00:02 And after this very inspiring presentation by Intermit,
00:06 uh,
00:06 we will start the second
00:08 session part of the session.
00:11 Um,
00:11 my name is Manuella Francisco,
00:13 and I'm the global director of the Global Practice
00:17 for macro Trade and Investment.
00:19 I have with me a very distinguished panel of academics.
00:23 Let me start by presenting them,
00:25 uh,
00:26 from my left to the right.
00:28 Um,
00:28 we have Professor Ufuk
00:31 Ax sorry.
00:32 Professor Jung Sin Shin,
00:35 a professor
00:36 at the Washington University of Saint Louis,
00:40 we have Professor Ufuk
00:42 Akish,
00:43 apologies for my bad pronunciation,
00:46 Professor of Economics at University of Chicago.
00:50 We have Professor Michael Grubb,
00:53 professor of Energy and Climate change at UCL University College of London.
00:58 And we have Professor
01:00 right
01:00 next to me,
01:01 Professor John Friedman,
01:03 Chair of Economics department at Brown University.
01:07 The four papers you hear about today
01:10 present some of the theoretical and policy underpinnings
01:14 underpinnings that will help us better understand
01:17 the growth challenge in middle income economies.
01:20 We,
01:21 we will hear a presentation that is structured around
01:24 the 3 themes of the upcoming world development report
01:28 that
01:29 is structured around enterprise,
01:31 social mobility,
01:32 and energy transition.
01:36 We will start with Professor Akishit that
01:39 will focus on the role of creative destruction
01:43 by
01:44 generating economic growth through innovation and entrepreneurship,
01:48 which will form the general framework of the Schumpeterian growth of the
01:54 of the World Bank development report.
01:56 Over to you,
01:57 Professor Akus.
02:03 So good morning everyone.
02:05 Uh thanks to the organizers for this fantastic conference.
02:10 Uh,
02:11 it's always difficult to talk after Inarmid,
02:13 who is a great presenter,
02:14 and,
02:15 uh,
02:15 but the nice thing is that he also made a
02:18 nice transition to what I'm going to talk about.
02:21 So,
02:21 uh,
02:23 today I will talk about economic growth,
02:24 but from an angle of creative destruction.
02:28 The literature on creative destruction has made
02:31 enormous advances over the past 30 years,
02:34 I would say,
02:36 and
02:37 this,
02:37 you know,
02:37 once you start thinking about economic growth
02:39 through the lens of creative destruction,
02:41 you suddenly start uncovering many,
02:43 many facts because now you're able to
02:46 map the economic growth idea to micro-level data and.
02:51 The microdata analysis is guided by the Schumpeterian theory,
02:56 as I'm going to illustrate.
02:57 It's a,
02:57 it's a very nice and very informative angle that the literature has developed,
03:03 and this is also the main thrust of the of the next WDR.
03:07 And so led by Sean Mikal and Indermit,
03:11 we have a very large team working on the WDR.
03:14 A lot of them are here,
03:16 and I would like to give you a little bit of an idea about what we are working on.
03:20 Uh,
03:21 at this new WDR,
03:23 so first of all,
03:24 we are taking a comprehensive approach.
03:26 So we,
03:27 we try to question everything in an economy
03:30 and we try to say
03:32 how can we save the middle income countries
03:34 that are trapped in the middle income trap?
03:36 How can we save them out of that trap through technology and as Indermet also showed,
03:41 as you go
03:43 through the stages of development,
03:45 as you get closer to the frontier.
03:48 Rather than capital accumulation,
03:50 what starts to matter is technology development and productivity.
03:54 And of course when it comes to productivity and technology,
03:57 the question is how can we introduce new technologies in an economy.
04:02 Just focusing on firms would be a mistake because
04:05 of course firms are some abstract entities,
04:07 but in those entities there are individuals working and this.
04:11 The whole approach,
04:12 the comprehensive approach is quite crucial.
04:15 So the whole reasoning starts with a
04:18 pool of individuals in any society,
04:20 which
04:21 we are illustrating here at the bottom right.
04:24 I think the pointer is not working,
04:25 but so at the bottom right,
04:27 and of course individuals in any society are ranked according to their talents
04:33 at the time of the birth.
04:34 Of course children are born with heterogeneous talents.
04:38 And societies with their own systems
04:41 somehow let some of those kids rise up in the society.
04:44 It could be based on talent
04:47 or it could be based on
04:48 some
04:49 ethnic background or family characteristics.
04:52 It depends on the
04:53 system that exists in the society.
04:56 So then,
04:56 of course,
04:57 in an ideal scenario.
04:59 We would have the most talented kids sent
05:03 to schools
05:04 when it comes to innovation-led growth or technology-led growth here.
05:09 The education starts to become more technical
05:12 and so then whom are we educating in
05:16 our technical schools becomes an important question.
05:18 Who are being sent in the society into those technical schools?
05:22 So then
05:23 education
05:24 becomes a different notion rather than looking at the average years of schooling,
05:29 now we are trying to look at
05:30 these elites
05:32 selected in the society that are sent into those technical schools.
05:36 Who are they?
05:37 Are they going there based on their ability or based on some other characteristics?
05:42 So then once we send the kids into schools and train them,
05:45 some of them stay in the country and some of them leave.
05:48 So suddenly brain drain becomes also part of the equation.
05:51 How do we treat brain drain?
05:53 Should we turn our back
05:54 and just complain and whine about the brain drain,
05:57 or
05:58 should we somehow embrace it?
05:59 Indeed today Marta is going to talk about a
06:04 fantastic paper of hers in more detail about this.
06:07 But
06:07 brain drain is also part of the equation.
06:11 So once we have people trained in the society,
06:15 some sent abroad and some coming back,
06:17 now we have the workforce in the society and
06:20 firms are hiring those trained workforce
06:23 and start competing among themselves.
06:25 So what do they invest in?
06:26 Are they investing?
06:27 In new technologies or if so through
06:30 what incentives or through what market mechanisms
06:34 and then the firms will create some value added
06:37 and as a result we will have this cycle.
06:39 Now
06:40 what is the observation about middle income countries?
06:42 The observation is that
06:44 through this pipe
06:45 the water is coming very,
06:46 very weak at the end.
06:48 So clearly something is blocked in the system,
06:51 and the question is where.
06:52 It can be anywhere,
06:54 right?
06:54 That's why rather than just focusing on one side of the economy,
06:58 we are just taking this comprehensive approach
07:00 and try to
07:02 identify the problems and potential solutions and policy recommendations
07:06 through this approach.
07:08 So why the Schumpeterian
07:11 model?
07:12 Why the Schumpeterian approach?
07:13 Of course when it comes to economic growth,
07:15 there are many,
07:16 many theories.
07:17 Indeed we are not denying any
07:19 other theories.
07:20 Of course the institutions are important.
07:22 Capital accumulation through the solar model is important,
07:25 but now we are also trying to bring in the technology
07:28 into the equation.
07:30 When it comes to technology and innovation,
07:32 there are competing theories.
07:34 One of them,
07:34 of course,
07:34 is the Romer model,
07:36 which is the Nobel winning model by Paul Romer.
07:40 But when you think about the Roman model,
07:41 of course it was a brilliant model,
07:43 but the way that model operated is that
07:45 there is this continuum of varieties,
07:47 and through each innovation we are adding new varieties to the economy,
07:51 and
07:51 we just add more and more varieties,
07:54 and whenever somebody introduces a new variety,
07:56 that person becomes
07:57 the
07:58 Permanent monopolies in that market and produces forever.
08:02 The same person keeps producing
08:04 forever
08:05 and the Roman model.
08:06 Of course it's a brilliant model endogenis technological change.
08:11 It doesn't talk about exit and turnover,
08:13 which is exactly the problem,
08:15 as I'm going to illustrate in a second,
08:18 especially in middle income countries,
08:19 because once you start thinking about turnover
08:22 and exit,
08:23 there you suddenly realize there are many,
08:25 many frictions that are embedded into the economy and the data
08:29 and that you can only read through the Schumpeterian theory.
08:32 So what does the Schumpeterian theory do?
08:34 So there is a firm.
08:36 Rather than introducing a variety,
08:38 it introduces a better version of an existing
08:42 technology.
08:43 So
08:43 for instance,
08:44 firm 1 is the
08:45 current producer,
08:46 but then Firm 2 comes in with a better technology.
08:49 Firm 1 is out and Firm 2 is in.
08:51 And this way through this turnover in the economy.
08:54 Firms are just replacing each other and
08:56 they are introducing new versions of technologies.
09:00 Of course this was the first version of the Schumpeterian model,
09:04 but then
09:05 Since 1992,
09:07 after Agnonhovi,
09:08 the literature has evolved immensely.
09:10 Now creative destruction is not only
09:13 taking place in our existing theories between entrants and incumbents,
09:16 but also among incumbents.
09:19 Indeed,
09:19 when we look at the data,
09:21 50 to 75% of the productivity growth
09:24 is coming from successful factor reallocation among incumbents,
09:29 and we cannot
09:31 avoid this,
09:32 obviously,
09:32 and that's why the type of framework that we are.
09:35 Considering is way
09:37 beyond Ajonhoitz initial framework,
09:40 I don't have time unfortunately,
09:41 so that's why I'm not able to go into those details.
09:43 But basically
09:44 here we have
09:46 a creative destruction model where not only
09:48 entrants and incumbents compete among themselves,
09:51 but also incumbents also compete
09:54 among themselves.
09:55 So once you start with this logic,
09:57 suddenly you realize that we might be,
10:00 we might be living in many different scenarios in different industries,
10:04 for instance.
10:05 Entrants through their new technologies are trying to push the frontier forward.
10:10 What can happen is that of course incumbents will somehow join join the right here.
10:16 So either incumbents can also react positively
10:19 to incumbent pressure.
10:21 In this case there will be the pro-competitive effect.
10:23 Indeed
10:24 there were some interesting papers
10:26 that argued that China's pressure
10:28 on the US market had some pro-competitive effect
10:32 or some paper.
10:34 Also argued that it had a negative effect and then some papers
10:38 argued that indeed in the continental Europe it was a positive,
10:40 etc.
10:41 So as you can see,
10:43 it's a very empirical question,
10:45 but we need to be flexible for these types of
10:48 different hypotheses so that we can read the data better.
10:52 Alternatively,
10:53 of course,
10:54 incumbents can
10:55 can resist the change,
10:57 and they are not always going to introduce a
10:59 better quality when an entrant comes in with a
11:02 better technology.
11:03 I might just say,
11:04 Well,
11:04 I lost the game.
11:05 Let me just leave the market.
11:06 I might try to rely on some
11:08 other strategies.
