Evolutionary Economic Geography and relatedness

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[Clémentine Cottineau] Good afternoon everyone. Thank you very much  for joining this new session of the seminar.   I'm Clémentine and together with my colleague  Ruta from TUDelft we're very happy to see you   again to discuss analytical urban models and their  circulation. Once a month, and you may be used to   it now, we receive an expert of urban models under  circulation, and we ask them to dissect this model   with a general question in mind. The question is - What are the unforeseen consequences of urban   model simplifications on our understanding of  cities. Indeed, urban models are simplifications   of reality, but simplifications which correspond to  modeling choices, hence whether they're implicit or   explicit they have consequences on how we view,  how we represent and how we act on cities. So   tracing the discrepancies between model results  and empirical analysis back to the urban model's   core assumptions and design choices is what  we tried to do during this seminar session,   with the aim to provide guidance on how to  use classical urban models appropriately,   to avoid misleading conclusion, and ultimately  to create better models. Last month we heard  

Cécile Tannier present the unforeseen consequences  of fractal models on city and regional planning.   The recording is now available on our YouTube  channel urban models seminar, if you've missed it.   And today we're very happy to introduce Ron  Boschma to talk about Evolutionary Economic   Geography and more specifically the concept of  relatedness. So Ron is currently full professor   in regional economics at the department of Human  Geography and Planning of Utrecht University.  

He is also Professor in innovation study at UIS  Business School of Stavinger University in Norway.   He's been a member of several advisory boards for  the European Commission regarding innovation and   urban policy. He's also been involved with the  World Bank, the European Science Foundation, the   OECD, the National Government of Italy and many  Ministries in the Netherlands for his expertise   on regional diversification, smart specialization  policy, the geography of innovation, regional   resilience, the special networks of industries  and the relationship between agglomeration   externalities and regional growth. Together with  other scholars he's laid out the foundation for   evolutionary economic geography, and today he  will present one model of evolutionary economic   geography, and its impact on how relatedness  is approached in urban studies and planning. Using the concept of relatedness he  will focus on the theoretical empirical   and policy implications of evolutionary economic  geography and its circulation, in particular on how   cities renew themselves and how this process  deviates from conventional economics. Sorry.

We were very pleased and excited to hear Ron  Boschma the day of the seminar, unfortunately   a technical problem prevented us from recording  the first five minutes of his presentation.   I will therefore briefly summarize it with  the help of his slides. As an introduction,   Ron presented the structure of his talk, and  then went on to explain how evolutionary   economics came to be in the 80s from a  dissatisfaction with traditional economics.   The main differences relate to four points. The  relation to equilibrium analysis, and rather   evolutionary economics decided to focus  on a theory of change and dynamics.  

The second point was the departure from analyzing  rational agents, but rather focusing on their   bounded rationality, and find that they try to  be rational but within the limited knowledge   and the limited information that the operator within. The third point is the departure from   the knowledge that perfect replication can be  achieved, instead evolutionary economics defend   the idea that knowledge spreads imperfectly  through social interactions ,for example. The   fourth point was to stress the importance  of history in analyzing economic situations.   Ron also highlighted that despite these elements evolutionary economics was still a-spatial   in the 80s, and so that led him and colleagues  to consider space in an evolutionary perspective   leading to evolutionary economic geography. In  particular he insisted that evolutionary   economic geography enriched the existing  literature on economic externalities, so starting   from Jane Jacob's argument that urban diversity  is beneficial to urban economies he argued that   diversity alone is not sufficient. These diverse  activities also have to be related somehow  

and that's why they share a pool of  knowledge and capabilities within   which can then be beneficial to the economy.  Thank you. And I will leave Ron to continue now. [Ron Boschma] Okay. So the economic structure  must have activities that   that are part of the economic structure, must  have something in common right before that   can mean something to each other. And this is  captured by the notion of related variety, it was  

published in in a seminal article by colleagues  of mine in Utrecht and they and they have a simple   hypothesis, right, the higher the number of related  industries in the region the more opportunities to   learn, the more knowledge spillovers will take  place, the more new combinations will be made   and the higher urban urban growth. And indeed  you you include that in conventional urban   growth models and indeed uh related variety now and then. Okay next slide please. But of course then you take a static picture of a city and you look at the urban   structure right and you see, okay whether it's  diversity or whether there's related variety or   not, and you and you want to explain urban  uh development and urban growth. But an   urban structure is also evolving, and then it  became very interesting to see to what extent   can we or can we observe regular patterns in that  respect. And I think when we when we talk about urban  

models and to what extent they grow they have  to have to have to show a strong capability to   add new economic activities in order to compensate  for decline and stagnation and existing activities   within cities. So that's how city can grow and  that should be that will be part of the of the   modeling exercise. So we have to understand then  how new activities come into being in cities.   There's a lot of empirical work on that  and we know that new economic activities   do not start from scratch, but they're built on  existing capabilities that are already present   in the region. So those conditions which new  activities are feasible to develop in the city,   so they provide opportunities, but they  set also limits to what can be achieved   in this diversification process. So right here  history matters. A previous competences, previous  

