1
00:00:07,360 --> 00:00:09,959
Speaker 1: Hello, and welcome to Stephanomics, the podcast that brings the

2
00:00:09,960 --> 00:00:14,080
Speaker 1: global economy to you. This week, we're thinking about jobs, machines,

3
00:00:14,480 --> 00:00:17,880
Speaker 1: and COVID nineteen. At the start of this pandemic, a

4
00:00:17,880 --> 00:00:21,439
Speaker 1: lot of people decided globalization was dead, the companies would

5
00:00:21,480 --> 00:00:24,640
Speaker 1: pull their factories out of China and supply chains would shrink.

6
00:00:25,400 --> 00:00:28,360
Speaker 1: By and large companies haven't done that, but COVID nineteen

7
00:00:28,480 --> 00:00:30,440
Speaker 1: does seem to have brought forward the day when an

8
00:00:30,480 --> 00:00:32,599
Speaker 1: awful lot of the jobs we see around us are

9
00:00:32,640 --> 00:00:35,680
Speaker 1: done by a machine. In a few minutes, I'll be

10
00:00:35,720 --> 00:00:39,120
Speaker 1: talking about what the future holds with Harvard Economics chair

11
00:00:39,200 --> 00:00:43,199
Speaker 1: and deep thinker about all things labor related Richard Freeman.

12
00:00:44,080 --> 00:00:47,720
Speaker 1: Our chief European economist, Jamie Rush is also going to

13
00:00:47,800 --> 00:00:50,760
Speaker 1: explain why the answer to the biggest question in the

14
00:00:50,760 --> 00:00:54,680
Speaker 1: global economy right now is three point two trillion dollars.

15
00:00:55,640 --> 00:00:58,560
Speaker 1: I'll leave you to work out with the questions, but

16
00:00:58,680 --> 00:01:02,120
Speaker 1: first here's u s A economy reporter Olivia Rockman on

17
00:01:02,160 --> 00:01:05,160
Speaker 1: the truth about robots and jobs in the year of

18
00:01:05,200 --> 00:01:22,200
Speaker 1: COVID nineteen. Adding more robots to factories, retail stores, or

19
00:01:22,240 --> 00:01:25,280
Speaker 1: mines was historically seen as a job killer by workers

20
00:01:25,360 --> 00:01:28,559
Speaker 1: and the unions that support them. But this year, automation

21
00:01:28,600 --> 00:01:31,440
Speaker 1: has allowed sectors of the economy to continue producing with

22
00:01:31,520 --> 00:01:35,200
Speaker 1: fewer people or without any at all, minimizing the coronavirus

23
00:01:35,240 --> 00:01:39,240
Speaker 1: risk for workers. Unions have recognized that automation will continue

24
00:01:39,240 --> 00:01:42,240
Speaker 1: to accelerate to avoid massive outbreaks like the ones seen

25
00:01:42,319 --> 00:01:46,080
Speaker 1: in US meatpacking plants earlier this year. But while they're

26
00:01:46,120 --> 00:01:49,400
Speaker 1: all for protecting workers, their concern is widespread and permanent

27
00:01:49,480 --> 00:01:52,600
Speaker 1: job loss. In many cases, once a company invests the

28
00:01:52,640 --> 00:01:56,320
Speaker 1: cash to implement automation, it's unlikely that they'll take their

29
00:01:56,320 --> 00:02:00,720
Speaker 1: workers back. A highway in Pennsylvania cut five toll worker

30
00:02:00,840 --> 00:02:04,760
Speaker 1: jobs earlier this year after putting an electronic system into place,

31
00:02:05,560 --> 00:02:07,800
Speaker 1: and it doesn't plan to bring any of the employees back.

32
00:02:08,160 --> 00:02:11,799
Speaker 1: Marcus Casey, an economist at the University of Illinois at Chicago,

33
00:02:12,040 --> 00:02:15,799
Speaker 1: spoke with me about how eliminating jobs can exacerbate inequality

34
00:02:16,200 --> 00:02:19,280
Speaker 1: those workers aren't taught new skills. High skill jobs that

35
00:02:19,400 --> 00:02:22,560
Speaker 1: require creative or human touch, so to speak, will remain

36
00:02:22,919 --> 00:02:26,600
Speaker 1: um even in cases where pay is not necessarily much higher.

37
00:02:27,120 --> 00:02:31,799
Speaker 1: For example, skill workers say professors like myself may see

38
00:02:31,800 --> 00:02:35,480
Speaker 1: our pay is relatively flat in the future, However, among

39
00:02:35,600 --> 00:02:40,560
Speaker 1: people who aren't necessarily as skilled, because there will be

40
00:02:40,600 --> 00:02:44,000
Speaker 1: fewer jobs, you might see pay actually decrease because of

41
00:02:44,040 --> 00:02:50,400
Speaker 1: increased competition in those sectors. One solution to the inequality

42
00:02:50,440 --> 00:02:53,919
Speaker 1: problem is increased investment in reskilling, which can help factory

43
00:02:53,919 --> 00:02:58,000
Speaker 1: workers transition into jobs in healthcare, for example, where automation

44
00:02:58,120 --> 00:03:01,800
Speaker 1: is more difficult to implement. That's US President electro Biden's

45
00:03:01,800 --> 00:03:06,240
Speaker 1: plan ensure that employers give workers impacted by automation advanced

