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The Comparative Economics of Knowledge Economy in Africa: Policy Benchmarks, Syndromes, and Implications

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Abstract

The paper complements the scarce literature on knowledge economy (KE) in Africa by comparing KE dynamics within Africa in order to assess best and worst performers based on fundamental characteristics of the continent’s development. The five dimensions of the World Bank’s Knowledge Economy Index (KEI) are employed, notably education, information and communication technology, innovation, economic incentives, and institutional regime. The empirical evidence is based on a five-step novel approach with data from 53 African countries for the period 1996–2010. Limitations of the beta catch-up approach are complemented with the sigma convergence strategy. Based on the determined fundamental characteristics, computed dynamic benchmarks, policy syndromes, and syndrome-free scenarios, we establish that landlocked, low-income, conflict-affected, Sub-Saharan African, nonoil-exporting, and French civil law countries are generally more predisposed to lower levels of KE, whereas English common law, openness to sea, absence of conflicts, North African, and middle-income countries are characteristics that predispose certain nations to higher KE. Broad and specific policy implications are discussed in detail.

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Notes

  1. According to the author, such syndromes are thought to have considerably contributed to the deplorable postindependence economic prosperity of the African continent. Within the framework of this study, “policy syndrome” refers to unappealing trends or positive KE deviations between benchmark and frontier fundamental characteristics. Therefore, growing dispersions in a given KE component reflects “policy syndromes” (PS), while a tendency showing reducing dispersions is qualified as a “syndrome-free” (SF) trend.

  2. As far as we have reviewed, the current extant of literature has focused on, among others broad discussions on KE (Rooney 2005; Lin 2006; Anyanwu 2012), information and communication technologies (Butcher 2011), education (Ford 2007; Weber 2011; Wantchekon et al. 2014), institutional regime and economic incentives (Cogburn 2003; Letiche 2006; Saxegaard 2006; Andrés and Asongu 2013a; Nguena and Tsafack 2014), innovation (Oyelaran-Oyeyinka and Sampath 2007; Carisle et al. 2013), intellectual capital and economic development (Wagiciengo and Belal 2012; Preece 2013), research and development (German and Stroud 2007; Sumberg 2005), indigenous knowledge systems (Raseroka 2008; Lwoga et al. 2010), intellectual property rights (Zerbe 2005; Lor and Britz 2005; Myburgh 2011; Andrés and Asongu 2013a, b; Andrés et al. 2014; Asongu 2013a), KE in the transformation of space (Moodley 2003; Maswera et al. 2008), spatiality in knowledge production (Bidwell et al. 2011; Neimark 2012), and catch-up in KE in light of the East Asian miracle (Lucas 1988, 1993; Bezmen and Depken 2004; Andrés et al. 2014; Kim et al. 2012; Andrés and Asongu 2013a, b).

  3. Learning from the past (Fosu 2010), Fosu (2012, 2013a) has substantially documented lessons and strategies on achieving development success. Such lessons are drawn from the emerging Asian giants of China and India (Singh 2013; Yao 2013; Santos-Paulino 2013), East Asia and the Pacific (Lee 2013; Jomo and Wee 2013; Warr 2013; Thoburn 2013; Khan 2013), Sub-Saharan Africa (Robinson 2013; Subramanian 2013; Lundahl and Petersson 2013; Fosu 2013b; Naudé 2013), Latin America and the Caribbean (De Mello 2013; Solimano 2013; Trejos 2013; Pozo et al. 2013; Cardoso 2013), and the Middle East and North Africa (Looney 2013; Baliamoune-Lutz 2013; Nyarko 2013b; Drine 2013).

  4. We have provided theoretical justifications for the PCA in the section “Principal Component Analysis.” However, in light of this benchmarking justification, it is also relevant to highlight some empirical intuition for the PCA. The KE indexes are better representations of the KE dimensions because they may have different dynamics. For instance, many studies have recently been complementing KAOPEN with de facto capital openness or foreign direct investment (FDI) because the former may not quite account for the flow and actual ebb of cross border capital and its impact (Aizenman et al. 2009). And very recently, studies have found that China is de facto (FDI) open despite being de jure closed. This has been the object of discussions in research circles (Prasad and Wei 2007; Aizenman and Glick 2009; Shah and Patnaik 2009; Batuo and Asongu 2014).

  5. Accordingly, in line with Asongu (2014ad), in a standard dynamic GMM approach, the estimated lagged value is a from which 1 is subtracted to obtain β (β = a − 1). In this context, the information criterion for beta convergence is β < 0. Thus, in a bid to limit the arithmetic gymnastics, a could directly be reported and the second information criterion (0 < |a| < 1) used to determine convergence. These interpretations are consistent with recent convergence literature (Prochniak and Witkowski 2012a, p. 20; Prochniak and Witkowski 2012b, p. 23; Asongu 2013a, 2014a).

  6. Conditional convergence is contingent on the variables we choose and empirically test which may not reflect all the changes necessary for conditional convergence to occur. It should be noted that this form of catch-up is that in which countries differ in macroeconomic and institutional characteristic that determined the endogenous variable.

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Appendix

Appendix

Table 7 (Table 7).

Table 7 Definition of variables
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Table 8

Table 8 Summary statistics
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Table 9

Table 9 Correlation analysis
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Table 10

Table 10 Categorization of countries
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Table 11

Table 11 Derivation of benchmarks
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Asongu, S.A. The Comparative Economics of Knowledge Economy in Africa: Policy Benchmarks, Syndromes, and Implications. J Knowl Econ 8, 596–637 (2017). https://doi.org/10.1007/s13132-015-0273-4

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