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The relation between complexity and synergy in the case of China: different ways of predicting GDP growth in a complex and adaptive system

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  • Inga Ivanova

    (National Research University Higher School of Economics (NRU HSE))

Abstract

The effectiveness of the Triple Helix model of innovations can be evaluated in bits of information using the TH indicator of synergy based on information theory. However synergy, measured in bits of information can’t be straightforwardly interpreted in economic terms. The present paper is an attempt to establish a connection between synergy and other growth relating economic measure, such as complexity indices. The synergy distribution among 31 Chinese territorial districts is compared with corresponding distribution of complexity. The latter are calculated with three different complexity measures and on different datasets. Synergy and complexity show substantial linear relationship with each other. These complexity measures are further tested with their ability to predict future GDP per capita growth using employment, income, and investment data for 31 territorial districts of China and 19 industries. The results of regression analysis suggests that the accuracy of growth forecast can be substantially improved when exploiting links of different origin in bipartite networks in comparison with export oriented approach.

Suggested Citation

  • Inga Ivanova, 2022. "The relation between complexity and synergy in the case of China: different ways of predicting GDP growth in a complex and adaptive system," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 195-215, February.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:1:d:10.1007_s11135-021-01118-6
    DOI: 10.1007/s11135-021-01118-6
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