1 00:00:00,120 --> 00:00:02,040 Speaker 1: Back in the day, if you needed to get from 2 00:00:02,120 --> 00:00:04,520 Speaker 1: point A to point B and you didn't have your 3 00:00:04,519 --> 00:00:07,280 Speaker 1: own car, you maybe hail a taxi or pick up 4 00:00:07,280 --> 00:00:09,800 Speaker 1: the phone and call it dispatcher. It could be a 5 00:00:09,840 --> 00:00:13,880 Speaker 1: major hassle to get a ride, especially if it was raining. Today, 6 00:00:14,040 --> 00:00:17,360 Speaker 1: the customer is in control. Cars for hire are flooding 7 00:00:17,360 --> 00:00:20,360 Speaker 1: the roads thanks to companies like Uber and Lyft, but 8 00:00:20,920 --> 00:00:24,280 Speaker 1: that convenience comes at a cost, not for the consumer 9 00:00:24,640 --> 00:00:37,080 Speaker 1: but for the person behind the wheel. Welcome to Benchmark. 10 00:00:37,280 --> 00:00:40,880 Speaker 1: I'm Scott Landman, an economics editor with Bloomberg News in Washington, 11 00:00:41,320 --> 00:00:45,240 Speaker 1: and I'm Daniel Moss, economics writer and editor at Bloomberg 12 00:00:45,360 --> 00:00:48,800 Speaker 1: View in New York. So, Dan, I just wanted to 13 00:00:48,800 --> 00:00:51,479 Speaker 1: talk about it was so bad taking cabs in d 14 00:00:51,560 --> 00:00:54,040 Speaker 1: C when I first moved here about fifteen years ago. 15 00:00:54,880 --> 00:00:57,319 Speaker 1: You couldn't get one when you wanted to, or if 16 00:00:57,360 --> 00:00:59,920 Speaker 1: you were in a part of town that didn't really 17 00:01:00,360 --> 00:01:03,280 Speaker 1: get served by the cabs. It was just really, really hard, 18 00:01:03,320 --> 00:01:06,120 Speaker 1: and you get into arguments with cabbies about the zone 19 00:01:06,160 --> 00:01:09,040 Speaker 1: fare system, which was it was its own beast. It 20 00:01:09,120 --> 00:01:12,959 Speaker 1: was just pretty crazy. And now it's just a different story. 21 00:01:13,280 --> 00:01:16,520 Speaker 1: Using your app to get Uber or a Lift and 22 00:01:16,760 --> 00:01:20,080 Speaker 1: it's just been really great. But talking with the drivers, 23 00:01:20,240 --> 00:01:23,399 Speaker 1: they just seem to work really long hours. They don't 24 00:01:23,440 --> 00:01:25,319 Speaker 1: make much money. It's kind of true for both the 25 00:01:25,360 --> 00:01:28,880 Speaker 1: taxi cabs and Uber dan or are you finding things 26 00:01:28,920 --> 00:01:31,720 Speaker 1: to be pretty similar in New York To a point 27 00:01:32,240 --> 00:01:36,280 Speaker 1: where I live in New York City, it's very very 28 00:01:36,319 --> 00:01:41,039 Speaker 1: easy to hail a cab, their yellow cabs, green cabs, 29 00:01:41,440 --> 00:01:45,399 Speaker 1: all kinds of cabs really running on the street next 30 00:01:45,400 --> 00:01:49,480 Speaker 1: to our apartment building. It's very straightforward. Neither my wife 31 00:01:49,520 --> 00:01:53,120 Speaker 1: nor I have had to rely on Lift or Uber now. 32 00:01:53,240 --> 00:01:56,880 Speaker 1: Having said that, it's quite a contrast from the ten 33 00:01:57,000 --> 00:01:59,920 Speaker 1: years I spent living in suburban Maryland, where it was 34 00:02:00,400 --> 00:02:03,880 Speaker 1: very tough to call a cab and get them there 35 00:02:03,920 --> 00:02:08,839 Speaker 1: on time. Ride check companies in that context, we're far 36 00:02:08,960 --> 00:02:12,240 Speaker 1: far more helpful, And I can say, as a resident 37 00:02:12,240 --> 00:02:15,560 Speaker 1: of suburban Maryland now it's pretty easy to get a 38 00:02:15,800 --> 00:02:18,480 Speaker 1: ride share, Uber or Lift out in the in the 39 00:02:18,520 --> 00:02:23,600 Speaker 1: suburbs these days. Wood cabs right