Good public policy in a democracy relies on efficient and accurate information flows between individuals
with firsthand, substantive expertise and elected legislators. While legislators are tasked with the job
of making and passing policy, they are politicians and not substantive experts. To make well-informed
policy, they must rely on the expertise of others. Hearings on the Hill argues that partisanship and
close competition for control of government shape the information that legislators collect, providing
opportunities for party leaders and interest groups to control information flows and influence policy.
It reveals how legislators strategically use committees, a central institution of Congress, and their
hearings for information acquisition and dissemination, ultimately impacting policy development in
American democracy. Marshaling extensive new data on hearings and witnesses from 1960 to 2018, this book
offers the first comprehensive analysis of how partisan incentives determine how and from whom members
of Congress seek information.
Articles
Congress often relies on bureaucrats’ information for policy production. However,scholars lack an
empirical understanding of what drives information sharing between bureaucrats and legislators. We argue
that the partisan alignment between a bureaucrat and legislator determines the amount and type of
information transmitted. Using new comprehensive data on bureaucratic witnesses in committee hearings,
as well as a new measure of the informational content of testimonies, we show that less analytical
information is transmitted between a bureaucrat and legislator pair when the legislator is a
presidential out-partisan than a co-partisan, and that this effect is heightened when the bureaucrat is
a political appointee. At the aggregate hearing level, the collective amount of analytical information
from bureaucrats is lower under divided government than unified government but is offset by the
analytical information from non-bureaucratic witnesses. These dynamics provide a nuanced understanding
of the information transmission between bureaucrats and Congress.
Supervised learning has become a staple in social science research for quantifying abstract concepts
within textual data. However, a survey of recent studies reveals inconsistencies in reporting
practices and validation standards. To tackle this issue, we introduce a framework that delineates the
process of converting text into a quantitative measure, highlighting critical reporting decisions at
each stage. We emphasize the importance of clear and comprehensive validation in the process, allowing
readers to critically assess both the methodology and the derived measure. To showcase our framework,
we develop and validate a measure assessing the tone of questions directed at nominees during US
Senate confirmation hearings. This study contributes to the growing literature promoting transparency in
the application of machine learning methods.
How can we utilize state-of-the-art NLP tools to better understand legislative deliberation? Committee
hearings are a core feature of any legislature, and they offer an institutional setting which promotes
the exchange of arguments and reasoning that directly impact and shape legislation. We apply What Is
Being Argued (WIBA), which is an argument extraction and analysis framework that we previously
developed, to U.S. Congressional committee hearings from 2005 to 2023 (109th to 117th Congresses). Then,
we further expand WIBA by introducing new ways to quantify various dynamics of democratic deliberation.
Specifically, these extensions present a variety of summary statistics capturing how deliberative or
controversial a discourse was, as well as useful visualizations to the WIBA output that aid analyzing
arguments made during the legislative deliberation. Our application reveals potential biases in the
committee system, and how political parties control the flow of information in ‘hot topic’ hearings.
We propose WIBA, a novel framework and suite of methods that enable the comprehensive understanding of
"What Is Being Argued" across contexts. Our approach develops a comprehensive framework that detects:
(a) the existence, (b) the topic, and (c) the stance of an argument, correctly accounting for the
logical dependence among the three tasks. Our algorithm leverages the fine-tuning and prompt-engineering
of Large Language Models. We evaluate our approach and show that it performs well in all the three
capabilities. First, we develop and release an Argument Detection model that can classify a piece of
text as an argument with an F1 score between 79% and 86% on three different benchmark datasets. Second,
we release a language model that can identify the topic being argued in a sentence, be it implicit or
explicit, with an average similarity score of 71%, outperforming current naive methods by nearly 40%.
Finally, we develop a method for Argument Stance Classification, and evaluate the capability of our
approach, showing it achieves a classification F1 score between 71% and 78% across three diverse
benchmark datasets. Our evaluation demonstrates that WIBA allows the comprehensive understanding of What
Is Being Argued in large corpora across diverse contexts, which is of core interest to many applications
in linguistics, communication, and social and computer science. To facilitate accessibility to the
advancements outlined in this work, we release WIBA as a free open access platform.