events really have a big impact on what you can  do when diversifying and when renewing yourself.   So a city is more likely to diversify into new  activities that are so-called related to existing   activities in regions. So because there's provide  skills, knowledge, institutions, networks that are   still relevant and that can be used by those new  economic activities. So in that sense and this is   this is an urban model that we already did a long  time ago, it was published in 2007, I'm getting old.  So and that was actually showing that urban  development by looking at this type of rules,   urban development was depicted as a branching  process in which new combinations stem from   related local activities that share similar  capabilities, right. So and the more variety you   get it becomes a kind of self-reinforcing process  and therefore it will have consequences for city   size distributions etc. Okay, next slide please.  It it it really goes back to the notion of related  

diversification, and let me just just explain  a bit what i actually mean by that. So just   to visualize it and to put it in very simple  terms. Is city A is specialized in motorbikes   then diversifies into car making and then moves  into car trucks, right. And each time this city is   diversifying, it built on existing capabilities,  right. It might be engineering capabilities more   in general, so the capabilities that you need  to excel in motorbikes are still very relevant   and useful to move into car making, right? That's  why it is related to existing activities. Well,  

city B is a very different example of a  diversification process in which the city is   specialized in textiles that moves into aircraft  making and then evolves into pharmaceuticals.   And as you can imagine, each time you diversify in  this city the city has to transform its underlying   capabilities completely, because the the  the the the the capabilities that you need   to excel in textiles are of no use are of  no immediate relevance to build an aircraft   making industry. Right? So it's unrelated to the  to what happened before. So that's why and there   have been many studies on this, we have done  studies also with Cesar Hidalgo and others that   related diversification is the rule and related  diversification is the exception. This is this  

makes sense, right, because if you can still build  on existing capabilities more easy to diversify   and you have lower adjustment cost etc. etc. And  therefore you will... it's more likely to happen   when city renew themselves and  diversify themselves over time. Okay next slide   please. And this was the first concept or  first model that was was introduced or   published in a science article by Hidalgo, Klinger, Barabasi and Hausmann, and how countries   built comparative advantage in new export products.  So they were not looking at regions or cities  

but but it's the same type of principle. They  looked at country that was their unit of analysis   and, of course, they had the kind of hypothesis  saying okay national capabilities conditions, which   new export products will be feasible to develop.  It had the same argumentation I made before,   and they use the notion of product space, so just  to go a bit into how this relatedness framework   is operationalized in that that  science article they used a co-occurrence analysis   score and they looked at the co-occurrence  of products in export portfolios of countries.   So if you have a high frequency of two export  products that pop up in the export portfolio   of countries all the time, they must have  something in common, they might share similar   similar capabilities. Well if two export products  never pop up in ex portfolios of countries and  

not in combination, then you you define them as  being unrelated, they might not have anything   in common and that's how relatedness between  products is approached. And next slide please. And then you get this product space, right, in which  each uh each each node stands for an uh an export   product and if there's a link between two nodes  it uh they are related above a certain threshold.   If there is no link between  two nodes, they are not related.   And as you can see, and this is typical for a  network like that, you have many activities in   the core of the network, right that are related  with many other products in this product space.   So they share similar capabilities with many  other products, while they're also products more   at the periphery of this network, like on the top  right oil of fruits - those are products that are   related to only one or two other products, but not  related to any other product in the product space.

Could you go back to the previous slide. Yes. So,  you use that information to see - okay what is   the diversification process that takes place in  countries? How do they develop new export products   and are they related to existing export products  that are already present in the export portfolio   of countries? And then what that what what this  science article actually shows is that, indeed,   countries develop new export products that are  closely related to existing export products. So   so this this was already the first study that  actually provided empirical evidence for that.  

