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Fighting Software Piracy: Which Governance Tools Matter in Africa?

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Abstract

This article integrates previously missing components of government quality into the governance-piracy nexus in exploring governance mechanisms by which global obligations for the treatment of IPRs are effectively transmitted from international to the national level in the battle against piracy. It assesses the best governance tools in the fight against piracy and upholding of intellectual property rights (IPRs). The instrumentality of IPR laws (treaties) in tackling piracy through good governance mechanisms is also examined. Findings demonstrate that: (1) while all governance tools under consideration significantly decrease the incidence of piracy, corruption-control is the most effective weapon; (2) but for voice and accountability, political stability and democracy, IPR laws (treaties) are instrumental in tackling piracy through government quality dynamics of rule of law, regulation quality, government effectiveness, corruption-control, and press freedom. Hence, the need for a policy approach most conducive to expanding development is to implement an integrated system of both IPRs and corollary good governance policies. Moreover, our findings support the relevance of good governance measures in developing countries wishing to complement their emerging IPR regimes.

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Notes

  1. For an excellent introduction to the economics of intellectual property rights (see Besen and Raskind 1991).

  2. Varian (1998) defines an information good as anything can be digitized. Books, records, and computer programs fall into this category. Computer software receives protection under copyright law, though in recent years software developers (particularly in the USA) have been granted patent protection as well. Copyright protects form of expression (e.g., written material and artistic works), while patents protect underlying ideas used for industrial products or processes.

  3. However, it is not necessary for example to endow owners with strong rights to control distribution and restrict use so as to avoid depletion of goods that by their definition are non-excludable. On the contrary, restricting use can freeze ideas and stifle innovation. Indeed, a substantial body of the literature warns of the dangers of too much protection of IPRs. For instance, stronger IPRs may stifle incentives to innovate and introduce new technologies (Helpman 1993; Bessen and Maskin 2000; Maskus 2000; Shadlen et al. 2005). As sustained by Shadlen et al. (2005), with too much protection, the tragedy of the commons may be replaced by the tragedy of the anti-commons (Heller and Eisenberg 1998), since diminished access to upstream ideas can deter downstream innovation.

  4. Theoretically, there are several reasons why piracy will not be damaging to the copyright holders. As piracy enlarges the installed base of users (legal or illegal), it creates network effects that increase the consumers’ willingness to pay for the software, thereby potentially increasing the producers’ profits (Takeyama 1994; Shy and Thisse 1999). Another wave of papers assumes that copies can be made from originals so that producers of information goods can indirectly appropriate part of the consumer surplus (e.g., Johnson 1985; Liebowitz 1985; Besen and Kirby 1989; Varian 2000). Even in the absence of network effects, piracy may be profitable because of indirect appropriation.

  5. Another strand of the formal literature has focused on the fact that, stronger intellectual property rights protection should enhance economic growth by increasing returns to innovation and therefore, the incentives to innovate. At the aggregate level, a broader strand of the literature has investigated the intensity of the protection of the IPRs and its impact on economic growth. This empirical literature has largely documented that, IPRs protection has a positive effect on economic growth, often using cross section data (see for example, Falvey et al. 2006; Park and Ginarte 1997).

  6. “The Board remains ready and willing to support software copyright owners by intensifying enforcement efforts to reduce software piracy in our country and ensure that legitimate businesses reap the fruits of their labor as per the Kenya Copyright Board mandate” (Fripp 2011).

  7. The model assumes the possibility to determine a unique set of appropriate institutional arrangements in advance and then expects convergence towards those arrangements is inherently desirable (El-Bialy 2010). Countries applying the same formal rules will have very different performance characteristics, due to the fact that they have different informal norms and enforcement characteristics (North 1995). Hence, it is very hard to determine a unique set of appropriate formal and external institutional arrangements that could be implemented in all countries without taking the already existing informal or internal institutional set-up of each country into consideration. According to North (1996), this fact can explain the failure of some formal rules from successful Western economies when applied to developing countries.

  8. For instance, some considerable achievements were observed as piracy trends started to decline in North Africa.

  9. Unfortunately, we are unable to include a dummy variable to capture the effect of the methodology change because of issues in degrees of freedom (owing to constraints in data availability).

  10. Refer to BSA (2009) for measurement details. 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 about the reliability of piracy data. Also see Traphagan and Griffith (1998).

  11. Among the many researchers that have used this data are Andrés (2006), Banerjee et al. (2005), Goel and Nelson (2009), Andrés and Goel (2012), and Marron and Steel (2000).

  12. See first point on second strand in the “Software piracy, IPRs, and Governance in Africa” section. Also see, first strand depicting stylized facts from selected countries on how governments are fighting software piracy.

  13. An OIR test is only possible in the presence of overidentification. That is, the instruments must be higher than the endogenous explaining variables by at least one degree of freedom. In the cases of exact identification (instruments equal to endogenous explaining variables) and under identification (instruments less than endogenous explaining variables) an OIR test is by definition not possible.

  14. We expect both population growth and economic prosperity to be positively associated with software piracy.

  15. The freedom variable in the analysis is a time-dynamic dummy instrumental variable. It may take the values of 0 or 1 for the same country depending on the state of quality of freedom in a given period. Hence there is a substantial difference between freedom (an instrumental variable) and press freedom quality which an exogenous variable.

  16. See second point on second strand in the “Software piracy, IPRs, and Governance in Africa” section.

  17. Note that measurement error in the dependent variable, the piracy rate, causes inefficiency in the regression analysis. This makes the standard errors in the coefficients on the explanatory variables large and they lose statistical significance. If the measurement error in the dependent variable is systematically related to one or more of the explanatory variables, OLS estimates will be biased. Taking the natural log of the dependent variable lets the bias move into the error term if measurement error is systematic and persistent.

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Acknowledgments

The authors are highly indebted to the editor and referees for their very useful comments.

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Authors and Affiliations

  1. School of Business Administration, Al Akhawayn University, 53000, Ifrane, Morocco

    Antonio R. Andrés

  2. African Governance and Development Institute, B.P. 18 SOA/1365, Yaoundé, Cameroon

    Simplice A. Asongu

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  1. Antonio R. Andrés
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Correspondence to Simplice A. Asongu.

Appendix

Appendix

See Tables 4, 5, and 6.

Table 4 Summary statistics and presentation of countries
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Table 5 Correlation matrix of key variables (N = 121)
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Table 6 Variable definitions
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Andrés, A.R., Asongu, S.A. Fighting Software Piracy: Which Governance Tools Matter in Africa?. J Bus Ethics 118, 667–682 (2013). https://doi.org/10.1007/s10551-013-1620-7

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