11:09 And indeed,
11:09 in many,
11:10 many middle income countries this is a major problem as we see in the data.
11:15 Incumbents can also collude.
11:17 It's not only one incumbent that the entrants might be fighting against.
11:20 It could be that the incumbents might be colluding among themselves.
11:23 And once you start reading or approaching the data
11:27 or the whole growth story through this logic,
11:30 suddenly it opens up the avenue into many interesting questions.
11:35 Of course we are aware of the fact that
11:38 innovation might be too early or too soon for some countries or for some sectors.
11:44 And
11:46 technology can be upgraded not only through innovation,
11:49 but sometimes countries can also utilize their advantage of backwardness,
11:53 and we are not the first ones to say this.
11:54 Indeed,
11:55 Gar Shankron in his
11:56 1960s book had already made this point very strongly,
12:00 forcefully.
12:02 If a country is far from the frontier,
12:05 Rather than forming these really expensive R&D labs and
12:09 try to innovate and compete with the frontier countries,
12:12 they might as well open up to the world
12:15 and get connected to the outside world and try to
12:19 bring those technologies that have been invented
12:22 anywhere around the world to the country first
12:24 and start learning from it and building from it.
12:27 Do we see an example of this?
12:28 Yes,
12:29 I guess
12:31 Jungs will
12:32 talk more about this,
12:33 but
12:34 The way we are thinking about the economic growth process is in different stages.
12:40 So on the x axis you can see the proximity to the frontier.
12:43 As you go to the right,
12:44 a country gets closer to the world technology frontier.
12:47 As we go to the left,
12:48 it gets far away from the frontier.
12:50 And if a country is very far from the frontier.
12:53 You know all they might need at that point is having some more capital and some more
13:00 resources to build more
13:02 pipes,
13:03 more roads,
13:04 or more bridges.
13:05 But
13:06 as we are getting closer to the frontier now,
13:08 rather than capital accumulation,
13:10 now technology has to be evolving,
13:12 and
13:13 again,
13:14 it's the basic intuition of the solar model.
13:16 The Solar model says
13:17 you can grow just so much with the catch up growth,
13:20 but once you reach your steady state from that point on,
13:23 you need to upgrade your technology.
13:24 How are you going to do it?
13:26 What we argue here is that
13:28 if a country is a middle income country,
13:30 there's an opportunity to bring technologies from outside.
13:34 And how are you going to do it?
13:36 Well,
13:36 now the trade suddenly has a different meaning.
13:39 Rather than
13:40 being open means that rather than just trying to sell your goods,
13:43 it might mean
13:45 opportunities to learn from the world.
13:47 And also
13:49 through diaspora abroad,
13:50 a country can make use of the knowledge at the world technology frontier
13:55 and learn from it.
13:56 And of course as the country starts building on what it learned,
14:00 then
14:00 it can start innovating
14:02 on itself once it's ready to do the innovation.
14:06 Do we have an example?
14:07 Yes,
14:08 Korea's story,
14:08 of course Korea's story is
14:11 very,
14:12 very involved,
14:13 but one of the things that was shown by
14:16 Yong Honghim and his co-author in his job market paper this year,
14:19 they showed they digitized the Korean archives
14:22 and looked at the government contracts,
14:24 and what they showed is that
14:26 The Korean government in the beginning,
14:28 in the early 70s,
14:29 when they were making the huge leapfrog,
14:31 the government was very heavily subsidizing technology adoption from Japan.
14:36 So the country was of course was not fully ready to do its own innovation,
14:40 but there were a lot of technologies that were licensed from Japan,
14:44 and then they learned from those experiences and build on it.
14:47 So this is a very important observation,
14:48 I think.
14:49 Rather than
14:50 putting an unreasonable target to middle income countries,
14:53 we need to put reasonable targets first so that
14:55 they can use it as a stepping stone and then
14:58 jump.
14:59 So
15:00 I highlighted the Schumpeterian dynamics so much,
15:03 but
15:03 what do we see once we start thinking about the Schumpeterian dynamics?
15:07 What do we learn from the microdata
15:10 as something new?
15:11 The basic premise of the Schumpeterian theory says that
15:14 entrants will be more productive and replace the less productive incumbents.
15:19 Let's look at some
15:20 middle income countries that's totally randomly selected.
15:24 So
15:25 here
15:25 we are comparing
15:26 exiters and entrance productivity
15:30 in Turkey,
15:31 and
15:32 the blue line is showing you the productivity of the exiters and
15:36 the red line is showing you the productivity of the entrants.
15:40 So clearly the basic premise of the Schumpeterian theory
15:43 is not working in the case of the Turkish manufacturing sector.
15:47 So
15:47 this is something that we have to fix
15:50 as part of the story.
15:52 So rather than creating destruction,
15:54 maybe this is a destructive creation in that sense,
15:56 because it's lowering the productivity.
15:58 Indeed,
15:59 when you look at the total factor productivity evolution in Turkey since the 1970s,
16:03 there's
16:05 a steady decline.
16:06 So let's switch gears and let's look at another country that did well
16:11 for a while and for quite some time now it's doing quite poorly
16:15 and here in this study
16:18 what we did is we looked at the growth strategies of the market leaders.
16:24 As the firm is evolving and becoming the dominant market leader,
16:29 does it become more innovative,
16:30 or does it rely on other alternative strategies?
16:33 Number 1 here means market leader.
16:36 Number 20 means that's the 20th largest firm.
16:39 So as you go to the right,
16:40 the firm size gets smaller.
16:41 As you go to the left,
16:42 firm size increases.
16:44 What we see is very interesting as firms are becoming the dominant market leader.
16:49 Innovativeness goes down.
16:50 The red dots go down,
16:52 but the number of politicians that they hire
16:55 relative to the firm size increases,
16:57 so they are getting more tangled
16:59 to the political system
17:01 rather than trying to push the frontier.
17:02 Again,
17:03 it's the simple Schumpeterian logic.
17:06 Once I'm in the market,
17:08 I need to resist,
17:09 and that's exactly what we observe here.
17:12 When we look at the Indian case again,
17:14 we see a lack of creative destruction,
17:17 and here on the top
17:19 left figure you see the life cycle of firms in the US and India.
17:22 In the US firms are growing very rapidly.
17:24 There's an up or out type of
17:26 competitive dynamics
17:27 when it comes to India.
17:29 The average firm size conditional on survival is just flat.
17:33 It means that the small firms,
17:35 they are not only growing,
17:36 they are not getting kicked out either.
17:37 And as a result,
17:39 the bottom right figure here is showing.
17:41 The fraction of small firms in the economy
17:44 as the cohortages.
17:46 As the cohortages,
17:47 the dashed line shows that in the US,
17:49 small firms are eliminated very,
17:51 very rapidly.
17:52 In India,
17:53 small firms are not being eliminated,
17:55 and as a result,
17:56 the fraction of small firms remains.
17:58 So this means that
18:00 the fact that we are observing so many small firms
18:04 in the middle.
18:04 Countries
18:06 could be a symptom of
18:07 not only lack of financing for them,
18:10 but
18:11 it could be lack of competition by larger firms.
18:14 Maybe the problem is in the mid-sized or large sized firms,
18:17 and maybe they are not growing sufficiently
18:20 to drive out the small firms so that they can create high quality,
18:23 high paying jobs,
18:24 so that all these trying to be entrepreneurs.
18:27 Can get reallocated and earn decent wages in these growing firms,
18:32 and this is what we need to consider.
18:35 Indeed,
18:36 of course,
18:36 when it comes to middle income countries,
18:38 informality is a major concern
18:41 and here once you start thinking through the Schumpeterian lens,
18:45 informality is not a cause but a result,
18:48 and informality could be a result of
18:51 Lack of competition in the market.
18:52 And here,
18:53 for instance,
18:54 again when we look at some regulations and what we see
18:58 is that
18:59 regulations that exist in middle income countries
19:02 can even encourage more
19:04 informality.
19:05 But if we could normally push the firms to larger sizes,
19:10 we would automatically fight
19:12 against informality.
19:14 So I'm running out of time.
19:16 Just a few
19:18 issues on the human capital side.
19:20 The report
19:21 will also focus on human capital.
19:23 And here we look at the talent allocation.
19:25 Innovation and technological upgrade cannot happen
19:28 just by itself or firms' investment.
19:30 Firms should also have good
19:32 engineers and inventors.
19:34 Without having good players,
19:35 a team cannot win the championship.
19:38 With that logic,
19:39 we have to also have a strong workforce.
19:41 And of course if
19:42 in a society there is discrimination or financial
19:45 frictions and not having equal opportunities for people.
19:49 We might be leaving out important talent out,
19:51 so this is,
19:51 this is an important
19:54 consideration in the report.
19:55 And then we look at the data again,
19:57 it's not the average years of schooling that matters for technological progress.
20:02 It's technical training,
20:03 technical education,
20:06 even in the historical US,
20:07 where we should draw a lot of lessons from,
20:10 in the historical US we see that
20:12 college education was extremely important for innovativeness.
20:16 And
20:17 not only having college education,
20:19 also having liquid secondary market for technologies,
20:22 but today in many of the middle income countries
20:25 there's barely college graduates.
20:27 There's barely any regulation for how to protect
20:30 intellectual property.
20:31 So
20:32 we need to talk about these aspects.
20:34 And
20:35 in successful countries like Denmark today we see that PhD
20:39 is the strongest predictor of innovation.
20:42 It's that technical.
20:43 It's at that level,
20:45 basically.
20:46 And of course when it comes to human capital we
20:48 don't necessarily need to grow them within the country only.
20:51 We can also tap on the global talents.
20:54 China,
20:54 for instance,
20:55 80% of Chinese students go back to their country
20:59 after studying,
21:00 and can you imagine what Big spillover there is in the case of China.
21:04 The number of returnees to China exceeds 1 million a year.
21:09 And that way,
21:10 of course,
21:10 we are talking about a Chinese miracle,
21:12 but we have to also think about these spillovers,
21:15 global spillovers that are taking place
21:17 and what happens in middle income countries
21:20 right now.
21:20 Middle income countries,
21:22 when you look at for instance,
21:23 who is leaving a country and who is coming back,
21:26 it's typically the most productive
21:28 individuals or scientists who are leaving the country.
21:31 And the least productive
21:33 ones are coming back because of the bad policies,
21:36 but rather than trying to force
21:38 those
21:40 immigrants to come back,
21:42 one thing one can do is one can try to connect with them
21:45 and try to learn from them.
21:47 Indeed,
21:47 Martha Prato will talk in great detail about
21:49 this rather than turning our back to migrants,
21:54 to people abroad,
21:55 we can try to build a bridge from them and learn from them
21:58 to utilize the advantage of backwardness.
