|
Special Issue:
SPECIAL TOPIC— Interdisciplinary physics: Complex network dynamics and emerging technologies
|
| SPECIAL TOPIC—Interdisciplinary physics: Complex network dynamics and emerging technologies |
Prev
Next
|
|
|
Advantage of populous countries in the trends of innovation efficiency |
| Dan-Dan Hu(胡淡淡)1,2, Xue-Jin Fang(方学进)1,2, and Xiao-Pu Han(韩筱璞)1,2,† |
1 Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China;
2 Institute of Information Economy and Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China |
|
|
|
|
Abstract A flurry of studies indicates that population size has a positive effect on innovation, however, cross-country empirical evidence remains sparse. In this paper, we add to the literature by investigating the relationship between population size and innovation efficiency at the country level through constructing three relative indexes based on the datasets of patent applications and Research and Development (R&D) investment. Different from previous studies based on absolute innovation indicators, the relative indexes can reflect the core innovation efficiency of economies by excluding the impact from the difference of economic development level, with a view putting all economies into a comparable standard framework. For all of the three relative indexes, their long-term trends show significant correlations with population size, and the economy with a larger population usually has better and stable performance on the trends of innovation efficiency. In addition, we find that there is a critical population size, over which the economy would be more likely to have a spontaneous improvement on innovation efficiency. This study provides direct evidence in supporting the population size advantage on the trends of innovation efficiency at the economy level and provides new insight to understand the rapid development of innovation in a few populous countries.
|
Received: 20 July 2021
Revised: 22 January 2022
Accepted manuscript online: 17 February 2022
|
|
PACS:
|
89.65.Gh
|
(Economics; econophysics, financial markets, business and management)
|
| |
89.75.-k
|
(Complex systems)
|
| |
89.65.-s
|
(Social and economic systems)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62073112 and 61673151) and the Zhejiang Provincial Natural Science Foundation of China (Grant No. LGF18F030007). |
Corresponding Authors:
Xiao-Pu Han
E-mail: xp@hznu.edu.cn
|
Cite this article:
Dan-Dan Hu(胡淡淡), Xue-Jin Fang(方学进), and Xiao-Pu Han(韩筱璞) Advantage of populous countries in the trends of innovation efficiency 2022 Chin. Phys. B 31 068903
|
[1] Romer P M 1986 J. Polit. Econ. 94 1002
[2] Lucas Jr R E 1988 J. Monet. Econ. 22 3
[3] Grossman G M and Helpman E 1994 J. Econ. Perspect. 8 23
[4] Zhou X X, Cai Z M, Tan K H, Zhang L L, Du J T and Song M L 2021 Technol. Forecast. Soc. Change 167 120671
[5] Ribeiro L C, Ruiz R M, Albuquerque E M and Bernardes A T 2006 Int. J. Mod. Phys. C 17 247
[6] Wang S B, Zhao J L and Yu J N 2019 Int. J. Mod. Phys. C 30 1950012
[7] Grossman G M and Helpman E 1991 Innovation and Growth in the Global Economy (MIT Press)
[8] Romer P M 1990 J. Polit. Econ. 98 S71
[9] Bettencourt L M, Lobo J, Helbing D, Kühnert C and West G B 2007 Proc. Natl. Acad. Sci. U. S. A. 104 7301
[10] Boserup E 1981 Population and Technological Change: A Study of Long Term Trends (Chicago: University of Chicago Press) p. 255
[11] Galor O and Weil D N 2000 Am. Econ. Rev. 90 806
[12] Kremer M 1993 Q. J. Econ. 108 681
[13] Gomez-Lievano A, Patterson-Lomba O and Hausmann R 2017 Nat. Hum. Behav. 1 0012
[14] Liang J 2018 Demographics of Innovation:Why Demographics is a Key to the Innovation Race (John Wiley and Sons) p. 71
[15] Li R Q, Lu L Y, Cui T Y, Gu W W, Ma S D, Xu G and Stanley H E 2021 IEEE Access 9 48052
[16] Balland P A, Jara-Figueroa C, Petralia S G, Steijn M P A, Rigby D L and Hidalgo C A 2020 Nat. Hum. Behav. 4 248
[17] Hong I, Frank M R, Rahwan I, Jung W S and Youn H 2020 Sci. Adv. 6 eaba4934
[18] Shearmur R 2012 Cities 29 S9
[19] Bettencourt L M A, Lobo J, Strumsky D and West G B 2010 PloS One 5 e13541
[20] Pan W, Ghoshal G, Krumme C, Cebrian M and Pentland A 2013 Nat. Commun. 4 1961
[21] Lobo J, Bettencourt L M A, Strumsky D and West G B 2013 PloS One 8 e58407
[22] Gao J, Zhang Y C and Zhou T 2019 Phys. Rep. 817 1
[23] Li R, Dong L, Zhang J, Wang X R, Wang W X, Di Z and Stanley H E 2017 Nat. Commun. 8 1841
[24] Howitt P 1999 J. Polit. Econ. 107 715
[25] Wen Y 2015 FRB St. Louis Working Paper
[26] Acemoglu D and Linn J 2004 Q. J. Econ. 119 1049
[27] Gancia G, Müller A and Zilibotti F 2013 Advances in Economics and Econometrics: Tenth World Congress (Cambridge: Cambridge University Press)
[28] Beerli A, Weiss F J, Zilibotti F and Zweimüller J 2020 Chin. Econ. Rev. 60 101157
[29] Solow R M 1957 Rev. Econ. Stat. 39 312
[30] Gao J and Jefferson G H 2007 Asia Pac. Bus. Rev. 13 357
[31] Desmet K and Parente S L 2010 Int. Rev. Econ. 51 319
[32] Deane P M 1979 The First Industrial Revoluation (Cambridge University Press)
[33] Wang S, Fan J, Zhao D T and Wang S Y 2016 Technol. Anal. Strateg. Manag. 28 396
[34] Lee H Y and Park Y T 2005 Asian J. Technol. Innov. 13 207
[35] Guan J and Zuo K 2014 Scientometrics 100 541
[36] Hsu Y 2011 Afr. J. Bus. Manag. 5 1378
[37] Guede-Cid R, Rodas-Alfaya L, Leguey-Galán S and Cid-Cid A I 2021 J. Open Innov. 7 62
[38] Kontolaimou A, Giotopoulos I and Tsakanikas A 2016 Econ. Model. 52 477
[39] Liu X L and White S 2001 Res. Policy 30 1091
[40] Sharma S and Thomas V 2008 Scientometrics 76 483
[41] Aw B Y, Roberts M J and Xu D Y 2011 Amer. Econ. Rev. 101 1312
[42] Fang X J, Cui J Y, Hu D D and Han X P 2020 Acta Phys. Sin. 69 088905 (in Chinese)
[43] Keuschnigg M, Mutgan S and Hedström P 2019 Sci. Adv. 5 eaav0042
[44] Wang W, Du W B, Li W H, Tong L and Wang J E 2021 Chin. Phys. B 30 018901
[45] Hu J W, Gao S, Yan J W, Lou P and Yin Y 2020 Chin. Phys. B 29 088901
[46] Bai J H and Li J 2011 Innovation 13 142
[47] Wang E C and Huang W C 2007 Res. Policy 36 260 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
