Simulated data on income and voter turnout

A simulated cross-section used to demonstrate the DAGassist() workflow on an applied question: does higher income increase voter turnout? Because the data-generating process is known, the correct answer is known too, and the cost of a misspecified control set can be read directly off the output.

Usage

turnout_data

Format

A data frame with 5,000 rows and 7 columns:

turnout

Outcome. Voter turnout propensity.

income

Exposure. Caused by state, age, and industry.

state

Confounder: a common cause of income and turnout.

age

Confounder: affects income, turnout, and industry.

polint

Political interest. A mediator on the path income -> polint -> turnout. Adjusting for it removes the indirect effect and is a bad control for the total effect.

industry

A cause of income lying on the back-door path income <- industry <- age -> turnout. Safe to adjust for, but not required once age is included.

elect_comp

Election competitiveness. A neutral control on the outcome: it affects turnout only, so adjusting for it leaves the estimate unbiased and improves precision.

Source

Simulated by data-raw/make_data.R.

Details

Generated from turnout_dag with set.seed(42). Income raises turnout directly (0.30) and indirectly by raising political interest (0.50 * 0.40 = 0.20), so the true total effect is 0.50 and the true direct effect is 0.30.

Adjusting for {age, state} recovers the total effect; adjusting for {age, polint, state} recovers the direct effect. Regressing turnout on every available covariate returns roughly 0.28 — close to the direct effect, because conditioning on the mediator polint silently changes the estimand rather than producing an obviously wrong number.

See also

turnout_dag for the generating graph; toy_data for a smaller example covering every causal role.