voters
We work with partners to collect and process data from each state's voter file. We then join that data with consumer data (including high quality email addresses and phone numbers), census data, and our own predictive models to provide users with a dataset that can power a variety of targeting, messaging, outreach, and modeling use cases.
Column | Description |
|---|---|
voterbase_id | Unique voter ID |
state_voter_id | State supplied voter ID, format differs from state to state |
county_voter_id | County supplied voter ID, format differs by county and state |
first_name | First name in uppercase with spaces, numbers, and special characters removed. Accented characters have been replaced with non-accented versions |
middle_name | Middle name in uppercase with spaces, numbers, and special characters removed. Accented characters have been replaced with non-accented versions |
middle_init | Middle initial in uppercase |
last_name | Last name in uppercase with spaces, numbers, and special characters removed. Accented characters have been replaced with non-accented versions |
name_suffix | Name suffix in uppercase, i.e. JR, III |
dob | Date of birth formatted as date (YYYY-MM-DD). DOB comes from voter registration data and commercial data |
myob | Month and year of birth formatted as 6 digit integer (YYYYMM). MYOB comes from voter registration data and commercial data. Some states truncate DOB to the first of the month making MYOB better for matching in those cases |
yob | Month and year of birth formatted as 4 digit integer (YYYY). YOB comes from voter registration data and commercial data. Some states truncate DOB to the first of the year making YOB better for matching in those cases. |
age | Voter's age by end of current year, calculated by current_year - YOB |
modeled_age | Voter's age by end of current year, calculated by current_year - YOB, modeled if DOB is missing |
reg_date | Voter registration date formatted as date (YYYY-MM-DD). In situations where state or county updates reg date when a voter record is updated, reg_date is calculated as 30 days prior to earliest recorded vote date |
gender | Voter gender from voter registration data. M, F, or NULL |
ethnicity | Ethnicity, self reported on the voterfile where available, otherwise modeled. In cases where the model isn't confident, Null. AAPI, Black, Latino, Native American, White |
ethnicity_source | Ethnicity source: Voterfile, Commercial, Uncoded |
modeled_race_aapi | Indigo race model, score from 0-1 with the probability that a voter is AAPI |
modeled_race_black | Indigo race model, score from 0-1 with the probability that a voter is Black |
modeled_race_latino | Indigo race model, score from 0-1 with the probability that a voter is Hispanic or Latino/a |
modeled_race_native_american | Indigo race model, score from 0-1 with the probability that a voter is Native American |
modeled_race_white | Indigo race model, score from 0-1 with the probability that a voter is White |
state_fips | Registration state fips code, two digit fips as determined by the census formatted as a string |
state | State abbreviation |
county_fips | Registration address county fips code, three digit fips as determined by the census formatted as a string |
county | Registration address county name in uppercase |
precinct | Registration precinct name |
reg_address | Registration address |
reg_city | Registration city name |
reg_state | Registration state abbreviation |
reg_zip | Registration address zip5 as string |
reg_zip4 | Registration address zip4 as string |
reg_lat | Registration address latitude |
reg_long | Registration address longitude |
reg_latlong_accuracy | Accuracy of reg_lat and reg_long columns, ordered from most to least accurate: GeoMatch9Digit, GeoMatchRooftop, GeoMatchBuilding, RangeInterpolation, ExactMatch, AverageOfApartments, ParcelCenter, GeoMatch5Digit, KnownAlternateName, DirectionPrefixRemoved, DirectionSuffixRemoved, StreetCenter, Intersection |
mailing_address | Mailing address |
mailing_city | Mailing city name |
mailing_state | Mailing address state abbreviation |
mailing_zip | Mailing address zip5 as string |
mailing_zip4 | Mailing address zip4 as string |
phone | Best phone number for voter, prioritizing cell phones over landlines, 9 digits formatted as a string |
phone_type | Type of phone: Cell, Landline, VOIP |
phone_confidence_code | Confidence in quality of phone number High - high confidence phone number matched at the individual level Household - high confidence phone number matched at the household level Low - low confidence phone number |
phone_cell | Cell phone number, 9 digits formatted as a string |
phone_cell_confidence_code | Confidence in quality of phone_cell High - high confidence phone number matched at the individual level Household - high confidence phone number matched at the household level Low - low confidence phone number |
phone_landline | Landline phone number, 9 digits formatted as a string |
phone_landline_confidence_code | Confidence in quality of phone_landline High - high confidence phone number matched at the individual level Household - high confidence phone number matched at the household level Low - low confidence phone number |
party | Party identification based on voterfile |
party_3way | Party identification grouped into DEM, REP, and IND based on voterfile and modeled data |
party_source | Source of party and party_3way data: Voterfile, Modeled |
district_congressional_2020 | Congressional district, three digits zero padded, i.e. 002, 011, 024 |
district_congressional_2010 | 2010 congressional district, three digits zero padded, i.e. 002, 011, 024 |
district_stleg_upper_2020 | Upper state legislative district, state senate. For numeric districts, district names are three digits and zero padded, i.e. 003, 021, 041B. For non-numeric district names, strings are uppercase. |
