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A1. Demographic ProfileGeneral purpose dataset that includes 32,000+ ZIP codes covering population by ZIP code as of 2020, distribution by gender, by age group, by racial/ethnic background, median age by gender and by race, five-year population growth, total households, household size, owner-vs-renter households, and college degree-holders by race.
Index_code
Index_name
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
2020Tot_Pop_ZIP
Total population as of 2020
%Tot_Pop_NHW
% Population self-identified as Non-Hispanic White
%Tot_Pop_HSP
% Population self-identified as Hispanic
%Tot_Pop_ASN
% Population self-identified as Asian
%Tot_Pop_BLK
% Population self-identified as Black or African-American
%Tot_Pop_AIAN
% Population self-identified as American Indian and Alaska Native
%Tot_Pop_NHPI
% Population self-identified as Native Hawaiian and Pacific Islander
%Tot_Pop_TWM
% Population self-identified as having two or more races/ethnic background
5YG%_Pop
2015-2020 Five-year population growth (percentage)
5YG^%_Pop
Annualized 2015-2020 per-year population growth (percentage)
Tot_Hhlds
Total households
Housing units
Total housing units
%Pop_<18
Percentage of population under 18 years of age
%Pop_18-64
Percentage of population between 18 and 64 years of age
%Pop_65+
Percentage of population who are 65 and above
F_%Pop
Percentage of population female
M_%Pop
Percentage of population male
MedianAge_ALL
Median age of resident population
MedianAge_M_ALL
Median age of male resident population
MedianAge_F_ALL
Median age of female resident population
MedianAge_NHW
Median age of resident population self-identified as Non-Hispanic White
MedianAge_M_NHW
Median age of male resident population self-identified as Non-Hispanic White
MedianAge_F_NHW
Median age of female resident population self-identified as Non-Hispanic White
MedianAge_HSP
Median age of resident population self-identified as Hispanic
MedianAge_M_HSP
Median age of male resident population self-identified as Hispanic
MedianAge_F_HSP
Median age of female resident population self-identified as Hispanic
MedianAge_ASN
Median age of resident population self-identified as Asian
MedianAge_M_ASN
Median age of male resident population self-identified as Asian
MedianAge_F_ASN
Median age of female resident population self-identified as Asian
MedianAge_BLK
Median age of resident population self-identified as Black or African-American
MedianAge_M_BLK
Median age of male resident population self-identified as Black or African-American
MedianAge_F_BLK
Median age of female resident population self-identified as Black or African-American
MedianAge_AIAN
Median age of resident population self-identified as American Indian and Alaska Native
MedianAge_M_AIAN
Median age of male resident population self-identified as American Indian and Alaska Native
MedianAge_F_AIAN
Median age of female resident population self-identified as American Indian and Alaska Native
MedianAge_NHPI
Median age of resident population self-identified as Native Hawaiian and Pacific Islander
MedianAge_M_NHPI
Median age of male resident population self-identified as Native Hawaiian and Pacific Islander
MedianAge_F_NHPI
Median age of female resident population self-identified as Native Hawaiian and Pacific Islander
MedianAge_TWM
Median age of resident population self-identified as having two or more races/ethnic background
MedianAge_M_TWM
Median age of male resident population self-identified as having two or more races/ethnic background
MedianAge_F_TWM
Median age of female resident population self-identified as having two or more races/ethnic background
Avg_Hhld_Size_ALL
Average size of households – All types of households
Avg_Hhld_Size_OWN
Average size of households – owner households
Avg_Hhld_Size_RTR
Average size of households – renter households
Tot_Hhlds
Total households
OWN_%Tot_Hhlds
Share of households who are owner households
RTR_%Tot_Hhlds
Share of households who are renter households
%ALL_Hhlds_MCFHs
Among all households – % married couple family households
%NHW_Hhlds_MCFHs
Among Non-Hispanic White households – % married couple family households
%HSP_Hhlds_MCFHs
Among Hispanic households – % married couple family households
%ASN_Hhlds_MCFHs
Among Asian households – % married couple family households
%BLK_Hhlds_MCFHs
Among Black or African-American households – % married couple family households
%AIAN_Hhlds_MCFHs
Among American Indian and Alaska Native households – % married couple family households
%NHPI_Hhlds_MCFHs
Among Native Hawaiian and Pacific Islander households – % married couple family households
%TWM_Hhlds_MCFHs
Among Two or more races households – % married couple family households
%ALL_Pop25+_XCollege
Share of adults (age 25 and above) without a college degree
%ALL_Pop25+_College
Share of adults (age 25 and above) with a minimum of college degree
%ZIP_College+_NHW
Among adults college degree-holders, % those self-identified as Non-Hispanic White
%ZIP_College+_HSP
Among adults college degree-holders, % those self-identified as Hispanic
%ZIP_College+_ASN
Among adults college degree-holders, % those self-identified as Asian
%ZIP_College+_BLK
Among adults college degree-holders, % those self-identified as Black or African-American
%ZIP_College+_AIAN
Among adults college degree-holders, % those self-identified as American Indian and Alaska Native
%ZIP_College+_NHPI
Among adults college degree-holders, % those self-identified as Native Hawaiian and Pacific Islander
%ZIP_College+_TWM
Among adults college degree-holders, % those self-identified as having two or more races/ethnic background
A2. Geographic ProfileGeneral purpose dataset ideal for mapping and planning. Includes 32,000+ ZIP codes covering geo-coordinates, land and water area, county, and region affiliations, density for population, households and housing units, topography, time zone, and telephone area codes.
Index_code
Index_name
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
ST
U.S. State abbreviation
ZIP_INTPTLAT
Interpolate latitudes – horizontal geographic parameters that form a coordinate system, along with longitudes, to help locate the approximate center of the ZIP code area
ZIP_INTPTLONG
Interpolate longitude – vertical geographic parameters that form a coordinate system, along with latitudes, to help locate the approximate center of the ZIP code area
ALAND_SQMI
Measurement, in square miles, of the land portion of the ZIP code, per Census boundary
AWATER_SQMI
Measurement, in square miles, of the water portion of the ZIP code, per Census boundary. Water area includes inland, coastal, Great Lakes, and territorial seawater.
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
C_Name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
County_fips
The Federal Information Processing Standards (fips) codes issued by the American National Standards Institute to U.S. counties.
Pop_per_SQMI
Population per square mile of ZIP code land area, rounded to full integer
Hhlds_per_SQMI
Households per square mile of ZIP code land area, rounded to full integer
HUs_per_SQMI
Housing units per square mile of ZIP code land area, rounded to full integer
Time zone
AKS: Alaskan Standard Time
CST: Central Standard Time
EST: Eastern Standard Time
HST: Hawaii-Aleutian Standard Time
MST: Mountain Standard Time
PST: Pacific Standard Time
PST/MST: Malheur County of Oregon is divided into two time zones.
Topography
County-based broad categorization of an area’s natural features as classified by the United States Department of Agriculture (Note Alaska and Hawaii were excluded from this survey):
1: Flat plains
2: Smooth plains
3: Irregular plains, slight relief
4: Irregular plains
5: Tablelands, moderate relief
6: Tablelands, considerable relief
7: Tablelands, high relief
8: Tablelands, very high relief
9: Plains with hills
10: Plains with high hills
11: Plains with low mountains
12: Plains with high mountains
13: Open low hills
14: Open hills
15: Open high hills
16: Open low mountains
17: Open high mountains
18: Hills
Census_region
Detailed region the ZIP code falls within based on the classification by the Census Bureau:
R1D1: Region–Northeast, Division 1–New England
R1D2: Region–Northeast, Division 2–Middle Atlantic
R2D3: Region–Midwest, Division 3–East North Central
R2D4: Region–Midwest, Division 4–West North Central
R3D5: Region–South, Division 5–South Atlantic
R3D6: Region–South, Division 6–East South Central
R3D7: Region–South, Division 7–West South Central
R4D8: Region–West, Division 8–Mountain
R4D9: Region–West, Division 9–Pacific
BEA_region
Region the ZIP code falls within based on the state-based classification by the Bureau of Economic Analysis:
1: New England
2: Mideast
3: Great Lakes
4: Plains
5: Southeast
6: Southwest
7: Rocky Mountain
8: Far West
Area_code(s)
A list of area codes commonly present/used/ascribed to in the ZIP code
A3. Voter ProfileKnow your constituents and maximize swing voter conversion. 120+ indicators for 26,000+ ZIP codes. Data include voting-age citizen headcount, racial, gender, and educational background distributions; sources of citizenship (native-born vs. naturalized foreign-born); most-held jobs, highest-compensated occupation, and the highest share of households by income and age groups.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents
2020Tot_Pop_ZIP
Total population by ZIP code as of 2020 Census
Tot_VPOP
Total voter population —voting-age U.S. citizens, native and naturalized combined, age 18 and older
VPOP_%TotPop
%Total population in the ZIP code who are voter population
%VPOP_M
%Voter population who are male
%VPOP_F
%Voter population who are female
%VPOP_NHW
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as Non-Hispanic White
%VPOP_HSP
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as Hispanic
%VPOP_ASN
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as Asian
%VPOP_BLK
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as Black or African-American
%VPOP_AIAN
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as American Indian and Alaska Native
%VPOP_NHPI
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as Native Hawaiian and Pacific Islander
%VPOP_TWM
%Voter population (U.S. citizens, native and naturalized combined, age 18 and older) self-identified as having two or more races/ethnic background
%VPOP_FB
%Voter population who were foreign-born, naturalized citizens age 18 and over
%VPOP_NB
%Voter population who were U.S.-born age 18 and over
Tot_Pop25+
Total population age 25 and over
%Pop25+_
%Total population -age 25 and over- without a high school diploma
%Pop25+_HS
%Total population -age 25 and over- with a high school diploma (including GED) only
%Pop25+_Some/AB
%Total population -age 25 and over- some college (without graduation), up to an Associates degree only
%Pop25+_College+
%Total population -age 25 and over- with a Bachelor’s degree or more
Max_group_EDU
Most representative educational level, among adults age 25 and over, by ZIP code
Max_subgroup_EDU1
Most representative educational level by self-identified race and gender, among adults age 25 and over, by ZIP code
%Max_subgroup_EDU
As % total adult age 25+, the most representative educational level by self-identified race and gender by ZIP code
MAXMEAR$_M_EmpPop16+
Highest median income by occupation, for a male employed civilian age 16 and over; Median income above $250,000 shown as 250000+; Insufficient data shown as “-“.
MAXMEAR$_M_EmpPop16+_Occp2
Occupation with the highest median earnings, for a male employed civilian age 16 and over; *More than one occupation met the criteria
MAXMEAR$_F_EmpPop16+
Highest median income by occupation, for a female employed civilian age 16 and over; Median income above $250,000 shown as 250000+; Insufficient data shown as “-“.
