`R/rd-CFT15.R`

`CFT15.Rd`

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`

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.

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.

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.