and even more so uh there was also a kind of  uh inequality story uh attached to it because   rich countries have many more opportunities to  diversify and sustain economic growth because they   are specialized in all those products that are  more at the core of the of the previous network so   they have many opportunities to diversify right  so and of course you can apply this to cities   right you can apply that so instead of looking at  countries you can look at cities and also cities   have different opportunities diversification  opportunities depending on their history depending   on the type of capabilities they have produced  in the past again they provide opportunities   but again they also set limits to what can  be achieved in this diversification process   next slide please yes so okay here uh uh uh just  to explain the same principle right this is the   the same network i showed before the product  space but then in a more a simple version and   here we have uh again each node stands for an  activity a product or it might be a technology   uh if if there's a link between two nodes they're  related if there's no link they are not related   and the black nodes are the activities in which a  city is already specialized while the white notes   are products that the city might  develop as a new specialization   and this this network already provides a lot  of information what are the opportunities   of a city to diversify right so on the right  side there is a white note that is related to   three other notes that are already present  in the region so there are already a lot of   capabilities that might be relevant for this new  economic activity and therefore it will have a   high probability that it will develop in the near  future well the the the white note on the left   is also related to three other notes but  it is only but only one of those three is   present in the region so there are not  that many relevant capabilities present   in the region and therefore that activity a new  potential new activity is less likely to develop   in this particular city right so it is really  informative to see how cities can grow by adding   new economic activities but it depends  very much on the type of capabilities   they have accumulated in the past next  slide please yeah here so there's a kind of the the same science study right that just took  this slide from theirs so um on the y-axis there's   the probability of developing an export product  right and then uh there's a density matter which   this basically is a relatedness density measure  measuring the degree of relatedness with existing   export products so rightly you see there's  a positive relation between the two the more   related export products are present at the country  level the more likely you develop you will develop   a new export product that is related to the  one that already present in the country okay   next slide please and and this is a  very powerful tool right because then   you can really see okay what are diversification  opportunities of cities and regions here's just   with just one slide i took from a study that  we did we published in regional studies in 2019   and you can actually look at where are in general  diversification opportunities and which regions   have most of those opportunities given the  position of those firms sorry of those regions   in the product space or technology  space if you look at technologies   and what type of capabilities they have already  in-house um so this is more this is here we use   pattern data so just to explain a bit so what  you actually do is you look at which technologies   are already present in a region so in which  technologies regions are already specialized   and then you look for all other technologies  that are not yet present in the region in   which the region is not yet specialized what  is the average degree of relatedness between   the missing technologies and the technologies  that are already present and the wider the gap   the lower the diversification opportunity over  europe of a region in europe right and the the   more close the capabilities are that are uh that  are found in existing activities in the region   the more likely it will develop new technologies  that can actually build on and draw on existing   capabilities that are already present okay next  slide please well you can do that for general   right for for uh for cities and regions but you  can also uh you can find tune it you can make it   it depends also a bit on what what you're  interested in as a researcher or what policy   makers might be interested in this is this is  a research we did for the european commission   and i wanted to know about what are the potentials  of european regions develop a few technologies   and hydrogen technology might be one of them  right there's a lot of discussion about that   that might be the the future technology to move us  into green economies so they wanted to know okay   which regions have most potential so we looked  at the relatedness structure around hydrogen   technology and which technology which of those  related technologies are located where in europe   so the more dark-colored the region the more they  have strong capabilities to move into new hydrogen   technology right so if you want to have a european  policy on that uh your book policy can just   already target a number of reasons right but  you you should not try to develop this in   regions where there are no relevant capabilities  whatsoever to move into hydrogen technology and   this type of this type of maps can really provide  uh uh relevant information next slide please   but okay so far it's about what's about more  diversification opportunities in general but   you can also look at specific technologies  of specific industries or specific jobs   data are available you can you can actually  do that but but you also want to diversify   not only in new activities but also the ones  that make your economy more complex and this is   a notion here the economic complexity index has  been introduced by by by cesari dagger ricardo   houseman opposed were working at the harvard at  that time a paper published in 2009 and basically   i won't say too much on that given the time  but activities are considered complex when   being unique and relying on a wide range of  capabilities for example artificial challenges is   a very complex technology why is that because it  needs to combine many different capabilities and   the knowledge in which it is grounded is pretty  sophisticated right so um and when you master   a technology like that you will not have many  competitors right so it gives you a competitive   advantage as soon as you're able to master these  very complex technologies uh because you might be   the only one you might be one of the few and it's  really hard to to copy or to imitate that complex   knowledge by other cities or regions what  we what we know about this literature is   saying that the most complex activities tend to  concentrate in the larger cities right so there   is really a kind of urban bias of most complex  technologies most complex innovations to cities   and this is quite important because also  what studies now tend to show is that the   higher the the more complex activity is  uh the the higher the economic benefits   that it might uh give to uh cities or regions and  we just finished the paper for european reasons on   that in terms of grp growth