Welcome
You've reached Dan Pemstein's web site. I'm an assistant professor of political science and international studies at the University of Mississippi. I specialize in comparative legislative studies, European Union politics, political economy, and methodology. I am especially interested in how politicians manipulate information to coordinate policy compromises across lawmaking institutions, particularly within the European Union, and in how career ambition and party organization interact to determine behavior in parliaments. Additionally, I am a co-author of the Scythe Statistical Library, an open source C++ library for statistical computation, and a co-developer of the Unified Democracy Scores, a set of measures that leverage the efforts of a variety of experts to provide a composite scale of democracy, accompanied by estimates of measurement uncertainty.
Research
You can find preprints and reprints of my publications, copies of my current working papers/presentations, and some data below. Take a look at my CV if you want detail.
(P)reprints
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Political Ambition and Legislative Behavior in the European Parliament
(with Stephen Meserve and William Bernhard)
(2009) Journal of Politics 71(3): 1015-1032
[Appendix]
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Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type
(with James Melton and Stephen Meserve)
(2010) Political Analysis 18(4): 426-449.
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The Scythe Statistical Library: An Open Source C++ Library for Statistical Computation
(with Kevin Quinn and Andrew Martin)
(2011) Journal of Statistical Software 42(12): 1-26.
Work in Progress
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The Power of Suggestion: Commission Influence on Voting in the European Parliament
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Bureaucratic Mediation and European Lawmaking
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Predicting and Explaining Roll Call Sponsorship in the European Parliament with Debate Speech
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Candidate Selection in European Parliament Elections
(with Stephen Meserve and William Bernhard)
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Statistical Tests of Agenda Control Using Roll Call Data
(with Josh Clinton and Kosuke Imai)
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Election Timing and Financial Market Behavior
(with William Bernhard and David Leblang)
Data
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(with James Melton and Stephen Meserve)
A set of measures that leverage the efforts of a variety of experts to provide a composite scale of democracy, accompanied by estimates of measurement uncertainty. The scores are available for virtually every country in the world from 1946 through 2008.
Teaching
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Introduction to European Studies (Mississippi)
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Research Methods for International Studies Majors (Mississippi)
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Comparative Legislative Behavior (Mississippi)
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Introduction to Comparative Politics (UIUC)
Computing
Software
I write a lot of code. This includes statistical software, other research-related code, and some projects that are just for fun. I don't update this section as often as I should, and some of this stuff is pretty dated, but C++/Scythe implementations of various Bayesian samplers should end up here eventually.
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The Scythe Statistical Library
A C++ library for statistical computation co-authored with Kevin M. Quinn (University of California, Berkeley) and Andrew D. Martin (Washington University). Scythe includes a suite of matrix manipulation functions, a suite of pseudo-random number generators, and a suite of numerical optimization routines. Scythe sits under the hood of a number of R packages, most notably MCMCpack, and has been used in published work in fields ranging from political science to computational statistics, dentistry, finance, molecular ecology, and physics. Scythe is free software.
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A Perl module for viewing and modifying the info and comment fields of audio files encoded in the Ogg Vorbis compressed audio format.
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A Perl module wrapping the libao cross-platform audio library.
Snippets
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This tutorial on the UDS website demonstrates how to use posterior samples from MCMC in subsequent analyses. The example is intended to explain how to use the Unified Democracy Scores correctly, but is generally applicable. So, for example, if you want to use Clinton-Jackman-Rivers or Martin-Quinn style ideal point estimates in your research and want to correctly incorporate posterior uncertainty in the ideal point estimates into your inferences, this tutorial shows you how, using stata.
Unless specifically accompanied by a license, all source code on this page is distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 license.