22:01 And finally,
22:01 there will be also the energy part in the report,
22:05 but just to give you an idea about how we are thinking about this,
22:09 you know,
22:10 we can produce dirty technology in
22:12 the Schumpeterian sense or non-green technology,
22:15 let me put it that way.
22:16 But there can be also green alternatives,
22:18 green technologies to use.
22:20 What's the comparison here?
22:22 The key critical thing here is that
22:25 today we are living in a world where there is a race between green technologies
22:29 and non-green technologies,
22:30 and green technologies by construction
22:33 are less advanced.
22:34 We need to reconsider the government policies because
22:37 some government.
22:38 Policies that might have well intentions might be slowing down this transition.
22:43 Indeed we do see some evidence on this.
22:46 For instance,
22:46 the subsidies that are being provided to fossil fuels
22:50 in many countries
22:53 are just going against the transition,
22:54 and at best it will just slow down the transition.
22:57 So that's why
22:58 thinking about this creative destruction and clearly.
23:00 Energy transition is the poster child of creative destruction.
23:03 We are trying to replace
23:05 an old
23:06 energy or technology with a green one.
23:08 That's very much in the realm of Schumpeterian theory,
23:11 and these are some of the elements that we are considering in the new.
23:16 Sorry for the limited time,
23:18 but hopefully it gave you a good idea about what we are working on.
23:22 Thank you.
23:24 Many thanks Professor Akhijit.
23:26 So we will now hear from Professor Friedman.
23:30 Uh,
23:30 his presentation focused on intergenerational mobility around the world.
23:35 His work focused on social mobility and the importance of selecting,
23:39 developing,
23:40 and enabling
23:41 talents,
23:42 talented individuals.
23:45 Over to you.
23:45 Well,
23:46 thank you very much for the uh invitation to speak here.
23:48 Um,
23:49 I should note at the start that I'm not a development economist.
23:51 Uh,
23:52 I do not specialize in economic growth and so,
23:55 uh,
23:55 I think everyone in this room is gonna have a better
23:58 sense of the exact types of policies,
24:01 the exact types of analysis like what Ufuk
24:03 was talking about that are really going to,
24:06 uh,
24:06 help,
24:07 uh,
24:07 move development forward across the world.
24:11 What I wanna talk about is intergenerational mobility.
24:14 Now
24:15 intergenerational mobility,
24:17 uh,
24:18 featured in that,
24:19 uh,
24:19 extremely nice schematic that Ufuk started with
24:22 as part of economic growth directly in terms of
24:25 figuring out how to get,
24:28 uh,
24:28 the most diverse broad set of talent
24:31 into the position of being innovators or high productivity employees
24:36 in a given country through education
24:39 and I think that's a very important part of what intergenerational mobility does.
24:43 I also think that intergenerational mobility is uh
24:46 a broader part of the institutions of a country
24:50 which if not maintained appropriately can lead to real problems
24:55 um and I think there are many countries across the world today where
24:58 the
24:59 lack of intergenerational mobility or
25:00 perceived lack of intergenerational mobility,
25:03 the sense that too many people are born into situations from
25:06 which they just don't have a shot at moving up in.
25:09 Uh,
25:09 the income ladder or,
25:10 or in the social ladder
25:13 where parents feel like their kids do not have the
25:15 opportunity to have a better life than they do,
25:17 that is not only an economic problem that becomes a
25:20 social problem that becomes a political problem that can really
25:23 uh
25:24 cut the legs out from under what might otherwise be
25:28 a high growth,
25:29 high productivity regime.
25:31 So let me start with just
25:33 a couple of facts from
25:35 uh some great data that the World Bank has produced,
25:38 uh,
25:39 right,
25:39 people always think of the American dream as,
25:41 you know,
25:42 the possibility that,
25:43 you know,
25:43 uh,
25:43 people,
25:44 especially immigrants could come to this country,
25:46 um,
25:47 in the early 20th century and kind of no matter
25:49 what your background there was the opportunity for success,
25:52 and I think.
25:53 You know,
25:53 most people know at this point that,
25:55 you know,
25:55 that is not something that's really borne out in the day today.
25:58 Uh,
25:59 here,
25:59 uh,
26:00 the particular statistic being calculated is the probability that a
26:03 child born into parents who are in the bottom half
26:07 of the income distribution move into
26:09 a position by the time that they themselves
26:11 reach the labor market that's in the top quartile
26:15 and so,
26:15 you know,
26:15 obviously each of those are defined,
26:17 um,
26:17 locally.
26:18 And what you see is that um you know the
26:21 US is very much in the middle of the distribution of
26:25 um
26:25 middle income countries.
26:27 There are some countries like Brazil and Pakistan
26:30 who have lower rates of upper mobility,
26:32 but there are also many countries here like Jordan and Thailand which have
26:36 uh much higher rates of upper mobility than the United States.
26:40 Now,
26:40 these statistics though are still,
26:43 I think really only glossing the surface of what we can learn
26:47 about how to increase intergenerational mobility.
26:50 And so what I wanna talk about today is,
26:53 uh,
26:53 what I think are a very exciting set of new tools
26:57 that have come out of work that,
26:59 um,
26:59 my co-authors and I and and many others have done,
27:02 um,
27:02 across the world.
27:04 Um,
27:04 these are techniques I think that,
27:06 uh,
27:06 mostly were pioneered in developed countries,
27:09 but what's been very exciting is to see a lot of new work applying them to many,
27:14 especially,
27:15 uh,
27:15 middle income,
27:15 but,
27:16 but even,
27:16 um,
27:17 countries at the,
27:18 um.
27:19 Very lowest levels of the development ladder.
27:21 Um,
27:22 and I think that it's extremely important,
27:25 right,
27:25 especially in this middle income range,
27:27 to think about,
27:28 uh,
27:29 alongside growth and kind of elimination of,
27:32 of abject poverty,
27:33 to think about maintaining or increasing or not losing ground on social mobility,
27:38 um,
27:39 as we go along.
27:39 And so,
27:40 um,
27:40 over the next 10 minutes I'm just gonna
27:42 kind of give you a little bit of a,
27:43 a tour.
27:44 I'm gonna hit kind of 3 highlights of techniques that have been,
27:47 um,
27:47 pioneered that,
27:48 that I,
27:48 I think might be helpful,
27:50 uh,
27:50 to this end.
27:52 So first,
27:52 let me just start with this map of the United States.
27:54 This is a heat map
27:56 where in each
27:58 city area,
27:59 essentially,
28:00 which are each of the colored polygons,
28:02 we're measuring the average incomes in adulthood for children who grew up to
28:08 families at the 25th percentile,
28:09 kind of the.
28:10 Average below average family,
28:12 uh,
28:12 and the heat map colors in blue children who
28:15 are doing very well in red children who are,
28:18 are not doing as well
28:19 and what you see across the United States
28:21 is just there's an enormous amount of variation.
28:24 Children born in Charlotte,
28:26 uh,
28:26 down in the southeast of this country.
28:28 Um,
28:28 are only making $26,000 on average as adults.
28:31 Children born in Dubuque,
28:33 um,
28:33 which is part of that very blue region in the
28:36 upper,
28:37 uh,
28:37 Midwest of this country,
28:38 are earning nearly twice that amount.
28:41 Now,
28:41 of course you could look at this map and say fine.
28:43 The United States is the size of a continent,
28:45 you know,
28:46 of course,
28:46 intergenerational mobility is going to vary at this scale.
28:49 What I think might be surprising is that even when you zero into
28:55 a particular city to make it really local,
28:58 you still see this range of variation in mobility.
29:02 So this is the same map that I showed you at the national level,
29:06 but now focusing only on the New York metropolitan area.
29:09 We've now colored things at the census tract level.
29:12 So these are essentially neighborhoods.
29:14 There are about 4000 households per
29:18 census tract.
29:19 The colors are essentially the same.
29:22 Right,
29:22 so that same variation that you saw between the
29:24 southeast of the United States and the Upper Midwest,
29:26 so those are areas that are,
29:28 you know,
29:28 still within the same country,
29:29 but,
29:29 you know,
29:29 thousands of miles apart.
29:31 Here you see that same variation between neighborhoods that are in many cases,
29:35 literally across the street from one another.
29:37 Um,
29:38 and in particular I'm,
29:39 I'm gonna come back to this example later on,
29:41 um,
29:42 you know,
29:42 you see down in,
29:43 um,
29:44 in Brooklyn,
29:45 right,
29:45 there's some areas where there's,
29:46 I'm gonna kind of point it out here,
29:48 the pointer is not working,
29:49 but kind of right in,
29:51 in this region here,
29:52 um,
29:53 there's these little blue islands in what
29:56 is otherwise a very low mobility neighborhood.
29:58 Uh,
29:59 the blue areas are,
30:00 uh,
30:00 what's called the Nehemiah houses,
30:02 and they were developed in the 1980s on literally a plot of vacant land.
30:06 Uh,
30:06 by a local organization as an attempt to create kind of a new society moving away from
30:12 some very low mobility areas,
30:14 uh,
30:14 that were called the Van Dyke houses.
30:16 So I think the first lesson that I want people to take away
30:19 is that mobility is going to vary not only at a national level
30:23 but in an incredibly local level.
30:25 And so kind of that's the level of granularity
30:27 that one has to take when thinking about this problem.
30:30 Now of course the examples that I'm showing you
30:32 here from my work are in the United States,
30:34 but
30:34 um over the past 10 years we've seen very
30:36 similar analyses of many countries around the world.
30:39 So here are 4 examples from developed countries in Europe,
30:44 um,
30:44 which show kind of very,
30:45 very similar patterns.
30:47 More relevant I think to this audience here are examples from.
30:51 Um,
30:51 Asher et al.
30:52 on the right and from Allesina et al.
30:54 on the left,
30:55 um,
30:55 of course one has to use data creatively in
30:58 order to generate these types of very local maps.
31:01 Uh,
31:01 on the right they're using education as a,
31:03 as a measure of socioeconomic status.
31:05 On the left they're using literacy,
31:07 but,
31:07 uh,
31:08 I think the big picture here is that not
31:10 only is intergenerational mobility varying at a national level,
31:14 it's varying
31:15 at uh a subnational and even very local level,
31:18 uh,
31:19 within these countries.
31:20 So second,
31:22 um,
31:22 let me just come back,
31:23 um,
31:23 to this map here.
31:25 Uh,
31:25 this looks at intergenerational mobility overall pooling all different types of,
31:31 uh,
31:31 individuals
31:32 and what analysis in,
31:34 in this country has shown,
31:35 this is analysis by my co-authors,
31:37 um,
31:37 right.
