Initial Jobless Claims in the United States increased to 237 thousand in the week ending August 30 of 2025 from 229 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.

Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-08-30 about initial claims, headline figure, and USA.

The number of people filing for initial unemployment benefits by county and week.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

Continuing Jobless Claims in the United States decreased to 1940 thousand in the week ending August 23 of 2025 from 1944 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.

Context

The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

Content

Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

Update Frequency: Monthly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

Dataset Source: http://www.bls.gov/data/

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Clark Young from Unsplash.

Inspiration

What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

Employment Rate in the United States remained unchanged at 59.60 percent in August. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in India decreased to 5.20 percent in July from 5.60 percent in June of 2025. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

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

Unemployment Rate in Germany remained unchanged at 6.30 percent in August. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About Dataset: Walmart Sales Forecast

This dataset focuses on predicting weekly store sales at Walmart by examining holiday effects, temporal patterns, and other influential factors. The goal is to enable efficient stock planning, revenue calculations, and strategic decision-making by understanding patterns related to seasonal sales fluctuations. This machine learning model is developed based on resources from : https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/evaluation .

Dataset Overview

1. Test Data Contains 115,064 rows with information: Store, Department, Date, IsHoliday. "IsHoliday" indicates whether the week includes a special holiday. Holidays tend to show higher average sales than non-holiday periods.

2. Train Data Also contains 115,064 rows with Store, Department, Date, IsHoliday, Weekly Sales. Weekly sales are the recorded weekly sales for specific departments at certain stores.

3. Features Data Consists of 8,190 rows with variables such as Temperature, Fuel Price, CPI, Unemployment, Markdown 1-5, IsHoliday * Temperature: Average temperature (Fahrenheit) in a region. * Fuel Price: Can impact consumer spending and sales. * Markdowns 1-5: Promotional markdowns (missing values marked as NA). * CPI: Consumer Price Index (reflects inflation/deflation). * Unemployment: Unemployment rate in a region that affects consumer spending.

4.Store Data Includes details about Walmart stores such as store numbers, store types, and store sizes. Walmart has 45 stores categorized into 3 types: * Type A: Sizes from 39.690 to 219.622 * Type B: Sizes from 34.875 to 140.167 * Type C: Sizes from 39.690 to 42.988 The target variables for prediction are weekly sales, is holiday, and date. The other features are explored to identify patterns and generate insights to build accurate prediction models.

Modeling Objective

The goal is to predict the impact of holidays on weekly store sales. To achieve this, a Time Series modeling approach was applied using variables such as date, weekly sales, is holiday, lag features, rolling averages, and XGBoost. The evaluation metric used was Weighted Mean Absolute Error (WMAE), which emphasizes periods of higher significance, such as holidays.

Final Model Metrics: * Weighted Mean Absolute Error = 211 * Error rate relative to average weekly sales = ~1.32%.

The low error percentage highlights the model's accuracy in forecasting weekly sales and assessing seasonal fluctuations.

Insights

• The analysis provides actionable insights by identifying holiday effects on sales trends.
• This supports better stock planning, strategic financial planning, and risk management.

📢 Published on : My LinkedIn

The Mikrozensus special surveys of the year 1982 have put an emphasis on questions on employment and labour market: in June occupational history (Mikrozensus MZ8202) in September “job seeking/extra hours (Mikrozensus MZ8203) finally, in December professional development and retraining (Mikrozensus MT8204) On the topic of job seeking the last study had been conducted in September 1979 (Mikrozensus MZ7903). The survey was conducted to complement the monthly statistic on persons who filed for unemployment at an unemployment office. It gives additional information on people searching for a job who did not file for unemployment at any unemployment office and who are therefore not include in the monthly statistics. This includes all unemployed people (those who lost a job as well as housewives, students, etc.) who might be looking for job as well as those who want to change their workplace because they are dissatisfied with their current job. extra hours: These questions are posed for the first time in the Mikrozensus. Extra hours are all time-related supererogation including unpaid extra hours or those which are paid with compensatory time.

Unemployment Rate in Canada increased to 7.10 percent in August from 6.90 percent in July of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this survey was to investigate experience of education, work and unemployment among young people, and their attitudes towards various actual and possible government-sponsored schemes aimed at helping them. Main Topics: Attitudinal/Behavioural Questions In employment: type of work, nature of business, length of time in present job, whether received any training, job satisfaction, intention to leave, opinion of employer's treatment of young people, number of past employers, details of first full-time job, reasons for leaving. Whether considered returning to full-time education and reasons for not doing so. Whether career intentions have changed in past two years and why, assessment of usefulness of holiday jobs/work experience at school/voluntary work experience. Details of any periods of unemployment. Unemployed: whether registered at Employment Office/careers office, length of time seeking work, details of previous full-time job, reasons for leaving, number of jobs since leaving school, details of first job. Methods of finding work, number of applications made, reasons for losing last job applied for, number of jobs turned down and reasons. Whether unemployed people should receive more help and from whom, whether considered returning to full-time education, whether career intentions have changed in last two years, assessment of usefulness of work experience gained at school. Whether would be prepared to move to another area for a job, details of previous periods of unemployment. In full-time education: type of institution attended, by whom fees met, qualifications aimed at, reasons for taking course, length of time expect to continue. Whether ever been in full-time employment (details), whether ever been unemployed, reasons for returning to education, assessment of usefulness of work experience gained at school. Expected ease of securing a job, changes in career intentions in last two years. General: assessment of importance of government work/training schemes, assessment of a fair wage, sources of information on jobs (e.g. parents, teachers, job centre), whether friendly/useful, most important aspects of a job, anticipated weekly salary, from whom would seek advice concerning employment or training, whether would be prepared to take part in national community work scheme, whether such a scheme should be compulsory. Background Variables Age, sex, social class of respondent and parents, marital status, whether has children, accommodation, school-leaving age and year, country of birth, qualification, income bracket. Multi-stage stratified random sample involving selection, initially, of parliamentary constituencies, then polling districts and, within polling districts, clusters of addresses. A total of approximateley 36,000 addresses was drawn and these formed the initial screening sample that interviewers were to contact in order to establish whether or not the household contained any 16-19 year olds.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