46
00:03:06,320 --> 00:03:08,400
Speaker 1: notice and put them at the front of the line

47
00:03:08,520 --> 00:03:12,160
Speaker 1: for new jobs, as well as paid skills training. Currently,

48
00:03:12,160 --> 00:03:15,240
Speaker 1: though the United States, Chile, and Mexico spend the least

49
00:03:15,280 --> 00:03:19,040
Speaker 1: among O E c D countries on policies intended to

50
00:03:19,080 --> 00:03:22,679
Speaker 1: improve job readiness and expand employment. That isn't likely to

51
00:03:22,760 --> 00:03:26,280
Speaker 1: change quickly because health concerns are coming first, and Casey

52
00:03:26,320 --> 00:03:29,519
Speaker 1: says there will be consequences. We already have a long

53
00:03:29,639 --> 00:03:33,120
Speaker 1: term problem with people who are prime age and not working.

54
00:03:33,680 --> 00:03:37,440
Speaker 1: Our employment to population ratio has been declining for many decades,

55
00:03:38,200 --> 00:03:42,160
Speaker 1: and supposed in the future we go from a situation

56
00:03:42,200 --> 00:03:45,200
Speaker 1: where we have a sixty seven or sixty eight percent

57
00:03:45,280 --> 00:03:49,240
Speaker 1: employment of population rate to maybe a fifty employment to

58
00:03:49,320 --> 00:03:53,400
Speaker 1: population rate. Many of those people are young, prime age malls,

59
00:03:54,080 --> 00:03:58,120
Speaker 1: and our social insurance programs tend to direct money away

60
00:03:58,160 --> 00:04:05,480
Speaker 1: from that population. So I'm delighted now to be able

61
00:04:05,520 --> 00:04:09,680
Speaker 1: to talk to Richard Freeman. Herbert Asherman, share of economics

62
00:04:09,720 --> 00:04:12,640
Speaker 1: at Harvard, taught me a lot about labor markets quite

63
00:04:12,640 --> 00:04:17,040
Speaker 1: a long time ago. Richard, thanks for coming on Stephanomics.

64
00:04:17,680 --> 00:04:20,359
Speaker 1: We're talking this week about the impact that COVID nineteen

65
00:04:20,400 --> 00:04:23,480
Speaker 1: has had on workers in the US and Europe, and

66
00:04:23,520 --> 00:04:28,520
Speaker 1: also how fits into the longer term structural changes in

67
00:04:28,560 --> 00:04:31,600
Speaker 1: the labor market that you've spent a lot of your

68
00:04:31,640 --> 00:04:35,000
Speaker 1: life thinking about and studying. And one thing that's come

69
00:04:35,080 --> 00:04:38,320
Speaker 1: up a lot is the unequal way that this pandemic

70
00:04:38,360 --> 00:04:41,760
Speaker 1: has hit society's and households, with poorer people much more

71
00:04:41,800 --> 00:04:45,640
Speaker 1: likely to get sick and more likely to lose their jobs.

72
00:04:45,680 --> 00:04:49,680
Speaker 1: But we also know that fiscal stimulus, certainly in the US,

73
00:04:49,720 --> 00:04:54,799
Speaker 1: actually increased the disposable income of many lower wage Americans

74
00:04:54,839 --> 00:04:58,480
Speaker 1: in twenty and I think you've also had many parts

75
00:04:58,520 --> 00:05:00,960
Speaker 1: of the world are kind of out break of social

76
00:05:01,040 --> 00:05:04,279
Speaker 1: solidarity in many places, and maybe a new appreciation of

77
00:05:04,320 --> 00:05:07,080
Speaker 1: the value of people in some of these low page

78
00:05:07,120 --> 00:05:10,760
Speaker 1: jobs that we reminded are so important for keeping the

79
00:05:10,800 --> 00:05:16,040
Speaker 1: country going. So when you look back at do you

80
00:05:16,080 --> 00:05:18,159
Speaker 1: think it's going to mark a break with some of

81
00:05:18,160 --> 00:05:22,320
Speaker 1: those long term trends for labor or or more an acceleration.

82
00:05:24,080 --> 00:05:28,920
Speaker 1: My guess is it will more accelerate things because the

83
00:05:30,080 --> 00:05:35,039
Speaker 1: good outcomes that we had on the particularly through our

84
00:05:34,880 --> 00:05:43,360
Speaker 1: almost unanimous Cares Act that really provided the people lost

85
00:05:43,440 --> 00:05:48,279
Speaker 1: their jobs and the low income people are at least

86
00:05:49,040 --> 00:05:52,520
Speaker 1: you know, financial protection. That was a one time thing.

87
00:05:52,560 --> 00:05:57,920
Speaker 1: We may next few days plays something else, but that

88
00:05:57,960 --> 00:06:01,520
Speaker 1: will also be a short term policy. The longer term

89
00:06:01,600 --> 00:06:05,200
Speaker 1: thing is that we've created two different kinds of workers,

90
00:06:05,480 --> 00:06:11,719
Speaker 1: those who work at home using computer technologies and those

91
00:06:11,760 --> 00:06:18,320
Speaker 1: who work interacting with other humans and dangerous settings. And

92
00:06:18,640 --> 00:06:21,760
Speaker 1: so I think the real push is going to be

93
00:06:21,920 --> 00:06:27,400
Speaker 1: to automate more of those jobs and um to do

94
00:06:27,520 --> 00:06:32,680
Speaker 1: more work at home phenomenon um. And that's that benefits

95
00:06:32,720 --> 00:06:38,440
Speaker 1: one group of people, managers, professionals, and that harms another

96
00:06:38,480 --> 00:06:42,080
Speaker 1: group of people, the people we were told next years ago,

97
00:06:42,680 --> 00:06:44,840
Speaker 1: not not so long ago, where the we're going to

98
00:06:44,960 --> 00:06:47,200
Speaker 1: be the heart of the new economy wasn't gonna be,

99
00:06:47,600 --> 00:06:50,440
Speaker 1: you know, guys working in big factories. It was going

100
00:06:50,520 --> 00:06:53,120
Speaker 1: to be all the people who interact with other humans.

