Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

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?

Graph and download economic data for Employed full time: Wage and salary workers: Computer scientists and systems analysts occupations: 16 years and over (LEU0254477000A) from 2000 to 2010 about analysts, computers, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

This paper replicates "Economic Discontent as a Mobilizer: Unemployment and Voter Turnout" by Burden and Wichowsky, 2014. Using re-specified regression and multinomial logit models, we do not find that increases in the state unemployment rate are associated with increases in votes for Democrats. We also present more intuitive results from the logit models using predicted probabilities.

Content

2018 unemployment rate in percent, per county in the USA.

Acknowledgements

This data was downloaded on March 23, 2019 from GeoFRED https://geofred.stlouisfed.org/map/?th=rdpu&cc=5&rc=false&im=fractile&sb&lng=-90.000&lat=40.028&zm=5&sl&sv&am=Average&at=Not%20Seasonally%20Adjusted,%20Monthly,%20Percent&sti=1224&fq=Annual&rt=county&un=lin&dt=2018-01-01

How Can I Use the Data? In https://research.stlouisfed.org/fred_terms.html states that: "As long as you don’t engage in a prohibited/restricted use and do adhere to any applicable copyright restrictions, you are free to use FRED for your own non-commercial, educational, and personal uses."

This study examines the relationship between socio-economic factors and crime distribution using a dataset that includes variables such as unemployment rates, literacy rates, per capita income, and population density. The analysis explores how these factors influence crime rates across different regions, comparing urban and rural areas to identify variations in crime patterns due to economic and social disparities. Additionally, the study investigates cultural and psychological influences on criminal activities. The findings offer valuable insights for policymakers to develop more effective crime prevention strategies.This dataset supports the manuscript ‘Crime and Socio-Economic Inequalities: Leveraging Deep Learning and Generative AI for Comprehensive Analysis.’ It includes:- CrimeEconomicsData.csv: Original dataset with 114 observations across 10 socio-economic variables (Per Capita Income, Population Density, Unemployment, Literacy Rate, Happiness Index, Crime Rate).- supplementary_data.zip: Contains: - table_ii_metrics.csv: Performance metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC) for machine learning and deep learning models in Table II. - figure_2_confusion_matrices.csv: Confusion matrix data for each model, supporting Figure 2’s visualizations. - README.txt: Description of the files and their purpose.Preprocessed datasets are not included, as preprocessing steps (e.g., mean imputation, standardization, PCA) are detailed in the manuscript and can be replicated using CrimeEconomicsData.csv.

Context & Content

This dataset consists of the unemployment rate and education level of adults in the USA by county. That is, for each county in the USA, this dataset provides the count and percentage of unemployed adults as well as the count and percentage of adults of various educational backgrounds. Each county was been assigned one of four locale categories (City, Suburb, Town, Rural) according to its 2013 Urban Influence Code and their descriptions provided in UIC_codes.csv. From the descriptions of each of the codes and the descriptions of the locales "City", "Suburb", "Town", and "Rural" provided on page 2 of the locale user manual (locale_user_manual.pdf), each county was assigned one of four locales.

The unemployment rate data includes the count and percentage of unemployed adults for each county in the USA for each year from 2000-2020. The median household income for 2019 is also included. The education level data includes the count and percentage of adults with less than a high school diploma, a high school diploma only, some college, and a bachelor's degree/four years of college or more for the years 1970, 1980, 1990, 2000, and 2019. The Urban Influence Code data includes the UIC and locale description of each county in the USA and the locale user manual has been included as a PDF as strictly a reference file, to understand how each county was assigned a locale within the unemployment.csv and education.csv files.

Data Sources

Source for the unemployment rate and education level data by county: "County-level Data Sets." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/county-level-data-sets/

Source for Urban Influence Codes by county: "Urban Influence Codes." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/urban-influence-codes/#:~:text=The%202013%20Urban%20Influence%20Codes,to%20metro%20and%20micropolitan%20areas.&text=An%20update%20of%20the%20Urban,is%20planned%20for%20mid%2D2023.

Inspiration

This dataset was created to be used as an additional data source for the LearnPlatform COVID-19 Impact on Digital Learning Kaggle competition, but is suitable for other analyses related to unemployment rate and education level in the USA.

Are people under economic stress more or less likely to vote, and why? With large observational datasets and a survey experiment involving unemployed Americans, we show that unemployment depresses participation. But it does so more powerfully when the unemployment rate is low, less powerfully when it is high. Whereas earlier studies have explained lower turnout among the unemployed by stressing the especially high opportunity costs these would-be voters face, our evidence points to the psychological effects of unemployment and of campaign messages about it. When unemployment is high, challengers have an incentive to blame the incumbent, thus eliciting anger among the unemployed. Psychologists have shown anger to be an approach or mobilizing emotion. When joblessness is low, campaigns tend to ignore it. The jobless thus remain in states of depression and self-blame, which are demobilizing emotions.

