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Harmonizing IPRs on Software Piracy: Empirics of Trajectories in Africa

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Abstract

In the current efforts of harmonizing the standards and enforcement of IPRs protection worldwide, this paper explores software piracy trajectories and dynamics in Africa. Using a battery of estimation techniques that ignore as well as integrate short-run disturbances in time-dynamic fashion, we answer the big questions policy makers are most likely to ask before harmonizing IPRs regimes in the battle against software piracy. Three main findings are established. (1) African countries with low software piracy rates are catching-up their counterparts with higher rates; implying despite existing divergent IPRs systems, convergence in piracy rate could be a genuine standard-setting platform. (2) Legal origins do not play a very significant role in the convergence process. (3) A genuine timeframe for standardizing IPRs laws in the fight against piracy is most likely between a horizon of 4–8 years. In other words, full (100 %) convergence within the specified horizon will mean the enforcements of IPRs regimes without distinction of nationality and locality. Policy implications and caveats are discussed.

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Notes

  1. This stance has gained prominence in the debate over if ‘permission’ should be granted to enable ‘copying’ of life-saving pharmaceuticals, especially those used in the management of HIV/AIDS in developing countries most affected and least likely to afford such treatments.

  2. Additional support for the possibility that the changing strength of IPRs regimes is based on a nation’s level of development or current technological ability could be traced in the rapid growth witnessed by South-East Asia. Some evidence suggest that the ‘East Asian Miracle’ could have originated from weaker IPRs regimes at the early stages of these nations’ development in addition to their accumulation of capital. These nations’ capacity to absorb, replicate and duplicate foreign innovations might have contributed to their relatively high growth rates. Further evidence has suggested that, as these countries became significant producers of new technologies and innovations, their IPRs regimes tightened. While Nelson and Pack (1999) have postulated that the productive assimilation of existing (foreign) productive techniques and technologies ‘was a critical component of the success of these countries’, Maskus (2000) cautions that weaker protection of IPRs will not necessarily be beneficial for developing countries as it may cause them to remain dependent on older and less efficient outdated technologies.

  3. The BSA data primarily measures the piracy of commercial software. We are unaware of any publicly available cross-national data on end-user software piracy. See Png (2008) for a discussion on the reliability of piracy data. Also, see Traphagan and Griffith (1998).

  4. Among the many researchers who have used this data are Marron and Steel (2000), Banerjee et al. (2005), Andrés (2006), and Goel and Nelson (2009).

  5. Piracy is strongly associated with poverty: faction of the population with low income. A stance that is valid from economic and cultural considerations (Moores and Esichaikul 2011, p. 1).

  6. The rule of law and regulation quality have a correlation of 87 %. GDP growth and GDP per capita growth: 95 %. Money supply and liquid liabilities: 96 %. PC users and internet penetration: 84 %.

  7. As emphasized by Bezmen and Depken (2004), studies assessing the piracy development nexus are subject to potential endogeneity problems because it is likely that a nation’s level of development is a crucial factor in its choice of or adherence to a particular IPRs regime. This confirms an earlier position by Ginarte and Park (1997) who found strong evidence that the level of economic development explains the strength of patent protection provided by individual countries.

  8. ‘We also demonstrate that more plausible results can be achieved using a system GMM estimator suggested by Arellano and Bover (1995) and Blundell and Bond (1998). The system estimator exploits an assumption about the initial conditions to obtain moment conditions that remain informative even for persistent series, and it has been shown to perform well in simulations. The necessary restrictions on the initial conditions are potentially consistent with standard growth frameworks, and appear to be both valid and highly informative in our empirical application. Hence we recommend this system GMM estimator for consideration in subsequent empirical growth research’. Bond et al. (2001, pp. 3–4).

  9. While Narayan et al. (2011) have used Eq. (1) in the absence of fixed effects, this paper applies Eqs. (2) and (3) instead, in line with Fung (2009). The Fung (2009) approach has been used in recent African convergence literature (Asongu 2012a, b).

  10. In the one-step, the residuals are assumed to be homoscedastic.

  11. We have 6 two-year non-overlapping intervals: 2000; 2001–2002; 2003–2004; 2005–2006; 2007–2008; 2009–2010. There are also four three-year non-overlapping intervals: 2000–2001; 2002–2004; 2005–2007; 2008–2010. For both types of non-overlapping intervals, owing to the data and periodical constraints, the first interval is short of 1 year.

  12. Note that the second vector of determinants entails the second set of control variables as presented in Table 4 (regulation quality, per capita GDP growth, liquid liabilities, internet penetration, trade and FDI).

  13. This poses no issue because overall interpretations will not be based on the three-year NOI results. Their use enriches the analysis in providing trajectories and dynamics hitherto unexplored in convergence empirics.

  14. World Trade Organization (WTO)/Trade-Related Aspects of Intellectual Property Rights (TRIPS).

  15. WIPO: World Intellectual Property Organization.

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Acknowledgments

The author is highly indebted to the editor and referees for their very useful comments.

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  1. African Governance and Development Institute, P.O. Box 18 SOA, 1365, Yaoundé, Cameroon

    Simplice A. Asongu

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Correspondence to Simplice A. Asongu.

Appendix

Appendix

See Tables 5, 6 and 7.

Table 6 Variable definitions
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Table 7 Correlation matrix
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Asongu, S.A. Harmonizing IPRs on Software Piracy: Empirics of Trajectories in Africa. J Bus Ethics 118, 45–60 (2013). https://doi.org/10.1007/s10551-012-1552-7

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