the Barwood cabs that 40 00:02:23,639 --> 00:02:26,520 Speaker 1: you still see them around. But I think the company 41 00:02:26,919 --> 00:02:30,720 Speaker 1: isn't isn't doing well. They actually filed for bankruptcy. Well, 42 00:02:30,760 --> 00:02:35,400 Speaker 1: I think it's clear they're not doing well anyway, Let's 43 00:02:35,440 --> 00:02:38,520 Speaker 1: talk a little more broadly about who loses in the 44 00:02:38,639 --> 00:02:42,720 Speaker 1: ride share economy, and we have a distinguished guest who's 45 00:02:42,840 --> 00:02:45,600 Speaker 1: able to talk about that. Henry Farber is the Hughes 46 00:02:45,760 --> 00:02:49,240 Speaker 1: Rogers Professor of Economics and an associate of the Industrial 47 00:02:49,320 --> 00:02:52,920 Speaker 1: Relations Section at Princeton University. Dr Farber, thank you so 48 00:02:53,000 --> 00:02:55,600 Speaker 1: much for joining us. Well, thank you Scott for having 49 00:02:55,639 --> 00:02:59,120 Speaker 1: me so Dr Farber. First of all, you wrote a 50 00:02:59,280 --> 00:03:02,400 Speaker 1: study a few years ago about why it's so hard 51 00:03:02,440 --> 00:03:05,600 Speaker 1: to catch a taxi cab in the rain in New York. 52 00:03:05,880 --> 00:03:07,680 Speaker 1: Can you tell us just a little bit about that 53 00:03:07,760 --> 00:03:10,280 Speaker 1: and what you found. Yes, that that was a study 54 00:03:10,320 --> 00:03:15,040 Speaker 1: addressing something of a controversy in economics about how individuals 55 00:03:15,120 --> 00:03:19,160 Speaker 1: make decisions, and in particular, some people have argued that 56 00:03:20,160 --> 00:03:24,080 Speaker 1: taxi drivers or other workers set an earnings target for 57 00:03:24,120 --> 00:03:26,560 Speaker 1: the day, for example, and when they reach their target, 58 00:03:26,600 --> 00:03:29,760 Speaker 1: they go home. So some people had conjectured that it's 59 00:03:29,760 --> 00:03:32,000 Speaker 1: difficult to get a taxi in the rain because the 60 00:03:32,080 --> 00:03:35,680 Speaker 1: drivers do so well that they reached their target early 61 00:03:35,720 --> 00:03:39,040 Speaker 1: and then say, okay, good, I'm going home. Um. An 62 00:03:39,080 --> 00:03:43,200 Speaker 1: alternative view is that when it's easy to make money. 63 00:03:43,440 --> 00:03:45,880 Speaker 1: You say, gee, I, this is great. I'll make hay 64 00:03:45,880 --> 00:03:48,400 Speaker 1: while the sunshine, so to speak, even if it's raining, 65 00:03:48,880 --> 00:03:52,160 Speaker 1: and I will continue to drive. I took a look 66 00:03:52,160 --> 00:03:56,240 Speaker 1: at this using complete records on the driving history of 67 00:03:56,280 --> 00:04:00,480 Speaker 1: every taxi in New York for five years and was 68 00:04:00,520 --> 00:04:03,440 Speaker 1: able to figure out that in fact, in hours when 69 00:04:03,440 --> 00:04:06,400 Speaker 1: it's rains, the taxis are very busy, but they're not 70 00:04:06,480 --> 00:04:09,960 Speaker 1: making more money because they have to drive slower, So 71 00:04:10,000 --> 00:04:12,400 Speaker 1: it can't be the case that they're going home early. 72 00:04:13,240 --> 00:04:17,960 Speaker 1: What happens during rainy periods is there's more demand for taxis, obviously, 73 00:04:18,520 --> 00:04:20,960 Speaker 1: but it's also true drivers don't like to drive so 74 00:04:21,040 --> 00:04:24,840 Speaker 1: much in the rain. It's harder, and they they just 75 00:04:24,920 --> 00:04:27,720 Speaker 1: some of them just take a break. Now, the market 76 00:04:27,800 --> 00:04:30,400 Speaker 1: has probably changed in a few years since you put 77 00:04:30,480 --> 00:04:34,039 Speaker 1: that out. There's