MAXMEAR$_F_EmpPop16+_Occp2
Occupation with the highest median earnings, for a female employed civilian age 16 and over; *More than one occupation met the criteria
TOT_EmpPop16+
Total employed civilian residents age 16 and over
%TOT_EmpPop16+_M
%Total employed civilian residents, age 16 and over, who are male
%TOT_EmpPop16+_F
%Total employed civilian residents, age 16 and over, who are female
MAXOCCP_M_EmpPop16+2
Most representative occupation held by employed male civilian age 16+, by ZIP code
MAXOCCP_M_%MEmpPop16+
Male workers’ most-held occupation as % total employed male civilian age 16+
MAXOCCP_F_EmpPop16+2
Most representative occupation held by employed female civilian age 16+, by ZIP code
MAXOCCP_F_%FEmpPop16+
Female workers’ most-held occupation as % total employed female civilian age 16+
Most representative households, by income and householder age groups, as % total household counts
1. Education by race/ethnicity
NHW_M_
Non-Hispanic White male -age 25 and over- without a high school diploma
NHW_M_HS
Non-Hispanic White male -age 25 and over- with a high school diploma (including GED) only
NHW_M_Some/AB
Non-Hispanic White male -age 25 and over- some college (without graduation), up to an Associates degree only
NHW_M_COLLEGE+
Non-Hispanic White male -age 25 and over- with a Bachelor’s degree or more
NHW_F_
Non-Hispanic White female -age 25 and over- without a high school diploma
NHW_F_HS
Non-Hispanic White female -age 25 and over- with a high school diploma (including GED) only
NHW_F_Some/AB
Non-Hispanic White female -age 25 and over- some college (without graduation), up to an Associates degree only
NHW_F_COLLEGE+
Non-Hispanic White female -age 25 and over- with a Bachelor’s degree or more
HSP_M_
Hispanic male -age 25 and over- without a high school diploma
HSP_M_HS
Hispanic male -age 25 and over- with a high school diploma (including GED) only
HSP_M_Some/AB
Hispanic male -age 25 and over- with some college (without graduation), up to an Associates degree only
HSP_M_COLLEGE+
Hispanic male -age 25 and over- with a Bachelor’s degree or more
HSP_F_
Hispanic female -age 25 and over- without a high school diploma
HSP_F_HS
Hispanic female -age 25 and over- with a high school diploma (including GED) only
HSP_F_Some/AB
Hispanic female -age 25 and over- some college (without graduation), up to an Associates degree only
HSP_F_COLLEGE+
Hispanic female -age 25 and over- with a Bachelor’s degree or more
ASN_M_
Asian male -age 25 and over- without a high school diploma
ASN_M_HS
Asian male -age 25 and over- with a high school diploma (including GED) only
ASN_M_Some/AB
Asian male -age 25 and over- some college (without graduation), up to an Associates degree only
ASN_M_COLLEGE+
Asian male -age 25 and over- with a Bachelor’s degree or more
ASN_F_
Asian female -age 25 and over- without a high school diploma
ASN_F_HS
Asian female -age 25 and over- with a high school diploma (including GED) only
ASN_F_Some/AB
Asian female -age 25 and over- some college (without graduation), up to an Associates degree only
ASN_F_COLLEGE+
Asian female -age 25 and over- with a Bachelor’s degree or more
BLK_M_
Black or African-American male -age 25 and over- without a high school diploma
BLK_M_HS
Black or African-American male -age 25 and over- with a high school diploma (including GED) only
BLK_M_Some/AB
Black or African-American male -age 25 and over- some college (without graduation), up to an Associates degree only
BLK_M_COLLEGE+
Black or African-American male -age 25 and over- with a Bachelor’s degree or more
BLK_F_
Black or African-American female -age 25 and over- without a high school diploma
BLK_F_HS
Black or African-American female -age 25 and over- with a high school diploma (including GED) only
BLK_F_Some/AB
Black or African-American female -age 25 and over- some college (without graduation), up to an Associates degree only
BLK_F_COLLEGE+
Black or African-American female -age 25 and over- with a Bachelor’s degree or more
AIAN_M_
American Indian and Alaska Native male -age 25 and over- without a high school diploma
AIAN_M_HS
American Indian and Alaska Native male -age 25 and over- with a high school diploma (including GED) only
AIAN_M_Some/AB
American Indian and Alaska Native male -age 25 and over- with some college (without graduation), up to an Associates degree only
AIAN_M_COLLEGE+
American Indian and Alaska Native male -age 25 and over- with a Bachelor’s degree or more
AIAN_F_
American Indian and Alaska Native female -age 25 and over- without a high school diploma
AIAN_F_HS
American Indian and Alaska Native female -age 25 and over- with a high school diploma (including GED) only
AIAN_F_Some/AB
American Indian and Alaska Native female -age 25 and over- some college (without graduation), up to an Associates degree only
AIAN_F_COLLEGE+
American Indian and Alaska Native female -age 25 and over- with a Bachelor’s degree or more
NHPI_M_
Native Hawaiian and Pacific Islander male -age 25 and over- without a high school diploma
NHPI_M_HS
Native Hawaiian and Pacific Islander male -age 25 and over- with a high school diploma (including GED) only
NHPI_M_Some/AB
Native Hawaiian and Pacific Islander male -age 25 and over- some college (without graduation), up to an Associates degree only
NHPI_M_COLLEGE+
Native Hawaiian and Pacific Islander male -age 25 and over- with a Bachelor’s degree or more
NHPI_F_
Native Hawaiian and Pacific Islander female -age 25 and over- without a high school diploma
NHPI_F_HS
Native Hawaiian and Pacific Islander female -age 25 and over- with a high school diploma (including GED) only
NHPI_F_Some/AB
Native Hawaiian and Pacific Islander female -age 25 and over- some college (without graduation), up to an Associates degree only
NHPI_F_COLLEGE+
Native Hawaiian and Pacific Islander female -age 25 and over- with a Bachelor’s degree or more
TWM_M_
Male population -with two or more race/ethnicity background age 25 and over- without a high school diploma
TWM_M_HS
Male population -with two or more race/ethnicity background age 25 and over- with a high school diploma (including GED) only
TWM_M_Some/AB
Male population -with two or more race/ethnicity background age 25 and over- some college (without graduation), up to an Associates degree only
TWM_M_COLLEGE+
Male population -with two or more race/ethnicity background age 25 and over- with a Bachelor’s degree or more
TWM_F_
Female population -with two or more race/ethnicity background age 25 and over- without a high school diploma
TWM_F_HS
Female population -with two or more race/ethnicity background age 25 and over- with a high school diploma (including GED) only
TWM_F_Some/AB
Female population -with two or more race/ethnicity background age 25 and over- some college (without graduation), up to an Associates degree only
TWM_F_COLLEGE+
Female population -with two or more race/ethnicity background age 25 and over- with a Bachelor’s degree or more
2. Occupation
WCL_Type1
Management occupations
WCL_Type2
Business and finance occupations
WCL_Type3
Computer and mathematical occupations
WCL_Type4
Architecture and engineering occupations
WCL_Type5
Life, physical, and social science occupations
WCL_Type6
Community and social service occupations
WCL_Type7
Legal occupations
WCL_Type8
Educational instruction, and library occupations
WCL_Type9
Arts, design, entertainment, sports, and media occupations
WCL_Type10
Healthcare practitioners occupation
WCL_Type11
Healthcare technical occupation
PCL_Type1
Healthcare support occupations
PCL_Type2A
Protective service occupations: Firefighting and prevention, and other protective service workers including supervisors
PCL_Type2B
Protective service occupations: Law enforcement workers including supervisors
PCL_Type3
Food preparation and serving related occupations
PCL_Type4
Building and grounds cleaning and maintenance occupations
PCL_Type5
Personal care and service occupations
GCL_Type1
Sales and related occupations
GCL_Type2
Office and administrative support occupations
BCL1_Type1
Farming, fishing, and forestry occupations
BCL1_Type2
Construction and extraction occupations
BCL1_Type3
Installation, maintenance, and repair occupations
BCL2_Type1
Production occupations
BCL2_Type2
Transportation occupations
BCL2_Type3
Material moving occupations
A4. Gen-Z ProfileIdeal dataset for marketing consultants and business strategists. Includes 27,000+ ZIP codes with robust data for financial and demographic data for Gen-Z households whose heads of households are under age 25, and education level for the population between 18-24, as of 2020
Index_code
Index_name
ST
U.S. State abbreviation
LOCATION
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau. Excludes locations with less than 100 population below age 25
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
Tot_Pop_<25
Total population below age 25. Note for this dataset, the Gen-Z profile approximated by extracting characteristics of the population, or heads of households, below the age of 25, as of 2020
%Tot_Pop_<25
Percentage of total population younger than 25
M_Pop_<25
Male population below age 25
F_Pop_<25
Female population below age 25
GenZ_Gender_Ratio_M/F
Male-to-female ratio: population below age 25
Hhlds_Hhldr<25
Total households whose heads of households are younger than 25 years of age
%Hhlds_Hhldr<25
Percentage of households whose heads of households are younger than 25
MHhld$_Hhldr<25
Median income for households whose heads of households are younger than 25 years of age
%Hhldr<25_Hhld$_<25K
Percentage of Gen-Z households with annual income below $25,000
%Hhldr<25_Hhld$_25K-49K
Percentage of Gen-Z households with annual income between $25,000 and $49,999
%Hhldr<25_Hhld$_50K-99K
Percentage of Gen-Z households with annual income between $50,000 and $99,999
%Hhldr<25_Hhld$_100K-199K
Percentage of Gen-Z households with annual income between $100,000 and $199,999
%Hhldr<25_Hhld$_200K+
Percentage of Gen-Z households with annual income of $200,000 or more
Total 18-24
Total population age 18-24
%Pop18-24_HS-AB
Percentage of the population aged 18-24 with a high school diploma (including GED), and some college (without graduation), up to an Associates degree
%Pop18-24_BACH
Percentage of the population aged 18-24 with a Bachelor’s degree
%Pop18-24_GRAD
Percentage of the population aged 18-24 with a graduate degree
%OWNU_Hhldr<25 (Gen-Z)
Owner units as a percentage of total households whose heads of householders are younger than 25
%RTRU_Hhldr<25 (Gen-Z)
Renter units as a percentage of total households whose heads of householders are younger than 25
%Gen-Z_hhlds_NHW
Percentage of Gen-Z households self-identified as Non-Hispanic White
%Gen-Z_hhlds_HSP
Percentage of Gen-Z households self-identified as Hispanic
%Gen-Z_hhlds_ASN
Percentage of Gen-Z households self-identified as Asian
%Gen-Z_hhlds_BLK
Percentage of Gen-Z households self-identified as Black or African-American
%Gen-Z_hhlds_AIAN
Percentage of Gen-Z households self-identified as American Indian and Alaska Native
%Gen-Z_hhlds_NHPI
Percentage of Gen-Z households self-identified as Native Hawaiian and Pacific Islander
%Gen-Z_hhlds_TWM
Percentage of Gen-Z households self-identified as having two or more races/ethnic background
A5. Senior Financial PowerThis dataset provides comprehensive information and analytics on the financial clout of the population aged 65 and older. Indicators include financial status such as shares of senior households without a mortgage, with an annual income of $200,000 and more; average size of, and short-term (one-year) and medium-term (five-year) trending of, social security and retirement incomes; senior population and gender ratio at 65+, 75+, and 85+ age groups. Includes 31,000+ ZIP codes (ZIP codes with 10+ seniors).
Index_code
Index_name
ST
U.S. State abbreviation
LOCATION
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau. Excludes locations with less than ten senior resident population
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
Senior_Pop
Total population age 65 and above
%Pop_65+
Percentage of total population age 65 and above
%Pop_75+
Percentage of total population age 75 and above
%Pop_85+
Percentage of total population age 85 and above
M/F_Ratio_Pop_65+
Male-to-female ratio: senior population age 65 and above
M/F_Ratio_Pop_75+
Male-to-female ratio: senior population age 75 and above
M/F_Ratio_Pop_85+
Male-to-female ratio: senior population age 85 and above
Retiree/Worker_Ratio
Ratio of retired population (age 65+) to working-age population (age 16-64)
%Pop_65+_College+
Percentage of population age 65 and above with a minimum of college education
%Hhlds_Hhldr65+
Percentage of households whose heads of households age 65 and above
Median hhld income (all hhlds)
Median household income for all households
Median hhld income Hhldr65+
Median income for households whose heads of households age 65 and above
%Hhld$_200K+_Hhldr65+
Percentage of households with $200,000+ income have heads of households age 65 and above
Avg_Hhld$_Hhldr65+
Average income for households headed by someone age 65 and above
%XMTG-Hhlds_Hhldr65+
Percentage of homes free-and-clear from mortgage burden that are headed by householders age 65 and above
Avg_Hhld$_Type4
Average household Social Security income; Note: for households receiving SS income; households can have multiple types of income
1YChg_Avg_Hhld$_Type4
One-year change of average household Social Security income
5YChg_Avg_Hhld$_Type4
Five-year change (non annualized) of average household Social Security income
Avg_Hhld$_Type7
Average household retirement income; Note: for households receiving retirement income; households can have multiple types of income
1YChg_Avg_Hhld$_Type7
One-year change of average household retirement income
5YChg_Avg_Hhld$_Type7
Five-year change (non annualized) of average household retirement income
B1. Average Household Income by RaceData for more than 29,000 ZIP codes include average household income (in 2021 inflation-adjusted value) by racial/ethnic background (Hispanic, Non-Hispanic White, African-American, Asian, American Indian and Alaska Native, Native Hawaiian, and Pacific Islanders, or of two-plus background); one-year household income change by race provides a comprehensive measure of how each group fared financially, since COVID-19, on a ZIP code level.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
U.S. State abbreviation
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
2021_AvgHhld$_ALL
Average household income (in 2021 inflation-adjusted USD$) – All households
2021_AvgHhld$_NHW
Average household income (in 2021 inflation-adjusted USD$) – Non-Hispanic White households
2021_AvgHhld$_HSP
Average household income (in 2021 inflation-adjusted USD$) – Hispanic households
2021_AvgHhld$_ASN
Average household income (in 2021 inflation-adjusted USD$) – Asian households
2021_AvgHhld$_BLK
Average household income (in 2021 inflation-adjusted USD$) – Black or African-American households
2021_AvgHhld$_AIAN
Average household income (in 2021 inflation-adjusted USD$) – American Indian and Alaska Native households
2021_AvgHhld$_NHPI
Average household income (in 2021 inflation-adjusted USD$) – Native Hawaiian and Pacific Islander households
