Abstract Political trade-offs are inherent to public budgeting in modern democracies. Faced with limited resources, policymakers must annually decide which projects to fund and which to delay or abandon. In the German federal system, these trade-offs are especially pronounced at the Länder level: State governments have little control over their tax revenues, while the constitutional debt brake prohibits structural deficits. Consequently, Länder cabinets are compelled to reallocate spending in response to fiscal stress. This study is the first to systematically investigate budgetary trade-offs between policy sectors in the German states. It examines how declining revenues and rising interest payments alter the composition of subnational budgets and whether political parties pursue distinct spending priorities under fiscal pressure. To explicitly model budgetary trade-offs, the analysis employs compositional dependent variables and applies seemingly unrelated regressions to a comprehensive panel dataset. The findings show that fiscal deterioration crowds out investment in infrastructure, universities and research. In turn, spending on school-based education, domestic security, and social protection gains in relative importance. Yet parties matter under austerity. Bourgeois governments seek to preserve public investment, whereas left-wing majorities prioritise social policies, including early childhood education and care. By combining a theory-driven approach with innovative methodology, the article advances our understanding of how policymakers adapt to financial constraints in a federal setting with limited fiscal autonomy. It contributes to broader debates on public finance, party politics, and policy prioritisation under austerity.

Abstract Corruption indices can provide valuable insights into varying levels of misconduct across regions, but they may also encourage statistical discrimination by transferring group-level attributes onto individuals. This paper examines how information about within-country regional corruption affects perceptions and trust. Using a pre-registered online experiment, we matched participants with partners from three Russian regions that differ in their corruption rankings. Participants estimated how many individuals from each region would report a favorable outcome in a coin toss, and decided how much to trust them as first movers in a trust game. Knowing the corruption indices not only led participants to view individuals from more corrupt regions as more dishonest and less trustworthy, but this information also prompted them to see those from less corrupt regions as more honest and trustworthy. This widened the perceived differences in honesty and trustworthiness between residents from and less corrupt regions. When allowed to choose their sources of information, about half of the participants opted to view the corruption index, further magnifying these perception gaps. Our findings highlight how group-level corruption data can influence individual-level interactions and foster statistical discrimination.