1
00:00:05,900 --> 00:00:08,770
Speaker 1: Welcome to Copy Time, a podcast series on Markets and

2
00:00:08,779 --> 00:00:11,779
Speaker 1: Economies from Devi S Group Research. I'm Tau. We chief economist.

3
00:00:11,789 --> 00:00:14,439
Speaker 1: Welcoming you to our 133rd episode.

4
00:00:15,189 --> 00:00:18,509
Speaker 1: Today, we will touch on demographics, a topic that has

5
00:00:18,520 --> 00:00:22,909
Speaker 1: deep implications for economic growth, financial market outlook and social stability.

6
00:00:23,239 --> 00:00:27,010
Speaker 1: Our guest is Jennifer Schubach, an expert on the field

7
00:00:27,020 --> 00:00:31,370
Speaker 1: of political demography. She recently became the President and CEO

8
00:00:31,379 --> 00:00:35,639
Speaker 1: of Population Reference Bureau. She also has affiliations with the

9
00:00:35,650 --> 00:00:38,598
Speaker 1: Wilson Center and he Center for New Frontiers at the

10
00:00:38,610 --> 00:00:40,560
Speaker 1: Center for Strategic and International Studies.

11
00:00:40,979 --> 00:00:44,889
Speaker 1: Her latest book, 8 Billion and Counting how sex, death

12
00:00:44,900 --> 00:00:48,700
Speaker 1: and migration shape our world. Jennifer. A warm welcome to

13
00:00:48,709 --> 00:00:48,919
Speaker 1: Kovi

14
00:00:48,930 --> 00:00:50,860
Speaker 2: Time. Thank you very much for having me

15
00:00:50,869 --> 00:00:53,080
Speaker 1: and welcome back to Singapore. Yes,

16
00:00:53,259 --> 00:00:55,990
Speaker 2: it is as busy as always. I think that's right.

17
00:00:56,220 --> 00:01:00,810
Speaker 1: You were here, I believe about 1415 years ago. And

18
00:01:00,819 --> 00:01:01,830
Speaker 1: what brings you to Singapore?

19
00:01:02,180 --> 00:01:05,929
Speaker 2: I spoke yesterday at the Singapore Council on Women's Organization's

20
00:01:05,940 --> 00:01:10,199
Speaker 2: first annual summit uh for Action on Gender equality, which

21
00:01:10,269 --> 00:01:13,019
Speaker 2: is uh I argued that we need to look at

22
00:01:13,029 --> 00:01:15,849
Speaker 2: our population issues through a gendered lens and we'll ask

23
00:01:15,860 --> 00:01:18,639
Speaker 2: different questions than we might otherwise ask and get different answers.

24
00:01:18,769 --> 00:01:20,650
Speaker 2: So I think it sheds a lot of light on

25
00:01:20,660 --> 00:01:23,209
Speaker 2: what's happening with demographic dynamics, particularly in Asia.

26
00:01:23,360 --> 00:01:26,639
Speaker 1: Fantastic. I don't think the intersection on gender and demographics

27
00:01:26,650 --> 00:01:29,470
Speaker 1: are fully appreciated. So I'm glad you're taking a stab

28
00:01:29,480 --> 00:01:30,000
Speaker 1: at that. Yeah. And

29
00:01:30,010 --> 00:01:30,089
Speaker 2: you

30
00:01:30,099 --> 00:01:31,690
Speaker 2: think it would be because, you know,

31
00:01:32,319 --> 00:01:36,050
Speaker 2: biology and yet somehow it is not really on the

32
00:01:36,059 --> 00:01:37,260
Speaker 2: radar of a policy maker.

33
00:01:37,430 --> 00:01:41,449
Speaker 1: That's right. Great. So Jennifer, I want to take advantage

34
00:01:41,459 --> 00:01:44,149
Speaker 1: of your experience and expertise on demographics as much as

35
00:01:44,160 --> 00:01:46,319
Speaker 1: I can today. But I want to start with a

36
00:01:46,330 --> 00:01:49,940
Speaker 1: couple of technical questions. So when I look at UN

37
00:01:49,949 --> 00:01:53,980
Speaker 1: National Prospects Database, fantastic database. Great website. I use it

38
00:01:53,989 --> 00:01:54,699
Speaker 1: all the time

39
00:01:54,989 --> 00:01:58,639
Speaker 1: but it's not just one forecast for population, there are

40
00:01:58,650 --> 00:02:02,860
Speaker 1: probability bands around population projections, then there are variants, low,

41
00:02:02,870 --> 00:02:07,639
Speaker 1: medium high constant fertility, variant, instant replacement fertility. Can you

42
00:02:07,650 --> 00:02:12,168
Speaker 1: break down these various measures of demographic projections for us? Sure.

43
00:02:12,179 --> 00:02:14,960
Speaker 2: So you know, when you're thinking about how to approach

44
00:02:14,970 --> 00:02:17,149
Speaker 2: the variant, you want to think about how are you

45
00:02:17,160 --> 00:02:19,570
Speaker 2: going to use them? So

46
00:02:20,788 --> 00:02:23,240
Speaker 2: there's different uses for different ones. I'm a huge fan

47
00:02:23,250 --> 00:02:26,529
Speaker 2: of the constant fertility variant. It says if everything stays,

48
00:02:26,538 --> 00:02:28,589
Speaker 2: stays the way that it is right now, what does

49
00:02:28,600 --> 00:02:30,649
Speaker 2: the future look like? And this is what I love

50
00:02:30,660 --> 00:02:34,168
Speaker 2: about demography. I'm sorry that you're an economist because you

51
00:02:34,179 --> 00:02:37,210
Speaker 2: don't know what's happening tomorrow. But I do, right? Is it,

52
00:02:37,270 --> 00:02:39,330
Speaker 2: it's a real privilege to be able to look out

53
00:02:39,339 --> 00:02:42,369
Speaker 2: into the future, many years in the future and have

54
00:02:42,380 --> 00:02:44,179
Speaker 2: a sense of what it will be because so many

55
00:02:44,190 --> 00:02:46,289
Speaker 2: of the people of the future are already born.

56
00:02:46,639 --> 00:02:50,179
Speaker 2: So the constant fertility variant is useful when we want

57
00:02:50,190 --> 00:02:51,300
Speaker 2: to say what,

58
00:02:52,100 --> 00:02:54,570
Speaker 2: you know, if everything stayed the same, how does the

59
00:02:54,580 --> 00:02:56,710
Speaker 2: future look? So if you want to prepare for that

60
00:02:56,720 --> 00:02:59,638
Speaker 2: type of future, then you can do so. And then

61
00:02:59,649 --> 00:03:02,759
Speaker 2: as you start tweaking those underlying numbers, that's where you

62
00:03:02,770 --> 00:03:05,990
Speaker 2: get this rainbow of projections. So you know, the UN

63
00:03:06,000 --> 00:03:09,820
Speaker 2: actually has hundreds of projections just by tweaking those underlying variants.

64
00:03:10,229 --> 00:03:12,399
Speaker 2: Most people use the medium

65
00:03:13,169 --> 00:03:16,509
Speaker 2: and it is the one that the UN would say

66
00:03:16,520 --> 00:03:20,380
Speaker 2: is the most likely to happen. It is made with

67
00:03:20,389 --> 00:03:23,750
Speaker 2: a mathematical model and it's based on uh the past.

68
00:03:23,758 --> 00:03:25,789
Speaker 2: So how do we use the past to inform the future?

69
00:03:27,020 --> 00:03:29,800
Speaker 2: It's not my favorite though because my background is actually

70
00:03:29,809 --> 00:03:34,869
Speaker 2: politics and political science and I'm not sure that that not,

71
00:03:34,880 --> 00:03:38,520
Speaker 2: you know, failing to contextualize an economic, political and social

72
00:03:38,529 --> 00:03:41,279
Speaker 2: variables is really the way to think about the future.

73
00:03:41,570 --> 00:03:44,990
Speaker 1: I think I want to call this podcast, contextualizing demography.

74
00:03:45,419 --> 00:03:48,000
Speaker 1: And I think you want to revisit this issue over

75
00:03:48,009 --> 00:03:50,550
Speaker 1: and over again. I think I also am persuaded by

76
00:03:50,559 --> 00:03:54,330
Speaker 1: the constant fertility matter and I really found resonance in

77
00:03:54,339 --> 00:03:56,899
Speaker 1: it when I saw your TED talk where you had

78
00:03:56,910 --> 00:03:59,630
Speaker 1: these projections that, you know, this country's population go down

79
00:03:59,639 --> 00:04:02,029
Speaker 1: by 40% by the end of the century. If we

80
00:04:02,039 --> 00:04:03,110
Speaker 1: go with the constant

81
00:04:03,350 --> 00:04:07,009
Speaker 1: variant, uh constant fertility variant, and then to your point,

82
00:04:07,020 --> 00:04:09,720
Speaker 1: you can start preparing or if you don't like that outcome,

83
00:04:09,979 --> 00:04:10,720
Speaker 1: maybe you can change.

84
00:04:11,619 --> 00:04:14,619
Speaker 2: And I think, you know, it's really important if you

85
00:04:14,630 --> 00:04:18,010
Speaker 2: are someone who actually uses these projections in your work

86
00:04:18,019 --> 00:04:20,510
Speaker 2: to make a difference as you know, economists might do

87
00:04:21,079 --> 00:04:25,329
Speaker 2: to understand what, how those assumptions are tweaked for the future.

88
00:04:25,970 --> 00:04:28,440
Speaker 2: And there have been some things that, that I would

89
00:04:28,450 --> 00:04:31,369
Speaker 2: really disagree with. So if we look, for example, at

90
00:04:31,380 --> 00:04:35,839
Speaker 2: the difference between the medium and the constant fertility for

91
00:04:35,850 --> 00:04:41,790
Speaker 2: countries like Nigeria or Tanzania just over the next uh

92
00:04:41,899 --> 00:04:44,519
Speaker 2: what 16 years, just out to 2040

93
00:04:45,269 --> 00:04:49,369
Speaker 2: it makes a dramatic difference in the total number of people.

94
00:04:49,428 --> 00:04:53,350
Speaker 2: I think it's something like um the constant fertility variant

95
00:04:53,359 --> 00:04:55,640
Speaker 2: versus the medium for Nigeria, I think is a child

96
00:04:55,649 --> 00:04:58,178
Speaker 2: and a half different. That would be. Now think about

97
00:04:58,190 --> 00:05:00,539
Speaker 2: Singapore's total fertility rate is less than one child. So

98
00:05:00,549 --> 00:05:03,029
Speaker 2: where is, is a huge difference? And so there are

99
00:05:03,040 --> 00:05:05,820
Speaker 2: these assumptions that you can tweak that say, do you

100
00:05:05,829 --> 00:05:08,820
Speaker 2: expect fertility rates to go up where they are currently low?

101
00:05:09,089 --> 00:05:11,479
Speaker 2: And historically, the UN has put that in there? And

102
00:05:11,488 --> 00:05:13,339
Speaker 2: do you expect them to come down where they're high?

103
00:05:13,350 --> 00:05:14,059
Speaker 1: Correct?

104
00:05:14,339 --> 00:05:16,238
Speaker 1: OK. If I were to travel back in time to

105
00:05:16,250 --> 00:05:21,119
Speaker 1: your last visit to Singapore. So 2010, 2011 and we

106
00:05:21,130 --> 00:05:24,089
Speaker 1: opened up un prospect probably it was not as elegant

107
00:05:24,100 --> 00:05:25,459
Speaker 1: a website then. It, it

108
00:05:25,470 --> 00:05:26,730
Speaker 2: was such easier to use though.

109
00:05:27,500 --> 00:05:30,200
Speaker 1: And, uh, and, and we looked at the forecast sitting

110
00:05:30,209 --> 00:05:33,200
Speaker 1: at 2011 and we looked at the forecast for 2024.

111
00:05:33,209 --> 00:05:35,779
Speaker 1: How spot on with those forecasts would have been?

112
00:05:36,269 --> 00:05:39,760
Speaker 2: What I remember the most was how optimistic

113
00:05:40,339 --> 00:05:43,339
Speaker 2: they were. So they were optimistic in two ways. Um

114
00:05:43,350 --> 00:05:46,678
Speaker 2: I've always been a Japan watcher and I remember noticing

115
00:05:46,690 --> 00:05:50,070
Speaker 2: how uh the expectation was that Japan's total fertility rate

116
00:05:50,079 --> 00:05:51,238
Speaker 2: would rise in the future.

