close_elections_lmb: A close-elections regression discontinuity study from Lee,...

In causaldata: Example Data Sets for Causal Inference Textbooks

A close-elections regression discontinuity study from Lee, Moretti, and Butler (2004)

Description

This data comes from a close-elections regression discontinuity study from Lee, Moretti, and Butler (2004). The design is intended to test convergence and divergence in policy. Major effects of electing someone from a particular party on policy outcomes *in a close race* indicates that the victor does what they want. Small or null effects indicate that the electee moderates their position towards their nearly-split electorate.

Usage

close_elections_lmb

Format

A data frame with 13588 rows and 9 variables

state

ICPSR state code

district

district code

id

Election ID

score

ADA voting score (higher = more liberal)

year

Year of election

demvoteshare

Democratic share of the vote

democrat

Democratic victory

lagdemocrat

Lagged Democratic victory

lagdemvoteshare

Lagged democratic share of the vote

Details

This data is used in the Regression Discontinuity chapter of Causal Inference: The Mixtape by Cunningham.

Source

Lee, David S., Enrico Moretti, and Matthew J. Butler. 2004. “Do Voters Affect or Elect Policies: Evidence from the U.S. House.” Quarterly Journal of Economics 119 (3): 807–59.

References

Cunningham. 2021. Causal Inference: The Mixtape. Yale Press. https://mixtape.scunning.com/index.html.

causaldata documentation built on Oct. 25, 2024, 1:07 a.m.