101
00:06:54,440 --> 00:06:59,160
Speaker 1: If this COVID is a one off and we don't

102
00:06:59,200 --> 00:07:05,000
Speaker 1: see another another five six years, you know, maybe we'll

103
00:07:05,040 --> 00:07:08,640
Speaker 1: we'll just go back to a more normal phenomenon. But

104
00:07:09,279 --> 00:07:12,760
Speaker 1: there's a certainly a chunk of thinking in the scientific

105
00:07:12,800 --> 00:07:18,480
Speaker 1: community that this is just the first pandemic that we're

106
00:07:18,480 --> 00:07:21,920
Speaker 1: going to say, and they're more coming down the line.

107
00:07:22,480 --> 00:07:27,160
Speaker 1: We're entering an age of pandemics, that this is not

108
00:07:27,280 --> 00:07:31,520
Speaker 1: the first one that's gonna jump um, and the climate

109
00:07:31,600 --> 00:07:36,280
Speaker 1: change is gonna add more to this. So if if

110
00:07:36,360 --> 00:07:39,520
Speaker 1: this is a turning point, it's going to be a

111
00:07:39,640 --> 00:07:43,640
Speaker 1: turning point changing work and I think speeding up automation

112
00:07:44,680 --> 00:07:51,720
Speaker 1: and creating more problems for the lower skill people. Just

113
00:07:51,800 --> 00:07:53,640
Speaker 1: to dig into that a bit, because then obviously is

114
00:07:53,720 --> 00:07:57,000
Speaker 1: quite a lot of debate and pre dates COVID about

115
00:07:57,360 --> 00:08:02,160
Speaker 1: the impact that the next wave of automation and artificial

116
00:08:02,200 --> 00:08:06,560
Speaker 1: intelligence could have on on inequality and on and on jobs.

117
00:08:07,080 --> 00:08:10,960
Speaker 1: And there is an argument that says, actually, this next

118
00:08:10,960 --> 00:08:13,000
Speaker 1: wave is going to be kind of different, and that

119
00:08:13,320 --> 00:08:15,200
Speaker 1: the jobs that we see were being replaced a kind

120
00:08:15,200 --> 00:08:21,680
Speaker 1: of accountants quite high skilled workers UM that have done

121
00:08:21,840 --> 00:08:25,920
Speaker 1: well in the previous few decades, the middle middle manager

122
00:08:26,120 --> 00:08:29,680
Speaker 1: types potentially UM. And you might argue, I mean, if

123
00:08:29,720 --> 00:08:32,480
Speaker 1: we don't live permanently in pandemic, you know, those kind

124
00:08:32,520 --> 00:08:36,520
Speaker 1: of person to person to job jobs, hairdressing, all those things,

125
00:08:36,720 --> 00:08:39,240
Speaker 1: I mean, they hadn't they didn't go away in the pandemic,

126
00:08:39,280 --> 00:08:41,040
Speaker 1: and we're all trying to go back to our addresses

127
00:08:41,080 --> 00:08:44,240
Speaker 1: when we can. So I wonder whether that we're whether

128
00:08:44,280 --> 00:08:50,520
Speaker 1: you're exaggerating the impact of of COVID and not seeing

129
00:08:50,559 --> 00:08:53,480
Speaker 1: potentially the other side of this automation, which does seem

130
00:08:53,520 --> 00:08:58,160
Speaker 1: to have accelerated. Yeah. I've been sympathetic to the view

131
00:08:58,240 --> 00:09:02,520
Speaker 1: that the more skilled people would be replaceable by really

132
00:09:02,559 --> 00:09:07,880
Speaker 1: smart AI UM. But so far the evidence is that

133
00:09:07,880 --> 00:09:12,040
Speaker 1: that hasn't happened. And all the AI experts told us

134
00:09:12,040 --> 00:09:16,280
Speaker 1: accounting is gonna just die off, and in fact, the

135
00:09:16,320 --> 00:09:21,440
Speaker 1: accountants keep increasing, and of course the accountant profession gets

136
00:09:21,440 --> 00:09:26,160
Speaker 1: more computerized and people learn more skills. Yeah, it's hard,

137
00:09:26,200 --> 00:09:28,720
Speaker 1: too hard for me to now see that those are

138
00:09:28,720 --> 00:09:32,800
Speaker 1: the people who are going to be threatened as opposed

139
00:09:32,800 --> 00:09:38,360
Speaker 1: to the clerks in the stores. UM. And and if

140
00:09:38,360 --> 00:09:41,160
Speaker 1: we want to go to a store. I suppose I'd

141
00:09:41,240 --> 00:09:44,520
Speaker 1: rather see a robot. It's safer for me as a

142
00:09:44,559 --> 00:09:48,400
Speaker 1: consumer to deal with the robot that can't carry the disease.

