PIBMUNIC

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.

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

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

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"
)

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

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