Global software piracy: Does US Special 301 pressure matter?

Section 301 of the US Trade Act seeks to combat global software piracy, with non-compliance by partner countries potentially leading to trade sanctions. Examining data from 1994-2017 involving 83 countries, this article shows that the process has no significant impact on international software piracy overall. It finds that countries that are below-median in terms of development, State capacity, and institutional quality, are unable and/or unwilling to bend to such pressure. By contrast, multilateral enforcement of stronger intellectual property protection appears to be an effective instrument in curbing piracy.

Intellectual property piracy pertains to the duplication and sale of copyrighted goods – including software, books, videos, and sound recordings – without the copyright holder's permission. Piracy is a major problem on numerous counts. It discourages the production of original works by eroding the profits of the innovators and authorised producers. Pirated works tend to be of inferior quality (for example, in the case of books), or without official support and carrying malware (such as for software). Piracy also leads to loss of government revenue, to the extent pirated products are sold in the ‘grey’ market.

Section 301 of the US Trade Act

Combating piracy has become increasingly difficult in the internet age, where intellectual property protection is national in scope, but theft can occur across borders as well. One effort to reign in software piracy is Section 301 of the US Trade Act, 1974 (and its enhancements via the Trade Act, 1984, and the Omnibus Trade and Competitiveness Act, 1988). US authorities use these provisions to pressurize countries considered to be providing inadequate protection to US intellectual property.

To this end, organisations such as the Motion Pictures Association of America, the International Intellectual Property Alliance, or civil society, may lobby the US government to remedy intellectual property violations by a country. By 30 April each year, the US Trade Representative (USTR) publishes a Special 301 report placing countries on a ‘Watch List’, a ‘Priority Watch List’, or a ‘Priority Foreign Country’ list, indicating progressively greater US concern about higher degree of non-compliance. An action plan is proposed, and the progress monitored. If the response is deemed insufficient, the country could be moved to a higher ‘censure’ category, or be slapped with trade sanctions. But is such pressure effective?

Impact on global software piracy

In a new study (Kanwar 2025), I conduct virtually the first quantitative analysis of the influence of the Special 301 process on global software piracy. Further, my research is the first to explore the heterogeneities of this relationship along various characteristics of the US trade partner country.

The pressure placed on countries by US Special 301 likely has both a direct and an indirect effect on their piracy rates, with the latter operating via the strength of intellectual property protection that countries provide. Since the US Special 301 process is motivated by piracy in the trading partner countries, as well as inadequate levels of intellectual property protection in those countries, we cannot claim ‘exogeneity’ of the political pressure variable vis-à-vis piracy. To establish a causal relationship between the US Special 301 process and software piracy, I employ an empirical specification¹ which permits us to treat changes in the national piracy rate, changes in the Special 301 pressure, and (percentage) changes in intellectual property protection as jointly ‘endogenous’ (determined within the model). Using data for the period 1994-2017 involving 83 countries, I derive consistent and efficient estimates, controlling for a vector of exogenous (external) factors such as per capita income, education level, and trade share with the US, as well as unobserved heterogeneity (variation) across countries and over time.

Aggregate results

I find that US Special 301 pressure has an insignificant influence on international software piracy for the sample countries as a whole, contrary to conjectures in the literature (for example, Shiu (2006), USTR (1990), and later reports).² In fact, a one-unit shock to the intellectual property protection variable has a significant negative impact on the piracy measure, although this effect dies out by the fifth period. All these results are robust to several checks using alternative measures of the key endogenous and exogenous variables, and to alternative specifications of the estimated model.

The results are very plausible, and square up well with explanations proffered in the literature. The reasons underlying the insignificance of the piracy response to Special 301 pressure are probably multiple. First, such pressure may be insufficient to overcome domestic resistance in a trade partner, particularly if it is regarded as bullying (Sykes 1992). Second, although such pressure may result in stronger de jure intellectual property protection, it may not translate into stronger de facto protection (Sell 1995). Third, Special 301 pressure may not represent a credible threat, as few countries are raised to the ‘priority foreign country list’, and even fewer subjected to trade sanctions (Shadlen, Shrank and Kurtz 2005, Shiu 2006). Finally, the signalling value of resistance cannot be ruled out – countries which resist unilateral US measures (such as Special 301) are 25% less probable to face similar measures in the subsequent five years (Pelc 2010).

