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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census |
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Identification Number: |
doi:10.7910/DVN/TNNSXG |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2021-07-05 |
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Version: |
4 |
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Bibliographic Citation: |
Kenny, Christopher T.; Kuriwaki, Shiro; McCartan, Cory; Rosenman, Evan; Simko, Tyler; Kosuke, Imai, 2021, "Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census", https://doi.org/10.7910/DVN/TNNSXG, Harvard Dataverse, V4 |
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Citation |
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Title: |
Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census |
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Identification Number: |
doi:10.7910/DVN/TNNSXG |
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Authoring Entity: |
Kenny, Christopher T. (Harvard University) |
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Kuriwaki, Shiro (Harvard University) |
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McCartan, Cory (Harvard University) |
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Rosenman, Evan (Harvard University) |
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Simko, Tyler (Harvard University) |
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Kosuke, Imai (Harvard University) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Imai, Kosuke |
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Depositor: |
Kuriwaki, Shiro |
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Date of Deposit: |
2021-07-05 |
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Holdings Information: |
https://doi.org/10.7910/DVN/TNNSXG |
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Study Scope |
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Keywords: |
Law, Social Sciences, Census, Redistricting, BISG, Differential Privacy, TopDown algorithm, One Person One Vote |
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Abstract: |
Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureau’s latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral districts. We find that the DAS systematically undercounts the population in mixed-race and mixed-partisan precincts, yielding unpredictable racial and partisan biases. While the DAS leads to a likely violation of the “One Person, One Vote” standard as currently interpreted, it does not prevent accurate predictions of an individual’s race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census. |
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Notes: |
Description on the <a href = "https://alarm-redist.github.io/posts/2021-10-06-2021-10-06-das-published/">ALARM Website</a> |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan Rosenman, Tyler Simko, and Kosuke Imai. 2021. "The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census" <i>Science Advances</i> 7:41 |
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Identification Number: |
10.1126/sciadv.abk3283 |
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Bibliographic Citation: |
Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan Rosenman, Tyler Simko, and Kosuke Imai. 2021. "The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census" <i>Science Advances</i> 7:41 |
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000_README.md |
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Guide to downloading the file, data and code |
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text/markdown |
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enacted_out.Rds |
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application/gzip |
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sims_partisan_sum.rds |
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application/octet-stream |
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sims_race_sum.rds |
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application/octet-stream |
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README.md |
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text/markdown |
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al.Rds |
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application/gzip |
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al_2018.zip |
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application/zipped-shapefile |
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de.Rds |
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application/octet-stream |
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de_2020.zip |
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application/zipped-shapefile |
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er_geoids.Rds |
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application/gzip |
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er_int_paired_df.Rds |
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application/gzip |
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er_map.Rds |
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application/gzip |
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la.Rds |
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application/octet-stream |
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la_2018.zip |
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application/zipped-shapefile |
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ms.Rds |
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application/gzip |
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SW_VTD10.zip |
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application/zipped-shapefile |
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SW_VTD10_meta.pdf |
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application/pdf |
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Sen_2010_TRP1.zip |
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application/zipped-shapefile |
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Sen_2010_TRP1_meta.pdf |
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application/pdf |
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nc_adj.rds |
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application/gzip |
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nc_shp.rds |
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Revised 2022-07-01 adding v19 population variables |
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application/octet-stream |
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pa_exc.rds |
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application/octet-stream |
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pa_plans.rds |
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application/octet-stream |
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pa_shp.rds |
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application/octet-stream |
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pa_vtd_das_04.rds |
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application/octet-stream |
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pa_vtd_das_12.rds |
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application/octet-stream |
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pa_vtd_das_19.rds |
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application/octet-stream |
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pa_vtd_orig.rds |
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application/octet-stream |
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sc_adj.rds |
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application/octet-stream |
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sc_shp.rds |
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application/octet-stream |
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sc_2018.zip |
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application/zipped-shapefile |
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ut.Rds |
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application/octet-stream |
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ut_2018.zip |
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application/zipped-shapefile |
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wa.Rds |
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application/octet-stream |
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wa_2018.zip |
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application/zipped-shapefile |
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00_custom_functions.R |
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type/x-r-syntax |
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01_download_setup.R |
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02_compare_pop_states.R |
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03A_partisan_race_errors_appendix.R |
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DAS 4.5 version of 03 |
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type/x-r-syntax |
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03B_partisan_race_errors_v19.R |
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DAS-19.61 version of 03 |
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03_partisan_race_errors.R |
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04_partisan_sims.R |
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05_race_sims.R |
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06_enacted_plan_difference.R |
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Archive.zip |
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00_al_setup.R |
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00_de_setup.R |
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01_de_state_sen.R |
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00_vf_read_subset_Rockland.R |
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01_vf_geocode.R |
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02_vf_subset_ERCSD.R |
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03_19_east_ramapo.R |
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03_east_ramapo.R |
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04_19_east_ramapo_enacted_parity.R |
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04_east_ramapo_enacted_parity.R |
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00_la_setup.R |
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01_run_pop_tol_tests.R |
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02_run_precinct_tests.R |
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03_analyze_pop_tol.R |
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04_run_mmd_diff_sims.R |
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05_mmd_diff_analyze.R |
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LA_utils.R |
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xx_calculate_parity.R |
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00_ms_setup.R |
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01_ms_sims.R |
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01_prep_NC_data.R |
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02_sim_NC.R |
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04_diagnose-NC-sims.R |
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01_prep_PA_data.R |
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02_sim_PA.R |
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xx_calculate_parity.R |
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01_prep_SC_data.R |
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02_sim_SC-CD.R |
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03_sim_SC-HD.R |
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04_diagnose-SC-sims.R |
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05_viz_SC.R |
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00_ut_setup.R |
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00_wa_setup.R |
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01_wru-compute.R |
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move_wru-NC_data.R |
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select-dataverse-files.R |
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