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Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters.

ABSTRACT: Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are scarce. In this work, we use LinkedIn's employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reveal hierarchical structure by applying network community detection. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated workers and financial performance, compared to traditional aggregation units. Furthermore, our analysis of the skills of educated workers reveals richer insights into the relationship between the labor flow of educated workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide useful insights into the growth of the economy.

SUBMITTER: Park J

PROVIDER: S-EPMC6671949 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters.

Park Jaehyuk J Wood Ian B IB Jing Elise E Nematzadeh Azadeh A Ghosh Souvik S Conover Michael D MD Ahn Yong-Yeol YY

Nature communications 20190801 1

Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are scarce. In this work, we use LinkedIn's employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reve ...[more]

PMID: 31371716

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Project description:Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower complexity than virtual brains modeling normal neural function. We finally discuss the implications of our results for the neurobiology of health and disease.

Project description:Introduction The objective of this systematic review is to identify tuberculosis (TB) high-risk among the general population globally. The review was conducted using the following steps: elaboration of the research question, search for relevant publications, selection of studies found, data extraction, analysis, and evidence synthesis. Methods The studies included were those published in English, from original research, presented findings relevant to tuberculosis high-risk across the globe, published between 2017 and 2023, and were based on geospatial analysis of TB. Two reviewers independently selected the articles and were blinded to each other`s comments. The resultant disagreement was resolved by a third blinded reviewer. For bibliographic search, controlled and free vocabularies that address the question to be investigated were used. The searches were carried out on PubMed, LILACS, EMBASE, Scopus, and Web of Science. and Google Scholar. Results A total of 79 published articles with a 40-year study period between 1982 and 2022 were evaluated. Based on the 79 studies, more than 40% of all countries that have carried out geospatial analysis of TB were from Asia, followed by South America with 23%, Africa had about 15%, and others with 2% and 1%. Various maps were used in the various studies and the most used is the thematic map (32%), rate map (26%), map of temporal tendency (20%), and others like the kernel density map (6%). The characteristics of the high-risk and the factors that affect the hotspot’s location are evident through studies related to poor socioeconomic conditions constituting (39%), followed by high population density (17%), climate-related clustering (15%), high-risk spread to neighbouring cities (13%), unstable and non-random cluster (11%). Conclusion There exist specific high-risk for TB which are areas that are related to low socioeconomic conditions and spectacular weather conditions, these areas when well-known will be easy targets for intervention by policymakers. We recommend that more studies making use of spatial, temporal, and spatiotemporal analysis be carried out to point out territories and populations that are vulnerable to TB. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-16493-y.

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Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters.

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Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters.

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