Skip to main content
Log in

The effects of market size, wealth, and network effects on digital piracy and profit

  • Published:
European Journal of Law and Economics Aims and scope Submit manuscript

Abstract

The effect of digital piracy is often framed as a creator having to compete against unauthorized copies of their own creation, despite intellectual property rights laws. This framing has empirical and theoretical support, but the empirical findings often suggest that the magnitude and even sign of piracy effects depend on the characteristics of the software and the market. For example, piracy seems to have a larger negative effect on sales of high-profile works, but a smaller and perhaps even positive effect on lesser-known works. This paper seeks a theoretical explanation of differential piracy effects. It is unique in that it gives considerable focus to the market size, and also to budgetary limitations of the consumer base, motivated by high piracy rates in developing countries. The models imply that piracy is more likely to help developers when the market for the software is smaller; when network effects are neither too weak nor too strong; when piracy is neither to accessible nor too inaccessible; when the cost of piracy is relatively homogeneous; and when the consumer base is not too poor. All things considered, the inclusion of market size, consumer budgets, and heterogeneous piracy costs suggest that piracy is less likely to be beneficial to developers than previous literature suggest. Developer profit may be higher or lower with piracy, but buyer welfare is no worse, and is sometimes better, with piracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Some developers’ price discrimination might dissuade piracy, e.g. offering a student discount. But such efforts are relatively uncommon: the verification process is likely prohibitively cumbersome when such a large volume is in demand. Perhaps advances in artificial intelligence will streamline the process in time.

  2. Since 2011, a video game called Star Citizen has been in development. The developers have raised over $300 million in funding. As of writing, Star Citizen has yet to progress to the beta stage of production, and has “no timeline for a full release” (Sirani, 2020). Fortunately for this paper, Star Citizen is an extreme outlier.

  3. That said, one might take development expenditure to be a long-run measurement of the health of the software industry.

  4. One might also consider multiplicative network effects, e.g. \(qC(p,q) N\eta\). In this scenario, someone would receive no benefit from using the software if no one else is using it, thereby implicitly assuming that the good is a pure network good regardless of other considerations. I find that assumption to be overly restrictive, thus motivating the use of additive network effects.

  5. Many integrated development environments are open source and freely available for many popular programming languages. This is even true in some specialized applications: many video game development tools are free to acquire, although some take a percentage royalty under certain conditions (Moore, 2020).

  6. Quality \(q^*\) is increasing in N when N is small enough: as N approaches zero there is no incentive to develop anything.

  7. In this setup, zero quality is equivalent to zero development expenditure, so one might ask how a piece of software could exist with no development. This circle can easily be squared by supposing that there are some fixed costs required to develop the bare minimum, with subsequent development being variable in cost. A fixed cost, however, only amounts to inserting a constant into each profit function and therefore makes no difference when comparing profit.

  8. One might even argue that \(\kappa\) is lower when there are more pirates because there are more users providing pirate copies on peer-to-peer networks, thereby amplifying this argument.

  9. The upper bound of \(\kappa\) is slightly relaxed above unity as needed to preserve the mean piracy cost, although the conclusions don’t change otherwise.

References

  • Ahn, I., & Shin, I. (2010). On the optimal level of protection in DRM. Information Economics and Policy, 22(4), 341–353.

    Article  Google Scholar 

  • Bae, S. H., & Choi, J. P. (2006). A model of piracy. Information Economics and Policy, 18(3), 303–320.

    Article  Google Scholar 

  • Banerjee, D. (2013). Effect of piracy on innovation in the presence of network externalities. Economic Modelling, 33, 526–532.

    Article  Google Scholar 

  • Banerjee, D., & Chatterjee, I. (2010). The impact of piracy on innovation in the presence of technological and market uncertainty. Information Economics and Policy, 22(4), 391–397.

    Article  Google Scholar 

  • Belleflamme, P. (2003). Pricing Information Goods in the Presence of Copying. Edward Elgar.

    Google Scholar 

  • Blackburn, D., Eisenach, J. A., & Harrison, D. Jr. (2019). Impacts of digital video piracy on the US economy.

  • Breckon, N. (2008). Gears of War 2’s Bleszinski Responds to Fan Criticism: ’I’ll Always Love the PC’. Shacknews.

  • Commission, I., et al. (2013). The report of the commission on the theft of American intellectual property. The National Bureau of Asian Research.

