Regional unemployment rates used by the Employment Insurance program, by effective date, current month.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025. This map shows the unemployment rate in the United States in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group.The pop-up is configured to include the following information for each geography level:Unemployment rate (%)Population count of persons over the age of 16 within work forceCount of employed and unemployed civil population (over age 16)Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The number of people who are unemployed as a percentage of the active labour force (i.e. employed and unemployed).

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show unemployment numbers and percentages by Zip Code Tabulation Area in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop16P_e

# Population 16 years and over, 2017

Pop16P_m

# Population 16 years and over, 2017 (MOE)

InLabForce_e

# In labor force, 2017

InLabForce_m

# In labor force, 2017 (MOE)

pInLabForce_e

% In labor force, 2017

pInLabForce_m

% In labor force, 2017 (MOE)

CivLabForce_e

# In civilian labor force, 2017

CivLabForce_m

# In civilian labor force, 2017 (MOE)

pCivLabForce_e

% In civilian labor force, 2017

pCivLabForce_m

% In civilian labor force, 2017 (MOE)

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

pCivEmployed_e

% Civilian employed, 2017

pCivEmployed_m

% Civilian employed, 2017 (MOE)

Unemployed_e

# Civilian unemployed, 2017

Unemployed_m

# Civilian unemployed, 2017 (MOE)

pUnemployed_e

% Civilian unemployed, 2017

pUnemployed_m

% Civilian unemployed, 2017 (MOE)

ArmedForce_e

# In armed forces, 2017

ArmedForce_m

# In armed forces, 2017 (MOE)

pArmedForce_e

% In armed forces, 2017

pArmedForce_m

% In armed forces, 2017 (MOE)

NotLabForce_e

# Not in labor force, 2017

NotLabForce_m

# Not in labor force, 2017 (MOE)

pNotLabForce_e

% Not in labor force, 2017

pNotLabForce_m

% Not in labor force, 2017 (MOE)

pUnempOLabForce_e

% Unemployed as part of total labor force (including armed forces), 2017

pUnempOLabForce_m

% Unemployed as part of total labor force (including armed forces), 2017 (MOE)

UnempCivLabForce_e

# Civilian Unemployed, 2017

UnempCivLabForce_m

# Civilian Unemployed, 2017 (MOE)

pUnempCivLabForce_e

% Unemployment Rate, 2017

pUnempCivLabForce_m

% Unemployment Rate, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This service offers Esri's Updated Demographics, Census Data, Tapestry Segmentation, and Business Summary data for the United States. Updates are based on the decennial census, Infogroup business data, other public and proprietary data sources, and proprietary models.

All attributes are available at all geography levels: country, state, county, tract, block group, ZIP code, place, county subdivision, congressional district, core-based statistical area (CBSA), and designated market area (DMA).

There are over 2,100 attributes in categories such as: population, households, race and ethnicity, educational attainment, marital status, employment by industry and occupation, income, net worth, housing and home value, number of businesses and employees, sales, and many others. Key attributes from the 2010 Census such as population, are presented for reference. Some attributes such as population, income, and home value, are also projected five years to 2021.

Esri offers Updated Demographics for 2019 and 2024 and Tapestry Segmentation for 2019. Esri provides Census Data for geographies not supplied by the Census Bureau including ZIP Codes and DMAs.

To view ArcGIS Online items using this service, including the terms of use, visit http://goto.arcgisonline.com/demographics9/USA_Demographics_and_Boundaries_2019.

This map shows the unemployment rate in the United States in 2017 in a multiscale map by country, state, county, ZIP Code, tract, and block group.The pop-up is configured to include the following information for each geography level:Unemployment rate (%)Population count of persons over the age of 16 within work forceCount of employed and unemployed civil population (over age 16)The data shown is from Esri's 2017 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2017/2022) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. Additional Esri Resources:Esri DemographicsU.S. 2017/2022 Esri Updated DemographicsEssential demographic vocabulary

US County & Zipcode Historical Demographics

Easily lookup US historical demographics by county FIPS or zipcode in seconds with this file containing over 5,901 different columns including:

*Lat/Long *Boundaries *State FIPS *Population from 2010-2019 *Death Rate from 2010-2019 *Unemployment from 2001-2020 *Education from 1970-2019 *Gender and Age Population

Provided by bitrook.com to help Data Scientists clean data faster.

