The Productive Efficiency of Science and Technology Worldwide: A Frontier Analysis

Authors

  • Gustavo Ferro Universidad del CEMA (UCEMA) and CONICET https://orcid.org/0000-0002-1592-0163
  • Carlos A. Romero CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política (IIEP-BAIRES)

DOI:

https://doi.org/10.7160/eriesj.2021.140402

Keywords:

Knowledge, Science, Data Envelopment Analysis, Research, Productive Efficiency

Abstract

We are interested in how codified knowledge is produced around the globe (which inputs are used to produce scientific articles and patented inventions) and the efficiency of the process (how do the best performers produce more with the same inputs or produce the same with less inputs). Using a Data Envelopment Analysis (DEA) efficiency frontier approach, we aim to determine which countries are more efficient at producing codified knowledge. We proxy knowledge production by publications and patents, obtained through human (researchers) and non-human (R&D expenditure) resources. We built a 15-year database with more than 800 observations of these and other variables. Our findings enable us to distinguish efficiency by country, geographical region, and income area. We run four different specifications and correlate the results with partial productivity indexes seeking consistency. Under constant returns to scale, the most traditional producers of knowledge are not fully efficient. Instead, small countries with limited resources appear to be efficient. When we add environmental conditions, both sets of countries are efficient producers of knowledge outputs. High-income regions, on the one hand, and East Asia, North America, and Europe and Central Asia, on the other, are the most efficient regions at producing knowledge.

Author Biography

Carlos A. Romero, CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política (IIEP-BAIRES)

Doctor in Economics (Universidad Nacional de La Plata), MSc from the University of Warwick and Bachelor of Economics from the University of Buenos Aires (UBA). Researcher at the Interdisciplinary Institute of Economic Policy of Buenos Aires and professor of Industrial Organization at the Faculty of Economic Sciences of the UBA. He is dedicated to applied research using computed general equilibrium models, input-output analysis, network models, Data Envelopment Analysis, among other tools. He has published articles and participated in various research projects related to various sectors and areas of the economy: energy, tourism and public services regulation, regional, justice, among others.

References

Abramo, G. and D'Angelo, C. A. (2014) ‘How do you define and measure research productivity?’, Scientometrics, Vol. 101, No. 2, pp. 1129–1144. https://doi.org/10.1007/s11192-014-1269-8

Abramo, G., D’Angelo, C. A. and Di Costa, F. (2015) ‘A New Approach to Measure the Scientific Strengths of Territories’, Journal of the Association for Information Science and Technology, Vol. 66, No. 6, pp. 1167–1177. https://doi.org/10.1002/asi.23257

Abramo, G., D’Angelo, C. A. and Murgia, G. (2016) ‘The combined effects of age and seniority on research performance of full professors’, Science and Public Policy, Vol. 43, No. 3, pp. 301–319. https://doi.org/10.1093/scipol/scv037

Abramo, G., Costa, C. and D’Angelo, C. A. (2015) ‘A multivariate stochastic model to assess research performance’, Scientometrics, Vol. 102, pp. 1755–1772. https://doi.org/10.1007/s11192-014-1474-5

Aksnes, D. W., Sivertsen, G., van Leeuwen, T. N. and Wendt, K. K. (2017) ‘Measuring the productivity of national R&D systems: Challenges in cross-national comparisons of R&D input and publication output indicators’, Science and Public Policy, Vol. 44, No. 2, pp. 246–258. https://doi.org/10.1093/scipol/scw058

Archambault, É., Campbell, D., Gingras, Y., and Larivière, V. (2009) ‘Comparing bibliometric statistics obtained from the Web of Science and Scopus’, Journal of the American Society for Information Science and Technology, Vol. 60, No. 7, pp. 1320–1326. https://doi.org/10.1002/asi.21062

Bandola-Gill, J. (2019) ‘Between relevance and excellence? Research impact agenda and the production of policy knowledge’, Science and Public Policy, Vol. 46, No. 6, pp. 895–905. https://doi.org/10.1093/scipol/scz037

Banker, R., Charnes, A. and Cooper, W. W. (1984) ‘Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis’, Management Science, Vol. 30, No. 9, pp. 1078–1192. https://doi.org/10.1287/mnsc.30.9.1078

Battese, G. and Coelli, T. (1988) ‘Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data’, Journal of Econometrics, Vol. 38, No. 3, pp. 387–399. https://doi.org/10.1016/0304-4076(88)90053-X

Bornmann, L., Gralka, S., de Moya Anegón, F., and Wohlrabe, K. (2020) ‘Efficiency of Universities and Research-Focused Institutions Worldwide: An Empirical DEA Investigation Based on Institutional Publication Numbers and Estimated Academic Staff Numbers’, CESifo Working Paper, No. 8157.

