PIBMUNIC

Overview

PIBMUNIC (Produto Interno Bruto por Município - Gross Domestic Product by Municipality) is Brazil’s official municipal-level GDP data produced by IBGE. This dataset provides:

Gross Domestic Product (GDP): Total economic output at current market prices
Value Added: Gross value added by economic activity
Taxes and Subsidies: Net taxes on products
Sectoral breakdown: GDP disaggregated by economic sectors (agriculture, industry, services)
Municipal coverage: All Brazilian municipalities
Long time series: Historical data spanning multiple decades
Multi-level aggregation: Available at country, state, and municipality levels

PIBMUNIC is essential for understanding Brazil’s regional economic structure, identifying economic disparities, analyzing sectoral specialization, and assessing economic development across municipalities.

Data Source and Methodology

PIBMUNIC data is compiled by IBGE using: - Data from CEMPRE (firm registry) for employment and output - Production and consumption surveys - Tax and financial records - Trade and services data - National accounts framework aligned with international standards

For more information, visit IBGE National Accounts.

Available Dataset

pibmunic

Complete municipal GDP statistics with sectoral detail.

Coverage: All 5,570+ Brazilian municipalities
Variables: GDP at current prices, value added by sector, taxes, subsidies
Sectors included: Agriculture, industry, services, public administration
Measurement: Brazilian Real (R$) at current prices
Time period: Varies by year; typically 2002 onwards
Use cases:
- Identify richest and poorest municipalities
- Analyze regional economic disparities
- Assess sectoral specialization (agriculture vs. industry vs. services)
- Economic growth analysis by municipality
- Development planning and policy evaluation
- Correlate with social/environmental indicators

Function Parameters

1. dataset

Only one dataset is available:

dataset = "pibmunic"  # Municipal GDP data

2. raw_data

Controls whether to download original or processed data.

TRUE: Returns raw IBGE format
FALSE: Returns treated data with English variable names and standardized formatting

raw_data = FALSE  # logical

3. geo_level

Specifies geographic aggregation level.

"country": National aggregate
"state": State-level aggregation
"municipality": Most detailed level with all municipalities

geo_level = "municipality"  # character string

4. time_period

Specifies which year(s) to download.

time_period = 2020              # single year
time_period = c(2015, 2020)     # specific years
time_period = 2015:2020         # range of years

5. language

Output language for variable names.

"pt": Portuguese
"eng": English

language = "eng"  # character string

Examples

Example 1: Municipal GDP for a single year

# download treated municipal GDP data for 2020
pib_munic <- load_pibmunic(
  dataset = "pibmunic",
  raw_data = FALSE,
  geo_level = "municipality",
  time_period = 2020,
  language = "eng"
)

Example 2: State-level GDP over time

# download treated state-level GDP data for 2015 to 2020
pib_state <- load_pibmunic(
  dataset = "pibmunic",
  raw_data = FALSE,
  geo_level = "state",
  time_period = 2015:2020,
  language = "eng"
)

Example 3: Country-level GDP in Portuguese

# download treated country-level GDP data for 2010 to 2020 in Portuguese
pib_br <- load_pibmunic(
  dataset = "pibmunic",
  raw_data = FALSE,
  geo_level = "country",
  time_period = 2010:2020,
  language = "pt"
)

Data Notes

Variables Structure

Typical variables include: - gdp: Gross Domestic Product at current prices (R$) - value_added_agriculture: Farming and forestry sector - value_added_industry: Manufacturing and construction - value_added_services: Commerce, finance, transport, etc. - value_added_public_admin: Government services - net_taxes: Taxes minus subsidies - Additional detail depends on specific IBGE release

Current Prices

All values are in “current prices” (nominal values, not deflated)
For real (inflation-adjusted) comparisons, you need to apply price deflators
Comparisons across years should account for inflation

Data Coverage

All municipalities: Includes all 5,570+ Brazilian municipalities
Time lag: Data typically released 2+ years after reference year
Revisions: IBGE periodically revises historical data

Important Limitations

Current prices: Values not adjusted for inflation
Time lag: Recent years may not be available
Confidentiality: Some small municipalities may have aggregated data
Methodological changes: IBGE occasionally updates national accounts methodology
Municipal boundaries: Changed in 2021; affects historical comparisons