# 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 |>
      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 (filtered locally from cached complete dataset)
    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",
          descricao_tipo_localizacao == "IND\u00cdGENA" ~ "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 = ","))