117
00:05:51,940 --> 00:05:54,570
Speaker 2: Well, that certainly has not happened. In fact, you know,

118
00:05:54,579 --> 00:05:56,980
Speaker 2: most of these rates have gone lower and I think

119
00:05:56,988 --> 00:06:00,869
Speaker 2: there was a sense that surely it can't stay this

120
00:06:00,880 --> 00:06:03,649
Speaker 2: low for this long. So there, you know, they thought

121
00:06:03,660 --> 00:06:05,290
Speaker 2: it would go up and then there was also an

122
00:06:05,299 --> 00:06:08,640
Speaker 2: expectation that it would come down faster, basically in a

123
00:06:08,649 --> 00:06:11,410
Speaker 2: handful of Sub Saharan African countries where it's still high

124
00:06:11,420 --> 00:06:15,630
Speaker 2: because it really fell fast in most parts of the world.

125
00:06:15,850 --> 00:06:16,410
Speaker 2: Um

126
00:06:16,970 --> 00:06:19,570
Speaker 2: But there have been a handful of countries where it

127
00:06:19,579 --> 00:06:23,178
Speaker 2: has remained high. Some demographers call it a fertility stall.

128
00:06:23,190 --> 00:06:25,829
Speaker 2: I mean that in and of itself shows that there's

129
00:06:25,839 --> 00:06:30,390
Speaker 2: a bias towards assuming a rapid decline. Um but they

130
00:06:30,399 --> 00:06:32,750
Speaker 2: were also overly optimistic about how quickly those rates would

131
00:06:32,760 --> 00:06:33,219
Speaker 2: fall

132
00:06:33,640 --> 00:06:37,540
Speaker 1: so the 8 billion mark that is looming would have

133
00:06:37,549 --> 00:06:39,089
Speaker 1: been at a different point in time.

134
00:06:39,329 --> 00:06:39,470
Speaker 2: It

135
00:06:39,480 --> 00:06:42,928
Speaker 2: wasn't really because actually they kind of canceled each other out. So,

136
00:06:42,940 --> 00:06:44,820
Speaker 2: you know, we knew it was 12 to 13 years

137
00:06:44,829 --> 00:06:47,619
Speaker 2: roughly in there, which is, you know, pretty close.

138
00:06:49,988 --> 00:06:52,820
Speaker 1: I use a lot of these data for my work

139
00:06:52,829 --> 00:06:55,040
Speaker 1: here at D BS. And as you can imagine, as

140
00:06:55,279 --> 00:06:57,970
Speaker 1: a aging society in Singapore, this issue is very close

141
00:06:57,980 --> 00:07:00,859
Speaker 1: to our heart. Um One question that I sometimes confront

142
00:07:00,869 --> 00:07:05,589
Speaker 1: is the forecast on India versus Japan, China versus us.

143
00:07:05,660 --> 00:07:09,690
Speaker 1: Are they all comparable? Are all countries producing decently comparable

144
00:07:09,700 --> 00:07:10,140
Speaker 1: data?

145
00:07:10,309 --> 00:07:11,059
Speaker 2: Well,

146
00:07:12,000 --> 00:07:16,540
Speaker 2: no, not, not necessarily. Um And there are multiple groups

147
00:07:16,549 --> 00:07:19,320
Speaker 2: who actually put out population projection. So Population Reference Bureau

148
00:07:19,329 --> 00:07:21,910
Speaker 2: does one as well. We have our world population data

149
00:07:21,920 --> 00:07:24,859
Speaker 2: sheet which we put out for about 60 years, comes

150
00:07:24,869 --> 00:07:29,529
Speaker 2: out um in September most years and uh our demographers

151
00:07:29,540 --> 00:07:32,109
Speaker 2: do their own sorts of projections. And

152
00:07:32,760 --> 00:07:34,929
Speaker 2: so there are a lot of sources of data if

153
00:07:34,940 --> 00:07:37,959
Speaker 2: you were to open up, for example, um the methodology

154
00:07:37,970 --> 00:07:40,540
Speaker 2: for Afghanistan. So like how does the UN actually know

155
00:07:40,549 --> 00:07:43,070
Speaker 2: what's happening in Afghanistan? We know that this is a,

156
00:07:43,100 --> 00:07:44,829
Speaker 2: you know, there's a lot happening in this country. So

157
00:07:44,839 --> 00:07:47,010
Speaker 2: how are you supposed to figure out where the population is?

158
00:07:47,019 --> 00:07:50,380
Speaker 2: You'd have two paragraphs of them fig of pulling together

159
00:07:50,390 --> 00:07:54,649
Speaker 2: data sources, um USA ID funds a series of demographic

160
00:07:54,660 --> 00:07:55,320
Speaker 2: and health surveys,

161
00:07:55,404 --> 00:07:58,744
Speaker 2: the DH S that's a really solid source for um

162
00:07:58,755 --> 00:08:03,445
Speaker 2: inputs into these projections. Um But sometimes there's also a

163
00:08:03,454 --> 00:08:05,424
Speaker 2: little bit of picking up the phone and calling other

164
00:08:05,434 --> 00:08:08,904
Speaker 2: demographers and, and experts in these countries and these regions

165
00:08:08,915 --> 00:08:11,065
Speaker 2: to say, here's what the numbers say. What do you

166
00:08:11,075 --> 00:08:13,584
Speaker 2: think about these? So there's a bit of a human

167
00:08:13,595 --> 00:08:16,105
Speaker 2: touch in there outside of that medium variant that I really,

168
00:08:16,114 --> 00:08:17,975
Speaker 2: I think is actually very useful.

169
00:08:19,170 --> 00:08:22,790
Speaker 2: But just to so, so look

170
00:08:24,290 --> 00:08:26,140
Speaker 2: the sources will be somewhat different,

171
00:08:27,279 --> 00:08:29,739
Speaker 2: but they're going through the same filter.

172
00:08:30,429 --> 00:08:33,510
Speaker 2: So in my mind, I'm comfortable enough saying that they're

173
00:08:33,520 --> 00:08:37,059
Speaker 2: comparable and that it's the filter part that matters.

174
00:08:37,609 --> 00:08:40,330
Speaker 1: Are there ways of sort of triangulating the data? So

175
00:08:40,340 --> 00:08:44,569
Speaker 1: you bring me demographic data, I bring you tax collection

176
00:08:44,580 --> 00:08:48,940
Speaker 1: data and we can see that if a country's projection

177
00:08:48,950 --> 00:08:51,510
Speaker 1: is huge pickup and the labor force in the country

178
00:08:51,520 --> 00:08:53,539
Speaker 1: saying there's lots of people working, we should be seeing

179
00:08:53,549 --> 00:08:54,400
Speaker 1: that in other

180
00:08:54,789 --> 00:08:56,869
Speaker 2: and then some of that happens as well and they

181
00:08:56,880 --> 00:08:59,250
Speaker 2: could pull from all kinds of different record keeping.

182
00:08:59,710 --> 00:09:02,530
Speaker 2: Um You know, the hardest part is the migration piece

183
00:09:02,539 --> 00:09:05,280
Speaker 2: because by definition, these people are on the move. So

184
00:09:05,289 --> 00:09:08,190
Speaker 2: there's a lot of guessing that goes into the migration

185
00:09:08,200 --> 00:09:08,929
Speaker 2: part as well.

186
00:09:09,669 --> 00:09:14,590
Speaker 1: Jennifer, I was in Cambodia recently and they sort of,

187
00:09:14,650 --> 00:09:16,690
Speaker 1: you know, talk about the killing fields in the seventies

188
00:09:16,700 --> 00:09:19,900
Speaker 1: and how life expectancy collapsed there. I think to like

189
00:09:19,909 --> 00:09:24,140
Speaker 1: 23 or 24 there. It's come back amazingly well over

190
00:09:24,150 --> 00:09:25,978
Speaker 1: the last 40 years. So that kind of reflects the

191
00:09:25,989 --> 00:09:27,460
Speaker 1: resiliency of society that

192
00:09:27,859 --> 00:09:31,319
Speaker 1: a demographic shock need not be fatal. And there are

193
00:09:31,330 --> 00:09:34,599
Speaker 1: ways to rejuvenate. But also it sort of brings me

194
00:09:34,609 --> 00:09:36,989
Speaker 1: to this question that, you know, what are the shocks

195
00:09:37,000 --> 00:09:42,150
Speaker 1: that alter demographic trends? Did the pandemic change demographic in

196
00:09:42,159 --> 00:09:42,590
Speaker 1: the world?

197
00:09:42,599 --> 00:09:42,919
Speaker 2: Not

198
00:09:42,929 --> 00:09:46,849
Speaker 2: really. So obviously, for the people who died in their families,

199
00:09:46,859 --> 00:09:48,530
Speaker 2: it was very much a shock. But when we're talking

200
00:09:48,539 --> 00:09:52,469
Speaker 2: about in these aggregate numbers, it didn't. Um In fact,

201
00:09:52,479 --> 00:09:55,000
Speaker 2: I think already that's kind of worked its way out

202
00:09:55,010 --> 00:09:55,739
Speaker 2: of the data

203
00:09:56,590 --> 00:09:59,909
Speaker 2: in some part. It's because many of the deaths were

204
00:09:59,919 --> 00:10:03,010
Speaker 2: to people who were already finished with their reproductive years.

205
00:10:03,369 --> 00:10:08,179
Speaker 2: Um But even for fertility rates, they basically went to

206
00:10:08,190 --> 00:10:10,589
Speaker 2: where they were before. And in the U SI, I've

207
00:10:10,599 --> 00:10:12,840
Speaker 2: always talked about it as it just put the, it

208
00:10:12,849 --> 00:10:16,469
Speaker 2: pushed on the gas pedal directions we were already headed.

209
00:10:16,479 --> 00:10:17,099
Speaker 2: So maybe you

210
00:10:17,210 --> 00:10:19,510
Speaker 2: a little bit of an acceleration, you know, life expectance

211
00:10:19,520 --> 00:10:22,520
Speaker 2: in the US was already on the decline, which is insane.

212
00:10:22,530 --> 00:10:24,390
Speaker 2: By the way, it should not

213
00:10:24,580 --> 00:10:26,179
Speaker 1: for a wealthy industrial society.

214
00:10:26,200 --> 00:10:29,349
Speaker 2: Yeah, even generally like, you know, unless absent a giant war,

215
00:10:29,359 --> 00:10:32,669
Speaker 2: it is absolutely insane that it would be going down.

216
00:10:32,679 --> 00:10:34,119
Speaker 2: And so it just pushed on the gas pedal a

217
00:10:34,130 --> 00:10:36,799
Speaker 2: little bit. Fertility trends already headed down. Maybe you push

218
00:10:36,809 --> 00:10:39,260
Speaker 2: on the gas pedal a little bit. And most demographers

219
00:10:39,270 --> 00:10:41,929
Speaker 2: would say that people who were going to have babies,

220
00:10:42,309 --> 00:10:44,809
Speaker 2: they might have delayed a little bit and then picked

221
00:10:44,820 --> 00:10:45,429
Speaker 2: right back up.

222
00:10:45,900 --> 00:10:48,750
Speaker 1: What about the pandemic bump? People were all staying at

223
00:10:48,760 --> 00:10:52,049
Speaker 1: home and not working, didn't they have a lot of babies? No,

224
00:10:52,059 --> 00:10:54,190
Speaker 2: not this time. Yeah. And like some of the data

225
00:10:54,200 --> 00:10:56,728
Speaker 2: we've seen about even what power outages in the past

226
00:10:56,739 --> 00:10:58,989
Speaker 2: and then you can kind of trace up now.

227
00:10:59,869 --> 00:11:02,539
Speaker 1: Fascinating. So it seems to me that we have pretty

228
00:11:02,549 --> 00:11:07,390
Speaker 1: powerful trends in motion and we have seen policy experiments

229
00:11:07,400 --> 00:11:10,429
Speaker 1: here and there around the world. And so far,

230
00:11:11,140 --> 00:11:13,729
Speaker 1: the power of the trend seems to be overwhelming to

231
00:11:13,739 --> 00:11:16,219
Speaker 1: a large extent. Um I'm gonna come back to that

232
00:11:16,229 --> 00:11:17,689
Speaker 1: uh because I want to talk a little bit about

233
00:11:17,700 --> 00:11:22,159
Speaker 1: the issue related to Scandinavia and so on, but just

234
00:11:22,510 --> 00:11:26,200
Speaker 1: drivers of fertility. Uh I know it's a big subject but,

235
00:11:26,210 --> 00:11:28,280
Speaker 1: and I'm sure you get lots of questions on that.

