Education Funding: FUNDEB

This vignette covers FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), the main funding mechanism for basic education in Brazil. educabR provides two functions to access FUNDEB data: resource distribution and enrollment counts.

library(educabR)
library(dplyr)
library(ggplot2)

Overview

FUNDEB redistributes tax revenues to fund basic education across states and municipalities. The data is split into two components:

Function Source Description
get_fundeb_distribution() STN (National Treasury) Monthly transfers by state/municipality and funding source
get_fundeb_enrollment() FNDE (OData API) Enrollment counts used for funding calculation

FUNDEB Distribution

Resource distribution data shows how much money was transferred to each state and municipality, broken down by funding source and month.

Funding sources

FUNDEB aggregates revenue from multiple tax sources:

Source Description
FPE State Participation Fund
FPM Municipal Participation Fund
IPI Tax on Industrialized Products
ITR Rural Property Tax
VAAF Fiscal Value-Added (state portion)
VAAT Total Value-Added
VAAR FUNDEB Complementation
ICMS State Sales Tax
IPVA Vehicle Tax
ITCMD Inheritance and Donation Tax

Downloading distribution data

# All distribution data for 2023
dist_2023 <- get_fundeb_distribution(year = 2023)

# Filter by state
dist_sp <- get_fundeb_distribution(year = 2023, uf = "SP")

# Filter by funding source
dist_fpm <- get_fundeb_distribution(year = 2023, source = "FPM")

# Filter by destination (states or municipalities)
dist_estados <- get_fundeb_distribution(
  year        = 2023,
  destination = "estados"
)

Available years

Data is available from 2007 to 2026.

Data structure

dist <- get_fundeb_distribution(2023, uf = "SP")
glimpse(dist)

Example analysis: Monthly transfers by source

dist <- get_fundeb_distribution(2023, uf = "SP")

# Total monthly transfers by funding source
monthly <- 
  dist |>
  group_by(fonte, mes) |>
  summarise(total = sum(valor, na.rm = TRUE), .groups = "drop")

ggplot(monthly, aes(x = mes, y = total / 1e6, fill = fonte)) +
  geom_col() +
  labs(
    title = "FUNDEB Transfers to Sao Paulo by Source (2023)",
    x     = "Month",
    y     = "Total (millions R$)",
    fill  = "Source"
    ) +
  theme_minimal() +
  theme(legend.position = "bottom")

Example analysis: Comparing states

dist <- get_fundeb_distribution(2023)

# Total annual transfers by state
by_state <- 
  dist |>
  group_by(uf) |>
  summarise(total = sum(valor, na.rm = TRUE), .groups = "drop") |>
  arrange(desc(total)) |>
  head(10)

ggplot(by_state, aes(x = reorder(uf, total), y = total / 1e9)) +
  geom_col(fill = "darkgreen") +
  coord_flip() +
  labs(
    title = "Top 10 States by FUNDEB Transfers (2023)",
    x     = NULL,
    y     = "Total (billions R$)"
    ) +
  theme_minimal()

FUNDEB Enrollment

Enrollment data shows the number of students considered for FUNDEB funding calculation, broken down by municipality, education type, school network, and location.

Downloading enrollment data

# All enrollment data for 2023
mat_2023 <- get_fundeb_enrollment(year = 2023)

# Filter by state (applied at the API level for efficiency)
mat_sp <- get_fundeb_enrollment(year = 2023, uf = "SP")

# Limited rows for exploration
mat_sample <- get_fundeb_enrollment(year = 2023, n_max = 5000)

Available years

Data is available from 2007 to 2026. Data is fetched from the FNDE OData API with automatic pagination.

Data structure

The enrollment data contains these columns:

Column Description
ano_censo Census year
uf State code
municipio Municipality name
tipo_rede_educacao Education network type
descricao_tipo_educacao Education type description
descricao_tipo_ensino Teaching type description
descricao_tipo_turma Class type description
descricao_tipo_carga_horaria Class hours type
descricao_tipo_localizacao Location type (urban/rural)
qtd_matricula Number of enrollments 
mat <- get_fundeb_enrollment(2023, uf = "RJ")
glimpse(mat)

Example analysis: Enrollment by education type

mat <- get_fundeb_enrollment(2023, uf = "SP")

# Total enrollment by education type
by_type <- 
  mat |>
  group_by(descricao_tipo_educacao) |>
  summarise(total = sum(qtd_matricula, na.rm = TRUE), .groups = "drop") |>
  arrange(desc(total))

ggplot(by_type, aes(x = reorder(descricao_tipo_educacao, total), y = total / 1e3)) +
  geom_col(fill = "coral") +
  coord_flip() +
  labs(
    title = "FUNDEB Enrollment by Education Type - SP (2023)",
    x     = NULL,
    y     = "Enrollments (thousands)"
    ) +
  theme_minimal()

Example analysis: Urban vs rural enrollment

mat <- get_fundeb_enrollment(2023)

# Compare urban vs rural
by_location <- 
  mat |>
  group_by(uf, descricao_tipo_localizacao) |>
  summarise(total = sum(qtd_matricula, na.rm = TRUE), .groups = "drop")

ggplot(by_location, aes(x = uf, y = total / 1e3, fill = descricao_tipo_localizacao)) +
  geom_col(position = "dodge") +
  labs(
    title = "FUNDEB Enrollment: Urban vs Rural by State (2023)",
    x     = "State",
    y     = "Enrollments (thousands)",
    fill  = "Location"
    ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Combining distribution and enrollment

You can combine both datasets to analyze per-student funding across states.

# Total transfers by state
dist <- get_fundeb_distribution(2023)
transfers <- 
  dist |>
  group_by(uf) |>
  summarise(total_transfer = sum(valor, na.rm = TRUE), .groups = "drop")

# Total enrollment by state
mat <- get_fundeb_enrollment(2023)
enrollment <- 
  mat |>
  group_by(uf) |>
  summarise(total_students = sum(qtd_matricula, na.rm = TRUE), .groups = "drop")

# Per-student funding
funding <- 
  inner_join(transfers, enrollment, by = "uf") |>
  mutate(per_student = total_transfer / total_students) |>
  arrange(desc(per_student))

ggplot(funding, aes(x = reorder(uf, per_student), y = per_student)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "FUNDEB Per-Student Funding by State (2023)",
    x     = NULL,
    y     = "R$ per Student"
    ) +
  theme_minimal()