143
00:09:57,600 --> 00:10:01,760
Speaker 1: There was an old challenge that on the one hand,

144
00:10:02,360 --> 00:10:05,240
Speaker 1: we've talked a lot about the risk of automation and

145
00:10:05,280 --> 00:10:08,760
Speaker 1: the likelihood the automation is going to take jobs, at

146
00:10:08,800 --> 00:10:11,600
Speaker 1: least in the chunk of the workforce, and yet we

147
00:10:11,640 --> 00:10:15,400
Speaker 1: have this problem, ongoing problem, a very low labor productivity

148
00:10:15,720 --> 00:10:19,559
Speaker 1: relative to the past. So what's the answer to that,

149
00:10:19,600 --> 00:10:21,080
Speaker 1: Because on the face of it, it does seem a

150
00:10:21,080 --> 00:10:24,880
Speaker 1: conundrum that we're that the automation could be destroying jobs

151
00:10:24,920 --> 00:10:27,640
Speaker 1: but somehow not increasing our productivity the amount that we

152
00:10:27,679 --> 00:10:31,240
Speaker 1: can make per person. Yeah. Well than some of that

153
00:10:31,360 --> 00:10:34,280
Speaker 1: has to do with the sectors. Almost the ast bulk

154
00:10:34,360 --> 00:10:39,120
Speaker 1: of the first automation things have have been factories, and

155
00:10:39,320 --> 00:10:43,840
Speaker 1: productivity there has done reasonably well. So it's been in

156
00:10:43,880 --> 00:10:48,240
Speaker 1: the in the server sectors where it's been slower, and

157
00:10:48,280 --> 00:10:53,160
Speaker 1: that's where the workforce is shifted. Two. If we begin

158
00:10:53,240 --> 00:10:57,520
Speaker 1: to see more automation in the service sectors, we will

159
00:10:57,559 --> 00:11:03,439
Speaker 1: see productivity go up and there will be job problems

160
00:11:04,800 --> 00:11:06,520
Speaker 1: and the wages would go up in some of these

161
00:11:06,559 --> 00:11:09,840
Speaker 1: service sector jobs. The few that are left wages, so

162
00:11:09,880 --> 00:11:12,600
Speaker 1: at least we will have wage growth if even if

163
00:11:12,600 --> 00:11:16,080
Speaker 1: we've lost the jobs. No, No, correctly, So I I

164
00:11:16,240 --> 00:11:20,880
Speaker 1: don't think if if we have a reasonably well organized economy,

165
00:11:20,920 --> 00:11:23,800
Speaker 1: that we're going to see mass unemployment. These these things

166
00:11:24,080 --> 00:11:27,480
Speaker 1: eight million, hundred million jobs disappearing, and depending on which

167
00:11:27,520 --> 00:11:30,360
Speaker 1: country there or which area of the world they're talking about,

168
00:11:30,520 --> 00:11:33,840
Speaker 1: I mean that what should happen is the workers will

169
00:11:33,880 --> 00:11:38,160
Speaker 1: find some other jobs, and those that that will put

170
00:11:38,200 --> 00:11:42,600
Speaker 1: downward pressure in those labor markets. And so I think

171
00:11:42,600 --> 00:11:45,240
Speaker 1: the actual our concern should be much more what is

172
00:11:45,240 --> 00:11:49,120
Speaker 1: automation going to do to the structure of wages? And

173
00:11:49,200 --> 00:11:51,760
Speaker 1: that gets back to your earlier thing. Is it gonna

174
00:11:51,920 --> 00:11:56,320
Speaker 1: harm the wages and the accountants and the managers or

175
00:11:56,360 --> 00:12:00,199
Speaker 1: is it going to harm the wages of the clerks

176
00:12:00,240 --> 00:12:06,000
Speaker 1: in the Your restaurant then has no waiters or waitresses,

177
00:12:06,040 --> 00:12:09,120
Speaker 1: but just buttons. You have been in one in China

178
00:12:09,480 --> 00:12:11,840
Speaker 1: where you just pushiate buttons. I assume there are a lot,

179
00:12:12,040 --> 00:12:15,920
Speaker 1: must be more in Japan and Okay, then there's you

180
00:12:15,960 --> 00:12:22,439
Speaker 1: feel safer, um, you etcetera, and um and it's more

181
00:12:22,480 --> 00:12:28,920
Speaker 1: maybe it's more efficient, it's as well. And if that's

182
00:12:28,920 --> 00:12:30,400
Speaker 1: going to be the way of the world, then where

183
00:12:30,440 --> 00:12:33,080
Speaker 1: are the people who would have held those jobs going

184
00:12:33,080 --> 00:12:35,960
Speaker 1: to go? We we just said we have to worry

185
00:12:36,000 --> 00:12:39,320
Speaker 1: about them. I think, um, I would not. I would

186
00:12:39,320 --> 00:12:44,760
Speaker 1: not trust natural forces teaching your automation to take care

187
00:12:44,800 --> 00:12:49,120
Speaker 1: of the poorer and less skilled people. I think that's

188
00:12:49,160 --> 00:12:52,880
Speaker 1: something we as societies have to deal with. So at

189
00:12:52,960 --> 00:12:55,240
Speaker 1: one point you made them over the years, which I

190
00:12:55,240 --> 00:12:57,640
Speaker 1: think is probably more much more accepted now that when

191
00:12:57,640 --> 00:13:00,920
Speaker 1: you were first teaching me about it in the the Deities.

