Abstract
This paper aims to investigate the relationship between the production of scientific knowledge and level of income for a panel of 56 countries during the period 1996–2015. We argue that the accumulation of scientific knowledge is a key factor for the enhancement of educational and technological capabilities within an economy, and hence may have a positive impact on GDP per capita levels. We use academic publications in refereed journals (in all areas and specifically in engineering) as a proxy of scientific performance. As regards the impacts of scientific performance, we distinguish between high- and middle-income countries and, among the latter, between Asian and Latin America. The results show that academic publications are consistently and positively correlated with income per capita, for both middle and high-income countries. We also find non-linear effects in both groups. Those effects are lower for middle-income countries suggesting the presence of decreasing returns on academic performance. Finally, while Asian countries benefited from specialization in engineering research, no such effects were found for their Latin American peers.
Similar content being viewed by others
Notes
The term economic complexity (EC) was coined by Hidalgo and Hausmann (2009), who argue that EC is a predictor of economic growth potential. The economic complexity of a country is calculated based on the diversity of exports a country produces and their ubiquity, or the number of countries able to produce them (and the complexity of those countries). See http://atlas.cid.harvard.edu/.
As these two variables were highly correlated, we estimated their effects in separate regressions.
The ranking from Freedom House’s database includes two types of institutional systems features: political rights, e.g. functioning of government, political pluralism, and electoral process, and civil rights, e.g. rule of law, individual rights, freedom of expression. In this ranking each characteristic is considered individually and rated in decreasing order from 1 to 7. Thus, by adding these two variables, countries could be rated from 2 to 14.
We ran the GMM models with the “xtabond2” Stata command with the addition of “noleveleq” and “robust” commands for autocorrelation and heteroscedasticity adjustment. The “robust” command specifies the robust estimator of the covariance matrix of the parameter estimates. The resulting standard-error estimates are consistent in the presence of any pattern of heteroscedasticity and autocorrelation within panels. On the other hand, the “noleveleq” command specifies that the levels equation should be excluded from the estimation, yielding difference rather than system GMM (StataCorp 2015).
The Roodman (2009) “collapse” method specifies that xtabond2 should create one instrument for each variable and lag distance, rather than one for each time period, variable, and lag distance. In large samples, collapse reduces statistical efficiency, but in small samples it can avoid the bias that arises as the number of instruments climbs towards the number of observations (StataCorp 2015).
GMM estimations improve accuracy in providing results that are robust to heteroscedasticity and autocorrelation.
References
Abramovitz, M. (1986). Catching up, forging ahead, and falling behind. Journal of Economic History, 46(2), 386–406. https://doi.org/10.1017/s0022050700046209.
Acemoglu, D., & Robinson, J. A. (2010). Why is Africa poor? Economic History of Developing Regions, 25(1), 21–50. https://doi.org/10.1080/20780389.2010.505010.
Aghion, P., & Howitt, P. (2009). The Economics of Growth. . MIT Press.
Albuquerque, E. (2001). Scientific infrastructure and the catching up process: Notes about the role of science in developing countries. Revista Brasileira de Econonomia, 55(4), 545–566.
Archibugi, D., & Coco, A. (2005). Measuring technological capabilities at the country level: A survey and a menu for choice. Research Policy, 34(2), 175–194. https://doi.org/10.1016/j.respol.2004.12.002.
Arellano, M., & Bond, S. (1991). Some test of specification for panel data: Monte-Carlo evidence and application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968.
Cameron, A., & Trivedi, P. (2005). Microeconometrics: Methods and applications. . Cambridge University Press. https://doi.org/10.1017/CBO9780511811241.
Cimoli, M., Dosi, G., & Stiglitz, J. (2009). Industrial policy and development: The political economy of capabilities accumulation. Oxford and New York: Oxford University Press.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553.
Dahlman C.J. & Nelson R. (1995). Social Absorption capability, national innovation systems and economic development. In: B.H. Koo & D.H. Perkins (Eds.) Social capability and long-term economic growth (pp. 82–122). Palgrave Macmillan. doi: https://doi.org/10.1007/978-1-349-13512-7_5.
