Good afternoon and welcome to I think, the third in this term's series on the evolving economic thought.
And thank you for coming along fine afternoon. We've got Dr Penny Mealey to talk to us today.
And he's a postdoctoral research officer at the Institute for New Economic Thinking here, the Oxford Martin School,
and also a lot of other fellow on the school's programme on the post-COVID carbon transition.
She's also a research associate at the Penitentiary for Public Policy at Cambridge.
Her research looked at novel, data driven ways of analysing economic growth and development.
Occupational ability and the post carbon transition to better understand the economy as a complex system.
She also leads a project on practical wisdom in a complex world of exploring what the Aristotelian
notion of living well might mean in the context of the complex adaptive systems that shape our lives.
But, she tells me, is what comes after you've navigated knowledge?
So we're going to hear from Penny for about 40 minutes or so, and then there'll be a question and answer session.
So plenty of scope to explore her ideas so I can listen to the stage.
Thank you, Julianne, it's real pleasure to be here and a privilege to be part of this lecture series.
So the lenses and tools through which we observe our world ultimately shape how we understand it.
And one of the most famous examples of this, of course, is Galileo, who,
after looking through one of the world's earliest telescopes, saw things that we'd never seen before.
And on the basis of what he saw, he went on, or be it rather painfully and slowly to revolutionise the way that we understand our planetary systems.
But what about our socio economic systems?
In the previous theory, lecture of this series does inform us about the potential for analytical methods in complexity,
economics to shape and potentially even revolutionise the way we understand our economy.
And what I want to talk about today is some of the analytical methods that we've been
using to get some better insights into something that is notoriously difficult to see,
to measure and to quantify. And that is our knowledge.
So when you think about, you know,
why some places are more prosperous than others or why it is that you and I enjoy higher living standards than our ancestors,
many people such as Joe Mulcair and Ricardo Houseman have stressed that, you know,
we're not any wealthier because we, as individuals are any smarter or more intelligent.
We're wealthier because we as a society have become much better at collectively
cultivating and coordinating different types of knowhow in different people's brains.
The problem, of course, is that this tapestry of knowledge is actually quite challenging to measure.
As economists, we often analyse our world using production functions,
which tend to aggregate things into a sort of homogenous clumps of capital and labour,
and we throw in a factor or two to represent the improvement right in our technological progress or productivity.
And while these analytical methods are useful in some settings,
they don't really give us very meaningful insights into the type of capital that you'd need
to say set up a wind farm or the type of capital you need to develop a garments industry.
They don't tell us anything about the type of skills or know-how that you'd need
to build a bridge compared to what you'd need to perform open heart surgery.
Now, sometimes production functions. Do you distinguish between high skill and low skill human capital?
And while this is an important step forward,
it still doesn't really allow us to merely meaningfully distinguish between the very different types of knowhow that two highly skilled professionals,
such as a doctor and lawyer, might have. Both have been to school for the same number of years, but both know how to do very different things.
So a different approach,
which is largely being pioneered by people like Ricardo Houseman and Cesar Hidalgo have been to just sort of move away from these capital and
labour aggregates and instead try to use detailed data on what countries and regions produce in order to infer information about what they know.
So, for example, with detailed employment data, I could look at a place like Oxford and see that compared to the UK average.
Oxford has a lot more people employed in things like higher education, publishing of books, research and development.
Do these things sound familiar to you?
And on this basis, I might infer that maybe Oxford has some sort of industrial knowhow in these particular areas.
I could do the same thing with a place like Liverpool and see evidence of some very different types of industrial knowledge and capabilities.
We could also do this at the country level using data on countries and products.
So here what I'm showing you is the UK and you can see that compared to the global average,
the UK exports many more millions of dollars in terms of cars, medicaments, aircraft, parts and whisky,
whereas Nigeria is at full basket is much more concentrated in things like petroleum products,
which suggest that perhaps Nigeria has a very different set of productive capabilities.
The problem, of course, is that if we try to do this, let's say for all of the three hundred and eighty of the UK's local authorities,
we try and look at whether each of the UK's local authorities have some kind of industrial expertise in any of the 200.
70 odd categories that we class industries into, we end up with something like over hundred thousand possibilities to consider.
And if we do this for countries and we look at whether a given country has any comparative
advantage in any one of the five thousand or exports that we class products into,
we end up with something like million possibilities. So how can we avoid getting lost in these really high dimensional knowledge bases?
