| close_elections_lmb {causaldata} | R Documentation |
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.