Skip to main content
SpringerLink
Log in

The impact of research output on economic growth by fields of science: a dynamic panel data analysis, 1980–2016

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Whether research output significantly impacts on economic growth, and which research areas/fields of science matter the most to improve the economic performance of countries, stand as fundamental endeavors of scientific inquiry. Although the extant literature has analyzed the impact of research output on economic growth both holistically and by field, the impact of academic knowledge as a capital good (hard and social sciences) versus a final good (medical and humanities) has been largely neglected in analyses involving large sets of countries over a broad period of time. Based on a sample of 65 countries over 36 years (1980 to 2016), and employing system GMM dynamic panel data estimations, four main results are worth highlighting: (1) holistic research output positively and significantly impacts on economic growth; (2) both the academic knowledge of scientific areas that most resemble capital goods (physical sciences, engineering and technology, life sciences or social sciences) or final goods (base clinical, pre-clinical and health or arts and humanities) foster economic performance; (3) the global impact of research output is particularly high in the fields of engineering and technology, social sciences, and physics; and (4) the impact of research output on economic growth occurs mainly through structural change processes involving the reallocation of resources towards the industrial sector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: Authors’ elaboration

Fig. 2

Similar content being viewed by others

Notes

  1. Australia, Belgium, Chile, Czech Republic, Denmark, Estonia, Germany, Greece, Iceland, Ireland, Japan, Luxembourg, Netherlands, Norway, Portugal, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, Turkey.

  2. The perils associated with having many instruments related to observation are well documented in the literature. As noted by Roodman (2009, 139), “[s]imply by being numerous, instruments can overfit instrumented variables, failing to expunge their endogenous components and biasing coefficient estimates towards those from non-instrumenting estimators.”. An additional problem with instrument proliferation is that it impairs the Hansen test, providing implausibly perfect p values of 1.000, and thus failing to reject the null of joint instrument validity. The Sargan test is not as vulnerable to instrument proliferation but requires homoscedastic errors for consistency, which cannot be assumed in the system GMM context (Roodman 2009).

  3. In order to address the issue of instrument proliferation, which typically affects system GMM estimations (Roodman 2009), we restricted the number of instruments by limiting the number of lags of the endogenous variables entering the instrument set, so that the number of instruments does not (largely) exceed the number of groups. Such a ‘rule of thumb’ has been followed by other authors in this area of research, namely Laverde-Rojas and Correa (2019).

References

  • Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. SAGE Open. https://doi.org/10.1177/2158244019829575.

    Article  Google Scholar 

  • Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014). Causality and endogeneity: Problems and solutions. In D. V. Day (Ed.), The oxford handbook of leadership and organizations (pp. 93–117). New York: Oxford University Press.

    Google Scholar 

  • Antonelli, C., & Fassio, C. (2016). Academic knowledge and economic growth: are scientific fields all alike? Socio-Economic Review,3, 537–565.

    Article  Google Scholar 

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economics Studies,58(2), 277–297.

    Article  MATH  Google Scholar 

  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error component models. Journal of Econometrics,68, 29–51.

    Article  MATH  Google Scholar 

  • Audretsch, D. B., Bozeman, B., Combs, K. L., Feldman, M., Link, A. N., Siegel, D. S., Stephan, P., Tassey, G., Wessner, C. (2002). The economics of science and technology. Journal of Technology Transfer, 27, 155–203.

    Article  Google Scholar 

  • Australian Academy of Science (2015). The importance of advanced physical and mathematical sciences to the Australian economy (downloaded on 6 September 2018 from www.chiefscientist.gov.au).

  • Bacon, F. (1620). Novum Organum Scientiarum. In The Novum organon, or a true guide to the interpretation of nature (G. W. Kitchin, Trans.). Oxford: Oxford University Press (1855).

  • Balconi, M., Brusoni, S., & Orsenigo, L. (2010). In defence of the linear model: An essay. Research Policy,39(1), 1–13.

    Article  Google Scholar 

  • Barro, R. J., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics,104, 184–198.

    Article  Google Scholar 

  • Benneworth, P. (2015). Tracing how arts and humanities research translates, circulates and consolidates in society. How have scholars been reacting to diverse impact and public value agendas? Arts and Humanities in Higher Education,14(1), 45–60.

    Article  Google Scholar 

  • Bhullar, M. P. S., & Kaur, M. S. (2014). Analysis of role of knowledge management in economic growth. International Journal of Emerging Research in Management and Technology,3(5), 29–33.

    Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel-data models. Journal of Econometrics,87(1), 115–143.

    Article  MATH  Google Scholar 

  • Bodman, P., & Le, T. (2013). Assessing the roles that absorptive capacity and economic distance play in the foreign direct investment-productivity growth nexus. Applied Economics,45(8), 1027–1039.

    Article  Google Scholar 

  • Bond, S., Hoeffler, A., Temple, J. (2001). GMM estimation of empirical growth models, C.E.P.R. Discussion Papers, No. 3048. University of Bristol, Bristol, UK.

  • Buenstorf, G., & Heinisch, D. P. (2020). When do firms get ideas from hiring PhDs? Research Policy,49(3), 103913.

    Article  Google Scholar 

  • Bush, V. (1945). Science—The endless frontier. Washington, DC: US Government Printing Office.

    Book  Google Scholar 

  • CEBR (2016). Engineering and economic growth: A global view, A report by CEBR for the Royal Academy of Engineering (downloaded on 12 September 2018 from https://www.raeng.org.uk/publications/reports/engineering-and-economic-growth-a-global-view).

  • Coccia, M. (2019). Why do nations produce science advances and new technology? Technology in Society,59, 101–124.

    Article  Google Scholar 

  • De Moya-Anegón, F., & Herrero-Solana, V. (1999). Science in America Latina: A comparison of bibliometric and scientific-technical indicators. Scientometrics,46, 299–320.

    Article  Google Scholar 

  • De Solla Price, D. J. (1963). Little science, big science. New York: Columbia University Press.

    Book  Google Scholar 

  • Easterly, W., & Levine, R. (1997). Africa’s growth tragedy: policies and ethnic divisions. The Quarterly Journal of Economics,112(4), 1203–1250.

    Article  Google Scholar 

  • EBRD (2019). Introducing the EBRD Knowledge Economy Index, European Bank for Reconstruction and Development (EBRD), London.

  • Evans, P. (2000). Are innovation-based endogenous growth models useful? (downloaded on 5 September 2018 from http://economics.sbs.ohiostate.edu/pdf/evans/twoss31.pdf).

  • Feldman, M.P., Link, A.N., & Siegel, D. (2002). The Economics of Science and Technology. An Overview of Initiatives to Foster Innovation, Entrepreneurship, and Economic Growth.

  • Frank, C., & Nason, E. (2009). Health research: measuring the social, health and economic benefits. CMAJ: Canadian Medical Association Journal,180(5), 528–534.

    Article  Google Scholar 

  • Frantzen, D. (2000). Innovation, international technological diffusion and changing influence of R&D on productivity. Cambridge Journal of Economics,24(2), 193–210.

    Article  Google Scholar 

  • Gabardo, F. A., Pereima, J. B., & Einloft, P. (2017). The incorporation of structural change into growth theory: A historical appraisal. Economia,18(3), 392–410.

    Article  Google Scholar 

  • Gibson, A. G., & Hazelkorn, E. (2017). Arts and humanities research, redefining public benefit, and research prioritization in Ireland. Research Evaluation,26(3), 199–210.

    Article  Google Scholar 

  • Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson: New York University.

    Google Scholar 

  • Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so much more output per worker than others. The Quarterly Journal of Economics,114(1), 83–116.

    Article  Google Scholar 

  • Hart, P. W., & Sommerfeld, J. T. (1998). Relationship between growth in gross domestic product (GDP) and growth in the chemical engineering literature in five different countries. Scientometrics,42(3), 299–311.

    Article  Google Scholar 

  • Hatemi-J, A., Ajmi, A. N., El Montasser, G., Inglesi-Lotz, R., & Gupta, R. (2016). Research output and economic growth in G7 countries: new evidence from asymmetric panel causality testing. Applied Economics,48(24), 2301–2308.

    Article  Google Scholar 

  • Holm, P., Jarrick, A., & Scott, D. (2015). The value of the Humanities. In Humanities world report 2015, chapter 2 London: Palgrave Macmillan.

  • Hosang, M. (2014). Venture capital investment in the life sciences in Switzerland. Chimia,68(12), 847–849.

    Article  Google Scholar 

  • Howson, T., & Davies, G. (2018). From mind to market: Innovation insights from the welsh life sciences and health ecosystem. In Proceedings of the European conference on innovation and entrepreneurship, ECIE (vol. 2018, pp. 309–318).

  • Inglesi-Lotz, R., Balcilar, M., & Gupta, R. (2014). Time-varying causality between research output and economic growth in US. Scientometrics,100, 203–216.

