This is the replication data for "Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate", published in 2015 in Journal of Causal Inference. I use these data to teach about regression discontinuity designs.

CFT15

Format

A data frame with 1390 observations on the following 9 variables.

state

a numeric vector for the state. This is ultimately a categorical variable.

year

a numeric vector for the year of the election.

vote

a numeric vector for the Democratic vote share in the next election (i.e. six years later).

margin

a numeric vector for the Democratic party's margin of victory in the statewide election. This is the running variable, in RDD parlance.

class

a numeric vector for the class to which each Senate seat belongs.

termshouse

a numeric vector for the Democratic candidate's cumulative number of terms previously served in the U.S. House.

termssenate

a numeric vector for the Democratic candidate's cumulative number of terms previously served in the U.S. Senate.

population

a numeric vector for the population of the Senate seat's state.

treatment

a numeric vector that is 1 if margin > 0 and is 0 if margin < 0.

Source

Cattaneo, Matias D. and Brigham R. Frandsen and Rocio Titiunik. 2015. "Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate". Journal of Causal Inference 3(1): 1--24.

References

Cattaneo, Matias D. and Brigham R. Frandsen and Rocio Titiunik. 2015. "Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate". Journal of Causal Inference 3(1): 1--24.

Calonico, Sebastian and Matias D. Cattaneo and Max H. Farrell and Rocio Titiunik. 2017. "rdrobust: Software for regression-discontinuity designs". The Stata Journal 17(2):372--404.