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.
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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.
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The authors thank two anonymous referees, whose comments helped to improve our manuscript.
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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
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DOI: https://doi.org/10.1007/s11192-021-03973-4