    Article  Google Scholar 

  • Inglesi-Lotz, R., Chang, T., & Gupta, R. (2015). Causality between research output and economic growth in BRICS. Quality & Quantity,49, 167–176.

    Article  Google Scholar 

  • Inglesi-Lotz, R., & Pouris, A. (2013). The influence of scientific research output of academics on economic growth in South Africa: An autoregressive distributed lag (ARDL) application. Scientometrics,95, 129–139.

    Article  Google Scholar 

  • Jaffe, K., Caicedo, M., Manzanares, M., Gil, M., Rios, A., Florez, A., et al. (2013). Productivity in physical and chemical science predicts the future economic growth of developing countries better than other popular indices. PLoS ONE,8(6), 1–10.

    Article  Google Scholar 

  • Jin, J. C. (2009). Economic research and economic growth: Evidence from East Asian economies. Journal of Asian Economics,20(2), 150–155.

    Article  Google Scholar 

  • Jin, J. C. (2010). Research publications, economic growth and causality: Japan’s experience. Pacific Economic Review,15(5), 666–673.

    Article  Google Scholar 

  • Jin, J. C., & Jin, L. (2013). Research publications and economic growth: evidence from cross-country regressions. Applied Economics,45(8), 983–990.

    Article  Google Scholar 

  • Kumar, R. R., Stauvermann, P. J., & Patel, A. (2016). Exploring the link between research and economic growth: an empirical study of China and USA. Quality & Quantity,50(3), 1073–1091.

    Article  Google Scholar 

  • Laverde-Rojas, H., & Correa, J. C. (2019). Can scientific productivity impact the economic complexity of countries? Scientometrics,120(1), 267–282.

    Article  Google Scholar 

  • Lee, L. C., Lin, P. H., Chuang, Y. W., & Lee, Y. Y. (2011). Research output and economic productivity: A Granger causality test. Scientometrics,89(2), 465–478.

    Article  Google Scholar 

  • Leydesdorff, L., & Wagner, C. (2009). Macro-level indicators of the relations between research funding and research output. Journal of Informetrics,3(4), 353–362.

    Article  Google Scholar 

  • Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics,22(1), 3–42.

    Article  Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics,107(2), 407–437.

    Article  MATH  Google Scholar 

  • Missio, F., Jayme, F., Jr., & Oreiro, J. L. (2015). The structuralist tradition in economics: methodological and macroeconomics aspects. Revista de Economia Política,35(2), 247–266.

    Google Scholar 

  • Mokyr, J. (2018). The past and the future of innovation: Some lessons from economic history. Explorations in Economic History,69, 13–26.

    Article  Google Scholar 

  • Moral-Benito, E. (2012). Determinants of economic growth: A Bayesian panel data approach. The Review of Economics and Statistics,94(2), 566–579.

    Article  Google Scholar 

  • Muller, S. M. (2019). Reply to “Research incentives and research output”: a caution on quantity incentives and the use of economic models for higher education policy. Higher Education, 78, 1129–1138.

    Article  Google Scholar 

  • Ntuli, H., Inglesi-Lotz, R., Chang, T., & Pouris, A. (2015). Does research output cause economic growth or vice versa? Evidence from 34 OECD countries. Journal of the Association for Information Science and Technology,66(8), 1709–1716.

    Article  Google Scholar 

  • O’Mahony, M., & Timmer, M. P. (2009). Output, input and productivity measures at the industry level: The EU KLEMS Database. Economic Journal,119(538), F374–F403.

    Article  Google Scholar 

  • OECD/WTO. (2019). Aid for trade at a glance 2019: Economic diversification and empowerment. Paris: OECD Publishing. https://doi.org/10.1787/18ea27d8-en.

    Book  Google Scholar 

  • Perilla Jimenez, J. R. (2019). Mainstream and evolutionary views of technology, economic growth and catching up. Journal of Evolutionary Economics,29(3), 823–852.

    Article  Google Scholar 

  • Quatraro, F. (2009). Innovation, structural change and productivity growth: Evidence from Italian regions, 1980–2003. Cambridge Journal of Economics,33(5), 1001–1022.

    Article  Google Scholar 

  • Quatraro, F. (2010). The economics of structural change in knowledge. New York: Routledge.

    Google Scholar 

  • Reid, G. (2014). Why should the taxpayer fund science and research?. UK: NCUB—National Centre for Universities and Businesses.

    Google Scholar 

  • Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy,94(5), 1002–1037.

    Article  Google Scholar 

  • Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy,98(5), S71–S102.

