1
00:00:03,220 --> 00:00:06,580
Okay. Can you just start by saying your name and what your current position is?

2
00:00:07,390 --> 00:00:15,520
So my name is Kate O'Kane. I'm an associate professor in public policy at the University of Oxford at the School of Government.

3
00:00:15,820 --> 00:00:19,150
Yeah. And you also have a connection to the Centre for African Studies.

4
00:00:19,180 --> 00:00:22,420
Is that right? The Centre for the Study of African Economies.

5
00:00:23,110 --> 00:00:26,410
Yes. One of the researchers there. Yeah.

6
00:00:26,450 --> 00:00:29,530
Yeah. Okay. So first of all, just tell me a little bit about yourself.

7
00:00:29,950 --> 00:00:38,470
How did you find yourself in the in the research area that you are and what were the main kind of career milestones on your way here?

8
00:00:39,040 --> 00:00:44,259
So I work in labour market and social protection policy as an economist,

9
00:00:44,260 --> 00:00:50,260
so I study welfare systems and labour market support systems in poor countries.

10
00:00:50,500 --> 00:00:58,959
So the broad kind of idea is how do you get people in poor countries into gainful employment and into sort of more rewarding,

11
00:00:58,960 --> 00:01:09,100
more productive jobs, better livelihoods? So that area of policy has grown a huge amount in the last sort of 25 or 30 years in general,

12
00:01:09,100 --> 00:01:15,160
but particularly in poor countries because governments have started to expand their welfare system somewhat.

13
00:01:15,730 --> 00:01:19,270
So before there was no provision made for you know,

14
00:01:19,270 --> 00:01:25,059
there was sometimes provision made for if you are a pensioner or if you were a vulnerable child and you didn't have any support,

15
00:01:25,060 --> 00:01:30,790
but there was very little other other support. But as mobile phone provision has expanded,

16
00:01:31,000 --> 00:01:38,470
countries have started rapidly expanding who get some sort of basic pension or unemployment insurance relief.

17
00:01:38,480 --> 00:01:42,580
And so I work I mean, actually very much like medical trials work,

18
00:01:42,580 --> 00:01:51,760
we do big randomised trials with governments or NGOs and we test, you know, how you can improve those, those sorts of policies.

19
00:01:52,330 --> 00:01:58,450
And one of the big things that's been a kind of real revolution is just giving people cash grants without conditions.

20
00:01:58,990 --> 00:02:04,960
So even in wealthy countries, often to get you unemployment insurance, you're required to do certain things.

21
00:02:06,280 --> 00:02:11,560
There's actually a lot of evidence showing that's not particularly effective and it costs quite a bit of money to impose.

22
00:02:11,950 --> 00:02:19,150
So that's been the sort of big backdrop. Backdrop and that was kind of that revolution was all going on as I was was studying.

23
00:02:19,780 --> 00:02:25,270
And so it was a I grew up in South Africa, worked a lot with unemployed young people.

24
00:02:26,140 --> 00:02:31,270
So I started an NGO during my undergrad, which worked a lot with that population,

25
00:02:31,420 --> 00:02:35,020
trying to help young people on the Cape Flats to apply for university.

26
00:02:35,230 --> 00:02:38,440
And so I really had a real sense of, you know,

27
00:02:38,730 --> 00:02:45,490
the growing number of people who were educated in these settings as there's been free primary and secondary education.

28
00:02:46,060 --> 00:02:51,730
But the, you know, huge aspirations for formal employment, but people really struggled to get work.

29
00:02:52,000 --> 00:02:54,850
So that was what got me interested in in that area.

30
00:02:54,850 --> 00:03:02,379
And then I've, I came to Oxford to do my PhD and then, you know, as I started to have more control over my research agenda,

31
00:03:02,380 --> 00:03:05,710
that was that was the kind of direction that I that I went into.

32
00:03:06,160 --> 00:03:11,080
So when did you arrive in Oxford? So 2007 to do my master's originally.

33
00:03:11,440 --> 00:03:17,530
Then I worked in Ethiopia for a year between Masters and Ph.D., did my Ph.D. at Oxford as well.

34
00:03:18,040 --> 00:03:22,240
I was at Cambridge and at Princeton for a bit on fellowships, and then I came back.

35
00:03:23,020 --> 00:03:31,330
So I started at PSG, and in 2018 I was sort of right with, you know, when the pandemic hit, I was still a postdoc.

36
00:03:31,720 --> 00:03:36,400
And then during the pandemic, I actually got a faculty post and moved into the post that I'm in now.

37
00:03:36,760 --> 00:03:40,360
Mm hmm. And so the research you were doing that stepped before 2019?

38
00:03:40,360 --> 00:03:44,229
Well before 2020 at the moment, did it involve a lot of fieldwork?

39
00:03:44,230 --> 00:03:50,700
Where were you going? Back and forth? Yeah. So the mainly was it mainly South Africa that you continue to work in.

40
00:03:51,130 --> 00:03:57,850
So work there. And then also in Kenya. So working in in three big kind of trial sites.

41
00:03:58,540 --> 00:04:04,480
So the ones in Nairobi, the ones in Johannesburg, and then the one is a rural site in in western Kenya.

42
00:04:04,720 --> 00:04:14,350
And so I worked with a team of academics setting up a sort of a place where when, you know, it's often in western Kenya to say to 400 villages.

43
00:04:14,350 --> 00:04:18,910
And we collaborated with an NGO to test a big cash transfer program.

44
00:04:19,510 --> 00:04:24,280
So during my postdoc, that was the main thing I was I was working on the NGOs called GiveDirectly.

45
00:04:25,060 --> 00:04:33,370
And so the idea behind it is to try and strip out as much overhead as possible and to try and just

46
00:04:33,370 --> 00:04:39,010
transfer cash directly from donors who wanted to give it to poor people in developing countries.

47
00:04:39,910 --> 00:04:47,740
And they do it as a quite a big lump sum. So there's a it's about a year's worth of income for a person, an average person in Kenya.

48
00:04:48,010 --> 00:04:53,139
And poor rural households get this as a lump sum and they can use it to start

49
00:04:53,140 --> 00:04:58,660
businesses or to invest in a child's education or to improve their housing.

50
00:04:58,660 --> 00:05:01,900
There's all sorts of things it's given completely, unconditionally.

51
00:05:02,470 --> 00:05:07,290
And so. From 2018 to just before the pandemic.

52
00:05:07,290 --> 00:05:13,560
We were working on an evaluation of this program and tried to understand sort of what its its effects were.

53
00:05:14,370 --> 00:05:21,300
And we find, you know, very similarly to cash programs that have been run a lot in the developing world.

54
00:05:22,470 --> 00:05:27,270
There's this huge narrative that poor people waste the money and they're going to get excited

55
00:05:27,270 --> 00:05:31,620
and they get this grant and then they're going to just blow it on cigarettes and alcohol.

56
00:05:31,890 --> 00:05:34,240
There's absolutely no evidence that is true.

57
00:05:34,260 --> 00:05:42,990
There's meta analyses now of hundreds of studies around the world showing that actually people will spend money primarily on food,

58
00:05:43,260 --> 00:05:51,810
but they invest in assets. They often use the money to search for work or they'll start a stopped or improve economic activities that they're doing,

59
00:05:52,500 --> 00:05:57,270
and particularly when they get a big lump sum as opposed to just a small amount of welfare each month.

60
00:05:57,600 --> 00:06:05,160
That can actually be really life changing in helping people to either get into better jobs or to build up their businesses.

61
00:06:05,460 --> 00:06:11,070
So that was exactly what we saw in Kenya, you know, very similar to the trials that had been done.

62
00:06:11,070 --> 00:06:14,700
And then we were testing, in addition, trying to understand the effects.

63
00:06:14,820 --> 00:06:21,660
So our kind of unique bit was trying to understand what was going on within the households and how was

64
00:06:21,660 --> 00:06:26,820
that changing woman and children's position and their ability to negotiate over the cash transfer.

65
00:06:26,850 --> 00:06:27,930
So that was our new bid.

66
00:06:28,140 --> 00:06:35,370
But we were also finding very similar evidence to what other countries had found when they were doing these these sorts of programs.

67
00:06:35,910 --> 00:06:39,389
And did the NGO also provide advice? And what about banking?

68
00:06:39,390 --> 00:06:43,710
I mean, did you just literally give them a pile of cash or did not say that that's a good.

69
00:06:43,860 --> 00:06:51,810
So there's there's a mobile money system in Kenya called M-Pesa, which is on it works even on really basic mobile phones.

70
00:06:52,440 --> 00:06:58,320
And so the NGO gives if you don't have a mobile phone, they give you a mobile phone and they help you to register for it.

71
00:06:58,680 --> 00:07:04,079
And then the money, you don't have a bank account, but it's there's a system where people, you know,

72
00:07:04,080 --> 00:07:08,490
like even very poor vendors in the market, you can just transfer money between the phones.

73
00:07:09,000 --> 00:07:12,420
So basically replaces the bank system. India has got that as well.

