1 00:00:04,080 --> 00:00:07,200 Speaker 1: Hello, and welcome to Stephanomics, the podcast that brings the 2 00:00:07,200 --> 00:00:15,480 Speaker 1: global economy to you. Today, we're talking to the Harvard 3 00:00:15,480 --> 00:00:19,400 Speaker 1: economist and former U. S. Treasury Secretary Larry Summers about 4 00:00:19,400 --> 00:00:22,439 Speaker 1: the state of the economy and economic policy making, and 5 00:00:22,640 --> 00:00:25,680 Speaker 1: especially looking for his take on the spread of automation 6 00:00:25,880 --> 00:00:28,360 Speaker 1: and the impact it could have on employment and the 7 00:00:28,360 --> 00:00:31,880 Speaker 1: whole global economy. But first, we've got a report from 8 00:00:31,880 --> 00:00:35,720 Speaker 1: one of Bloomberg's most experienced US economy reporters, Craig Torres, 9 00:00:35,960 --> 00:00:39,320 Speaker 1: who's based in Washington, d C. He couldn't help noticing 10 00:00:39,360 --> 00:00:43,000 Speaker 1: that despite all the talk of robots destroying jobs, the U. S. 11 00:00:43,040 --> 00:00:45,520 Speaker 1: Economy still seemed to be creating a lot of them. 12 00:00:45,520 --> 00:00:48,320 Speaker 1: It made him want to see for himself how technology 13 00:00:48,400 --> 00:00:51,040 Speaker 1: was affecting the world of work and what it might 14 00:00:51,120 --> 00:01:04,920 Speaker 1: mean for the future of jobs. So, you know, a 15 00:01:05,080 --> 00:01:08,240 Speaker 1: real busy Monday, or a Tuesday like today after holiday 16 00:01:08,720 --> 00:01:15,160 Speaker 1: US in a day could be a certainly very busy Monday's. 17 00:01:17,000 --> 00:01:20,600 Speaker 1: I recently visited Washington Hospital Center, the biggest in the 18 00:01:20,720 --> 00:01:24,639 Speaker 1: nation's capital, with an emergency room that handles two forty 19 00:01:24,680 --> 00:01:28,120 Speaker 1: patients a day. I went there because I thought it 20 00:01:28,160 --> 00:01:30,400 Speaker 1: would help us start to solve one of the big 21 00:01:30,480 --> 00:01:34,119 Speaker 1: riddles in the US economy today. Why have we been 22 00:01:34,200 --> 00:01:40,480 Speaker 1: hiring so many people in recent years? Last year, the 23 00:01:40,600 --> 00:01:44,440 Speaker 1: US added two point seven million jobs. That happened at 24 00:01:44,480 --> 00:01:48,480 Speaker 1: a time when we hear a lot about artificial intelligence, robots, 25 00:01:48,960 --> 00:01:52,680 Speaker 1: and human less tasks in a variety of industries. I 26 00:01:52,800 --> 00:01:56,440 Speaker 1: suspected that there was something about technology that was labor 27 00:01:56,480 --> 00:02:04,720 Speaker 1: intensive and labor creating. I found it at Washington Hospital, 28 00:02:04,880 --> 00:02:07,480 Speaker 1: which is part of med Star Health, a big chain 29 00:02:07,560 --> 00:02:11,880 Speaker 1: in the d C region. They found a technological solution 30 00:02:11,960 --> 00:02:14,519 Speaker 1: that let them double the number of e ER patients 31 00:02:14,560 --> 00:02:18,680 Speaker 1: they could triage. It made doctors more efficient, but it 32 00:02:18,760 --> 00:02:22,120 Speaker 1: also created the need for more jobs, texts and nurses 33 00:02:22,160 --> 00:02:26,520 Speaker 1: to process the higher volume of patients. I met an 34 00:02:26,560 --> 00:02:30,200 Speaker 1: e R doctor named Ethan Booker who helped apply this 35 00:02:30,320 --> 00:02:37,280 Speaker 1: technology with strong results. So how many patients do you 36 00:02:37,280 --> 00:02:42,359 Speaker 1: see an emergency here every year? Eighty seven thousand patients? 37 00:02:43,200 --> 00:02:46,919 Speaker 1: Because that was a lot of humans and pain one 38 00:02:46,919 --> 00:02:51,200 Speaker 1: way or another. Med Stars emergency room has this role 39 00:02:51,320 --> 00:02:55,080 Speaker 1: known as the p I T or PIT that stands 40 00:02:55,120 --> 00:02:59,360 Speaker 1: for provider and triage. The pit physician's job is to 41 00:02:59,400 --> 00:03:01,400 Speaker 1: make a call all on the condition of a patient 42 00:03:01,560 --> 00:03:04,840 Speaker 1: rolling into the e R and get them routed into care. 43 00:03:05,639 --> 00:03:10,520 Speaker 1: Do they have a broken bone or just to sore back. Imagine, however, 44 00:03:10,600 --> 00:03:13,960 Speaker 1: trying to process more than two hundred patients a day 45 00:03:14,680 --> 00:03:18,040 Speaker 1: any e RS by definition somewhat chaotic with lots of 46 00:03:18,120 --> 00:03:22,360 Speaker 1: urgency in the air and distractions can be huge. You know, 47 00:03:22,400 --> 00:03:25,880 Speaker 1: the patients came in the nurse, the doctors got their 48 00:03:25,960 --> 00:03:28,720 Speaker 1: orders started, but that work over there, as you can imagine, 49 00:03:28,800 --> 00:03:31,680 Speaker 1: kind of embedded in right in the front door of 50 00:03:31,680 --> 00:03:34,680 Speaker 1: the emergency department was It was an entire nine hour 51 00:03:34,760 --> 00:03:38,920 Speaker 1: shift on your feet. Um there was tons of interruptions, 52 00:03:39,760 --> 00:03:43,360 Speaker 1: helping people with wayfinding, people coming back to you to 53 00:03:43,440 --> 00:03:45,840 Speaker 1: kind of understand where they were in the process, that 54 00:03:45,960 --> 00:03:49,400 Speaker 1: kind of stuff. So what they did was used technology 55 00:03:49,440 --> 00:03:53,000 Speaker 1: to create a remote pit. I walk with Dr Booker 56 00:03:53,040 --> 00:03:56,520 Speaker 1: through a maze of hallways. He opens a door and 57 00:03:56,640 --> 00:04:00,680 Speaker 1: inside I see Dr Jasmine Mallock quietly using a headset 58 00:04:00,720 --> 00:04:04,520 Speaker 1: to talk with a patient on the video monitor. It 59 00:04:04,640 --> 00:04:08,360 Speaker 1: is only about a five minute walk from the emergency room, 60 00:04:08,400 --> 00:04:11,440 Speaker 1: but it is a world away. There are no patients 61 00:04:11,480 --> 00:04:16,159 Speaker 1: on stretchers here, nobody's bleeding. The remoteness isn't the only 62 00:04:16,200 --> 00:04:19,640 Speaker 1: efficiency of this system. If the patient has been to 63 00:04:19,680 --> 00:04:23,320 Speaker 1: the hospital before, Malik has a medical record in front 64 00:04:23,320 --> 00:04:26,960 Speaker 1: of her, she can also order follow on testing immediately 65 00:04:26,960 --> 00:04:30,200 Speaker 1: with a click of a mouse. I talked to Booker 66 00:04:30,240 --> 00:04:35,000 Speaker 1: about the difference between working downstairs versus here upstairs in 67 00:04:35,080 --> 00:04:38,080 Speaker 1: a nine hour shift, going flat out on your feet 68 00:04:38,120 --> 00:04:40,840 Speaker 1: the whole time, UM, you might be able to get 69 00:04:40,839 --> 00:04:44,360 Speaker 1: to ninety patients. UM. The peak speed that you can 70 00:04:44,400 --> 00:04:47,760 Speaker 1: do on this is is. I've seen some hours where 71 00:04:47,800 --> 00:04:50,720 Speaker 1: patients where physicians of process twenty two patients in an hour. 72 00:04:51,040 --> 00:04:54,599 Speaker 1: Twenty two patients an hour. That would amount to around 73 00:04:54,640 --> 00:04:58,719 Speaker 1: two patients during a nine hour shift, more than double 74 00:04:58,760 --> 00:05:03,120 Speaker 1: what a doctor on the R floor could do. More 75 00:05:03,160 --> 00:05:07,440 Speaker 1: patients passing through triage means the hospital needs to hire 76 00:05:07,480 --> 00:05:11,039 Speaker 1: more people to process all of them. Here's Booker again. 77 00:05:11,360 --> 00:05:15,560 Speaker 1: I think it's labor enhancing. Certainly didn't replace anybody, and 78 00:05:16,440 --> 00:05:19,240 Speaker 1: in places in which we were successful with this, there 79 00:05:19,240 --> 00:05:31,279 Speaker 1: there's a need for more labor. There is another myth 80 00:05:31,360 --> 00:05:34,880 Speaker 1: to bust here, one that is also about technology and jobs. 81 00:05:35,960 --> 00:05:39,000 Speaker 1: Very rarely does a company like MedStar buy a piece 82 00:05:39,000 --> 00:05:42,440 Speaker 1: of technology off the shelf, plug it in and turn 83 00:05:42,560 --> 00:05:46,599 Speaker 1: it on a complex organization like a healthcare system, has 84 00:05:46,680 --> 00:05:50,039 Speaker 1: to adapt tech to its own needs, and in healthcare 85 00:05:50,279 --> 00:05:54,080 Speaker 1: there is a very high bar for security. So for 86 00:05:54,200 --> 00:05:59,039 Speaker 1: all the alluring stories about technology being instantly productivity enhancing 87 00:05:59,600 --> 00:06:02,640 Speaker 1: it rare, really is it takes hours of human labor 88 00:06:02,680 --> 00:06:06,800 Speaker 1: to configure it and make it safe. Med Star has 89 00:06:06,800 --> 00:06:10,239 Speaker 1: a whole innovation institute that is always looking for ways 90 00:06:10,279 --> 00:06:15,000 Speaker 1: to use technology to help doctors and patients. I spoke 91 00:06:15,040 --> 00:06:18,240 Speaker 1: with an executive there named John Locke. This is how 92 00:06:18,279 --> 00:06:21,680 Speaker 1: he describes that process. We have teams of people across 93 00:06:21,680 --> 00:06:24,320 Speaker 1: that organization working on kind of the next five to 94 00:06:24,400 --> 00:06:26,919 Speaker 1: ten years. And then how do we give people in 95 00:06:26,920 --> 00:06:29,640 Speaker 1: a big health system, and we're talking about plus people 96 00:06:30,320 --> 00:06:33,279 Speaker 1: a big organization, how do we give people room in 97 00:06:33,360 --> 00:06:36,159 Speaker 1: that to actually experiment and build some of these things out? 98 00:06:37,240 --> 00:06:42,280 Speaker 1: This technology strangely labor intensive. Yes, creating something new is 99 00:06:42,320 --> 00:06:46,000 Speaker 1: full of dissonance. It's not smooth and easy. If it 100 00:06:46,080 --> 00:06:49,479 Speaker 1: isn't smooth and easy, then why are millions of companies 101 00:06:49,520 --> 00:06:53,680 