ABSTRACT
Knowledge economy (KE) has been a central issue in the political-economic literature of advanced economies, but little research has focused on the transition towards a KE in Africa. Using a latent profile analysis, six clusters of the KE were found in the region. The clusters range from very prepared with good performance in all KE dimensions (institutional, education, and innovation output) to very unprepared with low performance in each KE dimension. Lastly, we offer policy recommendations that shed some light on the national and international economic policies towards a more knowledge-oriented environment. One such recommendation is that effective policies should consider both the similarities and dissimilarities of African knowledge economies. How precise that can be done is one direction future research can take.
Acknowledgements
We would like to thank four anonymous referees and the two editors for their constructive comments. We would also like to thank participants at the First International Conference on Regional Integration “Taking action to approaching regional integration: Africa, Mediterranean and European Union in a Global Age,” WEAI's Virtual International Conference, and the Future of Growth Conference for valuable comments on previous versions of this paper. Data collection assistance was provided by Ms. Nilufar Safarova. This paper is dedicated to the memory of both my parents for always loving and supporting me during my academic career: Francisco Rodríguez Gude and Esperanza Andrés González who died from COVID-19 in 2020. The usual caveat applies.
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Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
Notes
1 The OECD defines KBEs at a very general level as economies that are directly based on the production, distribution, and use of knowledge and information (OECD, Citation1996, p. 7).
2 They employ the Technological Readiness Index and the technological innovation index from the World Economic Forum, the Technological Advanced index edited by UNIDO (United Nations Industrial Development, the Global summary index from the European Commission, the Technological Activity Index (TAI) FROM UNCTAD, and ArCO (Archibugi & Coco, Citation2004).
3 Benchmarking can be defined as a sequence of activities that involves process and assessment (see Watson, Citation1993).
4 Recent applications of mixture models in different contexts can be found in Csereklyei et al. (Citation2017), Sulkowski and White (Citation2016), Alfo et al. (Citation2008), Seo and Thorson (Citation2016), Kumar (Citation2019), and Clement (Citation2020).
5 Available at https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
6 See for further details, Biernacki et al. (Citation2000).
7 Details available upon request.
8 This is a non-parametric statistical test that assesses the differences among three or more independently sampled groups on a single, non-normally distributed continuous variable,
9 R code is available upon request. The classification tree has been generated with rpart package (Therneau & Atkinson, Citation2019). rpart stands for recursive portioning and regression trees. In our context given that our variable is a factor then we deal with a classification tree. By default, rpart () function uses the Gini impurity measure to split the node. The higher the Gini coefficient, the more different instances within the node.
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Antonio Rodríguez Andrés
Dr. Antonio Rodríguez Andrés has held the position of associate professor of Economics at universities in Denmark, Colombia, Spain, Chile, Morocco, and Cyprus. He is currently an associate professor of Economics at VŠB Technical University of Ostrava since 2017. His research focuses on quantitative methods, applied health economics, and law and economics, with projects in the fields of globalization, trade, crime, gun control, innovation, copyright law, mental health, and income inequality. He is coauthor of more than 305 papers in international refereed journals, and he is part of the editorial board of The Economic and Labour Relations Review, PLOS ONE, and International Journal of Tourism Research.
Abraham Otero
Dr. Abraham Otero was born in Orense, Spain in 1978. He received the B.S. degree in physics in 2000 (with honors), and PhD degree in Computer Science from the University of Santiago de Compostela. In 2006 he moved to San Pablo CEU University, where he started the Biomedical Engineering Degree and Master programs. Currently he is Full Professor and Head of the Degree and Master of Biomedical Engineering at the University CEU San Pablo, and Visiting Professor at the University of Turabo, Puerto Rico. He is coauthor of 44 scientific papers, 52 contributions in conferences and symposiums, and 4 patents on topics related to machine learning and data analysis.
Voxi Heinrich Amavilah
Dr. Voxi Heinrich Amavilah is a consultant and an Adjunct Professor at Estrella Mountain College in Avondale, Arizona, USA, and has taught economics for over 16 years. He has worked on many public and private sector projects as an independent consultant. An expert on the new growth and technological change, Amavilah has served a referee for many academic journals such as Journal of Economic Growth, Applied Economics, Applied Economics Letters, Applied Financial Letters, Economics Letters, Economic Modelling, Journal of Economic Studies, Review of Development Economics, Social Science Research, and Progress in Development Studies, Energy Policy, Sustainability, Energies, Telecommunications Policy, to mention a few randomly. Dr. Amavilah has reviewed submissions for many commercial and academic publishers. He is member of two expert witness groups, and an invited subject editor for some online entities, including Wikipedia. He has published many articles, working papers, book manuscripts, book reviews, and comments and opinions. Before his current activities, Voxi has worked in the private sector as a mineral (resource) economist. Amavilah holds a Ph.D. in Mineral Economics from the Department of Mining and Geological Engineering at the University of Arizona, USA.