Heterogeneities in piracy response

It is conceivable that the effect of Special 301 pressure on software piracy varies across countries, depending on their level of development (financial and human capital resources), State capacity (ability to manage resources), institutional quality (extent to which institutions aid management), and trade dependence on the US (indicating potential US leverage).

Using the World Bank’s ranking of countries based on per capita income, I split the sample into ‘high-income’ (or ‘developed’) and ‘medium- and low-income’ (or ‘developing’) groups and re-estimate the baseline model separately for these two groups. This reveals that although developed countries exhibit some piracy decline in response to Special 301 pressure, developing nations witness a spurt in piracy, which dies out in a few periods. This result is robust to using the Human Development Index as a measure of development.

Next, I conduct the analysis again employing the O’Reilly and Murphy (2022) State capacity index, and re-estimating the baseline model separately for the above-median and below-median groups. I find that piracy declines in response to Special 301 pressure in the former group, but exhibits a spurt in the latter, albeit one that fades away in a few periods. Results remain unchanged when using the (average of the) governance indicators of Kaufmann, Kraay and Mastruzzi (2010) as a measure of State capacity. The concurrence of the ‘level of development’ and ‘State capacity’ results is not surprising – of the 42 developed countries in the sample, 38 were also in the above-median State capacity group.

Capturing institutional quality in terms of the (average of the) Freedom House sub-indices (Gwartney et al. 2018), I show that while countries with above-median institutional quality do not exhibit a significant response to stronger Special 301 pressure, those with below-median quality are not able to prevent an increase in piracy.

Finally, defining trade dependence on the US as the ratio of a country’s exports to the US and its total exports, I find that piracy does not decline in response to Special 301 pressure in either low-dependence or high-dependence countries. While this evidence contradicts the assertion that such pressure would be effective for countries that are ‘US-dependent’, note that the above-median trade share group includes several developed countries closely aligned to the US (for example, Canada, Germany, Ireland, Israel, Italy, Japan, New Zealand, Sweden, Switzerland, and the UK), and US authorities may have adopted a more measured, less aggressive stance towards them. The results remain unchanged when trade dependence is redefined as the ratio of a country’s total trade with the US and its total trade worldwide.

Conclusion

Thus, the study finds that differentials in the level of development, State capacity, and institutional quality make for a somewhat heterogeneous piracy response to Special 301 pressure across countries, with the ‘below-median’ countries unable and/or unwilling to bend to such pressure. Several actionable insights emerge from this evidence. One implication would be to focus the Special 301 programme on the ‘above-median’ group and, arguably, only the most egregious of the ‘below-median’ group, for there appears to be little gain in pressurising the bulk of the developing countries. Furthermore, given the result that stronger intellectual property protection significantly curbs piracy, the second insight is that the US would benefit from pressing for the protection of intellectual property in letter and spirit – for instance, via multilateral agreements.

Notes:

I use a panel vector autoregression specification, a method which analyses how multiple variables influence each other over time across different groups (in this case, countries).
This is confirmed by an ‘orthogonalised impulse response function’, which describes the evolution of the variable of interest over time in response to a unit shock in one of the endogenous variables (values of which are determined within the model, that is, are influenced by other variables in the model), such that the shock is orthogonal or unrelated to the other endogenous variables. This is done to study the effect of a change in one variable alone. Further, the ‘forecast error variance decomposition’ shows that the contribution of the Special 301 variable is quite trivial in explaining the overall forecast error variance. The forecast error variance decomposition is a technique to gauge the contribution of individual shocks to the variance (or uncertainty) of forecasts (or predictions) of a specific variable over time.