74
00:07:12,570 --> 00:07:15,420
And a lot of countries are getting them.

75
00:07:15,600 --> 00:07:22,050
And so with that capacity, it's made it actually very easy for governments to send money to people's mobile phones,

76
00:07:22,200 --> 00:07:25,680
which, you know, that then becomes very important during during COVID.

77
00:07:27,030 --> 00:07:33,330
But the NGO has been a leader in using that technology to transfer money to to the households.

78
00:07:34,200 --> 00:07:37,499
So, yeah, so that's that's how the how it gets done.

79
00:07:37,500 --> 00:07:43,200
And so then it sort of sits on the phone like kind of think of your phone as a credit card, basically, or a debit card.

80
00:07:43,950 --> 00:07:47,939
And it's every household in this say there's 400 villages in western Kenya.

81
00:07:47,940 --> 00:07:54,840
Did every household get the money? So it was targeted at the poorest 40% of the households.

82
00:07:55,470 --> 00:07:59,490
So we use what's called a proxy means test.

83
00:07:59,490 --> 00:08:07,140
So you get the kind of best measure of poverty tends to be what households spend every month.

84
00:08:07,800 --> 00:08:13,350
But researchers have developed a proxy for that, which tends to be sort of their basic assets.

85
00:08:13,350 --> 00:08:22,739
So the state of the House, the quality of the floor, that sort of so it's we use we would measure that before the NGO went to the households.

86
00:08:22,740 --> 00:08:26,850
They would measure that and then they would use that to target.

87
00:08:27,150 --> 00:08:30,090
I mean there's there's quite a kind of cut off in the communities.

88
00:08:30,090 --> 00:08:34,890
You can see at the very, very poorest people can't afford to put an iron roof on their houses.

89
00:08:35,580 --> 00:08:41,640
And so they have to redo it every year with the thatch and they'll have a mud floor and they won't have even a basic mobile phone.

90
00:08:41,850 --> 00:08:45,659
And that's the kind of very poorest people. And so that was who we were.

91
00:08:45,660 --> 00:08:53,670
We were targeting with this program. And I was interested whether you see a kind of lifting of all the chips,

92
00:08:53,720 --> 00:09:00,000
if everybody in the community is doing a bit better economically, does that have repercussions within the community?

93
00:09:00,300 --> 00:09:07,350
Yeah. So actually one of my colleagues in the economics department did the first study on this with with the same NGO,

94
00:09:07,350 --> 00:09:11,399
it's said in an adjacent area, and they do find that that's the case.

95
00:09:11,400 --> 00:09:20,280
It's as Kagame's told us in the 1950s, when you inject more, you know, particularly in a recession,

96
00:09:20,280 --> 00:09:31,950
but at any time when you inject more money into a community, it so if you have a very limited supply of goods and services, it can lead to inflation.

97
00:09:32,250 --> 00:09:35,700
But in this case, actually markets are working pretty well.

98
00:09:36,360 --> 00:09:40,979
And so when you give this big cash injection, people spend it because they're poor,

99
00:09:40,980 --> 00:09:45,240
they don't buy most champagne, they're buying locally produced goods.

100
00:09:45,840 --> 00:09:49,830
And so that actually kickstarts a process of economic growth in these communities.

101
00:09:50,340 --> 00:09:51,660
More businesses open.

102
00:09:52,620 --> 00:10:02,580
The businesses sort of make more make more profits, often because they're selling more goods and services and they reckon the multiple.

103
00:10:02,800 --> 00:10:09,250
The effect of aid that's given is around between two and 2.5, which is, you know,

104
00:10:09,310 --> 00:10:13,720
it really does kick start a kind of process of of economic growth, which is fantastic.

105
00:10:14,560 --> 00:10:23,140
So the NGO is actually on the basis of these different evaluations, is now looking at working with the governments of Kenya and Malawi.

106
00:10:23,650 --> 00:10:29,020
And they're going to be trying to do something like this at a national level to see if, you know,

107
00:10:29,020 --> 00:10:35,440
just a really big injection of cash distributed to the poorest people can kick start a process of economic growth.

108
00:10:36,460 --> 00:10:39,490
Very interesting. So let's get to go back now.

109
00:10:39,520 --> 00:10:48,010
Can you remember where you were or under what circumstances you first heard that something was going on in China and that might affect you?

110
00:10:48,520 --> 00:10:53,350
Yeah, I remember very vividly because my my fiancee worked on the vaccine team.

111
00:10:54,040 --> 00:10:57,460
And so in I was just after Valentine's Day.

112
00:10:57,610 --> 00:11:03,549
And so we'd gone away for a nice weekend and we started he started seeing the stuff coming

113
00:11:03,550 --> 00:11:09,640
out of China and he was absolutely freaking out as any infectious diseases doctor was.

114
00:11:10,150 --> 00:11:18,910
I thought he'd lost a little bit, to be honest. And so we and that's that first kind of couple of weeks he was really trying you know,

115
00:11:18,940 --> 00:11:24,610
there was just a lot of conversation trying to get people to pay attention to the issue internally in the UK.

116
00:11:25,270 --> 00:11:34,419
But then, you know, almost immediately we started to realise he started to realise that there was going to be both possibility for

117
00:11:34,420 --> 00:11:42,040
vaccine production and that it was very likely that developing countries weren't going to be able to buy it.

118
00:11:43,510 --> 00:11:48,819
And so yeah, so I think the first bits of the pandemic before I kind of got into like how is this

119
00:11:48,820 --> 00:11:54,340
going to apply to welfare systems actually about the first two months we spent.

120
00:11:55,270 --> 00:12:00,130
So he this is Sandy Douglas and he's got close.

121
00:12:00,190 --> 00:12:03,240
He has also been interviewed as part of this project Trust.

122
00:12:03,340 --> 00:12:12,459
Right. Adrian Hill, who runs the Jenner Institute, and then my old PhD supervisor's professor, Stefan Dirk on.

123
00:12:12,460 --> 00:12:16,900
He's a prof at the Public Policy School as well.

124
00:12:17,680 --> 00:12:22,750
And so he was at that time had just left as the chief economist at David.

125
00:12:23,440 --> 00:12:26,710
And so we've all worked, you know, they work much more.

126
00:12:26,720 --> 00:12:34,360
The whole DNA Institute's thing is trying to realising that there are places where there aren't markets for drugs.

127
00:12:34,450 --> 00:12:40,480
So, you know, the drugs work and they have benefits or drugs or vaccines, but no one can afford to pay for them.

128
00:12:41,260 --> 00:12:48,670
And so they've got this whole, you know, trying to have universities as a public service,

129
00:12:48,880 --> 00:12:53,440
trying to invent low cost, publicly available technologies that,

130
00:12:54,310 --> 00:13:02,830
you know, not for profit so others can use to get those those benefits up to poor countries is very similar to the sort of work we do.

131
00:13:04,210 --> 00:13:12,400
And so we had a lot of discussions and then we ended up writing a kind of big sort of policy note,

132
00:13:12,700 --> 00:13:23,950
but basically arguing that this issue was going to hit vaccines for poor countries and so that they might be under-investment in vaccines in general,

133
00:13:24,160 --> 00:13:29,350
but there was particularly likely to be underinvestment in vaccines that would go to poor countries.

134
00:13:29,860 --> 00:13:36,129
So that didn't need a fridge and that potentially could be administered in fewer

135
00:13:36,130 --> 00:13:39,790
doses because obviously the distribution systems in those countries are really,

136
00:13:39,790 --> 00:13:46,069
really limited. So yeah, so from February through March.

137
00:13:46,070 --> 00:13:54,990
So I guess by about March 6th we had written that note and we were talking with people in the UK government and at the Gates Foundation and,

138
00:13:55,180 --> 00:14:01,110
and Cepi and all of those sorts of places trying to just get this point out there.

139
00:14:02,080 --> 00:14:05,860
And I mean, I think this is COVID, I would guess, in video,

140
00:14:05,860 --> 00:14:15,400
but it ended up that the that was that note was quite influential in the World Bank and others

141
00:14:15,640 --> 00:14:21,820
setting up the sort of alliance to pool funding to help developing countries get vaccines.

142
00:14:22,420 --> 00:14:29,950
But it also was a big motivator for the AstraZeneca deal with Oxford was very good,

143
00:14:29,950 --> 00:14:33,070
you know, got a very low price for the vaccines for poor countries.

144
00:14:33,370 --> 00:14:40,570
And then they also did this amazing outlicensing where they manufactured it in a bunch of poor countries so that it could be easily distributed.

145
00:14:41,860 --> 00:14:47,170
Yeah. So I mean, this sort of on a personal level of the pandemic, there was this we don't work together.

146
00:14:47,200 --> 00:14:57,520
You know, we, we live together, but we, we suddenly realised that the economics and the science needed to go together to make this point.

147
00:14:58,240 --> 00:15:02,320
So we had a couple of really interesting.

148
00:15:02,360 --> 00:15:10,540
We can try to kind of hammer this, no doubt, together in language that made sense to both to both constituencies.