in terms of employment  growth so it's it's quite essential to move into   more complex activities but again it depends very  much on the type of capabilities that regions have   or cities have developed in  the past next slide please   yeah this is just one example uh uh taken from  the study by balana rigby why they were one of   the first to really show that the geography  of complex knowledge is heavily concentrated   in in just a few and a limited number of cities  uh he has uh shows the um the example of the us   and u.s counties and how they score on  on the knowledge complexity next slide also what's interesting to see  is that this type of technologies   and the more complex one they tend to concentrate  in the larger city to an increasing extent over   time right so here i just took this from  a paper published in nature communication   you can actually see a long period rate  from 1850 to 2000 so this is that this is   historical pattern data set that we have developed  in utrecht together with american colleagues   and and we use this information to see  to what extent uh um complex technologies   uh have uh concentrated in cities and to  what extent that has increased or decreased   over time and as you can actually see is um  on the y-axis there's the scaling component   right there's the beta if it's higher than one it  means that the technologies are disproportionately   concentrated uh in the larger cities  right given the share of the population   and here they made a distinction between  the 25 most complex technologies in the 25   least complex technologies at different time  periods and actually as you can actually see   the 25 percent most complex they even concentrated  more in urban cities than the least complex   activities and that is exactly what  you expect because an urban environment   will provide you with network  externalities will provide you   with many opportunities to diversify to to combine  different type of capabilities there on which   complex activities we lie and therefore  they are really a very good environment   to produce those most complex technologies  and as you can see the scaling component   is extremely high especially in more recent  times after the third industrial revolution   when you have a bat of between 1.6 and 1.8 for  the most 25 percent most complex technologies   so here again evidence that it matters  that you are capable of diversing in in   new technologies in this respect and especially in  the most complex technologies but it is hard for   many cities to do so only the the the the most  urbanized uh cities uh are the ones uh that are   that have that capability to do so next slide  please so this this uh we have also researched   more in detail looking at entries of complex  technologies and industries in regions in europe   and what we tend to show is that regions with a  higher gdp with a higher complexity level with   a higher population densely are more capable of  entering high complex activities in contrast to   lagging regions smaller cities that rely more  on low complex activities when diversifying so   so again not every city has the same capacity  to diversify into activities let alone to   diversify into the more complex ones well  of course every city every region would   express the uh the desire the ambition to move  into more complex activities because it will uh   bring more economic benefits to  a city uh um um only some cities   really have the capabilities to do so well  many other cities especially the smaller cities   uh they uh they lack those uh they like  the required capabilities next slide please   this is just uh uh the same study that i talked  about before in the in the previous slide this is   just okay this looks at entries of technologies  by the average degree of complexity of new   technologies so so which which technologies enter  the region and what is the average complexity of   those technologies right that is on the y-axis  of each panel and then on the x-axis we have made   three groups of regions right so on the left top  there are reasons why it's a gdp per capita that   there's a low gdp per capita a medium and with a  high gdp per capita and actually as you can see   the higher the gdp per capita the higher the  average complexity of uh of new technologies   entering a region right so there's actually  and the same is population density the same   as technological complexity levels of regions  um and we did that for entries of technologies   that is the the four panels on the top and  we did it for new industries right so which   which regions in europe are more capable to move  into new industries and again we see that the   average complexity of of of new of industries  that enter a region is very much influenced   by the by the level of gdp per capita by the  level of industrial complexity of a region by   the population density in the region and  so very strong evidence thanks next slide so here now we have two concepts which i think  need to be taken into account when looking at   urban models when looking when taking on  board urban development and urban growth   so relatedness on the one hand and complexity  on the other end right and see what kind of   what kind of opportunities of diversification  can we identify based on those two dimensions   right and this has also huge policy implications  because if you for example compare industry j and   industry phi as as potential entries  in a particular region in this case   region a you would go for industry j and not  for industry i because industry j you can   build on existing relatedness in the region so  there are relevant capabilities already present   in the region and when you succeed to develop  industry j it will add more complexity to your   economy with all kinds of economic benefits  right and this is in contrast to industry i   which almost has to start from scratch  right the score is very low on relatedness   so there are no relevant capabilities in which it  would build to develop this industry i and even if   it would succeed it would not add much complexity  to the to its local economy so um if you can   identify those diversification opportunities  in the region uh uh i would recommend to say   to policymakers hey go for industry j  and not for industry i next slide please   and here we have some examples right so here uh  this is for ill de france region paris region one   of the richest uh regions in europe um and um and  here we identify diversification opportunities in   industries again on the x-axis there's the degree  of relatedness on the y-axis there's the degree of   complexity and as you can see there's a positive  relationship between the two so apparently   uh il de france has many diversification  opportunities in the more complex industries   well what has very low diversification  opportunities in the least complex activities   this is of course what every  region would like to have   right that you can diversify into more complex  activity this is just because eel de france has   developed capabilities in the past on which it  can build in order to diversify and that allows   that enables the ill defrance region to  diversify into more complex industries next slide please same graph on the x-axis relatedness density on  the y-axis complexity this is for silasia region   a polish region right old coal mining region old  steel mining regions things like that we know that   old industrial