So one approach is to take some inspiration from our much loved libraries who's been to a library recently.
Anyone who has so many books that they actually don't need to set foot in a library because all I can see these people,
I was actually going to embarrass Eric by about this. But as he is not here, I won't say anything.
So when you go to your library, how do you find that books tend to be ordered?
Some people I know ordered their books by colour, which is a most beautiful display,
but usually libraries use something a little bit more practical, such as the Dewey Decimal System.
And what this tries to do is it tries to put books about similar topics close
together in the library shelf so that you can try to minimise the time that
somebody who is interested in a particular topic spends walking back and forth across the library so we can actually do a similar thing without data.
Using a nifty algorithm,
we can collapse these sort of high dimensional space of countries and the products that they export onto a single one dimensional vector,
which may be known to some of you as the Economic Complexity Index.
And what this does is it tries to put countries that have similar exports close together, you know, ordering.
And countries with this similar exports far apart.
So when you have a look at sort of the two ends of a library shelf, you can say at one end you've got countries like Japan, Germany,
United States, you have similar exports to each other and actually maximally different exports to countries like Libya and Nigeria and Cameroon.
And just like the Dewey Decimal System remembers these delightfully coloured tables laminated to their library.
So our algorithm puts out a similar sort of look up table,
which gives us a kind of rough indication of the types of products that countries along our library shelf tend to be concentrated in.
So it's also this this ordering is known as the economic the product complexity index.
And what you can see is that countries that one in some countries like Japan, Germany,
the United States tend to have exports that are more closely concentrated in things like machinery, electrical and chemical products.
While our countries at the other end of our library tend to have exports that are more concentrated on things like textiles and mineral products.
So what does this have to do with economics, you might ask? Well,
it turns out that this library ordering of countries in terms of the similarities of their exports is closely correlated with their per capita income.
It's also been shown that this ordering is more predictive of economic growth
than the variables that economists traditionally put in our growth regression.
So things like human capital, education, competitiveness, institutional quality and so on.
And what this really suggests is that the types of things that countries know how to export really matters for their prosperity.
Countries that know how to export things like machinery,
electrical and chemical products that do tend to be more technologically sophisticated tend to
have higher per capita income than countries that sort of export mineral products and textiles.
That involves less technologically sophisticated how. So what about places within countries such as the UK?
Again, we can take our detailed employment data and using our nifty algorithm,
we can collapse our employment data on UK local authorities and industries onto a single dimension that puts faces
with similar industries close together in our library shelf and places with these similar industries far apart.
And here we go. We can see you've got on one side of the shelf places like City of London, Tower Hamlets, Westminster,
who have similar industrial strength to each other and different industrial strength to places like Falkirk and Sidgmore in Dudley.
And again, we can use our Dewey Decimal System to get a rough sort of sense of the types of industries that these places are sort of specialised in.
And again, we find that what in what local authorities know how to do also matters for their economic prosperity.
Places that are more concentrated in things like finance,
insurance and information do tend to have higher earnings per capita than places that are concentrated in things like agriculture.
So I just want to highlight that this dimensionality reduction tool takes in no information about places,
income per capita or wages or even the classification of economic activity.
All it sees is this pattern of ones and zeros that indicate whether a place is specialised in something or not, but kind of like a compass.
It turns out to be particularly useful at navigating these high dimensional knowledge landscapes if you like,
and sort of pointing in the direction of activities that tend to be associated with higher levels of prosperity.
And so far, we've found that it as well as working with the export data, it also works in data on UK local authorities,
data on U.S. states, data on Chinese provinces and data in Mexican states.
One of the most interesting things we found about our compass of sorts is that
it also turns out to be particularly useful when knowledge landscapes shift.
So in some work with Ricardo Houseman and Darren Farmer, we apply the same dimensionality reduction approach to look at US Census data
stretching over one hundred and sixty years of development history from 1850 to 2010.
And quite remarkably, we found for every decade in which we had income data on record.
We find that similar correlations with per capita income and also our measured it turns out to be predictive of growth,
but we also saw something we didn't expect to see.
So here what I'm showing you is the trajectory of two groups of states that for a good hundred years,
they occupied a fairly similar position in our library ordering,
suggesting that they had fairly similar specialisations in economic activities, but also that they had fairly similar levels of per capita income.
