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.

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 |>
  mutate(mes = as.integer(format(as.Date(mes_ano), "%m"))) |>
  group_by(origem, mes) |>
  summarise(total = sum(valor, na.rm = TRUE), .groups = "drop")

ggplot(monthly, aes(x = factor(mes), y = total / 1e6, fill = origem)) +
  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") +
  scale_y_continuous(labels = scales::number_format(big.mark = ".", decimal.mark = ",")) +
  guides(fill = guide_legend(nrow = 1))

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 2018
mat_2018 <- get_fundeb_enrollment(year = 2018)

# Filter by state (applied at the API level for efficiency)
mat_sp <- get_fundeb_enrollment(year = 2018, uf = "SP")

# Limited rows for exploration
mat_sample <- get_fundeb_enrollment(year = 2018, n_max = 5000)

Available years

Data is available for 2017-2018. 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(2018, uf = "RJ")
glimpse(mat)

Example analysis: Enrollment by education type

mat <- get_fundeb_enrollment(2018, 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", width = .5) +
  coord_flip() +
  labs(
    title = "FUNDEB Enrollment by Education Type - SP (2018)",
    x     = NULL,
    y     = "Enrollments (thousands)"
  ) +
  theme_minimal() +
  scale_y_continuous(labels = scales::number_format(big.mark = ".", decimal.mark = ","))

Example analysis: Urban vs rural enrollment

mat <- get_fundeb_enrollment(2018)

# Compare urban vs rural
by_location <-
  mat |>
  mutate(
    location = case_when(
      descricao_tipo_localizacao == "URBANA"     ~ "Urban",
      descricao_tipo_localizacao == "RURAL"      ~ "Rural",
      descricao_tipo_localizacao == "QUILOMBOLA" ~ "Quilombola",
      grepl("GENA", descricao_tipo_localizacao)  ~ "Indigenous",
      .default = descricao_tipo_localizacao
    )
  ) |>
  group_by(uf, location) |>
  summarise(total = sum(qtd_matricula, na.rm = TRUE), .groups = "drop")

ggplot(by_location, aes(x = uf, y = total / 1e3, fill = location)) +
  geom_col(position = "dodge") +
  labs(
    title = "FUNDEB Enrollment: Urban vs Rural by State (2018)",
    x     = "State",
    y     = "Enrollments (thousands)",
    fill  = "Location"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels = scales::number_format(big.mark = ".", decimal.mark = ","))

Combining distribution and enrollment

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

# Total transfers by state
dist <- get_fundeb_distribution(2018)
transfers <- 
  dist |>
  group_by(uf) |>
  summarise(total_transfer = sum(valor, na.rm = TRUE), .groups = "drop")

# Total enrollment by state
mat <- get_fundeb_enrollment(2018)
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 (2018)",
    x     = NULL,
    y     = "R$ per Student"
  ) +
  theme_minimal() +
  scale_y_continuous(labels = scales::number_format(big.mark = ".", decimal.mark = ","))

Welcome to ClientVPS Mirrors

Education Funding: FUNDEB

Overview

FUNDEB Distribution

Funding sources

Downloading distribution data

Available years

Data structure

Example analysis: Monthly transfers by source

Example analysis: Comparing states

FUNDEB Enrollment

Downloading enrollment data

Available years

Data structure

Example analysis: Enrollment by education type

Example analysis: Urban vs rural enrollment

Combining distribution and enrollment