Professor Steve Goldsmith interviews Professor Ricardo Hausmann about the new Metroverse tool, a powerful data visualization of urban economies across the globe. Built for policy makers, economist, communities, and businesses, the tool places a city’s economy in context, showing how and where any specific urban economy can move into it's "adjacent possible" growth areas.
This episode is a conversation between Professor Steve Goldsmith and Ricardo Hausmann, the Rafik Hariri Professor of the Practice of International Political Economy and director of the Growth Lab, one of the most well-regarded and influential hubs for research on international development. Professor Hausmann discusses the Lab's new Metroverse tool, a powerful yet easy-to-use data visualization of urban economies across the globe. Built for policy makers, economist, communities, and businesses, the Metroverse places a city’s current technological and economic capabilities in context, showing how and where any specific urban economy can move into it's "adjacent possible" growth areas. Metroverse maps and visualizes "what a city is good at today to help understand what it can become tomorrow."
Listen below, or wherever you get your podcasts, to hear Professors Goldsmith and Hausman discuss data-driven policy making, diversification in metropolitan areas, and how mayors can leave their city better than they inherited it.
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Hello, this is Steve Goldsmith, professor of Urban Affairs at Harvard's Kennedy school. And you're listening to Data-Smart City Pod, where we bring on top innovators and experts to discuss the future of cities and how to become data smart.
This is Steve Goldsmith for another one of our podcasts with a fascinating interview and an important subject with Ricardo Hausmann. He's the director of the Growth Lab at Harvard Center For International Development. He was formally Director of Center for International Development and Chief Economist at the Inter-American Development Bank. He also was previously Minister of Planning in Venezuela and on the board of The Central Bank of Venezuela and chair of the IMF World Bank Development Committee. That's a lot of titles, Ricardo. Congratulations, and thanks for being with us.
Thank you. Thank you for having me.
So we have a number of NGO officials and city and state and international officials who listen and get ideas from the podcast and from the website, Data-Smart City Solutions. And your Metroverse analysis delivers really new insights on a city's technological capabilities, which will affect its growth prospects. So let's start first Ricardo, with what is Metroverse? What do you mean by that? And then we'll dig a little deeper into it after that.
So Metroverse is a website that allows you to analyze over a thousand cities across the world, and to compare them to each other, to focus on what it is that they do. And to deliver some tools as to where might the city go forward, what are it's sources of strength and weakness. And we can do that with over a thousand cities in over 70 countries, which allows a level of comparison that was unachievable before.
So let's talk about whether this is a national, sub-national, local tool and how customized is it. If folks came onto your website and looked at the wonderful work you've done, how applicable could it be to their communities?
Well, I mean, the idea... Right now, the tool is in its first version. We just put it out a couple of months ago, but right now you can see your metropolitan area and we've defined the metropolitan area in a consistent way using satellite data and so on, to what is a contiguous human settlement, but in future versions, you'll be able to choose your town, your municipality, or the whole metropolitan area. So right now you can look at our definition of what the integral city is, and you can look at what it does, what its occupational structure is. And we've developed this idea that the process of growth is really not just doing more of the same. That in the process of growing you change what you do.
That a rich place is not just a poor place that does more of the same, it does different things. It moves from making, I don't know, garments to making electronics to then making cars, to making movies, to making things that typically mobilize more knowledge. And in this tool you can see where is your city, and if you want in a technology space or in a knowledge space, and what are interesting things that are not too far away from its current capabilities that might create value for them.
So it allows you to think about what diversification strategies, what new things you want to bring in, leveraging what sources of strength, so that you can think or you can help the city imagine itself change for the better. And in that process, you want to be able to benchmark and learn from other cities that might be similar to you or might be aspirational for you. And you might want to know what it is that they do that I don't do. And the tool allows you to answer that, also.
