César A. Hidalgo
@cesifoti • 58,701 subscribers
Professor at Toulouse School of Economics & Director of the Center for Collective Learning. Founder Datawheel & https://t.co/2d5FR0ZOwx Latest book: The Infinite Alphabet
Videos
How will US tariffs on Chinese imports impact German or Indian exporters? You can now explore millions of these scenarios using the OEC Tariff Simulator. It's very simple. 1. Choose an importer (eg USA) 2. Choose an exporter (eg China) 3. Choose an additional tariff rate (eg 10%) The simulation generates three visualizations. The first shows the expected change between these two countries (eg a drop in exports from China to the US). The second shows the impact on other countries (eg if the US imports less from China, it may start importing more from Mexico, Germany, etc.). Now, here's where things get really interesting. Click on any of these countries, and you’ll see a third visualization showing the impact on each of their products. For example, clicking on Germany shows that tariffs on China benefit German car part exporters. Clicking on India shows that the benefit goes to those exporting house linens and mattresses. There are millions of scenarios to explore. Of course, these are just estimates from a statistical model (we don’t have a crystal ball), so please take them with a pinch of salt. We are working on improving the model and developing an interface that allows users to explore more complex scenarios. To play with the tariff explorer, search for the Observatory of Economic Complexity or visit OEC dot world.
César A. Hidalgo104,739 次观看 • 1 年前
**New DataViz Project** Curious about academic impact? Tired of rankings? Today we are introducing Rankless ( a new data exploration platform that can help you explore the academic impact of thousands of universities. All universities produce impact that is specific to certain topics and geographies, but rankings flatten that information. Rankless wants to change that. Consider a comparison between the University of Utah and the University of Vienna, two universities ranked similarly in the Shanghai ranking. These universities differ in their geographical and topical footprint. The University of Utah specializes in Neuroscience and Medicine whereas the University of Vienna specializes in Physics, Astronomy, and Environmental Sciences. Their geographic impact is also quite different. Utah receives a large fraction of citations from medical centers in the U.S., Canada, and Israel, whereas Vienna receives many citations from technical institutes in Austria, Germany, and Hungary. These differences are easy to explore in Rankless but hard to see in rankings. Rankless was developed by a talented team at the Center for Collective Learning at Corvinus University (Corvinus University of Budapest). It was brought to life by Endre Mark Borza (Endre Mark Borza), a Hungarian economist and data engineer at CCL with the help of Máté Barkóczi, a Hungarian designer form MOME, and Veronika Hamar executive director at CCL. By moving beyond rankings, Rankless offers a fresh perspective on how universities influence each geography and topic, emphasizing diverse forms of impact and providing a richer understanding of academic influence. To learn more visit
César A. Hidalgo124,686 次观看 • 2 年前
Los datos de comercio internacional son fundamentales. En este visualizador de datos, que lanzamos junto a la Fundación Cotec, pueden explorar el comercio de cada comunidad autónoma, provincia, y producto español. Son más de 7.000 perfiles interactivos actualizados mensualmente! 🇪🇸 🇪🇸
César A. Hidalgo68,991 次观看 • 1 年前
Everyone is good at something. This network shows the countries that are the top exporter of each product. Out of 1217 traded products. 🇨🇳China is the top exporter of 534. 🇩🇪Germany of 147 🇺🇸 USA of 97 🇯🇵Japan of 27 🇮🇹 Italy 27 🇮🇳India 25 🇨🇦Canada 24 🇳🇱Netherlands 24 🇮🇩Indonesia 21 🇫🇷 France 18 🇧🇷Brazil 17 🇪🇸Spain 16 🇹🇷Turkey 16 🇰🇷South Korea 13 🇦🇺Australia 13 🇹🇭Thailand 13 🇷🇺Russia 12 🇳🇿New Zealand 10 🇧🇪Belgium 9 🇿🇦South Africa 8 🇨🇭Switzerland 8 🇲🇽 Mexico 7 🇸🇦 Saudi Arabia 7 🇨🇱Chile 6 🇲🇾Malaysia 6 🇬🇧 United Kingdom 6 🇦🇷Argentina 6 🇦🇹 Austria 6 🇮🇪 Ireland 5 🇫🇮 Finland 4 🇻🇳Vietnam 4 🇵🇪Peru 4 🇩🇰 Denmark 3 🇳🇴Norway 3 🇲🇦Morocco 3 🇵🇹Portugal 3 🇦🇪United Arab Emirates 3 🇸🇪 Sweden 2 & more 👇
César A. Hidalgo126,500 次观看 • 2 年前
Today we are introducing 2 key features to JAIGP: AI Review & Open Prompting. AI review is part of 5-step process where papers get feedback from Reviewer3 & are evaluated based on their ability to address that feedback. This means they are not stuck in an endless AI review loop. Open prompting is a newer idea. We are making all prompts we used to create JAIGP open, and also, opening up the journal's rules to the community for suggestions. You can suggest the prompt we should run next! So, if you have opinions about AI generated papers, you can share them directly with us at
César A. Hidalgo13,374 次观看 • 2 个月前
Six years before Lex Fridman launched his first podcast, I ran an interview series at MIT called Cambridge Nights. While we never reached the same heights, the series was featured in the New York Times and earned a couple of Webby award nominations. We had remarkable guests and engaging conversations, as we tried to push a deep conversation format. If you're curious, check out the links in the comments.
César A. Hidalgo48,249 次观看 • 1 年前
New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.