Speaker 1: working so hard to put technology in place? I wanted 102 00:06:53,720 --> 00:06:57,560 Speaker 1: to take that question to Lonnie Jaffee, a guy with 103 00:06:57,640 --> 00:07:00,720 Speaker 1: a long career and technology firms who was now at 104 00:07:00,760 --> 00:07:05,000 Speaker 1: Insight Venture Partners in New York. They invest about twenty 105 00:07:05,000 --> 00:07:09,320 Speaker 1: billion dollars spread over a hundred and fifty companies, many 106 00:07:09,360 --> 00:07:12,720 Speaker 1: of them makers of software. In the technology industry, we 107 00:07:12,840 --> 00:07:19,040 Speaker 1: see continuous enormous levels of deflation at all times. So 108 00:07:19,240 --> 00:07:23,040 Speaker 1: you know, in the cost of a gigabyte of storage 109 00:07:23,040 --> 00:07:25,800 Speaker 1: was something like five hundred thousand dollars per gigabyte, and 110 00:07:25,840 --> 00:07:29,440 Speaker 1: today it's less than three cents per gigabyte. It's hard 111 00:07:29,480 --> 00:07:32,120 Speaker 1: to wrap our minds around that level of a price drop. 112 00:07:33,240 --> 00:07:36,640 Speaker 1: But if you are a user of technology like that, 113 00:07:36,960 --> 00:07:41,080 Speaker 1: as the prices drop below certain thresholds, entirely new business 114 00:07:41,120 --> 00:07:44,640 Speaker 1: models get unlocked that we're not previously feasible. And those 115 00:07:44,680 --> 00:07:48,480 Speaker 1: can not just unlock new pools of revenue and opportunity 116 00:07:48,520 --> 00:07:51,360 Speaker 1: with your end customers, it can also unlock new pools 117 00:07:51,360 --> 00:07:57,520 Speaker 1: of labor. The interesting takeaway from me is that cloud 118 00:07:57,560 --> 00:08:00,640 Speaker 1: computing is making it cheaper and easier to put powerful 119 00:08:00,720 --> 00:08:04,040 Speaker 1: software into the hands of a lot of employees wherever 120 00:08:04,080 --> 00:08:08,680 Speaker 1: they are. Low cost pervasive technology might actually tilt the 121 00:08:08,720 --> 00:08:11,960 Speaker 1: mix of capital and labor we need to produce GDP 122 00:08:13,000 --> 00:08:17,400 Speaker 1: in favor of labor. That suggests America's tremendous job growth 123 00:08:17,640 --> 00:08:23,160 Speaker 1: might not be such a riddle after all. But before 124 00:08:23,280 --> 00:08:26,840 Speaker 1: ending my search, I wanted to explore how technology might 125 00:08:26,880 --> 00:08:29,920 Speaker 1: be affecting another part of the economy that's been creating 126 00:08:29,920 --> 00:08:40,840 Speaker 1: more jobs than most, the hotel sector. Here Well, I'm 127 00:08:40,840 --> 00:08:51,319 Speaker 1: good chorus from Bloomberg the Well. Eater is the chief 128 00:08:51,360 --> 00:08:56,560 Speaker 1: Information and Digital officer at Hilton, the hotel group. I 129 00:08:56,640 --> 00:09:01,240 Speaker 1: met her at their headquarters in McClean, Virginia. Lodging is 130 00:09:01,280 --> 00:09:05,680 Speaker 1: an incredibly labor intensive industry. I have a son, a cousin, 131 00:09:06,000 --> 00:09:08,760 Speaker 1: and a close friend in the business, and everything they 132 00:09:08,760 --> 00:09:12,079 Speaker 1: tell me suggests that getting the customer experience just right 133 00:09:12,480 --> 00:09:16,120 Speaker 1: is very human intensive. But Eater told me it was 134 00:09:16,200 --> 00:09:19,800 Speaker 1: also a business in which technology could be truly transformational. 135 00:09:21,040 --> 00:09:25,320 Speaker 1: I think what's possible with technology in for Hilton is 136 00:09:25,760 --> 00:09:31,200 Speaker 1: nothing short of staggering. Technology, in my mind, has yet 137 00:09:31,240 --> 00:09:35,640 Speaker 1: to truly immerse itself into the integrated experience for customers 138 00:09:36,080 --> 00:09:39,199 Speaker 1: in as profound a way as is available to us. 139 00:09:39,960 --> 00:09:43,480 Speaker 1: Eater describes the options she wants to see widely available 140 00:09:43,520 --> 00:09:47,200 Speaker 1: to hotel guests in a digital age, such as checking 141 00:09:47,200 --> 00:09:49,960 Speaker 1: in on your smartphone app, which can also set your 142 00:09:50,040 --> 00:09:54,200 Speaker 1: room temperature or order a meal before you've arrived. That 143 00:09:54,360 --> 00:09:59,120 Speaker 1: personalized experience is only possible if customers share their preferences, 144 00:09:59,440 --> 00:10:02,040 Speaker 1: which means eater has to win their trust. On the 145 00:10:02,080 --> 00:10:06,520 Speaker 1: front of digital security, an area that also takes a 146 00:10:06,520 --> 00:10:09,240 Speaker 1: lot of labor, the thing that we have to perfect 147 00:10:09,960 --> 00:10:13,680 Speaker 1: is the ability for humans to interact around this technology, 148 00:10:13,720 --> 00:10:17,560 Speaker 1: so there isn't a stark contrast between the digital engagement 149 00:10:17,640 --> 00:10:20,240 Speaker 1: and the human engagement, so that the