31:38 Chetty and Nathan Hendren
31:39 along with uh Maggie Jones and Sonia Porter,
31:42 is that this is sharply divided the
31:43 United States between individuals of different races
31:47 and so here I'm showing a similar map
31:50 with black men on the left and white men on the right.
31:55 Um,
31:55 and looking at these maps,
31:57 right,
31:57 it might look like we're using two different
31:59 color schemes for the two different maps.
32:02 Uh,
32:02 that's actually not the case.
32:03 It's the same color scheme on the bottom.
32:05 It's just that upward mobility for,
32:07 um,
32:07 black individuals,
32:09 black men born into poverty on the left
32:12 is,
32:12 is basically everywhere less than,
32:15 uh,
32:15 the,
32:16 the same outcomes for,
32:18 uh,
32:18 white men born into families at the same level of poverty on the right.
32:22 And so I,
32:23 I think it's not just that overall mobility,
32:25 it's thinking about
32:26 um how do opportunities
32:28 exist for children from these uh other dimensions in society,
32:32 um.
32:33 Similarly,
32:34 uh,
32:34 these,
32:35 uh,
32:35 types of findings have come out in work
32:37 in developing countries.
32:39 So this is,
32:40 um,
32:40 uh,
32:40 a scatter plot that I made based on results from that same Allesina et al.
32:45 um,
32:45 analysis where
32:47 they've calculated mobility separately
32:49 for,
32:50 uh,
32:51 Christian individuals and Muslim individuals in
32:53 many different countries in Africa.
32:55 And so we have uh the upward mobility in
32:58 each country for Christians on the horizontal axis,
33:01 for Muslims on the vertical axis,
33:04 um,
33:04 and what you see,
33:05 uh,
33:05 relative to the 45 degree line is that almost all countries are
33:10 below and to the right.
33:11 And so what that's saying is that to a varying degree,
33:13 uh,
33:14 you see more upward mobility for Christian individuals than Muslim individuals.
33:18 So again,
33:19 you know,
33:19 that's the type of
33:20 uh equality of opportunity that I think matters quite a bit,
33:24 not only from an economic perspective,
33:26 but from a broader perspective as well.
33:29 Third,
33:30 um,
33:31 we've seen quite a bit of evidence that these types of local variation in
33:36 upward mobility is not just about sorting
33:39 of particular people to particular places,
33:41 but rather it's a causal effect in large part of the.
33:44 Itself.
33:45 So how do we know that?
33:47 Um,
33:47 we've learned that from an exposure design.
33:51 And so the idea here is that one's,
33:53 uh,
33:54 the,
33:54 the impact of one's neighborhood
33:56 varies by how long one's lived in it,
33:58 right?
33:58 It's really an exposure-based model.
34:01 And so let me come back to those two
34:03 neighborhoods in,
34:04 in Brooklyn that I started with the Nehemiah houses,
34:06 which are a very high upper mobility neighborhood.
34:08 The Van Dyke houses,
34:09 which
34:10 are a very low mobility neighborhood,
34:12 and essentially just across the street
34:13 from the Nehemiah houses.
34:15 The dash line at the top is our outcomes for children who grew up
34:20 their entire lives in the Nehemiah houses,
34:22 which is much higher than the outcome,
34:25 uh,
34:25 the dashed line at the bottom for children who
34:27 spent their entire lives in the Van Dyke houses.
34:29 So the idea of this causal design is to separate out,
34:33 you know,
34:33 maybe those kids living in those two different,
34:35 um,
34:35 uh,
34:36 sets of houses are very different for other reasons.
34:39 And so instead of just comparing kids who grew up entirely in one versus another,
34:42 we're going to look at those who moved from one to another at
34:45 a particular point in childhood.
34:47 And so first,
34:48 let me think about children who were born in the Van
34:51 Dyke houses and then moved at a very early age,
34:54 let's say age 2,
34:55 to the Nehemiah houses.
34:56 Those children have outcomes that look very similar,
34:59 uh,
34:59 slightly below,
35:00 but very similar to those children who grew up,
35:03 their entire lives in the Nehemiah houses.
35:05 And then we're just gonna repeat this,
35:07 uh,
35:07 children who moved from the Van Dyke houses to the MEA houses at,
35:10 at age 3,
35:11 age 4,
35:11 and so on.
35:12 And what you see is that the more time you spend
35:15 in the Van Dyke houses before moving to the Nehemiah houses,
35:19 that is the less opportunity that you have to be exposed to that,
35:23 uh,
35:23 higher mobility environment,
35:24 whatever that is in the Nehemiah houses,
35:27 the worse your outcomes get by.
35:29 The time you've not moved until you're in your early twenties,
35:33 moving at that age to the my houses seems to really have no effect.
35:37 Um,
35:37 and then in particular,
35:38 one can then look after age 24.
35:40 We're measuring incomes at age 24,
35:43 so this is essentially a placebo check to see,
35:45 well,
35:45 maybe the people who move at these different ages are different,
35:48 and here you basically find no effect.
35:50 Um,
35:50 and now this,
35:51 this mover's design,
35:52 which has this kind of.
35:53 Characteristic hockey stick,
35:55 uh,
35:55 for those of you who are from North America,
35:58 um,
35:58 type of pattern,
35:59 uh,
36:00 this too has shown up in a remarkably broad set of circumstances across the world,
36:05 you know,
36:05 here's something similar not only in Denmark and Australia on the top,
36:09 but you see very similar exposure patterns,
36:12 uh,
36:12 in Africa from the Allesina work and in uh Brazil,
36:16 um,
36:16 on the bottom.
36:17 And again you see very similar things where it's not
36:19 just that exposure to these local places is incredibly important,
36:23 it seems to be exposure during
36:25 childhood
36:26 is the most important thing.
36:28 And of course the exact definitions of what that means
36:30 are going to differ between places and and how one is
36:33 um measuring upward mobility,
36:36 um,
36:36 but this really does seem to be a very robust fact in many places across the world.
36:42 Now,
36:43 the fact that these local exposure to to place are causal,
36:47 then of course
36:48 just raises the question of,
36:49 well,
36:50 what is it about these local places that that is causal,
36:53 and here I think,
36:55 um,
36:56 you know,
36:56 again,
36:56 the answers are going to differ.
36:58 What do we get from looking in the US at these types of things?
37:03 It's not just raw growth that seems to matter actually.
37:06 This slide might be helpful.
37:07 This is a scatter plot of average growth across US cities on upward mobility
37:12 in the 30 largest cities.
37:14 There's just no correlation at all.
37:16 So just broad-based growth does not necessarily help.
37:20 What does seem to matter are these features that are much more about
37:24 not only human capital development and exposure,
37:27 you know,
37:28 especially exposure to abject poverty is not a good thing.
37:31 But you see that the social circumstances and the
37:34 networks seem to matter quite a bit as well.
37:37 And so in particular you see the third one here,
37:40 greater social capital.
37:41 What seems to be going on here is exposure not only to adults who
37:46 can
37:47 not only inspire.
37:48 you to give you information about the pathways that
37:50 are available that can help make you make the choices
37:53 that might maximize your potential as a child,
37:56 but also exposure to other peers of yours
38:00 that matters tremendously as well.
38:01 And so here's a map of the United States
38:04 from um.
38:05 Some of Raj Shetty's recent work on social capital,
38:08 this looks enormously like the picture of upward mobility that I showed you before,
38:13 um,
38:13 and so my sense is that these types of uh social exposures are going to matter,
38:17 uh,
38:17 in addition to all these more directly human capital focused activities,
38:22 you know,
38:22 here's a similar thing looking across
38:24 regions.
38:25 From the,
38:25 the Allesina work,
38:27 um,
38:27 again,
38:27 what pops out here is not only some of
38:30 those raw economic factors like distance to a railroad,
38:34 um,
38:34 but it's things like distance to a Protestant mission which is gonna,
38:37 gonna be a stand-in for a lot of the cultural differences that,
38:39 you know,
38:40 exist,
38:40 uh,
38:40 across these institutional differences that exist across these,
38:43 these places.
38:45 So,
38:45 um,
38:46 kind of three takeaways as,
38:47 as my time is ending.
38:49 Um,
38:49 this is the first talk I've ever given where like the luge,
38:52 my time is being measured down to 10,000 of a second.
38:55 It's extremely precise.
38:57 Um,
38:57 luge is the only Olympic event where time is measured in the 10,000 of a second.
39:01 Um,
39:02 so 3 takeaways.
39:03 First,
39:04 it's the local childhood environment,
39:06 I think that really plays the central role
39:08 for developing prospects for upward mobility,
39:11 and it's environment again,
39:13 local,
39:13 I think is the key word here.
39:14 It's really
39:15 about kind of who you're interacting with on a daily basis.
39:18 You shouldn't even think about this is at the level of a city.
39:20 It's more like the,
39:20 the neighborhood that you're living in.
39:22 Second.
39:23 What I think this shows is that one,
39:26 much like Ufuk was talking about,
39:27 you can use these large scale observational data
39:30 to make,
39:31 uh,
39:31 to,
39:31 to learn something about what types of
39:33 interventions might be most valuable and where,
39:36 uh,
39:36 right,
39:36 this is,
39:37 you know,
39:37 not going to be as precise or as direct as an RCT for a particular intervention,
39:43 but this is going to give you perhaps a better sense of like what bucket to look in.
39:47 And third,
39:48 I think it's again we have to move beyond these,
39:51 uh,
39:51 you know,
39:51 very traditional models of human capital development,
39:54 uh,
39:55 right?
39:55 I think it's really about a lot of these broader sociological forces,
39:59 um,
39:59 that have a lot to do with economic mobility and inequality as well.
40:02 Thank you very much.
40:04 Many thanks Professor Friedman for the
40:07 very interesting and sobering presentation.
40:09 We'll hear next from Professor Shin.
40:12 Uh,
40:12 Professor Shin will present,
40:14 uh,
40:14 a presentation focused on growth through creative destruction
40:18 from the point of view of firms,
40:21 uh,
40:21 focusing on the South Korean growth miracle.
40:24 Over to you.
40:25 Thank you.
40:26 So South Korea is
40:28 one of the well-known economic success stories.
40:31 It went from poor to rich in one person's lifetime,
40:35 actually more like 30 years.
40:37 And a lot of research has been done,
40:39 a lot of theories have been expounded on how they
40:42 made this miracle.
40:44 What has not been possible until now
40:46 was an analysis of what is happening at the micro level,
40:50 at the plant level.
40:52 Is there a systematic pattern that accompany
40:54 this macroeconomic transformation at the micro level?
40:57 So we're able to do that.
40:59 So this is the motivating figure.
41:02 The bottom line is the GDP per capita
41:04 at domestic constant prices.
41:07 So if you look at it,
41:08 two things,
41:09 it's a low scale.