192
00:13:01,480 --> 00:13:06,200
Speaker 1: Is the power of institutional change and the institutions rules,

193
00:13:06,360 --> 00:13:11,000
Speaker 1: social morays, and how they have affected what happens to

194
00:13:11,080 --> 00:13:14,520
Speaker 1: workers in the labor market, what happens to wages. You

195
00:13:14,520 --> 00:13:17,360
Speaker 1: know that it's not just these aren't unstoppable forces that

196
00:13:17,440 --> 00:13:20,040
Speaker 1: we can do nothing to control. We have a new

197
00:13:20,600 --> 00:13:25,000
Speaker 1: administration in the US. Given what you know, you know,

198
00:13:25,200 --> 00:13:29,280
Speaker 1: what could the Biden administration do to improve the outcomes

199
00:13:29,320 --> 00:13:33,840
Speaker 1: for labor in the next few years. I'll take three

200
00:13:33,960 --> 00:13:42,400
Speaker 1: things are one, Biden is incredibly committed to trade union reforms,

201
00:13:43,080 --> 00:13:48,280
Speaker 1: So he is I've never seen a president speaks so strongly,

202
00:13:49,080 --> 00:13:51,640
Speaker 1: and sometimes it seems like a voice from the past.

203
00:13:54,040 --> 00:13:57,480
Speaker 1: Sometimes a voice from the past. He was around in

204
00:13:57,520 --> 00:14:02,320
Speaker 1: the past too, We know that. So so he said

205
00:14:02,600 --> 00:14:06,320
Speaker 1: g F. D. R said that Americans encourage unions, and

206
00:14:06,360 --> 00:14:11,200
Speaker 1: that's what I'm gonna do, um and UM. And so

207
00:14:11,240 --> 00:14:15,240
Speaker 1: he has a there is a pretty wealth set out plan.

208
00:14:15,480 --> 00:14:17,360
Speaker 1: I don't know if they can get it through the

209
00:14:17,440 --> 00:14:22,320
Speaker 1: Congress that would indeed strengthen that, but they're certainly gonna try.

210
00:14:22,600 --> 00:14:27,840
Speaker 1: The second area that I think they will move is

211
00:14:27,920 --> 00:14:34,360
Speaker 1: in the occupational health and safety area, the ocean, which

212
00:14:34,840 --> 00:14:37,760
Speaker 1: should have stepped forward in the pandemic as a major

213
00:14:38,200 --> 00:14:42,720
Speaker 1: force making sure workers are protected. In the US current

214
00:14:42,840 --> 00:14:46,880
Speaker 1: debate or is where the Republicans want to give employers

215
00:14:47,080 --> 00:14:52,400
Speaker 1: who bring workers back to unsafe workplaces UM protection against

216
00:14:52,520 --> 00:14:55,480
Speaker 1: legal suits for you brought me back to an unsafe

217
00:14:55,840 --> 00:15:00,800
Speaker 1: workplace and that that's that that's insane, it's some level

218
00:15:01,480 --> 00:15:05,560
Speaker 1: UM and it will just cause more to disaster for everybody.

219
00:15:05,840 --> 00:15:08,760
Speaker 1: And so I think there'll be a big push on

220
00:15:08,840 --> 00:15:12,320
Speaker 1: the ocean. And we should be doing obviously more R

221
00:15:12,360 --> 00:15:16,400
Speaker 1: and D and how can we protect workers in workplaces? UM,

222
00:15:16,440 --> 00:15:20,200
Speaker 1: that would be a more natural thing to do the

223
00:15:20,760 --> 00:15:24,360
Speaker 1: third area that that that they that they will push

224
00:15:24,560 --> 00:15:28,400
Speaker 1: something with training people for the new technologies. And so

225
00:15:28,440 --> 00:15:31,680
Speaker 1: there's a there's a big move inside the parts of

226
00:15:31,680 --> 00:15:36,000
Speaker 1: the U. S. Government that they need really major uh

227
00:15:36,760 --> 00:15:41,200
Speaker 1: changes in training. Um. I think the administration will will

228
00:15:41,200 --> 00:15:44,480
Speaker 1: love this when it when they see what it's being planned.

229
00:15:45,560 --> 00:15:49,440
Speaker 1: So so they'll they'll be those those uh, those friends.

230
00:15:49,480 --> 00:15:53,920
Speaker 1: But there have to be major tax changes as well. Um.

231
00:15:54,000 --> 00:15:58,120
Speaker 1: And how the Biden administration will will be able to

232
00:15:58,160 --> 00:16:01,920
Speaker 1: reduce the tax cut given to the billionaires and their

233
00:16:01,960 --> 00:16:07,760
Speaker 1: friends and do something more for ordinary citizens. We'll see.