149
00:15:10,550 --> 00:15:15,080
So you're trying to really distil the complicated science down of, you know,

150
00:15:15,080 --> 00:15:21,430
what were the key the key parameters of a vaccine that were going to mean that some of them work for poor countries and some of them didn't.

151
00:15:21,440 --> 00:15:30,080
And then, you know, trying to explain some of the quite complicated economic problems in ways that the scientists would understand.

152
00:15:30,890 --> 00:15:35,420
So it was quite it was it was very challenging. I mean, quite a quite an interesting process,

153
00:15:35,420 --> 00:15:44,690
but it was a weird sort of serendipity that we happened to be able to have the interdisciplinary plenary discussion in the living room,

154
00:15:44,990 --> 00:15:48,200
which was quite, which was quite helpful. Yeah, Yeah.

155
00:15:48,200 --> 00:15:53,250
And I mean, great to then be able to connect Stefan and Adrian, who, you know,

156
00:15:53,270 --> 00:15:59,000
then subsequently of that, that connection is kind of staged and you know,

157
00:15:59,060 --> 00:16:07,040
it really was quite a, a useful nexus, I think, to connect those two very different worlds in a way that could get the resources.

158
00:16:07,490 --> 00:16:11,580
Because the big issue they were facing actually was money. It was, you know,

159
00:16:11,580 --> 00:16:15,680
and they've got these initial Treasury grants and then World Bank grants that

160
00:16:15,680 --> 00:16:20,780
would make sure that there was sort of money flowing into the vaccine manufacture.

161
00:16:20,780 --> 00:16:27,349
Right. For them to start at the beginning. So it was quite that was very exciting to be was nothing to do with my normal job.

162
00:16:27,350 --> 00:16:30,470
But that kind of took over for the first first couple of months.

163
00:16:30,850 --> 00:16:35,060
Mm hmm. But then then turning to your your normal job, as you called it.

164
00:16:36,680 --> 00:16:41,210
How did. I mean, from March, obviously, from mid-March. We were in lockdown here.

165
00:16:41,990 --> 00:16:50,390
Yeah. I mean, I guess unlike most of us, you had already done quite a lot of remote working because you you're international colleagues all the time.

166
00:16:50,750 --> 00:16:53,810
So did it make a big difference to how you were able to work?

167
00:16:55,250 --> 00:17:00,290
So, I mean, the the big thing that we were very lucky with, we weren't actually in the field.

168
00:17:00,590 --> 00:17:05,180
So. So usually when we'll do an evaluation with the government or an NGO,

169
00:17:05,420 --> 00:17:10,909
there's a period when you're actually giving the, the sort of policy intervention to people.

170
00:17:10,910 --> 00:17:17,540
So, you know, GiveDirectly is going around surveying 10,000 households and then giving them the cash transfers.

171
00:17:18,050 --> 00:17:21,860
So I didn't have a project of that kind in the field at that stage,

172
00:17:21,860 --> 00:17:28,340
which was which was really lucky because I had many colleagues who they were in the middle of trials and they had to shut them down.

173
00:17:29,930 --> 00:17:35,780
So I wasn't actually in that stage. We had finished this trial and we had the results and we were writing.

174
00:17:35,780 --> 00:17:38,959
We were writing them up and doing doing analysis.

175
00:17:38,960 --> 00:17:42,230
So my own work wasn't wasn't that affected?

176
00:17:42,230 --> 00:17:45,740
We had one or two projects that we that we stopped and we did have.

177
00:17:46,070 --> 00:17:51,139
We actually had some teams in the countries sitting who would normally have been doing the field work,

178
00:17:51,140 --> 00:17:58,640
but they didn't have they didn't have work at that stage. So that actually ended up being quite useful in some of what we what we ended up doing.

179
00:18:00,020 --> 00:18:05,089
So you must have realised, sorry, I shouldn't put words in your mouth.

180
00:18:05,090 --> 00:18:17,300
I mean, did it how soon did it become apparent that the policy response to the pandemic was going to have an impact on people in poor communities?

181
00:18:18,350 --> 00:18:22,709
So, I mean, we we. Yeah.

182
00:18:22,710 --> 00:18:29,490
So we finished. We finished that note. The note, the Gates vaccine note was like March 6th, seventh.

183
00:18:30,120 --> 00:18:36,030
And then after we finished that, I started chatting with two other colleagues,

184
00:18:36,150 --> 00:18:41,100
have a a colleague at Warwick and one at Queen Mary Clement and then Francois Gerard.

185
00:18:41,430 --> 00:18:45,059
And so they both work on Francois,

186
00:18:45,060 --> 00:18:50,430
works on unemployment insurance and claim all works on kind of public works and cash transfers very similarly to me.

187
00:18:51,300 --> 00:18:55,320
And we started talking. So the lockdown sort of rolled out.

188
00:18:55,620 --> 00:18:58,139
A lot of countries weren't locking down in March.

189
00:18:58,140 --> 00:19:09,510
And then by March 21st, this the countries that have started locking down and then we realised it was going to be and it was absolutely devastating.

190
00:19:09,960 --> 00:19:11,910
You know, these are not countries.

191
00:19:12,270 --> 00:19:20,010
I mean, in developed countries, many places put people on furlough, so they kept work, you know, they weren't working, but they kept being paid.

192
00:19:20,280 --> 00:19:24,390
The firms didn't want to keep them on. And so the government paid to take them on.

193
00:19:24,720 --> 00:19:30,660
That didn't happen in many poor countries. So they were just millions of people overnight who were laid off.

194
00:19:31,440 --> 00:19:38,819
And, you know, in urban areas was really difficult because often either I mean, you saw in India,

195
00:19:38,820 --> 00:19:50,070
people were laid off en masse and there was this huge migration out to rural areas with the virus in many countries tried to stop that happening.

196
00:19:50,070 --> 00:19:54,570
So they shut down and then they they closed down travel out of the cities.

197
00:19:54,900 --> 00:20:00,540
But then you have a whole lot of people in the cities who have no jobs and are not getting an income and they can't eat.

198
00:20:01,230 --> 00:20:08,460
So, you know, we you could see that happening very quickly. And so when that as soon as that started.

199
00:20:08,670 --> 00:20:14,670
So I think sort of from about the 21st of March, we started writing a paper about,

200
00:20:16,200 --> 00:20:19,560
you know, what could the social protection response be from countries.

201
00:20:20,070 --> 00:20:25,950
So it has so that that published and went up just as a blog initially and then we

202
00:20:25,950 --> 00:20:30,480
did some kind of podcasts and things around it in those first couple of weeks.

203
00:20:30,750 --> 00:20:33,569
But it was basically saying, you know,

204
00:20:33,570 --> 00:20:42,270
countries need to stop worrying so much about exactly whether they can target the poor and just get money out like you just and,

205
00:20:42,480 --> 00:20:47,490
you know, we had a three kind of different ways in which countries could could respond.

206
00:20:48,240 --> 00:20:53,010
You know, one was using the traditional unemployment insurance that the few former workers would have.

207
00:20:53,220 --> 00:20:59,340
So trying to extend that, make it more generous if countries could put people on on furlough.

208
00:21:00,000 --> 00:21:04,139
The second thing was just using the sort of existing social assistance programs.

209
00:21:04,140 --> 00:21:07,560
So they are cash and food relief programs.

210
00:21:07,800 --> 00:21:13,740
So we were saying try to use the infrastructure that's already paying people, but pay them more.

211
00:21:14,610 --> 00:21:19,230
And then we were also talking about informal community channels of support that people could use.

212
00:21:20,220 --> 00:21:25,620
And so that was quite a broad ranging paper in the sense of, you know, COVID.

213
00:21:25,620 --> 00:21:30,510
We covered different countries, but I started to realise I know South Africa very well,

214
00:21:31,290 --> 00:21:35,040
and we work quite a lot with a provider that works a lot with the government.

215
00:21:35,670 --> 00:21:40,230
And so, you know, I as soon as we finished that, so that took about a week.

216
00:21:40,770 --> 00:21:45,630
And then as soon as we'd finished that paper, I started to look at what was going on in South Africa.

217
00:21:46,290 --> 00:21:52,050
And then we started to think about about how we could get involved in that for that particular country case.

218
00:21:52,830 --> 00:21:57,780
And you do you already have channels of communication set up between you and the policymakers.

219
00:21:59,310 --> 00:22:06,530
So they. I didn't have at as high a level as we are.

220
00:22:06,920 --> 00:22:13,700
So I knew sort of personally the one of the president's economic advisers and then

221
00:22:13,700 --> 00:22:18,320
one of the people who worked in the presidency actually was a student at BSG.

222
00:22:19,220 --> 00:22:25,100
And so I knew I knew the treaty Makhanya, the president's economic advisor.

223
00:22:25,550 --> 00:22:31,790
And so I, I mean, this was this sort of unusual time, but I just wrote a four page note.

224
00:22:32,510 --> 00:22:38,570
And so the first the first kind of thing that was a real issue in the South African context is that the.