district_stleg_lower_2020 | Lower state legislative district, including state house or state assembly depending on the state. For numeric districts, district names are three digits and zero padded, i.e. 003, 021, 041B. For non-numeric district names, strings are uppercase. |
census_block_2020 | Census block ID, 15 digits formatted as a string |
ts_model_education_college_graduate | TargetSmart education model. Predicts the likelihood that an individual has attained a college-level or higher education. Scores are expressed from 0-1. A higher score represents a higher probability that a person's education level is college graduate or higher. |
ts_model_education_high_school_only | TargetSmart education model. Predicts the likelihood that an individual has not attained formal education beyond high school. Scores are expressed from 0-1. A higher score represents a higher probability that a person's education level is high school or lower. |
ts_model_religion_catholic | TargetSmart religion score. Predicts the likelihood that a voter identifies as Catholic. Scores are expressed from 0-1. A higher score indicates a higher likelihood to identify as Catholic. |
ts_model_religion_evangelical | TargetSmart religion score. Predicts the likelihood that a voter identifies as Evangelical. Scores are expressed from 0-1. A higher score indicates a higher likelihood to identify as Evangelical. |
ts_model_religion_jewish | TargetSmart religion score. Predicts the likelihood that a voter identifies as Jewish. Scores are expressed from 0-1. A higher score indicates a higher likelihood to identify as Jewish. |
ts_model_religion_mormon | TargetSmart religion score. Predicts the likelihood that a voter identifies as Mormon. Scores are expressed from 0-1. A higher score indicates a higher likelihood to identify as Mormon. |
ts_model_gunowner | TargetSmart gun owner score. Predicts the likelihood that an individual supports stricter gun control laws. Scores are expressed from 0-1. A higher score indicates a higher likelihood that a person supports stricter gun control laws. |
ts_model_veteran | TargetSmart veteran score. Predicts the likelihood that an individual is a military veteran or an active service member. Scores are expressed from 0-1. A higher score predicts a higher likelihood that the individual is a military veteran or an active service member. |
ts_model_ideology_score | TargetSmart ideology score. Predicts the likelihood that an individual supports liberal ideology. Scores are expressed from 0-1. A value of 1 represents those most likely (very liberal) and 0 represents those least likely (very conservative). |
ts_model_children_present_score | TargetSmart children present score. Predicts the likelihood that a voter lives in a household with children. Scores are expressed from 0-1. A higher score represents a higher probability that a person lives in a household with children. |
ts_model_marriage | TargetSmart marriage score. Predicts the likelihood that an individual is married. Scores are expressed from 0-1. A higher score represents a higher probability that a person is married. |
ts_model_income_rank | TargetSmart high income score. Predicts the likelihood that an individual has an income over $100,000. Scores are expressed from 0-1. A higher score represents a higher probability that a person would self-report income greater than $100,000. |
ts_model_homeowner | TargetSmart homeowner score. Predicts the likelihood that an individual owns a home. Scores are expressed from 0-1. A higher score represents a higher probability that person owns a home. |
fec_hh_contribution_count_rep | Total number of FEC contributions to Republicans in the household |
fec_hh_contribution_count_dem | Total number of FEC contributions to Democrats in the household |
fec_hh_contribution_count_total | Total number of FEC contributions in the household |
fec_hh_contribution_pct_dem | Percent of FEC contributions in the household made to Democrats |
modeled_turnout_midterm_primary | Indigo midterm primary turnout model, score from 0-1 with the probability that a voter will turn out to vote in a midterm primary election |
modeled_turnout_midterm_general | Indigo midterm general turnout model, score from 0-1 with the probability that a voter will turn out to vote in a midterm general election |
modeled_turnout_presidential_primary | Indigo presidential primary turnout model, score from 0-1 with the probability that a voter will turn out to vote in a presidential primary election |
modeled_turnout_presidential_general | Indigo presidential general turnout model, score from 0-1 with the probability that a voter will turn out to vote in a presidential general election |
modeled_race | Categorical race value based on Indigo race model. AAPI, Black, Latino, Native American, White, or NULL if model is low confidence |
modeled_dem_partisanship | Indigo partisanship model, score from 0-1 with the probability that a voter indentifies as a Democrat |
g12_reg* | 1 if voter was registered for given election, 0 if voter wasn't yet registered |
g12_voted* | 1 if voted, 0 if didn't vote |
g12_ballot_type* | Indicated method of voting if voted. Note that not all states report type of ballot for all historic elections, a null value indicates lack of reporting. Early, Absentee, Poll Vote, Unknown. |
p12_party* | Partisan primary voted in in given election D - Democrat, G - Green, I - Independent, L - Libertarian, N - No Party Preference, O - Other Party, R - Republican, U - Unknown |
* For general, primary, and presidential primary elections from 2012-present day, we have _voted, _election_date, and _ballot_type columns for each election. The naming convention for these columns in [election stage - g/p/pp][2 digit election year - 14/22/23]_[column type], i.e. pp09_voted, p14_party, g22_ballot_type.