234
00:16:08,320 --> 00:16:19,320
Speaker 1: Richard Freeman, thank you very much. Okay, thank you. Now,

235
00:16:19,960 --> 00:16:22,840
Speaker 1: what value would you put on the ability to lead

236
00:16:22,880 --> 00:16:25,760
Speaker 1: a normal life? While you might say it's priceless, but

237
00:16:25,920 --> 00:16:29,360
Speaker 1: Bloomberg economists say it's three trillion dollars. That's the net

238
00:16:29,360 --> 00:16:32,720
Speaker 1: benefit to the global economy of countries with vaccination programs

239
00:16:32,720 --> 00:16:35,800
Speaker 1: for COVID nineteen being able to get back to normal

240
00:16:36,120 --> 00:16:40,400
Speaker 1: in That analysis was carried out by Chief of Mere

241
00:16:40,480 --> 00:16:44,520
Speaker 1: economist Jamie rush Our, Chief Asia economists Chang Shu, and

242
00:16:44,680 --> 00:16:48,240
Speaker 1: senior global economist Beyond Van Roy and Jamie rush Is

243
00:16:48,600 --> 00:16:52,960
Speaker 1: with me. Now. Jamie briefly how did you come up

244
00:16:52,960 --> 00:16:56,440
Speaker 1: with this number just over three trillion dollars. So we

245
00:16:56,480 --> 00:16:58,720
Speaker 1: thought of it as a bounding exercise. We asked ourselves,

246
00:16:58,760 --> 00:17:00,840
Speaker 1: and what's the best that it be possible for these

247
00:17:00,840 --> 00:17:04,960
Speaker 1: economies of everything just happens to go just right. And

248
00:17:05,000 --> 00:17:07,080
Speaker 1: so the way we approached it is we divided the

249
00:17:07,119 --> 00:17:10,479
Speaker 1: world into three groups of countries. We thought that there

250
00:17:10,480 --> 00:17:12,680
Speaker 1: are some that have got a chance perhaps of achieving

251
00:17:12,720 --> 00:17:15,800
Speaker 1: herd immunity, some that would only be able to vaccinate

252
00:17:15,800 --> 00:17:19,720
Speaker 1: the vulnerable populations, and some who probably even struggle to

253
00:17:19,720 --> 00:17:22,480
Speaker 1: do that. So it's that first group of countries that

254
00:17:22,480 --> 00:17:25,960
Speaker 1: has the best chance of getting back to normal. There'll

255
00:17:26,000 --> 00:17:29,840
Speaker 1: still be cases in those countries, but widespread transmissions pretty unlikely.

256
00:17:30,400 --> 00:17:34,840
Speaker 1: That means very low fatalities, very low personal and societal risks.

257
00:17:34,920 --> 00:17:36,840
Speaker 1: So it's these countries which have a good chance of

258
00:17:36,880 --> 00:17:39,879
Speaker 1: getting back to normal um. So what we did to

259
00:17:40,119 --> 00:17:41,680
Speaker 1: work out of your countries in this group is we

260
00:17:41,800 --> 00:17:44,119
Speaker 1: just compare the number of those is that are on

261
00:17:44,320 --> 00:17:48,560
Speaker 1: order with the size of the population um and we

262
00:17:48,640 --> 00:17:53,320
Speaker 1: found that about sixteen major economies are able to do that,

263
00:17:53,400 --> 00:17:56,560
Speaker 1: and they're accounting for about half of world GDP and

264
00:17:56,600 --> 00:17:59,760
Speaker 1: a third of the population, so thinking that what's the

265
00:17:59,800 --> 00:18:02,040
Speaker 1: best that's possible for them, or maybe they can get

266
00:18:02,080 --> 00:18:05,320
Speaker 1: back to their pre crisis trend, just maybe. So we

267
00:18:05,440 --> 00:18:08,359
Speaker 1: estimate that in four Q and taking into account the

268
00:18:08,400 --> 00:18:12,200
Speaker 1: impact of fresh lockdowns that the last the last three

269
00:18:12,200 --> 00:18:14,680
Speaker 1: months of this, that's right, yep, the last few months.

270
00:18:14,680 --> 00:18:17,359
Speaker 1: We were looking at those and we think that on

271
00:18:17,400 --> 00:18:21,960
Speaker 1: average those sixteen economies are about six percent below their

272
00:18:22,000 --> 00:18:25,440
Speaker 1: pre crisis trend level of output. So if you're able

273
00:18:25,480 --> 00:18:28,560
Speaker 1: to vaccinate a very substantial portion of the population in

274
00:18:28,600 --> 00:18:31,480
Speaker 1: those countries, then maybe if everything goes right, you could

275
00:18:31,520 --> 00:18:35,680
Speaker 1: get back to that trend level. So six pc increase

276
00:18:35,720 --> 00:18:38,560
Speaker 1: in GDP, which is about three point two percent of

277
00:18:38,640 --> 00:18:43,080
Speaker 1: world GDP. In that second group with countries but just

278
00:18:43,160 --> 00:18:46,040
Speaker 1: able to vaccinate the vulnerable populations. We think there are

279
00:18:46,040 --> 00:18:49,119
Speaker 1: about eleven major economies and these are mostly emerging market

280
00:18:49,119 --> 00:18:51,960
Speaker 1: economies and they don't make up a huge amount of

281
00:18:51,960 --> 00:18:55,320
Speaker 1: world GDP. But we think that vaccinations in the vulnerable

282
00:18:55,400 --> 00:18:58,359
Speaker 1: could have a very big impact on the economy because

283
00:18:58,359 --> 00:19:01,720
Speaker 1: if you think of it, it will very significantly reduced fatalities,

284
00:19:01,960 --> 00:19:06,480
Speaker 1: so intensive care beds won't be overburdened the risk of

285
00:19:06,560 --> 00:19:10,080
Speaker 1: death most people will be very low, and so you

286
00:19:10,080 --> 00:19:12,480
Speaker 1: should still see a very meaningful boost in g d P.