César A. Hidalgo54,324 次观看 • 1 年前
Explore the publication history of millions of scholars in Rankless. Our latest update helps you see publication impact like you’ve never seen it before. In only a few clicks you can see which institutions & countries cite you the most and what papers explain those citations. There are many combinations to explore. Try it out. It’s addictive :) Rankless is a visualization engine for publication impact built at the Center for Collective Learning by Endre Borza. It has been a great journey. Google rankless or click on the link in the comment to visit.
César A. Hidalgo32,041 次观看 • 1 年前
Do you have what it takes to forecast International trade? Today, we are launching AI4trade, a global challenge where teams compete for cash prizes by submitting their best trade forecasts. But it comes with a couple of twists: 1. This is a future out-of-sample challenge, so teams will need to forecast data that will not exist at the time of submission (impossible to train to the test 👹). 2. Forecasts must be better than simply using the latest trade data available at the time of submission (e.g. using July data as a prediction for October numbers ⏰). This should be a fun global competition. People from all parts of the world are welcome to participate and all methods are allowed. We hope the challenge stimulates more work at the intersection of AI and economics. Link in the first comment: 👇
César A. Hidalgo22,901 次观看 • 10 个月前
*New Paper on AI & Democracy* Imagine two approaches to democracy. The one we have today, where citizens choose a professional politician to represent them and others. Or an augmented form of democracy, where each citizen controls a personalized AI that helps them participate in thousands of nuanced decisions. This second approach is the idea of Augmented Democracy I introduced six years ago at TED. In our latest paper we explore a simplified version of Augmented Democracy by combining off-the-shelf LLMs, such as ChatGPT, with data collected using a collaborative government program builder. This was an online game where people build a personalized government program using proposals extracted from the programs of the candidates of the 2022 presidential election in Brazil. So how accurate are these augmented forms of democracy? Imagine a user who gave us 40 answers. We can use the first 20 to fine-tune a model that we can test using the 20 answers the model didn’t see. We can then compare the accuracy of these predictions with the ones obtained by a “bundle” rule, which assumes that users that self-reported to be from the left or right always chose the proposals from the candidate that shares their political identity. This showed us that LLMs were more accurate at predicting policy preferences than the bundle rule, meaning that the preferences captured in the participation data were more nuanced than a left-right axis, and that the LLMs can capture some of that nuance. Also, the LLMs can choose among policies coming from the same candidate, which is something that we cannot do using a bundle rule. But can these LLMs help us complete the aggregate preferences of the population? Direct or unbundled forms of participation can result in incomplete data when people answer only a fraction of all questions. In our paper, we simulate this incompleteness by sampling the full dataset. We ask how close we can get to the full dataset by using a random sample, or a random sample augmented by predictions made by these LLMs. Overall, we find that LLM-augmented data gets much closer to the full dataset than a pure random sample. These results do not mean that augmented democracy technology is ready, but they means we are in a much better place to continue exploring this idea than six years ago. This paper was a collaborative effort with Jairo Gudino, PhD student at CCL at the University of Toulouse Capitole and Umberto Grandi from IRIT also at the University of Toulouse Capitole. We hope you find these results insightful!
César A. Hidalgo26,912 次观看 • 1 年前
This was a very special project for me. It is a video we created in collaboration with a musician, and that we premiered last year at a theater in Palma de Gran Canaria at an event attended by the King of Spain and the President of Italy. The video is a short introduction to economic complexity written to be played live by Carlos Almoril, an excellent classical guitarist. The result was a beautiful collaboration between art and science. A profound musical and visual journey through the rough waters of economic complexity. Hope you enjoy it.
César A. Hidalgo20,573 次观看 • 1 年前
Rankless is nominated for a Webby! We are competing for the best Science website of the year against big names such as the New York Academy of Sciences, Quanta Magazine, and the Howard Hughes Medical Institute. If you like what we are doing at Rankless, please vote for us using the link in the first comment :-) Rankless is developed by Endre Mark Borza at the Corvinus University of Budapest site of the Center for Collective Learning. Thanks 🙏
César A. Hidalgo18,855 次观看 • 1 年前
The US and China are negotiating a 55% tariff on Beijing and 10% tariffs on Washington. What can we expect from that? Yesterday, we released a new tariff simulator at the OEC that automatically builds a report for any scenario. So we ran our model. In this case, it predicts China's total exports in 2027 would remain flat ($2.42T) and its total imports will grow to $2.13T. China would export about half a trillion less to the US, while growing its exports by about 30 billion each to Hong Kong, Japan, South Korea, Vietnam, India, and Germany. China would also continue to grow its imports of oil and other energy products, such as gas, coal briquettes, and refined petroleum, from Russia. In this scenario the US would increase its exports by about 14% and reduce its imports by about 20%. Mexico and Canada would benefit enormously, as trade between the US and them would increase respectively by $85B and $123B. But what's fun about this tool, is that it automatically gives you a report for any country. Curious about how this scenario might affect Indonesia? Just select Indonesia and click on generate report. Vietnam? Brazil? Spain? Do the same. You can explore these scenarios at the new OEC tariff simulator reports:
César A. Hidalgo16,439 次观看 • 1 年前
Twelve years ago we asked the question: how can we put public data "on wheels"? Datawheel was born from vision that data alone doesn't get far. It needs vehicles to reach hearts and minds. Vehicles that make data easy to find, explore, and understand. Today, Datawheel turns twelve. In these twelve years, we have built dozens of solutions that are helping define the state of the art in public data distribution. Today we celebrate this journey. A journey with a great team and amazing clients. The journey to make public data all it can be. are some of the tools defining all that public data can be. Thank you!!!
César A. Hidalgo12,925 次观看 • 11 个月前
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