front desk team 150 00:10:20,240 --> 00:10:23,880 Speaker 1: member knows what you ask for within the application or 151 00:10:23,920 --> 00:10:28,800 Speaker 1: in the message, etcetera, and can respond above and around them. 152 00:10:29,800 --> 00:10:32,600 Speaker 1: So technology can be labor enhancing in a high touch 153 00:10:32,679 --> 00:10:36,240 Speaker 1: business like lodging, and there might also be a sweet 154 00:10:36,240 --> 00:10:41,040 Speaker 1: spot when human labor and technology come together and employees 155 00:10:41,080 --> 00:10:44,480 Speaker 1: are freed from rope tasks such as check in and 156 00:10:44,480 --> 00:10:46,480 Speaker 1: can use their time to tell you about a good 157 00:10:46,600 --> 00:10:51,400 Speaker 1: lunch spot or maybe about a nearby music festival. Hilton's 158 00:10:51,440 --> 00:10:55,400 Speaker 1: business is about people serving people. It is very much 159 00:10:55,400 --> 00:10:57,440 Speaker 1: the culture of the business. It is very much the 160 00:10:57,520 --> 00:11:02,640 Speaker 1: culture of the experience, and we believe that humans. I 161 00:11:02,679 --> 00:11:08,920 Speaker 1: believe that humans provide that unique differentiation. It's the it's 162 00:11:08,960 --> 00:11:14,360 Speaker 1: the human connection based on empathy, really warmth and generosity 163 00:11:14,840 --> 00:11:18,600 Speaker 1: that makes a hotel experience stand apart. So we are 164 00:11:18,640 --> 00:11:22,640 Speaker 1: not designing technology to replace humans. That will not be 165 00:11:22,720 --> 00:11:25,640 Speaker 1: Hilton's business. Um, and I'm pretty happy about that. I 166 00:11:25,760 --> 00:11:43,400 Speaker 1: haven't a lie humans for Bloomberg News. I'm Craig trust Now. 167 00:11:43,440 --> 00:11:45,880 Speaker 1: I'm delighted to say I'm joined by my friend and 168 00:11:45,960 --> 00:11:49,760 Speaker 1: former boss, Larry Summers, the Harvard professor and former US 169 00:11:49,840 --> 00:11:53,000 Speaker 1: Treasury secretary who also served as the director of President 170 00:11:53,000 --> 00:11:57,600 Speaker 1: Obama's National Economic Council. Welcome to Stephanomics. Good to be 171 00:11:57,679 --> 00:12:02,160 Speaker 1: with you, Stephanie. We have just heard from Craig Torres, 172 00:12:02,240 --> 00:12:05,120 Speaker 1: who had a fairly upbeat view of the impact of 173 00:12:05,200 --> 00:12:08,240 Speaker 1: new technology on jobs. And I know from talking to 174 00:12:08,280 --> 00:12:10,000 Speaker 1: him that the reason that Craig got into this was 175 00:12:10,040 --> 00:12:12,040 Speaker 1: that he had just been looking week after week with 176 00:12:12,080 --> 00:12:15,120 Speaker 1: the US very strong job growth and as we know, 177 00:12:15,360 --> 00:12:19,280 Speaker 1: going with that weak productivity growth. But at the same 178 00:12:19,360 --> 00:12:22,640 Speaker 1: time as we've had all of this argument and concern 179 00:12:22,760 --> 00:12:27,839 Speaker 1: around technology and automation, and it just felt like it 180 00:12:27,880 --> 00:12:30,640 Speaker 1: was going against the sort of robots are coming for 181 00:12:30,720 --> 00:12:33,640 Speaker 1: your job's rhetoric that we hear about so much. So, 182 00:12:33,800 --> 00:12:37,320 Speaker 1: where do you come out on this ongoing debate about 183 00:12:37,320 --> 00:12:40,960 Speaker 1: the impact of technology on jobs, I think your reporter 184 00:12:41,120 --> 00:12:46,000 Speaker 1: is more wrong than right. Um. I take the long view. 185 00:12:47,120 --> 00:12:51,000 Speaker 1: I was involved in discussions of automation when I was 186 00:12:51,040 --> 00:12:53,800 Speaker 1: an undergraduate in M I T in the nineteen seventies, 187 00:12:54,360 --> 00:12:58,000 Speaker 1: and then we heard the view that Craig Torres takes 188 00:12:58,160 --> 00:13:03,240 Speaker 1: that technology would create more productivity, and I would create 189 00:13:03,280 --> 00:13:06,040 Speaker 1: more spending power, and I would create more jobs and 190 00:13:06,480 --> 00:13:10,440 Speaker 1: all would be well. Um. When people were saying that 191 00:13:10,520 --> 00:13:15,280 Speaker 1: in the nineteen seventies, five percent of men between the 192 00:13:15,360 --> 00:13:19,760 Speaker 1: ages of twenty five and fifty four we're not working. 193 00:13:21,040 --> 00:13:25,560 Speaker 1: Now about thirteen percent of men between the ages of 194 00:13:26,240 --> 00:13:32,600 Speaker 1: five and fifty four are not working. And so technology 195 00:13:32,679 --> 00:13:38,080 Speaker 1: actually on net has led to a substantial amount of displacement. 