2021_AvgHhld$_TWM
Average household income (in 2021 inflation-adjusted USD$) – Two or more races households
1YChg%_AvgHhld$_ALL
One-year percentage change of average household income (2021 over 2020) – All households
1YChg%_AvgHhld$_NHW
One-year percentage change of average household income (2021 over 2020) – Non-Hispanic White households
1YChg%_AvgHhld$_HSP
One-year percentage change of average household income (2021 over 2020) – Hispanic households
1YChg%_AvgHhld$_ASN
One-year percentage change of average household income (2021 over 2020) – Asian households
1YChg%_AvgHhld$_BLK
One-year percentage change of average household income (2021 over 2020) – Black or African-American households
1YChg%_AvgHhld$_AIAN
One-year percentage change of average household income (2021 over 2020) – American Indian and Alaska Native households
1YChg%_AvgHhld$_NHPI
One-year percentage change of average household income (2021 over 2020) – Native Hawaiian and Pacific Islander households
1YChg%_AvgHhld$_TWM
One-year percentage change of average household income (2021 over 2020) – Two or more races households
TotHhlds_ALL
Total households by ZIP code
TotHhlds_NHW
Total households by ZIP code – Non-Hispanic White households
TotHhlds_HSP
Total households by ZIP code – Hispanic households
TotHhlds_ASN
Total households by ZIP code – Asian alone households
TotHhlds_BLK
Total households by ZIP code – Black or African-American households
TotHhlds_AIAN
Total households by ZIP code – American Indian and Alaska Native alone households
TotHhlds_NHPI
Total households by ZIP code – Native Hawaiian and Pacific Islander alone households
TotHhlds_TWM
Total households by ZIP code – Two or more races households
B2. Average Household Income by TypeDetailed average household income by type for more than 26,000 ZIP codes. Income categories include wage and salary earnings, unemployment benefits, workers’ compensation, Social Security, supplemental security income, public assistance, veterans’ payments, survivor benefits, disability benefits, pension or retirement income, interest income, dividends, rents, royalties, estates and trusts, educational assistance, and other types of income such as unemployment benefits, alimony, and child support, and Veterans’ payments. Average household income (in 2019 inflation-adjusted $) is the aggregate income, for the location per Census survey earned in the prior 12 months by household members, divided by the total number of households.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
U.S. State abbreviation
Z_code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_name
County name
Avg_hhld$
Mean household income$
Avg_hhld$_w_Type1
Mean household income$ (from all types of income) for those with wages or salary income; Note: households can have multiple types of income
Avg_hhld$_w_Type2
Mean household income$ (from all types of income) for those with self-employment income; Note: households can have multiple types of income
Avg_hhld$_w_Type3
Mean household income$ (from all types of income) for those with interest, dividends, or net rental income; Note: households can have multiple types of income
Avg_hhld$_w_Type4
Mean household income$ (from all types of income) for those with Social Security income; Note: households can have multiple types of income
Avg_hhld$_w_Type5
Mean household income$ (from all types of income) for those with Supplemental Security Income (SSI); Note: households can have multiple types of income
Avg_hhld$_w_Type6
Mean household income$ (from all types of income) for those with cash public assistance income or Food Stamps/SNAP; Note: households can have multiple types of income
Avg_hhld$_w_Type7
Mean household income$ (from all types of income) for those with retirement income; Note: households can have multiple types of income
Avg_hhld$_w_Type8
Mean household income$ (from all types of income) for those with other types of income such as unemployment benefits, alimony and child support, and Veterans’ payments; Note: households can have multiple types of income
Tot_hhlds
Total households
%hhlds_$Type1
%Households with wages or salary income; Note: households can have multiple types of income
%hhlds_$Type2
%Households with self-employment income; Note: households can have multiple types of income
%hhlds_$Type3
%Households with interest, dividends, or net rental income; Note: households can have multiple types of income
%hhlds_$Type4
%Households with Social Security income; Note: households can have multiple types of income
%hhlds_$Type5
%Households with Supplemental Security Income (SSI); Note: households can have multiple types of income
%hhlds_$Type6
%Households with cash public assistance income or Food Stamps/SNAP; Note: households can have multiple types of income
%hhlds_$Type7
%Households with retirement income; Note: households can have multiple types of income
%hhlds_$Type8
%Households with other types of income (with other types of income such as unemployment benefits, alimony and child support, and Veterans’ payments); Note: households can have multiple types of income
B3. Pay EqualizerKnow your rising living costs and stay ahead of inflation. As a company, how do you retain talent relocating to ZIP codes with a lower cost base? How do you plan to hire workers not based in the corporate home state or city? As an employee, what is your leverage for job relocation? How much discount in pay can you afford to accept? How does your current income compare with the minimum needed to own a home in your dream neighborhood? Empower yourself with data and make intelligent bargains with this data set. Get ready to negotiate. Including proprietary ZIP code-based urban-rural population density measures to aid your pay decision. Featuring GeoIQ’s proprietary Pay Equalizing Value (PEV) — a nifty wage estimation tool that provides estimates for ZIP code-based pay, in targeted, lower-bound, and upper-bound recommended dollar value. Indexed to the pre-tax pay of $100,000 for a wage earner residing and working in ZIP code 10001 (location with the highest numbers of registered business establishments). Metrics factored in to create the PEV (not included in the dataset): federal taxes, relevant state taxes, typical rental cost by ZIP code, and the estimated aggregate miscellaneous cost associated with housing by ZIP code such as broadband internet subscription, electricity, gas, water, heating oil outlay etc.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
U.S. State abbreviation
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
ST_Rk_GeoIQ_PEV
Ranked: by equalized pay in the said ZIP code, within associated states
County_Rk_GeoIQ_PEV
Ranked: by equalized pay in the said ZIP code, within associated counties
ZIP_UR_Scale (ZUR)
Proprietary ZIP code-based urban-rural population density measure:
ZUR_1
1. Metro_500+: ZIP code has at least 500 population per square mile and is part of a metropolitan area
ZUR_2
2. Metro_250-499: ZIP code has 250-499 population per square mile and is part of a metropolitan area
ZUR_3
3. Metro_100-249: ZIP code has 100-249 population per square mile and is part of a metropolitan area
ZUR_4
4. Metro_50-99: ZIP code has less than 25 population per square mile and is part of a metropolitan area
ZUR_5
5. Metro_25-49: ZIP code has 50-99 population per square mile and is part of a metropolitan area
ZUR_6
6. Metro_<25: ZIP code has 25-49 population per square mile and is part of a metropolitan area
ZUR_7
7. Micro_500+: ZIP code has at least 500 population per square mile and is part of a micropolitan area
ZUR_8
8. Micro_250-499: ZIP code has 250-499 population per square mile and is part of a micropolitan area
ZUR_9
9. Micro_100-249: ZIP code has 100-249 population per square mile and is part of a micropolitan area
ZUR_10
10. Micro_50-99: ZIP code has 50-99 population per square mile and is part of a micropolitan area
ZUR_11
11. Micro_25-49: ZIP code has 25-49 population per square mile and is part of a micropolitan area
ZUR_12
12. Micro_<25: ZIP code has less than 25 population per square mile and is part of a micropolitan area
ZUR_13
13. NonM_500+: ZIP code has at least 500 population per square mile and is neither part of metro-, nor a micropolitan area
ZUR_14
14. NonM_250-499: ZIP code has 250-499 population per square mile and is neither part of metro-, nor a micropolitan area
ZUR_15
15. NonM_100-249: ZIP code has 100-249 population per square mile and is neither part of metro-, nor a micropolitan area
ZUR_16
16. NonM_50-99: ZIP code has 50-99 population per square mile and is neither part of metro-, nor a micropolitan area
ZUR_17
17. NonM_25-49: ZIP code has 25-49 population per square mile and is neither part of metro-, nor a micropolitan area
ZUR_18
18. NonM_<25: ZIP code has less than 25 population per square mile and is neither part of metro-, nor a micropolitan area
GeoIQ_PEI
GeoIQ’s Pay Equalizing Index (PEI) is a proprietary escalation tool that provides estimates for ZIP code-based pay, indexed to the pre-tax pay of $100,000 for a wage earner working and residing in ZIP code 10001 (location with the highest numbers of registered business establishments). For indexes customized to your organization’s base pay, contact us at: https://geoiqgroup.net/contact/
GeoIQ_PEV
GeoIQ’s Pay Equalizing Value (PEV) is a proprietary wage estimation tool that provides estimates for ZIP code-based pay —in targeted, as well as the lower-bound, upper-bound recommended dollar value — indexed to the pre-tax pay of $100,000 for a wage earner residing and working in ZIP code 10001; Metrics factored in to create the PEV: federal taxes, relevant state taxes, typical rental cost by ZIP code, and the estimated aggregate miscellaneous cost associated with housing by ZIP code such as broadband internet subscription, electricity, gas, water, heating oil outlay etc.. For pay estimates customized to your organization’s needs, contact us at: https://geoiqgroup.net/contact/
GeoIQ_PEV_LB
Lower-bound recommended pay escalation value for the Pay Equalizing Value
GeoIQ_PEV_UB
Upper-bound recommended pay escalation value for the Pay Equalizing Value
C1. Future of DrivingKnow your local market and target carbon footprint: Including 27,000+ ZIP codes. Analytics to help you understand how the current stock of vehicles is used, and identify potential new buyers. Including shares of households by the number of vehicles they keep; households without a car; portion of cars used for commuting, and length of time they spent on the road; shares of drivers by gender; average tax amount for personal property (cars, boats, motorcycles etc.); car ownership by homeowners vs. renters. Also including the below county-level data: densities of car dealerships and repair shops.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census Bureau
State
Abbreviation of 50 U.S. states and the District of Columbia
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
CAR_per_capita
Number of cars divided by ZIP code total population
CAR_per_hhld
Number of cars divided by ZIP code total households
Agg_CARS
Aggregate total number of cars by ZIP code; including automobiles, vans, and light trucks of one-ton capacity or less that are intended for household members to use
%County_Agg_CARS_ZIP
Number of cars by ZIP code residents as a share of total number of cars in the county
‰State_Agg_CARS_ZIP
Number of cars by ZIP code residents as a share of total number of cars in the state
%Agg_CARS_Commute
%Total number of cars by ZIP code residents used for commuting purposes
%Agg_CARS_OWN_OHU
%Total vehicles in the ZIP code belong to home-owning households
%Agg_CARS_RTR_OHU
%Total vehicles in the ZIP code belong to home-renting households
Tot_Hhlds
Total households
%Tot_Hhlds_XCAR
%Total households without any vehicle
%Tot_Hhlds_1CAR
%Total households with one vehicle
%Tot_Hhlds_2CAR
%Total households with two vehicles
%Tot_Hhlds_3CAR
%Total households with three vehicles
%Tot_Hhlds_MCAR
%Total households with four or more vehicles
Total workers 16+
Total workers age 16 and over
%TotWorkers16+_CAR
%Total workers 16+ drove to work
%TotWorkers16+_CARalone
%Total workers 16+ drove to work —while driving alone
%TotWorkers16+_CARpooled
%Total workers 16+ drove to work —while car-pooling with other individual(s)
5YAvgAggTT_CAR
2016-2020 Five-year average of aggregate time —one-way in minutes— used on traveling for the purpose of driving to work
5YAvg_CARs_commute
2016-2020 Five-year average number of cars used to commute to work
%5YAvg_CARs_commute_M
%2016-2020 Five-year average number of cars used to commute to work —driven by male drivers
%5YAvg_CARs_commute_F
%2016-2020 Five-year average number of cars used to commute to work —driven by female drivers
5YAvgDailyTT_CAR
2016-2020 Five-year average of daily time —one-way in minutes— used on traveling for the purpose of driving to work
MedianAge_ALL
Median age of all resident population combined
MedianAge_NHW
Median age of resident population self-identified as Non-Hispanic White
MedianAge_HSP
Median age of resident population self-identified as Hispanic
MedianAge_ASN
Median age of resident population self-identified as Asian
MedianAge_BLK
Median age of resident population self-identified as Black or African-American
MedianAge_AIAN
Median age of resident population self-identified as American Indian and Alaska Native
MedianAge_NHPI
Median age of resident population self-identified as Native Hawaiian and Pacific Islander
MedianAge_TWM
Median age of resident population self-identified as having two or more races/ethnic background
MHhld$_ALL
Median household income – All households
MHhld$_NHW
Median household income – Non-Hispanic White households
MHhld$_HSP
Median household income – Hispanic households
MHhld$_ASN
Median household income – Asian households
MHhld$_BLK
Median household income – Black or African-American households
MHhld$_AIAN
Median household income – American Indian and Alaska Native households
MHhld$_NHPI
Median household income – Native Hawaiian and Pacific Islander households
MHhld$_TWM
Median household income – Two or more races households
County_CAR_dealers_p10SQMI
County-based indicator: total number of car dealerships —new and old combined— for every ten square miles within the county limit. Note dealerships classified in this category are primarily engaged in retailing vehicles but may also provide repair and maintenace services.
County_CAR_repairs_p10SQMI
County-based indicator: total number of car repair shops for every ten square miles within the county limit. Note shops classified in this category provide repair, maintenace, diagnostic, paint, interior technical services of automobiles.
2019_Avg_PROP$
Average personal property taxes by ZIP code is the total personal property taxes amount, divided by the number of returns with personal property taxes; Based on individual income tax returns filed with the IRS between January 2019-December 2019. Personal property usually refers to movable property (in contrast to immovable ones such as land and house), some of the most common examples include cars, boats, motorcycles and personal planes.
