The impact of software piracy on inclusive human development: evidence from Africa

Abstract

The study examines the effect of software piracy on inclusive human development in 11 African countries for which software piracy data is available for the period 2000–2010. The empirical evidence is based on instrumental variable panel Fixed Effects (FE) and Tobit models in order to control for the unobserved heterogeneity and limited range in the dependent variable. The modeling exercise is based on the inequality adjusted human development (IHDI) and its constituents. The following main findings are established. First, from the FE regressions, software piracy consistently improves the IHDI and its constituents. Within this framework, the positive relationship between inclusive human development and software piracy is driven by all its constituents. Second, for Tobit regressions, the positive relationship between software piracy and inclusive human development is confirmed exclusively in IHDI and literacy specifications. Within the latter framework, the positive relationship between software piracy and inclusive human is driven fundamentally by the literacy rate. Policy implications are discussed.

Keywords:

• Software piracy
• human development
• intellectual property rights
• panel data
• instrumental variables

JEL Classifications:

• K42
• O34
• O38
• O47
• O57

Acknowledgement

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

Notes

1. ‘The article agrees with the enounced thesis and tries to provide an explanation of it that relates to the fact that in specific circumstances property-rights can produce distinct market failures that affect the social cost and can consequently prevent attainment of social welfare’ (Ramello 2011, 123). Even religious institutions with the supreme mission of spreading the Gospel have resorted to copyright for intellectual rents (Carla and Ramello 2011). ‘While property rules reduce transaction costs in the standard case of bilateral monopoly over the exchange of information goods, they might increase transaction costs’ (Nicita and Ramello 2007, 767). This evolution may be in part be traceable to the phenomenon of ‘causal economic thinking’, highlighted by Fleury and Marciano (2013).

2. For example international trade flows, technology transfers and foreign direct investment.

3. Using IPR laws (treaties) as instrumental variables.

4. 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 (2010) for a discussion about the reliability of piracy data. Also see Traphagan and Griffith (1998).

5. Among the many researchers that have used this data are Andrés (2006a), Banerjee, Khalid, and Sturm (2005), Goel and Nelson (2009) and Marron and Steel (2000).

6. Given that there are some changes in the computation of indicators from the World Bank, we did not just add the years 2011 and 2013 to the original data-set. On the contrary, we collected a fresh data-set from the 2016 World Bank publication.