196 00:13:38,880 --> 00:13:41,560 Speaker 1: Whenever has happened in the last few years, I don't 197 00:13:41,559 --> 00:13:45,120 Speaker 1: think proves that much. And by the way, since productivity 198 00:13:45,120 --> 00:13:47,520 Speaker 1: growth has been very slow for the last few years, 199 00:13:47,840 --> 00:13:50,440 Speaker 1: I don't think it's been a period of particularly active 200 00:13:51,040 --> 00:13:55,439 Speaker 1: uh technological change, So I don't think that proves very much. 201 00:13:56,040 --> 00:14:00,280 Speaker 1: I think looking forward, we've got to recognize that almost 202 00:14:00,320 --> 00:14:04,680 Speaker 1: any activity that can be routinized can be mechanized, and 203 00:14:04,960 --> 00:14:09,560 Speaker 1: that has to put substantial pressure on many different job 204 00:14:09,640 --> 00:14:13,120 Speaker 1: categories and many different people. Now, there'll certainly be some 205 00:14:13,200 --> 00:14:15,720 Speaker 1: new jobs created, and probably some of the new jobs 206 00:14:15,720 --> 00:14:19,320 Speaker 1: that will be created will have high productivity and support 207 00:14:19,400 --> 00:14:23,760 Speaker 1: high wages. But for people who are really set up 208 00:14:23,800 --> 00:14:26,440 Speaker 1: to do routine work and not to do other work, 209 00:14:27,120 --> 00:14:29,640 Speaker 1: I think it's likely to be a very difficult period 210 00:14:30,760 --> 00:14:33,440 Speaker 1: going ahead. And do you think I mean to defend 211 00:14:33,440 --> 00:14:36,240 Speaker 1: Craig a little bit? Of course, he's not claiming that 212 00:14:36,320 --> 00:14:41,480 Speaker 1: the overwhelming effect of technology um will be positive for labor. 213 00:14:41,640 --> 00:14:43,720 Speaker 1: He was just he was kind of highlight he's highlighting 214 00:14:43,760 --> 00:14:49,360 Speaker 1: some examples where you could see technology kind of complimenting late. 215 00:14:49,560 --> 00:14:53,360 Speaker 1: And there's certainly people talk about these cases where now 216 00:14:53,400 --> 00:14:55,320 Speaker 1: you will have you know, there'll be more demand for 217 00:14:55,360 --> 00:14:58,200 Speaker 1: the very human jobs, and the very human jobs will 218 00:14:58,240 --> 00:15:02,960 Speaker 1: be more prized because you have all this sort of 219 00:15:02,960 --> 00:15:07,440 Speaker 1: technology supporting those human qualities. Your view is there doesn't exist, 220 00:15:07,520 --> 00:15:09,160 Speaker 1: and maybe we'll have more of them down the road, 221 00:15:09,280 --> 00:15:11,440 Speaker 1: But the ELVM, they're not going to be more than 222 00:15:11,480 --> 00:15:14,080 Speaker 1: a small piece of the story in this short I 223 00:15:14,120 --> 00:15:16,720 Speaker 1: take the long view. Five or some of the people 224 00:15:16,800 --> 00:15:20,240 Speaker 1: weren't working in the group where you'd expect everybody to 225 00:15:20,280 --> 00:15:23,160 Speaker 1: be working and then thirteen for some of the people, 226 00:15:23,840 --> 00:15:27,720 Speaker 1: uh weren't working. And it's been a pretty inexorable trend. 227 00:15:28,120 --> 00:15:30,840 Speaker 1: If you take that trend back to the nineteen forties, 228 00:15:31,640 --> 00:15:38,240 Speaker 1: the trend is even stronger. So I don't dispute the 229 00:15:38,320 --> 00:15:43,120 Speaker 1: idea that there will be some sets of jobs that 230 00:15:43,200 --> 00:15:47,160 Speaker 1: will become more productive, and that there will be some 231 00:15:47,320 --> 00:15:52,200 Speaker 1: jobs created, you know, being an eBay merchant, being a 232 00:15:52,240 --> 00:15:59,760 Speaker 1: Facebook programmer, building building sites. But I think the to 233 00:16:00,000 --> 00:16:02,880 Speaker 1: mentality of it, and particularly for the people who were 234 00:16:03,280 --> 00:16:06,760 Speaker 1: most on the margin of working versus not working, I 235 00:16:06,760 --> 00:16:10,920 Speaker 1: think it's likely to be difficult. You know, twenty years ago, 236 00:16:11,360 --> 00:16:14,760 Speaker 1: the big change that was happening in the global labor 237 00:16:14,840 --> 00:16:18,640 Speaker 1: market arguably was China and globalization. You know, was the 238 00:16:19,080 --> 00:16:22,000 Speaker 1: was the arrival of the Chinese labor force, if you like, 239 00:16:22,080 --> 00:16:24,320 Speaker 1: into the global economy. And I remember when I was 240 00:16:24,960 --> 00:16:28,520 Speaker 1: studying at Harvard and elsewhere, there was a consistent underestimation 241 00:16:29,080 --> 00:16:31,160 Speaker 1: of the impact that was going to have on the 242 00:16:31,200 --> 00:16:33,800 Speaker 1: global labor market. Now we have all of this evidence 243 00:16:33,800 --> 00:16:36,320 Speaker 1: from David Ort and all these other academic evidence about 244 00:16:36,800 --> 00:16:41,440 Speaker 1: how that wave of competition from China cost jobs here 245 00:16:41,480 --> 00:16:45,120 Speaker 1: in the US, and people didn't immediately bounce back, and 246 00:16:45,160 --> 00:16:49,680 Speaker 1: the costs were concentrated in a way that theory doesn't predict. 