  • Danaher, B., & Smith, M. D. (2014). Gone in 60 seconds: The impact of the Megaupload shutdown on movie sales. International Journal of Industrial Organization, 33, 1–8.

    Article  Google Scholar 

  • Danaher, B., Smith, M. D., & Telang, R. (2020). Piracy landscape study: Analysis of existing and emerging research relevant to intellectual property rights (IPR) enforcement of commercial-scale piracy. USPTO Economic Working Paper.

  • Doctorow, C. (2013). How writers lose when ‘Piracy’ gets harder. O’Reilly Media, Inc.

    Google Scholar 

  • Fenlon, W. (2016). PC piracy survey results: 35 percent of PC gamers pirate. PC Gamer.

    Google Scholar 

  • Fink, C., Maskus, K. E., & Qian, Y. (2016). The economic effects of counterfeiting and piracy: A review and implications for developing countries. The World Bank Research Observer, 31(1), 1–28.

    Google Scholar 

  • Gayer, A., & Shy, O. (2003). Internet and peer-to-peer distributions in markets for digital products. Economics Letters, 81(2), 197–203.

    Article  Google Scholar 

  • Gopal, R. D., Bhattacharjee, S., & Sanders, G. L. (2006). Do artists benefit from online music sharing? The Journal of Business, 79(3), 1503–1533.

    Article  Google Scholar 

  • Graham, L. (2016). Can video game piracy be stopped in two years?. CNBC.

  • Hardy, W. (2021). Displacement from piracy in the American comic book market. Information Economics and Policy, 57, 100927.

    Article  Google Scholar 

  • Henry-Nickie, M., Frimpong, K., & Sun, H. (2019). Trends in the Information Technology sector. Brookings.

  • Herz, B., & Kiljański, K. (2018). Movie piracy and displaced sales in Europe: Evidence from six countries. Information Economics and Policy, 43, 12–22.

    Article  Google Scholar 

  • ICC (2013). Long term impacts of counterfeiting and piracy on increased Foreign Direct Investment and employment in Kenya. International Chamber of Commerce.

  • ITA (2021). India-Protecting Intellectual Property.

  • Jha, A. K., & Rajan, P. (2021). Movie piracy: Displacement and its impact on legitimate sales in India. The Journal of World Intellectual Property, 24(3–4), 237–252.

    Article  Google Scholar 

  • Liebowitz, S. J. (2008). Research note-testing file sharing’s impact on music album sales in cities. Management Science, 54(4), 852–859.

    Article  Google Scholar 

  • Michaud, L. (2017). Video game Dematerialisation: the digital proportion of the market should reach 73,1 Billion EUR in 2021. IDATE DigiWorld.

  • Moore, D. M. (2020). 11 tools to get you started making video games. The Verge.

  • Peitz, M., & Waelbroeck, P. (2006). Piracy of digital products: A critical review of the theoretical literature. Information Economics and Policy, 18(4), 449–476.

    Article  Google Scholar 

  • Peitz, M., & Waelbroeck, P. (2006). Why the music industry may gain from free downloading–the role of sampling. International Journal of Industrial Organization, 24(5), 907–913.

    Article  Google Scholar 

  • Perez, S. (2019). Netflix may be losing \$192M per month from piracy, cord cutting study claims. Tech Crunch.

  • Peukert, C., Claussen, J., & Kretschmer, T. (2013). Piracy and movie revenues: Evidence from Megaupload. A tale of the long tail? In VfS Annual conference 2013 (Duesseldorf): Competition policy and regulation in a global economic order 79697, Verein für Socialpolitik/German Economic Association.

  • Reimers, I. (2016). Can private copyright protection be effective? Evidence from book publishing. The Journal of Law and Economics, 59(2), 411–440.

    Article  Google Scholar 

  • Rossen, J. (2013). How hollywood can capitalize on piracy. MIT Technology Review.

    Google Scholar 

  • Saltzman, M. (2021). E3 2021: Video games are bigger business than ever, topping movies and music combined. USA Today.

  • Shy, O., & Thisse, J.-F. (1999). A strategic approach to software protection. Journal of Economics & Management Strategy, 8(2), 163–190.

    Article  Google Scholar 

  • Sirani, J. (2020). 18 Games that seemingly vanished. IGN.

  • Smith, E. (2017). The incredibly technical history of digital rights management. Vice.

  • Smith, M. D., & Telang, R. (2012). Assessing the academic literature regarding the impact of media piracy on sales. Available at SSRN 2132153.

