Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
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We study the problem of measuring group differences in choices when the dimensionality of the choice set is large.We show that standard approaches suffer from a severe finite-sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias.We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance.Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century.
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Wisnewski-Parks, G. (2020) · Southern Communication Journal · doi:10.1080/1041794x.2020.1765006 · Cited by 11 · see citing works
Textual Analysis in FinancePreprint
Tim Loughran, B., & McDonald, B. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3470272 · Cited by 11 · see citing works
Communication and Bargaining Breakdown: An Empirical Analysis
Backus, M., Blake, T., Pettus, J., & Tadelis, S. (2020) · National Bureau of Economic Research · doi:10.3386/w27984 · Cited by 11 · see citing works
Kaufman, A. R. (2019) · Presidential Studies Quarterly · doi:10.1111/psq.12629 · Cited by 10 · see citing works
Reframing social categorization as latent structure learning for understanding political behaviour
Lau, T. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0136 · Cited by 10 · see citing works
Did U.S. Politicians Expect the China Shock?
Bombardini, M., Li, B., & Trebbi, F. (2020) · National Bureau of Economic Research · doi:10.3386/w28073 · Cited by 10 · see citing works
Immigration, Political Ideologies and the Polarization of American PoliticsPreprint
Dreher, A., Langlotz, S., Matzat, J., & Parsons, C. R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3754680 · Cited by 10 · see citing works
Corporate Donations and Political Rhetoric: Evidence from a National BanPreprint
Cagé, J., Pennec, C. L., & Mougin, E. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3894330 · Cited by 10 · see citing works
A data science approach to 138 years of congressional speeches
Tucker, E. C., Capps, C. J., & Shamir, L. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04417 · Cited by 9 · see citing works
Endogenous Network Formation in CongressPreprint
Canen, N., & Trebbi, F. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2823338 · Cited by 9 · see citing works
Partisan EntrepreneurshipPreprint
Engelberg, J., Guzmán, J., Lu, R., & Mullins, W. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3821106 · Cited by 9 · see citing works
A Tool Kit for Relation Induction in Text Analysis
Stoltz, D. S., Taylor, M. A., & Dudley, J. S. K. (2024) · Sociological Methods & Research · doi:10.1177/00491241241233242 · Cited by 8 · see citing works
From Feeds to Inboxes: A Comparative Study of Polarization in Facebook and Email News Sharing
Yoganarasimhan, H., & Yakovetskaya, I. (2024) · Management Science · doi:10.1287/mnsc.2023.04134 · Cited by 8 · see citing works
The Status Quo and Belief Polarization of Inattentive Agents: Theory and Experiment
Novák, V., Matveenko, A., & Ravaioli, S. (2024) · American Economic Journal Microeconomics · doi:10.1257/mic.20210352 · Cited by 8 · see citing works
YouTube and Traditional Media: Polarization in the Catalan Political Conflict
Marin, J. G. (2021) · Studies in digital politics and governance · doi:10.1007/978-3-030-71815-2_3 · Cited by 7 · see citing works
The Imperfect Beliefs Voting ModelPreprint
Ogden, B. G. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2431447 · Cited by 7 · see citing works
Narrative EconomicsPreprint
Shiller, R. J. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2896857 · Cited by 7 · see citing works
The Democrat-Republican presidential growth gap and the partisan balance of the state governments
Cahan, D., & Potrafke, N. (2021) · Public Choice · doi:10.1007/s11127-021-00912-y · Cited by 6 · see citing works
Sloman, S. J., Oppenheimer, D. M., & DeDeo, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246689 · Cited by 6 · see citing works
Coming Apart? Cultural Distances in the United States Over TimePreprint
Bertrand, M., & Kamenica, E. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3208730 · Cited by 6 · see citing works
A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social GroupsPreprint
Bhatt, A. M., Goldberg, A., & Srivastava, S. B. (2022) · doi:10.31235/osf.io/mqhxu · Cited by 6 · see citing works
Whitebox inside blackbox: Using interpretable language models to analyze financial narrativesPreprint
CAO, S. S., Kim, Y., Wang, A., & Xiao, H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3568504 · Cited by 6 · see citing works
Republican Support and Economic Hardship: The Enduring Effects of the Opioid Epidemic
Arteaga, C., & Barone, V. (2025) · National Bureau of Economic Research · doi:10.3386/w33904 · Cited by 6 · see citing works
Truglia, R. N. P., & Cruces, G. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2427148 · Cited by 6 · see citing works
A natural language measure of ideology in the Brazilian Senate
de Figueredo, F. C., Mueller, B., & Cajueiro, D. O. (2022) · Revista Brasileira de Ciência Política · doi:10.1590/0103-3352.2022.37.246618 · Cited by 5 · see citing works
