: required other bonds to pay them off, bonds that were 392 00:20:47,080 --> 00:20:51,760 Speaker 6: in anticipation of taxes that hadn't been levied yet. And literally, 393 00:20:51,800 --> 00:20:56,440 Speaker 6: one day it's like the Emperor's clothes. A young banker 394 00:20:56,880 --> 00:21:01,760 Speaker 6: went to one of the issuingomists for the City of 395 00:21:01,800 --> 00:21:05,960 Speaker 6: New York and said, you're issuing a bond in anticipation 396 00:21:06,040 --> 00:21:11,280 Speaker 6: of attacks. What tax are you anticipating collecting? And he 397 00:21:11,280 --> 00:21:13,960 Speaker 6: couldn't answer it. He wrote something down on a piece 398 00:21:14,000 --> 00:21:18,560 Speaker 6: of paper, and at that moment, this young lawyer working 399 00:21:18,600 --> 00:21:22,040 Speaker 6: for the banks realized that there was nothing holding up 400 00:21:22,119 --> 00:21:23,040 Speaker 6: these issuances. 401 00:21:23,359 --> 00:21:25,399 Speaker 5: As we've heard before, you know, in two. 402 00:21:25,240 --> 00:21:28,800 Speaker 6: Thousand and eight and whatever, the banks were selling bonds 403 00:21:29,040 --> 00:21:33,320 Speaker 6: that had not been carefully vetted and where the risk 404 00:21:33,480 --> 00:21:35,240 Speaker 6: was not accurately priced. 405 00:21:35,440 --> 00:21:39,440 Speaker 5: So and since that was a short term borrowing pattern. 406 00:21:39,080 --> 00:21:42,200 Speaker 6: That the city at that point was on such hard 407 00:21:42,320 --> 00:21:44,840 Speaker 6: times that they were borrowing short term just to pay 408 00:21:44,880 --> 00:21:49,280 Speaker 6: their payrolls. The moment that the marketplace saw that there 409 00:21:49,359 --> 00:21:52,439 Speaker 6: was some problems with the accounting and there was a 410 00:21:52,440 --> 00:21:56,439 Speaker 6: young controller, Harrison Golden, who was at odds with the 411 00:21:56,480 --> 00:22:00,600 Speaker 6: mayor publicly about the books. Also something that had never happened, 412 00:22:00,960 --> 00:22:03,960 Speaker 6: the banks just got cold feet. The marketplace dried up, 413 00:22:04,040 --> 00:22:06,560 Speaker 6: and there was no ability for the city to borrow 414 00:22:06,720 --> 00:22:10,840 Speaker 6: short term. Literally overnight, they made a bond sale and 415 00:22:11,160 --> 00:22:14,080 Speaker 6: they had no bids. So at that moment, now the 416 00:22:14,119 --> 00:22:17,359 Speaker 6: city is on a three month you know horizon, or 417 00:22:17,760 --> 00:22:20,639 Speaker 6: a one month horizon. There are there are obligations coming 418 00:22:20,720 --> 00:22:23,040 Speaker 6: due and they're chasing these obligations. 419 00:22:23,240 --> 00:22:25,680 Speaker 1: If you had a bond sale where no one bought 420 00:22:25,720 --> 00:22:29,800 Speaker 1: the bond, even today, you would be on the precipice 421 00:22:29,800 --> 00:22:31,000 Speaker 1: of disaster. 422 00:22:30,800 --> 00:22:33,320 Speaker 6: And maybe blundering. And you have a very educated audience. 423 00:22:33,600 --> 00:22:35,919 Speaker 6: I would say this was worse. I don't think this 424 00:22:35,960 --> 00:22:39,720 Speaker 6: can happen much worse. No, But because now this was 425 00:22:39,840 --> 00:22:42,440 Speaker 6: for short term borrowing to just get through next month. 