  • Takeyama, L. N. (1994). The welfare implications of unauthorized reproduction of intellectual property in the presence of demand network externalities. The Journal of Industrial Economics, 42, 155–166.

    Article  Google Scholar 

  • Takeyama, L. N. (2002). Piracy, asymmetric information and product quality revelation. In Paper to the 2002 SERCI annual congress. Available at www.serci.org, Last visited April 24, 2003. Citeseer.

  • Takeyama, L. N. (2009). Copyright enforcement and product quality signaling in markets for computer software. Information Economics and Policy, 21(4), 291–296.

    Article  Google Scholar 

  • Tanaka, T., et al. (2019). The effects of internet book piracy: Case of comics. Discussion paper. Institute for Economics Studies, Keio University.

  • USTR (2022). 2022 Special 301 report.

  • van der Meulen, R., & Pettey, C. (2018). Gartner says global IT spending to reach \$3.7 trillion in 2018.

  • Waldfogel, J. (2010). Music file sharing and sales displacement in the iTunes era. Information Economics and Policy, 22(4), 306–314.

    Article  Google Scholar 

  • Webb, K. (2019). The \$120 billion gaming industry is going through more change than it ever has before, and everyone is trying to cash in. Business Insider.

  • Weightman, W. (2018). China’s progress on intellectual property rights (yes, really). The Diplomat.

  • Whitten, S. (2019). The death of the DVD: Why sales dropped more than 86 percent in 13 years. CNBC.

  • Yoon, K. (2002). The optimal level of copyright protection. Information Economics and Policy, 14(3), 327–348.

    Article  Google Scholar 

  • Zentner, A. (2006). Measuring the effect of file sharing on music purchases. The Journal of Law and Economics, 49(1), 63–90.

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank Andrés Carvajal and the UC-Davis micro reading group for their feedback.

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to William M. Volckmann II.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1: Proofs for Section 2 (a world without piracy)

Proposition 1

If \(\eta < \mu /N\) and the threat of piracy does not exist, then price is higher when market size, consumer budgets, or network effects are larger. Furthermore, development expenditure is also larger when consumers budgets are larger.

Proof

Applying the quotient rule on \(p^*\) gives respective numerators of

$$\begin{aligned}&\mu ^2(N^2\eta ^3 + N^2\eta ^2 + 2N\eta ^2\mu + \eta \mu ^2 + \mu ^2),\\&\quad N^2(N^2\eta ^4 + 2N\eta ^3\mu + 2N\eta ^2\mu + \eta ^2\mu ^2 + 4\eta \mu ^2 + 2\mu ^2),\\&\quad N\mu ^2(N^2\eta ^2 + 2N^2\eta + 2N\eta \mu + \mu ^2), \end{aligned}$$

all of which are unambiguously positive.

With respect to quality and consumer budgets, the derivative is

$$\begin{aligned} \frac{\partial q^*}{\partial \mu } = \frac{N^2( 2N\eta ^2\mu + 3\eta \mu ^2 + 2\mu ^2- N^2\eta ^3)}{(N^2\eta ^2 + 2N\eta \mu + 2N\mu + \mu ^2)^2}, \end{aligned}$$

which is positive when \(2N\eta ^2\mu + 3\eta \mu ^2 + 2\mu ^2> N^2\eta ^3\). When \(\eta = \mu /N\), the condition can be written as \(4\mu /N + 2 > 0\), which is clearly true. Now increase \(\mu\) by any amount. The case requirement \(\eta < \mu /N\) holds; the left-hand side of the positivity requirement increases; whereas the right-hand side of the positivity requirement is constant; together establishing that the positivity requirement does in fact hold whenever \(\eta < \mu /N\) holds. \(\square\)

Proposition 2

If \(\eta < \mu /N\) and the threat of piracy does not exist, then development expenditure is decreasing in network effects. Furthermore, development expenditure approaches zero when network effects approach \(\mu /N\) and market size approaches \(\mu /\eta\).

Proof

Simply plug in \(\eta = \mu /N\) and \(N = \mu /\eta\) into Eq. (5), in which case the numerators are zero and denominators non-zero. \(\square\)

Proposition 3

If \(\eta < \mu /N\) and the threat of piracy does not exist, then expected profit is higher when market size, consumer budgets, or network effects are larger.

