1. 1 Determinants of Software Piracy: Economics, Institutions, and Technology Rajeev K. Goel & Michael A. Nelson

2. 2 Introduction This paper determines various influences on software piracy using a large sample of countries. Cross-sectional analysis uses data from 2004 to estimate the effects of economic, institutional and technical factors on piracy of software.

3. 3 Unique aspects of “soft technologies.” Inadequate intellectual property protection. Nearly half of the software acquired globally is pirated (Business Software Alliance, 2006).

4. 4 In our sample, software piracy rate was highest in Vietnam (92%) with some other nations close behind, and was lowest in the U.S. (21%). Share of software revenues in GDP ranges from 0.007 to 0.53. Singapore in 2004 was the most networked- capable nation while the Russian Federation was the least capable.

5. 5 Consideration of various factors influencing recent software piracy, especially the effects of pricing and diffusion of the Internet, may be considered as the main contribution to the literature still in its infancy. Do economic or non-economic influences have a greater bearing on the piracy of software across nations?

6. 6 Model and Data One can view the theoretical foundations of software piracy in the context of a pirate weighing the costs and benefits of such illegal action (Becker (1968)).

7. 7 The dependent variable is the percentage of software acquired illegally in a country in 2004 (PIRACY). Estimates of piracy are based on the difference between software applications installed in a country and software applications legally shipped to that country.

8. 8 Model PIRACY i = f (GDPpc i, LIT i, REVsh i, PF i, EF i, CORR i, CMlaw, TelP i, IntP i, NETuser i, PCpc i, NetRdy i, URBAN i ) (1) Economics: GDPpc, REVsh,TelP, IntP Institutions: PF, EF, CORR, CMlaw Technology: NETuser, PCpc, NetRdy Other: LIT, URBAN

9. 9 Variable Table 1 Variable Definitions, Summary Statistics and Data Sources [mean; std. dev.] PIRACY Software Piracy Rate, 2004 (Percentage of software acquired illegally) [60.27; 20.23 ] GDPpc GDP per capita, PPP, 2004 (constant 2000 International $, in natural logs) [9.13; 0.93] REVsh Software revenue share of GDP, 2004 [0.14; 0.13] EF Index of Economic Freedom, 2004 (Index ranges from 0 to 100 with larger values implying economic freedom) [62.07; 10.59] PF Index of Civil Liberties, 2004 (Index ranges from 0 to 60 with larger values implying more political freedom) [41.34; 14.75] CORR Corruption Perceptions Index, 2004 (Index ranges from -2.5 (poor record) to +2.5 (excellent record combating less corruption)) [0.37; 1.02]

10. 10 Variable Table 1 (Continued) Variable Definitions, Summary Statistics and Data Sources [mean; std. dev.] CMlaw Binary variable which equals 1 if the country's company law or commercial code is English common law, equals 0 otherwise [0.22; 0.42] NETuser Internet users per 1,000 people, 2004 [249.32; 219.25] PCpc Personal computers per 1,000 people, 2004 [229.19; 241.0] TelP Price basket for residential fixed line phone - monthly subscription fee, 1/5 th of installation charge and cost of local calls (US $ per month, 2004) [14.03; 8.60] IntP Price basket for Internet - Internet access charge for 20 hours/month (US $ per month, 2003) [24.50; 13.31] NetRdy Networked Readiness Index, 2004 [0.70; 0.55] LIT Literacy rate, 2005 (% of literate population age 15 and over) [89.23; 13.48] URBAN Percentage of urban population, 2004 [66.65; 18.4]

11. 11 Results Estimation results for 11 alternate specifications of model in (1) are in Table 2. OLS; t-statistics based on robust standard-errors. Ramsey’s RESET test performed well

