The Limits of Tolerance for Territorial Inequalities in Equitable Federalism: Parameters of Distributive Justice in the Brazilian Context

Thursday, 10 July 2025: 15:45
Location: ASJE015 (Annex of the Faculty of Legal, Economic, and Social Sciences)
Oral Presentation
Gabriel SANTANA MACHADO, Getulio Vargas Foundation, Brazil
Federative arrangements exhibit diverse relationships with territorial inequalities; however, the existing literature has not thoroughly examined their capacity to promote distributive justice. This empirical article aims to identify the limits of tolerance for territorial inequalities within equitable federalism, a type of federative arrangement normatively conceptualized based on John Rawls’s theory of Justice as Fairness. We begin with the premise that while territorial inequalities are tolerated in federal arrangements, there are limits to the acceptability of these inequalities in equitable federalism. Such inequalities should not be excessive and must ensure that subnational entities can provide comparable public policies to their populations. Consequently, this study seeks to establish fair parameters for the acceptability of inequalities within federations. The identification of tolerance limits for territorial inequalities in equitable federalism aims to determine the levels of expropriation of municipal revenues necessary for municipalities to maintain their revenue potential while also addressing the resource needs of less affluent entities through revenue redistribution. Empirical analyses will be conducted using revenue data from Brazilian municipalities. To achieve this objective, quantitative analyses will focus on the budgetary execution of revenues from all Brazilian municipalities between 2013 and 2022. This proposed article is part of a research agenda dedicated to examining the capacity of federative arrangements to promote distributive justice territorially, thereby ensuring that constituent units possess equitable conditions to provide public policies for their respective populations.