Proof

Using the quotient rule, the respective numerators are

$$\begin{aligned}&N\mu ^3(1 + 2\eta )(N + \mu + N\eta ),\\&\quad N^3\mu (1 + 2\eta )(N\eta ^2 + \mu \eta + \mu )\\&\quad N^2\mu ^2( N\mu + \mu ^2- N^2\eta ^2 - N^2\eta ). \end{aligned}$$

The first two for market size and consumer budgets are clearly positive. The third for \(\eta\) is positive as long as \(N\mu + \mu ^2 > N^2\eta ^2 + N^2\eta\). Suppose \(\eta = \mu /N\). Then the condition for positivity becomes an equality; but for any smaller \(\eta\), the left-hand side will remain constant whereas the right-hand side will decrease, thereby satisfying the constraint. \(\square\)

Proposition 4

Buyer welfare is weakly increasing with network effects when there is no piracy.

Proof

Buyer welfare is zero when \(\eta < \mu /N\). Buyer welfare is \((N\eta - \mu )/2\) when \(\eta \ge \mu /N\), which is positive over the interval and increasing linearly with \(\eta\). \(\square\)

Appendix 2: Proofs for Section 3 (a world with piracy)

Proposition 5

Piracy cannot be beneficial to developers when network effects are very weak or very strong.

Proof

When \(\eta =0\), there is no benefit to adding pirate users, but there is the cost of increasing quality to attract pirate users; therefore the developer will not bother incentivizing budget-constrained pirates when \(\eta =0\). By continuity, this is also true of sufficiently small \(\eta >0\): the benefit of adding pirate users is small, and adding them requires a relatively large cost in increasing quality to incentivize budget-constrained pirates. It can be concluded that piracy is harmful for developers when network effects are weak.

Now suppose \(\kappa < \mu /2\). Large \(\eta \ge \mu /N\) implies that piracy-exploiting profit becomes \((1 - \kappa /\mu )N\kappa\), which is strictly less than piracy-free profit \(N\mu /4\). Because the inequality is strict, it also holds for some smaller \(\eta\) as well.

Finally, suppose \(\kappa \ge \mu /2\). Large \(\eta \ge \max \{\kappa /N, \mu /N \}\) implies that profit is \(N\mu /4\) with or without piracy. \(\square\)

Proposition 6

If a consumer base is moderately budget-poor, then a range of network effects exists such that piracy is beneficial to developers. But if a consumer base is extremely budget-poor, then no strength of network effects exists such that piracy is beneficial to developers.

Proof

Suppose \(\mu /2 \le \kappa < \mu\). It follows that \(\kappa /N < \mu /N\). Let \(\eta = \mu /N\), so that piracy-free profit and piracy-exploiting profit are both \(N\mu /4\). Now reduce \(\eta\) by \(\epsilon\). It is no longer the case that \(\eta \ge \mu /N\), so piracy-free profit falls; but it is still true that \(\eta \ge \kappa /N\), so piracy-exploiting profit remains at \(N\mu /4\).

Now suppose \(\mu \le \kappa\). It follows that \(\mu /N \le \kappa /N\). Let \(\eta = \kappa /N\) so that piracy-free profit and piracy-exploiting profit are both \(N\mu /4\). Now reduce \(\eta\) by \(\epsilon\). It is no longer the case that \(\eta \ge \kappa /N\), so piracy-exploiting profit falls; but it is still true that \(\eta \ge \mu /N\), so piracy-free profit remains at \(N\mu /4\).

Piracy-exploiting profit falls at a faster or equal pace than piracy-free profit when

$$\begin{aligned} N\mu ^2(- N^2\eta ^2 - N^2\eta + N\mu + \mu ^2) \le (\kappa - N\eta )(N^2\eta ^2 + 2N\eta \mu + 2N\mu + \mu ^2)^2. \end{aligned}$$

The left-hand side is zero when \(\eta = \mu /N\), whereas the right-hand side is non-negative since \(\kappa /N \ge \mu /N\). When \(\eta =0\), the left-hand side is less than the right-hand side as an implication of \(\kappa \ge \mu\). The left-hand side is monotonically decreasing in \(\eta\) over this range, whereas the polynomial behavior of the right-hand side implies that the larger (i.e. relevant) vertex is concave, demonstrating continued dominance of piracy-free profit. \(\square\)

Proposition 7

Suppose the consumer base is budget-rich. If either the cost of piracy is sufficiently small or the market size is sufficiently large, then no range of network effects exists that makes piracy beneficial to developers. But if the cost of piracy is sufficiently large and the size of the market is sufficiently small, then a range of network effects exists that makes piracy beneficial to developers.