287
00:19:13,000 --> 00:19:14,760
Speaker 1: But because these economies that are in this group a

288
00:19:14,840 --> 00:19:18,080
Speaker 1: bit smaller, you only add an extra half percent to

289
00:19:18,200 --> 00:19:20,760
Speaker 1: world GDP. And and so it's those two groups that

290
00:19:20,760 --> 00:19:23,800
Speaker 1: account for the three trillion dollar boost. I mean, I

291
00:19:23,840 --> 00:19:26,520
Speaker 1: understand these are just kind of broad magnitudes. We're not

292
00:19:26,600 --> 00:19:30,720
Speaker 1: we're not saying that these exact numbers. But is there

293
00:19:30,800 --> 00:19:33,399
Speaker 1: are you making an allowance here for the impact on

294
00:19:33,640 --> 00:19:35,679
Speaker 1: of of lockdowns? I mean, is the assumption that you're

295
00:19:35,720 --> 00:19:38,240
Speaker 1: saying that there will be no further lockdowns whereas there

296
00:19:38,320 --> 00:19:41,440
Speaker 1: might have been lockdown what's the what's the assumption there?

297
00:19:41,480 --> 00:19:43,960
Speaker 1: As the base case we've we've just assumed that the

298
00:19:44,000 --> 00:19:48,600
Speaker 1: lockdowns are in place now, stay in place until until

299
00:19:48,680 --> 00:19:52,280
Speaker 1: the spring um and then the vaccine gradually allows these

300
00:19:52,280 --> 00:19:55,719
Speaker 1: to unwind. But it means that the countries that will

301
00:19:55,760 --> 00:19:57,800
Speaker 1: get the biggest boost in the vaccine will obviously be

302
00:19:57,880 --> 00:20:00,320
Speaker 1: the ones that have got the tightest lockdown condition or

303
00:20:00,359 --> 00:20:02,520
Speaker 1: the lowest levels of g d P, So like the

304
00:20:02,640 --> 00:20:05,880
Speaker 1: UK calls into that category, Spain as well, where there's

305
00:20:05,920 --> 00:20:09,480
Speaker 1: really a lot of scope for improvement if they can

306
00:20:09,520 --> 00:20:12,080
Speaker 1: they can they can move past the crisis phase of

307
00:20:12,119 --> 00:20:16,800
Speaker 1: the pandemic and what you say the UK, of course

308
00:20:17,240 --> 00:20:19,000
Speaker 1: this is to throw something else in the mix. But

309
00:20:19,160 --> 00:20:21,200
Speaker 1: of course the UK in those three months is also

310
00:20:21,320 --> 00:20:23,560
Speaker 1: going to be going through the sort of the final

311
00:20:23,680 --> 00:20:26,240
Speaker 1: stage of Brexit, is actually not going out of the

312
00:20:26,280 --> 00:20:30,640
Speaker 1: sort of transition period of Brexit where we were still

313
00:20:30,680 --> 00:20:34,600
Speaker 1: following all the European rules. You spend you spent quite

314
00:20:34,600 --> 00:20:36,240
Speaker 1: a lot of time thinking about what's going to happen

315
00:20:36,280 --> 00:20:38,760
Speaker 1: to the UK next year. Do you think we're going

316
00:20:38,800 --> 00:20:40,760
Speaker 1: to be able to see the effect of Brexit in

317
00:20:40,800 --> 00:20:42,760
Speaker 1: the numbers or is it just going to be crowded

318
00:20:42,760 --> 00:20:45,840
Speaker 1: out by this big boost from vaccinations and the bounce

319
00:20:45,880 --> 00:20:48,159
Speaker 1: back of the economy generally as you come out of

320
00:20:48,200 --> 00:20:51,280
Speaker 1: the pandemic. Well, I think if there is an impact

321
00:20:51,280 --> 00:20:54,600
Speaker 1: from Brexit, it's likely to happen just before there's a

322
00:20:54,600 --> 00:20:56,879
Speaker 1: boost from the pandemic, So you probably would see in

323
00:20:56,920 --> 00:20:58,840
Speaker 1: the numbers because it would happen in the first quarter

324
00:20:58,880 --> 00:21:02,080
Speaker 1: of this year. Um. And it's I don't think it's

325
00:21:02,119 --> 00:21:04,600
Speaker 1: that likely that that would be a huge proportion of

326
00:21:04,600 --> 00:21:08,560
Speaker 1: the population vaccinated by the end of them. Um. I

327
00:21:08,600 --> 00:21:10,560
Speaker 1: mean I guess that the interesting thing about Brexit is

328
00:21:10,600 --> 00:21:12,320
Speaker 1: that some of the policies that are in place to

329
00:21:12,359 --> 00:21:14,960
Speaker 1: deal with COVID are also going to work pretty well

330
00:21:15,080 --> 00:21:18,959
Speaker 1: for the Brexit shock, because both COVID and Brexit disruption

331
00:21:19,000 --> 00:21:23,040
Speaker 1: would be a pretty similar, uh supply side disruption to

332
00:21:23,080 --> 00:21:26,639
Speaker 1: the economy, something that's hopefully temporary that would eventually go away,

333
00:21:26,880 --> 00:21:30,560
Speaker 1: and so it calls for the same sort of policy settings.