regions more in general struggle  in building new economic activities in order to   compensate for the declining stagnations of their  existing manufacturing activities silesia is a   prime example of that and look at that now if you  look at the diversification opportunities there's   a negative relationship between relatedness and  complexity meaning that the highest potential   to diversify into industries in salazar  region are in the least complex activities   in the least complex industries so you might say  that this would show that this region is trapped   in a low complex economy because most  diversification opportunities are in   the least complex activities well there are  hardly any diversification opportunities in   the more complex activities which you  can find on the on the left top right   next slide please so it actually  matters what type of capabilities you   have produced in the past because again  going back to my my previous statement existing capabilities provide opportunities  diversified but the set also limits so far i only talked about what type of  capabilities are present in cities and regions   right because it might affect their  diversification opportunities but of course   cities and regions are connected to each other  so you have to build in also connectivity between   cities and you have to account for that but not  only that it's not just by being connected per se   but you also have to identify the nature of those  linkages because this might be quite important   so again if my topic would be again how do cities  grow and how do they diversify over time i know   i don't i i should not only look at the type of  capabilities that are present in the city itself   but i should also take in take on board the  type of linkages they have with all the cities   and what we have shown in a very recent paper  is you need to develop so-called complementary   interregional linkages because urbanized  urban urban diversification depends not   on linkages per se it's not just  a matter of being connected or not   but you need to have that sort of capacity to  exploit external knowledge so non-local knowledge   needs to be in my terminology needs to be related  to local knowledge in order to understand the   external knowledge and that you can actually  exploit and activate the knowledge that you have you have access to through  those inter-regional linkages   so what matters are external linkages that  provide access to capabilities in other   regions that the region is lacking but related  to existing capabilities of the region so   you might lack certain capabilities  to move into hydrogen technology but you might still connect to other cities  or regions in order to get access to the   capabilities that are needed to move into hydrogen  technologies but that are missing in the region   but you still have to understand that external  knowledge in other regions so that it should be   related to the type of capabilities  that are present in your own region   so just let me explain that a bit  more in detail next slide please   so here again i have my two notions right of  relatedness on the x-axis and complexity on the   y-axis and as i said before uh the more related  relatedness the more relevant capabilities are   present in the region so the more more easy it  is to diversify into a technology i in region a so i just to make it simple i just said okay there  are 10 technologies that are related to technology   i so they provide relevant capabilities but only  50 of them are are present in in region a and 50   is not so what this region a could do  is to connect to other regions to add   relatedness to the level of relatedness density  it has it has already already has so it already   has 50 of all related technologies in-house but  if it would connect to region b it would add 30 of the related technologies that  are still missing in the region   connected to region c would add 20 region  d would have 10 so you can you can map that   for all the all the regions that are not  region a right in your in your population and then you're going to see hey so these are  really technologies that are related to the ones   that are missing sorry the ones that  are already present in the region   that are needed to develop in this case a  new hydrogen technology next slide piece   so for example you can then make a really nice  analysis by saying okay i go back to my example of   il de france right the paris region and the paris  region wants to move into new hydrogen technology   and as i showed er in the in the previous  slide ilder france has some capabilities   in new hydrogen technologies but not all the  the the related technologies that are needed   to develop new hydrogen technologies and this map  then actually shows where are the complementary   technologies that are needed by il de france  to move into new hydrogen technologies   so the more dark colored a region is the more  it can provide complementary capabilities   that it can understand and that it can  exploit when linking to that region   and that would enhance the probability that  it will be successful in diversifying into   new hydrogen technology okay so this is this is  uh uh um so so this can already provide a lot of   information again if you'll defrance region wants  to go for hydrogen first of all look at the type   of capabilities that are present in the region  then look at yeah with what regions in europe   it is connected for example through uh research  and development linkages uh through label mobility   you can think about different uh channels that  might provide access to capabilities elsewhere and   actually what you can see is if the european  union wants to develop hydrogen technology and   it wants to uh focus on ill de france because eel  de france has strong capabilities but not all the   capabilities necessary to move into that it could  look around and and identify strategic partners   for il de france region given the type of  capabilities that present in other regions so here   you can actually see that there are quite some  french regions that uh have strong capabilities   in technologies related to hydrogen technologies  that are missing in the old france region so   in that sense macron could decide about  okay let's make a french policy right so   i i i connect to french regions uh because then  i can bring together all the capabilities needed   to move into hydrogen technologies right  this this is the type of information   that that is mapped here that can be used that  can be useful for policy makers to think about   what type of options do they have what kind of  diversification opportunities do they have again   do they have capabilities that that that  make them likely to move into hydrogen   they could go for it but regions that lack  any of those capabilities they they might   not make it they might not be successful so  to invest in hydrogen technologies in those   urban contexts would not make any sense would  be a complete waste of money and resources um what our analysis also shows that at least  you need to have some capabilities before you   can connect to other regions that can really help  you out uh getting access to missing technologies   so again if you have zero capabilities  in hydrogen it's not the case that you   can compensate for that by linking to other  regions you need to have a certain knowledge base   in order to make that to make interregional  linkages