247 00:16:50,360 --> 00:16:54,040 Speaker 1: Now we have that data, how is it. Does it 248 00:16:54,120 --> 00:16:57,640 Speaker 1: help us be more aware of what's coming down the 249 00:16:57,640 --> 00:17:01,480 Speaker 1: track with this new wave of technological change or do 250 00:17:01,520 --> 00:17:03,960 Speaker 1: you think a whole lot of different lessons are going 251 00:17:04,000 --> 00:17:07,120 Speaker 1: to come out of that. I'm not sure. I think 252 00:17:07,119 --> 00:17:11,600 Speaker 1: the David Otter work that you refer to is about 253 00:17:11,640 --> 00:17:15,560 Speaker 1: to be and is already being subject to a wave 254 00:17:15,640 --> 00:17:19,879 Speaker 1: of revisionism. UH. People are taking account of the extra 255 00:17:19,960 --> 00:17:24,080 Speaker 1: exports as well as the extra imports. People are taking 256 00:17:24,119 --> 00:17:25,879 Speaker 1: account of the fact that a lot of the goods 257 00:17:25,920 --> 00:17:29,879 Speaker 1: that we import from China then become part of products 258 00:17:29,920 --> 00:17:33,320 Speaker 1: that we sell that are cheaper because of the imports 259 00:17:33,359 --> 00:17:36,639 Speaker 1: from products so China. So we import, so we use 260 00:17:36,760 --> 00:17:41,040 Speaker 1: more people because we sell more of the product. People 261 00:17:41,040 --> 00:17:44,040 Speaker 1: are taking account of the fact that the imports from 262 00:17:44,119 --> 00:17:47,560 Speaker 1: China held the price level down and that enabled the 263 00:17:47,600 --> 00:17:51,960 Speaker 1: FED to pursue easier policies. None of those effects were 264 00:17:51,960 --> 00:17:56,400 Speaker 1: really contained in the David out Tour research that's so 265 00:17:56,520 --> 00:18:01,080 Speaker 1: frequently UH cited. So I think you're going to see 266 00:18:01,160 --> 00:18:08,640 Speaker 1: some revisionism towards blaming China rather less for the problems 267 00:18:08,720 --> 00:18:20,800 Speaker 1: that have existed in the United States. Moving on from this, 268 00:18:20,840 --> 00:18:22,840 Speaker 1: we should talk about, you know, where you think the 269 00:18:22,920 --> 00:18:25,280 Speaker 1: US economy is right now. I mean, there's a lot 270 00:18:25,320 --> 00:18:29,080 Speaker 1: of debate about how much structural change there's been in 271 00:18:29,160 --> 00:18:33,720 Speaker 1: the labor market and how much further unemployment can go. 272 00:18:34,680 --> 00:18:38,399 Speaker 1: The recent FED change in its attitude towards that in 273 00:18:38,440 --> 00:18:40,840 Speaker 1: a sense, you know, seemingly willing to give it more 274 00:18:40,920 --> 00:18:45,359 Speaker 1: time before continuing to raise interest rates. Where do you 275 00:18:45,400 --> 00:18:46,879 Speaker 1: stand on that? Do you think the FED should be 276 00:18:46,920 --> 00:18:51,639 Speaker 1: even slower, even more cautious. I'm glad to see the 277 00:18:51,760 --> 00:18:57,560 Speaker 1: FED embrace the kinds of ideas of secular stagnation that 278 00:18:57,680 --> 00:19:02,200 Speaker 1: I've been talking about for some years now. I think 279 00:19:02,240 --> 00:19:07,159 Speaker 1: they've recognized that in an economy where fundamentally there's a 280 00:19:07,240 --> 00:19:10,200 Speaker 1: higher propensity to save than there used to be and 281 00:19:10,320 --> 00:19:13,720 Speaker 1: a lower propensity to invest than there used to be, 282 00:19:14,359 --> 00:19:18,040 Speaker 1: interest rates that once would have been very stimulative now 283 00:19:18,160 --> 00:19:23,480 Speaker 1: actually can be consistent with UH contraction, and therefore they've 284 00:19:23,520 --> 00:19:27,520 Speaker 1: got to be very careful about overly contracting the economy. 285 00:19:28,000 --> 00:19:31,240 Speaker 1: I think that's a good recognition by the FED. It's 286 00:19:31,320 --> 00:19:37,720 Speaker 1: also been good that the FED has recognized that inflation 287 00:19:37,880 --> 00:19:43,480 Speaker 1: looks awfully quiescent, and that the combination of no increases 288 00:19:43,520 --> 00:19:50,119 Speaker 1: in the minimum wage and forever huge restraints on union power, 289 00:19:51,000 --> 00:19:57,320 Speaker 1: the ability of companies to outsource more ruthlessly, aggressive shareholders 290 00:19:57,440 --> 00:20:04,760 Speaker 1: maximizing profits, all that has operated to put downwards pressure 291 00:20:04,800 --> 00:20:08,640 Speaker 1: on wages. And ultimately it's very hard to generate very 292 00:20:08,720 --> 00:20:13,959 Speaker 1: much product price inflation without having wage price inflation. And 293 00:20:14,440 --> 00:20:16,360 Speaker 1: so I think J. Powell has been in the right 294 00:20:16,400 --> 00:20:20,440 Speaker 1: place on that as well. I wish he'd gotten there 295 00:20:20,920 --> 00:20:26,879 Speaker 1: before they raised rates UM in UH December. And I 296 00:20:26,960 --> 00:20:31,639 Speaker 1: wish the President wasn't making it harder to do the 297 00:20:31,760 --> 00:20:35,359 Speaker 1: right thing by making doing the right thing look like 298 00:20:35,480 --> 00:20:39,439 Speaker 1: it's a craven political act, as he puts pressure on 299 00:20:40,560 --> 00:20:43,640 Speaker 1: the FED. But the Fed has to do what's right, 300 00:20:45,359 --> 00:20:50,560 Speaker 1: whether it makes politicians happy or whether it makes politicians unhappy. 