Mapping software piracy: A global typology of development, institutions, and innovation

This paper employs Principal Component Analysis (PCA) and cluster analysis to examine how software piracy interrelates with economic development and innovation. Six indicators—the Software Piracy Index, Human Development Index (HDI), gross national income per capita (GNIpc), legal system quality, Global Innovation Index (GII), and Economic Complexity Index (ECI)—are tested for correlation and multicollinearity after dimensionality reduction. The first principal component (PC1) captures an axis of institutional and economic sophistication (positively loading on all variables except piracy, which loads negatively), containing over 80% of the total variance. The second component (PC2), dominated by GNIpc and ECI, delineates income-centric versus complexity-driven economies. Four clusters emerge: (1) low-development/high-piracy countries, (2) emerging-market hybrids, (3) resource-rich but institutionally narrow states, and (4) advanced, compliant nations. Correlation testing and multicollinearity checks ensured data suitability, and the elbow method guided cluster count selection via k-means and HCPC validation.