41:11 From say 1970 to 2000 in 30 years,
41:14 the GDP per capita
41:16 uh
41:16 goes up by a factor of 10,
41:19 from 700 to 7000.
41:20 This is remarkableable growth rate.
41:22 The other thing you notice is that the growth
41:24 rate kind of slows down over each decade.
41:27 In the 70s and eighties,
41:28 it's about 9% to 8% per year growth
41:31 and 202,010 is 4% and 3% per year and
41:35 Koreans call that a recession.
41:38 And then,
41:39 the blue line is manufacturing value-added per worker,
41:42 so that's your measure of productivity of the manufacturing sector.
41:45 Of course,
41:45 it's
41:46 higher than the GDP because manufacturing tend
41:48 to be more productive than service sector.
41:51 Uh
41:52 broad patterns are consistent.
41:54 The subtle differences that manufacturing
41:57 grew really productive in the 90s,
41:59 in the value added per worker sense,
42:01 despite the '97
42:03 East Asian financial crisis,
42:05 and then it really slowed down after 2010.
42:09 So,
42:10 we're using
42:11 newly digitized data,
42:13 manufacturing
42:14 census data.
42:16 We already had data from 1982 onwards,
42:19 but what is new is the previous period,
42:22 196,
42:23 1017,
42:23 1981.
42:24 So,
42:24 one limitation is that the panel dimension of the data
42:28 is available only from 1982,
42:30 and then we're gonna actually spend some time in the second half of my
42:34 presentation about that part.
42:35 So first,
42:36 we start with the first half,
42:38 the cross-section data only.
42:40 It starts from 1967.
42:43 Since
42:43 the cutoff of employment is 5,
42:47 we're only looking at plants with at least 5 employees.
42:51 Until 2007,
42:51 after that,
42:52 it becomes at least 10 employees.
42:54 So,
42:55 you look at the right figure,
42:56 the average size of the manufacturing plants rises dramatically
43:00 in the 1970s and collapses dramatically in the 1980s.
43:03 There's some difference.
43:04 1970s is the period of
43:07 heavy and chemical industry promotion,
43:09 so a lot of this rise is
43:11 compositional.
43:12 So
43:12 you're moving away from light manufacturing to heavy
43:15 and chemical industry and chemical industry obviously has much
43:18 more employees
43:19 per uh plant.
43:22 And then the
43:24 collapse in the 1980s,
43:25 actually,
43:26 that's not compositional.
43:27 So it is happening in basically all industries within having chemical industry,
43:31 within manufacturing and other industries.
43:33 So there's some differences in how it goes up and down.
43:36 There are different ways to look at size.
43:38 This is what fraction of employment
43:40 in total employment is in plants with at least 2 250 or 500 employees.
43:46 Same pattern goes up and down.
43:50 And then this is log log plot.
43:52 So some of you are very familiar with this plot.
43:54 It basically shows the entire distribution,
43:56 so it's sort of a reverse of the cumulative distribution function.
44:00 So
44:01 at any point on the x axis,
44:03 pick two,
44:03 that's the logo where it stands,
44:04 so that's 100,
44:05 and you ask of all the plants,
44:07 what fraction of plants are actually larger than 100 employees.
44:11 And it's,
44:12 it's,
44:12 it's moderately declining.
44:14 So solid line in the middle you see is 1967,
44:17 which is showing every decade.
44:19 And then you see that in 1977 it shifts out
44:23 quite a bit.
44:23 It's almost like a for short of stochastic dominance.
44:26 So it's basically what it is is that the entire distribution shifts to the right,
44:30 so all the plants are basically much larger in 1977 and 196967,
44:34 but that was the peak of the plant size distribution,
44:37 and then you see
44:38 1987 and 1997,
44:40 2007,
44:41 actually you see these things coming back,
44:43 so things are getting smaller.
44:44 So it's very
44:45 consistent throughout um
44:47 The entire distribution,
44:48 that's just something at the top.
44:50 So
44:51 what,
44:51 at this point you take a stock and
44:53 compare the picture of
44:55 manufacturing value added per worker and the size distribution changes.
44:59 What you see is that there's no clear pattern
45:01 either in levels because
45:03 value per worker continues to grow,
45:04 but the size ra goes up and down.
45:07 Or in terms of growth rate.
45:09 So we had a fast growth in the 70s and 80s and 90s,
45:12 but then
45:13 sometimes science division goes up
45:15 and comes down,
45:16 so there's no simpler relationship.
45:18 So it prompted us to
45:20 look at
45:21 it that's over time,
45:22 we don't see any clear pattern between size and
45:24 value per worker or the growth rate of that.
45:27 So we
45:27 wanted to see whether actually there's any pattern
45:30 across countries.
45:31 So more research is definitely needed.
45:33 uh we just went to OECD database and grabbed whatever we could.
45:37 On the left panel,
45:38 these are countries including South Korea that report in
45:41 terms of plants rather than farms or enterprises.
45:44 So what you see is that
45:46 Um,
45:47 you're attempting to make,
45:48 uh,
45:48 oh,
45:48 there's a downward sloping
45:50 pattern here.
45:51 First,
45:51 only 6 countries here,
45:53 and also you know that there are many other countries,
45:55 for example,
45:56 India,
45:57 who talked about
45:58 many other countries with a really small employment
46:01 and very low value added per worker,
46:03 so there's no simple relationship.
46:04 The right hand side is from same OECD data
46:07 uh looking at countries that report in terms of enterprises and firms,
46:11 you see that there's no clear pattern here either,
46:14 at least among these countries in terms of the
46:15 size of a farm measured by average employment per farm
46:19 and the value added per worker of the sector.
46:21 This is a manufacturing.
46:23 So there doesn't seem to be a very simple relationship between
46:26 size and productivity or growth.
46:29 OK.
46:29 The other thing we can do
46:31 with this
46:32 static data,
46:33 cross section data is
46:34 do simple
46:35 calculation of misallocation,
46:37 sharing clinotype.
46:39 Um
46:39 you can see I plot,
46:41 but let's talk about the right one.
46:43 So if you look at it really carefully,
46:44 oh,
46:45 the data from 69 to 77 is missing because those
46:48 years we don't have a capital at the plant level,
46:50 so we cannot do the calculation.
46:52 So what you see is actually
46:53 there seems to be a downward slope in degree of missile location.
46:57 So location efficiency is actually improving
47:00 between
47:01 1968 and up to mid 1980s,
47:03 but then it gets worse in the
47:06 1980s and later part of the 1980s and 191990s,
47:09 and it gets really bad in the 2000s.
47:11 I think the last phase is more about the issue of missile specification.
47:14 I will not read too much into that.
47:16 But to the extent that we think this is a pattern misallocation,
47:19 then you again don't see a really
47:21 clear pattern between the degree of misallocation and
47:23 either level or growth of the value added per worker,
47:26 because again,
47:27 value per worker in manufacturing is growing constantly
47:30 and then it was growing fast in the 70s
47:33 when misallocation is coming down,
47:35 but then it's also growing
47:38 fast when misallocation is getting worse.
47:39 So there doesn't seem to be a very clean pattern here.
47:42 This is just another way to look at,
47:45 now it's a popular way to look at degree of misallocation.
47:47 It's a coefficient of regress if you know of TFPR and TFPQ
47:51 at the plant level and then aggregating it.
47:54 Same pattern misallocation comes down
47:56 and then
47:57 it takes off in the late 90s and 2000s.
48:01 So,
48:01 overall,
48:02 in the 70s,
48:03 I'm just kind of adding a picture here from a paper of mine.
48:07 Overall,
48:08 the,
48:08 in the 70s,
48:09 we just saw that actually misallocation was coming down
48:12 in in Korea,
48:14 but there's something interesting pattern.
48:15 So this is
48:16 active heavy chemical industry promotion period.
48:19 So what we see is that if you actually look at
48:21 the industries and regions that were targeted by this government.
48:24 The policy,
48:25 there was a lot of increase in
48:27 misallocation within those industries and regions compared to industries
48:30 and regions that were not targeted with the policy.
48:33 So
48:34 the aggregate pattern
48:36 belies some of the more interesting features at the
48:39 more desaggregate level patterns.
48:42 Now,
48:42 in the remaining few minutes,
48:43 I'll talk about the dynamic part.
48:45 This is where really interesting things happen and it connects
48:48 very well with what Wolf was saying in terms of the
48:51 trumpeterian growth models.
48:53 So,
48:54 the first set of pictures I'll show you is this charming picture picture.
48:58 So this is the Davis Hettinger shoeching
49:02 picture.
49:03 So this is a histogram.
49:04 So what is a histogram?
49:05 So it's basically showing the
49:07 employment share of some beans,
49:09 and what are the beans?
49:10 The bean is
49:11 basically the growth rate of a plants.
49:14 So you see the number around 0,
49:15 so the employment cluster around 0.
49:17 So those are workers working in plants,
49:20 that's not really.
49:21 Growing or shrinking.
49:22 Around 11 means this
49:25 plant is growing really fast,
49:28 uh,
49:28 -1 means it's shrinking really fast.
49:30 2 is entry,
49:31 and -2 is exit.
49:33 So it kind of shows the improvement change.
49:35 It's a small change is important,
49:37 plant size
49:38 plant size is important too.
49:39 It's more big
49:41 plant size change is important.
49:43 And in the 80s,
49:44 you see the importers entry really big time,
49:47 right?
49:48 So this is the period where the employment
49:51 average size
49:52 measured by employment of plants was collapsing.
49:55 And that's because there's huge entry and
49:56 entrance obviously is smaller than incumbents.
49:59 So that's when in all sectors of the economy there's a huge spike of entry
50:02 and the average size is coming down.
50:04 But at the same time you see actually there's a lot of firms
50:07 adjusting upward,
50:08 a lot of growing firms.
50:10 But then the next period,
50:12 87 to 92 or 92 to 97,
50:14 slight different pattern.
50:15 You see that things are kind of shifted to the left,
50:18 so entry is not as important.
50:19 Now exit
50:21 actually plays a more important role,
50:22 like a minus 2,
50:23 and then the whole picture is to the left,
50:25 meaning that you see a lot of plants downsizing actually.
50:29 And I think this may be the secret to what was um
50:33 why it was possible for South Korea to sustain the economic growth so long
50:37 that
50:38 In the 70s,
50:39 the government had a really heavy chemical industry policy
50:42 and
50:42 when they stopped it in the 80s,
50:44 you have this fresh batch of new firms coming in
50:47 and that pushed a lot of bad firms,
50:50 big firms to actually
50:51 uh
50:52 downsize and exit in the
50:54 80s,
50:55 the later part of the 80s and early 90s.
50:57 And I think that's really important for understanding how the productivity
51:00 growth measured by valuable worker grew so fast in the 90s.
