Employment Rate in the United States decreased to 59.70 percent in May from 60 percent in April of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in the United States remained unchanged at 4.20 percent in May. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Labor Force Participation Rate in the United States decreased to 62.40 percent in May from 62.60 percent in April of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The number of employed persons in The United States decreased to 163273 Thousand in May of 2025 from 163969 Thousand in April of 2025. This dataset provides - United States Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: March 2025 (preliminary values at the county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: May 21st, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties.To better understand the different labor force statistics included in this map, see the diagram below from BLS:

🚀 Data Science Careers in 2025: Jobs and Salary Trends in Pakistan 🚀 Data Science is one of the fastest-growing fields, and by 2025, the demand for skilled professionals in Pakistan will only increase. If you’re considering a career in Data Science, here’s what you need to know about the top jobs and salary trends.

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work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

A comprehensive dataset of top occupations for H-1B Visa sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest trends and employer behavior regarding H-1B visa sponsorship.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Nigeria unemployment rate for 2023 was 3.07%, a 0.75% decline from 2022.

<ul style='margin-top:20px;'>

<li>Nigeria unemployment rate for 2022 was <strong>3.83%</strong>, a <strong>1.57% decline</strong> from 2021.</li>
<li>Nigeria unemployment rate for 2021 was <strong>5.39%</strong>, a <strong>0.32% decline</strong> from 2020.</li>
<li>Nigeria unemployment rate for 2020 was <strong>5.71%</strong>, a <strong>0.51% increase</strong> from 2019.</li>
</ul>Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Job Offers in the United States increased to 7391 Thousand in April from 7200 Thousand in March of 2025. This dataset provides the latest reported value for - United States Job Openings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Philippines unemployment rate for 2023 was 2.23%, a 0.37% decline from 2022.

<ul style='margin-top:20px;'>

<li>Philippines unemployment rate for 2022 was <strong>2.60%</strong>, a <strong>0.8% decline</strong> from 2021.</li>
<li>Philippines unemployment rate for 2021 was <strong>3.40%</strong>, a <strong>0.88% increase</strong> from 2020.</li>
<li>Philippines unemployment rate for 2020 was <strong>2.52%</strong>, a <strong>0.29% increase</strong> from 2019.</li>
</ul>Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

A detailed analysis of H-1B visa sponsorship trends, featuring data on labor certifications, top sponsoring employers, most common job titles, leading immigration law firms, key industries, and geographic distribution. This dataset provides valuable insights into employment-based immigration patterns, helping professionals, employers, and policymakers make informed decisions.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Valley township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Valley township, the median income for all workers aged 15 years and older, regardless of work hours, was $51,224 for males and $40,450 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 21% between the median incomes of males and females in Valley township. With women, regardless of work hours, earning 79 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetownship of Valley township.

- Full-time workers, aged 15 years and older: In Valley township, among full-time, year-round workers aged 15 years and older, males earned a median income of $67,054, while females earned $60,556, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Valley township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Valley township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Valley township median household income by race. You can refer the same here

Transport for NSW provides projections of employment at the small area (Travel Zone or TZ) level for NSW. The latest version is Travel Zone Projections 2024 (TZP24), released in January 2025.\r \r TZP24 replaces the previously published TZP22.\r \r The projections are developed to support a strategic view of NSW and are aligned with the NSW Government Common Planning Assumptions .\r \r TZP24 Employment Projections are for employed persons by place of work. They are provided by Industry using two breakdowns:\r \r *\t33 industry categories (equivalent to the ABS 1-digit Australia and New Zealand Standard Industrial Classification (ANZSIC) codes with the exception of Manufacturing which is at 2-digit level).\r \r *\t4 Broad Industry Categories (groupings of the above).\r \r The projections in this release, TZP24, are presented annually from 2021 to 2031 and 5-yearly from 2031 to 2066, and are in TZ21 geography.\r \r Please note, TZP24 is based on best available data as at early 2024, and the projections incorporate results of the National Census conducted by the ABS in August 2021.\r \r Key Data Inputs used:\r \r *\tTZP24 Workforce Projections\r \r *\tCensus 2021 Place of Work by Destination Zone - ABS\r \r *\tNSW Intergenerational Report - NSW Treasury\r \r *\tSA4 Employment by industry projections - Victoria University\r \r *\tFuture Employment Development Database (FEDD) - a custom dataset compiled by TfNSW between August 2023 and February 2024, that presents the number of jobs expected from major projects based on publicly available documents.\r \r For a summary of the TZP24 Projections method please refer to the TZP24 Factsheet .\r \r For more detail on the projection process please refer to the TZP24 Technical Guide .\r \r Additional land use information for population and workforce as well as Travel Zone 2021 boundaries for NSW (TZ21) and concordance files are also available for download on the Open Data Hub.\r \r Visualisations of the employment projections are available on the Transport for NSW Website .\r \r Cautions\r \r The TZP24 dataset represents one view of the future aligned with the NSW Government Common Planning Assumptions for population and employment projections.\r \r The projections are not based on specific assumptions about future new transport infrastructure, but do take into account known land-use developments underway or planned, and strategic plans.\r \r *\tTZP24 is a strategic state-wide dataset and caution should be exercised when considering results at detailed breakdowns.\r \r *\tThe TZP24 outputs represent a point in time set of projections (as at early -2024).\r \r *\tThe projections are not government targets.\r \r *\tTravel Zone (TZ) level outputs are projections only and should be used as a guide. As with all small area data, aggregating of travel zone projections to higher geographies leads to more robust results.\r \r *\tAs a general rule, TZ-level projections are illustrative of a possible future only.\r \r *\tMore specific advice about data reliability for the specific variables projected is provided in the “Read Me” page of the Excel format summary spreadsheets on the TfNSW Open Data Hub.\r \r *\tCaution is advised when comparing TZP24 with the previous set of projections (TZP22) due to addition of new data sources for the most recent years, and adjustments to methodology.\r \r Further cautions and notes can be found in the TZP24 Technical Guide.

Malaysia unemployment rate for 2023 was 3.86%, a 0.08% decline from 2022.

<ul style='margin-top:20px;'>

<li>Malaysia unemployment rate for 2022 was <strong>3.93%</strong>, a <strong>0.71% decline</strong> from 2021.</li>
<li>Malaysia unemployment rate for 2021 was <strong>4.64%</strong>, a <strong>0.1% increase</strong> from 2020.</li>
<li>Malaysia unemployment rate for 2020 was <strong>4.54%</strong>, a <strong>1.28% increase</strong> from 2019.</li>
</ul>Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

Private businesses in the United States hired 37 thousand workers in May of 2025 compared to 60 thousand in April of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.