Edquist, C. (2005). Systems of innovation: Perspectives and challenges. In J. Fagerberg, D. Mowery, & R. Nelson (Eds.), The Oxford handbook of inovation. (pp. 181–208). Oxford University Press.
Fagerberg, J., & Srholec, M. (2008). National innovation systems, capabilities and economic development. Research Policy, 37(9), 1417–1435. https://doi.org/10.1016/j.respol.2008.06.003.
Freeman, C. (1995). The ‘National System of Innovation’ in historical perspective. Cambridge Journal of Economics, 19(1), 5–24. https://doi.org/10.1093/oxfordjournals.cje.a035309.
Freitas, I. M. B., Marques, R. A., & Silva, E. M. (2013). University–industry collaboration and innovation in emergent and mature industries in new industrialized countries. Research Policy, 42(2), 443–453. https://doi.org/10.1016/j.respol.2012.06.006.
Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31(6), 899–933. https://doi.org/10.1016/s0048-7333(01)00152-4.
Giuliani, E., & Rabellotti, R. (2012). Universities in emerging economies: bridging local industry with international science—Evidence from Chile and South Africa. Cambridge Journal of Economics, 36(3), 679–702. https://doi.org/10.1093/cje/bes009.
Hatemi-J, A., Ajmi, A. N., El Montasser, G., Inglesi-Lotz, R., & Gupta, R. (2015). Research output and economic growth in G7 countries: New evidence from asymmetric panel causality testing. Applied Economics, 48(24), 2301–2308. https://doi.org/10.1080/00036846.2015.1117052.
Hernández-González, V., Sans-Rosell, N., Jové-Deltell, M. C., & Reverter-Masia, J. (2016). Comparison between web of science and Scopus, bibliometric study of anatomy and morphology journals. International Journal of Morphology, 34(4), 1369–1377.
Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575. https://doi.org/10.1073/pnas.0900943106.
Hira, A. (2009). Learning from the tigers-comparing innovation institutions in rapidly developing economies with Latin America. Problemas del desarrollo, 40(156). doi: https://doi.org/10.22201/iiec.20078951e.2009.156.7752.
Inglesi-Lotz, R., Balcilar, M., & Gupta, R. (2014). Time-varying causality between research output and economic growth in US. Scientometrics, 100(1), 203–216. https://doi.org/10.1007/s11192-014-1257-z.
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(1), 129–139. https://doi.org/10.1007/s11192-012-0817-3.
Jaffe, A. (1989). Real effects of academic research. American Economic Review, 79(5), 957–970.
Kim, L. (1997). Imitation to innovation: The dynamics of Korea’s technological learning. . Harvard Business School Press.
Kim, L., & Nelson, R. R. (2000). Technology, learning, and innovation: Experiences of newly industrializing economies. . Cambridge University Press.
Kim, Y. K., & Lee, K. (2015). Different impacts of scientific and technological knowledge on economic growth: Contrasting science and technology policy in East Asia and Latin America. Asian Economic Policy Review, 10(1), 43–66. https://doi.org/10.1111/aepr.12081.
Kline, S., & Rosenberg, N. (1986). An Overview of innovation. In R. Landau & N. Rosenberg (Eds.), The Positive sum strategy: Harnessing technology for economic growth. (pp. 275–306). National Academy of Sciences.
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 and Quantity, 50(3), 1073–1091. https://doi.org/10.1007/s11135-015-0191-3.
Kwon, K.-S. (2011). The co-evolution of universities’ academic research and knowledge-transfer activities: The case of South Korea. Science and Public Policy, 38(6), 493–503. https://doi.org/10.3152/030234211x1296031526793.
Lall, S. (1992). Technological capabilities and industrialization. World Development, 20(2), 165–186. https://doi.org/10.1016/0305-750x(92)90097-f.