51:03 So this creative destruction is playing an important role.
51:06 And you look at the last period,
51:09 2008 to 2013 to 2008,
51:12 it's very quiet here.
51:14 What you can see is that while entry and exit is not playing its important role,
51:18 and also there's a lot of implement here around zero,
51:20 meaning that farms are not really growing or shrinking,
51:23 so it's very quiet.
51:24 So this is a reduction in dynamism
51:26 or reduction in churning.
51:29 So the prior set of research we just showing you,
51:31 well this is how plant size is changing,
51:33 measured by employment.
51:35 But for productivity,
51:36 we,
51:36 we really care about the correlation,
51:38 meaning you want the productive farms to grow
51:40 and unproductive farms to shrink.
51:42 So I'm showing you some of the
51:45 regression results following
51:47 uh Dekker Her Jeremy Miranda approach.
51:51 And what I'm showing you is
51:53 whether
51:54 farms grow
51:55 in terms of employment or capital or investment
51:58 when they have a good productivity shock.
52:00 And the first row numbers are positive and significant,
52:03 so that's good news.
52:04 Yes,
52:04 so farms are responding to productivity shocks,
52:07 so
52:07 uh that's good for economic growth.
52:08 But what's really interesting is
52:10 the second panel when we do things
52:12 decade by decade.
52:14 So what you see here is that
52:16 especially the employment growth part,
52:18 1990s,
52:19 that's when
52:20 the productivity is growing the most.
52:22 That's when
52:23 farms are most sensitive in terms of product of shock.
52:26 And then
52:27 the number gets really small over time.
52:29 It's been 2010 it's really quiet,
52:31 so even if you have a good pery shock,
52:33 you're not going to raise your employment and capital very much.
52:36 So there's the same picture,
52:38 the reduction in dynamism.
52:39 Firms are not really responding to perry shocks,
52:41 and I think that really.
52:43 Responds very well with
52:44 what we saw in the overall pattern that things really
52:47 slowed down towards the end of the period 200,
52:50 2010 went from stopping
52:51 uh dynamic in terms of either churning or
52:54 how responsive they are to productivity shocks.
52:57 So I'm about to run out of time,
52:59 so let me wrap up.
53:01 So,
53:01 as you saw,
53:03 at the micro level,
53:04 there was no really simple
53:05 systematic pattern of correlation between what's happening at the micro level
53:09 and
53:10 macro
53:11 transformation of the Korean economy
53:13 because
53:14 uh the economy
53:16 continues to grow,
53:16 but the size decision could move upward and downward,
53:20 and then with misallocation measures,
53:22 there's no simple pattern there either.
53:24 What's really promising was the
53:26 Uh,
53:27 the later part when
53:28 the dynamism is slowing down in terms of churning or responsivity shocks,
53:32 that's when the aggregate economy really slows down in terms of growth.
53:36 And I think that's really relevant for thinking about middle income countries.
53:40 So
53:40 this is a very promising line of work.
53:42 The problem is that
53:43 it presents a very high hurdle
53:45 for data requirements,
53:47 right?
53:47 So now you need a panel dimension
53:49 and you need to cover enough variables to compute
53:52 the productivity reform and input uses and everything.
53:55 So it may not be the best research program for low
53:58 income countries that do not have the right data infrastructure,
54:01 but for middle income countries I think it's a promising avenue.
54:04 Uh because when productivity slows down,
54:06 you don't know where to look at,
54:07 but when you have this micro evidence,
54:09 then you can be guided by economic theory
54:11 and look at
54:12 um
54:13 the sources of these
54:14 barriers and frictions that's slowing farms down and reallocation down
54:18 and that will lead us to
54:19 uh policy prescription.
54:21 Thank you.
54:25 Many thanks Professor Shin.
54:26 It's always a pleasure to hear about the,
54:28 the Korean growth miracle.
54:31 Uh,
54:32 finally,
54:32 we will hear from Professor Grubb.
54:35 Professor Greb will focus on the very important topic of
54:39 economics and policies of energy transition over to you.
54:44 Well
54:44 thank you very much indeed for the invitation and this session.
54:48 It's been absolutely fascinating.
54:50 Uh,
54:51 the only problem is it's left me with about 76 or 7 minutes to coffee.
54:55 Uh,
54:56 I'll,
54:56 I'll do my best.
54:58 Um,
54:58 so I'm going to touch on,
55:01 uh,
55:01 climate change and energy transition,
55:04 um,
55:04 just to a word of introduction that,
55:07 uh,
55:08 first,
55:09 uh,
55:09 I'm also not a development economist.
55:13 I'll be covering more of an energy economist
55:17 and special interest in innovation.
55:19 I will give a brief background,
55:22 uh,
55:22 possibly about half the time on
55:24 the context of climate change scene setting
55:28 because obviously there's a broader conference,
55:30 and then move on to particularly the innovation
55:34 dynamics that we've seen and some implications.
55:38 That's partly because climate change issues are so often
55:41 couched in.
55:43 Everything's going horribly wrong and it's a very doom and gloom scenario.
55:47 I do want to inject a perception that it's more interesting than that,
55:52 not least when we reflect on two things,
55:55 which is that economic development,
55:58 two of the fundamental factors that we've touched on,
56:00 are innovation
56:02 and cheap primary energy.
56:04 The question has always been,
56:05 is there potentially a clash with climate change?
56:08 I will be drawing significantly upon aspects of the IPCC report.
56:14 Many of you,
56:15 I think,
56:15 will know,
56:17 a lot.
56:18 The broad background IPCC is a massive international process.
56:22 These assessments roll around
56:24 every 6 or 7 years,
56:26 uh,
56:26 and we had the 6th assessment,
56:28 uh,
56:28 last
56:29 published last year.
56:31 So I'll draw quite a bit on that.
56:33 I will start with the science
56:35 and risk dimensions of it.
56:38 On the left hand side,
56:40 we have one of those famous graphs of scenarios of global temperature
56:46 suggesting a very wide range
56:49 of possibilities.
56:52 So
56:52 apparently a lot of agency in terms of what happens to the temperature this summer,
56:58 this century.
57:00 On the right hand side
57:02 you have a fairly well known IPCC diagram called the five reasons for Concern,
57:09 which range from loss of
57:11 specific ecosystems on the left hand side
57:13 through on the right hand to globally substantial
57:16 human impact and global tipping points where entire
57:20 Earth systems collapse.
57:23 The good news is on the left hand side,
57:25 I no longer believe any of those high projections.
57:27 They're not going to happen.
57:29 The bad news is on the right hand side,
57:32 all of those reds and yellows have crept downwards every assessment,
57:37 and I should have said really
57:40 intuitively you can see the yellow
57:42 is where there is a significant probability of something somewhat worrying.
57:47 The red it's getting bad and the purple is getting catastrophic,
57:52 and those two roughly.
57:54 seem to sort of cancel out,
57:56 which means climate change
57:58 looks like just as severe a problem as it always has been,
58:01 except that it's a lot more immediate and prescient
58:05 as indeed we see in the the extreme impacts
58:09 we see around the world.
58:11 So we are actually in a zone where probably looking
58:15 at somewhere between 1.5 and 3 degree temperature rise.
58:21 But that is a huge range in terms of what it may imply for humanity.
58:27 It is also a huge range in terms of of
58:29 how demanding or what it implies for the energy system.
58:32 Now that's been the scientific framing around risks.
58:36 The other framing,
58:38 if you like,
58:38 is the economics one.
58:39 What an economist by and large
58:42 is most interested in
58:43 is the trade-offs potentially
58:46 and some numbers.
58:48 Well,
58:48 we've had
58:50 a
58:50 good 2 or 3 decades of economists trying
58:53 to attach the most significant number they think,
58:55 which is
58:56 the social cost of carbon emissions,
58:58 how much damage,
58:59 what's the dollar equivalent
59:00 of a ton of carbon.
59:02 Some,
59:03 I think
59:04 a lot of non-economists find this whole idea somewhat distasteful or dubious,
59:09 and there's been extraordinary ranges in the attempts to attach a number.
59:14 But nevertheless,
59:15 it is striking that although I wouldn't say there's been a lot of convergence,
59:21 there has been a clear trend.
59:23 The number keeps getting higher
59:25 and the range on the upside to some extent gets higher.
59:28 And here in yellow I've highlighted a paper published in Nature
59:34 a few months ago.
59:35 I think relevant not just because it was in
59:38 Nature and a serious effort and serious authors,
59:40 but it was a response
59:42 to the call of the US National Academy of Sciences a few years ago saying,
59:45 look,
59:46 we've learned lots of things
59:48 and that has not been adequately put through into revising estimates of the SCC,
59:53 uh,
59:53 the social cost of carbon.
59:55 Uh,
59:55 and indeed one of the,
59:56 if you like,
59:57 the most sort of
59:59 climate economic skeptics,
1:00:00 Richard Tol,
1:00:01 uh,
1:00:02 published to say,
1:00:02 yeah,
1:00:03 actually he recognizes the cost has gone up quite a lot.
1:00:07 If you fully cost at the kind of levels suggested in that most recent study,
1:00:11 which is
1:00:12 about 3 to 4 times what a lot of economists have been saying only a few years ago,
1:00:17 then basically the cost of climate damages
1:00:19 from burning coal is substantially higher than the cost of buying coal.
1:00:23 And in fact,
1:00:24 in oil,
1:00:25 you may be looking on the order of the equivalent of about $50 per barrel
1:00:30 of oil associated with the climate damages,
1:00:32 plus or minus still a a a sizeable range,
1:00:36 but
1:00:37 that's pretty significant.
1:00:39 In terms of what we then look on in longer term goals
1:00:44 and the framing context on emissions,
1:00:47 well,
1:00:47 arising from the Paris Agreement,
1:00:50 governments agreed two kinds of emission goals.
1:00:52 It's worth saying
1:00:54 one was the objective to stabilize temperatures,
1:00:58 effectively interpreting the original convention's
1:01:01 commitment to stabilize atmospheres.
1:01:04 At a level to avoid dangerous interference,
1:01:07 governments agreed that means well below 2 degrees and striving towards 1.5.
1:01:13 And
1:01:14 the vast majority of major countries around the world have
1:01:17 declared net zero emission goals between 2050 and 2070,
1:01:23 which roughly corresponds with that range.
1:01:26 The other emission
1:01:27 goals was to be expressed nationally
1:01:30 through the nationally determined contributions.
1:01:33 On the left is the chart from the IPCC report which says,
1:01:36 well,
1:01:37 the red is where we think we were going given policies as of 2020.
1:01:43 The purple
1:01:44 is what those NDCs
1:01:47 indicated as national ambitions to 2030.