225
00:22:39,830 --> 00:22:42,770
If you're destitute, if you're absolutely starving,

226
00:22:43,040 --> 00:22:48,650
you can go to a welfare office and they'll give you a food parcel, and they'll do that for a period of three months.

227
00:22:49,650 --> 00:22:54,510
The portion of people who were destitute suddenly ballooned.

228
00:22:55,260 --> 00:22:58,230
So they were in South Africa, has 50 million people.

229
00:22:58,650 --> 00:23:05,580
There were 9 million people who were saying they were going to bed hungry every night and they were trying to get food parcels out,

230
00:23:05,580 --> 00:23:14,640
but in the middle of the pandemic, so that at that stage in in April, they were about I think they were getting out about 1 million parcels a week.

231
00:23:15,060 --> 00:23:17,550
So just know one ninth of what they needed.

232
00:23:19,260 --> 00:23:26,879
And so the I was using the Evidence from Kenya project and then also from what is known in the rest of the world.

233
00:23:26,880 --> 00:23:33,030
So there's loads of major studies and things just basically saying you need to shift from food into cash.

234
00:23:33,240 --> 00:23:38,320
So, you know, the the thing that you can pay everybody quickly is cash.

235
00:23:38,640 --> 00:23:47,190
And in normal times, we sort of worry, well, if the government gives out cash to poor people, then maybe they're not going to use it well.

236
00:23:47,190 --> 00:23:53,850
So we must make sure they. I don't agree with this, but we must make sure that they get it as food so that they spend it properly.

237
00:23:55,920 --> 00:23:58,110
There's no actual evidence that actually.

238
00:23:58,620 --> 00:24:05,070
So what I was saying in the note was we know that if you give people cash, mostly they spend it on food anyway.

239
00:24:05,370 --> 00:24:15,120
Cash is more cost effective to distribute because you instead of trying to move fresh food around or bags of lentils around, you know,

240
00:24:15,210 --> 00:24:20,280
then you just in the cash through the banking system or through these mobile money transfers,

241
00:24:20,580 --> 00:24:27,150
it's less likely to get stolen, easier to, you know, you can have security on the channels of distribution.

242
00:24:27,450 --> 00:24:33,510
And, you know, there's lots of studies being done. It delivers similar gains in nutrition and it's less expensive.

243
00:24:33,870 --> 00:24:42,390
So kind of put that together in a note. And I sent it to Judy and said, you know, this is this is the evidence base.

244
00:24:43,080 --> 00:24:52,049
You know, what should we. This is what we think we should do. And then I also did a bunch of radio and TV adverts,

245
00:24:52,050 --> 00:24:58,950
and then there was another group of South African academics who were doing some modelling at the same time.

246
00:24:59,310 --> 00:25:01,890
So was in communication with with them.

247
00:25:02,850 --> 00:25:10,500
And so there was this whole sort of set of different pressure groups on the government suggesting that they should make this shift.

248
00:25:12,060 --> 00:25:17,490
And they did. And so they overnight well, not overnight.

249
00:25:17,490 --> 00:25:21,930
I mean, they worked through, I think it was sort of by the end of April.

250
00:25:22,930 --> 00:25:26,560
They the president's announced in South Africa announced this big relief package,

251
00:25:26,950 --> 00:25:32,670
and it was the biggest relief, biggest change to the welfare system since about 2004 and 2004.

252
00:25:32,680 --> 00:25:39,090
They started giving a child benefit. And this time they said they did two things.

253
00:25:39,100 --> 00:25:43,600
The first was existing people who were already getting government grants.

254
00:25:43,600 --> 00:25:49,180
So pensioners and people who were caregivers of a child, they increased the amount that they got.

255
00:25:50,260 --> 00:25:56,350
And then the second thing was they they put they said that anyone who was unemployed could

256
00:25:56,350 --> 00:26:01,810
apply by mobile phone to get an unconditional cash payment and they were going to get three.

257
00:26:02,050 --> 00:26:07,660
It's about $35 or £20 a month, and that had never existed before.

258
00:26:07,660 --> 00:26:13,420
They'd never been welfare for the able bodied before. So they set this up.

259
00:26:13,420 --> 00:26:22,420
It was absolutely astounding. They set up I think they've got 6 million applications in like the first three weeks for this grant.

260
00:26:22,870 --> 00:26:31,840
And then they started paying out the money, you know, and increased the and so that that change in policy, they reckon,

261
00:26:32,420 --> 00:26:41,200
stopped 5.6 million people from falling into food poverty in those first those first couple of months after the lockdown.

262
00:26:41,860 --> 00:26:51,249
So it was really an amazing, amazing change by government, but also amazing campaign by civil society and academics.

263
00:26:51,250 --> 00:26:53,290
So alongside the academic papers,

264
00:26:53,290 --> 00:26:59,740
there was this big pressure group which was the unions and various civil society organisations called Pay the Grants,

265
00:27:00,340 --> 00:27:07,390
which was basically arguing for this, the shift away from food parcels into the into the cash provision.

266
00:27:07,930 --> 00:27:12,760
Did they have the money in the Treasury or did they have to get money from international sources?

267
00:27:13,050 --> 00:27:17,950
No, that's a very that's a very good question. They they went.

268
00:27:18,980 --> 00:27:25,360
So there was I think there was obviously a lot of government spending that didn't happen in that in that period.

269
00:27:25,370 --> 00:27:30,139
So I think some of it came from from that. I mean, infrastructure projects and projects.

270
00:27:30,140 --> 00:27:33,800
And so I think there was a lot of a lot of stuff that just wasn't going to happen.

271
00:27:33,830 --> 00:27:38,090
Wasn't going to happen. It was an emergency budget.

272
00:27:38,090 --> 00:27:45,130
So they, you know, as many governments were doing. I don't think at that stage that they and I don't actually think South Africa has gone before.

273
00:27:45,950 --> 00:27:50,360
They later went for a World Bank loan, but I think that was for different purposes.

274
00:27:51,110 --> 00:27:54,200
There were massive issues, though, like countries that just didn't have.

275
00:27:54,470 --> 00:27:58,310
So they reallocated budget and they have cut other things subsequently.

276
00:27:58,310 --> 00:28:06,590
And but I mean, there definitely were countries, you know, Malawi, like really poor countries, just didn't have the ability to do this.

277
00:28:07,130 --> 00:28:11,450
So it has been, you know, looking across the different countries,

278
00:28:11,450 --> 00:28:16,399
it has been the case that it's more been in middle and lower middle income countries.

279
00:28:16,400 --> 00:28:20,060
So countries where there's some tax base and then they can redistribute.

280
00:28:21,560 --> 00:28:27,469
But I think then the IMF later on tried to help countries to borrow more easily.

281
00:28:27,470 --> 00:28:30,660
There's a system called special drawing rights that they reallocate.

282
00:28:30,980 --> 00:28:36,260
Yeah. But they basically did try to help countries to do some more of this.

283
00:28:36,440 --> 00:28:41,300
But it has been there has been inequality within poor countries and what they were able to do.

284
00:28:42,970 --> 00:28:48,190
And you tell me if I'm jumping ahead to that. One of the things you've been interested in doing,

285
00:28:48,190 --> 00:28:53,980
I gather your part of the Mind and Behaviour Research Group is actually building self-efficacy

286
00:28:54,610 --> 00:29:01,000
among poor people so that they feel more confident about using the money that they've got.

287
00:29:01,600 --> 00:29:03,990
Can you tell me a bit more about that? Yeah.

288
00:29:04,000 --> 00:29:14,380
So in my Ph.D., the one of the things that I worked on was these sort of interventions to boost people's aspirations and increase self-efficacy.

289
00:29:14,410 --> 00:29:18,879
So the particular intervention we designed was a set of videos,

290
00:29:18,880 --> 00:29:26,950
which is a set of successful life stories about people who were very similar to the people who were.

291
00:29:27,770 --> 00:29:33,219
So we did this in rural Kenya in the study that I was talking about and also in rural

292
00:29:33,220 --> 00:29:38,860
Ethiopia sits with farmers who often women who are often sort of of lower status.

293
00:29:39,640 --> 00:29:48,969
And so we show these stories which are about real life people who've managed to improve their own position

294
00:29:48,970 --> 00:29:56,170
through their business or through studying for a further educational qualification or something like that.

295
00:29:57,640 --> 00:30:01,090
So we show these life stories and we've seen in randomised trials,

296
00:30:01,090 --> 00:30:10,149
so we give those randomly to some villages but not others and they actually do have an amazing impact on on people's self-efficacy,

297
00:30:10,150 --> 00:30:15,130
their aspirations for the future and then on how much they invest for the future.

298
00:30:15,140 --> 00:30:25,030
So interestingly, so it's often investing in kids education, but also women putting more into their businesses,

299
00:30:25,600 --> 00:30:30,220
working a bit more in their businesses, and then that increases the business profitability.

300
00:30:30,700 --> 00:30:34,120
And so in the study in Kenya that I was talking about,

301
00:30:34,120 --> 00:30:40,600
we tested those sorts of interventions and we found those had these benefits for self-efficacy and empowerment.