    Article  Google Scholar 

  • Roodman, D. (2009). Practitioners’ corner: A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics,71(1), 135–158.

    Article  Google Scholar 

  • Rosenberg, N., & Birdzell, L. E. (1990). Science, technology and Western miracle. Scientific American,263(5), 42–54.

    Article  Google Scholar 

  • Rosenberg, N., & Nelson, R. R. (1994). American universities and technical advance in industry. Research Policy,23(3), 323–348.

    Article  Google Scholar 

  • Saviotti, P. P., & Pyka, A. (2004). Economic development by the creation of new sectors. Journal of Evolutionary Economics,14(1), 1–35.

    Article  Google Scholar 

  • Schot, J., & Steinmueller, W. E. (2018). Three frames for innovation policy: R&D, systems of innovation and transformative change. Research Policy,47(9), 1554–1567.

    Article  Google Scholar 

  • Schumpeter, J. A. (1912). The theory of economic development (10th ed.). New Brunswick: Transaction Publishers.

    Google Scholar 

  • Schumpeter, J. A. (1942). Capitalism, socialism and democracy (3rd ed.). London: George Allen and Unwin.

    Google Scholar 

  • Silva, E. G., & Teixeira, A. A. C. (2011). Does structure influence growth? A panel data econometric assessment of ‘relatively less developed’ countries, 1979–2003. Industrial and Corporate Change,20(2), 457–510.

    Article  Google Scholar 

  • Sjöö, K., & Hellström, T. (2019). University–industry collaboration: A literature review and synthesis. Industry and Higher Education,33(4), 275–285.

    Article  Google Scholar 

  • Solarin, S. A., & Yen, Y. Y. (2016). A global analysis of the impact of research output on economic growth. Scientometrics,108(2), 855–874.

    Article  Google Scholar 

  • Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics,70(1), 65–94.

    Article  Google Scholar 

  • Stephan, P. (1996). The economics of science. Journal of Economic Literature,34(3), 1199–1235.

    Google Scholar 

  • Teixeira, A. A. C. (2014). Evolution, roots and influence of the literature on National Systems of Innovation: a bibliometric account. Cambridge Journal of Economics,38(1), 181–214.

    Article  Google Scholar 

  • Teixeira, A. A. C., & Fortuna, N. (2011). Human capital, R&D, trade, and long-run productivity testing the technological absorption hypothesis for the Portuguese economy, 1960–2001. Research Policy,39(3), 335–350.

    Article  Google Scholar 

  • Teixeira, A. A. C., & Mota, L. (2012). A bibliometric portrait of the evolution, scientific roots and influence of the literature on University-Industry links. Scientometrics,93(3), 719–743.

    Article  Google Scholar 

  • Teixeira, A. A. C., & Queirós, A. S. S. (2016). Economic growth, human capital and structural change: A dynamic panel data analysis. Research Policy,45(8), 1636–1648.

    Article  Google Scholar 

  • Vinkler, P. (2008). Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics,74(2), 237–254.

    Article  Google Scholar 

  • Weinberger, J. (1976). Science and rule in Bacon’s Utopia: An introduction to the reading of the New Atlantis. American Political Science Review,70(3), 865–885.

    Article  Google Scholar 

  • Wolff, E. N. (2003). What’s behind the rise in profitability in the US in the 1980s and the 1990s? Cambridge Journal of Economics,27(4), 479–499.

    Article  Google Scholar 

  • World Bank. (2017). World development indicators. Washington, DC: World Bank.

    Google Scholar 

  • Yasgül, Y. S., & Güris, B. (2016). Causality between research output in the field of biotechnology and economic growth in Turkey. Quality & Quantity,50, 1715–1726.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CEF.UP, Faculty of Economics, University of Porto, Porto, Portugal

    Tânia Pinto

  2. CEF.UP, Faculty of Economics, University of Porto, INESC Tec, OBEGEF, R. Dr. Roberto Frias, 4200-464, Porto, Portugal

    Aurora A. C. Teixeira

Authors
  1. Tânia Pinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aurora A. C. Teixeira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aurora A. C. Teixeira.

Appendix

Appendix

See Table 5.

Table 5 Categorization of the distinct fields of research
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pinto, T., Teixeira, A.A.C. The impact of research output on economic growth by fields of science: a dynamic panel data analysis, 1980–2016. Scientometrics 123, 945–978 (2020). https://doi.org/10.1007/s11192-020-03419-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-020-03419-3

Keywords

Search

Navigation