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Explaining Software Piracy using a New Set of Indicators

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Abstract

Previous literature has used common aggregated indices as a proxy for intellectual property rights (IPR) institutional quality to assess their importance in determining piracy rates. The main contribution of this study is to empirically assess the relative importance of IPR institutional developments by differentiating between provision of laws protecting IPRs and their enforcement. These are observed through a factor analysis comprising different variables reflecting the copyright related laws, regulations, and agreements executed by 85 member countries of the WTO in addition to other economic and institutional characteristics of these countries. Using cross-country software piracy rates as a proxy for IPR violations from 1994 to 2010, our results indicate that per capita income, individualism, literacy, internet spread, the number of IPR laws and agreements in a country, and judicial independence significantly determine the level of a nation’s software piracy rates. On the other hand, international threats by the United States Trade Representative (USTR) do not have a significant impact on the prevailing piracy situation.

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Notes

  1. According to a standard definition, an information good is a commodity whose main market value is derived from the information which it contains (Shapiro and Varian, p. 3). For instance, books, news, records, telephone conversations, and computer programs fall into this category.

  2. For examples and case studies about second-best institutions, see: Rodrik (2008).

  3. As it is the prevailing case with China, with 100 % tariffs on its trade with the US

  4. USTR is an agency of over 200 people, a highly committed group of professionals who have decades of specialized experience in trade issues and regions of the world. They negotiate directly with foreign governments to create trade agreements, resolve disputes, and participate in global trade policy organizations. They also meet with governments, business groups, legislators, and public interest groups to gather input on trade issues and explain the president’s trade policy positions. The agency was founded in 1962 and has offices in Washington, Geneva, and Brussels (www.ustr.gov).

  5. BSA is the largest and most international IT industry group, with policy, legal, and/or educational programs in 80 countries. BSA’s member companies are some of the most innovative companies in the world, investing billions of dollars a year in local economies and delivering software solutions trusted by billions of people to help them be more productive, connected, and secure (www.bsa.org/country/BSA%20and%20Members.aspx)

  6. Available in Kaufmann et al. (2004).

  7. Kaufmann et al. (1999a, b). Governance atters. World Bank Policy Research

    Department Working Paper, The World Bank.

  8. The corruption perceptions index (CPI) has been widely used to measure cross-country corruption (see, Lambsdorf, J.: 2000, Background paper to the 2000 Corruption Perceptions Index, Transparency International (September), www.transparency.org. This index also provides a useful cross sectional comparisons of corruptions across countries.

  9. The WGI comprise six dimensions of governance: voice and accountability, political stability, and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption.

  10. For more details, see Ginarte and Park (1997a, b), p.286-287.

  11. See: UNESCO portal: http://portal.unesco.org/culture/en/ev.php-URL_ID=39069&URL_DO=DO_TOPIC&URL_SECTION=201.html and WIPO: http://www.wipo.int/clea/en/ and http://www.wipo.int/about-ip/en/ipworldwide/country.htm

  12. See: UNESCO portal: http://portal.unesco.org/culture/en/ev.php-URL_ID=39069&URL_DO=DO_TOPIC&URL_SECTION=201.html and WIPO: http://www.wipo.int/clea/en/

  13. There does exist high correlation (0.83) between the number of agreements signed by any particular country and their respective globalization level, measure by the globalization index (Dreher 2006).

  14. We thank one of the referees for raising this point.

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Acknowledgments

The authors would like to thank Sina Imhof, Felix Horbach, Matthias Dauner, Sang-Min Park, Professor Thomas Eger, and Professor Stefan Voigt for their valuable comments and suggestions. Our greatest gratitude goes to Florian Neumeier and Professor Bernd Hayo for their guidance and constructive suggestions regarding the empirics for the paper.

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Authors and Affiliations

  1. Institute of Law and Economics, Faculty of Law, University of Hamburg, Hamburg, Germany

    Nora El-Bialy

  2. Departamento de Administración de Empresas y Economía, Facultad de Ciencias Económicas y Jurídicas, Universidad Camilo José Cela, C/ Castillo de Alarcón 49- Urb. Villafranca del Castillo, 28692, Villanueva de la Cañada, Madrid, Spain

    Antonio Rodríguez Andrés

  3. Economics Department, School of Liberal Arts, Indiana University-Purdue University in Indianapolis, 425 University Boulevard, Cavanaugh Hall, Room 516, Indianapolis, IN, 46202, USA

    Ronia Hawash

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  1. Nora El-Bialy
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  2. Antonio Rodríguez Andrés
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Correspondence to Nora El-Bialy.

Appendix

Appendix

Table 2 The number of IPR-related laws issued and agreements signed by each country
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Table 3 Variable definitions, summary statistics, and data sources
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El-Bialy, N., Andrés, A.R. & Hawash, R. Explaining Software Piracy using a New Set of Indicators. J Knowl Econ 7, 526–544 (2016). https://doi.org/10.1007/s13132-014-0226-3

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