Check out what happens in 1970 1980. You can see a complete Richelle shelving, if you like,
of our library show where these two groups of states that used to have our fairly similar economic profiles suddenly become very different.
And to understand a little bit more about, you know, what underpins this change,
we can look at our Dewey Decimal System and see how this classification changes.
We can see that for the first hundred years of our data period,
professional technical and finance activities tends to be co-located in states
that also have concentrations in production or manufacturing industries.
But over this 1970 1990 period, you can see that actually these activities become very spatially segregated.
And. It is important to note that, you know, these ships we're seeing are not due to some artefact of the classification changes.
They are also not picked up in measures that economists traditionally used to look at structural change or things like that,
change in employment shares and so on and so forth. But we do find that this remarkable,
distinct divergence in knowledge turns out to be predictive of the future divergence in these two groups of states.
So in 1970, there was no significant difference between our red states and blue states.
But by 1990, the incomes of these states really become very different and remain different to that.
From a political perspective, it is interesting to note that our ordering of states in terms of their similarity
in their economic specialisations does tend to correlate very strongly with the
results of the recent US presidential election and actually explains more variance
in the Trump vote than variables related to educational attainment by state.
We find a similar thing when we look at how the UK local authority ordering correlate with our.
Our recent most recent Brexit referendum. So what this really seeks to show us is that both economically and politically,
what places know really matters and the segregation in knowledge could matter more than we realise.
Not just for places, but also for people to say Here,
what I'm showing you is a network where each node is an occupation and its occupations are linked to each
other on the basis of those the likelihood in which a worker can transition from one job to another.
So we've based this network on data from the US from 2010 to 2017, and we've also coloured the nerves by their broader occupation classification.
So straight away, you can see that people don't really switch jobs at random.
There's quite distinct clustering in the way that people transition from one job to another.
I wonder if anyone can see something else that is quite striking about this network.
How about now when you look at the proportion of male and female workers in each occupation,
you can see that the US labour force basically splits in two halves a male off and a female half.
And unfortunately, all the gender stereotypes still hold.
So this really illustrates the systemic and entrenched nature of occupational sex segregation,
both at the level of occupations, but also in terms of career paths and occupational mobility.
When we look at education, we see a similar distinct cluster pattern, but it cuts across the labour force in a slightly different way.
Wages also tend to be fairly clustered.
But one thing we've been looking at recently is the clustered pattern of occupations in these occupational mobility networks.
When you look at the estimated likelihood or susceptibility of automation,
which has been estimated by two Oxford Martin School fellows Coal Freight and Michael Osborne.
I'm going to come back to this in a little bit.
I just want to highlight that, you know, in economics, when we model labour markets, we often assume that labour is fairly mobile or flexible,
and we kind of assume that people are a bit like plasticine where if you kind of lose your job in one area,
you can fairly easily easily remould yourself into a job in another area.
But when you look at the empirical patterns associated with occupational mobility, you can see that this assumption is possibly not that realistic.
So to explore these job transition patterns a little bit more,
we looked at how similar occupations are in terms of the discrete tasks that workers in these occupations have to know how to do.
So, for example, a podiatrist has to know things like how to fabricate medical devices, how to prescribe medical treatment,
how to direct organisational operations,
while a police officer has to know how to investigate criminal matters and how to direct organisational operations.
A nurse, she says, two tasks in common with a podiatrist.
And in this example, she shows one task in common with a police officer.
So on the basis of this sort of overlap in the tasks people undertake, we construct a measure of occupational task similarity.
And what we find is that these occupational tasks similarity turns out to be fairly predictive of future job transitions.
It actually turns out to be more predictive of all the benchmarks that we could find.
So again, what this suggests is that even at the individual level, what workers know how to do,
particularly the things that they learn in their day to day jobs,
tends to be particularly important for conditioning where they can find jobs in the future.
Recently, Maria Del Rio Schneider, who's a DPhil student with us at Einat,
took this work a lot further, where she developed a new model of the labour market,
which tried to take this empirical occupational mobility network and see what would happen if we simulated how a shock in labour demand,
such as automation, propagates through this empirical network.
And what she found, somewhat not surprisingly,
is that compared to a world where you've got a fully flexible plasticine labour force out of some occupations,
once you take into account where they can switch in their network,
they're going to have a much harder time finding a job if their job is automated than if we just assume that they could switch jobs at random.