426 00:22:43,000 --> 00:22:47,960 Speaker 6: Right now, the next month is pretty well audited and managed. 427 00:22:48,119 --> 00:22:52,000 Speaker 6: If you had a bond for an airport reconstruction finance 428 00:22:52,280 --> 00:22:54,680 Speaker 6: or a you know, and you had no bids, that's 429 00:22:54,720 --> 00:22:55,320 Speaker 6: a crisis. 430 00:22:55,400 --> 00:22:57,440 Speaker 5: But this is existential in a different way. 431 00:22:57,520 --> 00:22:59,480 Speaker 6: That is literally, what are we going to do next 432 00:22:59,520 --> 00:23:02,280 Speaker 6: month when we have to pay off those other bonds 433 00:23:02,480 --> 00:23:04,040 Speaker 6: and we have payroll to meet. 434 00:23:04,240 --> 00:23:05,280 Speaker 5: So that's kind of. 435 00:23:05,200 --> 00:23:07,199 Speaker 6: Where they found themselves, and I think where there have 436 00:23:07,280 --> 00:23:10,439 Speaker 6: been reforms since then, very important ones that now the 437 00:23:10,480 --> 00:23:13,920 Speaker 6: city will never face those particular variables again. 438 00:23:14,240 --> 00:23:18,760 Speaker 2: For sure. The city finds himself on the precipice of disaster. 439 00:23:18,600 --> 00:23:24,200 Speaker 6: Right and the mayor goes to Albany to get some funding, 440 00:23:24,400 --> 00:23:27,440 Speaker 6: and he goes to Albanie to present his problem, which 441 00:23:27,480 --> 00:23:30,240 Speaker 6: is that they have in their own audits at the 442 00:23:30,280 --> 00:23:33,920 Speaker 6: Mayor's Budget Bureau, they have found that they are short 443 00:23:34,240 --> 00:23:36,720 Speaker 6: billions of dollars. They thought they had a deficit of 444 00:23:36,720 --> 00:23:38,440 Speaker 6: two billion, but they had a deficit. 445 00:23:38,080 --> 00:23:38,720 Speaker 5: Of six billion. 446 00:23:39,280 --> 00:23:43,720 Speaker 6: And they go to Albany with this kind of half 447 00:23:43,760 --> 00:23:47,800 Speaker 6: assed presentation to the governor and to the lawmakers there 448 00:23:48,200 --> 00:23:52,760 Speaker 6: with just weeks to go before their next obligation is 449 00:23:52,760 --> 00:23:57,000 Speaker 6: coming due. So it's total Keystone cops at city Hall. 450 00:23:57,440 --> 00:24:00,640 Speaker 6: The sort of farce and tragedy of it is that 451 00:24:00,720 --> 00:24:02,439 Speaker 6: the mayor had been controller. 452 00:24:02,840 --> 00:24:07,080 Speaker 5: Was this dignified, kindly guy, career. 453 00:24:06,840 --> 00:24:10,080 Speaker 6: Politician, had worked his way up through as precinct captain 454 00:24:10,320 --> 00:24:12,880 Speaker 6: and was part of the machine. You know, but our 455 00:24:12,920 --> 00:24:16,040 Speaker 6: film is really agnostic on a lot of these things. 456 00:24:16,080 --> 00:24:18,119 Speaker 6: You know, he was part of a machine that in 457 00:24:18,160 --> 00:24:21,160 Speaker 6: some ways worked, and you know there was a real 458 00:24:21,200 --> 00:24:26,560 Speaker 6: connection between the politics and the community when you know 459 00:24:26,720 --> 00:24:30,200 Speaker 6: when they were that connected, this guy, it all fell 460 00:24:30,280 --> 00:24:33,199 Speaker 6: on his lap a beam. He was sort of the 461 00:24:33,200 --> 00:24:36,000 Speaker 6: goat because he should have known. He was in charge 462 00:24:36,000 --> 00:24:39,320 Speaker 6: of the books, not immediately prior but earlier, and was 463 00:24:39,359 --> 00:24:41,480 Speaker 6: the mayor, and it sort of all fell on him. 464 00:24:41,520 --> 00:24:45,480 Speaker 6: But as our film really has tried to navigate carefully, 465 00:24:45,800 --> 00:24:48,320 Speaker 6: the mayor's malfeasance was playing. 