Dahyun Choi

Hello! I’m a Ph.D. candidate in the Department of Politics at Princeton University. I study how private and public organizations—including firms, advocacy groups, administrative agencies, and legislatures—strategically interact in American politics, focusing on how they produce, manage, and deploy expertise to shape policy outcomes and navigate complex regulatory environments. I also develop computational methods to measure and analyze their behavior and information management practices.

Publications & Manuscripts Under Review

Partisan Bias and the Resilience of High-Quality Science

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How do partisan bias and scientific credibility jointly shape the use of evidence in regulatory policymaking? I investigate this question using a novel dataset of 16,783 peer-reviewed studies evaluated by the Environmental Protection Agency (EPA) for the Integrated Science Assessments (ISAs), which inform the National Ambient Air Quality Standards (NAAQS). I find that Democratic administrations are 15.4% more likely to cite pro-regulatory studies, while Republican administrations are 17.5% less likely to do so. These effects correspond to a two-standard-deviation change in study quality measures. Yet partisan bias is moderated by evidentiary quality: high-quality studies are cited consistently across administrations, while lower-quality studies are less penalized when aligned with an administration’s policy agenda. Evidence on participant selection in the ISA process suggests a plausible mechanism underlying this pattern. Together, the findings suggest that while science retains epistemic authority, its application is shaped by political context within the administrative state.

Why Interest Groups With Divergent Goals Collaborate: Evidence From Climate Regulation.

Accepted, Economics and Politics [ | paper ]

Why do interest groups with contrasting interests and policy goals work together? I present a theory of collaborative policy production and show that interest groups can achieve higher policy gains through collaboration, even though their ideal policy goals may diverge significantly. To test theoretical results, I introduce original measurement strategies that reveal systematic patterns in which firms and environmental groups invest in joint efforts to improve fine-grained details of policy to achieve greenhouse gas emissions targets. The analysis, using public comments spanning 2010-2020, demonstrates that comments written jointly by environmental groups and firms contain more information that can contribute to the quality of policy implementation than individual efforts alone, despite compromises on policy preferences. These findings highlight the hidden dynamics of regulatory politics, wherein divergent political goals are reconciled for high-quality policy implementation.

Political Economy of Climate and the Environment, University of Pennsylvania

Frontiers in Money in Politics Research, University of Southern California

2023 American Political Science Association, Los Angeles

2023 Money in Politics Conference, Copenhagen Business School

Climate Pipeline Project, Brown University

2023 Midwest Political Science Association, Chicago

2022 Environmental Politics and Governance, Virtual conference

2022 International Political Economy Society, University of Pittsburgh

Fine-tuned Large Language Models Can Replicate Expert Coding Better than Trained Coders: A Study on Informative Signals Sent by Interest Groups

(with Brandon Stewart and Denis Peskoff). Revised & Resubmitted, Political Science Research and Methods [ ]

Understanding the political process in the United States requires examining how information is provided to politicians and the general public. While existing studies point to interest groups as strategic information providers, studying this aspect empirically has been challenging due to the need for expert-level annotation in measurement. We make two contributions. First, we demonstrate that fine-tuned large language models (LLMs) can replicate expert-level annotation in a specialized area above the accuracy of lightly-trained workers, crowd-workers, and zero-shot LLMs. Second, we quantify two types of interest group signals that are difficult to separate empirically using other means: 1) informative signals that help agents improve political decisions, and 2) associative signals that influence preference formation but lack direct relevance to the substantive topic of interest. We demonstrate the utility of this approach using two applications where our classifier generalizes out of distribution. This study shows methodologically the applicability of large language models for complex expert-driven measurement tasks but also shows substantively that interest groups strategically tailor the composition of signals under different institutional settings.