And not surprisingly, the jobs that tend to be most severely affected are the jobs in this,
this space up here where you can see how difficult it is for workers who currently have a high risk of automobile to in their job.
It's so far away to find a low risk blue job in this occupational mobility network.
We also had a look at what this picture looks like for brown jobs or dirty jobs,
which just jobs that have a much higher probability of being employed in emissions intensive industries.
And you can see that the picture doesn't look quite as stark.
There's a few more transition possibilities, although they do look like they might be at risk of automation as well.
But one of the problems with brown old dirty employment is that it tends to be fairly geographically concentrated.
And this also tends to exacerbate the employment impacts associated with trying to transition out of things like emissions intensive industries.
So these things kind of speak to some of the issues we face due to the tendency for knowledge to sort of get locked in.
And so some people I have been thinking quite hard about,
then how do we break out of existing knowledge structures and how might we diversify into new industries?
And some of the again pioneering people here have been recorded Houseman and Cesar Hidalgo,
who said, This is the place you are really good at making T-shirts.
It would probably be fairly easy or straightforward for you to transition into making something like blouses and trousers.
Why? Because they kind of involve similar types of skills and no housing materials and so on.
But suppose you wanted to transition from making T-shirts to making something like a solar panel or pharmaceuticals?
Well, this would require a much larger leap in the things that you'd have to learn how to do.
So to bring this intuition to the data, we can actually just look at the likelihood that say a country is able to export something like trousers,
given that it already exports something like a T-shirt.
And we can put a number on this and compare it to the likelihood that a country is able to export pharmaceuticals,
given that it can also export T-shirts. And by looking at what countries are already able to export,
we can use a bit of maths to then determine that the the exports of the products that they would find it most easily are easily able to transition to.
Given what we know that they already know how to do so just to show you an example of
the United Kingdom here what I am plotting each dot here is a product which is blue.
If the United Kingdom is currently exporting a larger share than the global average and grey otherwise on the y axis,
I'm plotting each product our position in our library ordering oil.
So that is a product complexity index. And so you've got kind of machinery.
Electrical and chemical products can be further up in our y axis and products related to textiles and mineral products for the down on the x axis.
What I'm showing you is our estimate of the difficulty for the UK to transition to these products,
given all the other products that we know can already competitively export.
So some of the product is already good at doing things like radiation apparatus, parts, spectrometers, microwave tubes.
But something a bit more interesting is the things that it might be able to transition into in the future.
And these are things like pacemakers, X-ray tubes, parts of nuclear reactors and from a sort of industrial strategy perspective,
we might ask, Well, why isn't the UK currently exporting these products?
Are there some kind of binding constraints that we might be able to remove that could open up these future growth opportunities?
Is it something like skill shortages holding us back, something to do with the regulatory landscape?
Could it be infrastructure deficiencies? And on the basis of our answers to these questions,
we could then possibly create fairly targeted policies to involve inform some kind of data driven industrial strategy.
Now, proponents of industrial strategy often remind us that growth has a direction as well as a right.
And so we might ask, well, what if we want to move in a green direction?
What kind of know how might be useful for transitioning to a green economy?
So in some work with Alex Taleban, we tackle this question by collecting data on green products or products with environmental benefits,
by searching for every single environmental goods classification we could find.
And we've just plotted them on the same space so he can see for the UK all the green products in our dataset.
Here are some of the products that the UK is currently competitively exporting.
So things like micro terms, spectrometers again chromatography.
These things tend to be used in environmental monitoring, particularly related to monitoring air pollution and also water treatment.
And we can also identify some of these green possible growth opportunities for the UK that are fairly close to the UK's existing capabilities.
Here you can see things like optical instruments, which tend to be used in solar concentrated so solar power.
And we've also got things like a gearing and speed changes, which tend to be used in the gearboxes of wind turbines.
But in this work,
one of the key things that came up quite dramatically is that some countries are much better positioned to thrive in the green economy and others.
So if I compare the UK's green opportunities to my home country in terms of the difficulty that these two countries face in transitioning,
given what Australia is currently good at exporting, so many of these great opportunities are just so far out of reach.
So when it comes to the green economy. The reality is, for many countries, small steps in knowledge simply isn't enough.
