Brazilian deprivation index (IBP)
Description
It provides deprivation measures for each Brazilian municipality and census sector and is used to evaluate health inequalities across the country. The 2010 Brazilian Population Census is the basis for calculating the deprivation measure, available from CIDACS. The IBP index combines three factors:
- the percentage of families with income per capita below half of the minimum wage;
- the percentage of illiterate people older than 7 years old;
- the percentage of people without adequate access to drinkable water, sewage, garbage collection, bathroom, or shower;
This data does not need to be updated and the complete documentation about the construction of the index is presented in Developing a small-area deprivation measure for Brazil: Technical report. The original data source and visualization can be found in Cidacs.
Data access information
The data and documentation are available at the University of Glasgow. The data and this report are distributed under the Creative Commons Share-Alike license (CC BY-SA 4.0) and can be freely used by researchers, policymakers, or members of the public.
Methods of data collection
A Python code is available on our Github directory to download the data from Cidacs. The database doesn’t need an update. Additional information on data collection methods can be found in Developing a small-area deprivation measure for Brazil: Technical report and Github.
Data-specific information for IBP
The IBP dataset has a total of 15 columns and shows a total of 5566 registries (rows) and a size of 734 KB. A code with more details about the variables, data processing, and analysis methods is presented in our Github directory.
Limitations of IBP dataset
The IBP dataset includes only 3 measures of deprivation excluding others like employment, crime, health, education, and access to public services. It is also exclusively related to the year 2010 making comparison along time impracticable. The different population and ethnic groups (eg, indigenous peoples, quilombolas, riverside populations) are also not considered and there are some biases inherent to rural areas.