Laboratory for Networks, Technology & Innovation (NeTI Lab)

Professor Hidalgo is a world renowned expert in the fields of economic complexity,international development, network science and data science. He holds an ANITI Chair on Natural and Artificial Intelligence at the University of Toulouse. He is a honorary professor at Manchester University and a professor of practice at Harvard University. Prior to joining the University of Toulouse, Professor Hidalgo was the director of the Collective Learning group at MIT (2010-2019) and a faculty associate at Harvard (2008-2012). He is the author of multiplehighly cited publications and books, including Why Information Grows (Basic Books, 2015) and The Atlas of Economic Complexity (MIT Press, 2014).

Personal website: https://chidalgo.com/

Relatedness has become a key concept for studying the diversification of firms, regions and countries. However, studies tend to treat relatedness as being time‐invariant or, alternatively, consider its evolution as exogenously given. This study argues that relatedness is inherently dynamic and endogenous to technological and economic developments. Using patent data, we test the extent to which relatedness between technologies developed along co‐location and differences in technological complexity in 1980–2010. Our results show that co‐located technologies are more likely to become related over time. Moreover, our results suggest that co‐location and complexity of technologies are conducive to the intensification of relatedness over time.

(a) The density of all the possible technological combinations on a complexity‐complexity plot (1980–2010), (b) The density of all observed technological combinations on a complexity‐complexity plot (1980–2010).

The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.

Adoption peak prediction on local scales with the Bass model. (A) The Bass DE model estimates on the monthly adoption trend and a smoothed empirical adoption trend (3-month moving average) are compared. (B) Times of adoption peaks vary across towns. (C) Estimated pi and qi result in same adoption peak with fixed pi, except in early adoption cases when qi is high. (D) Estimated peaks of adoption correlate with empirical peaks of adoption (p=0.74). (E) Prediction Error in town i. (F) Dots are point estimates of linear univariate regressions and bars depict standard errors. Dependent variable is scaled with its maximum value and independent variables are log-transformed with base 10.

Social connections that reach distant places are advantageous for individuals, firms and cities, providing access to new skills and knowledge. However, systematic evidence on how firms build global knowledge access is still lacking. In this paper, we analyse how global work connections relate to differences in the skill composition of employees within companies and local industry clusters. We gather survey data from 10% of workers in a local industry in Sweden, and complement this with digital trace data to map co-worker networks and skill composition. This unique combination of data and features allows us to quantify global connections of employees and measure the degree of skill similarity and skill relatedness to co-workers. We find that workers with extensive local networks typically have skills related to those of others in the region and to those of their co-workers. Workers with more global ties typically bring in less related skills to the region. These results provide new insights into the composition of skills within knowledge-intensive firms by connecting the geography of network contacts to the diversity of skills accessible through them.

Geography of respondents’ national (a) and global (b) networks. Node size represents number of connections. Links tend to connect the region to major cities or countries, but geographic proximity also matters.

We analyze high-impact publications from global leading science centers. We show that most high-impact articles are born in emerging Asian cities. At the same time, the proportions of US and EU centers can produce more high-impact articles.