236
00:11:28,409 --> 00:11:31,280
Speaker 1: So let's talk about what drives fertility rates.

237
00:11:31,289 --> 00:11:33,880
Speaker 2: It is a lot of things which actually is what

238
00:11:33,890 --> 00:11:37,580
Speaker 2: frustrates policymakers. And I'm sympathetic to that because as an academic,

239
00:11:37,859 --> 00:11:40,760
Speaker 2: you know, the the best study is when you can say,

240
00:11:40,770 --> 00:11:43,650
Speaker 2: which is the mo what are the most powerful explanatory

241
00:11:43,659 --> 00:11:45,960
Speaker 2: variables here? And then if you're a policymaker, you can

242
00:11:45,969 --> 00:11:48,010
Speaker 2: take that research and you say, well, let's tweak, let's

243
00:11:48,020 --> 00:11:51,199
Speaker 2: put a policy in place to address this and voila

244
00:11:51,210 --> 00:11:54,330
Speaker 2: problem solved, but that's not really how these decisions are made.

245
00:11:54,340 --> 00:11:59,229
Speaker 2: And I think anyone who has considered having Children

246
00:11:59,599 --> 00:12:02,950
Speaker 2: knows exactly what we mean. When we say that it's complicated.

247
00:12:02,960 --> 00:12:06,010
Speaker 2: It's never just about the money or the ti, you know, timing.

248
00:12:06,020 --> 00:12:09,250
Speaker 2: It's so many things. But if you think about fertility

249
00:12:09,260 --> 00:12:10,979
Speaker 2: rates as a big old pot of soup, there are

250
00:12:10,989 --> 00:12:12,919
Speaker 2: a few different ingredients that go in and that are

251
00:12:12,929 --> 00:12:17,679
Speaker 2: pretty standard. So modernization matters and modernization can be a

252
00:12:17,690 --> 00:12:21,770
Speaker 2: whole host of variables like um education rates for women.

253
00:12:21,780 --> 00:12:24,630
Speaker 2: So if you're talking about a country where fertility rates

254
00:12:24,640 --> 00:12:28,270
Speaker 2: are very high, let's say 56 Children per woman.

255
00:12:28,489 --> 00:12:32,619
Speaker 2: And you bring in um educational system where women especially

256
00:12:32,630 --> 00:12:35,070
Speaker 2: make it to secondary school. You're gonna see those rates

257
00:12:35,080 --> 00:12:39,108
Speaker 2: fall also in modernization, incomes are rising. So opportunity cost

258
00:12:39,119 --> 00:12:42,630
Speaker 2: goes up, uh more opportunities to earn income outside the home.

259
00:12:43,130 --> 00:12:44,630
Speaker 2: You know, all of that kind of comes in together

260
00:12:44,640 --> 00:12:49,010
Speaker 2: there more robust health systems, access to family planning. And

261
00:12:49,020 --> 00:12:50,109
Speaker 2: so that is one driver.

262
00:12:50,650 --> 00:12:53,468
Speaker 2: You also have government policy which and it's hard to

263
00:12:53,479 --> 00:12:56,319
Speaker 2: analytically separate some of these because the government policy will

264
00:12:56,330 --> 00:12:59,468
Speaker 2: often be let's put in schools or put in health clinics.

265
00:12:59,710 --> 00:13:02,400
Speaker 2: But I with modernization, I like to think of it

266
00:13:02,409 --> 00:13:06,849
Speaker 2: as processes that are in motion that that the government

267
00:13:06,859 --> 00:13:10,369
Speaker 2: may or may not have put in motion itself. Um

268
00:13:10,380 --> 00:13:13,309
Speaker 2: But government policy can change the trajectory of trends as

269
00:13:13,320 --> 00:13:16,369
Speaker 2: we know, think about China and many India, many other places.

270
00:13:16,570 --> 00:13:20,099
Speaker 2: And then there's the family planning piece and it really is,

271
00:13:20,429 --> 00:13:23,530
Speaker 2: it's often left out of discussions which I find puzzling

272
00:13:23,539 --> 00:13:28,309
Speaker 2: but just the technology of being able to decide how

273
00:13:28,320 --> 00:13:32,069
Speaker 2: many Children to have and when is really powerful.

274
00:13:33,369 --> 00:13:37,099
Speaker 1: I bring this up with some degree of trepidation and

275
00:13:37,109 --> 00:13:41,609
Speaker 1: hesitation because this is a podcast on numbers and, and

276
00:13:41,619 --> 00:13:45,069
Speaker 1: trends and policy. But let me go there anyway, uh

277
00:13:45,080 --> 00:13:48,020
Speaker 1: last week, there was an article came from Apple News.

278
00:13:48,030 --> 00:13:50,539
Speaker 1: I can't remember the source, but it was that there

279
00:13:50,549 --> 00:13:54,929
Speaker 1: is also something beyond all these macroeconomic societal factors. There

280
00:13:54,940 --> 00:13:56,709
Speaker 1: is this question of purpose that

281
00:13:56,989 --> 00:13:59,659
Speaker 1: there is a generational shift on sense of purpose and

282
00:13:59,669 --> 00:14:04,090
Speaker 1: people feel that between climate change and geopolitics, they're not

283
00:14:04,099 --> 00:14:06,770
Speaker 1: hopeful about the future and maybe that plays a role.

284
00:14:06,909 --> 00:14:08,969
Speaker 1: It's hard to put a finger on it. But what's

285
00:14:08,979 --> 00:14:10,000
Speaker 1: your thought on that? I think?

286
00:14:10,010 --> 00:14:12,260
Speaker 2: Absolutely. Yes. And you know, if I'm trying to keep

287
00:14:12,270 --> 00:14:15,609
Speaker 2: it simple, I kind of stick it under the modernization umbrella,

288
00:14:15,619 --> 00:14:16,770
Speaker 2: but it is different.

289
00:14:17,429 --> 00:14:19,330
Speaker 2: And it's funny because, you know, now that I'm the

290
00:14:19,340 --> 00:14:21,729
Speaker 2: CEO of PR B, the first time I ever learned

291
00:14:21,739 --> 00:14:25,159
Speaker 2: about the Population Reference Bureau was to attend, I mean,

292
00:14:25,169 --> 00:14:27,969
Speaker 2: this is well over 20 years ago, I guess, but

293
00:14:27,979 --> 00:14:30,690
Speaker 2: it was to attend a seminar that was trying to

294
00:14:30,700 --> 00:14:33,650
Speaker 2: explain why fertility rates were so low in eastern Europe

295
00:14:33,940 --> 00:14:36,940
Speaker 2: and I'll never forget it. Uh because they, they, they

296
00:14:36,950 --> 00:14:38,729
Speaker 2: talked about all these same kind of variables we mentioned

297
00:14:38,739 --> 00:14:40,840
Speaker 2: and they said really, there's just no people feel so

298
00:14:40,849 --> 00:14:42,090
Speaker 2: dismal about the future

299
00:14:42,409 --> 00:14:46,270
Speaker 2: and we do see echoes of that today. Um especially,

300
00:14:46,280 --> 00:14:48,450
Speaker 2: you know, anecdotally, you speak to a young person, they

301
00:14:48,460 --> 00:14:50,630
Speaker 2: talk about that all the time. Um

302
00:14:51,530 --> 00:14:54,630
Speaker 2: There, that is some of it. But as far as

303
00:14:54,640 --> 00:14:57,270
Speaker 2: how do we know for sure if that will shape

304
00:14:57,280 --> 00:15:00,750
Speaker 2: fertility behavior? We don't know yet. And that's because these

305
00:15:00,760 --> 00:15:04,150
Speaker 2: young people have not completed their fertility. And so there

306
00:15:04,159 --> 00:15:06,289
Speaker 2: are some papers that I've seen that say, yeah, they

307
00:15:06,299 --> 00:15:08,719
Speaker 2: may feel this way. But we all know you, you

308
00:15:08,729 --> 00:15:11,119
Speaker 2: have a cer certain set of way. You think the

309
00:15:11,130 --> 00:15:13,539
Speaker 2: world works and you're 20 you're 25

310
00:15:14,119 --> 00:15:16,679
Speaker 2: and then you think a lot differently about the world later.

311
00:15:16,690 --> 00:15:18,859
Speaker 2: So we will not know we have to do this

312
00:15:18,869 --> 00:15:21,469
Speaker 2: again in 10 years to say, did it actually drive

313
00:15:21,479 --> 00:15:23,000
Speaker 2: people's fertility behavior lower?

314
00:15:23,010 --> 00:15:23,030
Speaker 1: It

315
00:15:23,080 --> 00:15:24,780
Speaker 1: makes me a little sad because I'd like to think

316
00:15:24,789 --> 00:15:27,210
Speaker 1: that optimism should be at the younger age spectrum and

317
00:15:27,219 --> 00:15:29,700
Speaker 1: the midlife crisis and everything should be later. But it's

318
00:15:29,710 --> 00:15:32,099
Speaker 1: like we're hoping it's almost the opposite. Now, you're starting

319
00:15:32,109 --> 00:15:34,539
Speaker 1: out kind of jaded about the future. And then hopefully,

320
00:15:34,905 --> 00:15:37,434
Speaker 1: as you make money, as you get gainful employment, you

321
00:15:37,445 --> 00:15:38,294
Speaker 1: get more purpose.

322
00:15:38,304 --> 00:15:38,575
Speaker 2: Well, and I

323
00:15:38,585 --> 00:15:42,575
Speaker 2: think we're caught in this, this echo chamber because think

324
00:15:42,585 --> 00:15:44,234
Speaker 2: about the type of news that comes in our phones.

325
00:15:44,244 --> 00:15:46,325
Speaker 2: It's all that and it's right there. It's in our

326
00:15:46,335 --> 00:15:49,505
Speaker 2: faces all day long. And for young people, that's all

327
00:15:49,515 --> 00:15:52,825
Speaker 2: they're surrounded by. Whereas we didn't grow up with that

328
00:15:52,835 --> 00:15:55,054
Speaker 2: type of flow of information.

329
00:15:55,400 --> 00:15:57,119
Speaker 2: And there are, I, I was speaking to a group

330
00:15:57,130 --> 00:16:00,090
Speaker 2: of young people, um, actually young people in Asia the

331
00:16:00,099 --> 00:16:02,950
Speaker 2: other night, uh, in the US. And I, I said

332
00:16:02,960 --> 00:16:04,820
Speaker 2: to them, you know, there are places where you can

333
00:16:04,830 --> 00:16:08,849
Speaker 2: read good news. Um, Angus Hervey, who is out of Australia,

334
00:16:08,859 --> 00:16:11,450
Speaker 2: I think he was at uh TED the same time

335
00:16:11,460 --> 00:16:14,140
Speaker 2: I was and he has his whole shtick is, here's

336
00:16:14,150 --> 00:16:17,190
Speaker 2: the good news and it's real news. It's evidence based

337
00:16:17,200 --> 00:16:19,390
Speaker 2: and it's actually showing what has happened. Um Hannah Ritchie

338
00:16:19,400 --> 00:16:21,150
Speaker 2: was also at, at TED the same year as I

339
00:16:21,159 --> 00:16:22,979
Speaker 2: was and she, she talks about environment

340
00:16:23,440 --> 00:16:26,429
Speaker 2: and she had a book out. Um, what year earlier

341
00:16:26,440 --> 00:16:28,599
Speaker 2: this year, I think as well. And she says these

342
00:16:28,609 --> 00:16:31,119
Speaker 2: are all the things that have gotten better, but that

343
00:16:31,130 --> 00:16:34,080
Speaker 2: is like a, a grain of sand on a beach.

344
00:16:35,609 --> 00:16:40,020
Speaker 1: Come back to the issue of modernization. The theory has

345
00:16:40,030 --> 00:16:42,700
Speaker 1: been that when you look at a country like Sweden

346
00:16:42,710 --> 00:16:47,469
Speaker 1: or Norway, once you become very wealthy, then people start

347
00:16:47,479 --> 00:16:50,330
Speaker 1: behaving a little differently because there are so many safes

348
00:16:50,390 --> 00:16:53,429
Speaker 1: in place and they then do have babies. I would

349
00:16:53,440 --> 00:16:55,039
Speaker 1: have thought some of that would play out in Japan.

