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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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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.

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Notes

  1. https://ec.europa.eu/sfc/sites/sfc2014/files/2007/nace_rev_2.pdf. Accessed 25 January 2021.

  2. http://data.stats.gov.cn/easyquery.htm?cn=E0103. Accessed 25 January 2021.

  3. Eurostat/OECD (2009, 2011); cf. Laafia (2002, p. 7) and Leydesdorff et al. (2006, p. 186).

  4. https://unstats.un.org/unsd/publication/SeriesM/SeriesM_34rev4E.pdf. Accessed 25 January 2021.

  5. http://mcin.macrochina.com.cn Accessed 25 January 2021.

  6. https://ec.europa.eu/research/innovation-union/pdf/knowledge_intensive_business_services_in_europe_2011.pdf Accessed 25 January 2021.

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Acknowledgment

The author is grateful to Loet Leydesdorff for his valuable comments on a previous version of the manuscript and Kevin Dong for help in data collection. Inga Ivanova acknowledges support of the Basic Research Program at the National Research University Higher School of Economics (NRU HSE) and a subsidy granted by the Russian Academic Excellence Project ‘5-100’.

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  1. Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics (NRU HSE), 20 Myasnitskaya St., Moscow, 101000, Russia

    Inga Ivanova

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  1. Inga Ivanova
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Correspondence to Inga Ivanova.

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Appendix

Appendix

See Tables 10, 11, 12, and 13

Table 10 Triple Helix synergy for 31 territorial districts of China for all sectors, high- and medium- technology sectors, and KIS in mbits of information (data for 2010)
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Table 11 Province-industry relative complexity values for 31 Chinese territorial districts for three complexity measures—ECI, Fitness, MECI (employment data 2010)
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Table 12 Province-industry relative complexity values for 31 Chinese territorial districts for three complexity measures—ECI, Fitness, MECI (income data 2010)
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Table 13 Province-industry relative complexity values for 31 Chinese territorial districts for three complexity measures—ECI, Fitness, MECI (investment data 2010)
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Ivanova, I. The relation between complexity and synergy in the case of China: different ways of predicting GDP growth in a complex and adaptive system. Qual Quant 56, 195–215 (2022). https://doi.org/10.1007/s11135-021-01118-6

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