Cesar A. Hidalgo

Thanks!

This is one of the most fun papers that I have worked in a long time. If you want to geek out with the mathematical foundations of economic complexity theory, please give it a read.

We also used this opportunity to embed this model into a short-run equilibrium framework showing that prices increase concavely with the complexity of a product and that economies should converge to the wages of others with similar levels of complexity.

This allows us to easily generate output matrices as large as those observed in the empirical literature and calculate their ECI.

Our main model is a generalization of Kremer’s O-Ring model with factors that can be made specific to each economy and activity. In this model, the output of an economy in an activity is proportional to the probability that it is not missing the factors that the activity requires.

In the product space, the core-periphery structure tells us that capabilities are correlated (e.g. Singapore scores high across all capabilities & Mali scores low). In the case of the research space, the ring structure is given by the fact that each field is only similar to a few other fields.

We also show that our model explains differences in network structure, like those observed between the product space (a network with a core and a periphery) and the research space (which is shaped as a ring).

The idea that you can measure the presence of economic factors—no matter how they are defined--is an interesting departure. It provides a basis for the use of complexity measures in development. In the paper, we show this property is robust to noise & applies to other production functions.

In a single-factor model, ECI separates economies that are better or worse endowed with the factor. In a model with many factors, ECI tells us which economies have a higher probability of being endowed with many of them--regardless of what these factors are.

NEW PAPER: The Theory of Economic Complexity

The Economic Complexity Index or ECI is a widely used empirical tool with an unclear theoretical basis. In this paper, we change that by formally deriving ECI for several production functions and showing what it really does. 👇🧵

arxiv.org/html/2506.1882…

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¿Fuga de cerebros en EE UU? La oportunidad de oro que Europa está a punto de desaprovechar Muchos investigadores en el país norteamericano están más dispuestos que nunca a mudarse. Pero si queremos que consideren venirse, tenemos que ponernos serios

Los €500M anunciados por la UE para atraer talento a Europa, ¿son comparables con el pago anual del endowment de Harvard o Yale?

No, nuestro esfuerzo continental esta al nivel de Ohio State.

En esta columna pongo a Choose Europe en contexto y ofrezco unas soluciones.

elpais.com/ciencia/2025...

In higher-income countries, urban proximity has been shown to enhance workers’ skills and income, but less is known about low- and middle-income countries. This column examines how the income of initially poor workers in Brazil changes after urban migration. Wages go up immediately for migrants to both southern and northern Brazilian cities, but only in the wealthier southern cities do initially poor migrants see a longer-term positive effect. Workplace segregation appears to play a key role, as southern firms do more to bring skilled and unskilled workers together.

Cities in southern #Brazil appear to be places of opportunity for migrants, while northern cities offer immediate bonuses but show less of a permanent + trajectory. Workplace level segregation can explain this.
E Glaeser, M Viarengo, @cesifoti.bsky.social, R Barza
cepr.org/voxeu/column...
#EconSky

Open postdoc position at @ceu-dnds.bsky.social in Vienna in the Computational Human Dynamics Lab of @martonkarsai.bsky.social on inequalities in 15-minutes cities in collaboration with www.dut-coline.org. Check it out: careers.ceu.edu/job/Vienna-P...
@ic2s2.bsky.social @netscisociety.bsky.social

Optimizing Economic Complexity Viktor Stojkoski, and César Hidalgo, “Optimizing Economic Complexity”, TSE Working Paper, n. 24-1623, March 2025.

19. This was a great collaboration with the always amazing Viktor Stojkoski

You can download the working paper at:
www.tse-fr.eu/publications...

18. Our results show that there are more careful & principled ways to define diversification opportunities.

ECI optimization is a flexible method to achieve this, since the forecasting function & optimization constraint can be changed to accommodate to different criterion.

17. Relatedness-complexity diagrams have been around for a long time (in 2011 we published them under the name: Export-Opportunity Spectrum (sorry for that :-)).

But more recently, they have become a core tool for the consulting industry focused on smart specialization.

16. First, the algorithm leverages an economy's current specialization pattern better.

Second, it solves the portfolio problem by providing suggestions that balance related and unrelated diversification opportunities.

15. Interestingly, the strategy defined by this algorithm is quite different from the one obtained from relatedness-complexity diagrams.

14. Using these targets, we define an optimization problem that chooses the optimal combination of activities that an economy will need to specialize in to achieve a target level of complexity with "minimum effort."

13. We then use this forecast to estimate the target level of specialization an economy will need to achieve within a time period to be on track to specialize in a product.

12. The method starts by defining a model forecasting the evolution of comparative advantage as a function of multiple measures of relatedness and other variables.

11. In our new working paper, we develop an optimization method that addresses these limitations by defining an industrial diversification strategy that minimizes the effort required for an economy to achieve a target level of economic complexity.

10. Third. We know from theoretical work that optimal diversification strategies require targeting related and unrelated diversification opportunities. Relatedness-complexity diagrams do not provide that balance. There is an open question of how to determine that portfolio.

9. Ignoring these changes can lead to overestimating the potential contribution of an industry to the economy (what is hot today may be "passé" tomorrow).

8. This drop was not because cars became less sophisticated, but because many middle- and lower-income economies began exporting them (e.g. Thailand, Morocco, Turkey, South Africa). (Complexity is a relative measure of sophistication)

7. Second. Relatedness-complexity diagrams ignore the fact that these variables change over time.

These changes can be substantial. Between 1962 and 2016 cars fell from being the 17th product in terms of complexity to the 208th (out of 817 products).

6. First, relatedness is an incomplete measure of economic potential.

Estimating en economy’s probability of "entering" an activity requires a model with many variables. Using relatedness as "the" measure of potential confuses a model's component with the full model. Not good.

5. But while this makes sense in principle, it suffers from some important limitations in practice:

4. So the heuristic that everyone has been using (I am guilty of this too) is to trade-off these variables by focusing on an "efficient frontier." These are products that are relatively close and valuable.