350
00:16:55,049 --> 00:16:55,849
Speaker 1: That hasn't happened.

351
00:16:56,159 --> 00:16:59,099
Speaker 1: So, not quite a convincing argument.

352
00:16:59,510 --> 00:17:02,489
Speaker 2: Well, I think we moved the bar on ourselves. Uh,

353
00:17:02,500 --> 00:17:05,540
Speaker 2: you know, when I feel very fortunate, I think mine,

354
00:17:05,550 --> 00:17:07,739
Speaker 2: mine was like the last cohort of people who could

355
00:17:07,750 --> 00:17:10,569
Speaker 2: basically buy houses in our early twenties before things started

356
00:17:10,579 --> 00:17:12,619
Speaker 2: to get out of control. Um,

357
00:17:13,150 --> 00:17:16,089
Speaker 2: but even the expectations that younger people have today for

358
00:17:16,099 --> 00:17:19,290
Speaker 2: what they wanna achieve before they have Children, it has

359
00:17:19,300 --> 00:17:22,729
Speaker 2: really moved up, up, up. Um And I think, I mean,

360
00:17:22,739 --> 00:17:25,329
Speaker 2: we could go down a rabbit hole about a technology.

361
00:17:25,339 --> 00:17:28,479
Speaker 2: I do keep coming back to, I remember um when

362
00:17:28,489 --> 00:17:31,609
Speaker 2: my kids were very young was when Instagram was really

363
00:17:31,619 --> 00:17:35,050
Speaker 2: starting and the only messages you'd get about parenting basically

364
00:17:35,060 --> 00:17:36,560
Speaker 2: were how hard it was

365
00:17:37,099 --> 00:17:40,839
Speaker 2: or the perfect mom who is throwing the perfect party

366
00:17:40,849 --> 00:17:44,239
Speaker 2: that probably costs $500. Well, neither of those things is

367
00:17:44,250 --> 00:17:47,170
Speaker 2: very attractive when you're thinking about having Children. Uh You're

368
00:17:47,180 --> 00:17:50,030
Speaker 2: thinking just really about how incredibly expensive it is. And

369
00:17:50,040 --> 00:17:52,479
Speaker 2: so a lot of the people I was speaking with

370
00:17:52,489 --> 00:17:57,329
Speaker 2: actually yesterday at this summit were saying how the competition

371
00:17:57,680 --> 00:18:00,859
Speaker 2: for like who can, who can just be better, more educated,

372
00:18:00,869 --> 00:18:03,579
Speaker 2: have a higher income. It's really spiraled up and this

373
00:18:03,589 --> 00:18:05,420
Speaker 2: puts a downward pressure on fertility

374
00:18:05,430 --> 00:18:06,020
Speaker 2: itself,

375
00:18:06,359 --> 00:18:09,639
Speaker 1: correct? OK. This is a medical science question. I really

376
00:18:09,650 --> 00:18:11,458
Speaker 1: don't know if there's an answer to this description, but

377
00:18:11,469 --> 00:18:16,270
Speaker 1: just from a women's health perspective. Are women today asked

378
00:18:16,280 --> 00:18:18,319
Speaker 1: for a child? I know it's complicated because there's a

379
00:18:18,329 --> 00:18:21,899
Speaker 1: choice issue there. But just from a biological perspective, well,

380
00:18:21,910 --> 00:18:22,359
Speaker 2: there

381
00:18:22,464 --> 00:18:25,675
Speaker 2: waiting later. So, and that is just very simple stats,

382
00:18:25,685 --> 00:18:27,935
Speaker 2: you know, people are marrying later and then they're waiting

383
00:18:27,944 --> 00:18:29,604
Speaker 2: later to have their first child. And when you know,

384
00:18:29,614 --> 00:18:35,244
Speaker 2: sitting in Asia where mo you know, most marriages, I'm sorry,

385
00:18:35,255 --> 00:18:37,594
Speaker 2: most Children are born within a marriage.

386
00:18:38,079 --> 00:18:41,180
Speaker 2: That means that marriage becomes an incredibly important first step

387
00:18:41,189 --> 00:18:43,390
Speaker 2: before you have Children. So if people are waiting later

388
00:18:43,400 --> 00:18:46,469
Speaker 2: to get married, then they are starting their families later.

389
00:18:46,479 --> 00:18:49,189
Speaker 2: And so we do see um a lot more women

390
00:18:49,199 --> 00:18:53,270
Speaker 2: of older ages seeking assisted reproductive technology. And those, those,

391
00:18:53,280 --> 00:18:54,000
Speaker 2: you know, we see that around

392
00:18:54,069 --> 00:18:56,319
Speaker 2: world, I mean, in the US, for example, the only

393
00:18:56,329 --> 00:18:58,109
Speaker 2: age group, I think that was seeing an increase in

394
00:18:58,119 --> 00:19:00,849
Speaker 2: births was 40 to 45. But that's because they were

395
00:19:00,859 --> 00:19:01,139
Speaker 2: waiting

396
00:19:01,150 --> 00:19:01,569
Speaker 2: later.

397
00:19:01,750 --> 00:19:05,050
Speaker 1: So it's not health but health, technology and choice.

398
00:19:05,060 --> 00:19:07,449
Speaker 2: Yes, I think so. Now this is, you know, there

399
00:19:07,459 --> 00:19:10,020
Speaker 2: are other people who have said that it's infertility.

400
00:19:10,689 --> 00:19:13,649
Speaker 2: I don't buy it because if you study data just

401
00:19:13,660 --> 00:19:15,520
Speaker 2: on preferences, you see that preferences

402
00:19:15,530 --> 00:19:19,949
Speaker 1: shifted, correct. And that's, that's probably the most powerful one.

403
00:19:20,119 --> 00:19:23,239
Speaker 1: So let's talk about Japan a little bit. Japan has

404
00:19:23,250 --> 00:19:27,589
Speaker 1: had a demographic time bomb ticking away for the last

405
00:19:27,599 --> 00:19:30,949
Speaker 1: several decades. And we see stories of, you know, villages

406
00:19:30,959 --> 00:19:35,449
Speaker 1: getting depopulated, the country being forced to embrace certain aspects

407
00:19:35,459 --> 00:19:37,739
Speaker 1: of immigration that Japan was not really open to in

408
00:19:37,750 --> 00:19:38,229
Speaker 1: the past.

409
00:19:38,670 --> 00:19:41,650
Speaker 1: This is the bad stuff. But at the same time,

410
00:19:41,660 --> 00:19:43,709
Speaker 1: when I look at Japan's economic performance over the last

411
00:19:43,719 --> 00:19:46,530
Speaker 1: 2030 years, while we say there has been a lost decade,

412
00:19:46,540 --> 00:19:48,660
Speaker 1: but because they have had basically zero or negative population

413
00:19:48,670 --> 00:19:51,319
Speaker 1: growth rate, their per capita income has gone up. So

414
00:19:51,329 --> 00:19:54,920
Speaker 1: the Japanese who are around are significantly more prosperous today

415
00:19:54,930 --> 00:19:57,500
Speaker 1: than they were. While just looking at the demographic story,

416
00:19:57,510 --> 00:19:59,359
Speaker 1: it looks like a bad story. So how do we

417
00:19:59,369 --> 00:20:00,920
Speaker 1: net this out? I think

418
00:20:00,930 --> 00:20:03,880
Speaker 2: we had nothing but wild guesses about what aging would

419
00:20:03,890 --> 00:20:07,319
Speaker 2: look like and its effects on the economy. Now,

420
00:20:07,750 --> 00:20:10,530
Speaker 2: other people would say they're not wild guesses, they're based

421
00:20:10,540 --> 00:20:13,698
Speaker 2: in all these economic theories about productivity and labor and

422
00:20:13,709 --> 00:20:16,719
Speaker 2: all of that. But we didn't know because we've never

423
00:20:16,729 --> 00:20:19,930
Speaker 2: in human history had this type of demographic shift. So

424
00:20:19,939 --> 00:20:22,800
Speaker 2: we took our logic from the past, which by the way,

425
00:20:22,810 --> 00:20:24,040
Speaker 2: this logic was made

426
00:20:24,430 --> 00:20:27,540
Speaker 2: at a time when population growth seemed like it would

427
00:20:27,550 --> 00:20:29,800
Speaker 2: be infinite. So all of our economic theories, all of

428
00:20:29,810 --> 00:20:31,260
Speaker 2: our cases have to do with that. And when we

429
00:20:31,270 --> 00:20:33,750
Speaker 2: kind of say, well, it must be different in the future.

430
00:20:33,969 --> 00:20:36,630
Speaker 2: I think we also have applied what we know about

431
00:20:36,640 --> 00:20:40,969
Speaker 2: individual aging to populations that you lose your capacity to

432
00:20:40,979 --> 00:20:43,688
Speaker 2: be innovative. You have cognitive decline you'll be less of

433
00:20:43,699 --> 00:20:46,629
Speaker 2: a risk taker, you're tired and not productive. And we

434
00:20:46,640 --> 00:20:49,849
Speaker 2: put all of that on what population aging would look

435
00:20:49,859 --> 00:20:51,469
Speaker 2: like and what it would do to the economy.

436
00:20:51,709 --> 00:20:55,688
Speaker 2: And so now we were surprised that that didn't necessarily happen.

437
00:20:57,290 --> 00:21:00,420
Speaker 2: I think in Japan's case, they were great there. If

438
00:21:00,430 --> 00:21:02,420
Speaker 2: anybody was going to go first, I'm glad they went

439
00:21:02,430 --> 00:21:02,949
Speaker 2: first

440
00:21:03,510 --> 00:21:07,550
Speaker 2: because they do show what's possible. And I really, if,

441
00:21:07,560 --> 00:21:09,530
Speaker 2: if I really mean that I'm glad that they went

442
00:21:09,540 --> 00:21:12,349
Speaker 2: first because imagine a, a country, I'm not gonna name

443
00:21:12,359 --> 00:21:14,849
Speaker 2: any other country names here. But imagine a country where

444
00:21:14,890 --> 00:21:18,650
Speaker 2: technology was not so robust or the population wasn't so

445
00:21:18,660 --> 00:21:22,880
Speaker 2: educated or even consumption patterns or views on older people,

446
00:21:22,890 --> 00:21:25,660
Speaker 2: all of these things together made it a great case

447
00:21:25,670 --> 00:21:28,599
Speaker 2: to say, look what's possible. I think it, I I

448
00:21:28,609 --> 00:21:32,060
Speaker 2: saw some statistic that something like half of Japanese firms

449
00:21:32,069 --> 00:21:33,119
Speaker 2: still use um

450
00:21:33,719 --> 00:21:37,510
Speaker 2: workers who are over the age of 70. So these

451
00:21:37,520 --> 00:21:40,099
Speaker 2: are things that I don't think we really thoroughly accounted

452
00:21:40,109 --> 00:21:42,560
Speaker 2: for when we were making our predictions of the future.

453
00:21:42,569 --> 00:21:42,688
Speaker 2: We

454
00:21:42,699 --> 00:21:45,229
Speaker 1: just assume over 65 retired

455
00:21:45,800 --> 00:21:48,079
Speaker 2: productivity. That's why I would never use dependency ratio. I,

456
00:21:48,089 --> 00:21:50,020
Speaker 2: I mean, I guess when I first started, I used

457
00:21:50,030 --> 00:21:51,430
Speaker 2: it and I quickly threw it out and said, I

458
00:21:51,439 --> 00:21:54,560
Speaker 2: will never use this as a measure because you can't

459
00:21:54,569 --> 00:21:55,170
Speaker 2: compare Gree

460
00:21:55,839 --> 00:21:58,718
Speaker 2: with Japan in terms of dependency ratio. It's not that

461
00:21:58,729 --> 00:22:00,069
Speaker 2: there's a magical number, you become a

462
00:22:00,079 --> 00:22:00,619
Speaker 2: dependent.

463
00:22:00,630 --> 00:22:03,599
Speaker 1: Right. There's like 56 years difference in the official retirement age.

464
00:22:03,609 --> 00:22:05,150
Speaker 1: You got more de facto. Yes.

465
00:22:05,160 --> 00:22:05,390
Speaker 2: And

466
00:22:05,400 --> 00:22:07,739
Speaker 2: I don't, yeah, I don't even use official retirement age either.