302
00:30:41,060 --> 00:30:46,300
And then we also tested the cash transfers and then we tested the two together.

303
00:30:46,990 --> 00:30:54,520
And so interestingly, the cash transfers also have really big benefits for people's aspirations and self-efficacy.

304
00:30:54,940 --> 00:30:58,390
So just giving the money really empowers people.

305
00:30:58,930 --> 00:31:06,049
You know, we measure that and it has it's more expensive to give them the money, but it also has bigger benefits.

306
00:31:06,050 --> 00:31:07,150
So I think that was quite an.

307
00:31:07,490 --> 00:31:15,190
So one of the things we were interested in was, you know, if you add the video intervention onto the cash, can that help?

308
00:31:15,700 --> 00:31:22,569
And it does help. So it particularly increases the extent to which women own the businesses are

309
00:31:22,570 --> 00:31:26,440
able to control them and the extent to which there's investment in children.

310
00:31:26,980 --> 00:31:31,720
So it does help. But I think the big lesson for me actually from that was just the cash.

311
00:31:31,930 --> 00:31:38,500
You know, we always think of welfare as being disempowering and that it, you know, makes people dependent on it, doesn't it?

312
00:31:38,770 --> 00:31:44,650
But people's, you know, how well they think they're going to do in the future, how in control they feel of their life.

313
00:31:45,430 --> 00:31:49,419
You know, do they think that things are going to get better and it then changes?

314
00:31:49,420 --> 00:31:55,810
You know, one of the things it's doing there is a psychological channel and that's somehow, you know, so so people always puzzled.

315
00:31:56,290 --> 00:31:59,890
I give people money. Why do they keep working? In fact, they work more.

316
00:32:00,700 --> 00:32:08,950
But I think it's because of some of these psychological benefits. So I think that was a really quite profound thing that I didn't I didn't expect.

317
00:32:08,950 --> 00:32:18,460
And that's been a really important reason why I've argued for the scale up of this international welfare programs,

318
00:32:19,030 --> 00:32:23,200
because it's simple and it's it's fairly quick to scale up.

319
00:32:23,470 --> 00:32:29,530
And, you know, we already knew from our own work that it was going to have these very multifaceted benefits for people.

320
00:32:30,010 --> 00:32:35,960
The other thing is it improves mental health. So levels of depression also dropped massively with cash transfers.

321
00:32:35,980 --> 00:32:40,510
So, yeah, so I think it would be quite a powerful thing to be able to give.

322
00:32:41,020 --> 00:32:47,110
Yes. Yes. I mean, particularly physical health as well. If people are back and they're going, yes, I haven't worked on that, but they have you know,

323
00:32:47,290 --> 00:32:50,890
people can pay for medicine where they weren't able to do that before or pay for,

324
00:32:51,310 --> 00:32:58,420
you know, I mean, in in poor countries, it's also you'll often see people have a huge growth or a broken limb that hasn't been set,

325
00:32:58,420 --> 00:33:06,100
but for tiny amounts of money and they can't work. You know, we're seeing a version of that in the UK very sadly.

326
00:33:06,310 --> 00:33:11,500
So I think it's it really the just more resources can do an enormous amount.

327
00:33:13,030 --> 00:33:21,550
And so, I mean, so you're a lot of what you were doing early in the pandemic was essentially advocacy and promoting the findings of previous research.

328
00:33:22,000 --> 00:33:27,550
Did you go on to do more research as a consequence of the cash transfers?

329
00:33:28,660 --> 00:33:32,500
So we we haven't done so.

330
00:33:32,500 --> 00:33:36,130
We didn't do any work on the cash transfer programme itself.

331
00:33:36,790 --> 00:33:41,860
Some of the reason for that was, you know, in a normal time we would pilot a programme and.

332
00:33:41,900 --> 00:33:49,880
We would do a randomised trial during the piloting. During this program we said, look, the kind of duty of care in this situation is this.

333
00:33:50,300 --> 00:33:53,450
We think this is going to work and so we just have to give it to everybody.

334
00:33:53,750 --> 00:34:02,000
So I mean it's it's tricky now because now the government has been working with the government on an ongoing basis for the last three years or so,

335
00:34:02,360 --> 00:34:05,900
and now they want to scale. They want to keep the program going.

336
00:34:06,080 --> 00:34:11,120
So it's running with the whole country and they want to keep it going. We don't actually know what it did.

337
00:34:11,870 --> 00:34:16,910
You know, we can do some estimates, but they're not as good as the estimates we would normally have.

338
00:34:18,410 --> 00:34:21,139
So that's actually quite a and I think that happened with a lot of countries

339
00:34:21,140 --> 00:34:24,350
that very rapidly scaled things up and now they want to make them permanent.

340
00:34:24,950 --> 00:34:28,070
So we didn't actually study the cash transfer program,

341
00:34:28,700 --> 00:34:34,849
but we have been doing quite a lot of work on how you help workers get back into work because

342
00:34:34,850 --> 00:34:39,200
there were these big layoffs and now workers are trying to reconnect with their employers.

343
00:34:39,740 --> 00:34:45,020
So we've actually been on the more labour market side doing quite a bit of work on on

344
00:34:45,710 --> 00:34:51,140
low cost ways that employers can screen workers so that they can find the best ones.

345
00:34:52,070 --> 00:34:56,180
And then also how to help workers identify which jobs they'll be well suited to.

346
00:34:57,250 --> 00:35:02,350
And is that a matter of technology and platforms and just communication?

347
00:35:03,280 --> 00:35:10,360
So, yeah, so the main thing I mean, one of the things that happened during the pandemic was the labour market went online.

348
00:35:11,440 --> 00:35:19,660
So in many countries, if there wasn't, you know, these platforms like indeed, or the government has a job search platform here,

349
00:35:20,200 --> 00:35:25,840
they also have those in developing countries and they're pretty cost effective to to access.

350
00:35:26,110 --> 00:35:29,320
And so work seekers often use those firms often use those.

351
00:35:29,650 --> 00:35:33,490
But way more people started using them, which is great.

352
00:35:34,480 --> 00:35:39,310
You know, I think in general it's been a real impetus to get a lot of stuff online, which has been fantastic.

353
00:35:40,810 --> 00:35:46,060
Yeah, so we work with one of the providers in South Africa that went from, you know, during the pandemic.

354
00:35:46,060 --> 00:35:49,420
I think it went from having a few hundred thousand work seekers.

355
00:35:49,420 --> 00:35:55,510
Now they have 4 million jobseekers signed up on the platform. So we're doing a lot of work with that platform to try.

356
00:35:55,510 --> 00:36:03,459
And the one big study that that came out during COVID was often employers will do a lot of screening,

357
00:36:03,460 --> 00:36:09,670
so they'll look at your school qualifications and then they'll make you sit a numeracy test and can you

358
00:36:09,670 --> 00:36:15,790
answer the phone and lots of different and that's quite expensive and then people have to do that in person.

359
00:36:17,380 --> 00:36:22,420
And so we've been testing actually does that do those qualifications help?

360
00:36:22,420 --> 00:36:30,639
Do they make workers more productive? Or what we we in some employers think is that actually if you test people's soft skills,

361
00:36:30,640 --> 00:36:36,370
that actually probably accounts mostly for how well they do in the job and what kind of skills.

362
00:36:37,150 --> 00:36:46,060
So the when we test, it's actually some very simple self-reported measures about how do you structure your day,

363
00:36:46,090 --> 00:36:51,909
You know, in the last week, how often have you got done the things that you meant to do, you know, how good are you?

364
00:36:51,910 --> 00:36:56,320
Are you at persisting through a task, Those sorts of things you would think people would.

365
00:36:56,590 --> 00:37:06,700
People are very honest, which is very interesting. But yeah, so that actually predicts very well whether people will get hired into a job.

366
00:37:06,700 --> 00:37:11,740
And then what we're testing is does that that measure predict how long they stay in the job as well?

367
00:37:12,730 --> 00:37:18,520
But, you know, you can do that that measure. You can phone people and ask them can even get them to query it on their phone.

368
00:37:19,990 --> 00:37:27,700
And so we're using that as a way of screening people to try and cut the screening costs and help people move back into into work.

369
00:37:28,140 --> 00:37:38,110
Mm. And I've got to note that you've also had an interest in the way governments communicate with people and the message tracker.

370
00:37:38,530 --> 00:37:48,700
Oh, yes. Yeah. Yeah. So that was another thing we did during, during COVID was, was just looking at how Yeah.

371
00:37:48,700 --> 00:37:52,150
What sorts of messaging governments provide to people.

372
00:37:52,840 --> 00:37:59,560
So the big thing during lockdowns was how to encourage people to sort of stick to the rules.

373
00:38:00,460 --> 00:38:04,600
And then we just did it was again, a review of bit of the evidence,

374
00:38:04,600 --> 00:38:10,810
but finding that making moral appeals is actually much more effective than you would think.