466 00:24:48,600 --> 00:24:52,800 Speaker 5: The auditing was terrible, The books were a joke. 467 00:24:53,400 --> 00:24:58,280 Speaker 6: But the banks failed their clients and failed the system 468 00:24:58,640 --> 00:25:01,639 Speaker 6: by selling bonds that they should not have been selling. 469 00:25:01,960 --> 00:25:04,080 Speaker 5: A lot of other factors came into play. 470 00:25:04,160 --> 00:25:08,119 Speaker 6: One of the legacies is that the unions were overpaid, 471 00:25:08,160 --> 00:25:11,080 Speaker 6: and I think that's kind of the lasting narrative here 472 00:25:11,600 --> 00:25:13,760 Speaker 6: is that New York in nineteen seventy five is an 473 00:25:13,760 --> 00:25:16,760 Speaker 6: example of what happens when unions are doing too well. 474 00:25:16,840 --> 00:25:19,800 Speaker 5: We really wanted to kind of go into that. You know, 475 00:25:19,840 --> 00:25:21,240 Speaker 5: they were paid well. 476 00:25:21,520 --> 00:25:25,400 Speaker 6: Your average worker union worker lived in the city, could 477 00:25:25,560 --> 00:25:27,280 Speaker 6: raise his kids, could buy a house. 478 00:25:27,720 --> 00:25:30,280 Speaker 5: Were they the problem. Absolutely not. 479 00:25:30,680 --> 00:25:34,080 Speaker 6: Yes, there was too much money was being spent proportionally 480 00:25:34,200 --> 00:25:37,680 Speaker 6: to the other parts of the system. But they were 481 00:25:37,720 --> 00:25:42,000 Speaker 6: not the cause of this problem. Had the other teams 482 00:25:42,240 --> 00:25:46,800 Speaker 6: in this game, the banks, the city's budget process, the 483 00:25:46,880 --> 00:25:50,520 Speaker 6: state's budget process, had they worked correctly as well as 484 00:25:50,520 --> 00:25:52,960 Speaker 6: the unions did. Frankly, the unions did what you're supposed 485 00:25:53,000 --> 00:25:55,440 Speaker 6: to do. They fought for the best contracts for their membership. 486 00:25:55,600 --> 00:25:58,439 Speaker 6: If the city had fought back with its best team 487 00:25:58,800 --> 00:26:02,280 Speaker 6: to negotiate a contract that was equitable on both sides 488 00:26:02,320 --> 00:26:04,760 Speaker 6: and that was you know, I don't think this crisis 489 00:26:04,760 --> 00:26:08,399 Speaker 6: would have happened. But the legacy narrative is, this is 490 00:26:08,440 --> 00:26:09,960 Speaker 6: the folly of liberalism. 491 00:26:10,320 --> 00:26:12,200 Speaker 5: You're going to pay unions too much, you're going to 492 00:26:12,240 --> 00:26:12,840 Speaker 5: go bankrupt. 493 00:26:13,080 --> 00:26:16,920 Speaker 6: So what happens next the governor, who both the governor 494 00:26:16,960 --> 00:26:20,920 Speaker 6: and the mayor were rookies. The governor had just started 495 00:26:21,119 --> 00:26:24,320 Speaker 6: his term, he had been a longstanding member of Congress. 