probably more drivers on the streets in 78 00:04:34,080 --> 00:04:36,840 Speaker 1: New York. I guess just because of Uber and left 79 00:04:36,920 --> 00:04:41,479 Speaker 1: more cars out there. Would that effect the market? It 80 00:04:41,560 --> 00:04:44,400 Speaker 1: just seems to be pretty unfettered in terms of the 81 00:04:44,400 --> 00:04:47,479 Speaker 1: barriers to entry for for people trying to get in 82 00:04:47,640 --> 00:04:50,960 Speaker 1: as drivers. Well as you know, whereas most people know 83 00:04:51,240 --> 00:04:54,680 Speaker 1: that New York had a very heavily regulated taxi industry, 84 00:04:54,720 --> 00:04:57,840 Speaker 1: with the number of yellow cab medallion's capped at around 85 00:04:57,880 --> 00:05:02,320 Speaker 1: thirteen or fourteen thousand. And what it happened was when 86 00:05:02,440 --> 00:05:07,320 Speaker 1: Uber came in basically relaxed this constraint and allowed other 87 00:05:07,400 --> 00:05:12,440 Speaker 1: ways for people to get rides. Uh So, fundamentally, the 88 00:05:12,480 --> 00:05:17,080 Speaker 1: technology of being able to match riders and drivers was 89 00:05:17,120 --> 00:05:20,279 Speaker 1: a huge leap forward, and it really allowed essentially a 90 00:05:20,360 --> 00:05:23,839 Speaker 1: relaxation of the limit on the number of cars that 91 00:05:23,880 --> 00:05:26,359 Speaker 1: could be out there. And I think we're feeling that 92 00:05:26,480 --> 00:05:30,520 Speaker 1: today because apparently traffic is a lot worse. It takes 93 00:05:30,560 --> 00:05:34,159 Speaker 1: longer to get, for example, from Manhattan to LaGuardia, or 94 00:05:34,320 --> 00:05:38,680 Speaker 1: to just get around Manhattan because of the increase in 95 00:05:38,720 --> 00:05:42,000 Speaker 1: the number of cars on the road. Did taxi companies 96 00:05:42,040 --> 00:05:46,240 Speaker 1: see this disruption coming? What did they miss that allowed 97 00:05:46,279 --> 00:05:49,920 Speaker 1: this to first creep and then gallop up on them. 98 00:05:50,400 --> 00:05:54,320 Speaker 1: I don't think the taxi companies themselves foresaw this. The 99 00:05:54,839 --> 00:05:58,839 Speaker 1: technological leap happened in the sweep of history rather quickly 100 00:05:59,560 --> 00:06:02,320 Speaker 1: and frankly the real you know, some people who lost 101 00:06:03,160 --> 00:06:07,080 Speaker 1: were people who had invested in medallions whose value is 102 00:06:07,120 --> 00:06:11,240 Speaker 1: solely the result of the regulation of the number of 103 00:06:11,279 --> 00:06:15,040 Speaker 1: medallions and the price that can be charged for a 104 00:06:15,040 --> 00:06:19,640 Speaker 1: taxi ride. So if you're asking about winners and losers, uh, 105 00:06:19,880 --> 00:06:23,400 Speaker 1: certainly the losers were people who owned the medallions. Certainly 106 00:06:23,560 --> 00:06:26,880 Speaker 1: the losers were the people that owned medallions. And yet 107 00:06:27,360 --> 00:06:30,479 Speaker 1: it back in those days, given the limited number of 108 00:06:30,560 --> 00:06:33,520 Speaker 1: drivers on the road, you guess that drivers could still 109 00:06:33,520 --> 00:06:36,919 Speaker 1: earn a decent living doing what they did. And now 110 00:06:37,120 --> 00:06:39,960 Speaker 1: there's just so much competition that it makes it I 111 00:06:40,000 --> 00:06:42,960 Speaker 1: think a lot harder for drivers to earn a reasonable 112 00:06:43,000 --> 00:06:46,480 Speaker 1: living doing the same job. I've seen studies offering different 113 00:06:46,560 --> 00:06:51,279 Speaker 1: numbers for what kinds of wages drivers earned, but it 114 00:06:51,320 --> 00:06:54,840 Speaker 1: seems like they're all fairly low. They're not nobody's getting rich. 115 00:06:54,880 --> 00:06:59,200 Speaker 1: It's probably tougher, is that what is happening today, Henry 116 00:06:59,640 --> 00:07:04,680 Speaker 1: in heart. What's true is the because the most taxis 117 00:07:04,680 --> 00:07:07,720 Speaker 1: in New York City, most yellow cabs in New York historically, 118 00:07:08,320 --> 00:07:11,640 Speaker 1: we're not driven by the owners. They were leased daily 119 00:07:11,760 --> 00:07:16,120 Speaker 1: to drivers for us reasonably substantial sum of money, so 120 00:07:16,160 --> 00:07:21,600 Speaker 1: that even pre ride haling services, taxi drivers were not 121 00:07:21,720 --> 00:07:23,480 Speaker 1: making a lot of money, and so it was an 122 00:07:23,600 --> 00:07:28,040 Speaker 1: entry level occupation in recent years for new immigrants, and 123 00:07:28,720 --> 00:07:31,960 Speaker 1: the what the drivers could take in in revenue and 124 00:07:32,000 --> 00:07:35,520 Speaker 1: tips less what they had to pay in lease fees 125 00:07:35,560 --> 00:07:39,040 Speaker 1: and gasoline really didn't make it even then a very 126 00:07:39,120 --> 00:07:43,400 Speaker 1: highly paid occupation is they sit for disruption in the 127 00:07:43,520 --> 00:07:48,320 Speaker 1: riding industry. Hank will Cuba and Lift and companies of 128 00:07:48,360 --> 00:07:52,360 Speaker 1: that ilk be themselves disrupted soon. What will be the 129 00:07:52,480 --> 00:07:54,960 Speaker 1: new new new thing. Well, I don't know if there's 130 00:07:55,000 --> 00:07:58,239 Speaker 1: going to be a new technology, but my own view 131 00:07:58,560 --> 00:08:03,240 Speaker 1: is that what's really innovative here is the technology to 132 00:08:03,320 --> 00:08:06,360 Speaker 1: match the riders and passengers. And as was noted in 133 00:08:06,400 --> 00:08:09,800 Speaker 1: the introductory remarks, this is a bit less valuable in 134 00:08:09,840 --> 00:08:12,280 Speaker 1: a very dense place like New York City, But in 135 00:08:12,320 --> 00:08:14,640 Speaker 1: the rest of the country, when you live in a 136 00:08:15,080 --> 00:08:19,080 Speaker 1: medium sized city or even small towns, the ability to 137 00:08:19,120 --> 00:08:22,640 Speaker 1: have drivers at the ready to come out and pick 138 00:08:22,720 --> 00:08:27,400 Speaker 1: you up is really valuable. So Uber may be disrupted 139 00:08:27,440 --> 00:08:31,560 Speaker 1: and Lift may be disrupted by new entrants who use 140 00:08:31,680 --> 00:08:35,840 Speaker 1: the same technology, and you know, so I wouldn't. I'm 141 00:08:35,880 --> 00:08:39,439 Speaker 1: not bullish on Uber being able, for example, to corner 142 00:08:39,520 --> 00:08:43,200 Speaker 1: the market and ride helling. Let's talk a little bit 143 00:08:43,240 --> 00:08:48,080 Speaker 1: about the broader labor market, given that there seems to 144 00:08:48,120 --> 00:08:52,760 Speaker 1: be an oversupply of drivers at the moment, and yet 145 00:08:52,800 --> 00:08:57,079 Speaker 1: we have a labor market that overall is considered pretty 146 00:08:57,120 --> 00:08:59,960 Speaker 1: tight in the United States, with unemployment at four point 147 00:09:00,160 --> 00:09:05,720 Speaker 1: one percent. Is this a situation among higher car drivers 148 00:09:05,760 --> 00:09:09,160 Speaker 1: that can persist for a long period of time, or 149 00:09:09,679 --> 00:09:12,439 Speaker 1: you know, could have sort itself out. Could we actually 150 00:09:12,480 --> 00:09:16,680 Speaker 1: see the number of drivers uh diminish as the labor 151 00:09:16,720 --> 00:09:19,560 Speaker 1: market gets tighter. Well, I think that depends in part 152 00:09:19,600 --> 00:09:23,079 Speaker 1: on whether wages grow elsewhere in the economy. You know, 153 00:09:23,160 --> 00:09:25,719 Speaker 1: one of the conundrums in the current labor market at 154 00:09:26,240 --> 00:09:29,760 Speaker 1: really unprecedentedly low unemployment rates at least for quite a 155 00:09:29,880 --> 00:09:33,000 Speaker 1: number of years, is that wages have not been growing. 