Data Sources

All Data Combined Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Population Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Unemployment Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Zip FIPS Crosswalk Source:

https://data.world/niccolley/us-zipcode-to-county-state

County Boundaries Source:

https://public.opendatasoft.com/explore/dataset/us-county-boundaries/table/?disjunctive.statefp&disjunctive.countyfp&disjunctive.name&disjunctive.namelsad&disjunctive.stusab&disjunctive.state_name

Age Sex Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-agesex-**.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-agesex.pdf

Races Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-alldata.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf

Unemployment rates by zip code

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

In the dataset ´GLES Cross-Section 2013-2021, Sensitive Regional Data´, the recoded or deleted variables of the GLES Cross-Section Scientific Use Files, which refer to the respondents’ place of residence, are made available for research purposes. The basis for the assignment of the small-scale regional units are the addresses of the respondents. After geocoding, i.e. the calculation of geocoordinates based on the addresses, the point coordinates were linked to regional units (e.g. INSPIRE grid cells, municipality and district ids, postal codes). The regional variables of this dataset can be linked to the survey data of the pre- and post-election cross-sections of the GLES.

This data set contains the following sensitive regional variables (both ids and, if applying, names): - 3-digit key for the adminsitrative governmental district (Regierungsbezirk) (since 2013) - 3-digit key for spatial planning region (since 2013) - 5-digit key for (city-) districts (since 2013) - 9-digit key for municipalities (since 2021) - 8-digit general municipality key (AGS) (since 2013) - 12-digit regional key (Regionalschlüssel) (since 2021) - Zip code (since 2013) - Constituencies (since 2013) - NUTS-3 code (since 2013) - INSPIRE ID (1km) (since 2013) - municipality size (since 2013) - BIK type of municipality (since 2013)

This sensitive data is subject to a special access restriction and can only be used within the scope of an on-site use in the Secure Data Center in Cologne. Further information and contact persons can be found on our website: https://www.gesis.org/en/secdc

In order to take into account changes in the territorial status of the regional units (e. g. district reforms, municipality incorporations), the regional variables are offered as time-harmonized variables as of December 31, 2015 in addition to the status as of January 1 of the year of survey.

If you want to use the regional variables to add additional context characteristics (regional attributes such as unemployment rate or election turnout, for example), you have to send us this data before your visit. In addition, we require a reference and documentation (description of variables) of the data. Note that this context data may be as sensitive as the regional variables if direct assignment is possible. Due to data protection it is problematic if individual characteristics can be assigned to specific regional units – and therefore ultimately to the individual respondents – even without the ALLBUS dataset by means of a table of correspondence. Accordingly, the publication of (descriptive) analysis results based on such contextual data is only possible in a coarsened form.

Please contact the GLES User Service first and send us the filled GLES regional data form (see ´Data & Documents´), specifying exactly which GLES datasets and regional variables you need. Contact: gles@gesis.org

As soon as you have clarified with the GLES user service which exact regional features are to be made available for on-site use, the data use agreement for the use of the data at a guest workstation in our Secure Data Center (Safe Room) in Cologne will be sent to you. Please specify all data sets you need, i.e. both the ´GLES Sensitive Regional Data (ZA6828)´ and the Scientific Use Files to which the regional variables are to be assigned. Furthermore, under ´Specific variables´, please name all the regional variables you need (see GLES regional data form).

Unemployment rates by zip code in wide format

The Justice Equity Need Index (JENI), by Advancement Project California, offers a means to map out the disparate burden that criminalization and a detention-first justice model place on specific communities. The index includes the following indicators:System Involvement: The system-involved population by ZIP Code results in direct needs for justice equity, as measured by adult and youth probation. Indicators: Adult Probation (per 1,000 people); Youth Probation (per 1,000 people) Inequity Drivers: Root inequities across communities that contribute to racial and economic disparities as seen in incarceration and policing. Indicators: Black, Latinx, AIAN, and NHPI Percentages of Population (average percentile); Unemployment Rate (%); Population aged 25+ without a High School Diploma (%); Population below 200% of the Federal Poverty Level (%); Violent Crime Rate (per 1,000 people) Criminalization Risk: Conditions where the criminal justice system has historically taken a detention-first, prevention-last approach. Indicators: Mental Health Hospitalizations (per 1,000 people); Substance Use-Related Hospitalizations (per 1,000 people); Homelessness Rate (per 1,000 people) Learn more at https://www.catalystcalifornia.org/campaign-tools/maps-and-data/justice-equity-need-index.Supervisorial Districts, SPAs, and CSAs determined by ZIP Code centroid.

