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ABSTRACT
This paper aims to investigate the role of scientific research in the development of technology advancement and subsequent economic growth. The publication data collected from Science and Nature consists of 31,615 lines in Excel. Scholars from 3,790 universities and research institutes in 118 countries have published in the two premier journals over the period 2012–2019. Using these publication data, we explore two transmission channels: first, research output can be used for the development of innovations and inventions; second, such a technological advancement promotes economic growth. Instrumental variable (IV) estimation suggests that scientific publications have a causal effect on the enhancement of advanced technologies; the technology enhancement, in turn, has a causal effect on economic growth. Empirical evidence in direct support of these two relationships provide major incentives for governments and business firms to invest more towards future research in the fields of science and technology.
Acknowledgements
We thank Goen Chang, Young-Eun Hong, Ji-Eun Jung, and Jun-Soo Park for excellent research assistance and conference participants at the International Economics and Finance Society China Conference, the Hong Kong conference organized by Chu Hai College of Higher Education, and the 11th Hong Kong Economic Association Biennial Conference for comments. We also thank the editor and anonymous referees for their helpful feedback. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the institutions with which the authors are affiliated. The first author Eden Yu also gratefully acknowledges financial support from the Hong Kong RGC/IDS 13/16 grant.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets generated and analysed during the current study are not publicly available due to the fact that they constitute an excerpt of research in progress but are available from the corresponding author on request.
Acronyms
| 2SLS | = | two-stage least squares |
| DUM | = | dummy variable |
| E | = | educational attainment |
| FDI | = | foreign direct investment |
| GDP | = | gross domestic product |
| GDP12 | = | real per capita GDP in the initial year of 2012 |
| GDPGR | = | average annual growth rates of real per capita GDP |
| ICT | = | information and communication technology |
| IV | = | instrumental variable |
| K | = | physical capital |
| L | = | labour |
| LDCs | = | less developed countries |
| LE | = | educated labour force |
| MIT | = | Massachusetts Institute of Technology |
| NICs | = | newly industrialized countries |
| OLS | = | ordinary least squares |
| Patent | = | patent applications per million people |
| PI | = | principal investigator |
| PUB | = | published pages per million people in Science and Nature |
| R&D | = | research and development |
| SC | = | science citation index |
| TOEFL | = | Test of English as a Foreign Language |
| UK | = | United Kingdom |
| US | = | United States (of America) |
| WB | = | World Bank |
| WDI | = | World Development Indicators |
| Y | = | output |
Notes
1 Occasionally, we found some rare cases that the number of co-authors for an article could be as large as 300–500.
2 To respect the significance of the publications in the two premier journals, our page count computes up to 4 decimal points.
3 It should be noted that the identification of many institution names in German, French, Spanish, and other languages would not have been possible without Google search.
4 As is widely known, the Cobb-Douglas production function is the simplest form of a general production function, assuming constant returns to scale, and it is commonly used to measure the growth of nations. Economic growth is crucially dependent on labour, capital, and technology. Especially in modern times, technological development plays the most important role in the growth of nations, which is the main theme of our paper. Therefore, the C-D production function is appropriately adopted here to measure the role of technology in economic growth.:
5 Argentina’s GDPGR (−27.7%) was found to be an influential outlier.
6 The data source, WDI, does not publish consistent macro data for Taiwan; therefore, Taiwan was excluded from our sample.
7 The remaining 80 countries published once in a while over the sample period, and most of their publications were 0.5 pages or less because of joint appointments with American or European authors. Including all these countries in the sample would be biased and misleading because these countries have not continuously published by their own will and own research capabilities. And the sum of published pages by these many countries reached only 0.5% of total publications over time. Thus, these countries were not included for estimation.
8 More details on instrumental variable estimation will be discussed in Section IV below.
9 Native English speakers do not take TOEFL tests, so all six English-speaking countries (UK, U.S.A., Canada, Ireland, Australia, and New Zealand) were assigned the TOEFL score of 110 out of 120, assuming that not all of them would get a perfect score in the exam.
10 This is a bit smaller than the speed of convergence found in the literature: −0.75% points in Barro (Citation1991) and −0.61% points in Hanushek and Kimko (Citation2000). One possible explanation would be that the majority of our sample countries, compared with the two earlier studies, are research-active and more developed countries. Hence, the speed of convergence among these countries is expected to be slower than those in earlier studies. Additionally, beginning our sample in 2012 placed our study many years after the two previous studies. Hence, the speed of convergence in recent decades would definitely be slower than those in the 1960s and 1970s.
11 Aghion et al. (Citation2009), in particular, employed the state-level data of the United States and found that the growth effect of educational spending was greater in the states that were close to technology frontiers. More specifically, investments in ‘high-brow’ education enhanced innovations for more advanced technologies of the state, while ‘low-brow’ education improved the state economies that were behind the frontier technology.
12 In either case, the dummy variable DUM was not included, since the extreme value (Argentina’s GDPGR) was one of the four middle observations that were deleted for estimation.
13 The size of the growth effect is similar to the findings in Hanushek and Kimko (Citation2000), in which the growth effect would rise by approximately 0.3% points if 10% of the mean quality measure of secondary education were equally increased.
14 In contrast, the Patent itself, without using publications as an IV, was found to have little growth effect even in the same model. The result is not reported here but is available upon request.