1:01:52 Obviously way off track,
1:01:55 so the Paris Agreement embodies two
1:01:57 currently fundamentally inconsistent things a long term goal
1:02:02 and a nationally determined
1:02:05 trajectories which are not consistent with that long-term goal.
1:02:09 If we go further and say what
1:02:11 The model suggest to be the
1:02:14 often least cost trajectory?
1:02:16 Well,
1:02:16 it starts by radically changing course,
1:02:19 dropping emissions by
1:02:20 somewhere between
1:02:22 25%
1:02:23 and
1:02:24 at least 45%
1:02:26 by 2030.
1:02:27 That's only 7 years away,
1:02:29 to be honest,
1:02:29 I don't think that's going to happen.
1:02:33 So if I now start moving forward to the analytics,
1:02:35 because some of you may know that background,
1:02:38 um.
1:02:39 This is an economics conference.
1:02:41 There's plenty we can say about climate change economics.
1:02:46 Probably the strap line was best captured by Nicholas Stern,
1:02:49 who described climate change as the biggest market failure in history.
1:02:53 But it has also been described as the perfect moral storm
1:02:58 because of the ethical,
1:02:58 it raises ethic issues about our responsibilities
1:03:02 to other people,
1:03:03 other countries,
1:03:04 other generation,
1:03:05 and human beings do have a sort of
1:03:08 almost infinite capacity to to try and find some way
1:03:11 of saying it's not really my responsibility per se.
1:03:14 It's been described as a super wicked problem,
1:03:16 one that is from a social science perspective,
1:03:19 just really hard
1:03:21 to solve.
1:03:23 And it's also been described in terms of psychological distance.
1:03:26 Human beings kind of evolved,
1:03:28 didn't evolve
1:03:29 to even be able to conceive about something on this
1:03:32 scale,
1:03:33 globally,
1:03:34 intergenerationally,
1:03:36 planetary risks,
1:03:37 etc.
1:03:39 In the IPCC we try and also map out the corresponding analytic frameworks.
1:03:44 We're all here familiar with aggregate efficiency.
1:03:47 I'm going to say a few words about the ethics and equity dimensions more explicitly,
1:03:52 but really focus on the third
1:03:54 innovation and transition in the second half of my talks,
1:03:58 not to forget the psychology and political
1:04:01 dimensions of this.
1:04:03 So
1:04:05 just on the equity front briefly,
1:04:07 the world is unequal.
1:04:09 I don't have time to run you through the details.
1:04:11 That chart on the right shows different regions
1:04:14 vertically per capita emissions against population.
1:04:18 The the dark blue is fossil fuel CO2,
1:04:20 the orange
1:04:22 is land use,
1:04:23 uh,
1:04:23 which is a much more concentrated issue.
1:04:26 Broadly you see substantial variation vertically between countries,
1:04:30 but not just north-south,
1:04:32 also within countries at similar regions of development.
1:04:35 Two other points I'll draw out from this.
1:04:38 Uh,
1:04:38 one is,
1:04:39 yes,
1:04:39 it's partly about trade and offshoring,
1:04:42 but actually even our consumption footprint.
1:04:45 Uh,
1:04:45 that amplifies but doesn't fundamentally change the kind of conclusions there.
1:04:50 And also
1:04:52 it's not just about between countries.
1:04:54 In fact,
1:04:54 a little bit like,
1:04:55 uh,
1:04:56 John's presentation earlier,
1:04:59 there's inequalities at every step of
1:05:00 the system within countries internationally.
1:05:03 You can look at
1:05:03 roughly 10% of the richest households around the world
1:05:08 account for
1:05:09 probably over a third of emissions through their consumption patterns.
1:05:13 I'm not going to describe the chart on the left,
1:05:15 it's a more elaborated version of what I said,
1:05:18 greater disaggregation,
1:05:19 but this time plotted against Human Development Index.
1:05:22 What it really says is,
1:05:24 All
1:05:25 countries and regions will need to change,
1:05:28 but what and how they need to change will vary a lot.
1:05:32 But a lot of the discourse now
1:05:34 is really about in terms of the development,
1:05:38 paradox and challenges,
1:05:39 not just alignment and co-benefits,
1:05:42 but shifting development pathways towards sustainability.
1:05:46 So where does technology come into it?
1:05:49 Well,
1:05:50 we've seen big reductions in the cost of renewable energy,
1:05:53 as I'm sure you'll well know.
1:05:56 These are classic charts now
1:05:58 of solar and wind and below the expansion rates
1:06:02 exponentially at rates of 20 to 40% a year.
1:06:06 Generally now,
1:06:07 both solar and wind,
1:06:08 the two biggest renewables,
1:06:11 uh,
1:06:11 not everywhere,
1:06:12 but as a broad international aggregate,
1:06:15 are cheaper than fossil fuels as a way of generating electricity.
1:06:19 That is a pretty fundamental change
1:06:22 from what we looked at a few years ago.
1:06:25 How long can that exponential growth rate,
1:06:27 and by the way,
1:06:28 even highly numerical experts,
1:06:31 I find,
1:06:32 are not actually that good
1:06:34 at really thinking what does exponential growth mean.
1:06:37 I invite you in the coffee break,
1:06:38 pull out your computer,
1:06:39 stick in exponential growth at these rates,
1:06:41 look what happens within this decade.
1:06:43 It is really quite striking.
1:06:46 And the data suggests as published last Thursday by the IEA,
1:06:50 it ain't slowing down in a hurry.
1:06:52 Expansion from about 200 gigawatts a year pre-COVID,
1:06:58 then 300 this year,
1:07:00 the IEA estimates
1:07:02 400 to 500 gigawatts of renewable energy being installed.
1:07:07 That's a huge growth rate.
1:07:10 Um,
1:07:12 OK,
1:07:12 I'm going to have to skip the audience participation,
1:07:15 but
1:07:17 All technologies,
1:07:19 pretty much in energy have displayed two things.
1:07:22 All of these turned out to be much cheaper than anyone projected.
1:07:26 And they all actually involved government action at scale
1:07:30 over many years on both the technology resource mapping,
1:07:34 development,
1:07:35 and the demand pool and price developments.
1:07:38 So where does this take us in terms of wider transitions?
1:07:41 Uh,
1:07:41 I'll,
1:07:42 I'll draw on,
1:07:42 but I won't have time for the depth,
1:07:44 obviously
1:07:45 quite a major international program looking at these issues,
1:07:48 um,
1:07:49 involve partners,
1:07:50 particularly in India,
1:07:51 China,
1:07:52 and Brazil,
1:07:53 um,
1:07:54 and we looked at a few case studies.
1:07:56 Uh,
1:07:57 wind,
1:07:57 uh,
1:07:58 both Europe and Brazil
1:08:00 moved from 1% to 10% of,
1:08:03 uh,
1:08:03 more than 10%
1:08:04 within a decade of wind energy.
1:08:06 Uh,
1:08:07 India,
1:08:08 light lighting efficiency,
1:08:10 extraordinary transformation partly driven by bulk government procurement,
1:08:14 which resulted in what they call the cheapest lighting in history.
1:08:18 Uh,
1:08:18 that sort of echoes now what the IEA says about solar
1:08:22 PV,
1:08:23 which is the cheapest electricity in history.
1:08:26 So.
1:08:27 Yeah,
1:08:27 lots of good news stories.
1:08:29 What do we learn from those case studies?
1:08:31 Well,
1:08:32 as I said,
1:08:33 significant government action,
1:08:34 largely now self-sustaining,
1:08:37 wouldn't have been pursued by the traditional economic prescription,
1:08:41 competitive markets,
1:08:42 price carbon,
1:08:43 um,
1:08:44 and probably not justified by cost-benefit appraisal if you took all
1:08:47 of the projections of how much these things would cost.
1:08:50 But the common themes cumulative progress,
1:08:53 market-based innovation,
1:08:55 sustained and targeted support beyond the pure R&D.
1:09:00 Substantial uncertainties when when these efforts set out
1:09:05 and strong international dimensions,
1:09:07 the movements of technology frontiers from one region to another,
1:09:11 partly reflecting swings of policies.
1:09:14 The point being,
1:09:15 the traditional simplified stories of either it's all about
1:09:19 government R&D drops the price and then things diffuse,
1:09:22 the A in the bottom left,
1:09:24 nor
1:09:25 the let's just do it through the market,
1:09:27 flat-paced technology.
1:09:29 Neutrality
1:09:31 and then companies will look forward and say that's what I'm going to do.
1:09:35 That's not reality.
1:09:36 Most technologies iterate through repeated cycles of
1:09:40 expansion,
1:09:41 learning,
1:09:42 innovation,
1:09:42 expansion,
1:09:43 learning,
1:09:43 innovation,
1:09:45 cost scale economies,
1:09:46 learning,
1:09:47 etc.
1:09:48 Right,
1:09:49 innovation's a complex journey and with
1:09:51 just tripped 0 time,
1:09:53 I don't have much
1:09:54 chance to go through it.
1:09:56 This slide looks terribly complicated.
1:09:59 It basically says it's not just about technology.
1:10:02 Every stage of this process requires developments in
1:10:05 all of those six factors you can see on the left.
1:10:08 All of those to some degree have to develop along with the technology.
1:10:13 It's more of a sort of a map of a multi,
1:10:15 multi-level journey
1:10:17 to point to examine where might the big blockages occur
1:10:21 that would get in the way of a fuller transition.
1:10:24 So this will bring me towards final remarks.
1:10:29 And I should have said I know
1:10:31 I had a choice to make.
1:10:32 Either I could try and pretend to be a
1:10:34 development economist or I'd stick to what I know.
1:10:37 There are a lot of issues,
1:10:38 and in a second I'll wind up by by noting some of the barriers.
1:10:42 But basically
1:10:43 transitions always start small,
1:10:45 may take years after technology emerges through a phase of
1:10:48 market emergence before they really emerge into the mainstream.
1:10:53 The impact on incumbent industries is often barely visible during that process,
1:10:57 why?
1:10:57 Because exponential growth starts small,
1:10:59 looks small,
1:11:00 and the big,
1:11:00 big,
1:11:00 big boys don't really notice it.
1:11:03 But boy do they start to change things at the margin and then more fundamentally.
1:11:08 I've got another version which shows some of
1:11:09 the incumbent industries as opposed to technologies,
1:11:12 maybe jumping on the rising blue curve,
1:11:14 as was already alluded to.
1:11:16 But a key thing tends to be
1:11:19 at this sort of
1:11:21 phase in the middle that requires a reconfiguration often of market structures,
1:11:26 because of all energy markets ultimately are human creations,
1:11:30 and the kind of markets that suit fossil fuels don't necessarily adapt well
1:11:35 and most cost effectively for renewables.