C2. Inflation Reduction Act & Local HomesOn the ZIP code level, what shares of homes were built before 1980, or after the year 2000? Which energy source(s) do local homes tend to rely upon for Winter heating? And, what are the main natural disasters and climate risks on a county level? How many homes might need an energy-efficiency makeover? Take advantage of this comprehensive dataset and make intelligent estimates to maximize the Inflation Reduction Act. Data covers 32,000+ ZIP codes.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
TOT_Hhlds
Total households (ZIP codes with less than ten household counts were excluded)
%OWNU_TOT_Hhlds
Owner-occupied units as a percentage of total households
OWNU
Owner-occupied units
%OWNU_B_<=YR1980
% Owner-occupied units built in 1980 or earlier
%OWNU_B_>=YR2000
% Owner-occupied units built in 2000 or later
%OWNU_B_>=YR2010
% Owner-occupied units built in 2010 or later
%OWNU_B_YR2000-2009
% Owner-occupied units built between 2000 and 2009
%OWNU_B_YR1980-1999
% Owner-occupied units built between 1980-1999
%OWNU_B_YR1960-1979
% Owner-occupied units built between 1960-1979
%OWNU_B_YR1940-1959
% Owner-occupied units built between 1940-1959
%OWNU_B_<=YR1939
% Owner-occupied units built in 1939 or earlier
MAX_OWNU_B%
Predominant shares of time frame during which local homes were built – in percentage
MAX_OWNU_B
Predominant shares of time frame during which local homes were built – in one of the six broad demarcations: 1. Year 2010 or later, 2. 2000-2009, 3. 1980-1999, 4. 1960-1979, 5. 1940-1959, 6. 1939 or earlier. *Equal shares of two or more time frames met the criteria
%HFUEL_Type1
%Total households heated by: utility gas (via underground pipes)
%HFUEL_Type2
%Total households heated by: bottled, tank, or liquid propane gas
%HFUEL_Type3
%Total households heated by: electricity
%HFUEL_Type4
%Total households heated by: fuel oil, kerosene, etc.
%HFUEL_Type5
%Total households heated by: coal or coke
%HFUEL_Type6
%Total households heated by: wood
%HFUEL_Type7
%Total households heated by: solar energy
%HFUEL_Type8
%Total households heated by: other fuel
%HFUEL_Type9
%Total households heated by: no fuel used
Max_%HFUEL_Type
Predominant home-heating fuel of choice – in percentage
MAX_HFUEL_Type
Predominant home-heating fuel of choice – by fuel type.
*MAX_HFUEL_Type
Predominant home-heating fuel of choice – by fuel type. *Equal shares of two or more fuel sources met the criteria
%TOT_RTNS_ECRED
%Total individual tax returns filed that included those claiming energy credits; Based on individual income tax returns filed with the IRS between January 2019-December 2019
2019_Avg_ECRED$
Average energy credits amount by ZIP code is the total energy credits, divided by the number of returns with such a claim; Based on individual income tax returns filed with the IRS between January 2019-December 2019
Hhlds_per_SQMI
ZIP code-based households per square mile of land area, rounded to full integer
The following analytics —derived from the National Risk Index by FEMA— is provided for reference. Each ZIP code is assigned the risk likelihood score linked to that of the county encompassing the ZIP code. For full access, see: https://www.fema.gov/flood-maps/products-tools/national-risk-index.
Possibility of occurrence score:
5=Very High
4=Relatively High
3=Relatively Moderate
2=Relatively Low
1=Very Low
0=Not applicable, no rating, or insufficient data
AVLN_Score=0-5
County-level possibility of occurrence — Avalanche
CFLD_Score=0-5
County-level possibility of occurrence — Coastal Flooding
CWAV_Score=0-5
County-level possibility of occurrence — Cold Wave
DRGT_Score=0-5
County-level possibility of occurrence — Drought
ERQK_Score=0-5
County-level possibility of occurrence — Earthquake
HAIL_Score=0-5
County-level possibility of occurrence — Hail
HRCN_Score=0-5
County-level possibility of occurrence — Hurricane
HWAV_Score=0-5
County-level possibility of occurrence — Heat Wave
ISTM_Score=0-5
County-level possibility of occurrence — Ice Storm
LNDS_Score=0-5
County-level possibility of occurrence — Landslide
LTNG_Score=0-5
County-level possibility of occurrence — Lightning
RFLD_Score=0-5
County-level possibility of occurrence — Riverine Flooding
SWND_Score=0-5
County-level possibility of occurrence — Strong Wind
TRND_Score=0-5
County-level possibility of occurrence — Tornado
TSUN_Score=0-5
County-level possibility of occurrence — Tsunami
VLCN_Score=0-5
County-level possibility of occurrence — Volcanic Activity
WFIR_Score=0-5
County-level possibility of occurrence — Wildfire
WNTW_Score=0-5
County-level possibility of occurrence — Winter Weather
MAX=5_NDSTR_Type
The natural disaster scored as “very high” (score=5) for the county or county equivalents that the ZIP code resides in or is most closely associated with. *More than one type of natural disaster was scored as having very high possibility to occur (score=5) for the county. “-” None scored 5.
C3. Utility ReportHighly detailed and comprehensive data analytics on household utility/energy profiles for 31,000+ ZIP codes. Including energy credits; subscription and/or access to internet services via broadband, cellular, satellite, fiber optic, DSL, and cable; distribution of home heating methods (utility or LP gas, electricity, wood, coal, solar, fuel, or kerosene); homes without complete plumbing or kitchen facilities.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents
2020 Total households
Total households (ZIP codes with less than ten household counts were excluded)
%HFUEL_Type1
%Total households heated by: utility gas (via underground pipes)
%HFUEL_Type2
%Total households heated by: bottled, tank, or liquid propane gas
%HFUEL_Type3
%Total households heated by: electricity
%HFUEL_Type4
%Total households heated by: fuel oil, kerosene, etc.
%HFUEL_Type5
%Total households heated by: coal or coke
%HFUEL_Type6
%Total households heated by: wood
%HFUEL_Type7
%Total households heated by: solar energy
%HFUEL_Type8
%Total households heated by: other fuel
%HFUEL_Type9
%Total households heated by: no fuel used
%Hhlds_XPlmg
%Total households without plumbing (derived from survey question(s) asking to identify the below items in the household: hot and cold running water, bathtub/shower, sink with a faucet, stove/range, refrigerator etc)
OWN_Hhlds_XPlmg
Owner-households without plumbing facilities
RTR_Hhlds_XPlmg
Renter-households without plumbing facilities
%Hhlds_XKitn
%Total households without complete kitchen facilities (derived from survey question(s) asking to identify the below items in the household: hot and cold running water, bathtub/shower, sink with a faucet, stove/range, refrigerator etc)
OWN_Hhlds_XKitn
Owner-households without kitchen facilities
RTR_Hhlds_XKitn
Renter-households without kitchen facilities
2019_TOT_RTNS
Total individual tax returns filed with the IRS between January 2019-December 2019
2019_RTNS_ECRED
Total individual tax returns, that claimed energy credits, filed with the IRS between January 2019-December 2019
%TOT_RTNS_ECRED
%Total individual tax returns filed that included those claiming energy credits
2019_Avg_ECRED$
Average energy credits amount by ZIP code is the total energy credits, divided by the number of returns with such a claim; Based on individual income tax returns filed with the IRS between January 2019-December 2019
%Hhlds_INTSUB
%Households with internet subscription of any type
%Hhlds_INTSUB_DAL_ONLY
%Households with internet subscription – dial-up only subscription
%Hhlds_INTSUB_BDB
%Households with broadband internet subscription of any type
%Hhlds_INTSUB_BDBCEL
%Households with broadband internet subscription – cellular data plan
%Hhlds_INTSUB_BDBOTH
%Households with broadband internet subscription – fiber optic, DSL (digital subscriber line), cable
%Hhlds_INTSUB_BDBSTL
%Households with internet subscription – broadband satellite service
%Hhlds_INT_XSUB
%Households have internet access while without a subscription
%Hhlds_XINT
%Households without any type of internet access
%POP_Poverty
%Population in poverty
%POVPOP_M
%Population in poverty who are male
%POVPOP_F
%Population in poverty who are female
%POVPOP_Age<5
%Population in poverty who are children under the age of five
%POVPOP_Age65+
%Population in poverty who are seniors age 65 and more
MHhld$_ALL
Median household income – All households
C4. Commuting – Profile by CountyCommuting data offers critical information on America’s infrastructure, accommodations needed based on disability, poverty, and language status, and reveals insights regarding public transit budgetary planning, trends on employment choices and preferences, health and emergency response planning, public transit utilization, as well as local real estate market. Including more than 80 indicators for 3,100+ U.S. counties; county-level estimates of total commuters using public transportation, driving to work, and those working from home; percentage of commuters by race/ethnicity; population density and urban-rural level; and inequality gaps impacting commuter characteristics.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
C_code
County_report_geocode
C_name
Full name of the U.S. county including state in which it resides
M_name
Metropolitan area name
R_scale
Scale of rural level based on the cross-attributes of population density and whether or not a county is part of a metropolitan area:
1.M_500+: county has at least 500 population per square mile and is part of a metropolitan area
2.M_250-499: county has 250-499 population per square mile and is part of a metropolitan area
3.M_100-249: county has 100-249 population per square mile and is part of a metropolitan area
4.M_<50-99: county has 50-99 population per square mile and is part of a metropolitan area
5.M_<50: county has less than 50 population per square mile and is part of a metropolitan area
6.NonM_50+: county has at least 50 population per square mile and is not part of a metropolitan area
7.NonM_25-49: county has 25-49 population per square mile and is not part of a metropolitan area
8.NonM_<25: county has less than 50 population per square mile and is not part of a metropolitan area
Pop_density
County population per square mile of county land area, rounded to full integer
TotWorkers16+
Total workers age 16 and over
%TotWorkers16+_BLK
% Total workers age 16+ who are Black/African-American
%TotWorkers16+_ASN
% Total workers age 16+ who are Asian
%TotWorkers16+_HSP
% Total workers age 16+ who are Hispanic
%TotWorkers16+_NHW
% Total workers age 16+ who are Non-Hispanic White
TotWorkers16+_WFH
Total workers age 16+ worked from home
%TotWorkers16+_WFH
% Total workers age 16+ worked from home
%TotWorkers16+_WFH_BLK
% Total workers age 16+ worked from home who are Black/African-American
%TotWorkers16+_WFH_ASN
% Total workers age 16+ worked from home who are Asian
%TotWorkers16+_WFH_HSP
% Total workers age 16+ worked from home who are Hispanic
%TotWorkers16+_WFH_NHW
% Total workers age 16+ worked from home who are Non-Hispanic White
TotWorkers16+_PubTr
Total workers age 16+ went to work by public transportation
%TotWorkers16+_PubTr_ALL
% Total workers age 16+ went to work by public transportation
%TotWorkers16+_PubTr_BLK
% Total workers age 16+ went to work by public transportation who are Black/African-American
%TotWorkers16+_PubTr_ASN
% Total workers age 16+ went to work by public transportation who are Asian
%TotWorkers16+_PubTr_HSP
% Total workers age 16+ went to work by public transportation who are Hispanic
%TotWorkers16+_PubTr_NHW
% Total workers age 16+ went to work by public transportation who are Non-Hispanic White
CommutingPubTr disparity quotient – Non-Hispanic White
EstTotWorkers16+_PubTr_60+
Estimated total workers age 16+ went to work by public transportation who are older (age 60 and above)
EstTotWorkers16+_PubTr_LANGAssist
Estimated total workers age 16+ went to work by public transportation who lack English proficiency
EstTotWorkers16+_PubTr_
Estimated total workers age 16+ went to work by public transportation below poverty threshold
EstTotWorkers16+_PubTr_IST
Estimated total workers age 16+ went to work by public transportation commuted within the state
EstTotWorkers16+_PubTr_IC
Estimated total workers age 16+ went to work by public transportation commuted within county of residence
EstTotWorkers16+_PubTr_OC
Estimated total workers age 16+ went to work by public transportation commuted outside county of residence
EstTotWorkers16+_PubTr_OST
Estimated total workers age 16+ went to work by public transportation commuted outside the state
EstTotWorkers16+_DISAB_PubTr
Estimated total workers age 16+ with at least one disability who went to work by public transportation
%TotWorkers16+_PubTr_60+
% Total workers age 16+ went to work by public transportation who are older (age 60 and above)
%TotWorkers16+_PubTr_LANGAssist
% Total workers age 16+ went to work by public transportation who lack English proficiency
%TotWorkers16+_PubTr_
% Total workers age 16+ went to work by public transportation below poverty threshold
%TotWorkers16+_PubTr_IST
% Total workers age 16+ went to work by public transportation commuted within the state
%TotWorkers16+_PubTr_IC
% Total workers age 16+ went to work by public transportation commuted within county of residence
%TotWorkers16+_PubTr_OC
% Total workers age 16+ went to work by public transportation commuted outside county of residence
%TotWorkers16+_PubTr_OST
% Total workers age 16+ went to work by public transportation commuted outside the state
