governors: Election results and longevity of politicians
In davidkane9/PPBDS.data: Data for Preceptor's Primer for Bayesian Data Science

Description Usage Format Details Author(s) Source

This is a dataset corresponding to the paper "Longevity Returns to Political Office" by Barfort, Klemmensen & Larsen (2019). The purpose of this study was to find out whether and how winning an election influences the lifespan of politicians. To answer this question, the authors collected data on all U.S. gubernatorial elections from 1945 to 2012. In addition to the names and votes of the running candidates, the data includes information on their pre- and post-election life spans. See also the 'Details' section below.

governors

A tibble with 1,092 observations and 11 variables:

state: character variable indicating the state in which an election took place
year: integer variable indicating the year in which an election took place
first_name: character variable indicating the first name of a candidate
last_name: character variable indicating the last name of a candidate
party: character variable indicating a member's party
sex: character variable indicating a member's sex
died: date variable indicating the candidate's date of death
status: character variable indicating whether a candidate was the challenger or incumbent
win_margin: double variable indicating the percentage margin by which the election was won (positive values) or lost (negative values)
alive_post: integer variable indicating the number of days a candidate lived after the election took place
alive_pre: integer variable indicating the number of days a candidate lived before the election took place

To facilitate subsequent analysis, the raw data collected by the authors was edited in three ways. First, for a given election, only the two candidates who received the highest number of votes were included. Second, candidates with unknown dates of death were excluded, resulting in fewer observations for elections in recent years. Third, in a few instances, only the year of birth or death could be determined; in these cases, the date was taken to be July 1 of that year.

Table: Data summary


Name	governors
Number of rows	1092
Number of columns	11
_______________________
Column type frequency:
Date	1
character	6
numeric	4
________________________
Group variables	None

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
died	0	1	1946-12-21	2019-08-18	1996-07-20	683

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
state	0	1	4	14	0	50	0
first_name	0	1	2	11	0	299	0
last_name	0	1	3	11	0	615	0
party	0	1	8	11	0	3	0
sex	0	1	4	6	0	2	0
status	0	1	9	10	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	1964.85	13.38	1945.00	1954.00	1962.00	1974.00	2011	▇▆▃▂▁
win_margin	0	1	3.22	26.07	-82.43	-9.87	0.97	12.18	100	▁▃▇▁▁
alive_post	0	1	10309.56	4885.95	46.00	6418.75	10811.00	14123.75	22067	▃▆▇▆▂
alive_pre	0	1	18892.42	3181.47	11450.00	16561.00	18757.50	20995.75	30633	▂▇▆▂▁