496 00:26:24,560 --> 00:26:27,280 Speaker 6: They were both from Brooklyn, and they were both dropped 497 00:26:27,280 --> 00:26:30,000 Speaker 6: into this. The governor arrives in Albany and he learns 498 00:26:30,040 --> 00:26:33,159 Speaker 6: that there's this crisis in New York City and then 499 00:26:33,200 --> 00:26:35,919 Speaker 6: New York City is looking at like the possibility of 500 00:26:36,200 --> 00:26:42,160 Speaker 6: default within months, and he organizes a response. He builds 501 00:26:42,359 --> 00:26:46,080 Speaker 6: a team around, you know, my father and a few 502 00:26:46,080 --> 00:26:50,479 Speaker 6: others who kind of created an administration that was an 503 00:26:50,520 --> 00:26:55,480 Speaker 6: emergency finance administration without a doubt, the failure of democracy, 504 00:26:55,800 --> 00:27:00,240 Speaker 6: there should not be an emergency group of elders or no. 505 00:27:00,600 --> 00:27:04,280 Speaker 2: Well, but it worked, So tell us how well it worked. 506 00:27:04,280 --> 00:27:07,560 Speaker 5: And it worked partly I think because my father. 507 00:27:07,520 --> 00:27:11,120 Speaker 6: Really revered government and revered FDR and was pro government. 508 00:27:11,240 --> 00:27:14,040 Speaker 5: So he was not there to sort of just you. 509 00:27:13,960 --> 00:27:17,440 Speaker 1: Know, he was not there to elon musk the government 510 00:27:17,480 --> 00:27:18,919 Speaker 1: that was there to make it work. 511 00:27:19,000 --> 00:27:21,840 Speaker 2: So explain to us how they did it. 512 00:27:21,840 --> 00:27:24,879 Speaker 6: It was a disaster for the city. I mean there 513 00:27:24,920 --> 00:27:28,840 Speaker 6: were you know, thousands upon thousands of layoffs. People were 514 00:27:29,200 --> 00:27:33,080 Speaker 6: their lives were completely upended. Arguably the city got worse. 515 00:27:33,160 --> 00:27:35,439 Speaker 6: Part of the challenge for our film was, like we 516 00:27:35,520 --> 00:27:38,680 Speaker 6: framed around nineteen seventy five, well, nineteen seventy six was 517 00:27:38,760 --> 00:27:41,159 Speaker 6: arguably worse than seventy five. But you know, for the 518 00:27:41,200 --> 00:27:44,080 Speaker 6: purpose of our study of what we wanted to talk about, 519 00:27:44,119 --> 00:27:48,080 Speaker 6: which was this kind of process around this default, this 520 00:27:48,160 --> 00:27:52,480 Speaker 6: near bankruptcy, it worked to end there. But the consequence 521 00:27:52,520 --> 00:27:55,320 Speaker 6: of all of the layoffs in fire and in police 522 00:27:55,920 --> 00:28:00,320 Speaker 6: was rampant crime and arson. And so you you go 523 00:28:00,359 --> 00:28:02,320 Speaker 6: into the eighties and you go into crack, and you 524 00:28:02,400 --> 00:28:05,320 Speaker 6: go into you know, the consequences for the city were 525 00:28:05,600 --> 00:28:08,960 Speaker 6: far reaching across the board and not good. In a way, 526 00:28:09,080 --> 00:28:11,760 Speaker 6: you could say that this was one of the better 527 00:28:12,240 --> 00:28:16,560 Speaker 6: moments in the story, was where people came together that 528 00:28:16,640 --> 00:28:20,040 Speaker 6: the unions, the banks, the you know, antagonists. 529 00:28:20,320 --> 00:28:22,719 Speaker 5: The press was unbelievable. They were incredible. 530 00:28:22,760 --> 00:28:25,720 Speaker 6: They were reporting the story, you know, several times a day, 531 00:28:25,760 --> 00:28:28,479 Speaker 6: and everyone in the city knew what was going on. 532 00:28:28,640 --> 00:28:31,119 Speaker 5: It was quite an amazing feature of this story. 