gerda_covariates: Get County-Level Covariates from INKAR

In gerda: German Election Database (GERDA)

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()

Details

The dataset includes 20 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: Median income, purchasing power, low-income households

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

Value

A data frame with 11,200 rows and 22 columns containing county-level covariates for 400 German counties from 1995 to 2022. See gerda_covariates_codebook for variable descriptions.

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"))

gerda documentation built on Nov. 13, 2025, 1:07 a.m.