301 00:20:50,800 --> 00:20:57,120 Speaker 1: And on balance, I think the risks of excessive restraint 302 00:20:57,800 --> 00:21:01,800 Speaker 1: on the economy are are greater than the risks of 303 00:21:01,920 --> 00:21:09,240 Speaker 1: insufficient restraint. In addition to the considerations I already raised UM, 304 00:21:09,280 --> 00:21:11,399 Speaker 1: our goal in the United States is to have a 305 00:21:11,400 --> 00:21:16,680 Speaker 1: two symmetric inflation target. Symmetrics an important word. It means 306 00:21:16,720 --> 00:21:21,080 Speaker 1: that inflation is supposed to average two Why I ask 307 00:21:21,160 --> 00:21:27,400 Speaker 1: you the question if after ten years of recovery, when 308 00:21:27,400 --> 00:21:30,679 Speaker 1: the unemployment rate is lower than it's been in fifty years, 309 00:21:31,720 --> 00:21:35,439 Speaker 1: and we're in the eleventh year of expansion, if that's 310 00:21:35,480 --> 00:21:38,520 Speaker 1: not the time when we're gonna have inflation above two 311 00:21:38,520 --> 00:21:42,600 Speaker 1: percent after ten years, when we've had it below two percent, 312 00:21:43,240 --> 00:21:46,159 Speaker 1: when would such a time ever be? And it's like 313 00:21:46,320 --> 00:21:49,399 Speaker 1: the credibility of the FED with respect to an inflation 314 00:21:49,480 --> 00:21:53,920 Speaker 1: target actually depends on its ability to generate a bit 315 00:21:53,920 --> 00:21:58,520 Speaker 1: of acceleration of inflation and inflation expectations from here. I'm 316 00:21:58,520 --> 00:22:00,200 Speaker 1: not sure they're going to be able to do way, 317 00:22:00,600 --> 00:22:03,600 Speaker 1: but they should at least be quite trying. Larry Summons, 318 00:22:03,640 --> 00:22:05,679 Speaker 1: thanks very much for being on Stephanomics, and maybe one 319 00:22:05,720 --> 00:22:07,800 Speaker 1: of these days we'll get you back on the program. 320 00:22:07,800 --> 00:22:16,760 Speaker 1: Good to be with you. So I'm joined now by 321 00:22:17,080 --> 00:22:20,960 Speaker 1: one of our top FED reporters, Gina Smellek, who's based 322 00:22:21,080 --> 00:22:24,480 Speaker 1: in New York and she has the important job, among 323 00:22:24,600 --> 00:22:28,840 Speaker 1: many other things, of writing our economic research rap every 324 00:22:28,840 --> 00:22:33,919 Speaker 1: week for Bloomberg, and recently she wrote about poor Man's 325 00:22:33,960 --> 00:22:37,320 Speaker 1: monetary policy. Tell us about that, Gina, Right, So this 326 00:22:37,400 --> 00:22:40,280 Speaker 1: is this idea from city groups. Will Embroider in a 327 00:22:40,320 --> 00:22:43,119 Speaker 1: recent research note that central banks are not going to 328 00:22:43,160 --> 00:22:46,000 Speaker 1: have a lot of ammunition at their disposal come the 329 00:22:46,040 --> 00:22:48,720 Speaker 1: next procession. And the point he's making here is that 330 00:22:48,880 --> 00:22:51,880 Speaker 1: rates are still pretty low around the world. The US 331 00:22:52,000 --> 00:22:54,960 Speaker 1: is obviously a little bit above zero, but you look 332 00:22:55,000 --> 00:22:57,560 Speaker 1: at the euro Area in Japan and they're still sort 333 00:22:57,560 --> 00:23:01,600 Speaker 1: of lingering below And so come the next crisis, the 334 00:23:01,680 --> 00:23:04,000 Speaker 1: central banks are really going to be forced to rely 335 00:23:04,119 --> 00:23:06,320 Speaker 1: heavily on their balance sheets, sort of tinkering around the 336 00:23:06,400 --> 00:23:09,119 Speaker 1: edges of composition in size, and aren't going to have 337 00:23:09,160 --> 00:23:11,040 Speaker 1: a lot of the tools that have traditionally been at 338 00:23:11,080 --> 00:23:14,840 Speaker 1: their disposal, which you know, most centrally includes rate cuts. 339 00:23:15,880 --> 00:23:18,159 Speaker 1: And I guess what's interesting about this, and you mentioned 340 00:23:18,200 --> 00:23:21,399 Speaker 1: a few other bits of research, including the recent paper 341 00:23:21,480 --> 00:23:26,000 Speaker 1: that that Larry Summers um co wrote and produced for 342 00:23:26,040 --> 00:23:29,719 Speaker 1: the Brookings Institute conference, that it's sort of this debate 343 00:23:29,760 --> 00:23:32,320 Speaker 1: about not having enough ammunition for the next crisis is 344 00:23:32,400 --> 00:23:37,040 Speaker 1: kind of clashing, sort of colliding with debates around what 345 00:23:37,200 --> 00:23:39,879 Speaker 1: the best economic policy is right now in the US 346 00:23:40,080 --> 00:23:43,959 Speaker 1: and the sort of more progressive thinkers um talking about 347 00:23:44,400 --> 00:23:48,680 Speaker 