533 00:28:31,200 --> 00:28:33,920 Speaker 2: But you also had nighttime papers then too. 534 00:28:34,160 --> 00:28:36,640 Speaker 5: Absolutely, and they knew they knew what was going on. 535 00:28:36,680 --> 00:28:39,440 Speaker 6: You know, Victor Gotbam, these guys, Alice Schnanker, they were 536 00:28:39,480 --> 00:28:40,520 Speaker 6: known to the city. 537 00:28:40,400 --> 00:28:44,680 Speaker 5: They were characters in the city. Kind of a story. 538 00:28:44,880 --> 00:28:47,520 Speaker 6: I think it meant something to be a public person 539 00:28:47,680 --> 00:28:48,960 Speaker 6: in nineteen seventy five. 540 00:28:49,360 --> 00:28:51,080 Speaker 5: That's different from what it is today. 541 00:28:51,320 --> 00:28:54,080 Speaker 2: Ooh, tell me more. You know, these are the kind 542 00:28:54,120 --> 00:28:55,200 Speaker 2: of things I think about a lot. 543 00:28:55,280 --> 00:28:58,360 Speaker 6: To explain, well, I just think that in those days, 544 00:28:58,480 --> 00:29:01,000 Speaker 6: it was a matter of some p that you were 545 00:29:01,520 --> 00:29:04,640 Speaker 6: going to participate in civic life. And you know, my 546 00:29:04,720 --> 00:29:08,120 Speaker 6: father loved it. You know, he completely took to it. 547 00:29:08,200 --> 00:29:11,240 Speaker 6: He liked the attention, he liked the press, he liked 548 00:29:11,240 --> 00:29:13,680 Speaker 6: the game of it. But he did it because he 549 00:29:14,080 --> 00:29:17,560 Speaker 6: wanted to help and loved New York. And I think 550 00:29:17,600 --> 00:29:20,720 Speaker 6: now public first of all, politics has been so degraded 551 00:29:20,720 --> 00:29:24,040 Speaker 6: that nobody really goes into politics to play a role. 552 00:29:24,400 --> 00:29:26,960 Speaker 2: I mean, I would argue that that maybe. 553 00:29:26,720 --> 00:29:30,760 Speaker 6: Mondami absolutely, that's an example of a sort of a 554 00:29:30,880 --> 00:29:31,840 Speaker 6: new chapter. 555 00:29:32,080 --> 00:29:34,520 Speaker 5: And I think what we had was Trump. We had people. 556 00:29:34,560 --> 00:29:37,640 Speaker 6: You could become a public person through reality television. You 557 00:29:37,640 --> 00:29:41,000 Speaker 6: could become if you're wealthy, you're Mark Cuban, You're going 558 00:29:41,040 --> 00:29:43,040 Speaker 6: to own a team, You're gonna be on TV, on. 559 00:29:43,040 --> 00:29:44,000 Speaker 5: Shark Tank or whatever. 560 00:29:44,160 --> 00:29:47,320 Speaker 6: There's so many other ways for a wealthy person now 561 00:29:47,360 --> 00:29:50,360 Speaker 6: to become a sort of a public person. And I 562 00:29:50,440 --> 00:29:54,080 Speaker 6: love that, Momdami, that there's this idea of a celebrity 563 00:29:54,840 --> 00:29:57,400 Speaker 6: who's going to be the mayor and who has talent, 564 00:29:57,600 --> 00:29:59,520 Speaker 6: and that his talent is being put there. 565 00:29:59,640 --> 00:30:02,320 Speaker 5: So I wish him well and I hope he's going 566 00:30:02,400 --> 00:30:03,240 Speaker 5: to be a rock. 567 00:30:03,840 --> 00:30:06,920 Speaker 2: So you have some interesting cameos. I don't know how 568 00:30:06,960 --> 00:30:09,040 Speaker 2: you do it. It's a terrible job in your movie. 