Ricardo, years and years ago, I was the mayor of Indianapolis. And then after that, sometime later, I the Deputy Mayor for New York City, and both jobs focused on trying to grow opportunity for local residents. Let's go back to Indianapolis for a second. So at the time, then, I was influenced by Mike Porter's core competitive strategy for cities, trying to identify core competitive clusters, and then build those out. Will this tool help identify clusters like that, or is it a different set of kind of growth technologies we're looking at?
Well, a little bit of the approach that Michael Porter had was to tell a city, identify your strength and focus on them and try to complete the cluster of that industry. I think that the world has moved on, in the sense that value chains have become more global, less local, so that focusing on the idea that you're going to have all your value chain in your city on very few clusters has, I think, opened up another alternative, which is how do you get your city to participate in more of these value chains by developing to be a node in this network of industries that are located in several places.
So that's why we think that it will immediately tell you what clusters you have, but it will tell you how you can expand laterally into other things that might be related from a technological point of view might be related from a business ecosystem requirement point of view, but that would not emerge if you start looking at the value chain of the cluster.
And when you say you evaluate the tech capabilities inside the geography, I know that's data heavy, but can you translate maybe for us kind of what you mean by that?
Well, I think of a firm as a group of people that are implementing a basket of technologies. If you make cars, you have to know how to make cars. You have to know all the ins and outs of making cars. So the fact that you make cars is an indication that you are able to implement a set of technologies that are involved in car production. So, that's how we look at every industry. So, that's how we think of that industry being in informative of the capabilities that the place has. And we try to infer, from the different patterns of relatedness in the data, the idea that given that you can do X, you must have an easier time to do Y.
That is, how you can move from what you are to other things that are quote, unquote nearby, in what they call the adjacent possible. So we have a method to estimate the adjacent possible of the things you do to the things you don't do. And your potential strength, sometimes we call it your implied comparative advantage, not your revealed comparative advantage, in potential things you could be doing, but you're currently not doing.
One particular thing that interested me about your work and your website is the following. Taking a large amount of data and well visualizing it, is for those of us who have been elected officials, right, is a key to success, right? The narrative around the data visualization. So talk to us a little bit about how a user is able to visualize in his or her community, the data that they could access through your project.
So, I mean, data visualization, I fully agree with you is really, really critical. And we've put an enormous amount of effort into thinking about how to visualize the data. We benefited from the fact that before developing this tool, we developed a very similar tool that analyzes countries by international trade data. And so we say there, that a city has the capability to do something, if it's good enough to export it. And we developed a whole set of visualizations for that tool. And that tool is called The Atlas of Economic Complexity. And I encourage people just to Google Atlas of Economic Complexity and check it out. But after doing that, we always wanted go sub-national, both sub-national and global. So we wanted to keep this global picture that we can see the whole world, but you can go sub-nationally, so you can go to cities and aggregation there.
And so this tool in particular is powered by a global dataset of firms that's put together by Dun and Bradstreet, and we aggregate these firms into industries and cities. And as you say, it's massive amounts of data. So the way we make it user friendly is by posing very clear questions that we want the data to answer and having one graph, one picture, that answers that question in a very clear graphical way. So for example, the tool starts by asking the question, what does my city do? And it will show you the structure of employment of that city. Then it will ask you, what is my city particularly good at or particularly bad at? And it will to tell you, which are the industries that are overrepresented and which are the industries that are underrepresented. It asks a question, what are other cities that are like my city?
And it gives you a lot of flexibility on how you want to compare your city. You might want to compare cities of the same size in the same country or in the world as a whole. You want to compare it to cities that have more or less similar economic structure or similar levels of income, so you can choose a comparator set. You might want to choose a single city to compare yourself to, that you might think of as aspirational. That you might say, everybody, these days, they say that Silicon Valley is the only place in the world that doesn't want to become Silicon Valley, right? How are you different from Silicon Valley? What is it that they do that you don't do, et cetera? Right? So the comparison I think, is super informative. It gives you ideas of what you could become, or what's the difference between what you are and what you want to be.