467
00:22:07,750 --> 00:22:09,550
Speaker 2: I think about what's the effective retirement age,

468
00:22:10,140 --> 00:22:12,718
Speaker 1: but one bottom line would be that you should be

469
00:22:12,729 --> 00:22:13,790
Speaker 1: rich before you get old.

470
00:22:14,489 --> 00:22:14,729
Speaker 1: From a

471
00:22:15,349 --> 00:22:17,560
Speaker 2: perspective. I will say we don't know what is the

472
00:22:17,569 --> 00:22:20,469
Speaker 2: should part. So, so here's how I think about population aging,

473
00:22:20,479 --> 00:22:24,540
Speaker 2: they're always winners and losers. It, the winners and losers

474
00:22:24,550 --> 00:22:29,680
Speaker 2: will be different in different settings. Um Let's say, for example,

475
00:22:29,689 --> 00:22:34,000
Speaker 2: if you are a um a poor country and there's

476
00:22:34,010 --> 00:22:38,040
Speaker 2: population aging, the losers will be the families and the

477
00:22:38,050 --> 00:22:41,629
Speaker 2: older people themselves um who really might not have the

478
00:22:41,640 --> 00:22:42,839
Speaker 2: resources to go around.

479
00:22:43,390 --> 00:22:46,140
Speaker 2: Is it going to bankrupt the government budget? Maybe not,

480
00:22:46,189 --> 00:22:48,310
Speaker 2: maybe they are the winners because they never put in

481
00:22:48,319 --> 00:22:51,319
Speaker 2: place those systems in the first place versus maybe a

482
00:22:51,329 --> 00:22:54,040
Speaker 2: European style welfare State where there are all these extensive

483
00:22:54,050 --> 00:22:57,760
Speaker 2: promises to the population. Well, the winners and losers might

484
00:22:57,770 --> 00:22:59,780
Speaker 2: look different there. So it's, it's really all about those

485
00:22:59,790 --> 00:23:00,609
Speaker 2: trade offs.

486
00:23:00,800 --> 00:23:03,209
Speaker 1: All right. That's, that's fantastic. And I'm glad you brought

487
00:23:03,219 --> 00:23:07,349
Speaker 1: that up. Um It's hard to talk about demographic trends

488
00:23:07,359 --> 00:23:10,369
Speaker 1: without one corollary of demographic trend, which is migration

489
00:23:10,739 --> 00:23:16,170
Speaker 1: uh for opportunities for uh you know, push factors, you know,

490
00:23:16,229 --> 00:23:20,599
Speaker 1: people move around. So when you think about demographics in

491
00:23:20,609 --> 00:23:24,938
Speaker 1: dealing with demographic, uh you know, trans aging, for example,

492
00:23:25,280 --> 00:23:27,479
Speaker 1: how do you contextualize migration

493
00:23:28,410 --> 00:23:31,959
Speaker 2: most of the time when I get questions from, you know,

494
00:23:31,969 --> 00:23:34,989
Speaker 2: there's always a question from the audience on migration and

495
00:23:35,000 --> 00:23:38,760
Speaker 2: there's always an assumption that the flows of the future

496
00:23:38,770 --> 00:23:41,119
Speaker 2: will be huge. I think everywhere I go, it is

497
00:23:41,130 --> 00:23:43,819
Speaker 2: look what's to come. When several years ago, when people

498
00:23:43,829 --> 00:23:46,520
Speaker 2: started paying more and more attention to climate change, look

499
00:23:46,530 --> 00:23:50,188
Speaker 2: how many people will be displaced, the flows will be huge.

500
00:23:50,619 --> 00:23:52,630
Speaker 2: I don't think that at all

501
00:23:53,239 --> 00:23:56,430
Speaker 2: doesn't mean people won't need to migrate or won't want

502
00:23:56,439 --> 00:23:58,550
Speaker 2: to migrate. But I don't think that they will be

503
00:23:58,560 --> 00:24:01,670
Speaker 2: able to migrate. I don't see any if I do

504
00:24:01,680 --> 00:24:03,939
Speaker 2: a temperature check around the world. There's nothing that tells

505
00:24:03,949 --> 00:24:05,270
Speaker 2: me here they come.

506
00:24:05,750 --> 00:24:08,959
Speaker 2: Um And in fact, speaking of winners and losers, people

507
00:24:08,969 --> 00:24:11,280
Speaker 2: who will be displaced and maybe need to move, they

508
00:24:11,290 --> 00:24:14,270
Speaker 2: will be the losers. In this case, I see nothing

509
00:24:14,280 --> 00:24:17,889
Speaker 2: but borders and actually, as I think, you know, put

510
00:24:17,900 --> 00:24:21,339
Speaker 2: it in conversation with low fertility. So these low fertility,

511
00:24:21,349 --> 00:24:25,729
Speaker 2: high migration societies, there's a lot of discussion and conflict

512
00:24:25,739 --> 00:24:30,180
Speaker 2: about the right, the, you know, the right mix of migrants.

513
00:24:30,380 --> 00:24:30,930
Speaker 2: Um

514
00:24:31,510 --> 00:24:33,849
Speaker 2: And I know that in, in Asia, there's a lot

515
00:24:33,859 --> 00:24:37,250
Speaker 2: of discussion particularly in Korea and Japan about what role

516
00:24:37,260 --> 00:24:40,270
Speaker 2: will migration play in the future in compensating for those

517
00:24:40,280 --> 00:24:42,569
Speaker 2: labor force losses. And most of the time I hear

518
00:24:42,579 --> 00:24:44,729
Speaker 2: people talk about Europe and say, I'm not really sure

519
00:24:44,739 --> 00:24:46,530
Speaker 2: we actually want to go in that direction. Is there

520
00:24:46,540 --> 00:24:50,430
Speaker 2: another way to do it? Um I think most people

521
00:24:50,439 --> 00:24:53,829
Speaker 2: do not realize that 96% of the world's population still

522
00:24:53,839 --> 00:24:55,689
Speaker 2: lives in the country in which they were born.

523
00:24:56,430 --> 00:25:00,369
Speaker 2: And I think the reason that it gets so wrong

524
00:25:00,609 --> 00:25:04,550
Speaker 2: in the popular media is that the elite in the world,

525
00:25:04,560 --> 00:25:08,130
Speaker 2: they have moved you. We're, we're in Singapore right now.

526
00:25:08,140 --> 00:25:10,239
Speaker 2: This is a group of people whose bias would be

527
00:25:10,250 --> 00:25:13,089
Speaker 2: thinking everybody moves. I've given a talk in Silicon Valley.

528
00:25:13,099 --> 00:25:15,550
Speaker 2: I always ask this question. I mean, spoiler alert. If I,

529
00:25:15,560 --> 00:25:17,438
Speaker 2: if I come give a talk for anybody who's listening,

530
00:25:17,459 --> 00:25:20,050
Speaker 2: I'll say pop quiz, what proportion of the world's population

531
00:25:20,060 --> 00:25:21,669
Speaker 2: lives out the side of the country in which they're

532
00:25:21,680 --> 00:25:24,489
Speaker 2: born and in Silicon Valley they're like 50%.

533
00:25:24,750 --> 00:25:27,729
Speaker 2: And I said, yeah, that's because you all moved, you know,

534
00:25:27,739 --> 00:25:31,099
Speaker 2: but it's way lower. So it's been, you know, two

535
00:25:31,109 --> 00:25:34,550
Speaker 2: or 3% for, for decades. I, I think there will

536
00:25:34,560 --> 00:25:37,530
Speaker 2: be reasons for people to move but labor does not

537
00:25:37,540 --> 00:25:39,219
Speaker 2: flow like capital, that's for sure.

538
00:25:39,380 --> 00:25:39,599
Speaker 1: Of

539
00:25:39,609 --> 00:25:42,889
Speaker 1: course. Uh I also think that some of the migration

540
00:25:42,900 --> 00:25:46,599
Speaker 1: of people might be supplanted by migration of robots going forward.

541
00:25:46,609 --> 00:25:49,329
Speaker 1: I had this discussion, Jennifer a couple of years ago,

542
00:25:49,339 --> 00:25:52,208
Speaker 1: I was at us C and we were talking about

543
00:25:52,420 --> 00:25:54,089
Speaker 1: economic future of the world, but

544
00:25:54,579 --> 00:25:56,949
Speaker 1: in the context of China we talked about aging and

545
00:25:56,959 --> 00:25:58,459
Speaker 1: what that means for China. And there were a couple

546
00:25:58,469 --> 00:26:02,479
Speaker 1: of Chinese nationals in the audience and they came to

547
00:26:02,489 --> 00:26:04,530
Speaker 1: talk to me after and they said you do want

548
00:26:04,540 --> 00:26:07,079
Speaker 1: to talk about China in the context of automation and

549
00:26:07,089 --> 00:26:10,989
Speaker 1: robot organization because we are very much cognizant of the

550
00:26:11,000 --> 00:26:12,609
Speaker 1: fact that our labor force is not what it used

551
00:26:12,619 --> 00:26:15,909
Speaker 1: to be and we will embrace automation as enthusiastically more

552
00:26:15,920 --> 00:26:16,369
Speaker 1: than anybody

553
00:26:16,380 --> 00:26:16,709
Speaker 1: else.

554
00:26:17,089 --> 00:26:18,989
Speaker 2: Yeah, I believe that to be the case. I mean,

555
00:26:19,180 --> 00:26:21,208
Speaker 2: I think, I mean, I'd be curious, I'm gonna kind

556
00:26:21,219 --> 00:26:22,420
Speaker 2: of throw this back to you a little bit when

557
00:26:22,430 --> 00:26:24,050
Speaker 2: I think about automation

558
00:26:24,300 --> 00:26:28,359
Speaker 2: and, and aging, I tend to think that if it

559
00:26:28,369 --> 00:26:31,510
Speaker 2: displaces some high skilled workers, that's probably good actually, since

560
00:26:31,520 --> 00:26:33,119
Speaker 2: there'll be fewer in the future

561
00:26:33,680 --> 00:26:38,319
Speaker 2: where I get uncomfortable. If it displaces low skilled workers

562
00:26:38,329 --> 00:26:41,510
Speaker 2: in low income contexts, then we are in a different space,

563
00:26:41,520 --> 00:26:44,770
Speaker 1: right? What I would like to hope for is that

564
00:26:44,780 --> 00:26:49,290
Speaker 1: a highly automated future actually increases the work potential of

565
00:26:49,300 --> 00:26:52,410
Speaker 1: the low or the high skilled people that they can

566
00:26:52,420 --> 00:26:54,629
Speaker 1: harness those robots and do more stuff as opposed to

567
00:26:54,640 --> 00:26:57,189
Speaker 1: being fully replaced by the robots that a future should

568
00:26:57,199 --> 00:26:57,640
Speaker 1: be

569
00:26:58,369 --> 00:27:01,810
Speaker 1: characterized by even more capabilities and more options for us

570
00:27:01,819 --> 00:27:04,389
Speaker 1: to do things. Not like, you know, just that one

571
00:27:04,400 --> 00:27:06,050
Speaker 1: thing that was done by a human being would be

572
00:27:06,060 --> 00:27:08,280
Speaker 1: done by a robot. Why can we have a multiplicity

573
00:27:08,290 --> 00:27:11,310
Speaker 1: around that, but that might be techno optimistic.

574
00:27:11,380 --> 00:27:15,199
Speaker 2: But I'm the same way too and I mostly am optimistic.

575
00:27:15,209 --> 00:27:17,079
Speaker 2: And when I look at demographic trends, I, my goodness,

576
00:27:17,089 --> 00:27:19,419
Speaker 2: look at life expectancy. We've added a whole another life

577
00:27:19,430 --> 00:27:19,989
Speaker 2: to people

578
00:27:20,369 --> 00:27:22,550
Speaker 2: and, you know, if it is the case that it

579
00:27:22,560 --> 00:27:25,329
Speaker 2: gives us a better well being in the future, that's

580
00:27:25,339 --> 00:27:27,989
Speaker 2: wonderful and kind of marry this with what we know

581
00:27:28,000 --> 00:27:31,389
Speaker 2: about younger generations today and their feelings about work. They

582
00:27:31,400 --> 00:27:34,479
Speaker 2: really prioritize well being. Uh so I'm not sure they're

583
00:27:34,489 --> 00:27:36,430
Speaker 2: willing to work the same number of hours. So perhaps

584
00:27:36,439 --> 00:27:36,530
Speaker 2: it

585
00:27:36,619 --> 00:27:37,540
Speaker 2: a perfect marriage,

586
00:27:37,550 --> 00:27:41,400
Speaker 1: correct. So what is your sense of your conversations with

587
00:27:41,410 --> 00:27:44,550
Speaker 1: policymakers around the world? Like government of Singapore or government

588
00:27:44,560 --> 00:27:48,770
Speaker 1: of the US or elsewhere? How are governments dealing with

589
00:27:48,780 --> 00:27:52,780
Speaker 1: these extremely powerful and most overwhelming shifts in demographic trends?