Email for current draft

2023 American Political Science Association, Los Angeles

2022 Society for Political Methodology, Washington University in St. Louis

How Much Data Is Enough? A Design-aware Approach to Empirical Sample Complexity

(with Perry Carter)

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How much data is needed to ensure that a model performs reliably on new, unseen data? Despite its central importance to empirical research design, sample size decisions are often made heuristically—guided more by resource constraints than by principled diagnostics. Existing tools like power analysis and cross-validation offer limited insight into how predictive performance scales with sample size. We introduce a design-aware, empirical framework for estimating sample complexity bounds tailored to applied settings. By fitting smooth extrapolation functions to model performance from resampled pilot data, our method estimates the sample size needed to achieve researcher-specified generalization guarantees. Through applications to supervised learning tasks involving extensive human-annotated data, we show that generalization often stabilizes with as little as 10% of typical labeling costs. This approach provides a statistically grounded, interpretable diagnostic for generalization performance and a practical tool for political scientists designing data-intensive studies under resource constraints or design uncertainty

2023 American Political Science Association, Los Angeles

2023 Society for Political Methodology, Stanford University

Parallel Forces, Parallel Patterns 1868–2024: An Integrated Approach to Seat and Vote Shares in the House and Senate

(with Charles Cameron and Harry Paarsch) [ ]

The Constitution creates a distinctive electoral rhythm in federal elections through the staggered timing of House, Senate, and presidential contests. This rhythm generates recurring presidential party losses in midterm elections and possibly other patterns, such as gains in presidential years and apparent dependence across electoral cycles. Although midterm losses in the House are well documented, these dynamics are often studied in isolation, over short periods, or overlooked in the Senate. We present an integrated empirical model of congressional seat shares from 1868 to 2024, showing that midterm losses for the president’s party are consistent and robust in both chambers. We find little evidence of presidential-year gains but strong evidence of dependence. Extending the analysis to vote shares following the Seventeenth Amendment, we observe similar patterns across chambers. Our results reveal the persistent impact of electoral rhythms over more than a century and demonstrate the value of macro-level analysis in understanding institutional dynamics within the American constitutional order.

Regulatory Capacity and Corporate Political Disengagement

(with Kyuwon Le) [ ]

Although there are public concerns about the declining capacity of regulatory agencies and its impact on regulatory outcomes, such decline could also lead regulated firms to disengage themselves from politics. We examine whether and how firms reduce their campaign contributions in response to decreases in state-level regulatory capacity. To do so, we collect original datasets on the workforce size of U.S. state environmental agencies and leverage variations in workforce shocks that arise from the gap between actual and appropriated workforce sizes. Our analysis reveals that state environmental agencies' workforce shocks decrease firms' donations to state legislators, particularly to those in the majority party and the Democratic party, but do not affect firms' contributions to their ideological allies. We also find that existing state-level restrictions on corporate donations do not moderate firms' political responsiveness. Overall, this article provides a nuanced picture of how diminishing regulatory capacity could shape corporate political activities.

2024 American Political Science Association, Philadelphia

2024 Money in Politics Conference, Copenhagen Business School

Conference on Bureaucratic and Interest Group Politics, Princeton University

2024 Midwest Political Science Association, Chicago

Works in Progress

Collusive Production of Environmental Data: Private Interests and Local Governments

Politics of Academic Experts: Evidence from Antitrust Laws

(with Nolan McCarty)

Legislative Foundations of the Administrative State

(with Amy Jeon, Charles Cameron, and Charlie McWeeny)

Interest Group Ecologies and Ideological Niches

(with Charles Cameron)

Sample Complexity For Open-Ended Survey Responses

(with Perry Carter and Narrelle Gilchrist)

Software

scR (available on CRAN, with Perry Carter) [ ]

scR is an R package developed by Carter & Choi (2025) designed to help researchers determine how much data is needed for reliable generalization. It provides a design-aware empirical framework that estimates sample complexity using smooth extrapolation of model performance from pilot data. Additionally, scR offers theoretical guidance by calculating Vapnik-Chervonenkis Dimension (VCD). This interpretable diagnostic is particularly helpful for empirical researchers designing data-intensive studies under resource constraints or uncertainty. For more details, see Carter & Choi (2025), "How Much Data Is Enough? A Design-aware Approach to Empirical Sample Complexity" (doi:10.31219/osf.io/evrcj_v2).

Email for current draft

2023 American Political Science Association, Los Angeles
2022 Society for Political Methodology, Washington University in St. Louis