We need to learn how to make large leaps. Again, people like Mariana Mazzucato have talked,
therefore about the importance of mission orientated thinking and setting their lives ambitious goals.
As John F. Kennedy spoke about, setting in a man in the Moon was important.
Not because it's easy, but because it was hard, and because setting this audacious goal served to really organise the best of our skills and energies.
But something that I think is possibly less appreciated, particularly in economics,
is that sometimes little changes, sometimes little people can have really large effects.
And as a group of us recently wrote in a science commentary piece,
if we can become better at understanding how to identify areas in complex socio adaptive systems that are sensitive,
where small targeted intervention could be amplified through positive feedback effects,
then we could reach our desired destination much more easily and at much lower cost than we originally envisioned.
So just to wrap up. The space of knowledge.
That we've talked about today, as you can see. Although it used to be fairly difficult to explore empirically with new tools and new data,
we becoming much better at understanding and how to understand how to navigate some of its contours and charting out potential future pathways.
And since Galileo, you first looked out into the sky and saw what he saw,
our understanding of the planetary system has has really made extraordinary progress.
But one of the key differences, of course, with our planetary system and our socioeconomic system is that for us,
our societal knowledge, this is actually us.
We don't just explore or analyse this space, we we think and we feel it, and now ultimately we shape it.
And this reflexivity of the relationship can be challenging from an analytical perspective.
But more importantly, I think it serves magnify our responsibility.
Because we get to decide here how we choose to make this space more inclusive,
how we choose to involve as many places and people as possible and seek to ensure
that they can meaningfully contribute to our collective space of knowledge.
And I think if we could mitigate against some of the divisiveness in knowledge that we see is quite rampant in this world today,
this could be a type of knowledge that matters more than anything else.
Thank you. It's got some time for some questions I should just warn you, as this is being filmed and live webcast,
so if you don't want to be filmed in live webcast, don't ask a question.
And so anyway. Should I tell who am or just?
Yes, please.
OK, maybe Stefano Bove, I'm working as an expert on sustainable development enterprise and designing economic group at the European level.
And the question is, it's a very good design presentation, but who do you think you should implement?
All this approach is not the government. I mean, the government should know everything.
Data bring the data together and try to create a knowledge and therefore a strategy.
And second is that is the motivation is to have a more advanced tools that he can combine data and imaging to create a new economy,
then to use a tool already conditional. This is a wonderful question.
So you just on your first point. Who should be doing this?
Who should use this type of an analytical tools, I think.
Well, from my perspective in economics, as researchers are status,
very few mainstream economists tend to use these largely because they just novel data driven tools.
But I think if we started to get in within research, some of these more empirical results through,
then that could possibly challenge the way the mainstream economics approach tends to most things,
as you see, you know,
modelling the labour market could be drastically improved if we started to take some of these empirical patterns more seriously into account.
But moving beyond research, I think while research does inform government and government is always a one
way in which research shapes how the decision making process is undertaken.
I don't see why industry can also use these type of analytical tools for looking at,
you know, where are the new industrial opportunities that we might be able to move into?
I think individuals could also benefit from looking at, you know,
when you're thinking about your career moves or occupations, where are the job opportunities that are likely to be out there?
So I sort of see it as something that is offering benefits to many people,
and I hope over time it does become more widely used now to your second point.
Can you remind me of again amendment?
I guess I make a this that the government, the government has to develop some kind of data hub at the national level.
And also provide the tools in this data where Citizen Kane is, he use it?
So the question is, is this kind of centralised data hub with data in other ones, a tool,
then they can implement that everybody private and public sector, or they can then use to move and fix that one?
Tremendous. So in other words, what is missing is a strategy search strategy strategy.
Yes, I completely agree.
And so there are some websites that does make the export data that I showed you available and with fairly kind of easy to use interactive tools,
you can look up your country and the types of products that are closest to it and so on and so forth within regions, less so.
But, you know, with love and with resources to do something like this, to make it more available to people, for sure.
I've now got two questions. But the first one was actually just in terms of the data.
How easy is it to sort of collect this data and how constrained by what's actually available because I was thinking,
it's interesting, you know, your duty system of classification.
But if you are constrained by the categories and the way that we actually look to measure these things,
which presumably have been like that for a very long time. So even within that and the way things are classified, it must be difficult to.
