Replication Data for: Race, Legislative Speech, and Symbolic Representation in Congress (doi:10.7910/DVN/6RL6ID)

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Replication Data for: Race, Legislative Speech, and Symbolic Representation in Congress

doi:10.7910/DVN/6RL6ID

Harvard Dataverse

2024-05-01

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Vishwanath, Arjun, 2024, "Replication Data for: Race, Legislative Speech, and Symbolic Representation in Congress", https://doi.org/10.7910/DVN/6RL6ID, Harvard Dataverse, V1

Replication Data for: Race, Legislative Speech, and Symbolic Representation in Congress

doi:10.7910/DVN/6RL6ID

Vishwanath, Arjun (Vanderbilt University)

Arjun Vishwanath

Harvard Dataverse

Arjun Vishwanath

Vishwanath, Arjun

2023-12-01

https://doi.org/10.7910/DVN/6RL6ID

Social Sciences, congress; race; representation

We know little about the extent to which racial minorities are symbolically represented by members of Congress. This stands in contrast to a wealth of research analyzing the extent to which minorities are substantively and descriptively represented. This article provides the most comprehensive analysis of symbolic representation to date. Using data on legislators' speech from 105,875 newsletters and 620,838 floor speeches, I find that white legislators of both parties are more likely to symbolically represent blacks, Hispanics, and Asians if those groups are more populous in their constituency. However, these effects only hold cross-sectionally; using a difference-in-differences setup from redistricting shocks, I find that there is little within-legislator variation in speech patterns as their constituencies change. Lastly, I show that, unlike on the symbolic dimension, legislators' substantive representation is not influenced by group size. I conclude that white legislators are symbolically responsive to their constituents' identities in their speech patterns.

This dataset underwent an independent verification process, complying with the AJPS Verification Policy updated June 2023, which replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of the code. The verification process was carried out by the Cornell Center for Social Sciences at Cornell University. <br></br> The associated article has been awarded the Open Materials Badge. Learn more about the Open Practice Badges from the <a href="https://www.cos.io/">Center for Open Science</a>. <br></br> <img src="https://socialsciences.cornell.edu/sites/default/files/2024-04/materials_large_color.png" alt="Open Materials Badge " width="60" height="60"> <br></br> Open Materials Badge

Ansolabehere, Stephen; Schaffner, Brian, 2013, "CCES Common Content, 2012", https://doi.org/10.7910/DVN/HQEVPK, Harvard Dataverse, V9, UNF:5:Eg5SQysFZaPiXc8tEbmmRA== [fileUNF]

<br></br> Ansolabehere, Stephen; Schaffner, Brian F., 2017, "CCES Common Content, 2016", https://doi.org/10.7910/DVN/GDF6Z0, Harvard Dataverse, V4, UNF:6:WhtR8dNtMzReHC295hA4cg== [fileUNF]

<br></br> Cormack, Lindsey (2017). “DCInbox – Capturing Every Constituent E-newsletter from 2009 Onwards”. The Legislative Scholar 2 (1): 2-36

<br></br> Gentzkow, Matthew, Jesse M. Shapiro, and Matt Taddy (2019). “Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech”. Econometrica 87 (4): 1307-1340

<br></br> Schaffner, Brian; Ansolabehere, Stephen, 2015, "CCES Common Content, 2014", https://doi.org/10.7910/DVN/XFXJVY, Harvard Dataverse, V5, UNF:6:WvvlTX+E+iNraxwbaWNVdg== [fileUNF]

<br></br> Schaffner, Brian; Stephen Ansolabehere; Sam Luks, 2019, "CCES Common Content, 2018", https://doi.org/10.7910/DVN/ZSBZ7K, Harvard Dataverse, V6, UNF:6:hFVU8vQ/SLTMUXPgmUw3JQ== [fileUNF]

The <i>American Journal of Political Science</i> and the Cornell Center for Social Sciences are not responsible for the accuracy or quality of data uploaded within the <i>AJPS</i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.

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cces_lccr_bound.rds

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contains data from the 2012, 2014, 2016, and 2018 CCES

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Codebook.pdf

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describes the variables used in the analysis and contained in the datasets provided here

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cs_analyzed.rds

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contains floor speech data at the document-sentence level

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cs_analyzed_doc_level.rds

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contains floor speech data from cs_analyzed.rds aggregated at the document level

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cs_analyzed_legislator_term.rds

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contains the data from cs_analyzed.rds aggregated at the member-congress-chamber level

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cs_analyzed_legislator_term_all.rds

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contains the data from cs_analyzed.rds aggregated at the member-congress-chamber level but also includes speech on defense, immigration, and trade

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cs_lasso_out_of_sample_validation.rds

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contains a sample of floor speech sentences from the raw codings. Used to compare the Lasso model's predictions to the coders' classifications.

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generate_tables_and_figures.R

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contains the code necessary to reproduce all figures and tables reported in the main text and appendix

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ideology_data.RDS

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contains data on MCs' roll-call based ideology

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mturk_codings.rds

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contains pilot codings from MTurk

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newsletters_analyzed.rds

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contains newsletter data at the document-sentence level

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newsletters_analyzed_doc_level.rds

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contains newsletter data from newsletters_analyzed.rds aggregated at the document level

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newsletters_analyzed_legislator_term.rds

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contains the data from newsletters_analyzed.rds aggregated at the member-congress-chamber level

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newsletters_analyzed_legislator_term_all.rds

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newsletters_lasso_out_of_sample_validation.rds

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contains a sample of newsletter sentences from the raw codings. Used to compare the Lasso model's predictions to the coders' classifications.

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newsletter_count.rds

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contains newsletter data at the member-congress-chamber level

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ra_codings.rds

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contains pilot codings from research assistants

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README.txt

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README file

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