1:11:37 So this other chart takes uh actually a sort of
1:11:41 concept from a book that we published a few years ago around 3 pillars of policy
1:11:45 and turns it into a dynamic chart
1:11:48 that
1:11:48 one does need phases of strategic investment to foster emerging technologies,
1:11:52 businesses,
1:11:53 etc.
1:11:55 Then one needs to evolve,
1:11:56 refigugure infrastructure
1:11:58 and market structures to adapt suited to new technologies,
1:12:02 build up the scale and technologies,
1:12:04 supply chains,
1:12:05 accelerate global diffusion.
1:12:07 You may well also have issues of attending to standards,
1:12:10 behavior,
1:12:11 uh,
1:12:12 norms,
1:12:13 and maybe measures to address er laggards and and obstacles.
1:12:18 So,
1:12:18 um,
1:12:20 I'm going to finish the slideshow there with,
1:12:21 with one exception,
1:12:23 but let me just say,
1:12:24 of course,
1:12:25 uh,
1:12:26 capital finance are absolutely crucial.
1:12:30 Uh,
1:12:30 if you look generally in aggregate,
1:12:32 you'd say to meet the kind of transitions projected by the IPCC as needed,
1:12:36 we need to expand the pace of international investment or global investment
1:12:41 by a factor of between 3 and 4 compared with 2020 levels.
1:12:45 And if you look differentially,
1:12:48 of course that gap is bigger in much of the developing world.
1:12:52 I won't run you through
1:12:53 those numbers,
1:12:54 but what is required
1:12:56 to actually get such large scale investment
1:13:00 into what look like potentially
1:13:02 attractive
1:13:03 investments is not trivial,
1:13:05 and of course it depends a lot on the cost of capital.
1:13:08 Um,
1:13:09 I think all other presenters have presented econometric studies.
1:13:13 I've sort of got one in my back pocket which we expected to
1:13:16 give a more technology oriented answer to the cost of capital and it,
1:13:21 and you know,
1:13:21 cost of technologies including solar.
1:13:24 It basically said looking across countries,
1:13:26 it's access to capital markets,
1:13:28 the terms and the perceived country risks
1:13:30 that are dominant factors.
1:13:32 So still big issues that obviously the World Bank
1:13:35 and others need to figure their way through.
1:13:38 My concluding slide
1:13:40 is
1:13:41 just to do a simple diagram but conceptually still quite powerful
1:13:46 and and a set of messages.
1:13:48 If you look at this,
1:13:49 it's not just a new technology,
1:13:51 a few green technologies,
1:13:52 it's an completely different structure of our energy systems.
1:13:58 For all kinds of reasons I could go into,
1:14:00 the transition is already underway,
1:14:02 but so far it's driven
1:14:04 more by non-market policies,
1:14:07 and that is proportionately changing
1:14:10 as these mature
1:14:12 and driven more by
1:14:14 the centers of innovation.
1:14:15 That also has to change.
1:14:17 I think one still needs elements of those three pillars of policies that I flagged,
1:14:22 and we are talking about effectively industrial development strategies
1:14:26 that help to shift development pathways.
1:14:29 Um,
1:14:30 we do see growing social in social and innovation and engagement,
1:14:36 um.
1:14:38 There's a long way to go,
1:14:40 but I hope I've given you some signs of hope,
1:14:43 er,
1:14:43 as well as acknowledging we face massive challenges.
1:14:47 Thank you very much.
1:14:49 Many thanks Professor Grubb.
1:14:51 Excellent presentation.
1:14:53 Um,
1:14:54 we are over time,
1:14:55 but I would like to give the opportunity,
1:14:58 uh,
1:14:59 to the ones in the audience.
1:15:00 I don't know if we have questions online,
1:15:02 but
1:15:03 to give the opportunity to one or two questions,
1:15:06 uh,
1:15:06 before we go,
1:15:07 uh,
1:15:07 for the coffee break.
1:15:09 Um,
1:15:10 if you have a question,
1:15:12 please come to the mic,
1:15:14 be succinct,
1:15:15 and,
1:15:15 uh,
1:15:16 indicate who are,
1:15:17 uh,
1:15:18 directing the question to.
1:15:25 If there are no questions,
1:15:26 we can go for coffee right away.
1:15:31 You have a great can you please come to the mic?
1:15:37 Good
1:15:37 morning.
1:15:40 I was impressed by the presentation from.
1:15:44 The gentleman who presented on Korea.
1:15:47 Growth in industry,
1:15:48 but
1:15:49 what I did not
1:15:50 get from that presentation was the state of the political economy,
1:15:54 the culture,
1:15:54 and the dynamism,
1:15:55 because
1:15:57 when Oadr read about
1:15:58 the miraculous growth of South Korea,
1:16:01 they link it to the leadership at the time.
1:16:04 So I would have expected that
1:16:06 although light would have been thrown on a level of leadership,
1:16:10 commitment and trust
1:16:11 that engender growth during that process.
1:16:13 So can you share some light on that,
1:16:15 please?
1:16:15 Thank you.
1:16:17 Thank you for the question.
1:16:18 Uh,
1:16:18 obviously,
1:16:19 there's a very interesting story behind it.
1:16:21 In the 70s,
1:16:22 80s,
1:16:23 the really successful uh outcome
1:16:25 was
1:16:26 really
1:16:27 driven by close collaboration between
1:16:30 uh the government leadership and also some of the leading businessmen,
1:16:33 the founders of Samsung and Hyundai,
1:16:35 they're very special people.
1:16:38 Then,
1:16:38 as you noted,
1:16:39 the political economy problem.
1:16:41 Uh was formed because even though the government formally stopped
1:16:45 doing industrial policy in the 80s,
1:16:48 there was this ongoing implicit
1:16:50 support
1:16:51 and understanding
1:16:52 going on,
1:16:53 which was still fine,
1:16:54 but once you go to 2000,
1:16:56 that became really serious
1:16:58 problem
1:16:58 because as Wufu was saying,
1:17:00 now they became just too big.
1:17:02 They were so dominant in so many different industries
1:17:05 and then without any
1:17:07 uh explicit
1:17:09 Uh,
1:17:09 regulation
1:17:11 They were able to abuse their market power and enter
1:17:13 into many different industries and deter the entry of the
1:17:16 uh the entrepreneurial activities,
1:17:18 and that is a big part of the slowdown in productivity growth towards the end.
1:17:22 So,
1:17:23 it can,
1:17:23 we cannot really say that it's a good thing or a bad thing.
1:17:26 Sometimes a close relationship and cooperation
1:17:28 can help the whole economy lift the boat.
1:17:32 Everybody's sport,
1:17:33 but then sometimes actually if they start to use it in the wrong way,
1:17:36 then it could be very hurtful for the agri-economy,
1:17:39 just become a rent seeking
1:17:40 activity for some.
1:17:41 It's a very
1:17:42 complicated story.
1:17:46 Thank you.
1:17:54 Uh,
1:17:54 my question's for,
1:17:55 uh,
1:17:55 Professor Friedman regarding your presentation,
1:17:59 um,
1:17:59 as you talked about the
1:18:01 children of an earlier age going to the more elite,
1:18:04 um,
1:18:05 neighborhoods per se,
1:18:06 how would you reflect that on the reverse for
1:18:10 putting like
1:18:11 An institute in a lower income,
1:18:14 um,
1:18:15 neighborhood,
1:18:16 would you have the
1:18:18 same effect or an adverse effect?
1:18:22 So,
1:18:23 um,
1:18:24 are you,
1:18:24 are you asking about,
1:18:25 um,
1:18:27 The particular policies in order to help children rise up or more like
1:18:31 downward mobility as a flip of the side of the coin of the
1:18:35 of upward mobility?
1:18:37 OK.
1:18:38 Um,
1:18:38 yeah,
1:18:38 I think what the literature shows is that,
1:18:42 um,
1:18:44 You need two different things I think in order to give children from,
1:18:49 uh,
1:18:50 lower income or lower socioeconomic backgrounds opportunities.
1:18:54 One is just
1:18:56 the institutional pathways to get them into,
1:19:00 um,
1:19:01 better education,
1:19:03 better health,
1:19:04 um,
1:19:06 the types of things that are gonna propel them along a career.
1:19:09 But then also
1:19:11 what you need is the
1:19:13 social and network structure in order to make this something that they want to do,
1:19:18 right?
1:19:18 So for instance,
1:19:19 you know,
1:19:19 one example,
1:19:21 um.
1:19:22 You know,
1:19:22 in India,
1:19:24 the.
1:19:26 Admissions exams to some of the,
1:19:28 you know,
1:19:28 very elite,
1:19:29 um,
1:19:30 IIT IIM,
1:19:31 those types of places,
1:19:32 right?
1:19:32 There's uh,
1:19:33 I don't wanna like
1:19:34 say that that's the optimal system because I think it creates a lot of,
1:19:37 um,
1:19:38 inefficiencies as well,
1:19:39 but there's a sense that like this is something that everybody wants to do
1:19:42 and no matter what your background is,
1:19:43 there's this test.
1:19:44 All you need to do is do well on the test.
1:19:45 You don't need parental connections.
1:19:46 You don't need anything,
1:19:47 just like
1:19:48 do well on the test.
1:19:49 Um,
1:19:50 and
1:19:51 again whether that's the right system or not,
1:19:52 what that does do is it creates,
1:19:54 uh,
1:19:55 a situation where people from all sorts of different backgrounds
1:19:58 feel like this is they,
1:19:59 they know about this,
1:20:00 this is something that they
1:20:02 can think about doing,
1:20:03 um,
1:20:04 it,
1:20:04 it has the appearance of,
1:20:05 of something that anybody can get into,
1:20:07 um,
1:20:08 there may be children in,
1:20:10 you know,
1:20:10 lots of different
1:20:11 villages and neighborhoods that,
1:20:13 that have access to that,
1:20:14 um,
1:20:15 you need that in addition to the institutions that,
1:20:17 that do the training as well.
1:20:21 Thank you.
1:20:22 Uh,
1:20:23 with this,
1:20:23 I would like to conclude this session.
1:20:25 Uh,
1:20:26 I will not attempt to summarize what we
1:20:28 learned here today was a very rich discussion.
1:20:31 I would like just to say that I took,
1:20:34 uh,
1:20:35 This take away I think it's very important that countries uh adopt both policies
1:20:41 to support competition,
1:20:43 innovation,
1:20:44 and
1:20:44 equal opportunities
1:20:46 in order to achieve sustained growth.
1:20:49 Thank you so much to all the panelists.
1:20:53 And for the very good discussion.
1:20:57 And
1:20:58 we'll see you at the,
1:20:59 at the coffee break.
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