375
00:38:11,170 --> 00:38:18,820
So people really want to do the right thing. And if you appeal to those norms, that's that's usually quite likely to be effective.

376
00:38:19,130 --> 00:38:28,150
Mm hmm. And I mean, do you have any connection with the government response tracker, which is also within the ads levels spoken to our net?

377
00:38:28,150 --> 00:38:31,479
Relax. Yeah. We didn't actually during the pandemic.

378
00:38:31,480 --> 00:38:37,750
I mean, we didn't do as much on the government messaging as we we sort of there were a couple of things we started tracking at the beginning.

379
00:38:37,750 --> 00:38:43,840
And then once this cash grant stuff started, we we basically moved the whole team over into doing that,

380
00:38:43,840 --> 00:38:47,380
but we didn't really have enough resources, were doing all of the different things that.

381
00:38:49,730 --> 00:38:57,170
And so that I mean, that sounds amazing. And you mentioned that you're now working with other countries as well as as well as South Africa.

382
00:38:57,470 --> 00:39:04,490
Yeah. So and so. So the other main big one we've been working with is is Kenya.

383
00:39:05,240 --> 00:39:11,840
But I think I mean, very similar so that the original paper that we wrote on the social protection response,

384
00:39:11,840 --> 00:39:17,980
I think that has been quite widely used in a range of different a range of different countries too.

385
00:39:18,020 --> 00:39:23,179
I mean, we were not the only ones making that argument, but I think the yeah, I mean,

386
00:39:23,180 --> 00:39:29,870
the number of cash transfer programs has skyrocketed in during the pandemic, which has been really encouraging.

387
00:39:30,590 --> 00:39:33,649
I mean, it's still the impacts on poverty have still been huge.

388
00:39:33,650 --> 00:39:38,950
We've really gone very far backwards for meeting the Sustainable Development Goals, for example.

389
00:39:39,410 --> 00:39:42,559
But I think there was quite a lot I mean, the negative impacts have still been.

390
00:39:42,560 --> 00:39:51,440
Yeah. Yes. Yes. And how optimistic are you that that kind of government support will be stay

391
00:39:51,440 --> 00:39:56,060
in place in the future and and on non-pandemic and non-emergency situation.

392
00:39:56,720 --> 00:40:04,930
And I mean I think I think pretty optimistic like you know the South African case very well and it it is a battle, you know,

393
00:40:04,940 --> 00:40:12,049
because as the emergency sort of thing reduces, you know, to be honest,

394
00:40:12,050 --> 00:40:15,800
I think politically some of what they were worried about was literally food riots.

395
00:40:16,610 --> 00:40:22,340
You know, and as things have gone a bit back to normal and more people have been employed and so on, that recedes a little bit.

396
00:40:22,610 --> 00:40:27,590
And so suddenly they think, well, you know, is this going to be popular enough that we're going to put up taxes?

397
00:40:29,020 --> 00:40:36,050
And so I think those sorts of questions about redistributive policy do start to become more prominent.

398
00:40:38,000 --> 00:40:42,560
And there's pushback. And so actually, the South African government did drop the grant for a little bit.

399
00:40:44,090 --> 00:40:50,390
They did actually then have food riots because the economy wasn't yet back on its on its feet.

400
00:40:50,400 --> 00:40:57,750
So that was in sort of July 2001. And then we suddenly did a big 2000, 20, 20, 21, 21.

401
00:40:58,550 --> 00:41:02,840
And so then we suddenly did another big piece of modelling saying, well, we should bring the cash grant back.

402
00:41:02,840 --> 00:41:07,300
And they, they did. But I think there is, you know,

403
00:41:07,310 --> 00:41:13,190
it is then I think governments face a good pressure of they have to take people

404
00:41:13,190 --> 00:41:17,809
off the ground programs and those people are also voters and they can protest.

405
00:41:17,810 --> 00:41:23,240
And so I think, you know, I think there has been an expansion that's probably isn't going to you know,

406
00:41:23,580 --> 00:41:27,890
it will be politically very difficult for governments to to pull that back.

407
00:41:28,130 --> 00:41:32,510
If you're a fan of redistributive policy, as I am, then you think that's a really good thing.

408
00:41:32,720 --> 00:41:37,910
You're not a fan of the welfare state, then you think it's a bad thing. But, you know, obviously there's debate about that.

409
00:41:41,740 --> 00:41:43,710
So. Yeah.

410
00:41:43,720 --> 00:41:51,670
I mean, has it has working on this they are on these projects in the pandemic raised new questions that you're interested in working on in the future.

411
00:41:52,500 --> 00:41:56,980
Um yeah. I mean, I think I think the you.

412
00:41:59,130 --> 00:42:05,280
You're one of the things is is really that this sort of set of you know,

413
00:42:05,280 --> 00:42:11,610
if a lot of workers lose their jobs, how do you help people to make back into the economy quickly?

414
00:42:12,390 --> 00:42:18,270
Because I think we we do really worry that there's been a generation that's been really badly affected.

415
00:42:18,960 --> 00:42:22,800
So. In in the labour market literature, it's called scarring.

416
00:42:23,400 --> 00:42:27,090
So, you know, that has a psychological connotation.

417
00:42:27,090 --> 00:42:35,430
But I mean, it's it's really you know, we know that if you graduate during a recession or any of that kind of downturn,

418
00:42:35,790 --> 00:42:41,400
you're actually less likely to earn over your life and you have higher unemployment and so on.

419
00:42:42,210 --> 00:42:45,570
And we've seen that, you know, on a really big scale with lots of.

420
00:42:46,290 --> 00:42:48,989
So I think they really are worries about, you know,

421
00:42:48,990 --> 00:42:55,320
how is this generation that's coming of age going to do and then also the people who were in in schooling.

422
00:42:56,010 --> 00:43:03,120
But I think also it's a kind of more hopeful narrative of, well, maybe we've developed some tools that we didn't have before.

423
00:43:03,120 --> 00:43:08,459
So, you know, in particular this being able to work online at low cost, you know,

424
00:43:08,460 --> 00:43:16,860
and really I think it was quite encouraging for many governments and practitioners to have to say,

425
00:43:16,980 --> 00:43:20,520
you know, this problem is so massive and we have to deal do something now.

426
00:43:20,790 --> 00:43:26,219
And actually what people were able to do at big scale in my field was more than they'd

427
00:43:26,220 --> 00:43:30,600
been able to do for 20 years before because you're suddenly there was the political will.

428
00:43:31,560 --> 00:43:37,300
And so, I mean, I do think for people who are big advocates of real,

429
00:43:37,320 --> 00:43:42,780
really policy that's targeted at the very poorest, you know, it's not like homelessness in the UK.

430
00:43:42,780 --> 00:43:46,770
We can actually fix this if we just put enough money and time into it.

431
00:43:47,820 --> 00:43:53,760
And so I think I think in that sense, people's hand has been strengthened a bit because it it is possible.

432
00:43:53,760 --> 00:43:57,920
It's just a question of political will and resources. I mean,

433
00:43:57,950 --> 00:44:04,349
I think that raises a quick question in my mind about the balance for policymakers between listening to people like

434
00:44:04,350 --> 00:44:12,150
you who are producing an evidence base and taking making political decisions based on what they think voters want.

435
00:44:12,540 --> 00:44:17,130
Those two things aren't always aligned, as we know very well.

436
00:44:17,460 --> 00:44:21,180
Absolutely. And it depends if the bulk of your voting base is poor or not.

437
00:44:22,200 --> 00:44:28,499
And so, yeah, I mean, I think I think that is a that is a real a real tension that many governments are facing.

438
00:44:28,500 --> 00:44:39,840
And, you know, also the I think the other thing that was really encouraging that has been a powerful, positive lesson was, you know,

439
00:44:40,080 --> 00:44:45,000
even when we were accumulating this whole evidence base before the pandemic, you know,

440
00:44:45,000 --> 00:44:51,000
there was a good 20 years of work gone, had gone into how do we distribute cash by mobile phone?

441
00:44:51,000 --> 00:44:54,389
What does it do to, you know, what is the comparison to food aid?

442
00:44:54,390 --> 00:45:01,590
And it was there. And so I think there has been quite a a strengthening of the argument for that sort

443
00:45:01,590 --> 00:45:07,530
of research because it helped us to really pivot very quickly in policy terms.

444
00:45:08,190 --> 00:45:12,450
The other thing that's been helpful was sort of systems of data surveillance.

445
00:45:12,450 --> 00:45:22,770
We talked a lot about them for health, but actually the countries that did the best were often those who had a good economic surveillance system.

446
00:45:23,550 --> 00:45:27,180
If you say quality, and that's not what we call them, but you know,

447
00:45:27,180 --> 00:45:33,930
you knew how many people there were of different income levels, who was going to be the likeliest to be affected?

448
00:45:33,930 --> 00:45:40,589
Who should you target? How could you measure that? And so I think the country, South Africa was lucky.

449
00:45:40,590 --> 00:45:45,390
We had a really good data source that we could use called the National Income Dynamics Study,

450
00:45:46,110 --> 00:45:52,950
and that helped us to be able to map, you know, what would the policy look like and then how should we design it?