Proof

First consider a low cost of piracy. Specifically, suppose \(\mu /2 > \kappa\), and let \(\kappa =0\). Piracy-exploiting profit as given in Eq. (11) is either negative or zero, which is less than piracy-free profit as given by Eqs. (6) or (8). By continuity, this must also hold for small but positive \(\kappa\). This establishes the first part of the proposition.

Now consider market size. Suppose \(\kappa < \mu /2\) and take \(N \rightarrow \infty\). It follows that \(\eta \ge \mu /N\). This also implies that \(\eta \ge \kappa /N\) since \(\mu > \kappa\). In this scenario, piracy-free and piracy-exploiting profits are respectively \(N\mu /4\) and \((1-\kappa /\mu )N\kappa\). Denied piracy has higher profit because

$$\begin{aligned} \left( 1 - \dfrac{\kappa }{\mu } \right) N\kappa < \frac{N\mu }{4} \quad \implies \quad (\mu - 2\kappa )^2 > 0, \end{aligned}$$

the strict inequality justified by \(\kappa < \mu /2\). Because the inequality is strict, there is room for the preceding inequality to be maintained even for finite but large N by continuity.

Finally, consider a larger value of \(\kappa\) and a smaller value of N. Specifically, suppose \(\kappa = \mu /2\) and \(\eta = \kappa /N\). Piracy-exploiting profit is \(N\mu /4\), whereas piracy-free profit is given by Eq. (6). Piracy-exploiting profit is higher when \(0 < (N \eta - \mu )^2\), which is clearly true since \(\eta = \kappa /N < \mu /N\). By strictness of the inequality and by continuity, this conclusion must also hold for a range of \(\kappa\) around \(\mu /2\) and a range of \(\eta\) around \(\kappa /N\). \(\square\)

Proposition 8

Buyer welfare is weakly increasing with network effects when there is piracy.

Proof

First consider denied piracy. If piracy is denied and non-binding, then the outcome is exactly the same as with no piracy, so \(W_B=0\). If piracy is denied and binding, then p and q are constrained such that \(q = \kappa - N\eta\) and \(p = \mu \kappa /(\mu + N\eta )\), which also generates \(W_B=0\).

Now consider exploited piracy with a budget-rich consumer base. If \(\mu /2> \kappa\) and \(\eta < \kappa /N\), then \(W_B=0\). If \(\mu /2> \kappa\) and \(\eta \ge \kappa /N\), then \(W_B = N\eta - \kappa\), which is clearly increasing in \(\eta\).

Finally, consider exploited piracy with a budget-poor consumer base. If \(\mu /2 \le \kappa\) and \(\eta < \kappa /N\), then \(W_B = \kappa - \mu /2\). If \(\mu /2 \le \kappa\) and \(\eta \ge \kappa /N\), then \(W_B = N\eta - \mu /2\), which is clearly increasing in \(\eta\). \(\square\)

Proposition 9

Buyer welfare is no worse, and is sometimes better, with exploited piracy relative to a world with no piracy.

Proof

Let us first give expressions for buyer welfare. In a world without piracy, buyer welfare is zero when \(\eta \le \mu /N\) and is \((N\eta - \mu )/2\) otherwise. If the consumer base is budget-rich and \(\eta < \kappa /N\), then exploiting piracy gives zero buyer welfare; if \(\eta \ge \kappa /N\), then \(W_B = N\eta - \kappa\). If the consumer base is budget-poor and \(\eta < \kappa /N\), then exploiting piracy gives \(W_B = \kappa - \mu /2\); if \(\eta \ge \kappa /N\), then \(W_B = N\eta - \mu /2\).

Suppose the consumer base is budget-rich, and that \(\eta \ge \kappa /N\). Then \(W_B = N\eta - \kappa\) with exploited piracy, and is either \((N\eta - \mu )/2\) or zero without piracy. Exploited piracy gives weakly higher buyer welfare because \(N\eta - \kappa \ge 0\) and because \(\kappa < \mu\) implies that \(N\eta - \kappa > (N\eta - \mu )/2\).

