Initial Jobless Claims in the United States increased to 226 thousand in the week ending August 2 of 2025 from 219 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on continued claims at the town level, see the dataset "Continued Claims for Unemployment Benefits by Town" here: https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

For data on initial claims see the following two datasets:

"Initial Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits/j3yj-ek9y

"Initial Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

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

United States Google Search Trends: Government Measures: Unemployment Benefits data was reported at 10.000 Score in 15 May 2025. This stayed constant from the previous number of 10.000 Score for 14 May 2025. United States Google Search Trends: Government Measures: Unemployment Benefits data is updated daily, averaging 11.000 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 90.000 Score in 09 Jan 2023 and a record low of 0.000 Score in 21 Apr 2023. United States Google Search Trends: Government Measures: Unemployment Benefits data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s United States – Table US.Google.GT: Google Search Trends: by Categories.

Case data for investigations in industries marked by a generally low wage workforce, low complaint rates, and high violation rates

Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-08-02 about initial claims, headline figure, and USA.

Continuing Jobless Claims in the United States increased to 1974 thousand in the week ending July 26 of 2025 from 1936 thousand in the previous week. This dataset provides the latest reported value for - United States Continuing Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Pandemic Emerg Unemployment Compensation (PEUC): Cont'd Claims: US data was reported at 10.391 Person th in 28 Jan 2023. This records a decrease from the previous number of 12.095 Person th for 21 Jan 2023. United States Pandemic Emerg Unemployment Compensation (PEUC): Cont'd Claims: US data is updated weekly, averaging 244.379 Person th from Mar 2020 (Median) to 28 Jan 2023, with 149 observations. The data reached an all-time high of 6,220.492 Person th in 06 Mar 2021 and a record low of 0.000 Person th in 28 Mar 2020. United States Pandemic Emerg Unemployment Compensation (PEUC): Cont'd Claims: US data remains active status in CEIC and is reported by U.S. Department of Labor. The data is categorized under Global Database’s United States – Table US.G149: Unemployment Insurance: Weekly Pandemic Claims (Discontinued). [COVID-19-IMPACT]

Contains the annual summation of employee hours and coal production reported by mine operators where the average quarterly employment is greater than zero with grouping by calendar year, subunit code and mine ID. The subunit code identifies the location or operation of the mine relating to the: (01) Underground; (02) Surface at underground; (03) Strip, quarry, open pit; (04) Auger; (05) Culm bank/refuse pile; (06) Dredge; (12) Other mining; (17) Independent shops or yards; (30) Mill operation/preparation plant; (99) Office workers at mine site.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Iowa Law requires Iowa Workforce Development to establish a tax table for each year. The unemployment insurance rate table trigger formula is primarily based on the UI trust fund balance, unemployment benefit payment history and covered wage growth. The formula is designed to enable the trust fund to keep pace with potential liabilities as covered unemployment and wages grow. This dataset contains the contribution rate table and the average tax rate for employers subject to the Iowa Unemployment Insurance system. There are eight rate tables each having 21 ranks. Table one has highest average tax rate. Table eight has the lowest average tax rate. The highest average tax rate (based on taxable wages) was 3.38% in 1984 (Table 1). The lowest average tax rate was 0.94% in 1998 (Table 8). [Time Period: 1980-2018]

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on continued claims at the town level, see the dataset "Continued Claims for Unemployment Benefits by Town" here: https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

For data on initial claims see the following two datasets:

"Initial Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits/j3yj-ek9y

"Initial Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

Employment by Industry reports the total Number of Employers, the Annual Average Employment, and the Annual Average Wage by industry at the town, county, and state level. Industries included in this dataset vary from location to location. In as many locations as possible, five specific industry segments are consistently present (Construction, Manufacturing, Retail Trade, All Industries, Total Government) as well as the largest 3 out of the remaining segments for that location, ranked by Annual Average Employment. Not every location has data for every segment, and some may not have data for the five consistently reported segments. This data is from the Connecticut Department of Labor Quarterly Census of Employment and Wages (QCEW). The program produces a comprehensive tabulation of employment and wage information for workers covered by Connecticut Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program.