451
00:45:53,880 --> 00:45:58,380
And, you know, that that investment will have paid for itself many times over.

452
00:45:58,890 --> 00:46:03,750
I mean, you know, we're all now trying to make the case for better preparedness in the future.

453
00:46:04,680 --> 00:46:09,780
So, you know, I think but but optimistically, I hope that, you know,

454
00:46:09,780 --> 00:46:16,470
the lessons of data and of of building up an evidence base in in policy terms have been have been quite positive ones.

455
00:46:17,070 --> 00:46:23,100
Excellent. Excellent. Can I just turn a little bit to how living through the pandemic was for you?

456
00:46:23,610 --> 00:46:29,339
I mean, one question I'm asking everybody is how personally threatened did you feel by the virus?

457
00:46:29,340 --> 00:46:35,640
I mean, I think not it now, but back at the beginning, before there were any vaccines, how did you how did you feel about it?

458
00:46:36,420 --> 00:46:39,899
I mean, I'm very, very lucky, you know, healthy and young.

459
00:46:39,900 --> 00:46:44,100
So, you know, I don't it wasn't it wasn't really something.

460
00:46:44,220 --> 00:46:49,530
I mean, we were super safe. We were sitting in our little house in Headington on Skype calls a lot.

461
00:46:49,530 --> 00:46:54,509
But I don't think I mean, neither of us were kind of in frontline services.

462
00:46:54,510 --> 00:47:02,630
So I think that it was it was. Curiously detached in a way that I wouldn't normally you know,

463
00:47:02,660 --> 00:47:09,710
always when I'm doing a study or art meeting the providers and the people are getting the policy and

464
00:47:10,160 --> 00:47:14,840
you're really in the countries where I'm working and I couldn't back the the flights were closed.

465
00:47:14,840 --> 00:47:20,149
You couldn't get to Kenya or South Africa. And so that was that was very weird.

466
00:47:20,150 --> 00:47:25,040
I mean, I met all of the government teams we worked with a year and a half into the pandemic.

467
00:47:26,000 --> 00:47:33,530
So that was that was quite strange because it felt, you know, it was also this particularly beautiful spring time.

468
00:47:33,860 --> 00:47:39,080
And I knew that the world my world was like absolute carnage, but I couldn't see it.

469
00:47:39,650 --> 00:47:44,060
So it was this very strange kind of I was very safe and very lucky.

470
00:47:44,540 --> 00:47:52,570
But at the same time, you know, in my frame of reference, there was a lot going on that I felt like I really needed to to deal with, you know?

471
00:47:52,640 --> 00:47:57,440
And I think that was also weird. There was like lot of people were baking and doing salads.

472
00:47:58,520 --> 00:48:02,960
We had like upstairs was the cash grants and downstairs was the vaccine factory.

473
00:48:03,890 --> 00:48:10,280
So we did think that the did you feel that the fact that you had something to keep you busy, did that help to support your wellbeing?

474
00:48:12,680 --> 00:48:20,480
Yeah, absolutely. I mean, I think. You know, I think I didn't feel this.

475
00:48:20,840 --> 00:48:23,900
I only got to this sort of work part of it a little bit later.

476
00:48:23,930 --> 00:48:30,200
I mean, I think the March, April, May, for the for the vaccine people was absolutely punishing.

477
00:48:30,230 --> 00:48:30,620
I mean,

478
00:48:31,580 --> 00:48:42,270
I just remember like feedings and the desserts because you lost ten kilos in like a because you as I've just never seen anybody work like that day.

479
00:48:42,520 --> 00:48:47,629
Yeah, I was you know, I did kind of switch more into I was doing the domestic stuff,

480
00:48:47,630 --> 00:48:54,350
so I was going to kind of going out to get food and, you know, making sure that the things were fed and cleaned and that sort of.

481
00:48:54,530 --> 00:48:57,430
And it was literally just like taking food into his room.

482
00:48:57,440 --> 00:49:02,110
So I'd be up when I got up, and then I would take out the dinner plate and he would go to bed at two.

483
00:49:02,120 --> 00:49:06,229
And so, you know, I wasn't working that level of of things.

484
00:49:06,230 --> 00:49:10,400
Although then later with did you know we were we're also working quite hard.

485
00:49:11,690 --> 00:49:17,360
So I think that that was strange because because I think it was a very, you know,

486
00:49:17,360 --> 00:49:23,840
very intellectual in the sense that it was just you with your laptop, but it was this quite punishing period in a lot of ways.

487
00:49:24,230 --> 00:49:30,320
So I wouldn't have said it was was the highest levels of well-being we've had in, you know, also at the same time,

488
00:49:30,320 --> 00:49:37,970
it's like in some ways you feel like you've you've kind of qualified your whole life to try and be able to do something and you could.

489
00:49:38,600 --> 00:49:42,230
And so I think that was quite a quite a unique privilege, but it was also.

490
00:49:43,400 --> 00:49:50,120
I think was also quite, quite stressful. I mean, I didn't I didn't ever have that that feeling that I think the vaccine people did of,

491
00:49:50,120 --> 00:49:53,120
you know, literally every hour I sleep like people are dying.

492
00:49:54,080 --> 00:49:57,740
And I think that was, you know, that was tough for them.

493
00:49:58,370 --> 00:50:04,969
And there was this beginning point during the vaccine stuff where, you know, I mean, I think we did help with doing the funding note and things.

494
00:50:04,970 --> 00:50:10,700
But it was this weird because I you know, I care very deeply about what my contribution is to the world.

495
00:50:10,700 --> 00:50:14,210
And it was it was interesting as a feminist, there was this point was like,

496
00:50:14,630 --> 00:50:21,020
the best thing I can do right now is feed this man in the thing that he's doing.

497
00:50:21,800 --> 00:50:26,150
And and that was quite an interesting thing to sort of work through in your in your head.

498
00:50:27,080 --> 00:50:33,050
But, you know, I think I think I'm very, very proud of of what they did of what we ended up being able to do.

499
00:50:34,070 --> 00:50:37,399
I think I think people pulled together incredibly.

500
00:50:37,400 --> 00:50:42,889
And, you know, I had teams from all over, like just working round the round the clock.

501
00:50:42,890 --> 00:50:51,770
I think I think really people did get a lot from, you know, my whole team of young people just being able to contribute in some way.

502
00:50:52,550 --> 00:50:56,180
I think they really felt much more, much more playful.

503
00:50:56,180 --> 00:50:59,360
And it was we didn't know that we were going to achieve anything.

504
00:51:00,170 --> 00:51:04,879
But it really I think we do feel very pleased with with what we what we managed to do,

505
00:51:04,880 --> 00:51:07,850
obviously, and in collaboration with the with the government people.

506
00:51:08,540 --> 00:51:15,499
But yeah, we we were glad when that first bit of things was was over although I think that kind of yeah,

507
00:51:15,500 --> 00:51:19,459
I mean it's actually ended up being quite a long collaboration now on the social grant stuff.

508
00:51:19,460 --> 00:51:28,100
It's been nearly nearly three years of sort of policy work, so it has kept going a little bit less frenetic pace, but not, not much.

509
00:51:28,670 --> 00:51:33,800
Hmm. And has any of that experience changed your attitude or your approach to your work?

510
00:51:35,260 --> 00:51:43,110
Yeah. I mean, I think it's it's it's quite cheeky going back to the business of like.

511
00:51:44,280 --> 00:51:47,070
The the nicotine stuff of publishing papers.

512
00:51:48,180 --> 00:51:55,940
You know, I do I do feel like it's definitely reoriented me towards the core of what's important in my work, you know,

513
00:51:55,950 --> 00:52:02,189
And actually when it came down to it, it was you know, it was really important that the model was done right.

514
00:52:02,190 --> 00:52:12,540
But you also have to do it fast. And so I think I think there are ways in which some of what we do in academia isn't completely Yeah,

515
00:52:12,570 --> 00:52:20,399
you know, it's good we can dip into this emergency stuff. There is a longer, more drawn out process of producing the evidence.

516
00:52:20,400 --> 00:52:23,220
But, you know, that's it is also really important. It's right.

517
00:52:23,340 --> 00:52:28,620
You know, when I was looking up 20 trials on cash programs, I was very glad they were properly done.

518
00:52:29,250 --> 00:52:33,540
So, I mean, I think it's made me more conscious of the work that happens,

519
00:52:33,990 --> 00:52:39,209
that happens quickly, but also the importance of those those scientific standards.

520
00:52:39,210 --> 00:52:44,040
I mean, I think I think we I remember Stephane at some point sending me a link of,

521
00:52:44,940 --> 00:52:50,670
you know, he said there was an amazing MSF podcast about Ebola in West Africa.

522
00:52:50,970 --> 00:52:58,290
And they said, you know, it got it got fixed by someone from a specialist from Uganda with a spreadsheet.