Import competition, fast-track authority and U.S. policy toward China
Che, Y., & Xiao, R. (2020) · Journal of Comparative Economics · doi:10.1016/j.jce.2020.04.003 · Cited by 5 · see citing works
Praet, S., Aelst, P. V., Daelemans, W. M. P., Kreutz, T., Peeters, J., Walgrave, S., & Martens, D. (2021) · Computational Communication Research · doi:10.5117/ccr2021.2.004.prae · Cited by 5 · see citing works
Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19Preprint
Davis, S. J., Hansen, S. E., & Seminario-Amez, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3701687 · Cited by 5 · see citing works
The Rise of Niche ConsumptionPreprint
Neiman, B., & Vavra, J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3432536 · Cited by 5 · see citing works
“Compensate the Losers?” Economic Policy and the Origins of U.S. Partisan RealignmentPreprint
Kuziemko, I., Longuet-Marx, N., & Naidu, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4609864 · Cited by 5 · see citing works
de Mesquita, E. B., & Dziuda, W. (2023) · National Bureau of Economic Research · doi:10.3386/w31827 · Cited by 5 · see citing works
The UC Cliometric History Project and Formatted Optical Character RecognitionPreprint
Bleemer, Z. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3240266 · Cited by 4 · see citing works
Optimal Term-Length in DemocraciesPreprint
Gersbach, H., Jackson, M. O., & Tejada, O. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3615407 · Cited by 4 · see citing works
Real-time Price Discovery via Verbal Communication: Method and Application to FedspeakPreprint
Cram, R. G., & Grotteria, M. (2020) · Social Science Research Network · doi:10.2139/ssrn.3613702 · Cited by 27 · see citing works
Divide and Rule: How Political Elites Polarize SocietyPreprint
Alder, S., & Wang, Y. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3769435 · Cited by 4 · see citing works
Macro Shocks and Firm-Level Response Heterogeneity
Davis, S. J., Hansen, S. E., & Seminario-Amez, C. (2025) · National Bureau of Economic Research · doi:10.3386/w33929 · Cited by 4 · see citing works
Using Machine Learning Methods to Support Causal Inference in Econometrics
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Consistency of p-norm based tests in high dimensions: Characterization, monotonicity, domination
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Without Fear or Favor? The Social Reality of Partisan Language
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National or European Politicians? Gauging MEPs Polarity When Russia is ConcernedPreprint
Андреевна, Д. А., Алексеевна, Х. А., & Skougarevskiy, D. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2779532 · Cited by 3 · see citing works
Big Data for Sociological Research
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Media Competition and News DietsPreprint
Angelucci, C., Cagé, J., & Sinkinson, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3537040 · Cited by 3 · see citing works
Tell Me a Story: Quantifying Economic Narratives and Their Role during COVID-19Preprint
Borup, D., Hansen, J. W., Liengaard, B. D., & Schütte, E. C. M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3752116 · Cited by 3 · see citing works
Gender and Lawmaking in Times of Quotas. Evidence from the French ParliamentPreprint
Lippmann, Q. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3330617 · Cited by 3 · see citing works
What Drives Tax Policy? Political, Institutional and Economic Determinants of State Tax Policy
Robinson, S., & Tazhitdinova, A. (2023) · National Bureau of Economic Research · doi:10.3386/w31268 · Cited by 3 · see citing works
Yang, Y., Duan, H., Liu, J., & Tam, K. Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4961494 · Cited by 3 · see citing works
Cassidy, W., & Kempf, E. (2025) · National Bureau of Economic Research · doi:10.3386/w33810 · Cited by 3 · see citing works
- Authors
- Gentzkow, M., Shapiro, J., & Taddy, M.
- Affiliations
- National Bureau of Economic Research (US)
- ORCID
- Gentzkow, Matthew
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2016-07
- OpenAlex
- W2885916966
- Cited by
- 159
- References
- 76
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 18 · 2023: 15 · 2022: 10 · 2021: 26 · 2020: 31 · 2019: 19 · 2018: 12 · 2017: 9 · 2016: 4 · 2015: 1 · 2014: 2 · 2013: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Licence
- public-domain
- Locations
- National Bureau of Economic Research (publishedVersion, public-domain)
- Funders
- National Science Foundation
- Brown University
- University of Chicago
- Booth School of Business, University of Chicago
- Stanford Institute for Economic Policy Research
- Stanford Research Computing Center, Stanford University
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- finite-sample bias, machine learning estimators, congressional speeches, partisanship, party identification, political speeches
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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