156 00:09:34,000 --> 00:09:38,880 Speaker 1: And one you need to remember that many people who 157 00:09:39,000 --> 00:09:43,040 Speaker 1: drive for Uber and Lift and other car sharing services 158 00:09:43,600 --> 00:09:46,440 Speaker 1: are people who have other jobs and are simply looking 159 00:09:46,480 --> 00:09:50,040 Speaker 1: for ways to supplement their income when their first main 160 00:09:50,160 --> 00:09:54,320 Speaker 1: job has fixed hours or at least no additional earnings 161 00:09:54,360 --> 00:09:57,960 Speaker 1: opportunities for them. So people are saying, Gee, I have 162 00:09:58,040 --> 00:10:00,240 Speaker 1: a car, I have some time, I'd like a little 163 00:10:00,280 --> 00:10:02,720 Speaker 1: more money, let me go out here. My own view 164 00:10:02,800 --> 00:10:05,880 Speaker 1: is this could persist for quite a while because of 165 00:10:05,880 --> 00:10:10,839 Speaker 1: the flexibility that's built in Hank. Could the ranching changes 166 00:10:11,559 --> 00:10:16,000 Speaker 1: that Uber and Lift have forced on traditional taxi companies 167 00:10:16,160 --> 00:10:22,080 Speaker 1: ironically led to their salvation rather than the demise. I'm 168 00:10:22,120 --> 00:10:24,960 Speaker 1: not sure how to answer that. I would say that 169 00:10:25,760 --> 00:10:29,400 Speaker 1: certainly they'll have to adapt by offering better and different service, 170 00:10:29,640 --> 00:10:32,640 Speaker 1: and one important way they could do that is by 171 00:10:32,679 --> 00:10:37,720 Speaker 1: adopting the same kind of matching technology that that the 172 00:10:37,840 --> 00:10:41,679 Speaker 1: ride sharing services use, so that if you could if 173 00:10:41,720 --> 00:10:43,560 Speaker 1: you live in a town and you have an app 174 00:10:43,640 --> 00:10:46,000 Speaker 1: for the local taxi company, where you just punch up 175 00:10:46,280 --> 00:10:48,280 Speaker 1: I need a ride and a car can show up 176 00:10:48,280 --> 00:10:50,480 Speaker 1: in a few minutes. I think that would be a 177 00:10:50,480 --> 00:10:54,319 Speaker 1: real improvement. Quality of service could improve. I don't have 178 00:10:54,880 --> 00:10:59,440 Speaker 1: any evidence on that directly, but these companies could adapt, 179 00:10:59,720 --> 00:11:04,280 Speaker 1: or maybe these companies, the existing cab companies will start, 180 00:11:04,559 --> 00:11:07,040 Speaker 1: just as I said, start to operate more like the 181 00:11:07,120 --> 00:11:10,320 Speaker 1: ride sharing services, and maybe that's the future they We 182 00:11:10,360 --> 00:11:14,439 Speaker 1: don't need to save the companies in their existing form. 183 00:11:14,520 --> 00:11:18,040 Speaker 1: They need to adapt what's newly to what's newly possible 184 00:11:18,080 --> 00:11:21,160 Speaker 1: with the technology. And by say, what I mean is 185 00:11:21,600 --> 00:11:26,000 Speaker 1: it is without question, a better customer experience, the cars, 186 00:11:26,040 --> 00:11:32,320 Speaker 1: a cleaner the driver. As Polita, I'm told strangely enough, 187 00:11:32,520 --> 00:11:35,840 Speaker 1: that drivers would often refuse to take a passenger to Brooklyn. 