Abstract
Judges, scholars, and commentators decry inconsistent areas of judicially
created policy. This could hurt courts’ policy making efficacy, so why do
judges allow it to happen? I show judicially-created policy can become
inconsistent when judges explain rules in more abstract terms than they decide
cases. To do so, I expand standard case-space models of judicial decision
making to account for relationships between specific facts and broader
doctrinal dimensions. This model of judicial decision making as a process of
multi-step reasoning reveals that preference aggregation in such a context can
lead to inconsistent collegial rules. I also outline a class of preference
configurations on collegial courts (i.e., multi-member courts) in which this
problem cannot arise. These results have implications for several areas of
inquiry in judicial politics such as models of principal-agent relationships
in judicial hierarchies and empirical research utilizing case facts as
predictor variables.
Agenda Setting and Attention to Precedent in the US Federal Courts
Abstract
To what degree is judicial agenda setting top-down or bottom-up? Existing
studies lack evidence of the frequency or magnitude of these two processes. We
conceptualize the judicial agenda as the legal questions/rules receiving
judicial attention, measure it using citations to Supreme Court opinions, and
estimate vector autoregression models to identify how each level of court
initiates or responds to variation in attention to precedent at other levels
of the judiciary. The Supreme Court exerts some top-down control, but agenda
setting is more often bottom-up, revealing lower courts are more integral to
setting the federal judicial agenda than previously understood.
Ends Against the Middle: Measuring Latent Traits When Opposites Respond the Same Way for Antithetical Reasons
Abstract
Standard methods for measuring latent traits from categorical data assume that
response functions are monotonic. This assumption is violated when individuals
from both extremes respond identically but for conflicting reasons. Two survey
respondents may "disagree" with a statement for opposing motivations,
liberal and conservative justices may dissent from the same Supreme Court
decision but provide ideologically contradictory rationales, and in
legislative settings, ideological opposites may join together to oppose
moderate legislation in pursuit of antithetical goals. In this article, we
introduce a scaling model that accommodates ends against the middle responses
and provide a novel estimation approach that improves upon existing routines.
We apply this method to survey data, voting data from the United States
Supreme Court, and the 116th Congress, and show it outperforms standard
methods in terms of both congruence with qualitative insights and model fit.
This suggests that our proposed method may offer improved one-dimensional
estimates of latent traits in many important settings.
GPIRT: A Gaussian Process Model for Item Response Theory
Abstract
The goal of item response theoretic (IRT) models is to provide estimates of
latent traits from binary observed indicators and at the same time to learn
the item response functions (IRFs) that map from latent trait to observed
response. However, in many cases observed behavior can deviate significantly
from the parametric assumptions of traditional IRT models. Nonparametric IRT
models overcome these challenges by relaxing assumptions about the form of the
IRFs, but standard tools are unable to simultaneously estimate flexible IRFs
and recover ability estimates for respondents. We propose a Bayesian
nonparametric model that solves this problem by placing Gaussian process
priors on the latent functions defining the IRFs. This allows us to
simultaneously relax assumptions about the shape of the IRFs while preserving
the ability to estimate latent traits. This in turn allows us to easily extend
the model to further tasks such as active learning. GPIRT therefore provides a
simple and intuitive solution to several longstanding problems in the IRT
literature.
Working Papers
The Force of the Law
Abstract
(How much) does "the law" affect judges decisions? Though among the most
fundamental of questions in judicial politics, the effect of the law is
difficult to identify. A number of past studies have taken clever approaches
to tackling this difficult question; however, they offer mixed findings at
best, and are sometimes critiqued on methdological grounds. I provide new
evidence by taking a new perspective on the law: I conceptualize the law as
the mapping from cases to outcomes implied by all past court decisions. I use
that perspective to develop a strategy that exploits attributes of Gaussian
process classification to isolate the effect of the law on judges' votes from
the effect of judges' own preferences. I apply this strategy to data on First
Amendment Free Exercise cases at the U.S. Supreme Court. I find the justices
exhibit varying levels of deference to the implications of past decisions,
with some justices showing substantial effects and others essentially
unaffected. In addition to providing the best available evidence of the
constraining effect of law, I provide a measure of the legal status quo, an
important theoretical concept in judicial politics, and highlight past
studies' vulnerability to misspecification bias, a problem my modeling
strategy also solves.
Inference in Gaussian Process Models for Political Science
Abstract
Political scientists often seek to perform inference in settings where
knowledge about the functional form mapping predictors to outcomes is
imperfect or the traditional assumption of conditional independence of
observations does not hold. Recently Gaussian process (GP) models, a family
of machine learning techniques, have been used to study politics in such
settings; however, many inferential quantities of interest to political
science have either not been derived in the statistical and machine learning
literature the models hail from or have not been employed in the political
science literature yet. I provide practical guidance for applied researchers
to implement GP models for more accurate inference in their research,
including how to obtain quantities of interest to political scientists, the
derivations of which are novel to the GP model literature.
Political Competition and Judicial Independence: How Courts Fill the Void When Legislatures Are Ineffective (R&R'd at Journal of Law and Courts)
Abstract
Judicial insurance theory argues that independence increases in anticipation
of government turnover. We argue that political competition—without a
turnover—can drive increases in de facto judicial independence. Our
game-theoretic model reveals (1) increased competition impedes legislators'
ability to enact their platforms, and (2) policy-seeking activists will
increasingly demand judicial interventions. Utilizing a sample of democratic
states over time, our expectations find empirical support; as a country's
legislature (1) is increasingly fractionalized among parties or (2) has
increasing seat turnover, we observe increases in de facto independence. Our
findings provide a new perspective on the link between judicial independence
and political competition.