Accurate forecast of countries’ research output by macro-level indicators

Mueller, Christoph Emanuel

doi:10.1007/s11192-016-2084-1

Cited by 20 publications

(21 citation statements)

References 31 publications

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“…Just as gross domestic product (GDP) is a measure of economic wealth, the mean citation rate per scientific article could be used as a measure of the scientific wealth of nations (King, 2004;May, 1997). The link between economic and scientific wealth has been repeatedly demonstrated (Cimini et al, 2014;Cole & Phelan, 1999;Docampo & Bessoule, 2019;Gantman, 2012;Hatemi-J et al, 2016;King, 2004;Kumar et al, 2016;May, 1997;Mueller, 2016;Prathap, 2017;Rousseau & Rousseau, 1998;Vinkler, 2008Vinkler, , 2018, meaning that the richer the country, the more money is invested into research and development (R&D) activities, which, in return, is expected to increase the quality of scientific publications. Yet, the relationship between economic and scientific wealth is not as simple and straightforward as is often presented.…”

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confidence: 99%

Factors Predicting the Scientific Wealth of Nations

Allïk

Lauk

Realo

2020

Cross-Cultural Research

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It has been repeatedly demonstrated that economic affluence is one of the main predictors of the scientific wealth of nations. Yet, the link is not as straightforward as is often presented. First, only a limited set of relatively affluent countries is usually studied. Second, there are differences between equally rich countries in their scientific success. The main aim of the present study is to find out which factors can enhance or suppress the effect of the economic wealth of countries on their scientific success, as measured by the High Quality Science Index ( HQSI). The HQSI is a composite indicator of scientific wealth, which in equal parts considers the mean citation rate per paper and the percentage of papers that have reached the top 1% of citations in the Essential Science Indicators ( ESI; Clarivate Analytics) database during the 11-year period from 2008 to 2018. Our results show that a high position in the ranking of countries on the HQSI can be achieved not only by increasing the number of high-quality papers but also by reducing the number of papers that are able to pass ESI thresholds but are of lower quality. The HQSI was positively and significantly correlated with the countries’ economic indicators (as measured by gross national income and Research and Development expenditure as a percentage from GDP), but these correlations became insignificant when other societal factors were controlled for. Overall, our findings indicate that it is small and well-governed countries with a long-standing democratic past that seem to be more efficient in translating economic wealth into high-quality science.

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confidence: 99%

Factors Predicting the Scientific Wealth of Nations

Allïk

Lauk

Realo

2020

Cross-Cultural Research

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“…Tables 2, 3, 4 and 5 present the results with no spatial dependence. The analysis will focus 5 One important discussion in the literature as described in Mueller (2016) is the lagged (delayed) effect of R&D investment on research outcome. In the ''Appendix 2'', we present results using one year lagged on the explanatory variables.…”

Section: Resultsmentioning

confidence: 99%

“…As in any type of production process, the output of research is what it is produced and published. Publishing a new scientific output is a way of transmitting knowledge in the scientific community (Mueller 2016). To produce new articles, we need researchers who will raise hypothesis and test them in different ways, and submit their papers to peerreview journal for publication.…”

Section: Randd and Research Outcomesmentioning

confidence: 99%

“…Explanatory variables for scientific inputs in the World Bank model includes the number of researchers in R&D and R&D expenditures. The latter is calculated by multiplying the percent of R&D expenditures obtained from the World Bank website, such as in Mueller (2016), by the GDP in 2003 dollars. This dataset includes 31 countries in a panel of nine years from 2003 to 2011.…”

Section: Datamentioning

confidence: 99%

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Scientific output: labor or capital intensive? An analysis for selected countries

Erfanian

Neto

2017

Scientometrics

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Scientific research contributes to sustainable economic growth environments. Hence, policy-makers should understand how the different inputs-namely labor and capital-are related to a country's scientific output. This paper addresses this issue by estimating output elasticities for labor and capital using a panel of 31 countries in nine years. Due to the nature of scientific output, we also use spatial econometric models to take into account the spillover effects from knowledge produced as well as labor and capital. The results show that capital elasticity is closer to the labor elasticity. The results suggest a decreasing return to scale production of scientific output. The spatial model points to negative spillovers from capital expenditure and no spillovers from labor or the scientific output.

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“…Bornmann and Leydesdorf (2013) analysed normalized citation impact values of six large countries along a 30-years period, and found statistically significant trends mostly associated with the internationalization of research in some large continental European countries. Mueller (2016) reports on the use of a machine learning procedure to test a model based upon sixteen macro-level predictors representing "aspects of the research and economic system, of the political conditions, and of structural and cultural attributes of countries" to forecast countries research output across scientific disciplines. But, to evaluate the research output of a country or institution, the most widely used indicators are those found in databases such as Web of Science/Incites.…”

Section: Introductionmentioning

confidence: 99%

A new approach to the analysis and evaluation of the research output of countries and institutions

Docampo

Bessoule

2019

Scientometrics

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A plethora of bibliometric indicators is available nowadays to gauge research performance. The spectrum of bibliometric based measures is very broad, from purely size-dependent indicators (e.g. raw counts of scientific contributions and/or citations) up to size-independent measures (e.g. citations per paper, publications or citations per researcher), through a number of indicators that effectively combine quantitative and qualitative features (e.g. the h-index). In this paper we present a straightforward procedure to evaluate the scientific contribution of territories and institutions that combines size-dependent and scale-free measures. We have analysed in the paper the scientific production of 189 countries in the period 2006-2015. Our approach enables effective global and field-related comparative analyses of the scientific productions of countries and academic/research institutions. Furthermore, the procedure helps to identifying strengths and weaknesses of a given country or institution, by tracking variations of performance ratios across research fields. Moreover, by using a straightforward wealth-index, we show how research performance measures are highly associated with the wealth of countries and territories. Given the simplicity of the methods introduced in this paper and the fact that their results are easily understandable by non-specialists, we believe they could become a useful tool for the assessment of the research output of countries and institutions.

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Accurate forecast of countries’ research output by macro-level indicators

Cited by 20 publications

References 31 publications

Factors Predicting the Scientific Wealth of Nations

Factors Predicting the Scientific Wealth of Nations

Scientific output: labor or capital intensive? An analysis for selected countries

A new approach to the analysis and evaluation of the research output of countries and institutions

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