188 00:11:36,520 --> 00:11:39,280 Speaker 1: That's pretty much when heard of now, and you can 189 00:11:39,320 --> 00:11:43,479 Speaker 1: also flag a Brooklyn cab in some parts of Manhattan. 190 00:11:43,880 --> 00:11:48,120 Speaker 1: These sort of things will once completely off limits. Well, 191 00:11:48,280 --> 00:11:50,640 Speaker 1: let's be clear. The green cabs, which are that what 192 00:11:50,679 --> 00:11:54,000 Speaker 1: are called the borough cabs in New York City have 193 00:11:54,080 --> 00:11:57,400 Speaker 1: only been around for a few years, and they are 194 00:11:57,480 --> 00:12:01,560 Speaker 1: prohibited by regulation from picking up passengers on the west 195 00:12:01,600 --> 00:12:06,600 Speaker 1: side Below Street and on the east side below nineties 196 00:12:06,640 --> 00:12:10,120 Speaker 1: Street in Manhattan. To the extent they're they're picking up 197 00:12:10,200 --> 00:12:13,480 Speaker 1: passengers in those areas, they're really not supposed to. What 198 00:12:13,720 --> 00:12:16,800 Speaker 1: is true is I believe there are fewer yellow cabs 199 00:12:16,840 --> 00:12:18,880 Speaker 1: on the road at any given time than there used 200 00:12:18,880 --> 00:12:21,599 Speaker 1: to be because a lot of drivers don't want to 201 00:12:21,640 --> 00:12:25,240 Speaker 1: pay the rental daily rental fee because they can't make 202 00:12:25,240 --> 00:12:28,280 Speaker 1: as much money as they used to because of competition 203 00:12:28,400 --> 00:12:31,880 Speaker 1: from the ride hailing services. Professor, we were just talking 204 00:12:31,920 --> 00:12:36,160 Speaker 1: about the future and adapting, and one thing that's really 205 00:12:36,200 --> 00:12:39,440 Speaker 1: hanging over this industry. You can say it's a guerrilla 206 00:12:39,480 --> 00:12:42,080 Speaker 1: and elephant, a big knife, whatever you want to call it, 207 00:12:42,840 --> 00:12:47,360 Speaker 1: Autonomous vehicles, self driving cars. Sooner or later, they're coming, 208 00:12:47,480 --> 00:12:51,160 Speaker 1: whether it's ten years, twenty years. Who knows what are 209 00:12:51,320 --> 00:12:55,439 Speaker 1: all these drivers going to do for jobs once they 210 00:12:55,480 --> 00:12:59,440 Speaker 1: come along, not just taxis but also delivery vehicles, trucks, 211 00:12:59,600 --> 00:13:01,720 Speaker 1: you name. What is this due to the labor force? 212 00:13:02,280 --> 00:13:05,120 Speaker 1: This is this is really the sixty four thou dollar question. 213 00:13:05,280 --> 00:13:10,120 Speaker 1: In fact, um driving related jobs of all descriptions have 214 00:13:10,320 --> 00:13:13,720 Speaker 1: been good jobs for low skilled workers that are just 215 00:13:13,880 --> 00:13:16,960 Speaker 1: above the lowest skilled jobs in the economy. They can 216 00:13:17,000 --> 00:13:20,960 Speaker 1: be very quite skilled, they can be moderately skilled, and 217 00:13:21,040 --> 00:13:25,880 Speaker 1: it's not clear to me what people with these skills 218 00:13:26,160 --> 00:13:29,760 Speaker 1: or lack of better skills are going to do. We've 219 00:13:29,760 --> 00:13:32,440 Speaker 1: seen some real there's just a right now, there's a 220 00:13:32,440 --> 00:13:35,439 Speaker 1: lot of people interested in driving for these ride heiling 221 00:13:35,520 --> 00:13:39,400 Speaker 1: services because entry into that industry is just so legal. 