523
00:52:59,430 --> 00:53:05,430
And I do think that if you care about science and you worry about the kind of anti expert thing,

524
00:53:05,730 --> 00:53:13,380
I think it has been quite an affirming experience and saying, you know, the technical details actually really important in good policy.

525
00:53:14,700 --> 00:53:18,339
And so I think in that sense it's made me, um, you know,

526
00:53:18,340 --> 00:53:23,910
it was very draining but I think also feel much more committed to what what we're trying to do on a longer term basis.

527
00:53:24,180 --> 00:53:27,870
Mm hmm. I just read it with a minute to go.

528
00:53:27,870 --> 00:53:31,260
I don't. How do I tell you time when I can. I can go on if you feel good.

529
00:53:31,740 --> 00:53:35,580
Because the question that I did have a note to ask you about capacity building.

530
00:53:35,880 --> 00:53:42,830
Yeah. Because you know, it's all very well Oxford coming out to South Africa what to do.

531
00:53:42,850 --> 00:53:46,379
But clearly South Africa ought to have its own experts.

532
00:53:46,380 --> 00:53:55,530
Is that something that you've also been engaged with? Yeah. So, I mean, I think the I think our process was much more collaborative, actually.

533
00:53:55,530 --> 00:53:59,100
So we so the longer term work we've been doing now.

534
00:53:59,850 --> 00:54:06,090
So in the sort of rash of the pandemic and this big change in welfare policy, that was a bit you know,

535
00:54:06,360 --> 00:54:09,749
there was sort of two different two or three different teams who were working on it.

536
00:54:09,750 --> 00:54:13,890
So I had a team that was working with some researchers in Cape Town.

537
00:54:15,000 --> 00:54:19,559
And some in at Duke in North Carolina. And so that was our team.

538
00:54:19,560 --> 00:54:26,340
And then there was this other team that was at the University of Cape Town and Stellenbosch, and they had some researchers in Amherst.

539
00:54:28,350 --> 00:54:31,469
And then actually for the later stages of designing.

540
00:54:31,470 --> 00:54:36,800
So we're now working with the government on making this grant permanent. We actually put those teams together.

541
00:54:37,110 --> 00:54:43,350
So it's it's a pretty equal distribution of researchers between them.

542
00:54:43,710 --> 00:54:48,210
So the you know, we have more of the experience on the randomised trial side,

543
00:54:48,360 --> 00:54:55,110
so we know more of the evidence base from other countries and then the South Africans do a particular sort of fiscal modelling technique.

544
00:54:55,710 --> 00:55:01,060
So they've worked a lot more on the survey data and they've, you know, there's also slightly I do labour,

545
00:55:01,350 --> 00:55:03,450
there's some differences in expertise in the teams,

546
00:55:03,450 --> 00:55:09,270
but I mean I guess the question then you have to ask is how are those South African researchers there?

547
00:55:10,380 --> 00:55:18,470
And you know, there's been very long term really amazing engagements in building I mean, I'm a I'm a product of that.

548
00:55:18,480 --> 00:55:21,590
Like, you know, I am South African. I did my undergrad there.

549
00:55:22,290 --> 00:55:31,380
There's a lot of different sort of scholarships and programs that exist to help people do short and long term experience abroad.

550
00:55:31,830 --> 00:55:35,040
And then those people, a lot of them go back and teach in the universities.

551
00:55:35,040 --> 00:55:39,670
And, you know, that's that's so good.

552
00:55:39,780 --> 00:55:43,650
That means that there is this pretty strong economic capacity there.

553
00:55:44,910 --> 00:55:49,870
And the Treasury has also invested quite a lot. They've got quite a lot of their own economists.

554
00:55:49,880 --> 00:55:54,990
So I think our next stage is to kind of try and get it out of the universities into the Treasury.

555
00:55:56,250 --> 00:56:03,389
But, you know, at least in in the experience of doing the collaborative work, I didn't I didn't feel like we were capacity building.

556
00:56:03,390 --> 00:56:07,830
I mean, I was learning as much as, you know. But but that's unusual.

557
00:56:07,830 --> 00:56:12,750
You know, it's a country that's a bit more developed. And so that wouldn't be the case in many other places.

558
00:56:12,750 --> 00:56:17,840
I mean, we work a lot more on trying to do training and but I mean the research centre

559
00:56:17,880 --> 00:56:24,420
work and that's a huge part of what we do is trying to get African economists,

560
00:56:25,200 --> 00:56:34,409
you know, into economics, into PhD programs and then, you know, back to back to their own countries to to teach in the in the universities.

561
00:56:34,410 --> 00:56:41,459
So we've got a kind of long term engagement with that. And what about international organisations like the International Labour Organisation?

562
00:56:41,460 --> 00:56:53,610
I mean, is there are a move to try to get these messages spreading across the whole low income countries from a from a kind of UN based approach?

563
00:56:54,120 --> 00:57:01,979
Yeah, that's so a lot of the sort of economic interventions are through the World Bank, so we work very closely with them.

564
00:57:01,980 --> 00:57:09,600
So one of my collaborators runs the runs the Jobs Practice at the World Bank, and she's actually been so she's I mean, she's amazing.

565
00:57:10,410 --> 00:57:18,989
Iliana Carranza She sort of manages a like a lot of research projects that deliver.

566
00:57:18,990 --> 00:57:24,840
So she collaborates with academics to kind of get evidence delivered and then they do a

567
00:57:24,840 --> 00:57:30,480
lot of work synthesising that and trying to get it into how the bank designs operations.

568
00:57:30,780 --> 00:57:35,969
So I mean, that's probably the biggest dissemination channel because, you know,

569
00:57:35,970 --> 00:57:43,260
so when like most of these sorts of programs are run with some World Bank money and

570
00:57:43,260 --> 00:57:48,030
so then the bank's involved and designed how they designed and evaluated and so on.

571
00:57:49,140 --> 00:57:52,440
And the yeah, I mean, in terms of the ILO, in the UN system,

572
00:57:52,950 --> 00:57:58,200
there is there's quite a lot of work on what is done and they run some programs in countries.

573
00:57:58,200 --> 00:58:06,150
But it's I think the move in the field has been really to try and get bilateral donors and the World Bank to fund the government.

574
00:58:06,360 --> 00:58:10,049
And I really think that's much better because otherwise you set up a whole

575
00:58:10,050 --> 00:58:14,070
parallel system and you actually want the government to build its own capacity.

576
00:58:14,070 --> 00:58:19,160
You just want to be funding it and. Mm hmm.

577
00:58:19,790 --> 00:58:24,720
And. Is there anything you'd like to see change in the future?

578
00:58:24,960 --> 00:58:30,560
About how. How you were able to do the research that you do.

579
00:58:31,800 --> 00:58:36,300
Yeah. I mean, I think the.

580
00:58:37,310 --> 00:58:41,640
Yeah. You know, one of the the biggest things has actually been funding.

581
00:58:41,660 --> 00:58:48,200
So one of the downsides has been, you know, foreign aid was massively cut in the wake of the pandemic.

582
00:58:48,200 --> 00:58:56,300
So, you know, the US, the UK cut the budget from point seven, 2.5 of GDP.

583
00:58:56,750 --> 00:59:00,889
I mean, wasn't it wasn't that even before. Yeah, so that was just before.

584
00:59:00,890 --> 00:59:10,790
But then a lot of the, a lot of the cuts and it sort of started to come through in 2021 because they, you know,

585
00:59:10,790 --> 00:59:16,670
they sort of went to the downstream from there when the cut was made and then the budgets ended up being cut.

586
00:59:17,540 --> 00:59:20,570
So that's been devastating. I mean, the the.

587
00:59:21,970 --> 00:59:27,520
Most a lot of the research I spoke about that was funded in the period when the UK was funding research.

588
00:59:28,390 --> 00:59:32,070
And so I think the biggest thing would just be trying to get those programmes back up.

589
00:59:32,080 --> 00:59:39,910
Like if we could just go up 2.7 again, you know, both on the funding research side, but also,

590
00:59:40,570 --> 00:59:44,469
you know, many countries had programmes like the one I was talking about in South Africa.

591
00:59:44,470 --> 00:59:48,459
They had one that was funded by DFID and that doesn't exist anymore.

592
00:59:48,460 --> 00:59:53,560
I mean, those that's been one of the worst down sides of the of the pandemic.

593
00:59:53,560 --> 00:59:59,100
So I think it's not a research focussed thing, but I think that's that's the biggest thing I'm worried about now is that we are,

594
00:59:59,230 --> 01:00:05,710
you know, we're we just seem to be under a government that's not doesn't have those, those same priorities.

595
01:00:06,310 --> 01:00:10,450
Um, but yeah, I mean I think, I think it's been also.

596
01:00:11,700 --> 01:00:19,890
You know, it has changed things already in a good way in that we, being away from the countries, actually made us put the local teams in charge more.

597
01:00:19,900 --> 01:00:28,740
And I think that's been really good. Yeah. So yeah, no, I mean it's it's been a real injection into things but quite energising and positive I think.

598
01:00:29,980 --> 01:00:31,720
Excellent. Thank you very much.