%TotWorkers16+_DISAB_PubTr
% Total workers age 16+ with at least one disability who went to work by public transportation
%TotWorkers16+_PubTr_TTTW<30
% Total workers age 16+ went to work by public transportation on average spending less than 30 minutes —per trip— to commute
%TotWorkers16+_PubTr_TTTW30-49
% Total workers age 16+ went to work by public transportation on average spending more than 30 minutes but less than one hour —per trip— to commute
%TotWorkers16+_PubTr_TTTW60+
% Total workers age 16+ went to work by public transportation on average spending one hour or more —per trip— to commute
TotWorkers16+_PubTr_TTTWAvg
Average travel time to work —minutes per trip: workers age 16+ went to work by public transportation
Median$_Workers16+_PubTr
Median earnings of workers age 16+ who went to work by public transportation
TotWorkers16+_CTV_alone
Total workers age 16+ went to work driving a car/truck/van alone
TotWorkers16+_CTV_carpooled
Total workers age 16+ went to work carpooling a car/truck/van
%TotWorkers16+_CTV_alone_ALL
% Total workers age 16+ went to work driving a car/truck/van alone
%TotWorkers16+_CTV_carpooled
% Total workers age 16+ went to work carpooling a car/truck/van
%TotWorkers16+_CTValone_BLK
% Total workers age 16+ went to work driving a car/truck/van alone who are Black/African-American
%TotWorkers16+_CTValone_ASN
% Total workers age 16+ went to work driving a car/truck/van alone who are Asian
%TotWorkers16+_CTValone_HSP
% Total workers age 16+ went to work driving a car/truck/van alone who are Hispanic
%TotWorkers16+_CTValone_NHW
% Total workers age 16+ went to work driving a car/truck/van alone who are Non-Hispanic White
TotWorkers16+_CTValone_BLK
Total workers age 16+ went to work driving a car/truck/van alone who are Black/African-American
TotWorkers16+_CTValone_ASN
Total workers age 16+ went to work driving a car/truck/van alone who are Asian
TotWorkers16+_CTValone_HSP
Total workers age 16+ went to work driving a car/truck/van alone who are Hispanic
TotWorkers16+_CTValone_NHW
Total workers age 16+ went to work driving a car/truck/van alone who are Non-Hispanic White
EstTotWorkers16+_CTValone_IST
Estimated total workers age 16+ went to work driving a car/truck/van alone commuted within the state
EstTotWorkers16+_CTValone_IC
Estimated total workers age 16+ went to work driving a car/truck/van alone commuted within county of residence
EstTotWorkers16+_CTValone_OC
Estimated total workers age 16+ went to work driving a car/truck/van alone commuted outside county of residence
EstTotWorkers16+_CTValone_OST
Estimated total workers age 16+ went to work driving a car/truck/van alone commuted outside the state
%TotWorkers16+_CTValone_IST
% Total workers age 16+ went to work driving a car/truck/van alone commuted within the state
%TotWorkers16+_CTValone_IC
% Total workers age 16+ went to work driving a car/truck/van alone commuted within county of residence
%TotWorkers16+_CTValone_OC
% Total workers age 16+ went to work driving a car/truck/van alone commuted outside county of residence
%TotWorkers16+_CTValone_OST
% Total workers age 16+ went to work driving a car/truck/van alone commuted outside the state
TotWorkers16+_CTV_alone_TTTWAvg
Average travel time to work —minutes per trip: workers age 16+ went to work driving a car/truck/van alone
TotWorkers16+_CTV_carpooled_TTTWAvg
Average travel time to work —minutes per trip: workers age 16+ went to work carpooling a car/truck/van
Median$_Workers16+_CTV_alone
Median earnings of workers age 16+ who went to work driving a car/truck/van alone
Median$_Workers16+_CTV_carpooled
Median earnings of workers age 16+ who went to work carpooling a car/truck/van
C5. Digital Access – Profile by CountyMaximize the Bipartisan Infrastructure Bill. This comprehensive dataset includes county-level broadband subscription and at least one computing device; estimates of additional subscriptions needed to achieve 90% and 100% digital access rates. Proprietary digital access disparity gap and quotient further show the severity of the digital divide by race by county.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
C_code
County_report_geocode
C_name
Full name of the U.S. county including state in which it resides
M_name
Metropolitan area name
R_scale
Scale of rural level based on the cross-attributes of population density and whether or not a county is part of a metropolitan area:
1.M_500+: county has at least 500 population per square mile and is part of a metropolitan area
2.M_250-499: county has 250-499 population per square mile and is part of a metropolitan area
3.M_100-249: county has 100-249 population per square mile and is part of a metropolitan area
4.M_<50-99: county has 50-99 population per square mile and is part of a metropolitan area
5.M_<50: county has less than 50 population per square mile and is part of a metropolitan area
6.NonM_50+: county has at least 50 population per square mile and is not part of a metropolitan area
7.NonM_25-49: county has 25-49 population per square mile and is not part of a metropolitan area
8.NonM_<25: county has less than 50 population per square mile and is not part of a metropolitan area
Pop_density
County population per square mile of county land area, rounded to full integer
TotPopH
Total population in households
%PopH_BLK
% Total population in households who are Black/African-American
%PopH_ASN
% Total population in households who are Asian
%PopH_HSP
% Total population in households who are Hispanic
%PopH_NHW
% Total population in households who are Non-Hispanic White
PopHDA_ALL
Total Population in households with computer AND broadband access
%PopHDA_BLK
% All population in households with computer AND broadband access who are Black/African-American
%PopHDA_ASN
% All population in households with computer AND broadband access who are Asian
%PopHDA_HSP
% All population in households with computer AND broadband access who are Hispanic
%PopHDA_NHW
% All population in households with computer AND broadband access who are Non-Hispanic White
DA_gapBLK
Digital access gap – Black/African-American
DA_gapASN
Digital access gap – Asian
DA_gapHSP
Digital access gap – Hispanic
DA_gapNHW
Digital access gap – Non-Hispanic White
DA_qBLK
Digital access disparity quotient – Black/African-American
DA_qASN
Digital access disparity quotient – Asian
DA_qHSP
Digital access disparity quotient – Hispanic
DA_qNHW
Digital access disparity quotient – Non-Hispanic White
%ALLPopH_DA
% All population in households with computer AND broadband access
%BLKPopH_DA
% Black/African-American population in households with computer AND broadband access
%ASNPopH_DA
% Asian population in households with computer AND broadband access
%HSPPopH_DA
% Hispanic population in households with computer AND broadband access
%NHWPopH_DA
% Non-Hispanic White population in households with computer AND broadband access
EstHhlds_INT_subs_100
Estimated broadband subscriptions needed to achieve 100% internet access
EstHhlds_INT_subs_90
Estimated broadband subscriptions needed to achieve 90% internet access
PopH_X-INT|W-CMPT_ALL
Estimated population in households without internet service (but with computer)
PopH_X-INT|W-CMPT_BLK
Estimated population in households without internet service (but with computer) who are Black/African-American
PopH_X-INT|W-CMPT_ASN
Estimated population in households without internet service (but with computer) who are Asian
PopH_X-INT|W-CMPT_HSP
Estimated population in households without internet service (but with computer) who are Hispanic
PopH_X-INT|W-CMPT_NHW
Estimated population in households without internet service (but with computer) who are Non-Hispanic White
PopH_X-INT|W-CMPT_NHOP+AIAn
Estimated population in households without internet service (but with computer) who are Native Hawaiian, other Pacific Islander, American Indian and Alaska Native (alone) combined
EstHhlds_X-CMPT_ALL
Estimated number of households without a computing device
PopH_X-CMPT_ALL
Estimated population in households without a computer
PopH_X-CMPT_BLK
Estimated population in households without a computer who are Black/African-American
PopH_X-CMPT_ASN
Estimated population in households without a computer who are Asian
PopH_X-CMPT_HSP
Estimated population in households without a computer who are Hispanic
PopH_X-CMPT_NHW
Estimated population in households without a computer who are Non-Hispanic White
PopH_X-CMPT_NHOP+AIAn
Estimated population in households without a computer who are Native Hawaiian, other Pacific Islander, American Indian and Alaska Native (alone) combined
EstHhlds_X-INT|W-CMPT_BLK
Estimated number of households without internet service (but with computer) who are Black/African-American
EstHhlds_X-INT|W-CMPT_ASN
Estimated number of households without internet service (but with computer) who are Asian
EstHhlds_X-INT|W-CMPT_HSP
Estimated number of households without internet service (but with computer) who are Hispanic
EstHhlds_X-INT|W-CMPT_NHW
Estimated number of households without internet service (but with computer) who are Non-Hispanic White
EstHhlds_X-CMPT_BLK
Estimated number of households without a computer who are Black/African-American
EstHhlds_X-CMPT_ASN
Estimated number of households without a computer who are Asian
EstHhlds_X-CMPT_HSP
Estimated number of households without a computer who are Hispanic
EstHhlds_X-CMPT_NHW
Estimated number of households without a computer who are Non-Hispanic White
D1. Occupational Pay MatrixIdeal dataset for pay consultants, human resources, immigration services, and labor unions. Take advantage of this exceptionally detailed dataset on ZIP code-level earnings by residents and make pay decisions based on relevant local benchmarks. Including ZIP code-level median annual earnings by gender for various categories
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
MEAR$_TOT_EmpPop16+
Median earnings for employed civilian age 16 and over
MEAR$_M_EmpPop16+
Median earnings for male employed civilian age 16 and over – All occupations
MEAR$_M_EmpPop16+_WCL_Type1
Median earnings for male employed civilian age 16 and over – Management occupations
MEAR$_M_EmpPop16+_WCL_Type2
Median earnings for male employed civilian age 16 and over – Business and finance occupations
MEAR$_M_EmpPop16+_WCL_Type3
Median earnings for male employed civilian age 16 and over – Computer and mathematical occupations
MEAR$_M_EmpPop16+_WCL_Type4
Median earnings for male employed civilian age 16 and over – Architecture and engineering occupations
MEAR$_M_EmpPop16+_WCL_Type5
Median earnings for male employed civilian age 16 and over – Life, physical, and social science occupations
MEAR$_M_EmpPop16+_WCL_Type6
Median earnings for male employed civilian age 16 and over – Community and social service occupations
MEAR$_M_EmpPop16+_WCL_Type7
Median earnings for male employed civilian age 16 and over – Legal occupations
MEAR$_M_EmpPop16+_WCL_Type8
Median earnings for male employed civilian age 16 and over – Educational instruction, and library occupations
MEAR$_M_EmpPop16+_WCL_Type9
Median earnings for male employed civilian age 16 and over – Arts, design, entertainment, sports, and media occupations
MEAR$_M_EmpPop16+_WCL_Type10
Median earnings for male employed civilian age 16 and over – Healthcare practitioners occupation
MEAR$_M_EmpPop16+_WCL_Type11
Median earnings for male employed civilian age 16 and over – Healthcare technical occupation
MEAR$_M_EmpPop16+_PCL_Type1
Median earnings for male employed civilian age 16 and over – Healthcare support occupations
MEAR$_M_EmpPop16+_PCL_Type2A
Median earnings for male employed civilian age 16 and over – Protective service occupations: Firefighting and prevention, and other protective service workers including supervisors
MEAR$_M_EmpPop16+_PCL_Type2B
Median earnings for male employed civilian age 16 and over – Protective service occupations: Law enforcement workers including supervisors
MEAR$_M_EmpPop16+_PCL_Type3
Median earnings for male employed civilian age 16 and over – Food preparation and serving related occupations
MEAR$_M_EmpPop16+_PCL_Type4
Median earnings for male employed civilian age 16 and over – Building and grounds cleaning and maintenance occupations
MEAR$_M_EmpPop16+_PCL_Type5
Median earnings for male employed civilian age 16 and over – Personal care and service occupations
MEAR$_M_EmpPop16+_GCL_Type1
Median earnings for male employed civilian age 16 and over – Sales and related occupations
MEAR$_M_EmpPop16+_GCL_Type2
Median earnings for male employed civilian age 16 and over – Office and administrative support occupations
MEAR$_M_EmpPop16+_BCL1_Type1
Median earnings for male employed civilian age 16 and over – Farming, fishing, and forestry occupations
MEAR$_M_EmpPop16+_BCL1_Type2
Median earnings for male employed civilian age 16 and over – Construction and extraction occupations
MEAR$_M_EmpPop16+_BCL1_Type3
Median earnings for male employed civilian age 16 and over – Installation, maintenance, and repair occupations
MEAR$_M_EmpPop16+_BCL2_Type1
Median earnings for male employed civilian age 16 and over – Production occupations
MEAR$_M_EmpPop16+_BCL2_Type2
Median earnings for male employed civilian age 16 and over – Transportation occupations
MEAR$_M_EmpPop16+_BCL2_Type3
Median earnings for male employed civilian age 16 and over – Material moving occupations
MAXMEAR$_M_EmpPop16+
Highest median income by occupation, for a male employed civilian age 16 and over; Median income above $250,000 shown as 250000+; Insufficient data shown as “-“.