effective so so the two go together right   so you need to have strong local capabilities  and at the same time that makes you more more likely to to connect to other  regions that can mean something to you   okay i think this was my last flight i think  also given the the time yes i should stop   thanks clementine for sharing it thank you  very much and thank you very much ron uh this   was very inspiring it was nice to hear that you  have so much so many advice for uh planners and   in terms of this economy that's a really  important and really relevant topic   and it's very nice to see how this theory connects  with practice um so i heard that it's a lot about   the complexity and linkages uh that that makes  cities uh to complete to compete successfully   in this global economics and um i noticed you're  also working in stavanger in norway and it's um   quite the opposite like the city it's  economic base as far as i know it's   it's quite specialized in one area um how could  you explain cities like like star anger and   and others which specialize specializes in in one  area um yeah just could really reflect on this   of course it's a very relevant question right  it goes a bit back also to maybe to the silesia   reason right although that is an old industrial  reason but it's a bit more diverse than i would   say stavanger region which is very much focused  on oil and gas right so of course we have done the   studies for for the stavanger reason right and we  have used this type of methodology to say okay um   yeah we we have to move out of oil and gas right  so we are also because of the transition agenda   we cannot rely on oil and gas in the next decades  so we have to move on um well we can we can we   can do something completely new try to build  that from scratch so there's zero relatedness   but that of course uh um is very hard to do and  policy is most likely to fail because there is   nothing that you can that you can build on right  so that is the big question for a region like   stravanger so what you would then have to do  is to see okay the type of capabilities that   are linked to oil and gas can they be used to  diversify into new activities in the region   that is that is what is at stake right so so  you can identify those right so there are not   that many diversification opportunities because  if you remember the product space the network   i showed before oil that was on the top and  was at the periphery of the network so it is   not very much related to many other activities so  if you want to diversify and to move into those   related activities the stavanger region does not  have that many opportunities well il de france   has many diversification opportunities right and  that's why history matters that's why capabilities   again provide opportunities to set also limits  that's why uh il de france is super rich   and stavanger is also super rich but that is  not very likely to last very very long time right because they just have different  diversification opportunities that their   economies are very different yeah and  you have to take that into account   yeah very interesting well at the moment i think  it it has advantage already with oil and gas   in the current situation uh clementine  would you like to ask your question   yes if there's no question yet i had two questions  um one related to the question you just answered   so for a region like silesia for example when  you really have a trade-off between relatedness   and complexity what is what is your recommendation  for policy is it more important to become more   complex over time or is it more important to start  from relatedness and i can ask my question second   question afterwards if you prefer yeah that's a  very good question right so uh these are uh that   that is these are the reasons that struggle a lot  right so because as you said they might they might   still have diverse diversification opportunity  as i showed but those are in the low complex   activities right so um but for a reason like that  when they have high unemployment it might already   uh make a difference when adding new industries in  the region even if they are low complex right so   it will provide new jobs uh and and many old  industrial regions are are are really desperate   to move into new jobs right to provide new jobs  to the to the local population because many people   have become employed uh over time so uh uh so that  that would be my first answer right you can add   new activities because there are relevant  capabilities that you might exploit but then you   will end up in a low complex economy if you really  want to move into a more high complex economy then   you really have to make huge policy efforts right  then you have to make huge policy investments   in research and education infrastructure  in order to really lift at the level of   all economic activities so that you can escape  this trap of low complexity it's not easy to do   but our theoretical prediction and our and our our  model uh are saying that uh yeah you need to have   a huge policy intervention uh just because it is  much difficult to do right so that's why you need   the market will not do it the the you really need  to have strong policy intervention to build new   infrastructure to beat a more knowledge economy  which requires huge investments um so that can   happen but that might take a long time right so  this is not a recipe that will bring new jobs   in in two or three two or three years from now  this is this is a long-term process thank you   very much uh we've got a question thank you martin  if i can just uh ask my second question which is   more related to the urban modeling and the kind of  question that we try to address in this seminar i   had a question about the unfortunate consequences  of this model and the theory on policy advice   because you mentioned several times that history  matters but history also matters in the way that   new industries are added to cities sometimes it's  based on what was there and sometimes i imagine   there's also chance of where an industry was  created based on i don't know a common factor but   actually might not be so related but it just  co-occurs and so especially for a new industry   you might have chance that plays out so you have  some examples of such industries and such policy   that have failed because the models showed  co-occurrence and supposed relatedness where   really it was not necessary it was just chance  that made it appear in two different places   that there's a similar industry structure yes it's  it's true to some extent uh for resource-based uh   industries right so the one that we talked  about before oil and gas right so uh um and   coal mining in the past right so uh those natural  resource based industries uh of course uh um the   degree of relatedness with existing activities  should not be that high and but you can still   exploit it although you still need to bring  expertise to do so right so still relevant   capabilities might be necessary for example to  move into uh oil and gas it's not something that   you if you just find it you can exploit it but  you need to have strong expertise in order to   make that happen and still relevant capabilities  might might be might might be quite needed um   but we see we but we also observe now and then  unrelated diversification right so uh um so um   and this is this is quite a debate that is going  on right now think