gerda (version 0.5.0)

gerda_covariates: Get County-Level Covariates from INKAR

Description

Returns county-level socioeconomic and demographic covariates from INKAR. This function provides flexible access to the raw covariate data for advanced users who want to inspect or manipulate it before merging with county-level election data.

For most users, we recommend using add_gerda_covariates instead, which automatically performs the merge with correct join keys.

Note: These covariates are at the county (Kreis) level and should be merged with county-level GERDA data (e.g., federal_cty_harm).

Usage

gerda_covariates()

Value

A data frame with 11,200 rows and 32 columns containing county-level covariates for 400 German counties from 1995 to 2022. See gerda_covariates_codebook for variable descriptions.

Details

The dataset includes 30 socioeconomic and demographic variables:

• Demographics: Age structure, foreign population, gender

• Economy: GDP, sectoral composition, enterprise structure

• Labor Market: Unemployment rates (overall, youth, long-term)

• Education: School completion rates, students, apprentices

• Income: Purchasing power, low-income households

• Healthcare: Physician density, hospital beds, GP density

• Childcare: Coverage rates for under-3 and 3-6 age groups

• Housing: Building permits, rent levels, living space

• Transport: Cars per capita

• Public Finances: Municipal debt, tax revenue

County codes are formatted as 5-digit AGS codes matching GERDA's harmonized county codes (2021 boundaries).

See Also

• add_gerda_covariates for automatic merging (recommended)

• gerda_covariates_codebook for variable descriptions

Examples

# Get the covariates data
covs <- gerda_covariates()

# Inspect the data
head(covs)
summary(covs)

# Manual merge (advanced)
library(dplyr)
elections <- load_gerda_web("federal_cty_harm")
merged <- elections %>%
  left_join(covs, by = c("county_code" = "county_code", "election_year" = "year"))