Theoretical expectations regarding communication patterns between legislators and outside agents, such
as lobbyists, agency officials, or policy experts, often depend on the relationship between legislators’
and agents’ preferences. However, legislators and nonelected outside agents evaluate the merits of
policies using distinct criteria and considerations. We develop a measurement method that flexibly
estimates the policy preferences for a class of outside agents—witnesses in committee hearings—separate
from that of legislators’ and compute their preference distance across the two dimensions. In our
application to Medicare hearings, we find that legislators in the U.S. Congress heavily condition their
questioning of witnesses on preference distance, showing that legislators tend to seek policy
information from like-minded experts in committee hearings. We do not find this result using a
conventional measurement placing both actors on one dimension. The contrast in results lends support for
the construct validity of our proposed preference measures.
Several theories of policy change posit that the politics of defining and prioritizing problems differs
from the politics of devising and selecting solutions. The former involves simplifying through
heuristics like indicators and ideology while the latter incorporates policy analysis and expertise to a
greater degree. By employing two large datasets of U.S. congressional hearings to analyze policymakers'
behavior of sending political messages, which we call “grandstanding,” we offer two findings. First,
consistent with our hypotheses, grandstanding is more prevalent when committees are focused on new and
emerging problems than when committees examine proposed alternatives or the implementation of existing
policies. Second, the cognitive dynamics of problem solving and the incentives to grandstand vary
depending on policy issues considered in hearings. Our analysis helps put dissatisfaction with
contemporary U.S. policymaking in context: a rise in “messaging politics” derives at least in part from
an increased focus on contesting the problem space in agenda-setting venues.
Members of Congress often use committee hearings as venues for political grandstanding. What we do not
know is if members who engage in this behavior are electorally rewarded. Using a dataset of 12,820 House
committee hearing transcripts from the 105th to 114th Congresses, I nd that an increase in a member's
grandstanding tendency in a given Congress leads to an increased vote share in the following election.
The effect is stronger when voters are potentially more exposed to grandstanding. To further investigate
the causal path, I test mechanisms through which voters reward members' grandstanding e orts using the
Cooperative Congressional Election Study (CCES) panel survey data. The results show that the effect of
grandstanding tends to work through persuading non-supporters rather than mobilizing turnout of
supporters. An additional analysis shows that PAC donors and voters react differently to members'
grandstanding behavior, providing members with incentives to represent these two groups differently.
How are politicians informed and who do politicians seek information from? The role of information has
been at the center for research on legislative organizations but there is a lack of systematic empirical
work on the information that Congress seeks to acquire and consider. To examine the information flow
between Congress and external groups, we construct the most comprehensive dataset to date on 74,082
congressional committee hearings and 755,540 witnesses spanning 1960-2018. We show descriptive patterns
of how witness composition varies across time and committee, and how different types of witnesses
provide varying levels of analytical information. We develop theoretical expectations for why committees
may invite different types of
witnesses based on committee intent, inter-branch relations, and congressional capacity. Our empirical
evidence shows how certain institutional conditions can affect how much committees turn to outsiders for
information and from whom they seek information.
While congressional committee members sometimes hold hearings to collect and transmit specialized
information to the floor, they also use hearings as venues to send political messages by framing an
issue or a party to the public which I refer to as “grandstanding.” However, we lack clear understanding
of when they strategically engage in grandstanding. I argue that when committee members have limited
legislative power they resort to making grandstanding speeches in hearings to please their target
audience. Using 12,820 House committee hearing transcripts from the 105th to 114th Congresses and
employing a crowd-sourced supervised learning method, I measure a “grandstanding score” for each
statement that committee members make. Findings suggest that grandstanding efforts are made more
commonly among minority members under a unified government, and non-chair members of powerful
committees, and in committees with jurisdiction over policies that the president wields primary power,
such as foreign affairs and national security.
While US Congress assigns only the members of a majority party to committee chairs, some state
legislatures and other legislative bodies using a proportional representation system also consider
members of a minority party for the position to promote a bipartisan policy making practice. Although
previous literature investigates the effects of bipartisan rules and practices exploiting such
institutional variations, the informational benefit of having a minority partisan committee chair has
not been explored. By extending a recent study exploring conditions under which information transmission
from agents to a principal is improved, this research note theoretically examines the effect of the
committee chair’s majority partisan status on information acquisition and transmission via committee
hearings. Findings suggest that under some conditions, the floor can informationally benefit more from
having a chair representing a minority party in the chamber with opposite bias call a hearing than with
a chair representing a majority party.
It has been controversial whether incumbents are punished more for a bad economy than they are rewarded
for a good economy due to mixed results from previous studies on one or handful number of countries.