about all the asian tigers   right so they they also were stuck in the low  complex economy but they were able to move into   higher complex activities um asian countries are  very special case right because there was huge   policy intervention anyhow right this is how  those economies operate and this is that is how   they how they do it like also a country like  china right now right there's a huge policy   uh effort uh to make things work so you might say  the more a government is willing to invest happily   uh you can you can relax a bit this notion of  relatedness because it gives you a bit more   freedom to move into something new that might not  be immediately close to the capabilities that that   you already have right so uh um so so so there has  been some studies indeed that actually showed that   uh so um so strong government intervention  i think also about uh the arab countries   right so uh uh like uh like dubai right uh the uh  uh saudi arabia i mean they they of course they   have loads of money uh uh because they uh they  they have a lot of oil dollars that they can use   uh in order to move into something completely  new so they build those big cities right so   and that is completely disconnected from  their oil business so this is a typical   example of unrelated diversification but  these are quite exceptional cases uh this   will not happen in countries like like the uk or  or even not the us or or or or france or germany   because first of all we live in a democracy right  so so you always have to distribute resources   you can you will not make just a few people happy  but you have to make many people happy so you have   to distribute your resources over many activities  why in a dictatorship you can easily just focus on   one or two activities because you don't have  to find a consensus in your society to do so   so again also here the institutional context  the political system that you're part of also   makes can have an influence on this okay  so i think we have a question from martin   martin yes please uh and mute yourself yeah  uh thank you for this presentation and i was   just wondering you present low complexity  as something that is uh could be a trap   and a high complexity as more preferable but how  would you consider the need for low complexity   activities they need to be placed somewhere as  well like the forestry or food production and   so if all regions strive to  the highest complexity i mean   they're all competing for the same  ship area of all tasks and skills that   uh need to be done how would you consider that  yeah yeah sure it's not not not an agenda to move   away from low complex uh activities  but it is it is it is an agenda that   wants to make economies uh more complex on average  right so because that will bring more economic   benefits uh because the the definition of a  complex activity is that you don't have that many   competitors right the type of activities that you  refer to forestry i mean you can basically do that   well not in every country but in many  countries right so and so therefore uh your um   um your returns might not be that high because  you have to compete with many so that is the one   that's why you want to go into more  complex activities because if you master   those type of knowledge those type of capabilities  then you might be one of the few and then you can   really have high returns to investments so it's  it's not a recipe to give up your low complex   activities although i see in countries  that move into more high complex activities   that low complex activities might be  victim of that because it will just   uh yeah draw resources like capital and  labor away from from those complex activities okay thanks uh and we have another question that  gave us a message to me i think um so the person   asked if i'm not mistaken relatedness is built  on top of predefined economic categories right   for example the netherlands sba are they not  missing out on innovations and potent potential   synergies we have no idea exist for example an  uncategorized activity that is not yet categorized   exactly because they are highly innovative that's  a very good point right so uh um yes uh okay this   technology is not a problem right because um in  the pattern data sets that we use where we can   distinguish between 250 000 technologies right  so i i i i'm sure we always will miss a few uh   but i think that is uh pretty well covered um  miss industries is another story right there you   indeed have predefined categories for example in  the netherlands uh yeah we have um industry data   and um there is not even a separate industrial  category for renewable energy right so uh well   of course everybody's talking about renewable  energy but there is no industry category in our   classification uh scheme at the central barrow  statistics so so you're absolutely right so um um   the way you can circumvent that a bit to some  extent is uh taking uh um a recombinant re   approach right so that you say okay um going back  to two technologies right so okay two technologies   already existed there might be quite mature  mature technologies but those will be combined   right and they have never been combined before  that will will make that those two existing   uh knowledge domains will be connected and  integrated which will lead to a new category   in a way right which is not yet defined but  because we but we already can observe it in   the data so so most of what we are saying is that  what we see in terms of renewal and innovation   that we can read from the data right so so we  can actually see what new combinations are made   um and and and those might not be even uh be associated with an existing category right  but those will be new categories in in the future   but we can already see where the combinations  take place and in 10 years or 20 years from now   the statistical office will determine that there's  a new category so you're right so we always behind   in that respect but still by looking at  combinations we can already see what is happening   and and what what we can observe in reality right  so this is this is refilled by the data itself okay uh if i may ask one more question um i  was listening to some um other seminar and   no conference of you and there was you  talked about the concept of proximity   i maybe didn't hear it today today or mishear  it somewhere could you explain it a bit how   how it's related with uh concept of relatedness  or not in the context of this yeah it's very much   related right so because what you're actually uh  want to see is okay oh i i don't take activities   i okay i look at activities in economies and but  they are not isolated from each other they always   are in relation to each other right and that might  be captured by the notion of relatedness right so   they they require similar capabilities  that might be knowledge might be skills   or you might define them as proximities right  so cognitive i mean the relatedness notion comes   maybe closest to the notion of cognitive proximity  so what you what you tend to share is similarity   in in in in certain knowledge activities so you're  you're cognitive proximate so you see it is very   the two notions relatedness and proximity are  very much related but relatedness is not very   very well defined right because we just look at   at activities and then we see that there are more  often combined but we don't know how and