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How well are women represented on the different political levels?
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README.md

2019-09-women-in-politics

Preliminary Remarks

This document describes the pre-processing and exploratory analysis of the data set that is the basis of the article Die grössten Hürden für Frauen in der Schweizer Politik published on srf.ch.

SRF Data attaches importance to the fact that the data pre-processing and analysis can be reproduced and checked. SRF Data believes in the principle of open data, but also open and comprehensible methods. On the other hand, it should be possible for third parties to build on this preparatory work and thus generate further evaluations or applications.

R-Script & Data

The preprocessing and analysis of the data was conducted in the R project for statistical computing. The RMarkdown script used to generate this document and all the resulting data can be downloaded under this link. Through executing main.Rmd, the herein described process can be reproduced and this document can be generated. In the course of this, data from the folder input will be processed and results will be written to output.

SRF Data uses Timo Grossenbacher's rddj-template as the basis for its R scripts. If you have problems executing this script, it may help to study the instructions from the rddj-template.

GitHub

The code for the herein described process can also be freely downloaded from https://github.com/srfdata/2019-09-women-in-politics.

License

Creative Commons Lizenzvertrag
2019-09-women-in-politics by SRF Data is licensed under a Creative Commons Namensnennung - Attribution ShareAlike 4.0 International License.

Other projects

Code and data by SRF Data are available on https://srfdata.github.io.

Disclaimer

The published information has been carefully compiled, but does not claim to be up-to-date, complete or correct. No liability is assumed for damages arising from the use of this script or the information drawn from it. This also applies to contents of third parties which are accessible via this offer.

Data description of output files

candidates_per_gender_per_year.csv

Attribute Type Description
year Number Year of measurement
category Enum Either "running" or "elected"
share Number Share of women as decimal

candidates_per_gender_per_party_and_year.csv

Attribute Type Description
year Number Year of measurement
category Enum Either "running" or "elected"
party Number Party in question
n Number Number of candidates in that canton, party, year and category
share Number Share of women as decimal

candidates_detailed.csv

Attribute Type Description
party Number Party in question
canton Number Canton of measurement
year Number Year of measurement
n Number Number of candidates in that canton, party and year
share Number Share of women as decimal

Original source

Data for 2019

-> input/candidates_2019.csv

Information about the candidates running this year was acquired by smartvote. As we cannot distribute any proprietary material from them, we have reduced the columns to those necessary for this analysis: ID, name, gender, district (canton) and party. The export was done via their API on the 6th of September 2019 with 4596 unique candidates.

Partiese were simplified as follows:

  • SP = SP + JUSO + JSP
  • GPS = GPS + JG + JGBNW + ALG + BastA
  • GLP = GLP + JGLP
  • BDP = BDP + JBDP
  • CVP = CVP + JCVP + CSV
  • FDP = FDP + JFS + LDP + JLDP
  • SVP = SVP + JSVP

Gender Ratios by Party and Canton and Year

-> input/su-d-17.02.02.05.02.06.xlsx

Candidates running provided by the Federal Statistical Office.

-> input/je-d-17.02.02.02.01.02.xlsx

Candidates elected provided by the Federal Statistical Office.

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