569 00:30:09,560 --> 00:30:12,920 Speaker 1: And I want to talk about them. 570 00:30:13,200 --> 00:30:15,120 Speaker 2: Dick Cheney and Ronny. 571 00:30:15,120 --> 00:30:18,280 Speaker 5: Rubby and Cheney. Right, So, Okay, when you're making a movie, 572 00:30:18,360 --> 00:30:19,480 Speaker 5: you want a villain. 573 00:30:19,400 --> 00:30:22,480 Speaker 6: And you need celebrities too, and you need celebrities and 574 00:30:22,640 --> 00:30:23,360 Speaker 6: especially now. 575 00:30:23,640 --> 00:30:26,600 Speaker 5: And we really thought we were going to be working 576 00:30:26,600 --> 00:30:27,960 Speaker 5: around the mayor as the villain. 577 00:30:28,040 --> 00:30:30,920 Speaker 6: Who ultimately is I think I've made clear It became 578 00:30:31,000 --> 00:30:33,840 Speaker 6: a little bit of our sort of tragic hero, not 579 00:30:33,960 --> 00:30:35,880 Speaker 6: hero in the sense of saving the day, but just 580 00:30:35,960 --> 00:30:39,040 Speaker 6: the person you follow and you sympathize with. But long Behold, 581 00:30:39,120 --> 00:30:42,280 Speaker 6: we came across Dick Cheney and Donald Rumsfeld's who were 582 00:30:42,720 --> 00:30:46,520 Speaker 6: highly placed in Ford's white House, and Rumsfield was the 583 00:30:46,600 --> 00:30:50,120 Speaker 6: chief of staff, and they hated New York. They hated 584 00:30:50,680 --> 00:30:53,640 Speaker 6: first of all, they were Midwesterners, and there's an antipathy 585 00:30:53,680 --> 00:30:54,120 Speaker 6: from there. 586 00:30:54,200 --> 00:30:55,680 Speaker 5: That is a known fact. 587 00:30:56,120 --> 00:30:58,479 Speaker 6: We actually there is a story that's not in our 588 00:30:58,520 --> 00:31:02,440 Speaker 6: film around the fact that they actually were plotting against 589 00:31:02,440 --> 00:31:05,440 Speaker 6: New York in some real way in Congress to sort 590 00:31:05,480 --> 00:31:09,680 Speaker 6: of precipitate New York's demise so that Chicago might rise. 591 00:31:10,040 --> 00:31:12,640 Speaker 6: But yeah, there they were young at the beginning of 592 00:31:12,640 --> 00:31:16,280 Speaker 6: their careers, and this was the beginning of the demonizing 593 00:31:16,680 --> 00:31:19,040 Speaker 6: of the coastal elites. Maybe not the beginning, I know 594 00:31:19,120 --> 00:31:23,000 Speaker 6: Nixon did it, and they went after New York as 595 00:31:23,040 --> 00:31:28,560 Speaker 6: a as the profligate cautionary tale, and they hated Nelson Rockefeller, 596 00:31:28,880 --> 00:31:32,880 Speaker 6: who was a high spending Republican. In the nineteen seventy 597 00:31:32,920 --> 00:31:37,160 Speaker 6: six campaign for they pushed Rockfeller off the ticket and. 598 00:31:37,120 --> 00:31:39,240 Speaker 5: They went for Bob Dole, who was from Kansas. 599 00:31:39,600 --> 00:31:41,920 Speaker 6: The convention was in Kansas, and that was sort of 600 00:31:41,920 --> 00:31:45,280 Speaker 6: the beginning to my mind of the Republican you know, 601 00:31:45,520 --> 00:31:49,480 Speaker 6: veering into the flyover states, into the middle and pushing 602 00:31:49,520 --> 00:31:52,840 Speaker 6: out the coastal elites as the kind of the enemy 603 00:31:53,160 --> 00:31:54,000 Speaker 6: they were chasing. 604 00:31:54,080 --> 00:31:55,360 Speaker 5: Ronald Reagan to the. 605 00:31:55,360 --> 00:31:57,360 Speaker 2: Right created no fault divorce. 