César A. Hidalgo 🇭🇺 Profile picture
Apr 6, 2021 7 tweets 4 min read Read on X
Interested in China's regional economic diversification ?
In this new paper in Regional Studies, we explore the role of relatedness, & high-speed rail, in China's regional diversification. The paper was led by lead @gaojian08
(1/n) 🧵
Paper: tandfonline.com/eprint/GRXBNTC…
We start by verifying that Chinese provinces are more likely to (i) enter related activities & (ii) enter activities present in geographic neighbors. These are classic findings in economic geography that we reproduce using Chinese enterprise data & firm financial data. (2/N)
Then, we compare these spillover channels. What matters more? Having related industries? Or a geographic neighbor that is already in that industry? We find that these two channels work as substitutes. (3/N)
Having many related industries, or many geographic neighbors where the industry is present, predicts entries. But having both is not more predictive than having one. As long as you have one source of spillovers (relatedness or geography) the other one is less relevant. (4/N)
Finally we look at the role of high-speed rail in accelerating regional spillovers. During the last decades, China's high-speed rail went through several acceleration campaigns, which should facilitate spillovers among nearby regions. (5/N)
These acceleration campaigns followed existing roads and rail lines (connecting cities which have been connected for thousands of years). So they were not designed to connect a particular industries (e.g. to connect advertisement companies in Shanghai & Wuhan) (6/N)
We find that regions connected by rail grew more similar in terms of their industrial structure. These findings, suggest that high speed rail contributes to spillovers among neighboring regions. (7/N). You can get the paper at: centerforcollectivelearning.org/papers
&
tandfonline.com/eprint/GRXBNTC…

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More from @cesifoti

Aug 2, 2023
**New Paper**
Economic complexity methods are popular tools in industrial policy. Yet, despite their widespread adoption, these methods are sometimes misunderstood. In this new paper, I explain & explore the policy implications of economic complexity. /1
https://t.co/BQ6xLOA8cMbuff.ly/3YjBFbh
Image
First, why are economic complexity methods misunderstood?
A key part of the confusion comes from the predictive nature of these methods. The concept of relatedness, for instance, anticipates the probability that a country or region will succeed at an activity./2
So the knee-jerk reaction that people get when they encounter these methods is to develop a strategy that recommends what the method predicts. These are the products/industries that would be easiest to achieve. But this line of thought is both wrong & incomplete./3
Read 21 tweets
Jun 25, 2023
What is intelligence?
And how is it different from problem solving?

These questions are central in our current discussion on AI & were debated passionately this week at the Santa Fe Institute’s conference on collective intelligence.

But what did we learn?

🧵 .. 1/N

First, a disclaimer. In this thread I will focus on one idea, not all the ideas discussed in the conference, and will obviate other aspects of intelligence (eg multidimensional intelligence), not because these are not important, but because I want to communicate one point.
My focus will be on a distinction between intelligence & problem solving, because in my experience, when people are pushed to define intelligence on the fly they often gravitate towards a problem solving definitions of intelligence.
Read 23 tweets
May 14, 2023
AI hype is on full swing, to a large extent, because of language models.

But as a writer, I am not totally convinced about the “productivity boosts.”

You see, writing fulfills a dual purpose. On the one hand, we write to communicate. But on the other hand…. /1
we write to clarify our own ideas.

We write to learn in ways that cannot be accomplished by reading.

A big part of what motivates a writer to work on a book is knowing that at the end of the journey I’ll be a different person.
You write not because you are an expert, but to become one.

Writing is sincere. It pushes you to encounter your own incompetence, repeatedly. And when your own words look stupid & your ideas malformed, you must either abandon them or refine them. In that process you learn.
Read 8 tweets
Apr 22, 2023
¿Cuanto litio exporta Chile? ¿Qué tan "lejos" esta Chile de las baterías? Acá un hilo con varios datos sobre el litio y sus derivados.

1. Chile es el principal exportador mundial de carbonato de litio. En el 2022, exportó USD 7600M de este producto
oec.world/en/profile/cou… ImageImage
2. Este gran volumen exportador es un fenómeno reciente. Hasta finales del 2021 Chile exportaba ~USD 100M de carbonato de litio al mes. En Mayo del 2022 las exportaciones llegaron a USD 1400 millones mensuales! Hoy están alrededor de los USD 600M al mes. Image
3. Chile también exporta hidroxido de litio. Este es un producto de mayor valor agregado, que permite hacer mejores baterías, y es favorecido por los productores de vehículos eléctricos. Acá Chile exporta menos (~USD 60M al mes pero creciendo). El principal exportador es China. ImageImage
Read 14 tweets
Mar 7, 2023
What can LLMs teach us about economics?
Like everyone else, economists have been enjoying the foibles & virtues of large language models (LLMs). But can these models teach us something about the economy that we don’t already know?
weforum.org/agenda/2023/03… /1
I believe there is much that economists can learn, not by chatting with LLMs, but by deconstructing how they work. After all, LLMs are built on mathematical concepts that are powerful enough to simulate language. Maybe, these models work can become a new source of inspiration.
To understand how LLMs work, it is useful to start with the most primitive version of a language-generating model. Imagine using a large corpus of text to count the number of times each word, such as brown, is followed by another word, such as dog.
Read 19 tweets
Feb 28, 2023
Tesla just announced they are building a new plant in Monterrey.

Could we have predicted this using AI?

The data tells us why Monterrey and Nuevo Leon are an excellent fit for Tesla.

Thread 🧵 /1
@elonmusk
To do this, we will use datamexico.org, an official data distribution platform from the secretary of the economy, and tools from economic complexity, an academic field using machine learning to understand the evolution of economies.
Just like chatGPT knows that tea & coffee are related because they are used similarly in sentences, we can know how economic activities, such as the manufacture of electric vehicles, are related to others based on their patterns of collocations.
Read 12 tweets

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