[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