606 00:31:58,080 --> 00:31:58,760 Speaker 5: Well there's that. 607 00:31:58,880 --> 00:32:04,280 Speaker 6: There's that these two guys were young activist and very 608 00:32:04,560 --> 00:32:09,600 Speaker 6: very energized young conservatives in Ford's White House, and they 609 00:32:09,840 --> 00:32:13,400 Speaker 6: set the policy on New York. They and William Simon 610 00:32:13,520 --> 00:32:16,320 Speaker 6: and a few others to really hang New York out 611 00:32:16,320 --> 00:32:16,680 Speaker 6: to dry. 612 00:32:16,680 --> 00:32:18,880 Speaker 5: And there was a political side to that. 613 00:32:18,880 --> 00:32:20,760 Speaker 6: Which was as I said, they were they were worried 614 00:32:20,800 --> 00:32:24,120 Speaker 6: about the right wing threat from Reagan, and there was 615 00:32:24,200 --> 00:32:28,320 Speaker 6: also a sort of ideological moralist side. And Ford was 616 00:32:28,360 --> 00:32:31,880 Speaker 6: a Midwesterner and had you know, was a kind of 617 00:32:31,920 --> 00:32:36,160 Speaker 6: a sober minded guy, and New York just defended him 618 00:32:36,240 --> 00:32:39,040 Speaker 6: on some basic level. And they kind of used New 619 00:32:39,120 --> 00:32:41,800 Speaker 6: York as a you know, as a device and let 620 00:32:41,800 --> 00:32:43,360 Speaker 6: it let New York hang out to dry. 621 00:32:43,480 --> 00:32:44,680 Speaker 5: And then it backfired. 622 00:32:44,720 --> 00:32:48,240 Speaker 6: And you know, David Gergan wrote a speech for Ford 623 00:32:48,600 --> 00:32:51,560 Speaker 6: which was what the famous headline New York Ford to 624 00:32:51,560 --> 00:32:55,400 Speaker 6: city drop dead is derived from. Gergan wrote this speech 625 00:32:55,560 --> 00:32:58,480 Speaker 6: and in our film we have him talking about that. 626 00:32:58,600 --> 00:33:00,320 Speaker 6: He wrote a speech that was that he got a 627 00:33:00,360 --> 00:33:03,200 Speaker 6: call from Cheney asking for a very tough speech, and 628 00:33:03,240 --> 00:33:05,400 Speaker 6: he wrote one, but he didn't think it would actually 629 00:33:05,440 --> 00:33:07,680 Speaker 6: get you know, he thought it would get watered down, 630 00:33:07,800 --> 00:33:10,920 Speaker 6: this is where we would start here and soften it. 631 00:33:11,240 --> 00:33:13,280 Speaker 5: And the President read it and it was a really 632 00:33:13,320 --> 00:33:14,000 Speaker 5: tough speech. 633 00:33:14,080 --> 00:33:17,360 Speaker 6: I mean it was very, very blunt, and it totally 634 00:33:17,440 --> 00:33:22,000 Speaker 6: backfired on Ford and arguably cost him the election. Because 635 00:33:22,120 --> 00:33:25,920 Speaker 6: New York had been a Republican and was not a 636 00:33:26,360 --> 00:33:28,680 Speaker 6: it wasn't a given that New York would go Democrat 637 00:33:29,040 --> 00:33:32,160 Speaker 6: in nineteen seventy six those forty one in those days, 638 00:33:32,160 --> 00:33:34,800 Speaker 6: it was forty one electoral votes went to Jimmy Carter, 639 00:33:34,960 --> 00:33:37,240 Speaker 6: and a lot of that came from his New York 640 00:33:37,280 --> 00:33:41,080 Speaker 6: City policy. Arguably one of the worst blunders of a 641 00:33:41,240 --> 00:33:44,120 Speaker 6: presidential advisor that you could have you know, you could have. 642 00:33:44,400 --> 00:33:51,000 Speaker 1: Designed everything is so unbelievably stupid. Though, this is fascinating. 