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2022 2023 ADULT EDUCATION AGE ANXIETY APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESSES CARE OF DEPENDANTS CHRONIC ILLNESS COHABITATION CONDITIONS OF EMPLO... COVID 19 DEBILITATIVE ILLNESS DEGREES DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS FAMILIES FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HAPPINESS HEADS OF HOUSEHOLD HEALTH HIGHER EDUCATION HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS Labour and employment MANAGERS MARITAL STATUS NATIONAL IDENTITY NATIONALITY OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELL BEING HEALTH WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the third edition (July 2023), the SOC variables NSECM20, NSECMJ20, SC20LMJ, SC20LMN, SC20MMJ, SC20MMN, SC20SMJ, SC20SMN, SOC20M, SC2010M and the person income weight PIWTA22 were replaced with revised versions. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.For the fourth edition (August 2023) the previous IDREF variable was replaced with an updated version. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2021 2022 ADULT EDUCATION AGE ANXIETY APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESSES CARE OF DEPENDANTS CHRONIC ILLNESS COHABITATION CONDITIONS OF EMPLO... COVID 19 DEBILITATIVE ILLNESS DEGREES DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS FAMILIES FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HAPPINESS HEADS OF HOUSEHOLD HEALTH HIGHER EDUCATION HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS Labour and employment MANAGERS MARITAL STATUS NATIONAL IDENTITY NATIONALITY OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELL BEING HEALTH WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

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The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI. Industry level data from 1975 to 2000 is reflective of the Standard Industrial Classification (SIC) codes.

The National Database of Childcare Prices (NDCP) provides childcare prices at the county level in the United States. The NDCP is a new data source, and the most comprehensive federal source of childcare prices at the county level in the United States. The NDCP was developed to fill a need for local-level childcare price data, standardized across U.S. states. Most existing sources of childcare price data provide prices at the state level, yet parents must choose childcare providers that are in close proximity to their homes or workplaces. Therefore, state averages are unlikely to be good estimates of the prices parents encounter in the market. State average prices do not reflect the substantial variation in prices from one locale to the next within a state and underestimate prices in urban areas.The NDCP provides data on the price of childcare by children's age groups and care setting (home-based or center-based) at the median and 75th percentile over an 15-year period (2008-2022, inclusive) at the county level. The data were obtained from state Lead Agencies responsible for conducting market rate surveys (MRS) according to Child Care and Development Fund regulations. A MRS is the collection and analysis of prices charged by childcare providers for services in the priced market. All state Lead Agencies must conduct a survey and develop a report on local childcare prices in their state every three years. The Women's Bureau contracted with ICF to obtain reports and data from previously conducted surveys to develop the NDCP. The NDCP standardizes and harmonizes data across years and geographies for about 200 previously-conducted MRS. The NDCP also provides county-level demographic and economic data from the American Community Survey.The accompanying User Guide (U.S. Department of Labor, Women's Bureau National Database of Childcare Prices: Final Report) provides detailed information about the data sources, data collection strategy, standardization and imputation of the data, and data limitations to inform and assist researchers who may be interested in using the data for future analyses. The following items are provided in the User Guide as appendices.Appendix A: Data Collection Protocol and Decisions Made During Data Entry Process, Including State NuancesAppendix B: List of Imputations Performed for Each State and YearAppendix C: County-Level Data DictionaryAppendix D: Methods Used for Specific Demographic Variables – CountyAppendix E: State-Level Data DictionaryAppendix F: Methods Used for Specific Demographic Variables – StateAppendix G: 2008-2018 Imputations for County-Level Childcare Prices from Statewide DataAppendix H: Price Quintile Ranges for State-Level Price DatabaseAppendix I: Summary of Additional 2008-2018 Data Added as a Result of Additional In-Between Study Imputations

ETA awards approximately $8 billion in formula and discretionary grants each year to an average of 1,000 recipients. Financial reports for each active grant must be submitted quarterly on the ETA-9130 Financial Report through the Federal Reporting System, an online ETA-9130 reporting system for recipients to enter and certify quarterly financial data. This dataset contains the financial reports submitted by states and discretionary grant recipients through this process. This reporting supports the Department of Labor’s ability to measure fund utilization for performance accountability and assess compliance with statutory expenditure requirements. This information also helps measure successful outcomes for participants, ensure sound service delivery and reporting practices, and determine whether the federal funds achieved maximum benefit.