Introduction to the Special Issue (doi:10.7910/DVN/U1UBII)

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Introduction to the Special Issue

doi:10.7910/DVN/U1UBII

Harvard Dataverse

2010-02-16

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Gary Cox, 2010, "Introduction to the Special Issue", https://doi.org/10.7910/DVN/U1UBII, Harvard Dataverse, V1

Introduction to the Special Issue

doi:10.7910/DVN/U1UBII

Gary Cox (University of California, San Diego)

Political Analysis

2001

Harvard Dataverse

Murray Research Archive

2010

https://doi.org/10.7910/DVN/U1UBII

THE SPATIAL MODEL is the workhorse theory of modern legislative studies. Starting as a metaphor common in discussions of mass elections, the model’s basics were developed by Hotelling (1929) and Downs (1957) for use in the electoral arena and by Black (1948a,b) for use in legislative studies.> <br /><br /> In both its electoral and its legislative versions, the spatial model was originally purely deterministic. Voters were presumed to be faced by w issues, each issue represented by a dimension in a policy space, W (a subset of Euclidian w-space). Voters’ preferences were represented by utility functions defined overW, with each voter possessing a unique ideal or most-preferred point. When presented with a choice between rejecting a bill, and thereby retaining the status quo s ∈ W, or passing that bill, and thereby instating a new policy b ∈ W, a voter voted “aye” (for passage) if she preferred b to s, voting “nay” (against passage) if she preferred s to b. <br /><br /> It took some time before the somewhat parallel literature on scaling legislators’ roll call votes—which assumed probabilistic voting and focused on the empirical task of recovering legislators’ scale positions—merged with the spatial model. It did so most clearly in thework of Poole and Rosenthal (1985, 1997), whose NOMINATE algorithm was based explicitly on a spatial justification.<br /><br /> Taking Poole and Rosenthal, and Heckman and Snyder (1997), as the first generation of suc h efforts, this special issue presents five papers from what might be called the “second generation” of stochastic spatial modeling. The papers in this burgeoning literature can be viewed as chasing two related goals: a closer integration of the theoretical models with their estimation partners, yielding better estimators of key theoretical parameters.

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