624 research outputs found

    Litigating State Interests: Attorneys General as Amici

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    An important strain of federalism scholarship locates the primary value of federalism in how it carves up the political landscape, allowing groups that are out of power at the national level to flourish—and, significantly, to govern—in the states. On that account, partisanship, rather than a commitment to state authority as such, motivates state actors to act as checks on federal power. Our study examines partisan motivation in one area where state actors can, and do, advocate on behalf of state power: the Supreme Court. We compiled data on state amicus filings in Supreme Court cases from the 1979–2013 Terms and linked it up with data on the partisanship of state attorneys general (AGs). Focusing only on merits-stage briefs, we looked at each AG’s partisan affiliation and the partisanship of the AGs who either joined, or explicitly opposed, her briefs. If partisanship drives amicus activity, then we should see a strong negative relationship between the partisanship of AGs opposing each other and a strong positive relationship between those who cosign briefs. What we found was somewhat surprising. States agreed far more often than they disagreed, and—until recently—most multistate briefs represented bipartisan, not partisan, coalitions of AGs. Indeed, for the first twenty years of our study, the cosigners of these briefs were generally indistinguishable from a random sampling of AGs then in office. The picture changes after 2000, when the coalitions of cosigners become decidedly more partisan, particularly among Republican AGs. The partisanship picture is also different for the 6% of cases in which different states square off in opposing briefs. In those cases, AGs do tend to join together in partisan clusters. Here, too, the appearance of partisanship becomes stronger after the mid-1990s

    Exit polling and racial bloc voting: Combining individual-level and RĂ—\timesC ecological data

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    Despite its shortcomings, cross-level or ecological inference remains a necessary part of some areas of quantitative inference, including in United States voting rights litigation. Ecological inference suffers from a lack of identification that, most agree, is best addressed by incorporating individual-level data into the model. In this paper we test the limits of such an incorporation by attempting it in the context of drawing inferences about racial voting patterns using a combination of an exit poll and precinct-level ecological data; accurate information about racial voting patterns is needed to assess triggers in voting rights laws that can determine the composition of United States legislative bodies. Specifically, we extend and study a hybrid model that addresses two-way tables of arbitrary dimension. We apply the hybrid model to an exit poll we administered in the City of Boston in 2008. Using the resulting data as well as simulation, we compare the performance of a pure ecological estimator, pure survey estimators using various sampling schemes and our hybrid. We conclude that the hybrid estimator offers substantial benefits by enabling substantive inferences about voting patterns not practicably available without its use.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS353 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    The Scythe Statistical Library: An Open Source C++ Library for Statistical Computation

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    The Scythe Statistical Library is an open source C++ library for statistical computation. It includes a suite of matrix manipulation functions, a suite of pseudo-random number generators, and a suite of numerical optimization routines. Programs written using Scythe are generally much faster than those written in commonly used interpreted languages, such as R and \proglang{MATLAB}; and can be compiled on any system with the GNU GCC compiler (and perhaps with other C++ compilers). One of the primary design goals of the Scythe developers has been ease of use for non-expert C++ programmers. Ease of use is provided through three primary mechanisms: (1) operator and function over-loading, (2) numerous pre-fabricated utility functions, and (3) clear documentation and example programs. Additionally, Scythe is quite flexible and entirely extensible because the source code is available to all users under the GNU General Public License.

    MCMCpack: Markov Chain Monte Carlo in R

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    We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization.

    How Not to Lie with Judicial Votes: Misconceptions, Measurement, and Models

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    In Part I, we describe the formal spatial theory often invoked to justify the statistical approach. While spatial theory has the nice feature of synthesizing theory and empirics, legal scholars may remain skeptical of its strong assumptions. Fortunately, measurement models can be illuminating even if the spatial theory is questionable. To illustrate this, Part II provides a nontechnical overview of the intuition behind measurement models that take merits votes as an input and return a summary score of Justice-specific behavior as an output. Such scores provide clear and intuitive descriptive summaries of differences in judicial voting. Confusion abounds, however, and in Part III we clarify prevailing misconceptions of such scores. We discuss how these scores relate to ideology, explain how such models grapple with the complexity and dimensionality of judicial decisionmaking, illustrate the problems of intertemporal extrapolation and cardinal interpretation of the scores, and highlight other common abuses of such measures. In Part IV, we demonstrate how modern measurement methods are useful precisely because they empower meaningful examination, data collection, and incorporation of doctrine and jurisprudence. We argue that existing uses are simply a special case of a much more general measurement approach that works synergistically with the qualitative study of case law. We demonstrate in Part V how such measurement approaches-when augmented with jurisprudentially meaningful data-----can advance our understanding of courts, with case studies of the constitutional revolution of 1937, the dimensionality of the Supreme Court, the historical origins of the standing doctrine, statutory interpretation, and backlash against Supreme Court opinions. We conclude with thoughts on the chief virtues of model-based measurement and the study of law

    Assessing Preference Change on the U.S. Supreme Court

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    The foundation upon which accounts of policy-motivated behavior of Supreme Court justices are built consists of assumptions about the policy preferences of the justices. To date, most scholars have assumed that the policy positions of Supreme Court justices remain consistent throughout the course of their careers and most measures of judicial ideology—such as Segal and Cover scores—are time invariant. On its face, this assumption is reasonable; Supreme Court justices serve with life tenure and are typically appointed after serving in other political or judicial roles. However, it is also possible that the worldviews, and thus the policy positions, of justices evolve through the course of their careers. In this article we use a Bayesian dynamic ideal point model to investigate preference change on the US Supreme Court. The model allows for justices’ ideal points to change over time in a smooth fashion. We focus our attention on the 16 justices who served for 10 or more terms and completed their service between the 1937 and 2003 terms. The results are striking—14 of these 16 justices exhibit significant preference change. This has profound implications for the use of time-invariant preference measures in applied work.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116217/1/jleo07a.pd

    Applied Bayesian Inference in R using MCMCpack

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    http://deepblue.lib.umich.edu/bitstream/2027.42/116223/1/rnews06.pd

    The `Rehnquist’ Court (?)

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    http://deepblue.lib.umich.edu/bitstream/2027.42/116263/1/lawcourt05.pd

    The Role of Theory and Evidence in Media Regulation and Law: A Response to Baker and a Defense of Empirical Legal Studies

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    We thank Professor Baker for a stimulating response to an Article in which we offered empirical evidence of editorial viewpoint diversity in the face of media consolidation. We appreciate his praise of the Article as apply[ing] innovative statistical techniques and as far superior methodologically to most empirical studies he has seen. At the same time, Baker denies the policy relevance to our Article because empirical evidence is entirely irrelevant to the field of media regulation under his preferred normative theory. Baker argues sweepingly that the legal academy\u27s increased willingness to consider the perspectives of quantitative empiricists and positive theorists is malignant, and that law is best confined to normative theory and value-based inquiries - to the exclusion of positive investigation. Because of the provocative nature of the specific critiques of our Article and the general across-the-board indictment of positive scholarship and empirical legal studies, we respond
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