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Showing posts from April, 2012

Graphing Predicted Legislative Violence with Zelig & ggplot2

Update 18 January 2013: This example works for Zelig versions < 4. For versions from 4 you will likely have to use Zelig's simulation.matrix command to extract the matrix of expected values. In my previous post I briefly mentioned an early draft of a working paper (HERE) I've written that looks into the possible causes of violence between legislators (like the violence shown in this picture from the Turkish Parliament).

In this post I'm going to briefly discuss how I used Zelig's rare events logistic regression (relogit) and ggplot2  in R to simulate and plot the legislative violence probabilities that are in the paper. In this example I am plotting simulated probabilities at fitted values on three variables:
Age of democracy (Polity IV > 5)A dichotomous electoral proportionality variable where 1 is high proportionality, 0 otherwise (see here for more details)Governing parties' majority (as a % of total legislative seats. Data is from DPI.)

Background
I used…

Causes of Legislative Violence

Update (November 2013): Following really helpful comments from a number of people, I've refined the theory further. Please see the most updated version of the paper at SSRN. Update (6 May 2012): I've updated the framework a little since I first wrote this post. I've refined the focuses on majoritarian vs. consensual systems rather than fairness. Please see the most updated version of the paper at SSRN.

From: The Guardian
If you've ever wondered why physical fights sometimes break out between legislators, like the one in the above picture from the Ukrainian parliament, you might be interested in a working paper that I just put together. The working paper is called:
"Two Sword Lengths: Losers' Consent and Violence in National Legislatures".

The title refers to the rumor that in the UK House of Commons the government and opposition benches are two sword lengths apart to prevent actual duels.

You can find a PDF version of the paper here.

My main findings are …