12. 12 Table 2 Determinants of Software Piracy (Dependent variable: PIRACY) ABCDEFGHIJK GDPpc -9.85** (3.0) -10.6** (3.2) -8.86** (2.7) -7.7** (2.4) -9.74** (2.8) -7.74** (3.0) -7.65** (2.8) -8.18** (2.8) -10.0** (3.7) -8.28** (3.3) -8.12** (3.0) REVsh 15.75** (2.2) 18.89** (2.7) 9.14 (1.0) 9.05 (0.9) 20.78** (2.9) 1.95 (0.2) 1.51 (0.1) 5.89 (0.6) -11.12 (0.9) -1.25 (0.1) 5.98 (0.7) TelP -0.44** (3.0) -0.38** (2.1) -0.39* (1.9) -0.5** (2.2) -0.38** (2.2) IntP -0.09 (1.4) -0.12* (1.7) -0.10* (1.7) -0.11* (1.8) -0.10* (1.7) PF -0.20* (1.7) -0.30** (2.3) -0.18 (1.4) -0.32** (2.4) -0.23** (2.2) -0.26** (2.6) -0.43** (3.6) -0.24** (2.5) -0.26** (2.6) EF 0.44** (3.8) 0.59** (3.8) 0.65** (3.9) 0.27** (1.9) 0.22* (1.8) 0.27** (2.0) CORR -8.95** (3.9) -9.53** (3.9) -6.7** (2.2) -8.1** (3.0) -10.1** (3.9) -8.54** (3.4) -10.2** (4.5) -10.2** (4.0) -9.02** (4.0) -10.25** (4.2) CMlaw -1.90 (0.6) -2.98 (1.1) Notes: Variable definitions are provided in Table 1. All equations included a constant term. The figures in parentheses are heteroskedasticity- consistent t-statistics (in absolute value). * denotes statistical significance at the 10% level and ** denotes significance at 5% or better.

13. 13 Table 2 Determinants of Software Piracy (Dependent variable: PIRACY) ABCDEFGHIJK NETuser -0.02** (2.1) -0.02** (2.3) PCpc -0.02** (3.2) -0.01* (1.8) -0.01 (1.4) -0.02** (3.3) -0.02* (1.9) -0.01 (1.5) -0.02** (2.5) -0.02** (2.5) NetRdy -9.05** (3.9) LIT 0.31** (2.1) 0.37** (2.3) 0.28* (1.9) 0.15 (1.2) 0.38** (2.5) 0.21* (1.8) 0.03 (0.3) 0.23** (2.0) 0.26** (2.2) 0.24** (2.4) 0.23** (2.0) URBAN -0.07 (1.0) 0.005 (0.1) F-value42.4**41.1**39.7**38.2**36.2**61.9**57.6**48.3**82.2**61.9**55.2** Adj. R 2 0.850.860.840.830.860.840.830.840.830.850.84 N5754 81 80818680 Notes: Variable definitions are provided in Table 1. All equations included a constant term. The figures in parentheses are heteroskedasticity- consistent t-statistics (in absolute value). * denotes statistical significance at the 10% level and ** denotes significance at 5% or better.

14. 14 Economic influences Results for all models reported in Table 2 show that greater economic prosperity (GDPpc) lowers software piracy - consistent with earlier empirical literature.

15. 15 The size of software market (REVsh) makes piracy more lucrative and the resulting coefficient is positive in most instances and statistically significant in some. Higher prices for Internet access (IntP) and telephone charges (TelP) reduce software piracy by making it relatively more expensive to access the Internet - the elasticity of piracy with respect to telephone charges is more than double that with respect to Internet access.

16. 16 Institutional influences Greater economic freedom (EF) and greater political freedom (PF) have opposite influences on software piracy.

17. 17 More corrupt nations have greater piracy of computer software as pirates can bribe to avoid punishment and there is likely to be less social stigma attached to illegal acts. Adherence to the English Common Law system (CMlaw) does not seem to significantly affect piracy of software

18. 18 Technological influences The signs on Internet users (NETuser) and diffusion of personal computers (PCpc) are uniformly negative across all models, and in most cases are statistically significant. Network readiness of a country (NetRdy) reinforces the negative effects on piracy of NETuser and PCpc.

19. 19 Other influences Surprisingly, greater literacy (LIT), holding constant the stage of development, seems to enhance the piracy of software. On the issue of the effect of the digital divide between urban and rural areas on the piracy of software, we are unable to find any evidence (the coefficient on URBAN is insignificant).

20. 20 Overall, both economic and non-economic influences seem important in terms of their impact on software piracy. Greater literacy, market size, economic freedom and corruption all lead to greater piracy. On the other hand, greater economic prosperity, political freedom, Internet and telephone charges, and diffusion of computer technologies all mitigate piracy.

21. 21 Implications From a policy perspective, greater political freedom, controlling corruption, enhancing the networking of nations and increasing telephone and/or Internet access charges all could potentially reduce software piracy. On the other hand, greater economic freedom and greater literacy can have perverse effects. As nations become more free, as is the case with transition nations, they should be cognizant of the effects of greater economic- and political freedom on piracy of software.

22. 22 Further, inducing greater competition in telecom markets in many instances have led to lower telephone and/or Internet charges. Such moves can have the undesirable effect of increasing the piracy of software. Our findings uniquely indicate that greater diffusion of the Internet and computer technologies among the population, other things equal, actually promotes the legal use of software.

23. 23 Future research Given appropriate data, this line of investigation may be refined in the future by examining piracy differences across software types. Is there greater piracy of corporate versus consumer software?