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ZipRecruiter's Quest: Employment Landscape Redefined? (ZIP)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

Information on employment, income structure, taxes or fees paid and support received by persons living in the household.

Topics: The complete data set is subdivided in three parts:

A.Information about every individual person in the household

B. Information about the household as a whole and

C. Information about the sample points.

A. For every person in the household the following data were collected:

1. demographic information.

2. information on employment: hours worked each week; status in profession; side jobs and information on full-time employment.

3. savings plan: monthly savings amount according to the 624-DM law; form of savings; employee savings bonus.

4. refugee, exile, resettler: type of identification; year of arrival.

5. reductions in employment: degree and reasons for reduction in earning capacity.

6. training: type and begin of further education and retraining supported by the employment office.

7. income and taxes: type and level of monthly income; special payments received; level of monthly wage and income tax paid; social security contributions; church tax; type of health insurance and monthly premium.

8. social services received: child allowance; student aid; earnings-related unemployment benefit; unemployment benefit; short time work or bad-weather pay; allowances and support aid from the employment office; payments from support advances; support payments for members of the military or those performing community service in lieu of the military; payments for support by the Federal German Armed Forces subsequent to a voluntary period of service; payments for those persecuted by the Nazi regime; burden-sharing for war damages; payments of divorced to spouse and children; receipt of voluntary support payments by relatives outside of the home.

9. pensions: type of anticipated retirement income based on rights earned; detailed information on pensions derived from compulsory retirement insurance, pensions from life insurance policies as well as rights to a pension outside the compulsory retirement insurance.

B. The following data was collected for the entire household:

1. aggregate information on characteristics of the household regarding transfer payments charged for the individual persons and characteristics.

2. housing situation and house construction: ZIP (postal) code of main place of residence; age of residential building; form of housing; amount of rent or rental value of one´s own residence; monthly amount of interest for residential property; repayment rates; taking advantage of private or public loans; living space and furnishings; additional costs.

3. taxes: information on income tax return; utilization of diverse tax law possibilities to reduce taxable income and level of the amount; costs in connection with the acquisition of tax deduction property; level of wealth tax and property tax paid.

4. social services: information on receipt and level of housing benefit or welfare as well as reasons for not filing a corresponding application.

5. finances: household income; inheritances; possession of residential property; current market value of one´s own home; structure and current market value of household assets.

6. health area: number of visits to the doctor, general practitioner and specialist; stays in a health resort; inability to work due to illness; hospital stays; frequency of continued payment of wages received.

C. Information on sampling point: state; administrative district; size of municipality class; ZIP (postal) code; residential population of the municipality; male population of the residential community; proportion of Germans or foreigners in the population; number of households in municipality.

Demography: occupation according to ISCO; current or last occupational position; social origins; professional position of father when respondent was 15 years old; religious affiliation; party preference (Sunday question).

Building on social psychologist Marie Jahoda’s pioneering work, the psychological literature has shown that work fulfills both manifest functions (e.g., monetary returns) and latent functions (e.g., social contact). This article uses data from the German panel study “Labor market and social security” (PASS), which contains information on latent and manifest factors (from a shortened latent and manifest benefits, or LaMB, scale), as well as a large array of other variables for over 9,000 respondents. This probability-sampled data allowed for detailed analyses that have not been previously possible. We investigate differences in these factors by labor market status, among those employed, and among those unemployed. We identify considerable variation between status groups, suggesting that employment, overall, is important and that longer periods of unemployment lead to a gradual decay of the latent and manifest factors. Furthermore, regression analyses show that the LaMB measures account for approximately 70% of the partial correlations between unemployment and various well-being measures.

The construction of the Swedish CDB and the data collection followed a template developed within the GGP. The template provided detailed guidelines for the collection, preparation, and documentation of the indicators. The database covers 16 main areas: Demography, Economy and Social Aspects, Labour and Employment, Parental Leave, Pension, Childcare, Military, Unemployment, Tax Benefits, Housing, Legal Aspects, Education, Health, Elderly Care, Politics, Culture. Each of these main domains contains more detailed indicators at the national or subregional (Riskområde NUTS2) level. In total, there are 243 indicators. Many of these indicators were calculated using Swedish Register Data. These indicators were not available in publicly accessible statistics and the Swedish CDB is thus currently the only database to provide them. The Swedish CDB offers a rich and unique set of time-series indicators at the national and subregional level.