Related Experiment Video
Updated: Aug 5, 2026

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
Published on: July 29, 2020
[Is it possible to make municipal basic income programs universal in Brazil? An estimate with municipal finance data]
Vitória Alves Pereira Santos1, Matías Mrejen2
1Câmara Municipal do Rio de Janeiro, Rio de Janeiro, Brasil.
None:
This study investigates the fiscal feasibility of universalizing municipal basic income programs in Brazil by taking the Basic Citizenship Income program in Maricá and Araribóia Social Currency program in Niterói as models. Using data from the Unified Registry and the finances of 5,570 Brazilian municipalities, we estimated the cost of these programs as a proportion of total and social expenditures. The results show that, for most municipalities, the implementation of programs along the lines of the studied models would be significantly more costly than for the reference municipalities. This study observed a marked regional inequality in implementation capacity, with the Brazilian North and Northeast facing an excessive fiscal burden, whereas its South and Southeast showed greater viability, especially for the Niterói model. Simulating scenarios with fiscal efforts resembling those in Maricá and Niterói found a trade-off between coverage and benefit and very large regional inequalities in transfer value. The universalization of basic income programs in Brazil based exclusively on municipal resources is unlikely due to deep regional disparities and high fiscal cost. This study suggests the need for a financing model that goes beyond the municipal sphere, enjoying substantial federal support and a link to a broader debate on fiscal justice and income redistribution to ensure equal benefit distribution and the population's well-being.
Related Concept Videos
Distributions to Estimate Population Parameter
Levels of Use of a GIS
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Growth Models with Integration: Problem Solving
Random Sampling Method
Applications of Life Tables