Now suppose that the consumer base is budget-rich, and that \(\eta < \kappa /N\). There are three subcases. if \(\eta< \kappa /N < \mu /N\), then buyer welfare is zero in either case. If \(\kappa /N \le \eta \le \mu /N\), then buyer welfare with no piracy is zero but with exploited piracy is \(\max \{N\eta - \kappa ,0\}\). If \(\kappa /N < \mu /N \le \eta\), then buyer welfare with exploited piracy is \(N\eta - \kappa\) and without piracy is \((N\eta - \mu )/2\). Suppose no piracy gives higher welfare, which is equivalent to \(N\eta < 2\kappa - \mu\). The consumer base is assumed budget-rich, which implies that \(N\eta< 2\kappa - \mu < 0\), which cannot be the case since \(N\eta \ge 0\). It follows that buyer welfare is again highest with exploited piracy.

Now suppose that the consumer base is budget-poor, and that \(\eta < \kappa /N\). When \(\eta \le \min \{\kappa /N, \mu /N \}\), buyer welfare with exploited piracy is \(\kappa - \mu /2>0\) but is zero with no piracy. When \(\mu /N \le \eta < \kappa /N\), buyer welfare with exploited piracy is \(\kappa - \mu /2>0\), which is larger than buyer welfare of \((N\eta - \mu )/2\) without piracy whenever \(\eta< 2\kappa <N\), which is already satisfied as an implication of \(\eta < \kappa /N\).

Finally, suppose that the consumer base is budget poor, and that \(\eta \ge \kappa /N\). If \(\eta \ge \max \{\kappa /N, \mu /N\}\), then buyer welfare with exploited piracy is \(N\eta - \mu /2\), which is clearly larger than the no-piracy counter part of \((N\eta - \mu )/2\). When \(\kappa /N \le \eta < \mu /N\), buyer welfare with exploited piracy is \(N\eta - \mu /2 > 0\), which is greater than no-piracy buyer welfare of zero.

So in all possible cases, exploiting piracy leads to equal or greater, and sometimes strictly greater, buyer welfare than the case where piracy does not exist at all. \(\square\)

Appendix 3: Heterogeneous piracy cost

Let \(\Phi (\cdot )\) denote the truncated normal cumulative distribution function, and consider the market with a budget-rich consumer base that satisfies \(\sigma > p/\mu\). Budgets are still assumed uniformly distributed, so proportion \(1 - p/\mu\) of the market is under consideration. Of those users, the proportion who will rather purchase than pirate is given by \(1 - \Phi (p/\kappa )\), provided \(p \le q + C(p,q)N\eta\). This gives a proportion of buyers of

$$\begin{aligned} B(p,q) = \left[ 1 - \Phi \left( \frac{p}{\kappa } \right) \right] \left[ 1 - \frac{p}{\mu } \right] . \end{aligned}$$
(13)

For those with \(\gamma < p/\kappa\), piracy is so cheap that they’d rather pirate; and piracy is better than abstaining if \(\gamma \le [q + C(p,q)N\eta ]/\kappa\). Of these two constraints, \(\gamma < p/\kappa\) is tighter as an implication of \(p \le q + C(p,q)N\eta\), so it can be concluded that the proportion of pirates in this segment of the market is

$$\begin{aligned} \Phi \left( \frac{p}{\kappa }\right) \left[ 1 - \frac{p}{\mu }\right] . \end{aligned}$$

And now of the \(p/\mu\) proportion of users who cannot afford the software, those who satisfy \(\gamma \le [q + C(p,q)N\eta ]/\kappa\) will pirate. So in this segment of the market the proportion of pirates is

$$\begin{aligned} \Phi \left( \frac{q + CN\eta }{\kappa }\right) \left[ \frac{p}{\mu }\right] . \end{aligned}$$

Ergo the total proportion of pirates is

$$\begin{aligned} P(p,q) = \Phi \left( \frac{p}{\kappa }\right) \left[ 1 - \frac{p}{\mu }\right] + \Phi \left( \frac{q + C(p,q)N\eta }{\kappa }\right) \left[ \frac{p}{\mu }\right] , \end{aligned}$$
(14)

and the total proportion of users C(pq) is given by the sum of Eqs. (13) and (14), which can be simplified into

$$\begin{aligned} C(p,q) = \left[ 1 - \frac{p}{\mu } \right] + \Phi \left( \frac{q + C(p,q)N\eta }{\kappa }\right) \left[ \frac{p}{\mu }\right] . \end{aligned}$$
(15)

The preceding equation pins down C, which in turn allows B and P to be pinned down.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Volckmann, W.M. The effects of market size, wealth, and network effects on digital piracy and profit. Eur J Law Econ 55, 61–85 (2023). https://doi.org/10.1007/s10657-022-09750-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10657-022-09750-9

Keywords

JEL Classification

Navigation