Charnes, A., Cooper, W. W. and Rhodes, E. (1978) ‘Measuring the Efficiency of Decision- Making Units’, European Journal of Operational Research, Vol. 2, No. 6, pp. 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Choi, H. and Zo, H. (2019) ‘Assessing the efficiency of national innovation systems in developing countries’, Science and Public Policy, Vol. 46, No. 4, pp. 530–540. https://doi.org/10.1093/scipol/scz005

Cooper, W. W., Seiford, L. M. and Tone, K. (2007) Data Envelopment Analysis. A Comprehensive Text with Models, Applications, References and DEA-Solver Software, 2nd Edition, Boston, MA: Springer. https://doi.org/10.1007/978-0-387-45283-8

De Fraja, G. and Valbonesi P. (2012) ‘The Design of the University System’, Journal of Public Economics, Vol. 96, No. 3–4, pp. 317–330. https://doi.org/10.1016/j.jpubeco.2011.10.005

D’Este, P., Ramos-Vielba, I., Woolley, R. and Amara, N. (2018) ‘How do researchers generate scientific and societal impacts? Toward an analytical and operational framework’, Science and Public Policy, Vol. 45, No. 6, pp. 752–763. https://doi.org/10.1093/scipol/scy023

Elías, J., Ferro, G. and García, A. (2019) ‘A Quest for Quality: Creativity and Innovation in the Wine Industry of Argentina’, AAWE Working Paper 242, 09/2019.

Ferro, G. and D’Elia, V. (2020) ‘Higher Education Efficiency Frontier Analysis: A Review of Variables to Consider’, Journal of Efficiency and Responsibility in Education and Science, Vol. 13, No. 3, pp. 140–153. https://doi.org/10.7160/eriesj.2020.130304

Glänzel, W. (2003) Bibliometrics As A Research Field. A Course on Theory and Application of Bibliometric Indicators, Course Handouts.

Gralka, S., Wohlrabe, K. and Bornmann, L. (2019) ‘How to measure research efficiency in higher education? Research grants vs. publication output’, Journal of Higher Education Policy and Management, Vol. 41, No. 3, pp. 322–341. https://doi.org/10.1080/1360080x.2019.1588492

Groot, T. and García-Valderrama, T. (2006) ’Research Quality and Efficiency. An Analysis of Assessments and Management Issues in Dutch Economics and Business Research Programs’, Research Policy, Vol. 35, No. 9, pp. 1362–1376. https://doi.org/10.1016/j.respol.2006.07.002

Leydesdorff, L. and Wagner, C. (2009) ‘Macro-level indicators of the relations between research funding and research output’, Journal of Informetrics, Vol. 3, No. 4, pp. 353–362. https://doi.org/10.1016/j.joi.2009.05.005

Lewis, K. K. (1999) ‘Trying to Explain Home Bias in Equities and Consumption’, Journal of Economic Literature, Vol. 37, No. 2, pp. 571–608. https://doi.org/10.1257/jel.37.2.571

Lundvall, B.-Å. (2007) ‘National Innovation Systems—Analytical Concept and Development Tool’, Industry and Innovation, Vol. 14, No. 1, pp. 95–119. https://doi.org/10.1080/13662710601130863

Marxt, C. and Brunner, C. (2013) ‘Analyzing and Improving the National Innovation System of Highly Developed Countries—The Case of Switzerland’, Technological Forecasting and Social Change, Vol. 80, No. 6, pp. 1035–1049. https://doi.org/10.1016/j.techfore.2012.07.008

Njøs, R. and Jakobsen, S.-E. (2018) ‘Policy for Evolution of Regional Innovation Systems: The Role of Social Capital and Regional Particularities’, Science and Public Policy, Vol. 45, No. 2, pp. 257–268. https://doi.org/10.1093/scipol/scx064

Rhaiem, M. (2017) ‘Measurement and Determinants of Academic Research Efficiency: A Systematic Review of the Evidence’, Scientometrics, Vol. 110, No. 2, pp. 581–615. https://doi.org/10.1007/s11192-016-2173-1

Sahoo, B., Singh, R., Mishra, B. and Sankaran, K. (2015) ‘Research Productivity in Management Schools of India: A Directional Benefit-of-Doubt Model Analysis’, MPRA Paper, No. 67046. https://mpra.ub.uni-muenchen.de/67046/

Schmid, J. and Fajebe, A. (2019) ‘Variation in patent impact by organization type: An investigation of government, university, and corporate patents’, Science and Public Policy, Vol. 46, No. 4, pp. 589–598. https://doi.org/10.1093/scipol/scz010

Scimago (2020) Scimago Journal & Country Rank, [Online], Available: https://www.scimagojr.com/countryrank.php [20 Mar 2020].

Thelwall, M. and Fairclough, R. (2017) ‘The research production of nations and departments: A statistical model for the share of publications’, Journal of Informetrics, Vol. 11, No. 4, pp. 1142–1157. https://doi.org/10.1016/j.joi.2017.10.001

UNESCO (2020) UNESCO Institute for Statistics (UIS), [Online], Available: http://data.uis.unesco.org/ [20 Mar 2020].

WIPO (2020) Information Resources on Patents, [Online], Available: https://www.wipo.int/patents/en/ [20 Mar 2020].

World Bank (2020) World Bank Open Data, [Online], Available: https://data.worldbank.org/ [20 Mar 2020].

Additional Files

Published

2021-12-20

How to Cite

Ferro, G. and Romero, C. (2021) ’The Productive Efficiency of Science and Technology Worldwide: A Frontier Analysis ’, Journal on Efficiency and Responsibility in Education and Science, vol. 14, no. 4, pp. 217–230 https://doi.org/10.7160/eriesj.2021.140402.

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Section

Research Paper