And then it tells you, what are your areas of strength and weakness? What are your sources of opportunity and sources of threat. So a strength, weakness, opportunity, and threat analysis, a SWOT analysis, of all the industries in your city. So it helps you decide, where do I need to play defense? Where can I play offense? Et cetera. So if you are in investment promotion in your city and you're going to have a dialogue with a potential investor, you want to tell him, look, if you came to my city, you'll find the right ecosystem because you have all these other players who need an ecosystem similar to yours, and they're thriving in my city. So you should be here for these and those reasons, and you can build your case. And if you're concerned that one industry is under threat, you might figure out, okay, what do I do about it?
Or how do I move those human resources to other industries that may be related, may use similar technologies and similar human capabilities, but that are underrepresented and with growth potential? So it really, I think, makes the life of a city leader, it makes this life of a economic planner in a city, it makes the life of business, if you were going to invest in a business in a city. I mean, if you want to locate your business in some city, which city are you going to go to? What do you want to know about that city? If you are in a city, it will let you better understand your environment. So I think that information is always useful in very many ways and often unpredictable ways.
And in your last answer, you mentioned a number of potential users in a community. And at least in the U.S., the workforce boards generally are too dependent on backward facing bureau labor statistics data. Who would other users be in the community? Would workforce boards be one that could use the data?
Definitely, definitely. You'll have the employment composition of your city. You'll have the employment composition of every industry. You'll try to figure out if the city has the skills that a potential industry might need or want. So they would be part of it. But I also can imagine there's a lot of money cities and states spend in attracting businesses and providing in investment incentives and so on. They may better target those efforts at the industries that may be more catalyzing of change.
And we have a very interesting way of visualizing these patterns of technological relatedness, of ease of moving from one to the other, in something that might look say, like, you want to think of it as a forest where every industry is like a tree. It allows where in this forest is your city strongly present and what's in your neighborhood. And so all of these questions can be answered in a quite, I think, communicable way with the kinds of visualizations of the data that we're putting forward.
That’s quite interesting. We're about out of time. Let me ask you one other kind of slightly different question. So particularly in a post kind of post COVID recovery, there's a lot of focus on how underlying inequities that have been aggravated, right, and exposed. So does your tool help us at all with fair growth or equitable growth? And if so, in what ways?
I mean, that's something that we have on our to do list. The current tool doesn't have the kind of data we want to have. So here, there's a trade off between being global and so having the same data for everybody in the world, and being specific to a particular city. So if we were to do that, I would like to know if you want the ethnic composition of every business to figure out how the different ethnic communities of a city, how are they specialized in different businesses so that I understand what are the industry ethnicity connection, if you want. So it's not that all ethnicities are in all industries, they tend to specialize, and we don't necessarily understand how, when an industry gets in trouble or when an industry moves forward, who's benefiting and who's hurting. And I think that information is something that should be doable for the U.S. I don't think it's easy to do it simultaneously for the whole globe.
Let's close with this. You're kind of one of the world's best economists on growth strategies, and you're a professor. I'm a professor, but I'm just a politician. So let's assume that you're coming into my office as a mayor, give me a pitch for why I should use Metroverse.
The city is what it is. You got into this office, you inherited a city. You want to leave this office with a better city. So you want to imagine your city as being better than what you inherited. In what direction are you going to move the city? How are you going to create the jobs that people in the city want? How are you going to move to the city to a better place? This tool will allow you to see who you are and what you can become.
That was a terrific answer. I think you got the wrong job. You should have run! This has been excellent. This is an important tool, an important set of options for public leaders around the globe. We encourage folks to pay attention to it, and we'll send out a link with the podcast. We've been talking to Ricardo Hausmann, who's the director of the Growth Lab at Harvard and who has developed Metroverse. Thank you so much for your time today.
If you like this podcast, please visit us at datasmartcities.org, or follow us at Data-Smart Cities on Twitter. Find us on iTunes, Spotify, or wherever you get your podcast. This podcast was produced by Betsy Gardner and hosted by me, Steve Goldsmith. We're proud to serve as essential resource for cities interested in the intersection of government, data, and innovation. Thanks for listening.