Laverde-Rojas, H., & Correa, J. C. (2019). Can scientific productivity impact the economic complexity of countries? Scientometrics, 120, 267–282. https://doi.org/10.1007/s11192-019-03118-8.
Lee, K., & Kim, Y. K. (2017). Comparing the national innovation systems in East Asia and Latin America: Fast versus slow. In T. Clarke & K. Lee (Eds.), Innovation in the Asia Pacific. (pp. 63–82). Springer.
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. https://doi.org/10.1007/s11192-011-0476-9.
Liu, X., Schwaag Serger, S., Tagscherer, U., & Chang, A. Y. (2017). Beyond catch-up—Can a new innovation policy help China overcome the middle income trap? Science and Public Policy, 44(5), 656–669. https://doi.org/10.1093/scipol/scw092.
Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. https://doi.org/10.1016/0304-3932(88)90168-7.
Lundvall, J. (1995). National systems of innovation: Towards a theory of innovation and interactive learning. In K. Pavitt (Ed.), National systems of innovation. (pp. 85–106). Printer Publishers.
Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31(2), 427–264. https://doi.org/10.1016/S0048-7333(01)00139-1.
Mankiw, G., Romer, D., & Weil, D. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437.
Mazzoleni, R., & Nelson, R. R. (2007). Public research institutions and economic catch-up. Research Policy, 36(10), 1512–1528. https://doi.org/10.1016/j.respol.2007.06.007.
Mongeon, P., & Paul-Hus, A. (2015). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5.
Niosi, J., & Reid, S. E. (2007). Biotechnology and nanotechnology: Science-based enabling technologies as windows of opportunity for LDCs? World Development, 35(3), 426–438. https://doi.org/10.1016/j.worlddev.2006.11.004.
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), 1709–1796.
Pavitt, K. (1998). The social shaping of the national science base. Research Policy, 27(8), 793–805. https://doi.org/10.1016/s0048-7333(98)00091-2.
Pinto, T., & Teixeira, A. A. C. (2020). The impact of research output on economic growth by fields of science: a dynamic panel data analysis, 1980–2016. Scientometrics, 123, 945–978. https://doi.org/10.1007/s11192-020-03419-3.
Romer, P. (1990). Endogenous technological change. Journal of Political Economy, 98(5), 71–102.
Roodman, D. M. (2009). A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71(1), 135–158. https://doi.org/10.1111/j.1468-0084.2008.00542.x.
Santa, S., & Herrero-Solana, V. (2010). Cobertura de la ciencia de Ame´rica Latina y el Caribe en Scopus vs Web of Science. Investigación Bibliotecológica, 24(52), 13–27. https://www.redalyc.org/articulo.oa?id=33931820006.
Shapira, P. & Youtie, J. (2015). The economic contributions of nanotechnology to green and sustainable growth. In: Basiuk, V., Basiuk, E. (eds) Green processes for nanotechnology, 409–434. Springer. doi:https://doi.org/10.1007/978-3-319-15461-9_15.
Solarin, S. A., & Yen, Y. Y. (2016). A global analysis of the impact of research output on economic growth. Scientometrics, 108(2), 855–874. https://doi.org/10.1007/s11192-016-2002-6.
StataCorp. (2015). Stata 14 Base Reference Manual. Stata Press.
Vinkler, P. (2008). Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics, 74, 237–254. https://doi.org/10.1007/s11192-008-0215-z.
Yaşgül, Y. S., & Güriş, B. (2015). Causality between research output in the field of biotechnology and economic growth in Turkey. Quality & Quantity, 50(4), 1715–1726. https://doi.org/10.1007/s11135-015-0230-0.
Author information
Authors and Affiliations
Corresponding author
Additional information
The authors thank two anonymous referees, whose comments helped to improve our manuscript.
Rights and permissions
About this article
Cite this article
Jack, P., Lachman, J. & López, A. Scientific knowledge production and economic catching-up: an empirical analysis. Scientometrics 126, 4565–4587 (2021). https://doi.org/10.1007/s11192-021-03973-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-021-03973-4