590
00:27:53,890 --> 00:27:57,060
Speaker 2: No matter where I am, there's one important thing in common.

591
00:27:57,609 --> 00:27:59,770
Speaker 2: They just wanna put the genie back in the bottle

592
00:28:00,280 --> 00:28:02,989
Speaker 2: and that means how do we get more babies now?

593
00:28:03,000 --> 00:28:06,189
Speaker 2: Even Singapore, which I am a huge admirer of. Uh

594
00:28:06,199 --> 00:28:09,310
Speaker 2: and I think if you like incredibly innovative again, if

595
00:28:09,319 --> 00:28:11,329
Speaker 2: anybody has to go through these types of things first,

596
00:28:11,339 --> 00:28:12,839
Speaker 2: great for Singapore to do it because we can see

597
00:28:12,849 --> 00:28:13,630
Speaker 2: what will happen.

598
00:28:13,900 --> 00:28:18,560
Speaker 2: But the general conversation everywhere is tell us what's the secret,

599
00:28:18,569 --> 00:28:21,619
Speaker 2: what's the secret to more babies? There is no secret.

600
00:28:21,630 --> 00:28:24,130
Speaker 2: And if I could have anyone, you know, if we

601
00:28:24,140 --> 00:28:27,719
Speaker 2: could change anything. It would be, accept this and move

602
00:28:27,729 --> 00:28:32,189
Speaker 2: towards resilience, the first country that does that and starts

603
00:28:32,199 --> 00:28:35,280
Speaker 2: planning for a resilient society. Speaking of winners and losers,

604
00:28:35,290 --> 00:28:37,500
Speaker 2: I mean, that, that put all your eggs in that basket.

605
00:28:37,510 --> 00:28:40,449
Speaker 2: That's the place to follow. So I keep looking for

606
00:28:40,459 --> 00:28:42,959
Speaker 2: signs that someone gets it

607
00:28:43,380 --> 00:28:46,000
Speaker 2: and they've moved past it and they're just thinking about,

608
00:28:46,010 --> 00:28:48,560
Speaker 2: you know, retirement work, health and all of those things,

609
00:28:48,569 --> 00:28:51,520
Speaker 2: Singapore possibly comes the closest to that. There's a lot

610
00:28:51,530 --> 00:28:55,589
Speaker 2: of discussion about health span, uh health span versus um

611
00:28:55,609 --> 00:28:59,760
Speaker 2: lifespan about wealth span as well. And, and so I,

612
00:28:59,770 --> 00:29:03,359
Speaker 2: I see little bits of murmurings there. Uh But we're,

613
00:29:03,369 --> 00:29:04,800
Speaker 2: I think we're already behind.

614
00:29:05,859 --> 00:29:09,010
Speaker 1: I became a citizen of Singapore earlier this year and

615
00:29:09,020 --> 00:29:12,849
Speaker 1: immediately I was given access to certain facilities like SG

616
00:29:12,859 --> 00:29:16,619
Speaker 1: health where the idea is that the health span issue

617
00:29:16,770 --> 00:29:21,239
Speaker 1: that having a single general practitioner attached to you who

618
00:29:21,250 --> 00:29:23,780
Speaker 1: has your records or refusing to go to a different doctor,

619
00:29:23,790 --> 00:29:26,510
Speaker 1: the records digitally go to the other doctor and there's

620
00:29:26,520 --> 00:29:30,589
Speaker 1: a continuous case record and the focus is on preventative

621
00:29:30,599 --> 00:29:33,520
Speaker 1: care as opposed to spending a lot of money on hospitalization.

622
00:29:33,760 --> 00:29:36,770
Speaker 1: So I think that's one sensible approach to dealing with

623
00:29:36,780 --> 00:29:38,770
Speaker 1: a population that will be increasingly older going

624
00:29:38,780 --> 00:29:39,180
Speaker 1: forward.

625
00:29:39,189 --> 00:29:41,930
Speaker 2: Absolutely. And I really do think that, you know, Singapore

626
00:29:41,939 --> 00:29:44,020
Speaker 2: is pretty much the one place that is, and it

627
00:29:44,030 --> 00:29:47,459
Speaker 2: helps it small because you can have lots of experiments here, small,

628
00:29:47,469 --> 00:29:49,180
Speaker 2: well educated and innovative.

629
00:29:49,280 --> 00:29:52,189
Speaker 2: So, you know, if you're going to have resilience, that's perfect.

630
00:29:52,229 --> 00:29:54,599
Speaker 1: Let's jump outside of Singapore a little bit and go

631
00:29:54,609 --> 00:29:57,650
Speaker 1: to the rest of Southeast Asia. What's your sense? I mean,

632
00:29:57,660 --> 00:29:59,819
Speaker 1: the one story that we always talk about in this

633
00:29:59,829 --> 00:30:03,520
Speaker 1: region is Thailand, which is not a high income economy

634
00:30:03,530 --> 00:30:04,660
Speaker 1: but displaying

635
00:30:05,430 --> 00:30:07,459
Speaker 1: advanced economy type demographic dynamic.

636
00:30:07,500 --> 00:30:13,239
Speaker 2: Yeah. Well, I know it's really, I'm also curious about Thailand. I, what?

637
00:30:13,250 --> 00:30:16,869
Speaker 2: So again, my whole career I've heard and it was

638
00:30:16,880 --> 00:30:20,280
Speaker 2: always spoken about China old before, rich, old, before rich.

639
00:30:20,650 --> 00:30:23,459
Speaker 2: And so I've always tried to think why, what is,

640
00:30:23,469 --> 00:30:25,800
Speaker 2: what does that actually matter? Is it

641
00:30:25,875 --> 00:30:29,935
Speaker 2: about household savings and income security in old age? Well,

642
00:30:29,944 --> 00:30:32,474
Speaker 2: how could we offset that if there's a really strong,

643
00:30:32,494 --> 00:30:35,765
Speaker 2: you know, family support there. Is it about the types

644
00:30:35,775 --> 00:30:38,755
Speaker 2: of jobs that people will have? So I don't know

645
00:30:38,765 --> 00:30:40,994
Speaker 2: if I think Thailand, I think the total fertility rate

646
00:30:41,005 --> 00:30:44,055
Speaker 2: there is like Singapore, one child per woman. I don't

647
00:30:44,064 --> 00:30:47,564
Speaker 2: know if I buy that. All of these countries that

648
00:30:47,574 --> 00:30:51,285
Speaker 2: are seeing these demographic trends before they're rich are doomed.

649
00:30:51,295 --> 00:30:52,574
Speaker 2: What do you think I'm missing?

650
00:30:53,060 --> 00:30:55,300
Speaker 1: I think you make a very good point. I think

651
00:30:55,310 --> 00:30:58,560
Speaker 1: the view is from a very traditional growth model. You

652
00:30:58,569 --> 00:31:00,369
Speaker 1: need a certain amount of money and a certain amount

653
00:31:00,380 --> 00:31:02,760
Speaker 1: of people to get a certain amount of growth, assuming

654
00:31:02,770 --> 00:31:06,239
Speaker 1: productivity is sort of constant around that if you have

655
00:31:06,250 --> 00:31:08,430
Speaker 1: less number of people, you'll get less growth, less growth

656
00:31:08,439 --> 00:31:12,550
Speaker 1: is like them because disaster economies can think of and therefore,

657
00:31:12,560 --> 00:31:14,839
Speaker 1: you know, you are doomed. And so do you need

658
00:31:14,849 --> 00:31:18,229
Speaker 1: immigration or do you need to have ways to encourage

659
00:31:18,239 --> 00:31:19,680
Speaker 1: women to have more babies? That sort of stuff?

660
00:31:19,930 --> 00:31:23,270
Speaker 1: But I think that given your train of thought, I'm

661
00:31:23,280 --> 00:31:26,250
Speaker 1: sort of persuaded by the point that you don't necessarily

662
00:31:26,260 --> 00:31:29,650
Speaker 1: think about pushing back against a very overwhelming dynamic. It's

663
00:31:29,660 --> 00:31:31,250
Speaker 1: a bit like climate change and we can take a

664
00:31:31,260 --> 00:31:33,069
Speaker 1: lot of mitigating measures. But we basically have to deal

665
00:31:33,079 --> 00:31:35,020
Speaker 1: with the world that will be warmer tomorrow than it

666
00:31:35,030 --> 00:31:38,250
Speaker 1: is today and today is already warmer than yesterday. Similarly,

667
00:31:38,260 --> 00:31:41,680
Speaker 1: with aging, I think that again, I think you made

668
00:31:41,689 --> 00:31:43,229
Speaker 1: a very good point with respect to Japan.

669
00:31:43,959 --> 00:31:47,689
Speaker 1: We probably were optimistic about Japan's demographic trend 20 years

670
00:31:47,699 --> 00:31:50,130
Speaker 1: ago than what has turned out to be and might

671
00:31:50,140 --> 00:31:52,609
Speaker 1: as well then have a very conservative approach instead of

672
00:31:52,619 --> 00:31:56,670
Speaker 1: being overly optimistic about it. So plan with the view

673
00:31:56,680 --> 00:32:00,109
Speaker 1: that fertile der don't change and then see what are

674
00:32:00,119 --> 00:32:01,219
Speaker 1: the opportunities around that?

675
00:32:01,510 --> 00:32:03,859
Speaker 2: I think that climate, I use the climate change uh

676
00:32:03,869 --> 00:32:07,969
Speaker 2: all the time to say, you know, you sure let's

677
00:32:07,979 --> 00:32:10,380
Speaker 2: try to stop the warming of the world, but you're

678
00:32:10,390 --> 00:32:12,109
Speaker 2: not just going to sit there on your front porch

679
00:32:12,119 --> 00:32:14,619
Speaker 2: with the water lapping around your ankles and think, well,

680
00:32:14,630 --> 00:32:16,890
Speaker 2: how do we stop the world from warming, you're gonna

681
00:32:16,900 --> 00:32:18,900
Speaker 2: try to put your house up on stilts. And so

682
00:32:18,930 --> 00:32:22,310
Speaker 2: this adaptation and resilience is exactly um how we need.

683
00:32:22,319 --> 00:32:24,770
Speaker 2: And now can I ask you about growth though? I

684
00:32:24,780 --> 00:32:25,459
Speaker 2: uh so

685
00:32:26,550 --> 00:32:29,250
Speaker 2: I come at this from an environmental studies perspective actually,

686
00:32:29,449 --> 00:32:30,099
Speaker 2: um

687
00:32:31,439 --> 00:32:32,380
Speaker 2: And

688
00:32:33,290 --> 00:32:36,189
Speaker 2: when I look at the, we've already got 50 shrinking countries,

689
00:32:36,199 --> 00:32:39,260
Speaker 2: I think. So this, this is already happening. We're already there.