and and   and through which mechanisms proximity  dimensions you have many proximity domains   right so you have not only cognitive proximity  but of course you have geographical proximity   you might have social proximity right  so uh two activities might be combined   at some point of time because there are two people  working in in in those two type of activities   and but in two or five years ago they  worked for the same organizations so   there's social proximity then between  the two activities so they're more likely   to be combined because those people know  each other from and they they share a past that that has been referred to a social proximity  institutional proximity the same if you uh   activities that are operating in the  same institutional dimension like   like language but you're you're more likely  to make combinations right so so all those   proximity dimensions can be captured and  and and might be part of this relatedness   framework so so so they have they have a lot of  in common in in the end we are we are only after that it is not a coincidence what type of  activities you find in the same city and what   type of activities you find in the same region  is remarkable right so even at the zip code in cities activities are related so in your  own neighborhood the type of activities that   you find there are related to each other so this  is this is an empirical observation this is what   we also refer to as a principle of relatedness  you it's you can find it in every spatial skill   at the global scale the national scale the city  scale the subsidy scale the neighbor skill the zip   code level so activities have something in common  they are not just there by by coincidence no   they evolved because they are connected to other  activities and you would not even be aware of it   but this is what we observe in reality by  just looking at the data they have a lot   in common maybe it has something to do with  the local resources or local specificities   capabilities right to me and that and mo and  the most important capabilities are knowledge   right so uh because this is what also  with the most strong result we get   uh by looking at the most  knowledge intensive activities   right so the the ones that really depend  on uh on a high intensity of knowledge   in order to survive so so there there's there's  a strong knowledge dimension attached to that and talking about about uh geographical  proximities um also here you're saying   that being too close to one another is  not very good but also being too far exactly yeah so so in a way so um that's a very  good point that you uh that you address because   now i'm saying right the more close you are  the better right the more close you are can   also mean that you that you end up in a situation  of lock-in it goes a bit a bit back to the to the   low complexity trap that we talked about before  right so like like like a region like silesia   or a region like stavanger they become  so specialized in one principle activity   that all the resources all the capabilities are  concentrated on those and no other activities   have the opportunity to grow because those  those principal industries are so dominant   right so that means that geographical proximity  so if you want to establish something new next   to a big principal dominant industry that is just  around the corner it will be very hard to do right   so the here here comes the notion of login uh here  comes the notion of traps uh which you have to   try uh to avoid so that also means that we always  say okay you need related variety right it's not   about specialization it's not about getting  a very become a very specialized city that   you just depend on one or two principal  industries you always have to make sure   that you have a diversified set of industries  but they should be related they should   they should mean something to each other  and that you you might avoid to get out   of this risk of login but not necessarily  so you might still look at my my the slide   that i showed when uh this city a right  unrelated diversification it moves from   uh what was it motorbikes from cars to car  trucks you might say it's still locked in   in engineering capabilities in general right  at some point of time think about the the the   the evolution of the car industry right now the  the car industry the cars nowadays okay you still   need uh engineering capabilities to excel in car  making but but but cars nowadays are computers   right they're full of hardware and software so  you also need besides mechanical engineering   capabilities you also need electronic  capabilities and now moving into electric cars   not not all car manufacturers are very  good in moving into electric cars look at elon musk right so that's why he grew big  because he had very strong capabilities   in that type of technologies well  the established car manufacturers   were locked in in engineering capabilities  right so they missed out and were not really   capable of diversifying fully although they now  make a lot of efforts to to make that happen thank you i think we have uh two minutes  for questions i might have just a comment   so do you make use of for example the analogy  with like weak ties and strong ties in this   regard like it's similar did you recommend them  that you should build on your weak relatedness uh   yeah i mean so it's there's something in between  there right so it's uh uh i wrote a long time   ago this proximity article in which you say okay  there's an optimum right of proximity you should   not have too little proximity and you should not  have too much proximity so there's something in   between so it's about weak ties and strong ties  so so uh very weak dies will not uh help you out   very strong ties might might get you into a  lock-in process but you have to uh uh there's a   kind of optimum uh there um and that is that might  be true for one relationship right so if you and   i would collaborate in terms of research i mean  the first five years we will be very productive   right and we will have great articles we  will develop new ideas things like that but   at some point of time i cannot learn from you  anymore so our relationship becomes locked in   right so uh uh so and then we after five years we  will not really produce uh novel articles anymore   so you have to connect to other people in order  to avoid that right so here so so avoid that you   become too proximate in many dimensions also i  think at least i would i would recommend that not   not stay and work your whole life at the same  university or in the same location but yeah get   new experiences right so get out of your lock-in  situation uh because geographical proximity might   also be harmful for the innovation process if you  just stay too long and you're always connected   to the same people in the same department  in the same location i can only subscribe   a lot of sense thank you very much i'm sorry  we are reaching the end of this seminar thank   you again for for this presentation uh for  your slides that we might share in the future   and for our participants i might invite you for  our next session which will be on the fourth of   april we'll hear about shelling's model of urban  segregation with izak beninson and aerys hatna   so thank you everyone thank you ron thank you  ruta and i hope everyone has a good afternoon

2022-03-21

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