Absolutely. It is a real challenge existing classification systems.
There are some new approaches that try to use machine learning methods and even more detailed data, such as, you know,
the text in the job ads that people placed online and these sorts of things to try and get at an
alternative way of inferring information about the types of skills and know how that people have done.
Farmer and a few others have recently been to the onus in the UK.
As you know, how do we get better data and how to improve the quality of our data so that we can do a lot more,
not just and sort of analyse it, but in terms of modelling things we as doing spoke about last week.
We just really, as you said, hamstrung by the quality of the data.
And I think this has changed, though, and I think as we understand more the value of data and the importance for,
you know, prosperity and also navigating our pathway, more and more resources get spent.
That's that's yeah,
because I would have thought the class of the way things are classified might be hiding things that are actually happening in the data.
I'm sure they are. I'm sure they are. And with more detail, better data we could certainly see.
I'll say my second question is looking for data to see who else wants to.
This one here. Hi, Brad Barlow.
Do you feel it in the Christ Church, in theology and economics? So it.
I think economically, we usually think of human labour as one of the most substitutable were adaptable factors of production, if you will.
So it's really interesting to me what you talked about, the way that, you know,
people, the types of transitions they make are actually trapped in a sense.
But I wonder if you could speak to the intergenerational aspect because it's one thing to say that, you know,
someone who in their early years were trained in a certain way is likely to follow a certain path.
But what about the move to their next the next generation, so to speak?
And do you have any data to suggest those types of shifts where you see those kinds of transitions that wouldn't have been as common?
It's an excellent question. It is, and that some analysis we are currently doing, we don't have the results yet.
We were looking at how these occupational mobility patterns differ by cohort.
I don't unfortunately have the slides to show you,
but we do expect to see quite different patterns possibly cutting through in different directions across the labour markets.
But yeah, unfortunately, in the few months, we should have some more definitive answers on this question.
Yeah. Thank you, Anthony David.
I'm retired, I'm a former CEO of a tech company. I feel the ghost of Hans Rosling hovering somewhere here.
But my question really is about the validity of data when you take international comparisons because
I think the impact of multinationals in an increasingly globalised world must distort their picture.
Quite like you quoted Nigeria, for example, as having expertise in oil, but surely most of that is actually the activity of multinationals.
Yeah, it's a really good point. And again, with better data, with access to, for example, multinational companies supply chains,
we could do extraordinary things but gain access to supply chain data is something we've been
trying to do for a good year and a bit with much difficulty for understandable reasons.
But if you know any companies who would be willing to share their data with us, we would love to hear from them.
Thank you very much for your presentation. I was looking at the data from the reverse point of view because you were looking into
policy making to where you couldn't see a positive relationship between A and B.
And you can say and you showed that very clearly. I'm wondering whether one could reverse it and say if people, for example,
move unusually from an area or from a skill to another one, unusually not another, was that atypical?
Is there any way you can analyse that atypical data to find out why they've
done it and what skills they might have needed to achieve that unusual move?
I'm thinking particularly of an analysis of creativity and which isn't covered.
I think really by what you're saying so much and how that might be a metric for actual positive change where it might not otherwise be expected,
but might well be needed. I think that's a brilliant idea for sure.
Certainly, when we were looking at the US data over a long period, we were looking for anomalies.
The problem is a little bit where, you know, you've got small samples,
and so it's hard to infer too much from these, as you said, unexpected or rare occurrences at the country level.
I guess one of the most well-known and applauded divergence from the usual path is a country like South Korea who has made extraordinary progress,
and they actually did make quite a large leap in their knowledge.
And we know that that was underpinned by quite a focussed industrial strategy to really take them from what was
largely rice growing to sort of shipbuilding and then to the technological capabilities that they have today.
And I think you're absolutely right.
I think to the extent that we can get better data and see more and more instances where you see people really bucking the trend and doing unexpected,
extraordinary things, I think they are very informative. And then certainly a really helpful future direction for research.
Thank you. Good point. OK, there's a more.
I was just interested in that side that you showed with the U.S. states, with that divergence in the 1970s, 1980s.
So what, what actually was going on there? Bring it up again.
So. Oh, yeah, yeah.
Yeah, so here you've got two groups of states who diverge after being fairly similar to each other,
the states and blue states that are kind of on the East Coast with the exception of California and the states and better actually,
many of them are Rust Belt states.