MAXMEAR$_M_EmpPop16+_Occp
Occupation with the highest median earnings, for a male employed civilian age 16 and over; *More than one occupation met the criteria
MEAR$_F_EmpPop16+
Median earnings for female employed civilian age 16 and over – All occupations
MEAR$_F_EmpPop16+_WCL_Type1
Median earnings for female employed civilian age 16 and over – Management occupations
MEAR$_F_EmpPop16+_WCL_Type2
Median earnings for female employed civilian age 16 and over – Business and finance occupations
MEAR$_F_EmpPop16+_WCL_Type3
Median earnings for female employed civilian age 16 and over – Computer and mathematical occupations
MEAR$_F_EmpPop16+_WCL_Type4
Median earnings for female employed civilian age 16 and over – Architecture and engineering occupations
MEAR$_F_EmpPop16+_WCL_Type5
Median earnings for female employed civilian age 16 and over – Life, physical, and social science occupations
MEAR$_F_EmpPop16+_WCL_Type6
Median earnings for female employed civilian age 16 and over – Community and social service occupations
MEAR$_F_EmpPop16+_WCL_Type7
Median earnings for female employed civilian age 16 and over – Legal occupations
MEAR$_F_EmpPop16+_WCL_Type8
Median earnings for female employed civilian age 16 and over – Educational instruction, and library occupations
MEAR$_F_EmpPop16+_WCL_Type9
Median earnings for female employed civilian age 16 and over – Arts, design, entertainment, sports, and media occupations
MEAR$_F_EmpPop16+_WCL_Type10
Median earnings for female employed civilian age 16 and over – Healthcare practitioners occupation
MEAR$_F_EmpPop16+_WCL_Type11
Median earnings for female employed civilian age 16 and over – Healthcare technical occupation
MEAR$_F_EmpPop16+_PCL_Type1
Median earnings for female employed civilian age 16 and over – Healthcare support occupations
MEAR$_F_EmpPop16+_PCL_Type2A
Median earnings for female employed civilian age 16 and over – Protective service occupations: Firefighting and prevention, and other protective service workers including supervisors
MEAR$_F_EmpPop16+_PCL_Type2B
Median earnings for female employed civilian age 16 and over – Protective service occupations: Law enforcement workers including supervisors
MEAR$_F_EmpPop16+_PCL_Type3
Median earnings for female employed civilian age 16 and over – Food preparation and serving related occupations
MEAR$_F_EmpPop16+_PCL_Type4
Median earnings for female employed civilian age 16 and over – Building and grounds cleaning and maintenance occupations
MEAR$_F_EmpPop16+_PCL_Type5
Median earnings for female employed civilian age 16 and over – Personal care and service occupations
MEAR$_F_EmpPop16+_GCL_Type1
Median earnings for female employed civilian age 16 and over – Sales and related occupations
MEAR$_F_EmpPop16+_GCL_Type2
Median earnings for female employed civilian age 16 and over – Office and administrative support occupations
MEAR$_F_EmpPop16+_BCL1_Type1
Median earnings for female employed civilian age 16 and over – Farming, fishing, and forestry occupations
MEAR$_F_EmpPop16+_BCL1_Type2
Median earnings for female employed civilian age 16 and over – Construction and extraction occupations
MEAR$_F_EmpPop16+_BCL1_Type3
Median earnings for female employed civilian age 16 and over – Installation, maintenance, and repair occupations
MEAR$_F_EmpPop16+_BCL2_Type1
Median earnings for female employed civilian age 16 and over – Production occupations
MEAR$_F_EmpPop16+_BCL2_Type2
Median earnings for female employed civilian age 16 and over – Transportation occupations
MEAR$_F_EmpPop16+_BCL2_Type3
Median earnings for female employed civilian age 16 and over – Material moving occupations
MAXMEAR$_F_EmpPop16+
Highest median income by occupation, for a female employed civilian age 16 and over; Median income above $250,000 shown as 250000+; Insufficient data shown as “-“.
MAXMEAR$_F_EmpPop16+_Occp
Occupation with the highest median earnings, for a female employed civilian age 16 and over; *More than one occupation met the criteria
D2. Retail Money MatrixGet ready for Black Friday and stay ahead of the pack with this ZIP code-based dataset. Locate where the money is by various flexible measures —whether it is by shares of households making more than $200,000, the average price tag for real estate taxes, or the many sources of household income. Furthermore, gauge retail penetration and potential using our stores per square mile and aggregate household income scale. Draw informed conclusions on what products and services to offer. Revive retail consumption in every neighborhood.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census Bureau
State
Abbreviation of 50 U.S. states and the District of Columbia
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
Tot_Pop
Total population
Tot_Pop_ZIP%ST
Total population in the ZIP code as % that of state total
Agg_hhld$_ALL
Aggregate total household income
Agg_hhld$_ZIP%ST
Aggregate total household income in the ZIP code as % that of state total
Agg_RET$_ALL
Aggregate real estate taxes paid by ZIP code
RET$_ZIP%ST
Estimated total real estate taxes paid by ZIP code as % that of state estimated total
Tot_hhlds
Total households
Tot_Hhlds_ZIP%ST
Total households, within the ZIP code, as a percentage of state total household counts
Tot_Retails_ZIP
Total retail establishments by ZIP code
Retails_per_SQMI
Retail concentration – establishments for each square mile of land area of each ZIP code
Retails_per_1K_Pop
Retail concentration – establishments for every one thousand of each ZIP code’s resident population
Retails_per_100M_Agg_hhld$
Retail concentration – establishments for every $100 million of each ZIP code’s aggregate household income
Retails_per_1M_Agg_RET$
Retail concentration – establishments for each one million of each ZIP code’s aggregate real estate taxes paid
Tot_Retails_COUNTY
Total retail establishments by county
Retail_ZIP%COUNTY
Total retail establishments by ZIP code as % that of county total
%Agg_hhld$_NHW
% aggregate household income by Non-Hispanic White households
%Agg_hhld$_HSP
% aggregate household income by Hispanic households
%Agg_hhld$_ASN
% aggregate household income by Asian households
%Agg_hhld$_BLK
% aggregate household income by Black or African-American households
%Agg_hhld$_AIAN
% aggregate household income by American Indian and Alaska Native households
%Agg_hhld$_NHPI
% aggregate household income by Native Hawaiian and Pacific Islander households
%Agg_hhld$_TWM
% aggregate household income by Two or more races households
%ALL_Hhlds_$200K+
% households with more than $200,000 household income
MHhld$_ALL
Median household income – all households; Note: median income above $250,000 shown as 250000+
MHhld$_NHW
Median household income – Non-Hispanic White households; Note: median income above $250,000 shown as 250000+
MHhld$_HSP
Median household income – Hispanic households; Note: median income above $250,000 shown as 250000+
MHhld$_ASN
Median household income – Asian households; Note: median income above $250,000 shown as 250000+
MHhld$_BLK
Median household income – Black or African-American households; Note: median income above $250,000 shown as 250000+
MHhld$_AIAN
Median household income – American Indian and Alaska Native households; Note: median income above $250,000 shown as 250000+
MHhld$_NHPI
Median household income – Native Hawaiian and Pacific Islander households; Note: median income above $250,000 shown as 250000+
MHhld$_TWM
Median household income – Two or more races households; Note: median income above $250,000 shown as 250000+
%Agg_hhld$_Type1
% aggregate household incomes sourced from wages or salary income; Note: households can have multiple types of income
%Agg_hhld$_Type2
% aggregate household incomes sourced from self-employment income; Note: households can have multiple types of income
%Agg_hhld$_Type3
% aggregate household incomes sourced from interest, dividends, or net rental income; Note: households can have multiple types of income
%Agg_hhld$_Type4
% aggregate household incomes sourced from Social Security income; Note: households can have multiple types of income
%Agg_hhld$_Type5
% aggregate household incomes sourced from Supplemental Security Income (SSI); Note: households can have multiple types of income
%Agg_hhld$_Type6
% aggregate household incomes sourced from public assistance income or Food Stamps/SNAP; Note: households can have multiple types of income
%Agg_hhld$_Type7
% aggregate household incomes sourced from retirement income; Note: households can have multiple types of income
%Agg_hhld$_Type8
% aggregate household incomes sourced from other types of income such as unemployment benefits, alimony and child support, and Veterans’ payments; Note: households can have multiple types of income
%Hhlds_Income_Type1
% households with wages or salary income; Note: households can have multiple types of income
%Hhlds_Income_Type2
% households with self-employment income; Note: households can have multiple types of income
%Hhlds_Income_Type3
% households with interest, dividends, or net rental income; Note: households can have multiple types of income
%Hhlds_Income_Type4
% households with Social Security income; Note: households can have multiple types of income
%Hhlds_Income_Type5
% households with Supplemental Security Income (SSI); Note: households can have multiple types of income
%Hhlds_Income_Type6
% households with public assistance income or Food Stamps/SNAP; Note: households can have multiple types of income
%Hhlds_Income_Type7
% households with retirement income; Note: households can have multiple types of income
%Hhlds_Income_Type8
% households with other types of income such as unemployment benefits, alimony and child support, and Veterans’ payments; Note: households can have multiple types of income
D3. Tech Workers – Job and PayEssential dataset for tech industry HRs and recruiters. Make intelligent job-hunting, relocation, and salary estimates based on relevant local benchmarks. Find out: 1. Location-based (25,000+ ZIP codes available) trending of STEM/Tech/IT jobs in the pre-Pandemic three-year period, and the momentum between 2020-2021; 2. How much has the median pay changed; 3. Know your odds of hiring, by checking out total tech workers by ZIP code, gender ratio, and tech workforce significance —as a share of all local workers. Included as broad STEM occupational groups in this dataset: computer, engineering, mathematical, physics, life science, architecture, and social science.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
TOT_EmpPop16+_STEM
Total civilian residents age 16 and over, full- or part-time combined, employed in science, technology, engineering and mathematics (STEM/TECH) jobs
1YChg%_STEM_Jobs
2020-2021 One-year change in headcounts: civilian residents age 16 and over in (STEM/TECH) jobs, regardless of full- or part-time employment status
3YGTrend_Pre-Pandemic (2016-2019)
Pre-Pandemic 2016-2019 three-year trend in local STEM/TECH job-holders count
%STEM_Jobs_M
Share of total STEM/TECH job holders who are male, regardless of full- or part-time employment status
%STEM_Jobs_F
Share of total STEM/TECH job holders who are female, regardless of full- or part-time employment status
STEM_%ALL
Share of STEM/TECH job holders as a percentage of total employed civilian residents age 16 and over, regardless of full- or part-time employment status
MEAR$_EmpPop16+_STEM
2021 Median earnings for civilians age 16 and over, employed full-time, year-round, in science, technology, engineering and mathematics (STEM/TECH) occupations. Note median income above $250,000 shown as 250000+; median income below the 2021 one-person federal poverty guideline —determined by the U.S. Department of Health and Human Services— are labeled as “BPL”. The poverty threshold for one person is $16,090 in Alaska, $14,820 in Hawaii, $12,880 in the 48 contiguous states and the District of Columbia.
MEAR$_EmpPop16+_STEM_Type1
2021 Median earnings for civilians age 16 and over, employed full-time, year-round – Computer and mathematical occupations. Note median income above $250,000 shown as 250000+; median income below the 2021 one-person federal poverty guideline —determined by the U.S. Department of Health and Human Services— are labeled as “BPL”. The poverty threshold for one person is $16,090 in Alaska, $14,820 in Hawaii, $12,880 in the 48 contiguous states and the District of Columbia.
MEAR$_EmpPop16+_STEM_Type2
2021 Median earnings for civilians age 16 and over, employed full-time, year-round – Architecture and engineering occupations. Note median income above $250,000 shown as 250000+; median income below the 2021 one-person federal poverty guideline —determined by the U.S. Department of Health and Human Services— are labeled as “BPL”. The poverty threshold for one person is $16,090 in Alaska, $14,820 in Hawaii, $12,880 in the 48 contiguous states and the District of Columbia.
MEAR$_EmpPop16+_STEM_Type3
2021 Median earnings for civilians age 16 and over, employed full-time, year-round – Life, physical, and social science occupations. Note median income above $250,000 shown as 250000+; median income below the 2021 one-person federal poverty guideline —determined by the U.S. Department of Health and Human Services— are labeled as “BPL”. The poverty threshold for one person is $16,090 in Alaska, $14,820 in Hawaii, $12,880 in the 48 contiguous states and the District of Columbia.