222 00:13:40,320 --> 00:13:43,960 Speaker 1: So I'm sorry, not legal, but easy. There's vary some 223 00:13:44,120 --> 00:13:47,480 Speaker 1: very interesting recent work done which shows in fact that 224 00:13:47,559 --> 00:13:50,640 Speaker 1: it's also very difficult to raise the pay of these 225 00:13:50,640 --> 00:13:54,120 Speaker 1: workers in any meaningful way. For example, there was an 226 00:13:54,160 --> 00:13:58,520 Speaker 1: experiment at uber where they tried to raise the fees 227 00:13:58,559 --> 00:14:02,079 Speaker 1: they paid to drivers, and for a week or two 228 00:14:02,200 --> 00:14:05,160 Speaker 1: the drivers would get more money, but then other drivers 229 00:14:05,200 --> 00:14:07,400 Speaker 1: would say, hey, this is really good, and more so, 230 00:14:07,440 --> 00:14:11,120 Speaker 1: many drivers would be out there looking for passengers at 231 00:14:11,120 --> 00:14:15,280 Speaker 1: any point in time that the actual hourly earnings went 232 00:14:15,360 --> 00:14:18,840 Speaker 1: back to its original level. So it's very hard to 233 00:14:18,880 --> 00:14:22,320 Speaker 1: know both how to boost the pay of these drivers 234 00:14:22,360 --> 00:14:25,840 Speaker 1: and what will happen once you know, I agree with 235 00:14:25,880 --> 00:14:29,200 Speaker 1: you that autonomous vehicles appear to be in our future, 236 00:14:30,040 --> 00:14:33,600 Speaker 1: and there's going to have to be some thought generally, 237 00:14:33,720 --> 00:14:37,520 Speaker 1: not just for ride sharing drivers or taxi drivers, but 238 00:14:37,600 --> 00:14:41,360 Speaker 1: for delivery men, for over the road truckers, for bus drivers, 239 00:14:41,440 --> 00:14:44,680 Speaker 1: for all of this. Um, what are we gonna do 240 00:14:44,840 --> 00:14:47,000 Speaker 1: with these people? I don't have a good answer to that. 241 00:14:47,520 --> 00:14:51,800 Speaker 1: What about how the rise of Uber and lift and 242 00:14:51,880 --> 00:14:56,080 Speaker 1: ride sharing services fit into the broader shift in labor 243 00:14:56,120 --> 00:14:59,560 Speaker 1: markets and developed countries such as the US toward what 244 00:14:59,640 --> 00:15:02,960 Speaker 1: we like to call the gig economy. You know, it 245 00:15:02,960 --> 00:15:07,080 Speaker 1: seems like work is getting less tethered to one specific 246 00:15:07,160 --> 00:15:11,880 Speaker 1: employer or location and people are taking more jobs as 247 00:15:11,960 --> 00:15:16,720 Speaker 1: contract workers, which is basically what these Uber drivers are right. Yes, 248 00:15:17,720 --> 00:15:20,880 Speaker 1: this has been an important change that predates any talk 249 00:15:20,920 --> 00:15:25,040 Speaker 1: of the gig economy or ride sharing or any of that. Um. 250 00:15:25,080 --> 00:15:28,400 Speaker 1: There have been many professions like engineering, where companies higher 251 00:15:28,440 --> 00:15:32,240 Speaker 1: engineers on a contract basis used to be that a 252 00:15:32,320 --> 00:15:35,360 Speaker 1: worker who was an engineer, for example, and graduated college 253 00:15:35,360 --> 00:15:37,360 Speaker 1: would think if they could get a job with a 254 00:15:37,440 --> 00:15:40,880 Speaker 1: major company like General Electric or IBM, they'd have a career, 255 00:15:41,120 --> 00:15:43,440 Speaker 1: and they were company men, and that was how it worked. 256 00:15:44,000 --> 00:15:48,440 Speaker 1: Younger people today don't perceive their future in that way, 257 00:15:48,760 --> 00:15:54,000 Speaker 1: and I think, frankly, public policy needs to adjust because 258 00:15:54,760 --> 00:15:58,520 Speaker 1: long term employment was a source of important what we'll 259 00:15:58,600 --> 00:16:02,720 Speaker 1: call fringe benefits but call core benefits like pensions and 260 00:16:02,840 --> 00:16:08,080 Speaker 1: health insurance, and now that workers links with their employers 261 00:16:08,120 --> 00:16:11,560 Speaker 1: are more tenuous than they used to, this presents real 262 00:16:11,720 --> 00:16:15,760 Speaker 1: