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Dynamics in the Fitness-Income plane: Brazilian states vs World countries

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  • Felipe G Operti
  • Emanuele Pugliese
  • José S Andrade Jr.
  • Luciano Pietronero
  • Andrea Gabrielli

Abstract

In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-monetary metric called Fitness. In this paper, after an overview about Brazil as a whole and the comparison with the other BRIC countries, we introduce a new methodology based on the Fitness algorithm, called Exogenous Fitness. Combining the results with the Gross Domestic Product per capita (GDPp), we look at the dynamics of the Brazilian states in the Fitness-Income plane. Two regimes are distinguishable: one with high predictability and the other with low predictability, showing a deep analogy with the heterogeneous dynamics of the World countries. Furthermore, we compare the ranking of the Brazilian states according to the Exogenous Fitness with the ranking obtained through two other techniques, namely Endogenous Fitness and Economic Complexity Index.

Suggested Citation

  • Felipe G Operti & Emanuele Pugliese & José S Andrade Jr. & Luciano Pietronero & Andrea Gabrielli, 2018. "Dynamics in the Fitness-Income plane: Brazilian states vs World countries," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.
    • Handle: RePEc:plo:pone00:0197616
    DOI: 10.1371/journal.pone.0197616
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    References listed on IDEAS

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    Cited by:

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    3. Song, Pengcheng & Wang, Pang Paul & Zhang, Baozhen & Zhang, Xuan & Zong, Xiangyu, 2021. "Complexity economic indexes for the energy market: Evidence during extreme global changes," Energy Economics, Elsevier, vol. 96(C).
    4. Angelica Sbardella & Andrea Zaccaria & Luciano Pietronero & Pasquale Scaramozzino, 2021. "Behind the Italian Regional Divide: An Economic Fitness and Complexity Perspective," LEM Papers Series 2021/30, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Ivan Lee & Regina Fang-Ying Lin, 2020. "Economic Complexity of the City Cluster in Guangdong–Hong Kong–Macao Greater Bay Area, China," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    6. Patelli, Aurelio & Napolitano, Lorenzo & Cimini, Giulio & Gabrielli, Andrea, 2023. "Geography of science: Competitiveness and inequality," Journal of Informetrics, Elsevier, vol. 17(1).
    7. Bernardo Caldarola & Dario Mazzilli & Lorenzo Napolitano & Aurelio Patelli & Angelica Sbardella, 2023. "Economic complexity and the sustainability transition: A review of data, methods, and literature," Papers 2308.07172, arXiv.org, revised Mar 2024.
    8. Nicolò Barbieri & Davide Consoli & Lorenzo Napolitano & François Perruchas & Emanuele Pugliese & Angelica Sbardella, 2023. "Regional technological capabilities and green opportunities in Europe," The Journal of Technology Transfer, Springer, vol. 48(2), pages 749-778, April.
    9. Ibrahim Tuğrul Çınar & Ilhan Korkmaz & Tüzin Baycan, 2022. "Regions’ economic fitness and sectoral labor productivity: Evidence from Turkey," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(3), pages 575-598, June.
    10. Dario Mazzilli & Manuel Sebastian Mariani & Flaviano Morone & Aurelio Patelli, 2022. "Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms," Papers 2212.12356, arXiv.org, revised Mar 2024.
    11. Fritz, Benedikt & Manduca, Robert, 2021. "The Economic Complexity of US Metropolitan Areas," SocArXiv 2gw9c, Center for Open Science.

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