690
00:32:39,849 --> 00:32:43,589
Speaker 2: And yet I don't see anything changing in the conversation

691
00:32:43,599 --> 00:32:47,390
Speaker 2: about a bias towards growth, a fetish of growth actually,

692
00:32:48,930 --> 00:32:52,280
Speaker 2: what would it take or is it impossible for us

693
00:32:52,290 --> 00:32:55,099
Speaker 2: to move away from thinking that the ultimate marker of

694
00:32:55,109 --> 00:32:59,979
Speaker 2: success is positive growth. Even as a country's population is shrinking,

695
00:33:00,660 --> 00:33:05,319
Speaker 1: it's hard, it's easy to just stay fixated on national

696
00:33:05,329 --> 00:33:08,900
Speaker 1: accounts based numbers, you know, which is not a very

697
00:33:08,910 --> 00:33:12,300
Speaker 1: old construct basically began after the second world war when

698
00:33:12,310 --> 00:33:15,239
Speaker 1: we had the whole war economy input output and national

699
00:33:15,250 --> 00:33:18,000
Speaker 1: accounts made sense this much capital, this much labor. This

700
00:33:18,010 --> 00:33:20,400
Speaker 1: is the amount of value that we are creating. We

701
00:33:20,410 --> 00:33:24,680
Speaker 1: haven't gotten out of it. We as economists are culpable

702
00:33:24,689 --> 00:33:26,829
Speaker 1: of not being able to

703
00:33:27,170 --> 00:33:29,380
Speaker 1: inform the world that there are better ways of looking

704
00:33:29,390 --> 00:33:32,400
Speaker 1: at prosperity, social welfare and well being. So for me,

705
00:33:32,410 --> 00:33:35,410
Speaker 1: for example, Singapore's biggest achievement is not a very high

706
00:33:35,420 --> 00:33:37,400
Speaker 1: per capita income, but the fact that it has one

707
00:33:37,410 --> 00:33:39,510
Speaker 1: of the highest life expectancies in the world that its

708
00:33:39,520 --> 00:33:42,229
Speaker 1: population has some of the highest education attainments in the world.

709
00:33:42,280 --> 00:33:44,150
Speaker 1: So I would love for us to look at it

710
00:33:44,160 --> 00:33:47,660
Speaker 1: from a multi dimensional perspective. But I think policymakers find

711
00:33:47,670 --> 00:33:50,430
Speaker 1: it easier to deal with a dollar amount and that

712
00:33:50,439 --> 00:33:52,949
Speaker 1: being a proxy for success achievement,

713
00:33:53,439 --> 00:33:56,900
Speaker 1: very hard to get out of that. And uh so

714
00:33:56,910 --> 00:33:59,619
Speaker 1: I'm not going to say that there is some bright

715
00:33:59,630 --> 00:34:02,540
Speaker 1: new shining field of economics that's going to change the

716
00:34:02,550 --> 00:34:05,589
Speaker 1: way we look at it. Nobel Laureate, Aaren and Joe

717
00:34:05,599 --> 00:34:08,340
Speaker 1: Stiglitz a few years ago, maybe about 1012 years ago,

718
00:34:08,350 --> 00:34:11,659
Speaker 1: wanted to sort of, you know, break forth alternative measures

719
00:34:11,669 --> 00:34:14,389
Speaker 1: of GDP different ways of looking at it. It was

720
00:34:14,399 --> 00:34:17,370
Speaker 1: an interesting thought experiment didn't really go anywhere. I think

721
00:34:17,379 --> 00:34:20,459
Speaker 1: for the remainder of our mutual professional careers, we'll have

722
00:34:20,469 --> 00:34:21,500
Speaker 1: to deal with a point that

723
00:34:21,830 --> 00:34:25,060
Speaker 1: if population shrinks growth goes down and that's the worst

724
00:34:25,070 --> 00:34:25,679
Speaker 1: of all, the worst of

725
00:34:25,689 --> 00:34:30,379
Speaker 2: all. And it's a shame, I really do think, yeah, maybe,

726
00:34:30,389 --> 00:34:32,649
Speaker 2: maybe in the future, but I do find the same

727
00:34:32,659 --> 00:34:34,250
Speaker 2: thing when I talk to people in the private sector

728
00:34:34,260 --> 00:34:36,810
Speaker 2: about what does it look like to scale down

729
00:34:37,370 --> 00:34:40,830
Speaker 2: and the, the, the, the eyes just bug out and you,

730
00:34:40,840 --> 00:34:42,610
Speaker 2: you know, you'd be at the airport and what is

731
00:34:42,620 --> 00:34:45,389
Speaker 2: on the screen behind you, what is consumer spending look like?

732
00:34:45,399 --> 00:34:48,060
Speaker 2: And it's just all of these markers that we expect.

733
00:34:48,070 --> 00:34:50,110
Speaker 2: And I, I do find that it connects actually with

734
00:34:50,120 --> 00:34:52,350
Speaker 2: our views on the environment because again, if you're measuring

735
00:34:52,360 --> 00:34:54,189
Speaker 2: everything with spending

736
00:34:54,610 --> 00:34:59,750
Speaker 2: uh or with growth, then that really conflicts with what

737
00:34:59,760 --> 00:35:01,350
Speaker 2: we're trying to do environmentally as well.

738
00:35:01,360 --> 00:35:01,560
Speaker 1: How

739
00:35:01,570 --> 00:35:04,870
Speaker 1: do you reduce emission when you want to actually burn

740
00:35:04,879 --> 00:35:07,209
Speaker 1: more electricity to run the genii models that we want

741
00:35:07,219 --> 00:35:07,510
Speaker 1: to use

742
00:35:07,520 --> 00:35:07,790
Speaker 1: now?

743
00:35:07,800 --> 00:35:09,449
Speaker 2: Yeah, I mean, it's a little bit easier with the

744
00:35:09,459 --> 00:35:11,620
Speaker 2: environment actually than with aging because you say, well, there's

745
00:35:11,629 --> 00:35:15,110
Speaker 2: a huge, uh there's a lot of money to be made.

746
00:35:15,205 --> 00:35:17,104
Speaker 2: We make these shifts towards greater things.

747
00:35:17,594 --> 00:35:20,485
Speaker 1: So that's one thing that again is close to my

748
00:35:20,495 --> 00:35:23,594
Speaker 1: heart because as a bank, we think about what are

749
00:35:23,604 --> 00:35:28,004
Speaker 1: the financial opportunities around an increasingly silver population and what

750
00:35:28,014 --> 00:35:30,114
Speaker 1: kind of financial engineering options you can give them so

751
00:35:30,125 --> 00:35:33,104
Speaker 1: that whatever money they have, it can go longer than

752
00:35:33,114 --> 00:35:35,745
Speaker 1: what we have thought was necessary even 1015 years ago.

753
00:35:36,054 --> 00:35:38,884
Speaker 1: So that is something that I think banks, insurance companies,

754
00:35:38,895 --> 00:35:42,004
Speaker 1: pension funds in Singapore in the US in Europe will

755
00:35:42,014 --> 00:35:44,625
Speaker 1: keep on doing, going forward because

756
00:35:45,189 --> 00:35:48,340
Speaker 1: going back to my immediate response to you, which is

757
00:35:48,350 --> 00:35:50,729
Speaker 1: that the growth fetish is not going to go away.

758
00:35:51,899 --> 00:35:54,879
Speaker 1: So that's, it's what it is. Um I am sure

759
00:35:54,889 --> 00:35:59,050
Speaker 1: Jennifer that you get asked about China and its doomsday

760
00:35:59,060 --> 00:36:01,939
Speaker 1: scenarios are on aging all the time. How do you

761
00:36:01,949 --> 00:36:05,560
Speaker 1: respond to those people who think that China is destined

762
00:36:05,570 --> 00:36:08,359
Speaker 1: for really bad things because it's aging so rapidly.

763
00:36:08,870 --> 00:36:12,399
Speaker 2: Well, one thing I say is that China is not

764
00:36:12,409 --> 00:36:16,199
Speaker 2: as surprised as you might think China is. So if you,

765
00:36:16,209 --> 00:36:19,389
Speaker 2: you can read documents from 40 years ago where

766
00:36:20,110 --> 00:36:23,739
Speaker 2: those in charge said, all right, if we put in

767
00:36:23,750 --> 00:36:28,479
Speaker 2: place these policies that encourage or more than encourage lower

768
00:36:28,489 --> 00:36:31,850
Speaker 2: births in 40 years time, we will be an aging society.

769
00:36:32,010 --> 00:36:34,409
Speaker 2: So they knew that would happen. I think the surprise

770
00:36:34,419 --> 00:36:37,659
Speaker 2: part is they thought they could reverse it. Um And

771
00:36:37,669 --> 00:36:39,620
Speaker 2: you know, so, but they're not alone in that.

772
00:36:40,530 --> 00:36:42,449
Speaker 2: What I tend to say is

773
00:36:44,100 --> 00:36:46,799
Speaker 2: I actually think that democracies will be and this is not,

774
00:36:46,810 --> 00:36:48,399
Speaker 2: this is like a hard thing to have. Democracies are

775
00:36:48,409 --> 00:36:51,439
Speaker 2: way worse off when it comes to population aging, it

776
00:36:51,449 --> 00:36:54,388
Speaker 2: gets back to this winners and losers thing. The thing

777
00:36:54,399 --> 00:36:56,919
Speaker 2: about demography that I said, I love is that you

778
00:36:56,929 --> 00:37:01,699
Speaker 2: can see the future it requires though long term planning.

779
00:37:02,060 --> 00:37:05,359
Speaker 2: Are you in a political system that can long term plan?

780
00:37:05,370 --> 00:37:07,428
Speaker 2: I do not live in one, I live in one

781
00:37:07,439 --> 00:37:10,649
Speaker 2: where the before the election is over. You're already talking

782
00:37:10,659 --> 00:37:12,120
Speaker 2: about the next election.

783
00:37:12,419 --> 00:37:14,830
Speaker 2: So how do you long term plan to deal with

784
00:37:14,840 --> 00:37:17,879
Speaker 2: a shift in basically the center of gravity of your population,

785
00:37:17,889 --> 00:37:21,280
Speaker 2: you cannot do it. You also have electoral penalties. Think

786
00:37:21,290 --> 00:37:25,060
Speaker 2: about in France when people took to the streets because

787
00:37:25,070 --> 00:37:28,080
Speaker 2: they're trying to raise the blue collar work, you know,

788
00:37:28,090 --> 00:37:31,610
Speaker 2: from like what is it? 55 to 57 or 57

789
00:37:31,620 --> 00:37:34,739
Speaker 2: to 59. There are these huge penalties and checks on

790
00:37:34,750 --> 00:37:36,679
Speaker 2: that if you're in a system

791
00:37:36,889 --> 00:37:41,350
Speaker 2: that is less responsive to pressures from the population and

792
00:37:41,360 --> 00:37:43,989
Speaker 2: in a system that can plan longer term, I think

793
00:37:44,000 --> 00:37:47,158
Speaker 2: you have an advantage there. So I do not see

794
00:37:47,169 --> 00:37:49,739
Speaker 2: as much of a doomsday scenario as maybe other people do.

795
00:37:50,169 --> 00:37:53,330
Speaker 1: I remember in your TED talk, you had this striking

796
00:37:53,340 --> 00:37:56,870
Speaker 1: observation that in Romania Ceausescu tried very hard to force

797
00:37:56,879 --> 00:37:59,409
Speaker 1: people to have babies and it led to like rather

798
00:37:59,419 --> 00:38:01,939
Speaker 1: dire outcomes where people just abandoned the Children, they were

799
00:38:01,949 --> 00:38:05,659
Speaker 1: all ending up in orphanages and of course, very soon thereafter, again,

800
00:38:05,669 --> 00:38:08,989
Speaker 1: fertility rate collapse. So don't, don't try to force it

801
00:38:09,000 --> 00:38:11,429
Speaker 1: now too much because it doesn't really go anywhere.

802
00:38:11,750 --> 00:38:15,479
Speaker 1: The other point you made in your TED talk and

803
00:38:15,489 --> 00:38:17,729
Speaker 1: that will take us to India is that while we

804
00:38:17,739 --> 00:38:20,939
Speaker 1: think of India as this big tail wind characterized economy

805
00:38:20,949 --> 00:38:23,669
Speaker 1: with lots of young people, actually, the working age population

806
00:38:23,679 --> 00:38:24,770
Speaker 1: of India has already peaked, already

807
00:38:24,790 --> 00:38:25,259
Speaker 1: peaked.

808
00:38:26,030 --> 00:38:28,100
Speaker 2: And you know, if you ask back to

809
00:38:28,215 --> 00:38:31,274
Speaker 2: references, if you look at the data on what are,

810
00:38:31,284 --> 00:38:34,514
Speaker 2: what's the preferred fertility rate? It's even lower. I think

811
00:38:34,524 --> 00:38:38,235
Speaker 2: it last I saw was around 1.76 and then the

812
00:38:38,245 --> 00:38:40,715
Speaker 2: overall fertility rate was at two. So as soon as

813
00:38:40,725 --> 00:38:43,415
Speaker 2: people can act on those preferences, it's going to keep

814
00:38:43,425 --> 00:38:46,935
Speaker 2: going lower. Um Yeah, do,

815
00:38:48,110 --> 00:38:50,540
Speaker 2: of course, I came up in a time where everyone

816
00:38:50,550 --> 00:38:52,969
Speaker 2: was obsessed with the bricks and thinking about this is

817
00:38:52,979 --> 00:38:56,419
Speaker 2: the future when we start looking at is India, the

818
00:38:56,429 --> 00:38:59,760
Speaker 2: next China, which is a common question. And I'm doing

819
00:38:59,770 --> 00:39:03,600
Speaker 2: this with a demographic lens. I really notice a difference

820
00:39:03,610 --> 00:39:06,260
Speaker 2: in the human capital situation, particularly when it comes to women.