In April 2025, the agriculture and related private wage and salary workers industry had the highest unemployment rate in the United States, at eight percent. In comparison, government workers had the lowest unemployment rate, at 1.8 percent. The average for all industries was 3.9 percent. U.S. unemployment There are several factors that impact unemployment, as it fluctuates with the state of the economy. Unfortunately, the forecasted unemployment rate in the United States is expected to increase as we head into the latter half of the decade. Those with a bachelor’s degree or higher saw the lowest unemployment rate from 1992 to 2022 in the United States, which is attributed to the fact that higher levels of education are seen as more desirable in the workforce. Nevada unemployment Nevada is one of the states with the highest unemployment rates in the country and Vermont typically has one of the lowest unemployment rates. These are seasonally adjusted rates, which means that seasonal factors such as holiday periods and weather events that influence employment periods are removed. Nevada's economy consists of industries that are currently suffering high unemployment rates such as tourism. As of May 2023, about 5.4 percent of Nevada's population was unemployed, possibly due to the lingering impact of the coronavirus pandemic.

Peru Labour Market: Female: Unemployment Rate data was reported at 4.411 % in 2017. This records a decrease from the previous number of 4.564 % for 2016. Peru Labour Market: Female: Unemployment Rate data is updated yearly, averaging 4.719 % from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 6.293 % in 2002 and a record low of 3.642 % in 2015. Peru Labour Market: Female: Unemployment Rate data remains active status in CEIC and is reported by National Institute of Statistics and Information Science. The data is categorized under Global Database’s Peru – Table PE.G003: National Household Survey: Labour Market.

Unemployment rate of doctorate holders by fields of science

The product has been discontinued since: 10 Apr 2019.

Peru Labour Market: Male: Unemployment Rate data was reported at 3.840 % in 2017. This records a decrease from the previous number of 3.878 % for 2016. Peru Labour Market: Male: Unemployment Rate data is updated yearly, averaging 4.055 % from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 5.685 % in 2002 and a record low of 3.173 % in 2012. Peru Labour Market: Male: Unemployment Rate data remains active status in CEIC and is reported by National Institute of Statistics and Information Science. The data is categorized under Global Database’s Peru – Table PE.G003: National Household Survey: Labour Market.

The data project includes the variables, the questions and the algorithms (in SPSS) required for the derivation of the Unemployment Rate

The unemployment data is from the Population Census 2005-06 this data shown in the National Regional Profile has been smoothed by averaging the original estimates over the four quarters to June for each year. Particular care should be taken when interpreting estimates for regions where the estimated labour force is smaller than 1000 persons. The unemployment data has then been attributed to each Statistical Local Area and then rasterised. Capital cities have been masked out of this analysis.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The "Sysmiss" label in the Statistics section indicates the number of non-responding records for each variable, and the "Valid" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996. The census metropolitan area (CMA) variable has been expanded from the three largest CMAs in Canada to nine. Two occupation variables based on the 2016 National Occupation Classicifcation have been reintroduced: a generic 10- category variable (NOC_10) and a detailed 40-category variable (NOC_40). A new variable on immigrant status (IMMIG) has been introduced, which distingushes between recent immigrants and established immigrants. Fourteen variables related to family and spouse/partner's

Full edition for scientific use. The study "Causes and Consequences of Unemployment in the COVID-19 pandemic" addresses the financial and psychological consequences of unemployment for those affected in the second year of the Corona pandemic. The study is based on an Austria-wide standardised telephone survey of 1844 people aged 15 to 64. The interviews took place between 29 May and 11 July 2021. 1215 interviewees were unemployed at the time of the interview, 332 of them long-term unemployed, 629 interviewees were employed.

The data and programs replicate tables and figures from "Unemployment Volatility in a Generalised Staggered Nash Wage Bargaining Framework", by Kara. Please see the ReadMe file for additional details. The code includes the necessary scripts to reproduce the model simulation results presented in the paper. The folder also contains a ReadMe.pdf document that describes the code.

Peru Unemployment Rate: Lima Metropolitan data was reported at 6.867 % in Oct 2018. This records an increase from the previous number of 6.120 % for Sep 2018. Peru Unemployment Rate: Lima Metropolitan data is updated monthly, averaging 7.895 % from Mar 2001 (Median) to Oct 2018, with 212 observations. The data reached an all-time high of 13.002 % in Jan 2005 and a record low of 4.674 % in Oct 2015. Peru Unemployment Rate: Lima Metropolitan data remains active status in CEIC and is reported by National Institute of Statistics and Information Science. The data is categorized under Global Database’s Peru – Table PE.G004: Permanent Employment Survey: Labour Market. Unemployment Rate covers Lima Metropolitan only.

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.

AYX Stock Forecast: The Next Big Thing in Data Science and Analytics

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

The data contain variables describing labour force, unemployment, support measures, and open vacancies, sorted by the Employment and Economic Development Offices (TE Offices). Variables related to labour force and unemployment include the amount of labour force, unemployment rate, the number of unemployed job-seekers and laid-off workers, and the number of long-term unemployed and repeatedly unemployed. The data also include information on vacancies notified to Employment and Economic Development Office and filled vacancies. The data include information on the duration of the periods of unemployment during the year, and on the reasons why the periods of unemployment had ended (e.g. employment, measures of employment administration, labour market training, and unemployment pension). There are also data on the number of employment policy statements and job-seekers' individual action plans, as well as on the recipients of labour market subsidy. In addition, the dataset contains information on the job-seekers employed with the aid of employment administration measures, and on the job-seekers who have finished labour market adult education, subsidised employment, or labour market subsidy traineeship. The data also contain information on the job-seekers' situation 3 months after the implementation of various employment policy measures (e.g. wage subsidy and other employment subsidies, preparatory labour market training, vocational labour market training, labour market subsidy traineeship and self-motivated education). The dataset includes the man-years of the Employment and Economic Development Office sorted by employee groups.