1YChg%_MEAR$_EmpPop16+_STEM
2020-2021 change in median earnings for civilians age 16 and over, employed full-time, year-round, in science, technology, engineering and mathematics (STEM/TECH) occupations
1YChg%_MEAR$_EmpPop16+_STEM_Type1
2020-2021 change in median earnings for civilians age 16 and over, employed full-time, year-round – Computer and mathematical occupations
1YChg%_MEAR$_EmpPop16+_STEM_Type2
2020-2021 change in median earnings for civilians age 16 and over, employed full-time, year-round – Architecture and engineering occupations
1YChg%_MEAR$_EmpPop16+_STEM_Type3
2020-2021 change in median earnings for civilians age 16 and over, employed full-time, year-round – Life, physical, and social science occupations
D4. Blue-Collar Worker GridZIP code-level data that helps you maximize the Inflation Reduction Act: gauge the size of the local blue-collar workforce, define “good-paying” construction and manufacturing jobs, and estimate total vehicles used for commuting by blue-collar workers. Including the size of the local construction and manufacturing workforce; those who drive to work; breakdown by gender, by race; predominant blue-collar wages; and pay comparison by industry.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
TOT_EmpPop16+
Total employed civilian residents age 16 and over
%TOT_EmpPop16+_M
%Total employed civilian residents age 16 and over who are male
%TOT_EmpPop16+_F
%Total employed civilian residents age 16 and over who are female
TOT_EmpPop16+_CNS+MAN
Total civilian residents age 16 and over employed in construction and manufacturing industries combined (ZIP codes with less than ten workers in theose two industries were excluded from this dataset)
%TOT_EmpPop16+_CNS
%Total age 16+ employed civilian residents in the construction industry
%TOT_EmpPop16+_MAN
%Total age 16+ employed civilian residents in the manufacturing industry
%TOT_EmpPop16+_CNS+MAN
%Total age 16+ employed civilian residents worked in construction and manufacturing industries combined
%Tot_CNS+MAN_M
%Total construction and manufacturing workers who are male; Workers refer to employed civilian residents age 16+
%Tot_CNS+MAN_F
%Total construction and manufacturing workers who are female; Workers refer to employed civilian residents age 16+
%TOT_EmpPop16+_CNS+MAN_FTYR
Share of construction and manufacturing workers worked full-time, year-round
TotWorkers16+_M_CNS
Total male workers in the construction industry, age 16+ and regardless of part-time, full-time status
TotWorkers16+_F_CNS
Total female workers in the construction industry, age 16+ and regardless of part-time, full-time status
Job_Ratio_M/F_CNS
Male-to-female ratio: construction industry job-holders at ZIP code resident-level
TotWorkers16+_M_MAN
Total male workers in the manufacturing industry, age 16+ and regardless of part-time, full-time status
TotWorkers16+_F_MAN
Total female workers in the manufacturing industry, age 16+ and regardless of part-time, full-time status
Job_Ratio_M/F_MAN
Male-to-female ratio: manufacturing industry job-holders at ZIP code resident level
MEAR$_FTYR_EmpPop16+_ALL
Median earnings for civilians, age 16 and over, employed full-time, year-round — all industries; Note median income above $250,000 shown as 250000+.
MEAR$_FTYR_EmpPop16+_CNS
Local predominant construction industry worker pay: Median earnings for civilians, age 16+ employed full-time, year-round — in the construction industry; Note median income above $250,000 shown as 250000+.
Pay_Ratio_ZIP/ST_CNS
Median construction industry pay ratio: resident workers by ZIP code vs. by state
MEAR$_FTYR_EmpPop16+_MAN
Local predominant manufacturing industry worker pay: Media earnings for civilians, age 16+ employed full-time, year-round — in the manufacturing industry; Note median income above $250,000 shown as 250000+.
Pay_Ratio_ZIP/ST_MAN
Median manufacturing industry pay ratio: resident workers by ZIP code vs. by state
MEAR$_M_FTYR_EmpPop16+_CNS
Median construction industry earnings for male workers, age 16+ employed full-time, year-round; Note median income above $250,000 shown as 250000+.
MEAR$_F_FTYR_EmpPop16+_CNS
Median construction industry earnings for female workers, age 16+ employed full-time, year-round; Note median income above $250,000 shown as 250000+.
Pay_Ratio_M/F_CNS
Male-to-female median pay ratio for workers in the construction industry; ZIP code resident level
MEAR$_M_FTYR_EmpPop16+_MAN
Median manufacturing industry earnings for male workers, age 16+ employed full-time, year-round; Note median income above $250,000 shown as 250000+.
MEAR$_F_FTYR_EmpPop16+_MAN
Median manufacturing industry earnings for female workers, age 16+ employed full-time, year-round; Note median income above $250,000 shown as 250000+.
Pay_Ratio_M/F_MAN
Male-to-female median pay ratio for workers in the manufacturing industry; ZIP code-level
Median age, 16-64, ALL
Median age for the working-age population (age 16-64) by ZIP code
Median age, 16-64, MALE
Median age for the male working-age (age 16-64) population by ZIP code
Median age, 16-64, FEMALE
Median age for the female working-age (age 16-64) population by ZIP code
%CNS_Workers_CAR
%Construction industry workers drive to work; Including those who car-pooled to commute to workplace
%CNS_Workers_PTR
%Construction industry workers commuted to work via public transportation
%CNS_Workers_OTH
%Construction industry workers commuted to work via all other means combined (walking, bicycling, work-from-home, taxi etc.)
CARS_CNS_commuting
Estimated total vehicles (including cars, trucks, vans) used for commuting by workers in the construction industry
%MAN_Workers_CAR
%Manufacturing industry workers drive to work; Including those who car-pooled to commute to workplace
%MAN_Workers_PTR
%Manufacturing industry workers commuted to work via public transportation
%MAN_Workers_OTH
%Manufacturing industry workers commuted to work via all other means combined (walking, bicycling, work-from-home, taxi etc.)
CARS_MAN_commuting
Estimated total vehicles (including cars, trucks, vans) used for commuting by workers in the manufacturing industry
EmpPop16+_BCL
Estimated total blue-collar workers by ZIP code: civilians age 16 and more, in broadly defined blue-collar occupations including natural resources, construction, maintenance, production, transportation, and material moving occupations; Note not all blue-collar occupation-holders work in either construction or manufacturing industry, nor are all construction and manufacturing industry workers classified as holding blue-collar occupations.
%ST_EmpPop16+_BCL
%Total employed civilian residents age 16+ holding blue-collar jobs
%EmpPop16+_BCL_NHW
%Est. blue-collar job-holders in the ZIP code self-identified as Non-Hispanic White
%EmpPop16+_BCL_HSP
%Est. blue-collar job-holders in the ZIP code self-identified as Hispanic
%EmpPop16+_BCL_ASN
%Est. blue-collar job-holders in the ZIP code self-identified as Asian
%EmpPop16+_BCL_BLK
%Est. blue-collar job-holders in the ZIP code self-identified as Black or African-American
%EmpPop16+_BCL_AIAN
%Est. blue-collar job-holders in the ZIP code self-identified as American Indian and Alaska Native
%EmpPop16+_BCL_NHPI
%Est. blue-collar job-holders in the ZIP code self-identified as Native Hawaiian and Pacific Islander
%EmpPop16+_BCL_TWM
%Est. blue-collar job-holders in the ZIP code self-identified as having two or more races/ethnic background
E1. Health CoverageDemographics analytics to aid decision-makers in identifying the hot spots of coverage insecurity. Including ZIP code-level health coverage for the general population, by insurance programs (employed-sponsored, direct purchase, Medicare, Medicaid/means-tested public coverage, TRICARE/military health coverage, and VA Health Care), racial/ethnic background, household income, disability status, and whether a civilian worked full-time, part-time or didn’t work. Data covers 26,000+ ZIP codes.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
U.S. State abbreviation
Z_code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
Tot_CPOP
Total civilian noninstitutionalized population
%Tot_CPOP_XHCOV
Total civilian noninstitutionalized population without health insurance coverage
%Tot_CPOP_HCOV
Total civilian noninstitutionalized population with health insurance coverage
%Tot_CPOP_1HCOV
%civilian noninstitutionalized population with one type of health insurance coverage
%Tot_CPOP_2HCOV
%civilian noninstitutionalized population with two or more types of health insurance coverage
%PopHhlds_<$25K_XHCov
%civilian noninstitutionalized population — in households making less than $25,000 — without health insurance
%PopHhlds_$25K-49.9_XHCov
%civilian noninstitutionalized population — in households making between $25,000-$49,999 — without health insurance
%PopHhlds_$50K-74.9_XHCov
%civilian noninstitutionalized population — in households making between $50,000-$74,999 — without health insurance
%PopHhlds_$75K-99.9_XHCov
%civilian noninstitutionalized population — in households making between $75,000-$99,999 — without health insurance
%PopHhlds_$100K+_XHCov
%civilian noninstitutionalized population — in households making more than $100,000 — without health insurance
Tot_CPOP_1HCOV
Total civilian noninstitutionalized population with one type of health insurance coverage
%Tot_CPOP_1HCOV_EMPB
% civilian noninstitutionalized population with one type of health insurance coverage: employer-based health insurance only
%Tot_CPOP_1HCOV_DIRP
%civilian noninstitutionalized population with one type of health insurance coverage: direct-purchase health insurance only
%Tot_CPOP_1HCOV_MCAR
%civilian noninstitutionalized population with one type of health insurance coverage: Medicare coverage only
%Tot_CPOP_1HCOV_MAID
%civilian noninstitutionalized population with one type of health insurance coverage: Medicaid/means-tested public coverage only
%Tot_CPOP_1HCOV_TRIC
%civilian noninstitutionalized population with one type of health insurance coverage: TRICARE/military health coverage only
%Tot_CPOP_1HCOV_VAHC
%civilian noninstitutionalized population with one type of health insurance coverage: VA Health Care only
Tot_CPOP_2HCOV
Total civilian noninstitutionalized population with two or more types of health insurance coverage
Tot_CPOP65+_2HCOV
Total ivilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage
%Tot_CPOP65+_2HCOV_EMPB+DIRP
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: employer-based and direct-purchase coverage
%Tot_CPOP65+_2HCOV_EMPB+MCAR
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: employer-based and Medicare coverage
%Tot_CPOP65+_2HCOV_DIRP+MAID
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: direct-purchase and Medicare coverage
%Tot_CPOP65+_2HCOV_MCAR+MAID
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: Medicare and Medicaid/means-tested public coverage
%Tot_CPOP65+_2HCOV_OTH-PRIC
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: other private only combinations
%Tot_CPOP65+_2HCOV_OTH-PUBC
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: other public only combinations
%Tot_CPOP65+_2HCOV_OTH-HCOV
%civilian noninstitutionalized population age 65 and older with two or more types of health insurance coverage: other coverage combinations
XCITN_CPOP
Noncitizen civilian noninstitutionalized population; Note naturalized foreign-born population excluded
%XCIT_CPOP_XHCOV
%Noncitizen civilian noninstitutionalized population without health insurance
%Tot_CPOP_19-64_FTYR_XHCOV
%Total civilian noninstitutionalized population 19-64 worked full-time, year-round: without health insurance
%Tot_CPOP_19-64_LFTYR_XHCOV
%Total civilian noninstitutionalized population 19-64 worked less than full-time, year-round: without health insurance
%Tot_CPOP_19-64_XWORK_XHCOV
%Total civilian noninstitutionalized population 19-64 who did not work: without health insurance
TCPop_Disability
Civilian noninstitutionalized population with a disability
%Disability_TCPop
%civilian noninstitutionalized population with at least one disability
Tot_disabled_XHCOV
% civilian noninstitutionalized population, with a disability, without health insurance
%PRICOV_Tot_disabled_HCOV
% civilian noninstitutionalized population, with a disability, with private health insurance coverage
%PUBCOV_Tot_disabled_HCOV
% civilian noninstitutionalized population, with a disability, with public health insurance coverage
%NHW_CPop_HCOV
%Non-Hispanic White civilian noninstitutionalized population with health insurance
%HSP_CPop_HCOV
%Hispanic civilian noninstitutionalized population with health insurance
%ASN_CPop_HCOV
%Asian civilian noninstitutionalized population with health insurance
%BLK_CPop_HCOV
%Black or African-American civilian noninstitutionalized population with health insurance
%AIAN_CPop_HCOV
%American Indian and Alaska Native civilian noninstitutionalized population with health insurance
%NHPI_CPop_HCOV
%Native Hawaiian and Pacific Islander civilian noninstitutionalized population with health insurance
%TWM_CPop_HCOV
%Two or more races civilian noninstitutionalized population with health insurance
E2. DisabilityExceptionally detailed demographics custom-made to help decision-makers map out the economics of disability and spotlight the neglected intersection of race, age, poverty, and disability. ZIP code-level distribution by the types of disability —i.e. ambulatory, cognitive, self-care, hearing, and vision; disability status by each race/ethnicity in broadly categorized age groups; racial group(s) by age with the highest disabled headcount; percentages of disabled by employment status; the fraction of disabled population also inflicted with poverty. Data cover 25,000+ ZIP codes with disabled population data.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census Bureau
State
Abbreviation of 50 U.S. states and the District of Columbia
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
Tot_CPOP
Total civilian noninstitutionalized population (excludes ZIP codes with zero civilian population)
Tot_CPOP_Disability
Total civilian noninstitutionalized population with a disability (excludes ZIP codes with zero disabled civilian population)
%Disability_Tot_CPOP
% Total civilian noninstitutionalized population with a disability
Disability_M/F_Ratio
Male-to-female ratio: civilian population with disability
Disability_CPOP_<18
Civilian noninstitutionalized population age below 18 with a disability
Disability_CPOP_18-64
Civilian noninstitutionalized population age 18-64 with a disability
Disability_CPOP_65+
Civilian noninstitutionalized population age 65 and over with a disability
%Disability_Tot_CPOP_<18
% Civilian noninstitutionalized population age below 18 with a disability
%Disability_Tot_CPOP<18-64
% Civilian noninstitutionalized population age 18-64 with a disability
%Disability_Tot_CPOP_65+
% Civilian noninstitutionalized population age 65 and over with a disability
%Tot_Disability_XHCOV
% Total civilian noninstitutionalized population with a disability without any type of health coverage
%Tot_Disability_PRIC
% Disabled civilian noninstitutionalized population with PRIVATE health coverage; Note more than one type of insurance may exist
%Tot_Disability_PUBC
% Disabled civilian noninstitutionalized population with PUBLIC health coverage; Note more than one type of insurance may exist
Disability_<18_NHW
Non-Hispanic White civilian noninstitutionalized population age below 18 with a disability
Disability_19-64_NHW
Non-Hispanic White civilian noninstitutionalized population age 18-64 with a disability
Disability_65+_NHW
Non-Hispanic White civilian noninstitutionalized population age 65 and over with a disability
Disability_<18_HSP
Hispanic civilian noninstitutionalized population age below 18 with a disability
Disability_19-64_HSP
Hispanic civilian noninstitutionalized population age 18-64 with a disability
Disability_65+_HSP
Hispanic civilian noninstitutionalized population age 65 and over with a disability
Disability_<18_ASN
Asian civilian noninstitutionalized population age below 18 with a disability
Disability_19-64_ASN
Asian civilian noninstitutionalized population age 18-64 with a disability
Disability_65+_ASN
Asian civilian noninstitutionalized population age 65 and over with a disability
Disability_<18_BLK
Black/African-American civilian noninstitutionalized population age below 18 with a disability
Disability_19-64_BLK
Black/African-American civilian noninstitutionalized population age 18-64 with a disability
Disability_65+_BLK
Black/African-American civilian noninstitutionalized population age 65 and over with a disability
Disability_<18_AIAN
American Indian/Alaska Native civilian noninstitutionalized population age below 18 with a disability
Disability_19-64_AIAN
American Indian/Alaska Native civilian noninstitutionalized population age 18-64 with a disability
Disability_65+_AIAN
American Indian/Alaska Native civilian noninstitutionalized population age 65 and over with a disability
Disability_<18_NHPI
Native Hawaiian/Pacific Islander civilian noninstitutionalized population age below 18 with a disability
Disability_19-64_NHPI
Native Hawaiian/Pacific Islander civilian noninstitutionalized population age 18-64 with a disability
Disability_65+_NHPI
Native Hawaiian/Pacific Islander civilian noninstitutionalized population age 65 and over with a disability
Disability_<18_TWM
Civilian noninstitutionalized population, with two or more racial backgrounds, age below 18 with a disability
Disability_19-64_TWM
Civilian noninstitutionalized population, with two or more racial backgrounds, age 18-64 with a disability
Disability_65+_TWM
Civilian noninstitutionalized population, with two or more racial backgrounds, age 65 and over with a disability
MAX_Disability_Pop
Race/ethnicity group by age with the highest headcount of disabled civilian population. ZIP codes with insufficient data shown as “-“; *More than one race/ethnicity met the criteria.
