• https://publications.iadb.org/en/users-guide-brazilian-education-panel-databases

Huberts, Alyssa, et al. (2017). Brazilian Education Panel Databases: 1996-2015. IDB Open Data. https://doi.org/10.60966/an5lcays

1. English

What information does the dataset include?

The dataset contains annual indicators on: - Number and type of schools (federal, state, municipal, private)
- Student enrollment and distribution across urban and rural areas
- School infrastructure (electricity, water, sewage, internet access)
- Teacher qualifications and average years of education
- Performance indicators such as pass, failure, dropout, and overage rates
- Learning outcomes (Portuguese and Math scores from Prova Brasil)

These variables allow users to evaluate equity, performance, and resource allocation across Brazil’s 5,000+ municipalities.

What is the coverage period?

The data span from 1996–2016, providing more than 20 years of consistent education statistics. Some variables (such as Prova Brasil scores) are available only in select years (2005, 2007, 2009, 2011).

This summary reflects the latest year available in your file (2015).
The dataset is an IDB municipal-level panel covering 1996–2016 (with some missing years), focused on fundamental education and municipal totals/averages for facilities and ratios.

• 147,809 total

  • Municipal: 89,128
  • State: 25,703
  • Private: 32,473
  • Federal: 505

• 17,716,401 students

  • Municipal: 12,281,108
  • State: 2,405,347
  • Private: 2,961,271
  • Federal: 68,675

• Schools:

  • Urban: 100,394
  • Rural: 47,415

• Students:

  • Urban: 9,545,072
  • Rural: 8,171,329

• Average class size: 25.1 students per class

• Students per teacher: 24.7
• Public schools — students per teacher: 26.5

Municipal-level facility indicators (school-weighted national estimates):

• Internet access:
  ~68.2% of schools have internet → ~31.8% lack internet

• Sewage (any source):
  ~95.3% of schools have sewage → ~4.7% lack sewage

• System scale indicators (schools, enrollments, network types)

• Infrastructure (internet, water, electricity, sewage)
• Performance ratios (class size, students/teacher)

• Some performance outcomes (pass/fail/dropout/over-age; Prova Brasil in select years)

• Literacy rates

• PISA
• IDEB mechanics/scores
• Higher education (including EAD)
• GDP or federal budget shares for education
• Teacher salaries
• Patents
• ENEM-income correlations
• Bolsa Família impacts
• Prouni KPIs
• Fundeb reform impacts
• Human Capital Index
• “Top-ranked public high schools.”

• Public vs. private graduation rates (not included in this municipal panel)

• 147,809 schools

• 17.7M fundamental enrollments
• ~31.8% of schools lack internet
• ~4.7% lack sewage
• Average class size: 25.1
• Students per teacher: 24.7 (public: 26.5)

How were the data constructed?

The municipal dataset was collapsed from the school-level database using INEP codes and standardized nomenclature. It aligns with the 2015 INEP classification system, with adjustments for older school codes (pre-2006). All transformations and aggregation scripts are available upon request from the dataset authors.

What are the dataset’s limitations?

• Data gaps: Certain years and indicators (e.g., pass/fail/dropout rates between 2000–2002) are missing.
• Measurement differences: Before 2007, performance ratios were calculated manually; after 2006, they were merged from official INEP indicators.
• Reporting inconsistencies: Some schools double-counted students when transitioning between 8-year and 9-year education systems.
• Sampling coverage: Prova Brasil results were not universal before 2007 and only included schools with more than 20 students.

How can researchers use this dataset?

This panel supports analysis of: - Long-term education trends at the municipal level
- Infrastructure access and its relation to student outcomes
- Teacher qualifications and school quality disparities
- Policy impacts on dropout rates, class size, and learning outcomes
- Regional inequality in educational development

What policy decisions can this dataset inform?

The dataset helps guide evidence-based education policy, including: - Resource allocation across municipalities
- Design of teacher training and qualification programs
- Expansion of school infrastructure and technology access
- Evaluation of federal and municipal education programs
- Tracking progress toward educational equity and learning goals