334
00:21:30,680 --> 00:21:33,280
Speaker 1: So in the UK we have a large furlough scheme

335
00:21:33,400 --> 00:21:36,480
Speaker 1: to protect workers protect jobs that would work if there's

336
00:21:36,520 --> 00:21:39,600
Speaker 1: disruption for Brexit or if there's continued disruption for COVID.

337
00:21:39,640 --> 00:21:42,680
Speaker 1: So in some ways it's it's helpful those those policies

338
00:21:42,680 --> 00:21:47,000
Speaker 1: already in place. Um, when you think about what your

339
00:21:47,040 --> 00:21:51,200
Speaker 1: sort of base cases for next year, how much is

340
00:21:51,600 --> 00:21:55,639
Speaker 1: how much does the pace of vaccination affect where the

341
00:21:55,640 --> 00:21:58,400
Speaker 1: global economy ends up at the end of next year

342
00:21:58,680 --> 00:22:01,200
Speaker 1: relative to where it might have been. No, I think

343
00:22:01,240 --> 00:22:04,880
Speaker 1: I think the realistic base cases that a portion of

344
00:22:04,920 --> 00:22:09,119
Speaker 1: the population is vaccinated in advanced economies, and because it

345
00:22:09,160 --> 00:22:12,800
Speaker 1: starts with the vulnerable groups, that that reduces fatalities quite

346
00:22:12,800 --> 00:22:15,920
Speaker 1: substantially and allows people to go start living their lives

347
00:22:15,920 --> 00:22:18,399
Speaker 1: a bit more normally. I mean, the big unknown is

348
00:22:18,440 --> 00:22:20,920
Speaker 1: how the people that aren't in the vulnerable groups will

349
00:22:21,000 --> 00:22:25,800
Speaker 1: behave once the vulnerable people are vaccinated. That's the that

350
00:22:25,920 --> 00:22:30,120
Speaker 1: could be like the huge upside surprise to activity next year,

351
00:22:30,320 --> 00:22:32,320
Speaker 1: where it could just be a massive dempscaph and everyone

352
00:22:32,359 --> 00:22:34,639
Speaker 1: just stays and stikes are still scared. It's perfectly possibly

353
00:22:34,640 --> 00:22:36,960
Speaker 1: it goes either way. So I think that it's it's

354
00:22:37,080 --> 00:22:39,520
Speaker 1: um it's not a question even it's not a question

355
00:22:39,560 --> 00:22:43,320
Speaker 1: of the medicine. It's not even a question of kind

356
00:22:43,320 --> 00:22:46,560
Speaker 1: of the standard economics. It's a a what are people's

357
00:22:46,560 --> 00:22:48,920
Speaker 1: behavior response going to be to this? And to be honest,

358
00:22:48,960 --> 00:22:52,280
Speaker 1: we just don't know. I have to say anecdotally, my

359
00:22:52,359 --> 00:22:54,760
Speaker 1: sense is that people are dying to go on lots

360
00:22:54,800 --> 00:22:57,800
Speaker 1: of holidays to extend that they can and go and

361
00:22:58,320 --> 00:23:00,879
Speaker 1: restaurants and other things. Now, how long that will last?

362
00:23:01,600 --> 00:23:04,359
Speaker 1: I think, isn't it? Because if you think back, if

363
00:23:04,400 --> 00:23:06,479
Speaker 1: you think back to the summer, I mean, people were

364
00:23:06,480 --> 00:23:09,399
Speaker 1: clamoring to get out and go to restaurants, fly, go home.

365
00:23:09,400 --> 00:23:11,160
Speaker 1: And it's one of the reasons why the economy picked

366
00:23:11,240 --> 00:23:14,080
Speaker 1: up so fast over that period is that people were

367
00:23:14,119 --> 00:23:16,880
Speaker 1: willing to do things even though that risk still existed,

368
00:23:17,000 --> 00:23:20,359
Speaker 1: so I'm fairly optimistic that's something something will be a

369
00:23:20,400 --> 00:23:24,440
Speaker 1: fairly major boost as vaccines wrong. Now, Jamie Rush, thank

370
00:23:24,480 --> 00:23:32,840
Speaker 1: you very much, thanks for listening to Stephonomics. We'll be

371
00:23:32,840 --> 00:23:36,080
Speaker 1: back next week with more on all things economic. Remember

372
00:23:36,119 --> 00:23:38,919
Speaker 1: you can always find us on the Bloomberg Terminal, website,

373
00:23:39,000 --> 00:23:41,639
Speaker 1: app or wherever you get your podcasts, and you can

374
00:23:41,680 --> 00:23:44,680
Speaker 1: get a lot of news and analysis from Bloomberg Economics

375
00:23:44,760 --> 00:23:48,760
Speaker 1: during the week by following at Economics on Twitter. This

376
00:23:48,800 --> 00:23:51,720
Speaker 1: episode was produced by Magnus Henrison, with special thanks to

377
00:23:51,760 --> 00:23:56,159
Speaker 1: Olivia Rockman, Richard Freeman, Chang Shue, Beyond, Van Roy, and

378
00:23:56,280 --> 00:24:00,000
Speaker 1: Jamie Rush. Lucy Meekin is the executive producer of Stephonomics.

379
00:24:00,040 --> 00:24:14,560
Speaker 1: Send ahead of Blimpberg podcast Beast Francesco mm hm hm