1: modern monetary theory and wanting to run big, bigger deficits, 348 00:23:48,720 --> 00:23:51,480 Speaker 1: you know, in a sense for political reasons, but you've 349 00:23:51,520 --> 00:23:55,000 Speaker 1: got economists saying you might want to do it to 350 00:23:55,119 --> 00:23:57,399 Speaker 1: help prevent the next crisis as well. Is that that 351 00:23:57,440 --> 00:24:00,119 Speaker 1: must be kind of interesting for you to see the 352 00:24:00,119 --> 00:24:03,480 Speaker 1: sort of everyone's coming for coming to the same conclusion, 353 00:24:03,520 --> 00:24:05,840 Speaker 1: which is we need to borrow more, just at a 354 00:24:05,880 --> 00:24:08,080 Speaker 1: time when I would have thought that President Trump was 355 00:24:08,080 --> 00:24:10,880 Speaker 1: already borrowing quite a lot, right. It's so interesting. These 356 00:24:10,880 --> 00:24:14,240 Speaker 1: conversations are also closely interconnected. And there's I think this 357 00:24:14,400 --> 00:24:18,240 Speaker 1: growing idea that central banks are dealing with what's called 358 00:24:18,280 --> 00:24:20,639 Speaker 1: a very low neutral interest rate. So that's the one 359 00:24:20,680 --> 00:24:23,760 Speaker 1: that neither stokes nor slows growth. It's really low for 360 00:24:23,880 --> 00:24:27,280 Speaker 1: demographic reasons because populations are aging and people tend to 361 00:24:27,280 --> 00:24:30,320 Speaker 1: save as they age, and that pushes that sort of 362 00:24:30,400 --> 00:24:32,680 Speaker 1: rate setting that they can achieve down a little bit. 363 00:24:33,160 --> 00:24:35,600 Speaker 1: One way to get that higher is by running a 364 00:24:35,600 --> 00:24:38,600 Speaker 1: bigger budget deficit, especially if it can if doing so 365 00:24:38,760 --> 00:24:41,159 Speaker 1: results in sort of infrastructure spending and other things that 366 00:24:41,160 --> 00:24:44,359 Speaker 1: can raise the productive capacity of an economy. But that's 367 00:24:44,359 --> 00:24:45,919 Speaker 1: hard to do in a world where you already have 368 00:24:45,920 --> 00:24:49,360 Speaker 1: really high to GDP ratios, and so I think there 369 00:24:49,359 --> 00:24:52,640 Speaker 1: are all these conversations around what does fiscal responsibility mean 370 00:24:52,880 --> 00:24:55,199 Speaker 1: in the world of twenty nineteen, and what is it 371 00:24:55,200 --> 00:24:57,520 Speaker 1: going to mean come the next recession? Like how much 372 00:24:57,560 --> 00:25:00,320 Speaker 1: can we play with this stuff around the edges? And 373 00:25:00,600 --> 00:25:02,240 Speaker 1: at what point do we push ourselves to a point 374 00:25:02,280 --> 00:25:04,840 Speaker 1: where we're just overextended on this fiscal front. And I 375 00:25:04,840 --> 00:25:08,600 Speaker 1: think that conversation is really reaching a fever pitch in 376 00:25:08,640 --> 00:25:11,400 Speaker 1: places like the US now, and I don't think it's 377 00:25:11,400 --> 00:25:13,560 Speaker 1: going to fade in any way come the next crisis. 378 00:25:13,600 --> 00:25:15,480 Speaker 1: I think this is this is a debate that we're 379 00:25:15,480 --> 00:25:18,000 Speaker 1: going to have with us for the foreseeable future, and 380 00:25:18,080 --> 00:25:19,840 Speaker 1: I suspect we are going to be talking about it 381 00:25:20,000 --> 00:25:30,840 Speaker 1: many times. Thanks very much, Gina Smellick. Thank you, thanks 382 00:25:30,840 --> 00:25:33,760 Speaker 1: for listening to Stephanomics. Please join us next week for 383 00:25:33,800 --> 00:25:37,560 Speaker 1: another episode about the forces shaping the global economy. In 384 00:25:37,560 --> 00:25:39,959 Speaker 1: the meantime, you can find us on the Bloomberg Terminal 385 00:25:40,040 --> 00:25:44,000 Speaker 1: website or app, and wherever you get your podcasts. We'd 386 00:25:44,000 --> 00:25:45,760 Speaker 1: love it if you took the time to rate and 387 00:25:45,800 --> 00:25:48,840 Speaker 1: review the show so it can reach more listeners. For 388 00:25:48,920 --> 00:25:52,760 Speaker 1: more news and analysis from Bloomberg Economics, follow as economics 389 00:25:52,800 --> 00:25:55,639 Speaker 1: on Twitter. You can also find me on my Twitter 390 00:25:55,680 --> 00:25:59,600 Speaker 1: handle at my Stephanomics. The story in this episode was 391 00:25:59,640 --> 00:26:02,760 Speaker 1: reported and written by Craig Torres. It was produced by 392 00:26:02,800 --> 00:26:06,280 Speaker 1: Magnus Hendrickson and edited by Scott Lanman, who is also 393 00:26:06,400 --> 00:26:10,480 Speaker 1: the executive producer of Stephanomics. Craig's original article on this 394 00:26:10,560 --> 00:26:13,879 Speaker 1: topic appeared in Bloomberg business Week. It was edited by 395 00:26:13,920 --> 00:26:17,800 Speaker 1: Ben Holland and Christina lind Black Special thanks to Larry Summers. 396 00:26:18,400 --> 00:26:21,560 Speaker 1: Francesco Levy is the head of Bloomberg Podcasts.