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The role of complex analysis in modeling economic growth

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  • Angelica Sbardella
  • Emanuele Pugliese
  • Andrea Zaccaria
  • Pasquale Scaramozzino

Abstract

Development and growth are complex and tumultuous processes. Modern economic growth theories identify some key determinants of economic growth. However, the relative importance of the determinants remains unknown, and additional variables may help clarify the directions and dimensions of the interactions. The novel stream of literature on economic complexity goes beyond aggregate measures of productive inputs, and considers instead a more granular and structural view of the productive possibilities of countries, i.e. their capabilities. Different endowments of capabilities are crucial ingredients in explaining differences in economic performances. In this paper we employ economic fitness, a measure of productive capabilities obtained through complex network techniques. Focusing on the combined roles of fitness and some more traditional drivers of growth, we build a bridge between economic growth theories and the economic complexity literature. Our findings, in agreement with other recent empirical studies, show that fitness plays a crucial role in fostering economic growth and, when it is included in the analysis, can be either complementary to traditional drivers of growth or can completely overshadow them.

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  • Angelica Sbardella & Emanuele Pugliese & Andrea Zaccaria & Pasquale Scaramozzino, 2018. "The role of complex analysis in modeling economic growth," Papers 1808.10428, arXiv.org.
  • Handle: RePEc:arx:papers:1808.10428
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    References listed on IDEAS

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    Cited by:

    1. Tacchella, Andrea & Zaccaria, Andrea & Miccheli, Marco & Pietronero, Luciano, 2023. "Relatedness in the era of machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Antonietti, Roberto & Franco, Chiara, 2021. "From FDI to economic complexity: a panel Granger causality analysis," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 225-239.
    3. Castañeda, Gonzalo & Pietronero, Luciano & Romero-Padilla, Juan & Zaccaria, Andrea, 2022. "The complex dynamic of growth: Fitness and the different patterns of economic activity in the medium and long terms," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 231-246.
    4. 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.
    5. Ma, Xinxin & Zong, Xiangyu & Chen, Ximing, 2022. "Economic fitness and economy growth potentiality: Evidence from BRICS and OECD countries," Finance Research Letters, Elsevier, vol. 50(C).
    6. Francesco de Cunzo & Alberto Petri & Andrea Zaccaria & Angelica Sbardella, 2022. "The trickle down from environmental innovation to productive complexity," Papers 2206.07537, arXiv.org.
    7. Stefano Costa & Stefano De Santis & Giovanni Dosi & Roberto Monducci & Angelica Sbardella & Maria Enrica, 2023. "From organizational capabilities to corporate performances: at the roots of productivity slowdown," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 32(6), pages 1217-1244.
    8. Andrea Tacchella & Andrea Zaccaria & Marco Miccheli & Luciano Pietronero, 2021. "Relatedness in the Era of Machine Learning," Papers 2103.06017, arXiv.org.
    9. Massimiliano Fessina & Giambattista Albora & Andrea Tacchella & Andrea Zaccaria, 2022. "Which products activate a product? An explainable machine learning approach," Papers 2212.03094, arXiv.org.
    10. Cong, Yufei & Ren, Chunhong, 2023. "Does Natural resource dependency impede sustainable development? Exploring the non-linear consequence of economic complexity," Resources Policy, Elsevier, vol. 85(PB).

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