Disability_%Pop_<18_NHW
% Non-Hispanic White civilian noninstitutionalized population age below 18 with a disability
Disability_%Pop_18-64_NHW
% Non-Hispanic White civilian noninstitutionalized population age 18-64 with a disability
Disability_%Pop_65+_NHW
% Non-Hispanic White civilian noninstitutionalized population age 65 and over with a disability
Disability_%Pop_<18_HSP
% Hispanic civilian noninstitutionalized population age below 18 with a disability
Disability_%Pop_18-64_HSP
% Hispanic civilian noninstitutionalized population age 18-64 with a disability
Disability_%Pop_65+_HSP
% Hispanic civilian noninstitutionalized population age 65 and over with a disability
Disability_%Pop_<18_ASN
% Asian civilian noninstitutionalized population age below 18 with a disability
Disability_%Pop_18-64_ASN
% Asian civilian noninstitutionalized population age 18-64 with a disability
Disability_%Pop_65+_ASN
% Asian civilian noninstitutionalized population age 65 and over with a disability
Disability_%Pop_<18_BLK
% Black/African-American civilian noninstitutionalized population age below 18 with a disability
Disability_%Pop_18-64_BLK
% Black/African-American civilian noninstitutionalized population age 18-64 with a disability
Disability_%Pop_65+_BLK
% Black/African-American civilian noninstitutionalized population age 65 and over with a disability
Disability_%Pop_<18_AIAN
% American Indian/Alaska Native civilian noninstitutionalized population age below 18 with a disability
Disability_%Pop_18-64_AIAN
% American Indian/Alaska Native civilian noninstitutionalized population age 18-64 with a disability
Disability_%Pop_65+_AIAN
% American Indian/Alaska Native civilian noninstitutionalized population age 65 and over with a disability
Disability_%Pop_<18_NHPI
% Native Hawaiian/Pacific Islander civilian noninstitutionalized population age below 18 with a disability
Disability_%Pop_18-64_NHPI
% Native Hawaiian/Pacific Islander civilian noninstitutionalized population age 18-64 with a disability
Disability_%Pop_65+_NHPI
% Native Hawaiian/Pacific Islander civilian noninstitutionalized population age 65 and over with a disability
Disability_%Pop_<18_TWM
% Civilian noninstitutionalized population, with two or more racial backgrounds, age below 18 with a disability
Disability_%Pop_18-64_TWM
% Civilian noninstitutionalized population, with two or more racial backgrounds, age 18-64 with a disability
Disability_%Pop_65+_TWM
% Civilian noninstitutionalized population, with two or more racial backgrounds, age 65 and over with a disability
MAX_Disability_%Pop_<18
Race/ethnicity group with the highest share of disabled civilian population under 18. ZIP codes with insufficient data shown as “-“; *More than one race/ethnicity met the criteria.
MAX_Disability_%Pop_18-64
Race/ethnicity group with the highest share of disabled civilian population between age 18-64. ZIP codes with insufficient data shown as “-“; *More than one race/ethnicity met the criteria.
MAX_Disability_%Pop_65+
Race/ethnicity group with the highest share of disabled civilian population age 65 and over. ZIP codes with insufficient data shown as “-“; *More than one race/ethnicity met the criteria.
Tot_CPOP5+
Civilian noninstitutionalized population age 5 and over
% Tot_CPOP5+_ HEARd
% Civilian noninstitutionalized population, age 5 and over, with hearing difficulty
% Tot_CPOP5+_ VISNd
% Civilian noninstitutionalized population, age 5 and over, with vision difficulty
% Tot_CPOP5+_ COGNd
% Civilian noninstitutionalized population, age 5 and over, with cognitive difficulty
% Tot_CPOP5+_ AMBLd
% Civilian noninstitutionalized population, age 5 and over, with ambulatory difficulty
% Tot_CPOP5+_ SFCRd
% Civilian noninstitutionalized population, age 5 and over, with self-care difficulty
%TOT_EmpCPop18-64_disability
% Total employed civilian population, between 18-64, with a disability
%TOT_XEmpCPop18-64_disability
% Total unemployed civilian population, between 18-64, with a disability
%XLF_CPOP18-64_disability
% Total civilian population not in the labor force, between 18-64, with a disability
%Disability_<18_POV
% Total disabled civilian population under 18 living in poverty
%Disability_18-64_POV
% Total disabled civilian population between 18-64 living in poverty
%Disability_65+_POV
% Total disabled civilian population age 65 and more living in poverty
F1. PovertyAmerica confronts hundreds and thousands of ZIP codes where poverty is pervasive. Identify where poverty is with this exceptionally detailed ZIP code-level dataset. Data for more than 26,000 ZIP codes. Including poverty ratio, poverty distribution by gender, age group, racial/ethnic background, low-income households without health coverage, and by disability status. Also identified as poverty-coupling indicators: share of households without internet access, computer or cell phone, functioning plumbing, or kitchen.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
Abbreviation of U.S. states (includes DC)
Z_code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_name
ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_name
Name for county and county equivalents that the ZIP code resides in or most closely associated with
%POP_Poverty
%Population in poverty; listed in order of poverty ratio, from high to low, by county
%POVPOP_M
%Population in poverty who are male
%POVPOP_F
%Population in poverty who are female
%POVPOP_Age<5
%Population in poverty who are children under the age of five
%POVPOP_Age5-15
%Population in poverty between age 5-15
%POVPOP_Age16-64
%Population in poverty between age 16-64
%POVPOP_Age65+
%Population in poverty who are seniors age 65 and more
%NHW_POP_
Poverty ratio among resident population self-identified as Non-Hispanic White
%HSP_POP_
Poverty ratio among resident population self-identified as Hispanic
%ASN_POP_
Poverty ratio among resident population self-identified as Asian
%BLK_POP_
Poverty ratio among resident population self-identified as Black or African-American
%AIAN_POP_
Poverty ratio among resident population self-identified as American Indian and Alaska Native
%NHPI_POP_
Poverty ratio among resident population self-identified as Native Hawaiian and Pacific Islander
%TWM_POP_
Poverty ratio among resident population self-identified as having two or more races/ethnic background
%Disability_<18_POV
% Total disabled civilian population under 18 living in poverty
%Disability_18-64_POV
% Total disabled civilian population between 18-64 living in poverty
%Disability_65+_POV
% Total disabled civilian population age 65 and more living in poverty
%Hhlds_XPlmg
%Total households without plumbing (derived from survey question(s) asking to identify the below items in the household: hot and cold running water, bathtub/shower, sink with a faucet, stove/range, refrigerator etc)
%Hhlds_Xkitn
%Total households without complete kitchen facilities (derived from survey question(s) asking to identify the below items in the household: hot and cold running water, bathtub/shower, sink with a faucet, stove/range, refrigerator etc)
%Hhlds_XCOMP
%Total households without computing devices (includes desktop, laptop, smartphone, tablet, portable wireless computer and smartphone).
%Hhlds_XINT
%Total households without internet access of any type and speed
%Hhlds_XBDB
%Total households without broadband internet access. Broadband access —per Census survey questionnaire— refers to broadband (high speed) internet subscriptions such as cable, fiber optic or Digital Subscriber Line (DSL), satellite, cellular data plan for a mobile device but excludes any dial-up services. Note that the Federal Communications Commission defines high-speed broadband as 25/3 Mbps minimum, or download speeds of 25 megabits per second and upload speeds of 3 megabits per second the very least.
%PopHhlds_<$25K_XHCov
%Population in households making less than $25,000 per year without health insurance coverage
F2. Class DivisionDoes your income place you in the top 5% or bottom 20% of your neighborhood? How does your household income position relative to other households? In this dataset, households are divided into five tiers/classes to provide details. Find out those answers, and how has class division changed between 2015-2020. Ideal dataset for policymakers and activist groups.
Index_code
Index_name
Region
Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST
U.S. State abbreviation
Z_Code
ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name
ZIP code location name. Note more than one common location might be available for some ZIP codes
Tot_hhlds
Total households (excluding ZIP codes with less than 100 households as of 2020)
AvgHhld$_
Average household income for those in the bottom 20% – lowest/first quintile
AvgHhld$_P20
Average household income for those between 20-40th percentiles – second quintile
AvgHhld$_P40
Average household income for those between 40-60th percentiles – third quintile
AvgHhld$_P60
Average household income for those between 60-80th percentiles – fourth quintile
AvgHhld$_P80
Average household income for those in the top 20% – highest/fifth quintile
AvgHhld$_P95
Average household income for those in the bottom 5% – above 95% of all households by location
%Agg_Hhld$_
% Aggregate total income held by households in the bottom 20% – lowest/first quintile
%Agg_Hhld$_P20
% Aggregate total income held by households between 20-40th percentiles – second quintile
%Agg_Hhld$_P40
% Aggregate total income held by households between 40-60th percentiles – third quintile
%Agg_Hhld$_P60
% Aggregate total income held by households between 60-80th percentiles – fourth quintile
%Agg_Hhld$_P80
% Aggregate total income held by households in the top 20% – highest/fifth quintile
%Agg_Hhld$_P95
% Aggregate total income held by households in the bottom 5% – above 95% of all households by location
5YChg%_HhldForm
Five-year nominal percentage change of total household counts by ZIP code
5YChg%_AvgHhld$_
Five-year nominal percent change of average household income for those in the bottom 20% – lowest/first quintile
5YChg%_AvgHhld$_P20
Five-year nominal percent change of average household income for those between 20-40th percentiles – second quintile
5YChg%_AvgHhld$_P40
Five-year nominal percent change of average household income for those between 40-60th percentiles – third quintile
5YChg%_AvgHhld$_P60
Five-year nominal percent change of average household income for those between 60-80th percentiles – fourth quintile
5YChg%_AvgHhld$_P80
Five-year nominal percent change of average household income for those in the top 20% – highest/fifth quintile
5YChg%_AvgHhld$_P95
Five-year nominal percent change of average household income for those in the bottom 5% – above 95% of all households by location
5YDPts_%Agg_Hhld$_
Five-year percentage points difference in aggregate total income held by households in the bottom 20% – lowest/first quintile
5YDPts_%Agg_Hhld$_P20
Five-year percentage points difference in aggregate total income held by households between 20-40th percentiles – second quintile
5YDPts_%Agg_Hhld$_P40
Five-year percentage points difference in aggregate total income held by households between 40-60th percentiles – third quintile
5YDPts_%Agg_Hhld$_P60
Five-year percentage points difference in aggregate total income held by households between 60-80th percentiles – fourth quintile
5YDPts_%Agg_Hhld$_P80
Five-year percentage points difference in aggregate total income held by households in the top 20% – highest/fifth quintile
5YDPts_%Agg_Hhld$_P95
Five-year percentage points difference in aggregate total income held by households in the bottom 5% – above 95% of all households by location
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