643 00:33:51,200 --> 00:33:53,760 Speaker 1: The fact that these people keep coming back again and 644 00:33:53,800 --> 00:33:55,960 Speaker 1: again is pretty incredible, right. 645 00:33:55,880 --> 00:33:58,600 Speaker 6: And you know the fact that like this antipathy, now 646 00:33:58,640 --> 00:34:01,520 Speaker 6: we have Trump. Arguably in those days, the war against 647 00:34:01,600 --> 00:34:03,840 Speaker 6: New York was political, like it had there was some 648 00:34:03,920 --> 00:34:08,000 Speaker 6: political gain. With Trump, it's sort of vindictive. It's you know, 649 00:34:08,360 --> 00:34:09,760 Speaker 6: he's not running for reelection. 650 00:34:10,200 --> 00:34:13,319 Speaker 2: Well I think he thinks he is, but sure he might. 651 00:34:13,520 --> 00:34:14,640 Speaker 5: Right, Well, you're right, right. 652 00:34:14,719 --> 00:34:18,000 Speaker 6: I shouldn't say that, but on some level, the animus 653 00:34:18,080 --> 00:34:21,000 Speaker 6: is just from his own gut. You know, nothing he 654 00:34:21,040 --> 00:34:24,040 Speaker 6: does seems all that. It's much more intuitive and much 655 00:34:24,080 --> 00:34:26,880 Speaker 6: more of a you know, off of the bad vibe 656 00:34:26,880 --> 00:34:29,960 Speaker 6: of it. Anyway, So and now amazing to watch him 657 00:34:30,000 --> 00:34:34,440 Speaker 6: and Mamdami start their romance, start their romance. 658 00:34:34,520 --> 00:34:35,960 Speaker 5: Yeah, where do you think that's going to go? 659 00:34:36,239 --> 00:34:39,480 Speaker 2: I don't know. Mayor is such a terrible job. I 660 00:34:39,480 --> 00:34:41,719 Speaker 2: don't know. I can't begin to hope. I hope that 661 00:34:41,840 --> 00:34:43,320 Speaker 2: he can do it. 662 00:34:43,320 --> 00:34:45,560 Speaker 6: It always amazed me that Bloomberg, with all his money, 663 00:34:45,560 --> 00:34:46,839 Speaker 6: that he wanted to be mayor of New York. 664 00:34:46,880 --> 00:34:49,400 Speaker 5: To mean, there's a certain sweetness in that is like, no, 665 00:34:49,520 --> 00:34:52,560 Speaker 5: I want to drive a locomotive to Yeah, that's what 666 00:34:52,640 --> 00:34:54,720 Speaker 5: it is. But I always wanted to do because why 667 00:34:54,800 --> 00:34:55,759 Speaker 5: else would you do that? 668 00:34:55,840 --> 00:34:57,840 Speaker 2: It's crazy, It doesn't make any sense. 669 00:34:58,520 --> 00:35:02,040 Speaker 5: Thank you, Michael, thank you so much. Thanks for having us. 670 00:35:02,320 --> 00:35:04,920 Speaker 5: We're streaming on Amazon and Apple. 671 00:35:05,160 --> 00:35:07,320 Speaker 6: And that's some film that I made with Peter Yost, 672 00:35:07,320 --> 00:35:08,160 Speaker 6: who could not be here. 673 00:35:08,920 --> 00:35:13,279 Speaker 1: That's it for this episode of Fast Politics. Tune in 674 00:35:13,600 --> 00:35:19,040 Speaker 1: every Monday, Wednesday, Thursday and Saturday to hear the best 675 00:35:19,120 --> 00:35:23,440 Speaker 1: minds and politics make sense of all this chaos. If 676 00:35:23,440 --> 00:35:26,520 Speaker 1: you enjoy this podcast, please send it to a friend 677 00:35:26,960 --> 00:35:28,560 Speaker 1: and keep the conversation going. 678 00:35:29,040 --> 00:35:30,160 Speaker 2: Thanks for listening.