WP3 aims to understand the means and goals of political corruption, particularly concerning legislation, public spending, and elections. It focuses on creating large-scale, micro-level datasets to measure and trace corrupt practices in national and EU parliaments and public spending. A compendium of corruption schemes will be developed through critical literature reviews and workshops with experts. The work will also involve collecting and analyzing legislative and public spending data, using techniques like text mining and machine learning to identify corruption risks and validate corruption indicators. The impact of corruption will be traced through case studies, assessing its influence on businesses benefiting from corrupt regulatory processes and public procurement. Machine learning models will be developed to predict corruption in public contracts, with potential real-time applications for monitoring public procurement laws. Additionally, WP3 will examine how corruptly amassed resources, including campaign funds and media influence, affect electoral outcomes. This WP links corruption data to measurable impacts on legislation, spending, and elections, aiming to quantify and better understand political corruption’s systemic effects.