challenges and how to provide individuals with with the goods 263 00:16:15,760 --> 00:16:20,160 Speaker 1: and benefits that they need. And aren't people getting stuck 264 00:16:20,200 --> 00:16:23,880 Speaker 1: with things like non compete agreements where they can't even 265 00:16:24,000 --> 00:16:25,920 Speaker 1: get away from their jobs. I mean, at the same 266 00:16:25,920 --> 00:16:31,400 Speaker 1: time that the labor markets becoming less tethered to companies 267 00:16:31,440 --> 00:16:34,120 Speaker 1: like this, companies are still trying to keep those tethers 268 00:16:34,120 --> 00:16:36,800 Speaker 1: in some ways. I'm glad you mentioned that this is 269 00:16:36,840 --> 00:16:40,040 Speaker 1: an important trend in the labor market. Is frankly a 270 00:16:40,160 --> 00:16:44,640 Speaker 1: trend toward employers having more market power over their employees. 271 00:16:45,120 --> 00:16:47,960 Speaker 1: The employers appear to be less interested in having long 272 00:16:48,080 --> 00:16:51,520 Speaker 1: term employment relationships with their workers. They want to be 273 00:16:51,600 --> 00:16:54,720 Speaker 1: able to keep workers around as cheaply as possible, and 274 00:16:54,880 --> 00:16:57,120 Speaker 1: one of the ways they do that is to try 275 00:16:57,160 --> 00:16:59,600 Speaker 1: to make it harder for them to find other jobs. 276 00:16:59,640 --> 00:17:03,920 Speaker 1: And that's exactly what non compete agreements do. They say, look, 277 00:17:04,400 --> 00:17:06,480 Speaker 1: you can leave, but you can't work again in this 278 00:17:06,560 --> 00:17:10,640 Speaker 1: industry for a while. Um. There's also been a spade 279 00:17:10,640 --> 00:17:14,480 Speaker 1: of non poaching agreements across firms. There was a very 280 00:17:14,520 --> 00:17:17,520 Speaker 1: well known case a year or two ago involving some 281 00:17:17,600 --> 00:17:22,280 Speaker 1: Silicon Valley companies in high tech that was kind of 282 00:17:22,800 --> 00:17:27,720 Speaker 1: put together a bit by Steve Jobs at Apple saying that, gee, 283 00:17:28,080 --> 00:17:29,960 Speaker 1: you got we're all going to agree not to try 284 00:17:30,000 --> 00:17:32,920 Speaker 1: and hire each other's employees. This is simply a way 285 00:17:32,960 --> 00:17:37,640 Speaker 1: to keep labor costs down and presents real challenges to employees, 286 00:17:37,760 --> 00:17:41,520 Speaker 1: but and also frankly to the antitrust authorities in Washington. 287 00:17:41,880 --> 00:17:46,000 Speaker 1: All right, well, on that uplifting note, we'll end this here, 288 00:17:46,280 --> 00:17:48,360 Speaker 1: Dr Farber, thank you so much for taking the time 289 00:17:48,400 --> 00:17:53,160 Speaker 1: to speak with us today. All right, thank you. Benchmark 290 00:17:53,200 --> 00:17:55,720 Speaker 1: will be back next week. Until then, you can find 291 00:17:55,760 --> 00:17:59,120 Speaker 1: us on the Bloomberg terminal bloomberg dot com, our Bloomberg App, 292 00:17:59,280 --> 00:18:03,960 Speaker 1: and podcast destinations such as Apple Podcasts, Spotify or wherever 293 00:18:04,000 --> 00:18:06,560 Speaker 1: you listen to podcasts. Please take the time to rate 294 00:18:06,600 --> 00:18:09,119 Speaker 1: and review the show, and you can also find us 295 00:18:09,119 --> 00:18:12,280 Speaker 1: on Twitter. You can follow me at at scott Landman 296 00:18:12,760 --> 00:18:17,600 Speaker 1: Dan you're at Moss Underscore. Echo Benchmark is produced by 297 00:18:17,600 --> 00:18:21,280 Speaker 1: topor Foreheads. The head of Bloomberg Podcast is Francesco Levy. 298 00:18:21,560 --> 00:18:23,360 Speaker 1: Thanks for listening, See you next time.