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The Heterogeneous Dynamics of Economic Complexity

Overview of attention for article published in PLoS ONE, February 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
3 news outlets
twitter
11 tweeters
facebook
3 Facebook pages
reddit
1 Redditor

Citations

dimensions_citation
47 Dimensions

Readers on

mendeley
124 Mendeley
citeulike
1 CiteULike
Title
The Heterogeneous Dynamics of Economic Complexity
Published in
PLoS ONE, February 2015
DOI 10.1371/journal.pone.0117174
Pubmed ID
Authors

Matthieu Cristelli, Andrea Tacchella, Luciano Pietronero

Abstract

What will be the growth of the Gross Domestic Product (GDP) or the competitiveness of China, United States, and Vietnam in the next 3, 5 or 10 years? Despite this kind of questions has a large societal impact and an extreme value for economic policy making, providing a scientific basis for economic predictability is still a very challenging problem. Recent results of a new branch-Economic Complexity-have set the basis for a framework to approach such a challenge and to provide new perspectives to cast economic prediction into the conceptual scheme of forecasting the evolution of a dynamical system as in the case of weather dynamics. We argue that a recently introduced non-monetary metrics for country competitiveness (fitness) allows for quantifying the hidden growth potential of countries by the means of the comparison of this measure for intangible assets with monetary figures, such as GDP per capita. This comparison defines the fitness-income plane where we observe that country dynamics presents strongly heterogeneous patterns of evolution. The flow in some zones is found to be laminar while in others a chaotic behavior is instead observed. These two regimes correspond to very different predictability features for the evolution of countries: in the former regime, we find strong predictable pattern while the latter scenario exhibits a very low predictability. In such a framework, regressions, the usual tool used in economics, are no more the appropriate strategy to deal with such a heterogeneous scenario and new concepts, borrowed from dynamical systems theory, are mandatory. We therefore propose a data-driven method-the selective predictability scheme-in which we adopt a strategy similar to the methods of analogues, firstly introduced by Lorenz, to assess future evolution of countries.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Germany 2 2%
Portugal 2 2%
Taiwan 1 <1%
Mexico 1 <1%
Italy 1 <1%
Japan 1 <1%
China 1 <1%
Luxembourg 1 <1%
Other 3 2%
Unknown 107 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 35%
Researcher 19 15%
Student > Master 17 14%
Other 11 9%
Student > Bachelor 9 7%
Other 25 20%
Readers by discipline Count As %
Economics, Econometrics and Finance 31 25%
Physics and Astronomy 20 16%
Unspecified 18 15%
Social Sciences 10 8%
Computer Science 9 7%
Other 36 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 36. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 October 2017.
All research outputs
#371,615
of 11,850,001 outputs
Outputs from PLoS ONE
#7,517
of 130,358 outputs
Outputs of similar age
#10,960
of 230,266 outputs
Outputs of similar age from PLoS ONE
#190
of 2,861 outputs
Altmetric has tracked 11,850,001 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 130,358 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 230,266 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 2,861 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.