This paper makes an empirical contribution to this lingering question by conducting extensive tests on
whether this asymmetry hypothesis is a cross-nationally generalizable phenomenon using all currently
available modules of the Comparative Study of Electoral Systems survey from 122 elections in 42
representative democracies between 1996 and 2016, as well as macro-economic indicators and
individual-level economic perception. In general, this paper finds little support for the asymmetry
hypothesis; although the evidence of such asymmetric economic voting is found in some subpopulations
using certain economic indicators, these conditional effects are largely inconsistent, suggesting that
it is still safe to assume a linear relationship between economic conditions and support for the
incumbent.
In principle, committees hold hearings to gather and provide information to their principals, but some
hearings are characterized as political showcases. This article investigates conditions that moderate
committee members' incentives to hold an informative hearing by presenting a game-theoretic model and a
lab experiment. Specifically, it studies when committees hold hearings and which types of hearing they
hold by varying policy preferences of committee members and the principal and political gains from
posturing. Findings provide new insights to how preferences and power distribution affect individuals'
incentives to be informed when they make decisions as members of a committee in many contexts.
Working Papers
Morality shapes politics by defining right and wrong, but its instinctive appeal---often bypassing deliberation---
can exacerbate partisan hostility and echo chambers. Despite this significance, little is known about the extent to
which politics is moralized and the conditions associated with it. Analyzing a comprehensive dataset of 3.3 million
U.S. Congressional tweets from 2013 to 2022, we find a striking, decade-long rise in moral rhetoric. Regression analyses
show that members' use of moral rhetoric is closely tied to electoral incentives to mobilize co-partisans: members use
moral rhetoric---especially negative---more as elections near, during low-turnout midterms, and in more homogeneous
districts heavily composed of co-partisans. Furthermore, we find that, over time, the election–moralization link has
strengthened, and members increasingly represent more co-partisan seats---changes that can entrench incentives to moralize
politics. These patterns reflect strategic adaptation to evolving structural incentives, with far-reaching implications
for political communications in polarized democracies.
Legislatures rely on external sources for information, and the diversity of these information providers shapes whose voices are heard in policymaking.
We examine the gender and racial composition of witnesses invited to testify before the U.S. Congress from 1961 to 2022.
Using a new dataset covering over 750,000 witnesses, we document the persistent underrepresentation of women and racial minorities,
with substantial variation over time, across committees, and by issue area. We further show that witnesses of different gender and
race emphasize distinct aspects of the same policy issues in their testimonies. These differences are more or less pronounced depending
on the policy domain. Our analysis highlights not just whether Congress hears from a diverse set of voices, but how the demographic makeup
of information providers shapes the content of legislative discussions---revealing when diversity matters most for the information
environment that underpins lawmaking.
The increasing influence of China in both international and domestic contexts has emerged as a central consideration for U.S. policymakers.
Despite this, there remains a limited understanding of how China is portrayed by U.S. policymakers.
While most existing research infers U.S. political leaders’ intentions from policy outcomes and takes executive-centered accounts of
foreign policy formation, this study analyzes legislative discourse directly---offering a large-scale, systematic analysis of how U.S.
lawmakers actively engaged in the strategic redefinition of China. Using a comprehensive dataset of U.S. congressional committee hearing
transcripts spanning from 1997 to 2022, we examine the temporal and partisan dynamics shaping the topical focus, tone, and arguments
surrounding China. Our findings reveal a marked shift in congressional discourse, with the thematic focus pivoting from economic
engagement to security competition. This is accompanied by an increasingly negative tone suggesting a broader U.S. strategic transition
from engagement to containment. Moreover, we also find more nuanced partisan differences in topical focus and the tone which may contradict
common belief that Republicans are tougher on policy toward China. By illuminating these trends, this research offers critical insights
into the evolving legislative narratives and strategic considerations underpinning U.S. legislative perspectives on China, contributing
to a deeper understanding of the intersection between domestic politics and international relations.
Despite NLP advances, computational approaches for judging argument similarity face a fundamental challenge:
semantic-positional dissonance. Embedding models must distinguish between arguments sharing similar linguistic
characteristics yet advancing opposing positions, and conversely recognizing when diverse linguistic expression
across different cultural, societal, and philosophical contexts convey identical positions. This distinction
between content, rhetoric, and position is a complex issue that requires insight from both cognitive science
and computational social science. To address this challenge, we introduce ArguBias, a framework that systematically
identifies, evaluates, and improves similarity judgments for arguments containing cognitive bias structures.
First, we establish the ArguBias corpus, containing 8,000 annotated argument pairs facilitating the taxonomy
of previously unexamined cognitive bias structures in argumentation. Second, we conduct an extensive evaluation
of 10 state-of-the-art embedding models on their cognitive bias sensitivity. We find general-purpose text embedding
models struggle significantly with lexical overlap and confirmation bias when judging argument similarity.