The Mikrozensus survey in September 1984 is on the topics shopping habits and job seeking shopping habits: The question program consists of the following topics: - local supply, especially important for elderly people and in rural areas - time needed for shopping, often a problem for working persons - planning of expenses: Of interest is the consumer behaviour and the planning of the household expenses. - planned purchases: This questions is of special interest in connection with the information gathered in the survey from June (Mikrozensus MZ8402) on the households’ achieved level of equipment with durable consumer goods. job seeking On the topic of job seeking the last study had been conducted in September 1982 (Mikrozensus MZ8203). The survey was conducted to complement the monthly statistic on persons who filed for unemployment at an unemployment office. It gives additional information on people searching for a job who did not file for unemployment at any unemployment office and who are therefore not include in the monthly statistics. This includes all unemployed people (those who lost a job as well as housewives, students, etc.) who might be looking for job as well as those who want to change their workplace because they are dissatisfied with their current job. Apart from providing possibilities for comparing with and complementing the monthly unemployment statistics of the labour market administration, this special survey provides additional information that cannot be found in an administration statistic. Probability: Stratified: Disproportional Face-to-face interview

This Mikrozensus survey contains two special programs: smoking habits and job seeking Smoking habits: Smoking poses a public health problem since it is a risk factor in connection with several diseases (cancer, cardiovascular diseases, etc.).Therefore, it is an important aiming point for preventive measures. In 1972 a Mikrozensus special survey had already been conducted on this topic (Mikrozensus MZ7201). Since then a lot of public as well as private educational work on the dangers had been done, sometimes even active intervention. Proposed by the Federal Ministry of Health and environmental protection, the study on smoking habits is now repeated. Most questions from the 1972 survey were posed again to ensure comparability. In consequence, this study does not only help with the development of new preventive measures against the so called associated diseases but it is also an examination of the effectiveness of already existing measures. Job seeking: The survey was conducted to complement the monthly statistic on persons who filed for unemployment at an unemployment office. It gives additional information on people searching for a job who did not file for unemployment at any unemployment office and who are therefore not include in the monthly statistics. This includes all unemployed people (those who lost a job as well as housewives, students, etc.) who might be looking for job as well as those who want to change their workplace because they are dissatisfied with their current job. Some questions on the topic of job seeking were already posed in the Mikrozensus survey in June 1987 (Mikrozensus MZ7802)

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

These data are taken from the ANNUAL datasets from the Labour Force Survey (LFS) carried out by the Office for National Statistics (ONS), providing labour market data back to 1996 for the NUTS2 areas in Wales, and back to 2001 for the local authorities in Wales. The availability of local authority data is dependent upon on an enhanced sample (around 350 per cent larger) for the annual LFS, which commenced in 2001. For years labelled 1996 to 2004 in this dataset, the actual periods covered are the 12 months running from March in the year given to February in the following year (e.g. 2001 = 1 March 2001 to 28 February 2002). Since 2004, the annual data have been produced on a rolling annual basis, updated every three months, and the dataset is now referred to as the Annual Population Survey (APS). The rolling annual averages are on a calendar basis with the first rolling annual average presented here covering the period 1 January 2004 to 31 December 2004, followed by data covering the period 1 April 2004 to 31 March 2005, with rolling quarterly updates applied thereafter. Note therefore that the consecutive rolling annual averages overlap by nine months, and there is also a two-month overlap between the last period presented on the former March to February basis, and the first period on the new basis. The population can be broken down into economically active and economically inactive populations. The economically active population is made up of persons in employment, and persons unemployed according to the International Labour Organisation (ILO) definition. This report allows the user to access these data. Although each measure is available for different population bases, there is an official standard population base used for each of the measures, as follows. Population aged 16 and over: Economic activity level, Employment level, ILO unemployment level Population aged 16-64: Economic inactivity level 16-64 population is used as the base for economic inactivity. By excluding persons of pensionable age who are generally retired and therefore economically inactive, this gives a more appropriate measure of workforce inactivity. Rates for each of the above measures are also calculated in a standard manner and are available in the dataset. With the exception of the ILO unemployment rate, each rate is defined in terms of the shares of population that fall into each category. The ILO unemployment rate is defined as ILO unemployed persons as a percentage of the economically active population. Although each rate is available for the different population bases, there is an official standard population base used for each of the rates, as follows. Percentage of population aged 16-64: Economic activity, Employment,. Economic inactivity Percentage of economically active population aged 16 and over: ILO unemployment