Skip to main content

Disproportionality Data

So I was hunting around for some data on disproportional electoral outcomes (when the proportion of voters cast for political parties is not close to the proportion of legislative seats that they win).

Michael Gallagher keeps an updated version of his Least Squares (or Gallagher) Index of electoral disproportionality on his website, however it is in PDF format; very inconvenient for using in any stats project.

John Carey & Simon Hix have some nice data--that includes much of Gallagher's data and some countries he doesn't cover--in easy to use Stata format (here). This is the data from their recent Electoral Sweet Spot paper (see here). However it only goes to 2003.

I combined the best of these two data sets into one .csv file and am making it available so that hopefully others can use their research time for better things than copying and pasting data from a PDF file. You can easily import this data into R or Stata or whatever you may use.

The data set is downloadable HERE. More details on how I combined the data can be found there as well.

I couldn't stop myself from making a few descriptive figures with the data. The first is a map of average disproportionality between 2000 and 2011. The second plots disproportionality over time (you can see there hasn't been much change).


Gallagher Electoral Disproportionality Averaged Over Elections from 2000 through 2011



As always, the R code:

Comments

Popular posts from this blog

Do Political Scientists Care About Effect Sizes: Replication and Type M Errors

Reproducibility has come a long way in political science. Many major journals now require replication materials be made available either on their websites or some service such as the Dataverse Network. Most of the top journals in political science have formally committed to reproducible research best practices by signing up to the The (DA-RT) Data Access and Research Transparency Joint Statement.This is certainly progress. But what are political scientists actually supposed to do with this new information? Data and code availability does help avoid effort duplication--researchers don't need to gather data or program statistical procedures that have already been gathered or programmed. It promotes better research habits. It definitely provides ''procedural oversight''. We would be highly suspect of results from authors that were unable or unwilling to produce their code/data.However, there are lots of problems that data/code availability requirements do not address.…

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame.I've found the various R methods for doing this hard to remember and usually need to look at old blogposts. Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command–slide–to the DataCombine R package (v0.1.5).Building on the shift function TszKin Julian posted on his blog, slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data.Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996, 1997. ExamplesNot…

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines'). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values.So, I’ve been putting together the simPH R p…