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Latent Dirichlet allocation model for world trade analysis

International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and...

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Autores principales: Kozlowski, Diego, Semeshenko, Viktoriya, Molinari, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861422/
https://www.ncbi.nlm.nih.gov/pubmed/33539376
http://dx.doi.org/10.1371/journal.pone.0245393
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author Kozlowski, Diego
Semeshenko, Viktoriya
Molinari, Andrea
author_facet Kozlowski, Diego
Semeshenko, Viktoriya
Molinari, Andrea
author_sort Kozlowski, Diego
collection PubMed
description International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a well known technique in the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries’ exports of goods from 1962 to 2016. The results show that this technique can encode the main specialisation patterns of international trade. On the country-level analysis, the findings show the changes in the specialisation patterns of countries over time. As traditional international trade analysis demands expert knowledge on a multiplicity of indicators, the possibility of encoding multiple known phenomena under a unique indicator is a powerful complement for traditional tools, as it allows top-down data-driven studies.
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spelling pubmed-78614222021-02-12 Latent Dirichlet allocation model for world trade analysis Kozlowski, Diego Semeshenko, Viktoriya Molinari, Andrea PLoS One Research Article International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a well known technique in the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries’ exports of goods from 1962 to 2016. The results show that this technique can encode the main specialisation patterns of international trade. On the country-level analysis, the findings show the changes in the specialisation patterns of countries over time. As traditional international trade analysis demands expert knowledge on a multiplicity of indicators, the possibility of encoding multiple known phenomena under a unique indicator is a powerful complement for traditional tools, as it allows top-down data-driven studies. Public Library of Science 2021-02-04 /pmc/articles/PMC7861422/ /pubmed/33539376 http://dx.doi.org/10.1371/journal.pone.0245393 Text en © 2021 Kozlowski et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kozlowski, Diego
Semeshenko, Viktoriya
Molinari, Andrea
Latent Dirichlet allocation model for world trade analysis
title Latent Dirichlet allocation model for world trade analysis
title_full Latent Dirichlet allocation model for world trade analysis
title_fullStr Latent Dirichlet allocation model for world trade analysis
title_full_unstemmed Latent Dirichlet allocation model for world trade analysis
title_short Latent Dirichlet allocation model for world trade analysis
title_sort latent dirichlet allocation model for world trade analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861422/
https://www.ncbi.nlm.nih.gov/pubmed/33539376
http://dx.doi.org/10.1371/journal.pone.0245393
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