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Fighting Software Piracy: Some Global Conditional Policy Instruments

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Abstract

This study examines the efficiency of tools for fighting software piracy in the conditional distributions of software piracy. Our paper examines software piracy in 99 countries over the period 1994–2010, using contemporary and non-contemporary quantile regressions. The intuition for modelling distributions contingent on existing levels of software piracy is that the effectiveness of tools against piracy may consistently decrease or increase simultaneously with the increasing levels of software piracy. Hence, blanket policies against software piracy are unlikely to succeed unless they are contingent on initial levels of software piracy and tailored differently across countries with low, medium and high levels of software piracy. Our findings indicate that GDP per capita, research and development expenditure, main intellectual property laws, multilateral treaties, bilateral treaties, World Intellectual Property Organisation treaties, money supply and respect for the rule of law have negative effects on software piracy. Equitably distributed wealth reduces software piracy, and the tendency not to indulge in software piracy because of equitably distributed wealth increases with the increasing software piracy levels. Hence, the negative degree of responsiveness of software piracy to changes in income levels is an increasing function of software piracy. Moreover, the relationships between policy instruments and software piracy display various patterns: U-shape, Kuznets-shape, S-shape and negative thresholds. A negative threshold represents negative estimates with the increasing negative magnitude throughout the conditional distributions of software piracy. We also discuss the policy implications of our study.

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Notes

  1. According to this strand of literature, as nations develop, adoption of more stringent IPRs regimes would, inter alia (i) stimulate exports (Maskus and Penubarti 1995); (ii) favour technology transfers and innovation (Lee and Mansfield 1996); and (iii) increase the possibility of investment from multinational enterprises (Mansfield 1994; Seyoum 1996).

  2. We use the terms ‘software piracy’ and ‘piracy’ interchangeably throughout the paper.

  3. Intuitively, software piracy promotes pro-poor development by making pirated technologies available to less-developed countries but this piracy is harmful for copyright holders because they lose the benefit of having the copyright and they are mainly in the developed countries. The positive impact of piracy on scientific publications is because the scholars in third world countries are able to use the software that is pirated and which they would not be able to use if it was not pirated. Fighting individual piracy can also backfire on copyright holders because (i) commercial piracy can increase the copyright holders’ profits due to a higher population of consumers and (ii) considerable detection and prosecution of individual piracy can also reduce the profits of copyright holders (Tunca and Wu 2012). The last point while counter-intuitive can be partly explained by the fact that the substantial use of a product, even if pirated, could increase publicity and purchase of legal versions of the same product.

  4. If people think piracy is unfair, they are less likely to engage in the use of pirated software.

  5. Also see recent literature focusing on the effects of globalisation, in inter alia (i) trade (Shuaibu 2015); (ii) employment (Anyanwu 2014; Foster-McGregor et al. 2015); (iii) growth (Kummer-Noormamode 2014; Tumwebaze and Ijjo 2015) and (iv) welfare (Makochekanwa 2014).

  6. SIIA stands for Software and Information Industry Association.

  7. Data from the BSA primarily measures commercial software piracy. The interested reader can refer to Traphagan and Griffith (1998) and Png (2008) for more insights into the reliability of piracy data.

  8. These data have been used extensively in the piracy literature (Marron and Steel 2000; Banerjee et al. 2005; Andrés 2006a; Goel and Nelson 2009).

  9. According to the narrative, piracy is strongly associated with poverty or the proportion of the population in the low-income strata. This position is in accordance with the argument made by Moores and Esichaikul (2011, p. 1) that the motivations for software piracy is related with the cultural and economic circumstances of those indulging in software piracy.

  10. http://www.wipo.int/wipolex/en/.

  11. For evidence on how the data are collected, the interested reader can refer to the case of Indonesia: http://www.wipo.int/wipolex/en/profile.jsp?code=ID.

  12. The quantile estimator is disclosed without subscripts in Eq. (1) for the purpose of simplicity and ease of presentation.

  13. In other words, increasing income levels decrease the ability to pirate and the magnitude of the negative relationship increases with increasing piracy levels. For example, ceteris paribus a $500 average income (or GDP per capita) decreases software piracy more in countries where initial levels of software piracy are high compared to countries where initial levels of software piracy are low. Moreover, given that piracy levels are higher in low-income countries, ceteris paribus, the effect of an annual average income of $500 would have a higher decreasing effect in low-income countries than the same effect in high-income countries.

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Acknowledgments

The authors are indebted to the editor and reviewers for their constructive comments.

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Authors and Affiliations

  1. Department of Accounting, Finance and Economics, Faculty of Business, Oxford Brookes University, Oxford, UK

    Simplice A. Asongu, Pritam Singh & Sara Le Roux

  2. African Governance and Development Institute, Yaoundé, Cameroon

    Simplice A. Asongu

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  1. Simplice A. Asongu
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  2. Pritam Singh
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Correspondence to Simplice A. Asongu.

Appendix

Appendix

See Tables 3, 4, and 5.

Table 3 Definitions of variables
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Table 4 Summary statistics (1994–2010)
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Table 5 Correlation analysis (uniform sample, n = 486)
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Asongu, S.A., Singh, P. & Le Roux, S. Fighting Software Piracy: Some Global Conditional Policy Instruments. J Bus Ethics 152, 175–189 (2018). https://doi.org/10.1007/s10551-016-3291-7

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