821
00:39:06,389 --> 00:39:09,810
Speaker 2: And so education, you know that and, and I think

822
00:39:09,820 --> 00:39:13,719
Speaker 2: that's why is a gender lens useful again when you

823
00:39:13,729 --> 00:39:16,928
Speaker 2: have 1.4 billion people, that's a lot of women.

824
00:39:17,129 --> 00:39:20,549
Speaker 2: And you know, the be most highly educated women have

825
00:39:20,560 --> 00:39:23,090
Speaker 2: some of the lowest labor force participation rates. And so

826
00:39:23,270 --> 00:39:28,929
Speaker 2: I don't see the same um setting and backdrop to

827
00:39:28,939 --> 00:39:32,709
Speaker 2: capitalize on. We call the demographic window of opportunity that

828
00:39:32,719 --> 00:39:34,979
Speaker 2: I know you talk about all the time to reap

829
00:39:34,989 --> 00:39:37,859
Speaker 2: a demographic dividend. So my expectation would be you won't

830
00:39:37,870 --> 00:39:39,468
Speaker 2: reap as high of a demographic dividend.

831
00:39:40,100 --> 00:39:42,429
Speaker 1: Absolutely. I mean, this issue is something that I have

832
00:39:42,439 --> 00:39:44,979
Speaker 1: talked about a lot in recent years, which is India

833
00:39:44,989 --> 00:39:48,139
Speaker 1: has to raise its female participation rate in the labor force.

834
00:39:48,340 --> 00:39:53,060
Speaker 1: You would not get a demographic dividend. If you a

835
00:39:53,070 --> 00:39:56,219
Speaker 1: don't educate the young and b don't bring the women

836
00:39:56,229 --> 00:39:58,479
Speaker 1: into the labor force. And these are two challenges that

837
00:39:58,489 --> 00:40:00,280
Speaker 1: India still has not fully delivered on

838
00:40:01,219 --> 00:40:04,500
Speaker 1: uh Jennifer attitude. So we talked about initially, you know,

839
00:40:04,510 --> 00:40:09,120
Speaker 1: policymakers and how they're thinking. Uh but societal attitude toward aging,

840
00:40:09,129 --> 00:40:13,110
Speaker 1: are we becoming more respectful of people with gray hair.

841
00:40:13,120 --> 00:40:16,319
Speaker 1: Are we, you know, getting ready for a world where

842
00:40:16,330 --> 00:40:17,370
Speaker 1: there will be lots and lots of people who are

843
00:40:17,379 --> 00:40:17,959
Speaker 1: 65

844
00:40:18,090 --> 00:40:18,669
Speaker 2: you're a bank,

845
00:40:18,985 --> 00:40:20,985
Speaker 2: how much money is there to be made in? Trying

846
00:40:20,995 --> 00:40:26,725
Speaker 2: to look like you're not old, substantial, substantial. I don't

847
00:40:26,735 --> 00:40:29,495
Speaker 2: see it happening anywhere. I mean, you know, when you're

848
00:40:29,504 --> 00:40:32,604
Speaker 2: really looking for it, we can pluck a few things out.

849
00:40:32,614 --> 00:40:36,385
Speaker 2: Like look at the grand influencers. Uh you know, there

850
00:40:36,969 --> 00:40:40,429
Speaker 2: uh some of the modeling campaigns will change and maybe

851
00:40:40,439 --> 00:40:43,979
Speaker 2: your products change a bit, but I think there's still

852
00:40:43,989 --> 00:40:47,929
Speaker 2: a huge fear of aging and I'm, I don't see

853
00:40:47,939 --> 00:40:48,929
Speaker 2: that changing. So

854
00:40:48,939 --> 00:40:50,979
Speaker 1: we're aging, but we're still agist. Yeah,

855
00:40:51,250 --> 00:40:52,759
Speaker 2: we absolutely are.

856
00:40:53,959 --> 00:40:55,529
Speaker 1: Um So

857
00:40:56,669 --> 00:41:02,549
Speaker 1: just as a bottom line that we have powerful demographic forces,

858
00:41:02,810 --> 00:41:05,530
Speaker 1: we're not fully ready to embrace the fact that it's

859
00:41:05,540 --> 00:41:07,629
Speaker 1: heading in that direction, we still think that the policy

860
00:41:07,639 --> 00:41:11,310
Speaker 1: to occur or middle income provision there takes us there.

861
00:41:11,500 --> 00:41:14,840
Speaker 1: So then why do you remain somewhat constructive or optimistic

862
00:41:14,850 --> 00:41:15,610
Speaker 1: about the future?

863
00:41:16,399 --> 00:41:19,169
Speaker 2: That is a very good question. I think that being

864
00:41:19,179 --> 00:41:22,850
Speaker 2: a student of history and being so macro and generalist

865
00:41:22,860 --> 00:41:25,540
Speaker 2: is what makes me optimistic because when you zoom out

866
00:41:25,550 --> 00:41:28,639
Speaker 2: and think about where we were just a few decades ago,

867
00:41:29,330 --> 00:41:31,949
Speaker 2: oh, we're so much better off. So let's just think

868
00:41:31,959 --> 00:41:35,899
Speaker 2: about health and life expectancy. That's the one everyone knows.

869
00:41:35,909 --> 00:41:38,969
Speaker 2: Of course, and what we'll see all the time. Oh,

870
00:41:38,979 --> 00:41:41,389
Speaker 2: cancer rates are on the rise and all that. Well,

871
00:41:41,399 --> 00:41:43,229
Speaker 2: some of that's because we're actually living long enough to

872
00:41:43,239 --> 00:41:44,659
Speaker 2: be able to get cancer.

873
00:41:44,979 --> 00:41:48,919
Speaker 2: Uh So you're always gonna, you have to die of something. So,

874
00:41:48,929 --> 00:41:52,300
Speaker 2: the shift to non communicable diseases though is fantastic. That's

875
00:41:52,310 --> 00:41:54,439
Speaker 2: a marker of success. You don't want people to die

876
00:41:54,449 --> 00:41:57,189
Speaker 2: prematurely of them. But overall, it's a good thing and

877
00:41:57,199 --> 00:41:59,419
Speaker 2: I actually think the same about fertility rates as well

878
00:41:59,429 --> 00:42:04,120
Speaker 2: with ac at a really important caveat. So worldwide, two

879
00:42:04,129 --> 00:42:06,889
Speaker 2: out of three people on this planet of 8 billion

880
00:42:06,899 --> 00:42:09,939
Speaker 2: now live somewhere with below replacement fertility rates.

881
00:42:10,860 --> 00:42:14,080
Speaker 2: They're able to do so in part because they know

882
00:42:14,090 --> 00:42:16,560
Speaker 2: that if they have a fewer, fewer number of Children,

883
00:42:16,570 --> 00:42:19,719
Speaker 2: they're gonna live to reproductive ages. So that is a

884
00:42:19,729 --> 00:42:24,319
Speaker 2: vote actually of, of confidence in the future in one sense. And,

885
00:42:24,330 --> 00:42:29,839
Speaker 2: and so, you know, fertility behavior um is, is an

886
00:42:29,850 --> 00:42:31,919
Speaker 2: example of that. And here's the caveat though,

887
00:42:33,139 --> 00:42:35,419
Speaker 2: if you just told me about a society and tell

888
00:42:35,429 --> 00:42:36,959
Speaker 2: me the name of the country or anything like that.

889
00:42:36,969 --> 00:42:38,629
Speaker 2: And you said this is a country and the total

890
00:42:38,639 --> 00:42:41,199
Speaker 2: fertility rate is six. What do you know about it?

891
00:42:41,209 --> 00:42:43,159
Speaker 2: I would say there are a lot of things that

892
00:42:43,169 --> 00:42:45,689
Speaker 2: are really broken in that society. They probably have very

893
00:42:45,699 --> 00:42:50,198
Speaker 2: low female educational attainment, they probably have rampant child marriage,

894
00:42:50,389 --> 00:42:54,399
Speaker 2: poor health outcomes and probably an unstable government and they're poor.

895
00:42:55,979 --> 00:42:59,800
Speaker 2: If you tell me that a country has a fertility

896
00:42:59,810 --> 00:43:00,879
Speaker 2: rate of around one.

897
00:43:02,280 --> 00:43:04,419
Speaker 2: I also think that there may be, that may be

898
00:43:04,429 --> 00:43:07,709
Speaker 2: an indicator of some things about that society that are

899
00:43:07,719 --> 00:43:11,179
Speaker 2: broken and I think it goes beyond, but it's part

900
00:43:11,189 --> 00:43:13,570
Speaker 2: of it, what you said about optimism for the future.

901
00:43:13,580 --> 00:43:16,570
Speaker 2: I think there is pessimism for the future there. And

902
00:43:16,689 --> 00:43:20,020
Speaker 2: in those societies where it's really low, those gender relations

903
00:43:20,030 --> 00:43:24,020
Speaker 2: tend to be really broken themselves. And so, um if

904
00:43:24,030 --> 00:43:27,459
Speaker 2: you start looking at the household level, you see that

905
00:43:27,550 --> 00:43:29,100
Speaker 2: it's very unequal

906
00:43:29,510 --> 00:43:31,649
Speaker 2: uh the amount of time that men and women spend

907
00:43:31,659 --> 00:43:35,319
Speaker 2: on unpaid labor at home. And then women are often

908
00:43:35,330 --> 00:43:38,060
Speaker 2: highly educated in these societies and they are spending a

909
00:43:38,070 --> 00:43:40,989
Speaker 2: lot of time in work. So I would say they're

910
00:43:41,000 --> 00:43:43,610
Speaker 2: being asked to birth more, work more and care more

911
00:43:43,620 --> 00:43:47,909
Speaker 2: and increasingly they're saying no, just no. And you know,

912
00:43:47,919 --> 00:43:49,969
Speaker 2: you can get this from feminists in the region who

913
00:43:49,979 --> 00:43:53,158
Speaker 2: will say we're just gonna opt out of the system completely. So,

914
00:43:53,419 --> 00:43:56,729
Speaker 2: you know, we think about reproduction is reproducing the system

915
00:43:56,739 --> 00:43:59,250
Speaker 2: you're in. So if you're opting out of that,

916
00:43:59,550 --> 00:44:02,929
Speaker 2: I think that says something about the society itself might

917
00:44:02,939 --> 00:44:04,530
Speaker 2: not be serving their interests,

918
00:44:04,540 --> 00:44:08,179
Speaker 1: right? That's like ultra modern. You're basically saying no to

919
00:44:08,189 --> 00:44:09,409
Speaker 1: the biological imperative.

920
00:44:11,189 --> 00:44:13,009
Speaker 1: It's a bit of a sober note to end on.

921
00:44:13,020 --> 00:44:16,419
Speaker 1: But I, I really appreciate your insights, Jennifer. Thank you

922
00:44:16,429 --> 00:44:18,590
Speaker 1: so much for your time. Thank you so much. It

923
00:44:18,600 --> 00:44:21,340
Speaker 1: was great to have you and thanks very much to

924
00:44:21,350 --> 00:44:24,439
Speaker 1: our listeners as well. Copy Time was produced by Ken

925
00:44:24,449 --> 00:44:28,169
Speaker 1: Del Rich Violet Lee and Daisy Sharma provided additional assistance.

926
00:44:28,479 --> 00:44:33,090
Speaker 1: All 133 episodes of the podcast are available on youtube

927
00:44:33,100 --> 00:44:35,989
Speaker 1: as well as on all major platforms including Apple Google

928
00:44:36,000 --> 00:44:36,830
Speaker 1: and Spotify.

929
00:44:37,310 --> 00:44:40,129
Speaker 1: This podcast is for information only and does not constitute

930
00:44:40,139 --> 00:44:42,969
Speaker 1: any investment advice. Have a great day.