And what we see in this period is while they used to have fairly similar economic profiles, they've become very different.
And the way in which they become different is that you can see how professional
technical and finance activities tended to be concentrated in both states,
groups of states. But after this period, they these professional activities become much more closely concentrated in these blue states.
While as production and manufacturing activities that tended to stay in these Rust Belt states.
So in a paper, we'd be looking at trying to explain why did this happen?
Why didn't both of them move into professional services? What's interesting is that in nineteen fifty in nineteen sixty,
you can see that they have slight differences in the educational attainment of state populations in these Typekit states,
which didn't seem to matter for economic prosperity in this previous period.
But you can see, once we sort of transition into this knowledge economy,
a small advantage in your education actually had a huge impact in the trajectory that you took.
And so in some sense, we don't want to assign causality.
We have some indication that certainly these educational differences were significantly predictive and suggest,
you know, could be part of part of what happened.
Yeah, it does illustrate that certain small differences that make no difference in some periods of history,
given the sort of knowledge, landscape and things that are fit or not fit.
But in a different circumstance, once computers took off around this period and being slightly more educated,
having more cognitive skills did end up making quite a big difference for these places.
Thanks for the question. It certainly is. OK.
I don't think you mentioned the factor of mobility, both traditional constraints and fairly traditions and even technological advances,
for example, the network of bullet trains in China now. And how is that a factor in the mobility of of?
Sorry, could you just repeat that question?
Yeah, the factor of mobility, the rights of one country or culture mobility is much more widespread and more facile.
Whereas in other countries it's limited both for economic reasons or cultural reasons or simply, there's no trains.
Yeah, I think mobility could play a big role.
It's not something we've analysed specifically. I think that a lot of people have looked at in more the mobility of people in terms
of migration and how a record of housing in particular has a lot of work of how,
um, because it's quite difficult for once you've got knowledge in your brain to put new knowledge in your brain.
His argument is it's much easier to move people rather than retrain people and say they have some analysis to show that migration of people,
particularly diasporas, you really see this knowledge moving as people move across places and in terms of transport.
This is interesting because, you know, in some sense, we have become more much more mobile as a society in the in the last 30 years,
but we've also become much more geographically clustered and you have these much bigger divides between cities and rural places.
And so in some sense, while we thought mobility would allow us to kind of, you know, be much more,
we would expect knowledge to kind of be more equitably distributed because people can move around.
The opposite has occurred and the arguments tend to be, well, you know, with knowledge based industries, you know,
face to face agglomeration effects become much stronger where being in the same room with someone,
were you talking about creativity or ideas or whatever apparently matters so much more?
Whether that will continue in the future,
it's hard to say when we can kind of hologram into one place and sort of be in different areas and in a way that we can't,
whether these sort of segregation, spatial segregation, you know, it might dissipate.
I don't know. Is to make them.
Thank you very much for your talk, Penny.
I'm just looking at this slide, one of the things that occurs to me that may have been going on is either to peak oil shocks in the 70's,
you know, quadrupling of oil prices 74 and 79 and explain helping perhaps to explain that divergence.
And it makes me wonder,
do you have energy intensity data to interact with your product complexity data and wondering what that might mean for these transitions as well?
Yeah, it's it's it's a really good point. It is something we've looked at.
Definitely. There is a strong correlation with the energy shock as well, and particularly because we're looking at similarity.
We do see that places that had more access to natural resources and energy became more similar over this short period, as you would expect.
But it wasn't. It was a shock, and it's dissipated,
whereas you do see that these effects are quite they remain and particularly in southern states, you see them change quite a bit.
And in response to the energy shock, but then they returned to their initial state.
So that's why we thought maybe education is something that is a stronger driver.
But I think there are a few of these factors. And as I said, that's why we're not trying to assign causality too strongly.
OK, thank you very much. Before we close, I just want to draw your attention to two more lectures in this series.
Same time, same place, five o'clock next Thursday. Professor Danny Dorling, who name and all of you, I'm sure, is the human species slowing down.
And then the following week, Professor Diane Coyle, who is director of the Bennett Institute for Public Policy at Cambridge,
at the moment talking on changing technology, changing economics.
So both at five o'clock in this, in this room next to Thursdays, so do come along.
And now I just like if we could close with one final round of applause to thank Penny for her great tome.