Third, we demonstrate how minimally fine-tuning embedding models with the ArguBias corpus reduces their sensitivity
to cognitive bias structures by up to 11.6pp, and we show that this improvement carries over to other argument similarity benchmarks.
Voters learn the quality of the incumbent party through its policy outcomes. Previous studies mostly focused
on the incumbent’s strategy to make the policy outcome informative largely ignoring the role of the challenger.
We develop an agency model with two innovative features: both the incumbent and the challenger party can contribute
to the informativeness of the policy outcome, and each party can allocate its resources between revealing information
and improving its quality directly. We find that only the disadvantaged party is willing to invest in information revelation,
but it does so only if the quality gap between the two parties is sufficiently large. Unlike the conventional wisdom,
this finding suggests that a weak competition, instead of a close one, helps the voter make a better-informed decision.
In representative democracy, it is crucial to include the perspectives of those governed in policy
making. To analyze representation, research often links public policy preferences with legislators’
stances through surveys and votes. However, the scholarship lacks effective methods to gauge if
substantive policy ideas of the public gain lawmakers’ attention. This study combines Reddit discussions
on policy issues with U.S. House of Representatives’ hearing transcripts from 2005-2022 to develop an
innovative LLM-driven argument detection and stance classification framework called WIBA (“What is
Being Argued”). By applying WIBA, we visualize the overlap of arguments, identifying which communities
of interest are represented or overlooked in legislative deliberations and how the pattern of
representation varies across partisan and non-partisan policy issues. Our proposed approach shifts the
focus from organized interests to the arguments themselves, providing a deeper understanding of
democratic representation at the argument level.
Legislators sometimes make impressive political statements in an effort to create a viral moment. Each time one of them succeeds,
the incentive to make such statements grows stronger. A recent study analyzing representatives’ speaking patterns during
committee hearings show that they tend to engage in such messaging activities when they have less legislative power to move
their bills forward to compensate for their lack of legislative achievements and still gain voters’ support (Park 2021).
We develop a measure of senator grandstanding by analyzing senators’ speaking patterns in committee hearings from the
105th to 117th Congresses. We find that grandstanding is more common among minority members, which is consistent with
the previous findings from the House, but this effect of being in the minority party is larger in the Senate than in the House.
Findings also suggest that senators, on average, tend to grandstand more than representatives.
Whether congressional committees engage in genuine deliberation or mere position-taking—a question long debated
in the literature—has fundamental implications for democratic theory and institutional design. Yet empirical tests
of these competing theories have been limited by the absence of scalable methods to trace influence from testimony
to bill text. To address this gap, we develop a computational approach that combines mechanical text processing with
theory-driven AI analysis to identify plausible pathways through which testimony influences legislative changes.
Our method first classifies changes by type (substantive-technical, substantive-political, organizational-structural,
or symbolicexpressive), then measures influence along three dimensions (temporal uniqueness, marginal contribution,
and correspondence strength) while identifying specific mechanisms through which testimony may shape legislation (direct language,
technical details, problem framing, solution approach, or conceptual framework). Applied to a proof-of-concept analysis
of nuclear waste policy legislation where most changes were substantive-technical, the methodology reveals that these
technical provisions show testimony influence while organizational-structural changes do not, with problem framing emerging
as the dominant mechanism. The framework generates datasets at three levels of analysis (change-level, speech-change dyad,
and speaker-level), enabling researchers to test whether committees function as information-gathering bodies (Krehbiel 1991),
venues for position-taking (Mayhew 1974), or forums for genuine deliberation (Habermas 1996).
Scientific expertise plays an increasingly central role in policymaking, yet growing distrust toward science among Americans
raise questions about how Congress engages with expert witnesses. Drawing on a comprehensive dataset of 760,000 congressional
witnesses from 1961 to 2022 and committee hearing transcripts from 1997 to 2018, we identify those affiliated with universities
and think tanks and analyze legislators' interactions with them. Using the two measures of legislators’ questioning styles---grandstanding
and analytical engagement---we assess how issue polarization, partisanship, and constituency characteristics shape congressional
treatment of expertise. We find that both parties engage in more grandstanding when questioning research witnesses, particularly
those from think tanks and during hearings on polarized issues. Republicans consistently exhibit higher grandstanding and lower
analytical engagement, especially toward university-affiliated scholars. Members representing constituents with lower education
attainment tend to grandstand more toward researchers. These patterns raise concerns about democratic accountability and effective
functioning of U.S. Congress.